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+# Sphinx build info version 1
+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 292c2c4c18973b0c51527665131fea15
+tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/.doctrees/nbsphinx/DownloadCompose.ipynb b/.doctrees/nbsphinx/DownloadCompose.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Usage of download_compose Module\n",
+    "Tianzhe Zhou 2024/11/9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[CompOSE](https://compose.obspm.fr/table)(CompStar Online Supernovae Equations of State)\n",
+    "is a database that provides data tables for different state of the art equations of state (EoS) ready for further usage in astrophysical applications, nuclear physics and beyond.\n",
+    "\n",
+    "In this notebook, we will demonstrate how to use the `download_compose` module to download the data from the CompOSE database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DownloadCompose: Fetching data from https://compose.obspm.fr/table \n",
+      "...\n",
+      "DownloadCompose: Find 308 EOS data on website https://compose.obspm.fr/table\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, import the class that we need to use\n",
+    "from EOSgenerators.download_compose import DownloadCompose\n",
+    "\n",
+    "# Then, create an instance of the class\n",
+    "# This will automatically fetch the latest version of all available\n",
+    "# EOS names and their corresponding download links\n",
+    "dc = DownloadCompose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id =   1, name = HS(DD2) neutron matter (no electrons)\n",
+      "id =   2, name = HS(DD2) neutron matter (with electrons)\n",
+      "id =   3, name = HS(FSG) neutron matter (no electrons)\n",
+      "id =   4, name = HS(FSG) neutron matter (with electrons)\n",
+      "id =   5, name = HS(IUF) neutron matter (no electrons)\n",
+      "id =   6, name = HS(IUF) neutron matter (with electrons)\n",
+      "id =   7, name = HS(NL3) neutron matter (no electrons)\n",
+      "id =   8, name = HS(NL3) neutron matter (with electrons)\n",
+      "id =   9, name = HS(TM1) neutron matter  (no electrons)\n",
+      "id =  10, name = HS(TM1) neutron matter (with electrons)\n",
+      "id =  11, name = HS(TMA) neutron matter (no electrons)\n",
+      "id =  12, name = HS(TMA) neutron matter (with electrons)\n",
+      "id =  13, name = SFH(SFHo) neutron matter (no electrons)\n",
+      "id =  14, name = SFH(SFHo) neutron matter (with electrons)\n",
+      "id =  15, name = SFH(SFHx) neutron matter (no electrons)\n",
+      "id =  16, name = SFH(SFHx) neutron matter (with electrons)\n",
+      "id =  17, name = HS(DD2) (no electrons)\n",
+      "id =  18, name = HS(DD2) (with electrons)\n",
+      "id =  19, name = HS(FSG) (no electrons)\n",
+      "id =  20, name = HS(FSG) (with electrons)\n",
+      "id =  21, name = HS(IUF) (no electrons)\n",
+      "id =  22, name = HS(IUF) (with electrons)\n",
+      "id =  23, name = HS(NL3) (no electrons)\n",
+      "id =  24, name = HS(NL3) (with electrons)\n",
+      "id =  25, name = HS(TM1) (no electrons)\n",
+      "id =  26, name = HS(TM1) (with electrons)\n",
+      "id =  27, name = HS(TMA) (no electrons)\n",
+      "id =  28, name = HS(TMA) (with electrons)\n",
+      "id =  29, name = GROM(LS220L) (with Lambdas, no low densities)\n",
+      "id =  30, name = GROM(LS220L) (with Lambdas, with low densities)\n",
+      "id =  31, name = LS(LS220) (no low densities)\n",
+      "id =  32, name = LS(LS220) (with low densities)\n",
+      "id =  33, name = SFH(SFHo) (no electrons)\n",
+      "id =  34, name = SFH(SFHo) (with electrons)\n",
+      "id =  35, name = SFH(SFHx) (no electrons)\n",
+      "id =  36, name = SFH(SFHx) (with electrons)\n",
+      "id =  37, name = SHO(FSU1) (no electrons)\n",
+      "id =  38, name = SHO(FSU1) (with electrons)\n",
+      "id =  39, name = SHO(FSU2) (with electrons)\n",
+      "id =  40, name = SHO(FSU2) (no electrons)\n",
+      "id =  41, name = SHT(NL3) (no electrons)\n",
+      "id =  42, name = SHT(NL3) (with electrons)\n",
+      "id =  43, name = BHB(DD2L) (no electrons)\n",
+      "id =  44, name = BHB(DD2L) (with electrons)\n",
+      "id =  45, name = BHB(DD2Lphi) (with electrons)\n",
+      "id =  46, name = BHB(DD2Lphi) (no electrons)\n",
+      "id =  47, name = SHO(FSU1) (no electrons)\n",
+      "id =  48, name = SHO(FSU1) (with electrons)\n",
+      "id =  49, name = SHO(FSU2) (no electrons)\n",
+      "id =  50, name = SHO(FSU2) (with electrons)\n",
+      "id =  51, name = SHT(NL3) (no electrons)\n",
+      "id =  52, name = SHT(NL3) (with electrons)\n",
+      "id =  53, name = GDTB(DDHdelta)\n",
+      "id =  54, name = GM(GM1)\n",
+      "id =  55, name = OPGR(GM1Y4) (with hyperons)\n",
+      "id =  56, name = STOS(TM1) neutron matter (original version)\n",
+      "id =  57, name = STOS(TM1) neutron matter (new version)\n",
+      "id =  58, name = STOS(TM1L) neutron and Lambda matter\n",
+      "id =  59, name = STOS(TM1) zero temperature (new version)\n",
+      "id =  60, name = STOS(TM1) zero temperature (original version)\n",
+      "id =  61, name = STOS(TM1) with Lambda zero temperature\n",
+      "id =  62, name = STOS(TM1L) with Lambda hyperons\n",
+      "id =  63, name = STOS(TM1) (new version)\n",
+      "id =  64, name = STOS(TM1) (original version)\n",
+      "id =  65, name = OPGR(GM1Y5) (with hyperons)\n",
+      "id =  66, name = OPGR(GM1Y6) (with hyperons)\n",
+      "id =  67, name = OPGR(DDHdeltaY4) (with hyperons)\n",
+      "id =  68, name = APR(APR)\n",
+      "id =  69, name = BBB(BHF-BBB2)\n",
+      "id =  70, name = SFHPST(TM1B145), with electrons\n",
+      "id =  71, name = STOS(TM1) (new version) with electrons\n",
+      "id =  72, name = STOS(TM1L) with Lambda hyperons and electrons\n",
+      "id =  73, name = SFHPST(TM1B139) with electrons\n",
+      "id =  74, name = SFHPST(TM1B155) with electrons\n",
+      "id =  75, name = SFHPST(TM1B165) with electrons (B165)\n",
+      "id =  76, name = IOTSY(TM1Y30) Sigma potential 30 MeV\n",
+      "id =  77, name = IOTSY(TM1Y30pi) with pions, Sigma potential 30 MeV\n",
+      "id =  78, name = IOTSY(TM1Y90pi) with pions, Sigma potential 90 MeV\n",
+      "id =  79, name = IOTSY(TM1Y0pi) with pions, Sigma potential 0 MeV\n",
+      "id =  80, name = IOTSY(TM1Y-30pi) with pions, Sigma potential -30 MeV\n",
+      "id =  81, name = IOTSY(TM1Y90) , Sigma potential 90 MeV)\n",
+      "id =  82, name = IOTSY(TM1Y0), Sigma potential 0 MeV\n",
+      "id =  83, name = IOTSY(TM1Y-30), Sigma potential -30 MeV\n",
+      "id =  84, name = RG(SLY230a)\n",
+      "id =  85, name = RG(SLY2)\n",
+      "id =  86, name = RG(SLY9)\n",
+      "id =  87, name = RG(SKI2)\n",
+      "id =  88, name = RG(SkI3)\n",
+      "id =  89, name = RG(SkI4)\n",
+      "id =  90, name = RG(SkI5)\n",
+      "id =  91, name = RG(SkI6)\n",
+      "id =  92, name = RG(KDE0v1)\n",
+      "id =  93, name = RG(KDE0v)\n",
+      "id =  94, name = RG(SK255)\n",
+      "id =  95, name = RG(SK272)\n",
+      "id =  96, name = RG(SKa)\n",
+      "id =  97, name = RG(SKb)\n",
+      "id =  98, name = RG(SkMp)\n",
+      "id =  99, name = RG(SkOp)\n",
+      "id = 100, name = RG(Rs)\n",
+      "id = 101, name = NL3\n",
+      "id = 104, name = OMHN(DD2Y)\n",
+      "id = 105, name = TNTYST(KOST2) variational EoS (with electrons)\n",
+      "id = 106, name = TNTYST(KOST2) variational EoS (without electrons)\n",
+      "id = 107, name = TNTYST(KOST2) variational EoS at zero temperature (with electrons)\n",
+      "id = 108, name = TNTYST(KOST2) variational EoS at zero temperature (no electrons)\n",
+      "id = 109, name = TNTYST(KOST2) neutron matter (zero temperature)\n",
+      "id = 110, name = TNTYST(KOST2) neutron matter\n",
+      "id = 111, name = FYSS(TM1) (no electrons)\n",
+      "id = 112, name = FTNS(KOST2) variational EoS (no electrons)\n",
+      "id = 113, name = FYSS(TM1) (with electrons)\n",
+      "id = 114, name = FTNS(KOST2) variational EoS (with electrons)\n",
+      "id = 115, name = DNS(CMF) Hadronic (with electrons)\n",
+      "id = 116, name = DNS(CMF) Hadronic (cold neutron stars)\n",
+      "id = 117, name = DNS(CMF) Hadronic (no electrons)\n",
+      "id = 118, name = FOP(SFHoY) (with electrons)\n",
+      "id = 119, name = FOP(SFHoY) (no electrons)\n",
+      "id = 120, name = BL(chiral)\n",
+      "id = 121, name = BL(chiral) with crust\n",
+      "id = 123, name = RG(SLY4)\n",
+      "id = 134, name = RG(SLY4)\n",
+      "id = 136, name = PT(GRDF1_DD2)\n",
+      "id = 137, name = PT(GRDF2_DD2) old version\n",
+      "id = 138, name = DNS(CMF) hadronic  (cold neutron stars) with crust\n",
+      "id = 139, name = BHK(QHC18)\n",
+      "id = 140, name = BFH(QHC19-B)\n",
+      "id = 141, name = SRO(SLy4) SNA version\n",
+      "id = 142, name = OOS(DD2_FRG) (2+1 flavors)\n",
+      "id = 143, name = OOS(DD2_FRG) (2 flavors)\n",
+      "id = 144, name = SNSH(TM1e)\n",
+      "id = 145, name = SNSH(TM1e) zero temperature\n",
+      "id = 146, name = SNSH(TM1e) neutron matter\n",
+      "id = 147, name = RG(SLy4) with neutrino opacities\n",
+      "id = 148, name = RG(SLY4) with neutrino opacities, extended version\n",
+      "id = 149, name = SRO(APR) SNA version\n",
+      "id = 150, name = BFH(QHC19-A)\n",
+      "id = 151, name = BFH(QHC19-C)\n",
+      "id = 152, name = BFH(QHC19-D)\n",
+      "id = 154, name = MBB(DD2K ) (no electrons)\n",
+      "id = 155, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1 without leptons\n",
+      "id = 156, name = BBKF(DD2F-SF) quark-hadron model RDF 1.2 without leptons\n",
+      "id = 157, name = BBKF(DD2F-SF) quark-hadron model RDF 1.3 without leptons\n",
+      "id = 158, name = BBKF(DD2F-SF) quark-hadron model RDF 1.4 without leptons\n",
+      "id = 159, name = BBKF(DD2F-SF) quark-hadron model RDF 1.5 without leptons\n",
+      "id = 160, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6 without leptons\n",
+      "id = 161, name = BBKF(DD2F-SF) quark-hadron model RDF 1.7 without leptons\n",
+      "id = 162, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8 without leptons\n",
+      "id = 163, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9 without leptons\n",
+      "id = 164, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1\n",
+      "id = 165, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.2\n",
+      "id = 166, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.3\n",
+      "id = 167, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.4\n",
+      "id = 168, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.5\n",
+      "id = 169, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6\n",
+      "id = 170, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.7\n",
+      "id = 171, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8\n",
+      "id = 172, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9\n",
+      "id = 173, name = SRO(NRAPR) NSE version\n",
+      "id = 174, name = SRO(SkAPR) SNA version\n",
+      "id = 175, name = SRO(KDE0v1) SNA version\n",
+      "id = 176, name = SRO(LS220*) SNA version\n",
+      "id = 177, name = SRO(LNS) SNA version\n",
+      "id = 178, name = SRO(LS220) SNA version\n",
+      "id = 180, name = DS(CMF)-1\n",
+      "id = 181, name = DS(CMF)-2\n",
+      "id = 182, name = DS(CMF)-3\n",
+      "id = 183, name = DS(CMF)-4\n",
+      "id = 184, name = DS(CMF)-5\n",
+      "id = 185, name = DS(CMF)-6\n",
+      "id = 186, name = DS(CMF)-7\n",
+      "id = 187, name = DS(CMF)-8\n",
+      "id = 188, name = DS(CMF)-1 with crust\n",
+      "id = 189, name = DS(CMF)-2 with crust\n",
+      "id = 190, name = DS(CMF)-3 with crust\n",
+      "id = 191, name = DS(CMF)-4 with crust\n",
+      "id = 192, name = DS(CMF)-5 with crust\n",
+      "id = 193, name = DS(CMF)-6 with crust\n",
+      "id = 194, name = DS(CMF)-7 with crust\n",
+      "id = 195, name = DS(CMF)-8 with crust\n",
+      "id = 196, name = OOS(DD2-FRG) with vector interactions(2 flavors)\n",
+      "id = 197, name = OOS(DD2-FRG) with vector interactions(2+1 flavors)\n",
+      "id = 198, name = JJ(VQCD(APR)), intermediate\n",
+      "id = 199, name = JJ(VQCD(APR)), stiff\n",
+      "id = 200, name = JJ(VQCD(APR)), soft\n",
+      "id = 201, name = MBB(HSDD2K) with electrons\n",
+      "id = 202, name = MBB(BHBLphiK) with electrons\n",
+      "id = 203, name = GPPVA(FSU2R) NS crust\n",
+      "id = 204, name = GPPVA(NL3wrL55) NS crust\n",
+      "id = 205, name = SDGTT(QMC-A), no leptons\n",
+      "id = 206, name = SDGTT(QMC-A) with leptons\n",
+      "id = 207, name = GPPVA(TM1e) NS crust\n",
+      "id = 208, name = GPPVA(FSU2H) NS crust\n",
+      "id = 209, name = GPPVA(FSU2) NS crust\n",
+      "id = 210, name = GPPVA(DD2) NS crust\n",
+      "id = 211, name = GPPVA(DDME2) NS crust\n",
+      "id = 212, name = GPPVA(TW) NS crust\n",
+      "id = 213, name = GPPVA(FSU2H) NS unified inner crust-core\n",
+      "id = 214, name = GPPVA(FSU2R) NS unified inner crust-core\n",
+      "id = 215, name = GPPVA(FSU2) NS unified inner crust-core\n",
+      "id = 216, name = GMSR(SLy5)\n",
+      "id = 217, name = GPPVA(DD2) NS unified inner crust-core\n",
+      "id = 218, name = GPPVA(DDME2) NS unified inner crust-core\n",
+      "id = 219, name = GPPVA(TW) NS unified inner crust-core\n",
+      "id = 220, name = GPPVA(NL3wrL55) NS unified inner crust-core\n",
+      "id = 221, name = GPPVA(TM1e) NS unified inner crust-core\n",
+      "id = 222, name = GMSR(LNS5)\n",
+      "id = 223, name = GMSR(BSK16)\n",
+      "id = 224, name = GMSR(BSK14)\n",
+      "id = 225, name = GMSR(RATP)\n",
+      "id = 227, name = GMSR(F0)\n",
+      "id = 228, name = GMSR(H1)\n",
+      "id = 229, name = GMSR(H2)\n",
+      "id = 230, name = GMSR(H3)\n",
+      "id = 231, name = GMSR(H4)\n",
+      "id = 232, name = GMSR(H5)\n",
+      "id = 233, name = GMSR(H7)\n",
+      "id = 234, name = GMSR(DHSL59)\n",
+      "id = 235, name = GMSR(DHSL69)\n",
+      "id = 236, name = KBH(QHC21_AT)\n",
+      "id = 237, name = KBH(QHC21_BT)\n",
+      "id = 238, name = KBH(QHC21_CT)\n",
+      "id = 239, name = KBH(QHC21_DT)\n",
+      "id = 240, name = KBH(QHC21_A)\n",
+      "id = 241, name = KBH(QHC21_B)\n",
+      "id = 242, name = KBH(QHC21_C)\n",
+      "id = 243, name = KBH(QHC21_D)\n",
+      "id = 244, name = R(DD2YDelta) 1.1-1.1 (no electrons)\n",
+      "id = 245, name = R(DD2YDelta) 1.1-1.1 (with electrons)\n",
+      "id = 246, name = R(DD2YDelta) 1.2-1.1 (with electrons)\n",
+      "id = 247, name = R(DD2YDelta) 1.2-1.1 (no electrons)\n",
+      "id = 248, name = R(DD2YDelta) 1.2-1.3 (with electrons)\n",
+      "id = 249, name = R(DD2YDelta) 1.2-1.3 (no electrons)\n",
+      "id = 250, name = R(DD2YDelta) 1.1-1.1 (cold NS)\n",
+      "id = 251, name = R(DD2YDelta) 1.2-1.3 (cold NS)\n",
+      "id = 252, name = R(DD2YDelta) 1.2-1.1 (cold NS)\n",
+      "id = 253, name = PCP(BSK24)\n",
+      "id = 254, name = VGBCMR(D1MStar)\n",
+      "id = 255, name = VGBCMR(D1M)\n",
+      "id = 256, name = PCP(BSK22)\n",
+      "id = 257, name = PCP(BSK25)\n",
+      "id = 258, name = PCP(BSK26)\n",
+      "id = 259, name = DS(CMF)-1 Hybrid\n",
+      "id = 260, name = DS(CMF)-2 Hybrid\n",
+      "id = 261, name = DS(CMF)-3 Hybrid\n",
+      "id = 262, name = DS(CMF)-4 Hybrid\n",
+      "id = 263, name = DS(CMF)-5 Hybrid\n",
+      "id = 264, name = DS(CMF)-6 Hybrid\n",
+      "id = 265, name = DS(CMF)-7 Hybrid\n",
+      "id = 266, name = DS(CMF)-8 Hybrid\n",
+      "id = 267, name = DS(CMF)-1 Hybrid with crust\n",
+      "id = 268, name = DS(CMF)-2 Hybrid with crust\n",
+      "id = 269, name = DS (CMF)-3 Hybrid with crust\n",
+      "id = 270, name = DS (CMF)-4 Hybrid with crust\n",
+      "id = 271, name = DS (CMF)-5 Hybrid with crust\n",
+      "id = 272, name = DS (CMF)-6 Hybrid with crust\n",
+      "id = 273, name = DS (CMF)-7 Hybrid with crust\n",
+      "id = 274, name = DS(CMF)-8 Hybrid with crust\n",
+      "id = 275, name = ABHT(QMC-RMF1)\n",
+      "id = 276, name = ABHT(QMC-RMF2)\n",
+      "id = 277, name = ABHT(QMC-RMF3)\n",
+      "id = 278, name = ABHT(QMC-RMF4)\n",
+      "id = 279, name = XMLSLZ(DDME2)\n",
+      "id = 280, name = XMLSLZ(DD-LZ1)\n",
+      "id = 281, name = XMLSLZ(DDME-X)\n",
+      "id = 282, name = XMLSLZ(GM1)\n",
+      "id = 283, name = XMLSLZ(MTVTC)\n",
+      "id = 284, name = XMLSLZ(NL3)\n",
+      "id = 285, name = XMLSLZ(PK1)\n",
+      "id = 286, name = XMLSLZ(PKDD)\n",
+      "id = 287, name = XMLSLZ(TM1)\n",
+      "id = 288, name = XMLSLZ(TW99)\n",
+      "id = 289, name = DEJ(DD2-VQCD) intermediate\n",
+      "id = 290, name = DEJ(DD2-VQCD) soft\n",
+      "id = 291, name = DEJ(DD2-VQCD) stiff\n",
+      "id = 292, name = PCGS(PCSB0)\n",
+      "id = 293, name = LPB(chiral) with electrons\n",
+      "id = 294, name = LPB(chiral)\n",
+      "id = 295, name = PCGS(PCSB1)\n",
+      "id = 296, name = PCGS(PCSB2)\n",
+      "id = 297, name = ABHT(QMC-RMF1)\n",
+      "id = 298, name = ABHT(QMC-RMF2)\n",
+      "id = 299, name = ABHT(QMC-RMF3)\n",
+      "id = 300, name = ABHT(QMC-RMF4)\n",
+      "id = 301, name = PT(GRDF2-DD2) new version\n",
+      "id = 302, name = PT(GRDF2-DD2) cold NS\n",
+      "id = 303, name = HJJSTV(VQCD)-intermediate\n",
+      "id = 304, name = HJJSTV(VQCD) soft\n",
+      "id = 305, name = HJJSTV(VQCD) stiff\n",
+      "id = 306, name = BL(chiral) with unified crust\n",
+      "id = 307, name = CMGO(GDFM-I)\n",
+      "id = 308, name = CMGO (GDFM-II)\n",
+      "id = 309, name = KRT(FSU2H*)\n",
+      "id = 310, name = SPG(M1) unified NS EoS\n",
+      "id = 311, name = SPG(M1) Crust NS EoS\n",
+      "id = 312, name = SPG(M2) unified NS EoS\n",
+      "id = 313, name = SPG(M3) unified NS EoS\n",
+      "id = 314, name = SPG(M4) unified NS EoS\n",
+      "id = 315, name = SPG(M5) unified NS EoS\n",
+      "id = 316, name = SPG(M2) Crust NS EoS\n",
+      "id = 317, name = SPG(M3) Crust NS EoS\n",
+      "id = 318, name = SPG(M4) Crust NS EoS\n",
+      "id = 319, name = SPG(M5) Crust NS EoS\n",
+      "id = 320, name = TSO(DDLS(30)-Y)\n",
+      "id = 321, name = TSO(DDLS(70)-Y)\n",
+      "id = 322, name = TSO(DDLS(70)-N)\n",
+      "id = 323, name = TSO(DDLS(50)-Y)\n",
+      "id = 324, name = TSO(DDLS(50)-N)\n",
+      "id = 325, name = TSO(DDLS(30)-N)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now, we can print the list of available EOS names.\n",
+    "# Each EOS name is related to a specific identifier,\n",
+    "# which we can use later to actually download the EOS data file.\n",
+    "dc.print_eos_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloads\\compose\\316\\eos.t already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.t\n",
+      "downloads\\compose\\316\\eos.nb already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.nb\n",
+      "downloads\\compose\\316\\eos.thermo already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.thermo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now we pick the EOS with identifier 316 and download it.\n",
+    "id = 316\n",
+    "eosdir = dc.download_id(id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t Nucleon masses used within the CompOSE tables :\t m_n = 938.90000 MeV and m_p = 938.90000 MeV\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We use Compose_eos function to extract the EOS data from the downloaded file\n",
+    "from EOSgenerators import Compose_eos\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "eosdir = dc.eos_download_dir(id).as_posix()\n",
+    "eps, p, name = Compose_eos.read_compose(eosdir=eosdir, eosname=dc.eos_name(id))\n",
+    "\n",
+    "# Finally, we can plot the EOS data using matplotlib,\n",
+    "# to ensure that all the previous steps were successful.\n",
+    "plt.figure(dpi=100)\n",
+    "plt.plot(eps, p)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"Energy density [$g/cm^3$]\")\n",
+    "plt.ylabel(\"Pressure [$dyn/cm^2$]\")\n",
+    "plt.title(name)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.doctrees/nbsphinx/DownloadCompose_5_1.png b/.doctrees/nbsphinx/DownloadCompose_5_1.png
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index 0000000..244f4fb
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diff --git a/.doctrees/nbsphinx/UnitConventionForDeveloper.ipynb b/.doctrees/nbsphinx/UnitConventionForDeveloper.ipynb
new file mode 100644
index 0000000..9f238b7
--- /dev/null
+++ b/.doctrees/nbsphinx/UnitConventionForDeveloper.ipynb
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unit Convention for Developer\n",
+    "Tianzhe Zhou 2024/11/1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To set up a solid foundation for the development of the project, \n",
+    "we need to establish a unit convention for the project. \n",
+    "The unit convention is a set of rules that define the units of the physical quantities used in the project. \n",
+    "The unit convention is important because it ensures that all developers use the same units for the same physical quantities, which helps to avoid errors and inconsistencies throughout the whole project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fm = 1\n",
+    "hbarc = 0.197327053\n",
+    "c = 1  # speed of light\n",
+    "hbar = 1  # reduced Planck constant\n",
+    "\n",
+    "GeV = 1 / hbarc  # Giga-electronvolt\n",
+    "MeV = 1e-3 * GeV  # Mega-electronvolt\n",
+    "\n",
+    "g = 5.625e26 * MeV  # gram\n",
+    "kg = 1e3 * g  # kilogram\n",
+    "cm = 1e13 * fm  # centimeter\n",
+    "m = 100 * cm  # meter\n",
+    "km = 1e5 * cm  # kilometer\n",
+    "s = 3e10 * cm  # second\n",
+    "\n",
+    "dyn = g * cm / s**2  # dyne\n",
+    "dyn_cm_2 = dyn / cm**2  # dyne / cm^2\n",
+    "g_cm_3 = g / cm**3  # gram / cm^3\n",
+    "erg = dyn * cm  # ἐργον energy unit\n",
+    "\n",
+    "m_n = 939.565 * MeV  # mass of neutron\n",
+    "n0 = 0.16 / fm**3  # saturation density\n",
+    "\n",
+    "e0 = m_n * n0  # saturation energy density\n",
+    "G = 6.6743e-8 * dyn * cm**2 / g**2  # gravitational constant\n",
+    "Msun = 1.989e33 * g  # mass of sun\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first 4 lines come from a widely used unit convention in High Energy Physics (HEP),\n",
+    "which is called [Natural Unit System](https://en.wikipedia.org/wiki/Natural_units).\n",
+    "You should be awared that we will NEVER using the Natural Unit System explicity,\n",
+    "as long as you obey the following rules.\n",
+    "1. Whenever you define a [literal](https://www.ituonline.com/tech-definitions/what-is-literal-programming/) \n",
+    "physical quantity, you should always *multiply* the unit of that quantity.\n",
+    "```python\n",
+    "# example of rule 1:\n",
+    "m_n = 939.565 * MeV  # GOOD\n",
+    "m_n = 939.565        # BAD\n",
+    "m_n = 1.67e-27 * kg  # GOOD\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Whenever you want a physical quantity in cgs unit(or others unit system), \n",
+    "you should always *divide* the corresponding unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.670337777777778e-24\n"
+     ]
+    }
+   ],
+   "source": [
+    "# example of rule 2:\n",
+    "m_n = 939.565 * MeV  # mass of neutron\n",
+    "# if I want to know m_n in terms of gram, I need to\n",
+    "print(m_n / g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. When you need to plot, *devide* the unit of your axis. See the example below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. When you use physic formula, you leave the whole form as it was, no additional constants.\n",
+    "```python\n",
+    "# example of rule 4:\n",
+    "E = m * c**2     # GOOD\n",
+    "F = G*M*m / r**2 # GOOD\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we present a full procedure of how to use the unit convention in calculating MR relation.\n",
+    "See https://arxiv.org/pdf/astro-ph/0506417v1 Page 15 for more details."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.433772787333139e-26\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from numpy import pi\n",
+    "\n",
+    "K_nrel = hbar**2 / (15 * pi**2 * m_n) * (3 * pi**2 / (m_n * c**2)) ** (5 / 3)  # Rule 1\n",
+    "print(K_nrel / (cm**2 / erg ** (2 / 3)))  # Rule 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def EOS_P(e):\n",
+    "    return K_nrel * e ** (5 / 3)  # Rule 4\n",
+    "\n",
+    "\n",
+    "e_min = 1e4 * g_cm_3  # Rule 1\n",
+    "e_max = 1e18 * g_cm_3  # Rule 1\n",
+    "e_grid = np.geomspace(e_min, e_max, 10000)\n",
+    "p_grid = EOS_P(e_grid)\n",
+    "\n",
+    "\n",
+    "def EofP(P, e_grid, p_grid):\n",
+    "    return np.interp(P, p_grid, e_grid)\n",
+    "\n",
+    "\n",
+    "def PofE(E, e_grid, p_grid):\n",
+    "    return np.interp(E, e_grid, p_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import ode\n",
+    "\n",
+    "\n",
+    "# working with rescaled quantities\n",
+    "def TOV1(r, y, grids):\n",
+    "    p, m = y\n",
+    "    e_grid = grids[0]\n",
+    "    p_grid = grids[1]\n",
+    "\n",
+    "    e = EofP(p, e_grid, p_grid)\n",
+    "\n",
+    "    dpdr = -(e + p) * (m + 4 * pi * r**3 * p)  # Rule 4\n",
+    "    dpdr = dpdr / (r * (r - 2 * m))  # Rule 4\n",
+    "    dmdr = 4 * pi * r**2 * e  # Rule 4\n",
+    "\n",
+    "    return np.array([dpdr, dmdr])\n",
+    "\n",
+    "\n",
+    "def solveTOV1(rho_c, e_grid, p_grid):\n",
+    "    # Notice that we only rescale quantities inside this function\n",
+    "    rho_c = rho_c * G / c**2\n",
+    "    e_grid = e_grid * G / c**2\n",
+    "    p_grid = p_grid * G / c**4\n",
+    "\n",
+    "    Pmin = p_grid[0]\n",
+    "\n",
+    "    r = 4.441e-16 * cm  # Rule 1\n",
+    "    dr = 10.0 * cm  # Rule 1\n",
+    "\n",
+    "    p_c = PofE(rho_c, e_grid, p_grid)\n",
+    "\n",
+    "    P0 = p_c - (4 * pi / 3) * (p_c + rho_c) * (3 * p_c + rho_c) * r**2  # Rule 4\n",
+    "    m0 = 4 / 3 * pi * rho_c * r**3  # Rule 4\n",
+    "\n",
+    "    param = (e_grid, p_grid)\n",
+    "\n",
+    "    stateTOV = np.array((P0, m0))\n",
+    "    sy = ode(TOV1).set_integrator(\"dop853\")\n",
+    "    sy.set_initial_value(stateTOV, r).set_f_params(param)\n",
+    "    while sy.successful() and stateTOV[0] > Pmin:\n",
+    "        stateTOV = sy.integrate(sy.t + dr)\n",
+    "        dpdr, dmdr = TOV1(sy.t + dr, stateTOV, param)\n",
+    "        dr = 0.46 / (dmdr / stateTOV[1] - dpdr / stateTOV[0])\n",
+    "    # at the end of this function, we rescale the quantities back\n",
+    "    return stateTOV[1] * c**2 / G, sy.t\n",
+    "\n",
+    "\n",
+    "def compute_tov_properties(rho_c, e_grid, p_grid):\n",
+    "    M = np.zeros_like(rho_c)\n",
+    "    R = np.zeros_like(rho_c)\n",
+    "    for i in range(len(rho_c)):\n",
+    "        M[i], R[i] = solveTOV1(rho_c[i], e_grid, p_grid)\n",
+    "    return M, R\n",
+    "\n",
+    "\n",
+    "def cal_MR():\n",
+    "    density = np.geomspace(1e14 * g_cm_3, e_max, 100)  # Rule 1\n",
+    "    return compute_tov_properties(density, e_grid, p_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2560x1920 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "M, R = cal_MR()\n",
+    "\n",
+    "plt.figure(dpi=400)\n",
+    "plt.plot(R / km, M / Msun, \"ro-\", ms=2)  # Rule 3\n",
+    "plt.xlabel(\"Radius [km]\")\n",
+    "plt.ylabel(\"Mass [Msun]\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.doctrees/nbsphinx/UnitConventionForDeveloper_13_0.png b/.doctrees/nbsphinx/UnitConventionForDeveloper_13_0.png
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diff --git a/.doctrees/nbsphinx/test_Bayesian_inference_MITbag_EOS.ipynb b/.doctrees/nbsphinx/test_Bayesian_inference_MITbag_EOS.ipynb
new file mode 100644
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--- /dev/null
+++ b/.doctrees/nbsphinx/test_Bayesian_inference_MITbag_EOS.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "edb2cf2a",
+   "metadata": {},
+   "source": [
+    "## MIT bag EOS Inference pipline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc633d3",
+   "metadata": {},
+   "source": [
+    "The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state $p=\\frac{\\epsilon}{3}-\\frac{4B}{3}$. There is only one parameter, the \"bag constant\" $B$. This represents the vacuum energy density, which creates a \"bag\" in which quarks are confined. See [Chodos et al. (1974)](https://doi.org/10.1103/PhysRevD.9.3471).\n",
+    "\n",
+    "In this notebook, we use our Bayesian inference code to constrain the value of $B$ using observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acdcb64a",
+   "metadata": {},
+   "source": [
+    "### (a) Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6f20856",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import EOSgenerators.MITbag_EOS as MITbag\n",
+    "from TOVsolver.unit import MeV, fm, g_cm_3, dyn_cm_2, km, Msun\n",
+    "import TOVsolver.main as main"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edfa0f72",
+   "metadata": {},
+   "source": [
+    "### (b) Set up priors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38136542",
+   "metadata": {},
+   "source": [
+    "Next, we need to set up the priors. We first use a parameters array to specify the variable name. This process should be consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube is a set of random numbers from 0 to 1. This prior setting is the standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.\n",
+    "\n",
+    "We provided two options call from prior:\"normal_Prior\" and \"flat_prior\".\n",
+    "\n",
+    "We note that since we are doing Equation of state inference from mass-radius data of neutron star measurement. The central density of the star should be also sampled. Otherwise, this will be a partially-defined prior that did not span all of the parameter space, and proved to be different from full-scope inference.\n",
+    "\n",
+    "This request will randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of the compact star --- different equations of state will predict different upper bounds, so here we need to use the prior-setting EoS parameters computing the mass-radius curve for this equation of state, then find out the last stable point of this equation of state (first mass points that give the direvative to be negative). We can find that index with the len() function, then reset this max_d to be upper limit of this density range.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b87dd3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = ['B','d1']\n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = prior.flat_prior(20,100,cube[0])\n",
+    "\n",
+    "    B = params[0]\n",
+    "    \n",
+    "    epsilon,p = MITbag.MITbag_compute_EOS(B)\n",
+    "\n",
+    "    RFSU2R = [] \n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50) \n",
+    "    if all(x<y for x,y in zip(epsilon[:], epsilon[1:])) and all(x<y for x, y in zip(p[:], p[1:])):\n",
+    "        MR = main.OutputMR(\"\",epsilon,p).T  \n",
+    "        \n",
+    "    else:\n",
+    "        MR = []\n",
+    "    if len(MR) == False: \n",
+    "        params[1] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "   \n",
+    "        for i in range(len(MR[1])):\n",
+    "            RFSU2R.append(MR[1][i])\n",
+    "            MFSU2R.append(MR[0][i])   \n",
+    "            if i > 20 and MR[0][i] - MR[0][i-1]< 0:    \n",
+    "                break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[1] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[1] = 14.3 + (max_d - 14.3) * cube[1]\n",
+    "        #params[3] = 14.3 + (max_d - 14.3) * cube[3]\n",
+    "        #this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8e4822",
+   "metadata": {},
+   "source": [
+    "In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.\n",
+    "\n",
+    "Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement.\n",
+    "For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. \"d1\" for the centre density of this measurement, in the meantime add \"params[2]\" to this code.\n",
+    "\n",
+    "If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. \"d1\" and \"d2\" for the centre density of these two measurements, in the meantime add \"params[3]\" to the above code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ab6d43",
+   "metadata": {},
+   "source": [
+    "### (c) Set up likehood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfd12ff",
+   "metadata": {},
+   "source": [
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is  𝑀=1.4𝑀⊙\n",
+    "  and  𝑅=13\n",
+    "  km, With a 5% Mass radius measurement uncertainty, \n",
+    "  \n",
+    " so here\n",
+    " \n",
+    "      likelihood.MRlikihood_Gaussian\n",
+    "      \n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood. eg:\n",
+    "\n",
+    "1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into\n",
+    "\n",
+    "likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2.If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.\n",
+    "\n",
+    "3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Where x is sampled distribution from real measurements, the standard is\n",
+    "\n",
+    "kernel, chrip = x,\n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d3a8359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "      \n",
+    "    B, d1 = theta\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "\n",
+    "    epsilon,p = MITbag.MITbag_compute_EOS(B)\n",
+    "\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(epsilon,p,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3802c4",
+   "metadata": {},
+   "source": [
+    "### (d) Set up sampler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76bce7fb",
+   "metadata": {},
+   "source": [
+    "Here next, we define sampler, there is two different sampler we provided for. \n",
+    "\n",
+    "Considering where you need resume file:\n",
+    "\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f05849a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 400\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/test_Bayesian_inference_Strangeon_EOS.ipynb b/.doctrees/nbsphinx/test_Bayesian_inference_Strangeon_EOS.ipynb
new file mode 100644
index 0000000..3d57d0d
--- /dev/null
+++ b/.doctrees/nbsphinx/test_Bayesian_inference_Strangeon_EOS.ipynb
@@ -0,0 +1,408 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "edb2cf2a",
+   "metadata": {},
+   "source": [
+    "## Strangeon EOS inference pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc633d3",
+   "metadata": {},
+   "source": [
+    "This is an example notebook about how to use our tools to analysis a observation constraint on strangeon star equation of state.\n",
+    "\n",
+    "Here in this notebook, we are using a strangeon matter EOS.\n",
+    "\n",
+    "The following is the package that we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f6f20856",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/a9/CompactOject/InferenceWorkflow/Likelihood.py:314: SyntaxWarning: invalid escape sequence '\\('\n",
+      "  \"\"\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "from TOVsolver.constant import oneoverfm_MeV, m_rho, m_w,G,c\n",
+    "import TOVsolver.main as main"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b494a39",
+   "metadata": {},
+   "source": [
+    "The following is the strangeon matter EOS function.\n",
+    "\n",
+    "We note that because the parameter Nq is an integer in the strangeon matter EOS.\n",
+    "\n",
+    "In the following, we use a possible value of this parameter, Nq=18, as an example to carry out the Bayesian analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c9d879d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nq=18\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edfa0f72",
+   "metadata": {},
+   "source": [
+    "### Set up prior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38136542",
+   "metadata": {},
+   "source": [
+    "Next step, we need to set up the prior, first use parameters array to specify the variable name, this process should consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.\n",
+    "\n",
+    " We provided two options call from prior:\"normal_Prior\" and \"flat_prior\".\n",
+    " \n",
+    "Then the Parameters prior should all set.\n",
+    "\n",
+    "We note that since we are doing Equation of state Inference from mass-radius data of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.\n",
+    "\n",
+    "This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of compact star --- different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by\n",
+    "\n",
+    "strangeon.compute_EOS\n",
+    "Compute out EOS, put into\n",
+    "\n",
+    "main.OutputMR\n",
+    "\n",
+    "find out Mass Radius of this equation of state, then find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)\n",
+    "\n",
+    "and find out that index by len() function, then reset this max_d to be upper limit of this density range.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b87dd3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = [ 'epsilon', 'ns', 'd1']\n",
+    "# for two or more MR measurements, define \n",
+    "# parameters = ['epsilon', 'ns', 'd1', 'd2'] \n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = prior.flat_prior(10, 170,cube[0])   #epsilon=10-170MeV\n",
+    "    params[1] = prior.flat_prior(0.17,0.36,cube[1]) #ns=0.17-0.36fm^-3    \n",
+    "    \n",
+    "    epsilon = params[0]\n",
+    "    ns = params[1]\n",
+    "\n",
+    "    theta1 = np.array([Nq, epsilon, ns])\n",
+    "    n_min = 3 * theta1[2] / theta1[0]  \n",
+    "    n_max = 0.16 * 8 * 3 / theta1[0]   \n",
+    "    n_values = np.linspace(n_min, n_max, 2000) \n",
+    "    energy_density, pressure = Strangeon.compute_EOS(n_values, theta1)\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "    eps_total=energy_density\n",
+    "    \n",
+    "    pres_total=pressure\n",
+    "    \n",
+    "    RFSU2R = [] \n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50) \n",
+    "    if all(x<y for x,y in zip(eps_total[:], eps_total[1:])) and all(x<y for x, y in zip(pres_total[:], pres_total[1:])):\n",
+    "        MR = main.OutputMR('',energy_density,pressure).T  \n",
+    "        \n",
+    "    else:\n",
+    "        MR = []\n",
+    "    if len(MR) == False: \n",
+    "        params[2] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "   \n",
+    "        for i in range(len(MR[0])):\n",
+    "            RFSU2R.append(MR[1][i])\n",
+    "            MFSU2R.append(MR[0][i])   \n",
+    "            if i > 20 and MR[0][i] - MR[0][i-1]< 0:    \n",
+    "                break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[2] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[2] = 14.3 + (max_d - 14.3) * cube[2]\n",
+    "        #params[3] = 14.3 + (max_d - 14.3) * cube[3]\n",
+    "        #this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8e4822",
+   "metadata": {},
+   "source": [
+    "In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.\n",
+    "\n",
+    "Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement.\n",
+    "For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. \"d1\" for the centre density of this measurement, in the meantime add \"params[2]\" to this code.\n",
+    "\n",
+    "If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. \"d1\" and \"d2\" for the centre density of these two measurements, in the meantime add \"params[3]\" to the above code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ab6d43",
+   "metadata": {},
+   "source": [
+    "### Set up likehood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfd12ff",
+   "metadata": {},
+   "source": [
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is  𝑀=1.4𝑀⊙\n",
+    "  and  𝑅=13\n",
+    "  km, With a 5% Mass radius measurement uncertainty, \n",
+    "  \n",
+    " so here\n",
+    " \n",
+    "      likelihood.MRlikihood_Gaussian\n",
+    "      \n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood. eg:\n",
+    "\n",
+    "1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into\n",
+    "\n",
+    "likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2.If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.\n",
+    "\n",
+    "3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Where x is sampled distribution from real measurements, the standard is\n",
+    "\n",
+    "kernel, chrip = x,\n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d3a8359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "      \n",
+    "    epsilon, ns, d1 = theta \n",
+    "    # comment this line if you need two measuremnts.\n",
+    "    #epsilon, ns, d1, d2 = theta\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "\n",
+    "\n",
+    "    theta1 = np.array([Nq, epsilon, ns])\n",
+    "    n_min = 3 * theta1[2] / theta1[0]  \n",
+    "    n_max = 0.16 * 8 * 3 / theta1[0]   \n",
+    "    n_values = np.linspace(n_min, n_max, 2000) \n",
+    "    energy_density, pressure = Strangeon.compute_EOS(n_values, theta1)\n",
+    "\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(energy_density,pressure,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96d26ce7",
+   "metadata": {},
+   "source": [
+    "In the following, we will show how to modify the likehood_transform function when considering more observations.\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "\n",
+    "    Nq, epsilon, ns, d1 = theta \n",
+    "    theta = np.array([epsilon, ns]) \n",
+    "    n_min = 3 * theta[1] / Nq      #n_min=3*ns/Nq\n",
+    "    n_max = 0.16 * 8 * 3 / Nq      #n_max=0.16*8*3/Nq\n",
+    "    n_values = np.linspace(n_min, n_max, 100)  # 100 points between n_min and n_max\n",
+    "    energy_density_total, pressure_total = Strangeon_compute_EOS(n_values, theta)\n",
+    "    \n",
+    "    #1. This line is to compute MR likelihood from a Simulated MR measurement:\n",
+    "    \n",
+    "    probMRgaussian = MRlikihood_Gaussian(energy_density_total,pressure_total,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    #probMRgaussian = likelihood.MRlikihood_Gaussian(energy_density_total,pressure_total,(2.08,13,0.08,0.65),d2)\n",
+    "    \n",
+    "    #2. This is  a block that constrain from given real MR measurement, say J0030:\n",
+    "    \n",
+    "    #J0030 = numpy.loadtxt('data/PST_equal_sampled_MR.txt', delimiter=' ')\n",
+    "    #J30R_list, J30M_list = zip(*J0030)\n",
+    "    #J30R_list = numpy.array(J30R_list).T    \n",
+    "    #J30M_list = numpy.array(J30M_list).T\n",
+    "    #Rmin = J30R_list.min()\n",
+    "    #Rmax = J30R_list.max()\n",
+    "    #Mmin = J30M_list.min()\n",
+    "    #Mmax = J30M_list.max()\n",
+    "    #X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]\n",
+    "    #positions = numpy.vstack([X3.ravel(), Y3.ravel()])\n",
+    "    #values = numpy.vstack([J30R_list, J30M_list])\n",
+    "    #kernel3 = stats.gaussian_kde(values)\n",
+    "    #probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)\n",
+    "    \n",
+    "    #3. This is to compute the constraint from experiment of nuclearmatter\n",
+    "    # 250<K<400, 25<J<38, 30<L<86:\n",
+    "    # hint: since this K,J,L sampling don't need to sample central density so this \n",
+    "    # theta should be redifined.\n",
+    "    #probK = likelihood.Kliklihood(theta,250,400)\n",
+    "    #probJ = likelihood.Jliklihood(theta,250,400)\n",
+    "    #probL = likelihood.Lliklihood(theta,250,400)\n",
+    "    \n",
+    "    #4. This is block to cosntrain from a GW event, say GW170817, here the file of this\n",
+    "    # event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:\n",
+    "    #GW170817 = np.load('GW170817_McQL1L2weights.npy')\n",
+    "    #chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])\n",
+    "    #kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])\n",
+    "    #probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)\n",
+    "    \n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    #prob =  probGW#+ probMRJ0030 + probK + probJ + probL + probGW\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3802c4",
+   "metadata": {},
+   "source": [
+    "### Set up sampler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76bce7fb",
+   "metadata": {},
+   "source": [
+    "Here next, we define sampler, there is two different sampler we provided for. \n",
+    "\n",
+    "Considering where you need resume file:\n",
+    "\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f05849a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 400\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "240dec67",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/test_EOSgenerators.ipynb b/.doctrees/nbsphinx/test_EOSgenerators.ipynb
new file mode 100644
index 0000000..5faa8b4
--- /dev/null
+++ b/.doctrees/nbsphinx/test_EOSgenerators.ipynb
@@ -0,0 +1,996 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Equation of State Generators illustration notebook\n",
+    "\n",
+    "## RMF EOS \n",
+    "\n",
+    "First import all of the package that will be used here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import EOSgenerators.crust_EOS as crust\n",
+    "import EOSgenerators.RMF_EOS as RMF\n",
+    "import EOSgenerators.Polytrope_EOS as Polytrope\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import math\n",
+    "from TOVsolver.unit import g_cm_3, dyn_cm_2, km, Msun\n",
+    "from TOVsolver.maxium_central_density import maxium_central_density"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  (a) Load crust EOS and prepare interface EOS\n",
+    "\n",
+    "Define constants, and load the crust EOS into this notebook, since when we solve the equation of state to TOV equation, we need full scope of the equation of state. Here, crust eos file is 'Tolos_crust_out.txt', while we need to times g_cm_3 for energy density and dyn_cm_2 for pressure, that will be easier for treatment, no need worry about G and c.\n",
+    "\n",
+    "Then just connect the crust part with the interface part(the part between core equation of state and crust) by a PolyInterpolate function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example of RMF"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Defining the paramters\n",
+    "\n",
+    "Defining the free prameters that we need to compute the equation of state, you could check our intro part of documentation or just check the original paper of us about details. [Huang et al,2023](https://arxiv.org/abs/2303.17518)\n",
+    "\n",
+    "After defined the equation of state parameters, we could call the 'compute_EOS' function from RMF, to compute full core equation of state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m_sig = 495.0 / 197.33\n",
+    "m_w = 3.96544\n",
+    "m_rho = 3.86662\n",
+    "\n",
+    "g_sigma = math.sqrt(107.5751)\n",
+    "g_omega = math.sqrt(182.3949)\n",
+    "g_rho = math.sqrt(206.4260)\n",
+    "\n",
+    "kappa = 3.09114168 / 197.33\n",
+    "lambda_0 = -0.00168015405\n",
+    "zeta = 0.024\n",
+    "Lambda_w = 0.045\n",
+    "theta = np.array(\n",
+    "    [m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w]\n",
+    ")\n",
+    "eps, pres = RMF.compute_EOS(eps_com, pres_com, theta)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (c) Connecting Core and crust EOS\n",
+    "\n",
+    "These two line can easily connect the Equation of state we computed for core part by setting the parameters and the crust equation of state, to a full equation of state that prepared for next step output\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eps_total = np.array([*eps_com, *eps])\n",
+    "pres_total = np.array([*pres_com, *pres])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below is one of the possiblity, use our defined function, 'OutputMR' to compute out what the mass radius curve corresponding to this equation of state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR(\"\", eps_total, pres_total).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SIl4D2jdRv/ZNdGVakqIiHH777wUAAFBbNsMw6v+fx4Ocy+VSQkKCcnJyFB8fb3Y5QFDweAxlHDut9XtOaP2eE/r33pM6kVd83vdFhNnVLjlWHZrGKrVxtFonRqlVo7I/WzSKlDMssEHK4zGUlVekn04VaMeRXG0/7NK2I7nacSRXOQUl1dftsKtPaiP9vEdLjezeglY+AABQSaByRVCFvIMHD+qDDz7QZ599pu3bt+vIkSNKSkpS//79NXXqVPXt27fW1/J4PHrllVf0xhtvKCMjQ7GxsRoyZIiefvppdejQocb3EvKA2jmYXaD0Hce0emeWNuw9oVP51YciSWqdGKWerRvp0uZx6tAsTh2bxapNUrTCHA1vAhTDMHTEVaith1zasPek1mRkVdmtU5LCHTYN7tRUv+jdSkMvbarIcFr4AAAAIU+S9Oijj+qZZ55Ru3btNGjQIDVt2lQZGRn65JNPZBiG3nvvPd1xxx21utakSZM0d+5cdenSRSNHjtTRo0f1/vvvKzIyUuvWrVOXLl2qfS8hD6haidujb/ae1Iodx5S+47gyjp2u9tz4yDD1SU1Ur5RG6tm6kXq0TlDjWGc9Vut/J04X6es9J7R21wmt3ZWlAyfzK50T5wzTDd1b6M6+bdQzpVH9FwkAABoMQp6kjz76SMnJyRo4cKDP/tWrV+vaa69VXFycDh06JKez5i+KK1as0NChQzVw4EB99dVX3vOXLVumYcOGaeDAgVq5cmW17yfkAWcVFLu1cudxffnjES3bfqzaLozxkWHq27ax+l6SpKvaNlbnFvFy2G31XG39MQxDWw+79Mn3B/X3TYd0LLeo0jn92jXW/xvcXv3bN5bNZt3/FgAAoGqEvPMYMWKEvvzyS33zzTe6/PLLazz3zjvv1HvvvaeVK1fqmmuu8Tl2/fXX64svvtCOHTvUsWPHKt9PyEOoKyxxK33Hcf1z8yEt235UhSWeSufYbVKvlEYa3KmpBnVMVrdWCZYOdTVxewx9vfuEPv7+oL744bDyin0nl+neKkH/b3A7De/aPGT/GwEAEIoClSssM7tmeHi4JCks7PwfKT09XTExMerfv3+lYyNGjNAXX3yhlStXVhvygFDk8Rhav/eEPtp4UEt+OKLcotJK58Q6wzTk0qa6rnNTXdMhmclGznDYbRrQoYkGdGiiWaO66ZNNB/X6yt3ad6KsO+eWgzma/LeNapscoweGttfNPVvJTtgDAAAXyBIh78CBA1q6dKmaN2+u7t2713huXl6eDh8+rG7dusnhqDz5QfmkKxkZGQGpFQg2mSfz9cG3mfpw40EdzC6odDwpJkIjujbT8K7N1a9d44DPdhnsoiIcGnNlG91xeYo+/+GwXk3frR8PlU3Ysud4nn73/mYtXH9Af7i5q7q2TDC5WgAAEIyCPuSVlJRo3LhxKioq0rPPPltlcKsoJydHkpSQUPWXp/Jm0vLzauJy+c6k53Q6zzseEAgWx3IL9dD7m7VmV1alY7HOMI3o2lw39myh/u2bKLwBzn7Z0DnsNu/yCqsysvRq+i6t33NSkvTd/lO68eU1GndVqh4a3kkJUeEmVwsAAPyhqKhIRUVnx+mfmyf8JahDnsfj0a9//WutWrVKEydO1Lhx4+r1/ikpKT6vp0+frhkzZtRrDUCgJEVHKONYrve13SZd0zFZt/ZprWFdmrEMgJ/YbDYN6pisQR2TtTrjuKb//UftycqTx5De/nq//vWfw/r99Zfqtj6t6cIJAECQmz17tmbOnBnw+wRtyDMMQxMnTtTChQs1duxYvfbaa7V6X3kLXnUtdeVpurqWvooyMzN9BkjSigcrCXPYdftlKfrnfw7pjstTdNtlrdUsPtLssixtYIdkff7/DdSba/bq5WW7VFDi1om8Yk39v//og28z9ddf9lbLRlFmlwkAAC7QtGnT9NBDD3lfu1yuSg1H/hCUIc/j8WjChAmaP3++xowZowULFshur113sZiYGLVo0UJ79+6V2+2u1L2zfCze+RZEl8q6djK7Jqzs/qHt9dCwjrQg1SNnmEP/b3B7jerVSk9/uk2fbjksSfpm3ymNfGm1XhjdS4M7NTW5SgAAcCHqa3hX0A2kqRjwRo8erXffffe84/DONWjQIOXl5Wnt2rWVji1ZssR7DhDqIsMdBDyTtGwUpf+5q48W3tNXrRPLWu9O5Zfo7gXf6C9f7pDbY4nVbwAAQAAEVcjzeDy65557NH/+fN1+++1auHBhjQEvKytL27dvV1aW78QRkyZNkiQ98cQTKi4u9u5ftmyZlixZomuuuYblEwA0CAM6NNGnDwzUdZ3LWu8MQ3p5+S6NnbdBx3ILTa4OAAA0REG1GPqMGTM0c+ZMxcbG6sEHH6xyTbxRo0apV69ePudXNSHKxIkTNW/ePHXp0kUjR47U0aNH9f777ysyMlLr1q1Tly5dqq2DxdAB1DfDMPTGqj16dsnZVrzkOKfmjOmtvm0bm1wdAAC4ECyGLmnfvn2SpNOnT+vpp5+u8py0tDRvyKvJ66+/rh49euj111/XSy+9pNjYWN144416+umnacUD0ODYbDbdO6iderdJ1APvbdRRV5GO5xZp3Jv/1pw7e2t41+ZmlwgAABqIoGrJayhoyQNgpqzTRfr//neTdw1Dh92m527roVv6tDa5MgAAUBeByhVBNSYPACA1iXVq/t1X6Be9W0mS3B5DDy3erLfX7TO3MAAA0CAQ8gAgCIU77PrL7T31X1enevdN/8ePmrM8Q3TQAAAgtBHyACBI2e02zbypq+4f0t6777+/3Kk/fbaNoAcAQAgj5AFAELPZbHpkRCc9dsOl3n1zV+/Vs0t2mFgVAAAwEyEPACxg0jXtNPuW7rKdWbv+1fTdWvxNprlFAQAAUxDyAMAixlzZRn+4qav39WMfb9HaMzNwAgCA0EHIAwALGXd1mu7unyZJKvUYum/hd9p1LNfcogAAQL0i5AGAxTwxsouu69xUkpRbWKq7F3yjrNNFJlcFAADqCyEPACzGYbfpr7/sra4tyxZVzTxZoEnvfKvCErfJlQEAgPpAyAMAC4pxhunNX12h5vGRkqSNB7L1+Mc/mFwVAACoD4Q8ALCo5gmRenP85YqOcEiSPtz4k5b8eMTkqgAAQKAR8gDAwrq2TNCsUd28rx//eItOMD4PAABLI+QBgMX9oncrDevSTJKUdbpYT/79BxmGYXJVAAAgUAh5AGBxNptNf/pFdyVGh0uSPttyRP/8z2GTqwIAAIFCyAOAEJAc59QfK3TbfPKTH3TMVWhiRQAAIFAIeQAQIn7eo6V+3qOFJCmnoETTPtpCt00AACyIkAcAIeSPN3dTk1inJGnZ9mP6aONBkysCAAD+RsgDgBCSGBOh2bd0975+dsl2FkkHAMBiCHkAEGKGdWmm6zqXzbZ51FWkd77eZ25BAADArwh5ABCCpozoJJutbPuV9N1yFZaYWxAAAPAbQh4AhKBOzeM0qlcrSVJ2fonmrdpjckUAAMBfCHkAEKJ+d11HhdnLmvPmrdmrrNNFJlcEAAD8gZAHACGqTeNojbmyjSQpv9it/1mxy+SKAACAPxDyACCEPTC0vaLCHZKkv60/oJ9O5ZtcEQAAuFiEPAAIYU3jI3V3/zRJUrHboxeXZphbEAAAuGiEPAAIcfde007xkWGSpL9vOqicAmbaBAAgmBHyACDEJUSH69bLWkuSStyG0nccM7kiAABwMQh5AACN6Nrcu/3lj0dNrAQAAFwsQh4AQJenJioxOlySlL7jmApL3CZXBAAALhQhDwCgMIdd13VuJknKK3Zr3e4skysCAAAXipAHAJAkDafLJgAAlkDIAwBIkgZ2aOJdM++rrUfl9hgmVwQAAC4EIQ8AIEmKDHdoUMdkSdKJvGJtPHDK5IoAAMCFIOQBALyGd23m3V7ywxETKwEAABeKkAcA8Bp6aVM57DZJ0pdbj8ow6LIJAECwIeQBALwaRUfoyrQkSdKBk/nam5VnckUAAKCuCHkAAB9DLk32bq/YcdzESgAAwIUg5AEAfAzp1NS7vWL7MRMrAQAAF4KQBwDw0b5prFo1ipIkbdh7QnlFpSZXBAAA6oKQBwDwYbPZvF02S9yG1u7KMrkiAABQF4Q8AEAlPl02GZcHAEBQCavtiUOHDvXrjW02m5YtW+bXawIA/OPqdo0VEWZXcalH6TuOyTAM2Ww2s8sCAAC1UOuQl56eLpvN5rc1k/iyAAANV3REmK5u21grdx7X4ZxC7Tiaq0ubx5tdFgAAqIVahzxJ6tatm1566aWLvukDDzygH3/88aKvAwAInCGdkrVyZ1lXzRXbjxPyAAAIEnUKeQkJCRo0aNBF3zQhIeGirwEACKzBnZpK/9wqqWwphcmD25lcEQAAqI1ah7wePXqoQ4cOfrlp+/btdfr0ab9cCwAQGGlNYpTaOFr7T+RrU2a2ikrdcoY5zC4LAACcR61D3qZNm/x20/nz5/vtWgCAwLmsTaL2n8hXsdujbYdz1SulkdklAQCA82AJBQBAtXpWCHWbM7NNqwMAANQeIQ8AUK2KLXebCHkAAASFOk28ciFWr16tDRs2yO12q0ePHho+fLgcDsZ0AEAw6Nwi3rteHiEPAIDgELCWvAMHDuiqq67SqFGjtHbtWn333XeaOHGiunTpos2bNwfqtgAAP4oIs6try7KlE/Zm5Sk7v9jkigAAwPkEJOTl5ORo6NCh6tKliw4cOKCPP/5Yixcv1r59+zRmzBgNGTJEe/fuDcStAQB+1rN1I+82rXkAADR8AQl5zz77rFJSUvTWW28pJibGuz8sLEwzZszQ8OHDNWXKlEDcGgDgZ73bNPJub87MMa8QAABQKwEZk7d48WK98MIL3tdff/21SkpKdM0110iSpk+frj59+qi4uFgRERGBKAEA4Ce+k6+cMq8QAABQKwEJeZmZmWrfvr339U033aSTJ0/K7XZLkjp06KCioiIdO3ZMrVu3DkQJAAA/aZMUrcTocJ3KL9GmzGwZhiGbzWZ2WQAAoBoB6a7ZtGlTZWZmel+/9tpreuedd7yvMzMz5XA41Lhx40DcHgDgRzabzbte3qn8Eh04mW9uQQAAoEYBCXk33XSTXn31Ve/rW2+9VXfddZf39V/+8hcNHjxYUVFRgbg9AMDPKnbZ/M9PjMsDAKAhC0jIe+KJJ7R27Vo99dRTlY699dZbmj9/vp577rlA3BoAEACXNo/zbu85nmdiJQAA4HwCMiavefPmWrJkiW655RZ98MEHGj58uMLDw7Vq1Srt3btXH3/8sXr16hWIWwMAAiCtydmZkvedIOQBANCQBWwx9F69emn79u164oknJEkFBQW65557tGvXLg0fPjxQtwUABEBq0tmQtzeLkAcAQEMWkJa8chEREbrrrrt8xuMBAIJPVIRDLRIidTinkJY8AAAauIC15AEArCWtcVlrXnZ+ibLzi02uBgAAVKdOIW/27Nn67LPPdOjQoUDVAwBooHzH5bGMAgAADVWdums+/vjj3gVwk5KS1KtXL/Xs2dP7Z5cuXeRwOAJSKADAXJc0ifZu78vK81lWAQAANBwXPCbvxIkTWrZsmZYtW+YNfuHh4erSpUul8NeoUSN/1QsAMEl5d02JyVcAAGjI6hTyIiMjVVRUpA4dOmjs2LFq1qyZNm/erM2bN+s///mPcnNztWnTJm3atMkb/CQpJSVFPXv2VO/evTVjxgx/fwYAQD24hGUUAAAICjbDMIzannzo0CE9/vjjevfdd2UYhoYNG6ZnnnlGPXv2lCTt2bPHG/o2b96sTZs2af/+/WdvZrPJ7Xb7/1PUM5fLpYSEBOXk5Cg+Pt7scgCgXhSWuNX5qS9kGFLP1gn6+/0DzC4JAICgFqhcUaeQV27Lli2aOnWqlixZIrvdrrFjx2rWrFlq3bp1pXNdLpdP8Js7d65fCjcTIQ9AqOr/5+U6mF2g+MgwbZ4+3KfXBgAAqJsGFfLKLV++XFOmTNH333+vyMhI/fa3v9W0adOUkJDgtwIbIkIegFA1dt4GrdmVJUna+OQwJcVEmFwRAADBK1C54qLWyRs6dKi+++47vfvuu2rWrJmeffZZtWvXTi+88IJKSkr8VSMAoIFo0/jsDJsHTxWYWAkAAKiOXxZDv+uuu7Rjxw4999xzMgxDDz/8sDp16qT33nvPH5f3sXDhQt177726/PLL5XQ6ZbPZtGDBgjpdIz09XTabrdqf9evX+71uALCCxhVa7k6xIDoAAA3SBS+hcK6IiAg9/PDDmjBhgp5++mm9/PLLGjt2rD7//HO98847/rqNnnjiCe3fv19NmjRRixYtfCZ2qatBgwZp8ODBlfZXNbYQACAlRIV7t7ML6LEBAEBD5JeQ99NPP2nr1q368ccftXXrVm3dulUOh0OGYejIkSP+uIXXvHnz1KFDB6WmpurPf/6zpk2bdsHXGjx4MEs6AEAdJEafbcnLpiUPAIAGqU4h78CBAz5B7scff9S2bdt0+vRpSVL5HC52u13t27dX9+7dNXLkSL8WfN111/n1egCA2msUXaElL5+WPAAAGqI6hby0tDTvdNnlga5Zs2bq27evunfvru7du6tHjx7q0qWLIiMj/V+tn2VkZOill15Sfn6+UlNTNWzYMDVp0sTssgCgwWoUzZg8AAAaugvqrtm5c2eNHTtWd955p9q0aePvmurNokWLtGjRIu/rqKgozZw5U1OmTDGxKgBouCq25OXQkgcAQINU59k1DcPQtm3b9Pjjj6tdu3bq2rWrxowZoz//+c/6/PPPdejQoUDU6VfJycl67rnntG3bNuXl5engwYNauHChkpKSNHXqVL3++uu1uo7L5fL5KSoqCnDlAGCuRFryAAC4YEVFRZUyRCDUqSXvvffe06ZNm7R582Zt2rRJR44c0bZt27Rt2zYtXrzYe15SUpJ69uzp89OlSxeFh4fXcPX607VrV3Xt2tX7Ojo6WnfddZd69uypyy67TNOnT9fEiRNlt9ecgVNSUnxeT58+nYlcAFhafOTZXxvMrgkAQN3Mnj1bM2fODPh96hTyRo8erdGjR3tfHz9+3Cf0bd68WTt27NCJEye0fPlyLV++3DuGLywsTJ07d9amTZv8+gH8qVu3burbt69Wr16tXbt2qWPHjjWen5mZ6bMyvdPpDHSJAGCqMIdd8ZFhchWWMvEKAAB1NG3aND300EPe1y6Xq1LDkT9c1BIKycnJGjZsmIYNG+bdV1RUpB9//NEn/P3nP/9RTk6OtmzZctEFB1r5xCv5+fnnPTc+Pt4n5AFAKGgUHXEm5NFdEwCAunA6nfXSMOS3xdDLOZ1O9enTR3369PHZv2/fPm3evNnft/Or0tJSbdy4UTabLagnlAGAQEqMDteBk1JOQYk8HkN2u83skgAAQAV1nnjlQqWlpenmm2+ur9tJkrKysrR9+3ZlZWX57P/666+9S0CUKy0t1ZQpU7R//36NGDFCSUlJ9VkqAASNhDOTr3gMKbew1ORqAADAuWrdkveHP/xBbdq00fjx4y/6pgsWLNCBAwf01FNP1fm98+bN05o1ayTJ2/1z3rx5Sk9PlySNGjVKo0aNkiTNmTNHM2fOrDQhypgxY2Sz2dSvXz+1atVK2dnZWrVqlXbs2KE2bdrotddeu6jPBwBWllhhGYVT+cVKiG4Yk2oBAIAytQ55M2bM0IABA/wS8t58802tW7fugkLemjVr9Pbbb/vsW7t2rdauXSuprMWwPORVZ/Lkyfriiy+Unp6urKwshYWFqX379nr88cf18MMPKzExsc51AUCoqNgRwqj+NAAAYBKbcW6/xWrY7XYNGDBAq1atuuibDhw4UOvWrZPb7b7oa5nB5XIpISFBOTk5TLwCIOT88o2vtX7PSUnSjzNHKMbp9+HdAACEhEDlijr9Zv7222/Vtm3bi77pkSNHLvoaAABzHMstkiTFRDgIeAAANEB1+u1cWFioffv2+eXG5evnAQCCy3FXWchrGh9pciUAAKAqtQ55e/fuDWQdAIAgUFDsVm5R2YyayXGBX+cHAADUXa1DXmpqaiDrAAAEgWO5hd7tpoQ8AAAapHpbJw8AEPzKx+NJUtM4umsCANAQEfIAALV2zFUh5MXTkgcAQENEyAMA1BrdNQEAaPgIeQCAWjvqorsmAAANHSEPAFBrPi15dNcEAKBBIuQBAGrtuM/EK4Q8AAAaIkIeAKDWyideiQizKyEq3ORqAABAVQh5AIBaK++u2TTOKZvNZnI1AACgKoQ8AECtFJd6dCq/RJKUTFdNAAAarLC6nOxwOC7qZjabTaWlpRd1DQCAObILir3bjWMiTKwEAADUpE4hzzCMi7rZxb4fAGAeV0GJdzue8XgAADRYdQp5UllrXKdOnTRu3Djdcsstio2NDURdAIAGJqdCyGPSFQAAGq46hbwXXnhBf/vb3/Ttt9/qiSee0NNPP61f/OIXGjdunK677jrZ7QzxAwCrIuQBABAc6pTKHnzwQf373//W9u3bNW3aNDVt2lR/+9vfdP3116tVq1Z6+OGHtXHjxkDVCgAwESEPAIDgcEFNbx07dtSsWbO0Z88erVq1Svfcc4+Kior0wgsv6IorrlDXrl31zDPPKDMz09/1AgBMkpNPyAMAIBhcdP/KAQMG6I033tCRI0f0wQcf6MYbb9Tu3bv12GOP6ZJLLtH999/vjzoBACbLKTg7O3KjaEIeAAANld8G0UVEROjWW2/VJ598oq+++kopKSnyeDzauXOnv24BADAR3TUBAAgOdZ5dszpHjx7Ve++9p3fffVebNm2SYRiKjY3VgAED/HULAICJCHkAAASHiwp5BQUF+vjjj/Xuu+9q2bJlKi0tlcPh0PDhwzVu3Dj94he/UFRUlL9qBQCYKKfCYuiskwcAQMNV55BnGIaWLl2qhQsX6uOPP1ZeXp4Mw1Dv3r01btw4jRkzRs2aNQtErQAAE9GSBwBAcKhTyJsyZYoWLVqkI0eOyDAMpaSk6P7779e4cePUuXPnQNUIAGgAykNeZLhdzjCHydUAAIDq1Cnk/eUvf5HNZlOnTp00duxYDRo0SDabTadOndK6detqdY1+/fpdUKEAAHOVhzxa8QAAaNguaEzejh079OSTT9b5fTabTaWlpec/EQDQ4BDyAAAIDnUKeW3atJHNZgtULQCABqqo1K3CEo8kQh4AAA1dnULevn37AlQGAKAhy86vOOlKhImVAACA8/HbYugAAOs6nlvk3U6OI+QBANCQEfIAAOeVdfpsyGsS6zSxEgAAcD6EPADAeWWdPrsQOiEPAICGjZAHADivii15yXGEPAAAGjJCHgDgvLJy6a4JAECwIOQBAM7ruM+YPCZeAQCgISPkAQDOy2fiFbprAgDQoBHyAADnlZVbNvGKM8yuOGedllgFAAD1jJAHADiv8pa8JrFO2Ww2k6sBAAA1IeQBAGpU6vboZH5ZSx5dNQEAaPgIeQCAGp3MK5ZhlG0nM+kKAAANHiEPAFCj46yRBwBAUCHkAQBqlHW62LvNGnkAADR8hDwAQI1YCB0AgOBCyAMA1Mh3IXRCHgAADR0hDwBQo4OnCrzbjMkDAKDhI+QBAGq0Ye8JSZLDblPnFnEmVwMAAM6HkAcAqNax3ELtPHpaktS9VYLiIsNNrggAAJwPIQ8AUK2vd5/wbvdv39jESgAAQG0R8gAA1Vq362zI69euiYmVAACA2iLkAQCqtXZ3liQpIsyuy1ITTa4GAADUBiEPAFClzJP5+unMzJqXpyYqMtxhckUAAKA2CHkAgCqt3ZXl3e7XjvF4AAAEC0IeAKBK6ypMutKvPePxAAAIFoQ8AEAlhmF4Q16sM0w9WiWYXBEAAKgtQh4AoJKMY6eVdbpIktT3kiSFOfh1AQBAsOC3NgCgkorj8a5mPB4AAEGFkAcAqGSdzyLojMcDACCYEPIAAD5K3R6t31MW8pJiItSpWZzJFQEAgLog5AEAfPx4yKXcwlJJZV017XabyRUBAIC6IOQBAHys3c36eAAABDNCHgDAx9cVx+O1YzweAADBhpAHAPDyeAxt3H9KktQiIVKpjaNNrggAANQVIQ8A4JV5Kl95xW5JUrdWCbLZGI8HAECwIeQBALy2HXZ5tzu3iDexEgAAcKEIeQAAr62Hc73bXVqwdAIAAMGIkAcA8KIlDwCA4EfIAwB4lYe8mAiHUhKZdAUAgGBEyAMASJJchSX66VSBJOnSFvEsgg4AQJAi5AEAJEnbK4zH68x4PAAAghYhDwAgifF4AABYRdCFvIULF+ree+/V5ZdfLqfTKZvNpgULFtT5Oh6PR3PmzFGPHj0UFRWl5ORk3XHHHcrIyPB/0QAQBAh5AABYQ9CFvCeeeEJvvPGG9u/frxYtWlzwde677z498MADcrvdeuCBB3TDDTfoH//4h6644gpt3brVjxUDQHAoD3k2m3Rpc7prAgAQrIIu5M2bN0/79u3T8ePHdd99913QNVasWKG5c+dq4MCB2rhxo5599lm9/fbb+vTTT+VyuTR58mQ/Vw0ADZvbY2jH0bIxeWmNYxQdEWZyRQAA4EIFXci77rrrlJqaelHXmDt3riRp1qxZcjqd3v3XXnutRowYoVWrVmnnzp0XdQ8ACCZ7s/JUWOKRxKQrAAAEu6ALef6Qnp6umJgY9e/fv9KxESNGSJJWrlxZ32UBgGl8xuM1ZzweAADBLORCXl5eng4fPqxLLrlEDoej0vEOHTpIEhOwAAgpTLoCAIB1hNygi5ycHElSQkJClcfj4+N9zquJy+Xyee10On26fwJAsPAJeS0JeQAABEJRUZGKioq8r8/NE/4Sci15/pSSkqKEhATvz+zZs80uCQAuyLYzC6HHR4apZUKkydUAAGBNs2fP9skPKSkpAblPyLXklbfgVddSV56mq2vpqygzM9Pb8ieJVjwAQWlfVp6OuAolSV1bJshms5lcEQAA1jRt2jQ99NBD3tculysgQS/kQl5MTIxatGihvXv3yu12VxqXVz4Wr3xsXk3i4+N9Qh4ABKOl2456twd1SjaxEgAArK2+hneFZHfNQYMGKS8vT2vXrq10bMmSJd5zACAUVAx513VuZmIlAADAHywd8rKysrR9+3ZlZWX57J80aZIk6YknnlBxcbF3/7Jly7RkyRJdc8016tixY73WCgBmyMkv0Tf7TkmS0hpHq11yjMkVAQCAixV03TXnzZunNWvWSJK2bNni3Zeeni5JGjVqlEaNGiVJmjNnjmbOnKnp06drxowZ3msMGTJEEyZM0Lx589S7d2+NHDlSR48e1fvvv6/4+Hi9+uqr9fmRAMA06TuPye0xJJW14jEeDwCA4Bd0IW/NmjV6++23ffatXbvW2/UyLS3NG/Jq8vrrr6tHjx56/fXX9dJLLyk2NlY33nijnn76aVrxAISMpduOebevpasmAACWYDMMwzC7iGDjcrmUkJCgnJwcJl4BELRK3B71+eNXyi0sVUJUuL594jqFOyzdix8AgAYlULmC3+YAEKK+2XtSuYWlkqTBnZIJeAAAWAS/0QEgRH3FrJoAAFgSIQ8AQpBhGN6lE8LsNtbHAwDAQgh5ABCCMo6dVubJAklS37ZJio8MN7kiAADgL4Q8AAhBFRdAv/ZSumoCAGAlhDwACEFLtzIeDwAAqyLkAUCIyTpdpO8zsyVJHZvFqk3jaHMLAgAAfkXIA4AQs3z7MZWvkEorHgAA1kPIA4AQs6zieDxCHgAAlkPIA4AQUlji1uqMLElSk9gI9UppZG5BAADA7wh5ABBCthzMUX6xW5I0qGNTOew2kysCAAD+RsgDgBCycf8p7/YVaYkmVgIAAAKFkAcAIWTjgbMhr3cbQh4AAFZEyAOAEGEYhjYeyJYkxTnD1KFprLkFAQCAgCDkAUCIOJhdoOO5RZKkXm0ayc54PAAALImQBwAhorwVT6KrJgAAVkbIA4AQUXHSlT5tGplXCAAACChCHgCEiO8rTrqSQkseAABWRcgDgBBQWOLWj4dckqR2yTFKiA43uSIAABAohDwACAE/HMxRqceQJPVhPB4AAJZGyAOAEFBxfbw+qYQ8AACsjJAHACFg4/5s7zYteQAAWBshDwAsrmwR9LKWPBZBBwDA+gh5AGBxB7MLdOzMIug9U1gEHQAAqyPkAYDFVVwEnfXxAACwPkIeAFhcxUXQezPpCgAAlkfIAwCL+65CyOvDIugAAFgeIQ8ALOyYq1BbDuZIki5tHsci6AAAhABCHgBY2Iodx7zb13VuZmIlAACgvhDyAMDClm47G/Ku7dzUxEoAAEB9IeQBgEUVlri1JiNLktQkNkI9WzcytyAAAFAvCHkAYFFf7zmhghK3JGlIp6asjwcAQIgg5AGARS3bdtS7fS3j8QAACBmEPACwIMMwtPzMeLwIh10DOzQxuSIAAFBfCHkAYEHbDufqUE6hJOmqdo0V4wwzuSIAAFBfCHkAYEEVu2pex6yaAACEFEIeAFjQ0u1nl04YeikhDwCAUELIAwCLOZ5bpM2Z2ZKkS5vHqXVitLkFAQCAekXIAwCLWbGdBdABAAhlhDwAsJilFcbjDb2UpRMAAAg1hDwAsJDCErfW7MqSJDWOiVCvlEbmFgQAAOodIQ8ALGT9nhPKL3ZLkgZ3aiqH3WZyRQAAoL4R8gDAQlbuPO7dZukEAABCEyEPACzk4KkC73avNo3MKwQAAJiGkAcAFpJdUOLdToyOMLESAABgFkIeAFhITn5ZyIsMtysy3GFyNQAAwAyEPACwkOyCYklSoyha8QAACFWEPACwCMMwdOpMS16j6HCTqwEAAGYh5AGARRSWeFRc6pFEyAMAIJQR8gDAIsq7akp01wQAIJQR8gDAIk7lnZ1Zk5Y8AABCFyEPACzCpyWP5RMAAAhZhDwAsIjy5RMkWvIAAAhlhDwAsIiKC6E3iiLkAQAQqgh5AGARp/Irdtck5AEAEKoIeQBgEb7dNRmTBwBAqCLkAYBFZDMmDwAAiJAHAJbh012TdfIAAAhZhDwAsIjDOYWSJJtNSoyhJQ8AgFBFyAMAC/B4DO06dlqS1CYpWs4wh8kVAQAAsxDyAMACDmYXqKDELUnq0DTW5GoAAICZCHkAYAHlrXiS1KFZnImVAAAAsxHyAMACMo7lerdpyQMAILQR8gDAAnYerdCS15SWPAAAQhkhDwAsIKNCd812TWNMrAQAAJiNkAcAQc4wDO06WtZds3VilKIjwkyuCAAAmImQBwBB7nBOofKKy2bW7MikKwAAhDxCHgAEuYpdNZl0BQAAEPIAIMhlHD07s2Z7Qh4AACGPkAcAQS7jKGvkAQCAs4Iy5H3zzTe64YYblJiYqJiYGF155ZVatGhRrd+fnp4um81W7c/69esDWD0A+FfFNfJoyQMAAEE3BVt6erpGjBihiIgI/fKXv1RCQoI++ugj3XXXXdq3b58ee+yxWl9r0KBBGjx4cKX9rVu39mPFABA4hmF4x+S1ahSlWGfQ/bUOAAD8LKi+DZSWlmrChAmy2WxatWqVevfuLUmaPn26rr76ak2fPl233367OnToUKvrDR48WDNmzAhgxQAQWK6CUuUWlkoqC3kAAABB1V1z+fLl2r17t+68805vwJOkuLg4PfnkkyotLdX8+fNNrBAA6ldcZJgaRYdLKuu26fEYJlcEAADMFlQteenp6ZKk4cOHVzpWvm/lypW1vl5GRoZeeukl5efnKzU1VcOGDVOTJk38UisA1Ae73abLUxO1dNsxncov0Z6s02rflMlXAAAIZUEV8jIyMiSpyu6YiYmJatKkifec2li0aJHPhC1RUVGaOXOmpkyZcvHFAkA9uTwtSUu3HZMkfbPvFCEPAIAQF1TdNXNyciRJCQkJVR6Pj4/3nlOT5ORkPffcc9q2bZvy8vJ08OBBLVy4UElJSZo6dapef/31WtXjcrl8foqKimr/YQDAT65IS/Ruf7P3pImVAACAmhQVFVXKEIEQVCHPX7p27apHHnlEl156qaKjo9WyZUvddddd+uKLLxQREaHp06fL4/Gc9zopKSlKSEjw/syePbseqgcAX91aJcgZVvbX+Tf7CXkAADRUs2fP9skPKSkpAblPUIW88ha86lrrXC5Xta18tdGtWzf17dtXR48e1a5du857fmZmpnJycrw/06ZNu+B7A8CFcoY51DOlkSQp82SBjuQUmlsQAACo0rRp03zyQ2ZmZkDuE1Qhr3wsXlXj7k6dOqWsrKxaL59QnfKJV/Lz8897bnx8vM+P0+m8qHsDwIWq2GXzW1rzAABokJxOZ6UMEQhBFfIGDRokSfryyy8rHSvfV37OhSgtLdXGjRtls9nUpk2bC74OANS3y9OSvNvf7jtlYiUAAMBsQRXyrr32WrVt21aLFi3Spk2bvPtzc3P1xz/+UWFhYRo/frx3f1ZWlrZv366srCyf63z99dcyDN+1pEpLSzVlyhTt379fI0aMUFJSkgAgWPRpkyibrWz7m3205AEAEMqCagmFsLAwzZs3TyNGjNDAgQM1ZswYxcfH66OPPtLevXs1a9YsdezY0Xv+nDlzNHPmTE2fPl0zZszw7h8zZoxsNpv69eunVq1aKTs7W6tWrdKOHTvUpk0bvfbaayZ8OgC4cAlR4erULE7bj+Rq22GXcgtLFBcZbnZZAADABEHVkidJQ4YM0Zo1azRgwAAtXrxYr7zyiho3bqyFCxfq8ccfr9U1Jk+erLS0NKWnp+uvf/2r/va3v8npdOrxxx/Xpk2blJqaGuBPAQD+d8WZLpseQ/r+QLa5xQAAANPYjHP7LeK8ymfxzMnJCdhgSQCoq39sPqTfvve9JOm3Q9vroeGdTK4IAADUJFC5Iuha8gAAVas4w+bib3/S7uOnTawGAACYhZAHABbRIiFKfdo0kiQdcRXqtlfXaVNmtqk1AQCA+kfIAwALeW3sZercoqy7x6n8Eo15Y73SdxwzuSoAAFCfCHkAYCFN4yP1/r1X6aq2ZZOwFJS4NeHtb/XRxp9MrgwAANQXQh4AWEx8ZLgW3H2lbujeXJJU6jH00OLNemPVbpMrAwAA9YGQBwAWFBnu0Mtj+mjcVWeXhPnTZ9v19Kdb5fEwqTIAAFZGyAMAi3LYbfrDzV310LCO3n1zV+/Vwx9sVonbY2JlAAAgkAh5AGBhNptNv722g2bf0l12W9m+j78/qHve/lZ5RaXmFgcAAAKCkAcAIWDMlW306tjL5Awr+2t/1c7junPuep04XWRyZQAAwN8IeQAQIkZ0ba537+mr+MgwSdLmn3J022tfK/NkvsmVAQAAfyLkAUAIufKSJH1wXz81i3dKkvZm5emWV9dp6yGXyZUBAAB/IeQBQIjp1DxOH07up7bJMZKk47lFGv361/p69wmTKwMAAP5AyAOAENQ6MVof3tdPvds0kiTlFpXqrnnr9djHW5TFOD0AAIIaIQ8AQlRiTIT+NqGvhl7aVJLkMaRFGw5oyHPpem3lbhWVuk2uEAAAXAhCHgCEsOiIML0+7jL9/meXKtZZNiFLblGp/vz5dl33/Ep9tuWwDIPF0wEACCY2g9/edeZyuZSQkKCcnBzFx8ebXQ4A+MXx3CI9/9UOvf9NpjwVfjNcmZakJ3/eRd1bJ5hXHAAAFhSoXEHIuwCEPABWtu2wS7M+3aq1u85OxGKzSbf0bq2pP+ukZvGRJlYHAIB1EPIaEEIeAKszDEPLth3Tnz7bpj1Zed79UeEO3TeonSZd01ZREQ4TKwQAIPgR8hoQQh6AUFFc6tG76/frr0t3ylVY6t3fIiFSU3/WSTf3bCW73WZihQAABC9CXgNCyAMQak7lFeuvyzL07vr9clcYsNczpZGe+nlnXZaaZGJ1AAAEJ0JeA0LIAxCqdh07rT99tk3Ltx/z2f/zHi306PWXqnVitEmVAQAQfAh5DQghD0CoW51xXLP+tU07juZ690WE2TVhwCX6f0Pae5djAAAA1SPkNSCEPACQSt0evf9tpp7/cqdO5BV79zeJderB6zpoRJdmaspMnAAAVIuQ14AQ8gDgLFdhif5nxS7NX7NPxW6Pz7FLmsSo7yVJ6ts2SX0vaayWjaJMqhIAgIaHkNeAEPIAoLIDJ/I1+/Nt+vyHI9Wek5IUpb6XNFbfS5J0VdvGap0YJZuN2TkBAKGJkNeAEPIAoHqbMrP11dYj2rDnpDb/lK0Sd/W/ZlomRKpv27LQd+UlSbqkSQyhDwAQMgh5DQghDwBqp6DYre8PnNL6vSe1Yc8JfZ+ZreJST7XnN41z6spLktS3bWNddUmS2jeNJfQBACyLkNeAEPIA4MIUlrj1n59ytGHPCW3Ye1Lf7T+lghJ3tec3jokoC31ngl+nZnEsvg4AsAxCXgNCyAMA/ygu9WjLwRxt2HtCG/ac1Lf7TiqvuPrQlxAVrivSknTVmYlcurSMl4PQBwAIUoS8BoSQBwCBUer26MdDLm/o+/e+k8otLK32/DhnmC5PS/SO6+vWKkHhDns9VgwAwIUj5DUghDwAqB9uj6HtR1zasOekNuw9oX/vPalT+SXVnh8d4dBlqYm66kzo6946Qc4wRz1WDABA7RHyGhBCHgCYw+MxlHHstLelb8PeE8o6XVzt+c4wu/q0SfSu09e7TSNFhhP6AAANAyGvASHkAUDDYBiGdh/P8wl9R11F1Z4f4bCrZ0qC0hrHqEVCpJolRJb9GR+pFglRSowOZzZPAEC9IeQ1IIQ8AGiYDMPQgZP52rDnpNafCX4Hswtq/f6IMLuax0eW/SSc+Tlnu2mcU2GM+wMA+AEhrwEh5AFA8PjpVL63lW/D3pPafyL/oq5nt0lNYp0VWgArtwg2j49UVATdQgEANSPkNSCEPAAIXtn5xTqcU6gjOYU64irU4ZxCHc0p1GHXmT9zCuSqYUbP2kqICj/bClixNfDM6xYJkUqIonsoAISyQOWKML9dCQCAINAoOkKNoiPUuUX1v0zzi0u9IdDnzwrbx08XqaZ/Js0pKFFOQYl2HM2t9pzI8LLuoT4tgt5AWNYimBznZC1AAECdEPIAADhHdESY2ibHqm1ybLXnlLg9Op5bVNYSWN4i6NMyWKCjOUUqdnuqvUZhiUf7TuRrXw1dSB12m5JjnWru0yXUt4WwWXwks4YCALwIeQAAXIBwh10tG0WpZaOoas8xDEMn84qrDoIVAmFuUfXdQ90eo6z10FWoTZnV15MYHX6m9c/pbQU8d7xgfGQY3UMBIAQQ8gAACBCbzabGsU41jnWqW6uEas87XVR6TnfQgkpdRWtaD1CSTuWX6FR+ibYdrv6c6AhH1eMEK2w3iXHKTvdQAAhqhDwAAEwW6wxT+6axat+0+u6hxaUeHXVVbgmsOF7wqKtQpZ7qBwrmF7u1JytPe7Lyqj0nzG5Ts/hINYt3qkVClM94wcTocEVHOBQVHqboCEfZdoRD0RFhjBsEgAaEkAcAQBCICLMrJSlaKUnR1Z7j8Rg6kVdcqUXQJxTmFCq/2F3tNUo9hg5mF5xZXzC7TvVFRzgUHX42+EWdCYJVB0OHoiLCFB3uGxYrHi9/Hc66hABQJ4Q8AAAswm63KTnOqeQ4p7qr6u6hhmEot6j0zHIR1c8gejKv5u6h5you9ai41KNslfjjo/gId9gUFV45BFYOiWf2eYNlVcHx7PuiIhxyhtkZpwjAcgh5AACEEJvNpvjIcMVHhqtDs7hqzyssceuYq+hMS2CBjroK5SooVX6xWwUlZX/mF7tVUOxWfnH5ft99NfQcrZMSt6ESd6lf1i88l90mn1bHqPCzrYhVtURWFRbL98c4w3zeHxlOgARgDkIeAACoJDLcoTaNo9WmcfXdQ2tiGIaKSj1lga/ErYLic4OhW3nFpd5t7/GSc4Jj+fES3301jT2sC49RNvHN6RpmOL1QNpu8oS8qwqHo8LBqWxSja2iJ9D1+Zl+4g3GQAKpFyAMAAH5ns9kUGe5QZLhDiQG4frE3QPqGwfwKwbFiuDx7vOqWyPIwml/sVnFp9Wsb1oVhyHufQHCWj4M8T0tkecA82+JY9eQ5Z891KIxxkEBQI+QBAICgExFmV0SYXQkK9/u1S90eFZScLxiebVmsriXS9/1l+wpL/BMgJamo1KOiUo9O5ft/HGSEw+4bEs9piaw4Oc7ZYHm2JbI8LFbVEhnhoBsrEGiEPAAAgArCHHbFOeyKi/R/gPR4DN+xizW0RJ7bRfXcwOmzr7hU+SVuGX4aB1ns9qi4wKOcAv8HSIfd5tNNtXIX1XP2RTgUU8NsrZHhDtntNoXZbbLbbHLYK/xUeG23iXCJkEHIAwAAqCd2u00xzjDFOP3/Fax8HGSlbqt1aIksD6A++860ULr9NA7S7Smb4TU3AOMgz6eq4BfmsJ8Jh1KY3S67XeecY1OYo+x91YXJ8wbMc84JO7PPce57qnmfz70rvU9y2O1nzin7DA67yuo+83mq2lfV/byf98w2oTh4EfIAAAAsoOI4yKSYCL9e2zAMFbs957QengmKFbq2+kywU0VLZFWtlwXFbhW7/deNtSZujyG3DCkwwyQtx2bThYXaM8HY55wawmTFEOwNsuWh21YhyHoD+JnQGtDwXHXdjnPe5zin7oaCkAcAAIAa2Ww2OcMccoY51OjCJlytUanb4xMWq2uJPLeLal6xW0WlHnk8hko9Hrk9kscwVOox5PEYZaHOY8htVNj2GL7nGIZK3ZX3ud3nvM8w/NYdNlgYRtkSJlKIffCLULtQWx5gJRUXBKQOQh4AAABMFeawK95hV3wAxkH6k6dCYKxLmKy070yA9HikUo9HHsOQ2yO5zwTVsuucCa2eM9cwDO+255x7VbWvYk3nvq8uIbi0in2+Ibi8bkMe48zn8X6G0AuH5f8Na8tTlB+QOgh5AAAAQC3Y7TbZZVO4w+xKgoNhlAW/6sLkuSG0Ygj2GBUC7LnvqxAyPeecUzGc1uZ9NYVnb4A15NNafO6+8lBbVXg+X1AuNgKzXAkhDwAAAIDf2Ww2OWxlXRhRNZfLpYRn/X9dVroEAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWEpQh75tvvtENN9ygxMRExcTE6Morr9SiRYvqdA2Px6M5c+aoR48eioqKUnJysu644w5lZGQEqGoAAAAACLygC3np6ekaMGCAVq9erdtuu02TJ09WVlaW7rrrLv3pT3+q9XXuu+8+PfDAA3K73XrggQd0ww036B//+IeuuOIKbd26NYCfAAAAAAACJ6hCXmlpqSZMmCCbzaZVq1Zp7ty5+u///m9t3rxZXbt21fTp02vVErdixQrNnTtXAwcO1MaNG/Xss8/q7bff1qeffiqXy6XJkyfX+P6ioiKfPxG6ioqKNGPGDJ4FSOJ5gC+eB5TjWUBFPA+oKFC5IqhC3vLly7V7927deeed6t27t3d/XFycnnzySZWWlmr+/Pnnvc7cuXMlSbNmzZLT6fTuv/baazVixAitWrVKO3furPb9hDyUKyoq0syZM3kWIInnAb54HlCOZwEV8TygIkKeyrpqStLw4cMrHSvft3LlylpdJyYmRv379690bMSIEbW+DgAAAAA0NEEV8sq7Ynbo0KHSscTERDVp0uS83TXz8vJ0+PBhXXLJJXI4HJWOl1+bCVgAAAAABKMwswuoi5ycHElSQkJClcfj4+P1008/XfQ1Kp5XFcMwJEmHDx/22e90On26f8L6XC6Xz58IbTwPqIjnAeV4FlARz0NoKyoq8umaWZ4nyvOFvwRVyGsoSkpKJElXXnmlyZWgoUhJSTG7BDQgPA+oiOcB5XgWUBHPAyoqzxf+ElQhr7z1rbpWNpfLVW0LXV2uUfG8qqSlpWn37t0KDw+XzWbz7qclDwAAAEB1zm3JMwxDJSUlSktL8+t9girkVRwvd9lll/kcO3XqlLKystSvX78arxETE6MWLVpo7969crvdlcbl1TTur5zdblfbtm0v5CMAAAAAQEAF1cQrgwYNkiR9+eWXlY6V7ys/53zXycvL09q1aysdW7JkSa2vAwAAAAANjc3w9yi/ACotLVWnTp108OBBrV+/Xr169ZIk5ebm6uqrr9aOHTv0448/qmPHjpKkrKwsZWVlqUmTJmrSpIn3OitWrNDQoUM1cOBALV26VBEREZKkZcuWadiwYRo4cCBLKAAAAAAISkHVkhcWFqZ58+bJ4/Fo4MCBmjRpkh555BH17NlTP/74o2bMmOENeJI0Z84cde7cWXPmzPG5zpAhQzRhwgStXr1avXv31tSpU/WrX/1KI0eOVHx8vF599dVK9zYMQx999JGGDBmiFi1aKDo6Wp06ddK9996rPXv2BPyzo/4tXLhQ9957ry6//HI5nU7ZbDYtWLCg2vNdLpceeughpaamyul0KjU1VQ899BCzZ1lEbZ+HkpISffjhhxo/frw6d+6smJgYxcXFqW/fvnrllVfkdrvrv3j4VV3/bqho7969io2Nlc1m03333RfYQlEvLuR52Lt3ryZOnOj9fdGsWTMNGTJEH3zwQf0UjYCp6/OQkZGhu+++Wx06dFBUVJRatWqlYcOG6R//+Ef9FY2AOHjwoF588UUNHz5cbdq0UUREhJo3b65bb71VGzZsqPI9fv0uaQShDRs2GD/72c+MhIQEIyoqyrj88suNhQsXVjpv+vTphiRj+vTplY653W7jpZdeMrp27Wo4nU6jcePGxm233Wbs2LGjyns+9NBDhiSjRYsWxn333WdMnTrVGDFihGGz2Yy4uDhjy5Yt/v6YMFlqaqohyWjSpIl3e/78+VWee/r0aaNXr16GJGPYsGHG73//e+NnP/uZIcno1auXcfr06fotHn5X2+dh27ZthiQjLi7OuPnmm42pU6ca9957r9GyZUtDknHjjTcaHo+n/j8A/KYufzdU5PF4jEGDBhkxMTGGJOPee+8NfLEIuLo+D19++aURHR1tREdHG6NHjzamTZtm3HfffUa/fv2MSZMm1V/hCIi6PA/r1683oqKijLCwMOOWW24xfv/73xt33323kZCQYEgyZsyYUb/Fw69+//vfG5KMdu3aGb/+9a+NRx991Lj11lsNh8Nh2O124/333/c539/fJYMy5NW3w4cPG3a73UhLSzNycnJ8jr3wwguGJOPuu+82qToEyldffWXs27fPMAzDmD17do1/UT/11FOGJGPq1KlV7n/qqacCXS4CrLbPw08//WS88sorRl5ens/+06dPG5dffrkhyVi8eHF9lIwAqcvfDRX99a9/NcLCwoznn3+ekGchdXkeDhw4YMTHxxsdOnQw9u/fX+l4SUlJIEtFPajL83D99dcbkoy///3vPvv3799vxMfHG1FRUUZhYWGgS0aAfPjhh8aqVasq7V+1apURHh5uJCUl+fzv6+/vkkHVXdMs+/btk8fjUf/+/b2LpZcbOXKkJOnYsWNmlIYAuu6665Samnre8wzD0Lx58xQbG6unnnrK59i0adOUmJioN9980++LXKJ+1fZ5aNWqlSZPnqzo6Gif/TExMXrooYckiTG/Qa62z0JFu3bt0rRp0zR16lT17t07QJXBDHV5Hv70pz/J5XLptddeU5s2bSodDwsLqknPUYW6PA979uyRzWbTz372M5/9bdq0Ubdu3VRQUKDc3NxAlIl6cMstt2jgwIGV9g8cOFBDhgzRyZMntWXLFkmB+S5JyKuFDh06KCIiQmvXrq30f7bPPvtMkjR06FAzSkMDkJGRoUOHDql///6KiYnxORYZGalrrrlGBw8e1K5du0yqEA1FeHi4JL7IhRqPx6O7775bqamplX55I3QYhqHFixercePGGjp0qL777js9//zz+u///m8tXbpUHo/H7BJRz7p27SrDMCrNGp+ZmakffvhB3bt395k4ENZx7veBQHyX5JtGLTRu3FhPP/20pkyZos6dO+umm25SXFyctmzZoqVLl2rSpEl64IEHzC4TJjnf2ooV13esaf1FWN9bb70lSRo+fLjJlaA+vfjii1q3bp3WrFkjp9Npdjkwyd69e3Xy5EldccUVmjx5sl577TWf471799Y//vEPtW7d2qQKUd/++Mc/as2aNbrlllt08803q3379jp+/Lg++ugjpaamavHixWaXiAA4cOCAli5dqubNm6t79+6SAvNdkpBXS4888ohatmype++912f2zX79+mns2LHeRI7Qk5OTI0lKSEio8nh5F9/y8xCa3njjDX3++ecaOnSobrjhBrPLQT3ZuXOnnnjiCT344IO6+uqrzS4HJiof1rFx40Zt27ZN8+fP180336ycnBz96U9/0ty5c3Xbbbdp/fr1JleK+tKlSxetX79et99+u/7v//7Puz8xMdE74yaspaSkROPGjVNRUZGeffZZORwOSYH5Lkl3zVqaNWuWxo8fr2nTpikzM1OnT5/WmjVrVFpaqiFDhuijjz4yu0QADdSnn36q+++/X6mpqVq4cKHZ5aCeeDwejR8/Xi1bttSsWbPMLgcmK++O6Xa79cc//lHjx49XYmKi0tLS9MYbb6hv377asGGD1qxZY3KlqC/ffvutBgwYoKSkJH333XfKy8vTnj17dM899+ihhx7S7bffbnaJ8COPx6Nf//rXWrVqlSZOnKhx48YF9H6EvFpYvny5nnzySd1///167LHH1Lp1a8XExKh///7617/+paioKP3ud78zu0yYpPxfXar715XytU2q+9cZWNuSJUt06623qlmzZlq+fLlatGhhdkmoJy+99JLWr1+vefPmVZqIB6Gn4u+Am266qdLxG2+8UVLZF39YX0lJiUaPHi2bzaZPPvlEffr0UXR0tC655BI999xzGj16tD7++GOtWLHC7FLhB4ZhaOLEiVq4cKHGjh1bqbt2IL5LEvJq4dNPP5VUtoj6uZKTk9W9e3cdOHBAWVlZ9V0aGoCK/aSrcr5+1rCuL774QqNGjVKTJk20YsUKtW3b1uySUI82bdokwzA0ZMgQ2Ww270/575LXX39dNptNo0aNMrdQ1Iv27dt7u2Y1atSo0vHyfQUFBfVYFcyyfft27dmzR3379q3yH4HKJ/T77rvv6rs0+JnH49E999yjt956S2PGjNGCBQtkt/tGsEB8l2RMXi0UFxdLko4fP17l8fL9DKgPTR06dFDLli21du1a5eXl+cyKVFhYqFWrVqlly5Zq3769iVWivpUHvKSkJK1YsYL//UPQoEGDqpxJ9fDhw/rss8906aWXqn///iypECKcTqf69eun1atXa+vWrRowYIDP8a1bt0qS0tLSTKgO9Y3vlqHB4/FowoQJmj9/vkaPHq13333X+489FQXiuyQtebXQv39/SdLzzz9fqRn17bff1q5du3TZZZcpLi7OjPJgMpvNpgkTJuj06dP6wx/+4HNs9uzZOnXqlCZMmCCbzWZShahv5QEvMTFRK1asoBU3RN19992aN29epZ8pU6ZIKguB8+bN029+8xuTK0V9mTx5siRpxowZKioq8u7fvn27FixYoLi4uEprpsGaunXrpoSEBK1du7bSEgqHDh3SK6+8IkkaPHiwCdXBH8pb8ObPn6/bb79dCxcurDLgSYH5LmkzWKH5vNxut6677jqlp6crOTlZN910kxITE7V582Z99dVXcjqdWrp0aaV/lUNwmzdvnncA/JYtW7Rx40b179/f+68oo0aN8nazysvL04ABA7Rp0yYNGzZMl112mTZv3qzPP/9cvXr10po1ayqte4LgUtvnYfv27erVq5eKior0y1/+Up06dap0rbS0NI0fP74+y4cf1eXvhqqkp6dryJAhuvfeeyuNy0DwqcvzYBiG7rjjDv3f//2fOnXqpBEjRignJ0cffvih8vPz9c477+iuu+4y66PAD+ryPLz55puaMGGC7Ha7Ro4cqc6dO+vo0aP6+OOP5XK59Jvf/EZz5swx66PgIs2YMUMzZ85UbGysHnzwwSp7dowaNUq9evWSFIDvkgZqpbCw0HjmmWeMPn36GNHR0UZYWJjRqlUr48477zS2bNlidnkIgF/96leGpGp/pk+f7nN+dna28bvf/c5ISUkxwsPDjZSUFON3v/udkZ2dbc4HgF/V9nlYsWJFjedJMgYNGmTqZ8HFqevfDecqf0buvffe+ikYAVXX56GkpMR4/vnnja5duxpOp9OIj483hg8fbqSnp5vzAeBXdX0eli5davz85z83kpOTDYfDYcTHxxsDBw403n77bXM+APzmfM+CJGP+/Pk+7/Hnd0la8gAAAADAQhiTBwAAAAAWQsgDAAAAAAsh5AEAAACAhRDyAAAAAMBCCHkAAAAAYCGEPAAAAACwEEIeAAAAAFgIIQ8AAAAALISQBwAAAAAWQsgDAKAWBg8eLJvN5v1JS0urdE75MbN98sknPrXabDalp6ebXRYAoJ4Q8gAAlpKWllYp4ERGRuqSSy7R2LFj9c0331zU9bt166b+/fvriiuu8FPF/te4cWP1799f/fv3V3x8vNnlAADqmc0wDMPsIgAA8Je0tDTt379fHTp0UNOmTSVJOTk52rVrlwoLC+VwODR//nyNGzeuTtcdPHiwVq5cqRUrVmjw4MFVnlPeiteQfrXWpm4AgLXQkgcAsKTHHntMa9as0Zo1a7RlyxYdOnRIt912m9xut37zm9/o1KlTZpcIAEBAEPIAACEhMTFRb775pmJiYpSbm6svv/zS7JIAAAgIQh4AIGTEx8erY8eOkqR9+/bV230Nw9Bvf/tb2Ww2tW/fXvv37/fWUHESl3nz5ql3796Kjo5Wq1at9Nvf/la5ubmSJLfbrb/85S/q2rWroqKi1Lp1az366KMqLi6ut88BAAgOhDwAQEjJz8+XJEVHR9fL/dxut37961/r5ZdfVrdu3bRmzRqlpqZWOu/hhx/WxIkTlZubq3bt2unYsWN6+eWXNWrUKHk8Ht1222165JFHZBiGUlNTdejQIT3zzDOaOHFivXwOAEDwIOQBAEJGRkaGdu/eLUnq1atXwO9XXFys0aNHa8GCBbryyiu1cuVKNW/evNJ5Bw8e1JtvvqmlS5dq165d2rJli77//ns1btxYy5cv16233qpvv/1W33//vbZu3art27dr+fLlioiI0DvvvKOtW7cG/LMAAIIHIQ8AYHkul0tLly7VqFGjVFpaqv79+2vgwIEBvWd+fr5uuukmffjhhxo8eLCWLl2qpKSkKs8tLS3VjBkzdO2113r3devWTZMmTZJUtu7dyy+/7BNMBw8erFtuuUWStGTJksB9EABA0CHkAQAs6e677/auk5eQkKBhw4Zp+/btGj16tP75z38G9N45OTkaMWKElixZopEjR+rzzz9XXFxcje/59a9/XWlfeahLSkrSqFGjKh3v3bu3JGnPnj0XXTMAwDrCzC4AAIBAKF8nzzAMHTlyRHv27FF4eLiuuOIKJSYmBvTeQ4cO1caNG/XLX/5S77zzjsLDw2s8Pzk5ucpFy5OTkyVJ7dq1q/Z9knT69OmLrBgAYCW05AEALKl8nby1a9dq9+7dWrNmjeLi4vTII49o4cKFAb33rl27JEndu3c/b8CTqp8Epnxx9fMdb0iLrwMAzEfIAwCEhP79+2vu3LmSpAcffFAulytg9/r8888VGxurxx9/XC+++GLA7gMAQFUIeQCAkDFq1ChdddVVOnnypJ5//vmA3adfv37617/+pejoaP3ud7/TK6+8ErB7AQBwLkIeACCkPProo5Kkl156KaBj2QYNGqS///3vioyM1P33368333wzYPcCAKAiQh4AIKTcdNNN6ty5s06dOqVXX301oPe67rrr9NFHHyk8PFyTJk0K+FhAAAAkQh4AIMTYbDY98sgjkqTnn39ehYWFAb3f9ddfr8WLF8vhcGj8+PFavHhxQO8HAAAhDwAQcsaOHauWLVvqyJEjeuuttwJ+v5tvvlmLFi2SJN1111365JNPAn5PAEDoYp08AICl7Nu377znRERE6ODBg36/d01LGdx2220qLS312ZeWllbjewYPHlzj8fHjx2v8+PF1rhMAYG2EPAAA6uCBBx5QQkKCWrRooQ8++MDscqq0evVqTZs2TZK0ZcsWk6sBANQ3Qh4AAHXwww8/SJJSU1NNrqR6J06c0Nq1a80uAwBgEptRUz8QAAAAAEBQYeIVAAAAALAQQh4AAAAAWAghDwAAAAAshJAHAAAAABZCyAMAAAAACyHkAQAAAICFEPIAAAAAwEIIeQAAAABgIYQ8AAAAALAQQh4AAAAAWAghDwAAAAAs5P8HyoALffSIIckAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[1] / km, MR[0] / Msun, lw=2)\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Illustrating the power of Numba accelerated EoS Generation\n",
+    "\n",
+    "We shall now run the same code but with the Numba accelerated version, comparing the times for EoS Generation.\n",
+    "\n",
+    "Keep in mind all previous definitions are still valid, all we need to change is a single line of code.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import EOSgenerators.fastRMF_EoS as FastRMF\n",
+    "import time\n",
+    "\n",
+    "Tolos_crust_out = np.loadtxt(\"Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)\n",
+    "\n",
+    "m_sig = 495.0 / 197.33\n",
+    "m_w = 3.96544\n",
+    "m_rho = 3.86662\n",
+    "\n",
+    "g_sigma = math.sqrt(107.5751)\n",
+    "g_omega = math.sqrt(182.3949)\n",
+    "g_rho = math.sqrt(206.4260)\n",
+    "\n",
+    "kappa = 3.09114168 / 197.33\n",
+    "lambda_0 = -0.00168015405\n",
+    "zeta = 0.024\n",
+    "Lambda_w = 0.045\n",
+    "theta = np.array(\n",
+    "    [m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Comparing the time it takes to generate the EoS\n",
+    "\n",
+    "The reason why we test the time it takes to generate the EoS here is due to the fact that Numba is compiling the code, and as such the first run will take considerably longer to complete, but all other runs will be greatly faster.\n",
+    "\n",
+    "Warning: Do not rerun this cell, as the compilation time will be gone.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------------\n",
+      "|                                                                                    |\n",
+      "|                                Time Comparion Table                                |\n",
+      "|                                                                                    |\n",
+      "|                 Time for first run  with RMF-Normal  =    550 ms                   |\n",
+      "|                 Time for first run  with RMF-Fast    =   1027 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                 Time for   20  runs with RMF-Normal  =  10099 ms                   |\n",
+      "|                 Time for   20  runs with RMF-Fast    =    287 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                 Time for  all  runs with RMF-Normal  =  10649 ms                   |\n",
+      "|                 Time for  all  runs with RMF-Fast    =   1315 ms (Has Compilation) |\n",
+      "|                                                                                    |\n",
+      "|                 Time for   1   run  with RMF-Normal  =    505 ms                   |\n",
+      "|                 Time for   1   run  with RMF-Fast    =     14 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                                                                                    |\n",
+      "|                              Total Speed Up = 35.15 x                              |\n",
+      "--------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Normal RMF ####\n",
+    "\n",
+    "first_run_time_normal = time.time()\n",
+    "eps_fast, pres_fast = RMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "first_run_time_normal = time.time() - first_run_time_normal\n",
+    "\n",
+    "time_for_20_runs_normal = time.time()\n",
+    "for _ in range(20):\n",
+    "    eps, pres = RMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "time_for_20_runs_normal = time.time() - time_for_20_runs_normal\n",
+    "\n",
+    "\n",
+    "##### Fast RMF #####\n",
+    "\n",
+    "first_run_time = time.time()\n",
+    "eps_fast, pres_fast = FastRMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "first_run_time = time.time() - first_run_time\n",
+    "\n",
+    "time_for_20_runs = time.time()\n",
+    "for _ in range(20):\n",
+    "    eps_fast, pres_fast = FastRMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "time_for_20_runs = time.time() - time_for_20_runs\n",
+    "\n",
+    "\n",
+    "print(f\"--------------------------------------------------------------------------------------\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*32 + \"Time Comparion Table\" + \" \"*32 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for first run  with RMF-Normal  = {first_run_time_normal*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for first run  with RMF-Fast    = {first_run_time*1000       :6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   20  runs with RMF-Normal  = {time_for_20_runs_normal*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   20  runs with RMF-Fast    = {time_for_20_runs*1000       :6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for  all  runs with RMF-Normal  = {(first_run_time_normal + time_for_20_runs_normal)*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for  all  runs with RMF-Fast    = {(first_run_time        + time_for_20_runs       )*1000:6.0f} ms (Has Compilation) \" + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   1   run  with RMF-Normal  = {time_for_20_runs_normal*1000/20:6.0f} ms  \" + \" \"*17 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   1   run  with RMF-Fast    = {time_for_20_runs       *1000/20:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*30 + f\"Total Speed Up = {time_for_20_runs_normal/time_for_20_runs:.2f} x\" + \" \"*30 + \"|\" )\n",
+    "print(f\"--------------------------------------------------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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REREREZE+RCFPRERERESkD1HIExERERER6UMU8kRERERERPoQhTwREREREZE+RCFPRERERES6RUZGBhaLhYqKiu4eShs9dVznSyFPRERERKQ1jxsaqnvWl9t91iHPnz8fi8XCr371qzb1v/3tb1gsls780+o0jY2NREVF8fTTT5/29aVLlxIVFUVjY2OH33vKlCkUFBQQGhra4dfuCj7dPQARERERkR6lsRYKP+vuUbSVPA0cwWdt4ufnxzPPPMODDz5IeHh4h926sbERu93eYdc7X3a7nf/4j/9g5cqV/PSnPz0lrK5YsYK77rrrosbW1NSEr6/vWe8dGxt7wdftKTSTJyIiIiLSB1x11VXExsaydOnSs7Z75513GDVqFA6Hg+TkZJ577rk2rycnJ/P0008zf/58QkNDuf/++1m5ciVhYWG8++67DBs2jICAAG699VZqampYtWoVycnJhIeH873vfQ+Xy2Vea/Xq1UyaNIng4GBiY2O58847KS4uPu/3tGDBArKystiwYUOb+ieffMKhQ4dYsGABYAS+ESNG4Ofnx/Dhw/n9739vts3JycFisbBmzRpmzJiBn58fq1evJjc3l5tuuonw8HACAwMZNWoU7733HnD65Zrn8+f2y1/+knvvvZfg4GASExN55ZVXzvu9diSFPBERERGRPsBms/HLX/6SF154gby8vNO22bZtG7fddhtz585l9+7dLFmyhCeeeIKVK1e2affrX/+a0aNHs23bNp544gkAamtref755/nzn//M+++/T0ZGBjfffDPvvfce7733Hm+88QavvPIKb7/9tnmdxsZGnnrqKXbu3Mnf/vY3srOzmT9//nm/pzFjxnDppZeyYsWKNvXXX3+dyZMnM3r0aF599VV++tOf8otf/IJ9+/bxy1/+kieeeIJVq1a16fOTn/yE73//++zbt4/Zs2fz3e9+l4aGBjZs2MDu3bt55plnCAoKatef23PPPcekSZP44osv+M53vsOiRYvYv3//eb/fjqLlmiIiIiIifcQ3v/lNxo8fz+LFi3nttddOef03v/kNs2bNMoPb0KFD2bt3L7/+9a/bhK+vfe1r/OhHPzK/z8zMpKmpiZdeeom0tDQAbr31Vt544w2KiooICgpi5MiRzJw5k48//pjbb78dgHvvvde8RmpqKs8//zyTJ0/m5MmTZwxUX3Xvvffyox/9iBdffJGgoCBOnjzJX/7yF37zm98A8NRTT/Hcc89x8803A5CSksLevXt5+eWX+fa3v21e56GHHjLbABw9epRbbrmFMWPGmOM7k/P9c7v++uv5zne+Axih8r//+7/JyMhg+PDh5/VeO4pm8kRERERE+pBnnnmGVatWsXfv3lNe27dvH1OnTm1Tmzp1KocOHWqzzHLSpEmn9A0ICDADHsCAAQNITk5uE9YGDBjQZjnmF198wTe+8Q2SkpIIDg5mxowZgBGwztcdd9yB2+3mrbfeAuCtt97C4/Ewd+5cSkpKOHbsGAsWLCAoKMj8evrpp8nKympzna++p+9///s8/fTTTJ06lcWLF7Nr164zjuF8/9zGjh1r/rPFYiE2NvaClqd2FIU8EREREZE+5Morr2T27Nk89thjp7zm8XhO2cDE4/Gc0i4wMPCU2lc3KrFYLKetuZt3Aq2pqeGaa64hKCiI1atX89lnn/HXv/4V4IJ2xAwNDeXWW281l2yuWLGCW2+9lZCQEPNer776Kjt27DC/9uzZw5YtW876nu677z6OHDnCXXfdxe7du5k0aRIvvPDCacdwvn9uZ/vz6EparikiIiIi0sf86le/Yvz48QwdOrRNfeTIkWRmZrapbdq0iaFDh2Kz2Tp0DPv376e0tJRf/epXJCQkAPD5559f1LUWLFjAjBkzePfdd9m4cSO//OUvAWPmcODAgRw5coR58+Zd8HUTEhJYuHAhCxcu5NFHH+XVV1/le9/73intuvLPrSMo5ImIiIiI9DFjxoxh3rx5p8xM/fCHP+TSSy/lqaee4vbbb2fz5s28+OKLbXaj7CiJiYnY7XZeeOEFFi5cyJ49e3jqqacu6lrp6ekMHjyYu+++m8GDB3PllVeary1ZsoTvf//7hISEcN1119HQ0MDnn39OeXk5P/jBD854zYceeojrrruOoUOHUl5ezkcffcSIESNO27Yr/9w6gkKeiIiIiEhr9gDjXLqexPfU5ZPn8tRTT7FmzZo2tUsuuYQ1a9bws5/9jKeeeoq4uDh+/vOfX9COl+crOjqalStX8thjj/H8889zySWX8F//9V98/etfv6jr3XvvvTz22GM88sgjber33XcfAQEB/PrXv+bHP/4xgYGBjBkzhoceeuis13O5XHz3u98lLy+PkJAQrr32Wv77v//7tG278s+tI1g8p1tMKp2iqqqK0NBQKisrCQkJ6e7hiIiIiEgr9fX1ZGdnk5KSgp+fX3cPR/q4s/28tTc3aOMVERERERGRPkQhT0REREREpA9RyBMREREREelDFPJERERERET6EIU8ERERERGRPkQhT0RERESkFW0+L12hM3/OFPJERERERABfX18Aamtru3kk0h+0/Jy1/Nx1JB2GLiIiIiIC2Gw2wsLCKC4uBiAgIACLxdLNo5K+xuPxUFtbS3FxMWFhYdhstg6/h0KeiIiIiEiz2NhYADPoiXSWsLAw8+etoynkiYiIiIg0s1gsxMXFERMTQ1NTU3cPR/ooX1/fTpnBa6GQJyIiIiLyFTabrVM/hIt0pl678crq1at58MEHmTRpEg6HA4vFwsqVKy/oGjNmzMBisZz164033mjTJzk5+YxtFy5c2IHvUERERERE5ML12pm8xx9/nNzcXKKiooiLiyM3N/eCrzF//nxmzJhxSr2pqYmlS5ditVqZNWvWKa+Hhoby0EMPnVKfNGnSBY9BRERERESkI/XakLd8+XKGDBlCUlISv/rVr3j00Ucv+Brz588/bf2dd97B4/Fw/fXXEx8ff8rrYWFhLFmy5ILvJyIiIiIi0tl6bci76qqrOu3ay5cvB2DBggWddg8REREREZHO0GtDXmfJy8tj3bp1xMbGcsMNN5y2TUNDA6tWreL48eOEh4czZcoUxo0b18UjFREREREROZVC3lesWLECt9vN/Pnz8fE5/R9PYWHhKUs9r732Wt544w2ioqK6YJQiIiIiIiKn12t31+wMHo+HFStWAGdeqnnvvfeSkZFBSUkJVVVVbNmyheuuu47333+fr3/963g8nnPep6qqqs1XQ0NDh74PERERERHpPRoaGk7JCO2hkNfKRx99RHZ2Nunp6QwePPi0bX72s5+Rnp5OVFQUwcHBXHbZZbz77rtMmzaNzZs38957753zPgkJCYSGhppfS5cu7ei3IiIiIiIivcTSpUvb5IOEhIR2XU8hr5WWDVfuu+++C+pntVq55557ANi4ceM52x87dozKykrz62J2BhURERERkb7h0UcfbZMPjh071q7r6Zm8ZuXl5fz1r38lLCyMW2655YL7tzyLV1tbe862ISEhhISEXPA9RERERESk73E4HDgcjg67nmbymq1evZqGhgbmzZuHv7//BfffunUrAMnJyR08MhERERERkfPXL0JeaWkp+/fvp7S09IxtXnvtNeDsZ+Pt3buXioqKU+qZmZn85je/weFwcPPNN7d7vCIiIiIiIher1y7XXL58OZmZmQDs3r3brGVkZAAwZ84c5syZA8CLL77Ik08+yeLFi1myZMkp19q2bRs7d+7kkksuYcKECWe855o1a3j22WeZNWsWycnJOBwO9uzZw7p167BarSxbtozExMQOfZ8iIiIiIiIXoteGvMzMTFatWtWmtnHjRnPjk+TkZDPknUvLLN65NlyZOXMm+/btY/v27axfv576+noGDBjA7bffzsMPP8zkyZMv/I2IiIiIiIh0IIvnfA52kw5RVVVFaGgolZWV2nhFREREREROq725oV88kyciIiIiItJfKOSJiIiIiIj0IQp5IiIiIiLSfzkb4OhWqC7s7pF0mF678YqIiIiIiEi71JZBwQ4j6DVUgT0IHEHdPap200yeiIiIiIj0P2XZcOxTI+ABuJ2Q/wW4Xd07rg6gkCciIiIiIv2HywnHt0PJfsA4aKDR6TZeazwJhbu7b2wdRCFPRERERET6h4ZqOLoJThYB4HZ7+MeuAn7+7pfUNjqNNtUFUJ7bjYNsP4U8ERERERHp+6ryIXczNNYAUNPg5MWPD7P2izwKKutZsTEH8wjxkv1QV96Ng20fbbwiIiIiIiJ9l9tthLYK7+xc7okafp+RRelJ43k8iwUSIwLweIx/xuOG0kOQMLmbBt0+CnkiIiIiItI3NdVB/g6orzBLnxwqYfXWozhdxnN4QQ4f7p+eyuiBod5+QTEQO7Zrx9qBFPJERERERKTvqSk1jkdwNQHG5ip/3JpL5uFSs0lKVBCLZqQRGWhvrlggeihEpHb9eDuQQp6IiIiIiPQdHg+UHYHSg2appLqB3318mGPltWbta8NjuG1SAr625m1KbHaInwABEV094g6nkCciIiIiIn2DqwkKdkJNiVnacayC1zKPUNtonH9nt9n49pQkLk+N9PbzD4e48eDr18UD7hwKeSIiIiIi0vvVVxqHmTfVAeBye/jfHcf5x+4Cs0lsiB+LZgxmULi/t194CkQNBWvfOXhAIU9ERERERHq3imNQvNfYFROoqm/ilfVH2FdYZTaZmBTO/CnJBNibI5DVB2LHQHBsd4y4UynkiYiIiIhI7+R2QdGXUHXcLB0urmbZ+iOU1zYCYLVY+NbEQVw9cgAWi8VoZA+CgZeAPbA7Rt3pFPJERERERKT3aawxlmc2VAPg8Xj4cH8xaz47hqv5UPNQf18WpqcxdECwt19IPAwYDVZbd4y6SyjkiYiIiIhI71JdBIW7wW0cj1Df5GLlphw+yykzmwwdEMyD6amE+Tcfj2CxQvRwCE/qjhF3KYU8ERERERHpHTwe42iEsiNmKb+yjt9/nEVBZZ1Zu3Z0LDdPGIitZTMVHz/jeAT/sC4ecPdQyBMRERERkZ7P2WAcj1B7wix9ml3Gyk05NDiN4xH8fW3cMzWFiUnh3n4BURA3DnzsX71in6WQJyIiIiIiPVttGRTsMIIe0ORy8/a2PP69r8hsMig8gEUz0ogNaXXWXeRg46tlw5V+QiFPRERERER6rrJsKDkAGJuplNc0smxDFoeLT5pNrkiL5K7Lk3H4NC/PtPoas3dB0d0w4O6nkCciIiIiIj2PywlFu6G60CztLajilQ1HqK43NlzxsVq5c3IiVw6N8h6P4BcKcePBHtANg+4ZFPJERERERKRnaag2jkdorAHA7fbwzz2F/HVHHs2nIxAZ6GDRjDRSolqddReaADEjoWXDlX5KIU9ERERERHqOqnwo3AMeYzOVmgYnr2/MZsexCrPJ6PhQ7p+eSpBfc5yx2GDAKAgd2A0D7nkU8kREREREpPu53VCyHypyzVJuWQ0vfZxFyUljwxWLBb4+diA3jo3Dam1enukbYByP4BfSHaPukRTyRERERESkezXVQf4OqK8wS58cKuWPW3NpcrkBCHT48MD0VEYPDPX2C4qB2LFg8+3a8fZwCnkiIiIiItJ9akqN4xFcxmYqjU43b249yieHS8wmKZGBLJyRRlSQo7ligeihEJHa9ePtBRTyRERERESk63k8UHYESg+apZLqBn6fcZijZbVmbeawGG6/NAFfW/NmKja7sTwzIKKrR9xrKOSJiIiIiEjXcjVBwS6oKTZLO49VsDzzCLWNxoYrdpuVu6ckcUVqlLeff7hxPIKvH3JmCnkiIiIiItJ16iuN4xGa6gBwuT387458/rE732wyIMSP78xIY1B4q7PuwlMgami/Px7hfCjkiYiIiIhI16g4BsV7wWNsplJV38QrG46wr6DKbHJJYjj3TE0mwN4cVaw+EDsGgmO7Y8S9kkKeiIiIiIh0LrcLir6EquNm6XDxSZatz6K8thEAq8XCrRMHcc3IAVgszccj2IOM5+8cQd0x6l5LIU9ERERERDpPY42xPLOhGgCPx8OH+4tZ89kxXB4PAKH+vjx4ZRrDYoO9/YLjjBk8q607Rt2rKeSJiIiIiEjnqC6Cwt3gNo5HqG9ysWpzLp9mnzCbDB0QzIPpqYT5242CxQrRwyE8qTtG3Cco5ImIiIiISMfyeKD0EJRlmaX8yjp+/3EWBZV1Zu3aUbHcfMlAbC2bqfj4Qfx4YxdNuWgKeSIiIiIi0nGcDVCwE2q9s3Wf5pSxcmMODU7jeAQ/Xxv3Tk1mYlKrs+4CoiBuHPjYu3rEfY5CnoiIiIiIdIzaMijYYQQ9oMnl5u1tefx7X5HZZFB4AIvSU4kN9ff2ixxsfLVsuCLtopAnIiIiIiLtV5YNJQcAYzOV8ppGlm3I4nDxSbPJFamR3HVFMg6f5uWZVl9j9i4ouhsG3Hcp5ImIiIiIyMVzOaFoN1QXmqV9BVW8suEIVfXGhis+Vit3TE4gfWi093gER4hxPII94HRXlXZQyBMRERERkYvTUG0cj9BYA4Db7eGfXxby1y/yaD4dgchAB4tmpJESFejtF5oAMSOhZcMV6VAKeSIiIiIicuGq8qFwD3iMzVRqGp28npnNjmMVZpNR8aE8MD2VIL/m2GGxwoBREDqoGwbcfyjkiYiIiIjI+XO7oWQ/VOSapdyyGl76OIuSk8aGKxYL3DQ2nhvHxmOzNi/P9A0wlmf6hXTHqPsVhTwRERERETk/TfXG8sz6CrOUebiU1VtyaXK5AQh0+PDA9FRGDwz19guKgdixYPPt4gH3Twp5IiIiIiJybjUnoOALcBmbqTQ63fzp06NsOFRiNkmJDGThjDSighzNFQtEDYHItG4YcP+lkCciIiIiImfm8UDZESg9RMvxCCXVDby0/jC5J2rNZjOGRTP30kR8bc2bqdjsxvLMgIjTXFQ6k0KeiIiIiIicnqsJCnZBTbFZ2plXwfJPjlDbaGy4YrdZueuKJKakRXn7+YdD3Hjw9eviAQso5ImIiIiIyOnUVxrP3zXVAeBye/j7znze3ZVvNhkQ4sd3ZqQxKLzVWXfhyRA1TMcjdKNe+ye/evVqHnzwQSZNmoTD4cBisbBy5coLukZGRgYWi+WMX1u2bDltv88++4zrr7+e8PBwAgMDmTx5Mm+++WYHvCsRERERkR6g4hgc3WIGvKr6Jn7774NtAt4liWE8fsMIb8Cz+hjLM2NGKOB1s147k/f444+Tm5tLVFQUcXFx5ObmnrvTGaSnpzNjxoxT6oMGnXp+R0ZGBrNnz8ZutzN37lxCQ0NZu3Yt8+bNIycnh8cee+yixyEiIiIi0q3cLijeC5V5Zimr5CTLMrIoq20EwGqxcMvEgcweGYvF0nw8gj3ICHiOoO4YtXxFrw15y5cvZ8iQISQlJfGrX/2KRx999KKvNWPGDJYsWXLOdk6nk/vuuw+LxcKGDRuYMGECAIsXL+aKK65g8eLFfOtb32LIkCEXPRYRERERkW7RWAv526GhGgCPx8NH+4tZ8/kxnG5jw5UQP18eTE9jeGywt19wHAwYDbZeGy36nF47j3rVVVeRlJTUpff86KOPyMrK4s477zQDHkBwcDBPPPEETqeTFStWdOmYRERERETa7WQx5G4yA159k4tXP8nmzU+PmgFvaEwwi28a6Q14FivEjIT48Qp4PYz+bQCHDh3i+eefp7a2lqSkJK6++mqioqJOaZeRkQHANddcc8prLbX169d36lhFRERERDqMx2McjVCWZZbyK+t46eMs8ivrzNrsUbHcfMlAfFqetfNxGMsz/cO7esRyHhTygDfffLPNxin+/v48+eSTPPLII23aHTp0COC0yzHDw8OJiooy24iIiIiI9GjOBijYCbUnzNJnOWWs3JhDvdM4HsHP18a9U5OZmNTqrLuAKIgbBz72rh6xnKd+HfKio6P59a9/zY033khiYiIVFRV8/PHH/OQnP+HHP/4xISEhPPjgg2b7yspKAEJDQ097vZCQEPLy8k77WmtVVVVtvnc4HDgcjna8ExERERGRC1BbBgU7jKAHON1u3v48jw/2FZlNBoYF8J0ZqcSG+nv7RQ42vlo2XJEO0dDQQENDg/n9V/PCheq1z+R1hFGjRvGjH/2I4cOHExAQQHx8PPPmzeP999/HbrezePFi3G53h983ISGB0NBQ82vp0qUdfg8RERERkdMqz4Fjn5oBr7ymkV//60CbgHdFaiQ/vWG4N+BZfWHgRIgaooDXCZYuXdomHyQkJLTrev16Ju9MRo8ezWWXXcYnn3zC4cOHGTp0KOCdwWuZ0fuqqqqqM87ytXbs2DFCQkLM7zWLJyIiIiKdzuWEot1QXWiW9hVU8cqGI1TVNwHgY7Uyd3ICM4ZGe49HcIQYz9/ZA053VekAjz76KD/4wQ/M76uqqtoV9BTyzqBl45Xa2lqz1vIs3qFDh5g4cWKb9uXl5ZSWljJlypRzXjskJKRNyBMRERER6VQNJ43jERprAON4hH/uKWTtF3l4jM0ziQx0sHBGGqlRgd5+oYMgZpQON+9kHf34lv5tnYbT6WT79u1YLBYSExPNenp6OgDr1q07pU9LraWNiIiIiEiPUFVgHI/QHPBqGp28+PFh3tnuDXij4kN44saR3oBnsULsGONLAa/X6Rf/xkpLS9m/fz+lpaVt6ps3b8bT8pPdzOl08sgjj5Cbm8vs2bOJiPDuJDRr1ixSU1N588032bFjh1mvrq7mqaeewsfHh/nz53fmWxEREREROT9uNxTvMzZY8Ri7ZeaW1fD0u3vZcawCMB6v+/q4eP7frKEE+zUv8vMNgMQrjFk86ZV67XLN5cuXk5mZCcDu3bvNWstZdnPmzGHOnDkAvPjiizz55JMsXryYJUuWmNe44447sFgsTJkyhYEDB1JRUcGGDRs4cOAAiYmJLFu2rM09fXx8WL58ObNnz2b69OnccccdhISEsHbtWrKzs3n66afN5/dERERERLpNU70R7urKzVLm4VJWb8mlyWVsLBjo8OH+6SmMGRjm7RcUA7FjwebbteOVDtVrQ15mZiarVq1qU9u4cSMbN24EIDk52Qx5Z7Jo0SLef/99MjIyKC0txcfHh8GDB/PTn/6UH/7wh4SHn3q448yZM8nMzGTx4sWsWbOGxsZGRo0axVNPPcW8efM67P2JiIiIiFyUmhNQ8AW4jM1UGp1u/vTZUTYcLDGbJEcGsmhGGlFBLc+BWYydMyNStXtmH2DxfHW9onSalt03KysrtfGKiIiIiHQsjwfKjkDpIcD4iF9S3cBL6w+Te8K7meCModHMnZyIr635yS2bHeLGQ2Bk149ZTqu9uaHXzuSJiIiIiEgzVxMU7IKaYrO0K6+C5Z9kU9PoBMBus3LX5UlMGRzl7ecfbgQ8X78uHrB0JoU8EREREZHerL7KOB6hqQ4Al9vD/+3M5/925ZtNYoIdfGfGYBIiWp11F54MUcO0e2YfpJAnIiIiItJbVeZB0ZfgMTZTqa538uonWXyZX2U2uSQxjHumphBgb/7ob/WBAaMhJK47RixdQCFPRERERKS3cbuh+Esj5DXLKjnJsowsymobAbBaLNwycSCzR8ZiadlMxR4I8ZeAI6g7Ri1dRCFPRERERKQ3aayF/C+gwZit83g8fHygmLc+O4bTbWy4EuLny4PpaQyPDfb2C44zZvBsigB9nf4Ni4iIiIj0FieLjQ1W3MbxCA1OF6s25bI1+4TZZEhMEAvT0wgLsBsFixWihxnP4Em/oJAnIiIiItLTeTzG0QhlWWapoLKOlzKyOF5RZ9ZmjxrAzZcMwqdlMxUfB8RPMHbRlH5DIU9EREREpCdzNkLBDqj1ztZ9nlPGio051DtdAPj52rh3SjITkyO8/QIijeMRfOxdO17pdgp5IiIiIiI9VV258fydswEAp9vN29vy+GBvkdlkYFgA35mRSmyov7dfRBpEDYGWDVekX1HIExERERHpicpzoOSAeTxCeW0jy9Zncbj4pNnk8pRI7p6ShMPHZhSsvhA3FoJiumHA0lMo5ImIiIiI9CQuJxTtgeoCs7S/oJqXN2RRVW9suOJjtTB3ciIzhkZ7j0dwhBjP39kDTndV6UcU8kREREREeoqGk5C/HRprAON4hPe/LOSd7Xl4jNMRiAiws2jmYFKjAr39QgdBzCho2XBF+jWFPBERERGRnqCqwJjBczsBqG108npmNl8cqzCbjIoP4f7paQT7NX+Mt1ghZiSEJXTDgKWnUsgTEREREelObjeUHjCewWt2tKyWlzIOU1zdYNZuGhfPTWPjsVmbl2f6+hvLM/1Cu3jA0tMp5ImIiIiIdJemeuN4hLpys7TxcClvbMmlyWVsuBJo9+G+6SmMHRTm7RcYY2ywYvPt2vFKr6CQJyIiIiLSHWpOGAHP1QhAo9PNnz47yoaDJWaTpMhAFqWnER3saK5YjKMRIlJ1PIKckUKeiIiIiEhX8nig7AiUHgKM3VRKqht4aX0WuSdqzGbpQ6OZe2kidp/mzVRsvhA3AQIju2HQ0pso5ImIiIiIdBVXExTugpPFZmlXXgXLP8mmptHYcMXXZuWuy5OYOjjK288vzHj+zteviwcsvZFCnoiIiIhIV6ivgvwvoKkWAJfbw//tzOf/duWbTWKCHSyaMZjEiFZn3YUnQ9QwHY8g500hT0RERESks1XmQdGX4DE2U6mud/LqJ1l8mV9lNpmQEMY901IItLccj2CD2DEQEtcdI5ZeTCFPRERERKSzuF1QvNcIec2ySk6yLCOLslpjwxWLBW69ZBCzR8ViadlMxR4I8ZeAI6g7Ri29nEKeiIiIiEhnaKw1lmc2GLN1Ho+HjAPF/PmzYzjdxoYrIX6+PHhlGsPjgr39gmNhwBiw6aO6XBz95IiIiIiIdLSTxVCwC9xNADQ4XfxhUy5bsk+YTYbEBPFgehrhAfbmigVihhvP4Im0g0KeiIiIiEhH8XiMoxHKssxSQWUdL2VkcbyizqxdM3IAt0wchE/LZio+DogbDwERXTxg6YsU8kREREREOoKzAQp2Qq13tm5bThmvb8qhvskFgJ+vjXumJDMpuVWYC4iEuHFG0BPpAAp5IiIiIiLtVVsGBTuMoAc43W7Wbs/jX18WmU0GhvmzaEYacaH+3n4RaRA1xNh9RaSDKOSJiIiIiLRHWTaUHACMzVQqaht5ef0RDhZXm00uS4nk21OScPjYjILVF+LGQlBMNwxY+jqFPBERERGRi+FyQuEuOOmdrdtfUM3LG7Koqjc2XPGxWph7aQIzhsV4j0dwhED8BLAHnO6qIu2mkCciIiIicqEaquH4dmiqBYzjEf65p5C1X+ThMSb0iAiws3BGGmnRrc66Cx0EMaOgZcMVkU6gkCciIiIiciEqj0PRl+AxNlOpaXTyemY2O45VmE1GxYdw//Q0gv2aP25brDBglBHyRDqZQp6IiIiIyPlwu6FkH1QcNUu5ZTUsy8iiuNrYcMVigRvHxnPT2Hhs1ublmb4BxvJMv5DuGLX0Qwp5IiIiIiLn0lQH+V9AfaVZ+uRQKX/cmkuTyw1AoMOH+6enMGZgmLdfUAzEjgWbbxcPWPozhTwRERERkbM5WWKcf+c2NlNpdLr549ZcMg+Xmk2SIwNZNCONqKCWs+4sED0UIlK7YcDS3ynkiYiIiIicjscDJw4bX81Kqhv43ceHOVZea9ZmDItm7qWJ+NqaN1Ox2Y3lmQERX72iSJdQyBMRERER+SpnozF7V+udrfviWAWvZR6hrtHYcMVus/HtKUlcnhrp7ecfDnHjwdeviwcs4qWQJyIiIiLSWl055O8AZz0ALrebv+7I55+7C8wmsSF+LJoxmEHh/t5+4SkQNVTHI0i3U8gTEREREWlRngsl+8FjbKZSWdfEKxuOsL+wymwyKSmcb09JJsDe/FHa6mNsrhI8oDtGLHIKhTwREREREZcTivZAtXe27mBxNcsysqisMzZcsVks3DopgatHxGCxNB+P4Ag2nr+zB3bHqEVOSyFPRERERPq3hpPG8QiNJwHweDx8sLeIv2zLw+3xABAW4MvC9DSGxAR7+4XEw4DRYLV1x6hFzkghT0RERET6r6oCYwbP7QSgttHJyk05bMstN5sMjw3hgStTCfVvPuvOYoWYERCW2B0jFjknhTwRERER6X/cbig9AOU5ZimvvJbfZ2RRVFVv1q4fE8ec8fHYWjZT8fU3lmf6hXbxgEXOn0KeiIiIiPQvTfXG8sz6CrO0KauUNzbn0ugyNlwJsNtYMC2V8Qlh3n6BMRA3Fmy+XTtekQukkCciIiIi/UfNCSj4AlzGZipNLjd//vQoGQdLzCaJEQF8Z8ZgooMdzRULRA2BiFRo2XBFpAdTyBMRERGRvs/jgbIjUHoIMDZTKaluYNn6LHJO1JjNrhwSzR2TE7H7NC/PtPlC3AQIjDzNRUV6JoU8EREREenbXE1QsAtqis3SruMVLP8km5oGY8MVX5uVeZcnMX1wlLefX5jx/J2vXxcPWKR9FPJEREREpO+qrzSev2uqA8Dl9vB/O/N5d3c+zacjEBPsYOGMNJIiWp11F54MUcOgZcMVkV5EIU9ERERE+qaKo1C8DzzGZipV9U0s/+QIX+ZXmU0mJIRxz7QUAu3NH4utPsbZdyFx3TFikQ6hkCciIiIifYvbBUVfQtVxs5RVcpJlGVmU1TYCYLVYuOWSgcweFYulZTMVe5CxPNMR1B2jFukwvXb+efXq1Tz44INMmjQJh8OBxWJh5cqVF3SNzMxMfvjDHzJx4kQiIyPx8/Nj+PDh/OQnP6GiouK0fZKTk7FYLKf9WrhwYfvfmIiIiIhcvMYaOLrZDHgej4d/7yvi2ff3mwEvxM+XH14zjGtHx3kDXnAcJF6hgCd9Qq+dyXv88cfJzc0lKiqKuLg4cnNzL/gat956K6WlpUybNo27774bi8VCRkYGzz77LO+88w6bNm0iJibmlH6hoaE89NBDp9QnTZp0MW9FRERERDpCdREU7gK3sZlKfZOLVZtz+TT7hNlkaEwwD6anEhZgNwoWK0QPh/Ck7hixSKfotSFv+fLlDBkyhKSkJH71q1/x6KOPXvA1Hn74Ye6++27i4rxrrj0eD9/97nd56aWXePLJJ/nd7353Sr+wsDCWLFnSnuGLiIiISEfxeKDkAJRnm6X8yjp+/3EWBZV1Zm32qFhuvmQgPi2bqfj4Qfx48A/v4gGLdK5eG/Kuuuqqdl/jJz/5ySk1i8XCE088wUsvvcT69evbfQ8RERER6URN9VCwE+rKzNLW7DJWbcqhwekCwN/Xxj1TU5iY1CrMBURB3DjwsXf1iEU6Xa8NeZ3J19cXAB+f0//xNDQ0sGrVKo4fP054eDhTpkxh3LhxXTlEEREREaktM45HcBnP2jW53Pzl8zw+3F9kNhkUHsCiGWnEhrQ66y5ysPHV8jyeSB+jkHcar7/+OgDXXHPNaV8vLCxk/vz5bWrXXnstb7zxBlFRUaftIyIiIiIdqOwIlBwEjMPuTtQ0smx9FkdKTppNpqRF8R+XJ+HwaV6eafU1Zu+CorthwCJdRyHvK3bs2MGTTz5JTEwMP/7xj095/d577yU9PZ1Ro0bhcDjYu3cvTz75JP/85z/5+te/zsaNG727NJ1BVVVVm+8dDgcOh6ND34eIiIhIn+RqMjZXOVlslr7Mr+TVT7Kprm8CwMdm5c7JiVw5JMr7ucwv1Dgewde/O0YtclYNDQ00NDSY3381L1yoXnuEQmfIzs7mxhtvxOVy8ec///m0s3I/+9nPSE9PJyoqiuDgYC677DLeffddpk2bxubNm3nvvffOeZ+EhARCQ0PNr6VLl3bG2xERERHpW+qrIHeTGfDcbg//tzOf//73QTPgRQU5ePTa4aQPjfYGvLBESLhcAU96rKVLl7bJBwkJCe26nmbymuXm5jJz5kxKSkp45513mDlz5nn3tVqt3HPPPWRmZrJx40ZuuOGGs7Y/duwYISEh5veaxRMRERE5h8o844BzjxuAk/VOlmceYffxSrPJ2EFhLJiWQpCj+SOuxQaxoyEkvjtGLHLeHn30UX7wgx+Y31dVVbUr6CnkATk5OcycOZP8/Hz+8pe/cOONN17wNVpm/Wpra8/ZNiQkpE3IExEREZEzcLuh+Esj5DXLLq3hpYwsTtQYy9ssFvjm+EFcNzoWq7V59s43AAZeAo7g7hi1yAXp6Me3+n3Iy8nJYcaMGeTn5/PWW2/xjW9846Kus3XrVgCSk5M7cHQiIiIi/VhjrbF7ZoPxfJLH42H9wRL+9OkxnG5jRi/Yz5cHrkxlZFyrX6AHDYDYsWDr9x91pZ/qFz/5paWllJaWEhUV1eY5u5aAd/z4cd566y2++c1vnvU6e/fuJT4+nrCwsDb1zMxMfvOb3+BwOLj55ps74y2IiIiI9C8ni6FgF7iNZ+0anC7e2JzL5iMnzCaDY4J48Mo0IgJbzrqzQPQwiEjphgGL9By9NuQtX76czMxMAHbv3m3WMjIyAJgzZw5z5swB4MUXX+TJJ59k8eLFLFmyxLzGjBkzyM3N5fLLL2fXrl3s2rXrlPu0br9mzRqeffZZZs2aRXJyMg6Hgz179rBu3TqsVivLli0jMTGxU96viIiISL/g8UDpISjLMkuFlXX8PuMIxyu8j8VcPWIAt04ahI+1eR9BHwfEjYeAiC4esEjP02tDXmZmJqtWrWpT27hxIxs3bgSMZZMtIe9McnNzAdiyZQtbtmw5bZvWIW/mzJns27eP7du3s379eurr6xkwYAC33347Dz/8MJMnT774NyQiIiLS3zkboGAn1Hpn67bllPH6phzqm1wA+PnY+PbUZCYntwpzAZHG+Xc+2sxOBMDi8Xg83T2I/qKqqorQ0FAqKyu18YqIiIhIa7VlULDDCHqA0+3mnW15rNtbZDaJD/Vn0cw04kNbHYUQkQZRQ4zdV0T6iPbmhl47kyciIiIifURZNpQcAIy5h/LaRl5en8Wh4pNmk8tSIrn7iiT8fG1GweoLcWMhKKYbBizSsynkiYiIiEj3cDmhaDdUF5ql/QXVvLwhi6rmw819rBbmXprAjGEx3sPNHSEQPwHsAd0xapEeTyFPRERERLpeQ7VxPEJjDQBut4d/flnIX7/Io+VhoogAOwtnpJEWHeTtFzoIYkaC1dYNgxbpHRTyRERERKRrVeVD4R7wGJup1DQ6eT0zmx3HKswmo+JDuX96KsF+zR9XLVYYMMoIeSJyVgp5IiIiItI13G4o2QcVR81S7okaXsrIouSkseGKxQI3jY3nxrHx2KzNyzN9A4zlmX7auE7kfCjkiYiIiEjna6ozlmfWV5qlTw6VsHrrUZwuNwCBDh8emJ7K6IGh3n5BMRA7Fmy+XT1ikV5LIU9EREREOldNqXE8gsvYTKXR6eaPW3PJPFxqNkmJCmLRjDQiA+3NFYtxNEJkWtePV6SXU8gTERERkc7h8cCJLDhxyCwVV9fz+4+zOFZea9ZmDovh9ksT8LVZjYLNbizPDIj46hVF5Dwo5ImIiIhIx3M2QsFOqPXO1n1xtJzXNmZT12hsuGK32fj2lCQuT4309vMPh7jx4OvXxQMW6TsU8kRERESkY9VVGM/fOesBcLnd/PWLfP65p8BsEhvqx3dmDGZgmL+3X3gKRA0Fq7WLByzStyjkiYiIiEjHKc+Fkv3gMTZTqaxr4uUNWRworDabTEqOYP6UZPx9m8+6s/pA7BgIju2OEYv0ORcV8r72ta916CAsFgsffvhhh15TRERERLqQ2wWFu6HaO1t3sKiaZeuzqKwzNlyxWSx8a1ICV42IwWJpPh7BEWw8f2cP7I5Ri/RJFxXyMjIysFgseDyeDhmE+X9yEREREel9Gk4ayzMbTwLg8XhYt7eIt7fl4W7+vBgW4Mui9DQGxwR7+4XEw4DRYLV1x6hF+qyLXq45evRonn/++XYP4Hvf+x5ffvllu68jIiIiIt2gqgCK9oDbCUBto5MVG3PYfrTcbDIiNoQH0lMJ8Ws+685ihZgREJbYHSMW6fMuOuSFhoaSnp7e7gGEhoaeu5GIiIiI9CxuN5QegPIcs5RXXsvvM7Ioqqo3azeMiecb4+OxWZtXbvn4Gcsz/cO6drwi/chFhbyxY8cyZMiQDhnA4MGDOXnyZIdcS0RERES6QFO9cbh5nXe2blNWKW9szqXRZWy4EmC3cd+0VMYlhHn7BUZD3Diw+XbteEX6GYunox6sk3OqqqoiNDSUyspKQkJCuns4IiIiIheu5gQUfAEuYzOVRqebP392lPUHS8wmSZEBLEofTHSww9svaihEpIL2YhA5p/bmBh2hICIiIiLn5vFA2REoPQQYcwQl1Q28tD6L3BM1ZrMrh0Zzx6WJ2H2az7qz+RqHmwdGdf2YRfophTwREREROTtXExTsgppis7Qrr4Lln2RT02hsuOJrs3LX5UlMHdwqzPmFGc/f+fp18YBF+jeFPBERERE5s7oK4/m7pjoAXG4Pf9+Zz7u78s0mMcEOvjNjMAkRAd5+YUkQPRys1q4dr4jQIf+vq6qqIiAggMzMzI64nIiIiIj0BBVH4dhWM+BV1Tfx238fbBPwJiSE8cSNI70Bz2IzlmcOGKmAJ9JNznsmb82aNWd8rba2lvr6ev7973+Tn2/8n/62225r/+hEREREpOu5XVC4G6oLzNLh4mqWrT9CeW0jAFaLhVsmDmT2yFgsLZup2AMh/hJwBHXHqEWk2Xnvrmm1WrFYLJypeevXLBYLLper40bZR2h3TREREenxGk5C/hfQaBxx5fF4+GBvEW9vy8PV/Fkv1N+XB69MY1hssLdfcBwMGA02PQ0k0l5dtrvm6NGjKS0t5dlnn2X69OltXqusrGT8+PH8+c9/5rLLLrvgQYiIiIhID1CVD4V7wGP8sr620cnKTTlsy/WehzcsNpgHrkwlzN9uFCxWiB4G4cndMGAROZ3zDnlffPEFv/3tb/nud7/LnDlz+PWvf01MTAxghDyA2NhYkpKSOmekIiIiItI53G4o2Q8VuWbpaFktL2Ucpri6waxdPyaOOePjsbU8a+fjMHbP9A/v6hGLyFmc99OwNpuNH/7wh+zdu5eamhqGDh3Kb3/7Wy3LFBEREenNmurg2JY2AS/zcCm/fG+fGfAC7Da+/7Uh3HLJIG/AC4iCpGkKeCI90AVveTRw4EDefvtt/vznP/O73/2OcePG8dFHH3kfuBURERGR3uFkCeRshHpjVVaj083KjTms2JhNk8sNQFJkID+7cRTjEsK8/SKHwKBJ4GPvhkGLyLlc9JOx1157LXv27OEXv/gFd955Z0eOSUREREQ6k8cDpYegLMssFVXV81JGFsfKa83ajKHR3H5pInaf5nkBm69xPEJgFCLSc5337ppnc/ToUbKzs5kwYYJ2jTwL7a4pIiIi3c7ZAAU7ofaEWdqWW86KjdnUNRmP4dhtNu6eksgVqa3CnF8YxI8HX/+uHa9IP9Rlu2ueTWJiIomJiR1xKRERERHpLLVlULDDCHqA0+1m7fbj/OvLQrNJbKgf35kxmIFhrcJceDJEDdPh5iK9RLtDXlVVFX/729+4++67O2I8IiIiItIZyrKh5ABgLOIqr23k5fVZHCo+aTaZlBzB/CnJ+PvajILVB2LHQHBsNwxYRC5Wu5drHjhwgJEjR2qXzfOg5ZoiIiLS5VxNULgLThabpX0FVbyy4QhV9U0A+Fgt3DYpga8Nj/FupucINo5HsAd2x6hF+rUesVxTRERERHqg+irI/wKajM1U3G4P/9xTyF935NHya/6IADsLZ6SRFh3k7RcSDwNGg9XWDYMWkfZSyBMRERHpiyrzoOhL8BhHIZxscLL8kyPsPl5pNhkVH8r901MJ9mv+SGixQsxICEvojhGLSAdRyBMRERHpS9wuKN5rhLxm2aU1vJSRxYkaY8MViwW+MW4gN4yJw2ptXp7p628sz/QL7Y5Ri0gHUsgTERER6Ssaa4zlmQ3VAHg8HjIOFPPnz/Jwuo0ZvWA/X+6fnsKo+FZhLigGYsca5+CJSK+nkCciIiLSF1QXGRusuJ0A1De5+MPmXLZme8/DS4sOYmF6GhGB9uaKBaKHQkRqNwxYRDqLQp6IiIhIb+Z2Q+lBKM82S/mVdfz+4ywKKuvM2tUjB3DrxEH4tJx1Z7MbyzMDIrp6xCLSyRTyRERERHqrpnrjcPO6crO05cgJ/rA5lwancbyVn6+Ne6ckMzG5VZjzj4D48eDj6NrxikiX6JCQ186j9kRERETkQtWcMAKeqxGAJpebtz47xscHvOfhDQoPYFF6KrGh/t5+EWkQNcTYfUVE+qR2h7yBAwfypz/9qSPGIiIiIiLn4vFA2REoPQQYv2gvPdnAsowssk/UmM2mpEXxH5cn4fBpXp5p9YW4scYmKyLSp7U75AUFBXH77bd3xFhERERE5GycjcbmKjUlZmlXXgXLP8mmptHYcMXHZuU/Lktk2uAoLC2zdY4Q4/k7e0B3jFpEupieyRMRERHpDeoqjOMRnPUAuNwe/ndHPv/YnW82iQ5ysGhGGkmRgd5+oQnGAectG66ISJ+nkCciIiLS05XnQsl+8Bhn3VXVN/HK+iPsK6wym4xPCOPeaSkE2ps/3llsMGAUhA7sjhGLSDfqlJBXXFyMv78/wcHBnXF5ERERkf7B5YSiPVBdYJYOFlfz8vosKmqbALBaLNwycSCzR8Z6l2faA43lmQ59FhPpjzos5NXX1/PII4+wYsUK6uqMM1mioqIYN24c48ePN/93+PDh2Gy2jrqtiIiISN/UUG0sz2w0NlPxeDys21vE29vycDfvbB7q78vC9DSGDmgV5oJjYcAYsGnBlkh/ZfF00PkH/+///T9eeOEFwNiMxWq1UlXlXULQ8pslu93OqFGjGD9+PMuXL++IW/caVVVVhIaGUllZSUhISHcPR0RERHqqqnwo3AMe46y72kYnKzZms/1ohdlkeGwI91+ZQpi/3ShYrBA9DMKTu368ItKh2psbOizkJSYmcvz4cV577TXmz58PQElJCTt37mTnzp3s2LGDnTt3sn//fpxOJxaLBZfL1RG37jUU8kREROSs3G4o2QcVR81SblkNyzKyKK5uMGs3jInjG+PjsbVspuLjZxxu7h/exQMWkc7Q3tzQYdssVVZWMnz4cDPgAURHR3PVVVfxwx/+kDfeeINdu3Zx8uRJtm/fzuuvv96u+61evZoHH3yQSZMm4XA4sFgsrFy58oKv43a7efHFFxk7diz+/v5ER0dz2223cejQoTP2+eyzz7j++usJDw8nMDCQyZMn8+abb7bj3YiIiEi/11gLx7a0CXifHCpl6Xv7zYAXaPfh+7OGcPMlg7wBLzAakqYq4ImIqcMWa48dO5by8vJztrPb7YwfP57x48e3636PP/44ubm5REVFERcXR25u7kVdZ+HChbz66quMHDmS733vexQVFfHWW2+xbt06Nm3axMiRI9u0z8jIYPbs2djtdubOnUtoaChr165l3rx55OTk8Nhjj7XrfYmIiEg/dLIYCnaB29hMpcHp5o9bc9l4uNRskhQZyKL0NKKDHd5+kUMgMg1aNlwREaEDZ/IWLVrE/v372bdvX0dd8qyWL19OTk4OJSUlLFy48KKu8fHHH/Pqq68yffp0tm/fzrPPPsuqVav4xz/+QVVVFYsWLWrT3ul0ct9992GxWNiwYQOvvvoq//Vf/8XOnTsZNWoUixcvPusMoIiIiEgbHg+UHITj28yAV1hVzy/f29cm4M0YGs1/XjvcG/BsvjDoUogarIAnIqfosJB35513cvPNNzN37lwKCgrO3aGdrrrqKpKSktp1jVdffRWAp59+GofD+1uxWbNmMXv2bDZs2MDBgwfN+kcffURWVhZ33nknEyZMMOvBwcE88cQTOJ1OVqxY0a4xiYiISD/hbIC8z6Asyyxtyy3j6Xf3kldeC4DdZuO+6SncdUUydp/mj23+4ZA0DQKjumPUItILdFjIA3jzzTcZOHAgY8eO5YUXXiA/P78jL9/hMjIyCAwMZOrUqae8Nnv2bADWr1/fpj3ANddcc0r7llrr9iIiIiKnVVsGuRuh9gQATrebtz47yu8zsqhrMjamiw314/EbR3BFaqswF54MgyaDr183DFpEeosOeyavqKiIW2+9lU2bNuHxeHjooYd4+OGHSUlJMZ/BmzBhAuPHjyc+Pr6jbnvRampqKCgoYPTo0ac9t2/IkCEAbZZftvxzy2uthYeHExUVpeWaIiIicnZlR4wlmhgbnJfXNLJsQxaHi0+aTS5NjuDbU5Lx923+jGL1gdgxxhl4IiLn0GEh7+GHH2bjxo1YLBbGjh2Lr68vhw4dIisri6ysLNauXWu2jYyMZMKECfzrX//qqNtfsMrKSgBCQ0NP+3rLVqUt7c63T15e3jnv3fr8QACHw9FmuaiIiIj0Qa4mKNxlbLLSbG9BFa9sOEJ1vfE8no/Vwm2TEvja8BjzjGEcwRA/AeyB3TFqEekCDQ0NNDR4j0n5al64UB0W8tatW4fNZmPdunXMnDnTrGdnZ7Njxw7znLwdO3Zw9OhR/v3vf3fUrXudhISENt8vXryYJUuWdM9gREREpPPVV0L+F9BUB4Db7eEfuwv4353HaTmxOCLAzsIZaaRFB3n7hQyEAaPAeuqqIxHpO5YuXcqTTz7ZYdfrsJDndrsZNWpUm4AHkJKSQkpKCt/85jfNWkVFBTt37uyoW1+Ultm41jN1rbWk59azdufT50yzfK0dO3aszaGGmsUTERHpwyqOQfFe8LgBOFnvZHnmEXYf936eGB0fyv3TUwnya/5oZrFCzEgISzjdFUWkj3n00Uf5wQ9+YH5fVVV1ysTQheiwkDdx4kRycnLOq21YWBjp6ekddeuLEhgYSFxcHNnZ2bhcrlOeyzvd83etn9ObOHFim/bl5eWUlpYyZcqUc947JCTkok6uFxERkV7E7YKiPVDl3YjuSGkNyzKyOFFjLMuyWOAb4wZyw5g4rNbm5Zm+AcbyTD99VhDpLzr68a0O213z4YcfJisri40bN3bUJTtdeno6NTU1px1zy/OCrcNoyz+vW7fulPYtte4OryIiItIDNJyE3E1mwPN4PHy4r4hn/rnPDHjBfr784Kph3DQu3hvwgmIgaYoCnoi0S4eFvClTpvDoo48yd+5cduzY0VGX7RClpaXs37+f0tLSNvUHHngAgMcff5zGxkaz/uGHH/Kvf/2LK6+8kqFDh5r1WbNmkZqayptvvtnmPVZXV/PUU0/h4+PD/PnzO/W9iIiISA9XlW8EvEZjt8zaRifLNhzhzU+P4nQbD+ANjgniZzeOZGR8S5izQPQwGDjROOhcRKQdLB5Py+O+7WOz2YiNjaW0tBSLxcKiRYu47bbbmDx58mmPKGiv5cuXk5mZCcDu3bvZvn07U6dOZfDgwQDMmTOHOXPmALBkyRKefPLJ025wcv/997N8+XJGjhzJDTfcQFFREW+99RZ+fn5s2rSJkSNHtmn/8ccfM3v2bBwOB3fccQchISGsXbuW7Oxsnn76aX7605+eccwtz+xVVlZquaaIiEhf43Ybz95VHjNLx8pqeWl9FkVV9Wbt6pEDuHXiIHyszb9r93FA3HgIiOjiAYtIT9Xe3HBRz+Q99thjXHnllVx77bVmLSQkhIKCAvP7559/nueffx673c6oUaPMs/LGjRvHuHHj2h1yMjMzWbVqVZvaxo0bzaWXycnJZsg7m5dffpmxY8fy8ssv8/zzzxMUFMRNN93EL37xizazeC1mzpxJZmYmixcvZs2aNTQ2NjJq1Cieeuop5s2b1673JCIiIr1UY62xe2aDd9vzTw6V8setuTS5jA1X/H1t3DM1hYlJ4d5+AZEQN84IeiIiHeSiZvKsVivTpk1jw4YNZq2iooLq6mp27txpfu3atYvDhw/jdhv/cTPPe8HYdfPw4cMd8BZ6D83kiYiI9EHVRVC4G9zGWXcNTjd/3JrLxsPex0QSIwJYNCONmGA/b7+INIgaYuy+IiLSSrfM5Pn4+JjBrUVkZCQ33ngj//u//8uNN95o1uvq6ti9e7cZ+loCYHZ29sXcWkRERKRncLuh9CCUez/TFFTWsWz9EfLKa81a+tBo5l6aiN2neXmm1deYvQuK7uoRi0g/cVEhLyYmhuzsbDwejzk75/F4KC8vP6Wtv78/kydPZvLkyW3q53vcgoiIiEiP01QPBTugzvvZ59PsMlZtyqHe6QLAbrNx95RErkiN8vbzC4P48eDr36XDFZH+5aJ210xPT6ewsJBvf/vb7Nixg4vZuyU5Oflibi0iIiLSvWpKITfTDHhNLmN55ssbssyAFx/qzxM3jmgb8MKTIeEyBTwR6XQX9UzekSNHmDJlCsXFxVgsFgICAqipqWHgwIH8+Mc/ZuzYsYwdO5bw8PBzX6wf0TN5IiIivZjHAycOG1/NSqobeHl9FtknaszaFamR3HVFEg6f5t3FrT4QOwaCY7t6xCLSS7U3N1z0EQolJSX89re/5e9//zt79+4FaLN8E2DQoEHmbpotX0OGDLmY2/UJCnkiIiK9lLMBCnZC7QmztONYBa9lHqG20Zi987FZufPSRK4cGuX9POQIhvgJYA/sjlGLSC/VbSGvterqakJDQ0lNTeXrX/+6ucnKiRPe/xC2/MfO39+fMWPGMG7cOJYtW9beW/cqCnkiIiK9UG2Z8fydswEAl9vN2i+O8/6eQrNJTLCDhelpJEW2CnOhgyBmJFg7/rxgEenbekTIg9Mfq3D8+PE2Ryrs3LmTQ4cO4Xa7sVgsuFyujrh1r6GQJyIi0ot4PFB2BEoPAcbHpfKaRl7ZcISDxdVms0sSw7hnagoB9ub97Cw2GDAKQgd2w6BFpC/oliMUTufIkSNtZu4ABg4cyMCBA7n++uvNWn19vXmkgoiIiEiP5GqCgl1QU2yW9uZX8conR6iuN87Ds1ks3DopgatHxHiXZ9oDjeWZjuDuGLWICNCBM3lybprJExER6QXqKiD/C3DWA+Bye/jHrgL+vus4LZ+aIgLsPJiexuCYIG+/4DgYMBpsHfY7dBHpp3rMTJ6IiIhIr1eeAyUHwOMGoKq+ieWfHOHL/Cqzyej4UO6bnkqwX8vyTCvEjICwxG4YsIjIqRTyRERERFxOKNoN1d7NVA4VV/Py+iOU1zYCYLHAnPEDuX50HFZr8/JMX39jeaZfaHeMWkTktC4q5P385z8nMTGR+fPnt3sAK1eu5OjRo/zsZz9r97VERERELlh9lbE8s6kWMI6EWre3iLe35eFuXp8Z4ufLA1emMiKu1bKpoBiIHQs23+4YtYjIGV3UM3mn20nzYk2fPp1Nmzb1i5029UyeiIhID1OZB0VfmsszaxqdvJ6ZzY5jFWaToQOCefDKVMIC7M0VC0QPhYjUrh+viPQLeiZPRERE5EK5XUa4qzpulnJKa3hpfRalJxvM2vVj4pgzPh6b1WoUfBwQNx4CIrp4wCIi5++iQ97nn39Oamr7f4NVWFh47kYiIiIiHaWxxlie2WCcdefxeMg4WMKfPz2G023M6AXafVgwPYVxg8K8/QKiIG4c+NhPc1ERkZ7jokNefX09OTk5HTII82wZERERkc5UVQBFe8DtBKC+ycUfNueyNdt71m9KVBAL01OJCnJ4+0UOgcg0Y/cVEZEe7qJCXnZ2dkePQ0RERKTzuN1Qsh8qcs3S8Yo6Xso4TEFlvVmbNXwA35o0CF9b8/JMm91YnhkY2cUDFhG5eBcV8pKSkjp6HCIiIiKdo7EWCnZAfaVZ2pRVyhubj9LYvPGbn6+Ne6YkMym51bN2/uFGwPP169rxioi0kzZeERERkb7rZDEU7AJ3EwCNTjd/+vQoGw6VmE0GhQewaEYasSGtwlxEKkQN1fJMEemVFPJERESk7/F4oPQglB0xS0VV9byUkcWx8lqzduWQaO6YnIjdp3l5ptUX4sYaZ+CJiPRSCnkiIiLStzTVG8sz68rN0racMl7flEN9k7E8026z8h+XJzF1cJS3n1+osTzTHtC14xUR6WAKeSIiItJ31JwwAp6rEYAml5u3t+Xx731FZpPYUD8WpQ9mULi/t19YEkQPh5bz8EREejGFPBEREen9PB44kQUnDpmlEzWNLFufxZGSk2Ztckokd1+RhL+vzShYfWDAaAiJ6+oRi4h0GoU8ERER6d2cjVCwE2pLzdKu4xUs/ySbmgbjPDwfq5W5kxOYMTTaez6vI9hYnukI6oZBi4h0HoU8ERER6b1qy4zlmc4GAFxuN/+7I59/7C4wm0QFOViYnkZKVKC3X8hAGDAKrLYuHrCISOdrd8irra3lgw8+4NAhY3nE4MGDufrqqwkMDDxHTxEREZGL5PEYO2eWHgI8AFTUNfLKhiMcKKw2m41PCOPeqSkEOpo/8lisRrgLHdQNgxYR6RrtCnn/+Mc/uOeeezhx4kSbekREBK+++ipz5sxpz+VFRERETuVshMLdUFNslvYXVPPKJ1lU1hnn4VktFm6ZOJDZI2O9yzN9AyB+AviFdMeoRUS6jMXj8XgupuPevXuZOHEiDQ0NOBwOhgwZgsfj4fDhwzQ0NGC32/n0008ZO3ZsR4+516qqqiI0NJTKykpCQvQXjIiIyAWrK4f8HeCsB8Dt9vCP3QX8787jtHyiCQ+w82B6KkNigr39gmNhwBiw6UkVEen52psbLnqf4Oeee46GhgauvvpqcnJy2LVrF7t37yY7O5tZs2bR2NjIb37zm4u9vIiIiEhbZdlwdKsZ8Krqm/jthwf52w5vwBsVH8LPbhrpDXgWK8SMMGbwFPBEpJ+46Jm8wYMHc/z4cY4ePUp0dHSb14qLi0lMTCQuLo7s7OwOGWhfoJk8ERGRi+BqgsJdcNK7PPNgUTUvb8iiotZYnmmxwDfGDeT6MXHYrM3LM338jHDnH9YNgxYRuXjtzQ0X/Sut/Px8hgwZckrAA4iJiWHIkCEcPnz4Yi8vIiIiAnUVkP9Fm+WZ/9xTyN92HMfd/HvqED9fHrgylRFxrT4IBcZA3Fiw+XbDoEVEutdFh7z6+nrCwsLO+HpYWBiNjY0Xe3kRERHp78qyofQgeNwAVNc7eS3zCLuPV5pNhseGcP+VKYT525srFogaAhGpxvSeiEg/pMXpIiIi0rO4mozdM08WmaVDxdW8sv4IZbXGL5AtFrhxbDw3jY1vtTzTYRxuHhDRDYMWEek52hXyiouL+cMf/nDG1wDeeOMNzvTY3913392e24uIiEhfU19pLM9sqgOM5Zn/2lvI2u1tl2fePz2VkfGtl2dGQ+xY8LGf7qoiIv3KRW+8YrVavefOXMyNLRacTudF9++NtPGKiIjIWZTnQsl+c3nmyXonr23MZldehdlk6IBgHrgylfAALc8Ukb6r2zZeSUxMbFfIExEREQGM5ZlFe6C60CxllZxkWUZWm+WZ14+O5xvj47BZm0+A0vJMEZHTuuiQl5OT04HDEBERkX6pvtI43LypFgCPx8MHe4t4e1serubFRsF+vtw3LYXRA0O9/QKijN0zfRzdMGgRkZ5NG6+IiIhI96g4CsX7zOWZNQ1OXt+YzY5jFWaToTHNyzMDtTxTROR8KeSJiIhI13I5m5dnFpilI6U1LMvI4kRNg1m7bkwc3xwf712eabMbyzMDI7t4wCIivYtCnoiIiHSd+qrm3TO9yzP/va+Yt7cdw+k2lmcGOny4b1oKYweFefsFRELcOC3PFBE5Dwp5IiIi0jUqjkHxXu/yzEYnKzfmsP1oudkkLTqIB9PTiAxsdRRC5BCITNPyTBGR86SQJyIiIp3L5YTiL6Eq3yxll9awbH0WpSe9yzOvHRXLNy8ZiI+WZ4qItItCnoiIiHSehmpjeWZjDWAsz/xofzFrPs/D6TZm9ALtPtw7LYXxCWHefv4RxvJMX79uGLSISO+mkCciIiKdozIPivaCxwVAbaOTVZty+Ty3zGySGhXEg+mpRAW1etYuIs3YQVPLM0VELopCnoiIiHQstwuKvoSq42Yp94SxPLO42rs885qRA7j5kkH42lqWZ/o2L8+M6uIBi4j0LQp5IiIi0nEaqo3DzRtPAsbyzIyDJfz5s2M4XcbyzAC7jXunpjAhMdzbzz/cCHhaniki0m4KeSIiItIxKo8bM3jNyzPrmlz8YXMun2afMJukRAbyYHoa0cFaniki0lkU8kRERKR93C7jaITKPLN0tKyWZeuzKKqqN2tXjRjArRO/sjwzdhwERXf1iEVE+jSFPBEREbl4DSebd8/0Ls/ccKiUP316lKbm5Zn+vjbumZLMxOQIbz8tzxQR6TTW7h5Ae3z22Wdcf/31hIeHExgYyOTJk3nzzTfPu/+MGTOwWCxn/XrjjTfa9ElOTj5j24ULF3b0WxQREem5qvIhd5MZ8OqbXCz/JJs/bM4xA15SZAA/u2lk24AXkQqDJivgiYh0kl47k5eRkcHs2bOx2+3MnTuX0NBQ1q5dy7x588jJyeGxxx475zXmz5/PjBkzTqk3NTWxdOlSrFYrs2bNOuX10NBQHnrooVPqkyZNupi3IiIi0rucZnlmXnktL63PorDSuzzza8NjuG1Sgnd5ptUX4sZCUExXj1hEpF+xeDweT3cP4kI5nU6GDx9OXl4emzdvZsKECQBUV1dzxRVXcODAAfbu3cuQIUMu6vrvvPMOt956KzfddBN///vf27yWnJwMQE5OzgVft6qqitDQUCorKwkJCbmosYmIiHSr0yzPzDx8gje35tLYPHvn52vj21OSmdx69s4vDOLHg69/149ZRKSXaW9u6JXLNT/66COysrK48847zYAHEBwczBNPPIHT6WTFihUXff3ly5cDsGDBgnaPVUREpM+ozDtleeZrG7NZuSnbDHgJ4QE8cePItgEvPAUSLlPAExHpIr1yuWZGRgYA11xzzSmvtdTWr19/UdfOy8tj3bp1xMbGcsMNN5y2TUNDA6tWreL48eOEh4czZcoUxo0bd1H3ExER6fFcTij+0ngGr9mxslpe3pBFQavlmTOGRnP7pYnYfVotz4wdA8EDunrEIiL9Wq8MeYcOHQI47XLM8PBwoqKizDYXasWKFbjdbubPn4+Pz+n/eAoLC5k/f36b2rXXXssbb7xBVFTUOe9RVVXV5nuHw4HD4ThDaxERkW5UXwUFO6CxBmjePfNgKX/6zLt7pp+PjbuuSOLy1EhvP79QY/dMe0DXj1lEpJdpaGigoaHB/P6reeFC9crlmpWVlYCxAcrphISEmG0uhMfjMZd5nmmp5r333ktGRgYlJSVUVVWxZcsWrrvuOt5//32+/vWvcz6POCYkJBAaGmp+LV269ILHKiIi0ukqjsHRzWbAq2108son2fxhi3f3zMSIAJ64aWTbgBeeDAmXK+CJiJynpUuXtskHCQkJ7bper9x45ZprruGDDz7g0KFDDB48+JTX09LSyMvLa5OGz8eHH37IVVddRXp6urkk9Hy43W7S09PJzMzk3XffPeMyz5YHKI8dO9bmAUrN5ImISI/ickLRHqguMEu5J2pYtj6L4mrv361fGx7DtyYmfGV55mgIju3qEYuI9Gqnm8lLSEi46I1XeuVyzZYZvDPN1rWEqQvVsuHKfffdd0H9rFYr99xzD5mZmWzcuPGMIa9FSEiIdtcUEZGeqb4S8ndAUy1grHL5+EAxb32Wh9PdfLi53cY9V3zlcHMtzxQRuWgdPenTK0Ney7N4hw4dYuLEiW1eKy8vp7S0lClTplzQNcvLy/nrX/9KWFgYt9xyywWPqeVZvNra2gvuKyIi0iOU50LJfvAYYa6m0ckfNuXweW652SQlMpAH09OIDm71YSQ8GaKGgbVXPgUiItLn9Mr/GqenpwOwbt26U15rqbW0OV+rV6+moaGBefPm4e9/4Vs8b926FfCeoyciItJruJrg+HbjgPPmgJddWsPP/29vm4B39YgB/OS64d6AZ/WFgRMhZoQCnohID9Irn8lzOp0MGzaM48ePs2XLFsaPHw+0PQz9yy+/ZOjQoQCUlpZSWlpKVFTUGXe/HD9+PDt37mT79u1tzt5rbe/evcTHxxMWFtamnpmZydVXX43H4+HgwYMkJiaetr8OQxcRkR6nrsLYPbOpDjCWZ36wr5h3th3D6TY+IgTafbhnWgoTEsK8/fzDIW6czr4TEekE7c0NvXK5po+PD8uXL2f27NlMnz6dO+64g5CQENauXUt2djZPP/20GfAAXnzxRZ588kkWL17MkiVLTrnetm3b2LlzJ5dccskZAx7AmjVrePbZZ5k1axbJyck4HA727NnDunXrsFqtLFu27IwBT0REpMcpy4bSg+bs3ckGJys2ZrPjWIXZJDU6iAevTCUqqNXyzIhUiByi2TsRkR6qV4Y8gJkzZ5KZmcnixYtZs2YNjY2NjBo1iqeeeop58+Zd0LVee+014NwbrsycOZN9+/axfft21q9fT319PQMGDOD222/n4YcfZvLkyRf9fkRERLqMqwkKd8HJYrN0uPgkr2w4woka7+5u146O5ZsTBuLTEuZsvhA7DoKiu3rEIiJyAXrlcs3eSss1RUSk29WVG7tnOusBcLs9rNtbyNrtx3E1fyQIcviwYFoKYweFefv5RzQvz/Tr+jGLiPQz/XK5poiIiFwgjwfKs6HkIGCEuep6J69lHmH3ce+RRENignjgyjQiAu3evhFpEDUELJYuHrSIiFwMhTwREZG+ztkIhbuhxrs882BRNa9sOEJ5bSNg5LfrR8fzjfFx2MzlmXZj9i7w9JuWiYhIz6SQJyIi0pfVlhm7ZzqNZ+3cbg/v7SngbzuO0/LARoifL/dNT2FUfKi3X0CkEfB8Ou5wXhER6RoKeSIiIn2RxwNlR6D0EC3LMyvrmngt8whf5leZzYbHhnD/9BTCAlqWZ1ogcjBEpml5pohIL6WQJyIi0tc4G6BgF9SWmqV9BVW8+skRKuuaACO/3TQ2nhvHxmOzNoc5HwfEjYeAiG4YtIiIdBSFPBERkb6k5gQU7jSXZ7rcHt7dlc//7co3l2eG+vty//RURsS12rEtIArixmp5pohIH6CQJyIi0hd4PHDisPHVrKK2kVc/yWZ/oXd55qj4EO6bnkqIn29zxWLsnBmRquWZIiJ9hEKeiIhIb9dUBwU7jTPwmn2ZX8nyT7Kpqvcuz/zm+EFcNzoWq5Znioj0aQp5IiIivVl1kXE8gtsIc063m7/tyOefuwvMJuEBdh64MpWhA4K9/QJjIHYM+Ni/ekUREenlFPJERER6I7cbSvZDRa5ZKqlu4JVPjnCk5KRZGzMwlAXTUgn2a/kr3wLRQyE8RcszRUT6KIU8ERGR3qaxBvJ3QIP3WbttOWWs2JxDXaMLAB+rhVsuGcRVIwa0Wp7pB/HjwT+868csIiJdRiFPRESkN6k8DsV7we0EoNHp5q3PjpJxsMRsEh3k4MH0NFKiAr39gmIgdizYfL96RRER6WMU8kRERHoDl9MId1XHzVJ+ZR0vrz9CXnmtWbs0OYK7r0giwN78V7zFCtHDIDy5iwcsIiLdRSFPRESkp6uvgvwvoMkIcx6Ph8zDJ3hz61EaXcbyTLvNyh2TE5k+JApLy7N2vgHG8ky/0G4auIiIdAeFPBERkZ6sPNfYYMXjBqC20cnqLUfZmn3CbDIwzJ8H09MYGObv7RcSDzGjwKa/6kVE+hv9l19ERKQncjVB4S44WWyWsktreGVDFsXVDWYtfWg0cy9NxO5jNQoWGwwYBaEDu3rEIiLSQyjkiYiI9DS1Zcbh5s56wFie+cHeIt7ZnofT7QHA39fGt6ckc2lyq4PMHcHG4eaOoG4YtIiI9BQKeSIiIj2FxwNlR6D0EGCEuep6J69vzGZXXoXZLCUqiAevTCU62OHtG5YE0cPBau3aMYuISI+jkCciItITOBuM2bta77N2+wurefWTLCpqm8zataNj+eaEgfi0hDmrL8SOgeABXT1iERHpoRTyREREultNqRHwXI0AuNxu3t1VwP/tysdjTOgR7OfLgmnJjBkY5u3nHw5x48DX/9RriohIv6WQJyIi0l3cbjhxyFii2aysppFXPznCwaJqszYiNoT7pqcQFmD39o1Ig6gh0HJcgoiISDOFPBERke7QWGvM3tVXmKUdxyp4fWM2NQ1OAKwWC98YH891o+OwWZvDnM1ubK4SGNn1YxYRkV5BIU9ERKSrVRVA0R5wG2GuyeXmnW15fLCvyGwSEWDngfRUhsQEe/sFREHcWPBxfPWKIiIiJoU8ERGRruJ2QfE+qDxmlgqr6nllQxa5J2rN2oSEMOZPTSHI0fLXtAWih0J4ipZniojIOSnkiYiIdIWGasjfAY0nzdKWIyd4Y3Mu9U4XAD5WK7dfOoiZw2KwtIQ5X39jeaZ/WJcPWUREeieFPBERkc5Wngsl+8HjBqDB6eKPW4+y8XCp2SQ2xI8H0lNJigj09guOhQGjwebb1SMWEZFeTCFPRESkszgboWg3nCw2S0fLanl5QxaFlfVmbUpaFPMuS8TP12YULFaIGQlhCV09YhER6QMU8kRERDpDbRkU7DAOOQc8Hg8f7i/mL5/n4XQbM3oOHxv/cXkiU9KivP3sQRA/HhzBp15TRETkPCjkiYiIdCS3G04chrIss1RV38SKjTnsyqswawnhASxMTyU2tNVB5qGDjBk8q60LBywiIn2NQp6IiEhHOc3Zd1/mV/JaZjaVdU1m7eqRA7jlkkH42qxGwepjPHsXEtfFAxYRkb5IIU9ERKQjVOVD0Zdtzr772xfHef/LQrNJsJ8v905LZuzAMG8/v1Bj90x7QNeOV0RE+iyFPBERkfZwOaF4L1QdN0tFVfW8suEIOSdqzNqo+BDunZZCmL/d2zciFSKHgNXalSMWEZE+TiFPRETkYtVVGMszm4yDzD0eD5uzTvDHrUdbnX1n4eZLBnH1iAFYrc1n39nsEDcOAqPOcGEREZGLp5AnIiJyoTweKM+GkoOAB4DaRiertxxla/YJs9mAED8emJ5KclSrs+8CYyB2DPjYERER6QwKeSIiIheiqR4Kd0Ot9yDzrJKTvLLhCKUnG8za1MFR3Dn5K2ffRQ+D8OQuHrCIiPQ3CnkiIiLn62QxFO4Cl7FTpsvt4f09hfxtx3HcHmNGz9/Xxl1XJHNZSoS3nz3Q2FzFL6QbBi0iIv2NQp6IiMi5uN1Qsh8qcs1SeU0jr2Ye4UBhtVlLiw7i/umpRAc7vH1DEyBmhM6+ExGRLqOQJyIicjYNJ6FgBzR4w9wXR8tZsSmHmgbjuASLBW4YE8/Xx8Vha9kp0+oLsaMhOLYbBi0iIv2ZQp6IiMiZVByD4n3gMXbKbHS6eevzo2QcKDGbRATYWTA9leGxwd5+/uHG7pm+/l09YhEREYU8ERGRU7iajM1VThaZpbzyOl7ZkMXxijqzdkliGN+ekkKQo+WvUwtEDobINGN6T0REpBso5ImIiLRWW2acfeesB4yz7z4+UMxbn+fhdLkBsNus3H5pAulDo7G0hDkfP2P2LiDiTFcWERHpEgp5IiIiYJx9d+IwnMii5ey76nonKzdls+NYhdlsUHgAD16ZSnxYq6WYwbEwYDTYfLt2zCIiIqehkCciItJYaxyNUFdulvYVVLE88wgVtU1mbdbwAdw6cRB2n+bNVSw2Y+fMsISuHrGIiMgZKeSJiEj/VpUPRV+C29gp0+l287878vnnngKaj74j2M+Xe6YkMy4hzNvPEWIsz3QEdf2YRUREzkIhT0RE+idXkxHuqgvMUkl1A69sOMKR0pNmbURcCAumpRAeYPf2DU+GqGHQclyCiIhID6KQJyIi/c9pNlfZnHWCP356lPom47gEm8XCnAkDuXZULFZr8+YqNjvEjoWg6O4auYiIyDkp5ImISP/hdhubq5QdoWVzlZoGJ29syeWznDKzWUywg/uvTCM1KtDbNzAaYseAj6OLBy0iInJhFPJERKR/aKwxZu/qK83S/oJqXss8Qllto1mbkhbFHZMTCLA3/xVpsULUUGOJps6+ExGRXqBXP0zw2Wefcf311xMeHk5gYCCTJ0/mzTffPO/+GRkZWCyWM35t2bKlU+4rIiJdrOIY5Gw0A16Ty83b2/P4rw/2mwEvwG7jwfQ0FkxL8QY83wBIvBwiUhTwRESk1+i1M3kZGRnMnj0bu93O3LlzCQ0NZe3atcybN4+cnBwee+yx875Weno6M2bMOKU+aNCgTr2viIh0MmcjFO2Bk0VmqaCyjlc/ySb3RI1ZGxYbzIJpqUQGttpcJXQQRI8AW6/9q1JERPopi8fTskF07+F0Ohk+fDh5eXls3ryZCRMmAFBdXc0VV1zBgQMH2Lt3L0OGDDnrdTIyMpg5cyaLFy9myZIlnX7fqqoqQkNDqaysJCQk5MLetIiIXJiaE1C4E5wNgLG5yoZDpfz502M0urybq3zzkoFcMzIWW8vmKlZfiB1tHHAuIiLSDdqbG3rlcs2PPvqIrKws7rzzTjNoAQQHB/PEE0/gdDpZsWJFn7mviIhcALcbivdD3qdmwKuud/Lix4f5w+YcM+DFhvrx2PUjuG50nDfgBURC8jQFPBER6dV65RqUjIwMAK655ppTXmuprV+//ryvd+jQIZ5//nlqa2tJSkri6quvJioqqtPvKyIiHayh2thcpaHaLO05XsnrG7OprGsyazOGRnPbpQk4fGxGwWKFqCEQrmfvRESk9+uVIe/QoUMAp10WGR4eTlRUlNnmfLz55pttNk7x9/fnySef5JFHHumU+1ZVVbX53uFw4HBoS24RkXYpz4WS/eBxA9DodLN2ex4f7PM+jxfs58u3pyQzISHM288eCHHjwU/L6EVEpHs0NDTQ0NBgfv/VvHCheuVyzcpKY3e00NDQ074eEhJitjmb6Ohofv3rX7Nv3z5qamo4fvw4q1evJiIigh//+Me8/PLLnXLfhIQEQkNDza+lS5ees4+IiJyBswHytkHxXjPg5ZXX8Yv39rUJeKPiQ1ly08i2AS8sEZKmKuCJiEi3Wrp0aZt8kJCQ0K7r9cqZvI4yatQoRo0aZX4fEBDAvHnzGDduHBMnTmTx4sXcf//9WK0dm4WPHTvW5gFKzeKJiFykkyVQuAtcxjEIHo+HD/cX85dteThdRuDzsVn51sRBfG1YDNaWZ+9svhA7FoJiumvkIiIipkcffZQf/OAH5vdVVVXtCnq9MuS1zKSdadasZTeaizV69Gguu+wyPvnkEw4fPszQoUM79L4hISHaXVNEpD3cLmNpZsVRs1RR18iKzBz25Hv/Gz0wLIAHrkxhUHiAt29gNMSOAR/9gk1ERHqGjn58q1cu12x5Ju50z7+Vl5dTWlp6zuMTzqVl45Xa2touva+IiJxDXTnkZLYJeDuOVbDk73vbBLyrRgzg8RtGeAOexQoxI2DQJAU8ERHp03plyEtPTwdg3bp1p7zWUmtpczGcTifbt2/HYrGQmJjYZfcVEZGzcLuh9BAc3QpNxi/gGpwu/rA5hxc+OkR1vbF7Zqi/Lw9fNZQ7Jidi92n+a84RDElTIDy5mwYvIiLSdXrtYejDhg3j+PHjbNmyhfHjxwNtDyX/8ssvzWWWpaWllJaWEhUV1eZohM2bN3P55ZdjabVdttPp5JFHHuG3v/0t1157Lf/85z8v+r5fpcPQRUQuUmONcTRCvXemLqvkJMs/OUJxtXc3svEJYXx7SjIhfr7evuEpEDUUOvj5ahERkc7S3tzQK5/J8/HxYfny5cyePZvp06dzxx13EBISwtq1a8nOzubpp59uE7RefPFFnnzySRYvXsySJUvM+h133IHFYmHKlCkMHDiQiooKNmzYwIEDB0hMTGTZsmXtuq+IiHSA8lwoOQAe4xBzp9vNu7sK+MeuAtzNv6e022zcfukg0odGe39x5+MwNlcJPPXcUxERkb6sV4Y8gJkzZ5KZmcnixYtZs2YNjY2NjBo1iqeeeop58+ad1zUWLVrE+++/T0ZGBqWlpfj4+DB48GB++tOf8sMf/pDw8PBOua+IiJyHpnoo2gM1JWapoLKO5Z9kk3OixqylRgWxYHoKsSF+3r5BMTBgDPjYu3LEIiIiPUKvXK7ZW2m5pojIeaouNAKey3jOzuPx8PGBYv7yeR6NzUcjWC0Wvj4unuvHxGJrWYppsRmbq4S173whERGR7tQvl2uKiEgf5WqC4n1Qddwsldc2smJjDl+22jkzNsSP+6ankhIV6O3rFwZxY8HeqiYiItIPKeSJiEjPUFtmbK7irDdLn+eU8YctudQ0OM3a14bHcOvEBBwtO2digaghEJEKrTbSEhER6a8U8kREpHu53VB6EMqzzVJto5M3tx5l85ETZi3U35d7piYzZmCYt689EOLGgV9oFw5YRESkZ1PIExGR7lNfBYW7oKHaLO0vrOb1zGxO1HiPRpiUFM5dlycT5Nfqr62wJIgeBlZbV45YRESkx1PIExGRrufxGDN3pYfAY2yk0uRy89cvjrNubyEtW4L5+9q487JErkiN1NEIIiIi50khT0REulZTHRTsgroys3SsrJblmdnkldeataEDglkwLYWoIIe3b3AsDBgNNl9ERETk9BTyRESk61Qcg5L94DY2UnG5PXywt5C/fpGP023M6PlYrdx8yUCuHjEAq7V59s7qCwNGQkh8d41cRESk11DIExGRzneag81LTzbwWmY2B4u8z+MNCg/g/ukpDAoP8PYNiITYMeDr35UjFhER6bUU8kREpHNV5UPRXnB7DzbPPFzKnz87Rn2TCzBOPpg9MpY5Ewbia2s52NwKUUMhPFlHI4iIiFwAhTwREekczkYo/hKqC81SeW0jqzblsPu492DzyEAH905LYXhssLevI9g4GsERjIiIiFwYhTwREel41UXG8kxXI2DM3n2aU84ft+RS0+g92Hzq4CjmXppAgL3VX0cRqRA5BKzWr15VREREzoNCnoiIdBxXExTvNZZoNquud7J6Sy6f53p30wz19+XbVyQzLiHM29fX3zgaISCiCwcsIiLS9yjkiYhIx6gpNQ42d3oPMf/iaDl/2JxLVX2TWZucEsm8yYltDzYPGQgxI8Gmv5ZERETaS3+biohI+7icUHoAKo6apZpGJ3/69Cibs06YtSCHD/MuT2JycquZOpvd2DkzKKYrRywiItKnKeSJiMjFqy0zZu+a6szSnuOVrNqUQ1lto1kbnxDGXVckEeZv9/YNjjNm73zsiIiISMdRyBMRkQvndkHpISjPNkv1TS7+su0YGQe8Z+H5+9q4Y3IiU9IisbQcg2DzhZhREBLX1aMWERHpFxTyRETkwtRVGLN3jTVm6WBRNa9nZlNy0vs83si4EOZPTSEysNVMXWAMxI4GH0cXDlhERKR/UcgTEZHz43ZDWRacyAI8ADQ63fxtRx7r9hbhMUrYbTZuu3QQM4ZGe2fvrD4QMwJCB3XP2EVERPoRhTwRETm3+koo3A0N1WYpu7SG1zKzKaj0Po83JCaIe6amMCDEz9s3IMqYvfP178oRi4iI9FsKeSIicmZuN5w4DGVHaJm9a3K5+ceuAv6xuwB38/Sdj83KzRMGctWIAdiszbN3FhtED4OwRGiZ0RMREZFOp5AnIiKnV1dhzN41njRLuSdqWLExh2PltWYtKTKQ+6alEB/WaqbOP9w4GsEe2IUDFhEREVDIExGRrzJ3zsyh9ezdu7sK+OfuAlzNs3c2i4WbxsVz3ZhYfKxWo6/FClFDIDxFs3ciIiLdRCFPRES8asuM2bsm70xddmkNKzZmc7zC++zdoPAA7p2aTFJkq5k6RwjEjQVHcFeOWERERL5CIU9ERJpn7w42z94ZGp1u/r4zn/e/LDB3zrRZLNwwNo7rx8Tha2uevcMCkYMhIhVaZvRERESk2yjkiYj0d6eZvTtcfJKVm7IpqKw3a4kRAdwzNYXEiABvX3uQMXvnF9qVIxYREZGzUMgTEemvXE4oPQAVR81Sg9PN377I44N93nPvfKxWbhoXx7WjWz17hwUi0yAiTbN3IiIiPYxCnohIf1RzAop2Q5P3ObuDRdWs2JhNcXWDWUuJDOSeaSkMbL1zpiMYYseCX0hXjlhERETOk0KeiEh/4nJCyX6oPGaWGpwu3tl2nI8OtJq9s1mZMz6ea0YOwNZ658yIND17JyIi0sMp5ImI9Bc1pcazd07vc3b7CqpYuSmH0pPe2bu06CDmT00mPrT17F2Ice6dZu9ERER6PIU8EZG+ztUExfug6rhZqmty8fbnx8g4WGLW7DYr35wwkFkjBmCzNp9xZ7F6d87UuXciIiK9gkKeiEhfVl0IRV+Cq9Es7TleyR8253Kixjt7NzQmmPlTkxkQ4uft6xdmzN45grpwwCIiItJeCnkiIn1RUz0Ufwkni81SbaOTv3yex4ZD3tk7h4+NWy4ZyMxhMVhbz95FDYHwFM3eiYiI9EIKeSIifYnHY2yqUnIA3E6zvP1oOau35FJZ12TWhseGMH9KMtHBDm9//3Bj9s4e2JWjFhERkQ6kkCci0lc0nISiPVBXbpYqaht589OjbMv11vx8bNw6aRDpQ6Jbzd7ZIHoohCVp9k5ERKSXU8gTEent3G4oz4YTh8HjBsDj8ZB5uJQ1nx+jttFlNh0zMJS7rkgmMtDu7e8fAbGjNXsnIiLSRyjkiYj0ZnUVxuxdQ7VZKqqq5w+bc9lfWGXWgv18mXtpApelRGCxtJ69GwZhiZq9ExER6UMU8kREeiO3C0oPQnkuYJxg7nK7Wbe3iP/dkU+Ty202vSItktsnJRLs1+o/+QFRMGAU2AO6eOAiIiLS2RTyRER6m5PFxrEIrQ41zz1Rw8pNORwtqzVrkYEO7r4iidEDQ719rb4QMwJCB3bliEVERKQLKeSJiPQWTfVQvBdOFpmlBqebv+88zrovi3B7jBk9iwWuGjGAOeMH4udr8/YPjjMCno/jq1cWERGRPkQhT0Skp/N4oOKosTyz1bEIX+ZXsnpLLsXV3kPNB4UH8O0pyaRGtdpExcfPWJoZFNOVoxYREZFuopAnItKT1VcZG6vUV5qlqvom3vr0GFuyT5g1H6uVm8bFMXtULL42q7d/WBJEDQWb/nMvIiLSX+hvfRGRnsjtgtJDUJ5Dy8YqbrdxLMLb2/KoafTO6A2NCeauKUnEh/p7+9sDYcBoCIjo2nGLiIhIt1PIExHpaU6zsUp+RR1/2JzDoeKTZi3Q7sO3JiUwNS2y1aHmVohIg4hUsFq/emURERHpBxTyRER6itNsrNLodPPurgL+9WUBTrfHrF+RGsltlyYQ4ufr7e8XZhxq7gjuwkGLiIhIT6OQJyLS3TweY1nmicPn3FglJtjBXZcnMzI+xNvf6gvRQyE0QYeai4iIiEKeiEi3qi0zZu8aqs1SZV0Tb312jK1tNlaxcN3oOK4fE4fdp9UyzOBYiB4Bvn5dOWoRERHpwRTyRES6g7MRSvZD1XGz5HZ72HCohHe251Hb6DLrp91YxdcfYkZBUHRXjlpERER6AYU8EZGu5PFA5TEoOQjuJrOcU1rD6q1HyS796sYqg5iaFuXdWAULRKRA5GCw2hARERH5ql699dpnn33G9ddfT3h4OIGBgUyePJk333zzvPtnZmbywx/+kIkTJxIZGYmfnx/Dhw/nJz/5CRUVFaftk5ycjMViOe3XwoULO+idiUifVFcBRzcbO2c2B7yaBiert+Ty9Ht72wS8K1Ijefqbo5k+JNob8PzCIGkKRA9TwBMREZEz6rUzeRkZGcyePRu73c7cuXMJDQ1l7dq1zJs3j5ycHB577LFzXuPWW2+ltLSUadOmcffdd2OxWMjIyODZZ5/lnXfeYdOmTcTExJzSLzQ0lIceeuiU+qRJkzrirYlIX+NqgtKDUHHULHk8HjZlneAv2/KorvfO6MWH+jPvsiSGx7XaIVMbq4iIiMgFsHg8Hs+5m/UsTqeT4cOHk5eXx+bNm5kwYQIA1dXVXHHFFRw4cIC9e/cyZMiQs17nmWee4e677yYuLs6seTwevvvd7/LSSy/xne98h9/97ndt+iQnJwOQk5NzweOuqqoiNDSUyspKQkJCzt1BRHo3j8d45q5kvxH0muWV17J6S26bM+8cPja+Pi6OWSMG4GvTxioiIiL9WXtzQ69crvnRRx+RlZXFnXfeaQY8gODgYJ544gmcTicrVqw453V+8pOftAl4ABaLhSeeeAKA9evXd+zARaT/qKuAo1ugcLcZ8OqaXLz12VGe/L+9bQLexKRwnpozmmtHx3kDnm8ADJwE8RMU8EREROSC9MrlmhkZGQBcc801p7zWUmtPQPP1NQ4X9vE5/R9PQ0MDq1at4vjx44SHhzNlyhTGjRt30fcTkT7E2WAszazMM0sej4fPcsp56/OjVNR6Z/Righ3MuyyJ0QNDvf0tVohIg4hUsPbK38P9/+3deXSU9b3H8fdMkpmsk30hbCHIGpagiKikGBHQ2lKPeisRe0XU1p7eS7W37VFbQRSl0tqDLW2tcgCr9VquRbSt2KgYNMhiK4LsAZNAAmQh+zbJZOb+8ZBlskCAZIZMPq9z5gSe58nzfKND4JPf7/f9iYiIiJf1y5CXk5MD0OV0zMjISGJiYlqvuRhr164Fug6RAKdPn2bhwoVux26++WZeffVVYmJiznv/qqoqt99brVasVuvFFSsilwenEyry4cwxt66ZJyvqeX3XcQ6eavtzH+Bn5taJg5ibkuC+511IHMSNA0uwJysXERERL7Pb7djt9tbfd8wLF6pf/pi4srISMBqgdMVms7Vec6G++OILli1bRlxcHD/96U87nV+0aBFZWVmUlJRQVVXFjh07uOWWW3jvvfeYN28ePVniOHToUMLDw1tfK1asuKhaReQyUVsK+dnG2ruWrpmNDt7YdZyl7+x3C3iTh0Tw9Lcm8M3JiW0Bzz8QEq+EIVcp4ImIiAxAK1ascMsHQ4cOvaT79cvGK3PmzOH9998nJyeHK664otP5kSNHUlBQ4JaGeyI3N5e0tDRKS0vZvHkz6enpPfo8p9PJzJkzyc7O5u9//zu33nprl9e1LKA8ceKE2wJKjeSJ9FONdVByEGqKWw81O11sO1rKxt2Fbl0zo0OsZFwzjClDI9o+32SGyBEQPVJbIoiIiAxgXY3kDR069KIbr/TL6ZotI3jdjda1hKkLkZ+fT3p6OiUlJfz1r3/tccADMJvN3HfffWRnZ7Nt27ZuQ14Lm82m7poi/ZmzGcq+Ml4uZ+vho8U1vL7rOPlnaluPWfzMfL2rqZnB0RA3HqyhnqxcRERELkO9PejTL0Ney1q8nJwcrrrqKrdz5eXllJaWct111/X4fnl5eaSnp3Py5En+7//+j2984xsXXFPLWry6uroL/lwR6UeqTkLJYXA0tB6qqGvkzX8XsP2rM26XTk2K4ttThxIdYmk76G81tkSwuXf2FREREekt/XJN3syZMwHIzMzsdK7lWMs155OXl8cNN9xAYWEhf/nLX/jWt751UTXt3LkTaNtHT0R8TH055G+HU3taA15Ts5PN+07xs7f2uQW8IZHB/GTOWL4/c2RbwDOZjY6ZSV9TwBMREZE+1S/X5DkcDsaMGUNhYSE7duwgNTUVcN8Mff/+/YwePRqA0tJSSktLiYmJcet+2THg3X777ed87oEDB0hMTCQiIsLteHZ2NrNnz8blcnHkyBGGDRvW5edrM3SRfqixDkoPQ/Vpt8N7Cyp447MTFFW1jeiFWPy5bUoiM0fH4td++4OQ2LNdM0M8VbWIiIj0Y5eaG/rldE1/f3/WrFnD3LlzSUtLIyMjA5vNxsaNG8nNzWX58uWtAQ9g9erVLFu2jKVLl/Lkk0+2Hr/hhhvIz89n+vTp7N27l71793Z6VvvrN2zYwMqVK5k1axZJSUlYrVb27dtHZmYmZrOZF198sduAJyL9TLMDyo5BeZ7buruC8no2/OsE+0+2rQk2mWDmqFhumzKEsMB231YDgo1wFxrnwcJFRERkoOuXIQ8gPT2d7Oxsli5dyoYNG2hsbCQlJYWnn36aBQsW9Oge+fn5AOzYsYMdO3Z0eU37kJeens7Bgwf5/PPP2bp1Kw0NDcTHx3PXXXfxyCOPMG3atEv+ukTEy1wuqDxhbGje3NYds7K+ibe/KOTjnBLaz38YFRdKxrRhDI9uN0pn9jemZkaO0IbmIiIi4nH9crpmf6XpmiKXudpSKD4IjTWthxodTt4/WMS7X56ioam59Xh0iJU7rhrCtKRITCZT2z1siRAzBgICPVm5iIiI+JABOV1TRKRX2auNjpm1Ja2HXC4Xu/LK+eu/CzhT27ZvTWCAH1+fOIjZ4+Ldt0Sw2oypmcFRnqxcREREpBOFPBEZuJrqoTQHqgrdDh8rqeGNz07wVUnbiJ7JBF8bFcu3UgcTHhTQdrFfgDFyFz7EuEhERETEyxTyRGTgcTQaG5lX5Ls1VSmptrNxdyG7ct33u0tJtPHtqcMYEhnUdtBkhsgkiBoJfvpWKiIiIpcP/ctERAYOZ7PRLbMsF5xtTVVq7A7e/fIUHx4qxtHcFvoGhQfx7auHMDEx3H3dXViCMXpnCfZg8SIiIiI9o5AnIr7P5YLKAjiTA4629XV2h5MPDhaxed8p6hvbmqqEBQbwrdREvjYqxn2/u8AIiBsLQZEeLF5ERETkwijkiYhvqy4yNjNvrG091Ox0su3oGd7eU0hFXduIXoCfmVnj4rh14iCCLe2+PfoHQuwYo3OmiIiIyGVOIU9EfFPtGWOvu4aK1kMul4vdJyrY+HkBpyobWo+bTDBjZCzzUhOJCrG03aN1v7skMPt5rnYRERGRS6CQJyK+pa4MzhyFOvfmKUeKqnnz3wUca9cxEyB1aAR3XDmExIh2TVUwGd0yY0aBv9UDRYuIiIj0HoU8EfEN9RXGdgh1pW6HC8rr2fh5AXsKKtyOXxEXyp1XDWFUXJj7fULjIGY0WDscFxEREeknFPJEpH9rqDLCXW2x2+HTlfW8s+cUu/LO4HK1HU8MD+KOq4YweUiHjplBkUa402bmIiIi0s8p5IlI/2SvNsJdTZHb4ZJqO3/be5JPj5W6hbuoYAvzpgzmuuRo/Mztwp0l1Ah3YfEeKlxERESkbynkiUj/Yq8x1txVn3I7fKa2kb/vPcm2nFKa26W7sMAAbp4Qz41j4rH4t9sOwd9qhDvbYKPzioiIiIiPUMgTkf7BXg1njkH1aaAtxFXUNfKPL0/x8ZFSHM62jcyDLX7cnDKIG8fFERTQrjOmOQCikyFiuDpmioiIiE9SyBORy1t9BZQdgxr3NXdVDU1s/vIUHx0uoam5LdwFBfgxe3w8s8fHu+91ZzIbWyFEJYNfgGdqFxEREfEChTwRuTzVlRkjdx26ZVY1NJG5v4gth4qxO5pbj1v9/Zg1Lo65KQmEWjuEu/ChRrgLCPRU9SIiIiJeo5AnIpeX2lIj3NWXuR0uq23kn/tP8/GREhrbjdwF+Jm5cWwsN08YhC2w/Qjd2b3uokdCQBAiIiIiA4VCnohcHmqKjYYqDZVuh0uq7by77xSfHj3jtubO32zma6NjuHXiICKCLe0+wwS2RIi+AizBHipeRERE5PKhkCci3uN0QvVJKMuFxhq3Uycr63l37yl25pbhbNct0+JnZuboWOamJBAZYnG/X9ggiBkFlhBPVC8iIiJyWVLIExHPa26CiuNQkQ8Ou9up/LJa3t17in8fL3fb5y4wwI8bx8Yxe3x8h2mZQGi8Ee6sYR4oXkREROTyppAnIp7TWGcEu4oT4GprmuJyuThaUsPmL0+zp6DC7VNCrP7MHhfPjWPjCLF2+JYVGm9Mywy0eaB4ERERkf5BIU9E+l59BZTnQnUR7fe4a3a6+OJ4Oe8dKOKrEvfpmrbAAOZOiOeG0XEEtt/nDhOEJRgNVTRyJyIiItKJQp6I9A2Xy2imUp7XqVOm3dFMdk4p7x8ooqTGfbpmVLCFWyYmMOOKWCz+5rYTJrPRUCUqWWvuRERERM5BIU9EepejEaoKjDV3TfVupyrqG9lysISsw8XUNjrczg2JDGbu+HiuHhFFgF+HcBc+FKJGaCsEERERkR5QyBOR3tFQCeX5UH0KXE63Uycr6vnn/tPs+KrMbRsEgJREG3PGJ5CSaMNkMrWdMPlBxDAj3PlbPfEViIiIiPgEhTwRuXhOJ9ScNsJdQ0WHUy72n6rkw4PFfFnovvedn8nEtOQo5oxPYFhUh73szAEQORwihoN/hy0SREREROS8FPJE5MI11RsdMiuPG9shtFNrd7DtaCkfHS6muNp9vV2QxY+Zo2OZNTaeqI573AUEQeQICB8CZj9ERERE5OIo5IlIzzidUFtirLerKaF9l0ww9rfLOlTCjq/O0NjsPiUzOsTKTePjSRsVQ1BAhwAXGGFMyQyNh/bTNUVERETkoijkici52WugqhAqC6C50e1UU7OTz49XsOVQEUeLazp96vhBNtLHxjF5SDh+ZrP7ydA4Y+QuOKovqxcREREZcBTyRKQzZ7PRQKWyAOrLO50uq23k45wSth4uoarBfbpmYIAf14+M4YaxsSSGd+iGafKD8MEQmaRtEERERET6iEKeiLSpLzeCXfVpcLpvcdDU7GTPiQo+ySll/6lKXO6zNUkMD+LGsbFMH9nFlEx/q9FIJXyomqmIiIiI9DGFPJGBrrHWCHVVhcavOygoryc7p4QduWVUdxi1M5tMTBkWwY1j4hmTEOq+BQJAUJTRKTMkDjpO1xQRERGRPqGQJzIQNTUY0zGrTxn723VQ1+jgs9xyPjlaSm5p57V20SFWrr8imrRRsZ27ZLZMyYwYBtawvvoKRERERKQbCnkiA0VzkzFiV30K6sro2B3T6XRxpKiG7GMl/DuvvFOHTH+zmSuHR5J2RQxjE8IwmzuM2llCjGBnGwJ++tYiIiIi4i36l5iIL2t2GNseVJ+E2lJwuQc3l8vF8bI6duaWsSu3jPK6xk63GBoZzIxRMUxPjibU2vFbhglCYyEiCUKi++7rEBEREZEeU8gT8TWORqgpgppiqOsc7ABOVzWw66syduWd4VRlQ6fzQRY/po+IZsYVMQyPDu681i4g2Ni03DYYAgL76isRERERkYugkCfiCxrr2oJdfTkdp2ICVNQ1siuvnF1fnSH3TOcGK34mEymDw7lmRBRXDovE4t+hUYrJD8LijQ6Z2ttORERE5LKlkCfSXzVUGaGu5jTYq7u8pKy2kS9OlPN5fgWHiqo6bXsAMDo+jGtGRDF1eBShgV18SwgMN0btwgaBX0AvfxEiIiIi0tsU8kT6C0ejMf2yttT46LB3ednJynp251ew+3h5lyN2AMOigrkmOYqrk6KJ7tgdE4wwZxtsvAJtvflViIiIiEgfU8gTuVy5XNBQYYS62pIutzoAoytmXlkdnx8vZ/fxck53scYOIC7MyjUjopmWHEVieFDnC0x+EBoHtkQIjtG+diIiIiL9lEKeyOWksdbY3qCuFGrPgLOpy8sampo5fLqaLwsr+eJERZddMcHojDllWARXDotkSGRQ5wYqmCAkFmyDIDQezH69/AWJiIiIiKcp5Il4i8sFjTVGqKsvMz42dx3WXC4XBeX17D9Zyb7CKnKKq3E4Oy+wM5lgVGwYU4ZFMGVYJLFh1q6fHRR1NtglgH8X0zVFREREpN9SyBPxFJcL7FVtoa6+3NigvBs1DQ4OnKpi38lK9p+spKKu62v9zWbGJ9qYMjSC1GER2AK7aY4SGGF0xwwbBAFdTNcUEREREZ+gkCfSVxrrjHV0DZXG2rqGKnA1d3t5XaODo8U1HC6q5vDpGvLO1HTZDRMgJtTKhEQbKYPDGTfIRlBAV9MsTRAcbayzC43XfnYiIiIiA4RCnkhvcDR2CHQV5xylA2Ok7khxNUdOV3OkuJrjZXXdhjqLn5kxCWFMGBzOhEQb8bbALtbXYTRPCYmBsARjrZ22PBAREREZcBTyRC6E02mso7NXt70aq7vdzqCFy+WitKaR3NJacoqNkbrCirpzfs7giCAmDLYxITGCK+JCO29O3sLPYgS6sARj5E7NU0REREQGNIU8ka44ndBUa0y5bKwx1tLZq43f081wWzvVDQ5yz9SQW1JLbmkteWfqqG7ofmTPZILBEcGMiQ9jdHwooxPCul9bhwmCIo0Ru5AYsNqMG4iIiIiIoJAnA5nTCU11xqux9uzHs6HO0fVec12psTsoKK/n+Jlavio1Ql1pzblH9kwmGB4VwpiEUEbF2xgVF0qo9Rx/HAOCjL3rQmKN0To//dEVERERka7pX4riO1wucDqMqZPNjW0vR8uv7cY6uebGs9c00ZNRuRaNDienKuspLK+noKKegvJ6Civquu162V6o1Z8RMSHGKzaEK2JDCbac44+fOeDsaF20Ee6soT2uU0REREQGtn4d8j777DOWLl3K9u3baWxsJCUlhYcffpi77767x/dwOp38/ve/56WXXiInJ4fQ0FDS09N55plnGDVqVJ89V3qgj0Nbd2rsDoqqGiiubqCoys6pygYKy+spqmrA2V1nlHYsfmaGRwefDXWhjIgJISbU0nWjlBZ+AcbedUGRxkidNUxTMEVERETkovTbkJeVlcXcuXOxWCzMnz+f8PBwNm7cyIIFC8jLy+Pxxx/v0X0eeughXn75ZcaPH89///d/U1RUxF/+8hcyMzP59NNPGT9+fK891263u33sMy6X8aLdx5bjdHHO7SNdH+vxtR0+upxnL3Oepwbn2eDW+6Gto0aHk2q7g6r6JiPMVdlbA11RdQO1dkeP7xVi8WdwZBBDIoMYEmkEu8ERgfiZu2mSAjQ1NfHGm29x18LvYwmPh+AoI9TJgGO321mxYgWPPfYYVms3G9eLz9P7QEDvA2mj94LApecGk8vVg6GJy4zD4WDs2LEUFBSwfft2pkyZAkB1dTXXXnsthw8f5sCBA92OxLX46KOPuPHGG0lLS+P9999v/YP04YcfMnv2bNLS0ti6dWuvPbegoIChQ4dycudGBsXF4R6+OM/vO4Yt2q7pKtANIC2hrbq+iSp7EzX1DiPENTRR3eCgpsFBVYOD6oYmahocNDi636uuO/5mM4PCA8+GuSAj2EUEExEccO4ROjC2NQi0QWA4BIZT1eRHeEwClZWV2Gy2i/yqxRdUVVURHh6u98IAp/eBgN4H0kbvBYG23HDixAmGDBlywZ/fL0fytmzZwrFjx7jvvvtagxZAWFgYTzzxBPPnz2fdunU8++yz57zPyy+/DMDy5cvdflIya9Ys5s6dy3vvvceRI0cYPXp0rz7XZK8Buzam7o4nQlt3ooItxNkCibNZibdZibcFEW+zEhdmxf8co3NtTMb6ucBwCIwwPnacellV1Wv1ioiIiIh01C9DXlZWFgBz5szpdK7lWPsRuHPdJyQkhOuvv77TuZaQt3Xr1taQ11vPbXa5aGp2GrMZMfZQa/14dkDOeXZEztnumAtwOl24cOF0unBiNIg0rnHhdBm/drpcrcfbXhgvZxfXuoxnO53Gc53OdsdcLpo7nTc+upzt793uXk7ja2yr0Umz0/gam51t9TW72v/aSa29uddDW3uhVn/CAv0JswYQFuRPmNWf6NCWMBdIbFgg1u72ouuKOcAIcK2vUGM7A+1TJyIiIiJe1C9DXk5ODkCX0yIjIyOJiYlpvaY7tbW1nDp1igkTJuDn1/kf5S33bn+fS31uy8zYH23Yg0VzrC9ZiMUIbaHWAEID/QiztvzavzXQhQYGEGr1J9Tqd851cgD2hga6nPVsMkFAiBHiLGeDnCUEAjqMxjZh7K13HlVnR/KqNKI34Om9IKD3gRj0PpAWei8MTHa73W393alTp4C2/HCh+mXIq6ysBCA8PLzL8zabjYKCgku+R/vreuO5TU1Gq/0Nv/v1OWuTgWHo0KHeLkEuE3ovCOh9IAa9D6SF3gsCbfnhQvXLkNdfJSUlcezYMQIC3Jt1WK1WdU8SERERERmgOo7kuVwumpqaSEpKuqj79cuQ1zKS1n6Urb2WrkSXeo/21/XGc81mM8nJyeesS0RERERE5FJcQJeJy0dX6+ValJeXU1paet7tE0JCQhg0aBC5ubk0N3du9NHV+rveeK6IiIiIiEhf6pchb+bMmQBkZmZ2OtdyrOWa892ntraWbdu2dTr3z3/+s9N9euu5IiIiIiIifaXfboY+ZswYCgsL2bFjB6mpqYD7puT79+9v3fqgtLSU0tJSYmJiiImJab1P+83QP/jgAywWC3DuzdAv5LkiIiIiIiKe1i9H8vz9/VmzZg1Op5O0tDS++93v8uMf/5jJkyezf/9+nnzySbegtXr1asaNG8fq1avd7pOens4DDzzAJ598wpQpU/jpT3/Kvffey6233orNZuMPf/jDJT23xWeffcbXv/51IiMjCQkJYdq0abz++ut98x9HLkuvvfYa3/ve95g6dSpWqxWTycT69eu9XZZ4WGFhIatWrWLOnDkMGzYMi8VCQkICd9xxBzt37vR2eeIhFRUVLF68mGuvvZaEhASsViuDBw/mxhtv5K9//etFt8uW/m/lypWYTCZMJhM7duzwdjniQUlJSa3/7zu+HnroIW+XJx721ltvMXv2bKKjowkKCmLEiBFkZGRw4sSJHt+jXzZeASOgZWdns3TpUjZs2EBjYyMpKSk8/fTTLFiwoMf3+eMf/8ikSZP44x//yG9+8xtCQ0P55je/yTPPPNNlYLvQ52ZlZTF37lwsFgvz588nPDycjRs3smDBAvLy8nj88ccv6b+D9A8///nPyc/PJyYmhkGDBpGfn+/tksQLfvvb3/Lcc88xcuRIZs+eTVxcHDk5OWzatIlNmzbxv//7v3z729/2dpnSx0pLS1m7di3Tp0/ntttuIyoqiuLiYv72t79x55138uCDD/LSSy95u0zxsIMHD7JkyRJCQkKorT3/nqvie8LDw3n44Yc7HZ86darnixGvcLlcPPTQQ7z00kuMHDmS+fPnExYWxsmTJ9m6dSv5+fk93lqjX07X7C8cDgdjx46loKCA7du3M2XKFMB9eueBAwfUrGUA+OCDDxg1ahTDhw/nF7/4BY899hjr1q1j4cKF3i5NPGjjxo3ExsaSlpbmdvyTTz5h1qxZrd/ItaWKb2tubsblcuHv7/5z1urqaqZPn86BAwfYt28fKSkpXqpQPK25uZlrr70Wk8nE6NGjee2119i+fTvTp0/3dmniIS1t8vPy8rxah3jXb37zG374wx/ygx/8gBdeeAE/Pz+38w6Ho9PfHd3pl9M1+4stW7Zw7Ngx7r777taABxAWFsYTTzyBw+Fg3bp1XqxQPOWmm25i+PDh3i5DvOz222/vFPAA0tLSSE9Pp6ysjC+//NILlYkn+fn5dfmXdFhYGHPnzgXg6NGjni5LvOi5555jz549rF27ttM/6kRkYKivr2fZsmUkJyezatWqLr8X9DTgQT+ertkfZGVlATBnzpxO51qOtW/sIiIDV0BAAHBh38DFtzQ0NLBlyxZMJhPjx4/3djniIfv27WPZsmX8/Oc/1+jtAGe323nllVcoLCwkMjKS6667jsmTJ3u7LPGQ999/n7KyMhYuXEhzczPvvPMOR44cISIigptuuokrrrjigu6nf030oa722msRGRlJTExMl3vuicjAcvz4cT744AMSEhKYOHGit8sRD6moqGDVqlU4nU6Ki4t59913OXHiBEuXLtU0/gHC4XCwcOFCxo0bx6OPPurtcsTLTp8+3WkZx80338yrr77q1h1efNO//vUvwPhh7+TJkzl8+HDrObPZzCOPPMKvfvWrHt9PIa8PVVZWAsZC2q7YbDYKCgo8WZKIXGaampr4zne+g91uZ+XKlZqqNYBUVFSwbNmy1t8HBATwy1/+kv/5n//xYlXiSc8++yx79uxh586draP5MjAtWrSImTNnkpKSgtVq5cCBAyxbtozNmzczb948tm3bhslk8naZ0oeKi4sBeP7557nyyivZtWsX48aNY/fu3Xz3u9/l+eefZ+TIkXz/+9/v0f20Jk9ExEucTieLFi3i448/5sEHH+Q73/mOt0sSD0pKSsLlcuFwOMjNzeWpp57iZz/7GXfccQcOh8Pb5Ukf27NnD8uXL+fHP/4xV155pbfLES9bsmQJM2fOJCYmhrCwMK655hr+/ve/M2PGDLZv3867777r7RKljzmdTgAsFgubNm3i6quvJjQ0lLS0NN58803MZjPPP/98j++nkNeHWkbwWkb0Oqqqqup2lE9EfJvL5eLBBx/ktdde45577uHFF1/0dkniJX5+fiQlJfHoo4+yfPly3nrrLV5++WVvlyV97N5772XkyJE8+eST3i5FLlNms5n77rsPgG3btnm5GulrLZlg6tSpJCYmup1LSUkhOTmZY8eOUVFR0aP7KeT1oZY1FV2tuysvL6e0tFTrLkQGIKfTyf3338/atWvJyMhg/fr1mM36dixtTblaGneJ79qzZw+HDh0iMDDQbePrV155BaB1S4VNmzZ5t1Dxqpa1eHV1dV6uRPramDFjAIiIiOjyfMvx+vr6Ht1Pa/L60MyZM1mxYgWZmZnMnz/f7VxmZmbrNSIycDidTh544AHWrVvHXXfdxauvvqp1eNLq5MmTgLqsDgT3339/l8c//vhjcnJymDdvHrGxsa37p8nAtHPnTgC9DwaA9PR0AA4ePNjpXFNTE0ePHiUkJITY2Nge3U9/i/ShWbNmkZyczOuvv87ixYtJTU0FjA1vn376afz9/bUZtsgA0jKCt379ev7jP/6D1157TQFvAPriiy8YMWJEp+n6ZWVlPP744wDccsst3ihNPGjNmjVdHl+4cCE5OTk89thj2gx9gDhw4ACJiYmdRnCys7P59a9/jdVq5fbbb/dOceIxI0eOZM6cOWRmZrJmzRoeeOCB1nO/+MUvqKio4J577unxDwEV8vqQv78/a9asYe7cuaSlpZGRkYHNZmPjxo3k5uayfPlyRo8e7e0yxQPWrFlDdnY2QOtm12vWrGmdknXbbbdx2223eak68ZSnnnqK9evXExoayujRo1m+fHmna2677bbWHwiJb1q/fj1r1qwhPT2d4cOHExISQn5+Pv/4xz+oqanhjjvu4O677/Z2mSLiIRs2bGDlypXMmjWLpKQkrFYr+/btIzMzE7PZzIsvvsiwYcO8XaZ4wO9//3uuu+46HnzwQTZt2sTYsWPZvXs3W7ZsYfjw4fzyl7/s8b0U8vpYeno62dnZLF26lA0bNtDY2EhKSgpPP/00CxYs8HZ54iHZ2dmt6yxabNu2rXUhdVJSkkLeAJCXlwdATU0NzzzzTJfXJCUlKeT5uDvvvJPKykp27NjBxx9/TF1dHVFRUcyYMYP//M//ZP78+WqVLjKApKenc/DgQT7//HO2bt1KQ0MD8fHx3HXXXTzyyCNMmzbN2yWKh4wcOZJ//etfLFmyhPfee4/MzEwSEhL4wQ9+wJIlS4iLi+vxvUwul8vVh7WKiIiIiIiIB6mdm4iIiIiIiA9RyBMREREREfEhCnkiIiIiIiI+RCFPRERERETEhyjkiYiIiIiI+BCFPBERERERER+ikCciIiIiIuJDFPJERERERER8iEKeiIiIiIiID1HIExERERER8SEKeSIiIgOYw+Fg0qRJmEwm3njjDW+XIyIivUAhT0REZAB74YUXKCkp8XYZIiLSixTyREREBqjCwkKWLVvGc8895+1SRESkFynkiYiInEdeXh4mk8nt9eSTT3Z7/bFjx1iwYAGDBw8mICAAk8nEo48+6rmCe+iHP/wh8+bN42tf+1q316Smprp93TfccIPnChQRkYvi7+0CRERE+gur1crUqVMBGDZsWJfXFBcXc/3111NUVERCQgJXX301ZrP5nEHKG9577z0yMzM5fPgwdru92+umTJlCaGgolZWV7Nu3z4MViojIxVLIExER6aGEhASys7PPec0LL7xAUVERd999N3/605/w8/PzUHU919DQwH/913+xdOlSBg0aRF5eXrfXrlu3DoCsrCzS09M9VKGIiFwKhTwREZFe9PbbbwPwox/9yCMBr7q6msLCwvNeN2jQIMLDwwF49tlnsVgsLF68uK/LExERL1DIExER6SXV1dUcOHCA4OBgpkyZ4pFn/uMf/yAjI+O8161bt46FCxeSn5/PypUr+fOf/0xtbS0AVVVVANTV1VFZWdkaBkVEpH9S4xUREfFpLpeLt99+m2984xskJSVhtVqJiYkhNTWVRYsWcfr06Ut+Rm5uLiaTCZvNhsvloq6uDj8/v9ZmJWVlZQBERka2/v7dd99l9uzZREZGEh0dTUZGhlstr7/+OjNmzMBmszF06FAWL15MXV1dp2fPnz8fl8t13tfChQtba7Xb7dx5551ERkYSGRnJ5MmTAbj//vuJj4+/5P8eIiLiXRrJExERn+V0OsnIyGDDhg0AJCcnk5qaSnl5OYcOHWLv3r2sWrXqkp9z5swZrr/+ek6ePElubi6JiYmMGDECgIiICKKiojh+/DgVFRUkJCTwq1/9ihUrVpCcnMzQoUPZv38/b7zxBsePHycrK4sFCxbw5ptvMnbsWAYPHsyhQ4f47W9/S3NzM7/73e8uqdbU1FQ++ugjt2OnT58mIyODJ554gptuuumS7i8iIt6nkCciIj7rT3/6Exs2bGDy5Mm8/vrrjB8/vvWc3W5n79692Gy2S37O1KlTyc7O5pFHHmHVqlX85Cc/4eGHH3a7Zs+ePYARCF955RW2bt3a2nHzww8/ZPbs2Xz66afMmzePgoIC9u7dy4QJEwBYu3Yt999/P+vWrWP16tWYTKaLrjUiIqLTNggtjVfGjx9/2XUBFRGRC6fpmiIi4rOysrIAWLJkiVvAA2M7hKuvvrpXn9cS5FqmP3Z1DuCdd95xC1OzZs1i4sSJAOzatYvNmze3BjyARYsWERoaSn19PUVFRb1as4iI+B6N5ImIiM9KTEwEYPXq1YwaNYoxY8ZgsVj67Hl79+4FYNKkSZ3OtYS8hx56iKuuuqrT+YCAAACeeOIJhgwZ0um82Wz8XDYoKKjX6m2RlJSEy+Xq9fuKiIh3aCRPRER81uOPP84999zDp59+yqRJk7BarZhMJj744INef1ZBQQFnzpxhyJAhREdHdzrfEgDvuuuuTudcLhc5OTmYTCbuvPPOTueLi4upqqoiJiZGnS9FROS8NJInIiI+6/Dhw62dLUeMGEFCQgJms7nLkbZLda6pmnV1dRw9ehSLxcK0adM6nc/NzaWqqork5OQuR/HOdW8REZGOFPJERMQnbd68mXnz5nHVVVfx+eefd1qT19vOFcS+/PJLnE4nKSkprdMy2/viiy8Ao/Plue7d3XkREZH2FPJERMQnLV68GIBNmzaRkJDQ58/rSdOV7kLa+UJey3mN5ImISE9oTZ6IiPic4uJijh49SlhYmEcCHpw75LWsxzvfSN2UKVPOeV4jeSIi0hMKeSIi4nNsNhv+/v6Ul5fz1FNPYbfb3c4fPHiQt99+u9eeV19fT05ODkFBQYwaNarT+fOtqTvXSJ7dbufQoUNYLBbGjh3bazWLiIjvUsgTERGfExgYyPe+9z0Ali5dSlxcHKmpqUyaNImYmBjGjx/Pp59+2mvPa1lzN3HixNatDtprGcnrKuSVl5dz/PhxoqOju2y6cuDAARwOR7fr+URERDrSmjwREfFJL7zwAikpKfz5z39m//797Nu3j4iICIYNG0ZGRgb33ntvrz2rZaSuq66dLZ0zk5KSiIiI6PZzzzeVU+vxRESkp0wu7X4qIiJyTnl5eYwYMYLhw4eTl5fn7XK8Iisri/T0dGbOnElWVpa3yxERkXPQSJ6IiEgPnT59mhkzZgCwaNEiFi1a5OWK+t59991HTk4OlZWV3i5FRER6SCFPRESkh+x2O9u2bQPgpptu8nI1nrF79+7WKaMiItI/aLqmiIiIiIiID1F3TRERERERER+ikCciIiIiIuJDFPJERERERER8iEKeiIiIiIiID1HIExERERER8SEKeSIiIiIiIj5EIU9ERERERMSHKOSJiIiIiIj4EIU8ERERERERH6KQJyIiIiIi4kMU8kRERERERHyIQp6IiIiIiIgP+X8Gv4UNNRwO7QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eps_total_fast = np.array([*eps_com, *eps_fast])\n",
+    "pres_total_fast = np.array([*pres_com, *pres_fast])\n",
+    "\n",
+    "compare_fig1, ax = plt.subplots(1, 1, figsize=(9,6))\n",
+    "\n",
+    "ax.plot(eps_total_fast, pres_total_fast, lw=2, label=\"Fast Version\")\n",
+    "ax.plot(eps_total, pres_total, lw=6, label=\"Normal Version\", alpha=0.3)\n",
+    "ax.set_ylabel(r\"P $[fm^{-4}]$\", fontsize=16)\n",
+    "ax.set_xlabel(r\"$\\epsilon$ $[fm^{-4}]$\", fontsize=16)\n",
+    "ax.set_xlim(0, 6)\n",
+    "ax.set_ylim(0, 2)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "plt.legend()\n",
+    "compare_fig1.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MR_fast = main.OutputMR(\"\", eps_total_fast, pres_total_fast).T\n",
+    "\n",
+    "compare_fig2, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "\n",
+    "ax.plot(MR_fast[1] / km, MR_fast[0] / Msun, lw=2, label=\"Fast Version\")\n",
+    "ax.plot(MR[1] / km, MR[0] / Msun, lw=6, label=\"Normal Version\", alpha=0.3)\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "plt.legend()\n",
+    "compare_fig2.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polytrope EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.integrate import ode\n",
+    "import EOSgenerators.Polytrope_EOS as Polytrope\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "from TOVsolver.unit import  km, Msun, MeV,fm,g_cm_3,dyn_cm_2, G,c "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The polytropic equation of state (Polytrope) is the most commonly used EOS in astrophysics, its advantage is assumed to be capable to simulate all EOS shape with given parameters. Here, we provide one of the computation for polytrope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Connecting outer crust with polytropic part"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For polytrope, we first connecting it with crust as we did in RMF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define a set of parameters, first three in theta are the parameters control the slope of each polytrope, and the last two are transition point from one segment to another. Details see the documentation about polytrope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gammas = np.array([1.03, 2.62, 1.38])\n",
+    "rho_ts = np.array([8.087e13, 1.8078e15]) * g_cm_3\n",
+    "theta = np.append(gammas, rho_ts)\n",
+    "\n",
+    "eps_set = np.logspace(11.7, 15.6, 100, base=10) * g_cm_3\n",
+    "pres_out = Polytrope.compute_EOS(eps_set, theta, eps_crust_T_out[-1], pres_crust_T_out[-1])\n",
+    "\n",
+    "eps_total = np.hstack((eps_crust_T_out, eps_set))\n",
+    "pres_total = np.hstack((pres_crust_T_out, pres_out))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS\n",
+    "\n",
+    "Here below we use the strangeon matter EOS to compute the mass radius curve.\n",
+    "\n",
+    "The following code calculates the mass and radius of the strange stars for the given EOS of the polytrope using our main.OutputMR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR('',eps_total, pres_total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[:,1]/km , MR[:,0]/Msun, lw=2)\n",
+    "# ax.scatter(R /km, M / Msun, c='r')\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Strangeon matter EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.integrate import ode\n",
+    "from EOSgenerators import Strangeon_EOS as Strangeon\n",
+    "\n",
+    "from TOVsolver.unit import  km, Msun, MeV,fm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The strangeon matter EOS describes the strongly interacting matter in the solid state, and the compact star can be a self-bound quark star composed of strangeon matter. Therefore, in the following we present the bare quark star EOS without a crust.\n",
+    "\n",
+    "Note that the strangeon matter parameters we input are in the natural unit system. The quentity returned by the Strangeon_compute_EOS function is in the Geometric Unit System. Thus, here, the parameter epsilon and ns are in the units of $MeV/fm^{-3}$. So follow the unite conversion rule, if we are using this unit, we just time this unit to do compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the EOS parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the strangeon matter EOS that we will use to calculate the energy densities and pressures of the system.\n",
+    "The following is an example of how to define the theta and Nq.\n",
+    "For example, theta=[50, 0.24] means the example values for epsilon and ns:\n",
+    "epsilon in units of MeV, ns in units of $fm^-3$.\n",
+    "Nq is an integer, e.g. Nq=9, 12, 15, 18, 21, 24, 27\n",
+    "\n",
+    "For the definition of parameter n, the minimum value is 3*ns/Nq, the maximum value is about 0.16*8*3/Nq.\n",
+    "Then we can generate,for example, 1000 points for the input of the srangeon matter EOS. Special note: If you are using this EOS, need a very fine grid for the EOS. From our test, 1000 points for this EOS is the minimum requirement\n",
+    "\n",
+    "Once we define the EOS parameters already, we can use the Strangeon_compute_EOS function to calcute the energy_density and pressure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "n values: [0.04       0.04008671 0.04017342 ... 0.21315991 0.21324662 0.21333333]\n",
+      "Energy densities: [  204.61935484   205.0631334    205.50734538 ... 46616.1606005\n",
+      " 46711.98281964 46807.96367981]\n",
+      "Pressures: [0.00000000e+00 1.98864796e-01 4.00749542e-01 ... 1.88749504e+05\n",
+      " 1.89139331e+05 1.89529799e+05]\n"
+     ]
+    }
+   ],
+   "source": [
+    "Nq=18\n",
+    "epsilon=50\n",
+    "ns=0.24\n",
+    "theta = np.array([Nq, epsilon, ns])\n",
+    "n_min = 3 * theta[2] / theta[0]  \n",
+    "n_max = 0.16 * 8 * 3 / theta[0]   \n",
+    "n_values = np.linspace(n_min, n_max, 2000) \n",
+    "\n",
+    "energy_densities, pressures = Strangeon.compute_EOS(n_values, theta)\n",
+    "print(\"n values:\", n_values)\n",
+    "print(\"Energy densities:\", energy_densities)\n",
+    "print(\"Pressures:\", pressures) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below we use the strangeon matter EOS to compute the mass radius curve.\n",
+    "\n",
+    "The following code calculates the mass and radius of the strange stars for the given EOS of the strange matter EOS. Since our energy_density and pressure are all in same unit $MeV/fm^{-3}$ so all we need is to time this unit\n",
+    "\n",
+    "Input a given central pressure and central energy density, you will obtain the radius where the pressure is zero and the mass at that radius."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Solve the TOV equations using each central pressure and energy density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "MR= main.OutputMR('',energy_densities* MeV/fm**3   , pressures* MeV/fm**3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[:,1]/km , MR[:,0]/Msun, lw=2)\n",
+    "\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "# ax.set_xlim(8.0, 20.0)\n",
+    "# ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Speed of sound EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from EOSgenerators import SpeedofSound_EOS\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "from TOVsolver.unit import g_cm_3, dyn_cm_2, km, Msun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Speed of sound EOS describes the core EOS of a compact star, \n",
+    "so it should be connected with the crust EOS to form a full EOS.\n",
+    "See https://arxiv.org/abs/1812.08188 for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the EOS parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To construct the Speed of sound EOS, we need to specify the outer crust EOS and \n",
+    "the interface EOS, and then connect them with the core EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_last = eps_com[-1]\n",
+    "y_last = pres_com[-1]\n",
+    "dydx_last = (pres_com[-1] - pres_com[-2]) / (eps_com[-1] - eps_com[-2])\n",
+    "CS_EOS = SpeedofSound_EOS.compute_EOS(x_last, y_last, dydx_last)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to generate the parameters of the Speed of sound EOS, we need a list\n",
+    "of 5 uniform random numbers between 0 and 1.\n",
+    "\n",
+    "After generate the parameters, we use function `check_a` to check if the parameters\n",
+    "are valid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "cs_a = CS_EOS.gen_a((0.2, 0.2, 0.3, 0.4, 0.5))\n",
+    "print(CS_EOS.check_a(cs_a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below we use the speed of sound EOS to compute the mass radius curve.\n",
+    "\n",
+    "First we calculate the core EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_e_min = x_last\n",
+    "core_e_max = 2e16 * g_cm_3\n",
+    "n_core_e = 1000\n",
+    "core_e = np.geomspace(core_e_min, core_e_max, n_core_e)\n",
+    "core_p = CS_EOS.cal_core_p(core_e, cs_a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we concat the core EOS with the crust EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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YOYRiwcohAAAAAMClpGXn65bp622Orq8R4K0143uoog/7714pVg4BAAAAAIBS6a3v422CIUmaOqgFwVAJQDgEAAAAAACcasvvp/TVtkSbsV7Naqhfy1omdYS/IhwCAAAAAABOk5NfqIlL4mzGKni569XbW8hisZjUFf6KcAgAAAAAADjNK8v36I/UTJux5/s2VVCVCiZ1hAsRDgEAAAAAAKf4ZnuS3eNk4cGVNapzA3MagkOEQwAAAAAAoNgtizmqFxw8TvbusDZyd+NxspLEw+wGAAAAAACAc+UVWHXwZIaOp+UoPbdAeQVWSZJhGDIkyZAMGTIMyZD+++///Vr/nWcY/7vGMP68t/Hf+/x3mgoNQ3FH0hQZe8yuj9cHt1RIDX+nv19cHcIhAAAAAADKoISUdH0fd1xr4lO0+0iaCqyGqf080r2R7mwXZGoPcIxwCAAAAACAMsJqNbR6zwnN++UPbfn9tNntFLmvU31N7NfM7DZwEYRDAAAAAACUAZsTUvXW9/GKO5JmditFArw99MJtzXR3RD2OrS/BCIcAAAAAACjFUtJzNOW73fp+1/HLzq1Z0VsVfTzl5eEmi0WyyPLff0uyWGT581///fdff/3n4F9/bbH8+Vr636/13+t8PNwU0bCq7mwXpKp+Xk553yg+hEMAAAAAAJRChmFoafQRTV22R2nZ+Q7nBPp7aUDrOro5rIbaBFdWRR9PF3eJ0oBwCAAAAACAUiY7r1AvLd2lxb8lO6w3qu6np3qF6LZWteXt4e7i7lDaEA4BAAAAAFCKHDyZob/N/037TqTb1ar6een5vk01rH2QPNzdTOgOpRHhEAAAAAAApcQvCal6dP4OpecU2NUGtqmjqQObq5q/twmdoTQjHAIAAAAAoBRYvCNZLyyJVX6hYTPu7+2ht4a00oDWdUzqDKUd4RAAAAAAACWYYRj6cG2Cpv+4367WrFaAPrqnnRpV9zehM5QVhEMAAAAAAJRQhmHore/j9Z8Nv9vVbm1RS++PCJevFxtO4/oQDgEAAAAAUAJZrYamLt+tz389bFd7uFtDTewXJjc3iwmdoawhHAIAAAAAoIQptBqatCROC7cn2YxbLNIrg1poVOcG5jSGMolwCAAAAACAEsQwDL201D4YcrNI7w5rozvbBZnUGcoqwiEAAAAAAEoIwzD0RuRefbXNNhjycLNo5si26t+6tkmdoSwjHAIAAAAAoISY+dMBzd70h82Yp7tFH93TXr2b1zSpK5R1bmY3AAAAAAAApNkbf9eMNQdsxtzdLPrwrnYEQ3AqwiEAAAAAAEy25LdkvR6512bMYpHeHdZat7asZVJXKC8IhwAAAAAAMNHmhFRNWBRrN/7a7S11R1s2n4bzEQ4BAAAAAGCS+OPn9NgXO1RgNWzGX+jXTPd2qm9SVyhvCIcAAAAAADDB8bQcPTA3Sum5BTbjY7o21GM9GpvUFcojwiEAAAAAAFwsPSdfo+du07G0HJvxfi1r6cXbwkzqCuUV4RAAAAAAAC5UaDX09Fc7FX883Wa8ff0qen9EuNzcLCZ1hvKKcAgAAAAAABeatipeP+87aTPWMNBPs0d1kI+nu0ldoTwjHAIAAAAAwEWW/Jas/2z43Wasqp+X5j3QUVX8vEzqCuUd4RAAAAAAAC6wM/GMXlgSZzPm6W7Rx/e2V/1qfiZ1BRAOAQAAAADgdMfTcvToFzuUV2C1GX/t9paKaFjVpK6APxEOAQAAAADgRDn5hXrki+1KSc+1GR99YwONjKhnUlfA/xAOAQAAAADgRJO/26XY5DSbsS4h1fRSf46sR8lAOAQAAAAAgJMsjErU19uTbcbqV6ugf93dTh7ufCRHycDvRAAAAAAAnCAuOU0vf7fbZszPy12zR3VQ5QqcTIaSg3AIAAAAAIBidjYrT4//n/0G1G8Pba0mNQNM6gpwjHAIAAAAAIBiZLUaGrcwWslnsm3GH+zSUANa1zGpK+DiCIcAAAAAAChG//o5QT/vO2kz1qF+FU28rZlJHQGXRjgEAAAAAEAx2XjgpKav2W8zFujvpX/e3U6ebECNEorfmQAAAAAAFIOU9ByNWxgtw/jfmJtF+uCutqpVyce8xoDLIBwCAAAAAOA6Wa2Gxn8do9SMPJvx5/s2042NA03qCrgyhEMAAAAAAFyn/2z4XRsPpNqM9WxaXY92b2RSR8CVIxwCAAAAAOA67Dh8Ru+u3mczVrOit94d1kZubhaTugKuHOEQAAAAAADXKC07X09/tVOF1v9tNGSxSDNGtFU1f28TOwOuHOEQAAAAAADXwDAMTVoSpyNns23Gn+rVRJ0bVzOpK+DqEQ4BAAAAAHANvtqWpMi4YzZjEQ2q6uleISZ1BFwbwiEAAAAAAK7SwZMZenXFbpuxyhU8NWNkuDzc+aiN0oXfsQAAAAAAXIX8QqvGLYxWTr7VZvydoW1Up7KvSV0B145wCAAAAACAq/Dh2gTFJqfZjI3qXF+9m9c0qSPg+hAOAQAAAABwhX5LPKN//ZxgM9a4up8m9gszqSPg+hEOAQAAAABwBTJzC/TswmibY+s93Cx6f0S4fL3cTewMuD6EQwAAAAAAXIE3Vu7VoVNZNmPP3NxErYMqm9MQUEwIhwAAAAAAuIy18Sf05dZEm7F29Srr8Zsam9QRUHwIhwAAAAAAuITTmXmasCjOZqyCl7umD+fYepQN/C4GAAAAAOASpizbrdSMXJuxlwc0V4NAP5M6AooX4RAAAAAAABexatcxLY85ajN2S1gNjewYbFJHQPEjHAIAAAAAwIEzmXl6aekum7HKFTz15p2tZLFYTOoKKH6EQwAAAAAAOPDK8t1KzcizGZs6sIVqBPiY1BHgHIRDAAAAAABc4Mc9J7Q02v5xstvD65jUEeA8hEMAAAAAAPzF2aw8TfrW9nSyij4eeuMOHidD2UQ4BAAAAADAX7y6Yo9OptueTjZ5YAvVrMjjZCibCIcAAAAAAPivtfEntOS3IzZjNzWtriHt6prUEeB8hEMAAAAAAEjKzC3QS9/ank4W4O2hf3A6Gco4wiEAAAAAACTNWLNfR9NybMZeGhCm2pV8TeoIcA3CIQAAAABAubf7aJrmbD5kM9a5UTUN7xBsTkOACxEOAQAAAADKtUKroUnf7lKh1Sga83J30+t3tORxMpQLhEMAAAAAgHLty62HFZN01mbs8Zsaq3F1f3MaAlyMcAgAAAAAUG6dOJejaav22Yw1CvTT4zc1NqkjwPUIhwAAAAAA5darK/YoPbfAZuz1O1rKx9PdpI4A1yMcAgAAAACUSz/vS1Fk7DGbsTvb1dWNjQNN6ggwB+EQAAAAAKDcyc4r1MtLd9mMVa7gqRdvCzOpI8A8hEMAAAAAgHLng7UHlHwm22ZsYr9mqubvbVJHgHkIhwAAAAAA5cq+4+n6ZMPvNmMRDapqWPtgkzoCzEU4BAAAAAAoN6xWQ5O+jVOB1Sga83S36I07WsrNzWJiZ4B5CIcAAAAAAOXGwu1J2nH4jM3YI90bqUnNAJM6AsxHOAQAAAAAKBdOpufqHyv32ozVq1pBT/VqYlJHQMlAOAQAAAAAKBfeiNyjczkFNmOvD24pH093kzoCSgbCIQAAAABAmbfpQKqWRh+1GRvUpo66h1Y3qSOg5CAcAgAAAACUaTn5hXppaZzNWICPh14aEGZSR0DJQjgEAAAAACjTPvo5QYdOZdmM/f3WZqoR4GNSR0DJQjgEAAAAACizElLS9e/1B23G2tarrLsj6pnUEVDyEA4BAAAAAMokq9XQpCW7lF9oFI25u1n05h2t5OZmMbEzoGQhHAIAAAAAlElfb0/StkOnbcYe6tpQYbUrmtQRUDIRDgEAAAAAypyT6bl6c+Vem7GgKr565pYmJnUElFyEQwAAAACAMue1FXt0LqfAZuz1wS1VwcvDpI6AkotwqBQ4e/asnn76aXXu3Fm1atWSt7e36tatq169emnx4sUyDOO65gMAAABAWbJuX4qWxRy1GRvUpo5ualrDpI6Ako1wqBRITU3VnDlz5Ofnp8GDB2v8+PHq16+fdu/eraFDh+rRRx+9rvkAAAAAUFZk5hbopaW7bMYq+njo5QHNTeoIKPksBstISrzCwkIZhiEPD9vlj+np6erUqZP27NmjXbt2qUWLFtc0vzgkJycrODhYkpSUlKSgoKBiuzcAAAAAXKmXlsZp/pZEm7G37mylkRxdjzLCGZ+/WTlUCri7u9sFPZIUEBCgvn37SpISEhKueT4AAAAAlAUb9p+0C4YiGlbV8A7BJnUElA6EQ5eRkpKiFStWaPLkyerXr58CAwNlsVhksVg0evToq7pXYmKinnvuOYWFhcnPz09Vq1ZVRESE3n33XWVlZV11bzk5OVq7dq0sFouaN7/8EsmrnQ8AAAAApUVadr7+vjjWZszX013vDG0tNzeLSV0BpQPbtF9GzZo1i+U+kZGRuueee5SWllY0lpWVpaioKEVFRWn27NlauXKlGjVqdNF7nD17VjNmzJDValVKSopWrlyppKQkTZkyRU2a2B/HeLXzAQAAAKA0MgxDU77bpWNpOTbjk25rpvrV/EzqCig9CIeuQnBwsMLCwrR69eqrui4mJkbDhw9XVlaW/P39NXHiRPXs2VPZ2dlasGCBPvnkE+3bt0/9+/dXVFSU/P39Hd7n7NmzeuWVV4p+7enpqXfeeUfjx48vlvkAAAAAUBotiErS0mjb08m6hgTqnhvqm9QRULoQDl3G5MmT1bFjR3Xs2FE1a9bUoUOH1LBhw6u6x9ixY5WVlSUPDw+tXr1anTt3Lqr16tVLTZo00YQJExQfH6/p06dr8uTJDu/ToEEDGYahwsJCJSUlacGCBXrxxRf1yy+/6Ouvv7bbZ+hq5wMAAABAabP7aJqmLNttMxbg7aG3eZwMuGLsOXQZr7zyigYMGHDNj5dFRUVp3bp1kqQxY8bYBEPnjR8/XmFhYZKkGTNmKD8//5L3dHd3V4MGDfTCCy/o9ddf17fffqtPPvmk2OYDAAAAQGmQkp6jRz7fobwCq834tKGtVbeyr0ldAaUP4ZCTLV26tOj1Aw884HCOm5ubRo0aJUk6c+ZMUZh0Jfr06SNJV3zN1c4HAAAAgJIoK69AY+Zt15Gz2TbjD3RpoH6tapvUFVA6EQ452caNGyVJfn5+at++/UXn9ejRo+j1pk2brvj+R4/++VztlT4idrXzAQAAAKCkSc/J1+g5UYo7kmYz3rZeZU3sF2ZSV0DpRTjkZHv37pUkhYSEXDKQadasmd0150VHR9uccnbe6dOnNWnSJElSv379rnk+AAAAAJQWv5/M0F2fbNG2Q6dtxutVraBPRnWQlwcfc4GrxfIRJ8rJyVFqaqokKSgo6JJzq1SpIj8/P2VmZiopKcmmNm/ePM2ePVs9e/ZU/fr15efnp8OHDysyMlIZGRkaMmSI7r777muefyWSk5MvWT927NhV3Q8AAAAAroZhGJq/NVFvRO5RTr7tHkOVK3hq7gMdFejvbVJ3QOlGOORE6enpRa8vdjz9X50PhzIyMmzGhw4dqrS0NG3ZskUbNmxQVlaWqlatqq5du2rUqFEaOXKkLBbLNc+/EsHBwVc1HwAAAACKS8q5HD2/KFbr95+0qwX6e2n+QzeocfXLf+YC4BjhkBPl5OQUvfby8rrsfG/vP1Pu7GzbDdW6du2qrl27XvHXvdr5AAAAAFBSrYw7pknfxulslv2pzo0C/fTJ/R0IhoDrRDjkRD4+PkWv8/LyLjs/NzdXkuTrW/KOXLzwUbcLHTt2TBERES7qBgAAAEBZdy4nX1O/260lO484rI/qXF8T+4XJ18vdxZ0BZQ/hkBMFBAQUvb7wUTFHMjMzJV3ZI2iudrk9kwAAAACguPxyMFXPfR2jo2k5drUaAd6aNrS1bmpaw4TOgLKJcMiJfHx8FBgYqNTU1Mtu6HzmzJmicIj9fQAAAACURzn5hXr3h32avekPh/X+rWrr9cEtVcXv8tt2ALhyhENOFhYWpo0bNyohIUEFBQUXPc4+Pj7e5hoAAAAAKE92H03TuIXR2n/C/qmLAB8PvXZ7S90eXueqD9cBcHluZjdQ1p3fGDozM1M7duy46Lz169cXve7SpYvT+wIAAACAkqDQauijdQka/K/NDoOhzo2qadXY7hrcti7BEOAkhENONnjw4KLXc+fOdTjHarXq888/lyRVrlxZPXv2dEVrAAAAAGCqxFNZGvGfXzVt1T7lFxo2NS8PN73UP0z/99ANqlu55B3aA5QlhENOFhERoW7dukmSPv30U/366692c9577z3t3btXkvTMM8/I09PTpT0CAAAAgCsZhqGFUYnqN3ODth8+Y1dvXruiVjzVVQ91ayQ3N1YLAc7GnkOXsWnTJiUkJBT9OjU1teh1QkKC5s2bZzN/9OjRdveYOXOmunTpouzsbPXp00eTJk1Sz549lZ2drQULFmjWrFmSpNDQUI0fP94p7wMAAAAASoLUjFy9sDhOa/aesKu5WaTHejTW2FtC5eXBWgbAVSyGYRiXn1Z+jR49Wp999tkVz7/Yf87ly5fr3nvv1blz5xzWQ0NDFRkZqZCQkGvq02zJyclFp6wlJSUpKCjI5I4AAAAAlDQ/7jmhFxbH6lRmnl2tXtUKmj68jTo0qGpCZ0Dp4YzP36wccpGBAwcqNjZWM2fOVGRkpJKTk+Xl5aWQkBANGzZMTz75pCpUqGB2mwAAAABQ7DJyC/Ta8j1auD3JYX1kx2C9NKC5/L35iAqYgZVDKBasHAIAAADgSNSh03r262glnc62q1Xz89JbQ1qrd/OaJnQGlE6sHAIAAAAAlAp5BVa9v2a/Pl5/UI6WJNwSVlNvDWmlQH9v1zcHwAbhEAAAAACgWO07nq5xC6O155j9nqt+Xu6aMrCFhnUIksXCSWRASUA4BAAAAAAoFlaroTmb/9C0H/Ypr8BqV+9Qv4qmDw9XvWrstwqUJIRDAAAAAIDrduRstp77Oka//n7KrubpbtG43qF6tHtjubuxWggoaQiHAAAAAADXzDAMLY0+oslLdys9t8CuHlrTX++PCFeLOpVM6A7AlSAcAgAAAABck9OZeXppaZxWxh23q1ks0pguDfVc36by8XQ3oTsAV4pwCAAAAABw1dbGn9DfF8fpZHquXa1OJR+9O7yNbmwcaEJnAK4W4RAAAAAA4Ipl5Bbojcg9+mpbksP6nW3rasqgFqrk6+nizgBcK8IhAAAAAMAV2fbHaY3/JlpJp7PtapUreOqNwa3Uv3VtEzoDcD0IhwAAAAAAl5RbUKjpq/dr1sbfZRj29Z5Nq+vtIa1Vo6KP65sDcN0IhwAAAAAAF7X7aJqeXRijfSfS7WoVvNz18oDmGtkxWBYLR9QDpRXhEAAAAADATkGhVf/Z8LtmrNmv/EL75UIdG1TRu8PaqH41PxO6A1CcCIcAAAAAADYOpWbq2a+j9VviWbual7ubxvcJ1UPdGsndjdVCQFlAOAQAAAAAkCQZhqH/25qoNyL3Kju/0K4eVrui3h/RRs1qVTShOwDOQjgEAAAAANDxtBxNWByrDftP2tXcLNJjPRrrmVuayNvD3YTuADgT4RAAAAAAlHPLYo7q5aW7lJadb1erX62Cpg9vo/b1q5rQGQBXIBwCAAAAgHLqTGaeXv5ul1bEHnNYv7dTPU3sFyY/bz46AmUZ/w8HAAAAgHLo530p+vuiWKWk59rVagR4a9rQ1rqpaQ0TOgPgaoRDAAAAAFCOZOYW6I2Ve/Xl1kSH9YFt6ui121uocgUvF3cGwCyEQwAAAABQTmw/dFrPfh2jxNNZdrVKvp56bXBLDWpTx4TOAJiJcAgAAAAAyrjcgkLNWHNA/1l/UFbDvt4jtLqmDW2tmhV9XN8cANMRDgEAAABAGbb32DmNWxit+OPpdjVfT3e92D9M99xQTxaLxYTuAJQEhEMAAAAAUAYVWg3N2vC7pv+4T/mF9suF2tWrrOnDw9Ug0M+E7gCUJIRDAAAAAFDGHD6VqfFfx2j74TN2NU93i8b1DtWj3RvL3Y3VQgAIhwAAAACgzDAMQ19tS9LrkXuUlVdoV29WK0DTh4ereZ2KJnQHoKQiHAIAAACAMiDlXI4mLI7Vun0n7WoWi/Ro98Ya17uJvD3cTegOQElGOAQAAAAApdyK2KN6aekunc3Kt6vVq1pB7w1vo44NqprQGYDSgHAIAAAAAEqps1l5mvzdbi2LOeqwfvcN9fTibWHy8+ajH4CL408IAAAAACiFNuw/qecXxejEuVy7WvUAb00b0lo9m9UwoTMApQ3hEAAAAACUIll5BfrHynh9seWww3r/VrX1+uCWquLn5eLOAJRWhEMAAAAAUErsOHxG47+O1qFTWXa1ij4eem1wSw1qU0cWC0fUA7hyhEMAAAAAUMLlFVg186f9+ve6g7Ia9vVuTQL1ztA2qlXJx/XNASj1CIcAAAAAoATbdzxd4xZGa8+xc3Y1H083vXhbmO7tVJ/VQgCuGeEQAAAAAJRAhVZDn276Xe/+sF95hVa7ett6lTV9eLgaBvqZ0B2AsoRwCAAAAABKmMRTWXrumxhtO3TarubhZtG43qF6tHsjebi7mdAdgLKGcAgAAAAASgjDMPTltkS9EblXWXmFdvXQmv6aPjxcLetWMqE7AGUV4RAAAAAAlADH0rI1YVGsNh5ItatZLNLD3Rrp2d6h8vF0N6E7AGUZ4RAAAAAAmMgwDH2784imLNut9JwCu3pQFV+9N6yNbmhUzYTuAJQHhEMAAAAAYJLUjFy9+G2cfth9wmH97hvqadJtYfL35qMbAOfhTxgAAAAAMMGqXcc06dtdOp2ZZ1erWdFbbw9prZua1jChMwDlDeEQAAAAALhQWla+pizbpaXRRx3W72hbV1MHtlClCp4u7gxAeUU4BAAAAAAusm5fiv6+OFYnzuXa1ar5eemNO1rq1pa1TegMQHlGOAQAAAAATpaRW6A3Ivfqq22JDut9mtfUm3e2UqC/t4s7AwDCIQAAAABwqi2/n9Jz38Qo+Uy2XS3Ax0Ov3t5Cg8PrymKxmNAdABAOAQAAAIBT5OQX6p0f9mnO5j9kGPb17qHV9faQVqpdydf1zQHAXxAOAQAAAEAxi046q2e/jtbvJzPtahW83PVS/+a6KyKY1UIASgTCIQAAAAAoJnkFVn3w0wF9tC5BVgerhSIaVNW7w9qoXrUKrm8OAC6CcAgAAAAAisHeY+f07Ncx2nvsnF3Ny8NNE/o21YNdGsrNjdVCAEoWwiEAAAAAuA4FhVb9Z8PvmrFmv/IL7ZcLtQ6qpOnD2yikRoAJ3QHA5REOAQAAAMA1OngyQ+O/jlF00lm7moebRc/c3ESP39RYHu5urm8OAK4Q4RAAAAAAXCWr1dDcXw7pnR/ilZNvtas3rRmg94a3Ucu6lUzoDgCuDuEQAAAAAFyFQ6mZen5RjKIOnbGruVmkR3s01thbmsjbw92E7gDg6hEOAQAAAMAVsFoNzfvlkKZdZLVQw0A/vTusjdrXr2JCdwBw7QiHAAAAAOAyDqVmasKiWG07dNphffSNDfT3W5vJ14vVQgBKH8IhAAAAALgIq9XQZ78e0turHK8WCq7qq2lD2qhz42omdAcAxYNwCAAAAAAcOHwqU88vitW2PxyvFhrVub7+fmsz+XnzsQpA6cafYgAAAADwF1aroc9/PaS3V+1Tdn6hXT2oiq+mDW2tGxsHmtAdABQ/wiEAAAAA+K/EU1l6flGMtl5ktdB9nerrhX6sFgJQtvAnGgAAAIByz2o1NH/rYb31fbyy8i6yWmhIa90YwmohAGUP4RAAAACAci3p9J+rhbb87ni10L2d6umFfmHyZ7UQgDKKP90AAAAAlEuXWy1Ut/Kfewt1YbUQgDKOcAgAAABAuXP4VKb+vjj2oquF7r6hnibdxmohAOUDf9IBAAAAKDcKrYbmbv5D767ep5x8q129bmVfvT2ktbo2YbUQgPKDcAgAAABAubD/RLqeXxSrmKSzDuusFgJQXvGnHgAAAIAyLa/Aqn+vO6h//nxA+YWGXb1uZV+9NaSVujWpbkJ3AGA+wiEAAAAAZVZM0ln9fXGs4o+nO6zf37m+nr+1GauFAJRr/AkIAAAAoMzJyS/U+z/u1ycbf5fVfrGQGgX66e2hrdWxQVXXNwcAJQzhEAAAAIAyZevvp/T3xbE6dCrLrubuZtEj3RvpmZubyMfT3YTuAKDkIRwCAAAAUCak5+Tr7VXxmr8l0WE9rHZFvTO0tVrWreTizgCgZCMcAgAAAFDq/bT3hF5euktH03Lsal7ubnrmliZ6pHsjebq7mdAdAJRshEMAAAAASq0T53I0ddlufb/ruMN6u3qVNW1oa4XUCHBxZwBQehAOAQAAACh1rFZD/7f1sKat2qf03AK7uq+nu57v21T339hA7m4WEzoEgNKDcAgAAABAqRJ//JwmLonTzsSzDutdQwL1jztbKbhqBdc2BgClFOEQAAAAgFIhO69QM386oNkbf1eBg/Ppq/p56aX+YbqjbV1ZLKwWAoArRTgEAAAAoMTbsP+kXlq6S4mn7Y+nl6Sh7YM06bYwVfXzcnFnAFD6EQ4BAAAAKLFOpufq9cg9+i76qMN6w0A/vXFHS93YONDFnQFA2UE4BAAAAKDEKSi06v+2Jurd1fuUnmO/4bSnu0WP92isv/UMkY+nuwkdAkDZQTgEAAAAoETZcfiMXl66S3uOnXNY79igit68o5Wa1OR4egAoDoRDAAAAAEqEUxm5entVvL7enuywXtHHQxNvC9OIDsFy43h6ACg2hEMAAAAATFVoNfTltkS9sype5xw8QiZJd7Stq4m3NVONAB8XdwcAZR/hEAAAAADTRCed1ctLdynuSJrDemhNf712e0vd0KiaizsDgPKDcAgAAACAy504l6Npq/Zp8W+OHyHz83LXuN6huv/GBvJ0d3NxdwBQvhAOAQAAAHCZnPxCzd74uz5ad1BZeYUO5wxqU0cv9g9TzYo8QgYArkA4BAAAAMDpDMPQyrjjenPlXh05m+1wTuPqfnrt9pa6MSTQxd0BQPlGOAQAAADAqXYdSdOry/do26HTDut+Xu566uYmerBLQ3l58AgZALga4RAAAAAApzh6Nlvv/7hfi35LlmHY1y0WaXj7YI3vG8opZABgIsIhAAAAAMUqLStfH61P0LzNh5RbYHU4J6JhVU0e0Fwt61ZycXcAgAsRDgEAAAAoFjn5hfrsl0P6188JOpdT4HBOUBVfvXhbmG5tWUsWi8XFHQIAHOGB3hLu7Nmzevrpp9W5c2fVqlVL3t7eqlu3rnr16qXFixfLcLQ+V1JUVJRuu+02ValSRX5+foqIiNCXX37p4u4BAABQHhRaDX0dlaSe767TP76PdxgM+Xm5a8KtTbXm2R7q16o2wRAAlCCsHCrhUlNTNWfOHHXq1EmDBw9W1apVlZKSouXLl2vo0KF6+OGHNWvWLJtr1q1bp759+8rLy0sjR45UpUqVtGTJEt1zzz06dOiQJk2aZNK7AQAAQFlitRpaEXdMH/x0QAkpGQ7neLhZdPcN9fRUryaqHuDt4g4BAFfCYlxs6QlKhMLCQhmGIQ8P2xwvPT1dnTp10p49e7Rr1y61aNFCklRQUKBmzZopOTlZv/76q9q2bVs0v3Pnztq3b5/27NmjJk2aFGufycnJCg4OliQlJSUpKCioWO8PAACAksNqNbRy1zHNXHNABy4SCknSgNa19VyfpmoQ6OfC7gCgbHPG528eKyvh3N3d7YIhSQoICFDfvn0lSQkJCUXja9eu1cGDB3X33XcXBUPn57/88ssqKCjQ3Llznd84AAAAyhyr1dDKuGPqN3Ojnvxy50WDoRsbV9OyJ7von3e3IxgCgFKAx8ouISUlRdu2bdO2bdsUFRWlqKgonTp1SpJ0//33a968eVd8r8TERH3wwQeKjIxUYmKivL29FRISouHDh+tvf/ubKlSocFW95eTkaO3atbJYLGrevHnR+Lp16yRJffr0sbvm/Nj69euv6msBAACgfCsotGrlruP66OcExR9Pv+i85rUr6oV+zdStSSB7CgFAKUI4dAk1a9YslvtERkbqnnvuUVpaWtFYVlZWUeA0e/ZsrVy5Uo0aNbroPc6ePasZM2bIarUqJSVFK1euVFJSkqZMmWLziNiBAwckyeFjY1WqVFFgYGDRHAAAAOBScvIL9c32JM3a+LuSTmdfdF6zWgEae0sT9WleS25uhEIAUNoQDl2h4OBghYWFafXq1Vd1XUxMjIYPH66srCz5+/tr4sSJ6tmzp7Kzs7VgwQJ98skn2rdvn/r376+oqCj5+/s7vM/Zs2f1yiuvFP3a09NT77zzjsaPH28z73wAValSJYf3qVixopKTk6/qPQAAAKB8ScvK1xdbDmnu5kM6lZl30XlNawbomVua6NYWhEIAUJoRDl3C5MmT1bFjR3Xs2FE1a9bUoUOH1LBhw6u6x9ixY5WVlSUPDw+tXr1anTt3Lqr16tVLTZo00YQJExQfH6/p06dr8uTJDu/ToEEDGYahwsJCJSUlacGCBXrxxRf1yy+/6Ouvv3a4LxEAAABwNRJSMvT5r4e0eEeyMvMKLzqvSQ1/PXNLE93WsjahEACUAU5PFF599VVnfwlJumiocj3+ulLnWkRFRRXtATRmzBibYOi88ePHa+7cudq7d69mzJihiRMnytPT86L3dHd3V4MGDfTCCy/I3d1dEyZM0CeffKLHH39c0v9WDP31Eba/Onfu3EVXFQEAAKD8sVoN/bwvRfN+OaSNB1IvObdNUCU91qOx+rSoJXdCIQAoM5weDk2dOtUlm9E5Ixy6XkuXLi16/cADDzic4+bmplGjRmnixIk6c+aM1q1bp969e1/R/fv06aMJEyZo3bp1ReHQ+b2GDhw4oPbt29vMP3PmjFJTU3XjjTdew7sBAABAWXI6M09LfkvW578eVuLprEvO7R5aXY/1aKTOjaqx0TQAlEEuexbJMAyn3buk/gW1ceNGSZKfn59dUPNXPXr0KHq9adOmKw6Hjh49Kkk2j5T16NFD//jHP7R69WqNHDnSZv75/ZL++vUAAABQfhRaDW08cFLfbE/W6j3HlV948e/R3SxS/9Z19Gj3RmpZl5XnAFCWuSwc2rVrl82R68V1z9atWxfrPYvT3r17JUkhISGX3BOoWbNmdtecFx0drYYNG9o9Cnb69GlNmjRJktSvX7+i8ZtvvlmNGjXSl19+qaefflrh4eGSpPT0dL322mvy8PDQ6NGjr+dtAQAAoJQ5fCpTi3cka9GOZB1Ny7nk3CoVPDUyop7u7VRfdSv7uqhDAICZSvUuxiV1xZAk5eTkKDX1z2e2g4KCLjm3SpUq8vPzU2ZmppKSkmxq8+bN0+zZs9WzZ0/Vr19ffn5+Onz4sCIjI5WRkaEhQ4bo7rvvLprv4eGh2bNnq2/fvurWrZvuuusuVaxYUUuWLNEff/yh119/XaGhoVf9fi53wtmxY8eu+p4AAABwnuNpOVoRe1TLY48pJunsZec3r11Ro29soEHhdeTj6e78BgEAJUapDodKsvT09KLXFzue/q/Oh0MZGRk240OHDlVaWpq2bNmiDRs2KCsrS1WrVlXXrl01atQojRw50i4k69mzpzZt2qQpU6bo66+/Vl5enlq0aKHXXntN99xzzzW9n+Dg4Gu6DgAAAK5z4lyOftxzQstjjmrbodO63M4OXh5u6teylu7tVF8d6lcp0T98BQA4j9PDoZ9//lmSrvoI+CvRsGHDovuXNDk5/1uu6+Xlddn53t7ekqTs7Gyb8a5du6pr165X/fUjIiL0/fffX/V1AAAAKD2sVkNxR9K0Nj5Fa+NTFHfE8Ym1F2pZt6JGdAjWoDZ1VanCxU/KBQCUD04Ph5y5+XGFChVK7ObKPj4+Ra/z8vIuOz83N1eS5OtbMp/rvvBxtwsdO3ZMERERLuoGAACg/MrILdCmAyf/GwidVGpG7hVdV9XPSwNb19bwjsFqUYcNpgEA/8NjZU4SEBBQ9PrCR8UcyczMlHRlj6CZ4XL7JgEAAMA58gqs+nZnsjYnnNKBlAwlpKRf8pSxvwrw9lDflrU0sE0ddWlcTR7ubk7uFgBQGhEOOYmPj48CAwOVmpp62c2cz5w5UxQOsbcPAAAAztt/Il3jFkZr99FzV3xNgLeHejStroFt6qhHaHU2lwYAXBbhkBOFhYVp48aNSkhIUEFBwUWPs4+Pj7e5BgAAAOWb1WpozuY/NO2HfcorsF52fqPqfrq5WQ31alZTHRpUkScrhAAAV8Gp4VB+fr7i4uLk4eGhVq1aXfT0g9jYWEVHR2vUqFHObMflunbtqo0bNyozM1M7duzQDTfc4HDe+vXri1536dLFVe0BAACgBEo6naXnvonR1j9OX3SOp7tFNzSspl7NaqhXsxpqEOjnwg4BAGWN036ksGjRItWpU0cdO3ZU27ZtFRwcrC+//NLh3G+//VYPPPCAs1oxzeDBg4tez5071+Ecq9Wqzz//XJJUuXJl9ezZ0xWtAQAAoIQxDENfb09Sv5kbHQZDTWr467XbW2jBI520c3IfzX/oBj3YtSHBEADgujklHNq2bZtGjhypc+fOqXfv3rrtttt06tQp3XfffXr88ced8SVLpIiICHXr1k2S9Omnn+rXX3+1m/Pee+9p7969kqRnnnlGnp4cJQoAAFDepGbk6pEvdmjColhl5BbY1CwW6aGuDbX8qa66r3MDdWpUTf7e7A4BACg+TvlbZdq0aXJzc9PatWuLHpNKTEzUfffdp1mzZik7O1tz58696GNmJcWmTZuUkJBQ9OvU1NSi1wkJCZo3b57N/NGjR9vdY+bMmerSpYuys7PVp08fTZo0ST179lR2drYWLFigWbNmSZJCQ0M1fvx4p7wPAAAAlFyrdx/XxCVxOpWZZ1erW9lX7w5ro86Nq5nQGQCgvHBKOLR582YNHjzYZv+cevXq6aefftIDDzygzz//XIWFhfr8889LdEA0e/ZsffbZZw5rmzdv1ubNm23GHIVDbdu21cKFC3Xvvffq3LlzmjRpkt2c0NBQRUZGKiAgoFj6BgAAQMmXnpOvV5fv0Tc7HJ9sO7R9kKYMbK4AH1aWAwCcyynh0OnTp9WkSRP7L+bhoc8//1xeXl6aO3eurFarvvjiC2e0UKIMHDhQsbGxmjlzpiIjI5WcnCwvLy+FhIRo2LBhevLJJ1WhQgWz2wQAAIAL5BVY9cPu43rr+3gdOZttV6/m56U372ylvi1qmdAdAKA8cko4VKtWLaWkpDisWSwWffrppzIMQ/PmzZPValVISIgz2rhu8+bNs3t07FrVr19f06dP1/Tp04vlfgAAAChdMnML9PH6g/pqW6JSM+wfIZOk3s1r6h93tlKgv7eLuwMAlGdOCYeaNWtmczy7I59++qmkPwMYHqcCAABAWbbj8Gk9+3WMDp/Kclj39/bQlIHNNbR9UInedgEAUDY55bSyfv36KSEhwW5Pnr86v4Lo/vvvV3p6ujPaAAAAAEyVV2DV26viNezjXy8aDN3QsKq+f6abhnUIJhgCAJjCKSuHhg8frhMnTujkyZOXnGexWDRnzhzVr19fhw8fdkYrAAAAgCnij5/TuIUx2nvsnMN6RMOqurdTfQ1oVVtuboRCAADzWAzDMMxuAqVfcnKygoODJUlJSUkKCgoyuSMAAABzFFoNzd74u95bvV95hVa7evv6VfTmHa3UtBZbKwAArp4zPn87ZeUQAAAAUB4lnc7S+K9jtO3Qabuap7tF43qH6tHujeXOSiEAQAlCOAQAAABcp/ScfH21LVEz1xxQZl6hXb1pzQBNH9FGLepUMqE7AAAujXAIAAAAuEanMnI1a8Pv+nJrotJzC+zqFov0SLdGerZPqLw93E3oEACAyysR4dDp06eVlpamSpUqqWrVqma3AwAAAFzW6t3HNXFJnE5l5jmsB1Xx1fTh4YpoyPe3AICSzdRwaPHixXr55Ze1b9++orGKFSuqdevWCg8PL/qnZcuW8vT0NLFTAAAA4E/pOfl6ZfkeLdqRfNE5IzoE6+WBzeXvXSJ+FgsAwCWZ9rfVsmXLNGzYMFksFv31wLS0tDRt3LhRmzZtKhrz8PBQs2bNbAKjnj17mtE2AAAAyrFfD57Sc9/E6MjZbLuaxSL1bV5Lj/ZopLb1qpjQHQAA18a0cOjNN9+UJBmGoTvuuEO33HKLrFarEhISFBMTo5iYGJ05c0aSlJ+fr7i4OO3atUvz58+XxWJRQYH9M90AAACAM+TkF+rdH/bp081/6C8/1yzSu3lNTbotTA0D/VzfHAAA18m0cCg2NlYWi0V/+9vf9OGHHzqck5SUpOjoaEVHRysmJkbR0dH6448/bFYaAQAAAM6060iaxi2M1oGUDLuav7eHpgxsrqHtg2SxcDw9AKB0Mi0c8vPzU25urkaMGHHROcHBwQoODtbAgQOLxtLT0xUbG+uKFgEAAFBO5RVYFZ10Vmv2ntCcTX+owGr/w8lOjarq3WFtFFSlggkdAgBQfEwLh8LCwrR582a5u1/dkZ4BAQHq0qWLk7oCAABAeZZ0OktvrYrXz/EpysordDjHy8NNE/o21YNdGsrNjdVCAIDSz82sL3z//ffLMAz9/PPPZrUAAAAASPpzH8yFUYm6dcYGRcYeu2gw1KJORa14qqse6taIYAgAUGaYFg7dd999atOmjd5//30dP37crDYAAABQzp1Mz9XDn2/X3xfHKfMioZCbRXqqV4i+/VsXhdYMcHGHAAA4l2nhkJeXlxYuXCgPDw/dcsst2rNnj1mtAAAAoJxateuY+s7YoDV7UxzWm9euqIe6NlTk0900vk9TeXmY9u0zAABOY9qeQ5IUGhqqtWvXqkuXLurYsaOefPJJDR8+XO3btzezLQAAAJRxaVn5emXFbi357YjD+siOwXq+b1NV8/d2cWcAALiexTDxXPjXX39db775pnJzc2UYRtHxn9WrV1d4eLjCw8PVtm1bhYeHKzQ0lONBS7Dk5GQFBwdLkpKSkhQUFGRyRwAAAPYMw9CymKN6bcUepWbk2dUD/b301p2tdUvzmiZ0BwDA5Tnj87dpK4fmzp2ryZMn24ydz6lSUlL0448/6scffyyq+fr6qnXr1kWh0SOPPOLSfgEAAFC6paTn6IXFcVob7/gRsltb1NIbd7RktRAAoNwxLRz68MMPJUne3t4aP368br75Zrm7u+vAgQOKiYnRzp07FRMTo4yMDElSVlaWtmzZoq1bt8pisRAOAQAA4Ip9H3dMk76N05msfLtagLeHpg5qoTvb1WWlOgCgXDItHDpw4IAsFotefPFFvfTSS0Xj3bt3t5mXkJCgnTt3Kjo6uujfnG4GAACAK5GWna9Xlu3Wkp2O9xa6JaymXr29hepU9nVxZwAAlBymhUN+fn7KyspSnz59LjkvJCREISEhGjZsWNFYSorjpcAAAADAeZsTUvX8NzE6mpZjVwv099brg1vq1pa1TOgMAICSxbRwKDw8XD/++KOysrKu+toaNWo4oSMAAACUBTn5hXp7Vbzmbj7ksN6vZS29cUcrVfXzcm1jAACUUG5mfeEHH3xQhmFoxYoVZrUAAACAMiY2+az6f7DRYTAU4OOh90e00Uf3tCMYAgDgL0wLh4YPH64+ffro3//+t+Li4sxqAwAAAGVAQaFVM9cc0J0f/aKDJzPt6jc2rqYfxnbXHW2D2HQaAIALmBYOLVy4UC+//LJatWqlXr16admyZWa1AgAAgFKq0Gpow/6TGvLxr3p/zX4VWA2bureHm6YMbK75Y25g02kAAC7CtD2H7rrrrqKf2hiGoTvuuEN9+/bViBEjdNttt6l69epmtQYAAIAS7mxWnj7d9IcW7UjWMQcbTktSq7qV9P6INgqpEeDi7gAAKF1MC4ekP0Ohv77+4Ycf9MMPP0iS6tSpo7Zt2yo8PFxt27ZV27Zt1aBBA5M6BQAAQEmxbl+KJiyKVUp6rsO6u5tFT/YM0ZO9QuTpbtpCeQAASg3TwqEDBw4oOjpaMTExRf9OSkoqqh85ckRHjx5VZGRk0VilSpWKwqL33nvPjLYBAABgkqy8Ar0RuVf/tzXxonMaBfpp+ohwhQdXdl1jAACUchbjr8t3THbmzBmbsCg6Olp79+5VXl6ezTyLxaLCwkKTuoQjycnJCg4OliQlJSUpKCjI5I4AAEBZ8lviGT27MFqHTmU5rLeoU1HDOwRrRMdg+Xi6u7g7AABcxxmfv019rOxCVapU0U033aSbbrqpaKygoEB79uyxCY1iYmLMaxIAAAAuk1dg1Qc/HdBH6xJkdfAjzR6h1fX3W5upeZ2Krm8OAIAyokSFQ454eHiodevWat26te677z6z2wEAAICL7D+RrnELo7X76Dm7mq+nu17sH6Z7bqjH0fQAAFwn03boi4+P14033qinn35a+/fvN6sNAAAAlDBWq6HZG3/XgA83OQyG2tarrJXPdNO9neoTDAEAUAxMWzm0cOFCbdmyRYmJiZo2bZpZbQAAAKAEST6Tpee+idGW30/b1TzcLBrXO1SPdm8kD04hAwCg2JgWDq1evVoWi0WjR4+Wj4/PZecnJCRo5cqVCg8PV9euXeXmxjcEAAAAZYVhGFr82xG9smy30nML7OpNavjr/RHhalm3kgndAQBQtpkWDh06dEiS1L179yua36BBA7399ts6fvy4Vq5cqb59+zqxOwAAALjKqYxcTfo2Tj/sPmFXs1ikMV0a6rm+TTmFDAAAJzFt+c2pU6ckSTVr1ryi+R4eHhoyZIgMw9Dy5cud2RoAAABcZM2eE+o7Y4PDYKhuZV99+VAnvTSgOcEQAABOZFo4VLVqVUnSmTNnrviaHj16SJK2bNnilJ4AAADgGhm5Bfr7olg99Pl2pWbk2dWHtg/S92O7qXPjaiZ0BwBA+WJaONSsWTNJ0i+//HLF1zRo0ECSdPToUWe0BAAAABfY9sdp3TpjgxZuT7KrVfXz0sf3tte7w9qooo+nCd0BAFD+mBYO9enTR4ZhaNasWcrPz7+ia84fVXr+kTQAAACUHrkFhfrHyr0aMetXJZ/JtqvfElZDP4ztrltb1jKhOwAAyi/TwqExY8bI19dXSUlJeuSRR67omn379kmSAgICnNkaAAAAitmeo+d0+z836z8bfpdh2Nb8vNw1bUhrfTKqg6oHeJvTIAAA5Zhp4VD16tU1depUGYahzz//XAMGDFBqauolr/nkk08kSS1atHBFiwAAALhOBYVW/XvdQd3+r02KP55uV49oUFWrxnbX8I7BRavEAQCAa5kWDknS888/rzFjxsgwDH3//fdq3LixJk6cqB07dtjMS05O1v33369169bJYrFo8ODB5jQMAACAK1JQaNWS35LV+/0NentVvPILbZcLebm7adJtzfTVI50UXLWCSV0CAABJshjGhQt7Xe/NN9/UlClTVFhYWPQTI09PT9WsWVN5eXlKSUmRJBmGocaNGys6Olp+fn5mtowLJCcnKzg4WJKUlJSkoKAgkzsCAABmKCi0alnMUX24NkF/pGY6nBNWu6LeH9FGzWpVdHF3AACUfs74/G3qyqHzJk2apOjoaPXr10/SnyFQXl6ekpKSdOLECRmGIcMw1KxZM61cuZJgCAAAoATadSRN/T/YpGe/jnEYDLlZpMdvaqylT9xIMAQAQAniYXYD57Vo0UKRkZE6ceKEVqxYobi4OB0/flw5OTmqU6eOevbsqTvvvFPu7u5mtwoAAIC/KCi06j8bfteMNfvtHh87r1XdSpoysLk6NKjq4u4AAMDluCQc2rFjh9q3b39Fc2vWrKkxY8Y4uSMAAAAUh0OpmXr262j9lnjWYb11UCWNvaWJejatwYbTAACUUC4Jhzp27Kg6deqof//+GjhwoG655Rb5+Pi44ksDAADACQzD0JfbEvX6ir3Kzi+0qzerFaAJtzYlFAIAoBRw2WNlR48e1ezZszV79mz5+PioV69eGjhwoAYMGKA6deq4qg0AAABcp5RzOZqwOFbr9p20q7lZpL/dFKKnb24iL48Ssb0lAAC4DJf8jZ2cnKyPP/5Yt912m3x8fJSdna3IyEg9/vjjCg4OVvv27TV16lS7I+wBAABQskTGHlOfGRscBkMNqlXQN4/dqOf6NiUYAgCgFHH5UfbZ2dlas2aNVqxYocjISB09evTPRv673LhWrVo2j5/5+vq6sj1cI46yBwCgbEvLzteU73ZpafRRh/V7bqinSbeFyc+7xJx3AgBAmeSMz98uD4cutGPHDi1fvlwrVqzQb7/99mdT/w2KePys9CAcAgCg7NqckKrnvonRsbQcu1r1AG9NG9paPZvWMKEzAADKnzIZDv3V0aNHtWLFCi1fvlxr165Vdna2pP+FReHh4Ro4cKAGDhx4xaefwTUIhwAAKHty8gv11vfxmvfLIYf1/q1q6/XBLVXFz8u1jQEAUI6V+XDor3JycrRmzRotX778oo+fDRgwQH/729/Upk0bM1uFCIcAAChrYpPPatzCaB08mWlXC/Dx0Gu3t9Tt4XU4iQwAABdzxufvEvtQuI+PjwYMGKABAwZI+vPxs/Orinbu3Kljx45p9uzZqlu3LuEQAABAMSkotOpfPx/Uh2sPqMBq/zPELiHV9M7QNqpTmX0hAQAoK0psOHSh9u3bq3379poyZUrR42crVqxQhQoVzG4NAACgTPj9ZIbGfR2jmKSzdjVvDze90K+Z7u/cQG5urBYCAKAsKbGPlaF04bEyAABKL8Mw9MWWw3pz5V7l5Fvt6q3qVtL7I9oopEaACd0BAIC/KlePlQEAAMD5jqfl6PlFMdp4INWu5u5m0RM9Q/RUrxB5uruZ0B0AAHAFU8Oh7OxszZkzR7/88osyMjJUrVo1NW3aVK1bt1Z4eLhq165tZnsAAABl2rKYo3p56S6lZefb1RoF+mn6iHCFB1d2fWMAAMClTAuHUlNT1b17d+3bt++icwIDAxUeHm7zT7NmzTgVAwAA4DqczcrTy9/t1vKYow7r93eurxf6hcnXy93FnQEAADOYFg69+OKLio+PlyR5e3urSZMmys7O1qFDh1RYWChJOnnypNasWaM1a9YUXefj46NWrVppy5YtpvQNAABQmq3ff1ITFsXoxLlcu1rNit56Z2gbdQ+tbkJnAADALKaFQ5GRkbJYLGrevLl++OEH1alTR5KUl5enXbt2KTo6Wjt37tTOnTsVGxurjIwMSX8+ihYVFWVW2wAAAKXS4VOZ+tfPCfp6e7LD+qA2dfTa7S1VqYKnizsDAABmM/WxMkl6+eWXi4IhSfLy8lK7du3Url07m/kHDhzQzp07i0IjAAAAXN7eY+f073UHtSL2qKwOzqit5Oup1we31MA2deyLAACgXDAtHKpZs6aSk5PVpEmTK5rfpEkTNWnSRMOHD3dyZwAAAKVfVl6B3ly5V/O3JF50TrcmgXpnaBvVquTjws4AAEBJY9qZpJ07d5YknThxwqwWAAAAyqTfEs+o/webLhoM+Xi66bXbW+jzByMIhgAAgHnh0JgxY2QYhr777juzWgAAAChT8gutem/1Pg399y/6IzXTru7n5a5HujfS+ud76r7ODTgBFgAASDLxsbLevXvrzjvv1Ny5czVmzBh16NDBrFYAAABKvYSUdI1dGK1dR87Z1Xw93fVoj0YafWMDVa7gZUJ3AACgJDMtHFqyZIlefPFFHT9+XL1799Ynn3yioUOHmtUOAABAqWS1Gpr3yyG9vSpeuQVWu3p4cGW9PyJcDQP9TOgOAACUBqaFQ0OHDpXFYpG7u7sKCgo0YsQI9erVS8OHD9eAAQNUu3Zts1oDAAAoFY6ezdbzi2K0OeGUXc3DzaJnbm6ix29qLA9303YSAAAApYBp4ZAkGYahgoKCotdr167V2rVrJf15mlnbtm3Vtm1btWvXTm3btlXDhg3NbBcAAKBEMAxDS6OPaPJ3u5WeU2BXb1zdTzNGtFWroEomdAcAAEob08KhuLg4xcTE2Pzz15PLjh8/rlWrVmnVqlVFYxUrVlR4eLjatWun9957z4y2AQAATHUmM08vLd2lyLhjDusPdGmgv9/aTD6e7i7uDAAAlFYWwzAMs5s47+TJk4qOjrYJjOLj44tWF51nsVhUWFhoUpdwJDk5WcHBwZKkpKQkBQUFmdwRAABlz7p9KZqwKFYp6bl2tdqVfPTusDbqEhJoQmcAAMBVnPH529THyi5UvXp19e7dW7179y4ay8/P1+7du20Co9jYWBO7BAAAcK2svAK9uXKv5m9JdFi/o21dTR3UQpV8PV3cGQAAKAtMC4feeecdjR07Vp6el/4mxtPTU+Hh4QoPD3dNYwAAACXIb4lnNP7rGP2RmmlXq1zBU28MbqX+rTnIAwAAXDvTjq74+9//rrCwMC1ZssSsFgAAAEqs/EKrpq/ep6H//sVhMNQjtLp+GNudYAgAAFw308Ihd3d3/f777xo2bJh69Oih3377zaxWAAAASpSElHTd+dEv+mBtgqwX7A7p6+mu1we31LwHOqpmRR9zGgQAAGWKaeFQbGys+vbtK8MwtGnTJkVEROiBBx7Q0aNHzWoJAADAVFaroTmb/lD/DzYp7kiaXT08uLJWPtNN93aqL4vFYkKHAACgLDItHAoLC9P333+vFStWKDQ0VFarVZ9//rmaNm2q1157TdnZ2Wa1BgAA4HJHz2brvjlb9eqKPcotsNrUPNwsGt87VIse66yGgX4mdQgAAMoq08Kh82677Tbt2rVLM2bMUJUqVZSZmampU6cqNDRU8+fPN7s9AAAApzIMQ0t3HlHfGRu0OeGUXb1xdT99+7cueurmJvJwN/1bNwAAUAaViO8w3N3d9fTTT+vAgQN68skn5e7uriNHjuj+++9XRESENm/ebHaLAAAAxe5MZp6e/HKnxi6MVnpOgV39gS4NFPl0N7UKqmRCdwAAoLwoEeHQeVWqVNEHH3ygmJiYov2Itm/fru7du2vEiBE6dOiQ2S0CAAAUi3X7UtR3xgZFxh2zq9Wu5KP/e+gGTRnYQj6e7iZ0BwAAypMSFQ6dd34/opUrV6pZs2YyDEOLFi1SWFiYJk6cqPT0dLNbBAAAuCZZeQV6aWmcRs+NUkp6rl19cHgdrRrbXV1CAk3oDgAAlEclMhw679Zbb1VcXJzeeecdeXh4KDc3V9OmTVOTJk00a9YsGYZx+ZsAAACUEDsTz6j/B5s0f0uiXa1yBU/96+52mjGyrSr5eprQHQAAKK88zG7gQoZhaPfu3YqKitL27dsVFRWl2NhYFRQUyGKxyDAMpaSk6PHHH9dHH32kf/7zn+ratavZbQMAAFxUfqFVH/50QP9ad1CFVvsfbnUPra53hrZWzYo+JnQHAADKO9PDoQMHDhSFQFFRUYqOjlZWVlZR/a+rgywWi8LCwlSjRg2tX79esbGx6tGjhx577DFNnz5d3t7eZrwFAACAi0pISde4hTGKO5JmV/P1dNek/mG694Z6slgsJnQHAABgYjjUu3dvbd++XefOnSsau/AxsWrVqikiIkKdOnVSp06ddMMNN6hixYqSpKioKE2YMEHr16/Xxx9/rJ07d+rHH3+Un5+fS98HAACAI1aroc9+PaS3vo9XboHVrh4eXFnvjwhXw0C+dwEAAOYyLRz66aefbH7t4eGh1q1bFwVBnTp1UkhIyEWv79ixo37++WfNnj1bTzzxhLZu3ap3331XU6ZMcXbrAAAAl3T0bLaeXxSjzQmn7GoebhY9c3MTPX5TY3m4l+jtHwEAQDlhWjhUp04dmyCoQ4cO8vG5+ufsH3roIR09elRTp07VwoULCYcAAIBpDMPQd9FH9fJ3u5SeU2BXb1zdTzNGtFWroEomdAcAAOCYaT+uSk5O1qJFi/Tcc8+pa9eu1xQMnTdo0CBJ0qFDh4qpu5LlyJEjmjFjhvr06aN69erJy8tLtWrV0pAhQ7R161a7+fPmzZPFYrnkPzfffLMJ7wQAgLLrbFaenvxqp8YujHYYDI2+sYEin+5GMAQAAEoc0zekLg4BAQGSpNzcXJM7cY4PP/xQb7/9tho3bqzevXurRo0aOnDggJYuXaqlS5fqq6++0vDhw4vmh4eHX3QF1aJFi7R792717dvXVe0DAFDmrduXogmLYpWSbv+9SO1KPnpnaBt1bRJoQmcAAACXZzEu3AW6mN1zzz1q06aNwsPDFR4erho1ahT718jOztb8+fO1c+dOffTRR8V+f7MtWbJE1atXV7du3WzGN27cqJtvvlkBAQE6evToZU9ry8vLU506dZSWlqbk5GTVrFmz2HpMTk5WcHCwJCkpKUlBQUHFdm8AAEqinPxCLYs5qkXbk7Xt0GmHcwaH19Ert7dUJV9PF3cHAADKKmd8/nZ6OOTm5mZzNGvNmjUVHh5uExiFhoZyfOs16tu3r1avXq2oqCh16NDhknMXLlyokSNHavDgwfr222+LtQ/CIQBAebL90Gk9+3WMEk9nOaxX8vXUG3e01IDWdVzcGQAAKOuc8fnb6Y+VhYaGKiEhQVbrn0e4Hj9+XD/88IN++OGHojm+vr5q1aqVTWjUunVrVahQwdntXVZKSoq2bdumbdu2KSoqSlFRUTp16s+TR+6//37Nmzfviu+VmJioDz74QJGRkUpMTJS3t7dCQkI0fPhw/e1vf7um9+vp+edPIj08Lv8/5aeffirpz028AQDA1csrsGrGmv36eP1BWS/y47XuodX1ztDWqlnx2vdTBAAAcCWnrxySpJycHMXFxSkmJqbon9jYWJ07d+5/jVywcshisSgkJMRulVHt2rWd3a5dHxdzNeFQZGSk7rnnHqWlpTmsN23aVCtXrlSjRo2uuLfExESFhoaqSpUqSk5Olru7+0XnHj58WI0aNVLt2rV1+PDhS869FqwcAgCUdftPpGvsgmjtOXbOYb2qn5fG9Q7VvTfUY0U0AABwmlK5ckiSfHx81LFjR3Xs2NFm/ODBg3riiSe0evVqXZhRGYah/fv368CBA/rmm2+KxgMDA9W2bVutWrXKFa3bCA4OVlhYmFavXn1V18XExGj48OHKysqSv7+/Jk6cqJ49eyo7O1sLFizQJ598on379ql///6KioqSv7//Ze+Zn5+v++67T7m5uZo2bdplw565c+fKarXqgQceKPZgCACAssxqNTRn8x+a9sM+5RVY7erhwZX1aPdGujmsprw8TDsIFgAA4JqZdlqZ1WrVs88+qx9//FGNGzfW888/r1atWqlixYo6fvy4tm7dqmXLlmnbtm021508eVI//vijy/qcPHlyUbBVs2ZNHTp0SA0bNryqe4wdO1ZZWVny8PDQ6tWr1blz56Jar1691KRJE02YMEHx8fGaPn26Jk+efMn7Wa1WPfjgg9qwYYMefvhh3XfffZedP3fuXFksFj344INX1TsAAOXZkbPZev6bGP1y8JRdzcPNonG9Q/VYj8Zyd2OlEAAAKL1c8liZIzNnztS4ceMUERGh9evXX/SkraioKD399NPaunWr/P39dddddyk+Pl7r1693ccd/+ms4dCWPlUVFRSkiIkKS9Oijj+rjjz+2m2O1WtWyZUvt3btXVapU0YkTJ4r2ErqQYRh66KGHNGfOHN1777367LPP5OZ26Z9S/vDDD7r11lt18803a82aNVfwLq8ej5UBAMoSwzC0NPqIJn+3W+k5BXb1JjX89f6IcLWsW8mE7gAAQHnmjM/fpq19/vTTT2WxWDRx4sRLHsHesWNHbdq0SaNGjVJGRoYOHjxoWjB0LZYuXVr0+oEHHnA4x83NTaNGjZIknTlzRuvWrXM4z2q1asyYMZozZ47uuusuzZs377LBkMRG1AAAXI0zmXl68sudGrcwxmEwNKZrQy1/qivBEAAAKDNMC4cOHjwoSapXr95l57q7u2v27Nlq06aNfv75Z3311VfObq/YbNy4UZLk5+en9u3bX3Rejx49il5v2rTJrm61WvXQQw9p7ty5GjFihL744osr2jvo1KlT+u6771S1alXdcccd1/AOAAAoP9btS1HfGRsUGXfMrlanko++fOgGvTyguXw82b8PAACUHabtOeTv76+cnBwlJSWpbdu2l53v4eGhp556Sg899JC++uor3XXXXS7o8vrt3btXkhQSEnLJ4+abNWtmd81551cMzZs3T8OGDdP8+fOveFPpL774Qnl5ebr33nsvuULrcpKTky9ZP3bM/ptoAABKi6y8Av1jZby+2HLYYf2OtnU1dVALVfJ1/Ng3AABAaWZaONSxY0d9//33+vLLLzVo0KAruqZ169aSpN9++82ZrRWbnJwcpaamStJlnwGsUqWK/Pz8lJmZqaSkJJvaq6++qnnz5snf31+hoaF6/fXX7a4fPHiwwsPD7caL65Gy888zAgBQ1kQnndWzC6P1e2qmXa1yBU+9MbiV+reubUJnAAAArmFaODR69GitXLlS33zzjYYMGaJhw4Zd9pr09HRJfz4qVRqc71fSFR1Pfz4cysjIsBk/dOiQJCkjI0NvvPGGw2sbNGhgFw5t27ZNu3btUkREhFq1anV1zQMAUMblF1r1z7UJ+ufPCSq02p/P0T20ut4Z2lo1K/qY0B0AAIDrmBYODR06VLfccovWrFmju+++W/v379eECRMuekqXJM2dO1eSVKFCBVe1eV1ycnKKXnt5eV12/vnHvrKzs23G582bd9lT0RyJiIhQcR1Gd+FqpgsdO3as6FQ2AABKuoMnM/TswmjFJKfZ1Xw83fRi/+a694Z6slg4oh4AAJR9poVDkvTNN9/o5ptv1m+//abJkydr9uzZeuSRRzRo0CC1aNGiaN7Bgwf16quvav78+bJYLLrxxhtN7PrK+fj87yeNeXl5l52fm5srSfL19XVaT9eKo+kBAGWBYRj6Ysthvblyr3LyrXb1NkGVNH1EuBpXv/yKXwAAgLLC1HCoUqVK2rx5s5544gnNmTNHhw8f1ksvvaSXXnpJvr6+CgwM1OnTp5WZ+b89ADw8PPTiiy+a2PWVCwgIKHp94aNijpx/n1fyCBoAALg6Saez9OLSXdqw/6Rdzd3Noqd6heiJniHydDftMFcAAABTmP7dj7e3t2bPnq2NGzfq5ptvlmEYMgxDWVlZSkxMVEZGRtFY5cqVtWDBAnXq1Mnstq+Ij4+PAgMDJV3+tK8zZ84UhUNs/gwAQPE5mZ6rqct2q9d76xwGQ40C/bTk8Rs19pZQgiEAAFAumbpy6K+6dOmiH3/8UceOHdOqVasUExOj48ePKzs7WzVq1FCnTp00bNgwVaxY0exWr0pYWJg2btyohIQEFRQUXPQ4+/j4eJtrAADA9TEMQ4t/O6JXlu1Wem6Bwzn3d66vF/qFydfL3cXdAQAAlBwlJhw6r3bt2nrggQfMbqPYdO3aVRs3blRmZqZ27NihG264weG89evXF73u0qWLq9oDAKBMOpaWrVeW7dGq3ccd1msEeOudYW3UI7S6izsDAAAoeVg77WSDBw8uen3+tLULWa1Wff7555KkypUrq2fPnq5oDQCAMufXg6c0eu42dXlrrcNgyMvdTaNvbKDV47oTDAEAAPwX4ZCTRUREqFu3bpKkTz/9VL/++qvdnPfee0979+6VJD3zzDPy9PR0aY8AAJR22XmFmvLdLt31yRat23dSVsN+zu3hdbT2uR6aOqiFKlfwcn2TAAAAJZTTHytLTEyUJNWtW1fu7sX7PH9hYaGOHDkiSapXr16x3vu8TZs2KSEhoejXqampRa8TEhI0b948m/mjR4+2u8fMmTPVpUsXZWdnq0+fPpo0aZJ69uyp7OxsLViwQLNmzZIkhYaGavz48U55HwAAlFVxyWkau3CnDp7MdFiv6uelN+9opVtb1nJxZwAAAKWDxTAMBz9bKz5ubm5yc3NTbGysmjdvXqz33r17t1q1aiU3NzcVFDjeaPJ6jR49Wp999tkVz7/Yf87ly5fr3nvv1blz5xzWQ0NDFRkZqZCQkGvq02zJyclFp6wlJSUpKCjI5I4AAGVdQaFVH68/qBlrDqjAwVIhf28P3R5eR8/c0kQ1AnxM6BAAAKD4OePzt0s2pHZy/uT0+xeHgQMHKjY2VjNnzlRkZKSSk5Pl5eWlkJAQDRs2TE8++aQqVKhgdpsAAJQKiaeyNO7raO04fMau5uPppuf7NtNdEcGq4FXizt4AAAAocVyycshisWjXrl3FfkT7+ZVDFotFhYWFxXpvXB1WDgEAXMEwDH2zPVmvLN+tzDz7v/tbB1XS+yPC1bi6vwndAQAAOF+pXTkkSX369Cn2jZbz8/OL9X4AAKDkOpWRq4lL4rR6zwm7mptFerJniJ66uYk83TlvAwAA4Gq47LGy8xtHAwAAXK218Sc0YVGcUjNy7Wr1q1XQ9OHhal+/igmdAQAAlH5OD4fuv/9+Z38JAABQRmXlFeiNyL36v62JDusjOwbr5QHN5efN3kIAAADXyunfSc2dO9fZXwIAAJRB0UlnNW5htP5ItT+ivpqfl94a0lq9m9c0oTMAAICyhR+zAQCAEqWg0Kp//pygD9cmqNDBEfU3N6uht4a0VvUAbxO6AwAAKHsIhwAAQInxR2qmxi6MVkzSWbuar6e7Xh7QXHdFBMtisbi+OQAAgDKKcAgAAJjOMAx9uS1Rr6/Yq+x8+yPqw4Mr6/0R4WoY6GdCdwAAAGUb4RAAADDVyfRcvbA4Vj/Fp9jV3N0serpXEz3Rs7E8OKIeAADAKQiHAACAaX7cc0IvLI7Vqcw8u1rDQD+9PyJc4cGVXd8YAABAOUI4BAAAXC4zt0CvLt+jhduTHNbv7VRPk24LUwUvvlUBAABwNr7jAgAALrXj8BmNWxitxNNZdrVAf2+9M7S1ejarYUJnAAAA5RPhEAAAcIn8Qqs++OmA/vVzghycUK8+zWvqH3e2UjV/jqgHAABwJcIhAADgdAdPZmjcwmjFJqfZ1fy83DVlYAsN6xDEEfUAAAAmIBwCAABOYxiGvthyWG+u3KucfKtdvX39Knp/eLjqVatgQncAAACQCIcAAICTpJzL0fOLYrV+/0m7moebRWNvaaLHenBEPQAAgNkIhwAAQLFbteuYJi6J05msfLta4+p+mjGirVoFVTKhMwAAAFyIcAgAABSb9Jx8TV22R4t/S3ZYv79zfb3QL0y+Xu4u7gwAAAAXQzgEAACKxbY/TuvZr6OVfCbbrlYjwFvvDGujHqHVTegMAAAAl0I4BAAArktegVXvr9mvj9cflOHgiPp+LWvpzTtaqYqfl+ubAwAAwGURDgEAgGu2/0S6xi6I1p5j5+xq/t4eemVQC93Zri5H1AMAAJRghEMAAOCqWa2G5v1ySG+tildegf0R9RENquq94W0UXJUj6gEAAEo6wiEAAHBVjqfl6PlFMdp4INWu5ulu0bO9m+qR7o3k7sZqIQAAgNKAcAgAAFyxFbFH9eK3u5SWbX9EfZMa/np/RLha1uWIegAAgNKEcAgAAFxWWna+pi7brW93HnFYf7BLQ024tal8PDmiHgAAoLQhHAIAAJf068FTGv91tI6m5djValX00bvD2qhrk0ATOgMAAEBxIBwCAAAO5RYU6r3V+/XJxt8dHlE/oHVtvT64pSpX4Ih6AACA0oxwCAAA2Ik/fk5jF0Qr/ni6XS3Ax0OvD26pQW3qcEQ9AABAGUA4BAAAilithuZs/kPTVu1TXqH9EfWdGlXVe8PDVbeyrwndAQAAwBkIhwAAgAoKrVoRe0wfrUvQ/hMZdnUvdzc937epxnRtKDeOqAcAAChTCIcAACjnog6d1oRFsfojNdNhvVmtAL0/IlxhtSu6uDMAAAC4AuEQAADlVF6BVTN/2q9/rzsoq4MNpy0W6eFujfRs71COqAcAACjDXBoOHTx4UF9++aXi4+NVWFio2rVrq3v37rrtttvk7e3tylYAACjXElLSNXZhtHYdOeew3qSGv165vYVubMwR9QAAAGWdy8Kh//znP3r66adVUFBgM/7BBx8oODhYH3/8sW699VZXtQMAQLlkGIa+2HJYb0TuVW6B/YbTLetW1FO9mqh3WE32FgIAACgnXBIORUVF6YknnpDVav9NqCQlJiZq0KBBWrRokQYNGuSKlgAAKHdSzuXo+UWxWr//pF3N092icb1D9Wj3xnInFAIAAChXXBIOffjhh7JarbJYLOrfv7/GjBmjOnXq6MiRI1qxYoW++OIL5efn68EHH1R8fLwCA1nCDgBAcVq165gmLonTmax8u1pIDX/NGBGulnUrmdAZAAAAzOaScGjTpk2yWCzq16+fli1bVjTesWNHDR48WKNGjVLfvn115swZzZo1S5MmTXJFWwAAlHnpOfl6ZfkeLdqR7LA++sYGeqFfMzacBgAAKMfcXPFFjh07Jkl69NFHHda7d++u8ePHyzAMLV682BUtAQBQ5m0/dFq3fbDRYTBUI8Bbnz0YoamDWhAMAQAAlHMuCYdyc3MlSQ0aNLjonLvuukuSFBcXp7y8PFe0BQBAmZRXYNU7P8Rr+H9+VdLpbLt6v5a19MPY7uoRWt2E7gAAAFDSuPQoe3f3i/9kMiQkRJJUWFiokydPqm7duq5qCwCAMiMhJUNjF+50eES9v7eHpg5qoSHt6spiYdNpAAAA/Mml4dCleHt7F71OT083sRMAAEqf80fUv7lyr3Ly7U8H7VC/it4fEa7gqhVM6A4AAAAlmUvDoSv9KeXFjrwHAAD2LnVEvYfbn0fUP9aDI+oBAADgmEvDoa5du6pNmzYKDw8v+qd58+by8CgxC5gAAChVOKIeAAAA18tlqYxhGDpz5ozWr1+v9evXF417enqqefPmCg8PLxrLz7f/BhcAAPxPRm6BXlm2W99wRD0AAACuk0vCoY8++kjR0dGKjo7Wrl27lJWVVVTLy8tTTEyMYmJiih4769Chgxo3bqzWrVurdevWatOmjVq3bq369eu7ol0AAEq07YdOa9zX0Q5PIqsR4K13hrXhJDIAAABcMYthGIYrv6BhGNq3b19RWBQdHa2YmBidOHHCvrkL9igKCAhQq1at1KZNG/3zn/90Vcu4AsnJyQoODpYkJSUlKSgoyOSOAKDsyS+0auaaA/poXYKsDv72vrVFLf3jzlaq4ufl+uYAAADgEs74/O3ycOhiTpw4oZ07d9qERgkJCQ43p7ZYLCosLDShS1wM4RAAONfBkxkauyBacUfS7Gr+3h6aMrC5hrYP4oh6AACAMs4Zn79LzE7QNWvW1K233qpbb721aCw7O1sxMTE2gdGuXbuUnW2/jB4AgLLIMAwtjErSK8v3KDvf/gcjHFEPAACA61ViwiFHfH191alTJ3Xq1KlozDAM7d+/38SuAABwjbNZeXphcZxW7T5uV+OIegAAABSXEh0OOWKxWNS0aVOz2wAAwKl+PXhK4xZG6/i5HLta4+p+mjGirVoFcUQ9AAAArl+pC4cAACjL8gutev/H/fr3+oNytCvgPTfU00v9m8vXiyPqAQAAUDwIhwAAKCEOpWbqmQU7FZNsv+l05QqeentIa/VtUcuEzgAAAFCWEQ4BAGAywzC0aEeypizbraw8+02nu4RU0/Th4apZ0ceE7gAAAFDWEQ4BAGCitOx8Tfo2TpGxx+xqnu4WPdenqR7u1khubDoNAAAAJyEcAgDAJNv+OK1xC6N15Gy2Xa1RoJ9mjmTTaQAAADgf4RAAAC5WUGjVBz8d0D9/TpDVwabTIzsGa/LA5qrgxV/TAAAAcD6+6wQAwIWSTmfpmQU79VviWbtaJV9PvXVnK/VrVdv1jQEAAKDcIhwCAMBFvt2ZrJeX7lZGboFdrVOjqpo+PFx1Kvua0BkAAADKM8IhAACc7FxOviYv3aWl0Uftah5uFo3rHarHejSWO5tOAwAAwASEQwAAONGOw2f0zIKdSj5jv+l0/WoVNHNkW4UHV3Z9YwAAAMB/EQ4BAOAEhVZD//o5QTN/OqBCB7tOD20fpKmDWsjfm7+KAQAAYC6+IwUAoJgln8nSuIXRijp0xq4W4OOhN+9opYFt6pjQGQAAAGCPcAgAgGK0LOaoXvw2Tuk59ptOd2xQRe+PCFdQlQomdAYAAAA4RjgEAEAxyMgt0JTvdmvxb8l2NXc3i565uYn+dlNjebi7mdAdAAAAcHGEQwAAXKfopLN6ZsFOHT6VZVcLruqrGSPaqn39KiZ0BgAAAFwe4RAAANeo0Gro4/UH9f6P+1XgYNPpO9rW1au3t1CAj6cJ3QEAAABXhnAIAIBrcPRstsYtjNbWP07b1fy9PfT64JYa3LauCZ0BAAAAV4dwCACAq/R93DG9sCROadn5drW29Spr5oi2qleNTacBAABQOhAOAQBwhbLyCvTq8j1aEJVkV3OzSE/2DNHTNzdh02kAAACUKoRDAABcgbjkND2zYKd+T820q9Wt7Kv3R4QromFVEzoDAAAArg/hEAAAl2C1Gvpk4+96d/U+5Rfabzo9oHVtvXFHK1XyZdNpAAAAlE6EQwAAXMSJczl69utobU44ZVer4OWuV29vqSHt6spisZjQHQAAAFA8CIcAAHBg9e7j+vviWJ3Jst90uk1QJc0c2VYNAv1M6AwAAAAoXoRDAAD8RU5+of6xcq8++/WwXc1ikR7v0VjjeofKk02nAQAAUEYQDgEA8F9/pGbqyS9/0+6j5+xqtSv5aPrwcHVuXM2EzgAAAADnIRwCAEDSd9FHNGlJnDLzCu1q/VrW0j/ubKXKFbxM6AwAAABwLsIhAEC5lpVXoKnLduvr7cl2NW8PN00d1EIjOwaz6TQAAADKLMIhAEC5te94up788jcdSMmwqzWp4a9/3t1OTWsFmNAZAAAA4DqEQwCAcscwDC2IStLUZbuVW2C1qw/vEKSpg1qoghd/TQIAAKDs47teAEC5kp6Tr0nf7tLymKN2NT8vd715ZyvdHl7XhM4AAAAAcxAOAQDKjdjks3rqq506fCrLrtaiTkX98+52ahjoZ0JnAAAAgHkIhwAAZZ5hGJqz+ZDe+n6v8gsNu/roGxto4m3N5O3hbkJ3AAAAgLkIhwAAZdqZzDw9vyhWa/aesKtV9PHQtKFtdGvLWiZ0BgAAAJQMhEMAgDIr6tBpPf3VTh1Ly7Grta1XWR/e1VZBVSqY0BkAAABQchAOAQDKHKvV0L/XH9T0H/er0Gr/GNljPRprfJ9Qebq7mdAdAAAAULIQDgEAypSU9Bw9uzBGmxJS7WrV/Lz03vA2uqlpDRM6AwAAAEomwiEAQJmx6UCqxi6MVmpGrl2tc6NqmjEyXDUr+pjQGQAAAFByEQ4BAEq9gkKrZqw5oH+tS5BxwVNkbhZp7C2heqJniNzdLOY0CAAAAJRghEMAgFLt6NlsPf3VTm0/fMauVrOitz4Y2VY3NKpmQmcAAABA6cBOnCXckSNHNGPGDPXp00f16tWTl5eXatWqpSFDhmjr1q1XdI9p06bJYrHIYrFoy5YtTu4YAFznxz0ndNsHGx0GQ72a1dD3z3QnGAIAAAAug5VDJdyHH36ot99+W40bN1bv3r1Vo0YNHThwQEuXLtXSpUv11Vdfafjw4Re9fu/evZo8ebL8/PyUmZnpws4BwHlyCwr11vfxmrv5kF3Nw82iF/o104NdGsqNx8gAAACAyyIcKuEiIiK0YcMGdevWzWZ848aNuvnmm/X444/r9ttvl7e3t921hYWFuv/++9WmTRuFhoZq/vz5rmobAJzm8KlMPfnlTsUdSbOrBVf11Yd3tVN4cGXXNwYAAACUUjxWVsLdeeeddsGQJHXr1k09e/bU6dOnFRcX5/Dat99+WzExMZozZ47c3d2d3SoAON2ymKPq/8Emh8HQba1qacVT3QiGAAAAgKtEOHQJKSkpWrFihSZPnqx+/fopMDCwaO+e0aNHX9W9EhMT9dxzzyksLEx+fn6qWrWqIiIi9O677yorK+ua+vP09JQkeXjYLwDbtWuXXnnlFb300ktq0aLFNd0fAEqK7LxCTVwSq6e/2qmM3AKbmpeHm14f3FL/urudKvl6mtQhAAAAUHrxWNkl1KxZs1juExkZqXvuuUdpaf/7SXdWVpaioqIUFRWl2bNna+XKlWrUqNEV3zMxMVFr1qxRrVq11KpVK5taQUGBRo8erbCwML3wwgvF8h4AwCwHTqTriS9/0/4TGXa1RtX99M+72ql5nYomdAYAAACUDawcukLBwcHq06fPVV8XExOj4cOHKy0tTf7+/nrjjTf0yy+/6KefftLDDz8sSdq3b5/69++vjAz7Dz6O5Ofn67777lNubq6mTZtm98jYm2++WfQ42fnVRQBQ2hiGoa+jkjTwn5scBkND2gVp+ZNdCYYAAACA68TKoUuYPHmyOnbsqI4dO6pmzZo6dOiQGjZseFX3GDt2rLKysuTh4aHVq1erc+fORbVevXqpSZMmmjBhguLj4zV9+nRNnjz5kvezWq168MEHtWHDBj388MO67777bOoxMTF6/fXX9dxzz6ldu3ZX1SsAlBQZuQV68ds4fRd91K5Wwctdr93eUkPaB5nQGQAAAFD2sHLoEl555RUNGDDgmh8vi4qK0rp16yRJY8aMsQmGzhs/frzCwsIkSTNmzFB+fv5F72cYhh5++GHNnz9f9957rz7++GO7Offff78aN26sqVOnXlPPAGC2XUfSNOCDjQ6DoWa1ArT8qa4EQwAAAEAxIhxyoqVLlxa9fuCBBxzOcXNz06hRoyRJZ86cKQqTLmS1WjVmzBjNmTNHd911l+bNmyc3N/v/+WJiYhQfHy8fH5+izbMtFos+++wzSVLnzp1lsVhsegOAksAwDM3b/Ifu/OgXHTplv1H/fZ3qa+kTXdS4ur8J3QEAAABlF4+VOdHGjRslSX5+fmrfvv1F5/Xo0aPo9aZNm9S7d2+butVq1UMPPaS5c+dqxIgR+uKLLy56NP2YMWMcjm/YsEEHDhzQoEGDVL16dTVo0OAq3w0AOE9aVr6eXxSj1XtO2NUCfDw0bUhr9WtV24TOAAAAgLKPcMiJ9u7dK0kKCQlxeNz8ec2aNbO75rzzK4bmzZunYcOGaf78+RcNhiRp9uzZDsdHjx6tAwcOaOLEierUqdPVvA1JUnJy8iXrx44du+p7AoAk7Th8Rk9/tVNHzmbb1doEV9Y/72qr4KoVTOgMAAAAKB8Ih5wkJydHqampkqSgoEvvjVGlShX5+fkpMzNTSUlJNrVXX31V8+bNk7+/v0JDQ/X666/bXT948GCFh4cXW++OBAcHO/X+AMofq9XQfzb8rndX71Oh1bCrP9K9kZ7r01ReHjwBDQAAADgT4ZCTpKenF73297/8/hjnw6ELj7M/dOiQJCkjI0NvvPGGw2sbNGjg9HAIAIpTakaunv06Rhv2n7SrVangqenDw9WzWQ0TOgMAAADKH8IhJ8nJySl67eXlddn53t7ekqTsbNvHKubNm6d58+Zddz/Xe58LVzRd6NixY4qIiLjm+wMoP35JSNUzC6N1Mj3XrhbRsKo+GNlWtSr5mNAZAAAAUD4RDjmJj8//Ptjk5eVddn5u7p8fknx9fZ3W0/W43KNxAHA5BYVWffDTAX34c4KMC54is1ikp3o10dO9QuThzmNkAAAAgCsRDjlJQEBA0esLHxVzJDMzU9KVPYIGAKVN4qksPfdNjLYdOm1XqxHgrRkjw3Vj40ATOgMAAABAOOQkPj4+CgwMVGpq6mVP+jpz5kxROMTGzwDKEqvV0Pyth/XW9/HKyiu0q3cPra7pw9so0N/bhO4AAAAASIRDThUWFqaNGzcqISFBBQUFFz3OPj4+3uYaACgLEk9l6flFMdr6h/1qIQ83i57v21QPd2skNzeLCd0BAAAAOI+NHZyoa9eukv58ZGzHjh0Xnbd+/fqi1126dHF6XwDgTFaroc9+OaS+MzY4DIbqVvbVwkc769EejQmGAAAAgBKAcMiJBg8eXPR67ty5DudYrVZ9/vnnkqTKlSurZ8+ermgNAJzi8KlM3fXJFk1ZtlvZ+faPkd19Qz39MK672tevYkJ3AAAAABwhHHKiiIgIdevWTZL06aef6tdff7Wb895772nv3r2SpGeeeUaenp4u7REAioNhGJq/5bBunbHxoquF5o+5QW/e0Ur+3jzRDAAAAJQkfId+CZs2bVJCQkLRr1NTU4teJyQkaN68eTbzR48ebXePmTNnqkuXLsrOzlafPn00adIk9ezZU9nZ2VqwYIFmzZolSQoNDdX48eOd8j4AwJly8gs16ds4LfntiMP6PTfU08TbwgiFAAAAgBLKYhiGYXYTJdXo0aP12WefXfH8i/2nXL58ue69916dO3fOYT00NFSRkZEKCQm5pj5LguTk5KKT1pKSkhQUFGRyRwBcIel0lh6bv0O7j9r/+Va3sq+mDW2tLiEcUQ8AAAAUF2d8/ubHuC4wcOBAxcbGaubMmYqMjFRycrK8vLwUEhKiYcOG6cknn/z/9u47OsoyYf/4NSmkEjoJPVSJSE8iCogBRVBURJoKGEFewLWABV9ZF9FVAQs/dnVRMEAQ1BVRUcFlrQgoYugghLL0AIYACaSXeX5/+GaWYRJIcJJnJs/3cw7nTO77nskVuE8yuXiKgoODzY4JAOWybl+aHvlgs85mF7jMcbQQAAAA4D04cghuwZFDgHUYhqG5aw7olVXJsl/0EySkmq9eH9pR/a5pYE44AAAAoIrjyCEAgKky8wo1edk2fbnjpMtci7ohmjuyq1qHVzchGQAAAIArRTkEACiTA6cyNW7xJu1LzXSZu/nqcL0+tKPCArnjIgAAAOBtKIcAAJf19a7f9PiHW3U+r9Bp3GaTHr+pjf4U10o+PjaT0gEAAAD4IyiHAAClstsNzf52n/7+7T6XubBAP/3tns6Ku6q+CckAAAAAuAvlEACgRBk5BZr4zy36fs8pl7m2EdU1d2RXNasTYkIyAAAAAO5EOQQAcJF88pzGLd6kw6ezXebu6NhQM+5ur+Bq/AgBAAAAqgLe2QMAnHyx7bgmL9uunIIip3FfH5ue6d9WY3o0l83G9YUAAACAqoJyCAAgSSossmvmqmS9s/agy1ydkGp6894uuq5lHROSAQAAAKhIlEMAAJ3OzNMjH2zRT/857TLXsXENvTWiqxrWDDIhGQAAAICKRjkEABa3/Vi6xi/epOMZuS5zw2OaaNod7RTo72tCMgAAAACVgXIIACxs6cajenb5TuUX2p3G/X1tev6Oa3TvtU1NSgYAAACgslAOAYAF5Rfa9fwXv+q9DUdc5iLCAjVnRBd1aVrLhGQAAAAAKhvlEABYzG/ncjVhySZtPpLuMhcbWVv/uK+L6lUPqPxgAAAAAExBOQQAFpJ06Iweem+zTp3Pc5l7oHukptwaJX9fHxOSAQAAADAL5RAAWIBhGHp3/WH9dcUuFdoNp7lAfx9NH9Red3VubFI6AAAAAGaiHAKAKi63oEhTPt2hTzanuMw1rhWkuSO7ql3DGiYkAwAAAOAJKIcAoAo7eiZb45ds0q/Hz7nM9WxdV2/c01k1g6uZkAwAAACAp6AcAoAqat2+ND3ywWadzS5wmftTXEs9fvNV8vWxmZAMAAAAgCehHAKAKsYwDM1dc0CvrErWRZcXUkg1X70+tJP6XRNhTjgAAAAAHodyCACqkMy8Qk1etk1f7jjpMteiXojmjeyqVvWrm5AMAAAAgKeiHAKAKuLAqUyNW7xJ+1IzXeZuvjpcs4Z2VPVAfxOSAQAAAPBklEMAUAV8ves3Pf7hVp3PK3Qat9mkJ25uo4dubCUfri8EAAAAoASUQwDgxex2Q7O/3ae/f7vPZa5GkL/+NryTbryqvgnJAAAAAHgLyiEA8FIZ2QWa+OEWfb/nlMtc24jqmjcyWk3rBJuQDAAAAIA3oRwCAC+UfPKcxi3epMOns13m7uzUUNMHtVdwNb7FAwAAALg8fnMAAC/z+bbjenrZduUUFDmN+/rYNOXWKI3uHimbjesLAQAAACgbyiEA8BKFRXbNXJWsd9YedJmrE1JNb97bRde1rGNCMgAAAADejHIIALzA6cw8Pfz+Fq0/cNplrmOTmnp7RBc1qBFkQjIAAAAA3o5yCAA83PZj6Rq/eJOOZ+S6zA2PaaLn72ynAD9fE5IBAAAAqAoohwDAgy1NOqpnP9up/EK703g1Xx89f2c73RPb1KRkAAAAAKoKyiEA8ED5hXY9/8Wvem/DEZe5iLBAvTWiizo3rWVCMgAAAABVDeUQAHiY387lavySTdpyJN1lLrZ5bf3j3i6qVz2g8oMBAAAAqJIohwDAgyQdOqMJSzYrLTPPZW509+Z65ta28vf1MSEZAAAAgKqKcggAPIBhGHp3/WH9dcUuFdoNp7lAfx/NGNRBAzs3MikdAAAAgKqMcggATJZbUKQpn+zQJ1tSXOaa1A7S2yO6ql3DGiYkAwAAAGAFlEMAYKKjZ7I1fskm/Xr8nMvcDW3q6e/DO6lmcDUTkgEAAACwCsohADDJ2n2n9MgHW5SeXeAy93BcK026uY18fWwmJAMAAABgJZRDAFDJDMPQ2z8c0Kv/TtZFlxdSaICfXhvSUf2uiTAnHAAAAADLoRwCgEqUmVeopz7apn/tPOky16JeiOaNjFar+qEmJAMAAABgVZRDAFBJDpzK1LjFm7QvNdNlru/V4Xp9aEdVD/Q3IRkAAAAAK6McAoBK8PWu3/T4h1t1Pq/Qadxmk57se5Um9GopH64vBAAAAMAElEMAUIHsdkOzv92nv3+7z2WuRpC//n5PZ/VqU8+EZAAAAADwO8ohAKggGdkFmvjhFn2/55TLXFSDMM0d0VVN6wSbkAwAAAAA/otyCAAqQPLJcxq3eJMOn852mbuzU0PNGNRBQdV8TUgGAAAAAM4ohwDAzT7fdlxPL9uunIIip3FfH5v+fGuUHugeKZuN6wsBAAAA8AyUQwDgJoVFds1clax31h50masbWk1v3ttF3VrUMSEZAAAAAJSOcggA3OB0Zp4efn+L1h847TLXqUlNvTWiixrUCDIhGQAAAABcGuUQAPxBO45laNzijTqekesyd09sE027o50C/Li+EAAAAADPRDkEAH/Aqp0nNfHDLcotsDuNV/P10fN3ttM9sU1NSgYAAAAAZUM5BABXwDAMzV1zQDNXJcswnOciwgL11ogu6ty0ljnhAAAAAKAcKIcAoJzyC+36y/Kd+nDjUZe52Oa19Y97u6he9QATkgEAAABA+VEOAUA5ZGQXaPySTSVeeHpYdBP9deA1qubnY0IyAAAAALgylEMAUEaH0rI0elGSDpzKchq32aSn+7XVuBtayGazmZQOAAAAAK4M5RAAlMEvB8/ofxZvVHp2gdN4oL+PZg/rrH7XRJiUDAAAAAD+GMohALiMTzYf09Mfb1dBkfOVp+tXD1DC/dHq0LimOcEAAAAAwA0ohwCgFHa7of/3zV698d1+l7moBmGaf3+0GtYMMiEZAAAAALgP5RAAlCC3oEhPfLRNK7efcJnr07a+/n5PZ4UE8C0UAAAAgPfjNxsAuMip83ka++5GbT2a7jI3untz/fm2KPn6cOFpAAAAAFUD5RAAXGDPyfManZiklPQcp3FfH5um3dFOI7s1MykZAAAAAFQMyiEA+D8/7D2lP723WZl5hU7j1QP89OZ9XdSrTT2TkgEAAABAxaEcAgBJi9cf0rQvdqnI7nxHssa1grQgPkZtwqublAwAAAAAKhblEABLK7IbenHlLi388ZDLXOemNfXOqGjVDQ2o/GAAAAAAUEkohwBYVmZeoR79YIu+S051mRvQoYFeG9JRgf6+JiQDAAAAgMpDOQTAko6n52h0YpKST553mXu0dytNvKmNfLgjGQAAAAALoBwCYDnbjqbrwXc36tT5PKfxar4+mjm4ve7q3NikZAAAAABQ+SiHAFjKqp0nNPHDrcotsDuN1wr217xR0YqJrG1SMgAAAAAwB+UQAEswDENv/3BAM1clu8y1qBeihfExalYnxIRkAAAAAGAuyiEAVV5+oV3PLt+hpRuPucxd37KO3rqvq2oE+5uQDAAAAADMRzkEoEpLz87X+CWb9POBMy5zw2Oa6K8Dr5G/r48JyQAAAADAM1AOAaiyDqZlaUxikg6kZTmN22zSM/3bamzPFrLZuCMZAAAAAGujHAJQJW04cFrjlmxSenaB03igv49mD+usftdEmJQMAAAAADwL5RCAKmfZpmN65pPtKigynMbrVw/Q/Ptj1L5xDZOSAQAAAIDnoRwCUGXY7YZmfb1Xb36/32Xu6gZhmh8frQY1gkxIBgAAAACei3IIQJWQW1CkJ5Zu08odJ1zmboqqr78N76yQAL7lAQAAAMDF+E0JgNdLPZ+rse9u0raj6S5zY3o015Rbo+Trw4WnAQAAAKAklEMAvFryyXMak7hRKek5TuO+PjY9f0c7jejWzKRkAAAAAOAdKIcAeK3Ve1L18PtblJlX6DRePcBPc0Z0Uc/W9UxKBgAAAADeg3IIgFd6d/0hTfv8V9mdb0imxrWCtDA+Rq3Dq5sTDAAAAAC8DOUQAK9SWGTXiyt3K/GnQy5zXZrW1LxR0aobGlD5wQAAAADAS1EOAfAamXmFeuT9zfp+zymXuTs6NtQrgzso0N/XhGQAAAAA4L0ohwB4hZT0HI1JTFLyyfMuc4/1aa2JN7WWzcYdyQAAAACgvCiHAHi8rUfT9eCijUrLzHMar+bro1cGd9DAzo1MSgYAAAAA3o9yCIBH+3LHCU36cKvyCu1O47VDqmnuyK6KiaxtUjIAAAAAqBoohwB4JMMwNGf1f/Tqv/e4zLWsF6IF8TFqVifEhGQAAAAAULVQDgHwOPmFdk35dIeWbTrmMte9VR3Nua+ragT5m5AMAAAAAKoeyiEAHuVsVr7GL9mkDQfPuMzdE9tEL9x5jfx9fUxIBgAAAABVE+UQAI9xMC1LoxOTdDAty2ncZpOm9I/Sgz2bc0cyAAAAAHAzyiEAHuHnA6c1fskmpWcXOI0H+ftq9vBOuqVdhEnJAAAAAKBq49wML5CSkqLZs2erb9++atq0qapVq6aIiAjdfffd2rBhQ4nPWbJkicaNG6fo6GgFBATIZrMpMTGxcoMDZfTRxqMaOX+DSzEUHhagj8ZfRzEEAAAAABWII4e8wBtvvKGZM2eqZcuWuvnmm1W/fn3t27dPy5cv1/Lly/XBBx9o6NChTs959tlndfjwYdWtW1cNGjTQ4cOHTUoPlM5uN/TaV3s0Z/V/XObaNQzT/PtjFFEj0IRkAAAAAGAdlENeIDY2VmvWrFHPnj2dxteuXas+ffpowoQJuvPOOxUQEOCYS0hIUOvWrdWsWTPNmDFDzzzzTGXHBi4pJ79IT3y0VV/uOOkyd1NUuP42vJNCAvgWBQAAAAAVjd+8vMCgQYNKHO/Zs6fi4uL01VdfaceOHYqOjnbM3XTTTZUVDyi31PO5Grtoo7Ydy3CZe7BHcz1za5R8fbjwNAAAAABUBq45dBmpqalasWKFpk6dqv79+6tu3bqy2Wyy2WyKj48v12sdOXJETz75pKKiohQSEqLatWsrNjZWr732mrKzs68on7+/vyTJz4+eD95h94lzuusfP7kUQ74+Nr18V3s9O+BqiiEAAAAAqEQ0CpcRHh7ultdZuXKl7rvvPmVk/PcX4uzsbCUlJSkpKUkJCQn68ssv1aJFizK/5pEjR/TNN98oIiJC7du3d0tOoCJ9n5yqh9/frKz8Iqfx6oF+mnNfF/VsXc+kZAAAAABgXRw5VA5NmjRR3759y/28bdu2aejQocrIyFBoaKheeukl/fTTT/r22281duxYSdKePXt02223KTMzs0yvWVBQoJEjRyovL0+vvPKKfH19y50LqEyJPx7UmEVJLsVQk9pB+mTC9RRDAAAAAGASjhy6jKlTpyomJkYxMTEKDw/XoUOH1Lx583K9xsSJE5WdnS0/Pz999dVXuu666xxzvXv3VuvWrTV58mQlJydr1qxZmjp16iVfz263a/To0VqzZo3Gjh2rkSNHXtHXBlSGwiK7/rpilxatd71jXtdmtTRvZFfVCQ0o4ZkAAAAAgMrAkUOX8fzzz2vAgAFXfHpZUlKSVq9eLUkaM2aMUzFU7IknnlBUVJQkafbs2SooKCj19QzD0NixY7VkyRKNGDFCb7/99hXlAirD+dwCPfjuxhKLoTs7NdR7D15LMQQAAAAAJqMcqmDLly93PH7ggQdKXOPj46NRo0ZJks6ePesoky5mt9s1ZswYLViwQPfcc48SExPl48M/ITzTsbPZGvzWeq3ec8plbuJNrTV7WCcF+nM6JAAAAACYjWahgq1du1aSFBISoq5du5a6rlevXo7H69atc5m32+168MEHtXDhQg0bNkyLFy/mOkPwWFuOnNXAf/ykPb+ddxqv5uujvw3vpIk3tZHNxh3JAAAAAMATcM2hCrZ7925JUqtWrS55u/m2bdu6PKdY8RFDiYmJGjJkiJYsWVLpxdCxY8cuOX/ixIlKSgJPt3L7CT2+dKvyCu1O47VDqmneyK6KjqxtUjIAAAAAQEkohypQbm6u0tLSJEmNGze+5NpatWopJCREWVlZOnr0qNPcCy+8oMTERIWGhqpNmzZ68cUXXZ4/cOBAderUyfFxQkKC4wikHTt2OMaKT1kbOHCgBg4cWOavpUmTJmVeC2syDENzVv9Hr/57j8tcq/qhWnB/jJrWCTYhGQAAAADgUiiHKtD58/89pSY0NPSy64vLoYtvZ3/o0CFJUmZmpl566aUSnxsZGelUDq1bt06LFi1yWvPjjz/qxx9/dKwvTzkEXEpeYZGmfLJTH292PcKsR6u6+sd9XVQjyN+EZAAAAACAy6EcqkC5ubmOx9WqVbvs+oCA3+/alJOT4zSemJioxMTEcn3uK3nOpVx8NNPFTpw4odjYWLd9PniPs1n5Grdkk345eMZl7p7Ypnrhznby9+XyZgAAAADgqSiHKlBgYKDjcX5+/mXX5+XlSZKCgoIqLNOVutxpcbCmA6cyNToxSYdOZzuN22zSn2+N0pgezbnwNAAAAAB4OMqhClS9enXH44tPFStJVlaWpLKdggaYbf1/Tmv8kk3KyClwGg/y99Xf7+msm68ONykZAAAAAKA8KIcqUGBgoOrWrau0tLTL3u3r7NmzjnKIiz/D0y3deFRTPtmhQrvhNB4eFqD598fomkY1TEoGAAAAACgvLgRSwaKioiRJ+/fvV2FhYanrkpOTXZ4DeBq73dDMVcmavGy7SzHUrmGYPvtTD4ohAAAAAPAylEMVrEePHpJ+P2Vs06ZNpa774YcfHI+7d+9e4bmA8srJL9Kf3t+st1b/x2Xu5qvD9dH46xRRI7CEZwIAAAAAPBnlUAW78HbxCxcuLHGN3W7Xu+++K0mqWbOm4uLiKiMaUGap53I1bN56/WvnSZe5/7mhhd4e0VXB1ThLFQAAAAC8EeVQBYuNjVXPnj0lSfPnz9f69etd1rz++uvavXu3JOmxxx6Tv79/pWYELmXX8XMa+I8ftf1YhtO4r49NL9/VXlNujZKvD3ckAwAAAABvxX/1X8a6deu0f/9+x8dpaWmOx/v371diYqLT+vj4eJfX+Nvf/qbu3bsrJydHffv21ZQpUxQXF6ecnBz985//1Lx58yRJbdq00RNPPFEhXwdwJdbtS9P4JZuUmed8vazqgX56676u6tG6rknJAAAAAADuYjMMw7j8MuuKj4/XokWLyry+tL/OL774QiNGjNC5c+dKnG/Tpo1WrlypVq1aXVFOsx07dsxxl7WjR4+qcePGJifCH/X5tuN6YulWFRQ57+mmtYO1ID5arepXNykZAAAAAFhXRfz+zWllleT222/X9u3bNWnSJLVp00bBwcGqWbOmoqOjNXPmTG3ZssVriyFUPfPXHdSjH2xxKYaim9XSpw9dTzEEAAAAAFUIRw7BLThyqGoovlX93DUHXOZubR+hWUM7KdDf14RkAAAAAACpYn7/5ppDACRJBUV2Pb1suz7ZkuIyd/91zTT19nZceBoAAAAAqiDKIQDKyivUhPc2a83eUy5zT91ylR66saVsNoohAAAAAKiKKIcAi0vLzNPoxKQSb1U/Y1B7DYluYlIyAAAAAEBloBwCLOzI6WyNWrBBh05nO40H+vtozn1d1LttuEnJAAAAAACVhXIIsKidKRmKX5iktMw8p/Fawf5aEB+jzk1rmZQMAAAAAFCZKIcAC1q3L03jFm9UVn6R03ijmkF6d0ysWtYLNSkZAAAAAKCyUQ4BFvP5tuN6YulWFRQZTuNtI6pr0ehYhYcFmpQMAAAAAGAGyiHAQuavO6i/rtjlMt6tRW3NGxWtsEB/E1IBAAAAAMxEOQRYgN1uaOaqZM1dc8Bl7rb2DTRrWEcF+PmakAwAAAAAYDbKIaCKKyiy6+ll2/XJlhSXufuva6apt7eTr4/NhGQAAAAAAE9AOQRUYVl5hZrw3mat2XvKZe6pW67SQze2lM1GMQQAAAAAVkY5BFRRaZl5Gp2YpO3HMpzGfX1smjGovYZENzEpGQAAAADAk1AOAVXQkdPZGrVggw6dznYaD/L31Zz7uiiubX2TkgEAAAAAPA3lEFDF7EzJUPzCJKVl5jmN1wr214L4GHVuWsukZAAAAAAAT0Q5BFQh6/aladzijcrKL3Iab1QzSO+OiVXLeqEmJQMAAAAAeCrKIaCK+Gxrip78aJsKigyn8agGYUp8IEbhYYEmJQMAAAAAeDLKIaAKSFh7QC+u3O0y3q1Fbc0bFa2wQH8TUgEAAAAAvAHlEODF7HZDM1cla+6aAy5zt7VvoFnDOirAz9eEZAAAAAAAb0E5BHipgiK7nl62XZ9sSXGZi78+UlMHXC0fH5sJyQAAAAAA3oRyCPBCWXmFmvDeZq3Ze8pl7qlbrtJDN7aUzUYxBAAAAAC4PMohwMukZeZpdGKSth/LcBr39bFpxqD2GhLdxKRkAAAAAABvRDkEeJEjp7M1asEGHTqd7TQe5O+rOfd1UVzb+iYlAwAAAAB4K8ohwEvsTMlQ/MIkpWXmOY3XCvbXgvgYdW5ay6RkAAAAAABvRjk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+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "full_e = np.concatenate((eps_com, core_e[1:]))\n",
+    "full_p = np.concatenate((pres_com, core_p[1:]))\n",
+    "plt.figure(dpi=200)\n",
+    "plt.plot(full_e / g_cm_3, full_p / dyn_cm_2)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(r\"$\\epsilon\\quad [g/cm^3]$\")\n",
+    "plt.ylabel(r\"$P\\quad [dyn/cm^2]$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we compute the mass radius curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MR = main.OutputMR(\"\", full_e, full_p)\n",
+    "plt.figure(dpi=200)\n",
+    "plt.plot(MR[:, 1] / km, MR[:, 0] / Msun)\n",
+    "plt.xlim(6, 17)\n",
+    "plt.ylim(0, 2)\n",
+    "plt.xlabel(\"Radius [km]\")\n",
+    "plt.ylabel(\"Mass [Msun]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MIT bag model EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state $p=\\frac{\\epsilon}{3}-\\frac{4B}{3}$. There is only one parameter, the \"bag constant\" $B$. This represents the vacuum energy density, which creates a \"bag\" in which quarks are confined. See [Chodos et al. (1974)](https://doi.org/10.1103/PhysRevD.9.3471)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from EOSgenerators import MITbag_EOS\n",
+    "from TOVsolver.unit import MeV, fm, g_cm_3, dyn_cm_2, km, Msun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the parameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A \"typical\" value of 57 $MeVfm^{-3}$ for the bag constant is taken from [Alcock (1986)](http://dx.doi.org/10.1086/164679)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy density range: 1.1554421785237932 to 2.888605446309483\n",
+      "pressure range: 0.0 to 0.5777210892618965\n"
+     ]
+    }
+   ],
+   "source": [
+    "B = 57\n",
+    "\n",
+    "epsilon,p = MITbag_EOS.MITbag_compute_EOS(B)\n",
+    "\n",
+    "print('energy density range:',epsilon[0],'to',epsilon[-1])\n",
+    "print('pressure range:',p[0],'to',p[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Computing the mass-radius relation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute a mass-radius relation for this EOS by solving the TOV equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import TOVsolver.main as main\n",
+    "import TOVsolver.EoS_import as EoS_import\n",
+    "from itertools import repeat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR('',epsilon,p).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MR[1]/km, MR[0]/Msun,lw=2,label=f'B = {B:.2f} MeV $fm^{-3}$')\n",
+    "ax.set_ylabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.doctrees/nbsphinx/test_EOSgenerators_11_0.png b/.doctrees/nbsphinx/test_EOSgenerators_11_0.png
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diff --git a/.doctrees/nbsphinx/test_EOSgenerators_39_0.png b/.doctrees/nbsphinx/test_EOSgenerators_39_0.png
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diff --git a/.doctrees/nbsphinx/test_EOSgenerators_55_0.png b/.doctrees/nbsphinx/test_EOSgenerators_55_0.png
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diff --git a/.doctrees/nbsphinx/test_EOSgenerators_66_0.png b/.doctrees/nbsphinx/test_EOSgenerators_66_0.png
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diff --git a/.doctrees/nbsphinx/test_Inference.ipynb b/.doctrees/nbsphinx/test_Inference.ipynb
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+++ b/.doctrees/nbsphinx/test_Inference.ipynb
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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RMF EOS inference pipeline\n",
+    "\n",
+    "This is an example notebook about how to use our tools to analysis a observation/nuclear constraint on neutron star equation of stat. \n",
+    "\n",
+    "Here in this notebook, we are using a RMF EoS model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Package we need:\n",
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "\n",
+    "from TOVsolver.constant import oneoverfm_MeV, m_rho, m_w,G,c\n",
+    "import TOVsolver.main as main\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "import EOSgenerators.fastRMF_EoS as RMF"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to define the crust, this crust and interface is unversial for all the equation of state here, we just change the core part of equation of state and that is come from the RMF computation. Crust as below, \"Tolos_crust_out.txt\" is BPS crust model. Then as in README, since that file energy_density and presurssure all in MeV/fm3, so we need some conversion. then just call \n",
+    "\n",
+    "   ```sh\n",
+    "   crust.PolyInterpolate\n",
+    "   ```\n",
+    "\n",
+    "To finish the interface and make it ready to connect with core part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Tolos_crust_out = np.loadtxt('Tolos_crust_out.txt', delimiter='  ')\n",
+    "Tolos_crust_out = np.loadtxt('Tolos_crust_out.txt', delimiter=None, comments='#', usecols=(0, 1, 2, 3, 4))\n",
+    "eps_crust_out = Tolos_crust_out[:,3] * G / c**2\n",
+    "pres_crust_out = Tolos_crust_out[:,4] * G / c**4\n",
+    "\n",
+    "eps_crust, pres_crust = crust.PolyInterpolate(eps_crust_out, pres_crust_out)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up prior\n",
+    "\n",
+    "Next step, we need to set up the prior, first use parameters array to specify the variable name, should consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling. We provided \n",
+    "\n",
+    "\"normal_Prior\" and \"flat_prior\"\n",
+    "\n",
+    "two options call from prior. Here then the Parameters prior should all set\n",
+    "\n",
+    "------------------\n",
+    "\n",
+    "However, since we are doing Equation of state Inference from mass radius of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.\n",
+    "\n",
+    "This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of neutron star ---  different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by\n",
+    "\n",
+    "```sh\n",
+    "RMF.compute_EOS\n",
+    "```\n",
+    "\n",
+    "Compute out EOS, put into\n",
+    "\n",
+    "```sh\n",
+    "main.OutputMR\n",
+    "```\n",
+    "\n",
+    "find out Mass Radius of this equation of state, find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)\n",
+    "\n",
+    "found out that index by len() function, then reset this max_d to be upper limit of this density range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = ['g_sigma', 'g_omega','g_rho', 'kappa', 'lambda_0', 'zeta', 'Lambda_w','d1']\n",
+    "# for two or more MR measurements, define d2 or more depend on complexity.\n",
+    "# parameters = ['g_sigma', 'g_omega','g_rho', 'kappa', 'lambda_0', 'zeta', 'Lambda_w','d1', 'd2']\n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = math.sqrt(prior.normal_Prior(107.5, 7.5,cube[0]))\n",
+    "    params[2] = math.sqrt(prior.flat_prior(75,210,cube[2]))\n",
+    "    params[1] = math.sqrt(prior.flat_prior(150,210,cube[1]))\n",
+    "    params[3] = prior.normal_Prior(2.525/oneoverfm_MeV, 1.525/oneoverfm_MeV,cube[3])\n",
+    "    params[4] = prior.normal_Prior(0.0045, 0.0205,cube[4])\n",
+    "    params[5] = prior.flat_prior(0,0.04,cube[5])\n",
+    "    params[6] = prior.flat_prior(0,0.045,cube[6])\n",
+    "    \n",
+    "    g_sigma = params[0]\n",
+    "    g_omega = params[1]\n",
+    "    g_rho = params[2]\n",
+    "\n",
+    "    kappa = params[3]\n",
+    "    lambda_0 = params[4]\n",
+    "    zeta = params[5]\n",
+    "    Lambda_w = params[6]\n",
+    "    m_sig = 495 / oneoverfm_MeV\n",
+    "\n",
+    "    theta = np.array([m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa,\n",
+    "                         lambda_0, zeta, Lambda_w])\n",
+    "    \n",
+    "    ep, pr = RMF.compute_EOS(eps_crust, pres_crust, theta)\n",
+    "\n",
+    "    eps_total = np.hstack((eps_crust,ep))\n",
+    "    \n",
+    "    pres_total = np.hstack((pres_crust,pr))\n",
+    "    \n",
+    "    RFSU2R = []\n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50)\n",
+    "    if all(x<y for x,y in zip(eps_total[:], eps_total[1:])) and all(x<y for x, y in zip(pres_total[:], pres_total[1:])):\n",
+    "        MR = main.OutputMR('',eps_total,pres_total).T\n",
+    "        if len(MR[1]) == False: \n",
+    "            params[7] = 0\n",
+    "            #params[8] = 0\n",
+    "            # this line for showing how to add one more observation\n",
+    "        else:\n",
+    "   \n",
+    "            for i in range(len(MR[1])):\n",
+    "                RFSU2R.append(MR[0][i])\n",
+    "                MFSU2R.append(MR[1][i])   \n",
+    "                if i > 20 and MR[1][i] - MR[1][i-1]< 0:\n",
+    "                    break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[7] = 0\n",
+    "        # params[8] = 0\n",
+    "        # this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[7] = 14.3 + (max_d - 14.3) * cube[7]\n",
+    "        # params[8] = 14.3 + (max_d - 14.3) * cube[8]\n",
+    "        # this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up likelihood\n",
+    "\n",
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is $M = 1.4 M_{\\odot}$ and $R = 13$ km, With a 5% Mass radius measurement uncertainty, so here \n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.MRlikihood_Gaussian\n",
+    "   ```\n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/EoS_inference/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood.\n",
+    "eg:\n",
+    "1. If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into \n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2. If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement\n",
+    "\n",
+    "3. If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "   likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "   likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "   ```\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4. If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "Where x is sampled distribution from real measurements, the standard is \n",
+    "\n",
+    "kernel, chrip = x, \n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is  [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling.\n",
+    "\n",
+    "\n",
+    "\n",
+    "5. To calculate the log-likelihood for the pure neutron matter (PNM) equation of state (EoS) using chiral effective field theory (chiEFT) constraints, the function\n",
+    "   \n",
+    "     `likelihood.chiEFT_PNM(EoS_PNM, type=\"Gaussian\", contraint_quantity=\"e\", enlargement=0)`\n",
+    "\n",
+    "EoS_PNM is a 3D array of number density (rho), energy density (e) and pressure (p) with unit fm^-3, MeV.fm^-3 and MeV.fm^-3.  \n",
+    "\n",
+    "compares PNM EoS data with chiEFT-derived constraints for either energy per neutron \\( E/N \\) or pressure \\( p \\),\n",
+    "depending on the specified `contraint_quantity` parameter (\"e\" for energy or \"p\" for pressure). \n",
+    "\n",
+    "The likelihood model can be either \"Gaussian\" or \"Super Gaussian\" with an optional `enlargement` factor for the Super Gaussian to flatten its peak. e.g., 0.05 for 5% enlargement. \n",
+    "\n",
+    "The function calculates the sum of log-likelihoods over densities of 0.08, 0.12, and 0.16 fm\\(^-3\\), utilizing energy constraints from Huth et al. (Nature, 2022) and pressure constraints from Hebeler et al. (ApJ, 2013). This enables a statistical comparison of the PNM EoS data against chiEFT-derived reference values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "    \n",
+    "    # g_sigma, g_omega,g_rho, kappa, lambda_0, zeta, Lambda_w, d1, d2 = theta\n",
+    "    g_sigma, g_omega,g_rho, kappa, lambda_0, zeta, Lambda_w, d1 = theta # comment this line if you need two measuremnts.\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "    m_sig = 495 / oneoverfm_MeV\n",
+    "    m_w = 3.96544\n",
+    "    m_rho = 3.86662\n",
+    "    theta1 = np.array([m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w])\n",
+    "    ep, pr = RMF.compute_EOS(eps_crust, pres_crust, theta1)\n",
+    "\n",
+    "    eps_total = np.hstack((eps_crust,ep))\n",
+    "    pres_total = np.hstack((pres_crust,pr))\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    # probMRgaussian1 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(1.4,13,0.07,0.65),d1)\n",
+    "    # probMRgaussian2 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(2.0,12,0.1,0.6),d2)\n",
+    "    # probMR = probMRgaussian1 + probMRgaussian2\n",
+    "    \n",
+    "    # Same could be extended to more distributions. Notice the prior definition should be change accordingly\n",
+    "    # by define more density parameters like here d2.\n",
+    "    \n",
+    "    #1. This line is to compute MR likelihood from a Simulated MR measurement:\n",
+    "    \n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    #2. This is  a block that constrain from given real MR measurement, say J0030:\n",
+    "    #J0030 = numpy.loadtxt('data/PST_equal_sampled_MR.txt', delimiter=' ')\n",
+    "    #J30R_list, J30M_list = zip(*J0030)\n",
+    "    #J30R_list = numpy.array(J30R_list).T    \n",
+    "    #J30M_list = numpy.array(J30M_list).T\n",
+    "    #Rmin = J30R_list.min()\n",
+    "    #Rmax = J30R_list.max()\n",
+    "    #Mmin = J30M_list.min()\n",
+    "    #Mmax = J30M_list.max()\n",
+    "    #X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]\n",
+    "    #positions = numpy.vstack([X3.ravel(), Y3.ravel()])\n",
+    "    #values = numpy.vstack([J30R_list, J30M_list])\n",
+    "    #kernel3 = stats.gaussian_kde(values)\n",
+    "    #probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)\n",
+    "    \n",
+    "    #3. This is to compute the constraint from experiment of nuclearmatter\n",
+    "    # 250<K<400, 25<J<38, 30<L<86:\n",
+    "    # hint: since this K,J,L sampling don't need to sample central density so this \n",
+    "    # theta should be redifined.\n",
+    "    #probK = likelihood.Kliklihood(theta,250,400)\n",
+    "    #probJ = likelihood.Jliklihood(theta,250,400)\n",
+    "    #probL = likelihood.Lliklihood(theta,250,400)\n",
+    "    \n",
+    "    #4. This is block to cosntrain from a GW event, say GW170817, here the file of this\n",
+    "    # event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:\n",
+    "    #GW170817 = np.load('GW170817_McQL1L2weights.npy')\n",
+    "    #chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])\n",
+    "    #kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])\n",
+    "    #probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)\n",
+    "\n",
+    "\n",
+    "    #5. This block constraints pure neutron matter EOS\n",
+    "    ## For pure neutron matter alpha = 0\n",
+    "    #theta1    = np.append(theta, 0)\n",
+    "    #rho, e, p = RMF.get_eos_alpha(theta1)\n",
+    "    #EoS_PNM   = np.array([rho, e*oneoverfm_MeV, p*oneoverfm_MeV]) # energy density and pressure are converted from fm^-4 to MeV fm^-3\n",
+    " \n",
+    "    #probPNM = likelihood.chiEFT_PNM(EoS_PNM, type=\"Super Gaussian\", contraint_quantity=\"e\", enlargement=0.1) # 10% enlargement \n",
+    "\n",
+    "    prob =  probMRgaussian #+ probMRJ0030 + probK + probJ + probL + probGW + probPNM \n",
+    "    return prob"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up sampler\n",
+    "\n",
+    "Here next, we define sampler, there is two different sampler we provided for. Considering where you need resume file:\n",
+    "\n",
+    "```sh\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "```\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 2000\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Posterior plot\n",
+    "Since we only run our code at my labtob so here is the result corner plot(still have a lot flucatuation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1810x1810 with 64 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import corner\n",
+    "\n",
+    "figure = corner.corner(samples,labels=[ r\"$g_{\\sigma}$\", r\"$g_{\\omega}$\",r\"$g_{\\rho}$\",r\"$\\kappa$\", r\"$\\lambda_0$\",r\"$\\zeta$\",r\"$\\Lambda_w$\",\"d1\",\"d2\"],\n",
+    "                    smooth=0.9,\n",
+    "                    label_kwargs=dict(fontsize=22),\n",
+    "                    title_kwargs=dict(fontsize=22),\n",
+    "                    quantiles=[0.16, 0.84],\n",
+    "                    levels=(1 - np.exp(-0.5), 1 - np.exp(-2), 1 - np.exp(-9 / 2.)),\n",
+    "                    plot_density=False,\n",
+    "                    plot_datapoints=False,\n",
+    "                    fill_contours=True,\n",
+    "                    show_titles=True,\n",
+    "                    max_n_ticks=3,\n",
+    "                    title_fmt= '.2f')\n",
+    "                       #,range=[(10,80),(0.56,0.64),(0.10, 0.2),(150,600),(40,80),(50,75)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4481115b400f107b26d360e6549f546bb0e8bc1af70e4e66085bfa77a017a39"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.doctrees/nbsphinx/test_Inference_11_0.png b/.doctrees/nbsphinx/test_Inference_11_0.png
new file mode 100644
index 0000000..ad4ba2e
Binary files /dev/null and b/.doctrees/nbsphinx/test_Inference_11_0.png differ
diff --git a/.doctrees/nbsphinx/test_TOVsolver.ipynb b/.doctrees/nbsphinx/test_TOVsolver.ipynb
new file mode 100644
index 0000000..6a479e3
--- /dev/null
+++ b/.doctrees/nbsphinx/test_TOVsolver.ipynb
@@ -0,0 +1,260 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TOVsolver module guide\n",
+    "\n",
+    "TOV solver part we have multiple function, we can generate a Mass radius function from 'Test_EOS.csv' file here easily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mass Radius file will be generated and stored as  2-d array. The first column is Radius, second one is mass\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import TOVsolver.main as main\n",
+    "import TOVsolver.EoS_import as EoS_import\n",
+    "from matplotlib import pyplot\n",
+    "import matplotlib.pyplot as plt\n",
+    "import TOVsolver.constant\n",
+    "from itertools import repeat\n",
+    "import numpy\n",
+    "# Main has two functions\n",
+    "#   Output.MRT() returns the mass, radius, tidal\n",
+    "#   OutputC_s() returns the speed of sound\n",
+    "# Using the main functions with either arrays of density and pressure or a .csv with density and pressure columns\n",
+    "#   Function argument is either (filename = ) OR (density = , pressure = )\n",
+    "#   Example: main.OutputMR(filename = 'file.csv') OR main.OutputMR(density = array1, pressure = array2)\n",
+    "MR = main.OutputMR(\"Test_EOS.csv\").T\n",
+    "# Plot the Mass-Radius curve\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MR[0], MR[1],lw=2)\n",
+    "ax.set_ylabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.set_xlim(8., 20.)\n",
+    "ax.set_ylim(0,3)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can generate the tidal property from it also,like tidal deformability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mass Radius and tidal will be generated as the 3-d array. The first column is Radius, second one is mass,last is tidal\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# try solve tidal property\n",
+    "MRT = main.OutputMRT(\"Test_EOS.csv\").T\n",
+    "# Plot the Radius and Tidal curve\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "        \n",
+    "ax.plot(MRT[0], MRT[2],lw=2)\n",
+    "ax.set_ylabel(r'$\\Lambda$', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.set_xlim(8., 20.)\n",
+    "ax.set_ylim(0,1000)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot out Tidal with mass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9evXSDz/84GxbVFSktLQ0Pffcc+rWrZvGjx+v5ORkPffcc0pLS1NRUd1Nc5966imNGjVKhw8f1v33369hw4YpMzNTqampWrFiRTO+UgBAYyVGh8rPxyaJbRQAANZmMwzDMLuIppSbm6sOHTooPj5eGzduVHj4z8Nznn/+eY0fP15jxozRm2++KUmaNm2aZsyYoYkTJ+qZZ55xtq05PnXqVE2fPt15PCsrS8nJyercubPWrl2riIgISdKWLVuUkpKi9u3ba/v27fLz86u3voKCAkVERCg/P9+lNgCA+/3q+XRtzz0lPx+btswYLLufr9klAQC8SHNlAcv15O3Zs0cOh0Opqal1/uBuvPFGSdKRI0ckVQ/rnDt3rkJDQzV16lSXtpMmTVJUVJTeeOMN1c7B8+bNU2VlpSZPnuwMeJLUo0cP3XXXXdq1a5eWL1/urpcHADgPyT8tvlLpMJR1uNDkagAAcA/LhbykpCQFBAQoMzNTp065zrn47LPPJEnXXHONpOpeuYMHDyo1NVUhISEubQMDAzVgwAAdOHBA2dnZzuM1wzEHDRpU57kHDx4sSVq5cmWTvR4AQNNhhU0AgDeof0yhB2vdurVmzpypP//5z+revbtuuukmhYWFafPmzfryyy/1u9/9Tg8++KCk6pAnVQfD+tQcz8rKcrkdGhqqmJiY07YHALQ8tUPeVkIeAMCiLBfypOqFV2JjY3Xffffp5Zdfdh7v27ev7rjjDvn7+0uS8vPzJcll2GVtNcM9a9rV3I6Ojm50+4b8cuVOu90uu91+xvsBAM6d6wqbhDwAgHuVlZWprKzM+fvpVu9vSpYbrilJTz75pEaPHq1JkyYpJydHhYWFysjIUGVlpQYOHKiPPvrI7BIVFxeniIgI58+sWbPMLgkALK91qF3twqv/QW3boVOy2NpjAIAWZtasWS7f+ePi4prleS0X8pYvX66//OUv+sMf/qDHHntMHTt2VEhIiFJTU/Xvf/9bQUFBGj9+vKSfe/Aa6nmrSdq1e/pqVsNpbPuG5OTkKD8/3/kzadKkxr9IAMA5qxmymV9SoYP5pSZXAwCwskmTJrl858/JyWmW57VcyPvPf/4jSRo4cGCdc23bttXFF1+sffv26dixY2ecQ1ffnL2kpCQVFhYqNze3Ue0bEh4e7vLDUE0AaB4ui6+wXx4AwI3sdnud7/3NwXIhr7y8XJJ09OjRes/XHLfb7UpKSlJsbKwyMzPrbHpeWlqq9PR0xcbGKjEx0Xk8LS1NkrRs2bI6j7106VKXNgCAlieZFTYBABZnuZCXmpoqSXr22WfrDKt8++23lZ2drT59+igsLEw2m01jx45VYWGhZsyY4dJ21qxZOnnypMaOHSubzeY8PmbMGPn5+WnmzJkuj79lyxa988476tKli3OLBgBAy+PSk5dLyAMAWI/NsNis86qqKl133XVasWKF2rZtq5tuuklRUVHauHGjvvjiC9ntdn355Zfq16+fJKmoqEj9+vXThg0bdP3116tPnz7auHGjlixZol69eikjI6POHnozZ87UlClTFB8fr6FDh6qoqEiLFi1SSUmJli5dWu9Q0RrNtcs9AKB+VQ5DPaZ9rtIKhzq1DtaKPzd8zQYAoCk1VxawXMiTqpcqfeGFF/T+++9r+/btKi8vV7t27ZSWlqZJkybpoosucmmfn5+v6dOna/HixcrNzVVMTIyGDh2qadOmNbiIyrvvvqvnn39eW7ZsUUBAgK666irNmDFDl19++WlrI+QBgPl++7cMbdyfL5tN+uHxwQqxW3JHIQBAC0PIsyhCHgCYb9JHm7RobfUKZx/+vq/6JESZXBEAwBs0Vxaw3Jw8AADOpDuLrwAALIyQBwDwOrVD3lZCHgDAYgh5AACvc2FMmPM2PXkAAKsh5AEAvE5YoL/iWwVLknbknlKVg+npAADrIOQBALxS9/bVvXnF5VXae7zI5GoAAGg6hDwAgFdyXXzllImVAADQtAh5AACvlMwKmwAAiyLkAQC8EtsoAACsipAHAPBKHaOCFBboJ4ltFAAA1kLIAwB4JZvNpu4x1b15h/JLlVdcbnJFAAA0DUIeAMBrJceyKToAwHoIeQAAr1WzjYIkbT1IyAMAWAMhDwDgtdhGAQBgRYQ8AIDX6touTL4+NkmssAkAsA5CHgDAawX6+6pzmxBJUvaRQpVXOkyuCACA80fIAwB4tZohm+VVDu06WmhyNQAAnD9CHgDAq7EpOgDAagh5AACvVnsbBUIeAMAKCHkAAK9WexsFVtgEAFgBIQ8A4NWiwwLVJjRAUvWG6IZhmFwRAADnh5AHAPB6NfPyThSV68ipMpOrAQDg/BDyAABeL7nW4itbmZcHAPBwhDwAgNervcLm1oOEPACAZyPkAQC8HtsoAACshJAHAPB6nduGKMCv+q9EQh4AwNMR8gAAXs/f10dd24VKknYfK1JJeZXJFQEAcO4IeQAASOoeUz1k02FIOw6zXx4AwHMR8gAAEPPyAADWQcgDAEBSciwhDwBgDYQ8AAD083BNiW0UAACejZAHAICkiGB/dYgMkiRtzz0lh8MwuSIAAM4NIQ8AgJ/UzMsrLKvU/pMlJlcDAMC5IeQBAPCT5PZhzttbmZcHAPBQhDwAAH5Se4VNQh4AwFMR8gAA+AnbKAAArICQBwDAT+JbBSskwFcSIQ8A4LkIeQAA/MTHx6YLf+rN23+yRPklFSZXBADA2SPkAQBQS/dai69spzcPAOCBCHkAANTCvDwAgKcj5AEAUEuyS8g7ZWIlAACcG0IeAAC1dIsJk81WfZttFAAAnoiQBwBALcEBfrqgdYgkacfhU6qscphcEQAAZ4eQBwDAL3SPrR6yWV7p0O5jRSZXAwDA2SHkAQDwC7Xn5TFkEwDgaQh5AAD8Qu1tFAh5AABPQ8gDAOAXurPCJgDAgxHyAAD4hZjwQEUF+0tirzwAgOch5AEA8As2m83Zm3f0VJmOniozuSIAABqPkAcAQD1ch2zSmwcA8ByEPAAA6kHIAwB4KkIeAAD1SCbkAQA8FCEPAIB6JEaHyt/XJoltFAAAnoWQBwBAPQL8fNSlbagkadfRIpVWVJlcEQAAjUPIAwCgAcmx1UM2qxyGso8UmlwNAACNQ8gDAKABteflbT3IkE0AgGcg5AEA0IDaK2wyLw8A4CkIeQAANIBtFAAAnoiQBwBAA1qFBCgmPFBSdcgzDMPkigAAODNCHgAAp9G9fZgkqaC0UgfySkyuBgCAMyPkAQBwGq5DNk+ZWAkAAI1DyAMA4DRqtlGQpPX7TppYCQAAjUPIAwDgNFIuaOW8nZl9zMRKAABoHEIeAACnER0WqAtjquflbTqQr5NF5SZXBADA6RHyAAA4g36JbSRJhiGt2nXc5GoAADg9Qh4AAGfQv2tb5+2M7KMmVgIAwJkR8gAAOIOUTq0U4Fv9V+bXWcfYLw8A0KIR8gAAOIOgAF9d1ilKkrT/ZIn2Hi82uSIAABpGyAMAoBH6JbVx3v6aVTYBAC0YIQ8AgEbon1hrXl4W8/IAAC0XIQ8AgEboERuuqGB/SdKq7OOqrHKYXBEAAPUj5AEA0Ag+Pjb1/WkrhVNlldq4P9/kigAAqB8hDwCARuqf+PO8vIws5uUBAFomQh4AAI1Ue/EV9ssDALRUhDwAABqpY1SwOrcJkSSt35enwrJKkysCAKAuQh4AAGehpjev0mFo9a7jJlcDAEBdhDwAAM5Cv9rz8tgvDwDQAhHyAAA4C1d2aS1fH5sk6Wv2ywMAtECEPAAAzkJ4oL96xUVKknYdLdLBvBJzCwIA4BcIeQAAnKV+bKUAAGjBCHkAAJylAV1/DnlfMy8PANDCEPIAADhLPTtGKszuJ0nKzD4mh8MwuSIAAH5GyAMA4Cz5+froyi6tJUknisq19VCByRUBAPAzQh4AAOegfxJbKQAAWiZCHgAA54DFVwAALRUhDwCAc3BBmxB1iAySJK3dc0KlFVUmVwQAQDVCHgAA58Bmszl788orHVq7+4TJFQEAUI2QBwDAOerHvDwAQAtk6ZD38ccf6/rrr1fr1q0VFBSkCy64QCNGjFBOTo5Lu4KCAk2YMEEJCQmy2+1KSEjQhAkTVFDQ8GppCxcuVEpKikJCQhQVFaUbbrhB69atc/dLAgC0IKmJbWSzVd/+mnl5AIAWwpIhzzAM3Xfffbrlllu0e/duDR8+XA8//LD69++vVatWae/evc62RUVFSktL03PPPadu3bpp/PjxSk5O1nPPPae0tDQVFRXVefynnnpKo0aN0uHDh3X//fdr2LBhyszMVGpqqlasWNGMrxQAYKZWIQG6KDZCkrTtUIGOniozuSIAACQ/swtwh5deekmvvfaaHnjgAb3wwgvy9fV1OV9ZWem8PXv2bG3YsEETJ07UM8884zw+bdo0zZgxQ7Nnz9b06dOdx7OysjRt2jR17dpVa9euVURE9V/uDz30kFJSUjR27Fht375dfn6W/KMFAPxCv6Q22nwgX5K0atcx/bZXB5MrAgB4O8v15JWUlGj69Onq3Lmznn/++ToBT5IzgBmGoblz5yo0NFRTp051aTNp0iRFRUXpjTfekGEYzuPz5s1TZWWlJk+e7Ax4ktSjRw/ddddd2rVrl5YvX+6mVwcAaGn619pKgSGbAICWwHIh74svvtCJEyc0ZMgQVVVV6aOPPtLTTz+tV155RdnZ2S5ts7KydPDgQaWmpiokJMTlXGBgoAYMGKADBw643K9mOOagQYPqPPfgwYMlSStXrmziVwUAaKn6dIpSoH/1X6cZWcdc/mEQAAAzWG5MYc3iJ35+furZs6d27NjhPOfj46Px48frf//3fyVVhzxJSkpKqvexao5nZWW53A4NDVVMTMxp25/JLxd1sdvtstvtZ7wfAKBlsfv5KuWC1krfeVS5BaXKPlKopHZhZpcFAGgBysrKVFb283zt0y3s2JQs15N35MgRSdKcOXMUHh6utWvX6tSpU0pPT1fXrl01Z84cvfzyy5Kk/PzqORS1h13WFh4e7tKu5vbZtG9IXFycIiIinD+zZs1q5CsEALQ0DNkEANRn1qxZLt/54+LimuV5LRfyHA6HJCkgIECffPKJLr/8coWGhqp///5avHixfHx8NGfOHJOrlHJycpSfn+/8mTRpktklAQDOUf+u7JcHAKhr0qRJLt/5f7mVm7tYbrhmTS/bZZddptjYWJdzPXr0UOfOnZWdna28vDxn24Z63mq6U2v33EVERJxV+4aEh4c7e/4AAJ6tW7swtQ2z6+ipMq3+8bjKKx0K8LPcv6MCAM6SWVOyLPc3ULdu3SRJkZGR9Z6vOV5SUnLGOXT1zdlLSkpSYWGhcnNzG9UeAGB9NptN/X4asllcXqX1+06aXBEAwJtZLuQNHDhQkrRt27Y65yoqKpSdna2QkBC1bdtWSUlJio2NVWZmZp1Nz0tLS5Wenq7Y2FglJiY6j6elpUmSli1bVufxly5d6tIGAOA9+iUyZBMA0DJYLuR16dJFgwYNUnZ2tubOnety7umnn1ZeXp5uvvlm+fn5yWazaezYsSosLNSMGTNc2s6aNUsnT57U2LFjZbPZnMfHjBkjPz8/zZw502XY5pYtW/TOO++oS5cuuuaaa9z7IgEALU6/JBZfAQC0DDbDghv67Nq1S3379tWRI0d044036sILL9T69eu1fPlyJSQkaPXq1c4tEIqKitSvXz9t2LBB119/vfr06aONGzdqyZIl6tWrlzIyMursoTdz5kxNmTJF8fHxGjp0qIqKirRo0SKVlJRo6dKlzt7E+hQUFDjn9TEnDwCsZdBzK7XzcKF8bNL6vwxSRLC/2SUBAFqQ5soCluvJk6p789atW6fRo0fru+++04svvqisrCw98MADWrt2rcsedyEhIVqxYoXGjx+v7du3a86cOfrhhx80fvx4rVixok7Ak6TJkydrwYIFio6O1ssvv6z33ntPffv2VWZm5mkDHgDA2voltpUkOQxp1S568wAA5rBkT15LRk8eAFjXV9uPaMxb30qSRl4Rr6duvtjkigAALQk9eQAAeJgrOreSv2/1PO4M5uUBAExCyAMAoIkEB/ipT0KUJGnfiWLtO15sckUAAG9EyAMAoAn1T2rrvP119lETKwEAeCtCHgAATchlvzyGbAIATEDIAwCgCV3UIUIRQdVbJ2RmH1OVg/XNAADNi5AHAEAT8vWxKTWxtSSpoLRSm/bnmVsQAMDrEPIAAGhiNfvlSQzZBAA0P0IeAABNrH/Sz/Pyvs4m5AEAmhchDwCAJhbXKlidWgdLktbvO6miskqTKwIAeBNCHgAAbtDvp968iipDa3YfN7kaAIA3IeQBAOAGteflfc28PABAMyLkAQDgBld1aS0fW/VtFl8BADQnQh4AAG4QEeSvnnGRkqSsI4U6lF9ibkEAAK9ByAMAwE36J/68yia9eQCA5kLIAwDATfol1dovj60UAADNhJAHAICb9I6PVEiAryQpM/uYHA7D5IoAAN6AkAcAgJv4+/roqi6tJUnHCsu1PfeUyRUBALwBIQ8AADfqV3teXvZREysBAHgLQh4AAG5Ue14e++UBAJoDIQ8AADfq0jZE7SMCJUlrd59QaUWVyRUBAKyOkAcAgBvZbDbnkM2ySofW7TlpckUAAKsj5AEA4Gb9kn6el/c18/IAAG5GyAMAwM1S2RQdANCMCHkAALhZm1C7esSGS5K2HCzQ8cIykysCAFgZIQ8AgGZQe8hm5q7jJlYCALA6Qh4AAM2gf+LPWylkZDEvDwDgPoQ8AACawWWdomT3q/5rNyPrmAzDMLkiAIBVEfIAAGgGgf6+SrmglSTpYH6pdh0tMrkiAIBVEfIAAGgm/VxW2WTIJgDAPQh5AAA0k9qLr2Rks5UCAMA9CHkAADST7jHhahMaIEla/eMJVVQ5TK4IAGBFhDwAAJqJj4/NuTF6YVmlNuTkmVsQAMCSCHkAADSj2vPyvs5iyCYAoOkR8gAAaEb9k9gvDwDgXoQ8AACaUUxEoBKjQyVJG/fnK7+kwuSKAABW4/aQd+DAAc2ePVuXXHKJu58KAACPUDNks8ph6Jtdx02uBgBgNX7ueNDCwkJ9+OGHmj9/vlasWCHDMNzxNAAAeKT+SW301qo9kqSM7KP61UUx5hYEALCUJgt5DodDy5Yt0/z58/Xpp5+qpKTEGe4uv/xyjRgxoqmeCgAAj3ZF59by87Gp0mEog8VXAABN7LxD3vr16zV//nwtWrRIR44ccQa77t27a8SIERo5cqQ6d+583oUCAGAVoXY/XZoQpbW7T2jP8WLlnChWXKtgs8sCAFjEOYW8/fv3691339X8+fO1bds2SZJhGIqPj9fw4cM1cuRI5uABAHAa/RPbaO3uE5KkjOxjGpESb3JFAACraHTIKyws1OLFizV//nytXLlShmHIMAy1bdtWt912m0aOHKm+ffu6s1YAACyjX1IbzflipyQpI4uQBwBoOo0Oee3atVNpaakMw1B4eLiGDBmikSNH6tprr5Wvr687awQAwHIu6Rip8EA/FZRWKnPXMVU5DPn62MwuCwBgAY0OeSUlJbLZbOrdu7feeOMN9erVy41lAQBgbb4+NvXt0kafb8lVXnGFfjiQr55xkWaXBQCwgEbvk9ejRw8ZhqH169erT58+uvTSS/XXv/5V+/btc2d9AABYVr+kNs7bGdmssgkAaBqNDnmbN2/W+vXrNX78eMXExGjDhg169NFHdcEFF6h///76+9//riNHjrizVgAALKV/rZD3ddZREysBAFhJo0OeJPXs2VNz5sxRTk6OPv/8cw0fPlxBQUHKzMzUQw89pA4dOmjw4MF66623lJ+f766aAQCwhITWIYprFSRJ+m7vSRWXV5pcEQDACs4q5Dnv5OOjQYMG6d1339Xhw4c1b948DRw4UIZh6IsvvtC9996rmJgY3XzzzXr//fdVUlLS1HUDAGAJ/ZPaSpIqqgyt+WlLBQAAzsc5hbzaQkJCdPfdd+vLL7/Uvn379PTTT6tHjx4qKyvTp59+qpEjRyo6OlqjRo1qinoBALCU/om15uVlMS8PAHD+zjvk1RYbG6uJEydq06ZN+v777/XII4+oXbt2Kioq0nvvvdeUTwUAgCX07dJGNTsnEPIAAE2hSUNebb169dKzzz6r/fv367PPPtOIESPc9VQAAHisiGB/XdwxUpK04/ApHSkoNbcgAIDHc1vIcz6Bj49+9atfacGCBe5+KgAAPFLtIZtf05sHADhPbg95AADg9NgvDwDQlAh5AACY7NL4KAUH+EqqDnmGYZhcEQDAkxHyAAAwWYCfj67s3FqSdPRUmXYcPmVyRQAAT0bIAwCgBejHVgoAgCZCyAMAoAXoX2teXjohDwBwHgh5AAC0AInRoYqNCJQkZWYfU24+WykAAM4NIQ8AgBbAZrNpaJ+OkqQqh6H3v80xuSIAgKci5AEA0EIMT4mXj6369qK1+1RZ5TC3IACARyLkAQDQQsRGBumaC9tJknILSrV8+xGTKwIAeCJCHgAALcioK+Odtxes2WdiJQAAT0XIAwCgBUlLaquOUUGSpPSdR7XveLHJFQEAPA0hDwCAFsTHx6aRV/zcm/fu2r0mVgMA8ESEPAAAWphhl8XJ37d6BZZ/rtuvssoqkysCAHgSQh4AAC1Mm1C7fnVRe0nSiaJyff5DrskVAQA8CSEPAIAW6I7aQzZXswALAKDxCHkAALRAKRe0UmJ0qCRp7Z4T2pF7yuSKAACegpAHAEALZLPZNKpWb97CNSzAAgBoHEIeAAAt1C2XdlSgf/Vf1R99f0DF5ZUmVwQA8ASEPAAAWqiIIH/d1DNWknSqrFL/2nDQ5IoAAJ6AkAcAQAt2x5UJztvvrmEBFgDAmRHyAABowS7pGKmLO0RIkjYfyNfGnDxzCwIAtHiEPAAAWrg7rqy1nQILsAAAzoCQBwBAC/ebnrEKC/STJP1r40HlF1eYXBEAoCUj5AEA0MIFB/jp1ks7SpJKKxz6aP1+kysCALRkhDwAADzAyCtqD9ncJ8MwTKwGANCSEfIAAPAAXduFKeWCVpKk7COFWrP7hMkVAQBaKkIeAAAeYlSt3rwFq1mABQBQP0IeAAAe4lcXxah1SIAkaemWXB09VWZyRQCAloiQBwCAh7D7+eq2y+IkSRVVhv75XY7JFQEAWiJCHgAAHmRkSrxsturbC9fsU5WDBVgAAK4IeQAAeJD41sEakNRWkrT/ZInSs46aXBEAoKUh5AEA4GFqL8DyLguwAAB+gZAHAICHuebCaLWPCJQkLd9+RAfySkyuCADQkhDyAADwMH6+Php+eXVvnsOQ3lu7z+SKAAAtCSEPAAAPNDwlTr4+1SuwvPdtjiqqHCZXBABoKbwi5M2ePVs2m002m02rV6+ut01BQYEmTJighIQE2e12JSQkaMKECSooKGjwcRcuXKiUlBSFhIQoKipKN9xwg9atW+eulwEAgFO78EBd372dJOnoqTJ9sfWwyRUBAFoKy4e8bdu2aerUqQoJCWmwTVFRkdLS0vTcc8+pW7duGj9+vJKTk/Xcc88pLS1NRUVFde7z1FNPadSoUTp8+LDuv/9+DRs2TJmZmUpNTdWKFSvc+IoAAKh2x5UJztvvrmEBFgBANUuHvKqqKt19993q2bOnbr755gbbzZ49Wxs2bNDEiRO1bNkyPf3001qyZImmTp2qDRs2aPbs2S7ts7KyNG3aNHXt2lWbNm3SnDlz9Oqrr2rVqlXy8/PT2LFjVVlZ6e6XBwDwcn27tFan1sGSpMzs4/rxaKHJFQEAWgJLh7xnnnlGGzdu1JtvvilfX9962xiGoblz5yo0NFRTp051OTdp0iRFRUXpjTfekGH8vNnsvHnzVFlZqcmTJysiIsJ5vEePHrrrrru0a9cuLV++3D0vCgCAn/j42DTqip978xauYQEWAICFQ94PP/yg6dOna8qUKerRo0eD7bKysnTw4EGlpqbWGdIZGBioAQMG6MCBA8rOznYerxmOOWjQoDqPN3jwYEnSypUrm+BVAABwekP7dFSAX/Vf54u/36/SiiqTKwIAmM2SIa+yslKjR49W9+7d9eijj562bVZWliQpKSmp3vM1x2va1dwODQ1VTExMo9oDAOAuUSEB+vXF7SVJecUV+s+mQyZXBAAwmyVD3lNPPeUcpunv73/atvn5+ZLkMuyytvDwcJd2NbfPpn19CgoKXH7KyspO2x4AgIaMujLeeZsFWACg5SgrK6vzvb85WC7kbdy4UU8++aT+9Kc/6dJLLzW7nAbFxcUpIiLC+TNr1iyzSwIAeKhL46N0YUyYJOn7fXnaerB5vkQAAE5v1qxZLt/54+LimuV5LRfy7r77bnXp0kWPP/54o9rX9Mg11PNWk7Zr99xFREScVfv65OTkKD8/3/kzadKkRtULAMAv2Ww2jWI7BQBocSZNmuTynT8nJ6dZntdyIW/jxo3avn27AgMDnRug22w2vf3225Kkq666SjabTZ988omkM8+hq2/OXlJSkgoLC5Wbm9uo9vUJDw93+bHb7Wf3QgEAqOXm3h0UElC9kvQn6w+osIytfADAbHa7vc73/ubg1yzP0ozuvffeeo+np6crKytLN910k9q2batOnTpJqg5jsbGxyszMVFFRkcsKm6WlpUpPT1dsbKwSExOdx9PS0vTNN99o2bJluuuuu1yeZ+nSpc42AAA0l1C7n37bu4MWrtmnovIqfbL+gMtm6QAA72Ezam8AZ2GjR4/W22+/rW+++UZXXnmly7lp06ZpxowZmjhxop555pk6x6dOnarp06c7j+/cuVM9evRQ586dtXbtWufQzC1btiglJUXt27fX9u3b5edXN0MXFBQ4h3s2V5IHAHiHLQfzdeOLGZKkC2PCtOTh/rLZbCZXBQCo0VxZwHI9eedi4sSJ+te//qXZs2dr/fr16tOnjzZu3KglS5aoV69emjhxokv7rl276vHHH9eUKVN0ySWXaOjQoSoqKtKiRYtUUVGh119/vd6ABwCAO/WIjVDv+Eit35en7bmn9P2+PPVJiDK7LABAM7PcnLxzERISohUrVmj8+PHavn275syZox9++EHjx4/XihUr6mySLkmTJ0/WggULFB0drZdfflnvvfee+vbtq8zMTA0cONCEVwEAgDTqChZgAQBv5zXDNVsKhmsCANyptKJKVzz1X+WXVCjAz0drJl2rqJAAs8sCAKj5sgA9eQAAWEigv6+G9ukoSSqvdOjD7/ebXBEAoLkR8gAAsJiRV8Q7b7+7Zp8cDgbtAIA3IeQBAGAxXdqGqm+X1pKk3ceK9M2Px02uCADQnAh5AABYUO098hasZgEWAPAmhDwAACzo+uR2ahtmlyQt23pYhwtKTa4IANBcCHkAAFiQv6+Phl8eJ0mqchh6/9sckysCADQXQh4AABY1PCVePrbq24vW7lNllcPcggAAzYKQBwCARXWIDNI1F0ZLkg7ll+qrHUdNrggA0BwIeQAAWNioK35egOXdNSzAAgDegJAHAICFDejaVh2jgiRJK3ceVc6JYpMrAgC4GyEPAAAL8/WxaURK9ebohiEtXLvP5IoAAO5GyAMAwOKGXRYnf9/qFVg++DZH5ZUswAIAVkbIAwDA4tqG2TW4R4wk6XhRuT7fkmtyRQAAdyLkAQDgBVwWYFnNAiwAYGWEPAAAvMCVnVupS9sQSdKa3SeUdfiUyRUBANyFkAcAgBew2WwuvXnz6c0DAMsi5AEA4CVu7dNRQf6+kqT3vs3R4YJSkysCALgDIQ8AAC8REeSvO6+q7s0rr3To5RW7TK4IAOAOhDwAALzI7wZ0dvbmLVy7T7n59OYBgNUQ8gAA8CJtQu26u28nSdW9ef9YkW1uQQCAJkfIAwDAy/xuQGeFBPw0N29tjg7mlZhcEQCgKRHyAADwMq1CAn7uzaty6O9f0ZsHAFZCyAMAwAuN699ZoXY/SdIH63K0/2SxyRUBAJoKIQ8AAC8UFRKgMamdJEkVVYb+/hUrbQKAVRDyAADwUmP7dVbYT715/1yXo5wT9OYBgBUQ8gAA8FIRwf4a0+8CSVKlw9DfljM3DwCsgJAHAIAXu7ffBQoLrO7NW/z9fu09XmRyRQCA80XIAwDAi0UE+Wtsv86SpCqHoZfozQMAj0fIAwDAy43p10nhP/Xmfbz+gPYcozcPADwZIQ8AAC8XHuivcf1/7s17cXmWyRUBAM4HIQ8AAGh0aidFBvtLkj5Zf0A/Hi00uSIAwLki5AEAAIXV6s1zGNKL/6U3DwA8FSEPAABIku7u20lRP/Xm/WvjQWUfoTcPADwRIQ8AAEiSQu1++t2ALpLozQMAT0bIAwAATnddlaBWIQGSpP/bdFBZh0+ZXBEA4GwR8gAAgFOI3U/3p1XPzTMM6Xl68wDA4xDyAACAizuuTFCb0OrevM82H9KOXHrzAMCTEPIAAICL4AA/3Z9WPTfPMKQX/rvT5IoAAGeDkAcAAOoYdUWC2oTaJUmfbc7VtkMFJlcEAGgsQh4AAKgjKMBX/3N1F+fvz39Jbx4AeApCHgAAqNfIK+IVHVbdm7d0y2FtOZhvckUAgMYg5AEAgHoF+v+yN4+VNgHAExDyAABAg4anxCsmPFCS9MXWw9q8n948AGjpCHkAAKBBgf6+emAgc/MAwJMQ8gAAwGkNuzxOsRHVvXn/3X5EG3PyzC0IAHBahDwAAHBadj9f/c/AROfv9OYBQMtGyAMAAGc07LI4dYgMkiR9teOovt930uSKAAANIeQBAIAzCvDz0R+uqd2bx0qbANBSEfIAAECjDO3TUR2jqnvz0nce1Xd76c0DgJaIkAcAABrF39dHD17D3DwAaOkIeQAAoNFuubSj4lsFS5K+zjqmb/ecMLkiAMAvEfIAAECj/bI377kv6M0DgJaGkAcAAM7Kzb07qFPr6t68VbuOa/WPx02uCABQGyEPAACcFT9fHz14TZLzd3rzAKBlIeQBAICz9ttesbqgTYgkac3uE1q165jJFQEAahDyAADAWfPz9dHD1/7cm/f8F1kyDMPEigAANQh5AADgnPymZ6y6tK3uzVu754RW7WJuHgC0BIQ8AABwTnx9bHqoVm/es1/spDcPAFoAQh4AADhnv74kVonRoZKk7/ae1NdZzM0DALMR8gAAwDnz9bHpketqrbT5Jb15AGA2Qh4AADgvN1zUXt3ahUmS1u/L08qdR02uCAC8GyEPAACcFx8fmx6u1Zs367PtKq2oMrEiAPBuhDwAAHDeftUjRj1iwyVJOw6f0jOfbze5IgDwXoQ8AABw3nx8bPrf23oqwK/6q8W8zD36avsRk6sCAO9EyAMAAE2ie/twPfb/LnT+/ufFG3X0VJmJFQGAdyLkAQCAJnN3304a2K2tJOlYYbn+9M+NcjhYbRMAmhMhDwAANBmbzaa/3tZTbULtkqSVO49q3qo95hYFAF6GkAcAAJpUm1C75gzr6fz9mSXbteVgvokVAYB3IeQBAIAml9a1rcb2u0CSVF7l0EOL1quknG0VAKA5EPIAAIBb/PlX3ZTcvnpbhV1Hi/TEf7aaXBEAeAdCHgAAcAu7n69eHNFbQf6+kqSFa/bp8x9yTa4KAKyPkAcAANwmMTpU036T7Pz90Y826VB+iYkVAYD1EfIAAIBb3X55nP7fRTGSpLziCk14f6Oq2FYBANyGkAcAANzKZrNp1i0Xq31EoCTpmx+P69X0XSZXBQDWRcgDAABuFxkcoOdu7yWbrfr3Z5ft1IacPFNrAgCrIuQBAIBmcWXn1nrg6kRJUqXD0MPvrVdhWaXJVQGA9RDyAABAs3n4uiT1iouUJO09Xqxpn24xtyAAsCBCHgAAaDb+vj56cXhvhdr9JEkffr9fn244YHJVAGAthDwAANCs4lsH64khPZy/T/n4B+WcKDaxIgCwFkIeAABodjf37qghvWIlSafKKvXI+xtUWeUwuSoAsAZCHgAAMMWMIRcprlWQJOm7vSf10vJskysCAGsg5AEAAFOEB/rr+dt7y9enel+Fl5Zn6ds9J0yuCgA8HyEPAACYpk9ClB65NkmS5DCkR97boPySCpOrAgDPRsgDAACm+p+BiUrp1EqSdCCvRI99vFmGYZhcFQB4LkIeAAAwla+PTc8N76XwwOptFf6z6ZAWf7ff5KoAwHMR8gAAgOk6RAbp6Vsvcf4+7V9btPtYkYkVAYDnIuQBAIAW4YaL2+v2y+IkScXlVXr4vfUqr2RbBQA4W5YLeQcOHNDzzz+vQYMGKT4+XgEBAYqJidGtt96qNWvW1HufgoICTZgwQQkJCbLb7UpISNCECRNUUFDQ4PMsXLhQKSkpCgkJUVRUlG644QatW7fOXS8LAACvMPU3yercJkSStGl/vp79YqfJFQGA57EZFpvZ/Oijj+qZZ55Rly5dlJaWpujoaGVlZemTTz6RYRhatGiRhg0b5mxfVFSkfv36acOGDbr++ut16aWXauPGjfr888/Vq1cvZWRkKCQkxOU5nnrqKU2ePFnx8fEaOnSoCgsL9d5776m0tFRLly7V1Vdf3WB9BQUFioiIUH5+vsLDw931xwAAgMfavD9ft7ycqYoqQzabtODeK5Sa2MbssgDgvDVXFrBcyPvoo4/Utm1b9e/f3+X4119/rWuvvVZhYWE6ePCg7Ha7JGnatGmaMWOGJk6cqGeeecbZvub41KlTNX36dOfxrKwsJScnq3Pnzlq7dq0iIiIkSVu2bFFKSorat2+v7du3y8/Pr976CHkAAJzZa+m79NRn2yVJ7cLtWvLwALUKCTC5KgA4P82VBSw3XPOWW26pE/AkqX///ho4cKBOnDihzZs3S5IMw9DcuXMVGhqqqVOnurSfNGmSoqKi9MYbb7gs4zxv3jxVVlZq8uTJzoAnST169NBdd92lXbt2afny5W56dQAAeIex/Tqr30+9d4cLyjRx8UY5HJb6d2kAcBvLhbzT8ff3lyRnL1tWVpYOHjyo1NTUOkMyAwMDNWDAAB04cEDZ2dnO4ytWrJAkDRo0qM7jDx48WJK0cuVKd5QPAIDX8PGx6dlhPZ29d19uO6In/rOV/fMAoBG8JuTt27dPX375pWJiYnTxxRdLqg55kpSUlFTvfWqO17SruR0aGqqYmJhGtW9IQUGBy09ZWdnZvSAAACwuOjxQzw7rKV8fmyRpXuYevZb+o8lVAUDjlZWV1fne3xy8IuRVVFTozjvvVFlZmWbPni1fX19JUn5+viS5DLusrWacbE27mttn074hcXFxioiIcP7MmjWr8S8IAAAvcXW3aM26+WLn77OWbNdH37NROgDPMGvWLJfv/HFxcc3yvPWvDmIhDodD99xzj9LT0zVu3DjdeeedZpckScrJyXGZbFmzEAwAAHA17PI4HTlVqv9dVr2dwsTFm9Q61K60rm1NrgwATm/SpEmaMGGC8/eCgoJmCXqW7skzDEPjxo3TggULdMcdd+iVV15xOV/TI9dQz1tNd2rtnrua1XAa274h4eHhLj+EPAAAGvbAwETdeWWCJKnSYej3C77Tpv155hYFAGdgt9vrfO9vDpYNeQ6HQ/fee6/efPNNjRgxQm+99ZZ8fFxf7pnm0NU3Zy8pKUmFhYXKzc1tVHsAAHD+bDabHr+ph37Vo3pOfHF5lcbM+1Z7jhWZXBkAtDyWDHkOh0Njx47VvHnzdPvtt2v+/PnOeXi1JSUlKTY2VpmZmSoqcv1LorS0VOnp6YqNjVViYqLzeFpamiRp2bJldR5v6dKlLm0AAEDT8fWx6fnhvZTSqZUk6XhRue6et1ZHT7F4GQDUZrmQV9ODN2/ePN12221asGBBvQFPqv5XwbFjx6qwsFAzZsxwOTdr1iydPHlSY8eOlc1mcx4fM2aM/Pz8NHPmTJdhm1u2bNE777yjLl266JprrnHPiwMAwMsF+vvq9bsuU9d2oZKkvceLdc9b36qwrNLkygCg5bAZFttw5vHHH9f06dMVGhqqhx9+2LknXm1DhgxRr169JElFRUXq16+fNmzYoOuvv159+vTRxo0btWTJEvXq1UsZGRl19tCbOXOmpkyZovj4eA0dOlRFRUVatGiRSkpKtHTpUg0cOLDB+pprl3sAAKzsUH6JbvnHKh3KL5Uk9U9qozfuvlwBfpb792sAFtJcWcByIW/06NF6++23T9tm3rx5Gj16tPP3/Px8TZ8+XYsXL1Zubq5iYmI0dOhQTZs2rcFFVN599109//zz2rJliwICAnTVVVdpxowZuvzyy0/73IQ8AACaRtbhUxr6yjfKL6mQJN3cu4Pm3NZTPj62M9wTAMxByLMoQh4AAE3n2z0ndMfcNSqrdEiS7kvrrEn/r7vJVQFA/ZorCzCmAQAAeKzLO7XSiyN6q6bz7tWVP+qNjN3mFgUAJiPkAQAAjza4R4xm/PYi5+9P/Hur/m/jQRMrAgBzEfIAAIDHu+PKBD107c/71P7xg41alX3MxIoAwDyEPAAAYAnjr0vS8MvjJEnlVQ79bv532nIw/wz3AgDrIeQBAABLsNlsenLIRbque7QkqbCsUqPnfaucE8UmVwYAzYuQBwAALMPP10cvjbhUveMjJUlHT5Xp7jfX6kRRubmFAUAzIuQBAABLCQrw1Zt3X67ObUMkST8eK9I9b32r4vJKkysDgOZByAMAAJYTFRKgd+5JUXSYXZK0ISdPf1i4XpVVDpMrAwD3I+QBAABL6hgVrLfvSVGY3U+StHz7ET328WYZhmFyZQDgXoQ8AABgWd3bh+u1uy5TgG/1V54P1u3Xs1/sNLkqAHAvQh4AALC0q7q01rO395TNVv37S8uz9dwXO+nRA2BZhDwAAGB5v74kVtN+nez8/YX/Zmnav7bI4SDoAbAeQh4AAPAKo1Mv0JQbuzt/f+ebvXrovfUqr2QxFgDWQsgDAABeY2z/zppzW0/5+lSP3fz3pkO69222VwBgLYQ8AADgVW7t01Gv3dlHdr/qr0FfZx3TyNfX6CQbpgOwCEIeAADwOtd2b6cFY69QeGD19gobcvJ026vf6FB+icmVAcD5I+QBAACvdHmnVnr/vqvU9qcN07OPFGroy99o19FCkysDgPNDyAMAAF6re/twfXh/XyW0DpYkHcgr0W2vfKNN+/PMLQwAzgMhDwAAeLX41sH65/1XqXv7cEnSiaJyjXhttTKzj5lcGQCcG0IeAADwetFhgXr/viuVckErSVJReZXGzPtWn20+ZHJlAHD2CHkAAACSwgP99c49KbqueztJUnmVQw8s/F4L1+wzuTIAODuEPAAAgJ8E+vvqlTsu1dA+HSVJhiE99vFm/f2rbBmGYXJ1ANA4hDwAAIBa/Hx99Nehl+h3Azo7j/116Q498e9tcjgIegBaPkIeAADAL9hsNj12Q3dN+n8XOo+9mblbf/znRlVUOUysDADOjJAHAADQgPvSumj2rZfIx1b9+8frD+i++d+ppLzK3MIA4DQIeQAAAKcx7PI4vXxHHwX4VX9tWr79iO58Y43yiytMrgwA6kfIAwAAOIPBPWL09pgUhdr9JEnr9p7Ub/6Woa0HC0yuDADqIuQBAAA0wlVdWuu9312pNqEBkqR9J4p18z8ytfi7/SZXBgCuCHkAAACNdFGHCH3yQKou6RghSSqrdOhP/9yoxz7erLJK5ukBaBkIeQAAAGehY1SwPrjvKo1IiXceW7hmn4a98o0O5JWYWBkAVCPkAQAAnKVAf1/NuuVi/XXoJbL/tCDLxv35+vWLXyt951GTqwPg7Qh5AAAA5+i2y+L00f/0VXyrYEnSyeIK3T1vrV76bxYbpwMwDSEPAADgPPSIjdD//aGfrr0wWpJkGNKcL3Zq3Dvr2GYBgCkIeQAAAOcpIthfr991mf40qKtsP22c/t/tR/Sbv2Voy8F8c4sD4HUIeQAAAE3Ax8emP1yTpHfuSVFUsL+k6m0WbvnHKv1zXY7J1QHwJoQ8AACAJtQ/qa3+/VB/9ay1zcKfF2/SpI82q7SCbRYAuB8hDwAAoIl1iAzSB/dfpVFX/LzNwqK1+zTs1W+0/2SxiZUB8AaEPAAAADew+/lq5s0Xa85tPZ3bLGzan69fv5ShFTuOmFwdACsj5AEAALjRrX066uP/SVVC6+ptFvKKKzR63rea8slmFZVVmlwdACsi5AEAALhZcmy4/vWHfrque7Tz2ILV+/T/Xvhaa3efMLEyAFZEyAMAAGgGEUH+eu3Oy/TEb3soyN9XUvXqm7e/9o2e+PdWFmUB0GQIeQAAAM3Ex8emO6/qpCUP99flnaIkVW+e/kbGbt3w4tdav++kyRUCsAJCHgAAQDPr1CZE7/3uKk25sbsCflqU5cejRbr15VWa/fl2lVXSqwfg3BHyAAAATODrY9PY/p312UP9nHvqOQzpHyt26bd/y9QPB/JNrhCApyLkAQAAmCgxOkwf/r6v/jy4m/x9bZKk7bmnNOTvmXrhyyxVVDlMrhCApyHkAQAAmMzP10cPDEzUpw/0U/f24ZKkSoeh577cqVv+sUo7D58yuUIAnoSQBwAA0EIkx4br0wdS9eA1ifL1qe7V23wgX79+MUOvrNylSnr1ADQCIQ8AAKAFCfDz0R8HddNHv++rxOhQSVJ5lUNPL9mum/6Wqe/2sgIngNMj5AEAALRAPeMi9e8H++l3AzrLVt2pp62HCnTry6s0cfFGHS8sM7dAAC0WIQ8AAKCFCvT31WM3dNfi+/s65+pJ0gfr9mvg/67Q/NV7VeUwTKwQQEtkMwyDK0MzKigoUEREhPLz8xUeHn7mOwAAAEiqrHJoweq9mrNsp06VVTqPX9whQk8MuUi94iLNKw5AozRXFqAnDwAAwAP4+fpodOoF+u+f0nRL7w7O45sP5Ovmf2Rq0kebdLKo3MQKAbQUhDwAAAAPEh0WqGdv76X3f3elurULkyQZhrRobY4GzlmhRWv3ycEQTsCrMVyzmTFcEwAANJWKKofeXrVHz32xU0XlVc7jPeMi9eRvL9LFHSNMrA7ALzVXFiDkNTNCHgAAaGqHC0o18z/b9K+NB12O33hxez10bZK6xYSZVBmA2gh5FkXIAwAA7rJq1zFN/XSLso8Uuhwn7AEtAyHPogh5AADAncorHZq/eq9eXrFLx36xlx5hDzAXIc+iCHkAAKA5lJRX6d01e/XKyh8Je0ALQcizKEIeAABoToQ9oOUg5FkUIQ8AAJihobBns0m/7RmrCdd3U3zrYBMrBKyPkGdRhDwAAGCmhsKev69NI1Li9YdrEhUdFmhihYB1EfIsipAHAABagpLyKi1YvVf/WJGtk8UVzuNB/r66t98F+l1aZ4UH+ptYIWA9hDyLIuQBAICWpKC0QnPTf9TcjN0qrrWhemSwv/7n6i6666pOCvT3NbFCwDoIeRZFyAMAAC3R0VNl+vtX2Xp3zV5VVP389TAmPFAPXJOo2/p0JOwB54mQZ1GEPAAA0JLlnCjWc1/s1McbDqj2t8Q2oQEa3beT7rgyQZHBAeYVCHgwQp5FEfIAAIAn2J5boP9dukNfbjvicjw4wFcjUuJ1b78LFBsZZFJ1gGci5FkUIQ8AAHiSHw7k65WVu/TZ5kNy1PrW6Odj0029YnXfgC7sswc0EiHPogh5AADAE+07Xqy5GT/q/W9zVFbpcDl3Xfdo/f7qRPVJiDKpOsAzEPIsipAHAAA82fHCMr39zV69880e5dXaekGSUi5opf+5uovSuraVzWYzqUKg5SLkWRQhDwAAWEFRWaXe/zZHr3/9ow7ll7qcS24frj9ck6hf9YiRjw9hD6hByLMoQh4AALCS8kqHPt1wQK+s3KVdR4tcznVrF6YHr03UDRe1J+wBIuRZFiEPAABYkcNhaNnWXP1jxS5t2p/vci4pOlQPXZukGy8m7MG7EfIsipAHAACszDAMpWcd0wtf7tT3+/JczvWIDddjN3RXamIbc4oDTEbIsyhCHgAA8AaGYSgj+5he+DJL6/aedDk3oGtbTb6hO1svwOsQ8iyKkAcAALyJYRj6OuuYnl6yXVsPFTiP+/nY9Puru+iBgYkK9Pc1sUKg+RDyLIqQBwAAvJHDYehfGw/qr0t36EBeifN457YheuH23rq4Y4SJ1QHNo7mygI/bHhkAAAD4iY+PTUN6d9B//5imh65Nkr9v9QIsPx4t0q0vr9L81XtNrhCwDkIeAAAAmk2gv68mXN9V/36wvy75qfeuvMqhv3zygz5Zf8Dk6gBrIOQBAACg2XWLCdPi+/tqTGon57G/fPqDSiuqzCsKsAhCHgAAAEwR4Oejqb9OVuuQAEnSqdJK7TledIZ7ATgTQh4AAABM85dPf9DxonJJUqC/j2Ijg0yuCPB8hDwAAACYYvexIi1Yvc/5+58HX6jwQH8TKwKsgZAHAAAAU3z43X7n7fvSOuvefheYWA1gHYQ8AAAANLsqh6EPv68Oeb4+NgIe0IT8zC4AAAAA3qGwrFJbDxZo0/48rdx5VIfySyVJV3dtq+iwQJOrA6yDkAcAAIDzZhiGTpVV6uipMh0uKNWhvFIdyi/RgbxS7T1epN3Hipyh7pduu6xjM1cLWBsh7zx8++23mjZtmr755huVl5erR48eeuSRRzRy5EizSwMAADhnDkd1YMsvrlBeSbnyiiuUV1Kh/OLq2ydq/ltUrpPF5TpeWK6jhWUqr3Sc1fO0CbXr5t6xGpQc46ZXAngnQt45WrFihQYPHqyAgAANHz5cERER+uijjzRq1Cjt2bNHjz32WL33Kysrc/kvvE9ZWZlmzZqlSZMmyW63m10OmhnvP/gMeDcz3/+C0godOFmiY4VlOnqqTMcKy3SssFzHTpU5Q1t+SYXyisuVX1Ihh9G0zx8Z7K8L2oSoe/twXRQbod7xkbowJkw2m61pn6iF4xrg3ZorC9gMw2ji/4Wtr7KyUhdeeKH279+vb775Rr1795YknTp1SldddZV27NihrVu3Kikpqc599+/fr7i4OOXk5KhjR4YmeKOCggJFREQoPz9f4eHhZpeDZsb7Dz4D3s2M939jTp7eyNitzzYfUmVTJzdJNpsUFRygtqF2tQmr/m/bMLvaRwQpNjJQ7SOClNA6WJHBAU3+3J6Ia4B3a64sQE/eOVi+fLl27dqlMWPGOAOeJIWFhekvf/mLhg8frnnz5umpp54ysUoAAODNDMPQPW99q692HG1Ue5tNCg/0V2SwvyKD/BURHKCIoOrbkcH+igiq/okMDlCrkJ/+Gxyg8CB/+fp4V28c0NIR8s7BihUrJEmDBg2qc67m2MqVK5uzJAAAABc2m00XtAl1hrxWIQG65sJotQu3q81PvW1tQqt/WocQ1gArIeSdg6ysLEmqdzhmVFSU2rRp42zzSzWjYw8dOuRy3G63My7bSxQUFLj8F96F9x98Brxbc7//Qy9upfQtezUyJV6/7hmrQH/felo5pKpSFRXWv/IlmhbXAO9SVlbmMv+uJgO4e8YcIe8c5OfnS5IiIiLqPR8eHq79+/fXe66iokKSlJKS4p7i4DHi4uLMLgEm4v0HnwHv1tzv/3+b9dnQGFwDvFtNJnAXQl4z69Spk3bt2iV/f3+X1aToyQMAAACs5Zc9eYZhqKKiQp06dXLr8xLyzkFND15Nj94v1ayaVB8fHx917tzZbbUBAAAA8G4+ZhfgiWrm4tU37+7kyZM6duxYvfP1AAAAAMDdCHnnIC0tTZK0bNmyOudqjtW0AQAAAIDmxGbo56CyslLdunXTgQMHtHr1avXq1UuS62boW7ZsUdeuXc0tFAAAAIDXoSfvHPj5+Wnu3LlyOBzq37+/br75ZnXu3FmRkZHasmWL2rVrp3Xr1p3VYzocDv3tb3/TJZdcoqCgILVt21bDhg1rcCsGtBzffvutbrjhBkVFRSkkJEQpKSlauHBho++/YsUK2Wy2Bn9Wr17txupxvhYsWKD77rtPl112mex2u2w2m956662zfhyuAZ6pKd5/rgGe68CBA3r++ec1aNAgxcfHKyAgQDExMbr11lu1Zs2as3osrgGeqak+A1wHPFNeXp4eeughXXXVVYqJiZHdbleHDh10zTXX6MMPPzyrbRKa+hrAwivnaODAgcrIyNCDDz6oTz75RJLUpk0bpaSkaNu2bRo1apT27Nmjxx57rFGPd//99+v1119XcnKyHnzwQR0+fFjvv/++li1bplWrVik5OdmNrwbnasWKFRo8eLACAgI0fPhwRURE6KOPPjrr91+qHuJ79dVX1znesWPHJqwYTW3KlCnau3ev2rRpo/bt22vv3r3n9DhcAzxTU73/EtcAT/TSSy/pmWeeUZcuXXT99dcrOjpaWVlZ+uSTT/TJJ59o0aJFGjZsWKMei2uAZ2rKz4DEdcDTHDt2TG+++aauvPJKDRkyRK1atdKRI0f0f//3fxo6dKjGjRun1157rVGP1eTXAAPnrKKiwujSpYtht9uN77//3nm8oKDA6NGjh+Hn52fs3LnzjI+zfPlyQ5LRv39/o7S01Hn8yy+/NGw2mzFgwAC31I/z01Tv/1dffWVIMqZNm+bGauEuX3zxhbFnzx7DMAxj1qxZhiRj3rx5Z/UYXAM8V1O8/1wDPNeHH35opKen1zmenp5u+Pv7G61atXL5f7ohXAM8V1N9BrgOeKbKykqjoqKizvGCggIjOTnZkGT88MMPZ3wcd1wDGK55HpYvX65du3Zp5MiR6t27t/N4WFiY/vKXv6iyslLz5s074+O8/vrrkqQnn3zSZa+8a6+9VoMHD1Z6erp27tzZ9C8A56Wp3n94tuuuu04JCQnn9RhcAzxXU7z/8Fy33HKL+vfvX+d4//79NXDgQJ04cUKbN28+4+NwDfBcTfUZgGfy9fWVn1/dgZFhYWEaPHiwJCk7O/uMj+OOawAh7zysWLFCkjRo0KA652qOrVy5slGPExISotTU1Drnaj4gjXkcNK+mev9rZGVl6cUXX9TTTz+tRYsW6dixY01SJ1o+rgGQuAZYjb+/vyTV+wXwl7gGWNPZfAZqcB2whtLSUi1fvlw2m61RwyzdcQ1gTt55qJkIWd+eeFFRUWrTps0ZJ0sWFRXp0KFDuuiii+Tr61vn/On25IO5muL9r23hwoUuC7YEBQVp+vTp+vOf/3z+xaLF4hqAGlwDrGPfvn368ssvFRMTo4svvvi0bbkGWNPZfAZq4zrgmfLy8vT888/L4XDoyJEj+uyzz5STk6Np06adce9sd10D6Mk7D/n5+ZKkiIiIes+Hh4c725zPY9Ruh5ajKd5/SWrbtq3++te/atu2bSoqKtKBAwe0YMECtWrVShMnTtSrr77apHWjZeEaAK4B1lJRUaE777xTZWVlmj17dr1f2mrjGmA9Z/sZkLgOeLq8vDxNnz5dTzzxhF599VXl5ubqr3/9q6ZNm3bG+7rrGkBPHmCyHj16qEePHs7fg4ODNWrUKPXs2VN9+vTRtGnTNG7cOPn48G8ygBVxDbAOh8Ohe+65R+np6Ro3bpzuvPNOs0tCMzvXzwDXAc/WqVMnGYahqqoq5eTk6L333tPkyZO1atUqffDBB2c1ZLep8Ek5DzWJu6FkXVBQ0GAqP5vHqN0OLUdTvP+nc9FFF+mKK67Q4cOHGzVpF56JawAawjXAsxiGoXHjxmnBggW644479MorrzTqflwDrONcPwOnw3XAs/j6+qpTp0569NFH9eSTT+rjjz92LqrSEHddAwh55+F0Y2RPnjypY8eOnXEcbkhIiNq3b6/du3erqqqqzvnTzfuCuZri/T+TNm3aSJKKi4vP63HQcnENwOlwDfAMDodD9957r958802NGDFCb731VqN7XLgGWMP5fAbOhOuAZ6pZhK9mob6GuOsaQMg7D2lpaZKkZcuW1TlXc6ymzZkep6ioSJmZmXXOLV26tNGPg+bVVO9/QyorK/X999/LZrMpPj7+nB8HLR/XANSHa4BncDgcGjt2rObNm6fbb79d8+fPb9QcrNq4Bni2pvgMNITrgOc6ePCgpMatruqWa8BZ76wHp4qKCqNz586G3W431q9f7zxeezPsHTt2OI8fPXrU2LZtm3H06FGXx6m9AWJZWZnzOJugtmxN9f6vWrXKcDgcdR77kUceMSQZv/rVr9z6OtB0zrQZNtcAazvX959rgOeqqqoyRo8ebUgybrvttno3Ra6Na4D1NNVngOuAZ1q/fr2Rl5dX5/jx48eNXr16GZKM+fPnO4835zXAZhiGcXaxELV99dVXGjx4sOx2u0aMGKHw8HB99NFH2r17t5588klNnjzZ2fbxxx/X9OnTNW3aND3++OMujzNu3DjNnTtXycnJuvHGG3X48GG9//77CgwM1KpVqxq1xwaaX1O8/506dZLNZlPfvn3VoUMH5eXlKT09XTt27FB8fLzS09PZbLkFmzt3rjIyMiRJmzdv1vfff6/U1FQlJiZKkoYMGaIhQ4ZI4hpgRU3x/nMN8Fw172loaKgefvjhev/FfsiQIerVq5dLe64B1tFUnwGuA57pkUce0dy5czVw4EAlJCQoJCREe/fu1X/+8x8VFhbq1ltv1QcffOAcutuc1wBW1zxPAwcOVEZGhqZNm6YPPvhA5eXl6tGjh5544gmNGjWq0Y/z6quv6pJLLtGrr76qF198UaGhofrNb36jmTNnqmvXrm58BTgfTfH+//73v9fnn3+uFStW6NixY/Lz81NiYqImT56sP/7xj4qKinLzq8D5yMjI0Ntvv+1yLDMz0znkolOnTs4v+afDNcAzNcX7zzXAc+3Zs0eSVFhYqJkzZ9bbplOnTs4v+KfDNcAzNdVngOuAZxo6dKjy8/O1evVqpaenq7i4WK1atVK/fv101113afjw4bLZbI16rKa+BtCTBwAAAAAWwsIrAAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAATWDPnj2y2WwuP48//rjZZdXRq1cvlxqvvvpqs0sCADQxQh4AwPI6derkDDV//OMfT9v2hRdecAlBZ8tutys1NVWpqamKj4+vc/69995zefw9e/Y0+FgHDhxQcHCws+3MmTPPup5f6t27t1JTU3XRRRed92MBAFomP7MLAACgOS1cuFCzZ8+Wr69vvecXLFhwXo8fExOjjIyMBs9v2LDB5fctW7aoU6dO9badMmWKSkpKnL/37NnzvGqTpHnz5kmSVqxYoYEDB5734wEAWh568gAAXqNbt27Kzc3Vl19+We/5HTt2aN26derWrZvbati4caMkqXv37pKkrVu31ttu8+bNeuedd5SUlOTsUezVq5fb6gIAWAchDwDgNe644w5JDffWzZ8/X5J05513uq2Gmp68mloaCnkTJ06Uw+HQHXfcIcMw1KpVK3Xs2NFtdQEArIOQBwDwGmlpaYqLi9PHH3+soqIil3OGYejdd99VUFCQbrnlFrc8/5EjR5SbmysfHx+NHDlSUvVwzV9avny5Pv/8c1133XWKiYmRRC8eAKDxCHkAAK9hs9k0atQoFRUV6eOPP3Y5l5GRoT179mjIkCEKCwtzy/PX9OIlJSWpU6dOateunbZt2ybDMJxtDMPQn//8Z9lsNs2ePds5vLMp5uMBALwDIQ8A4FVqhmLWDM2s0RxDNX8Z2C6++GIVFhYqJyfH2WbhwoX6/vvvNWrUKPXu3dt5H3ryAACNRcgDAHiV5ORk9e7dW//973916NAhSVJZWZn++c9/Kjo6Wtdff73bnrumJ692yJN+npdXVlamKVOmyG6368knn5RhGNq0aZPLfQAAOBNCHgDA69x5552qqqrSokWLJEn//ve/lZeXpxEjRsjPz327C9XXkyf9PC/vb3/7m/bs2aMHH3xQCQkJ+vHHH3Xq1Cn5+/srOTnZbXUBAKyFkAcA8DojRoyQr6+vc4hmzX9rVrx0h9LSUu3YsUPSzyHvkksukVTdk5eXl6ennnpKUVFReuyxxyT9HAqTk5Pl7+/vttoAANZCyAMAeJ2YmBhdd9112rBhg9LT07VkyRJdeOGFuuyyy9z2nFu2bFFlZaXLVgjJycny8fHR1q1bNXPmTJ04cUKPPfaYoqKiJKlR8/EOHTqkP/zhD4qLi5PdbldiYqIee+wx5eXlue21AABaNkIeAMAr1Sywcuedd6q8vNytC65IdefjSVJQUJASExO1adMmvfTSS0pISNCDDz7oPH+mlTU3bNignj17av/+/Zo7d67Wr1+vZ599VqtWrdKll16qPXv2uO31AABaLkIeAMAr3XzzzQoNDdW+ffucWyu4U0OB7eKLL1ZxcbHKysr05JNPym63O8/VBMP6evLKysp08803a9y4cfrkk080ePBgJScn66abbtLy5cuVlpam22+/3WV7BgCAd3Df7HIAAFqw4OBg/fGPf1RGRoaSkpKUkJDg1uerrydPkm688Ubl5eWpXbt2LkEzPz9fe/furfc+krR48WIFBAToiSeeqHPOx8dHL730kuLi4pSenq60tLQmfCUAgJaOkAcA8FqPP/54sz1XQ1shjBkzRmPGjKnTvqbnLy4uTq1atapzfs2aNRo0aJB8fKoH5SxevFj333+/Pv30U6Wmpio0NFRpaWlas2YNIQ8AvAzDNQEAcLPdu3crPz9ffn5+jd4K4Uzz8SorK11W3CwtLdXx48dVUVHhPObv76+qqqrzqBwA4IkIeQAAuFlNYLvwwgtd5tw15j4Nrax5ySWX6Ouvv3b+fscdd8gwDF199dWSpIqKCq1atcq5TQMAwHsQ8gAAlrdnzx4ZhqF+/fo1qn3Hjh1lGEaTLVoyZMgQGYahzZs3N/o+c+fOlWEY9c65k6Thw4frxx9/1BtvvFHv+ZkzZyo4OFiDBg06p5oBAJ6LOXkAADSh3NxcZ5i85557dM8997jleSIjI7Vw4ULdfPPN+u677/SnP/1JcXFxys7O1vTp0/X5559r+fLldTZRHzNmjLKyspSfn++WugAA5qMnDwCAJlRWVqbMzExlZmZq3759bn2uwYMHKyMjQzt37lRSUpICAgLUq1cvlZWVad26dbr00kvr3Gf9+vXKzMzUDz/84NbaAADmsRlsoAMAgMcrKirS8ePHFR0drcDAQLPLAQCYiJAHAAAAABbCcE0AAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFkLIAwAAAAALIeQBAAAAgIUQ8gAAAADAQgh5AAAAAGAhhDwAAAAAsBBCHgAAAABYCCEPAAAAACyEkAcAAAAAFvL/AXMvCOMEkltCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Mass and Tidal curve\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MRT[1], MRT[2],lw=2)\n",
+    "ax.set_ylabel(r'$\\Lambda$', fontsize=16)\n",
+    "ax.set_xlabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlim(0., 3.)\n",
+    "ax.set_ylim(0,1000)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Can generate the speed of sound from the given equation of state. This do not need to solve TOV equation, the speed of sound is defined by the derivative of P(rho) curve, this quantity is very of interest, since whether there is a upper limit of this speed of sound (except for the casaulity limit c_s < 1 ) is a very interesting and hot topic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from TOVsolver.constant import c,G \n",
+    "# here we plotting out speed of sound with Test_EOS, the density is in g/cm3\n",
+    "C_s,rho = main.OutputC_s(\"Test_EOS.csv\")\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(rho, C_s,lw=2)\n",
+    "ax.set_xlabel(r'$\\epsilon$', fontsize=16)\n",
+    "ax.set_ylabel(r'C_s', fontsize=16)\n",
+    "plt.xscale(\"log\")\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This package can easily integrated into a Baysian inference flow, to do bayesian inference, Here, we generated several (50) EoSs from RMF model, and try to use a loop to compute out all of their MRT property. That could be a in-between step of doing bayesian inference of neutron star EoS. Remember these EoS could also be polytrope, or anything that generate from your own EoS computation code. Next step for us will be integrate our EoS computation into this package and also the Bayesian analysis."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4481115b400f107b26d360e6549f546bb0e8bc1af70e4e66085bfa77a017a39"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Bayesian Inference smapler setup module &mdash; CompactObject 1.9.1 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Bayesian Inference smapler setup module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.BayesianSampler.UltranestSampler"><code class="docutils literal notranslate"><span class="pre">UltranestSampler()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.BayesianSampler.UltranestSamplerResume"><code class="docutils literal notranslate"><span class="pre">UltranestSamplerResume()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+  <section id="bayesian-inference-smapler-setup-module">
+<span id="bayesian-sampler"></span><h1>Bayesian Inference smapler setup module<a class="headerlink" href="#bayesian-inference-smapler-setup-module" title="Link to this heading"></a></h1>
+<p>Functions to start run Bayesian inference by calling UltraNest package</p>
+<dl class="py function" id="module-InferenceWorkflow.BayesianSampler">
+<dt class="sig sig-object py" id="InferenceWorkflow.BayesianSampler.UltranestSampler">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.BayesianSampler.</span></span><span class="sig-name descname"><span class="pre">UltranestSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">likelihood</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">live_points</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_calls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/BayesianSampler.html#UltranestSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.BayesianSampler.UltranestSampler" title="Link to this definition"></a></dt>
+<dd><p>UltraNest based nested sampler by given likelihood prior, and parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>parameters</strong> (<em>array</em>) – parameters array that want to be constrained.</p></li>
+<li><p><strong>likelihood</strong> (<em>array</em>) – theta as input. likelihood function defined by user.</p></li>
+<li><p><strong>prior</strong> (<em>array</em>) – cube as input, prior function defined by user. please check our test_inference.ipynb</p></li>
+<li><p><strong>prior.</strong> (<em>to check how to define likelihood and</em>)</p></li>
+<li><p><strong>step</strong> (<em>int</em>) – as a step sampler, define this inference want to devided to how many steps.</p></li>
+<li><p><strong>live_points</strong> (<em>int</em>) – define how many live points will be used to explore the whole</p></li>
+<li><p><strong>space.</strong> (<em>parameters</em>)</p></li>
+<li><p><strong>max_ncalls</strong> (<em>int</em>) – define after how many steps the sampler will stop work.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>equal weighted samples of whole posteior parameter space,
+this run will generate a dirctory as ‘output’, please check the run# folder, and the
+chain dirctory, there is a ‘equal_weighted_samples’ file, that is same with flat_samples here.
+It will be easier to check if you are using clusters to do this inference.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>flat_samples (array)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.BayesianSampler.UltranestSamplerResume">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.BayesianSampler.</span></span><span class="sig-name descname"><span class="pre">UltranestSamplerResume</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">likelihood</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsteps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">live_points</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_calls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/BayesianSampler.html#UltranestSamplerResume"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.BayesianSampler.UltranestSamplerResume" title="Link to this definition"></a></dt>
+<dd><p>UltraNest based nested sampler by given likelihood prior, and parameters. (resume true verion
+could restart you run from your previous stopped results)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>parameters</strong> (<em>array</em>) – parameters array that want to be constrained.</p></li>
+<li><p><strong>likelihood</strong> (<em>array</em>) – theta as input. likelihood function defined by user.</p></li>
+<li><p><strong>prior</strong> (<em>array</em>) – cube as input, prior function defined by user. please check our test_inference.ipynb</p></li>
+<li><p><strong>prior.</strong> (<em>to check how to define likelihood and</em>)</p></li>
+<li><p><strong>step</strong> (<em>int</em>) – as a step sampler, define this inference want to devided to how many steps.</p></li>
+<li><p><strong>live_points</strong> (<em>int</em>) – define how many live points will be used to explore the whole</p></li>
+<li><p><strong>space.</strong> (<em>parameters</em>)</p></li>
+<li><p><strong>max_ncalls</strong> (<em>int</em>) – define after how many steps the sampler will stop work.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>equal weighted samples of whole posteior parameter space,
+this run will generate a dirctory as ‘output’, please check the run# folder, and the
+chain dirctory, there is a ‘equal_weighted_samples’ file, that is same with flat_samples here.
+It will be easier to check if you are using clusters to do this inference.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>flat_samples (array)</p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toctree-l2"><a class="reference internal" href="#introduction">Introduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#contributing-an-equation-of-state-eos">Contributing an Equation of State (EOS)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#contributing-inference-components">Contributing Inference Components</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#guidelines-for-key-functions-and-permissions">Guidelines for Key Functions and Permissions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#acknowledgements">Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#contact">Contact</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+             
+  <section id="call-for-contributions">
+<h1>Call for Contributions<a class="headerlink" href="#call-for-contributions" title="Link to this heading"></a></h1>
+<section id="introduction">
+<h2>Introduction<a class="headerlink" href="#introduction" title="Link to this heading"></a></h2>
+<p>If you are interested in contributing to the <strong>CompactObject</strong> project, please send an email to <a class="reference external" href="mailto:chun&#46;h&#37;&#52;&#48;wustl&#46;edu">chun<span>&#46;</span>h<span>&#64;</span>wustl<span>&#46;</span>edu</a> to request collaboration. You can also open an issue on our GitHub repository at <a class="reference external" href="https://github.com/ChunHuangPhy/CompactObject/issues">Issue page</a> so that the community can track the status of collaborations.</p>
+<p>We are always eager to expand our scope to include the most equation of state (EOS) computations currently available in the community, as well as the most relevant constraints. If you have your own EOS and wish to contribute, please follow the steps outlined below.</p>
+</section>
+<section id="contributing-an-equation-of-state-eos">
+<h2>Contributing an Equation of State (EOS)<a class="headerlink" href="#contributing-an-equation-of-state-eos" title="Link to this heading"></a></h2>
+<ol class="arabic">
+<li><p><strong>Open an Issue</strong>
+Visit our GitHub issues page at <a class="reference external" href="https://github.com/ChunHuangPhy/CompactObject/issues">Issue page</a> and open a new issue to suggest the EOS you would like to contribute.</p></li>
+<li><p><strong>Request Collaboration</strong>
+Send an email to <cite>chun.h&#64;wustl.edu</cite> to request collaborator access to the repository.</p></li>
+<li><p><strong>Commit the EOS Script</strong>
+- Create a <cite>.py</cite> script that computes the EOS. The script should:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Input the EOS parameter values.</p></li>
+<li><p>Output the pressure and density in CGS units.</p></li>
+</ul>
+</div></blockquote>
+<ul class="simple">
+<li><p>For unit conventions, please refer to the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/UnitConventionForDeveloper.html">UnitConventionForDeveloper</a>.</p></li>
+<li><p>Name the main compute function as <cite>compute_EOS</cite>.</p></li>
+<li><p>Name the file as <cite>(YourEOSName)_EOS.py</cite>.</p></li>
+</ul>
+</li>
+<li><p><strong>Create a Demonstration Notebook</strong>
+Develop a Jupyter notebook that demonstrates:
+- How to compute the EOS.
+- Defining your EOS parameters.
+- Integrating your EOS with our default TOV solver (<cite>main.outputMR</cite>).
+- Ensuring the TOV solver correctly solves the EOS and outputs a mass-radius curve plot.</p></li>
+</ol>
+</section>
+<section id="contributing-inference-components">
+<h2>Contributing Inference Components<a class="headerlink" href="#contributing-inference-components" title="Link to this heading"></a></h2>
+<p>If you have developed your own likelihood, prior, sampler, or any component related to inference that is suitable for addition to our functionalities, we highly recommend contacting us. Follow these steps to contribute:</p>
+<ol class="arabic simple">
+<li><p><strong>Open an Issue</strong>
+Visit our GitHub issues page at <a class="reference external" href="https://github.com/ChunHuangPhy/CompactObject/issues">Issue page</a> and open a new issue to suggest changes to the inference components.</p></li>
+<li><p><strong>Request Collaboration</strong>
+Send an email to <cite>chun.h&#64;wustl.edu</cite> to request collaborator access to the repository.</p></li>
+<li><p><strong>Commit the Inference Script</strong>
+- Create a <cite>.py</cite> script within the <cite>InferenceWorkflow</cite> directory.
+- Name the script after the function you are contributing.</p></li>
+<li><p><strong>Create a Demonstration Notebook</strong>
+Develop a Jupyter notebook that demonstrates:
+- How to use your prior, likelihood, or sampler.
+- Include sampling outputs to ensure compatibility with the current structure of the package.</p></li>
+</ol>
+</section>
+<section id="guidelines-for-key-functions-and-permissions">
+<h2>Guidelines for Key Functions and Permissions<a class="headerlink" href="#guidelines-for-key-functions-and-permissions" title="Link to this heading"></a></h2>
+<p>The <strong>TOVsolver</strong> module is fundamental to all inference and computation within <strong>CompactObject</strong>. Modifying this module requires careful consideration, as changes may necessitate significant code restructuring.</p>
+<ul class="simple">
+<li><p><strong>Permissions:</strong>
+Only original contributors have permission to modify the <strong>TOVsolver</strong> code. All commits related to the <strong>TOVsolver</strong> module require detailed reviews by the original members of the package.</p></li>
+<li><p><strong>Local Changes:</strong>
+If you clone the repository locally and need to make changes to the <strong>TOVsolver</strong>, proceed with caution to avoid extensive code rebuilding.</p></li>
+</ul>
+</section>
+<section id="acknowledgements">
+<h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Link to this heading"></a></h2>
+<p>We welcome feedback and contributions to expand the functionalities of <strong>CompactObject</strong>. Your support helps enhance the tool for the entire research community.</p>
+</section>
+<section id="contact">
+<h2>Contact<a class="headerlink" href="#contact" title="Link to this heading"></a></h2>
+<p>For inquiries, contributions, or to be featured in our publications list, please contact us at <a class="reference external" href="mailto:chun&#46;h&#37;&#52;&#48;wustl&#46;edu">chun<span>&#46;</span>h<span>&#64;</span>wustl<span>&#46;</span>edu</a>.</p>
+</section>
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+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+             
+  <section id="postprocessing-module-cornerplot">
+<span id="corner-plot-module"></span><h1>Postprocessing module: Cornerplot<a class="headerlink" href="#postprocessing-module-cornerplot" title="Link to this heading"></a></h1>
+<p>Function to plot overlapping corner plots with input as two or three 2d posterior arrays</p>
+</section>
+
+
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+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
+        </div>
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+      <li class="breadcrumb-item active">Usage of download_compose Module</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/DownloadCompose.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Usage-of-download_compose-Module">
+<h1>Usage of download_compose Module<a class="headerlink" href="#Usage-of-download_compose-Module" title="Link to this heading"></a></h1>
+<p>Tianzhe Zhou 2024/11/9</p>
+<p><a class="reference external" href="https://compose.obspm.fr/table">CompOSE</a>(CompStar Online Supernovae Equations of State) is a database that provides data tables for different state of the art equations of state (EoS) ready for further usage in astrophysical applications, nuclear physics and beyond.</p>
+<p>In this notebook, we will demonstrate how to use the <code class="docutils literal notranslate"><span class="pre">download_compose</span></code> module to download the data from the CompOSE database.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># First, import the class that we need to use</span>
+<span class="kn">from</span> <span class="nn">EOSgenerators.download_compose</span> <span class="kn">import</span> <span class="n">DownloadCompose</span>
+
+<span class="c1"># Then, create an instance of the class</span>
+<span class="c1"># This will automatically fetch the latest version of all available</span>
+<span class="c1"># EOS names and their corresponding download links</span>
+<span class="n">dc</span> <span class="o">=</span> <span class="n">DownloadCompose</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+DownloadCompose: Fetching data from https://compose.obspm.fr/table
+...
+DownloadCompose: Find 308 EOS data on website https://compose.obspm.fr/table
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now, we can print the list of available EOS names.</span>
+<span class="c1"># Each EOS name is related to a specific identifier,</span>
+<span class="c1"># which we can use later to actually download the EOS data file.</span>
+<span class="n">dc</span><span class="o">.</span><span class="n">print_eos_list</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+id =   1, name = HS(DD2) neutron matter (no electrons)
+id =   2, name = HS(DD2) neutron matter (with electrons)
+id =   3, name = HS(FSG) neutron matter (no electrons)
+id =   4, name = HS(FSG) neutron matter (with electrons)
+id =   5, name = HS(IUF) neutron matter (no electrons)
+id =   6, name = HS(IUF) neutron matter (with electrons)
+id =   7, name = HS(NL3) neutron matter (no electrons)
+id =   8, name = HS(NL3) neutron matter (with electrons)
+id =   9, name = HS(TM1) neutron matter  (no electrons)
+id =  10, name = HS(TM1) neutron matter (with electrons)
+id =  11, name = HS(TMA) neutron matter (no electrons)
+id =  12, name = HS(TMA) neutron matter (with electrons)
+id =  13, name = SFH(SFHo) neutron matter (no electrons)
+id =  14, name = SFH(SFHo) neutron matter (with electrons)
+id =  15, name = SFH(SFHx) neutron matter (no electrons)
+id =  16, name = SFH(SFHx) neutron matter (with electrons)
+id =  17, name = HS(DD2) (no electrons)
+id =  18, name = HS(DD2) (with electrons)
+id =  19, name = HS(FSG) (no electrons)
+id =  20, name = HS(FSG) (with electrons)
+id =  21, name = HS(IUF) (no electrons)
+id =  22, name = HS(IUF) (with electrons)
+id =  23, name = HS(NL3) (no electrons)
+id =  24, name = HS(NL3) (with electrons)
+id =  25, name = HS(TM1) (no electrons)
+id =  26, name = HS(TM1) (with electrons)
+id =  27, name = HS(TMA) (no electrons)
+id =  28, name = HS(TMA) (with electrons)
+id =  29, name = GROM(LS220L) (with Lambdas, no low densities)
+id =  30, name = GROM(LS220L) (with Lambdas, with low densities)
+id =  31, name = LS(LS220) (no low densities)
+id =  32, name = LS(LS220) (with low densities)
+id =  33, name = SFH(SFHo) (no electrons)
+id =  34, name = SFH(SFHo) (with electrons)
+id =  35, name = SFH(SFHx) (no electrons)
+id =  36, name = SFH(SFHx) (with electrons)
+id =  37, name = SHO(FSU1) (no electrons)
+id =  38, name = SHO(FSU1) (with electrons)
+id =  39, name = SHO(FSU2) (with electrons)
+id =  40, name = SHO(FSU2) (no electrons)
+id =  41, name = SHT(NL3) (no electrons)
+id =  42, name = SHT(NL3) (with electrons)
+id =  43, name = BHB(DD2L) (no electrons)
+id =  44, name = BHB(DD2L) (with electrons)
+id =  45, name = BHB(DD2Lphi) (with electrons)
+id =  46, name = BHB(DD2Lphi) (no electrons)
+id =  47, name = SHO(FSU1) (no electrons)
+id =  48, name = SHO(FSU1) (with electrons)
+id =  49, name = SHO(FSU2) (no electrons)
+id =  50, name = SHO(FSU2) (with electrons)
+id =  51, name = SHT(NL3) (no electrons)
+id =  52, name = SHT(NL3) (with electrons)
+id =  53, name = GDTB(DDHdelta)
+id =  54, name = GM(GM1)
+id =  55, name = OPGR(GM1Y4) (with hyperons)
+id =  56, name = STOS(TM1) neutron matter (original version)
+id =  57, name = STOS(TM1) neutron matter (new version)
+id =  58, name = STOS(TM1L) neutron and Lambda matter
+id =  59, name = STOS(TM1) zero temperature (new version)
+id =  60, name = STOS(TM1) zero temperature (original version)
+id =  61, name = STOS(TM1) with Lambda zero temperature
+id =  62, name = STOS(TM1L) with Lambda hyperons
+id =  63, name = STOS(TM1) (new version)
+id =  64, name = STOS(TM1) (original version)
+id =  65, name = OPGR(GM1Y5) (with hyperons)
+id =  66, name = OPGR(GM1Y6) (with hyperons)
+id =  67, name = OPGR(DDHdeltaY4) (with hyperons)
+id =  68, name = APR(APR)
+id =  69, name = BBB(BHF-BBB2)
+id =  70, name = SFHPST(TM1B145), with electrons
+id =  71, name = STOS(TM1) (new version) with electrons
+id =  72, name = STOS(TM1L) with Lambda hyperons and electrons
+id =  73, name = SFHPST(TM1B139) with electrons
+id =  74, name = SFHPST(TM1B155) with electrons
+id =  75, name = SFHPST(TM1B165) with electrons (B165)
+id =  76, name = IOTSY(TM1Y30) Sigma potential 30 MeV
+id =  77, name = IOTSY(TM1Y30pi) with pions, Sigma potential 30 MeV
+id =  78, name = IOTSY(TM1Y90pi) with pions, Sigma potential 90 MeV
+id =  79, name = IOTSY(TM1Y0pi) with pions, Sigma potential 0 MeV
+id =  80, name = IOTSY(TM1Y-30pi) with pions, Sigma potential -30 MeV
+id =  81, name = IOTSY(TM1Y90) , Sigma potential 90 MeV)
+id =  82, name = IOTSY(TM1Y0), Sigma potential 0 MeV
+id =  83, name = IOTSY(TM1Y-30), Sigma potential -30 MeV
+id =  84, name = RG(SLY230a)
+id =  85, name = RG(SLY2)
+id =  86, name = RG(SLY9)
+id =  87, name = RG(SKI2)
+id =  88, name = RG(SkI3)
+id =  89, name = RG(SkI4)
+id =  90, name = RG(SkI5)
+id =  91, name = RG(SkI6)
+id =  92, name = RG(KDE0v1)
+id =  93, name = RG(KDE0v)
+id =  94, name = RG(SK255)
+id =  95, name = RG(SK272)
+id =  96, name = RG(SKa)
+id =  97, name = RG(SKb)
+id =  98, name = RG(SkMp)
+id =  99, name = RG(SkOp)
+id = 100, name = RG(Rs)
+id = 101, name = NL3
+id = 104, name = OMHN(DD2Y)
+id = 105, name = TNTYST(KOST2) variational EoS (with electrons)
+id = 106, name = TNTYST(KOST2) variational EoS (without electrons)
+id = 107, name = TNTYST(KOST2) variational EoS at zero temperature (with electrons)
+id = 108, name = TNTYST(KOST2) variational EoS at zero temperature (no electrons)
+id = 109, name = TNTYST(KOST2) neutron matter (zero temperature)
+id = 110, name = TNTYST(KOST2) neutron matter
+id = 111, name = FYSS(TM1) (no electrons)
+id = 112, name = FTNS(KOST2) variational EoS (no electrons)
+id = 113, name = FYSS(TM1) (with electrons)
+id = 114, name = FTNS(KOST2) variational EoS (with electrons)
+id = 115, name = DNS(CMF) Hadronic (with electrons)
+id = 116, name = DNS(CMF) Hadronic (cold neutron stars)
+id = 117, name = DNS(CMF) Hadronic (no electrons)
+id = 118, name = FOP(SFHoY) (with electrons)
+id = 119, name = FOP(SFHoY) (no electrons)
+id = 120, name = BL(chiral)
+id = 121, name = BL(chiral) with crust
+id = 123, name = RG(SLY4)
+id = 134, name = RG(SLY4)
+id = 136, name = PT(GRDF1_DD2)
+id = 137, name = PT(GRDF2_DD2) old version
+id = 138, name = DNS(CMF) hadronic  (cold neutron stars) with crust
+id = 139, name = BHK(QHC18)
+id = 140, name = BFH(QHC19-B)
+id = 141, name = SRO(SLy4) SNA version
+id = 142, name = OOS(DD2_FRG) (2+1 flavors)
+id = 143, name = OOS(DD2_FRG) (2 flavors)
+id = 144, name = SNSH(TM1e)
+id = 145, name = SNSH(TM1e) zero temperature
+id = 146, name = SNSH(TM1e) neutron matter
+id = 147, name = RG(SLy4) with neutrino opacities
+id = 148, name = RG(SLY4) with neutrino opacities, extended version
+id = 149, name = SRO(APR) SNA version
+id = 150, name = BFH(QHC19-A)
+id = 151, name = BFH(QHC19-C)
+id = 152, name = BFH(QHC19-D)
+id = 154, name = MBB(DD2K ) (no electrons)
+id = 155, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1 without leptons
+id = 156, name = BBKF(DD2F-SF) quark-hadron model RDF 1.2 without leptons
+id = 157, name = BBKF(DD2F-SF) quark-hadron model RDF 1.3 without leptons
+id = 158, name = BBKF(DD2F-SF) quark-hadron model RDF 1.4 without leptons
+id = 159, name = BBKF(DD2F-SF) quark-hadron model RDF 1.5 without leptons
+id = 160, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6 without leptons
+id = 161, name = BBKF(DD2F-SF) quark-hadron model RDF 1.7 without leptons
+id = 162, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8 without leptons
+id = 163, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9 without leptons
+id = 164, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1
+id = 165, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.2
+id = 166, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.3
+id = 167, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.4
+id = 168, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.5
+id = 169, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6
+id = 170, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.7
+id = 171, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8
+id = 172, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9
+id = 173, name = SRO(NRAPR) NSE version
+id = 174, name = SRO(SkAPR) SNA version
+id = 175, name = SRO(KDE0v1) SNA version
+id = 176, name = SRO(LS220*) SNA version
+id = 177, name = SRO(LNS) SNA version
+id = 178, name = SRO(LS220) SNA version
+id = 180, name = DS(CMF)-1
+id = 181, name = DS(CMF)-2
+id = 182, name = DS(CMF)-3
+id = 183, name = DS(CMF)-4
+id = 184, name = DS(CMF)-5
+id = 185, name = DS(CMF)-6
+id = 186, name = DS(CMF)-7
+id = 187, name = DS(CMF)-8
+id = 188, name = DS(CMF)-1 with crust
+id = 189, name = DS(CMF)-2 with crust
+id = 190, name = DS(CMF)-3 with crust
+id = 191, name = DS(CMF)-4 with crust
+id = 192, name = DS(CMF)-5 with crust
+id = 193, name = DS(CMF)-6 with crust
+id = 194, name = DS(CMF)-7 with crust
+id = 195, name = DS(CMF)-8 with crust
+id = 196, name = OOS(DD2-FRG) with vector interactions(2 flavors)
+id = 197, name = OOS(DD2-FRG) with vector interactions(2+1 flavors)
+id = 198, name = JJ(VQCD(APR)), intermediate
+id = 199, name = JJ(VQCD(APR)), stiff
+id = 200, name = JJ(VQCD(APR)), soft
+id = 201, name = MBB(HSDD2K) with electrons
+id = 202, name = MBB(BHBLphiK) with electrons
+id = 203, name = GPPVA(FSU2R) NS crust
+id = 204, name = GPPVA(NL3wrL55) NS crust
+id = 205, name = SDGTT(QMC-A), no leptons
+id = 206, name = SDGTT(QMC-A) with leptons
+id = 207, name = GPPVA(TM1e) NS crust
+id = 208, name = GPPVA(FSU2H) NS crust
+id = 209, name = GPPVA(FSU2) NS crust
+id = 210, name = GPPVA(DD2) NS crust
+id = 211, name = GPPVA(DDME2) NS crust
+id = 212, name = GPPVA(TW) NS crust
+id = 213, name = GPPVA(FSU2H) NS unified inner crust-core
+id = 214, name = GPPVA(FSU2R) NS unified inner crust-core
+id = 215, name = GPPVA(FSU2) NS unified inner crust-core
+id = 216, name = GMSR(SLy5)
+id = 217, name = GPPVA(DD2) NS unified inner crust-core
+id = 218, name = GPPVA(DDME2) NS unified inner crust-core
+id = 219, name = GPPVA(TW) NS unified inner crust-core
+id = 220, name = GPPVA(NL3wrL55) NS unified inner crust-core
+id = 221, name = GPPVA(TM1e) NS unified inner crust-core
+id = 222, name = GMSR(LNS5)
+id = 223, name = GMSR(BSK16)
+id = 224, name = GMSR(BSK14)
+id = 225, name = GMSR(RATP)
+id = 227, name = GMSR(F0)
+id = 228, name = GMSR(H1)
+id = 229, name = GMSR(H2)
+id = 230, name = GMSR(H3)
+id = 231, name = GMSR(H4)
+id = 232, name = GMSR(H5)
+id = 233, name = GMSR(H7)
+id = 234, name = GMSR(DHSL59)
+id = 235, name = GMSR(DHSL69)
+id = 236, name = KBH(QHC21_AT)
+id = 237, name = KBH(QHC21_BT)
+id = 238, name = KBH(QHC21_CT)
+id = 239, name = KBH(QHC21_DT)
+id = 240, name = KBH(QHC21_A)
+id = 241, name = KBH(QHC21_B)
+id = 242, name = KBH(QHC21_C)
+id = 243, name = KBH(QHC21_D)
+id = 244, name = R(DD2YDelta) 1.1-1.1 (no electrons)
+id = 245, name = R(DD2YDelta) 1.1-1.1 (with electrons)
+id = 246, name = R(DD2YDelta) 1.2-1.1 (with electrons)
+id = 247, name = R(DD2YDelta) 1.2-1.1 (no electrons)
+id = 248, name = R(DD2YDelta) 1.2-1.3 (with electrons)
+id = 249, name = R(DD2YDelta) 1.2-1.3 (no electrons)
+id = 250, name = R(DD2YDelta) 1.1-1.1 (cold NS)
+id = 251, name = R(DD2YDelta) 1.2-1.3 (cold NS)
+id = 252, name = R(DD2YDelta) 1.2-1.1 (cold NS)
+id = 253, name = PCP(BSK24)
+id = 254, name = VGBCMR(D1MStar)
+id = 255, name = VGBCMR(D1M)
+id = 256, name = PCP(BSK22)
+id = 257, name = PCP(BSK25)
+id = 258, name = PCP(BSK26)
+id = 259, name = DS(CMF)-1 Hybrid
+id = 260, name = DS(CMF)-2 Hybrid
+id = 261, name = DS(CMF)-3 Hybrid
+id = 262, name = DS(CMF)-4 Hybrid
+id = 263, name = DS(CMF)-5 Hybrid
+id = 264, name = DS(CMF)-6 Hybrid
+id = 265, name = DS(CMF)-7 Hybrid
+id = 266, name = DS(CMF)-8 Hybrid
+id = 267, name = DS(CMF)-1 Hybrid with crust
+id = 268, name = DS(CMF)-2 Hybrid with crust
+id = 269, name = DS (CMF)-3 Hybrid with crust
+id = 270, name = DS (CMF)-4 Hybrid with crust
+id = 271, name = DS (CMF)-5 Hybrid with crust
+id = 272, name = DS (CMF)-6 Hybrid with crust
+id = 273, name = DS (CMF)-7 Hybrid with crust
+id = 274, name = DS(CMF)-8 Hybrid with crust
+id = 275, name = ABHT(QMC-RMF1)
+id = 276, name = ABHT(QMC-RMF2)
+id = 277, name = ABHT(QMC-RMF3)
+id = 278, name = ABHT(QMC-RMF4)
+id = 279, name = XMLSLZ(DDME2)
+id = 280, name = XMLSLZ(DD-LZ1)
+id = 281, name = XMLSLZ(DDME-X)
+id = 282, name = XMLSLZ(GM1)
+id = 283, name = XMLSLZ(MTVTC)
+id = 284, name = XMLSLZ(NL3)
+id = 285, name = XMLSLZ(PK1)
+id = 286, name = XMLSLZ(PKDD)
+id = 287, name = XMLSLZ(TM1)
+id = 288, name = XMLSLZ(TW99)
+id = 289, name = DEJ(DD2-VQCD) intermediate
+id = 290, name = DEJ(DD2-VQCD) soft
+id = 291, name = DEJ(DD2-VQCD) stiff
+id = 292, name = PCGS(PCSB0)
+id = 293, name = LPB(chiral) with electrons
+id = 294, name = LPB(chiral)
+id = 295, name = PCGS(PCSB1)
+id = 296, name = PCGS(PCSB2)
+id = 297, name = ABHT(QMC-RMF1)
+id = 298, name = ABHT(QMC-RMF2)
+id = 299, name = ABHT(QMC-RMF3)
+id = 300, name = ABHT(QMC-RMF4)
+id = 301, name = PT(GRDF2-DD2) new version
+id = 302, name = PT(GRDF2-DD2) cold NS
+id = 303, name = HJJSTV(VQCD)-intermediate
+id = 304, name = HJJSTV(VQCD) soft
+id = 305, name = HJJSTV(VQCD) stiff
+id = 306, name = BL(chiral) with unified crust
+id = 307, name = CMGO(GDFM-I)
+id = 308, name = CMGO (GDFM-II)
+id = 309, name = KRT(FSU2H*)
+id = 310, name = SPG(M1) unified NS EoS
+id = 311, name = SPG(M1) Crust NS EoS
+id = 312, name = SPG(M2) unified NS EoS
+id = 313, name = SPG(M3) unified NS EoS
+id = 314, name = SPG(M4) unified NS EoS
+id = 315, name = SPG(M5) unified NS EoS
+id = 316, name = SPG(M2) Crust NS EoS
+id = 317, name = SPG(M3) Crust NS EoS
+id = 318, name = SPG(M4) Crust NS EoS
+id = 319, name = SPG(M5) Crust NS EoS
+id = 320, name = TSO(DDLS(30)-Y)
+id = 321, name = TSO(DDLS(70)-Y)
+id = 322, name = TSO(DDLS(70)-N)
+id = 323, name = TSO(DDLS(50)-Y)
+id = 324, name = TSO(DDLS(50)-N)
+id = 325, name = TSO(DDLS(30)-N)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now we pick the EOS with identifier 316 and download it.</span>
+<span class="nb">id</span> <span class="o">=</span> <span class="mi">316</span>
+<span class="n">eosdir</span> <span class="o">=</span> <span class="n">dc</span><span class="o">.</span><span class="n">download_id</span><span class="p">(</span><span class="nb">id</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+downloads\compose\316\eos.t already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.t
+downloads\compose\316\eos.nb already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.nb
+downloads\compose\316\eos.thermo already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.thermo
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We use Compose_eos function to extract the EOS data from the downloaded file</span>
+<span class="kn">from</span> <span class="nn">EOSgenerators</span> <span class="kn">import</span> <span class="n">Compose_eos</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">eosdir</span> <span class="o">=</span> <span class="n">dc</span><span class="o">.</span><span class="n">eos_download_dir</span><span class="p">(</span><span class="nb">id</span><span class="p">)</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span>
+<span class="n">eps</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">Compose_eos</span><span class="o">.</span><span class="n">read_compose</span><span class="p">(</span><span class="n">eosdir</span><span class="o">=</span><span class="n">eosdir</span><span class="p">,</span> <span class="n">eosname</span><span class="o">=</span><span class="n">dc</span><span class="o">.</span><span class="n">eos_name</span><span class="p">(</span><span class="nb">id</span><span class="p">))</span>
+
+<span class="c1"># Finally, we can plot the EOS data using matplotlib,</span>
+<span class="c1"># to ensure that all the previous steps were successful.</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">dpi</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Energy density [$g/cm^3$]&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Pressure [$dyn/cm^2$]&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+         Nucleon masses used within the CompOSE tables :         m_n = 938.90000 MeV and m_p = 938.90000 MeV
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/DownloadCompose_5_1.png" src="_images/DownloadCompose_5_1.png" />
+</div>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2023, Chun Huang, Nicole Obsborn, Nathan Whitsett.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/DownloadCompose.ipynb b/DownloadCompose.ipynb
new file mode 100644
index 0000000..39097d4
--- /dev/null
+++ b/DownloadCompose.ipynb
@@ -0,0 +1,459 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Usage of download_compose Module\n",
+    "Tianzhe Zhou 2024/11/9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[CompOSE](https://compose.obspm.fr/table)(CompStar Online Supernovae Equations of State)\n",
+    "is a database that provides data tables for different state of the art equations of state (EoS) ready for further usage in astrophysical applications, nuclear physics and beyond.\n",
+    "\n",
+    "In this notebook, we will demonstrate how to use the `download_compose` module to download the data from the CompOSE database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DownloadCompose: Fetching data from https://compose.obspm.fr/table \n",
+      "...\n",
+      "DownloadCompose: Find 308 EOS data on website https://compose.obspm.fr/table\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, import the class that we need to use\n",
+    "from EOSgenerators.download_compose import DownloadCompose\n",
+    "\n",
+    "# Then, create an instance of the class\n",
+    "# This will automatically fetch the latest version of all available\n",
+    "# EOS names and their corresponding download links\n",
+    "dc = DownloadCompose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id =   1, name = HS(DD2) neutron matter (no electrons)\n",
+      "id =   2, name = HS(DD2) neutron matter (with electrons)\n",
+      "id =   3, name = HS(FSG) neutron matter (no electrons)\n",
+      "id =   4, name = HS(FSG) neutron matter (with electrons)\n",
+      "id =   5, name = HS(IUF) neutron matter (no electrons)\n",
+      "id =   6, name = HS(IUF) neutron matter (with electrons)\n",
+      "id =   7, name = HS(NL3) neutron matter (no electrons)\n",
+      "id =   8, name = HS(NL3) neutron matter (with electrons)\n",
+      "id =   9, name = HS(TM1) neutron matter  (no electrons)\n",
+      "id =  10, name = HS(TM1) neutron matter (with electrons)\n",
+      "id =  11, name = HS(TMA) neutron matter (no electrons)\n",
+      "id =  12, name = HS(TMA) neutron matter (with electrons)\n",
+      "id =  13, name = SFH(SFHo) neutron matter (no electrons)\n",
+      "id =  14, name = SFH(SFHo) neutron matter (with electrons)\n",
+      "id =  15, name = SFH(SFHx) neutron matter (no electrons)\n",
+      "id =  16, name = SFH(SFHx) neutron matter (with electrons)\n",
+      "id =  17, name = HS(DD2) (no electrons)\n",
+      "id =  18, name = HS(DD2) (with electrons)\n",
+      "id =  19, name = HS(FSG) (no electrons)\n",
+      "id =  20, name = HS(FSG) (with electrons)\n",
+      "id =  21, name = HS(IUF) (no electrons)\n",
+      "id =  22, name = HS(IUF) (with electrons)\n",
+      "id =  23, name = HS(NL3) (no electrons)\n",
+      "id =  24, name = HS(NL3) (with electrons)\n",
+      "id =  25, name = HS(TM1) (no electrons)\n",
+      "id =  26, name = HS(TM1) (with electrons)\n",
+      "id =  27, name = HS(TMA) (no electrons)\n",
+      "id =  28, name = HS(TMA) (with electrons)\n",
+      "id =  29, name = GROM(LS220L) (with Lambdas, no low densities)\n",
+      "id =  30, name = GROM(LS220L) (with Lambdas, with low densities)\n",
+      "id =  31, name = LS(LS220) (no low densities)\n",
+      "id =  32, name = LS(LS220) (with low densities)\n",
+      "id =  33, name = SFH(SFHo) (no electrons)\n",
+      "id =  34, name = SFH(SFHo) (with electrons)\n",
+      "id =  35, name = SFH(SFHx) (no electrons)\n",
+      "id =  36, name = SFH(SFHx) (with electrons)\n",
+      "id =  37, name = SHO(FSU1) (no electrons)\n",
+      "id =  38, name = SHO(FSU1) (with electrons)\n",
+      "id =  39, name = SHO(FSU2) (with electrons)\n",
+      "id =  40, name = SHO(FSU2) (no electrons)\n",
+      "id =  41, name = SHT(NL3) (no electrons)\n",
+      "id =  42, name = SHT(NL3) (with electrons)\n",
+      "id =  43, name = BHB(DD2L) (no electrons)\n",
+      "id =  44, name = BHB(DD2L) (with electrons)\n",
+      "id =  45, name = BHB(DD2Lphi) (with electrons)\n",
+      "id =  46, name = BHB(DD2Lphi) (no electrons)\n",
+      "id =  47, name = SHO(FSU1) (no electrons)\n",
+      "id =  48, name = SHO(FSU1) (with electrons)\n",
+      "id =  49, name = SHO(FSU2) (no electrons)\n",
+      "id =  50, name = SHO(FSU2) (with electrons)\n",
+      "id =  51, name = SHT(NL3) (no electrons)\n",
+      "id =  52, name = SHT(NL3) (with electrons)\n",
+      "id =  53, name = GDTB(DDHdelta)\n",
+      "id =  54, name = GM(GM1)\n",
+      "id =  55, name = OPGR(GM1Y4) (with hyperons)\n",
+      "id =  56, name = STOS(TM1) neutron matter (original version)\n",
+      "id =  57, name = STOS(TM1) neutron matter (new version)\n",
+      "id =  58, name = STOS(TM1L) neutron and Lambda matter\n",
+      "id =  59, name = STOS(TM1) zero temperature (new version)\n",
+      "id =  60, name = STOS(TM1) zero temperature (original version)\n",
+      "id =  61, name = STOS(TM1) with Lambda zero temperature\n",
+      "id =  62, name = STOS(TM1L) with Lambda hyperons\n",
+      "id =  63, name = STOS(TM1) (new version)\n",
+      "id =  64, name = STOS(TM1) (original version)\n",
+      "id =  65, name = OPGR(GM1Y5) (with hyperons)\n",
+      "id =  66, name = OPGR(GM1Y6) (with hyperons)\n",
+      "id =  67, name = OPGR(DDHdeltaY4) (with hyperons)\n",
+      "id =  68, name = APR(APR)\n",
+      "id =  69, name = BBB(BHF-BBB2)\n",
+      "id =  70, name = SFHPST(TM1B145), with electrons\n",
+      "id =  71, name = STOS(TM1) (new version) with electrons\n",
+      "id =  72, name = STOS(TM1L) with Lambda hyperons and electrons\n",
+      "id =  73, name = SFHPST(TM1B139) with electrons\n",
+      "id =  74, name = SFHPST(TM1B155) with electrons\n",
+      "id =  75, name = SFHPST(TM1B165) with electrons (B165)\n",
+      "id =  76, name = IOTSY(TM1Y30) Sigma potential 30 MeV\n",
+      "id =  77, name = IOTSY(TM1Y30pi) with pions, Sigma potential 30 MeV\n",
+      "id =  78, name = IOTSY(TM1Y90pi) with pions, Sigma potential 90 MeV\n",
+      "id =  79, name = IOTSY(TM1Y0pi) with pions, Sigma potential 0 MeV\n",
+      "id =  80, name = IOTSY(TM1Y-30pi) with pions, Sigma potential -30 MeV\n",
+      "id =  81, name = IOTSY(TM1Y90) , Sigma potential 90 MeV)\n",
+      "id =  82, name = IOTSY(TM1Y0), Sigma potential 0 MeV\n",
+      "id =  83, name = IOTSY(TM1Y-30), Sigma potential -30 MeV\n",
+      "id =  84, name = RG(SLY230a)\n",
+      "id =  85, name = RG(SLY2)\n",
+      "id =  86, name = RG(SLY9)\n",
+      "id =  87, name = RG(SKI2)\n",
+      "id =  88, name = RG(SkI3)\n",
+      "id =  89, name = RG(SkI4)\n",
+      "id =  90, name = RG(SkI5)\n",
+      "id =  91, name = RG(SkI6)\n",
+      "id =  92, name = RG(KDE0v1)\n",
+      "id =  93, name = RG(KDE0v)\n",
+      "id =  94, name = RG(SK255)\n",
+      "id =  95, name = RG(SK272)\n",
+      "id =  96, name = RG(SKa)\n",
+      "id =  97, name = RG(SKb)\n",
+      "id =  98, name = RG(SkMp)\n",
+      "id =  99, name = RG(SkOp)\n",
+      "id = 100, name = RG(Rs)\n",
+      "id = 101, name = NL3\n",
+      "id = 104, name = OMHN(DD2Y)\n",
+      "id = 105, name = TNTYST(KOST2) variational EoS (with electrons)\n",
+      "id = 106, name = TNTYST(KOST2) variational EoS (without electrons)\n",
+      "id = 107, name = TNTYST(KOST2) variational EoS at zero temperature (with electrons)\n",
+      "id = 108, name = TNTYST(KOST2) variational EoS at zero temperature (no electrons)\n",
+      "id = 109, name = TNTYST(KOST2) neutron matter (zero temperature)\n",
+      "id = 110, name = TNTYST(KOST2) neutron matter\n",
+      "id = 111, name = FYSS(TM1) (no electrons)\n",
+      "id = 112, name = FTNS(KOST2) variational EoS (no electrons)\n",
+      "id = 113, name = FYSS(TM1) (with electrons)\n",
+      "id = 114, name = FTNS(KOST2) variational EoS (with electrons)\n",
+      "id = 115, name = DNS(CMF) Hadronic (with electrons)\n",
+      "id = 116, name = DNS(CMF) Hadronic (cold neutron stars)\n",
+      "id = 117, name = DNS(CMF) Hadronic (no electrons)\n",
+      "id = 118, name = FOP(SFHoY) (with electrons)\n",
+      "id = 119, name = FOP(SFHoY) (no electrons)\n",
+      "id = 120, name = BL(chiral)\n",
+      "id = 121, name = BL(chiral) with crust\n",
+      "id = 123, name = RG(SLY4)\n",
+      "id = 134, name = RG(SLY4)\n",
+      "id = 136, name = PT(GRDF1_DD2)\n",
+      "id = 137, name = PT(GRDF2_DD2) old version\n",
+      "id = 138, name = DNS(CMF) hadronic  (cold neutron stars) with crust\n",
+      "id = 139, name = BHK(QHC18)\n",
+      "id = 140, name = BFH(QHC19-B)\n",
+      "id = 141, name = SRO(SLy4) SNA version\n",
+      "id = 142, name = OOS(DD2_FRG) (2+1 flavors)\n",
+      "id = 143, name = OOS(DD2_FRG) (2 flavors)\n",
+      "id = 144, name = SNSH(TM1e)\n",
+      "id = 145, name = SNSH(TM1e) zero temperature\n",
+      "id = 146, name = SNSH(TM1e) neutron matter\n",
+      "id = 147, name = RG(SLy4) with neutrino opacities\n",
+      "id = 148, name = RG(SLY4) with neutrino opacities, extended version\n",
+      "id = 149, name = SRO(APR) SNA version\n",
+      "id = 150, name = BFH(QHC19-A)\n",
+      "id = 151, name = BFH(QHC19-C)\n",
+      "id = 152, name = BFH(QHC19-D)\n",
+      "id = 154, name = MBB(DD2K ) (no electrons)\n",
+      "id = 155, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1 without leptons\n",
+      "id = 156, name = BBKF(DD2F-SF) quark-hadron model RDF 1.2 without leptons\n",
+      "id = 157, name = BBKF(DD2F-SF) quark-hadron model RDF 1.3 without leptons\n",
+      "id = 158, name = BBKF(DD2F-SF) quark-hadron model RDF 1.4 without leptons\n",
+      "id = 159, name = BBKF(DD2F-SF) quark-hadron model RDF 1.5 without leptons\n",
+      "id = 160, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6 without leptons\n",
+      "id = 161, name = BBKF(DD2F-SF) quark-hadron model RDF 1.7 without leptons\n",
+      "id = 162, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8 without leptons\n",
+      "id = 163, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9 without leptons\n",
+      "id = 164, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1\n",
+      "id = 165, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.2\n",
+      "id = 166, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.3\n",
+      "id = 167, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.4\n",
+      "id = 168, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.5\n",
+      "id = 169, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6\n",
+      "id = 170, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.7\n",
+      "id = 171, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8\n",
+      "id = 172, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9\n",
+      "id = 173, name = SRO(NRAPR) NSE version\n",
+      "id = 174, name = SRO(SkAPR) SNA version\n",
+      "id = 175, name = SRO(KDE0v1) SNA version\n",
+      "id = 176, name = SRO(LS220*) SNA version\n",
+      "id = 177, name = SRO(LNS) SNA version\n",
+      "id = 178, name = SRO(LS220) SNA version\n",
+      "id = 180, name = DS(CMF)-1\n",
+      "id = 181, name = DS(CMF)-2\n",
+      "id = 182, name = DS(CMF)-3\n",
+      "id = 183, name = DS(CMF)-4\n",
+      "id = 184, name = DS(CMF)-5\n",
+      "id = 185, name = DS(CMF)-6\n",
+      "id = 186, name = DS(CMF)-7\n",
+      "id = 187, name = DS(CMF)-8\n",
+      "id = 188, name = DS(CMF)-1 with crust\n",
+      "id = 189, name = DS(CMF)-2 with crust\n",
+      "id = 190, name = DS(CMF)-3 with crust\n",
+      "id = 191, name = DS(CMF)-4 with crust\n",
+      "id = 192, name = DS(CMF)-5 with crust\n",
+      "id = 193, name = DS(CMF)-6 with crust\n",
+      "id = 194, name = DS(CMF)-7 with crust\n",
+      "id = 195, name = DS(CMF)-8 with crust\n",
+      "id = 196, name = OOS(DD2-FRG) with vector interactions(2 flavors)\n",
+      "id = 197, name = OOS(DD2-FRG) with vector interactions(2+1 flavors)\n",
+      "id = 198, name = JJ(VQCD(APR)), intermediate\n",
+      "id = 199, name = JJ(VQCD(APR)), stiff\n",
+      "id = 200, name = JJ(VQCD(APR)), soft\n",
+      "id = 201, name = MBB(HSDD2K) with electrons\n",
+      "id = 202, name = MBB(BHBLphiK) with electrons\n",
+      "id = 203, name = GPPVA(FSU2R) NS crust\n",
+      "id = 204, name = GPPVA(NL3wrL55) NS crust\n",
+      "id = 205, name = SDGTT(QMC-A), no leptons\n",
+      "id = 206, name = SDGTT(QMC-A) with leptons\n",
+      "id = 207, name = GPPVA(TM1e) NS crust\n",
+      "id = 208, name = GPPVA(FSU2H) NS crust\n",
+      "id = 209, name = GPPVA(FSU2) NS crust\n",
+      "id = 210, name = GPPVA(DD2) NS crust\n",
+      "id = 211, name = GPPVA(DDME2) NS crust\n",
+      "id = 212, name = GPPVA(TW) NS crust\n",
+      "id = 213, name = GPPVA(FSU2H) NS unified inner crust-core\n",
+      "id = 214, name = GPPVA(FSU2R) NS unified inner crust-core\n",
+      "id = 215, name = GPPVA(FSU2) NS unified inner crust-core\n",
+      "id = 216, name = GMSR(SLy5)\n",
+      "id = 217, name = GPPVA(DD2) NS unified inner crust-core\n",
+      "id = 218, name = GPPVA(DDME2) NS unified inner crust-core\n",
+      "id = 219, name = GPPVA(TW) NS unified inner crust-core\n",
+      "id = 220, name = GPPVA(NL3wrL55) NS unified inner crust-core\n",
+      "id = 221, name = GPPVA(TM1e) NS unified inner crust-core\n",
+      "id = 222, name = GMSR(LNS5)\n",
+      "id = 223, name = GMSR(BSK16)\n",
+      "id = 224, name = GMSR(BSK14)\n",
+      "id = 225, name = GMSR(RATP)\n",
+      "id = 227, name = GMSR(F0)\n",
+      "id = 228, name = GMSR(H1)\n",
+      "id = 229, name = GMSR(H2)\n",
+      "id = 230, name = GMSR(H3)\n",
+      "id = 231, name = GMSR(H4)\n",
+      "id = 232, name = GMSR(H5)\n",
+      "id = 233, name = GMSR(H7)\n",
+      "id = 234, name = GMSR(DHSL59)\n",
+      "id = 235, name = GMSR(DHSL69)\n",
+      "id = 236, name = KBH(QHC21_AT)\n",
+      "id = 237, name = KBH(QHC21_BT)\n",
+      "id = 238, name = KBH(QHC21_CT)\n",
+      "id = 239, name = KBH(QHC21_DT)\n",
+      "id = 240, name = KBH(QHC21_A)\n",
+      "id = 241, name = KBH(QHC21_B)\n",
+      "id = 242, name = KBH(QHC21_C)\n",
+      "id = 243, name = KBH(QHC21_D)\n",
+      "id = 244, name = R(DD2YDelta) 1.1-1.1 (no electrons)\n",
+      "id = 245, name = R(DD2YDelta) 1.1-1.1 (with electrons)\n",
+      "id = 246, name = R(DD2YDelta) 1.2-1.1 (with electrons)\n",
+      "id = 247, name = R(DD2YDelta) 1.2-1.1 (no electrons)\n",
+      "id = 248, name = R(DD2YDelta) 1.2-1.3 (with electrons)\n",
+      "id = 249, name = R(DD2YDelta) 1.2-1.3 (no electrons)\n",
+      "id = 250, name = R(DD2YDelta) 1.1-1.1 (cold NS)\n",
+      "id = 251, name = R(DD2YDelta) 1.2-1.3 (cold NS)\n",
+      "id = 252, name = R(DD2YDelta) 1.2-1.1 (cold NS)\n",
+      "id = 253, name = PCP(BSK24)\n",
+      "id = 254, name = VGBCMR(D1MStar)\n",
+      "id = 255, name = VGBCMR(D1M)\n",
+      "id = 256, name = PCP(BSK22)\n",
+      "id = 257, name = PCP(BSK25)\n",
+      "id = 258, name = PCP(BSK26)\n",
+      "id = 259, name = DS(CMF)-1 Hybrid\n",
+      "id = 260, name = DS(CMF)-2 Hybrid\n",
+      "id = 261, name = DS(CMF)-3 Hybrid\n",
+      "id = 262, name = DS(CMF)-4 Hybrid\n",
+      "id = 263, name = DS(CMF)-5 Hybrid\n",
+      "id = 264, name = DS(CMF)-6 Hybrid\n",
+      "id = 265, name = DS(CMF)-7 Hybrid\n",
+      "id = 266, name = DS(CMF)-8 Hybrid\n",
+      "id = 267, name = DS(CMF)-1 Hybrid with crust\n",
+      "id = 268, name = DS(CMF)-2 Hybrid with crust\n",
+      "id = 269, name = DS (CMF)-3 Hybrid with crust\n",
+      "id = 270, name = DS (CMF)-4 Hybrid with crust\n",
+      "id = 271, name = DS (CMF)-5 Hybrid with crust\n",
+      "id = 272, name = DS (CMF)-6 Hybrid with crust\n",
+      "id = 273, name = DS (CMF)-7 Hybrid with crust\n",
+      "id = 274, name = DS(CMF)-8 Hybrid with crust\n",
+      "id = 275, name = ABHT(QMC-RMF1)\n",
+      "id = 276, name = ABHT(QMC-RMF2)\n",
+      "id = 277, name = ABHT(QMC-RMF3)\n",
+      "id = 278, name = ABHT(QMC-RMF4)\n",
+      "id = 279, name = XMLSLZ(DDME2)\n",
+      "id = 280, name = XMLSLZ(DD-LZ1)\n",
+      "id = 281, name = XMLSLZ(DDME-X)\n",
+      "id = 282, name = XMLSLZ(GM1)\n",
+      "id = 283, name = XMLSLZ(MTVTC)\n",
+      "id = 284, name = XMLSLZ(NL3)\n",
+      "id = 285, name = XMLSLZ(PK1)\n",
+      "id = 286, name = XMLSLZ(PKDD)\n",
+      "id = 287, name = XMLSLZ(TM1)\n",
+      "id = 288, name = XMLSLZ(TW99)\n",
+      "id = 289, name = DEJ(DD2-VQCD) intermediate\n",
+      "id = 290, name = DEJ(DD2-VQCD) soft\n",
+      "id = 291, name = DEJ(DD2-VQCD) stiff\n",
+      "id = 292, name = PCGS(PCSB0)\n",
+      "id = 293, name = LPB(chiral) with electrons\n",
+      "id = 294, name = LPB(chiral)\n",
+      "id = 295, name = PCGS(PCSB1)\n",
+      "id = 296, name = PCGS(PCSB2)\n",
+      "id = 297, name = ABHT(QMC-RMF1)\n",
+      "id = 298, name = ABHT(QMC-RMF2)\n",
+      "id = 299, name = ABHT(QMC-RMF3)\n",
+      "id = 300, name = ABHT(QMC-RMF4)\n",
+      "id = 301, name = PT(GRDF2-DD2) new version\n",
+      "id = 302, name = PT(GRDF2-DD2) cold NS\n",
+      "id = 303, name = HJJSTV(VQCD)-intermediate\n",
+      "id = 304, name = HJJSTV(VQCD) soft\n",
+      "id = 305, name = HJJSTV(VQCD) stiff\n",
+      "id = 306, name = BL(chiral) with unified crust\n",
+      "id = 307, name = CMGO(GDFM-I)\n",
+      "id = 308, name = CMGO (GDFM-II)\n",
+      "id = 309, name = KRT(FSU2H*)\n",
+      "id = 310, name = SPG(M1) unified NS EoS\n",
+      "id = 311, name = SPG(M1) Crust NS EoS\n",
+      "id = 312, name = SPG(M2) unified NS EoS\n",
+      "id = 313, name = SPG(M3) unified NS EoS\n",
+      "id = 314, name = SPG(M4) unified NS EoS\n",
+      "id = 315, name = SPG(M5) unified NS EoS\n",
+      "id = 316, name = SPG(M2) Crust NS EoS\n",
+      "id = 317, name = SPG(M3) Crust NS EoS\n",
+      "id = 318, name = SPG(M4) Crust NS EoS\n",
+      "id = 319, name = SPG(M5) Crust NS EoS\n",
+      "id = 320, name = TSO(DDLS(30)-Y)\n",
+      "id = 321, name = TSO(DDLS(70)-Y)\n",
+      "id = 322, name = TSO(DDLS(70)-N)\n",
+      "id = 323, name = TSO(DDLS(50)-Y)\n",
+      "id = 324, name = TSO(DDLS(50)-N)\n",
+      "id = 325, name = TSO(DDLS(30)-N)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now, we can print the list of available EOS names.\n",
+    "# Each EOS name is related to a specific identifier,\n",
+    "# which we can use later to actually download the EOS data file.\n",
+    "dc.print_eos_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloads\\compose\\316\\eos.t already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.t\n",
+      "downloads\\compose\\316\\eos.nb already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.nb\n",
+      "downloads\\compose\\316\\eos.thermo already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.thermo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now we pick the EOS with identifier 316 and download it.\n",
+    "id = 316\n",
+    "eosdir = dc.download_id(id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t Nucleon masses used within the CompOSE tables :\t m_n = 938.90000 MeV and m_p = 938.90000 MeV\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We use Compose_eos function to extract the EOS data from the downloaded file\n",
+    "from EOSgenerators import Compose_eos\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "eosdir = dc.eos_download_dir(id).as_posix()\n",
+    "eps, p, name = Compose_eos.read_compose(eosdir=eosdir, eosname=dc.eos_name(id))\n",
+    "\n",
+    "# Finally, we can plot the EOS data using matplotlib,\n",
+    "# to ensure that all the previous steps were successful.\n",
+    "plt.figure(dpi=100)\n",
+    "plt.plot(eps, p)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"Energy density [$g/cm^3$]\")\n",
+    "plt.ylabel(\"Pressure [$dyn/cm^2$]\")\n",
+    "plt.title(name)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+  <section id="eos-solver">
+<span id="eos"></span><h1>EOS solver<a class="headerlink" href="#eos-solver" title="Link to this heading"></a></h1>
+<p>Functions to compute multiple Equation of state from given parameters, like polytrope, speed of sound, RMF etc.</p>
+</section>
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+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Import Equation of State</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.EoS_import.EOS_check"><code class="docutils literal notranslate"><span class="pre">EOS_check()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.EoS_import.EOS_import"><code class="docutils literal notranslate"><span class="pre">EOS_import()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.EoS_import.file_read"><code class="docutils literal notranslate"><span class="pre">file_read()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
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+  <section id="import-equation-of-state">
+<span id="eos-importing"></span><h1>Import Equation of State<a class="headerlink" href="#import-equation-of-state" title="Link to this heading"></a></h1>
+<p>Function to import equation of state given by user.</p>
+<dl class="py function" id="module-TOVsolver.EoS_import">
+<dt class="sig sig-object py" id="TOVsolver.EoS_import.EOS_check">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.EoS_import.</span></span><span class="sig-name descname"><span class="pre">EOS_check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/EoS_import.html#EOS_check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.EoS_import.EOS_check" title="Link to this definition"></a></dt>
+<dd><p>file_read</p>
+<p>Checks that the derivative (drho/dp) is positive.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>density</strong> (<em>array</em>) – numpy 1Darray. Density array to be checked.</p></li>
+<li><p><strong>pressure</strong> (<em>array</em>) – numpy 1Darray. Pressure array to be checked.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>two arrays, one corresponding to density and one corresponding to pressure or ends the function and prints
+invalid equation of state.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.EoS_import.EOS_import">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.EoS_import.</span></span><span class="sig-name descname"><span class="pre">EOS_import</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/EoS_import.html#EOS_import"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.EoS_import.EOS_import" title="Link to this definition"></a></dt>
+<dd><p>Imports density and pressure from csv or array, checks them, and returns them.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file_name</strong> (<em>string</em><em>, </em><em>optional</em>) – string. CSV file to be opened.</p></li>
+<li><p><strong>density</strong> (<em>array</em><em>, </em><em>optional</em>) – numpy 1Darray. Passed into a check function and returned if valid.</p></li>
+<li><p><strong>pressure</strong> (<em>array</em><em>, </em><em>optional</em>) – numpy 1Darray. Passed into a check function and returned if valid.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>checked density and pressure.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.EoS_import.file_read">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.EoS_import.</span></span><span class="sig-name descname"><span class="pre">file_read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_file</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/EoS_import.html#file_read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.EoS_import.file_read" title="Link to this definition"></a></dt>
+<dd><p>Reads a csv file of denisty and pressure given by the user.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input_file</strong> (<em>string</em>) – string. File to be opened and parsed.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>two 1Darray numpy arrays, one corresponding to density and one corresponding to pressrure.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Likelihood function definition module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.Likelihood.Jliklihood"><code class="docutils literal notranslate"><span class="pre">Jliklihood()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.Likelihood.Kliklihood"><code class="docutils literal notranslate"><span class="pre">Kliklihood()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.Likelihood.Lliklihood"><code class="docutils literal notranslate"><span class="pre">Lliklihood()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.Likelihood.MRlikihood_Gaussian"><code class="docutils literal notranslate"><span class="pre">MRlikihood_Gaussian()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.Likelihood.MRlikihood_kernel"><code class="docutils literal notranslate"><span class="pre">MRlikihood_kernel()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.Likelihood.Masslikihood_Gaussian"><code class="docutils literal notranslate"><span class="pre">Masslikihood_Gaussian()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.Likelihood.TidalLikihood_kernel"><code class="docutils literal notranslate"><span class="pre">TidalLikihood_kernel()</span></code></a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+  <section id="likelihood-function-definition-module">
+<span id="likelihood-function"></span><h1>Likelihood function definition module<a class="headerlink" href="#likelihood-function-definition-module" title="Link to this heading"></a></h1>
+<p>Functions to define Likelihood functions from Astro observation and nuclear experiments</p>
+<dl class="py function" id="module-InferenceWorkflow.Likelihood">
+<dt class="sig sig-object py" id="InferenceWorkflow.Likelihood.Jliklihood">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.Likelihood.</span></span><span class="sig-name descname"><span class="pre">Jliklihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">J_low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">J_up</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/Likelihood.html#Jliklihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.Likelihood.Jliklihood" title="Link to this definition"></a></dt>
+<dd><p>Computing likelihood from a hard cut constraint of J.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>theta</strong> (<em>array</em>) – An array representing the parameters used to determine a RMF model in the</p></li>
+<li><p><strong>case</strong> (<em>Lagrangian. In this</em>)</p></li>
+<li><p><strong>parameters.</strong> (<em>the RMF model is defined by 7</em>)</p></li>
+<li><p><strong>K_low</strong> (<em>float</em>) – lower bound of this J constraint.</p></li>
+<li><p><strong>K_up</strong> (<em>float</em>) – upper bound of this J constraint.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>likelihood feed back for this given paramter set-up.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>likelihood (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.Likelihood.Kliklihood">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.Likelihood.</span></span><span class="sig-name descname"><span class="pre">Kliklihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">K_low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">K_up</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/Likelihood.html#Kliklihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.Likelihood.Kliklihood" title="Link to this definition"></a></dt>
+<dd><p>Computing likelihood from a hard cut constraint of K.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>theta</strong> (<em>array</em>) – An array representing the parameters used to determine a RMF model in the</p></li>
+<li><p><strong>case</strong> (<em>Lagrangian. In this</em>)</p></li>
+<li><p><strong>parameters.</strong> (<em>the RMF model is defined by 7</em>)</p></li>
+<li><p><strong>K_low</strong> (<em>float</em>) – lower bound of this K constraint.</p></li>
+<li><p><strong>K_up</strong> (<em>float</em>) – upper bound of this K constraint.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>likelihood feed back for this given paramter set-up.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>likelihood (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.Likelihood.Lliklihood">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.Likelihood.</span></span><span class="sig-name descname"><span class="pre">Lliklihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L_low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L_up</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/Likelihood.html#Lliklihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.Likelihood.Lliklihood" title="Link to this definition"></a></dt>
+<dd><p>Computing likelihood from a hard cut constraint of L.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>theta</strong> (<em>array</em>) – An array representing the parameters used to determine a RMF model in the</p></li>
+<li><p><strong>case</strong> (<em>Lagrangian. In this</em>)</p></li>
+<li><p><strong>parameters.</strong> (<em>the RMF model is defined by 7</em>)</p></li>
+<li><p><strong>K_low</strong> (<em>float</em>) – lower bound of this L constraint.</p></li>
+<li><p><strong>K_up</strong> (<em>float</em>) – upper bound of this L constraint.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>likelihood feed back for this given paramter set-up.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>likelihood (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.Likelihood.MRlikihood_Gaussian">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.Likelihood.</span></span><span class="sig-name descname"><span class="pre">MRlikihood_Gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pres_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/Likelihood.html#MRlikihood_Gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.Likelihood.MRlikihood_Gaussian" title="Link to this definition"></a></dt>
+<dd><p>Computing likelihood from a simulation gaussian distribution of MR measurement</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eps_total</strong> (<em>array</em>) – the energy density of full EoS in MeV/fm3, times a G/c**2 factor</p></li>
+<li><p><strong>pres_total</strong> (<em>array</em>) – the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</p></li>
+<li><p><strong>x</strong> (<em>float array</em>) – [Mvalue, Rvalue, Mwidth, Rwidth], Mvalue is the Mass center value of this</p></li>
+<li><p><strong>measurement</strong> (<em>this Mass</em>)</p></li>
+<li><p><strong>it</strong> (<em>Rvalue is the Radius center of</em>)</p></li>
+<li><p><strong>of</strong> (<em>Mwidth is the 1-sigma width</em>)</p></li>
+<li><p><strong>measurement</strong></p></li>
+<li><p><strong>measurement.</strong> (<em>Rwidth is the 1-sigma width</em><em> of </em><em>this radius</em>)</p></li>
+<li><p><strong>d1</strong> (<em>float</em>) – the sampled density of this measurement</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>likelihood feed back for this given paramter set-up.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>likelihood (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.Likelihood.MRlikihood_kernel">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.Likelihood.</span></span><span class="sig-name descname"><span class="pre">MRlikihood_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pres_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/Likelihood.html#MRlikihood_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.Likelihood.MRlikihood_kernel" title="Link to this definition"></a></dt>
+<dd><p>Computing likelihood from a distribution of MR measurement</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eps_total</strong> (<em>array</em>) – the energy density of full EoS in MeV/fm3, times a G/c**2 factor</p></li>
+<li><p><strong>pres_total</strong> (<em>array</em>) – the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</p></li>
+<li><p><strong>x</strong> (<em>kde.kernel</em>) – the distribution kernel of MR measurement.</p></li>
+<li><p><strong>d1</strong> (<em>float</em>) – the sampled density of this measurement</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>likelihood feed back for this given paramter set-up.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>likelihood (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.Likelihood.Masslikihood_Gaussian">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.Likelihood.</span></span><span class="sig-name descname"><span class="pre">Masslikihood_Gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pres_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/Likelihood.html#Masslikihood_Gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.Likelihood.Masslikihood_Gaussian" title="Link to this definition"></a></dt>
+<dd><p>Computing likelihood from a simulation gaussian distribution of Mass measurement</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eps_total</strong> (<em>array</em>) – the energy density of full EoS in MeV/fm3, times a G/c**2 factor</p></li>
+<li><p><strong>pres_total</strong> (<em>array</em>) – the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</p></li>
+<li><p><strong>x</strong> (<em>float array</em>) – [Mvalue, Mwidth], Mvalue is the Mass center value of this</p></li>
+<li><p><strong>measurement</strong> (<em>simulated</em>)</p></li>
+<li><p><strong>measurement.</strong> (<em>Mwidth is the 1-sigma width</em><em> of </em><em>this Mass</em>)</p></li>
+<li><p><strong>d1</strong> (<em>float</em>) – the sampled density of this measurement</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>likelihood feed back for this given paramter set-up.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>likelihood (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.Likelihood.TidalLikihood_kernel">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.Likelihood.</span></span><span class="sig-name descname"><span class="pre">TidalLikihood_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pres_total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/Likelihood.html#TidalLikihood_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.Likelihood.TidalLikihood_kernel" title="Link to this definition"></a></dt>
+<dd><p>Computing likelihood from a distribution of Gravitational wave measurement</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eps_total</strong> (<em>array</em>) – the energy density of full EoS in MeV/fm3, times a G/c**2 factor</p></li>
+<li><p><strong>pres_total</strong> (<em>array</em>) – the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</p></li>
+<li><p><strong>x</strong> (<em>kde.kernel</em>) – containing kernelGW and chirp, kernelGW is the distribution kde.kernel</p></li>
+<li><p><strong>measurement</strong> (<em>sampled from full GW</em>)</p></li>
+<li><p><strong>mass</strong> (<em>in</em><em> [</em><em>chrip</em>)</p></li>
+<li><p><strong>M2/M1</strong></p></li>
+<li><p><strong>M1</strong> (<em>tidal of</em>)</p></li>
+<li><p><strong>sequence.</strong> (<em>tidal</em><em> of </em><em>M2</em><em>]</em>)</p></li>
+<li><p><strong>solely.</strong> (<em>chrip mass is the sampling from chrip mass term in GW events</em>)</p></li>
+<li><p><strong>d1</strong> (<em>float</em>) – the sampled density of this measurement</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>likelihood feed back for this given paramter set-up.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>likelihood (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
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+             
+  <section id="rmf-eos-solver">
+<span id="rmf-eos"></span><h1>RMF EOS solver<a class="headerlink" href="#rmf-eos-solver" title="Link to this heading"></a></h1>
+<p>Functions to compute RMF Equation of state from given parameters.</p>
+<dl class="py function" id="module-EOSgenerators.RMF_EOS">
+<dt class="sig sig-object py" id="EOSgenerators.RMF_EOS.Energy_density_Pressure">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.RMF_EOS.</span></span><span class="sig-name descname"><span class="pre">Energy_density_Pressure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/RMF_EOS.html#Energy_density_Pressure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.RMF_EOS.Energy_density_Pressure" title="Link to this definition"></a></dt>
+<dd><p>Generate pressure and energy density two EOS ingredient from given RMF term and given parameters,</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array</em>) – An array that consists of the initial values of sigma, omega, rho, and chemical</p></li>
+<li><p><strong>function.</strong> (<em>potential obtained from the initial_values</em>)</p></li>
+<li><p><strong>rho</strong> (<em>float</em>) – The central density from which the computation of the equation of state begins.</p></li>
+<li><p><strong>theta</strong> (<em>array</em>) – An array representing the parameters used to determine a RMF model in the</p></li>
+<li><p><strong>case</strong> (<em>Lagrangian. In this</em>)</p></li>
+<li><p><strong>parameters.</strong> (<em>the RMF model is defined by 10</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>EOS ingredient, energy density in g/cm3
+pressure (float): EOS ingredient, pressure in dyn/cm3</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>energy_density (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="EOSgenerators.RMF_EOS.compute_EOS">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.RMF_EOS.</span></span><span class="sig-name descname"><span class="pre">compute_EOS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps_crust</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pres_crust</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/RMF_EOS.html#compute_EOS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.RMF_EOS.compute_EOS" title="Link to this definition"></a></dt>
+<dd><p>Generate core part equation of state, main function, from RMF model,</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eps_crust</strong> (<em>array</em>) – the energy density of crust EoS in MeV/fm3, times a G/c**2 factor</p></li>
+<li><p><strong>pres_crust</strong> (<em>array</em>) – the pressure from crust EoS model in MeV/fm3, times a G/c**4 factor</p></li>
+<li><p><strong>theta</strong> (<em>array</em>) – An array representing the parameters used to determine a RMF model in the</p></li>
+<li><p><strong>case</strong> (<em>Lagrangian. In this</em>)</p></li>
+<li><p><strong>parameters.</strong> (<em>the RMF model is defined by 10</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>EOS ingredient, energy density in g/cm3
+pressure (float): EOS ingredient, pressure in dyn/cm3</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>energy_density (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="EOSgenerators.RMF_EOS.functie">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.RMF_EOS.</span></span><span class="sig-name descname"><span class="pre">functie</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/RMF_EOS.html#functie"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.RMF_EOS.functie" title="Link to this definition"></a></dt>
+<dd><p>iterate the the sigma, omega, rho term and chemical potential of electron and neutron at
+any given density</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array</em>) – initial sigma omega rho and chemical potential from initial_values function</p></li>
+<li><p><strong>args</strong> (<em>array</em>) – paramters of determine a RMF model in lagrangian, here we have 10 parameters.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>sigma term in lagrangian
+omega (float): omega term in lagrangian
+rho_03 (float): rho term in lagrangian
+mu_n (float): chemical potential of neutron matter
+mu_e (float): chemical potential of electron portion</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>sigma (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="EOSgenerators.RMF_EOS.initial_values">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.RMF_EOS.</span></span><span class="sig-name descname"><span class="pre">initial_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rho</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/RMF_EOS.html#initial_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.RMF_EOS.initial_values" title="Link to this definition"></a></dt>
+<dd><p>Outputs the the sigma, omega, rho term and chemical potential of electron and neutron at
+given initial density</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rho</strong> (<em>float</em>) – given nuclear density</p></li>
+<li><p><strong>theta</strong> (<em>array</em>) – paramters of determine a RMF model in lagrangian, here we have 10 parameters.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>sigma term in lagrangian
+omega (float): omega term in lagrangian
+rho_03 (float): rho term in lagrangian
+mu_n (float): chemical potential of neutron matter
+mu_e (float): chemical potential of electron portion</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>sigma (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
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+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Solve TOV Equation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.solver_code.TOV_def"><code class="docutils literal notranslate"><span class="pre">TOV_def()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.solver_code.m1_from_mc_m2"><code class="docutils literal notranslate"><span class="pre">m1_from_mc_m2()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.solver_code.solveTOV"><code class="docutils literal notranslate"><span class="pre">solveTOV()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.solver_code.solveTOV_tidal"><code class="docutils literal notranslate"><span class="pre">solveTOV_tidal()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.solver_code.tidal_deformability"><code class="docutils literal notranslate"><span class="pre">tidal_deformability()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
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+</ul>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+             
+  <section id="solve-tov-equation">
+<span id="tov-solving"></span><h1>Solve TOV Equation<a class="headerlink" href="#solve-tov-equation" title="Link to this heading"></a></h1>
+<p>Function to solve the TOV equation from EOS equation of state given by user.</p>
+<dl class="py function" id="module-TOVsolver.solver_code">
+<dt class="sig sig-object py" id="TOVsolver.solver_code.TOV_def">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.solver_code.</span></span><span class="sig-name descname"><span class="pre">TOV_def</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">r</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inveos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ad_index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/solver_code.html#TOV_def"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.solver_code.TOV_def" title="Link to this definition"></a></dt>
+<dd><p>a function that packing the whole TOV equations set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>r</strong> (<em>float</em>) – raius as integrate varible</p></li>
+<li><p><strong>y</strong> (<em>psudo-varible</em>) – containing pressure, mass, h and b as intergarte varibles</p></li>
+<li><p><strong>equation</strong> (<em>to solve out the TOV</em>)</p></li>
+<li><p><strong>inveos</strong> – the invert of the eos, pressure and energy density relation to integrate</p></li>
+<li><p><strong>interpolate.</strong> (<em>and</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The array that contains all the Stars’ masses, in M_sun as a
+Units.
+Radius (array): The array that contains all the Stars’s radius, in km.
+Tidal Deformability (array): The array that contains correpsonding Tidal property,
+These are dimension-less.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Mass (array)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.solver_code.m1_from_mc_m2">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.solver_code.</span></span><span class="sig-name descname"><span class="pre">m1_from_mc_m2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/solver_code.html#m1_from_mc_m2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.solver_code.m1_from_mc_m2" title="Link to this definition"></a></dt>
+<dd><p>a function that feed back the companion star mass from GW event measurement.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mc</strong> (<em>float</em>) – chrip mass of a GW event, unit in solar mass.</p></li>
+<li><p><strong>m2</strong> (<em>float</em><em> or </em><em>numpy array</em>) – the determined mass for one of the star, this</p></li>
+<li><p><strong>EoS.</strong> (<em>is computed from sampling of</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the companion star mass in solar mass.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>m1 (float or numpy array)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.solver_code.solveTOV">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.solver_code.</span></span><span class="sig-name descname"><span class="pre">solveTOV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center_rho</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">energy_density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/solver_code.html#solveTOV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.solver_code.solveTOV" title="Link to this definition"></a></dt>
+<dd><p>Solve TOV equation from given Equation of state in the neutron star
+core density range</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>center_rho</strong> (<em>array</em>) – This is the energy density here is fixed in main</p></li>
+<li><p><strong>np.logspace</strong> (<em>that is</em>)</p></li>
+<li><p><strong>energy_density</strong> (<em>array</em>) – Desity array of the neutron star EoS, in MeV/fm^{-3}</p></li>
+<li><p><strong>simiplicity</strong> (<em>Notice here for</em>)</p></li>
+<li><p><strong>magnitude</strong> (<em>we omitted G/c**4</em>)</p></li>
+<li><p><strong>so</strong></p></li>
+<li><p><strong>MeV/fm^{-3}</strong><strong>)</strong><strong>*G/c**4</strong> (<em>(</em><em>value in</em>)</p></li>
+<li><p><strong>are</strong> (<em>could convert to the energy density we</em>)</p></li>
+<li><p><strong>using</strong></p></li>
+<li><p><strong>magnitude.</strong> (<em>please check the Test_EOS.csv to double check the order of</em>)</p></li>
+<li><p><strong>pressure</strong> (<em>array</em>) – Pressure array of neutron star EoS, also in nautral unit</p></li>
+<li><p><strong>MeV/fm^{-3}</strong> (<em>with</em>)</p></li>
+<li><p><strong>Test_EOS.csv</strong> (<em>still please check the</em>)</p></li>
+<li><p><strong>is</strong> (<em>the conversion</em>)</p></li>
+<li><p><strong>dyn/cm3</strong><strong>)</strong><strong>*G/c**4.</strong> (<em>(</em><em>value in</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The array that contains all the Stars’ masses, in M_sun as a
+Units.
+Radius (array): The array that contains all the Stars’s radius, in km.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Mass (array)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.solver_code.solveTOV_tidal">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.solver_code.</span></span><span class="sig-name descname"><span class="pre">solveTOV_tidal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center_rho</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">energy_density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/solver_code.html#solveTOV_tidal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.solver_code.solveTOV_tidal" title="Link to this definition"></a></dt>
+<dd><p>Solve TOV equation from given Equation of state in the neutron star
+core density range</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>center_rho</strong> (<em>array</em>) – This is the energy density here is fixed in main</p></li>
+<li><p><strong>np.logspace</strong> (<em>that is</em>)</p></li>
+<li><p><strong>energy_density</strong> (<em>array</em>) – Desity array of the neutron star EoS, in MeV/fm^{-3}</p></li>
+<li><p><strong>simiplicity</strong> (<em>Notice here for</em>)</p></li>
+<li><p><strong>magnitude</strong> (<em>we omitted G/c**4</em>)</p></li>
+<li><p><strong>so</strong></p></li>
+<li><p><strong>MeV/fm^{-3}</strong><strong>)</strong><strong>*G/c**4</strong> (<em>(</em><em>value in</em>)</p></li>
+<li><p><strong>are</strong> (<em>could convert to the energy density we</em>)</p></li>
+<li><p><strong>using</strong></p></li>
+<li><p><strong>magnitude.</strong> (<em>please check the Test_EOS.csv to double check the order of</em>)</p></li>
+<li><p><strong>pressure</strong> (<em>array</em>) – Pressure array of neutron star EoS, also in nautral unit</p></li>
+<li><p><strong>MeV/fm^{-3}</strong> (<em>with</em>)</p></li>
+<li><p><strong>Test_EOS.csv</strong> (<em>still please check the</em>)</p></li>
+<li><p><strong>is</strong> (<em>the conversion</em>)</p></li>
+<li><p><strong>dyn/cm3</strong><strong>)</strong><strong>*G/c**4.</strong> (<em>(</em><em>value in</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The array that contains all the Stars’ masses, in M_sun as a
+Units.
+Radius (array): The array that contains all the Stars’s radius, in km.
+Tidal Deformability (array): The array that contains correpsonding Tidal property,
+These are dimension-less.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Mass (array)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.solver_code.tidal_deformability">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.solver_code.</span></span><span class="sig-name descname"><span class="pre">tidal_deformability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Rns</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/solver_code.html#tidal_deformability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.solver_code.tidal_deformability" title="Link to this definition"></a></dt>
+<dd><p>Compute Tidal deformability from y2, neutron star mass and raius</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y2</strong> (<em>array</em>) – midiate varrible that computing tidal</p></li>
+<li><p><strong>Mns</strong> (<em>array</em>) – neutron star mass in g/cm3</p></li>
+<li><p><strong>Rns</strong> (<em>array</em>) – neutron star radius in cm.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>neutron star tidal deformability with unit-less.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tidal_def (array)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
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+  <section id="Unit-Convention-for-Developer">
+<h1>Unit Convention for Developer<a class="headerlink" href="#Unit-Convention-for-Developer" title="Link to this heading"></a></h1>
+<p>Tianzhe Zhou 2024/11/1</p>
+<p>To set up a solid foundation for the development of the project, we need to establish a unit convention for the project. The unit convention is a set of rules that define the units of the physical quantities used in the project. The unit convention is important because it ensures that all developers use the same units for the same physical quantities, which helps to avoid errors and inconsistencies throughout the whole project.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fm</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">hbarc</span> <span class="o">=</span> <span class="mf">0.197327053</span>
+<span class="n">c</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># speed of light</span>
+<span class="n">hbar</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># reduced Planck constant</span>
+
+<span class="n">GeV</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">hbarc</span>  <span class="c1"># Giga-electronvolt</span>
+<span class="n">MeV</span> <span class="o">=</span> <span class="mf">1e-3</span> <span class="o">*</span> <span class="n">GeV</span>  <span class="c1"># Mega-electronvolt</span>
+
+<span class="n">g</span> <span class="o">=</span> <span class="mf">5.625e26</span> <span class="o">*</span> <span class="n">MeV</span>  <span class="c1"># gram</span>
+<span class="n">kg</span> <span class="o">=</span> <span class="mf">1e3</span> <span class="o">*</span> <span class="n">g</span>  <span class="c1"># kilogram</span>
+<span class="n">cm</span> <span class="o">=</span> <span class="mf">1e13</span> <span class="o">*</span> <span class="n">fm</span>  <span class="c1"># centimeter</span>
+<span class="n">m</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">cm</span>  <span class="c1"># meter</span>
+<span class="n">km</span> <span class="o">=</span> <span class="mf">1e5</span> <span class="o">*</span> <span class="n">cm</span>  <span class="c1"># kilometer</span>
+<span class="n">s</span> <span class="o">=</span> <span class="mf">3e10</span> <span class="o">*</span> <span class="n">cm</span>  <span class="c1"># second</span>
+
+<span class="n">dyn</span> <span class="o">=</span> <span class="n">g</span> <span class="o">*</span> <span class="n">cm</span> <span class="o">/</span> <span class="n">s</span><span class="o">**</span><span class="mi">2</span>  <span class="c1"># dyne</span>
+<span class="n">dyn_cm_2</span> <span class="o">=</span> <span class="n">dyn</span> <span class="o">/</span> <span class="n">cm</span><span class="o">**</span><span class="mi">2</span>  <span class="c1"># dyne / cm^2</span>
+<span class="n">g_cm_3</span> <span class="o">=</span> <span class="n">g</span> <span class="o">/</span> <span class="n">cm</span><span class="o">**</span><span class="mi">3</span>  <span class="c1"># gram / cm^3</span>
+<span class="n">erg</span> <span class="o">=</span> <span class="n">dyn</span> <span class="o">*</span> <span class="n">cm</span>  <span class="c1"># ἐργον energy unit</span>
+
+<span class="n">m_n</span> <span class="o">=</span> <span class="mf">939.565</span> <span class="o">*</span> <span class="n">MeV</span>  <span class="c1"># mass of neutron</span>
+<span class="n">n0</span> <span class="o">=</span> <span class="mf">0.16</span> <span class="o">/</span> <span class="n">fm</span><span class="o">**</span><span class="mi">3</span>  <span class="c1"># saturation density</span>
+
+<span class="n">e0</span> <span class="o">=</span> <span class="n">m_n</span> <span class="o">*</span> <span class="n">n0</span>  <span class="c1"># saturation energy density</span>
+<span class="n">G</span> <span class="o">=</span> <span class="mf">6.6743e-8</span> <span class="o">*</span> <span class="n">dyn</span> <span class="o">*</span> <span class="n">cm</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">g</span><span class="o">**</span><span class="mi">2</span>  <span class="c1"># gravitational constant</span>
+<span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span> <span class="o">*</span> <span class="n">g</span>  <span class="c1"># mass of sun</span>
+<br/></pre></div>
+</div>
+</div>
+<p>The first 4 lines come from a widely used unit convention in High Energy Physics (HEP), which is called <a class="reference external" href="https://en.wikipedia.org/wiki/Natural_units">Natural Unit System</a>. You should be awared that we will NEVER using the Natural Unit System explicity, as long as you obey the following rules. 1. Whenever you define a <a class="reference external" href="https://www.ituonline.com/tech-definitions/what-is-literal-programming/">literal</a> physical quantity, you should always <em>multiply</em> the unit of that quantity.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># example of rule 1:</span>
+<span class="n">m_n</span> <span class="o">=</span> <span class="mf">939.565</span> <span class="o">*</span> <span class="n">MeV</span>  <span class="c1"># GOOD</span>
+<span class="n">m_n</span> <span class="o">=</span> <span class="mf">939.565</span>        <span class="c1"># BAD</span>
+<span class="n">m_n</span> <span class="o">=</span> <span class="mf">1.67e-27</span> <span class="o">*</span> <span class="n">kg</span>  <span class="c1"># GOOD</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="2">
+<li><p>Whenever you want a physical quantity in cgs unit(or others unit system), you should always <em>divide</em> the corresponding unit.</p></li>
+</ol>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># example of rule 2:</span>
+<span class="n">m_n</span> <span class="o">=</span> <span class="mf">939.565</span> <span class="o">*</span> <span class="n">MeV</span>  <span class="c1"># mass of neutron</span>
+<span class="c1"># if I want to know m_n in terms of gram, I need to</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">m_n</span> <span class="o">/</span> <span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+1.670337777777778e-24
+</pre></div></div>
+</div>
+<ol class="arabic simple" start="3">
+<li><p>When you need to plot, <em>devide</em> the unit of your axis. See the example below.</p></li>
+</ol>
+<ol class="arabic simple" start="4">
+<li><p>When you use physic formula, you leave the whole form as it was, no additional constants.</p></li>
+</ol>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># example of rule 4:</span>
+<span class="n">E</span> <span class="o">=</span> <span class="n">m</span> <span class="o">*</span> <span class="n">c</span><span class="o">**</span><span class="mi">2</span>     <span class="c1"># GOOD</span>
+<span class="n">F</span> <span class="o">=</span> <span class="n">G</span><span class="o">*</span><span class="n">M</span><span class="o">*</span><span class="n">m</span> <span class="o">/</span> <span class="n">r</span><span class="o">**</span><span class="mi">2</span> <span class="c1"># GOOD</span>
+</pre></div>
+</div>
+<p>Now we present a full procedure of how to use the unit convention in calculating MR relation. See <a class="reference external" href="https://arxiv.org/pdf/astro-ph/0506417v1">https://arxiv.org/pdf/astro-ph/0506417v1</a> Page 15 for more details.</p>
+<img alt="image.png" 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" />
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">pi</span>
+
+<span class="n">K_nrel</span> <span class="o">=</span> <span class="n">hbar</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mi">15</span> <span class="o">*</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="n">m_n</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="n">m_n</span> <span class="o">*</span> <span class="n">c</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span> <span class="o">**</span> <span class="p">(</span><span class="mi">5</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># Rule 1</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">K_nrel</span> <span class="o">/</span> <span class="p">(</span><span class="n">cm</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">erg</span> <span class="o">**</span> <span class="p">(</span><span class="mi">2</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)))</span>  <span class="c1"># Rule 2</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+6.433772787333139e-26
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">EOS_P</span><span class="p">(</span><span class="n">e</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">K_nrel</span> <span class="o">*</span> <span class="n">e</span> <span class="o">**</span> <span class="p">(</span><span class="mi">5</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># Rule 4</span>
+
+
+<span class="n">e_min</span> <span class="o">=</span> <span class="mf">1e4</span> <span class="o">*</span> <span class="n">g_cm_3</span>  <span class="c1"># Rule 1</span>
+<span class="n">e_max</span> <span class="o">=</span> <span class="mf">1e18</span> <span class="o">*</span> <span class="n">g_cm_3</span>  <span class="c1"># Rule 1</span>
+<span class="n">e_grid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">geomspace</span><span class="p">(</span><span class="n">e_min</span><span class="p">,</span> <span class="n">e_max</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
+<span class="n">p_grid</span> <span class="o">=</span> <span class="n">EOS_P</span><span class="p">(</span><span class="n">e_grid</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">EofP</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">PofE</span><span class="p">(</span><span class="n">E</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">E</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">ode</span>
+
+
+<span class="c1"># working with rescaled quantities</span>
+<span class="k">def</span> <span class="nf">TOV1</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">grids</span><span class="p">):</span>
+    <span class="n">p</span><span class="p">,</span> <span class="n">m</span> <span class="o">=</span> <span class="n">y</span>
+    <span class="n">e_grid</span> <span class="o">=</span> <span class="n">grids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">p_grid</span> <span class="o">=</span> <span class="n">grids</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">e</span> <span class="o">=</span> <span class="n">EofP</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">)</span>
+
+    <span class="n">dpdr</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">e</span> <span class="o">+</span> <span class="n">p</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">m</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">pi</span> <span class="o">*</span> <span class="n">r</span><span class="o">**</span><span class="mi">3</span> <span class="o">*</span> <span class="n">p</span><span class="p">)</span>  <span class="c1"># Rule 4</span>
+    <span class="n">dpdr</span> <span class="o">=</span> <span class="n">dpdr</span> <span class="o">/</span> <span class="p">(</span><span class="n">r</span> <span class="o">*</span> <span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">m</span><span class="p">))</span>  <span class="c1"># Rule 4</span>
+    <span class="n">dmdr</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">pi</span> <span class="o">*</span> <span class="n">r</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="n">e</span>  <span class="c1"># Rule 4</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">dpdr</span><span class="p">,</span> <span class="n">dmdr</span><span class="p">])</span>
+
+
+<span class="k">def</span> <span class="nf">solveTOV1</span><span class="p">(</span><span class="n">rho_c</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">):</span>
+    <span class="c1"># Notice that we only rescale quantities inside this function</span>
+    <span class="n">rho_c</span> <span class="o">=</span> <span class="n">rho_c</span> <span class="o">*</span> <span class="n">G</span> <span class="o">/</span> <span class="n">c</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">e_grid</span> <span class="o">=</span> <span class="n">e_grid</span> <span class="o">*</span> <span class="n">G</span> <span class="o">/</span> <span class="n">c</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">p_grid</span> <span class="o">=</span> <span class="n">p_grid</span> <span class="o">*</span> <span class="n">G</span> <span class="o">/</span> <span class="n">c</span><span class="o">**</span><span class="mi">4</span>
+
+    <span class="n">Pmin</span> <span class="o">=</span> <span class="n">p_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="n">r</span> <span class="o">=</span> <span class="mf">4.441e-16</span> <span class="o">*</span> <span class="n">cm</span>  <span class="c1"># Rule 1</span>
+    <span class="n">dr</span> <span class="o">=</span> <span class="mf">10.0</span> <span class="o">*</span> <span class="n">cm</span>  <span class="c1"># Rule 1</span>
+
+    <span class="n">p_c</span> <span class="o">=</span> <span class="n">PofE</span><span class="p">(</span><span class="n">rho_c</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">)</span>
+
+    <span class="n">P0</span> <span class="o">=</span> <span class="n">p_c</span> <span class="o">-</span> <span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="n">pi</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">p_c</span> <span class="o">+</span> <span class="n">rho_c</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">p_c</span> <span class="o">+</span> <span class="n">rho_c</span><span class="p">)</span> <span class="o">*</span> <span class="n">r</span><span class="o">**</span><span class="mi">2</span>  <span class="c1"># Rule 4</span>
+    <span class="n">m0</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">/</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">pi</span> <span class="o">*</span> <span class="n">rho_c</span> <span class="o">*</span> <span class="n">r</span><span class="o">**</span><span class="mi">3</span>  <span class="c1"># Rule 4</span>
+
+    <span class="n">param</span> <span class="o">=</span> <span class="p">(</span><span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">)</span>
+
+    <span class="n">stateTOV</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">P0</span><span class="p">,</span> <span class="n">m0</span><span class="p">))</span>
+    <span class="n">sy</span> <span class="o">=</span> <span class="n">ode</span><span class="p">(</span><span class="n">TOV1</span><span class="p">)</span><span class="o">.</span><span class="n">set_integrator</span><span class="p">(</span><span class="s2">&quot;dop853&quot;</span><span class="p">)</span>
+    <span class="n">sy</span><span class="o">.</span><span class="n">set_initial_value</span><span class="p">(</span><span class="n">stateTOV</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span><span class="o">.</span><span class="n">set_f_params</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
+    <span class="k">while</span> <span class="n">sy</span><span class="o">.</span><span class="n">successful</span><span class="p">()</span> <span class="ow">and</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">Pmin</span><span class="p">:</span>
+        <span class="n">stateTOV</span> <span class="o">=</span> <span class="n">sy</span><span class="o">.</span><span class="n">integrate</span><span class="p">(</span><span class="n">sy</span><span class="o">.</span><span class="n">t</span> <span class="o">+</span> <span class="n">dr</span><span class="p">)</span>
+        <span class="n">dpdr</span><span class="p">,</span> <span class="n">dmdr</span> <span class="o">=</span> <span class="n">TOV1</span><span class="p">(</span><span class="n">sy</span><span class="o">.</span><span class="n">t</span> <span class="o">+</span> <span class="n">dr</span><span class="p">,</span> <span class="n">stateTOV</span><span class="p">,</span> <span class="n">param</span><span class="p">)</span>
+        <span class="n">dr</span> <span class="o">=</span> <span class="mf">0.46</span> <span class="o">/</span> <span class="p">(</span><span class="n">dmdr</span> <span class="o">/</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">dpdr</span> <span class="o">/</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="c1"># at the end of this function, we rescale the quantities back</span>
+    <span class="k">return</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">c</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">G</span><span class="p">,</span> <span class="n">sy</span><span class="o">.</span><span class="n">t</span>
+
+
+<span class="k">def</span> <span class="nf">compute_tov_properties</span><span class="p">(</span><span class="n">rho_c</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">):</span>
+    <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">rho_c</span><span class="p">)</span>
+    <span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">rho_c</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rho_c</span><span class="p">)):</span>
+        <span class="n">M</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">R</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">solveTOV1</span><span class="p">(</span><span class="n">rho_c</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">M</span><span class="p">,</span> <span class="n">R</span>
+
+
+<span class="k">def</span> <span class="nf">cal_MR</span><span class="p">():</span>
+    <span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">geomspace</span><span class="p">(</span><span class="mf">1e14</span> <span class="o">*</span> <span class="n">g_cm_3</span><span class="p">,</span> <span class="n">e_max</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>  <span class="c1"># Rule 1</span>
+    <span class="k">return</span> <span class="n">compute_tov_properties</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">e_grid</span><span class="p">,</span> <span class="n">p_grid</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">M</span><span class="p">,</span> <span class="n">R</span> <span class="o">=</span> <span class="n">cal_MR</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">dpi</span><span class="o">=</span><span class="mi">400</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">R</span> <span class="o">/</span> <span class="n">km</span><span class="p">,</span> <span class="n">M</span> <span class="o">/</span> <span class="n">Msun</span><span class="p">,</span> <span class="s2">&quot;ro-&quot;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># Rule 3</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Radius [km]&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Mass [Msun]&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/UnitConventionForDeveloper_13_0.png" src="_images/UnitConventionForDeveloper_13_0.png" />
+</div>
+</div>
+</section>
+
+
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diff --git a/UnitConventionForDeveloper.ipynb b/UnitConventionForDeveloper.ipynb
new file mode 100644
index 0000000..9f238b7
--- /dev/null
+++ b/UnitConventionForDeveloper.ipynb
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unit Convention for Developer\n",
+    "Tianzhe Zhou 2024/11/1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To set up a solid foundation for the development of the project, \n",
+    "we need to establish a unit convention for the project. \n",
+    "The unit convention is a set of rules that define the units of the physical quantities used in the project. \n",
+    "The unit convention is important because it ensures that all developers use the same units for the same physical quantities, which helps to avoid errors and inconsistencies throughout the whole project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fm = 1\n",
+    "hbarc = 0.197327053\n",
+    "c = 1  # speed of light\n",
+    "hbar = 1  # reduced Planck constant\n",
+    "\n",
+    "GeV = 1 / hbarc  # Giga-electronvolt\n",
+    "MeV = 1e-3 * GeV  # Mega-electronvolt\n",
+    "\n",
+    "g = 5.625e26 * MeV  # gram\n",
+    "kg = 1e3 * g  # kilogram\n",
+    "cm = 1e13 * fm  # centimeter\n",
+    "m = 100 * cm  # meter\n",
+    "km = 1e5 * cm  # kilometer\n",
+    "s = 3e10 * cm  # second\n",
+    "\n",
+    "dyn = g * cm / s**2  # dyne\n",
+    "dyn_cm_2 = dyn / cm**2  # dyne / cm^2\n",
+    "g_cm_3 = g / cm**3  # gram / cm^3\n",
+    "erg = dyn * cm  # ἐργον energy unit\n",
+    "\n",
+    "m_n = 939.565 * MeV  # mass of neutron\n",
+    "n0 = 0.16 / fm**3  # saturation density\n",
+    "\n",
+    "e0 = m_n * n0  # saturation energy density\n",
+    "G = 6.6743e-8 * dyn * cm**2 / g**2  # gravitational constant\n",
+    "Msun = 1.989e33 * g  # mass of sun\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first 4 lines come from a widely used unit convention in High Energy Physics (HEP),\n",
+    "which is called [Natural Unit System](https://en.wikipedia.org/wiki/Natural_units).\n",
+    "You should be awared that we will NEVER using the Natural Unit System explicity,\n",
+    "as long as you obey the following rules.\n",
+    "1. Whenever you define a [literal](https://www.ituonline.com/tech-definitions/what-is-literal-programming/) \n",
+    "physical quantity, you should always *multiply* the unit of that quantity.\n",
+    "```python\n",
+    "# example of rule 1:\n",
+    "m_n = 939.565 * MeV  # GOOD\n",
+    "m_n = 939.565        # BAD\n",
+    "m_n = 1.67e-27 * kg  # GOOD\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Whenever you want a physical quantity in cgs unit(or others unit system), \n",
+    "you should always *divide* the corresponding unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.670337777777778e-24\n"
+     ]
+    }
+   ],
+   "source": [
+    "# example of rule 2:\n",
+    "m_n = 939.565 * MeV  # mass of neutron\n",
+    "# if I want to know m_n in terms of gram, I need to\n",
+    "print(m_n / g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. When you need to plot, *devide* the unit of your axis. See the example below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. When you use physic formula, you leave the whole form as it was, no additional constants.\n",
+    "```python\n",
+    "# example of rule 4:\n",
+    "E = m * c**2     # GOOD\n",
+    "F = G*M*m / r**2 # GOOD\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we present a full procedure of how to use the unit convention in calculating MR relation.\n",
+    "See https://arxiv.org/pdf/astro-ph/0506417v1 Page 15 for more details."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.433772787333139e-26\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from numpy import pi\n",
+    "\n",
+    "K_nrel = hbar**2 / (15 * pi**2 * m_n) * (3 * pi**2 / (m_n * c**2)) ** (5 / 3)  # Rule 1\n",
+    "print(K_nrel / (cm**2 / erg ** (2 / 3)))  # Rule 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def EOS_P(e):\n",
+    "    return K_nrel * e ** (5 / 3)  # Rule 4\n",
+    "\n",
+    "\n",
+    "e_min = 1e4 * g_cm_3  # Rule 1\n",
+    "e_max = 1e18 * g_cm_3  # Rule 1\n",
+    "e_grid = np.geomspace(e_min, e_max, 10000)\n",
+    "p_grid = EOS_P(e_grid)\n",
+    "\n",
+    "\n",
+    "def EofP(P, e_grid, p_grid):\n",
+    "    return np.interp(P, p_grid, e_grid)\n",
+    "\n",
+    "\n",
+    "def PofE(E, e_grid, p_grid):\n",
+    "    return np.interp(E, e_grid, p_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import ode\n",
+    "\n",
+    "\n",
+    "# working with rescaled quantities\n",
+    "def TOV1(r, y, grids):\n",
+    "    p, m = y\n",
+    "    e_grid = grids[0]\n",
+    "    p_grid = grids[1]\n",
+    "\n",
+    "    e = EofP(p, e_grid, p_grid)\n",
+    "\n",
+    "    dpdr = -(e + p) * (m + 4 * pi * r**3 * p)  # Rule 4\n",
+    "    dpdr = dpdr / (r * (r - 2 * m))  # Rule 4\n",
+    "    dmdr = 4 * pi * r**2 * e  # Rule 4\n",
+    "\n",
+    "    return np.array([dpdr, dmdr])\n",
+    "\n",
+    "\n",
+    "def solveTOV1(rho_c, e_grid, p_grid):\n",
+    "    # Notice that we only rescale quantities inside this function\n",
+    "    rho_c = rho_c * G / c**2\n",
+    "    e_grid = e_grid * G / c**2\n",
+    "    p_grid = p_grid * G / c**4\n",
+    "\n",
+    "    Pmin = p_grid[0]\n",
+    "\n",
+    "    r = 4.441e-16 * cm  # Rule 1\n",
+    "    dr = 10.0 * cm  # Rule 1\n",
+    "\n",
+    "    p_c = PofE(rho_c, e_grid, p_grid)\n",
+    "\n",
+    "    P0 = p_c - (4 * pi / 3) * (p_c + rho_c) * (3 * p_c + rho_c) * r**2  # Rule 4\n",
+    "    m0 = 4 / 3 * pi * rho_c * r**3  # Rule 4\n",
+    "\n",
+    "    param = (e_grid, p_grid)\n",
+    "\n",
+    "    stateTOV = np.array((P0, m0))\n",
+    "    sy = ode(TOV1).set_integrator(\"dop853\")\n",
+    "    sy.set_initial_value(stateTOV, r).set_f_params(param)\n",
+    "    while sy.successful() and stateTOV[0] > Pmin:\n",
+    "        stateTOV = sy.integrate(sy.t + dr)\n",
+    "        dpdr, dmdr = TOV1(sy.t + dr, stateTOV, param)\n",
+    "        dr = 0.46 / (dmdr / stateTOV[1] - dpdr / stateTOV[0])\n",
+    "    # at the end of this function, we rescale the quantities back\n",
+    "    return stateTOV[1] * c**2 / G, sy.t\n",
+    "\n",
+    "\n",
+    "def compute_tov_properties(rho_c, e_grid, p_grid):\n",
+    "    M = np.zeros_like(rho_c)\n",
+    "    R = np.zeros_like(rho_c)\n",
+    "    for i in range(len(rho_c)):\n",
+    "        M[i], R[i] = solveTOV1(rho_c[i], e_grid, p_grid)\n",
+    "    return M, R\n",
+    "\n",
+    "\n",
+    "def cal_MR():\n",
+    "    density = np.geomspace(1e14 * g_cm_3, e_max, 100)  # Rule 1\n",
+    "    return compute_tov_properties(density, e_grid, p_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5v8cDAAAAAMOOsg0AQHfGjy9v/4QJlckBQOd23jlpbk4OPLBn+y67LNlll+RPf0qampL6+mTatGTSpOJtfX0xFwAAAADgFUb3dwAAgAFv9dXL219bW5kcAKzaWmslV1yRnHNO8tnPJsuXl7bvL39J3vKWZOXKju9raEjmzk2mTk3mzEkmT65sZgAAAABgUHKyDQBAV37xi+R3vytvxqxZlUgCQHdGjEhOOCG5+eZk001L39dZ0eaVGhuT6dOTBQvKywcAAAAADAnKNgAAq3LTTcn735+0t/d+xtSpTkIA6GvTpiW33568852Vm9nWlsycmbS0VG4mAAAAADAoKdsAAHSmpSV5z3uSl17q/YyamuL6EQD63vrrJ9dck5x2WnHiTSW0tSWzZ1dmFgAAAAAwaCnbAAC81r33Ju96V7JkSe9n1NQk8+cntbWVywVAz4walXzxi8mvf12UbyqhsTFpaqrMLAAAAABgUFK2AQB4pX/+s7h25PHHez+jri5ZuDCZMaNyuQDovf/4j+Jaqbe+tTLz5s2rzBwAAAAAYFAa3d8BAAAGjKeeKoo2f/tbaet33z1ZsSJpbU0mTChOsZk1K5k8uaoxAeiFTTdNfve75AtfSM46q7xZLS2VyQQAAAAADErKNgAASfL888m++yZ3313a+lNOSU4/vbqZAKisMWOSM89Mfvaz5O9/7/2c1tbKZQIAAAAABh3XSAEALF2aHHRQ0thY2vpjj01OO626mQConk03LW//hAmVyQEAAAAADErKNgDA8LZyZfKhDyW//nVp6w85JPnOd5IRI6qbC4Dqqa0tb//y5Ul7e2WyAAAAAACDjrINADC8NDUl9fXJtGnJpEnF6QaXXVba3ne+M/nRj5JRo6qbEYDqmjWrvP233pocdljy4osViQMAAAAADC6j+zsAAECfaG4uSjalXhX1WrvtllxxRbLaapXNBUDfmzIlmTq1938mJMlPfpLcf3/yi18kG21UsWgAAAAAwMDnZBsAYOhbsCCZPr33X1Tdbrvk6quTNdesbC4A+s+cOUlNTXkzbrkl2XXX5I9/rEwmAAAAAGBQULYBAIa25uZk5sykra13+zfbLLn++mS99SqbC4D+NXlyMn9++YWbhx5K3vrW5KqrKpMLAAAAABjwlG0AgKGtvr73RZv1109uuCF53esqmwmAgWHGjGThwuJKqXI8/3yy//7JWWcl7e2VyQYAAAAADFij+zsAAEDV3HZb76+OSpKzz0622aZyeQAYeGprk4aGpKkpmTcvaWlJWluTCROSNdZIfve7ZPny7ue0tyef/nTy5z8n552XrLZa9bMDAAAAAP1C2QYAGLrmzStv/623JkccUZksAAxsU6YUr9dqaEgOOCB57LHS5vzgB8l99yVXXOEKQgAAAAAYolwjBQAMXS0t/bsfgMGvrq4oX+6wQ+l7br452W235J57qpcLAAAAAOg3yjYAwNC1ZEl5+1tbK5MDgMFt882TRYuSffctfc999yVTpybXX1+9XAAAAABAv1C2AQCGrvHjy9s/YUJlcgAw+I0fn/zyl8kJJ5S+59lnk332SebOrV4uAAAAAKDPKdsAAEPXuuuWt7+2tjI5ABgaRo1Kzj47ueCCZPTo0vasWJHU1xev5curmw8AAAAA6BPKNgDA0PTnPye/+115M2bNqkgUAIaYj360uB5qnXVK3zN3bnEN1TPPVC0WAAAAANA3lG0AgKHn6aeT97wneeGF3s+YOjWZPLlymQAYWt7+9uSWW5Jttil9z/XXJ3V1yS9+UZx0M21aMmlS8ba+PmlurlpcAAAAAKBySjz3GgBgkFi+PDnkkOR//7f3M2pqihMIAKArb3xj0tiYvO99yY03lrbnnnuSAw/s+LyhofizZ+rUZM4chU8AAAAAGMCcbAMADC2f+Uxyww29319Tk8yfn9TWVi4TAEPXOusk11yTHHtsZeY1NibTpycLFlRmHgAAAABQcco2AMDQMW9ecs45vd9fV5csXJjMmFG5TAAMfWPGFKfSnHtuMrICf81ua0tmzkxaWsqfBQAAAABUnGukAIChobExOfro0tZOmpSMH5+0tiYTJhSn2Mya5coOAHpvxIjkuOOSrbcurjNcsqS8eW1tyezZxfVSAAAAAMCAomwDAAx+//xncuCBydKl3a99+9uTX/+6OIUAACrt3e8uCjL77Zc88EB5sxobk6amZMqUymQDAAAAACrCNVIAwOD24ovJAQckjz7a/dottkjmz1e0AaC63vzm5JZbkunTy581b175MwAAAACAilK2AQAGr/b25Kijiu/6784aayS//GWy3nrVzwUA66+f3Hhj8bYcLS2VyQMAAAAAVIyyDQAweJ11VnLJJaWt/fGPk0mTqpsHAF5p7Nhkww3Lm9HaWpksAAAAAEDFKNsAAIPTtdcmn/1saWtPP724agoA+tr48eXtnzChMjkAAAAAgIoZ3d8BAABK0tSUXHRRcZ3GE08k999fXCPVnYMPTk4+uerxAKBTtbVJQ0Pv96+2WuWyAAAAAAAV4WQbAGBga25O6uqSXXZJ5s4tvmD5178mK1Z0v3fHHYuCzogRVY8JAJ2aNau8/TfdVJzQVkrBFAAAAADoE8o2AMDAtWBBMn160tjY870TJya//GWyxhqVzwUApZoyJZk6tbwZp56aHHdcaUVTAAAAAKDqlG0AgIGpuTmZOTNpa+v53tGjkyuuSDbfvPK5AKCn5sxJamrKmzF3bnLooclLL1UmEwAAAADQa6P7OwADx913353Fixfn4YcfznPPPZeampqsv/762W677bLzzjtnzJgx/R2xZE8++WSampry2GOP5fHHH09bW1vGjx+fzTbbLG95y1uyxRZb9HdEALpTX9+7ok1SfFHzbW+rbB4A6K3Jk5P583tfIn3Zz36WPPlkcuWVyfjxlcsHAAAAAPSIss0w99BDD+U73/lOLrnkkjz88MOrXDd+/Pjst99+Of7447PLLrv0YcLStba2Zs6cObnyyivT0tKSlStXrnLtFltskcMPPzzHHntsNt544z5MCUBJbrutd1dHvWzy5MplAYBKmDEjWbgwmT27vD/jfvObZM89k2uvTTbYoGLxAAAAAIDSuUZqmFq5cmXOOOOMbLvttjnzzDO7LNokyZIlS3LppZdm1113zQc/+ME8++yzfZS0eytXrsxZZ52VLbbYIieddFKampq6LNokyQMPPJAvf/nLeeMb35ivf/3r3a4HoI/Nm9e/+wGgGmprk4aGolRaX59Mm5bssEPxdo89Sp/T0pK89a3JAw9ULysAAAAAsEpOthmG2traMnPmzCxYsKBX+y+++OLccsst+fWvf503vOENlQ3XQ88991wOO+yw/OpXv+rV/ueffz6f//znc/PNN+eKK67IuHHjKpwQgF5paenf/QBQTVOmFK/Xuvzy5LDDkqVLu5/xv/9blHR+/evkLW+pfEYAAAAAYJWcbDPMrFixIgcffPAqizZjxozJjjvumL333ju77rpr1lxzzU7X3Xvvvdlrr73y6KOPVjNul1544YXsscceXRZtXv/612fq1Kl529velm233TYjR3b+U/66667Lvvvum6Wl/KM2ANW3ZEl5+1tbK5MDAPrSwQcX10ONH1/a+kcfTd72tuT3v69uLgAAAADgVZRthpnTTjstV199dYfna6+9dr75zW/m8ccfzx133JEbb7wxt9xyS5566qlcfvnl2WabbTrseeCBB3LooYf22xVMH/3oR9PSyckFY8aMyQknnJC//OUv+fvf/56GhobcfPPNueeee/Loo4/m3HPPzcSJEzvsu+mmm3Lcccf1RXQAulPqFxlXZcKEyuQAgL62117JTTclG2xQ2vpnn03e+c7kF7+oZioAAAAA4BWUbYaRO++8M1/72tc6PN98883T1NSUT37yk1lrrbVe9b4xY8bkoIMOSktLS/7zP/+zw97f/e53+d73vle1zKsyb968/OQnP+nwfIMNNsgtt9ySs88+u9OC0Prrr5/jjjsu99xzT3bbbbcO7z///PNzww03VCUzAD0waVJ5+2trK5MDAPpDbW2ycGGyxRalrX/ppeSgg5Lvf7+6uQAAAACAJMo2w8qnPvWprFix4lXP1lhjjVx77bXZaqututy7xhpr5Oc//3ne/OY3d3jfKaeckueff76iWbuyfPnynH766R2e19TU5MYbb8zOO+/c7Yz11lsvN9xwQ6eFnE984hP9dloPAP/nX/8qb/+sWRWJAQD9Zuuti8LNjjuWtn7lyuSoo5KvfS1pb69uNgAAAAAY5pRthomWlpZOT2w55ZRTst1225U0Y9y4cbngggs6PH/yySdz/vnnl52xVJdddlnuv//+Ds+/9KUvZVIPTkIYP358Lrroog7P77nnnlxxxRXlRASgHJddlvzyl73fP3VqMnly5fIAQH/ZeOPk5puTPfYofc9JJyXHH1+UbwAAAACAqlC2GSY6u+pp3XXXzXHHHdejOXV1dZ1eJ9WXZZtf/OIXHZ5NmDAhs2fP7vGsurq67Lnnnh2en3feeb1IBkDZ7r8/Ofro3u+vqUnmzq1cHgDob2utlVx3XXLggaXvOffc5LDDkqVLq5cLAAAAAIYxZZthYPny5Zk/f36H54cffnjGjRvX43lHHXVUh2f33HNPbr/99l7l64n29vbcdNNNHZ4feOCBWXPNNXs184Mf/GCHZzfddFP+8Y9/9GoeAL20dGny/vcnS5b0bn9NTTJ/flJbW9lcANDfXv4zrpO/i63SZZcl++2XPPdc9XIBAAAAwDClbDMM3HLLLXn66ac7PD/ooIN6NW/ffffN6quv3uH5tdde26t5PfHXv/41Tz75ZIfn06ZN6/XMurq6Ds/a29v75McDwCucdFJy222921tXlyxcmMyYUdlMADBQjBqVfO97ycknl77n+uuT3XZLPvKRZNq0ZNKk4m19fdLcXL2sAAAAADDEKdsMA7/97W87PBs3blynJZNS1NTUdFpu6ezjVNq//vWvTp9vv/32vZ657bbbZtSoUR2e33jjjb2eCUAPXXttctZZ3a9bbbVk8uRkhx3+/cXCpqZk0SIn2gAw9I0YkXz5y8U1USNGlLbn7ruTH/wgaWhIFi8u3s6dm0yZUpRVlW4AAAAAoMeUbYaBpqamDs922mmnjBkzptczd9111w7PmvvgH2mfeOKJTp+vvfbavZ45YsSIrLXWWh2ed/bfDYAqePjhpJMr/Tp1ySVFueauu4qTbL7znaJ8AwDDyXHHJT/5SVLG3+mSJI2NyfTpyYIFlckFAAAAAMOEss0w8Mc//rHDs0mTJpU1s7P9zzzzTP7+97+XNbc7L774YqfPx44dW9bcmpqaDs/uv//+tLa2ljUXgG6sWJEccUSyijLlq3zsY8nBB1c/EwAMBoccklx9dbLGGuXNaWtLZs5MWloqkwsAAAAAhgFlmyFu2bJleeihhzo833rrrcuau6r99913X1lzu7OqE2yeffbZsuY+88wznT6v9o8HYNj7+teTUq4h3GGH5JvfrH4eABhM/uM/kptuSiZOLG9OW1sye3ZFIgEAAADAcDC6vwNQXQ899FBWrlzZ4fmmm25a1txV7X/wwQfLmtud9dZbr9Pnjz76aK9nLlmyJC+88EKn73vwwQez884793p2KRoaGsraf9ddd1UoCUAfW7gwOfXU7tetvnpy2WXFWwDg1aZMKf5Mfec7k7/9rfdzGhuLqxqnTKlcNgAAAAAYopRthrjHHnus0+cbbrhhWXNXtf/xxx8va253Nttss06f33rrrZkxY0avZt56662rfN+q/vtV0rRp06r+MQAGnKeeSg49tLhGqjvnnpu8+c3VzwQAg9U22ySLFiWTJhV/xvbWvHnKNgAAAABQAtdIDXFPreIfWtdaa62y5o4aNSrjxo3r8PzJJ58sa253Nt5442y11VYdnl9//fW9ntnV3mr/eACGpfb25KMfTf7+9+7XHnJI8pGPVD8TAAx2m2ySdPJ3pR5paalMFgAAAAAY4pRthrjnnnuu0+drrrlm2bM7m/H888+XPbc7e+65Z4dnt9xyS1p68Q/DbW1t+cEPfrDK9/fFjwdg2DnvvOTKK7tft8UWyfe+l4wYUf1MADAUvPhieftbWyuTAwAAAACGOGWbIW7ZsmWdPh89uvwbxMaMGdPh2dKlS8ue250jjjii0+fHHXdcVq5c2aNZp556ap544olVvr8vfjwAw0JTU1Jfn+y4Y/G2O6NHJ5ddlpR5EhsADCvjx5e3f8KEyuQAAAAAgCGu/MYFA9qKFSs6fT5q1KiyZ3c2Y/ny5WXP7c4ee+yRurq6NDQ0vOr5okWLcsIJJ+Rb3/pWSXPmz5+fs88+u8s1PS3v9MaiRYvK2n/XXXflmGOOqVAagAprbi7KNY2NPdt3xhnJrrtWJxMADFW1tclr/p7UI33w9x8AAAAAGAqUbYa4VZ1gU4lSTGczOjvtphrOOOOM7LXXXh3KMN/+9rfzr3/9K9/61rey4YYbdrr3pZdeyllnnZVTTz11lWWkl9XU1FQs86rU1dVV/WMA9IsFC5KZM5O2tp7te9e7khNOqE4mABjKZs1K5s7t/f7GxuRLX0pOPdU1jgAAAADQBWWbIW7s2LGdPl/V9VI90dkVS6v6eJW2xx575PTTT8/JJ5/c4X2XXXZZFixYkP333z/veMc7summm2bMmDH517/+lUWLFuWKK67IP//5z1ft2WabbfLPf/4zzz///Kuejxs3rqo/DoAhq7m5d0Wb9dZL5s1LRrrpEgB6bMqUZOrUnp8o90qnnZa89FLyta8p3AAAAADAKijbDHHjx4/v9Hlra2vZs5csWdLh2YQJE8qeW6ovfOELefDBB/P973+/w/uee+65XHLJJbnkkku6nbP++uvnpz/9aXbeeecO79tggw0qkhVg2Kmv73nRJkk22KB4AQC9M2dOMn167/4cftnXv54sXZqcdZbCDQAAAAB0wreND3Hrrbdep8+feeaZsua+9NJLeemll0r+eNUwYsSIXHDBBTnrrLMyatSoXs3Yeuutc9NNN2Wttdbq9P0bb7xxOREBhqfbbuv9d9T/+c9JU1Nl8wDAcDJ5cjJ/flLulbjf/GbyiU8kr7m6FwAAAABQthnyNtpoo06f/+tf/ypr7iOPPNKjj1dNJ554Yu666668733vy4gSv+uypqYmJ5xwQlpaWrL99tt3uFbqZdtss00lowIMD/Pm9e9+ABjuZsxIFi4srpQqx5w5ybHHKtwAAAAAwGu4RmqI22STTTJ27NgOp9D8/e9/L2vuQw891OnzLbbYoqy5vbXddtvlpz/9ac4888z8+te/zg033JA///nPeeKJJ/LUU09l5MiR2WijjTJp0qS8853vzPvf//5MnDjx/9/f3NzcYebqq6+erbfeui9/GABDQ0tL/+4HAJLa2qShoTgxbt684s/X1tZkwoTidcMNyYoV3c85//ziSqnvfz/p5YmiAAAAADDUKNsMcSNGjMhWW22Vu++++1XP77333rLmrmp/f5dTNttssxx11FE56qijerSvoaGhw7PddtstI0c6/Amgx5YsKW9/a2tlcgAAyZQpxeu1fvGL5H3vS5Yt637GRRcVhZt585LR/hkBAAAAADQJhoGdd965w7M77rijrJmd7X/d6173qtNiBpNFixZ1eLbXXnv1QxKAIWD8+PL2T5hQmRwAwKodcEBy5ZXJ2LGlrb/00uTQQ0sr5wAAAADAEKdsMwzstttuHZ7dfffdaS3j5IBVnQQzGP3+97/v9Fqsgw46qB/SAAwBm21W3v7a2srkAAC6tu++ya9+ldTUlLZ+/vziNJylS6ubCwAAAAAGOGWbYeAd73hHh2crVqzIjTfe2Kt5jz/+eG6//faSPs5gcOGFF3Z4tvPOO2f77bfvhzQAg9yyZUmZp6dl1qxKJAEASvHOdyZXX52MG1fa+l/8Innve5O2tqrGAgAAAICBTNlmGNhuu+2y1VZbdXj+05/+tFfz5s+fn5UrV77q2YgRI7Lvvvv2al5/uv/++/Ozn/2sw/Pjjz++78MADAXf+Ebyl7/0fv/UqcnkyZXLAwB0b6+9kuuuS9Zcs7T1V1+d7L9/8uKL1c0FAAAAAAOUss0wcfjhh3d4duWVV+af//xnj+a0t7dn7ty5HZ7vueeeef3rX9/rfP3l2GOPTdtrviNzq622ygc+8IF+SgQwiN11V3L66b3fX1OTdPJnDADQB3bfPbn++mTChNLWX399cQ3V889XNxcAAAAADEDKNsPE0UcfndVWW+1Vz5YtW5aTTz65R3PmzZuXu+++u8Pz+vr6kmfMmjUrI0aM6PB68MEHe5SlXF/72tdy/fXXd3h+zjnnZMyYMX2aBWDQW7asuP5p2bLe7a+pSebPT2prKxoLAOiBurrkN79J1l67tPW/+13y7ncnS5ZUNRYAAAAADDTKNsPEJptskg9/+MMdnl900UWZP39+STP++te/dnq90g477JADDjigzIQ999xzz/VqX3t7e0477bScdNJJHd53xBFHZL/99is3GsDw81//lbS09G5vXV2ycGEyY0ZlMwEAPTdlSlGiWW+90tb/z/8k//mfybPPVjcXAAAAAAwgyjbDyJe//OWsu+66HZ4fccQR+clPftLl3jvuuCN77713nu3kH1DPPffcjBzZ9z+V9ttvvxx00EG55ppr8tJLL5W0Z9GiRdltt93ypS99qcP7dthhh06vyAKgG4sXJ6ed1v26ddZJpk5NdtghmTYtqa9PmpqSRYucaAMAA8lOOyU33ZRssEFp6xsakne8I3n66WqmAgAAAIABY3R/B6DvTJw4MRdeeGEOPPDAVz1/6aWXcuihh+ZHP/pRjjnmmNTV1WXixIlZsmRJ/vjHP+aSSy7JRRddlGWdXA3yyU9+Mm9/+9v76ofwKsuWLcvPf/7z/PznP88aa6yRPfbYIzvttFO23377rLfeellzzTXz9NNP57HHHsvixYtz1VVX5b777ut01hve8IZcd911GT9+fB//KAAGuVKvjxo5Mrn22mS33fokFgBQph12KAo3e++dPPJI9+ubmpK99kpuuCGZOLHq8QAAAACgPynbDDMHHHBAvva1r+ULX/hCh/ddd911ue6660qeNWPGjHzjG9+oZLxee/7553PNNdfkmmuu6fHenXbaKddcc0023njjKiQDGOLOPDNpbu5+3ac+pWgDAIPNdtslN99clGj+8Y/u199xR/L2tye/+U3pp+IAAAAAwCDkGqlh6POf/3zOOeecjBo1qtczDj/88Fx++eUZM2ZMBZP1rVGjRuX4449PQ0ODog1AbyxenHRyLV8Hb3pTaddMAQADzxvfWBRuNt+8tPWLFxd/9k+Zkkya9O+rI0sp5wIAAADAIKFsM0wdf/zxWbhwYSZPntyjfRtvvHEuvvjiXHzxxRk7dmyV0pVmn332yWabbdbjfauttloOOeSQ3HnnnTnnnHNSU1NThXQAQ9zy5aVfH3XRRYnfawFg8Npyy6Jws+WWpa1/+umiXLN4cdLQkMydW5Rv6uqUbgAAAAAYEka0t7e393cI+teNN96YH//4x7nxxhvzz3/+s8P711577ey+++45+OCDc8ghh/R7yea1/vznP2fRokW57bbb8pe//CUPPvhgnnrqqTz//PMZPXp0JkyYkDe84Q1585vfnLe//e3ZZ599MnHixP6OXTENDQ2ZNm3aq54tWrQodXV1/ZQIGBa+9rXkpJO6X/eZzyQD5MpBAKBM//hHsvfeyb339n5GTU0yf34yY0blcgEAAAAwZAyWr38r2/AqzzzzTB5++OE8//zzqampycSJE12xNMANlt9sgCFk8eKktrb7U23e9Kbk9tudagMAQ8kjjxSFmz//ufczamqShQuLzycAAAAA4BUGy9e/R/d3AAaWtddeO2uvvXZ/xwBgoFq+PDnyyNKuj/rhDxVtAGCo2Xjj5Kabkne8I7nrrt7NaGtLZs8urpgCAAAAgEFoZH8HAAAGkTPPTJqaul934onJ1KnVzwMA9L0NNkh+97tkm216P6OxsbTPKQAAAABgAFK2AQC61tSU1NcnO+2UnHRS9+u33TY57bSqxwIA+tF66yVve1t5M+bNq0wWAAAAAOhjrpECADrX3FyUbBobS98zcmRy0UXJ6qtXLRYAMED86U/l7W9pqUwOAAAAAOhjyjYAQEcLFiQzZyZtbT3bd8IJro8CgOFiyZLy9re2ViYHAAAAAPQx10gBAK/W3Ny7os3mmyenn16dTADAwDN+fHn7x42rTA4AAAAA6GPKNgDAq9XX97xokyRrrun6KAAYTmpry9v/4IPJs89WJAoAAAAA9CVlGwDg3267LWls7N3eP/0paWqqbB4AYOCaNau8/Y89luyzT/LccxWJAwAAAAB9RdkGAPi3efP6dz8AMHhMmZJMnVrejEWLkv32S154oTKZAAAAAKAPKNsAAP/W0tK/+wGAwWXOnKSmprwZN92UHHhg766xBAAAAIB+oGwDAPzbkiXl7W9trUwOAGBwmDw5mT+//MLN9dcn73tfsnRpZXIBAAAAQBUp2wAA/zZ+fHn7J0yoTA4AYPCYMSNZuLD8K6Wuuio57LBk+fLK5AIAAACAKhnd3wEAgAGktjZpaChvPwAw/Lz8OURTUzJvXnG1ZGtrUcTddtvkhhuSf/yj+zmXX56stlryox8lo0ZVPzcAAAAA9IKyDQDwb3vumcyd2/v9s2ZVKgkAMBhNmVK8Xuuhh5K3vS158MHuZ1x6aXEt1QUXJCMdyAsAAADAwONfrQCAQnt7ct55vd8/dWoyeXLl8gAAQ8frX5/89rfJ615X2vof/CA57rji8xMAAAAAGGCUbQCAwmWXFV8E642amvJOxAEAhr4ttig+19hoo9LW//d/J5/6lMINAAAAAAOOsg0AkDz7bHLCCb3bW1OTzJ+f1NZWNhMAMPS88Y3Jb36TTJxY2vpvfjM55ZTqZgIAAACAHlK2AQCSL34xefTRnu+rq0sWLkxmzKh8JgBgaNp+++TGG5N11ilt/Ve/mnzlK9XNBAAAAAA9MLq/AwAA/ez225M5c7pft9FGxXehT5hQnGIza1YyeXLV4wEAQ9COOybXX5/svXfS2tr9+lNOKU7T+9Snqp8NAAAAALqhbAMAw9nKlcmxxxZvu7LBBsndd5f+HegAAN2ZMiW59trkne9Mnn+++/Wf/nRRuKmvr342AAAAAOiCa6QAYDj7wQ+SW27pft1ZZynaAACVN21acvXVyeqrl7b+uOOSCy6obiYAAAAA6IayDQAMV088kXz2s92ve9vbksMPr34eAGB42mOP5Be/SFZbrbT1xxyTXHxxVSMBAAAAQFeUbQBguPrc55Knnup6zejRydy5yYgRfZMJABie3vnO5Ioris89utPensyalfzsZ1WPBQAAAACdUbYBgOFo0aLkwgu7X/fJTyY77FD9PAAAM2Ykl12WjBrV/dqVK5NDD01++cvq5wIAAACA11C2AYDhZvny5Nhju1/3utclX/xi9fMAALzsoIOSH/2otFP1VqxIZs5Mrr22+rkAAAAA4BVKOJ8ZABhS5sxJ7ryz+3Xf/nay5prVzwMA8EqHHpq89FLy4Q93v3bZsmS//ZJtt01GjkzGj09qa5Mjj0wmT65+VgAAAACGJSfbAMBw8vDDpZ1W8+53JwceWP08AACdOfLI5LzzSlu7YkVy993J4sVJQ0Myd24yZUpSV5c0N1c3JwAAAADDkrINAAwnJ56YLFnS9ZqxY5PvfKe06xsAAKrlYx9Lzjmn9/sbG5Pp05MFCyqXCQAAAACibAMAQ19TU1Jfn7z5zclll3W//vOfT7baqvq5AAC6c/zxyRln9H5/W1syc2bS0lKxSAAAAAAwur8DAABV0txclGwaG0vfs9VWyWc/W71MAAA99bnPFaWZ007r3f62tmT27OKKKQAAAACoACfbAMBQtGBBcW1CT4o2STJnTlJTU51MAAC9deqpyRFH9H5/Y2Nx2h8AAAAAVICyDQAMNc3NxXUJbW0927f33sm73lWdTAAA5RgxIhk/vrwZ8+ZVJgsAAAAAw56yDQAMNfX1PS/aJMnTT1c+CwBApdx+e3n7W1oqkwMAAACAYU/ZBgCGkttu6/nVUS9raXG9AgAwcC1ZUt7+1tbK5AAAAABg2FO2AYChpNzrEVyvAAAMVOVeI1XufgAAAAD4P8o2ADCUlHs9gusVAICBqra2vP1PPZWsXFmZLAAAAAAMa8o2ADCUuF4BABiqZs0qb/9f/pKceGLS3l6ROAAAAAAMX8o2ADCUlHs9woQJlckBAFBpU6YkU6eWN+Nb30rOOKMicQAAAAAYvpRtAGAoKfd6hXL3AwBU05w5SU1NeTNOOik5//zK5AEAAABgWFK2AYChpNzrFcrdDwBQTZMnJ/Pnl1+4+djHkssvr0wmAAAAAIYdZRsAGEpaW3u/d+rU4gtYAAAD2YwZycKF5V0p1d6eHHpocsMNlcsFAAAAwLAxur8DAAAVsmJFcuKJvdtbU5PMnVvZPAAA1VJbmzQ0JE1Nybx5SUtLUTqeMCHZZJNkwYKkra3rGcuWJQcemPz2t8muu/ZNbgAAAACGBGUbABgqfvSj5I47er6vpqa4jqG2tuKRAACqasqU4vVa119fnICzbFnX+59/Ptlnn+R//ifZbrvqZAQAAABgyHGNFAAMBc8/n5x0Us/31dUV1zDMmFH5TAAA/eWd70x+/ONkxIju1z75ZLH+73+vfi4AAAAAhgQn2wDAUHDmmckjj3S9ZuTIZJttknXXLU6xmTUrmTy5T+IBAPS5972vKNJ8/OPdr/3HP4rCzf/8T7L++tXPBgAAAMCgpmwDAIPdww8XZZvufPGLyamnVj8PAMBAceyxReHmlFO6X/uXvyTvfnfyu98l48dXPxsAAAAAg5ZrpABgsDv55OSFF7pes8kmyac+1Td5AAAGkpNOSj7xidLWNjcnBxyQtLVVNRIAAAAAg5uyDQAMZnfckVx0UffrvvrVZI01qp0GAGDgGTEiOeec5LDDSlv/298Wa1esqG4uAAAAAAYtZRsAGKza25MTTyzedmWnnZIPfrBPIgEADEgjRyY//GGy776lrf/5z5OPfaz7z7MAAAAAGJaUbQBgsFqwoPjO6+6cfXbxBSYAgOFszJjkZz9L3vrW0tZ///vJF75Q3UwAAAAADEq+8gYAg9GyZcmnP939uv32S/baq/p5AAAGg3HjkquuSiZNKm39179eFJcBAAAA4BWUbQBgMPre95K//KXrNaNHJ2ee2Td5AAAGi3XWSX7962SLLUpb/6lPJRddVNVIAAAAAAwuyjYAMNg880zypS91v+5jH0u23bbaaQAABp+NN05uuCHZcMPS1n/0o8kvf1ndTAAAAAAMGso2ADDYfPWryZNPdr1mrbWSU0/tmzwAAIPRVlsVJ9ystVb3a1esSA45JLn55urnAgAAAGDAU7YBgMHkgQeSc8/tft3JJycTJ1Y/DwDAYLbjjslVVyU1Nd2vfemlZL/9kttvr34uAAAAAAY0ZRsAGAyampL6+mSXXZKlS7teu8UWyXHH9U0uAIDBbvfdk/nzk1Gjul+7ZEnyrnclf/1r9XMBAAAAMGCN7u8AAEAXmpuLkk1jY+l7vvGNZOzY6mUCABhqZsxIfvjD5IMf7H7tY48lkycn22xTnHYzfnxSW5sceWTxHAAAAIAhz8k2ADBQLViQTJ/es6LNtGnJwQdXLxMAwFB1xBHJOeeUtnbJkqIUvXhx0tCQzJ2bTJmS1NUVzwEAAAAY0pRtAGAgam5OZs5M2tp6tu/oo5MRI6qTCQBgqDv++OSkk3q/v7GxKEsvWFCxSAAAAAAMPMo2ADAQ1df3vGiTJN/9buWzAAAMJ1/+cnLMMb3f39ZWlKZbWiqXCQAAAIABRdkGAAaa227r2dVRr9TYmDQ1VTYPAMBwMmJEcS1UOVdztrUls2dXLhMAAAAAA4qyDQAMNPPm9e9+AIDhbtSo5Mc/TnbZpfczlKABAAAAhixlGwAYaMq9csCVBQAA5Rs7Ntl55/JmKEEDAAAADEnKNgAw0CxZUt7+1tbK5AAAGO7uuqu8/UrQAAAAAEOSsg0ADDTjx5e3f8KEyuQAABjulKABAAAA6ISyDQAMNLW1/bsfAICCEjQAAAAAnVC2AYCBZtas/t0PAECh3BLztttWJgcAAAAAA4qyDQAMNOV8B/TUqcnkyZXLAgAwnJVbYl64sPyrqAAAAAAYcJRtAGCgOfXU3u2rqUnmzq1sFgCA4WzKlKLM3Fv33pscfHCybFnlMgEAAADQ75RtAGAgufPO5LLLer6vpiaZP7/8qw4AAHi1OXOKz7V66/rrk6OPTtrbK5cJAAAAgH6lbAMAA8kpp/R8T11dcUXBjBmVzwMAMNxNnlyUmssp3Fx0UfKlL1UqEQAAAAD9TNkGAAaKW29NfvWr7tdtvXUybVpSX580NSWLFjnRBgCgmmbMKMrN5Vwpdfrpyfe/X7lMAAAAAPSb0f0dAAD4Pyef3P2amTOTn/2s+lkAAHi12tqkoaEoO8+bl7S0JK2tyRprJPfdlzzxRPczPvaxZJNNkn32qX5eAAAAAKpG2QYABoKbb05uuKHrNSNHFt8RDQBA/5kypXi90iOPFFd7/u1vXe9dsaIoT998c8cZAAAAAAwarpECgP7W3p6cdFL36444InnTm6qfBwCAntl44+S665J11ul+7QsvJPvum9x/f/VzAQAAAFAVyjYA0N9+/etk4cKu14wenXzxi32TBwCAnnvTm5Jf/SoZO7b7tY89lrzrXaVdPQUAAADAgKNsAwD9qb09Ofnk7td99KPJlltWPw8AAL03fXpyySXJiBHdr/3rX5P3vCd58cXq5wIAAACgopRtAKA/XXll0tzc9ZqamtIKOQAA9L+DDkrOOae0tQ0NyWGHJStWVDcTAAAAABWlbAMA/WXFiuSUU7pf9/GPJ5tuWv08AABUxv/7f8kJJ5S29sork+OPL048BAAAAGBQULYBgP5y2WXJ3Xd3vWaNNZLPfrZv8gAAUDlnnpm8732lrZ0zJznrrOrmAQAAAKBilG0AoD8sW5acemr3644/Ptlgg6rHAQCgwkaOTObNS972ttLWf+YzyU9+Ut1MAAAAAFSEsg0A9IeLLkruu6/rNWuvnXzqU32RBgCAaqipSX7xi2S77Upb/6EPJb/7XVUjAQAAAFA+ZRsA6Gttbcnpp3e/7tOfLgo3AAAMXuusk1x7bbLxxt2vXbYsOfDAZPHi6ucCAAAAoNeUbQCgr51/fvKPf3S9Zv31k098om/yAABQXZtvnlxzTbLmmt2vffbZ5N3vTv75z+rnAgAAAKBXlG0AoK80NSXHHJOceGL3az//+dK+GAMAwOCw007JFVcko0d3v/Yf/0j22aco3gAAAAAw4CjbAEC1NTcndXXJLrsUp9osX971+k03TY49tm+yAQDQd975zuT73y9t7Z13JgcdlCxdWt1MAAAAAPSYsg0AVNOCBcn06UljY+l7TjklqampXiYAAPrPhz6UfPnLpa39zW+Sj3wkaW+vbiYAAAAAekTZBgCqpbk5mTkzaWsrfc+IEcUVAwAADF0nnZQcfXRpa3/842I9AAAAAANGCReFAwC9Ul/fs6JNUnzX8vHHJw0NVYkEAMAAMGJEMndu8s9/Jldf3f36M85IFi0qrpRasiQZPz6prU2OPDKZPLn6eQEAAAB4FSfbAEA13HZbz66OeqXGxqSpqbJ5AAAYWEaPTn7602TKlNLW33xzUchevLh4O3dusbeurjhREQAAAIA+o2wDANUwb17/7gcAYOBbY41kwYJkyy17P6OxMZk+vZgDAAAAQJ9QtgGAamhp6d/9AAAMDhtumFx7bbLeer2f0daWzJzpc0gAAACAPqJsAwDVsGRJeftbWyuTAwCAgW+bbZKrrkpqano/o60tmT27cpkAAAAAWCVlGwCohvHjy9s/YUJlcgAAMDjU1SU/+UkyYkTvZzQ2Jk1NlcsEAAAAQKeUbQCgGmpr+3c/AACDzwEHJLvvXt6MefMqEgUAAACAVVO2AYBqmDWrf/cDADA4LVtW3v6WlsrkAAAAAGCVlG0AoBp22CEZM6Z3e6dOTSZPrmweAAAGhyVLytvf2lqZHAAAAACskrINAFTDRRf17ruSa2qSuXMrHgcAgEFi/Pjy9k+YUJkcAAAAAKySsg0AVNqyZcnXv97zfTU1yfz5SW1t5TMBADA4lPu5oM8lAQAAAKpO2QYAKu3SS5O//a1ne+rqkoULkxkzqpMJAIDBYdas8vbvv39FYgAAAACwaso2AFBJK1YkX/ta9+s22SSZNi2pr0+ampJFi3wXMgAAyZQpydSpvd9/6qlJW1vl8gAAAADQwej+DgAAQ8oVVyT33tv1mk03Te67Lxk7tm8yAQAwuMyZk0yf3rvSzKJFyYc/nFxySTJiROWzAQAAAOBkGwComPb25Ktf7X7dpz+taAMAwKpNnpzMn5/U1PRu/09+knzpSxWNBAAAAMC/KdsAQKUsWJDceWfXa9ZfPznqqL7JAwDA4DVjRrJwYe+vlDr99OTiiyubCQAAAIAkyjYAUBnt7clXvtL9uhNOSMaNq34eAAAGv9rapKEhue22pL4+mTYtefObk7XWKm3/Rz6S/P731c0IAAAAMAyN7u8AADAk/OY3ya23dr1m7bWTj3+8T+IAADCETJlSvF72zDPJW9+a3H131/uWLUsOPDBpbEze+MaqRgQAAAAYTpxsAwCVUMqpNp/4RDJhQvWzAAAwtK29dnGF6frrd7/2qaeSffct3gIAAABQEco2AFCuP/whufnmrtessUZRtgEAgErYYovkl79Mxo7tfu1f/5q8973J0qXVzwUAAAAwDCjbAEC5vvrV7td8/OPJeutVPwsAAMNHXV0yb15pa2++OTn66KS9vbqZAAAAAIYBZRsAKEdzc3LddV2vGTs2OeGEvskDAMDwcsghpV1pmhTFnK99rbp5AAAAAIYBZRsAKEcpp9ocdVSy0UbVzwIAwPD0hS8kH/pQaWtPPjn56U+rmwcAAABgiFO2AYDe+tOfkiuv7HrN6NHJpz/dN3kAABieRoxIzj8/2WOP0tZ/6ENJQ0N1MwEAAAAMYco2ANBTTU1JfX2y557dr/3Qh5LNNqt6JAAAhrnVVkuuuCJ54xu7X/vSS8n++ycPPFD9XAAAAABDkLINAJSquTmpq0t22SWZOzd54omu148cmXzuc32TDQAA1lsvufrqZN11u1/7+OPJvvsmzzxT9VgAAAAAQ42yDQCUYsGCZPr0pLGx9D3vf3+y9dbVywQAAK/1xjcWV52OGdP92j//OZk5M1m2rPq5AAAAAIYQZRsA6E5zc/FFiLa2nu3bf//q5AEAgK687W3JhReWtvbGG5PZs5P29upmAgAAABhClG0AoDv19T0v2iTJOedUPgsAAJTiiCOSU04pbe0FFyRnn13dPAAAAABDiLINAHTlttt6dnXUKzU2Jk1Nlc0DAAClOu205AMfKG3tZz5TXD8FAAAAQLeUbQCgK/Pm9e9+AADorREjkh/8IKmr635te3ty2GHK4gAAAAAlULYBgK60tPTvfgAAKEdNTfLLXyZbbNH92hdfTPbbL3nooernAgAAABjElG0AoCtLlpS3v7W1MjkAAKC31l8/ufrqZK21ul/76KPJjBnlfx4MAAAAMIQp2wBAV8aPL2//hAmVyQEAAOXYbrvkiiuS0aO7X3vnnckhhyTLl1c/FwAAAMAgpGwDAF2pre3f/QAAUCl7752cd15pa6+9tvhcdtq0ZNKk4m19fdLcXN2MAAAAAIOAsg0AdGXWrP7dDwAAlfTRjyaf+Uxpa++6K2loSBYvLt7OnZtMmZLU1SndAAAAAMOasg0AdGXKlGT77Xu3d+rUZPLkyuYBAIBynXFG8t739n5/Y2MyfXqyYEHlMgEAAAAMIso2ANCdiRN7vqempvjOXwAAGGhGjkwuvjjZZZfez2hrS2bOTFpaKpcLAAAAYJBQtgGArvzlL8n//E/P9tTUJPPnJ7W11ckEAADlGjcu+dWvks026/2MtrZk9uzKZQIAAAAYJJRtAKAr55yTtLeXvr6uLlm4MJkxo3qZAACgEjbaqLgKaty43s9obEyamiqXCQAAAGAQULYBgFV5/PFk3rzu102enNTXF19kWLTIiTYAAAwekyYle+1V3oxSPmcGAAAAGEJG93cAABiwzjuvOBq/Kx/4QHLppX2TBwAAquHJJ8vb39JSmRwAAAAAg4STbQCgM21tyZw53a878cTqZwEAgGpasqS8/a2tlckBAAAAMEgo2wBAZy6+uLhGqit77llcIQUAAIPZ+PHl7Z8woTI5AAAAAAYJZRsAeK2VK5NvfrP7dU61AQBgKKit7d/9AAAAAIOMsg0AvNa11yb33NP1mm23TfbZp2/yAABANc2aVd7+97+/IjEAAAAABgtlGwB4rbPO6n7NCSckI/0xCgDAEDBlSjJ1au/3z5mTtLdXLg8AAADAAOerhADwSi0tyU03db1m/fWTI47okzgAANAn5sxJamp6t/eyy5Kvfa2yeQAAAAAGMGUbAHils8/ufs3s2cnqq1c/CwAA9JXJk5P583tfuDn55OTnP69sJgAAAIABStkGAF720EPJT3/a9ZqamuTjH++bPAAA0JdmzEgWLuz9lVJHHJHccUdFIwEAAAAMRMo2APCyb387WbGi6zUf/GBxjRQAAAxFtbVJQ0Ny221JfX0ybVqy/fbJuHHd733hheQ970n+9a/q5wQAAADoR6P7OwAADAitrckFF3S/7oQTqp8FAAD625Qpxetlf/tbsssuyeOPd73voYeSAw9Mfvvb3l9JBQAAADDAOdkGAJLk+98vCjdd2W+/ZNtt+yYPAAAMJJtvnlx5ZTJmTPdrGxqSY45J2turnwsAAACgHyjbAMCyZcUVUt058cTqZwEAgIHqrW9Nzj+/tLU/+lFy1lnVzQMAAADQT5RtABjempqSffdN/v73rtdNnpy87W19kwkAAAaqWbOST32qtLWf/Wxy1VVVjQMAAADQH5RtABiempuTurpkl12SG27ofv2nPpWMGFH9XAAAMNB9/evJPvt0v669PTn00GTx4upnAgAAAOhDyjYADD8LFiTTpyeNjaWtX3/95OCDq5sJAAAGi1Gjkp/8JNl+++7XPvdcst9+yeOPVz8XAAAAQB9RtgFgeGluTmbOTNraSt/z9NPJnXdWLxMAAAw2EyYUV0Stt173ax98sCivL11a9VgAAAAAfUHZBoDhpb6+Z0WbJFm+PJk9uzp5AABgsNpyy+Tyy5PRo7tf+/vfJx//eHG1FAAAAMAgp2wDwPBx222lXx31Wo2NSVNTZfMAAMBgt+eeydy5pa298MLk3HOrGgcAAACgLyjbADB8zJvXv/sBAGAoOvro5BOfKG3tCSck111X3TwAAAAAVaZsA8Dw0dLSv/sBAGCoOvvs5J3v7H7dypXJIYck99xT/UwAAAAAVaJsA8DwsWRJeftbWyuTAwAAhprRo5Of/jTZZpvu17a2Jvvtlzz1VPVzAQAAAFSBsg0Aw8f48eXtnzChMjkAAGAoWnvt5Kqrirfd+d//TWbOTJYtq3YqAAAAgIpTtgFg+Kit7d/9AAAw1G2zTTJ/fjJqVPdrf/vb5Pjjqx4JAAAAoNKUbQAYPmbN6t/9AAAwHLzjHcm3v13a2v/+7+IFAAAAMIgo2wAwfEyZkmy0Ue/2Tp2aTJ5c2TwAADBUffzjycc+VtraT3wi+c1vqpsHAAAAoIKUbQAYPp57rnj1VE1NMndu5fMAAMBQNWJEcu65ydvf3v3aFSuSmTOTv/61+rkAAAAAKkDZBoDh45JLel62qalJ5s9PamurkwkAAIaqMWOKz6W32qr7tU8/ney3X/LMM1WPBQAAAFAuZRsAhof29mTOnJ7tqatLFi5MZsyoTiYAABjq1lsvueqqZMKE7tf+5S/J+9+fLF9e/VwAAAAAZRjd3wEAoE/8/vfJ4sVdr6mpKU6wqa1NZs1KJk/uk2gAADCkbbddctllRYl95cqu1/7618lmmxUlnfHji8/NjzzS5+YAAADAgOJkGwCGh1JOtTnvvOIkm+98xz/mAwBAJb373clZZ5W29pFHiqJ8Q0Myd24yZUpx6mRzc3UzAgAAAJRI2QaAoe8f/0iuvLLrNeutlxxySN/kAQCA4ej445MPf7h3exsbk+nTkwULKhoJAAAAoDeUbQAY+r73vWTFiq7XfPSjyeqr900eAAAYjkaMKE6T3H333u1va0tmzkxaWiqbCwAAAKCHlG0AGNpeeik5//yu14wcmXzsY32TBwAAhrPVVkuuuCLZfPPe7W9rS2bPrmwmAAAAgB5StgFgaLv88uSxx7peM2NG8oY39EkcAAAY9tZfPznjjN7vb2xMmpoqlwcAAACgh5RtABja5szpfk19ffVzAAAA/7ZwYXn7582rTA4AAACAXhjd3wEYOO6+++4sXrw4Dz/8cJ577rnU1NRk/fXXz3bbbZedd945Y8aM6e+IJVuyZEmam5vz0EMP5Zlnnklra2tqamqy9tprZ+LEidlxxx3zBqdYwNDX3Fx812tXtt022XvvvskDAAAUWlr6dz8AAABAGZRthrmHHnoo3/nOd3LJJZfk4YcfXuW68ePHZ7/99svxxx+fXXbZpQ8Tlu7RRx/ND3/4w1x66aW5++67s3Llyi7XT5w4Me9+97tz9NFHZ/r06X2UEuhTc+d2v2b27GSkg94AAKBPLVlS3v7W1srkAAAAAOgFX10cplauXJkzzjgj2267bc4888wuizZJcVLMpZdeml133TUf/OAH8+yzz/ZR0u61tbXl85//fF7/+tfnC1/4QhYvXtxt0SZJnnjiiVx88cXZfffdM23atPz5z3/ug7RAn3nyyeTSS7tes+aayYc+1Dd5AACAfxs/vrz9EyZUJgcAAABALyjbDENtbW3Zf//984UvfCEvvvhij/dffPHF2XXXXfPggw9WPlwPPfTQQ9l5553z9a9/PcuXL+/1nIaGhuy0006Z5853GDouvDB56aWu13zwg/6RHgAA+kNtbXn7d9qpIjEAAAAAesM1UsPMihUrcvDBB+fqq6/u9P1jxozJ9ttvn4kTJ2bJkiW5++6789xzz3VYd++992avvfbKokWLstFGG1U7dqcefvjhvP3tb8999923yjXjx4/PG9/4xqy99tp58cUX8+ijj+aBBx7odO3SpUvz4Q9/OCNHjswRRxxRrdhAX1ixIvnv/+5+3ezZ1c8CAAB0NGtWade+rspTT1UsCgAAAEBPOdlmmDnttNM6Ldqsvfba+eY3v5nHH388d9xxR2688cbccssteeqpp3L55Zdnm2226bDngQceyKGHHlrSlU3VcNRRR62yaHPYYYelsbExzzzzTJqbm/Ob3/wmixYtyv3335/HH3883/72t7Ppppt22Ldy5cocc8wxqyzkAIPE1Vcnf/tb12v22ivZfvu+yQMAALzalCnJ1Km933/ZZckPflC5PAAAAAA9oGwzjNx555352te+1uH55ptvnqampnzyk5/MWmut9ar3jRkzJgcddFBaWlryn//5nx32/u53v8v3vve9qmVelauvvjrXXHNNh+c1NTW56qqr8uMf/zi77bZbRo7s+FN84sSJ+cQnPpG77ror//Ef/9Hh/S+++GJOPPHEquQG+kBTU1Jf3/26UtYAAADVM2dOUlPT+/3HHps0NlYuDwAAAECJlG2GkU996lNZsWLFq56tscYaufbaa7PVVlt1uXeNNdbIz3/+87z5zW/u8L5TTjklzz//fEWzdueiiy7q9PkFF1yQGTNmlDRjnXXWyZVXXpntttuuw/uuuuqqPP300+VEBPpac3NSV5fsskvy0ENdr33965P99uubXAAAQOcmT07mz+994Wbp0uS9700efriyuQAAAAC6oWwzTLS0tOSGG27o8PyUU07ptGzSmXHjxuWCCy7o8PzJJ5/M+eefX3bGUi1btizXXntth+c777xzDj/88B7NWmONNfLVr361w/Ply5d3+jGAAWrBgmT69NK/q/Xtb09Gj65uJgAAoHszZiQLF/b+SqlHHkkOPDBpa6tsLgAAAIAuKNsME51d9bTuuuvmuOOO69Gcurq6Tq+T6suyzSOPPNLpSToHHXRQr+bts88+GTduXIfn9913X6/mAX2suTmZObNn/7j+s58lLS3VywQAAJSutjZpaEhuu6247nXatGSHHZKddkrGjOl+/623Jh/7WNLeXvWoAAAAAImyzbCwfPnyzJ8/v8Pzww8/vNOSSXeOOuqoDs/uueee3H777b3K11P/+te/On2+/fbb92re2LFjs+WWW3Z4/sgjj/RqHtDH6ut7/l2sbW3J7NnVyQMAAPTOlCnJd75TnHRz113J7bcX10yVYt685Nxzq5sPAAAA4P8o2wwDt9xyS55++ukOz3t7Esy+++6b1VdfvcPzvrp2qX0V36m25ppr9nrmhAkTOjwbNWpUr+cBfeS220q/Ouq1GhuTpqbK5gEAACpr//2T008vbe2JJya/+U118wAAAABE2WZY+O1vf9vh2bhx41JXV9ereTU1NZk2bVpJH6caNtxww06fP/nkk72e+cQTT5T8cYABZN68/t0PAABU30knJaV8w9CKFcn73pfcf3/1MwEAAADDmrLNMNDUyckNO+20U8aUcu/5Kuy6664dnjU3N/d6Xk9svvnmnRZhbr311l7Ne/LJJ/O///u/HZ5PnTq1V/OAPtTS0r/7AQCA6hs5MrnoomTSpO7XPvVUcRrOc89VPRYAAAAwfCnbDAN//OMfOzybVMo/UHWhs/3PPPNM/v73v5c1t1Tvfe97Ozz76U9/mhdffLHHsy666KKsXLnyVc822GCD7L777r3OB/SRJUvK29/aWpkcAABAda25ZvKLXyTrrtv92sWLk1mzktf8XR8AAACgUpRthrhly5bloYce6vB86623Lmvuqvbfd999Zc0t1fHHH5/VVlvtVc8efvjhfOELX+jRnHvvvTdf+cpXOjw/8cQTM3bs2LIyAn1g/Pjy9k+YUJkcAABA9W25ZfKznyWjRnW/9oorkq9+tfqZAAAAgGFpdH8HoLoeeuihDqe2JMmmm25a1txV7X/wwQfLmluqbbbZJqeffno+97nPver5t771rSTJN77xjQ5lnNf6wx/+kA984AN55plnXvV86tSpOeGEEyoZt0sNDQ1l7b/rrrsqlAQGodrapJxfQ7W1lcsCAABU3957J2efnRx/fPdrv/jF5C1vKa6VAgAAAKggZZsh7rHHHuv0+YYbbljW3FXtf/zxx8ua2xOf/exn889//jPf+c53XvX8W9/6Vq644oocddRR2WuvvbLttttm7bXXTltbWx599NHceuut+clPfpJrrrmmQxFp5513ztVXX53Ro/vul8a0adP67GPBkDNrVjJ3bnn7AQCAweUTn0huvz2ZN6/7tYcfntxyS7L99tXPBQAAAAwbrpEa4p566qlOn6+11lplzR01alTGjRvX4fmTTz5Z1tyeOvfcc3PhhRdm7bXXftXzhx56KF/84hczffr0rL/++hkzZkzGjx+fN77xjTnssMOyYMGCVxVtRo8eneOOOy4LFy7MuqXc/w4MDFOmJBMn9m7v1KnJ5MmVzQMAAFTfiBHJd7+b7Lpr92ufe6442ebpp6ufCwAAABg2lG2GuOeee67T52uuuWbZszub8fzzz5c9t6c+/OEP58EHH8wZZ5yRHXfcsUd7t9hii3zmM5/Jvffem3PPPTerr756lVICVfH000lra8/31dSUdyIOAADQv2pqkiuvTDbeuPu1//u/yfvfnyxfXv1cAAAAwLCgbDPELVu2rNPnlbgmacyYMR2eLV26tOy5vbX66qtn7bXXzsiRpf+0fuyxx/LXv/41d911V9rb26uYDqiKH/846envOzU1yfz5SW1tdTIBAAB9Y5NNkp//PFltte7XXn998vnPVz8TAAAAMCyU37hgQFuxYkWnz0eNGlX27M5mLO/j7xJbuXJlzjzzzJxxxhl59tlne7z/+eefz5VXXpkrr7wyb3nLW3LhhRdmypQpVUi6aosWLSpr/1133ZVjjjmmQmlgEGlvT84/v2d76uqSOXMUbQAAYKiYOrW4UurDH+5+7VlnJTvtlBx2WNVjAQAAAEObss0Qt6oTbCpRiulsRmen3VRLa2tr3vOe9+Tmm2/u8L7Ro0fn3e9+d/bcc89stdVWWXfddfPiiy/msccey6233pqrr746999//6v23HnnnZk2bVrOO++8fOQjH+mrH0bq6ur67GPBkHLLLcnixV2vWWONZMcdi3LNrFnJ5Ml9Eg0AAOhDRx6Z3H578p3vdL/2ox9N3vQmfzcAAAAAyqJsM8SNHTu20+erul6qJzq7MmpVH6/SXnrppbzrXe9KQ0NDh/cdcsghOfPMM/P617++072HH354vvWtb+VnP/tZ6uvr8+STT/7/71u2bFmOOuqojBs3Lh/4wAeqlh+ogAsu6H7N+ecnhx5a/SwAAED/Ovvsooz/u991va6tLTnggKSpKdlwwz6JBgAAAAw9I/s7ANU1fvz4Tp+3traWPXvJkiUdnk2YMKHsuaU46aSTOi3afPnLX85ll122yqLNy0aOHJn3v//9uf322/OGN7zhVe9rb2/PMccck7/97W+VjAxUUmtrctllXa9Zd93kve/tmzwAAED/GjMm+dnPktf8Hb9T//hHctBBSSffRAQAAABQCmWbIW699dbr9PkzzzxT1tyXXnopL730Uskfr5L+9re/5Vvf+laH5+9///tz8skn92jW61//+vzqV7/qcN3WkiVL8qUvfamMlEBVXXpp8sILXa/54AeTmpq+yQMAAPS/iROTX/wiGTeu+7ULFyaf+ETVIwEAAABDk7LNELfRRht1+vxf//pXWXMfeeSRHn28Svrud7+bFStWvOrZmDFj8vWvf71X8yZNmpRZs2Z1eH7ppZfm6aef7tVMoMpKuULqqKOqnwMAABhYdtwxueii0tZ+73vJd79b1TgAAADA0KRsM8RtsskmGTt2bIfnf//738ua+9BDD3X6fIsttihrbimuv/76Ds/e+ta3ZvPNN+/1zMMOO6zDs6VLl+b3v/99r2cCVdLcnLS0dL1m2rRk++37Jg8AADCwzJyZnHRSaWuPOy7xd38AAACgh5RthrgRI0Zkq6226vD83nvvLWvuqvZvvfXWZc3tzooVK/LHP/6xw/Pp06eXNXfatGkZMWJEh+e33357WXOBKijlVJujj65+DgAAYOA6/fRkv/26X7d8eXLwwUmZ35QEAAAADC/KNsPAzjvv3OHZHXfcUdbMzva/7nWvy8SJE8ua252nn366wxVSSbLhhhuWNXe11VbLOuus0+H5E088UdZcoMKeey659NKu16y1VvGdrAAAwPA1cmTy4x8nb3pT92sffzw54IDkhReqHgsAAAAYGpRthoHddtutw7O77747ra3/H3t3Hmd1Xe8P/HVYZARBXHMpzbTM6w5IgJi5VJaYJlJqLmhmC9TVX93balm2XrvVTeimZkrmFrhUWJqamSGjMLibS+aC+y64DCKc3x/nQuLgnDMz58yZOfN8Ph7n8eN8zvv9Pa/pV9zHfHl/P59Fnb7m3LlzK/qeanv11VdXuz5w4MAuX3t111i+fHmXrwtU0W9+kyxe3H7Nxz6WDB7cPXkAAICea9iw5Le/LQ3kl3Pjjcnmmyfbb186lnbq1NIRtgAAAACrYdimD9h7773brC1btixXXnllp6735JNPrvZ4pdV9T7Wtt956q13v6g40xWIxTz/9dJv1DTbYoEvXBarstNPK1zhCCgAAWOEd70jOPz9ZzdHRbTz1VHLbbcncucn06cmoUcnYsYZuAAAAgDYM2/QB22yzTbbccss26xdccEGnrjdz5sw2O74UCoXsu+++nbpeRwwcOHC1xz3Nnz+/S9e98cYbV7trjmEb6EFuvTW5/vr2a3bZJdlxx+7JAwAA9A777JN8//ud621uTsaPT2bPrm4mAAAAoFczbNNHHHbYYW3WLr744jz88MMduk6xWMz06dPbrL/nPe/JW97ylk7n64iRI0e2Wbvqqqu6dCzWxRdfvNr1XXbZpdPXBKrs9NPL13ziE7XPAQAA9D7/8R/JIYd0rre1NZk0KVmwoLqZAAAAgF7LsE0fceyxx2aNNdZYZW3p0qX52te+1qHrzJgxI3fccUeb9alTp1Z8jcmTJ6dQKLR53X///RX177PPPm3WFi9enP/+7/+uOMNrPfHEE/npT3/aZn3dddc1bAM9xcsvJ2ef3X7NWmslBx/cPXkAAIDepVBIfvGLZOedO9ff2ppMmVLdTAAAAECvZdimj9hkk01y9NFHt1k/66yzMnPmzIqucc899+S4445rs77ddtvlgAMO6GLCyk2cODEDBw5ss/7d7343l19+eYeutWTJkkycOHG1u+J85CMfSb9+/icCPcKsWclzz7Vfc8ghydCh3RIHAADohQYPTr75zc73NzcnXTzGGgAAAGgMJgn6kJNOOinrrrtum/XDDz885513Xru9N910U/baa688//zzbT776U9/2q1DKW9961vzyU9+ss36q6++mv333z+nnHJKli9fXvY699xzT3bdddf87W9/a/PZ4MGDc8IJJ1QlL9BF8+cnX/xi+TpHSAEAAOV08CGdNmbMqE4OAAAAoFczbNOHrL/++jnjjDParC9ZsiSHHnpoPvCBD+SSSy7J448/nmXLluW5557LNddck2OPPTajR4/OwoUL2/Qef/zx2WOPPboj/iq+8Y1vZPPNN2+zvmTJknzuc5/L1ltvne9///uZO3dunnjiiSxdujSLFy/Ovffem/POOy8HH3xwttlmm7S0tKz2+ieddFI22WSTWv8YQHtaWpKxY5NddkkefbT92h13TEaN6p5cAABA77VgQX37AQAAgIYwoN4B6F4HHHBAvvvd7+YrX/lKm88uu+yyXHbZZRVfa8KECfnBD35QzXgVW3/99XP55Zdn/Pjxeeqpp9p8/o9//CNf/vKXO3Xtz33uc/l//+//dTUi0BWzZyeTJiWtrZXVjx2bFAq1zQQAAPR+ixd3rX81x1ADAAAAfY+dbfqgL3/5y/nxj3+c/v37d/oahx12WGbNmpWBAwdWMVnHbL311pkzZ05Gjx5dlesNGjQoP/jBD/KTn/ykKtcDOqmlpWODNkly1lmeMAUAAMobOrRr/cOGVScHAAAA0KsZtumjjjvuuMyZMycjR47sUN/GG2+cs88+O2effXYGDRpUo3SVe8c73pE5c+bkhz/8YTbbbLNOXaN///454IADMn/+/Pznf/5nCnbHgPqaOrVjgzZJqX7KlNrkAQAAGseIEV3r33nn6uQAAAAAejXDNn3Yu971rsyfPz9XXHFFjjzyyGy66aarrRs+fHj222+/zJgxI/fdd18OO+ywLn3vWWedlWKx2Ob11re+tVPXGzBgQD7/+c/nvvvuy+9///tMmTIlo0aNyhprrPGGPZtsskn233///OAHP8gDDzyQiy++ONttt10nfyKgaubNS5qbO9fb3JzMn1/dPAAAQGOZPLneCQAAAIAGMKDeAai/vffeO3vvvXeS5LnnnssjjzySF198MU1NTVl//fWz8cYb1zlhZfr165cJEyZkwoQJSZJXX301zz77bJ577rksWrQoa6yxRoYPH5511lkna621Vp3TAqs1Y0bX+0eNqk4WAACg8YwalYwZ0/kh/5/9LJkwIdlnn+rmAgAAAHoVwzasYvjw4Rk+fHi9Y1TFgAEDssEGG2SDDTaodxSgUgsW1LcfAABofNOmJePHd/z42iQpFpNDDintqrnlltXPBgAAAPQKjpECoOdYvLhr/YsWVScHAADQuEaOTGbOTJqaOtf/3HPJAQckL7xQzVQAAABAL2LYBoCeY+jQrvUPG1adHAAAQGObMCGZM6d0pFRn3HZb8vGPl3a6AQAAAPocwzYA9BwjRtS3HwAA6DtGjEjmzk3mzUumTk3GjUu22y7ZfvukXwW3zH7zm+SHP6x9TgAAAKDHMWwDQM8xeXJ9+wEAgL5n1KjklFNKO93cemtyyy3Jz35WWe+XvpRccUVt8wEAAAA9jmEbAHqOUaOSLbfsXO+YMcnIkdXNAwAA9E3HHpscc0z5uuXLk4MPTu67r/aZAAAAgB7DsA0APcs663S8p6kpmT69+lkAAIC+qVBIpk1LRo8uX/vMM8mBByYvvVT7XAAAAECPYNgGgJ7jwQeTlpaO9TQ1JTNnJiNG1CYTAADQNw0alFx4YbLhhuVrb7qptBtOsVjzWAAAAED9GbYBoOc4++yO3ZweOzaZMyeZMKF2mQAAgL7rzW8uDfcPGFC+9pxzkv/5n9pnAgAAAOrOsA0APUOxmMyY0X5NoVDaxn3q1GT+/OS66+xoAwAA1Na73538+MeV1X7hC8nVV9c2DwAAAFB3FTyWAwDdoLk5ueee9msmTiw9VQoAANCdpkwpDfyXe0Bg2bLkIx8pHY+72Wbdkw0AAADodna2AaBnOOus8jVHHlnzGAAAAG0UCsn//m8ycmT52qeeSg48MHn55drnAgAAAOrCsA0A9ffyy8kFF7Rfs+GGyfvf3z15AAAAXm/NNZOLLkrWX798bUtL8qlPlY7LBQAAABqOYRsA6u93v0uef779mo99LBk4sHvyAAAArM5mmyW/+U3Sv3/52l/9Kpk+vfaZAAAAgG5n2AaA+psxo3zN5Mk1jwEAAFDWHnskP/xhZbXHH59ce21t8wAAAADdzrANAPX1yCPJ5Ze3X7PTTskOO3RLHAAAgLL+/d9Lu2+W8+qryUEHJQ89VPtMAAAAQLcxbANAfZ1zTrJ8efs1Rx7ZPVkAAAAqUSgkp51WejCgnCeeSCZOTJYsqXksAAAAoHsYtgGgforF8kdIDRiQHHpo9+QBAACo1ODByUUXJeuuW772hhuSKVNKvwMBAAAAvZ5hGwDqZ8GC5Pbb26/54AeTDTfsnjwAAAAdscUWyfnnJ/0quMV2xhml3XAAAACAXs+wDQD1U25Xm8QRUgAAQM/23vcm3/9+ZbWf/Wxy3XW1zQMAAADUnGEbAOrjlVeSc89tv2bddZN99+2ePAAAAJ31hS8kH/lI+bqlS5OJE5NHHql9JgAAAKBmDNsAUB+XXpo8/XT7NYcckgwa1D15AAAAOqtQSH75y2T77cvXPvZYctBBpQcQAAAAgF7JsA0A9VHJEVKTJ9c8BgAAQFUMGZJcfHEyfHj52rlzk3//95pHAgAAAGrDsA0A3e+KK5Lf/a79mn/7t2TkyO7JAwAAUA1bbpmcd15pp5tyfv7z5Iwzap8JAAAAqDrDNgB0n5aWZOzY5H3vS4rF9muPPLKyG9QAAAA9yT77JN/5TmW1xx6b7LBD6fipceOSqVNLvzcBAAAAPZphGwC6x+zZyfjxSXNzZfVvelNt8wAAANTKl76UTJxYvm758uTWW5PbbisdLTV9ejJqVOkhBUM3AAAA0GMZtgGg9lpakkmTktbWyns+9alkwYLaZQIAAKiVQiE588zS8bid0dxcelhh9uzq5gIAAACqwrANALU3dWrHBm2SUv2UKbXJAwAAUGtDhyYXX5wMG9a5/tbW0kMLHkIAAACAHsewDQC1NW9e5UdHvV5zczJ/fnXzAAAAdJd3vCM555zO93sIAQAAAHokwzYA1NaMGfXtBwAAqKcJE5Jjj+18v4cQAAAAoMcxbANAbXV1y3NbpgMAAL3dgAFd6/cQAgAAAPQohm0AqK3Fi7vWv2hRdXIAAADUy403dq3fQwgAAADQoxi2AaC2hg7tWv+wYdXJAQAAUC8eQgAAAICGYtgGgNoaMaK+/QAAAPXmIQQAAABoKIZtAKityZPr2w8AAFBvHkIAAACAhmLYBoDaGjUqedvbOtc7ZkwycmR18wAAAHS3rj5EMGlSVWIAAAAA1WHYBoDaW3fdjvc0NSXTp1c/CwAAQHcbNar0MEFnnXJKUixWLw8AAADQJYZtAKitRx9NWlo61tPUlMycaat0AACgcUybVvpdpzNmzUr++7+rmwcAAADoNMM2ANTWBRd07AnMsWOTOXOSCRNqlwkAAKC7jRxZeqigswM3X/xi8uc/VzcTAAAA0CmGbQCorXPPLV8zenQydWoyf35y3XV2tAEAABrThAmlhws6c6TU8uXJRz+aLFxY/VwAAABAhwyodwAAGtg//pHMm9d+zYc+lPz2t92TBwAAoN5GjEjmzi09bDBjRrJgQfL888mDDyaLF7ff+9RTyUEHJX/9azJoUPfkBQAAANowbANA7Zx3XvmaQw+tfQ4AAICeZtSo0muFRx4pDeI8/nj7fTfckHzuc8mpp9Y2HwAAAPCGHCMFQG0Ui+WPkBoyJNlvv+7JAwAA0JNtskkyc2YyoIJn4047LTnjjNpnAgAAAFbLsA0AtXHzzcmdd7Zfc8AByeDB3RIHAACgx9ttt+SHP6ysdsqU0lFUAAAAQLczbANAbZTb1SZJDjmk9jkAAAB6k899rrLjdpcsSSZOTJ56qvaZAAAAgFUYtgGg+pYvT84/v/2a9dZL3ve+7skDAADQWxQKpWOitt++fO2DDyYHH5y8+mrtcwEAAAArGbYBoPrmzEkWLmy/ZtKkZODA7skDAADQmwwZklx0UbL22uVrr7oq+drXap8JAAAAWMmwDQDV5wgpAACArtlqq+TXv66s9gc/KA3nAAAAAN3CsA0A1bV0aTJzZvs1b35zMn589+QBAADorSZMSL7xjcpqjzwyufPO2uYBAAAAkhi2AaDarrgiefrp9msOPjjp5/8EAQAAlPX1rycf/GD5uhdeSD784WTx4tpnAgAAgD7Ov3QCUF3nnVe+5tBDa58DAACgEfTrVzpO6m1vK197553JUUclxWLtcwEAAEAfZtgGgOp56aXk4ovbr3nnO5OdduqWOAAAAA1hnXWSiy5K1lyzfO2FFyYnn1z7TAAAANCHGbYBoDrmzy9tWf7ii+3XHXJIUih0TyYAAIBGseOOyWmnVVb75S8nV11V2zwAAADQhxm2AaBrWlqSsWOTXXZJ/vSn8vWHHFL7TAAAAI3osMOSz362fN3y5cnBBycPPlj7TAAAANAHGbYBoPNmz07Gj0+amyur32qr5O1vr20mAACARvbDHya77lq+7qmnkokTk9bW2mcCAACAPsawDQCd09KSTJrUsRu399+fLFhQs0gAAAANb401kpkzk402Kl87f35lO+EAAAAAHWLYBoDOmTq1409IvvpqMmVKbfIAAAD0FRtvXBq4GTCgfO0vflF6AQAAAFVj2AaAjps3r/Kjo16vubn0dCUAAACdN3588qMfVVY7ZUpyww21zQMAAAB9iGEbADpuxoz69gMAAFDacfRjHytf98oryUEHJU8+WftMAAAA0AcYtgGg4xYsqG8/AAAASaGQnHZassMO5WsXLkwOPrh0vC8AAADQJYZtAOi4xYu71r9oUXVyAAAA9HWDBycXXZQMH16+9s9/Tr761ZpHAgAAgEZn2AaAjhs6tGv9w4ZVJwcAAADJllsm55xT2ummnP/6r+TCC2ufCQAAABqYYRsAOm7EiPr2AwAAsKoPfjD5xjcqq508Ofn732saBwAAABqZYRsAOm7y5Pr2AwAA0NYJJyT77lu+7oUXSg9BbLttMm5cMnVq0tJS+3wAAADQIAzbANBxo0YlY8Z0rnfMmGTkyOrmAQAAIOnXLzn77NKxUuW0tiZ33JHMnZtMn176PW/sWEM3AAAAUAHDNgB0zrRppRu5HdHUVLqJCwAAQG2ss05y0UXJmmt2vLe5ORk/Ppk9u/q5AAAAoIEYtgGgczbZJFm+vPL6pqZk5szSVuUAAADUzg47JL/4Red6W1uTSZOSBQuqmwkAAAAaiGEbADrn4osrrx07NpkzJ5kwoXZ5AAAA+JdDD00+97nO9ba2JlOmVDcPAAAANBDDNgB0zqxZ7X9eKCSf+EQyf35y3XV2tAEAAOhuBx/c+d7m5tLvcwAAAEAbhm0A6Lgnnkiuuab9mg9+MDnttGTkyO7JBAAAwKrOOadr/TNmVCcHAAAANBjDNgB03MUXJ8uXt18zaVL3ZAEAAGD1Fiyobz8AAAA0KMM2AHTczJntfz5wYPKhD3VPFgAAAFZv8eKu9S9aVJ0cAAAA0GAM2wDQMU8+mfzlL+3X7L13ss463RIHAACANzB0aNf6hw2rTg4AAABoMIZtAOiY3/42Wbas/ZqDDuqeLAAAALyxESO61r/zztXJAQAAAA3GsA0AHVPuCKn+/ZP99++eLAAAALyxyZO71r90aVViAAAAQKMxbANA5Z5+OrnqqvZr9torWW+97skDAADAGxs1KhkzpvP9p5+e/PGP1csDAAAADcKwDQCV+93vHCEFAADQm0ybljQ1da63WEw+9rHkvvuqmwkAAAB6OcM2AFSukiOkDjigW6IAAABQgZEjS7/LdXbg5tlnk4kTk5dfrm4uAAAA6MUM2wBQmWefTa68sv2a97wn2WCDbokDAABAhSZMSObM6fyRUjfemEyZUtrpBgAAADBsA0CFfve7ZOnS9mscIQUAANAzjRiRzJ2bzJuXTJ2ajBuXbLddssMOSb8KbhGeeWbyi1/UPicAAAD0AgPqHQCAXmLWrPY/79cv+fCHuycLAAAAnTNqVOn1Wqefnhx7bPneqVOTnXZKdtmlJtEAAACgt7CzDQDlPf988qc/tV/z7ncnb3pT9+QBAACgeo45Jjn66PJ1r7xS2tH0qadqnwkAAAB6MMM2AJT3P/9TuqnaHkdIAQAA9E6FQjJtWrLzzuVrH3ww+djHkmXLap8LAAAAeijDNgC8sZaWZOzY5BvfKF974IG1zwMAAEBtrLlmcuGFyTrrlK/905+SE0+seSQAAADoqQzbALB6s2cn48cnzc3la/v1Kw3mAAAA0HttsUVyzjmlnW7K+fa3S783AgAAQB9k2AaAtlpakkmTktbWyuqXLy/VL1hQ21wAAADU1gc+UNnupkly2GHJvffWNg8AAAD0QIZtAGhr6tTKB21WaG1NpkypTR4AAAC6zwknlIZuynn++WTixOSll2qfCQAAAHoQwzYArGrevMqOjlqd5uZk/vzq5gEAAKB79euX/PrXyVvfWr725puTT386KRZrHgsAAAB6CsM2AKxqxoz69gMAAFB/666bXHhhMmhQ+dpf/So59dTaZwIAAIAewrANAKtasKC+/QAAAPQMI0Yk//u/ldV+7nPJ9dfXNg8AAAD0EIZtAFjV4sVd61+0qDo5AAAAqL+jjko+8YnydUuXJgcdlDz5ZO0zAQAAQJ0ZtgFgVUOHdq1/2LDq5AAAAKBn+OlPk1Gjytc99FByyCHJsmW1zwQAAAB1ZNgGgFWNGFHffgAAAHqWpqZk1qxk3XXL1151VXLCCbXPBAAAAHVk2AaAVU2eXN9+AAAAep7NN0/OOy8pFMrXfu97yW9/W/tMAAAAUCeGbQBY1ahRyfbbd653zJhk5Mjq5gEAAKBneN/7km99q7LaI45I7rmntnkAAACgTgzbANDWu97V8Z6mpmT69OpnAQAAoOf4yleSCRPK1y1alBx4YPLii7XPBAAAAN3MsA0Abc2b17H6pqZk5sxkxIja5AEAAKBn6NcvOfvs5G1vK197223JsccmxWLtcwEAAEA3MmwDwKruvz+5+ebK68eOTebMqezJRgAAAHq/4cOTiy4qPXhRzrnn2gUVAACAhmPYBoBV/fa35Wu22SaZOjWZPz+57jo72gAAAPQ1O+6YnHpqZbXHH1/63REAAAAaxIB6BwCghyk3bDNkSLJgQWVPMAIAANC4jjgiaW5O/vd/26979dVk0qTS75JvelP3ZAMAAIAasrMNAP/yzDPJX//afs0++xi0AQAAoOTHP05Gjy5f98gjycEHlwZvAAAAoJczbAPAv1x6abJsWfs1++/fPVkAAADo+QYNSmbNStZfv3ztX/6SfOUrNY8EAAAAtWbYBoB/KXeEVP/+yb77dk8WAAAAeoe3vCU5//ykXwW3Gk8+Odl662T77ZNx45KpU5OWltpnBAAAgCoybANASWtrctll7de8+93Juut2Tx4AAAB6j732Sr7zncpq7747ue22ZO7cZPr0ZNSoZOxYQzcAAAD0GoZtACi56qrkxRfbr3GEFAAAAG/ki1/s/O+Nzc3J+PHJ7NnVzQQAAAA1YNgGgJJyR0glhm0AAAB4Y4VCMmNGstVWnetvbU0mTUoWLKhuLgAAAKgywzYAJMuXJ7/7Xfs1O+6YvPWt3RIHAACAXmrttZOLLkrWXLNz/a2tyZQp1c0EAAAAVWbYBoDk+uuTxx9vv8auNgAAAFRi++2TL32p8/3Nzcn8+dXLAwAAAFVm2AaAyo6QOuCAmscAAACgQTzxRNf6Z8yoTg4AAACoAcM2ACSXXNL+55ttluy0U3ckAQAAoBEsWFDffgAAAKghwzYAfd2sWcldd7Vf86EPJYVC9+QBAACg91u8uGv9ixZVJwcAAADUgGEbgL6qpSUZOzaZNKl8rSOkAAAA6IihQ7vWP2xYdXIAAABADRi2AeiLZs9Oxo9Pmpsrq+/qE4kAAAD0LSNG1LcfAAAAasiwDUBf09JS2s2mtbXynkMOSRYsqF0mAAAAGsvkyV3r/+hHqxIDAAAAasGwDUBfM3VqxwZtklL9lCm1yQMAAEDjGTUqGTOm8/3/+79JsVi9PAAAAFBFhm0A+pJ58yo/Our1mpuT+fOrmwcAAIDGNW1a0tTUud5zz01+9rPq5gEAAIAqMWwD0JfMmFHffgAAAPqOkSOTmTM7P3Bz/PGdf2AEAAAAasiwDUBfsmBBffsBAADoWyZMSObM6dyRUkuXJpMmJU8+Wf1cAAAA0AWGbQD6ksWLu9a/aFF1cgAAANB3jBiRzJ1bOtp46tRk3Lhk222TtdYq3/vQQ8khhyTLltU+JwAAAFTIsA1AXzJ0aNf6hw2rTg4AAAD6nlGjklNOKe10c9ttyR13JOuvX77vqquSr3+99vkAAACgQoZtAPqSESPq2w8AAAArvOUtyfnnJ/0quEX53e8mv/td7TMBAABABQzbAPQlkyfXtx8AAABea6+9kpNOqqz2iCOSe++tbR4AAACogGEbgL5k1KhkzJjO9Y4Zk4wcWd08AAAA8KUvJfvtV77u+eeTiROTl16qfSYAAABoh2EbgL7m29/ueE9TUzJ9evWzAAAAQL9+ya9+lbztbeVrb745+cxnkmKx9rkAAADgDRi2AehrHn20Y/VNTcnMmcmIEbXJAwAAAMOHJxdeWPodtJwZM5LTT695JAAAAHgjhm0A+po//KHy2rFjkzlzkgkTapcHAAAAkmSnnZL//d/Kaj/72WT+/JrGAQAAgDdi2AagL3n11eSyy9qvGTYsmTq1dNPyuuvsaAMAAED3mTw5OfbY8nWvvJIcdFDy9NM1jwQAAACvZ9gGoC+5/vrk2Wfbr/n615NTTklGjuyeTAAAAPBa//M/lf1O+sADycc+lixbVvtMAAAA8BqGbQD6kksvLV/zwQ/WPgcAAAC8kaam5MILk3XXLV97+eXJSSfVPhMAAAC8hmEbgL7kD39o//Mttkje+c7uyQIAAABvZPPNk3POSQqF8rXf+lbyxz/WPhMAAAD8H8M2AH3FQw8lN9/cfs0HP1jZjUwAAACotX32Sb7xjfJ1xWLpOKn77695JAAAAEgM2wD0HeV2tUmSffetfQ4AAACo1AknJB/4QPm6Z59NDjooaW2tfSYAAAD6PMM2AH1FuWGbNddM3vOebokCAAAAFenXLzn77NKxUuW0tCSf+1ztMwEAANDnGbYB6AuWLEmuvLL9mj33LA3cAAAAQE+y3nrJhRcma6xRvvb005Mzz6x9JgAAAPq0AfUO0J6jjz663hG6rFAo5Iwzzqh3DKCv++tfkxdfbL/mgx/sniwAAADQUSNHJtOmJcceW772M59Jdtop2XnnmscCAACgb+rRwzZnnXVWCoVCvWN0WrFYNGwD9AyXXlq+Zt99a58DAAAAOuuYY5K5c8vvXNPamkycWDpWap11uicbAAAAfUqvOEaqWCz2yhdAj/GHP7T/+bbbJptv3j1ZAAAAoDMKhWT69NKuNeXcd19yxBHJ8uU1jwUAAEDf0yuGbQqFQq98AfQI99xTerXHEVIAAAD0BmuumVx4YTJ8ePna2bOT732v5pEAAADoe3rFsE29d6ixqw3QqzlCCgAAgEbytrclZ59dWe0JJyRXXFHbPAAAAPQ5A+odoFKFQiFf/epXs+WWW9Y7SrvuvffefPvb3653DIB/Oe+89j9fe+1k3LjuyQIAAADVMGFC8tWvJt/5Tvt1xWJyyCHJggXJZpt1TzYAAAAaXq8ZtkmSCRMmZPTo0fWO0a7rr7/esA3QM7S0JJ/+dDJvXvt1u+ySDBzYPZkAAACgWr75zeT665Mrr2y/7umnk0mTkr/+NRk0qHuyAQAA0NB6xTFSAHTQ7NnJ+PHlB22S5JprSvUAAADQm/Tvn5x7bvKWt5SvveGGZPPNk+23L+3uOnVq6SEVAAAA6ATDNgCNpqWl9MRea2tl9UuXluoXLKhtLgAAAKi2DTZIZs6sbMfWxx9PbrstmTs3mT49GTUqGTvW0A0AAAAdZtgGoNFMnVr5oM0Kra3JlCm1yQMAAAC19K53JT/5Sed6m5tLO8Pa8RUAAIAOMGwD0EjmzSvdKOyM5uZk/vzq5gEAAIDu8OlPJ4cd1rne1lY7vgIAANAhA+odoD2bbbZZCoXCyveDBg2qY5rKDBo0qE1ugG4zY0bX+0eNqk4WAAAA6C6FQvLznyc33VQ6KqqjVuz4Ondu1aMBAADQeHr0sM39999f7wgdttNOO/XK3ECD6OpTeJ7iAwAAoLcaMiQ58cTkoIM6179ix1cPoQAAAFCGY6QAGsnixV3rX7SoOjkAAACgHq6+umv9Xd0xFgAAgD7BsA1AIxk6tGv9w4ZVJwcAAADUgx1fAQAA6AaGbQAayYgR9e0HAACAerLjKwAAAN3AsA1AI5k8ub79AAAAUE92fAUAAKAbGLYBaCSjRiU779y53jFjkpEjq5sHAAAAupMdXwEAAOgGhm0AGs2++3a8p6kpmT69+lkAAACgO3V1x9bdd69KDAAAABqbYRuARnP33R2rb2pKZs709B4AAAC936hRpZ1bO+uEE5LFi6uXBwAAgIZk2AagkSxbllx5ZeX1Y8cmc+YkEybULhMAAAB0p2nTSg+WdMaddyaf+ERSLFY3EwAAAA3FsA1AI1mwIHnmmfZrttgimTo1mT8/ue46O9oAAADQWEaOLO3g2tmBmwsuSH760+pmAgAAoKEMqHeA7nD11Vfn2muvTUtLS+6+++48//zzef7559Pa2trlaxcKhbz66qtVSAlQBVdcUb7m0kuTbbapfRYAAAColwkTSju5TpmSNDd3vP8LXygdSbXrrtXPBgAAQK/XsMM2y5Yty49//OP87Gc/ywMPPLByvWgLWKCR/elP7X/+5jcn73xn92QBAACAehoxIpk7t7Sz64wZpd1gFy1Knn46efTR9ntffTX5yEdKPW96U/fkBQAAoNdoyGGbO++8M4ceemhuvvnmNsM1hUKhat9jcAfoURYvLh0L1Z73vS+p4t+DAAAA0OONGlV6rbB0abLHHqWdb9rzyCPJIYeUHmwZ0JC3UQEAAOikfvUOUG333ntv9thjj5WDNoVCYZUXQMO65prSDcP2vO993ZMFAAAAeqqBA5MLLkg23LB87dVXJ1/7Wu0zAQAA0Ks01LDNsmXLst9+++Xxxx9PsuouNsViseovgB6l3BFShUKy117dkwUAAAB6sk03Tc4/P+lXwe3RH/wgueSSmkcCAACg92ioYZvTTz89d95552qHbMaNG5ef/OQn+dvf/pbHHnssL730UpYvX97l17Jly+r4EwO8RrlhmxEjkvXX754sAAAA0NPtsUfyve9VVnvkkck//lHbPAAAAPQaDXXY8LRp01Z5XywW8+Y3vzm//OUvs/fee9cpFUA3eOCB5K672q9xhBQAAACs6j/+I5k7t/zONYsWJRMnlmoHD+6WaAAAAPRcDbOzzcMPP5w77rhj5a42xWIx66+/fv7yl78YtAEa3xVXlK8xbAMAAACrKhSSs85KttqqfO0ttySf+lTieHkAAIA+r2F2trnhhhtW/rlYLKZQKOTEE0/M2972tjqm6l3uuOOO3HbbbXnkkUfywgsvpKmpKRtssEG22Wab7Lzzzhk4cGC9IwJvpNwRUkOGJGPHdk8WAAAA6E3WXju58MJkzJjk5Zfbrz377GTXXZNPfrJ7sgEAANAjNcywzRNPPLHK+wEDBuTwww+vU5reY+HChTnllFNyzjnn5JFHHnnDuqFDh2a//fbLcccdl1122aUbE7Z11lln5aijjqr59xx55JE566yzav490GXLliVXXdV+zXvekwwa1C1xAAAAoNfZYYfk1FOTI44oX/u5zyUjRiR1vkcGAABA/TTMMVLPPPPMKu+33nrrDB06tE5per7ly5fne9/7XrbeeuucfPLJ7Q7aJMnixYtz7rnnZvTo0TniiCPy/PPPd1NSoKxf/zp53d+BbThCCgAAANp3+OGlY6LKeeWV5KCDkqeeqn0mAAAAeqSGGbYZ9JodGwqFQjbYYIM6punZWltbs//+++crX/lKXi63Ne5qnH322Rk9enTuv//+6ofrQd7+9rfXOwK0r6WldDTU5Mnlaw3bAAAAQHk/+UllO9Y8+GDysY+VdpsFAACgz2mYYZuNNtpolfedGSLpC5YtW5aDDjoos2fPXu3nAwcOzI477pi99toro0ePzlprrbXaurvvvjt77rlnHnvssVrGrZv+/ftnciUDDFAvs2cn48cnzc3lawuF5J57ap8JAAAAertBg5JZs5L11itf+6c/JSedVPtMAAAA9DgD6h2gWrbffvuVfy4Wi3n88cfrmKbn+uY3v5lLL720zfrw4cPz9a9/PUcffXTWXnvtletLly7N7373u3zlK1/J3XffvUrPfffdl0MPPTRXXnll+vXrvrmtfffdN3Pnzq3KtZ588sl86EMfarO+zz77ZNNNN63Kd0DVtbQkkyYlra2V1ReLyUc+ksyZUzpTHgAAAHhjm22WnHNO8oEPlH6nbs+3vpW8612lWgAAAPqMhhq22XjjjVfutHL//ffn0UcfzcYbb1znZD3HLbfcku9+97tt1jfffPNcddVV2XLLLdt8NnDgwEycODH77LNPJk6cmMsvv3yVz6+++uqceuqp+fSnP12z3K+3wQYbVO2YsB/+8IerXT/mmGOqcn2oialTKx+0WaG1NZkyJanSoBoAAAA0tPe/PznxxOQb32i/rlgsHSe1YEHy1rd2RzIAAAB6gIY5RipJJk+enOJrnja56KKL6pim5/nCF76QZa87R3rIkCH54x//uNpBm9fXXXTRRdl2223bfHbCCSfkxRdfrGrW7nLGGWe0Wdtoo40yYcKEOqSBCsybV9nRUavT3JzMn1/dPAAAANCovva1ynasefbZ5KCDOv5gDAAAAL1WQw3bHH/88VlnnXVSKBRSLBbz3e9+Ny+//HK9Y/UICxYsyBVXXNFm/YQTTsg222xT0TUGDx6c008/vc36008/ndNOO63LGbvbnDlzcuedd7ZZP/LIIzNgQMNs+kSjmTGjvv0AAADQV/Trl/z618nmm5evbWlJ/v3fa58JAACAHqGhhm3WX3/9/OQnP0mxWEyhUMhjjz2WyZMn1ztWj3Dqqae2WVt33XXz2c9+tkPXGTt2bN7//ve3We+Nwza/+MUvVrv+8Y9/vJuTQAcsWFDffgAAAOhL1l03ufDCZI01yteedlpy1lk1jwQAAED9NdSwTZIcfvjh+eIXv7jyOKlZs2bl0EMPzQsvvFDnZPXz6quvZubMmW3WDzvssAwePLjD1/vEJz7RZu3OO+/MjTfe2Kl89bB48eLV/mey++675+1vf3sdEkGFFi/uWv+iRdXJAQAAAH3FyJHJtGmV1X7608lNN9U0DgAAAPXXcMM2SfK9730vJ510UgqFQpLkggsuyPbbb5+f//zneemll+qcrvtdf/31efbZZ9usT5w4sVPX23fffbPmmmu2Wf/jH//YqevVw3nnnZcXX3yxzfoxxxxThzTQAUOHdq1/2LDq5AAAAIC+5JhjkqOOKl/X2ppMnJg891zNIwEAAFA/A+odoJq+9a1vrfJ+1113zbXXXpskeeCBBzJlypQcd9xxGTFiRLbddtusu+66GTJkSJe/9+tf/3qXr1FLf/7zn9usDR48OGPHju3U9ZqamjJu3LhcddVVbb7nK1/5Sqeu2d1Wd4TU8OHDc9BBB9UhDXTAiBHJ3Lld6wcAAAA6plBIpk8vHc98883t1/7zn8kRRySXXJL0a8hnHQEAAPq8hhq2OfHEE1fuZvNaK9aKxWJeeeWVXH/99bn++uur9r09fdhm/vz5bdZ22mmnDBw4sNPXHD16dJthm5aWlk5frzvdeuutmTdvXpv1j33sY2lqaqpDIuiAyZNLN/e60g8AAAB03JprJhdeWDpW6vnn26/9/e+TH/wg+fKXuycbAAAA3aohH60oFosrXyveJ6Whm0KhsMrnXX31Bjev5mmb7bffvkvXXF3/c889lwcffLBL1+0Oq9vVJnGEFL3EqFHJmDGd6x0zpnRDEAAAAOicLbdMzj67stqvfS153cNqAAAANIaGHLZZMVTz2le5zzvz6g2WLl2ahQsXtlnfaqutunTdN+q/9957u3TdWluyZEl+/etft1kfOXJkdtppp+4PBJ0xbVrSv3/HepqaurYjDgAAAFCy335JJUepL1+eHHJI8tBDtc8EAABAt2q4YZtq7lrTCLvaLFy4MMuXL2+zvummm3bpum/Uf//993fpurV28cUX55lnnmmzblcbepURI5KhQyuvb2pKZs4s9QEAAABd961vJXvtVb7uySeTSZOSV16pfSYAAAC6zYB6B6imb3zjG/WO0OM88cQTq11/05ve1KXrvlH/k08+2aXr1trqjpAaPHhwDj300DqkKZk7d26X+m+99dYqJaHXuOuu5LnnKqsdO7a0E45BGwAAAKie/v2Tc88t/b798MPt1zY3J1/4QvLTn3ZPNgAAAGrOsE2DW90uLkmy9tprd+m6/fv3z+DBg/PSSy+tsv7000936bq1dP/99+fPf/5zm/VJkyZl2LBhdUhUMm7cuLp9N73U1VeXr/noR5P/+I9k5Mja5wEAAIC+aMMNk1mzkne/O1m6tP3aU05JLrssGTSotFvtiBHJUUf5vR0AAKCXarhjpFjVCy+8sNr1tdZaq8vXXt01XnzxxS5ft1bOOOOM1R7/5Qgpep3VDI2tYt11S0/XuWEHAAAAtTVmTPKjH1VWe889yW23JXPnJtOnJ6NGlXakbWmpbUYAAACqzrBNg1v6Bk/VDBjQ9U2NBg4c2GbtlR56/vTy5ctz1llntVl/5zvfmfHjx3d/IOis5cuTv/yl/Zrdd0/6+esdAAAAusWUKckhh3Sut7k5GT8+mT27upkAAACoKf8a2+CWLVu22vX+/ft3+dqru8arr77a5evWwmWXXZaHHnqozfrHP/7xOqSBLrj99uSpp9qv2XPP7skCAAAAJIVCcvrpybbbdq6/tTWZNClZsKC6uQAAAKiZrm9vQo/2RjvYVGMoZnXXWN1uNz3BL37xizZrAwcOzBFHHFGHNKu67rrrutR/66235pOf/GSV0tDjlTtCKkn22KP2OQAAAIB/GTIkufDC0tFQb3Cse7taW0s75MydW/1sAAAAVJ1hmwY3aNCg1a6/0fFSHbG6I6Pe6Pvq6Yknnsjs1WzFu99++2XDDTesQ6JVjR07tt4R6E2uvrr9zzfcMPm3f+ueLAAAAMC/bL118tWvJl/+cuf6m5uT+fNLAzsAAAD0aI6RanBDhw5d7fqiRYu6fO3Fixe3WRs2bFiXr1ttM2bMWO1w0THHHFOHNNAFy5Yl11zTfs0ee5S2rwYAAAC632qOMe+QGTOqkwMAAICaMmzT4NZbb73Vrj/33HNduu6SJUuyZMmSir+vnn75y1+2WXvLW96S97///XVIA11w001Juf/tOkIKAAAA6mfBgvr2AwAA0C0M2zS4jTbaaLXrjz/+eJeu++ijj3bo++rlb3/7W+68884260cddVT69fNff3qZckdIJYZtAAAAoJ5WsxN0h1RhN2oAAABqb0C9A1TTt771rbp879e//vW6fG8lNtlkkwwaNKjNLjQPPvhgl667cOHC1a5vscUWXbputf3iF79os9avX78cffTRdUgDXVRu2GaTTZK3v717sgAAAABtvcGR7hXrgUe0AwAA0FZDDduceOKJKRQK3f69PXnYplAoZMstt8wdd9yxyvrdd9/dpeu+Uf9WW23VpetW0+LFizNz5sw263vvvXc233zzOiSCLli6NPnrX9uv2XPPpA5/BwIAAAD/Z8SIZO7crvUDAADQ4zXkOTrFYrHbXr3Bzjvv3Gbtpptu6tI1V9f/5je/Oeuvv36XrltN5557bl566aU268ccc0wd0kAX/frXyQsvtF/jCCkAAACor8mTu9b/kY9UJQYAAAC11ZDDNoVCoVtevcW73vWuNmt33HFHFnXhDOi5q3lCZ3XfU09nnHFGm7X1118/+++/fx3SQCe1tCRjxyaVHH1m2AYAAADqa9SoZMyYzvdPn570kgf8AAAA+rKGG7ap1a41vXVXm6R0bNLrLVu2LFdeeWWnrvfkk0/mxhtvrOh76uXWW2/NvHnz2qwfccQRWWONNeqQCDph9uxk/Pikubl8baGQ3H577TMBAAAA7Zs2LWlq6lzvBRckP/1pdfMAAABQdQPqHaCavvGNb3T5GkuXLs3TTz+dhx9+OHPnzs3TTz+dpLRbTrFYzJAhQzJlypSsueaaXf6u7rLNNttkyy23zL333rvK+gUXXJADDzyww9ebOXNmli9fvspaoVDIvvvu26Wc1XT66aevdt0RUvQaLS3JpElJa2tl9cViqX7OHOe7AwAAQD2NHJnMnNmx3+tf6wtfKO2Qs+uu1c8GAABAVRi2KePaa6/Nf//3f+d3v/tdCoVCXnrppVx66aW59NJLs9lmm1X9+2rlsMMOyze/+c1V1i6++OI8/PDD2XTTTSu+TrFYzPTp09usv+c978lb3vKWLueshiVLluScc85psz5u3Lhss802dUgEnTB1asdvyLW2JlOmJKs55g0AAADoRhMmlB6ImTKlsh1rX+vVV5OPfCRZsCB505tqkw8AAIAuabhjpKptt912yyWXXJLf/OY3GTp0aJLk9ttvz2677ZYHH3ywzukqd+yxx7Y5Pmnp0qX52te+1qHrzJgxI3fccUeb9alTp1Z8jcmTJ6dQKLR53X///R3K8kYuuuiiPPPMM23W7WpDrzFvXsdvxK3Q3JzMn1/dPAAAAEDHjRhReiBm3rzSQzXjxiXbbpv83z3Gdj3ySHLwwaXBGwAAAHocwzYVOuigg/Lb3/42AwcOTKFQyMKFC7PffvtlyZIl9Y5WkU022SRHH310m/WzzjorM2fOrOga99xzT4477rg269ttt10OOOCALiasnjPOOKPN2tChQ/ORj3ykDmmgE2bMqG8/AAAAUD2jRiWnnFLa6ea225K//z3ZcMPyfX/5S/KVr9Q8HgAAAB1n2KYDdt9995x44okpFospFAq57bbb8q1vfavesSp20kknZd11122zfvjhh+e8885rt/emm27KXnvtleeff77NZz/96U/Tr1/P+K/Sfffdlz//+c9t1g855JAMGTKkDomgExYsqG8/AAAAUDubbpqcf35Syf20k09OLrqo9pkAAADokJ4xIdGLHHfccXnT/52VXCwW89Of/jTPPvtsnVNVZv3111/tri9LlizJoYcemg984AO55JJL8vjjj2fZsmV57rnncs011+TYY4/N6NGjs3Dhwja9xx9/fPbYY4/uiF+RM844I8Visc26I6ToVRYv7lr/okXVyQEAAADUxh57JN/9bmW1kycnd99d0zgAAAB0jGGbDmpqasq+++67cqDjpZdeyvnnn1/nVJU74IAD8t03+EX+sssuy4c//OFstNFGGTBgQNZZZ5285z3vyemnn56lS5e2qZ8wYUJ+8IMf1DpyxZYtW5azzjqrzfoOO+yQXXbZpfsDQWdVcnZ7e4YNq04OAAAAoHb+8z+TSo5mX7w4mTgxefHFmkcCAACgMoZtOmG33XZLkhQKhSTJn/70p3rG6bAvf/nL+fGPf5z+/ft3+hqHHXZYZs2alYEDB1YxWddcdtllefjhh9us29WGXmfEiPr2AwAAALVXKCRnnZW8/e3la2+7LTn22GQ1OzoDAADQ/QbUO0BvtPHGG6/8c7FYzK233lrHNJ1z3HHHZezYsZkyZUpaWloq7tt4443zX//1XznssMNqmK5z7rzzzrz//e9fZa1QKPTIrNCuyZOT6dO71g8AAAD0fGuvnVx4YfKudyUvv9x+7bnnJmPHJlOndk82AAAA3lChWPQ4REdde+212X333VMoFFIsFjNkyJAsXry43rE67corr8yvf/3rXHnllavdGWb48OHZbbfdctBBB+WjH/1oBg0aVIeUvJG5c+dm3Lhxq6xdd911GTt2bJ0SURVjxybNzR3vGzMmmTu3+nkAAACA2jnnnKSSB8YGDkyuuaZ03wAAAKAB9ZZ//7azTSc8+eSTq7xfunRpnZJUx95775299947SfLcc8/lkUceyYsvvpimpqasv/76q+zkA3STadNKN8468vdLU1PXdsQBAAAA6uNjH0uuuy752c/ar1u6NJk0KVmwINlww+7JBgAAQBuGbTph7ut2jVh33XXrlKT6hg8fnuHDh9c7BjByZGmXmmuvray+qSmZOTMZMaK2uQAAAIDa+NGPkpaW5Prr2697+OHkkEOSyy9PBri9CwAAUA/96h2gt3n55Zdz/vnnp1AorFzb0FMkQLUVi8m991ZWO3ZsMmdOMmFCbTMBAAAAtTNoUOlBmvXXL1/75z8nJ5xQ+0wAAACslmGbDvr85z+fhx9+OElSLBZTKBR63NlgQAO4777kkUfar9ltt2T+/NI203a0AQAAgN7vLW9Jzj8/6VfBbdvvfz/57W9rnwkAAIA2DNtU6Nlnn83kyZNz6qmnrrKrTZJ84AMfqFMqoGFVcnzUT35SOm4KAAAAaBx77ZWcdFJltUcckfzjH7XNAwAAQBsO9V2N5cuXZ/HixXnsscdyyy235LLLLsuFF16YxYsXr9zNJkkKhUK22mqr7LvvvnVODDScv/2t/c/XWivZYYfuyQIAAAB0ry99KWluTn7/+/brFi1KJk5M5s5NBg/unmwAAAA01rBN//79a3btYrGYJCsHbVYM3fzgBz+o6fcCfVS5nW3GjUsGNNRf4QAAAMAK/folv/pVMmpUcu+97dfeckvyqU8lM2Ykr9uRGwAAgNpoqGOkisVizV6FQqHN8VHf+MY3csABB9TnhwUa1xNPJHfd1X7Nbrt1TxYAAACgPoYPTy68MFlzzfK1Z5+d/PznNY8EAABASUMN2yRZORRT7dcKxWIxgwYNyn//93/n61//eh1/UqBhzZlTvsawDQAAADS+HXesfIjm3/89ueGG2uYBAAAgSQMO21Tba3e3WWONNXLwwQfnpptuyvHHH1/vaECjKneE1MCByejR3ZMFAAAAqK8jjkg++cnydUuXJgcdlDz1VO0zAQAA9HED6h2g2orFYpev0b9//wwdOjRrr712Ntlkk4wYMSK77LJL9ttvv6yzzjpVSAnQjnLDNqNGVbaFNAAAANAY/ud/kgULknnz2q9buDA55JDkssuS/v27JxsAAEAf1FDDNsuXL693BICueeGF5MYb269xhBQAAAD0LYMGJbNmJSNGJE8/3X7tlVcm3/hG8u1vd082AACAPsgxUgA9SXNzsmxZ+zXjx3dPFgAAAKDn2Gyz5Nxzk0KhfO13vpP8/ve1zwQAANBHGbYB6EkuuKB8za671j4HAAAA0PO8733JN79ZWe3hhyf//Gdt8wAAAPRRhm0AeoKWlmTs2OQXv2i/bsstk3XX7Z5MAAAAQM/z1a8m++5bvu7555OJE5OXX659JgAAgD7GsA1Avc2eXToaqrm5fO3995fqAQAAgL6pX7/k7LOTLbYoX3vTTclnPpMUizWPBQAA0JcYtgGop5aWZNKkpLW1svply0r1CxbUNhcAAADQc62zTnLhhUlTU/nas85Kttoq2X77ZNy4ZOrU0v0IAAAAOs2wDUA9TZ1a+aDNCq2tyZQptckDAAAA9A4775z87GeV1f7zn8lttyVz5ybTpyejRpWOszZ0AwAA0CkD6h2gp3rooYfyt7/9LY899liWLVuWTTfdNKNGjcpWW21V72hAo5g3r7Kjo1anuTmZP790cwwAAADom446KrnuuuQXv+h4b3Nz6VjrmTOTCROqnw0AAKCBGbZ5nXnz5uU///M/89e//nW1n++yyy45+eSTs9tuu3VzMqDhzJjR9X7DNgAAANC3nXJKcuONndulprW1dFz1nDnJiBHVzwYAANCgGuoYqSVLluTf/u3f8ra3vW3la+rUqRX3n3vuudltt93y17/+NcVicbWvG264IXvssUe+9a1v1fAnAfqEBQvq2w8AAAD0fk1NyaxZybrrdq7fcdUAAAAd1lDDNpdffnnuvPPOPPDAA7n//vvzwAMP5MADD6yod968eTnqqKPyyiuvpFgsplAovOFr+fLl+eY3v5n/+Z//qfFPBDS0xYu71r9oUXVyAAAAAL3bW9+afP3rne9fcVw1AAAAFWmoYZvf//73q7zfYostsueee1bUe8wxx2Tp0qUrB2pWeO2uNisUCoUUi8X853/+Z+66667qhAf6nqFDu9Y/bFh1cgAAAAC93z33dK2/q8ddAwAA9CENNWwzd+7clYMwhUIhEyZMqKjvoosuyq233tpmyGbQoEHZe++9c/DBB2eXXXZZZeAmSZYuXZrjjz++qj8D0Id09Sx0Z6kDAAAAKziuGgAAoNs0zLDNCy+8kL///e+rrH3wgx+sqPe0005b+ecVAzVjxozJP/7xj/zpT3/Kueeem+uvvz7XXnttNtxwwyRZOZhz+eWX57777qvGjwD0NZMn17cfAAAAaByOqwYAAOg2DTNsc++997bZeWbnnXcu2/fEE0/kqquuWmVXm6FDh+biiy/OJptsskrtrrvumvPPP7/N95x77rldSA70WaNGJWPGdK53zJhk5Mjq5gEAAAB6L8dVAwAAdJuGGba5//77V3m/wQYbZIMNNijb96c//SnLli1LkpXHTx199NF505vetNr63XffPe973/tW1ibJNddc07XwQN81bVoyYEDHepqakunTa5MHAAAA6J0cVw0AANBtGmbY5rHHHlvl/cYbb1xR39VXX91m7cgjj2y356CDDlr552KxmJtvvrmi7wJoY+TIju1Q09SUzJzpBhgAAACwqq4eN73//lWJAQAA0Bc0zLDNSy+9tPLPhUIhwyrc9vSvf/3rKkdIvfnNb86OO+7Ybs+oUaNWef/UU0/l2Wef7UBagP9TLCav25nrDY0dm8yZk0yYUNNIAAAAQC/UleOqk+T730/+bwdwAAAA2tcwwzYvv/zyKu+bmprK9jz11FO59957k/zrCKk999yzbN9b3/rWNmvPPfdcRTkBVvHAA8njj7df8573JPPnJ9ddZ0cbAAAA4I1Nm1baFbczrroqOeGE6uYBAABoUA0zbDNw4MBV3r92p5s3MmfOnDZr7373u8v2DR06tM3aokWLyvYBtNHcXL7mxz/u2FFTAAAAQN80cmTp+OnODtx873vJJZdUNRIAAEAjaphhm7XXXnvln4vFYh599NGyPddcc02btfHjx5ftW7p0aZu1YrFYtg+gjXLDNkOGJNtt1z1ZAAAAgN5vwoTSMdSdPVLqyCOTu++ubiYAAIAG0zDDNuutt94q7x988MG0tra22/OHP/whhUJh5fsNN9wwb3/728t+17PPPttmbciQIRUmBXiNcsM2u+ySDBjQPVkAAACAxjBiRDJ3bjJvXjJ1ajJuXOlhnnXWKd+7aFFy4IHJiy/WPicAAEAv1TDDNtu9bueHZcuW5S9/+csb1t9yyy25+/+e0CgWiykUCtl9990r+q6nnnqqzdprd9YBqEhra7JgQfs1nX0KDQAAAGDUqOSUU0o73dx6a3Lvvcnb3la+7/bbk098IrGbNwAAwGo1zLDNO97xjja7y5xyyilvWL+6z/bYY4+KvuvWW29d5X1TU1M23HDDinoBVrrxxmQ1x9KtwrANAAAAUC3rrJNcdFHS1FS+9rzzkmnTap8JAACgF2qYYZtCoZAJEyas3KWmWCzmsssuy49+9KM2tX/6059y5plnrnKEVP/+/bP//vtX9F0LXrcTxRZbbNG18EDfVO4IqSR517tqnwMAAADoO3bcMTn11Mpq/9//K+2KAwAAwCoaZtgmSY488siVf14xcPMf//Ef2XPPPfOjH/0op59+ej7+8Y9nv/32y/Lly5P86wip973vfdloo40q+p5rr712ld7XH2EFUJFywzZvfWtS4d9LAAAAABU74ojk058uX/fqq8mkScljj9U+EwAAQC8yoN4BqmmfffbJbrvtlmuvvTaFQmHlwM0111yTa665ZmXdiiGZ1/ra175W0XcsXLgw8+bNW3ntJBk7dmz1fgig7yg3bOMIKQAAAKBWfvzjZMGC5Prr26979NHkox9NrrwyGTiwe7IBAAD0cA21s02S/PznP8/aa6+98v2KoZjXvl47aFMoFDJ58uSMqfAftWfNmtVmzbAN0GGPPJI8+GD7NYZtAAAAgFoZNCiZNSvZYIPytX/9a/KlL9U+EwAAQC/RcMM222yzTS699NIMGzZs5c4zK3a5WfFaoVgsZo899sjPfvaziq9/+umnr3KNddZZJ6NGjareDwA0vpaWZI89ytcNH17zKAAAAEAf9uY3J+efn/Sr4Dbxj36U/OY3tc8EAADQCzTcsE2SjBs3Ln//+99zyCGHZI011mizs02xWMzw4cPzne98J5dffnkGDRpU0XVnz56dO++8c5Udct7//venXyW/jAIkyezZyfjxyd13l6/91KdK9QAAAAC1sueeyfe+V1nt0Ucnf/97bfMAAAD0AgPqHaBWNtpoo5xzzjlZvHhx/vKXv2ThwoV5/vnnM3z48Gy77bYZO3ZsBnbwjOHbbrst+++//yprRx11VDVjA42spSWZNClpba2svrW1VD9nTjJiRG2zAQAAAH3Xf/xH0tycXHxx+3Uvvph8+MPJDTckw4Z1TzYAAIAeqGGHbVYYOnRo9ttvv6pc60vOJQa6YurUygdtVmhtTaZMSebOrU0mAAAAgEIhOfPM5Pbby+/Ge9ddpR1uZs4s9QEAAPRBzj8C6A7z5pWeEOuM5uZk/vzq5gEAAAB4rbXXTi66KBk8uHzthRcm//3ftc8EAADQQxm2AegOM2bUtx8AAACgnG23Tc44o7LaL34xufrq2uYBAADooQzbAHSHBQvq2w8AAABQiYMPTo47rnzd8uWl2ocfrnkkAACAnsawDUB3WLy4a/2LFlUnBwAAAEA5//Vfyfjx5eueeCKZNCl55ZXaZwIAAOhBDNsAdIehQ7vWP2xYdXIAAAAAlDNwYPKb3yQbbVS+du7c5POfr30mAACAHsSwDUB3GDGivv0AAAAAHbHxxqWBm/79y9dOm5b8+te1zwQAANBDGLYB6A6TJ9e3HwAAAKCjdtst+eEPK6s99tjklltqmwcAAKCHGFDvAJV429veVu8Ib6hQKOTee++tdwygpxs1KhkzJmlu7njvmDHJyJHVzwQAAABQzr//e+l+xgUXtF/38svJgQcm8+cnw4d3SzQAAIB66RXDNvfff38KhUKKxWK9o7RRKBTqHQHoLaZNS3bZJenI32VNTcn06bXLBAAAANCeQiH5xS+SW29N7rij/dp7702OOCK55JKkn03VAQCAxtWrfuMpFAo96gXQIe98Z8fqm5qSmTOTESNqkwcAAACgEmutlVx0UTJ0aPna3/8++d73ap8JAACgjnrVsA1Ar3bzzZXvajN2bDJnTjJhQm0zAQAAAFRi662Ts86qrPaEE5I//ammcQAAAOqpVw3bFIvFHvUC6JCWlvI1H/946Wzz666zow0AAADQsxx4YPKf/1m+rlhMDj00eeCB2mcCAACogwH1DtARhUIhxWIxhUIhu+22W/bYY496RwKo3Pz57X+++ealM9ABAAAAeqrvfCeZNy+5+ur2655+OjnooOTaa0tHZQMAADSQXjVss2LQplgs5tprr83ChQszefLkTJ48OW95y1vqHQ+gfeV2thk5sntyAAAAAHTWgAHJ+eeXduR9+OH2a+fPLz1ctOGGydChpZ6jjnIPBAAA6PV61TFShUJh5f9bLBZz//3358QTT8wWW2yRffbZJ7/5zW/yyiuv1DklwGq8+GLy97+3XzNqVPdkAQAAAOiKDTdMZs1KBg4sX/vEE8lttyVz5ybTp5fuf4wdW9lx2wAAAD1Urxi2+dKXvpRNNtkkxWIxxWIxyb8Gb4rFYpYvX54rrrgihxxySDbeeON89rOfzYIFC+oZGWBVN92ULF/efo2nugAAAIDeYsyY5Cc/6Vxvc3Myfnwye3ZVIwEAAHSXXjFs893vfjcPPPBALr300hx44IEZOHDgysGbQqGwcqebYrGYZ599Nj/72c+yyy67ZKeddsopp5ySZ555pt4/AtDXVfK0lmEbAAAAoDf59KeTww/vXG9razJpUuKhSQAAoBfqFcM2SdKvX7984AMfyKxZs/LII4/kRz/6UXbYYYc2QzevHby55ZZbctxxx2XTTTfNRz/60fzxj39cuTMOQLcqN2zz1rcm663XLVEAAAAAqqJQSH7+82SHHTrX39qaTJlS3UwAAADdoNcM27zWuuuum+OOOy433XRT5s2bl0996lNZe+2133DwZsmSJZk1a1YmTJiQzTbbLF/72tfyj3/8o94/BtCXzJ/f/ud2tQEAAAB6o8GDkxNP7Hx/c3P5+yYAAAA9TK8ctnmtkSNH5mc/+1keffTR/PrXv85ee+2VJCt3sHn9bjcPP/xwvve972XrrbfO7rvvnl/96ld56aWX6vkjAI3uxReTv/+9/RrDNgAAAEBvddVVXeufMaM6OQAAALpJrx+2WWHQoEE59NBDc8UVV+S+++7L17/+9Wy++ebtHjP1t7/9LUcddVQ23njjfOITn8h1111X7x8DaDQtLcm73pWUO8Ju2LDuyQMAAABQbQsW1LcfAACgmzXMsM1rbbbZZjnxxBPzz3/+M1dccUUOPvjgDBo0qM3gzYr3ixcvzi9/+cvstttu2WabbXLyySfnscceq/ePAfR2s2cn48cnt99evvYLXyjVAwAAAPQ2ixd3rX/RourkAAAA6CYNOWzzWnvttVfOPffcPProo5k2bVpGjhzZ7m43d911V770pS9ls802y3777ZdLLrkky5Ytq/ePAfQ2LS3JpElJa2tl9a2tpXpPcgEAAAC9zdChXeu34y8AANDLNPywzQprr712PvOZz2TevHm55ZZb8rnPfS7rrbfeGw7evPrqq7n00kszceLEXHrppfWOD/Q2U6dWPmizQmtrMmVKbfIAAAAA1MqIEfXtBwAA6GZ9Ztjmtbbbbrv85Cc/ycMPP5zf/OY32WeffdKvX782gzcAnTJvXtLc3Lne5uZk/vzq5gEAAACopcmTu9a//fZViQEAANBd+uSwzQoDBw7MQQcdlD/84Q958MEH87WvfS0DBgyodyygt5sxo779AAAAAN1p1KhkzJjO959wQvLQQ9XLAwAAUGN9ethmhfnz5+ekk07KtGnTsmzZsnrHAXq7BQvq2w8AAADQ3aZNS5qaOtf7xBPJpEnJkiXVzQQAAFAjfXbY5qmnnsqPf/zj7LDDDnnXu96VU089Nc8991yKxWK9owG93eLFXetftKg6OQAAAAC6y8iRycyZnR+4aW5OjjuuqpEAAABqpU8N2yxfvjyzZ8/OgQcemE033TRf+MIXctttt6VYLKZYLKZQKKRQKCTJyqGbHXfcMVtttVU9YwO9zdChXesfNqw6OQAAAAC604QJyZw5nT9S6uc/T848s7qZAAAAamBAvQN0h7vuuiu//OUvc/bZZ+fxxx9P8q9hmhXDNa9dW2eddXLooYfm6KOPzs4779z9gYHebcSIZO7crvUDAAAA9EYr7ovMn5/MmFE6LnvRouSFF5L77y/f/+lPJzvsUNopBwAAoIdq2GGbF154Ieeff35++ctf5vrrr0+SVY6Iev2QTb9+/fLe9743Rx99dA444ICsscYa3Z4ZaBCTJyfTp3etHwAAAKA3GzWq9FqhWEwOOSS54IL2+5YsSQ48MGlpSdZfv7YZAQAAOqnhhm2uueaa/PKXv8yFF16Yl19+OUn7u9i87W1vy+TJkzN58uS8+c1v7v7AQOMZNaq0XXJzc8d7x4zx5BYAAADQeAqF5Be/SG67Lbn99vZrH3ywNJhz2WVJ//7dkw8AAKADGmLYZuHChZkxY0bOOuus3HfffUlWv4vNirXBgwdn4sSJOfroo7P77rt3f2Cg8U2bVhqcefXVynuamrq2Iw4AAABAT7bWWsnFF5ceVFq0qP3aK69Mvva15Hvf655sAAAAHdBrh21eeeWVXHTRRTnzzDNz1VVXpVgstntMVJK8613vytFHH52DDz44Q4cO7fbMQB8ycmTpjPIbbqisvqkpmTmz1AMAAADQqN7+9uTXv04+9KHytd//frLLLqVjpQAAAHqQXjds09LSkjPPPDPnnXdennvuuSTtHxP1pje9KYcffniOPvrovPOd7+z2vEAf9sQTldWNHVvaCcegDQAAANAX7LdfcsIJyUknla898sjk3/4tcW8XAADoQXrFsM3TTz+ds88+O2eeeWZuu+22JKs/JmrF+oABA/LBD34wRx99dPbdd9/0d64v0N2efz65//72a9797uRHPyrtggMAAADQl3zjG8m8eclll7Vf98ILyYc/XNo92G7lAABAD9Erhm022WSTvPrqq2WPifq3f/u3HHXUUTn88MOz4YYbdntOgJVuvbV8zbe/bdAGAAAA6Jv690/OOScZNSq57772a++8MznqqNIR3K+5LwwAAFAv/eodoBJLly5d+edCoZBCoZBisZhisZihQ4fmE5/4RObOnZvbbrstn//85w3aAPV3883la3bYofY5AAAAAHqqdddNLr44WXPN8rUXXpicfHLtMwEAAFSgV+xss8KKIZtCoZDddtstRx99dCZOnJjBgwcnSZYvX16XXP369YqZJaA73XJL+59vvnmy9trdkwUAAACgp9pxx+S005LDDy9f++UvJyNGJHvvXftcAAAA7ehVwzYrjosqFov529/+lr/97W85+uij65qpUCjk1VdfrWsGoAcqN2yz447dkwMAAACgpzvssOSGG5JTTmm/bvny5OCDk5aW0oNMAAAAddJrt2RZcYxUT3gBrGL58uTWW9uvcYQUAAAAwL/88IfJrruWr3v66WTixKS1tfaZAAAA3kCvHbYpFAp1fwGs1j//mbz4Yvs1hm0AAAAA/mWNNZKZM5ONNipf29KSTJmSeBASAACok141bGPQBejxWlqSffctX9e/f+2zAAAAAPQmG2+czJqVDBhQvvaXv0xOP732mQAAAFaj1wzb1PuoKMdHAWXNnp2MH5/cfXf52o99rFQPAAAAwL/sumvy4x9XVjt1anL99bXNAwAAsBoVPCJQf1dffXW9IwC0r6UlmTSp8vPCW1tL9XPmJCNG1DYbAAAAQG8yZUpyww3J2We3X7d0aTJxYum+zJve1D3ZAAAA0kuGbXbfffd6RwBo39SplQ/arNDaWrp5NHdubTIBAAAA9EaFQvLznye33JLcfHP7tQ8/nHz0o8mVV1Z2/BQAAEAV9JpjpAB6rHnzkubmzvU2Nyfz51c3DwAAAEBvN3hwctFFyTrrlK+95prki1+sfSYAAID/Y9gGoKtmzKhvPwAAAEAjetvbknPPLe10U86PfpRccEHtMwEAAMSwDUDXLVhQ334AAACARrXPPsm3vlVZ7dFHJ7fdVts8AAAAMWwD0HWLF3etf9Gi6uQAAAAAaERf+UryoQ+Vr3vppeTAA5Pnnqt5JAAAoG8zbAPQVUOHdq1/2LDq5AAAAABoRP36Jb/6VfL2t5evveee5IgjkuXLa58LAADoswzbAHTViBH17QcAAABodGuvnVx0UTJkSPna3/8++e53a58JAADoswzbAHTV5Mn17QcAAADoC7bbLvnlLyur/frXkz/+sbZ5AACAPsuwDUBXjRqVjBnTud4xY5KRI6ubBwAAAKBRfeQjyec/X76uWEw+9rHkn/+sfSYAAKDPMWwDUA3TpiUDB3asp6kpmT69NnkAAAAAGtX3v5+85z3l6559Ntl++2TbbZNx45KpU5OWlprHAwAAGl+PHrZ529vetvK15ZZb5uabb653pLJuuummNrmBPmDkyGTChMrrm5qSmTOTESNqlwkAAACgEQ0YkFxwQfLmN5evfeml5I47krlzSw89jRqVjB1r6AYAAOiSAfUO0J77778/hUIhxWIxhUIhS5YsqXekspYsWdImN9BHvPJKZXVjx5Z2wjFoAwAAANA5G26YzJqVvPvdld+TWaG5ORk/vvQgVEcengIAAPg/PXpnG4Be5e9/b//z7bdP5s9PrrvOoA0AAABAV73rXaUHmjqjtTWZNClZsKC6mQAAgD7BsA1ANbz8cnLffe3XfPKTpeOmAAAAAKiOT3wi+fjHO9fb2ppMmVLdPAAAQJ9g2AagGu66KykW26/ZZpvuyQIAAADQl0ye3Pne5ubSTsQAAAAdYNgGoBrKHSGVGLYBAAAAqIXzz+9a/4wZ1ckBAAD0GQPqHaAjLr300tx55531jtGue++9t94RgHooN2yz9trJRht1TxYAAACAvmTBgvr2AwAAfU6vGbYpFov59re/Xe8YAKt3xx3tf/5v/5YUCt2TBQAAAKAvWby4a/2LFlUnBwAA0Gf0mmGbpDRwA9AjlXsCyhFSAAAAALUxdGjX+ocNq04OAACgz+hX7wCVKBQKve4F9BEtLcmYMcl997Vf19WbPgAAAACs3ogRXevfaaeqxAAAAPqOHj9sUywWe+0LaHCzZyfjxyfXX1++9n//t1QPAAAAQHVNnty1/meeqUoMAACg7+jRx0gdeeSR9Y4AsHotLcmkSUlra2X1r7xSqp8zp+tPWwEAAADwL6NGlXYebm7uXP/55ycf+EByxBHVzQUAADSsHj1sc+aZZ9Y7AsDqTZ1a+aDNCq2tyZQpydy5tckEAAAA0FdNm1bagbij92tW+OQnk223TUaOrG4uAACgIfX4Y6QAepx58zr/pFRzczJ/fnXzAAAAAPR1I0cmM2cmTU2d629tTQ48MHnyyermAgAAGpJhG4COmjGjvv0AAAAAtDVhQukI7zFjOtf/4IPJRz+avPpqdXMBAAANx7ANQEctWFDffgAAAABWb8SI0hHe8+aVjgEfNy7Zbrtk552TgQPL9199dfLFL9Y+JwAA0KsNqHcAgF5n8eKu9S9aVJ0cAAAAAKzeqFGl12tdckny4Q+X7/3Rj0rHUh16aE2iAQAAvZ+dbQA6aujQrvUPG1adHAAAAABU7oADkhNOqKz2mGOSm26qZRoAAKAXM2wD0FEjRtS3HwAAAIDOOfHE5IMfLF/38sulXXCefrrmkQAAgN7HsA1AR02eXN9+AAAAADqnX7/knHOSrbYqX3v//ckhhyTLltU8FgAA0LsYtgHoqFGjkjFjOtc7ZkzpzG8AAAAA6mP48OSSS5IhQ8rXXnFF8tWv1joRAADQyxi2AeiMadOSpqaO9TQ1JdOn1yYPAAAAAJXbdtvkrLMqq/3BD5Lf/KamcQAAgN7FsA1AZ4wc2bGbLE1NycyZyYgRtcsEAAAAQOUOOij50pcqqz3qqOTWW2ubBwAA6DUM2wB01rhxldWNHZvMmZNMmFDbPAAAAAB0zLe/nbzvfeXrXnop+fCHk2efrX0mAACgxzNsA9BZ//hH+Zpf/Sq57jo72gAAAAD0RP37J+edl2yxRfnae+9NPvaxZNmy2ucCAAB6NMM2AJ1VybCN3WwAAAAAerZ1100uuSQZPLh87R//mHzjGzWPBAAA9GyGbQA669572/98vfWSddbpniwAAAAAdN4OOyRnnFFZ7Xe+k1x0UW3zAAAAPZphG4DOKrezzZZbdk8OAAAAALru4IOTL3yhstojj0zuuKO2eQAAgB7LsA1AZ910U/ufb7VVt8QAAAAAoEq+971kr73K173wQnLAAcnzz9c8EgAA0PMYtgHoqJaWZOzY5NZb26+r5JxvAAAAAHqOAQOS889PNt+8fO099ySHHZYsX177XAAAQI9i2AagI2bPTsaPT5qby9fOmFGqBwAAAKD3WH/95OKLk6am8rWzZyff+lbtMwEAAD2KYRuASrW0JJMmJa2tldUvXVqqX7CgtrkAAAAAqK6dd05OP72y2m9+M/nd72qbBwAA6FEM2wBUaurUygdtVmhtTaZMqU0eAAAAAGrnsMOS446rvPbOO2saBwAA6DkM2wBUYt68yo6OWp3m5mT+/OrmAQAAAKD2/uu/kt13L1+3eHHy4Q8nixbVPhMAAFB3hm0AKjFjRn37AQAAAOh+Awcmv/lN8uY3l6+9887kyCOT5ctrnwsAAKgrwzYAlViwoL79AAAAANTHhhsmF12UDBpUvvaSS5LvfrfmkQAAgPoybNOOxx57LDfddFNaWlry2GOP1TsOUE+LF3et3xbCAAAAAL3XLrskP/95ZbVf/3ryhz/UNg8AAFBXhm1eZ/HixfnmN7+ZrbbaKptuumlGjhyZ0aNHZ9NNN81mm22WL33pS3n66afrHRPobkOHdq1/2LDq5AAAAACgPiZPTqZMKV9XLCaHHpr84x81jwQAANTHgHoHqLbPfOYzaW1tXfl+++23z/HHH19R7+2335599903CxcuTLFYbPP5Qw89lJNPPjkzZszIr371q7z3ve+tWm6ghxsxIpk7t2v9AAAAAPRuP/pRcvPNyd/+1n7d888nBxyQNDcna63VLdEAAIDu01DDNnPnzs3Pf/7zFAqFlWtnnnlmRb2PP/549t577zz++ONJsso1XqtYLObxxx/P/vvvn8svvzy77bZb14MDPd/kycn06V3rBwAAAKB3W2ONZObMZOTI5JFH2q+9/fZk882TTTYp7Zo8YkRy1FGlXgAAoFdrqGOkLr744pV/LhaLWXfddXPIIYdU1PupT30qjz/+eAqFwhsO2iRZ+Xlra2smTpyYZ599tsu5gV5g1KhkzJjO9Y4Z4yYKAAAAQKPYaKPkwgtLgzflPPNMctttpR2Tp08v3WMaOzZpaal9TgAAoGYaatjmb/+3dWexWEyhUMg+++yTgQMHlu2bP39+fvvb37YZsikWixk0aFDe9KY3pV+/fm2Olnr66adzwgknVO8HAHq2adOSpqaO9TQ1dW1HHAAAAAB6njFjOn/Pp7k5GT8+mT27upkAAIBu0zDDNkuXLs2NN964ysDMhAkTKuo95ZRTVnlfLBaz/vrrZ9asWVm8eHEeeeSRPPPMM/n2t7+d/v37JyntcFMsFnPGGWfkueeeq9rPAfRgI0eWtgn+v78HympqKtWPGFHbXAAAAAB0v2OOST75yc71trYmkyYlCxZUNxMAANAtGmbY5p///GeWLFmyytro0aPL9r388su5+OKLVw7pFIvF9O/fP7Nnz86BBx64crhm6NCh+cpXvpKf/OQnq+xw88orr2TmzJlV/EmAHm3ChGTXXcvXjR2bzJlTqgcAAACgMf3P/5TuA3VGa2syZUp18wAAAN2iYYZt7r///lXer7XWWtliiy3K9l111VV54YUXkvzr+KmJEydml112WW39Zz7zmWy33XarrF122WWdCw30TosWtf/5QQcl111nRxsAAACARjdoUPK1r3W+v7k5mT+/enkAAIBu0TDDNg899NAq7zfbbLOK+v7yl7+0WTvmmGPa7TnssMNWDuYUi8XceOONFecEGsCDD7b/+ahR3ZMDAAAAgPr7wx+61j9jRnVyAAAA3aZhhm1W7E6TJIVCIWuvvXZFfa8fthk2bFj22GOPdnvGjx+/yvsHHnggL730UmVBgd7thReSZ55pv6bCYT8AAAAAGsCCBfXtBwAAul3DDNu8fthlyJAhFfXcfPPNK3eoKRQK2XPPPdO/f/92+97+9re3WXv66ac7FhjonRYuLF9j2AYAAACg71i8uGv95Y4sBwAAepyGGbYpFAqrvG9tbS3bc8MNN2TZsmWrrO22225l+1a3a84ivxBB42tpSQ48sHzdc8/VPAoAAAAAPcTQoV3rHzasOjkAAIBu0zDDNsNe8wtJsVjMM+WOeUny17/+tc1aJcM2/fq1/Y9tyZIlZfuAXmz27GT8+OTOO8vXHnRQqR4AAACAxjdiRNf6d965OjkAAIBu0zDDNsOHD1/l/T//+c+yPZdffvkq74cMGZKdK/jF5rnV7FoxePDgsn1AL9XSkkyalFSwY1aSUt2kSc7bBgAAAOgLJk/uWv8rr1QlBgAA0H0aZthm6623XuV9a2tr5s2b94b1jz76aK6//voUCoUUi8UUCoWMHTt2tbvWvN7qds0Z2tWtQoGea+rUygdtVmhtTaZMqU0eAAAAAHqOUaOSMWM633/66cnFF1cvDwAAUHMNM2yz3XbbpX///qusnXXWWW9Yf8YZZ2T58uWrrO2xxx4Vfdddd921yvv+/ftno402qiwo0LvMm5c0N3eut7k5mT+/unkAAAAA6HmmTUuamjrff8QRye23Vy8PAABQUw0zbDNo0KDstttuK3epKRaLOeOMM3Lddde1qX3ggQfywx/+MIVCYZX1CRMmVPRdLS0tq7x/y1ve0mbQB2gQM2bUtx8AAACAnm/kyGTmzM4P3LzwQrL//smzz1Y3FwAAUBMNM2yTJIcddtjKPxcKhbzyyit573vfm5NOOikLFizIPffck7PPPjvvfve7s2jRoiRZOZyz0047Zbvttqvoe66//vpVvued73xndX8QoOdYsKC+/QAAAAD0DhMmJHPmdP5IqXvvTQ4+OFm2rLq5AACAqmuoYZtDDz00b33rW1e+LxQKefnll3PiiSdml112yTvf+c5Mnjw5CxcubLOrzec///mKvmPRokW5+uqrV+6ekyTvete7qvYzAD3M4sVd6/+/wT4AAAAA+oARI5K5c0tHk0+dmowbl2y3XbLDDkm/Cm7H/+lPyZe/XPucAABAlzTUsE1TU1NOOeWUVdZWDMW89vXaQZtCoZDRo0fn0EMPreg7fve73+WVV15ZZW3s2LFdDw/0TEOHdq1/2LDq5AAAAACg9xg1KjnllNJON7femtx8c3LmmZX1nnxycu65tc0HAAB0SUMN2yTJvvvum5/+9Kcrd51JSgM1r32tUCwWs/HGG2fmzJkVX3/GjBmrvF9jjTWy6667dj040DONGFHffgAAAAAawxFHJP/+75XVfvzjjicHAIAerOGGbZJkypQpmT17djbffPM2u9q89vXe9743N9xwQ9785jdXdN2bb745V1111cqBnUKhkN133z2DBw+u5Y8D1NPkyfXtBwAAAKBx/PCHyZ57lq9rbU0+/OHkiSdqnwkAAOiwAfUOUCsf/OAHc/fdd+fPf/5zLr/88ixcuDDPP/98hg8fnm233Tb77rtvRo4c2aFrnnbaaVl77bVXWfvwhz9czdhATzNqVDJmTNLc3PHeMWOSDv49AwAAAEADGzAgueCCZJddkvvvb7/2wQeTSZOSK69MBg7slngAAEBlCsXXnrcE9Dpz587NuHHjVlm77rrrMnbs2DolakAtLcn48aUniirV1FQ6k9sxUgAAAAC83s03J+PGJS+9VL52ypRk2rTaZwIAgB6gt/z7d0MeIwVQVSNHJjNnJv37V1bf1FSqN2gDAAAAwOrsuGNy5pmV1U6fnpxxRm3zAAAAHWLYBqASEyaUjpQqZ+zY0o42EybUPhMAAAAAvddHPpJ8+cuV1X7mM5075hwAAKgJwzYAlSq3re9hhyXXXWdHGwAAAAAqc9JJyQc/WL7ulVeSAw9MHnmk9pkAAICyDNsAVOqxx9r/fIcduicHAAAAAI2hf//k3HOTd7yjfO2jj5YGblpba58LAABol2Gbdjz22GO56aab0tLSksfK/SM70NhefTV58sn2azbaqHuyAAAAANA41l47+e1vk6FDy9def30yZUpSLNY+FwAA8IYM27zO4sWL881vfjNbbbVVNt1004wcOTKjR4/Opptums022yxf+tKX8vTTT9c7JtCdWlqSMWPK1y1aVPssAAAAADSed74zOeecpFAoX/vLXybTp9c+EwAA8IYG1DtAtX3mM59J62u20dx+++1z/PHHV9R7++23Z999983ChQtTXM2TAQ899FBOPvnkzJgxI7/61a/y3ve+t2q5gR5q9uxk0qTKtuf9/OeTzTdPJkyofS4AAAAAGst++yXf+lZywgnla487Ltluu+Q976l1KgAAYDUaathm7ty5+fnPf57Ca6b/zzzzzIp6H3/88ey99955/PHHk2SVa7xWsVjM448/nv333z+XX355dtttt64H7yHuuOOO3HbbbXnkkUfywgsvpKmpKRtssEG22Wab7Lzzzhk4cGC9I3bKq6++mttvvz133XVXHn300bzwwgvp379/1lprrWy88cbZcsst8853vjNNTU31jkpP09JS+aBNkixZUqqfMycZMaK22QAAAABoPF/9anLTTcmFF7Zft2xZ6T7U/Pmlh78AAIBu1VDDNhdffPHKPxeLxay33no55JBDKur91Kc+lccff/wNh2xWWPF5a2trJk6cmLvuuivrrLNO50PX2cKFC3PKKafknHPOySOPPPKGdUOHDs1+++2X4447Lrvssks3JuycZcuW5Q9/+EPOOuusXHnllVlU5nifgQMHZvvtt8+73/3u7Lvvvnn3u9+dNdZYo5vS0mNNnVr5oM0Kra2lc7Pnzq1NJgAAAAAaV6GQnHVWctddyW23tV/71FPJAQeUHvwaPLg70gEAAP+nX70DVNPf/va3JKVBm0KhkH322aei3Vjmz5+f3/72t20GbYrFYgYNGpQ3velN6devX5ujpZ5++umcUMmWnj3Q8uXL873vfS9bb711Tj755HYHbZJk8eLFOffcczN69OgcccQRef7557spacddeuml2WGHHfKhD30oF110UdlBmyRZunRpFixYkJ/85Cd573vfm3PPPbcbktKjzZuXNDd3rre5ufRUEQAAAAB01FprJZdcklTykOdNNyUf/3jyunvXAABAbTXMsM3SpUtz4403rjIwM2HChIp6TznllFXeF4vFrL/++pk1a1YWL16cRx55JM8880y+/e1vp3///klKO9wUi8WcccYZee6556r2c3SH1tbW7L///vnKV76Sl19+ucP9Z599dkaPHp3777+/+uG64OWXX87HP/7xTJgwIXfccUe949DbzZhR334AAAAA+q4tt0x+85ukXwW38M8/Pzn55NpnAgAAVmqYYZt//vOfWbJkySpro0ePLtv38ssv5+KLL145pFMsFtO/f//Mnj07Bx544MrhmqFDh+YrX/lKfvKTn6yyw80rr7ySmTNnVvEnqa1ly5bloIMOyuzZs1f7+cCBA7Pjjjtmr732yujRo7PWWmuttu7uu+/Onnvumccee6yWcSv27LPPZu+9984vf/nLN6wZPHhwttxyy4wbNy7vec97suOOO2a99dbrxpT0KgsW1LcfAAAAgL5t770rH6L50peSyy6rbR4AAGClhhm2ef0uK2uttVa22GKLsn1XXXVVXnjhhST/On5q4sSJ2WWXXVZb/5nPfCbbbbfdKmuX9aJfYr75zW/m0ksvbbM+fPjw/OhHP8qTTz6Zm266KVdeeWWuv/76PPPMM5k1a1be8Y53tOm57777cuihh2b58uXdEf0Nvfzyy9l3331z3XXXtfls0KBB+cxnPpMrr7wyzz//fP7xj39kzpw5ufrqq3PTTTflqaeeygMPPJDzzjsvhxxySIYNG1aHn4AeafHirvVXcHwZAAAAALTr+OOTww4rX1csJoccktxzT+0zAQAAjTNs89BDD63yfrPNNquo7y9/+UubtWOOOabdnsMOO2zlYE6xWMyNN95Ycc56uuWWW/Ld7363zfrmm2+e+fPn5/jjj8/aa6+9ymcDBw7MxIkTs2DBgrz//e9v03v11Vfn1FNPrVnmSnz84x/P3Llz26y/973vzW233Zbp06dnr732yoABA1bbv9lmm+Xggw/Oueeem0ceeSSnnnpqxf/9oYENHdq1foNbAAAAAHRVoZCcdloycmT52ueeSw44oOsPkQEAAGU1zLDNit1pkqRQKLQZGnkjrx+2GTZsWPbYY492e8aPH7/K+wceeCAvvfRSZUHr6Atf+EKWLVu2ytqQIUPyxz/+MVtuuWW7vUOGDMlFF12Ubbfdts1nJ5xwQl588cWqZq3U2WefnfPOO6/N+kc/+tH88Y9/zFZbbdWh6w0ZMiTHHnts9txzz2pFpLcaMaK+/QAAAACQJGuumVx8cbLhhuVr77gjOfzwpM67kQMAQKNrmGGb1w+7DBkypKKem2++eeUONYVCIXvuuWf69+/fbt/b3/72NmtPP/10xwJ3swULFuSKK65os37CCSdkm222qegagwcPzumnn95m/emnn85pp53W5Ywd9fzzz+cLX/hCm/W99torv/71r8v+/yO0a/Lk+vYDAAAAwApveUsya1byBrt3r+K3v02+9a3aZwIAgD6sYYZtCoXCKu9bW1vL9txwww1tdnrZbbfdyvatbtecRYsWle2rp9Ud9bTuuuvms5/9bIeuM3bs2NUeJ1WPYZuTTz45TzzxxCprTU1N+fnPf/6GR0ZBxUaNSsaM6VzvmDGVbe0LAAAAAJXabbfklFMqq/3mN5NLLqlpHAAA6MsaZthm2LBhK/9cLBbzzDPPlO3561//2matkmGbfv3a/se2ZMmSsn318uqrr2bmzJlt1g877LAMHjy4w9f7xCc+0WbtzjvvzI033tipfJ3x4osv5mc/+1mb9eOPP77DR0fBG5o2LWlq6lhPU1MyfXpt8gAAAADQt33yk8lq7s+u1uGHJ7ffXts8AADQRzXMsM3w4cNXef/Pf/6zbM/ll1++yvshQ4Zk5513Ltv33HPPtVnrzNBKd7n++uvz7LPPtlmfOHFip6637777Zs0112yz/sc//rFT1+uMCy+8sM3PVCgUcuyxx3ZbBvqAkSOTmTOTQYMqq29qKtWPGFHbXAAAAAD0TYVC6QGxcePK177wQrLrrsno0cn225d6pk5NWlpqnxMAABpcwwzbbL311qu8b21tzbx5896w/tFHH83111+fQqGQYrGYQqGQsWPHrnbXmtdb3a45Q4cO7XjobvLnP/+5zdrgwYMzduzYTl2vqakp41bzy9zqvqdWzj///DZre+65Z9761rd2Wwb6iAkTkrPOKl+3007JnDmlegAAAAColTXWSC68MNl00/K1zz+fzJuX3HZbMnduaUfmUaOSsWMN3QAAQBc0zLDNdtttl/79+6+ydlY7/0B+xhlnZPny5aus7bHHHhV911133bXK+/79+2ejjTaqLGgdzJ8/v83aTjvtlIEDB3b6mqNHj26z1tJNv5wtWbIkV199dZv1D33oQ93y/fRBlfzv+9JL7WgDAAAAQPfYaKPk4osr35H59Zqbk/Hjk9mzq5sLAAD6iIYZthk0aFB22223lbvUFIvFnHHGGbnuuuva1D7wwAP54Q9/mEKhsMr6hAp3pHj9UMlb3vKWNoM+PcnNN9/cZm377bfv0jVX1//cc8/lwQcf7NJ1K9Hc3JzW1tY262PGjFlt/bJly/LPf/4zCxYsyB133JEnn3wyy5Ytq3VMGslqjmFrY511ap8DAAAAAFbYZZfk1FM739/amkyalCxYUL1MAADQRzTMsE2SHHbYYSv/XCgU8sorr+S9731vTjrppCxYsCD33HNPzj777Lz73e/OokWLkmTlcM5OO+2U7bbbrqLvuf7661f5nne+853V/UGqaOnSpVm4cGGb9a3+P3t3Hmd1Xe8P/DXsoiBuV0XNTLPIJR0mRUJzX3HL3VxwywoyTS21zEqzm3ZzCcxME0IzxVxxu5JlhkyJo6mRWqaJ4o7IkoMs8/tjfnrFGZwzc86ZM8vz+XicB8xnPp/3eY3Z7TK85vPdaKOi5i7v/DPPPFPU3ELUNfOHv759+2aLLbZ47+MFCxbk8ssvz4477pgVV1wxG264YYYOHZpNNtkk//Vf/5W+fftmm222ydlnn91sGQne8/DDyWmnffieqqpkxoz2yQMAAAAA7zr66ORrX2v7+fr6ZPTo0uUBAIBuolelA5TS4YcfnvPOOy///ve/kzQWYd5+++1897vfzXe/+9339r1bsHm/U089taD3mDt3bn7/+9+/d3tOVVVVtt5665J9DaU2c+bMJo/LSpJ1Cnme74dY3vnnnnuuqLmF+Nvf/tZk7eMf/3j69OmTJPnVr36V008/Pa+++upyZyxZsiS1tbWpra3Neeedl/333z/nnntuNtlkk7LlXp5p06YVdf7xxx8vURKamDy58ad7mrlJaRkNDY3X7k6alBR4QxYAAAAAlMSPf5xMnZpMn96287W1jWdrakqbCwAAurAuVbbp169ffvrTn2afffZ5b+3dUsz7vb9oU1VVla222iqHH354Qe9x22235Z133llmxjbbbFNk8vJZXuFkzTXXLGru8s6/9tprRc0tRHOPqho8eHCWLFmSE044IVdffXWrZ95888256667ctVVVxX870KpDB8+vF3fjwI9/HBhRZt3vXvt7tSpSXV1ebMBAAAAwLt69Uo237ztZZskmTBB2QYAAFqhSz1GKkn22muvXHrppcsUbKqqqpZ5vauhoSFrr712Jk2aVPD8CRMmLPNxnz598tnPfrb44GUye/bsZtdXXnnloub27Nkz/fv3b7L+xhtvFDW3EC+99FKTtcGDB2fUqFFtKtq8q76+Pl/4whdywQUXFBOPrmLMmMKLNu9y7S4AAAAAlfD3vxd3vq6uNDkAAKCb6HJlmyQZPXp0Jk+enPXXXz8NDQ3Lfe2yyy75y1/+knXXXbeguX/961/zu9/97r3CTlVVVT73uc81WzrpKObPn9/s+korrVT07OZmLFiwoOi5LZkzZ06TtXvuuSfXXHPNMmsDBgzIqaeemvvvvz8vvfRSFi5cmJdeein3339/TjvttAwcOLDZ+WeeeWbuvvvuckSns3joocbrc9vi3Wt3AQAAAKC9zJtX3Pm5c0uTAwAAuoku9Rip99tzzz3z9NNP57777ss999yTmTNn5q233sqgQYOyySabZK+99srQoUNbNfOKK65ociPM/vvvX8rYJbdo0aJm13v1Kv4/+t69ezdZe+edd4qe25KFCxc2WfvgbTe77bZbJkyY0ORxV2uttVbWWmutbLfddjnttNNyzDHH5K677lpmz9KlS/OFL3whTz31VFZfffXSfwF0fB+4wapN5127CwAAAEB7GTCguPPL+cFEAACgeV22bJM0Fkp23XXX7LrrriWZN27cuIwbN64ks9rLkiVLml3v2bNn0bObm7F48eKi57akpffYc889c8sttzRbBnq/NddcM7feemsOOOCA3H777ct8bvbs2bnkkkty7rnnFp23JQ8++GBR5x9//PGceOKJJUpDkuKvzXXtLgAAAADtqbo6mTatuPMAAEDBunTZhuXfYFOKUkxzM1oquJTCh73Hf/3Xf2XChAkF5+jdu3euvvrqbLrppnn55ZeX+dzYsWPzzW9+sySP3Pow22yzTVnn0wau3QUAAACgMxk1KinmB0UPPLBkUQAAoDvoUekAlFffvn2bXV/e46Vao7lHRi3v/UqpT58+y/3c17/+9VY/+mm11VbL17/+9Sbrc+bMyR/+8IfWxqMrcO0uAAAAAJ1JTU0ybFjbz194YbKcW9IBAICmlG26uAHLKQ3MLcHNG/Oauf1jYDuUDAYNGtTseo8ePXLssce2aeZxxx3X7GOxlG26qWKvzXXtLgAAAADtbezYpF+/tp29447kW98qbR4AAOjClG26uNVWW63Z9Tlz5hQ1d+HChVm4cGHB71dKy7u5ZsiQIVljjTXaNHPVVVfNJpts0mS9tra2TfPo5EaNqux5AAAAAGitoUOTSZPaXrj50Y+Sa68tbSYAAOiilG26uLXWWqvZ9VdeeaWouS+99FKr3q+U1llnnWbXt9hii6LmfvrTn26yVuw/JzqpYq7dHTas8RsbAAAAANDeRo5Mpk5t+/e2jjsueeih0mYCAIAuSNmmixs8eHD69u3bZP35558vau7MmTObXd9ggw2KmluI5b3HqquuWtTc5m7leeONN4qaSSfWlmt3+/VLxo0rTx4AAAAAKER1dTJtWmNpZsyYZPjwZJNNkgEDWj67cGGy337JrFlljwkAAJ2Zsk0XV1VVlQ033LDJ+tNPP13U3OWd32ijjYqaW4ghQ4Y0u96/f/+i5q644opN1ubPn1/UTDqx1l67269f4/7q6vLmAgAAAIBC1NQkP/1p4003TzyR/OMfyXrrtXxu1qxk//2Tt98uf0YAAOikukXZpqGhIX/84x/zox/9KPvvv38233zzfOQjH8nAgQPTs2fPol+XXXZZpb/ED7Xllls2WXv00UeLmtnc+XXXXTerr756UXMLMXQ5j+iZO3duUXPfeuutJmurrLJKUTPp5EaOTO6+u+V9Q4Y0ftNi5MjyZwIAAACAtlhzzeTWW5MVVmh571/+knzxi0lDQ/lzAQBAJ9Sr0gHKaf78+bn88sszbty4ZR6b1FDCPyBUVVWVbFa5bL311rn22muXWZsxY0bmzp2bgQMHtmnmtGnTmn2f9rDxxhtnjTXWyGuvvbbM+quvvlrU3ObOt0d5iA6ukJ/g6dHDNx4AAAAA6Pi23DKZMCE5+OCW915zTbL55snpp5c/FwAAdDJd9mabhx9+OFtssUW++c1v5t///ncaGhreeyWNJZliX53Fzjvv3GRtyZIlmTJlSpvmvfbaa3nkkUcKep9yqKqqyq677tpk/aGHHipqbnPn119//aJm0slNntz4jOqW/O1vyYgRjfsBAAAAoCM76KDkO98pbO83v5nccUd58wAAQCfUJcs2d9xxRz772c/m2WefTUNDw4cWZd5fwFmeDxZ13tVZCjdDhgzJhhtu2GT9+uuvb9O8SZMmZenSpcusVVVVZa+99mrTvLY44IADmqw9//zzeeqpp9o075///GeeffbZJus77LBDm+bRBTz8cOM3HhYuLGx/fX3j/rq68uYCAAAAgGKdc06y//4t72toSA47LPn738ufCQAAOpEuV7Z58skn84UvfCHvvPNOkv8rxLy/MNNceebDPv/+gs6HzejIjjjiiCZrN998c1588cVWzWloaMi4ceOarG+//fZZb7312pyvtUaOHJk11lijyfrPfvazNs277LLLml1vr9t66IDGjGks0LRGfX0yenR58gAAAABAqfTokfzqV42PiWrJvHnJPvsks2eXPxcAAHQSXa5sc9xxx2Xu3LlNbq/Zb7/9csstt2TWrFlZtGhRkv8r4lRVVaW2tjaLFi3Kq6++mr///e+55ppr8qUvfSmDBg1aplDTq1evfOc738nixYuzdOnSLF26NF/5ylfa94tsgy9+8Yvp06fPMmuLFi3Kt7/97VbNmTBhQmbMmNFkfcyYMQXPGDVqVLO3DT333HMFz+jdu3e++tWvNlm//PLL88QTTxQ8J0lmzJjRbEln6NCh2XLLLVs1iy7ioYeS2tq2na2tTaZPL20eAAAAACi1lVZKbr01WX31lvf+85/JIYckixeXPxcAAHQCXapsc//992fatGnL3ELTp0+f3Hjjjbnpppuyzz77ZK211krPnj2bPd+zZ8+svvrq+cQnPpHDDz88l112WV544YVcfPHFWXHFFVNVVZUlS5bk3HPPzb777pv61t56UUGDBw/Oscce22R9/PjxmTRpUkEz/vGPf+Tkk09usr7ppptmv/32KzJh65100klZc801l1lbuHBhDj744Lz88ssFzXjllVdy8MEHN/uf5TnnnFOSnHRCEyZU9jwAAAAAtIePfjT57W+TXr1a3jtlSnLqqWWPBAAAnUGXKtuMHTv2vd83NDSkqqoql19+eT7/+c+3eeYKK6yQk046KY888kg++clPvvfoqDvvvDOHHXZYKWK3m3PPPTerrrpqk/Ujjzwy11133YeeffTRR7PTTjvlrbfeavK5Sy+9ND16tP+/SiuvvHIuvvjiJut///vfM2LEiPzhD3/40PP3339/RowYkb/97W9NPrfrrrtm7733LlFSOp26usqeBwAAAID2st12ybhxhe299NLkyivLmwcAADqBLlW2uf/++1NVVfVe0WbEiBE5+uijSzJ7ww03zJQpU7LBBhu89x633XZbLr300pLMbw+rr756rrrqqibrCxcuzOGHH5499tgjt9xyS1555ZUsWbIkc+bMyf33358vfvGL2WqrrTJz5swmZ0855ZTssMMO7RG/WYceemhOPPHEJuvPPPNMdthhh2y77ba58MILc9ttt+WBBx7IbbfdlgsvvDDbbbddtt9++/zzn/9scvZjH/tYi+Ujurh584o7P3duaXIAAAAAQHv44heT0aML2/uVryR/+lN58wAAQAdXwN2QncPf//73vP766+89QipJvvKVr5T0PdZee+1ceeWV2Wmnnd4r3HznO9/JMccckwEDBpT0vcplv/32y/nnn5+zzjqryefuvvvu3H333QXPGjlyZH70ox+VMl6bjB07Nm+88UZuvPHGJp/705/+lD+14g9+G2ywQe64445mbwCiGyn2v88DB5YmBwAAAAC0l4suSv7+9+S++z5836JFyec/nzz0ULL++u2TDQAAOpguc7PNU0891WRt9913L/j8okWLCtq3ww47ZLfddktDQ0OSZN68eZk4cWLB79MRnHnmmbnooovSs2fPNs844ogjcuONN6Z3794lTNY2vXr1ym9+85ucccYZy5StWmvHHXfMQw89lE9+8pMlTEenVF1d2fMAAAAA0N56905uuCH52Mda3vvaa8m++yYLFpQ/FwAAdEBdpmwze/bsZT5ef/31s/LKKxd8vr6+vuC9Bx10UJK8V+y49dZbCz7bUZx88smZOnVqhg4d2qpza6+9diZOnJiJEyemb9++ZUrXej179swPf/jD1NbWtvqxVptvvnl++9vfZsqUKVlttdXKlJBOZdSoyp4HAAAAgEpYbbXkttsKu/n5r39Njj46Wbq0/LkAAKCD6ZJlm6qqqqy55pofuv+DRZHWlG223Xbb937f0NCQ2trags92JFtvvXWmT5+ee++9N0cffXTWWWedZvcNGjQoe++9dyZMmJBnn302RxxxRFHvO378+DQ0NDR5ffSjHy1qbpJstdVWue+++/K3v/0tP/jBD7L77rtngw02yIorrpiePXtmtdVWyyc+8YnsvPPO+cEPfpA///nPefTRR/P5z3++qFtx6GJqapJhw9p2dtiwpJUlNgAAAADoMDbZJLn22qSQ75f+9rfJeeeVPxMAAHQwvSodoFQWL168zMcDBw780P0DBgzIG2+88d7Hr7zySsHvtdZaay3z8fz58/Pyyy83We8sdt555+y8885Jkjlz5mTWrFlZsGBB+vXrl9VXXz1rr712hRO23qc+9al86lOfqnQMOrOxY5MRI5JWFPHSr18yblz5MgEAAABAe9h77+T885Mzz2x57znnNBZ0Djig/LkAAKCD6DI32wz4wLWW//nPfz50/wfLODNnziz4vfr169dk7f3Fnc5s0KBB+dSnPpXPfOYz2WyzzTpl0QZKYujQZNKkxmdVF6Jfv8b91dXlzQUAAAAA7eGb30wOO6ywvUcd1fhYKQAA6Ca6TNlmlVVWee/3DQ0Neeuttz50/1prrZWGhob3Pp4xY0bB7/X66683WVu4cGHB54FOYuTI5LjjWt630krJlVc27gcAAACArqCqKrnqqsZHrrfkP/9J9tknefXV8ucCAIAOoMuUbTbaaKNlPn7ppZc+dP9mm22WJKmqqkpDQ0OmTp1a8Hs99NBDTdYGDRpU8Hmgk5g8ufEbCi2ZPz85/vjG/QAAAADQVaywQnLLLclaa7W89/nnkwMPTN55p+yxAACg0rpM2eaTn/zkMh/Pnj37Qws3m2+++TIfv/TSS3nggQcKeq9f/epXTdbWWGONgs4CncTDDycHHZQsWlTY/vr6xv11deXNBQAAAADtaZ11Ggs3ffu2vPeBB5LRo5P33SoPAABdUZcp2wwcODAf+9jHlln764c8I3bXXXdtsnbOOecs82ip5tx77725+eabU1VV9d7axhtvnAEDBrQyMdChjRnTWKBpjfr6xm8mAAAAAEBXsvXWyS9+UdjeK69Mxo0rbx4AAKiwLlO2SZIddthhmbLMPffcs9y9G264Ybbaaqs0NDS89yip+++/P0cffXQWLFjQ7Jnbb789Bx100Hsfv3t2l112Kd0XAVTeQw8ltbVtO1tbm0yfXto8AAAAAFBpRx6ZnH56YXtPPjn53e/KGgcAACqpS5VtdtxxxyR5rzxz2223fej+r3zlK+/9/t0z1157bTbYYIOceOKJufjii3PllVfm+9//frbZZpvst99+mTt37jIzevTokTFjxpT+iwEqZ8KEyp4HAAAAgI7ohz9M9tij5X1LljQ+cv2f/yx/JgAAqIBelQ5QSnvttVf69OmTRYsWJUmee+65PPjggxk+fHiz+4866qhcfvnlqa2tTVVV1XuFm9dffz1XXnllk/3v3mTz/t+PGjUqG2+8cfm+KKD91dVV9jwAAAAAdEQ9eybXXdf4WKmnnvrwvW++meyzT+NN0AMHtk8+AABoJ13qZpuBAwdmt912e+9RUg0NDfmf//mfDz0zfvz4rLnmmu99/P7SzQdf7xZt3vXpT386Y8eOLf0XAlTWvHnFnf/ADVgAAAAA0GWsvHJy++3JoEEt7/3735P110822ywZPjwZMyZ5+OGyRwQAgHLrUjfbJMkPfvCD7LPPPu993LNnzw/dv/HGG2fKlCnZY4898sILL7xXqPlgseb9GhoassMOO+TGG29M3759SxMc6DgGDCjuvJ/UAQAAAKAr+/jHkxtuSHbfPVm69MP3zpnT+EqSadOSceOSYcOSsWOToUPLnRQAAMqiy5VtNt1002y66aatOrPJJpvkySefzHnnnZef//znefPNN5e7d7311su3vvWtHHfccS0WeYBOqrq68Q/+xZwHAAAAgK5sl12Sn/wkOfnk1p+trU1GjEgmTUpGjix5NAAAKLcuV7Zpq/79++f888/PueeemwceeCAPP/xwXnnllbz11ltZeeWVM3jw4Gy77bYZqmkPXd+oUY0/YVPMeQAAAADo6k46KXnsseSXv2z92fr65KCDkqlT/fAaAACdjrLNB/Ts2TPbb799tt9++0pHASqlpqbxKtva2tafHTbM9bcAAAAAdA9VVclllyVPPpk8+GDrz9fXJ6NHF3fLNAAAVECPSgcA6JDGjk369WvdmX79irsRBwAAAAA6m759k7PPbvv52tpk+vTS5QEAgHagbAPQnKFDk9NPb92Z00935S0AAAAA3c/kycWdnzChNDkAAKCdKNsANOfhh5MLL2zdmQsvTOrqypMHAAAAADqqYr8n5ntqAAB0Mso2AM0ZM6bxmdGt8e4zpgEAAACgO5k3r7jzc+eWJgcAALQTZRuAD3roocZnRbeFZ0wDAAAA0N0MGFDc+YEDS5MDAADaibINwAcV+4xoz5gGAAAAoDupri7u/MYblyYHAAC0E2UbgA/yjGkAAAAAKNyoUcWdnzYtWbCgJFEAAKA99Kp0gEIce+yxlY6wXFVVVbnqqqsqHQMoJc+YBgAAAIDC1dQkw4a1/dHsTz2VHHVUMmlS0sPPCAMA0PF1irLN+PHjU1VVVekYTTQ0NCjbQFfkGdMAAAAA0DpjxyYjRiT19W07f9NNyXe+k5x3XmlzAQBAGXSqinhDQ0OHegFdVLHPmC72PAAAAAB0NkOHNt5M069f22f84AfJNdeULhMAAJRJpyrbVFVVdagX0EUV+4zpYs8DAAAAQGc0cmQydWrjI6Xa6rjjkmnTSpcJAADKoFM8RupdbpMB2kUxz5geNqzxp3gAAAAAoDuqrm4sy0yfnkyYkNTVJXPnJiutlDzzTPLaax9+/p13kv32S/7yl2T99dslMgAAtFanKttUVVWloaEhVVVVqa6uzkorrVTpSEBX1dZnTB9ySHnyAAAAAEBnUlPT+Hq/F15IttoqeemlDz/76qvJ3ns33pIzYED5MgIAQBt1qrJN8n+Fm6eeeioHHnhgjjnmmGy77baVjgV0NUOHJj/8YXLKKa07d+aZyXbbNf4EDwAAAADwf9ZdN7n11sbvn7X0Q26PP54cfnhyyy1Jz57tEg8AAArVo9IB2mr+/PmZMGFCtt9++3ziE5/If//3f2fWrFmVjgV0Jddf3/oz9fXJ6NGlzwIAAAAAXcFnPtP4eKlCTJ7c+MNtAADQwXSKss2wYcPS0NCQhoaGJI2327x7w01DQ0P+8Y9/5Fvf+lbWX3/97LXXXrnxxhuzaNGiCqcGOrWHHkpqa9t2tra28ZnUAAAAAEBTBx+cfO97he298MLk6qvLmwcAAFqpU5RtHnzwwTz55JM57bTTsuaaa75Xsnm3dPNu8WbJkiW5++67c8ghh2Tw4ME55ZRT8thjj1U6PtAZFfrTNeU6DwAAAABd2dlnJ4ceWtjeE09M/vjH8uYBAIBW6BRlmyTZeOONc8EFF2TmzJm59dZbs++++6ZXr17Lve3mjTfeyKWXXpott9wyNTU1+dnPfpY5c+ZU9osAOo+6usqeBwAAAICurKoq+eUvk622annvokXJ5z+f/Otf5c8FAAAF6DRlm3f17Nkze++9d26++ea88MILueCCCzJkyJDl3nbT0NCQurq6jBkzJoMHD87hhx+ee++9t9JfBtDRzZtX3Pm5c0uTAwAAAAC6qhVWSG65JVl33Zb3vvFGMnJk8tZbZY8FAAAt6XRlm/dbY401ctppp+WJJ57ItGnTcvzxx2fAgAHLLd7U19fn+uuvz+67756PfvSj+e53v5vnnnuu0l8G0BENGFDc+YEDS5MDAAAAALqytddObrst6d+/5b1//3tyyCHJ4sXlzwUAAB+iU5dt3m/rrbfOFVdckZdeeinjx4/P5z73uSRZ7mOmnn/++Zx77rnZaKONsuOOO+baa69NfX19Jb8EoCOprq7seQAAAADoLrbcMrn22sL23nNPctpp5c0DAAAt6DJlm3etsMIKOeqoo/L73/8+//znP3PWWWdl3XXXXe5tN0uXLs3999+fo446KmuvvXa+/OUv5y9/+Uulvwyg0kaNqux5AAAAAOhO9tsv+eEPC9t7ySXJz39e1jgAAPBhulzZ5v022GCDnHfeeXnuuedy11135cADD0yfPn2aFG/e/fitt97KFVdckW222SabbrppfvKTn+S1116r9JcBVEJNTTJsWNvOfuITydChpc0DAAAAAF3dN7+ZHHVUYXtHj07uu6+8eQAAYDm6dNnmXVVVVdltt91yww03ZNasWbn44ouzxRZbLPe2m4aGhsyYMSOnn3561l133dx///2V/hKAShg7NunTp/XnnnsuqasreRwAAAAA6NKqqpIrrkg++9mW9y5Zkhx4YPL00+XPBQAAH9Atyjbvt8oqq+Skk05KXV1dHn744XzlK1/JoEGDllu8Wbx4cd56661KxwYqYejQZIMNWn9u4cLGn6wBAAAAAFqnb9/kppuS9ddvee+bbyZ77934KwAAtKNuV7Z5vy233DJjx47NSy+9lOuuuy677LJLqqqqKh0L6Cgeeih56qm2na2tTaZPL20eAAAAAOgO/uu/kttvT1ZaqeW9Tz+dHHRQsmhR+XMBAMD/163LNu96/202AO+ZMKGy5wEAAACgu9pss+Q3v2l8tFRLfve75KSTkoaG8ucCAIB087LNI488kpNOOilrr712DjvssNx7773vPU4KIHV1lT0PAAAAAN3ZXnslP/5xYXsvvzwZO7a8eQAA4P/rVekA7W327Nm55pprcvXVV+exxx5LkmXKNR+84aZXr14ZNGhQe8cEOoJ584o7P3duaXIAAAAAQHd1yinJjBnJVVe1vPfkk5OPfzzZffeyxwIAoHvrFmWbhoaG3H333fnlL3+Z22+/PYsWLWpSsHn/3iTZdNNNc8wxx+TII4/M6quv3u6ZgQ5gwIDizg8cWJocAAAAANBdVVUll12W/POfyf33f/jepUuTQw5JamuTIUPaJx8AAN1Sly7b/OMf/8jVV1+dX/3qV3nppZeS/F+ZprmCzaBBg3LooYfm2GOPTU1NTfsHBjqW6upk2rTizgMAAAAAxenTJ/ntb5Ott06eeebD986dm4wcmfz5z4kfpAUAoEy6XNnmP//5T66//vr88pe/zIMPPpik6WOi3tXQ0JCqqqrsuOOOOfbYY3PAAQekb9++7Z4Z6KBGjUrGjWv7+Q03LFkUAAAAAOjWVlstuf32ZNiwlh/f/q9/JQcckNx7b2NRBwAASqxHpQOUyp/+9Kcce+yxWWuttXL88cfnwQcfTENDw3uFmndf76595CMfyTnnnJNnn302U6ZMyeGHH65oAyyrpqbxD+9tdeaZSV1d6fI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",
+      "text/plain": [
+       "<Figure size 2560x1920 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "M, R = cal_MR()\n",
+    "\n",
+    "plt.figure(dpi=400)\n",
+    "plt.plot(R / km, M / Msun, \"ro-\", ms=2)  # Rule 3\n",
+    "plt.xlabel(\"Radius [km]\")\n",
+    "plt.ylabel(\"Mass [Msun]\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+             
+  <h1>Source code for EOSgenerators.RMF_EOS</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">optimize</span>
+<span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+<span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+<span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+<span class="n">dyncm2_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.6022e33</span><span class="p">)</span>
+<span class="n">gcm3_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.7827e12</span><span class="p">)</span>
+<span class="n">oneoverfm_MeV</span> <span class="o">=</span> <span class="mf">197.33</span>
+
+<span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+<span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+<span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+<span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+
+<span class="n">J_B</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span>
+<span class="n">b_B</span> <span class="o">=</span> <span class="mi">1</span>
+
+<span class="n">m_l</span> <span class="o">=</span> <span class="p">[</span><span class="n">m_e</span><span class="p">,</span> <span class="n">m_mu</span><span class="p">]</span>
+<span class="n">m_b</span> <span class="o">=</span> <span class="p">[</span><span class="n">m_p</span><span class="p">,</span> <span class="n">m_n</span><span class="p">]</span>
+
+<span class="n">Matrix_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span>
+
+<span class="n">Matrix_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">],[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">]])</span>
+
+<div class="viewcode-block" id="initial_values">
+<a class="viewcode-back" href="../../RMF_EOS.html#EOSgenerators.RMF_EOS.initial_values">[docs]</a>
+<span class="k">def</span> <span class="nf">initial_values</span><span class="p">(</span><span class="n">rho</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Outputs the the sigma, omega, rho term and chemical potential of electron and neutron at </span>
+<span class="sd">    given initial density</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        rho (float): given nuclear density</span>
+<span class="sd">        theta (array): paramters of determine a RMF model in lagrangian, here we have 10 parameters.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        sigma (float): sigma term in lagrangian</span>
+<span class="sd">        omega (float): omega term in lagrangian</span>
+<span class="sd">        rho_03 (float): rho term in lagrangian</span>
+<span class="sd">        mu_n (float): chemical potential of neutron matter</span>
+<span class="sd">        mu_e (float): chemical potential of electron portion</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">theta</span>
+
+    <span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+    <span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+    <span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    <span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    
+    <span class="n">rho_0</span> <span class="o">=</span> <span class="mf">0.1505</span>
+
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">g_sigma</span><span class="o">*</span><span class="n">rho</span><span class="o">/</span><span class="p">(</span><span class="n">m_sig</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">rho_03</span> <span class="o">=</span> <span class="o">-</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho</span><span class="o">/</span><span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="p">(</span><span class="n">m_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+    <span class="n">omega</span> <span class="o">=</span> <span class="n">rho</span><span class="o">*</span><span class="p">((((</span><span class="n">m_w</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_omega</span><span class="p">)</span><span class="o">+</span>\
+    <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="p">((</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">g_omega</span><span class="p">))</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.</span><span class="p">))</span>
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="n">m_n</span><span class="o">-</span><span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span>
+    <span class="n">mu_n</span> <span class="o">=</span> <span class="n">m_eff</span> <span class="o">+</span> <span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span> <span class="o">+</span> <span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+    <span class="n">mu_e</span> <span class="o">=</span> <span class="mf">0.12</span><span class="o">*</span><span class="n">m_e</span><span class="o">*</span><span class="p">(</span><span class="n">rho</span><span class="o">/</span><span class="n">rho_0</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">2</span><span class="o">/</span><span class="mf">3.</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">omega</span><span class="p">,</span> <span class="n">rho_03</span><span class="p">,</span> <span class="n">mu_n</span><span class="p">,</span> <span class="n">mu_e</span></div>
+
+
+
+<div class="viewcode-block" id="functie">
+<a class="viewcode-back" href="../../RMF_EOS.html#EOSgenerators.RMF_EOS.functie">[docs]</a>
+<span class="k">def</span> <span class="nf">functie</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;iterate the the sigma, omega, rho term and chemical potential of electron and neutron at </span>
+<span class="sd">    any given density</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        x (array): initial sigma omega rho and chemical potential from initial_values function</span>
+<span class="sd">        args (array): paramters of determine a RMF model in lagrangian, here we have 10 parameters.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        sigma (float): sigma term in lagrangian</span>
+<span class="sd">        omega (float): omega term in lagrangian</span>
+<span class="sd">        rho_03 (float): rho term in lagrangian</span>
+<span class="sd">        mu_n (float): chemical potential of neutron matter</span>
+<span class="sd">        mu_e (float): chemical potential of electron portion</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">m_sig</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">m_w</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">m_rho</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+    <span class="n">g_sigma</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+    <span class="n">g_omega</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+    <span class="n">g_rho</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
+    <span class="n">kappa</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
+    <span class="n">lambda_0</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span>
+    <span class="n">zeta</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span>
+    <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span>
+    <span class="n">rho</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span>
+
+    <span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+    <span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+    <span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    <span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+
+    <span class="n">J_B</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span>
+    <span class="n">b_B</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="n">m_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_e</span><span class="p">,</span> <span class="n">m_mu</span><span class="p">])</span>
+    <span class="n">m_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_p</span><span class="p">,</span> <span class="n">m_n</span><span class="p">])</span>
+
+    <span class="n">Matrix_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span>
+
+    <span class="n">Matrix_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">],[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">]])</span>
+    
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">omega</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">rho_03</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+    <span class="n">mu_n</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+    <span class="n">mu_e</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+    
+
+    <span class="n">rho_B_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">rho_SB_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q_list</span> <span class="o">=</span> <span class="p">[]</span>
+    
+    
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="n">m_n</span> <span class="o">-</span> <span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span>
+    <span class="c1">#i = 0</span>
+    <span class="c1">#j = 0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_b</span><span class="p">)):</span>
+    <span class="c1">#while i &lt; len(Matrix_b):</span>
+    
+        <span class="n">mu_b</span> <span class="o">=</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">mu_e</span>
+        
+        <span class="n">E_fb</span> <span class="o">=</span> <span class="n">mu_b</span> <span class="o">-</span> <span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span>
+        
+        <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="n">E_fb</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span>
+        <span class="k">if</span> <span class="n">k_fb_sq</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">k_fb_sq</span><span class="p">,</span> <span class="n">a_min</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">a_max</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+            <span class="n">E_fb</span> <span class="o">=</span> <span class="n">m_eff</span>
+        
+        <span class="n">k_fb</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fb_sq</span><span class="p">)</span>
+        
+        <span class="n">rho_B</span> <span class="o">=</span> <span class="p">((</span><span class="mi">2</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">b_B</span><span class="o">*</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">rho_SB</span> <span class="o">=</span> <span class="p">(</span><span class="n">m_eff</span><span class="o">/</span><span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="p">(</span><span class="n">E_fb</span><span class="o">*</span><span class="n">k_fb</span> <span class="o">-</span> \
+        <span class="p">(</span><span class="n">m_eff</span><span class="o">**</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">E_fb</span> <span class="o">+</span> <span class="n">k_fb</span><span class="p">)</span><span class="o">/</span><span class="n">m_eff</span><span class="p">))</span>
+        
+        <span class="n">rho_B_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rho_B</span><span class="p">)</span>
+        <span class="n">rho_SB_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rho_SB</span><span class="p">)</span>
+        
+        <span class="n">Q_B</span> <span class="o">=</span> <span class="p">((</span><span class="mf">2.</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">q_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Q_B</span><span class="p">)</span>
+        <span class="c1">#i += 1</span>
+        
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_l</span><span class="p">)):</span>
+        <span class="c1">#while j &lt; len(Matrix_l):</span>
+        
+        <span class="n">mu_l</span> <span class="o">=</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">mu_e</span>
+        <span class="n">E_fl</span> <span class="o">=</span> <span class="n">mu_l</span>
+        
+        <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="n">E_fl</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span>
+        <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">k_fl_sq</span><span class="p">,</span> <span class="n">a_min</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">a_max</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">k_fl</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fl_sq</span><span class="p">)</span>
+        
+        <span class="n">Q_L</span> <span class="o">=</span> <span class="p">((</span><span class="mf">2.</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span><span class="o">*</span><span class="n">Matrix_l</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">k_fl</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+        <span class="n">q_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Q_L</span><span class="p">)</span>
+        
+        <span class="c1">#rho_l = k_fl**3 / (3.*(math.pi**2))</span>
+        <span class="c1">#rho_list.append(rho_l)</span>
+
+    
+    <span class="n">f</span> <span class="o">=</span> <span class="p">[(</span><span class="n">sigma</span><span class="o">*</span><span class="p">(</span><span class="n">m_sig</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_sigma</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">rho_SB_list</span><span class="p">)</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">3</span><span class="p">])</span>
+         <span class="o">+</span><span class="p">(</span><span class="n">kappa</span><span class="o">*</span><span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span> <span class="o">+</span> <span class="p">(</span><span class="n">lambda_0</span><span class="o">*</span><span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">/</span><span class="mf">6.</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
+         
+        <span class="p">(</span><span class="n">omega</span><span class="o">*</span><span class="p">(</span><span class="n">m_w</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_omega</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">rho_B_list</span><span class="p">)</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">4</span><span class="p">])</span> <span class="o">+</span> <span class="p">(</span><span class="n">zeta</span><span class="o">*</span><span class="p">(</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="p">)</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">/</span><span class="mf">6.</span>\
+        <span class="o">+</span><span class="mf">2.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="p">(</span><span class="n">rho_03</span><span class="o">*</span><span class="n">g_rho</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
+       
+        <span class="p">(</span><span class="n">rho_03</span><span class="o">*</span><span class="p">(</span><span class="n">m_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_rho</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">rho_B_list</span><span class="p">)</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">5</span><span class="p">]</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">2</span><span class="p">])</span><span class="o">+</span><span class="mf">2.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="p">(</span><span class="n">omega</span><span class="o">*</span><span class="n">g_omega</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> 
+             
+        <span class="p">(</span><span class="n">rho</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">rho_B_list</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
+        
+        <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">q_list</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">]</span>
+        
+    <span class="k">return</span> <span class="n">f</span></div>
+
+
+
+<div class="viewcode-block" id="Energy_density_Pressure">
+<a class="viewcode-back" href="../../RMF_EOS.html#EOSgenerators.RMF_EOS.Energy_density_Pressure">[docs]</a>
+<span class="k">def</span> <span class="nf">Energy_density_Pressure</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">rho</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate pressure and energy density two EOS ingredient from given RMF term and given parameters,</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        x (array): An array that consists of the initial values of sigma, omega, rho, and chemical </span>
+<span class="sd">        potential obtained from the initial_values function.</span>
+<span class="sd">        rho (float): The central density from which the computation of the equation of state begins.</span>
+<span class="sd">        theta (array): An array representing the parameters used to determine a RMF model in the </span>
+<span class="sd">        Lagrangian. In this case, the RMF model is defined by 10 parameters.</span>
+
+
+<span class="sd">    Returns:</span>
+<span class="sd">        energy_density (float): EOS ingredient, energy density in g/cm3</span>
+<span class="sd">        pressure (float): EOS ingredient, pressure in dyn/cm3</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">sigma</span><span class="p">,</span> <span class="n">omega</span><span class="p">,</span> <span class="n">rho_03</span><span class="p">,</span> <span class="n">mu_n</span><span class="p">,</span> <span class="n">mu_e</span> <span class="o">=</span> <span class="n">x</span>
+    
+    <span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">theta</span>
+
+    <span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+    <span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+    <span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    <span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+
+    <span class="n">J_B</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span>
+    <span class="n">b_B</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="n">m_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_e</span><span class="p">,</span> <span class="n">m_mu</span><span class="p">])</span>
+    <span class="n">m_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_p</span><span class="p">,</span> <span class="n">m_n</span><span class="p">])</span>
+    
+    <span class="n">energy_b</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">energy_l</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">multi</span> <span class="o">=</span> <span class="mi">0</span>
+    
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="n">m_n</span> <span class="o">-</span> <span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_b</span><span class="p">)):</span>        
+        <span class="n">mu_b</span> <span class="o">=</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">mu_e</span>
+        
+        <span class="n">E_fb</span> <span class="o">=</span> <span class="n">mu_b</span> <span class="o">-</span> <span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span>
+        
+        <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="n">E_fb</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span>
+        <span class="k">if</span> <span class="n">k_fb_sq</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">E_fb</span> <span class="o">=</span> <span class="n">m_eff</span>
+          
+        <span class="n">k_fb</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fb_sq</span><span class="p">)</span>
+
+        <span class="n">rho_B</span> <span class="o">=</span> <span class="p">((</span><span class="mf">2.</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span><span class="o">*</span><span class="n">b_B</span><span class="o">*</span><span class="p">(</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        
+        <span class="n">multi</span> <span class="o">=</span> <span class="n">multi</span> <span class="o">+</span> <span class="n">mu_b</span><span class="o">*</span><span class="n">rho_B</span>
+        <span class="n">energy_baryon</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mf">8.</span><span class="o">*</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="n">k_fb</span><span class="o">*</span><span class="p">(</span><span class="n">E_fb</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span>\
+        <span class="o">+</span> <span class="p">(</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="n">E_fb</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">k_fb</span> <span class="o">+</span> <span class="n">E_fb</span><span class="p">)</span><span class="o">/</span><span class="n">m_eff</span><span class="p">)</span><span class="o">*</span><span class="n">m_eff</span><span class="o">**</span><span class="mi">4</span><span class="p">)</span>
+
+        <span class="n">energy_b</span> <span class="o">=</span> <span class="n">energy_b</span> <span class="o">+</span> <span class="n">energy_baryon</span>
+        
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_l</span><span class="p">)):</span>
+        
+        <span class="n">mu_l</span> <span class="o">=</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">mu_e</span>
+        
+        <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="n">mu_l</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span>
+        <span class="k">if</span> <span class="n">k_fl_sq</span> <span class="o">&lt;</span> <span class="mf">0.0</span><span class="p">:</span>
+            <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="n">k_fl</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fl_sq</span><span class="p">)</span>
+        
+        <span class="n">rho_l</span> <span class="o">=</span> <span class="n">k_fl</span><span class="o">**</span><span class="mi">3</span> <span class="o">/</span> <span class="p">(</span><span class="mf">3.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        
+        <span class="n">multi</span> <span class="o">=</span> <span class="n">multi</span> <span class="o">+</span> <span class="n">mu_l</span><span class="o">*</span><span class="n">rho_l</span>
+        <span class="n">energy_lepton</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mf">8.</span><span class="o">*</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="n">k_fl</span><span class="o">*</span><span class="p">(</span><span class="n">mu_l</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span>\
+        <span class="o">+</span><span class="n">mu_l</span><span class="o">*</span><span class="p">(</span><span class="n">k_fl</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">-</span><span class="p">(</span><span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">**</span><span class="mi">4</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">k_fl</span><span class="o">+</span><span class="n">mu_l</span><span class="p">)</span><span class="o">/</span><span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
+        
+        <span class="n">energy_l</span> <span class="o">=</span> <span class="n">energy_l</span> <span class="o">+</span> <span class="n">energy_lepton</span>
+        
+    <span class="n">sigma_terms</span> <span class="o">=</span>  <span class="mf">0.5</span><span class="o">*</span><span class="p">((</span><span class="n">sigma</span><span class="o">*</span><span class="n">m_sig</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">kappa</span><span class="o">*</span><span class="p">((</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">3</span><span class="p">))</span><span class="o">/</span><span class="mf">6.</span>\
+                    <span class="o">+</span> <span class="p">(</span><span class="n">lambda_0</span><span class="o">*</span><span class="p">((</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">4</span><span class="p">))</span><span class="o">/</span><span class="mf">24.</span>
+        
+    <span class="n">omega_terms</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">((</span><span class="n">omega</span><span class="o">*</span><span class="n">m_w</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span><span class="p">(</span><span class="n">zeta</span><span class="o">*</span><span class="p">((</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="p">)</span><span class="o">**</span><span class="mi">4</span><span class="p">))</span><span class="o">/</span><span class="mf">8.</span>
+        
+    <span class="n">rho_terms</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">((</span><span class="n">rho_03</span><span class="o">*</span><span class="n">m_rho</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span> <span class="o">+</span> <span class="mf">3.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="p">((</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    
+    <span class="n">energy_density</span> <span class="o">=</span> <span class="n">energy_b</span> <span class="o">+</span> <span class="n">energy_l</span> <span class="o">+</span> <span class="n">sigma_terms</span> \
+    <span class="o">+</span> <span class="n">omega_terms</span> <span class="o">+</span> <span class="n">rho_terms</span>
+        
+    <span class="n">Pressure</span> <span class="o">=</span> <span class="n">multi</span> <span class="o">-</span> <span class="n">energy_density</span>
+
+    <span class="k">return</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">Pressure</span></div>
+
+
+<div class="viewcode-block" id="compute_EOS">
+<a class="viewcode-back" href="../../RMF_EOS.html#EOSgenerators.RMF_EOS.compute_EOS">[docs]</a>
+<span class="k">def</span> <span class="nf">compute_EOS</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">,</span> <span class="n">pres_crust</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate core part equation of state, main function, from RMF model,</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        eps_crust (array): the energy density of crust EoS in MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_crust (array): the pressure from crust EoS model in MeV/fm3, times a G/c**4 factor</span>
+<span class="sd">        theta (array): An array representing the parameters used to determine a RMF model in the </span>
+<span class="sd">        Lagrangian. In this case, the RMF model is defined by 10 parameters.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        energy_density (float): EOS ingredient, energy density in g/cm3</span>
+<span class="sd">        pressure (float): EOS ingredient, pressure in dyn/cm3</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.05</span>
+    <span class="n">rho_0</span> <span class="o">=</span> <span class="mf">0.1505</span>
+    
+    <span class="n">x_init</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">initial_values</span><span class="p">(</span><span class="mf">0.1</span><span class="o">*</span><span class="n">rho_0</span><span class="p">,</span> <span class="n">theta</span><span class="p">))</span>
+    <span class="n">Energy</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">Pressure</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">125</span><span class="p">):</span>
+    
+        <span class="n">rho</span> <span class="o">=</span> <span class="n">i</span><span class="o">*</span><span class="n">dt</span><span class="o">*</span><span class="n">rho_0</span>
+        <span class="n">arg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">rho</span><span class="p">)</span>
+        <span class="n">sol</span> <span class="o">=</span> <span class="n">optimize</span><span class="o">.</span><span class="n">root</span><span class="p">(</span><span class="n">functie</span><span class="p">,</span> <span class="n">x_init</span> <span class="p">,</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;lm&#39;</span><span class="p">,</span><span class="n">args</span> <span class="o">=</span> <span class="n">arg</span><span class="p">)</span>
+    
+        <span class="n">Re</span><span class="o">=</span> <span class="n">Energy_density_Pressure</span><span class="p">(</span><span class="n">x_init</span><span class="p">,</span> <span class="n">rho</span><span class="p">,</span><span class="n">theta</span><span class="p">)</span>
+    
+        <span class="n">Energy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Re</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">oneoverfm_MeV</span><span class="o">/</span><span class="n">gcm3_to_MeVfm3</span><span class="p">)</span>
+        <span class="n">Pressure</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Re</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">oneoverfm_MeV</span><span class="o">/</span><span class="n">dyncm2_to_MeVfm3</span><span class="p">)</span>
+    
+        <span class="n">x_init</span> <span class="o">=</span> <span class="n">sol</span><span class="o">.</span><span class="n">x</span>
+        
+    <span class="n">Energy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Energy</span><span class="p">)</span>
+    <span class="n">Pressure</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Pressure</span><span class="p">)</span>
+    
+    <span class="n">end</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">Energy</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">Energy</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mi">2</span> <span class="o">&gt;</span> <span class="nb">max</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">)</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">&gt;</span><span class="mi">18</span><span class="p">:</span>
+            <span class="n">end</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">2</span>
+            <span class="k">break</span>
+        <span class="n">end</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="n">ep</span> <span class="o">=</span> <span class="n">Energy</span><span class="p">[</span><span class="n">end</span><span class="p">::]</span> <span class="o">*</span> <span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">pr</span> <span class="o">=</span> <span class="n">Pressure</span><span class="p">[</span><span class="n">end</span><span class="p">::]</span> <span class="o">*</span> <span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mi">4</span>
+    
+    <span class="k">return</span> <span class="n">ep</span><span class="p">,</span> <span class="n">pr</span></div>
+
+</pre></div>
+
+           </div>
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diff --git a/_modules/EOSgenerators/crust_EOS.html b/_modules/EOSgenerators/crust_EOS.html
new file mode 100644
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for EOSgenerators.crust_EOS</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">TOVsolver.constant</span> <span class="k">as</span> <span class="nn">constant</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.EoS_import</span> <span class="kn">import</span> <span class="n">EOS_import</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<div class="viewcode-block" id="PolyInterpolate">
+<a class="viewcode-back" href="../../crust_EOS.html#EOSgenerators.crust_EOS.PolyInterpolate">[docs]</a>
+<span class="k">def</span> <span class="nf">PolyInterpolate</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">,</span> <span class="n">pressure_crust</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Polytrope connecting crust part of equation of state to core part</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        eps_crust (array): the energy density of crust EoS in MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_crust (array): the pressure from crust EoS model in MeV/fm3, times a G/c**4 factor</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        eps_combine (float): EOS ingredient, combined crust and inter-crust part  energy density in</span>
+<span class="sd">        MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_combine (float): EOS ingredient, combined crust and inter-crust part pressure in MeV/fm3,</span>
+<span class="sd">        times a G/c**2 factor</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="n">constant</span><span class="o">.</span><span class="n">c</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="n">constant</span><span class="o">.</span><span class="n">G</span>
+    
+    <span class="n">xs_polytro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">11.7</span><span class="p">,</span> <span class="mf">14.28</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+    <span class="n">ys_polytropic</span> <span class="o">=</span> <span class="p">(</span><span class="mf">4.75764e29</span> <span class="o">+</span> <span class="mf">9.04238e13</span> <span class="o">*</span> <span class="n">xs_polytro</span><span class="o">**</span><span class="p">(</span><span class="mi">4</span><span class="o">/</span><span class="mi">3</span><span class="p">))</span> <span class="o">*</span> <span class="n">G</span> <span class="o">/</span> <span class="n">c</span><span class="o">**</span><span class="mi">4</span>
+    <span class="n">xs_polytropic</span> <span class="o">=</span> <span class="n">xs_polytro</span> <span class="o">*</span> <span class="n">G</span> <span class="o">/</span> <span class="n">c</span><span class="o">**</span><span class="mi">2</span>
+
+    <span class="n">eps_combine</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">,</span> <span class="n">xs_polytropic</span><span class="p">)</span>
+    <span class="n">pres_combine</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pressure_crust</span><span class="p">,</span> <span class="n">ys_polytropic</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">eps_combine</span><span class="p">,</span> <span class="n">pres_combine</span></div>
+
+</pre></div>
+
+           </div>
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+<li class="toctree-l1"><a class="reference internal" href="../../Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for EOSgenerators.fastRMF_EoS</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
+<span class="kn">from</span> <span class="nn">NumbaMinpack</span> <span class="kn">import</span> <span class="n">lmdif</span><span class="p">,</span> <span class="n">hybrd</span><span class="p">,</span> <span class="n">minpack_sig</span>
+<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">njit</span><span class="p">,</span> <span class="n">jit</span><span class="p">,</span> <span class="n">cfunc</span>
+
+<span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+<span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+<span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+<span class="n">dyncm2_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.6022e33</span><span class="p">)</span>
+<span class="n">gcm3_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.7827e12</span><span class="p">)</span>
+<span class="n">oneoverfm_MeV</span> <span class="o">=</span> <span class="mf">197.33</span>
+
+<span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+<span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+<span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+<span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+
+<span class="n">J_B</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span>
+<span class="n">b_B</span> <span class="o">=</span> <span class="mi">1</span>
+
+<span class="n">m_l</span> <span class="o">=</span> <span class="p">[</span><span class="n">m_e</span><span class="p">,</span> <span class="n">m_mu</span><span class="p">]</span>
+<span class="n">m_b</span> <span class="o">=</span> <span class="p">[</span><span class="n">m_p</span><span class="p">,</span> <span class="n">m_n</span><span class="p">]</span>
+
+<span class="n">Matrix_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span>
+
+<span class="n">Matrix_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">],[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">]])</span>
+
+<span class="c1"># defining the function to optimize with numba-minpack</span>
+
+<span class="nd">@cfunc</span><span class="p">(</span><span class="n">minpack_sig</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">myfunc</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">fvec</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;iterate the the sigma, omega, rho term and chemical potential of electron and neutron at </span>
+<span class="sd">    any given density(fast version)</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        x (array): initial sigma omega rho and chemical potential from initial_values function</span>
+<span class="sd">        fvec (array): iterated sigma sigma omega rho and chemical potential for output.</span>
+<span class="sd">        args (array): paramters of determine a RMF model in lagrangian, here we have 10 parameters.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        sigma (float): sigma term in lagrangian</span>
+<span class="sd">        omega (float): omega term in lagrangian</span>
+<span class="sd">        rho_03 (float): rho term in lagrangian</span>
+<span class="sd">        mu_n (float): chemical potential of neutron matter</span>
+<span class="sd">        mu_e (float): chemical potential of electron portion</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">m_sig</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">m_w</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">m_rho</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+    <span class="n">g_sigma</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+    <span class="n">g_omega</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+    <span class="n">g_rho</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
+    <span class="n">kappa</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
+    <span class="n">lambda_0</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span>
+    <span class="n">zeta</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span>
+    <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span>
+    <span class="n">rho</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span>
+
+    <span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+    <span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+    <span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    <span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+
+    <span class="n">J_B</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span>
+    <span class="n">b_B</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="n">m_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_e</span><span class="p">,</span> <span class="n">m_mu</span><span class="p">])</span>
+    <span class="n">m_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_p</span><span class="p">,</span> <span class="n">m_n</span><span class="p">])</span>
+
+    <span class="n">Matrix_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span>
+
+    <span class="n">Matrix_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">],[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span><span class="p">]])</span>
+    
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">omega</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">rho_03</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+    <span class="n">mu_n</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+    <span class="n">mu_e</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+
+    <span class="n">rho_B_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">rho_SB_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">q_list</span> <span class="o">=</span> <span class="p">[]</span>
+    
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="n">m_n</span> <span class="o">-</span> <span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_b</span><span class="p">)):</span>    
+        <span class="n">mu_b</span> <span class="o">=</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">mu_e</span>
+        
+        <span class="n">E_fb</span> <span class="o">=</span> <span class="n">mu_b</span> <span class="o">-</span> <span class="n">g_omega</span> <span class="o">*</span> <span class="n">omega</span> <span class="o">-</span> <span class="n">g_rho</span> <span class="o">*</span> <span class="n">rho_03</span> <span class="o">*</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span>
+        
+        <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="n">E_fb</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span>
+        <span class="k">if</span> <span class="n">k_fb_sq</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">E_fb</span> <span class="o">=</span> <span class="n">m_eff</span>
+        
+        <span class="n">k_fb</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fb_sq</span><span class="p">)</span>
+        
+        <span class="n">rho_B</span> <span class="o">=</span> <span class="p">((</span><span class="mi">2</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">b_B</span><span class="o">*</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">rho_SB</span> <span class="o">=</span> <span class="p">(</span><span class="n">m_eff</span><span class="o">/</span><span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="p">(</span><span class="n">E_fb</span><span class="o">*</span><span class="n">k_fb</span> <span class="o">-</span> \
+        <span class="p">(</span><span class="n">m_eff</span><span class="o">**</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">E_fb</span> <span class="o">+</span> <span class="n">k_fb</span><span class="p">)</span><span class="o">/</span><span class="n">m_eff</span><span class="p">))</span>
+        
+        <span class="n">rho_B_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rho_B</span><span class="p">)</span>
+        <span class="n">rho_SB_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rho_SB</span><span class="p">)</span>
+        
+        <span class="n">Q_B</span> <span class="o">=</span> <span class="p">((</span><span class="mf">2.</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">q_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Q_B</span><span class="p">)</span>
+        
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_l</span><span class="p">)):</span>        
+        <span class="n">mu_l</span> <span class="o">=</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">mu_e</span>
+        <span class="n">E_fl</span> <span class="o">=</span> <span class="n">mu_l</span>
+        
+        <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="n">E_fl</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span>
+        
+        <span class="k">if</span> <span class="n">k_fl_sq</span> <span class="o">&lt;</span> <span class="mf">0.0</span><span class="p">:</span>
+            <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="mf">0.0</span>
+    
+        <span class="n">k_fl</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fl_sq</span><span class="p">)</span>
+        
+        <span class="n">Q_L</span> <span class="o">=</span> <span class="p">((</span><span class="mf">2.</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span><span class="o">*</span><span class="n">Matrix_l</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">k_fl</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+        <span class="n">q_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Q_L</span><span class="p">)</span>
+        
+    <span class="n">sum1</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">sum2</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">sum3</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">sum4</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">sum5</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rho_SB_list</span><span class="p">)):</span>
+        <span class="n">sum1</span> <span class="o">=</span> <span class="n">sum1</span> <span class="o">+</span> <span class="n">rho_SB_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">3</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rho_B_list</span><span class="p">)):</span>
+        <span class="n">sum2</span> <span class="o">=</span> <span class="n">sum2</span> <span class="o">+</span> <span class="n">rho_B_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">4</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rho_B_list</span><span class="p">)):</span>
+        <span class="n">sum3</span> <span class="o">=</span> <span class="n">sum3</span> <span class="o">+</span> <span class="n">rho_B_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">5</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">Matrix_b</span><span class="p">[:,</span><span class="mi">2</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rho_B_list</span><span class="p">)):</span>
+        <span class="n">sum4</span> <span class="o">=</span> <span class="n">sum4</span> <span class="o">+</span> <span class="n">rho_B_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">q_list</span><span class="p">)):</span>
+        <span class="n">sum5</span> <span class="o">=</span> <span class="n">sum5</span> <span class="o">+</span> <span class="n">q_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+    <span class="n">fvec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">sigma</span><span class="o">*</span><span class="p">(</span><span class="n">m_sig</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_sigma</span> <span class="o">-</span> <span class="n">sum1</span> <span class="o">+</span><span class="p">(</span><span class="n">kappa</span><span class="o">*</span><span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="mf">2.</span>\
+              <span class="o">+</span> <span class="p">(</span><span class="n">lambda_0</span><span class="o">*</span><span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">/</span><span class="mf">6.</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">fvec</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">omega</span><span class="o">*</span><span class="p">(</span><span class="n">m_w</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_omega</span> <span class="o">-</span> <span class="n">sum2</span> <span class="o">+</span> <span class="p">(</span><span class="n">zeta</span><span class="o">*</span><span class="p">(</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="p">)</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">/</span><span class="mf">6.</span>\
+              <span class="o">+</span> <span class="mf">2.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="p">(</span><span class="n">rho_03</span><span class="o">*</span><span class="n">g_rho</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">fvec</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">rho_03</span><span class="o">*</span><span class="p">(</span><span class="n">m_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_rho</span> <span class="o">-</span> <span class="n">sum3</span> <span class="o">+</span> <span class="mf">2.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="p">(</span><span class="n">omega</span><span class="o">*</span><span class="n">g_omega</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
+    
+    <span class="n">fvec</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">rho</span> <span class="o">-</span> <span class="n">sum4</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">fvec</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum5</span><span class="o">**</span><span class="mi">2</span>
+
+    
+<span class="n">funcptr</span> <span class="o">=</span> <span class="n">myfunc</span><span class="o">.</span><span class="n">address</span> <span class="c1"># address in memory to myfunc</span>
+
+<span class="c1"># defining the initial values and E-P functions with numba</span>
+<div class="viewcode-block" id="initial_values">
+<a class="viewcode-back" href="../../fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.initial_values">[docs]</a>
+<span class="nd">@jit</span>
+<span class="k">def</span> <span class="nf">initial_values</span><span class="p">(</span><span class="n">rho</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Outputs the the sigma, omega, rho term and chemical potential of electron and neutron at </span>
+<span class="sd">    given initial density (fast version)</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        rho (float): given nuclear density</span>
+<span class="sd">        theta (array): paramters of determine a RMF model in lagrangian, here we have 10 parameters.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        sigma (float): sigma term in lagrangian</span>
+<span class="sd">        omega (float): omega term in lagrangian</span>
+<span class="sd">        rho_03 (float): rho term in lagrangian</span>
+<span class="sd">        mu_n (float): chemical potential of neutron matter</span>
+<span class="sd">        mu_e (float): chemical potential of electron portion</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">theta</span>
+
+    <span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+    <span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+    <span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    <span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    
+    <span class="n">rho_0</span> <span class="o">=</span> <span class="mf">0.1505</span>
+
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">g_sigma</span><span class="o">*</span><span class="n">rho</span><span class="o">/</span><span class="p">(</span><span class="n">m_sig</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">rho_03</span> <span class="o">=</span> <span class="o">-</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho</span><span class="o">/</span><span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="p">(</span><span class="n">m_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+    <span class="n">omega</span> <span class="o">=</span> <span class="n">rho</span><span class="o">*</span><span class="p">((((</span><span class="n">m_w</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">g_omega</span><span class="p">)</span><span class="o">+</span>\
+    <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="p">((</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">g_omega</span><span class="p">))</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.</span><span class="p">))</span>
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="n">m_n</span><span class="o">-</span><span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span>
+    <span class="n">mu_n</span> <span class="o">=</span> <span class="n">m_eff</span> <span class="o">+</span> <span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span> <span class="o">+</span> <span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+    <span class="n">mu_e</span> <span class="o">=</span> <span class="mf">0.12</span><span class="o">*</span><span class="n">m_e</span><span class="o">*</span><span class="p">(</span><span class="n">rho</span><span class="o">/</span><span class="n">rho_0</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">2</span><span class="o">/</span><span class="mf">3.</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">omega</span><span class="p">,</span> <span class="n">rho_03</span><span class="p">,</span> <span class="n">mu_n</span><span class="p">,</span> <span class="n">mu_e</span></div>
+
+
+<div class="viewcode-block" id="Energy_density_Pressure">
+<a class="viewcode-back" href="../../fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.Energy_density_Pressure">[docs]</a>
+<span class="nd">@njit</span>
+<span class="k">def</span> <span class="nf">Energy_density_Pressure</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">rho</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate pressure and energy density two EOS ingredient from given RMF term and given parameters,</span>
+<span class="sd">    (fast version)</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        x (array): An array that consists of the initial values of sigma, omega, rho, and chemical </span>
+<span class="sd">        potential obtained from the initial_values function.</span>
+<span class="sd">        rho (float): The central density from which the computation of the equation of state begins.</span>
+<span class="sd">        theta (array): An array representing the parameters used to determine a RMF model in the </span>
+<span class="sd">        Lagrangian. In this case, the RMF model is defined by 10 parameters.</span>
+
+
+<span class="sd">    Returns:</span>
+<span class="sd">        energy_density (float): EOS ingredient, energy density in g/cm3</span>
+<span class="sd">        pressure (float): EOS ingredient, pressure in dyn/cm3</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">sigma</span><span class="p">,</span> <span class="n">omega</span><span class="p">,</span> <span class="n">rho_03</span><span class="p">,</span> <span class="n">mu_n</span><span class="p">,</span> <span class="n">mu_e</span> <span class="o">=</span> <span class="n">x</span>
+    
+    <span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">theta</span>
+
+    <span class="n">m_e</span> <span class="o">=</span> <span class="mf">2.5896</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**-</span><span class="mi">3</span>
+    <span class="n">m_mu</span> <span class="o">=</span> <span class="mf">0.53544</span>
+    <span class="n">m_n</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+    <span class="n">m_p</span> <span class="o">=</span> <span class="mf">4.7583690772</span>
+
+    <span class="n">J_B</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="mf">2.</span>
+    <span class="n">b_B</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="n">m_l</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_e</span><span class="p">,</span> <span class="n">m_mu</span><span class="p">])</span>
+    <span class="n">m_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_p</span><span class="p">,</span> <span class="n">m_n</span><span class="p">])</span>
+    
+    <span class="n">energy_b</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">energy_l</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">multi</span> <span class="o">=</span> <span class="mi">0</span>
+    
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="n">m_n</span> <span class="o">-</span> <span class="p">(</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_b</span><span class="p">)):</span>        
+        <span class="n">mu_b</span> <span class="o">=</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">mu_e</span>
+        
+        <span class="n">E_fb</span> <span class="o">=</span> <span class="n">mu_b</span> <span class="o">-</span> <span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="n">Matrix_b</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span>
+        
+        <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="n">E_fb</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span>
+        <span class="k">if</span> <span class="n">k_fb_sq</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">k_fb_sq</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">E_fb</span> <span class="o">=</span> <span class="n">m_eff</span>
+          
+        <span class="n">k_fb</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fb_sq</span><span class="p">)</span>
+
+        <span class="n">rho_B</span> <span class="o">=</span> <span class="p">((</span><span class="mf">2.</span><span class="o">*</span><span class="n">J_B</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span><span class="o">*</span><span class="n">b_B</span><span class="o">*</span><span class="p">(</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">6.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        
+        <span class="n">multi</span> <span class="o">=</span> <span class="n">multi</span> <span class="o">+</span> <span class="n">mu_b</span><span class="o">*</span><span class="n">rho_B</span>
+        <span class="n">energy_baryon</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mf">8.</span><span class="o">*</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="n">k_fb</span><span class="o">*</span><span class="p">(</span><span class="n">E_fb</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span>\
+        <span class="o">+</span> <span class="p">(</span><span class="n">k_fb</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="n">E_fb</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">k_fb</span> <span class="o">+</span> <span class="n">E_fb</span><span class="p">)</span><span class="o">/</span><span class="n">m_eff</span><span class="p">)</span><span class="o">*</span><span class="n">m_eff</span><span class="o">**</span><span class="mi">4</span><span class="p">)</span>
+
+        <span class="n">energy_b</span> <span class="o">=</span> <span class="n">energy_b</span> <span class="o">+</span> <span class="n">energy_baryon</span>
+        
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Matrix_l</span><span class="p">)):</span>
+        
+        <span class="n">mu_l</span> <span class="o">=</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">mu_n</span> <span class="o">-</span> <span class="n">Matrix_l</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">mu_e</span>
+        
+        <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="n">mu_l</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span>
+        <span class="k">if</span> <span class="n">k_fl_sq</span> <span class="o">&lt;</span> <span class="mf">0.0</span><span class="p">:</span>
+            <span class="n">k_fl_sq</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="n">k_fl</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">k_fl_sq</span><span class="p">)</span>
+        
+        <span class="n">rho_l</span> <span class="o">=</span> <span class="n">k_fl</span><span class="o">**</span><span class="mi">3</span> <span class="o">/</span> <span class="p">(</span><span class="mf">3.</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        
+        <span class="n">multi</span> <span class="o">=</span> <span class="n">multi</span> <span class="o">+</span> <span class="n">mu_l</span><span class="o">*</span><span class="n">rho_l</span>
+        <span class="n">energy_lepton</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mf">8.</span><span class="o">*</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="n">k_fl</span><span class="o">*</span><span class="p">(</span><span class="n">mu_l</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span>\
+        <span class="o">+</span><span class="n">mu_l</span><span class="o">*</span><span class="p">(</span><span class="n">k_fl</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span><span class="o">-</span><span class="p">(</span><span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">**</span><span class="mi">4</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">k_fl</span><span class="o">+</span><span class="n">mu_l</span><span class="p">)</span><span class="o">/</span><span class="n">m_l</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
+        
+        <span class="n">energy_l</span> <span class="o">=</span> <span class="n">energy_l</span> <span class="o">+</span> <span class="n">energy_lepton</span>
+        
+    <span class="n">sigma_terms</span> <span class="o">=</span>  <span class="mf">0.5</span><span class="o">*</span><span class="p">((</span><span class="n">sigma</span><span class="o">*</span><span class="n">m_sig</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">kappa</span><span class="o">*</span><span class="p">((</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">3</span><span class="p">))</span><span class="o">/</span><span class="mf">6.</span>\
+                    <span class="o">+</span> <span class="p">(</span><span class="n">lambda_0</span><span class="o">*</span><span class="p">((</span><span class="n">g_sigma</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span><span class="o">**</span><span class="mi">4</span><span class="p">))</span><span class="o">/</span><span class="mf">24.</span>
+        
+    <span class="n">omega_terms</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">((</span><span class="n">omega</span><span class="o">*</span><span class="n">m_w</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span><span class="p">(</span><span class="n">zeta</span><span class="o">*</span><span class="p">((</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="p">)</span><span class="o">**</span><span class="mi">4</span><span class="p">))</span><span class="o">/</span><span class="mf">8.</span>
+        
+    <span class="n">rho_terms</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">((</span><span class="n">rho_03</span><span class="o">*</span><span class="n">m_rho</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span> <span class="o">+</span> <span class="mf">3.</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="p">((</span><span class="n">g_rho</span><span class="o">*</span><span class="n">rho_03</span><span class="o">*</span><span class="n">g_omega</span><span class="o">*</span><span class="n">omega</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    
+    <span class="n">energy_density</span> <span class="o">=</span> <span class="n">energy_b</span> <span class="o">+</span> <span class="n">energy_l</span> <span class="o">+</span> <span class="n">sigma_terms</span> \
+    <span class="o">+</span> <span class="n">omega_terms</span> <span class="o">+</span> <span class="n">rho_terms</span>
+        
+    <span class="n">Pressure</span> <span class="o">=</span> <span class="n">multi</span> <span class="o">-</span> <span class="n">energy_density</span>
+
+    <span class="k">return</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">Pressure</span></div>
+
+
+<span class="c1"># define a function that computes the EoS </span>
+
+<div class="viewcode-block" id="compute_EOS">
+<a class="viewcode-back" href="../../fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.compute_EOS">[docs]</a>
+<span class="nd">@njit</span>
+<span class="k">def</span> <span class="nf">compute_EOS</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">,</span> <span class="n">pres_crust</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate core part equation of state, main function, from RMF model (fast version)</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        eps_crust (array): the energy density of crust EoS in MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_crust (array): the pressure from crust EoS model in MeV/fm3, times a G/c**4 factor</span>
+<span class="sd">        theta (array): An array representing the parameters used to determine a RMF model in the </span>
+<span class="sd">        Lagrangian. In this case, the RMF model is defined by 10 parameters.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        energy_density (float): EOS ingredient, energy density in g/cm3</span>
+<span class="sd">        pressure (float): EOS ingredient, pressure in dyn/cm3</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.05</span>
+    <span class="n">rho_0</span> <span class="o">=</span> <span class="mf">0.1505</span>
+    
+    <span class="n">x_init</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">initial_values</span><span class="p">(</span><span class="mf">0.1</span><span class="o">*</span><span class="n">rho_0</span><span class="p">,</span> <span class="n">theta</span><span class="p">))</span>
+    <span class="n">Energy</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">Pressure</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">125</span><span class="p">):</span>
+
+        <span class="n">rho</span> <span class="o">=</span> <span class="n">i</span><span class="o">*</span><span class="n">dt</span><span class="o">*</span><span class="n">rho_0</span>
+        <span class="n">args</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">rho</span><span class="p">)</span>
+        <span class="n">xsol</span><span class="p">,</span> <span class="n">fvec</span><span class="p">,</span> <span class="n">success</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="n">lmdif</span><span class="p">(</span><span class="n">funcptr</span><span class="p">,</span> <span class="n">x_init</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span>
+        <span class="n">Re</span> <span class="o">=</span> <span class="n">Energy_density_Pressure</span><span class="p">(</span><span class="n">x_init</span><span class="p">,</span> <span class="n">rho</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+
+        <span class="n">Energy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Re</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">oneoverfm_MeV</span><span class="o">/</span><span class="n">gcm3_to_MeVfm3</span><span class="p">)</span>
+        <span class="n">Pressure</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Re</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">oneoverfm_MeV</span><span class="o">/</span><span class="n">dyncm2_to_MeVfm3</span><span class="p">)</span>
+
+        <span class="n">x_init</span> <span class="o">=</span> <span class="n">xsol</span>
+    <span class="n">Energy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Energy</span><span class="p">)</span>
+    <span class="n">Pressure</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Pressure</span><span class="p">)</span>
+    
+    <span class="n">end</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">Energy</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">Energy</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mi">2</span> <span class="o">&gt;</span> <span class="nb">max</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">)</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">&gt;</span><span class="mi">18</span><span class="p">:</span>
+            <span class="n">end</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">2</span>
+            <span class="k">break</span>
+        <span class="n">end</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="n">ep</span> <span class="o">=</span> <span class="n">Energy</span><span class="p">[</span><span class="n">end</span><span class="p">::]</span> <span class="o">*</span> <span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">pr</span> <span class="o">=</span> <span class="n">Pressure</span><span class="p">[</span><span class="n">end</span><span class="p">::]</span> <span class="o">*</span> <span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mi">4</span>
+    
+    <span class="k">return</span> <span class="n">ep</span><span class="p">,</span> <span class="n">pr</span></div>
+
+
+</pre></div>
+
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+             
+  <h1>Source code for InferenceWorkflow.BayesianSampler</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">ultranest</span>
+<span class="kn">import</span> <span class="nn">ultranest.stepsampler</span>
+
+<div class="viewcode-block" id="UltranestSampler">
+<a class="viewcode-back" href="../../BayesianSampler.html#InferenceWorkflow.BayesianSampler.UltranestSampler">[docs]</a>
+<span class="k">def</span> <span class="nf">UltranestSampler</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span><span class="n">likelihood</span><span class="p">,</span><span class="n">prior</span><span class="p">,</span><span class="n">step</span><span class="p">,</span><span class="n">live_points</span><span class="p">,</span><span class="n">max_calls</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;UltraNest based nested sampler by given likelihood prior, and parameters.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        parameters (array): parameters array that want to be constrained.</span>
+<span class="sd">        likelihood (array): theta as input. likelihood function defined by user.</span>
+<span class="sd">        prior (array): cube as input, prior function defined by user. please check our test_inference.ipynb</span>
+<span class="sd">        to check how to define likelihood and prior.</span>
+<span class="sd">        step (int): as a step sampler, define this inference want to devided to how many steps.</span>
+<span class="sd">        live_points (int): define how many live points will be used to explore the whole</span>
+<span class="sd">        parameters space.</span>
+<span class="sd">        max_ncalls (int): define after how many steps the sampler will stop work.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        flat_samples (array): equal weighted samples of whole posteior parameter space,</span>
+<span class="sd">        this run will generate a dirctory as &#39;output&#39;, please check the run# folder, and the</span>
+<span class="sd">        chain dirctory, there is a &#39;equal_weighted_samples&#39; file, that is same with flat_samples here.</span>
+<span class="sd">        It will be easier to check if you are using clusters to do this inference.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">sampler</span> <span class="o">=</span> <span class="n">ultranest</span><span class="o">.</span><span class="n">ReactiveNestedSampler</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span><span class="n">log_dir</span><span class="o">=</span><span class="s1">&#39;output&#39;</span><span class="p">)</span>
+    <span class="n">sampler</span><span class="o">.</span><span class="n">stepsampler</span> <span class="o">=</span> <span class="n">ultranest</span><span class="o">.</span><span class="n">stepsampler</span><span class="o">.</span><span class="n">SliceSampler</span><span class="p">(</span>
+        <span class="n">nsteps</span><span class="o">=</span><span class="n">step</span><span class="p">,</span>
+        <span class="n">generate_direction</span><span class="o">=</span><span class="n">ultranest</span><span class="o">.</span><span class="n">stepsampler</span><span class="o">.</span><span class="n">generate_mixture_random_direction</span><span class="p">,</span>
+        <span class="c1"># adaptive_nsteps=False,</span>
+        <span class="c1"># max_nsteps=400</span>
+    <span class="p">)</span>
+
+    <span class="n">result</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">min_num_live_points</span><span class="o">=</span><span class="n">live_points</span><span class="p">,</span><span class="n">max_ncalls</span><span class="o">=</span> <span class="n">max_calls</span><span class="p">)</span>
+    <span class="n">flat_samples</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;samples&#39;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">flat_samples</span></div>
+
+
+<div class="viewcode-block" id="UltranestSamplerResume">
+<a class="viewcode-back" href="../../BayesianSampler.html#InferenceWorkflow.BayesianSampler.UltranestSamplerResume">[docs]</a>
+<span class="k">def</span> <span class="nf">UltranestSamplerResume</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span><span class="n">likelihood</span><span class="p">,</span><span class="n">prior</span><span class="p">,</span><span class="n">nsteps</span><span class="p">,</span><span class="n">live_points</span><span class="p">,</span><span class="n">max_calls</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;UltraNest based nested sampler by given likelihood prior, and parameters. (resume true verion</span>
+<span class="sd">    could restart you run from your previous stopped results)</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        parameters (array): parameters array that want to be constrained.</span>
+<span class="sd">        likelihood (array): theta as input. likelihood function defined by user.</span>
+<span class="sd">        prior (array): cube as input, prior function defined by user. please check our test_inference.ipynb</span>
+<span class="sd">        to check how to define likelihood and prior.</span>
+<span class="sd">        step (int): as a step sampler, define this inference want to devided to how many steps.</span>
+<span class="sd">        live_points (int): define how many live points will be used to explore the whole</span>
+<span class="sd">        parameters space.</span>
+<span class="sd">        max_ncalls (int): define after how many steps the sampler will stop work.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        flat_samples (array): equal weighted samples of whole posteior parameter space,</span>
+<span class="sd">        this run will generate a dirctory as &#39;output&#39;, please check the run# folder, and the</span>
+<span class="sd">        chain dirctory, there is a &#39;equal_weighted_samples&#39; file, that is same with flat_samples here.</span>
+<span class="sd">        It will be easier to check if you are using clusters to do this inference.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">sampler</span> <span class="o">=</span> <span class="n">ultranest</span><span class="o">.</span><span class="n">ReactiveNestedSampler</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">likelihood</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span><span class="n">log_dir</span><span class="o">=</span><span class="s1">&#39;output&#39;</span><span class="p">,</span><span class="n">resume</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">sampler</span><span class="o">.</span><span class="n">stepsampler</span> <span class="o">=</span> <span class="n">ultranest</span><span class="o">.</span><span class="n">stepsampler</span><span class="o">.</span><span class="n">SliceSampler</span><span class="p">(</span>
+        <span class="n">nsteps</span><span class="o">=</span><span class="n">nsteps</span><span class="p">,</span>
+        <span class="n">generate_direction</span><span class="o">=</span><span class="n">ultranest</span><span class="o">.</span><span class="n">stepsampler</span><span class="o">.</span><span class="n">generate_mixture_random_direction</span><span class="p">,</span>
+        <span class="c1"># adaptive_nsteps=False,</span>
+        <span class="c1"># max_nsteps=400</span>
+    <span class="p">)</span>
+
+    <span class="n">result</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">min_num_live_points</span><span class="o">=</span><span class="n">live_points</span><span class="p">,</span><span class="n">max_ncalls</span><span class="o">=</span><span class="n">max_calls</span><span class="p">)</span>
+    <span class="n">flat_samples</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;samples&#39;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">flat_samples</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
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diff --git a/_modules/InferenceWorkflow/Likelihood.html b/_modules/InferenceWorkflow/Likelihood.html
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+  <h1>Source code for InferenceWorkflow.Likelihood</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">TOVsolver.constant</span> <span class="k">as</span> <span class="nn">constant</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.solver_code</span> <span class="k">as</span> <span class="nn">TOV_solver</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.EoS_import</span> <span class="k">as</span> <span class="nn">EoS_import</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.crust_EOS</span> <span class="k">as</span> <span class="nn">crust</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.RMF_EOS</span> <span class="k">as</span> <span class="nn">RMF</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interpolate</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+
+<span class="n">oneoverfm_MeV</span> <span class="o">=</span> <span class="n">constant</span><span class="o">.</span><span class="n">oneoverfm_MeV</span>
+<span class="n">c</span> <span class="o">=</span> <span class="n">constant</span><span class="o">.</span><span class="n">c</span>
+<span class="n">G</span> <span class="o">=</span> <span class="n">constant</span><span class="o">.</span><span class="n">G</span>
+
+<div class="viewcode-block" id="MRlikihood_kernel">
+<a class="viewcode-back" href="../../Likelihood.html#InferenceWorkflow.Likelihood.MRlikihood_kernel">[docs]</a>
+<span class="k">def</span> <span class="nf">MRlikihood_kernel</span><span class="p">(</span><span class="n">eps_total</span><span class="p">,</span><span class="n">pres_total</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">d1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computing likelihood from a distribution of MR measurement</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        eps_total (array): the energy density of full EoS in MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_total (array): the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</span>
+<span class="sd">        x (kde.kernel): the distribution kernel of MR measurement.</span>
+<span class="sd">        d1 (float): the sampled density of this measurement</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        likelihood (float): likelihood feed back for this given paramter set-up.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">kernel</span> <span class="o">=</span> <span class="n">x</span>
+    
+    <span class="k">if</span> <span class="n">d1</span> <span class="o">==</span><span class="mi">0</span> <span class="p">:</span>
+        <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span>   <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eps_total</span><span class="p">[:],</span> <span class="n">eps_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pres_total</span><span class="p">[:],</span> <span class="n">pres_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
+            <span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMRpoint</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span><span class="n">eps_total</span><span class="p">,</span><span class="n">eps_total</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">likelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">kernel</span><span class="o">.</span><span class="n">evaluate</span><span class="p">((</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
+    <span class="k">if</span> <span class="n">likelihood</span> <span class="o">&lt;=</span> <span class="o">-</span><span class="mf">1e101</span><span class="p">:</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">likelihood</span></div>
+
+    
+<div class="viewcode-block" id="TidalLikihood_kernel">
+<a class="viewcode-back" href="../../Likelihood.html#InferenceWorkflow.Likelihood.TidalLikihood_kernel">[docs]</a>
+<span class="k">def</span> <span class="nf">TidalLikihood_kernel</span><span class="p">(</span><span class="n">eps_total</span><span class="p">,</span><span class="n">pres_total</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">d1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computing likelihood from a distribution of Gravitational wave measurement</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        eps_total (array): the energy density of full EoS in MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_total (array): the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</span>
+<span class="sd">        x (kde.kernel): containing kernelGW and chirp, kernelGW is the distribution kde.kernel </span>
+<span class="sd">        sampled from full GW measurement, in [chrip mass, M2/M1, tidal of M1, tidal of M2] sequence.</span>
+<span class="sd">        chrip mass is the sampling from chrip mass term in GW events solely.</span>
+<span class="sd">        d1 (float): the sampled density of this measurement</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        likelihood (float): likelihood feed back for this given paramter set-up.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">kernelGW</span><span class="p">,</span><span class="n">chrip</span> <span class="o">=</span> <span class="n">x</span>
+    
+    <span class="k">if</span> <span class="n">d1</span> <span class="o">==</span><span class="mi">0</span> <span class="p">:</span>
+        <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span>   <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eps_total</span><span class="p">[:],</span> <span class="n">eps_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pres_total</span><span class="p">[:],</span> <span class="n">pres_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
+            <span class="n">MRT</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMRTpoint</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span><span class="n">eps_total</span><span class="p">,</span><span class="n">eps_total</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+            <span class="n">M1</span> <span class="o">=</span> <span class="n">TOV_solver</span><span class="o">.</span><span class="n">m1_from_mc_m2</span><span class="p">(</span><span class="n">chrip_mass</span><span class="p">,</span> <span class="n">MRT</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">Tidal_line</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMRT</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">eps_total</span><span class="p">,</span><span class="n">eps_total</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MRT</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">Tidal_line</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">chrip_mass</span> <span class="o">=</span> <span class="n">chrip</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">MTspline</span> <span class="o">=</span> <span class="n">interpolate</span><span class="o">.</span><span class="n">interp1d</span><span class="p">(</span><span class="n">Tidal_line</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">Tidal_line</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">point</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">chrip_mass</span><span class="p">],</span> <span class="p">[</span><span class="n">MRT</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span> <span class="n">M1</span><span class="p">],[</span><span class="n">MTspline</span><span class="p">(</span><span class="n">M1</span><span class="p">)],</span> <span class="p">[</span><span class="n">MRT</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]]])</span>
+            <span class="n">likelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">kernelGW</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">point</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">likelihood</span> <span class="o">&lt;=</span> <span class="o">-</span><span class="mf">1e101</span><span class="p">:</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">likelihood</span></div>
+
+
+<div class="viewcode-block" id="MRlikihood_Gaussian">
+<a class="viewcode-back" href="../../Likelihood.html#InferenceWorkflow.Likelihood.MRlikihood_Gaussian">[docs]</a>
+<span class="k">def</span> <span class="nf">MRlikihood_Gaussian</span><span class="p">(</span><span class="n">eps_total</span><span class="p">,</span><span class="n">pres_total</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">d1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computing likelihood from a simulation gaussian distribution of MR measurement</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        eps_total (array): the energy density of full EoS in MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_total (array): the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</span>
+<span class="sd">        x (float array): [Mvalue, Rvalue, Mwidth, Rwidth], Mvalue is the Mass center value of this </span>
+<span class="sd">        simulated measurement, Rvalue is the Radius center of it, Mwidth is the 1-sigma width of</span>
+<span class="sd">        this Mass measurement, Rwidth is the 1-sigma width of this radius measurement.</span>
+<span class="sd">        d1 (float): the sampled density of this measurement</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        likelihood (float): likelihood feed back for this given paramter set-up.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">Mvalue</span><span class="p">,</span> <span class="n">Rvalue</span><span class="p">,</span> <span class="n">Mwidth</span><span class="p">,</span><span class="n">Rwidth</span> <span class="o">=</span> <span class="n">x</span>
+    
+    <span class="n">sigma_x</span> <span class="o">=</span> <span class="n">Rwidth</span>
+    <span class="n">sigma_y</span> <span class="o">=</span> <span class="n">Mwidth</span>
+    
+    <span class="k">if</span> <span class="n">d1</span> <span class="o">==</span><span class="mi">0</span> <span class="p">:</span>
+        <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span>   <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eps_total</span><span class="p">[:],</span> <span class="n">eps_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pres_total</span><span class="p">[:],</span> <span class="n">pres_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
+            <span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMRpoint</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span><span class="n">eps_total</span><span class="p">,</span><span class="n">eps_total</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fx</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">sigma_x</span><span class="o">*</span><span class="n">sigma_y</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">Rvalue</span><span class="p">,</span> <span class="mf">2.</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">sigma_x</span><span class="p">,</span><span class="mf">2.</span><span class="p">))</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">Mvalue</span><span class="p">,</span> <span class="mf">2.</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">sigma_y</span><span class="p">,</span><span class="mf">2.</span><span class="p">)))</span>
+            <span class="n">likelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">fx</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">likelihood</span> <span class="o">&lt;=</span> <span class="o">-</span><span class="mf">1e101</span><span class="p">:</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">likelihood</span></div>
+
+    
+<div class="viewcode-block" id="Masslikihood_Gaussian">
+<a class="viewcode-back" href="../../Likelihood.html#InferenceWorkflow.Likelihood.Masslikihood_Gaussian">[docs]</a>
+<span class="k">def</span> <span class="nf">Masslikihood_Gaussian</span><span class="p">(</span><span class="n">eps_total</span><span class="p">,</span><span class="n">pres_total</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">d1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computing likelihood from a simulation gaussian distribution of Mass measurement</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        eps_total (array): the energy density of full EoS in MeV/fm3, times a G/c**2 factor</span>
+<span class="sd">        pres_total (array): the pressure from full EoS model in MeV/fm3, times a G/c**4 factor</span>
+<span class="sd">        x (float array): [Mvalue, Mwidth], Mvalue is the Mass center value of this </span>
+<span class="sd">        simulated measurement, Mwidth is the 1-sigma width of this Mass measurement. </span>
+<span class="sd">        d1 (float): the sampled density of this measurement</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        likelihood (float): likelihood feed back for this given paramter set-up.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">Mvalue</span><span class="p">,</span> <span class="n">Mwidth</span> <span class="o">=</span> <span class="n">x</span>
+    
+    <span class="n">sigma_y</span> <span class="o">=</span> <span class="n">Mwidth</span>
+    
+    <span class="k">if</span> <span class="n">d1</span> <span class="o">==</span><span class="mi">0</span> <span class="p">:</span>
+        <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span>   <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eps_total</span><span class="p">[:],</span> <span class="n">eps_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pres_total</span><span class="p">[:],</span> <span class="n">pres_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
+            <span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMRpoint</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span><span class="n">eps_total</span><span class="p">,</span><span class="n">eps_total</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">likelihood</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e101</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">&gt;=</span> <span class="n">Mvalue</span><span class="p">:</span>
+                <span class="n">likelihood</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">else</span><span class="p">:</span>    
+                <span class="n">fx</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">sigma_y</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">Mvalue</span><span class="p">,</span> <span class="mf">2.</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">sigma_y</span><span class="p">,</span><span class="mf">2.</span><span class="p">)))</span>
+                <span class="n">likelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">fx</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">likelihood</span> <span class="o">&lt;=</span> <span class="o">-</span><span class="mf">1e101</span><span class="p">:</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mf">1e101</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">likelihood</span></div>
+
+
+
+<div class="viewcode-block" id="Kliklihood">
+<a class="viewcode-back" href="../../Likelihood.html#InferenceWorkflow.Likelihood.Kliklihood">[docs]</a>
+<span class="k">def</span> <span class="nf">Kliklihood</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="n">K_low</span><span class="p">,</span><span class="n">K_up</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computing likelihood from a hard cut constraint of K.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">    </span>
+<span class="sd">        theta (array): An array representing the parameters used to determine a RMF model in the </span>
+<span class="sd">        Lagrangian. In this case, the RMF model is defined by 7 parameters. </span>
+<span class="sd">        K_low (float): lower bound of this K constraint.</span>
+<span class="sd">        K_up (float): upper bound of this K constraint.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        likelihood (float): likelihood feed back for this given paramter set-up.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span><span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">lambda_w</span> <span class="o">=</span> <span class="n">theta</span>
+    
+    <span class="n">m_sig</span> <span class="o">=</span> <span class="mi">495</span> <span class="o">/</span> <span class="n">oneoverfm_MeV</span>
+    <span class="n">m_w</span> <span class="o">=</span> <span class="mf">3.96544</span>
+    <span class="n">m_rho</span> <span class="o">=</span> <span class="mf">3.86662</span>
+    
+    <span class="n">m_b</span> <span class="o">=</span> <span class="mi">939</span>
+    <span class="n">E_per</span> <span class="o">=-</span><span class="mf">16.28</span>
+    <span class="n">m_sig</span> <span class="o">=</span><span class="mi">495</span><span class="o">/</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.55</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">()</span><span class="o">*</span><span class="mf">0.1</span><span class="p">)</span><span class="o">*</span><span class="n">m_b</span>
+    <span class="n">kappa_one</span> <span class="o">=</span> <span class="n">kappa</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_sig_one</span> <span class="o">=</span> <span class="n">m_sig</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_w_one</span> <span class="o">=</span> <span class="n">m_w</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">rho</span> <span class="o">=</span> <span class="mf">0.1505</span><span class="o">*</span> <span class="p">(</span><span class="mf">197.33</span><span class="o">**</span><span class="mi">3</span> <span class="p">)</span>
+    <span class="n">k_f</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="mf">3.</span><span class="o">*</span><span class="n">rho</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mf">3.</span><span class="p">)</span>
+    <span class="n">E_f</span> <span class="o">=</span> <span class="p">(</span><span class="n">k_f</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="n">g_w_omega</span> <span class="o">=</span> <span class="n">E_per</span> <span class="o">-</span> <span class="n">E_f</span> <span class="o">+</span> <span class="n">m_b</span>
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">m_b</span> <span class="o">-</span> <span class="n">m_eff</span>
+    <span class="n">m_sig_star_sqr_g_sig_sqr</span> <span class="o">=</span> <span class="p">(</span><span class="n">m_sig_one</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">g_sigma</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span> <span class="p">(</span><span class="n">kappa_one</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">lambda_0</span><span class="o">*</span><span class="n">sigma</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">m_w_star_sqr</span> <span class="o">=</span> <span class="n">m_w_one</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">zeta</span><span class="o">*</span><span class="p">(</span><span class="n">g_omega</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">g_w_omega</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="c1"># Components o f rho_s_prime</span>
+    <span class="n">term1</span> <span class="o">=</span> <span class="n">k_f</span><span class="o">/</span><span class="n">E_f</span>
+    <span class="n">term2</span> <span class="o">=</span> <span class="n">E_f</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">term3</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">term4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">k_f</span><span class="o">+</span><span class="n">E_f</span><span class="p">)</span><span class="o">/</span><span class="n">m_eff</span><span class="p">)</span>
+    <span class="n">rho_s_prime</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">term1</span><span class="o">*</span><span class="n">term2</span> <span class="o">-</span> <span class="n">term3</span><span class="o">*</span><span class="n">term4</span><span class="p">)</span>
+    <span class="n">dM_drho</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">m_eff</span><span class="o">/</span><span class="n">E_f</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="n">m_sig_star_sqr_g_sig_sqr</span><span class="o">+</span><span class="n">rho_s_prime</span><span class="p">))</span>
+    <span class="n">M_Ef</span> <span class="o">=</span> <span class="n">m_eff</span><span class="o">/</span><span class="n">E_f</span>
+    <span class="n">dEf_drho</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">k_f</span><span class="o">*</span><span class="n">E_f</span><span class="p">)</span>
+    <span class="n">dW0_drho</span> <span class="o">=</span> <span class="n">g_omega</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">m_w_star_sqr</span>
+    
+    <span class="n">K</span> <span class="o">=</span> <span class="mf">9.</span><span class="o">*</span><span class="n">rho</span><span class="o">*</span><span class="p">(</span><span class="n">dEf_drho</span><span class="o">+</span><span class="n">dW0_drho</span><span class="o">+</span><span class="p">(</span><span class="n">M_Ef</span><span class="o">*</span><span class="n">dM_drho</span><span class="p">))</span>
+    <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="n">K_low</span> <span class="o">+</span> <span class="n">K_up</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">width</span> <span class="o">=</span> <span class="p">(</span><span class="n">K_up</span> <span class="o">-</span> <span class="n">K_low</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">p_K</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="nb">abs</span><span class="p">(</span> <span class="n">center</span> <span class="o">-</span> <span class="n">K</span><span class="p">)</span><span class="o">**</span><span class="mf">10.</span><span class="o">/</span><span class="n">width</span><span class="o">**</span><span class="mf">10.</span>
+    <span class="k">return</span> <span class="n">p_K</span></div>
+
+
+<div class="viewcode-block" id="Jliklihood">
+<a class="viewcode-back" href="../../Likelihood.html#InferenceWorkflow.Likelihood.Jliklihood">[docs]</a>
+<span class="k">def</span> <span class="nf">Jliklihood</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="n">J_low</span><span class="p">,</span><span class="n">J_up</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computing likelihood from a hard cut constraint of J.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">    </span>
+<span class="sd">        theta (array): An array representing the parameters used to determine a RMF model in the </span>
+<span class="sd">        Lagrangian. In this case, the RMF model is defined by 7 parameters.</span>
+<span class="sd">        K_low (float): lower bound of this J constraint.</span>
+<span class="sd">        K_up (float): upper bound of this J constraint.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        likelihood (float): likelihood feed back for this given paramter set-up.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span><span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">theta</span>
+    
+    <span class="n">m_sig</span> <span class="o">=</span> <span class="mi">495</span> <span class="o">/</span> <span class="n">oneoverfm_MeV</span>
+    <span class="n">m_w</span> <span class="o">=</span> <span class="mf">3.96544</span>
+    <span class="n">m_rho</span> <span class="o">=</span> <span class="mf">3.86662</span>
+    
+    <span class="n">m_b</span> <span class="o">=</span> <span class="mi">939</span>
+    <span class="n">E_per</span> <span class="o">=-</span><span class="mf">16.28</span>
+    <span class="n">m_sig</span> <span class="o">=</span><span class="mi">495</span><span class="o">/</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.55</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">()</span><span class="o">*</span><span class="mf">0.1</span><span class="p">)</span><span class="o">*</span><span class="n">m_b</span>
+    <span class="n">kappa_one</span> <span class="o">=</span> <span class="n">kappa</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_sig_one</span> <span class="o">=</span> <span class="n">m_sig</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_w_one</span> <span class="o">=</span> <span class="n">m_w</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">rho</span> <span class="o">=</span> <span class="mf">0.1505</span><span class="o">*</span> <span class="p">(</span><span class="mf">197.33</span><span class="o">**</span><span class="mi">3</span> <span class="p">)</span>
+    <span class="n">k_f</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="mf">3.</span><span class="o">*</span><span class="n">rho</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mf">3.</span><span class="p">)</span>
+    <span class="n">E_f</span> <span class="o">=</span> <span class="p">(</span><span class="n">k_f</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">g_w_omega</span> <span class="o">=</span> <span class="n">E_per</span> <span class="o">-</span> <span class="n">E_f</span> <span class="o">+</span> <span class="n">m_b</span>
+    <span class="n">J_0</span> <span class="o">=</span> <span class="n">k_f</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="p">(</span><span class="mi">6</span><span class="o">*</span><span class="n">E_f</span><span class="p">)</span>
+    <span class="n">J_1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mf">8.</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">rho</span><span class="o">*</span><span class="n">g_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">m_rho</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="p">(</span><span class="n">g_w_omega</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">g_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+    <span class="n">J</span> <span class="o">=</span> <span class="n">J_0</span> <span class="o">+</span> <span class="n">J_1</span>
+    
+    <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="n">J_low</span> <span class="o">+</span> <span class="n">J_up</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">width</span> <span class="o">=</span> <span class="p">(</span><span class="n">J_up</span> <span class="o">-</span> <span class="n">J_low</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">p_J</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="nb">abs</span><span class="p">(</span> <span class="n">center</span> <span class="o">-</span> <span class="n">J</span><span class="p">)</span><span class="o">**</span><span class="mf">10.</span><span class="o">/</span><span class="n">width</span><span class="o">**</span><span class="mf">10.</span>
+    <span class="k">return</span> <span class="n">p_J</span></div>
+
+
+<div class="viewcode-block" id="Lliklihood">
+<a class="viewcode-back" href="../../Likelihood.html#InferenceWorkflow.Likelihood.Lliklihood">[docs]</a>
+<span class="k">def</span> <span class="nf">Lliklihood</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span><span class="n">L_low</span><span class="p">,</span><span class="n">L_up</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computing likelihood from a hard cut constraint of L.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">    </span>
+<span class="sd">        theta (array): An array representing the parameters used to determine a RMF model in the </span>
+<span class="sd">        Lagrangian. In this case, the RMF model is defined by 7 parameters. </span>
+<span class="sd">        K_low (float): lower bound of this L constraint.</span>
+<span class="sd">        K_up (float): upper bound of this L constraint.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        likelihood (float): likelihood feed back for this given paramter set-up.</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span><span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">theta</span>
+    
+    <span class="n">m_sig</span> <span class="o">=</span> <span class="mi">495</span> <span class="o">/</span> <span class="n">oneoverfm_MeV</span>
+    <span class="n">m_w</span> <span class="o">=</span> <span class="mf">3.96544</span>
+    <span class="n">m_rho</span> <span class="o">=</span> <span class="mf">3.86662</span>
+    
+    <span class="n">m_b</span> <span class="o">=</span> <span class="mi">939</span>
+    <span class="n">E_per</span> <span class="o">=-</span><span class="mf">16.28</span>
+    <span class="n">m_sig</span> <span class="o">=</span><span class="mi">495</span><span class="o">/</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_eff</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.55</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">()</span><span class="o">*</span><span class="mf">0.1</span><span class="p">)</span><span class="o">*</span><span class="n">m_b</span>
+    <span class="n">kappa_one</span> <span class="o">=</span> <span class="n">kappa</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_sig_one</span> <span class="o">=</span> <span class="n">m_sig</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">m_w_one</span> <span class="o">=</span> <span class="n">m_w</span><span class="o">*</span><span class="n">oneoverfm_MeV</span>
+    <span class="n">rho</span> <span class="o">=</span> <span class="mf">0.1505</span><span class="o">*</span> <span class="p">(</span><span class="mf">197.33</span><span class="o">**</span><span class="mi">3</span> <span class="p">)</span>
+    <span class="n">k_f</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="mf">3.</span><span class="o">*</span><span class="n">rho</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mf">3.</span><span class="p">)</span>
+    <span class="n">E_f</span> <span class="o">=</span> <span class="p">(</span><span class="n">k_f</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">J_0</span> <span class="o">=</span> <span class="n">k_f</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="p">(</span><span class="mi">6</span><span class="o">*</span><span class="n">E_f</span><span class="p">)</span>
+    <span class="n">J_1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mf">8.</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">rho</span><span class="o">*</span><span class="n">g_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">m_rho</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="p">(</span><span class="n">g_w_omega</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">g_rho</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+    <span class="n">J</span><span class="o">=</span> <span class="n">J_0</span> <span class="o">+</span> <span class="n">J_1</span> 
+    <span class="n">g_w_omega</span> <span class="o">=</span> <span class="n">E_per</span> <span class="o">-</span> <span class="n">E_f</span> <span class="o">+</span> <span class="n">m_b</span>
+    <span class="n">m_star_sqr_E_f_sqr</span> <span class="o">=</span> <span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">E_f</span><span class="o">**</span><span class="mi">2</span> 
+    <span class="n">rho_m_star</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">rho</span><span class="o">/</span><span class="n">m_eff</span> 
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">m_b</span> <span class="o">-</span> <span class="n">m_eff</span>
+    <span class="n">m_sig_star_sqr_g_sig_sqr</span> <span class="o">=</span> <span class="p">(</span><span class="n">m_sig</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">g_sigma</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span>\
+    <span class="p">(</span><span class="n">kappa</span><span class="o">*</span><span class="n">sigma</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">lambda_0</span><span class="o">*</span><span class="n">sigma</span><span class="o">**</span><span class="mi">2</span>
+    
+    <span class="c1"># Components o f rho_s_prime</span>
+    <span class="n">term1</span> <span class="o">=</span> <span class="n">k_f</span><span class="o">/</span><span class="n">E_f</span>
+    <span class="n">term2</span> <span class="o">=</span> <span class="n">E_f</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">term3</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">m_eff</span><span class="o">**</span><span class="mi">2</span>
+    <span class="n">term4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">k_f</span><span class="o">+</span><span class="n">E_f</span><span class="p">)</span><span class="o">/</span><span class="n">m_eff</span><span class="p">)</span>
+    <span class="n">rho_s_prime</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">term1</span><span class="o">*</span><span class="n">term2</span> <span class="o">-</span> <span class="n">term3</span><span class="o">*</span><span class="n">term4</span><span class="p">)</span>
+    <span class="n">dM_drho</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">m_eff</span><span class="o">/</span><span class="n">E_f</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="n">m_sig_star_sqr_g_sig_sqr</span><span class="o">+</span><span class="n">rho_s_prime</span><span class="p">))</span>
+    <span class="n">m_w_star_sqr</span> <span class="o">=</span> <span class="n">m_w</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">zeta</span><span class="o">*</span><span class="p">(</span><span class="n">g_omega</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">g_w_omega</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">L_0</span> <span class="o">=</span> <span class="n">J_0</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="p">(</span><span class="n">m_star_sqr_E_f_sqr</span><span class="o">*</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span><span class="p">(</span><span class="n">rho_m_star</span><span class="o">*</span><span class="n">dM_drho</span><span class="p">))))</span>
+    <span class="n">L_1</span> <span class="o">=</span> <span class="mf">3.</span><span class="o">*</span><span class="n">J_1</span><span class="o">*</span><span class="p">(</span><span class="mf">1.</span><span class="o">-</span> <span class="mf">32.</span><span class="o">*</span><span class="p">(</span><span class="n">g_omega</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">m_w_star_sqr</span><span class="p">)</span><span class="o">*</span><span class="n">g_w_omega</span><span class="o">*</span><span class="n">Lambda_w</span><span class="o">*</span><span class="n">J_1</span><span class="p">)</span>
+    <span class="n">L</span><span class="o">=</span> <span class="n">L_0</span> <span class="o">+</span> <span class="n">L_1</span>
+    
+    <span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="n">L_low</span> <span class="o">+</span> <span class="n">L_up</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">width</span> <span class="o">=</span> <span class="p">(</span><span class="n">L_up</span> <span class="o">-</span> <span class="n">L_low</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">p_L</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="nb">abs</span><span class="p">(</span> <span class="n">center</span> <span class="o">-</span> <span class="n">J</span><span class="p">)</span><span class="o">**</span><span class="mf">10.</span><span class="o">/</span><span class="n">width</span><span class="o">**</span><span class="mf">10.</span>
+    <span class="k">return</span> <span class="n">p_L</span></div>
+
+</pre></div>
+
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
+        </div>
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+      <li class="breadcrumb-item active">InferenceWorkflow.prior</li>
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+      </li>
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+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for InferenceWorkflow.prior</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">scipy.stats</span>
+
+<div class="viewcode-block" id="normal_Prior">
+<a class="viewcode-back" href="../../prior.html#InferenceWorkflow.prior.normal_Prior">[docs]</a>
+<span class="k">def</span> <span class="nf">normal_Prior</span><span class="p">(</span><span class="n">center</span><span class="p">,</span><span class="n">width</span><span class="p">,</span><span class="n">random</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate a normal prior distribution for a given parameter,</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        center (float): center value of this gaussian distribution.</span>
+<span class="sd">        width (float): width of this gaussian distribution, this is the 1-sigma width.</span>
+<span class="sd">        random (float): random number generated to do inference, this is follow the </span>
+<span class="sd">        definition of baysian workflow of UltraNest, here default to be cube[i]</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        ppf (float): ppf of this distribution function</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">random</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="flat_prior">
+<a class="viewcode-back" href="../../prior.html#InferenceWorkflow.prior.flat_prior">[docs]</a>
+<span class="k">def</span> <span class="nf">flat_prior</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">up</span><span class="p">,</span><span class="n">random</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate a flat prior distribution for a given parameter,</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        low (float): lower bound of this flat distribution.</span>
+<span class="sd">        up (float): upper bound of this flat distribution.</span>
+<span class="sd">        random (float): random number generated to do inference, this is follow the </span>
+<span class="sd">        definition of baysian workflow of UltraNest, here default to be cube[i]</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        ppf (float): ppf of this distribution function</span>
+<span class="sd">        </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">low</span> <span class="o">+</span> <span class="p">(</span><span class="n">up</span> <span class="o">-</span> <span class="n">low</span><span class="p">)</span> <span class="o">*</span> <span class="n">random</span></div>
+
+
+
+
+</pre></div>
+
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+<li class="toctree-l1"><a class="reference internal" href="../../Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+      <li class="breadcrumb-item active">TOVsolver.EoS_import</li>
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for TOVsolver.EoS_import</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">csv</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+
+<div class="viewcode-block" id="EOS_import">
+<a class="viewcode-back" href="../../EoS_import.html#TOVsolver.EoS_import.EOS_import">[docs]</a>
+<span class="k">def</span> <span class="nf">EOS_import</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">density</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">pressure</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;EOS_import</span>
+
+<span class="sd">    Imports density and pressure from csv or array, checks them, and returns them.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        file_name (string, optional): string. CSV file to be opened.</span>
+<span class="sd">        density (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+<span class="sd">        pressure (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        array: checked density and pressure.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">file_name</span><span class="p">:</span>
+        <span class="n">density_checked</span><span class="p">,</span> <span class="n">pressure_checked</span> <span class="o">=</span> <span class="n">EOS_check</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">density_checked</span><span class="p">,</span> <span class="n">pressure_checked</span>
+
+    <span class="n">input_file</span> <span class="o">=</span> <span class="n">file_name</span>
+
+    <span class="n">density</span><span class="p">,</span> <span class="n">pressure</span> <span class="o">=</span> <span class="n">file_read</span><span class="p">(</span><span class="n">input_file</span><span class="p">)</span>
+
+    <span class="n">density_checked</span><span class="p">,</span> <span class="n">pressure_checked</span> <span class="o">=</span> <span class="n">EOS_check</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">density</span><span class="p">,</span> <span class="n">pressure</span></div>
+
+
+<div class="viewcode-block" id="file_read">
+<a class="viewcode-back" href="../../EoS_import.html#TOVsolver.EoS_import.file_read">[docs]</a>
+<span class="k">def</span> <span class="nf">file_read</span><span class="p">(</span><span class="n">input_file</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;file_read</span>
+
+<span class="sd">    Reads a csv file of denisty and pressure given by the user.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        input_file (string): string. File to be opened and parsed.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        array: two 1Darray numpy arrays, one corresponding to density and one corresponding to pressrure. </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    
+    <span class="n">data_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">density_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">pressure_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">input_file</span><span class="p">)</span> <span class="k">as</span> <span class="n">csvfile</span><span class="p">:</span>
+        <span class="n">file_read</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="n">csvfile</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
+        <span class="n">data_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">row</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">file_read</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">:</span>
+        <span class="n">density_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="n">pressure_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+
+    <span class="c1"># Make the lists numpy arrays </span>
+    <span class="n">density_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">density_list</span><span class="p">)</span>
+    <span class="n">pressure_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pressure_list</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">density_array</span><span class="p">,</span> <span class="n">pressure_array</span></div>
+
+
+<div class="viewcode-block" id="EOS_check">
+<a class="viewcode-back" href="../../EoS_import.html#TOVsolver.EoS_import.EOS_check">[docs]</a>
+<span class="k">def</span> <span class="nf">EOS_check</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;file_read</span>
+
+<span class="sd">    Checks that the derivative (drho/dp) is positive.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        density (array): numpy 1Darray. Density array to be checked.</span>
+<span class="sd">        pressure (array): numpy 1Darray. Pressure array to be checked.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        array: two arrays, one corresponding to density and one corresponding to pressure or ends the function and prints</span>
+<span class="sd">        invalid equation of state.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">dp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">pressure</span><span class="p">)</span> <span class="c1"># dy</span>
+    <span class="n">drho</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">density</span><span class="p">)</span> <span class="c1"># dx</span>
+
+    <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">drho</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;This is not a valid equation of state, 0&quot;</span><span class="p">)</span>
+            <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">()</span>
+
+    <span class="n">dpdrho</span> <span class="o">=</span> <span class="n">dp</span><span class="o">/</span><span class="n">drho</span> <span class="c1"># dydx</span>
+
+    <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">dpdrho</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">value</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1">#print(&quot;This is a valid equation of state&quot;)</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;This is not a valid equation of state&quot;</span><span class="p">)</span>
+            <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">()</span>
+            
+        
+
+    <span class="k">return</span> <span class="n">density</span><span class="p">,</span> <span class="n">pressure</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../EOS.html">EOS solver</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">TOVsolver.main</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for TOVsolver.main</h1><div class="highlight"><pre>
+<span></span><span class="c1"># Python packages</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">UnivariateSpline</span>
+<span class="kn">from</span> <span class="nn">scipy.constants</span> <span class="kn">import</span> <span class="n">pi</span>
+<span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">odeint</span><span class="p">,</span> <span class="n">ode</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">optimize</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">repeat</span>
+<span class="kn">import</span> <span class="nn">csv</span>
+
+
+<span class="c1"># Import files</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.solver_code</span> <span class="k">as</span> <span class="nn">TOV_solver</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.EoS_import</span> <span class="k">as</span> <span class="nn">EoS_import</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.speed_of_sound</span> <span class="k">as</span> <span class="nn">speed_of_sound</span>
+
+<span class="c1"># Global Variables</span>
+<div class="viewcode-block" id="OutputMR">
+<a class="viewcode-back" href="../../main.html#TOVsolver.main.OutputMR">[docs]</a>
+<span class="k">def</span> <span class="nf">OutputMR</span><span class="p">(</span><span class="n">input_file</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">density</span><span class="o">=</span><span class="p">[],</span><span class="n">pressure</span><span class="o">=</span><span class="p">[]):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Outputs the mass, radius, and tidal deformability</span>
+<span class="sd">    Args:</span>
+<span class="sd">        file_name (string, optional): string. CSV file to be opened.</span>
+<span class="sd">        density (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+<span class="sd">        pressure (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        MR (tuple): tuple with mass, radius.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+    <span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+    <span class="n">dyncm2_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.6022e33</span><span class="p">)</span>
+    <span class="n">gcm3_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.7827e12</span><span class="p">)</span>
+    <span class="n">oneoverfm_MeV</span> <span class="o">=</span> <span class="mf">197.33</span>
+    <span class="c1">#############This is something we need to change, like the input for this EOS import should</span>
+    <span class="c1">############# be one file contatining Whole EOS. that first column is density and second is pressure</span>
+    <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span> <span class="o">=</span> <span class="n">EoS_import</span><span class="o">.</span><span class="n">EOS_import</span><span class="p">(</span><span class="n">input_file</span><span class="p">,</span><span class="n">density</span><span class="p">,</span><span class="n">pressure</span><span class="p">)</span>
+    <span class="c1">############# Lets the user only input the EOS file path, then this EOS_import should have file</span>
+    <span class="c1">############# as input. and the outputMR should have a file as input too?</span>
+    
+    <span class="n">Radius</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">Mass</span> <span class="o">=</span> <span class="p">[]</span>
+    
+    <span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">14.3</span><span class="p">,</span> <span class="mf">15.6</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+<span class="c1">#This following step is to make a dicision whether the EOS ingredients is always increase. We can do that outsie of this main to the </span>
+<span class="c1">#EOS import.</span>
+<span class="c1">#if   all(x&lt;y for x, y in zip(eps_total_poly[:], eps_total_poly[[1:])) and all(x&lt;y for x, y in zip(pres_total_poly[j][:], pres_total_poly[j][1:])):</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">density</span><span class="p">)):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">Radius</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">Mass</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="c1">#This is sentense is for avoiding the outflow of the result, like when solveTOV blow up because of ill EOS, we need to stop</span>
+        <span class="k">except</span> <span class="ne">OverflowError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="c1">#print(&quot;This EOS is ill-defined to reach an infinity result, that is not phyiscal, No Mass radius will be generated.&quot;)</span>
+            <span class="k">break</span>
+    <span class="n">MR</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">Radius</span><span class="p">,</span> <span class="n">Mass</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+    <span class="c1">#print(&quot;Mass Radius file will be generated and stored as  2-d array. The first column is Radius, second one is mass&quot;)</span>
+    
+    <span class="k">return</span> <span class="n">MR</span></div>
+
+
+<div class="viewcode-block" id="OutputMRT">
+<a class="viewcode-back" href="../../main.html#TOVsolver.main.OutputMRT">[docs]</a>
+<span class="k">def</span> <span class="nf">OutputMRT</span><span class="p">(</span><span class="n">input_file</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">density</span><span class="o">=</span><span class="p">[],</span><span class="n">pressure</span><span class="o">=</span><span class="p">[]):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Outputs the mass, radius, and tidal deformability</span>
+<span class="sd">    Args:</span>
+<span class="sd">        file_name (string, optional): string. CSV file to be opened.</span>
+<span class="sd">        density (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+<span class="sd">        pressure (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        MRT (tuple): tuple with mass, radius, and tidal deformability. </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+    <span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+    <span class="n">dyncm2_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.6022e33</span><span class="p">)</span>
+    <span class="n">gcm3_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.7827e12</span><span class="p">)</span>
+    <span class="n">oneoverfm_MeV</span> <span class="o">=</span> <span class="mf">197.33</span>
+    <span class="c1">#############This is something we need to change, like the input for this EOS import should</span>
+    <span class="c1">############# be one file contatining Whole EOS. that first column is density and second is pressure</span>
+    <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span> <span class="o">=</span> <span class="n">EoS_import</span><span class="o">.</span><span class="n">EOS_import</span><span class="p">(</span><span class="n">input_file</span><span class="p">,</span><span class="n">density</span><span class="p">,</span><span class="n">pressure</span><span class="p">)</span>
+    <span class="c1">############# Lets the user only input the EOS file path, then this EOS_import should have file</span>
+    <span class="c1">############# as input. and the outputMR should have a file as input too?</span>
+    
+    <span class="n">Radius</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">Mass</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">tidal</span> <span class="o">=</span> <span class="p">[]</span> 
+    <span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">14.3</span><span class="p">,</span> <span class="mf">15.6</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+<span class="c1">#This following step is to make a dicision whether the EOS ingredients is always increase. We can do that outsie of this main to the </span>
+<span class="c1">#EOS import.</span>
+<span class="c1">#if   all(x&lt;y for x, y in zip(eps_total_poly[:], eps_total_poly[[1:])) and all(x&lt;y for x, y in zip(pres_total_poly[j][:], pres_total_poly[j][1:])):</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">density</span><span class="p">)):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">Radius</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV_tidal</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">Mass</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV_tidal</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">tidal</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV_tidal</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">2</span><span class="p">])</span>
+    <span class="c1">#This is sentense is for avoiding the outflow of the result, like when solveTOV blow up because of ill EOS, we need to stop</span>
+        <span class="k">except</span> <span class="ne">OverflowError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="c1">#print(&quot;This EOS is ill-defined to reach an infinity result, that is not phyiscal, No Mass radius will be generated.&quot;)</span>
+            <span class="k">break</span>
+    <span class="n">MRT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">Radius</span><span class="p">,</span> <span class="n">Mass</span><span class="p">,</span><span class="n">tidal</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+    <span class="c1">#print(&quot;Mass Radius and tidal will be generated as the 3-d array. The first column is Radius, second one is mass,last is tidal&quot;)</span>
+
+    <span class="k">return</span> <span class="n">MRT</span></div>
+
+
+<div class="viewcode-block" id="OutputC_s">
+<a class="viewcode-back" href="../../main.html#TOVsolver.main.OutputC_s">[docs]</a>
+<span class="k">def</span> <span class="nf">OutputC_s</span><span class="p">(</span><span class="n">input_file</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">density</span><span class="o">=</span><span class="p">[],</span><span class="n">pressure</span><span class="o">=</span><span class="p">[]):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calls function to open csv (if needed) and check equation of state validity.</span>
+<span class="sd">        Then calls function to calculate speed of sound.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        file_name (string, optional): string. CSV file to be opened.</span>
+<span class="sd">        density (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+<span class="sd">        pressure (array, optional): numpy 1Darray. Passed into a check function and returned if valid.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        C_s (array): numpy 1D array. List of speeds of sound.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span> <span class="o">=</span> <span class="n">EoS_import</span><span class="o">.</span><span class="n">EOS_import</span><span class="p">(</span><span class="n">input_file</span><span class="p">,</span><span class="n">density</span><span class="p">,</span><span class="n">pressure</span><span class="p">)</span>
+    <span class="n">C_s</span> <span class="o">=</span> <span class="n">speed_of_sound</span><span class="o">.</span><span class="n">speed_of_sound_calc</span><span class="p">(</span><span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">C_s</span></div>
+
+
+<div class="viewcode-block" id="OutputMRpoint">
+<a class="viewcode-back" href="../../main.html#TOVsolver.main.OutputMRpoint">[docs]</a>
+<span class="k">def</span> <span class="nf">OutputMRpoint</span><span class="p">(</span><span class="n">central_density</span><span class="p">,</span><span class="n">energy_density</span><span class="p">,</span><span class="n">pressure</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Outputs the mass, radius, and tidal deformability (single point)</span>
+<span class="sd">    Args:</span>
+<span class="sd">        central_density (float): central density that we want to compute</span>
+<span class="sd">        density (array, optional): numpy 1Darray. Density of EoS</span>
+<span class="sd">        pressure (array, optional): numpy 1Darray. pressure of EoS</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        MR (tuple): tuple with mass, radius.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+    <span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+    <span class="n">dyncm2_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.6022e33</span><span class="p">)</span>
+    <span class="n">gcm3_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.7827e12</span><span class="p">)</span>
+    <span class="n">oneoverfm_MeV</span> <span class="o">=</span> <span class="mf">197.33</span>
+    
+    <span class="n">Radius</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">Mass</span> <span class="o">=</span> <span class="p">[]</span>
+    
+<span class="c1">#This following step is to make a dicision whether the EOS ingredients is always increase. We can do that outsie of this main to the </span>
+<span class="c1">#EOS import.</span>
+<span class="c1">#if   all(x&lt;y for x, y in zip(eps_total_poly[:], eps_total_poly[[1:])) and all(x&lt;y for x, y in zip(pres_total_poly[j][:], pres_total_poly[j][1:])):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">Radius</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV</span><span class="p">(</span><span class="n">central_density</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">Mass</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV</span><span class="p">(</span><span class="n">central_density</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="c1">#This is sentense is for avoiding the outflow of the result, like when solveTOV blow up because of ill EOS, we need to stop</span>
+    <span class="k">except</span> <span class="ne">OverflowError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;This EOS is ill-defined to reach an infinity result, that is not phyiscal, No Mass radius will be generated.&quot;</span><span class="p">)</span>
+    <span class="n">MR</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">Radius</span><span class="p">,</span> <span class="n">Mass</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+    
+    <span class="k">return</span> <span class="n">MR</span></div>
+
+
+<div class="viewcode-block" id="OutputMRTpoint">
+<a class="viewcode-back" href="../../main.html#TOVsolver.main.OutputMRTpoint">[docs]</a>
+<span class="k">def</span> <span class="nf">OutputMRTpoint</span><span class="p">(</span><span class="n">central_density</span><span class="p">,</span><span class="n">energy_density</span><span class="p">,</span><span class="n">pressure</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Outputs the mass, radius, and tidal deformability (single point)</span>
+<span class="sd">    Args:</span>
+<span class="sd">        central_density (float): central density that we want to compute</span>
+<span class="sd">        density (array, optional): numpy 1Darray. Density of EoS</span>
+<span class="sd">        pressure (array, optional): numpy 1Darray. pressure of EoS</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        MRT (tuple): tuple with mass, radius and tidal.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+    <span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+    <span class="n">dyncm2_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.6022e33</span><span class="p">)</span>
+    <span class="n">gcm3_to_MeVfm3</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="mf">1.7827e12</span><span class="p">)</span>
+    <span class="n">oneoverfm_MeV</span> <span class="o">=</span> <span class="mf">197.33</span>
+    
+    <span class="n">Radius</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">Mass</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">tidal</span> <span class="o">=</span> <span class="p">[]</span> 
+<span class="c1">#This following step is to make a dicision whether the EOS ingredients is always increase. We can do that outsie of this main to the </span>
+<span class="c1">#EOS import.</span>
+<span class="c1">#if   all(x&lt;y for x, y in zip(eps_total_poly[:], eps_total_poly[[1:])) and all(x&lt;y for x, y in zip(pres_total_poly[j][:], pres_total_poly[j][1:])):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">Radius</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV_tidal</span><span class="p">(</span><span class="n">central_density</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">Mass</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV_tidal</span><span class="p">(</span><span class="n">central_density</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">tidal</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TOV_solver</span><span class="o">.</span><span class="n">solveTOV_tidal</span><span class="p">(</span><span class="n">central_density</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)[</span><span class="mi">2</span><span class="p">])</span>
+    <span class="c1">#This is sentense is for avoiding the outflow of the result, like when solveTOV blow up because of ill EOS, we need to stop</span>
+    <span class="k">except</span> <span class="ne">OverflowError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;This EOS is ill-defined to reach an infinity result, that is not phyiscal, No Mass radius will be generated.&quot;</span><span class="p">)</span>
+    <span class="n">MRT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">Radius</span><span class="p">,</span> <span class="n">Mass</span><span class="p">,</span><span class="n">tidal</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+        
+    <span class="k">return</span> <span class="n">MRT</span></div>
+
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2023, Chun Huang, Nicole Obsborn, Nathan Whitsett.</p>
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+<li class="toctree-l1"><a class="reference internal" href="../../publications.html">Publications</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../cluster.html">Computation Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../starthere.html">Start Here!</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">TOVsolver.solver_code</li>
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+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for TOVsolver.solver_code</h1><div class="highlight"><pre>
+<span></span><span class="c1"># Python packages</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">UnivariateSpline</span>
+<span class="kn">from</span> <span class="nn">scipy.constants</span> <span class="kn">import</span> <span class="n">pi</span>
+<span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">odeint</span><span class="p">,</span> <span class="n">ode</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">optimize</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">repeat</span>
+
+<div class="viewcode-block" id="m1_from_mc_m2">
+<a class="viewcode-back" href="../../TOV_solver.html#TOVsolver.solver_code.m1_from_mc_m2">[docs]</a>
+<span class="k">def</span> <span class="nf">m1_from_mc_m2</span><span class="p">(</span><span class="n">mc</span><span class="p">,</span> <span class="n">m2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;a function that feed back the companion star mass from GW event measurement.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        mc (float): chrip mass of a GW event, unit in solar mass.</span>
+<span class="sd">        m2 (float or numpy array): the determined mass for one of the star, this </span>
+<span class="sd">        is computed from sampling of EoS.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        m1 (float or numpy array): the companion star mass in solar mass.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">m2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">m2</span><span class="p">)</span>
+    <span class="n">num1</span> <span class="o">=</span> <span class="p">(</span><span class="mf">2.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span> <span class="o">*</span> <span class="n">mc</span><span class="o">**</span><span class="mf">5.</span>
+    <span class="n">denom1</span> <span class="o">=</span> <span class="p">((</span><span class="mi">9</span> <span class="o">*</span> <span class="n">m2</span><span class="o">**</span><span class="mf">7.</span> <span class="o">*</span> <span class="n">mc</span><span class="o">**</span><span class="mf">5.</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">3.</span><span class="p">)</span> <span class="o">*</span> 
+              <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="mi">27</span> <span class="o">*</span> <span class="n">m2</span><span class="o">**</span><span class="mf">14.</span> <span class="o">*</span> <span class="n">mc</span><span class="o">**</span><span class="mf">10.</span> <span class="o">-</span> 
+                         <span class="mf">4.</span> <span class="o">*</span> <span class="n">m2</span><span class="o">**</span><span class="mf">9.</span> <span class="o">*</span> <span class="n">mc</span><span class="o">**</span><span class="mf">15.</span><span class="p">)))</span><span class="o">**</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">))</span>
+    <span class="n">denom2</span> <span class="o">=</span> <span class="mf">2.</span><span class="o">**</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span> <span class="o">*</span> <span class="mf">3.</span><span class="o">**</span><span class="p">(</span><span class="mf">2.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span> <span class="o">*</span> <span class="n">m2</span><span class="o">**</span><span class="mf">3.</span>
+    <span class="k">return</span> <span class="n">num1</span> <span class="o">/</span> <span class="n">denom1</span> <span class="o">+</span> <span class="n">denom1</span> <span class="o">/</span> <span class="n">denom2</span></div>
+
+
+<span class="k">def</span> <span class="nf">pressure_adind</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">epsgrid</span><span class="p">,</span> <span class="n">presgrid</span><span class="p">):</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">presgrid</span><span class="p">,</span> <span class="n">P</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">eds</span> <span class="o">=</span> <span class="n">epsgrid</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span>  <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">P</span> <span class="o">/</span> <span class="n">presgrid</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mf">3.</span> <span class="o">/</span> <span class="mf">5.</span><span class="p">)</span>
+        <span class="n">adind</span> <span class="o">=</span> <span class="mf">5.</span> <span class="o">/</span> <span class="mf">3.</span> <span class="o">*</span> <span class="n">presgrid</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">eds</span> <span class="o">/</span> <span class="n">epsgrid</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mf">5.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">eds</span> <span class="o">*</span> <span class="p">(</span><span class="n">eds</span> <span class="o">+</span> <span class="n">P</span><span class="p">)</span> <span class="o">/</span> <span class="n">P</span>
+    <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">presgrid</span><span class="p">):</span>
+        <span class="n">eds</span> <span class="o">=</span> <span class="n">epsgrid</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">P</span> <span class="o">/</span> <span class="n">presgrid</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mf">3.</span> <span class="o">/</span> <span class="mf">5.</span><span class="p">)</span>
+        <span class="n">adind</span> <span class="o">=</span> <span class="mf">5.</span> <span class="o">/</span> <span class="mf">3.</span> <span class="o">*</span> <span class="n">presgrid</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">eds</span> <span class="o">/</span> <span class="n">epsgrid</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mf">5.</span> <span class="o">/</span> <span class="mf">3.</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">eds</span> <span class="o">*</span> <span class="p">(</span><span class="n">eds</span> <span class="o">+</span> <span class="n">P</span><span class="p">)</span> <span class="o">/</span> <span class="n">P</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ci</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">presgrid</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">/</span> <span class="n">presgrid</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">epsgrid</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">/</span> <span class="n">epsgrid</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">eds</span> <span class="o">=</span> <span class="n">epsgrid</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">P</span> <span class="o">/</span> <span class="n">presgrid</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">ci</span><span class="p">)</span>
+        <span class="n">adind</span> <span class="o">=</span> <span class="n">ci</span> <span class="o">*</span> <span class="n">presgrid</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">eds</span> <span class="o">/</span> <span class="n">epsgrid</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">ci</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">eds</span> <span class="o">*</span> <span class="p">(</span><span class="n">eds</span> <span class="o">+</span> <span class="n">P</span><span class="p">)</span> <span class="o">/</span> <span class="n">P</span>
+    <span class="k">return</span> <span class="n">adind</span>
+<span class="k">def</span> <span class="nf">TOV</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">inveos</span><span class="p">):</span>
+    <span class="n">pres</span><span class="p">,</span> <span class="n">m</span> <span class="o">=</span> <span class="n">y</span>
+    
+    <span class="c1">#eps = 10**inveos(np.log10(pres))</span>
+    <span class="n">eps</span> <span class="o">=</span> <span class="n">inveos</span><span class="p">(</span><span class="n">pres</span><span class="p">)</span>
+    <span class="n">dpdr</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">eps</span> <span class="o">+</span> <span class="n">pres</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">m</span> <span class="o">+</span> <span class="mf">4.</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">3.</span> <span class="o">*</span> <span class="n">pres</span><span class="p">)</span>
+    <span class="n">dpdr</span> <span class="o">=</span> <span class="n">dpdr</span><span class="o">/</span><span class="p">(</span><span class="n">r</span><span class="o">*</span><span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="mf">2.</span><span class="o">*</span><span class="n">m</span><span class="p">))</span>
+    <span class="n">dmdr</span> <span class="o">=</span> <span class="mf">4.</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">eps</span>
+    
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">dpdr</span><span class="p">,</span> <span class="n">dmdr</span><span class="p">])</span>
+
+<div class="viewcode-block" id="TOV_def">
+<a class="viewcode-back" href="../../TOV_solver.html#TOVsolver.solver_code.TOV_def">[docs]</a>
+<span class="k">def</span> <span class="nf">TOV_def</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span><span class="n">inveos</span><span class="p">,</span><span class="n">ad_index</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;a function that packing the whole TOV equations set</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        r (float): raius as integrate varible</span>
+<span class="sd">        y (psudo-varible): containing pressure, mass, h and b as intergarte varibles</span>
+<span class="sd">        to solve out the TOV equation</span>
+<span class="sd">        inveos: the invert of the eos, pressure and energy density relation to integrate</span>
+<span class="sd">        and interpolate.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        Mass (array): The array that contains all the Stars&#39; masses, in M_sun as a </span>
+<span class="sd">        Units.</span>
+<span class="sd">        Radius (array): The array that contains all the Stars&#39;s radius, in km.</span>
+<span class="sd">        Tidal Deformability (array): The array that contains correpsonding Tidal property, </span>
+<span class="sd">        These are dimension-less.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">pres</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span><span class="n">h</span><span class="p">,</span><span class="n">b</span> <span class="o">=</span> <span class="n">y</span>
+    
+    <span class="c1">#energy_density = 10**inveos(np.log10(pres))</span>
+    <span class="n">eps</span> <span class="o">=</span> <span class="n">inveos</span><span class="p">(</span><span class="n">pres</span><span class="p">)</span>
+    <span class="n">dpdr</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">eps</span> <span class="o">+</span> <span class="n">pres</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">m</span> <span class="o">+</span> <span class="mf">4.</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">3.</span> <span class="o">*</span> <span class="n">pres</span><span class="p">)</span>
+    <span class="n">dpdr</span> <span class="o">=</span> <span class="n">dpdr</span><span class="o">/</span><span class="p">(</span><span class="n">r</span><span class="o">*</span><span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="mf">2.</span><span class="o">*</span><span class="n">m</span><span class="p">))</span>
+    <span class="n">dmdr</span> <span class="o">=</span> <span class="mf">4.</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">eps</span>
+    <span class="n">dhdr</span> <span class="o">=</span> <span class="n">b</span>
+    
+    <span class="n">dfdr</span> <span class="o">=</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">m</span> <span class="o">/</span> <span class="n">r</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">h</span> <span class="o">*</span> \
+        <span class="p">(</span><span class="o">-</span><span class="mf">2.</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="p">(</span><span class="mf">5.</span> <span class="o">*</span> <span class="n">eps</span> <span class="o">+</span> <span class="mf">9.</span> <span class="o">*</span> <span class="n">pres</span> <span class="o">+</span> <span class="p">(</span><span class="n">eps</span> <span class="o">+</span> <span class="n">pres</span><span class="p">)</span><span class="o">**</span><span class="mf">2.</span> <span class="o">/</span>
+                        <span class="p">(</span><span class="n">pres</span><span class="o">*</span><span class="n">ad_index</span><span class="p">))</span> <span class="o">+</span> <span class="mf">3.</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">m</span> <span class="o">/</span> <span class="n">r</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">m</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span>
+         <span class="mf">4.</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">r</span> <span class="o">*</span> <span class="n">pres</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> \
+        <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">b</span> <span class="o">/</span> <span class="n">r</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">r</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> \
+        <span class="p">(</span><span class="o">-</span><span class="mf">1.</span> <span class="o">+</span> <span class="n">m</span> <span class="o">/</span> <span class="n">r</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">eps</span> <span class="o">-</span> <span class="n">pres</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">dpdr</span><span class="p">,</span> <span class="n">dmdr</span><span class="p">,</span> <span class="n">dhdr</span><span class="p">,</span> <span class="n">dfdr</span><span class="p">])</span></div>
+
+
+<div class="viewcode-block" id="tidal_deformability">
+<a class="viewcode-back" href="../../TOV_solver.html#TOVsolver.solver_code.tidal_deformability">[docs]</a>
+<span class="k">def</span> <span class="nf">tidal_deformability</span><span class="p">(</span><span class="n">y2</span><span class="p">,</span> <span class="n">Mns</span><span class="p">,</span> <span class="n">Rns</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compute Tidal deformability from y2, neutron star mass and raius</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        y2 (array): midiate varrible that computing tidal</span>
+<span class="sd">        Mns (array): neutron star mass in g/cm3</span>
+<span class="sd">        Rns (array): neutron star radius in cm.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        tidal_def (array): neutron star tidal deformability with unit-less.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">C</span> <span class="o">=</span> <span class="n">Mns</span> <span class="o">/</span> <span class="n">Rns</span>
+    <span class="n">Eps</span> <span class="o">=</span> <span class="mf">4.</span> <span class="o">*</span> <span class="n">C</span><span class="o">**</span><span class="mf">3.</span> <span class="o">*</span> <span class="p">(</span><span class="mf">13.</span> <span class="o">-</span> <span class="mf">11.</span> <span class="o">*</span> <span class="n">y2</span> <span class="o">+</span> <span class="n">C</span> <span class="o">*</span> <span class="p">(</span><span class="mf">3.</span> <span class="o">*</span> <span class="n">y2</span> <span class="o">-</span> <span class="mf">2.</span><span class="p">)</span> <span class="o">+</span>
+                        <span class="mf">2.</span> <span class="o">*</span> <span class="n">C</span><span class="o">**</span><span class="mf">2.</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">+</span> <span class="n">y2</span><span class="p">))</span> <span class="o">+</span> \
+        <span class="mf">3.</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">C</span><span class="p">)</span><span class="o">**</span><span class="mf">2.</span> <span class="o">*</span> <span class="p">(</span><span class="mf">2.</span> <span class="o">-</span> <span class="n">y2</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">C</span> <span class="o">*</span> <span class="p">(</span><span class="n">y2</span> <span class="o">-</span> <span class="mf">1.</span><span class="p">))</span> <span class="o">*</span> \
+        <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">C</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">C</span> <span class="o">*</span> <span class="p">(</span><span class="mf">6.</span> <span class="o">-</span> <span class="mf">3.</span> <span class="o">*</span> <span class="n">y2</span> <span class="o">+</span> <span class="mf">3.</span> <span class="o">*</span> <span class="n">C</span> <span class="o">*</span> <span class="p">(</span><span class="mf">5.</span> <span class="o">*</span> <span class="n">y2</span> <span class="o">-</span> <span class="mf">8.</span><span class="p">))</span>
+    <span class="n">tidal_def</span> <span class="o">=</span> <span class="mf">16.</span> <span class="o">/</span> <span class="p">(</span><span class="mf">15.</span> <span class="o">*</span> <span class="n">Eps</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">C</span><span class="p">)</span><span class="o">**</span><span class="mf">2.</span> <span class="o">*</span>\
+        <span class="p">(</span><span class="mf">2.</span> <span class="o">+</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">C</span> <span class="o">*</span> <span class="p">(</span><span class="n">y2</span> <span class="o">-</span> <span class="mf">1.</span><span class="p">)</span> <span class="o">-</span> <span class="n">y2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">tidal_def</span></div>
+
+
+<span class="c1"># Function solves the TOV equation, returning mass and radius</span>
+<div class="viewcode-block" id="solveTOV_tidal">
+<a class="viewcode-back" href="../../TOV_solver.html#TOVsolver.solver_code.solveTOV_tidal">[docs]</a>
+<span class="k">def</span> <span class="nf">solveTOV_tidal</span><span class="p">(</span><span class="n">center_rho</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Solve TOV equation from given Equation of state in the neutron star </span>
+<span class="sd">    core density range</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        center_rho(array): This is the energy density here is fixed in main</span>
+<span class="sd">        that is np.logspace(14.3, 15.6, 50)</span>
+<span class="sd">        energy_density (array): Desity array of the neutron star EoS, in MeV/fm^{-3}</span>
+<span class="sd">        Notice here for simiplicity, we omitted G/c**4 magnitude, so </span>
+<span class="sd">        (value in MeV/fm^{-3})*G/c**4, could convert to the energy density we are</span>
+<span class="sd">        using, please check the Test_EOS.csv to double check the order of magnitude.</span>
+<span class="sd">        </span>
+<span class="sd">        pressure (array): Pressure array of neutron star EoS, also in nautral unit</span>
+<span class="sd">        with MeV/fm^{-3}, still please check the Test_EOS.csv, the conversion is </span>
+<span class="sd">        (value in dyn/cm3)*G/c**4.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        Mass (array): The array that contains all the Stars&#39; masses, in M_sun as a </span>
+<span class="sd">        Units.</span>
+<span class="sd">        Radius (array): The array that contains all the Stars&#39;s radius, in km.</span>
+<span class="sd">        Tidal Deformability (array): The array that contains correpsonding Tidal property, </span>
+<span class="sd">        These are dimension-less.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1">#eos = UnivariateSpline(np.log10(eps), np.log10(pres), k=1, s=0)</span>
+    <span class="c1">#inveos = UnivariateSpline(np.log10(pressure), np.log10(energy_density), k=1, s=0)</span>
+    <span class="c1">#We could change this to Double Log Interpolation。</span>
+
+
+    <span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+    <span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+    <span class="n">eos</span> <span class="o">=</span> <span class="n">UnivariateSpline</span><span class="p">(</span><span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">inveos</span> <span class="o">=</span> <span class="n">UnivariateSpline</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">Pmin</span> <span class="o">=</span> <span class="n">pressure</span><span class="p">[</span><span class="mi">20</span><span class="p">]</span>
+    
+    <span class="n">r</span> <span class="o">=</span> <span class="mf">4.441e-16</span>
+    <span class="n">dr</span> <span class="o">=</span> <span class="mf">10.</span>
+    <span class="n">rmax</span> <span class="o">=</span> <span class="mi">50</span> <span class="o">*</span> <span class="mf">1e5</span>
+    <span class="n">rhocent</span> <span class="o">=</span> <span class="n">center_rho</span> <span class="o">*</span> <span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mf">2.</span>
+    <span class="c1">#pcent = 10**eos(np.log10(rhocent))</span>
+    <span class="n">pcent</span> <span class="o">=</span> <span class="n">eos</span><span class="p">(</span><span class="n">rhocent</span><span class="p">)</span>
+    <span class="n">P0</span> <span class="o">=</span> <span class="n">pcent</span> <span class="o">-</span> <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">pi</span><span class="o">/</span><span class="mf">3.</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">pcent</span> <span class="o">+</span> <span class="n">rhocent</span><span class="p">)</span> <span class="o">*</span><span class="p">(</span><span class="mf">3.</span><span class="o">*</span><span class="n">pcent</span> <span class="o">+</span> <span class="n">rhocent</span><span class="p">)</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">2.</span>
+    <span class="n">m0</span> <span class="o">=</span> <span class="mf">4.</span><span class="o">/</span><span class="mf">3.</span> <span class="o">*</span><span class="n">pi</span> <span class="o">*</span><span class="n">rhocent</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">3.</span>
+    <span class="n">h0</span> <span class="o">=</span> <span class="n">r</span><span class="o">**</span><span class="mf">2.</span>
+    <span class="n">b0</span> <span class="o">=</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">r</span>
+    <span class="n">stateTOV</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">P0</span><span class="p">,</span> <span class="n">m0</span><span class="p">,</span> <span class="n">h0</span><span class="p">,</span><span class="n">b0</span><span class="p">])</span>
+    <span class="n">ad_index</span> <span class="o">=</span> <span class="n">pressure_adind</span><span class="p">(</span><span class="n">P0</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">)</span>
+    <span class="n">sy</span> <span class="o">=</span> <span class="n">ode</span><span class="p">(</span><span class="n">TOV_def</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span><span class="o">.</span><span class="n">set_integrator</span><span class="p">(</span><span class="s1">&#39;dopri5&#39;</span><span class="p">)</span>
+    
+    <span class="c1">#have been modified from Irida to this integrator</span>
+    <span class="n">sy</span><span class="o">.</span><span class="n">set_initial_value</span><span class="p">(</span><span class="n">stateTOV</span> <span class="p">,</span> <span class="n">r</span><span class="p">)</span><span class="o">.</span><span class="n">set_f_params</span><span class="p">(</span><span class="n">inveos</span><span class="p">,</span><span class="n">ad_index</span><span class="p">)</span>
+    
+    <span class="k">while</span> <span class="n">sy</span><span class="o">.</span><span class="n">successful</span><span class="p">()</span> <span class="ow">and</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&gt;</span><span class="n">Pmin</span><span class="p">:</span>
+        <span class="n">stateTOV</span> <span class="o">=</span> <span class="n">sy</span><span class="o">.</span><span class="n">integrate</span><span class="p">(</span><span class="n">sy</span><span class="o">.</span><span class="n">t</span><span class="o">+</span><span class="n">dr</span><span class="p">)</span>
+        <span class="n">dpdr</span><span class="p">,</span> <span class="n">dmdr</span><span class="p">,</span> <span class="n">dhdr</span><span class="p">,</span> <span class="n">dfdr</span><span class="o">=</span> <span class="n">TOV_def</span><span class="p">(</span><span class="n">sy</span><span class="o">.</span><span class="n">t</span><span class="o">+</span><span class="n">dr</span><span class="p">,</span> <span class="n">stateTOV</span><span class="p">,</span> <span class="n">inveos</span><span class="p">,</span><span class="n">ad_index</span><span class="p">)</span>
+        <span class="n">dr</span> <span class="o">=</span> <span class="mf">0.46</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="n">stateTOV</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">dmdr</span> <span class="o">-</span> <span class="mf">1.</span><span class="o">/</span><span class="n">stateTOV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">dpdr</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.</span><span class="p">)</span>
+    <span class="n">Mb</span> <span class="o">=</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">Rns</span> <span class="o">=</span> <span class="n">sy</span><span class="o">.</span><span class="n">t</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">Rns</span> <span class="o">*</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">/</span><span class="n">stateTOV</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> 
+    <span class="n">tidal</span> <span class="o">=</span> <span class="n">tidal_deformability</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">Mb</span><span class="p">,</span> <span class="n">Rns</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">Mb</span><span class="o">*</span><span class="n">c</span><span class="o">**</span><span class="mf">2.</span><span class="o">/</span><span class="n">G</span><span class="o">/</span><span class="n">Msun</span><span class="p">,</span> <span class="n">Rns</span><span class="o">/</span><span class="mf">1e5</span><span class="p">,</span><span class="n">tidal</span></div>
+
+
+
+<div class="viewcode-block" id="solveTOV">
+<a class="viewcode-back" href="../../TOV_solver.html#TOVsolver.solver_code.solveTOV">[docs]</a>
+<span class="k">def</span> <span class="nf">solveTOV</span><span class="p">(</span><span class="n">center_rho</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Solve TOV equation from given Equation of state in the neutron star </span>
+<span class="sd">    core density range</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        center_rho(array): This is the energy density here is fixed in main</span>
+<span class="sd">        that is np.logspace(14.3, 15.6, 50)</span>
+<span class="sd">        energy_density (array): Desity array of the neutron star EoS, in MeV/fm^{-3}</span>
+<span class="sd">        Notice here for simiplicity, we omitted G/c**4 magnitude, so </span>
+<span class="sd">        (value in MeV/fm^{-3})*G/c**4, could convert to the energy density we are</span>
+<span class="sd">        using, please check the Test_EOS.csv to double check the order of magnitude.</span>
+<span class="sd">        </span>
+<span class="sd">        pressure (array): Pressure array of neutron star EoS, also in nautral unit</span>
+<span class="sd">        with MeV/fm^{-3}, still please check the Test_EOS.csv, the conversion is </span>
+<span class="sd">        (value in dyn/cm3)*G/c**4.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        Mass (array): The array that contains all the Stars&#39; masses, in M_sun as a </span>
+<span class="sd">        Units.</span>
+<span class="sd">        Radius (array): The array that contains all the Stars&#39;s radius, in km.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1">#eos = UnivariateSpline(np.log10(energy_density), np.log10(pres), k=1, s=0)</span>
+    <span class="c1">#inveos = UnivariateSpline(np.log10(pres), np.log10(energy_density), k=1, s=0)</span>
+    <span class="c1">#We could change this to Double Log Interpolation。</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="mf">3e10</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="mf">6.67428e-8</span>
+    <span class="n">Msun</span> <span class="o">=</span> <span class="mf">1.989e33</span>
+
+    <span class="n">eos</span> <span class="o">=</span> <span class="n">UnivariateSpline</span><span class="p">(</span><span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">inveos</span> <span class="o">=</span> <span class="n">UnivariateSpline</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">energy_density</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">Pmin</span> <span class="o">=</span> <span class="n">pressure</span><span class="p">[</span><span class="mi">20</span><span class="p">]</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="mf">4.441e-16</span>
+    <span class="n">dr</span> <span class="o">=</span> <span class="mf">10.</span>
+    <span class="n">center_rho</span> <span class="o">=</span> <span class="n">center_rho</span> <span class="o">*</span> <span class="n">G</span><span class="o">/</span><span class="n">c</span><span class="o">**</span><span class="mf">2.</span>
+    
+    <span class="c1">#pcent = 10**eos(np.log10(rhocent))</span>
+    <span class="n">pcent</span> <span class="o">=</span> <span class="n">eos</span><span class="p">(</span><span class="n">center_rho</span><span class="p">)</span>
+    <span class="n">P0</span> <span class="o">=</span> <span class="n">pcent</span> <span class="o">-</span> <span class="p">(</span><span class="mf">2.</span><span class="o">*</span><span class="n">pi</span><span class="o">/</span><span class="mf">3.</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">pcent</span> <span class="o">+</span> <span class="n">center_rho</span><span class="p">)</span> <span class="o">*</span><span class="p">(</span><span class="mf">3.</span><span class="o">*</span><span class="n">pcent</span> <span class="o">+</span> <span class="n">center_rho</span><span class="p">)</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">2.</span>
+    <span class="n">m0</span> <span class="o">=</span> <span class="mf">4.</span><span class="o">/</span><span class="mf">3.</span> <span class="o">*</span><span class="n">pi</span> <span class="o">*</span><span class="n">center_rho</span><span class="o">*</span><span class="n">r</span><span class="o">**</span><span class="mf">3.</span>
+    <span class="n">stateTOV</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">P0</span><span class="p">,</span> <span class="n">m0</span><span class="p">])</span>
+    
+    <span class="n">sy</span> <span class="o">=</span> <span class="n">ode</span><span class="p">(</span><span class="n">TOV</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span><span class="o">.</span><span class="n">set_integrator</span><span class="p">(</span><span class="s1">&#39;dopri5&#39;</span><span class="p">)</span>
+    
+  
+    <span class="c1">#have been modified from Irida to this integrator</span>
+    <span class="n">sy</span><span class="o">.</span><span class="n">set_initial_value</span><span class="p">(</span><span class="n">stateTOV</span> <span class="p">,</span> <span class="n">r</span><span class="p">)</span><span class="o">.</span><span class="n">set_f_params</span><span class="p">(</span><span class="n">inveos</span><span class="p">)</span>
+    
+    <span class="k">while</span> <span class="n">sy</span><span class="o">.</span><span class="n">successful</span><span class="p">()</span> <span class="ow">and</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&gt;</span><span class="n">Pmin</span><span class="p">:</span>
+        <span class="n">stateTOV</span> <span class="o">=</span> <span class="n">sy</span><span class="o">.</span><span class="n">integrate</span><span class="p">(</span><span class="n">sy</span><span class="o">.</span><span class="n">t</span><span class="o">+</span><span class="n">dr</span><span class="p">)</span>
+        <span class="n">dpdr</span><span class="p">,</span> <span class="n">dmdr</span> <span class="o">=</span> <span class="n">TOV</span><span class="p">(</span><span class="n">sy</span><span class="o">.</span><span class="n">t</span><span class="o">+</span><span class="n">dr</span><span class="p">,</span> <span class="n">stateTOV</span><span class="p">,</span> <span class="n">inveos</span><span class="p">)</span>
+        <span class="n">dr</span> <span class="o">=</span> <span class="mf">0.46</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="n">stateTOV</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">dmdr</span> <span class="o">-</span> <span class="mf">1.</span><span class="o">/</span><span class="n">stateTOV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">dpdr</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">1.</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">stateTOV</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">c</span><span class="o">**</span><span class="mf">2.</span><span class="o">/</span><span class="n">G</span><span class="o">/</span><span class="n">Msun</span><span class="p">,</span> <span class="n">sy</span><span class="o">.</span><span class="n">t</span><span class="o">/</span><span class="mf">1e5</span>  </div>
+
+</pre></div>
+
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+<li class="toctree-l1"><a class="reference internal" href="../../Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
+        </div>
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+      <li class="breadcrumb-item active">TOVsolver.speed_of_sound</li>
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+      </li>
+  </ul>
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for TOVsolver.speed_of_sound</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.constant</span> <span class="kn">import</span> <span class="n">c</span><span class="p">,</span><span class="n">G</span>
+
+<div class="viewcode-block" id="speed_of_sound_calc">
+<a class="viewcode-back" href="../../speed_of_sound.html#TOVsolver.speed_of_sound.speed_of_sound_calc">[docs]</a>
+<span class="k">def</span> <span class="nf">speed_of_sound_calc</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">pressure</span><span class="p">):</span>
+
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that calculates the speed of sound by taking the gradient of the euqation of state.</span>
+<span class="sd">    Args:</span>
+<span class="sd">        density (array): numpy 1Darray. </span>
+<span class="sd">        pressure (array): numpy 1Darray.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        speed_of_sound (array): numpy 1Darray.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">speed_of_sound</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="c1">#density = density*c**2/G</span>
+    <span class="c1">#pressure = pressure*c**4/G</span>
+    
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">density</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+        <span class="n">speed_of_sound</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+    <span class="n">d2</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">C_s</span><span class="o">=</span> <span class="p">[]</span>
+    <span class="c1">#eps2 = []</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">speed_of_sound</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">&gt;</span> <span class="mf">1.5e-14</span><span class="p">:</span>
+            <span class="n">d2</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="n">C_s</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">speed_of_sound</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">C_s</span><span class="p">,</span><span class="n">d2</span></div>
+
+</pre></div>
+
+           </div>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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+          <a href="../index.html">CompactObject</a>
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+
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+      <li class="breadcrumb-item active">Overview: module code</li>
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+           <div itemprop="articleBody">
+             
+  <h1>All modules for which code is available</h1>
+<ul><li><a href="EOSgenerators/RMF_EOS.html">EOSgenerators.RMF_EOS</a></li>
+<li><a href="EOSgenerators/crust_EOS.html">EOSgenerators.crust_EOS</a></li>
+<li><a href="EOSgenerators/fastRMF_EoS.html">EOSgenerators.fastRMF_EoS</a></li>
+<li><a href="InferenceWorkflow/BayesianSampler.html">InferenceWorkflow.BayesianSampler</a></li>
+<li><a href="InferenceWorkflow/Likelihood.html">InferenceWorkflow.Likelihood</a></li>
+<li><a href="InferenceWorkflow/prior.html">InferenceWorkflow.prior</a></li>
+<li><a href="TOVsolver/EoS_import.html">TOVsolver.EoS_import</a></li>
+<li><a href="TOVsolver/main.html">TOVsolver.main</a></li>
+<li><a href="TOVsolver/solver_code.html">TOVsolver.solver_code</a></li>
+<li><a href="TOVsolver/speed_of_sound.html">TOVsolver.speed_of_sound</a></li>
+</ul>
+
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index 0000000..4e86a3d
--- /dev/null
+++ b/_sources/BayesianSampler.rst.txt
@@ -0,0 +1,9 @@
+.. _Bayesian_Sampler:
+
+Bayesian Inference smapler setup module
+=======================================
+
+Functions to start run Bayesian inference by calling UltraNest package
+
+.. automodule:: InferenceWorkflow.BayesianSampler
+   :members:
diff --git a/_sources/Contributor.rst.txt b/_sources/Contributor.rst.txt
new file mode 100644
index 0000000..48ff756
--- /dev/null
+++ b/_sources/Contributor.rst.txt
@@ -0,0 +1,74 @@
+Call for Contributions
+======================
+
+Introduction
+------------
+
+If you are interested in contributing to the **CompactObject** project, please send an email to `chun.h@wustl.edu <mailto:chun.h@wustl.edu>`_ to request collaboration. You can also open an issue on our GitHub repository at `Issue page <https://github.com/ChunHuangPhy/CompactObject/issues>`_ so that the community can track the status of collaborations.
+
+We are always eager to expand our scope to include the most equation of state (EOS) computations currently available in the community, as well as the most relevant constraints. If you have your own EOS and wish to contribute, please follow the steps outlined below.
+
+Contributing an Equation of State (EOS)
+---------------------------------------
+
+1. **Open an Issue**  
+   Visit our GitHub issues page at `Issue page <https://github.com/ChunHuangPhy/CompactObject/issues>`_ and open a new issue to suggest the EOS you would like to contribute.
+
+2. **Request Collaboration**  
+   Send an email to `chun.h@wustl.edu` to request collaborator access to the repository.
+
+3. **Commit the EOS Script**  
+   - Create a `.py` script that computes the EOS. The script should:
+     - Input the EOS parameter values.
+     - Output the pressure and density in CGS units.
+   - For unit conventions, please refer to the `UnitConventionForDeveloper <https://chunhuangphy.github.io/CompactObject/UnitConventionForDeveloper.html>`_.
+   - Name the main compute function as `compute_EOS`.
+   - Name the file as `(YourEOSName)_EOS.py`.
+
+4. **Create a Demonstration Notebook**  
+   Develop a Jupyter notebook that demonstrates:
+   - How to compute the EOS.
+   - Defining your EOS parameters.
+   - Integrating your EOS with our default TOV solver (`main.outputMR`).
+   - Ensuring the TOV solver correctly solves the EOS and outputs a mass-radius curve plot.
+
+Contributing Inference Components
+---------------------------------
+
+If you have developed your own likelihood, prior, sampler, or any component related to inference that is suitable for addition to our functionalities, we highly recommend contacting us. Follow these steps to contribute:
+
+1. **Open an Issue**  
+   Visit our GitHub issues page at `Issue page <https://github.com/ChunHuangPhy/CompactObject/issues>`_ and open a new issue to suggest changes to the inference components.
+
+2. **Request Collaboration**  
+   Send an email to `chun.h@wustl.edu` to request collaborator access to the repository.
+
+3. **Commit the Inference Script**  
+   - Create a `.py` script within the `InferenceWorkflow` directory.
+   - Name the script after the function you are contributing.
+
+4. **Create a Demonstration Notebook**  
+   Develop a Jupyter notebook that demonstrates:
+   - How to use your prior, likelihood, or sampler.
+   - Include sampling outputs to ensure compatibility with the current structure of the package.
+
+Guidelines for Key Functions and Permissions
+--------------------------------------------
+
+The **TOVsolver** module is fundamental to all inference and computation within **CompactObject**. Modifying this module requires careful consideration, as changes may necessitate significant code restructuring.
+
+- **Permissions:**  
+  Only original contributors have permission to modify the **TOVsolver** code. All commits related to the **TOVsolver** module require detailed reviews by the original members of the package.
+
+- **Local Changes:**  
+  If you clone the repository locally and need to make changes to the **TOVsolver**, proceed with caution to avoid extensive code rebuilding.
+
+Acknowledgements
+----------------
+
+We welcome feedback and contributions to expand the functionalities of **CompactObject**. Your support helps enhance the tool for the entire research community.
+
+Contact
+-------
+
+For inquiries, contributions, or to be featured in our publications list, please contact us at `chun.h@wustl.edu <mailto:chun.h@wustl.edu>`_.
diff --git a/_sources/Cornerplot.rst.txt b/_sources/Cornerplot.rst.txt
new file mode 100644
index 0000000..5cc0ba5
--- /dev/null
+++ b/_sources/Cornerplot.rst.txt
@@ -0,0 +1,9 @@
+.. _Corner_plot_module:
+
+Postprocessing module: Cornerplot
+======================================
+
+Function to plot overlapping corner plots with input as two or three 2d posterior arrays
+
+.. automodule:: postprocessing.Cornerplot
+   :members:
diff --git a/_sources/DownloadCompose.ipynb.txt b/_sources/DownloadCompose.ipynb.txt
new file mode 100644
index 0000000..39097d4
--- /dev/null
+++ b/_sources/DownloadCompose.ipynb.txt
@@ -0,0 +1,459 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Usage of download_compose Module\n",
+    "Tianzhe Zhou 2024/11/9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[CompOSE](https://compose.obspm.fr/table)(CompStar Online Supernovae Equations of State)\n",
+    "is a database that provides data tables for different state of the art equations of state (EoS) ready for further usage in astrophysical applications, nuclear physics and beyond.\n",
+    "\n",
+    "In this notebook, we will demonstrate how to use the `download_compose` module to download the data from the CompOSE database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DownloadCompose: Fetching data from https://compose.obspm.fr/table \n",
+      "...\n",
+      "DownloadCompose: Find 308 EOS data on website https://compose.obspm.fr/table\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, import the class that we need to use\n",
+    "from EOSgenerators.download_compose import DownloadCompose\n",
+    "\n",
+    "# Then, create an instance of the class\n",
+    "# This will automatically fetch the latest version of all available\n",
+    "# EOS names and their corresponding download links\n",
+    "dc = DownloadCompose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id =   1, name = HS(DD2) neutron matter (no electrons)\n",
+      "id =   2, name = HS(DD2) neutron matter (with electrons)\n",
+      "id =   3, name = HS(FSG) neutron matter (no electrons)\n",
+      "id =   4, name = HS(FSG) neutron matter (with electrons)\n",
+      "id =   5, name = HS(IUF) neutron matter (no electrons)\n",
+      "id =   6, name = HS(IUF) neutron matter (with electrons)\n",
+      "id =   7, name = HS(NL3) neutron matter (no electrons)\n",
+      "id =   8, name = HS(NL3) neutron matter (with electrons)\n",
+      "id =   9, name = HS(TM1) neutron matter  (no electrons)\n",
+      "id =  10, name = HS(TM1) neutron matter (with electrons)\n",
+      "id =  11, name = HS(TMA) neutron matter (no electrons)\n",
+      "id =  12, name = HS(TMA) neutron matter (with electrons)\n",
+      "id =  13, name = SFH(SFHo) neutron matter (no electrons)\n",
+      "id =  14, name = SFH(SFHo) neutron matter (with electrons)\n",
+      "id =  15, name = SFH(SFHx) neutron matter (no electrons)\n",
+      "id =  16, name = SFH(SFHx) neutron matter (with electrons)\n",
+      "id =  17, name = HS(DD2) (no electrons)\n",
+      "id =  18, name = HS(DD2) (with electrons)\n",
+      "id =  19, name = HS(FSG) (no electrons)\n",
+      "id =  20, name = HS(FSG) (with electrons)\n",
+      "id =  21, name = HS(IUF) (no electrons)\n",
+      "id =  22, name = HS(IUF) (with electrons)\n",
+      "id =  23, name = HS(NL3) (no electrons)\n",
+      "id =  24, name = HS(NL3) (with electrons)\n",
+      "id =  25, name = HS(TM1) (no electrons)\n",
+      "id =  26, name = HS(TM1) (with electrons)\n",
+      "id =  27, name = HS(TMA) (no electrons)\n",
+      "id =  28, name = HS(TMA) (with electrons)\n",
+      "id =  29, name = GROM(LS220L) (with Lambdas, no low densities)\n",
+      "id =  30, name = GROM(LS220L) (with Lambdas, with low densities)\n",
+      "id =  31, name = LS(LS220) (no low densities)\n",
+      "id =  32, name = LS(LS220) (with low densities)\n",
+      "id =  33, name = SFH(SFHo) (no electrons)\n",
+      "id =  34, name = SFH(SFHo) (with electrons)\n",
+      "id =  35, name = SFH(SFHx) (no electrons)\n",
+      "id =  36, name = SFH(SFHx) (with electrons)\n",
+      "id =  37, name = SHO(FSU1) (no electrons)\n",
+      "id =  38, name = SHO(FSU1) (with electrons)\n",
+      "id =  39, name = SHO(FSU2) (with electrons)\n",
+      "id =  40, name = SHO(FSU2) (no electrons)\n",
+      "id =  41, name = SHT(NL3) (no electrons)\n",
+      "id =  42, name = SHT(NL3) (with electrons)\n",
+      "id =  43, name = BHB(DD2L) (no electrons)\n",
+      "id =  44, name = BHB(DD2L) (with electrons)\n",
+      "id =  45, name = BHB(DD2Lphi) (with electrons)\n",
+      "id =  46, name = BHB(DD2Lphi) (no electrons)\n",
+      "id =  47, name = SHO(FSU1) (no electrons)\n",
+      "id =  48, name = SHO(FSU1) (with electrons)\n",
+      "id =  49, name = SHO(FSU2) (no electrons)\n",
+      "id =  50, name = SHO(FSU2) (with electrons)\n",
+      "id =  51, name = SHT(NL3) (no electrons)\n",
+      "id =  52, name = SHT(NL3) (with electrons)\n",
+      "id =  53, name = GDTB(DDHdelta)\n",
+      "id =  54, name = GM(GM1)\n",
+      "id =  55, name = OPGR(GM1Y4) (with hyperons)\n",
+      "id =  56, name = STOS(TM1) neutron matter (original version)\n",
+      "id =  57, name = STOS(TM1) neutron matter (new version)\n",
+      "id =  58, name = STOS(TM1L) neutron and Lambda matter\n",
+      "id =  59, name = STOS(TM1) zero temperature (new version)\n",
+      "id =  60, name = STOS(TM1) zero temperature (original version)\n",
+      "id =  61, name = STOS(TM1) with Lambda zero temperature\n",
+      "id =  62, name = STOS(TM1L) with Lambda hyperons\n",
+      "id =  63, name = STOS(TM1) (new version)\n",
+      "id =  64, name = STOS(TM1) (original version)\n",
+      "id =  65, name = OPGR(GM1Y5) (with hyperons)\n",
+      "id =  66, name = OPGR(GM1Y6) (with hyperons)\n",
+      "id =  67, name = OPGR(DDHdeltaY4) (with hyperons)\n",
+      "id =  68, name = APR(APR)\n",
+      "id =  69, name = BBB(BHF-BBB2)\n",
+      "id =  70, name = SFHPST(TM1B145), with electrons\n",
+      "id =  71, name = STOS(TM1) (new version) with electrons\n",
+      "id =  72, name = STOS(TM1L) with Lambda hyperons and electrons\n",
+      "id =  73, name = SFHPST(TM1B139) with electrons\n",
+      "id =  74, name = SFHPST(TM1B155) with electrons\n",
+      "id =  75, name = SFHPST(TM1B165) with electrons (B165)\n",
+      "id =  76, name = IOTSY(TM1Y30) Sigma potential 30 MeV\n",
+      "id =  77, name = IOTSY(TM1Y30pi) with pions, Sigma potential 30 MeV\n",
+      "id =  78, name = IOTSY(TM1Y90pi) with pions, Sigma potential 90 MeV\n",
+      "id =  79, name = IOTSY(TM1Y0pi) with pions, Sigma potential 0 MeV\n",
+      "id =  80, name = IOTSY(TM1Y-30pi) with pions, Sigma potential -30 MeV\n",
+      "id =  81, name = IOTSY(TM1Y90) , Sigma potential 90 MeV)\n",
+      "id =  82, name = IOTSY(TM1Y0), Sigma potential 0 MeV\n",
+      "id =  83, name = IOTSY(TM1Y-30), Sigma potential -30 MeV\n",
+      "id =  84, name = RG(SLY230a)\n",
+      "id =  85, name = RG(SLY2)\n",
+      "id =  86, name = RG(SLY9)\n",
+      "id =  87, name = RG(SKI2)\n",
+      "id =  88, name = RG(SkI3)\n",
+      "id =  89, name = RG(SkI4)\n",
+      "id =  90, name = RG(SkI5)\n",
+      "id =  91, name = RG(SkI6)\n",
+      "id =  92, name = RG(KDE0v1)\n",
+      "id =  93, name = RG(KDE0v)\n",
+      "id =  94, name = RG(SK255)\n",
+      "id =  95, name = RG(SK272)\n",
+      "id =  96, name = RG(SKa)\n",
+      "id =  97, name = RG(SKb)\n",
+      "id =  98, name = RG(SkMp)\n",
+      "id =  99, name = RG(SkOp)\n",
+      "id = 100, name = RG(Rs)\n",
+      "id = 101, name = NL3\n",
+      "id = 104, name = OMHN(DD2Y)\n",
+      "id = 105, name = TNTYST(KOST2) variational EoS (with electrons)\n",
+      "id = 106, name = TNTYST(KOST2) variational EoS (without electrons)\n",
+      "id = 107, name = TNTYST(KOST2) variational EoS at zero temperature (with electrons)\n",
+      "id = 108, name = TNTYST(KOST2) variational EoS at zero temperature (no electrons)\n",
+      "id = 109, name = TNTYST(KOST2) neutron matter (zero temperature)\n",
+      "id = 110, name = TNTYST(KOST2) neutron matter\n",
+      "id = 111, name = FYSS(TM1) (no electrons)\n",
+      "id = 112, name = FTNS(KOST2) variational EoS (no electrons)\n",
+      "id = 113, name = FYSS(TM1) (with electrons)\n",
+      "id = 114, name = FTNS(KOST2) variational EoS (with electrons)\n",
+      "id = 115, name = DNS(CMF) Hadronic (with electrons)\n",
+      "id = 116, name = DNS(CMF) Hadronic (cold neutron stars)\n",
+      "id = 117, name = DNS(CMF) Hadronic (no electrons)\n",
+      "id = 118, name = FOP(SFHoY) (with electrons)\n",
+      "id = 119, name = FOP(SFHoY) (no electrons)\n",
+      "id = 120, name = BL(chiral)\n",
+      "id = 121, name = BL(chiral) with crust\n",
+      "id = 123, name = RG(SLY4)\n",
+      "id = 134, name = RG(SLY4)\n",
+      "id = 136, name = PT(GRDF1_DD2)\n",
+      "id = 137, name = PT(GRDF2_DD2) old version\n",
+      "id = 138, name = DNS(CMF) hadronic  (cold neutron stars) with crust\n",
+      "id = 139, name = BHK(QHC18)\n",
+      "id = 140, name = BFH(QHC19-B)\n",
+      "id = 141, name = SRO(SLy4) SNA version\n",
+      "id = 142, name = OOS(DD2_FRG) (2+1 flavors)\n",
+      "id = 143, name = OOS(DD2_FRG) (2 flavors)\n",
+      "id = 144, name = SNSH(TM1e)\n",
+      "id = 145, name = SNSH(TM1e) zero temperature\n",
+      "id = 146, name = SNSH(TM1e) neutron matter\n",
+      "id = 147, name = RG(SLy4) with neutrino opacities\n",
+      "id = 148, name = RG(SLY4) with neutrino opacities, extended version\n",
+      "id = 149, name = SRO(APR) SNA version\n",
+      "id = 150, name = BFH(QHC19-A)\n",
+      "id = 151, name = BFH(QHC19-C)\n",
+      "id = 152, name = BFH(QHC19-D)\n",
+      "id = 154, name = MBB(DD2K ) (no electrons)\n",
+      "id = 155, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1 without leptons\n",
+      "id = 156, name = BBKF(DD2F-SF) quark-hadron model RDF 1.2 without leptons\n",
+      "id = 157, name = BBKF(DD2F-SF) quark-hadron model RDF 1.3 without leptons\n",
+      "id = 158, name = BBKF(DD2F-SF) quark-hadron model RDF 1.4 without leptons\n",
+      "id = 159, name = BBKF(DD2F-SF) quark-hadron model RDF 1.5 without leptons\n",
+      "id = 160, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6 without leptons\n",
+      "id = 161, name = BBKF(DD2F-SF) quark-hadron model RDF 1.7 without leptons\n",
+      "id = 162, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8 without leptons\n",
+      "id = 163, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9 without leptons\n",
+      "id = 164, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.1\n",
+      "id = 165, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.2\n",
+      "id = 166, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.3\n",
+      "id = 167, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.4\n",
+      "id = 168, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.5\n",
+      "id = 169, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.6\n",
+      "id = 170, name = BBKF(DD2F-SF) quark-hadron model  RDF 1.7\n",
+      "id = 171, name = BBKF(DD2-SF) quark-hadron model  RDF 1.8\n",
+      "id = 172, name = BBKF(DD2-SF) quark-hadron model  RDF 1.9\n",
+      "id = 173, name = SRO(NRAPR) NSE version\n",
+      "id = 174, name = SRO(SkAPR) SNA version\n",
+      "id = 175, name = SRO(KDE0v1) SNA version\n",
+      "id = 176, name = SRO(LS220*) SNA version\n",
+      "id = 177, name = SRO(LNS) SNA version\n",
+      "id = 178, name = SRO(LS220) SNA version\n",
+      "id = 180, name = DS(CMF)-1\n",
+      "id = 181, name = DS(CMF)-2\n",
+      "id = 182, name = DS(CMF)-3\n",
+      "id = 183, name = DS(CMF)-4\n",
+      "id = 184, name = DS(CMF)-5\n",
+      "id = 185, name = DS(CMF)-6\n",
+      "id = 186, name = DS(CMF)-7\n",
+      "id = 187, name = DS(CMF)-8\n",
+      "id = 188, name = DS(CMF)-1 with crust\n",
+      "id = 189, name = DS(CMF)-2 with crust\n",
+      "id = 190, name = DS(CMF)-3 with crust\n",
+      "id = 191, name = DS(CMF)-4 with crust\n",
+      "id = 192, name = DS(CMF)-5 with crust\n",
+      "id = 193, name = DS(CMF)-6 with crust\n",
+      "id = 194, name = DS(CMF)-7 with crust\n",
+      "id = 195, name = DS(CMF)-8 with crust\n",
+      "id = 196, name = OOS(DD2-FRG) with vector interactions(2 flavors)\n",
+      "id = 197, name = OOS(DD2-FRG) with vector interactions(2+1 flavors)\n",
+      "id = 198, name = JJ(VQCD(APR)), intermediate\n",
+      "id = 199, name = JJ(VQCD(APR)), stiff\n",
+      "id = 200, name = JJ(VQCD(APR)), soft\n",
+      "id = 201, name = MBB(HSDD2K) with electrons\n",
+      "id = 202, name = MBB(BHBLphiK) with electrons\n",
+      "id = 203, name = GPPVA(FSU2R) NS crust\n",
+      "id = 204, name = GPPVA(NL3wrL55) NS crust\n",
+      "id = 205, name = SDGTT(QMC-A), no leptons\n",
+      "id = 206, name = SDGTT(QMC-A) with leptons\n",
+      "id = 207, name = GPPVA(TM1e) NS crust\n",
+      "id = 208, name = GPPVA(FSU2H) NS crust\n",
+      "id = 209, name = GPPVA(FSU2) NS crust\n",
+      "id = 210, name = GPPVA(DD2) NS crust\n",
+      "id = 211, name = GPPVA(DDME2) NS crust\n",
+      "id = 212, name = GPPVA(TW) NS crust\n",
+      "id = 213, name = GPPVA(FSU2H) NS unified inner crust-core\n",
+      "id = 214, name = GPPVA(FSU2R) NS unified inner crust-core\n",
+      "id = 215, name = GPPVA(FSU2) NS unified inner crust-core\n",
+      "id = 216, name = GMSR(SLy5)\n",
+      "id = 217, name = GPPVA(DD2) NS unified inner crust-core\n",
+      "id = 218, name = GPPVA(DDME2) NS unified inner crust-core\n",
+      "id = 219, name = GPPVA(TW) NS unified inner crust-core\n",
+      "id = 220, name = GPPVA(NL3wrL55) NS unified inner crust-core\n",
+      "id = 221, name = GPPVA(TM1e) NS unified inner crust-core\n",
+      "id = 222, name = GMSR(LNS5)\n",
+      "id = 223, name = GMSR(BSK16)\n",
+      "id = 224, name = GMSR(BSK14)\n",
+      "id = 225, name = GMSR(RATP)\n",
+      "id = 227, name = GMSR(F0)\n",
+      "id = 228, name = GMSR(H1)\n",
+      "id = 229, name = GMSR(H2)\n",
+      "id = 230, name = GMSR(H3)\n",
+      "id = 231, name = GMSR(H4)\n",
+      "id = 232, name = GMSR(H5)\n",
+      "id = 233, name = GMSR(H7)\n",
+      "id = 234, name = GMSR(DHSL59)\n",
+      "id = 235, name = GMSR(DHSL69)\n",
+      "id = 236, name = KBH(QHC21_AT)\n",
+      "id = 237, name = KBH(QHC21_BT)\n",
+      "id = 238, name = KBH(QHC21_CT)\n",
+      "id = 239, name = KBH(QHC21_DT)\n",
+      "id = 240, name = KBH(QHC21_A)\n",
+      "id = 241, name = KBH(QHC21_B)\n",
+      "id = 242, name = KBH(QHC21_C)\n",
+      "id = 243, name = KBH(QHC21_D)\n",
+      "id = 244, name = R(DD2YDelta) 1.1-1.1 (no electrons)\n",
+      "id = 245, name = R(DD2YDelta) 1.1-1.1 (with electrons)\n",
+      "id = 246, name = R(DD2YDelta) 1.2-1.1 (with electrons)\n",
+      "id = 247, name = R(DD2YDelta) 1.2-1.1 (no electrons)\n",
+      "id = 248, name = R(DD2YDelta) 1.2-1.3 (with electrons)\n",
+      "id = 249, name = R(DD2YDelta) 1.2-1.3 (no electrons)\n",
+      "id = 250, name = R(DD2YDelta) 1.1-1.1 (cold NS)\n",
+      "id = 251, name = R(DD2YDelta) 1.2-1.3 (cold NS)\n",
+      "id = 252, name = R(DD2YDelta) 1.2-1.1 (cold NS)\n",
+      "id = 253, name = PCP(BSK24)\n",
+      "id = 254, name = VGBCMR(D1MStar)\n",
+      "id = 255, name = VGBCMR(D1M)\n",
+      "id = 256, name = PCP(BSK22)\n",
+      "id = 257, name = PCP(BSK25)\n",
+      "id = 258, name = PCP(BSK26)\n",
+      "id = 259, name = DS(CMF)-1 Hybrid\n",
+      "id = 260, name = DS(CMF)-2 Hybrid\n",
+      "id = 261, name = DS(CMF)-3 Hybrid\n",
+      "id = 262, name = DS(CMF)-4 Hybrid\n",
+      "id = 263, name = DS(CMF)-5 Hybrid\n",
+      "id = 264, name = DS(CMF)-6 Hybrid\n",
+      "id = 265, name = DS(CMF)-7 Hybrid\n",
+      "id = 266, name = DS(CMF)-8 Hybrid\n",
+      "id = 267, name = DS(CMF)-1 Hybrid with crust\n",
+      "id = 268, name = DS(CMF)-2 Hybrid with crust\n",
+      "id = 269, name = DS (CMF)-3 Hybrid with crust\n",
+      "id = 270, name = DS (CMF)-4 Hybrid with crust\n",
+      "id = 271, name = DS (CMF)-5 Hybrid with crust\n",
+      "id = 272, name = DS (CMF)-6 Hybrid with crust\n",
+      "id = 273, name = DS (CMF)-7 Hybrid with crust\n",
+      "id = 274, name = DS(CMF)-8 Hybrid with crust\n",
+      "id = 275, name = ABHT(QMC-RMF1)\n",
+      "id = 276, name = ABHT(QMC-RMF2)\n",
+      "id = 277, name = ABHT(QMC-RMF3)\n",
+      "id = 278, name = ABHT(QMC-RMF4)\n",
+      "id = 279, name = XMLSLZ(DDME2)\n",
+      "id = 280, name = XMLSLZ(DD-LZ1)\n",
+      "id = 281, name = XMLSLZ(DDME-X)\n",
+      "id = 282, name = XMLSLZ(GM1)\n",
+      "id = 283, name = XMLSLZ(MTVTC)\n",
+      "id = 284, name = XMLSLZ(NL3)\n",
+      "id = 285, name = XMLSLZ(PK1)\n",
+      "id = 286, name = XMLSLZ(PKDD)\n",
+      "id = 287, name = XMLSLZ(TM1)\n",
+      "id = 288, name = XMLSLZ(TW99)\n",
+      "id = 289, name = DEJ(DD2-VQCD) intermediate\n",
+      "id = 290, name = DEJ(DD2-VQCD) soft\n",
+      "id = 291, name = DEJ(DD2-VQCD) stiff\n",
+      "id = 292, name = PCGS(PCSB0)\n",
+      "id = 293, name = LPB(chiral) with electrons\n",
+      "id = 294, name = LPB(chiral)\n",
+      "id = 295, name = PCGS(PCSB1)\n",
+      "id = 296, name = PCGS(PCSB2)\n",
+      "id = 297, name = ABHT(QMC-RMF1)\n",
+      "id = 298, name = ABHT(QMC-RMF2)\n",
+      "id = 299, name = ABHT(QMC-RMF3)\n",
+      "id = 300, name = ABHT(QMC-RMF4)\n",
+      "id = 301, name = PT(GRDF2-DD2) new version\n",
+      "id = 302, name = PT(GRDF2-DD2) cold NS\n",
+      "id = 303, name = HJJSTV(VQCD)-intermediate\n",
+      "id = 304, name = HJJSTV(VQCD) soft\n",
+      "id = 305, name = HJJSTV(VQCD) stiff\n",
+      "id = 306, name = BL(chiral) with unified crust\n",
+      "id = 307, name = CMGO(GDFM-I)\n",
+      "id = 308, name = CMGO (GDFM-II)\n",
+      "id = 309, name = KRT(FSU2H*)\n",
+      "id = 310, name = SPG(M1) unified NS EoS\n",
+      "id = 311, name = SPG(M1) Crust NS EoS\n",
+      "id = 312, name = SPG(M2) unified NS EoS\n",
+      "id = 313, name = SPG(M3) unified NS EoS\n",
+      "id = 314, name = SPG(M4) unified NS EoS\n",
+      "id = 315, name = SPG(M5) unified NS EoS\n",
+      "id = 316, name = SPG(M2) Crust NS EoS\n",
+      "id = 317, name = SPG(M3) Crust NS EoS\n",
+      "id = 318, name = SPG(M4) Crust NS EoS\n",
+      "id = 319, name = SPG(M5) Crust NS EoS\n",
+      "id = 320, name = TSO(DDLS(30)-Y)\n",
+      "id = 321, name = TSO(DDLS(70)-Y)\n",
+      "id = 322, name = TSO(DDLS(70)-N)\n",
+      "id = 323, name = TSO(DDLS(50)-Y)\n",
+      "id = 324, name = TSO(DDLS(50)-N)\n",
+      "id = 325, name = TSO(DDLS(30)-N)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now, we can print the list of available EOS names.\n",
+    "# Each EOS name is related to a specific identifier,\n",
+    "# which we can use later to actually download the EOS data file.\n",
+    "dc.print_eos_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloads\\compose\\316\\eos.t already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.t\n",
+      "downloads\\compose\\316\\eos.nb already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.nb\n",
+      "downloads\\compose\\316\\eos.thermo already exists, skip download https://compose.obspm.fr/download/1D/Crust/M2/eos.thermo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now we pick the EOS with identifier 316 and download it.\n",
+    "id = 316\n",
+    "eosdir = dc.download_id(id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t Nucleon masses used within the CompOSE tables :\t m_n = 938.90000 MeV and m_p = 938.90000 MeV\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We use Compose_eos function to extract the EOS data from the downloaded file\n",
+    "from EOSgenerators import Compose_eos\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "eosdir = dc.eos_download_dir(id).as_posix()\n",
+    "eps, p, name = Compose_eos.read_compose(eosdir=eosdir, eosname=dc.eos_name(id))\n",
+    "\n",
+    "# Finally, we can plot the EOS data using matplotlib,\n",
+    "# to ensure that all the previous steps were successful.\n",
+    "plt.figure(dpi=100)\n",
+    "plt.plot(eps, p)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"Energy density [$g/cm^3$]\")\n",
+    "plt.ylabel(\"Pressure [$dyn/cm^2$]\")\n",
+    "plt.title(name)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/EOS.rst.txt b/_sources/EOS.rst.txt
new file mode 100644
index 0000000..069099d
--- /dev/null
+++ b/_sources/EOS.rst.txt
@@ -0,0 +1,9 @@
+.. _EOS:
+
+EOS solver 
+=====================
+
+Functions to compute multiple Equation of state from given parameters, like polytrope, speed of sound, RMF etc.
+
+.. automodule:: EOSgenerators.EOS
+   :members:
diff --git a/_sources/EoS_import.rst.txt b/_sources/EoS_import.rst.txt
new file mode 100644
index 0000000..9efc23a
--- /dev/null
+++ b/_sources/EoS_import.rst.txt
@@ -0,0 +1,9 @@
+.. _EoS_importing:
+
+Import Equation of State
+=====================
+
+Function to import equation of state given by user.
+
+.. automodule:: TOVsolver.EoS_import
+   :members:
diff --git a/_sources/Likelihood.rst.txt b/_sources/Likelihood.rst.txt
new file mode 100644
index 0000000..67ba7c4
--- /dev/null
+++ b/_sources/Likelihood.rst.txt
@@ -0,0 +1,9 @@
+.. _Likelihood_function:
+
+Likelihood function definition module
+=====================================
+
+Functions to define Likelihood functions from Astro observation and nuclear experiments
+
+.. automodule:: InferenceWorkflow.Likelihood
+   :members:
diff --git a/_sources/RMF_EOS.rst.txt b/_sources/RMF_EOS.rst.txt
new file mode 100644
index 0000000..0bb1340
--- /dev/null
+++ b/_sources/RMF_EOS.rst.txt
@@ -0,0 +1,9 @@
+.. _RMF_EOS:
+
+RMF EOS solver 
+=====================
+
+Functions to compute RMF Equation of state from given parameters.
+
+.. automodule:: EOSgenerators.RMF_EOS
+   :members:
diff --git a/_sources/TOV_solver.rst.txt b/_sources/TOV_solver.rst.txt
new file mode 100644
index 0000000..8b8b2a7
--- /dev/null
+++ b/_sources/TOV_solver.rst.txt
@@ -0,0 +1,9 @@
+.. _TOV_solving:
+
+Solve TOV Equation
+=====================
+
+Function to solve the TOV equation from EOS equation of state given by user.
+
+.. automodule:: TOVsolver.solver_code
+   :members:
diff --git a/_sources/UnitConventionForDeveloper.ipynb.txt b/_sources/UnitConventionForDeveloper.ipynb.txt
new file mode 100644
index 0000000..9f238b7
--- /dev/null
+++ b/_sources/UnitConventionForDeveloper.ipynb.txt
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unit Convention for Developer\n",
+    "Tianzhe Zhou 2024/11/1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To set up a solid foundation for the development of the project, \n",
+    "we need to establish a unit convention for the project. \n",
+    "The unit convention is a set of rules that define the units of the physical quantities used in the project. \n",
+    "The unit convention is important because it ensures that all developers use the same units for the same physical quantities, which helps to avoid errors and inconsistencies throughout the whole project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fm = 1\n",
+    "hbarc = 0.197327053\n",
+    "c = 1  # speed of light\n",
+    "hbar = 1  # reduced Planck constant\n",
+    "\n",
+    "GeV = 1 / hbarc  # Giga-electronvolt\n",
+    "MeV = 1e-3 * GeV  # Mega-electronvolt\n",
+    "\n",
+    "g = 5.625e26 * MeV  # gram\n",
+    "kg = 1e3 * g  # kilogram\n",
+    "cm = 1e13 * fm  # centimeter\n",
+    "m = 100 * cm  # meter\n",
+    "km = 1e5 * cm  # kilometer\n",
+    "s = 3e10 * cm  # second\n",
+    "\n",
+    "dyn = g * cm / s**2  # dyne\n",
+    "dyn_cm_2 = dyn / cm**2  # dyne / cm^2\n",
+    "g_cm_3 = g / cm**3  # gram / cm^3\n",
+    "erg = dyn * cm  # ἐργον energy unit\n",
+    "\n",
+    "m_n = 939.565 * MeV  # mass of neutron\n",
+    "n0 = 0.16 / fm**3  # saturation density\n",
+    "\n",
+    "e0 = m_n * n0  # saturation energy density\n",
+    "G = 6.6743e-8 * dyn * cm**2 / g**2  # gravitational constant\n",
+    "Msun = 1.989e33 * g  # mass of sun\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first 4 lines come from a widely used unit convention in High Energy Physics (HEP),\n",
+    "which is called [Natural Unit System](https://en.wikipedia.org/wiki/Natural_units).\n",
+    "You should be awared that we will NEVER using the Natural Unit System explicity,\n",
+    "as long as you obey the following rules.\n",
+    "1. Whenever you define a [literal](https://www.ituonline.com/tech-definitions/what-is-literal-programming/) \n",
+    "physical quantity, you should always *multiply* the unit of that quantity.\n",
+    "```python\n",
+    "# example of rule 1:\n",
+    "m_n = 939.565 * MeV  # GOOD\n",
+    "m_n = 939.565        # BAD\n",
+    "m_n = 1.67e-27 * kg  # GOOD\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Whenever you want a physical quantity in cgs unit(or others unit system), \n",
+    "you should always *divide* the corresponding unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.670337777777778e-24\n"
+     ]
+    }
+   ],
+   "source": [
+    "# example of rule 2:\n",
+    "m_n = 939.565 * MeV  # mass of neutron\n",
+    "# if I want to know m_n in terms of gram, I need to\n",
+    "print(m_n / g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. When you need to plot, *devide* the unit of your axis. See the example below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. When you use physic formula, you leave the whole form as it was, no additional constants.\n",
+    "```python\n",
+    "# example of rule 4:\n",
+    "E = m * c**2     # GOOD\n",
+    "F = G*M*m / r**2 # GOOD\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we present a full procedure of how to use the unit convention in calculating MR relation.\n",
+    "See https://arxiv.org/pdf/astro-ph/0506417v1 Page 15 for more details."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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MOAceIWg266FqWJP2OvM//W5TO2bgEkGSPIGBysPQyKL2+gwn3pImnOlgLyCNGu1RL5DySTiznBC4prYJc+Mtk1J9rqtNWysK3dPMhs/0wAorLbuXI5mieVJzOnxZCzuSX/Kfq6oxZcrzmtvWsGi9Odrum5Vhoek3G9oWdZ37IcMu/QyPgBchAAGdEUD8qjNNhYJCgLMC0uQXE5wLEAS/wbirasED/Xpda3nfIdMJl1YF8ZXptXkE333qbbXt0zaRJe1PuZMqv+j4fQEflR9BQV+/xWbcaUfHHWttzSdNmh79kfGGitPl84Ii+bVK972aLn/HHRhcmQlvCZ5D38O/HfSWz5VJOz3960Q7e4HAptMl1SwOacLF7iWEBEEYl+h16k2gatt++RGn9hso7YC/+Z9OiI3T/oPoNzvjZQhAQJcEEL/qUmuhrBDgpkDS5431zCiCslZPfpXJfq7tUJTpfRVUWnZfcUNUyf2FjfkEz3XURe0Dd2LXtC/E7GjUaM+XLAc0dMw+T3MeVcD7fKanLJDbSkSJ8dl/JCZlN1hWJL8a9lz/WeNBYp8sqGzIRLA8C8+DOtKpHHl9YjGBhuRXOmZ/0wLMkLSiTZZ/Q8DJzb9qlAoC3BZA/Mrt9kHpIKALAl/39TQnCZ550yNqyaaSyGPe9gKCIIs3WaochX7d1c2QJCt126c805ZqXRO2+ZSQ36S+3cWsxaDMYRI+/OdsTJqWHfYk02OD6Nhz3UoYkUzGQrYfpLBgswNZDy6lSU9G1jCVz5HluP6llpCe/vyvl708n0LYYs7jDKO+2Ltbx3do2bxj/zkX3jCduVJR+I2dcwd069C6ZYsOfabsvaN8L6tU9W+Ptg3p1KK5d9//nXgnksmSvz/Z/u+UAT6d2rbrPnXddfacEvKdEsKf7F87f+ygXh3beXfoPmrd2beqrSm+O7+RpuRXWUp2MkGQFTrtUt1F9S2ANQhAAAJaBBC/aoHB0xCAQGYFREeHMZe2bev8ozbKiv6ws5s5RZCCUjPORiofL/L6pOICoqjHiu9aAzHxzTn1+ATBt+p0Oevxa/TdGaWFZGG3f0O0Hl+5OPJlOtrv/L5tWzZn/7Fl655jD35kubNYlhC4xkWe/GpeYeRT7QH31yMDmPFsBGFSeuA97bN0BZ8a1bT3ruC4dzMaWJqXG3gl4MK4Nk1Hrjz3MSpZKgnf0a+ywNThv4cqSbTJEWd6uPU8+yn+8RIPgbDs2NVL2zZqv/JUQBwteXNgcCkTiz6bUmb5TUWTir4enduxlIV9i9Eb7gQyQ81+BhzoWrX0+CPh6Z3HdNiS5pqSX2WytNnWyErd9iJ+VXsj4gkIQOD3Aohff2+ELSAAgQwE0qbHomr0OcLqOaTjH452tCBJQZ3Bx6NU40hR8CZXUyrj/tHoe7PKCinSoO62zN1MSbmQwbt9jEkNRVLehjvLiuTXar0OqcSVqkWUJtwbXMWECWApy8E7gtMjReXNJEHzver99zRJJov4X0tbkjIvXsH9fzfSs4DD9vYwIgVNVUbOSfxWtGo1+5FEJgvc0l5IEgIb9+0v5Fm44tdT6lgQpEmPdYGK00lFH9Z2rywUFhu4yV8p+hSdGu3QeNINRUIzHXOirabkV3lhRRenOPFIwr7BQp2aTkEZGssQgEB+CiB+zU99nBsCeiCQ9GmDqymTl2ntOO2tInhhKhZ/bb67MUmVdJvjH8OehFWa9GBIFRPKxOOw2nxb6Sb01/86lKRIw/ZLXiiCp/RXM1oSMePDSINuq1V6DTPaIz9fi9/TvxwjSBr4rNWc/JpWOvEteZ80QZB2dWZrjOqTPm/0cJsTKJZJE2/1K2dEUAV7rXutpJdy5wjVmR/oLwtbuG1g8hZEJ0dV45HC1vP90n5uiN9eWL/S93pYkqIFRXeXNDMlqapddionFfx6v69d+fJzLkWlFVUWeW1iUT5ZoukqjV3sSZ93eRUSUCauW1+zfvUoDoAFCEAAAloFEL9qpcELEIBAZgS+7mWSXyljhyaNawzd8SEtVIq9u65rIaGwote8xz/TYiGVwyUwQRtlN+lkjMrTqiuikNO9q1sKrRuuvB6admSZTJb09sqavq0GnArRcqmeDlviZUsZOKx9phgxpnpcDqxJRbHfwkI+B766fmBGbfncWARp1v6fS28+hYSF/0xQMxPH/Xj/9OQEj6LyWQgIgirgMWLrnVdBrNH6dFyQ38vvtEwW92JJdUOyQOUx/umhp0wmi93aoyRFWYw88FPJIP6d38soiUyaHDC5dgHSoGYGbvSv811LGhKURee5R8+dO3vyyO61S2YP796kevXGU3co3wNWfGduQ43Jr2nnpT8cH13BjF+q8cyHSnfclSZ+PrNqWNvBvqz7HaTthf8hAAEIMAKIX/E+gAAE/kRAdHhoJSb5tfac15/PDXNzajVwxqIFU3u6l7MuXnfEiqsRWiJMmUwWfLCvBY/fePItlU5btbIkf7+/dGBDe/PCbl2GzVywYMown2a1K1Z367fp6he1GC91Z0nUsTZ2AsvqE19lfGi1c+XhE6IDgyrwKL7Q0MTcwtLKxs7e3s7GytLC3NTYUMDnm/VYq/glwBRKmnizX3kTvsDA2NTcwtLa1t7e1trS3NRIyOeVbLYqTAMEM3uuAcme4YGOu9S9pCGvQLMjmkJ/UZhvowI807LDMrhL7c8Lo+35hNCm/pj5ixYtXrxs5TrffSduPw9RSiSQK9Kh/zazZWZ+vaI686sKMf317oae9YpbFK3tM3zaggUzBndv6VixUvMBy+4Fo1NWRQorEIAASwDxKwsEqxCAQBYEpMl+Y2qZK2Z+lUp++F0+vH3bnjPXX/z4XQQiiTrZoYjQymHqm0xEmUk/3149vm/rpq37T1x5/vmXUl+shtKGnRxqyxc0m/VAQ1ynYXO9fCp+V9+yFFVwzKH0C/oymezb2ZH2fLJU87Uar+l/OzHYikdV6b5P0wS0qUohO7sK5fNehWeIm/ITQn3mV03W4m+vbx3d67t5657TV5+ExGZ4XE374zkIQOAvFED8+hc2OqoMgRwTSApaV9eEIigbTTO//vYsSWfGOfCFVf73MGev8kdv61mab9Zo1198I6bU9GLTJgeDlTvAY3b0KUtRBQbtYObPSgzaPXzqcaVGEp2f6MgjDXusyygH98fxQRY8smz7HewOV6UDyWSyyKsTiqQlv0oT788atuovbg1VGqxBAAI5IYD4NScUcQwI/K0CX/b0MGNmfm12NINhWNpxEgK3NLIWugw+lUGHn/a9Nb+S9NnX3drAdfhZXblJleZq/NmzCW9W1DIibZxnKkeNkqjj3vYCg8LdrkRKZTLxnXlNu/4vIP084vcz6luSBg7rnmf0c0IUutPDmle0yTL1/teYNwd6ew2/wdxqS+K3zJNPCJrOZLrAo25Na9xrfw42cXqZsQQBCPytAohf/9aWR70hkAMCSQcHV5TP/Drno3I3XxaOLLq90N3U0mO/2syxWTiGyqZJ5yc5GRdur3Vol8rG+rpCf9jS0YhJfr2knJrBpLea84o2+d93iSz29eaWDUbci1LMKiAThe90t+SZlB36KCH9SU1Aceem1TMydlj5gJn2NeUhlfx8sH96g4qOUw68l1/+p1+v9zYghT3XB9OyyN0D3WacUUljSNsP/0MAAhDIpgDi12zCYTcIQECa9GRUTTOC4NUZceF3ya5atei4u0OrF3DseygyJ/Ie41+vr29t2WXFy4wTZLWWRk9eEJ0ZW5MvKDHzgurcDnTksSkNbIo2Gj6+n6db111+Kq9G35xcQsh3GngigwloU3ik4q8HZrQuW6JW1xHT/pk7Y/SAjm4uddoNWXY9ML2PlY59MqdFmaIOXUb2b9ZiyH6N6bZ6go1qQAAC+SGA+DU/1HFOCOi4QFK4/8kDO1dObm7HJwiCV95zwsYdew+feaJ5pqzfVfb7nfkO1mWnn/n+uw1/97r4w78tilVpt+GLcq/j73bSy9fFvz49DwjXxCD+/v7R7XsBESqTaskN6Nh3/i+/qz+vBUgc+9XvzvVrN+49exuu5ddL/Ofn9x6+DNPyqpbj4mkIQAACmRBA/JoJJGwCAQioCNBfjk92q9/Q3aNZy9be3q1bNPN0d2tYr3HbBc8zHf2oHE8mebmrZ4mS3ofe/UmoE3t5nkdxh1H3lGfVVz0N1iAAAQhAQD8EEL/qRzuiFhDQdYHY6/9rW8F55K1v2UukFT/f0ati9d6nAv8kAtZ1Q5QfAhCAwN8igPj1b2lp1BMCnBcQPTv8z4K977ORuipNerx84rIH2ZoDgfMsKCAEIAABCLAFEL+yRbAOAQhAAAIQgAAEIMBlAcSvXG4dlA0CEIAABCAAAQhAgC2A+JUtgnUIQAACEIAABCAAAS4LIH7lcuugbBCAAAQgAAEIQAACbAHEr2wRrEMAAhCAAAQgAAEIcFkA8SuXWwdlgwAEIAABCEAAAhBgCyB+ZYtgHQIQgAAEIAABCECAywKIX7ncOigbBCAAAQhAAAIQgABbAPErWwTrEIAABCAAAQhAAAJcFkD8yuXWQdkgAAEIQAACEIAABNgCiF/ZIliHAAQgAAEIQAACEOCyAOJXLrcOygYBCEAAAhCAAAQgwBZA/MoWwToEIAABCEAAAhCAAJcFEL9yuXVQNghAAAIQgAAEIAABtgDiV7YI1iEAAQhAAAIQgAAEuCyA+JXLrYOyQQACEIAABCAAAQiwBRC/skWwDgEIQAACEIAABCDAZQHEr1xuHZQNAhCAAAQgAAEIQIAtgPiVLYJ1CEAAAhCAAAQgAAEuCyB+5XLroGwQgAAEIAABCEAAAmwBxK9sEaxDAAIQgAAEIAABCHBZAPErl1sHZYMABCAAAQhAAAIQYAsgfmWLYB0CEIAABCAAAQhAgMsCiF+53DooGwQgAAEIQAACEIAAWwDxK1sE6xCAAAQgAAEIQAACXBZA/Mrl1kHZIAABCEAAAhCAAATYAohf2SJYhwAEIAABCEAAAhDgsgDiVy63DsoGAQhAAAIQgAAEIMAWQPzKFsE6BCAAAQhAAAIQgACXBRC/crl1UDYIQAACEIAABCAAAbYA4le2CNYhAAEIQAACEIAABLgsgPiVy62DskEAAhCAAAQgAAEIsAUQv7JFsA4BCEAAAhCAAAQgwGUBxK9cbh2UDQIQgAAEIAABCECALYD4lS2CdQhAAAIQgAAEIAABLgsgfuVy66BsEIAABCAAAQhAAAJsAcSvbBGsQwACEIAABCAAAQhwWQDxK5dbB2WDAAQgAAEIQAACEGALIH5li2AdAhCAAAQgAAEIQIDLAohfudw6KBsEIAABCEAAAhCAAFsA8StbBOsQgAAEIAABCEAAAlwWQPzK5dZB2SAAAQhAAAIQgAAE2AKIX9kiWIcABCAAAQhAAAIQ4LIA4lcutw7KBgEIQAACEIAABCDAFkD8yhbBOgQgAAEIQAACEIAAlwUQv3K5dVA2CEAAAhCAAAQgAAG2AOJXtgjWIQABCEAAAhCAAAS4LID4lcutg7JBAAIQgAAEIAABCLAFEL+yRbAOAQhAAAIQgAAEIMBlAcSvXG4dlA0CEIAABCAAAQhAgC2A+JUtgnUIQAACEIAABCAAAS4LIH7lcuugbBCAAAQgAAEIQAACbAHEr2wRrEMAAhCAAAQgAAEIcFkA8SuXWwdlgwAEIAABCEAAAhBgCyB+ZYtgHQIQgAAEIAABCECAywKIX7ncOigbBCAAAQhAAAIQgABbAPErWwTrEIAABCAAAQhAAAJcFkD8yuXWQdkgAAEIQAACEIAABNgCiF/ZIliHAAQgAAEIQAACEOCyAOJXLrcOygYBCEAAAhCAAAQgwBZA/MoWwToEIAABCEAAAhCAAJcFEL9yuXVQNghAAAIQgAAEIAABtgDiV7YI1iEAAQhAAAIQgAAEuCyA+JXLrYOyQQACEIAABCAAAQiwBRC/skWwDgEIQAACEIAABIpZuSwAACAASURBVCDAZQHEr1xuHZQNAhCAAAQgAAEIQIAtgPiVLYJ1CEAAAhCAAAQgAAEuCyB+5XLroGwQgAAEIAABCEAAAmwBxK9sEaxDAAIQgAAEIAABCHBZAPErl1sHZYMABCAAAQhAAAIQYAsgfmWLYB0CEIAABCAAAQhAgMsCiF+53DooGwQgAAEIQAACEIAAWwDxK1sE6xCAAAQgAAEIQAACXBZA/Mrl1kHZIAABCEAAAhCAAATYAohf2SJYhwAEIAABCEAAAhDgsgDiVy63DsoGAQhAAAIQgAAEIMAWQPzKFsE6BCAAAQhAAAIQgACXBRC/crl1UDYIQAACEIAABCAAAbYA4le2CNYhAAEIQAACEIAABLgsgPiVy62DskEAAhCAAAQgAAEIsAUQv7JFsA4BCEAAAhCAAAQgwGUBxK9cbh2UDQIQgAAEIAABCECALYD4lS2CdQhAAAIQgAAEIAABLgsgfuVy66BsEIAABCAAAQhAAAJsAcSvbBGsQwACEIAABCAAAQhwWQDxK5dbB2WDAAQgAAEIQAACEGALIH5li2AdAhCAAAQgAAEIQIDLAohfudw6KBsEIAABCEAAAhCAAFsA8StbBOsQgAAEIAABCEAAAlwWQPzK5dZB2SAAAQhAAAIQgAAE2AKIX9kiWIcABCAAAQhAAAIQ4LIA4lcutw7KBgEIQAACEIAABCDAFkD8yhbBOgQgAAEIQAACEIAAlwUQv3K5dVA2CEAAAhCAAAQgAAG2AOJXtgjWIQABCEAAAhCAAAS4LID4lcutg7JBAAIQyKxAUtiz+wE/6cxuju0gAAEI6LAA4lcdbjwUHQIQgECaQMzufhWqdNuXkLaO/yEAAQjosQDiVz1uXFQNAhDQFQFJeNCnWLW+U6n455eQX5mpgyhsr5dd4fHHIjRtLIoMfvvk4dP3IVHJml7GcxCAAAR0TgDxq841GQoMAQjonYDk0z+NrQoUcWjRZeDEGf/Mn//PjEkje7R1K29fpPf6V2phrXr1Jf6rWliVH/44Qar8Gh33ZvvUTnWdG7bvNXTs2OFdmlS0tqvSefy2gEiVzZR3wTIEIAABnRBA/KoTzYRCQgACei0gCZrdqCCh8iANbRzGbn4cn4l6S5NfTHAu6DX7kUR5Y/GH5W2rtp5+ISK90zXh+kJPM4oq4jrd/xdCWGUsLEMAAjomgPhVxxoMxYUABPRQQBI0x71yu+Fj+nZr37p1u259Ry3cePp9pEo4mkGtI69PKmXRcOeH9EBVJpPFPp5XwZASWHvue5/+vCTyQHMrHkGaD9oRlsEB8RIEIAABjgsgfuV4A6F4+SsgifoS8Oj+43chMZkNJfK3vDi7jgpIguZ4NNj4SpSt4kfv7FO+QrstEarv0Vi/hVUMKUpYdeWjJMVhpQlnOxYWEoSg/ZLXmUhLUOyHBQhAAALcEkD8yq32QGk4I5D04tjcVk4Vqji5eXo0qFLU3NiyTNM+C68HYXg3Z5pInwryB/GrKGxPM9siEzSM3Ep4e/PI8ZuBykFx/KtlNY1IUlBm4c3MJCboEzHqAgEI6JUA4le9ak5UJocERPdXt69Wb8SpgJiUA0ol3y8u7VBISBrau296mPpkDp0Lh4GATKYUvyZGfXn51O9dSEzm+kcl/itbWFUY8UR15JYW07gzE5z4JL9al+1hqp21WrbH0xCAAAQ4KoD4laMNg2Llo0DCh41uFdoe+yxWLUP0oWHVeARp6zQtIAljX1RtuLumIxkgTPxad+qmlQPbNe/Ye9iEccM6NqpU3rnb2stfMo5ipcnPxzsXbM4auaWpOSTxX84s7VzU0MK19/rXGLyliQjPQUC3BKSS2E+vnjx4HBD+Kz3HXbeq8CelRfz6J3rYVy8F6LcbvA1Iyr7W+IeqX/PR92eXE5KkoPySO7j2yv2m16kMEEnQHDfbmp1Xv45K+2lER56eUs/IqOz4Ax8zCGF/XptY0qIRa+SWatuIn/gO82xYp2rJggKeWR2f5fc+IwdGVQhrENA1AWli4N5ZnR3KV6zd2LNJ/RqFzAxty9Yf+u/pkMS0DxBdq1E2yov4NRto2EW/BeiXq1sZkARl6r7vi0oXrCh0S31TkiDNRxyI1m8CpdrF+Z/19T39UjmHUulVzi7mbgYIHf/m2Bbfa29jc67+ojd3bger9utLfh5uaScwKNT+TJjK+1DppNE7+pSr2J49cktpA5XF2E8XpzQvbWBats+y69EZBMUqO2EFAhDgloA06dWiVtW8Ru8JTOthSQq/O8e7DJ/klfVc9DL2bwlhEb9y632J0nBBgI5/s3fJvE1nXieqlibWb0ElQ5IUlJ537S/pf42+vLiFlbBQ7zV+uhW/5nYGiDTp9bK2ZYxsG668/i0X40DJx1kNLQnSoMvK96rvxNQ1UejuprZFJh6P1PiqxifpX9d6VzAmKMuuK/yRAauRCE9CgNsCEv813jU7bAlX/QOWRF3uVd6YIA28Zt3jdvlzrHSIX3OMEgfitACd+DP04yu/hy8+ZepunJrqIro2y5VPkuYVR2ZurIymY+jQc3TUhXlelgLrLkvv6Vq0nhcZIHTs09meRYWWLkuv5EAIGxfy8unbn2qhcNSqNnYkQVXvfVjTG0fit6K5VZbfjaLTY2pQBGFQuNsV3IVLEyuegwCnBejwJV62JGXmOfmS6iez6NJUZx5BFKg8ltPlz7nCIX7NOUsciYsCotMz3EvYWRoKSJK5uRFVs9+x7HUlJn3e26KIgDIsN+1EiFqcwcWa/1mZ4q7929yCb+Ix/nyU6q/8PzusTCaT/PwS8PDO7YcvP0WrXi7/4yMrDpBHGSDJEdeGOVkJreqtuv1Tce5sLCR/P+pd1IAyrLbiQfpErfLjxK/rWJgkqCo9DqofVpr8fJxTweazH2tpn7ibG0a2bzdwx/0o1X0lz1Z48QkCadyqLFiDgI4I0F8WeNqQBFm86cpvKn/89Nv1bQQEwbPqoCM1+dNiIn79U0Hsz20B+lvArbOnj22d3caW+dI27rM5OBvRp1T0dknrEjxhsYGbn2cv/OW2Eqt04he7ehcR8qt32RasLfGStUcmVun4wEOLBtSvWKxczQbN27T2qFvR0sisZI3mE1dfDs/pQDbPMkASPuxqXszAsHCLfQHZHxSVELjaxZikDKssv6d6kNT8AWHbxS/UgVNGbu0K1DzumI473saGeceX77iTddD7C92YFwycNmTzdgnqZcEzEIBA3glEvTq+eN6qK29Y+ffim7Nd+QRhVm543hUlX8+E+DVf+XHyvBKIvDaxmIAgDZw3BmQ5/pSKP/sOcjCxdJh+SOlGnHlV8rw/T9j1WVXNeQWrDr/7M8fGAYhCLwyrX7n50NX3vyqiKTrs8WbvcsYkySteb+Lt0JyLlLWS5UYGCP31/ITyRjw7x/EPs3s5Xpr0eKSTw7zL7DyEpC/b3Kx4Apvmh4LUcVJGbm39qdIBk15zaeLtARWMDQqUH+H7RvUH2891nYqRBGlfZ/Y7zaFv+kGwBAEI6IqANMlvrKM5SfIbjL2sK2X+w3Iifv1DQOzOUQFp8ssNEwcNSH30a+NUiCQIXoHK7fqkPSf/f/ya2xlXQCr+tKV/TbtKXfY8ZV2HzXg/XX1VFHKsU1ljfoHaax5mO1FYre508NpOFdxHn/imNrfLl6ODmH5xgqrQZn0O9vWqlYB5ItcyQBKu/ONmRPJr+uxSvZynsRQan6Q/HB7q0njKXaV5BqTiz5t7VhIISwzf+VY1AGWOIArd5WlbZNKJDEZuSZ6s7+jQbO7DcJXYN+zWrKomlMCyzn93/p45NDSa40kI6JMAHbDTx5pHWlTsf1X1T16fKsmqC+JXFghW9URAKn63e8G0KSmPySPdyxgSBGFVuc341KdS/5u/85FM+0Mq+ripr0O15vMepQcmkvdXdl58k+VOXO0n4dArUvH7pa2KU1SBLv97rqVfLzulTXi/ytmUIgiyYKV+l1T7WSVRB5tb8ZgA1qD6f09Z2Z/ZOZe2fXI1A0Sa6D+5ng1J2fbf8k491tRWJNXnE57sHlWvRt2eExZt3L5r88qZnV2K2VZoufTUR02dpJIn//OyqjjqqdrvAdVjxt73HV2/SvWWfSYsXb9jz65N80e3KWVqWKpO390PI7JbTtUzYA0CEOCAQGzAZvdCQtMS3gdeqY7p4kDZcq8IiF9zzxZH5ooA/etUh8ICgjTsse5z5r+2paLADb3rNBmy56tKdmbkmk51lrHyFLlS0T8sh+TJmjYFKLKw65y3KlX+w8PKIq+ML8J0shIEZTPppErOljTxas9SzE+LXJ1VNw8yQGL8ltYypwTWTXa9zv5vG/Gvj1cOblwyf8HKzQevPgqMUek5TW8FabL/OMeCLeY8ycxvDKn4x7PrJ3ZtWrVg7rxlq3dcfPyVNStc+nGxBAEI6KBAQtAxnyoWhZ2GnAv8u/64Eb/q4LsVRc6iQNSNySWEJGngsPZZZnv4pEnv1/ao03L8wbchYYpHaPCngAfrvMq0OZVziaFZrEoubp7wdlMjGz5lVH353ZzLHJCXVxR+vGNJQ5IgDQt7H1e9K6804VR7OwETv/KLTD8XlxvVy6sMkPgz4x35JFWu1brcToSIuDqhhKXbbo09s7khiGNCAAJcFUj4eLhrFRsXn7UBMTk2XIGrdWWXC/ErWwTreicgubeAGXBtUmbww4RM/YVLk97+17WSQD7hlrzbUOUfiyrjnudo9yQnwOngNR1LUqSg/qgLuXH9KTHM/8yxM/4h7N8P8S+X1jBioAU27c9HZKp1ssSVlxkgovCjbYsZEDz74Xuy0M2fperIN47e3rtshfbbtI3cyvoBsQcEIKCTAnEfDnapVrbrwquRimsxkq/ntp/QycpkvdCIX7Nuhj10S4AOX9aCmQS+Yudd8kvXoi/+Vw/v3rxug++J669jFH/26ZWSPJffP1YlaE1fIUu32ax/P3RDzo4qJiAFNq2OfdVy0TrdJweXJP4rmhuQBEEKG0+6lsGlL7EoMTE+40eiSMzODcnzDBDJk1WtTEnSvMKQ+7n2FhGF7PSwKTo5o5FbOdhAOBQEIKBZQCqJCbi2e/HM8UP69xs6dvq/q/fcDVS5K7NUkvgzNCjA/+GNy2eO7Nux8/SL1NQiOvrN/UsHfTdu2Lr73IMgxYQsKadJjvp47diuDevW++45fv99Rr9S497v7+zkMmW/yjwikoh9rWpP0FxivXsW8aveNSkqpCpA/zrRzp6ZOqvrqvdf7qzp3rhu8+6j5y1btWL+IEdbg0K1+p18n/2ERdVT6eqaNMl/gosFSQo8p93OSwtJ1PW+lU1IgirqOt1PU8BHx7/dPbNzjaLmPC194ek/KwiCpAxq+OxU9EPkSwYIHXerfyUTgjTyXvhUwy+jHHiDSJ4sb2ZVcZTfb0Zu5cCZcAgIQECLgDjwyvL2NawFZuU7jVmw5cCJU0d2Lp/e361Kec8hvh/iUq8jpef9yz+nSjRfFyOlv1xf0dWzcbveo+ctXzl3bLuy5kKbim033YlgTkR/P7ekR93azfqMmrV85b+jOtQ045tUb/3PPU3zCfx6s7uDc4N/jrwIVeS3hYV+/fTu+n+dq7XboqXY+vY04ld9a1HUhyUQeV0+86uw4sApPRp4Tb3yUfFzl/6ws7s5RZbw/F+e9jmyypf/q/TH/X2seISgoNfhL3nX+Zoc8Wh+27I80rC69wKl6R3SOSRR96Y2KcZEriRJ8fh8Pp9HpS4wK3w+RZJUyhLzr8DArGj7uddTWjf/MkAkj5Y1NSAJwyLdLuXGEH86bEnzIt4L/XInOE7HxxIEIKBFIOnx1l4ljKgC5drvfCSPO9O2i/Db4FXSrPbg4yk/xqWShMjvIQF39/hUNyMJokTzta+vzPXuoDylHf12l48VjzQq1vV86PfjU1q0Hbc/KC38ldHft/YoRxFU5Y6+31X/4H8F7GxT1kTLj3p+48k300qk5/8jftXzBv7rqye+M68Rc7chUljafc5z1U6+uBdLqhuSlEmTgyF5F7dxrUWkSc+ZzleC5zLkTG5kvqrUl/55buWYfj27tPasU8pSaFyk4ZwDzzWfVPJ5fddyZkXqT1p39nV4Ai2TSZNfjK/bYHPa6H5pwoVuFVqf1Jwym58ZIMkRR72LCAnSsO0if3ZCg4pFNlck8bEMBx4QgEB+CIRdm1bRhKKElRbdUJlBWRJ1tgszSpXgWbQ5rTLAV3R2vAOPIIq69e3rPfxWhEooSv860daOmRvHw6dby8FHWOkC308Ps+ERlGnjfZ+UvqEkn+T3j1W++KS0TBoN8P2WHzD5cE7Er/mAjlPmnQAdtsTLlmSGB3ntV7t5VuyjuRUMyezdlCvvqpDLZwo9NcyeT1AG1VY9YQ+uyoUziz4+uXL+3Lmzp4/uWDO7S+0iBmalWw5Z+TCUNcmp5PnmTsUr970UnP58/KtlLjXG+KeNnIv1W+BQk5sD6UQXJjvxScKk9IB7v3J+RFouNAoOCQEIZEpAmuQ/3tmCJMiSzVaFqQSiMlH4jsYWzFTWPPPmJ74r/8QUX59Vl08QlKBA73WByi/IZDJp0oNhVU2ZqQXNGqnfCzrWb2FlQ5LgF599KVfmZslUnTm8EeJXDjcOivbHAnTMcW/m162B90J/1U8b5tBfdvuYkATPorXqz+U/PqsOHYAOXeldlCTIoo2X5H0ShVT8aWOPSnySNC7SZM2tH4pPdjruSm8nz23+yh/ZEr/lzWr1OZI2xot+v7lDudYbVfvTueIe92JpDSOKoCwH7wxRVIorhUM5IACB7AqEnxpmy2PGm3Zcrn5XvLhbm0a2btpmwqbHihw1+XlS41fSqP6OQKVuVPlr0uSXE10KEARhV2++ch9rSgET3q50NCIJymLM4Rye0zC7ANzaD/Ert9oDpclZgcirE4ryCdLAYd1z9c7FpIODK1IEYVt7zkf2p0rOloK7R4t/tdzBhCJIkx7rgvIl0hKF7m9hz8z/KrTz2vtG0Ubi+HjVJhF/mO1WuN/WkDRK0cHBlZwHn8rL0WZpp87E/+L3Mxta5devgkyUD5tAAALZEBAdG1GFYiarLjHnivKv64wPlRq/Cmy6XI1nX5CRJr+Sx69khc67036cpx8t4f0qJyOSIM1HHlDJVUjf4u9eQvz6d7e/ntdefPufBnyCMC077LHazK/SpIfDq5kSBL/x5FvpV6n1HIRVPfHN2a58guBZNj+abxnAMVt8SjJfCQRVudN21dyw9NLGPf+3ukWdre/S4lXx++n1rNyn3OZqw0me/9fSgCQoQ8f1aXPmpFcGSxCAgC4K0GGLm1oz9wo0qLkm07fCkclS41fDYn1uq38NpcavVPXeh9M+3dJp0uJX0+H7Eb+msyiWEL8qKLCgdwJ06OKmNiRBVfHZpz5IKCX5lfklfTnzv6T1ikia9Gh4dVOCIEu3XKctcMz9CkseL/FIubksz9zzsOYwWnRlmotl5bHP0pJf6dgT3raGXrMfqeeE5H6BM3WGpC9bG1rwCFLQdMY9zhYyUzXBRhCAQIqA+PWUOhby+LXONpVJVzMGQvyasU/2X0X8mn077MlxAUnU0da2AoI06rXhi9rFcfG1mUxOvUmZIZm8KRfHK5uN4kXfmV5aSBKkUS4nD4gDTi3o6d3SZ/S6Z6zhtfJCf93eWZgyfFbLbwk67nqf8iY1+x1VXF9LeLOilpHQY/pd7oaGkk9zG1uRBFGwxuQArvYSZ+M9g10gkOMCyb++vnh8/+nr4DjVpCFtJ0r4+en5w3uPXnz6lcd///T3pV42TPwqrLLikSLZSVsxFc8jflVQ5PAC4tccBsXhuCMQeXlcYSb5tdb6l+wrM9KkB0OZUZ/8eqMvpbwmTQy8ffcjdwqf+yURX51RhxkVa6h0XT4Xzpr0xbexFTMslyD5DcZdUY/lvvh2yjB+pYP29bLkm/bfFqYoXeS1iUX5PCfO5r8yBU3NXSENaqz2y/xXnaKKWICA/gv8en9yUgeX8hUd3T09XKsXMzG0qNyw1+pzgezP61QJ0btLK3q4Va9Rz7vfsJGDe3nXd/Gcc+SDWt9E7rmJzoxjZsIiKNuJJ2IyfRrEr5mmyuKGiF+zCIbNdUZAfGtOPT5BmJUb/kTtZkU/L48rwidIQYWl91KHigYfHliv/1GdqdwfFzRt3ABRsGbudhCm5mnIA9hy7X3VEjkkT5d5puQPUKZue4LUemAkQQs87SgjV1+l6c8iTg+z5pHFmq76kccdMFlhj741tSTTvS1os+B5Hn7FZqWI2BYC+ScQ83y9R5W6M/e/TPtMkHy+taJpCSOSZ9tF8bmsKB797fg0d2trp9lH0rLg6bAjU92t7NqcClP70FDsldMLP+UDgglC0GTaHW2fPRGPD+y/rTyBFuLXnG6GtOMhfk2TwP96JkB/XehprSX5VXRiZDWKIKwcprxJ7Q+M2Nit1pSTUXpmkEF1koLW1TGhCIKqNeC4lt6ODPbOwkvi7/u8rJk7SBgUdFx8UfljPeUgcb49U8ZvkSWbrlRPf415MKe8AVWwxuRXSj23EaeGWfEIo+J9bquN581CyXJ5UzruQrcSQoIgSzRdxbp9Ti6fGYeHAOcFJEGLmlcavP0j66dd6PkxxYUkZVxz1SPlSagSri/wMBUWH3vws2L7pM/r65qQyn0QeVFn8Yd5nvYkQZhXHPFEbTAWUwA6bEXbiqMOfFcqDOJXJYwcXUT8mqOcOBhnBCSRh1va8gnSsOf69I+8tNJFrmhtRxD8RhNvpARFv/wWudab/jpteFDaZvr8/+fdPqbMrVmNem8KVnwl5E6Fo/YMrFyi0Wz/nxq6SZIjjrQpzMyfRRlVXXpLfY7DSN9eZSmCX2+MSuJB9O1ppYQkKSgz7zqXx95FrGjNfNXxrdqdU7kfT+4w46gQ0B0BUejWBuY8foHq/15V6TWQJj0awQwq5btNuqGoTYzf4uomvBKeK4KVP0IkXw7MGdBvws7AvP3cjnmxrqEtn6DM2y96rP7LP+TSxJrOk5+phLaii1OceAQhtPe5ofZ7W5rsN7aWOUGQlbvvU+T3Kyoe/2pZDWb+LNPh+zD/gEIlfQHxa7oFlvRJ4OfFMYW0zvwqOjWmBkUIO69gcqeSI26Naey64MpPfar+7+qSdGBQBYrpFM2L7Mzk75eHOBd27PTv3c8qH9FS0Rff/tX4JEEJigzY+Fz9yyDh7do6BXikoOzCmypxqvj7bg9LHkHwag8/r77X7+qeZ6+Lr81kMowJfsmszBaZZ8XDiSCQbwJJnzbUM6MI0rD7GtWZp+nwf5tZEwRZscuetMJFbe9dltI8DDdtkzz9n/56Y2G9QgaUsGjHWadC06Nn8cfzs+tUdNvwIDU1ViqK/RYW/OH52VH1rEmCoIQVJ+17EPgl9FsU07Us+vUjLCTo6bm5tZmPMsK8Qq/DD959DQ2PYbLdxL8iwoKDXp2a36KAfL5Zh65r7r0OCg2P5PDHXZ62QcrJEL/mAzpOmQcCgVs7GpBkwWoTnqd/vqSfNvHz8V41bYrW7jtr6gAPZ9dxO14qXZ1O30xfl6TJrybVZm76YlC09y21LoHcqHVyxMNl/epaW5Vt1nPcim2Hz106u3/zQp86hXkkZVW++dJTHzUmk33y7WRAkna1Z7Enq5F8WtTUjiQFDcZd4fIHeviR/vKvH4H3whe53MmdG42GY0Ig9wQSnh5ZtuC/U0FxKlP6S5Ofj3M0TxlZm3Ju+tepjkWEzJD/h0kyWdyHhxcP7D5w6cH7qHz9y4/7dHVBn/r2JgK7Co069R81bvRA74ZVa7oPP/o8vaM08sr4YgYGxqYFLK1s7Ozt7W2tLS3MjAwElbruSZQxd5k2EBqZpb9YsICZsYHAdMjuaGny8/HOlgZGJuYWaXsWtDA1NhSaex2PwAdJ+ntSD+PX6PtL3WtWrVbDwdG5tmv9Ro3d3d0bu9V3rePsWKtGtSoNem8JZ39Vxh+d0qR6tRq1HJ3ruDZo1Lhxw/p1nGrVbD3lQroTlnRNgI7/ePHQsafBKh1+ypWQJgbfPb13x97Tz0K0bqO8vT4tS37ub1aQ+dFfxH0p6y7euVlNOvLDtc2LJvZq38K9YYPGnq26DZiy7tD979pH59PxQZcOHnz0RUMDxQffP3L4plLnR24WPLvHTvjwn7Mxk6VRvsP22OweBPulCEhFMV9eP7lz6/6rT5E5+x0e+/rgtBm71O/eyZKPj/j47MHtOw/8P4TFZr4Aoqgvzx/dfeAfGJWU+Z1YZ/7T1fiQVwHByumkGR9QHB3y7smdm3effsj7GDHm8fxKhhQpKLvgeupfTMo0f6SR++6X12Z0a91z5Jy1mzctGu9dtnDlXgvO/1DOKMi4WrnwanL0x5unD23fvHnXwVP33yK0zAXiDA+ph/GrODrw1sUzJ47u3/hPh8KpA5utPAcv3n7g2OmzF+8GpN9mPU2GDnt+fuWg2oYkM8ePYcFK3gNnrN15/PEnfOOkCeF//RKIujG5hJAkCF6tASfytRdDv1jVaiNNvOxT0pAgCIuq419oug6gtgee0CAQGXB8Rl+PMna2let4tmnbsmGtsuUcO6+9GpIj8aBU9HJGQ1uhTddrWi9EJAWcXdqtQVlzQzP7IoUtjHgkaVC4otuwRUcCY1T6DlWLLg66sb5/05qly1Su09irmZtjmcK2pR1aTtt0S1MeuOquObkmenl0ioudgeOgk7//S6ejbm6b3KpWibLObfoPHdq7nWvpEtXaDdc8bXNOllFxLPrr2k6lKNKg7uCjiqmifxwdUIAiKINarTt32eqnSJGXvD0+ooyhoeuQo/l37xVFubGQPwJ6GL8qIGMezCnHfElnZgpx0e2FTYyNS3Scsuu14u9GcSAsQECvBOj3m9rJ/zIEHZa+yZEgQK94crAy4vcz6lsyo9PMmh1Tn3ohB0+kr4eivMkNmQAAIABJREFUo66s7F7azLhmu/nXAxUdCuJPN5e6V2yw0S/zfYragET3ljYzJQmN96Zn9qG/n5zpUbS0+4xtt74lMn8r0sTQa9vGOdsLSYKyrtx13zNFRKV8irhr/2tf1aWP7/XP6VEjHfP08KwGxcwqt1ryIqPAV/k42VwWx0cFvbx9cO30Tg3LmlBM7uVvpxmRJn3YMsjZzKjc2N2vFdc7YgKP9alpWbB8ux2P0i+LZ7NMv99N9Hh9Byu+oFqH9YFKMx6G7u5uwFzDELDvt0eHLvGyJfklpp7+8ftjYwt9FNDj+FXybIWXgAlffzfIg46+sbpbrTr99j2N1McmRp0gwBJQzMJtM/GEIiZgbYPVHBGI39K9uPzLt8LSu38ebOVIkXTnIHTkuX88LXiC2v0PflO6TBz/ZncXx6ImAr7zkDPp0WG2qhX3arVrQeYinZb4VfxkQ4fiFbuf/MA+T8TDxU6WzKxwZmW6n/2qVDimGHTQsaHlawy9q6ljMPr5mvrWBrUHHY9ip7FlqwIadhIdH1GVR/KMC5Z09vCZvWqcS6amyYs5NbG2AWXeaYkfq6rxH3w9CgnMyvS6xJ5mVXRjdV8vjybumXw0adpj1ikN5U19ShxwaHhZUwv34fuDVa9U/Dg+SH4nZvW/INFZ5m4CVMXOO3P554D2UuOVfBXQ4/g1cl3Hokz4SlmM2K91aDn96/mqPg2aDvL9oJpFnq+NgpNDIFcFIld5M/M6kQY11z7Tnnyaq0X4Ww4uujLNhUk0Jk2H7Nb6KfS3YGStnvS7/X3t+aRl1VGPVMITyZOlHil5YSW81kRmcAH/d6eTit8tbOHg7FiE0hK/Jn8/1q5kpfnXVCZ4Sjtq0qWptfny/hGnAceUCyhN9h/nZNVlpbb7QomuTK8tNHPb/ZEV9aYd+I//T/r1MzI2dTwqHXeouQXvt/2vcc+W1DSljIr3uhGtDio6N6EWjxTUG3le9ReY5O2l9XNmzpyRycfM2asOP9NSOfGr/UPKWVcYtvFx2r0M0jeMvj2ttJAkDett/8AaZCu+PtuVTxCW1ScpTw6dvieW9F1Ab+NXacKl7iWYzDPKpPG+L5o/KSJf7OnZwHX0lqfqfzP63u6o398rIE1+McGZmXyAMvE8iovaufxGCNzcPiVVo+VcP6RqZB5bFHK4TXEDgjTtsY4dCEbenV/NnCL5BTsve/oHnZiSp2vbuw08sG8sc0dQjf2vH3d2s7Co1H7wjIOPfqq3XdyzxdXkYyb4Vt5nlEZVxL9cWsOowIj9GqNeBiDi3EhbgdX4YxoTDzIvlKktMxe/Jp0YWZ0iyAodd/xSD19lsogLo+z5hMA6l+50JXqxd1Clsk3X3AhTICd+vrbr5KuUGkp+HmhuxSMNaq7xZ/3YFt9MiV+rTUT8mql3g95tpLfx66+H/5SXf29YO01nT77DtGKC374x9et02fEwQu/aFBWCQEYC0oRznYsKCYIQFu6VN5NnZVQafX/tx4nBTP8Xwfvzi936TqVcP/HNOfX5JCGwbncuQkNIFRf+9m3wH6W+JLzf0sypz+XwxLPjtcWv8n5H5r7HhHGpgQ/URncp/o4Ifgnl+X1/Xhxjz+c5DdSamhNxdqStoOC4o1yJX+m4S92ZUYYCj+l3Nf4eSPy4xsWYJEjzwTvClBspJ5ZFz3cPcKw98MwHRc4tc9TPO3yaTbmVenzJ54VN7Uh+4ennVCaBlslE8rajKnTwVe7/zolS4Ri6IaCv8WtGya/SpI87xzat32HJUy7fPV033j8ope4JiL9vb2TGTIrNuimr7tVEF0ocfWtKSeaHNFm6zeY/Crh0obI5VUY65mL30oYEQRZpvDRUY0j1h2eSBC1v5zL+cDCdGgNp7H8VnZ/oyPz0YIbfeakPv5Mm3RtYyYR5mTQfeTB9eFOMvOuEEpYassVf9YJ7SqFFl6e5CAt4HNRyVfAPa8baPTP9r2l9PYKOy98pekCVj0PHHW3F3AKa5zL0LCs7VnmzrC+L/Lb3dW445tyrkDDFIzQ46O3DRe3KD9quuAUr/XF/74I8fuPJt1QSCOjQf5nxW0UmHlNsmfUiYA9dFtDX+FU5+VWlhzUu6MwoT5ceC69gmgFdft+i7NkXSL0nIUEUdluSK8FB9oumh3vKryYzaZL29RfkScSiD4bfz4ywYyJHfsPx11RClpypnOTF1m71e+6VD69K6cPTGL/K4l/v7FjFSmBYuN3sS9FqkV16/yuv8LSz6V2DdNyVnkzwTZCUee1uSx6EqIR8CYHbPQsZNxh9IW+S1jITv8pv9cKkww/YrjlFW5p4qXsJg5x+D4uebOtVzID5Ia3+IAWqo7Ukn1Z1KG1UqOWBd4puWvrz2TGlDYQuffd9y41fODnzTsNRcldAP+NXpeRXN6XkV/G7c3PrF7XosuKt2mdR7irj6BDgjkDa5K9EqVYbNaa7caeoelASUfi2hqbMl7RFlXGYAjZzDcr0UMo7PoXdVn+mZQnPTyzy8XSpXrOWo3P99gNm7rv99U8ilsRPu1s5dT0TkjIoIqP4VV5aWqLl20KR/8or4HVUZWy+5NGKFvIrHEwQa2RXe7Lv41/ygySFXBjsYlvBa0lArIakiMzhZG2rTMSv4puz6zHj4cgCow6l9yIrn0aaeKtfeSOCIEzKDHmUkDMlT/y4oZ55Su+2evhKUOYtTqrmjdCxz/7Xs5Zd8foj5q7bsWvronFty9qV6jjtaIjqZAXKxcay3gvoZ/yacgWHIAhrx2kpya9SSfipea2ZOfsIqnzbTUgc0Pt3NiqoTSDi1FAr5ouDrNRtn6I3Q9vGeP4PBdKuvRJGxfvdzaHv/j8sEud3j/qvbSGmy5o0Gbb3+73/unn6LLnxPoaWyZJ+vtoxuamN0LLRgG2B2cOkg9d1dRm++1NaUPrb+FWblvju/Eby+Qeoaj57WZNhScVBG/tUZ2YtTXmQBlW8Jm/dPL1h6eLNx+wNyl7JtRUkw+czEb+KDg+txPzAoizHHtGckitNupuSKcGzbH8xryJvTdUSf39//8SuNf8uWb3v9J0PGdy4T9POeE7/BPQyflVOfj0nkslE327P6VC/3dBeNeQdIZSxy6aXKtd09K9dU2okTXwwu10DR4ea2X84OLo2G3c1XPMEDvrqpt/1Ct/fy0g+70+mbsmj3xa5XztpwpkOhQTMaDl7nxtqY4By//w5c4a8/CSRJgdMrs3Mj0FQFr1mTPLuu1PlAjEdeWiEA58UVGm/iT3v6u/rSr/b28e18zalW4hnM36VRF/qUY7pkhTaNtunaYCwjP55ZVWvcspdjKRho1En8vh+p5mIX5N29iktj1+1TokgTXowpAqT6UuaND/5JzOW/b6BsAUEsiCgl/GrcvLrj5B7q73rNpl/IjCZ/rGxa0l5ug2//phLOhTARt5e7t24bs3KZYvaWTcacyELzSv+sGv2iP79+mb/0a//8EmbXuAycxbQub7pp60dmNkHCJ7rqEu5kFzI9erncfmkCRe7lWC8eVYdLuVn39Wf1TsPP0mkSQ+HVTNl3qGkgU2JVrvZs37KROG7m1rzCKpA11Uv07pRM1U7UeiRjs5tjwYpv+uzF78mXJpRj7kBl0HZ8QcVXbnsMtC/Atb2rm5mYizvppX3xJKCMg1HHHsRw94019YzEb8mbPVhbrFBUFrvZqJoEdK4yeHwLJHnWsVwYAjIZHoYv6Ynvxo5DZ/Z3bXxsDPvUoeBRlybKL/tOyG073hed2a+TI4Kun/j1Dg3a5I07LHuM963EPgDAfrt+jZMf2BuDY75g6Lp467SxOu95aN5KNPWZ/E7MBNNLE16MLSqPH4lyJJeazTketFf5jexYtIxS/W/k/mZk+hvvn3qDNjyXjX+yk78Gvl4cS1zihIU6bfeX0s/CP315lKP0oXcBm19/fPbtXWDq1vL7wUpD2IFlg5zTn5VLYZWl8TQ22tnT5m7/lKYctStdXP2C5mJX317lPhd/Krof8WM0WxhrOejgB7Gr4rkV5Iyb9B/63vlPg/x6+n1CjJ/q6RxlxWvM/kJko/Nk35q8espdSxIA4d1z1lzOKdvwp0l+ac0/skjgSy2O/1qdcuU+JU9H01WDpRHdePYabIilLqtNOnOwIop116bHlOa5T4bh/pLdmHuX+VoLm95QdNZDzUN1Ura2acUcyWNV3jq6Uz2ZdIfjw6t23atWspBluNX0bfzvauYUQbFB27UFryKn+8eVLaAfdel9xSTDMR/vjyzQ1Vj5ruHeQisGvm+0DS5lmobS5Nfz2pkI//CMmw177EmCtUd1NYyEb8m7e5XRp4/oHVKWqX3cItTGm7QpXZWHXkitTHwn5KAjjRdajH1L36V+K/wkv/U5bFu6yevMf1ue1dz5o+VtKw2QYeGA4tCfRsV4JmUHfo4D3P/s/1WVvpzwGKuC2SxmegXq5rL41dBk2l3svGNmHK6XK8VJ0+QRWpmc6Y3MSV30KjxgdQx79k4zN+0C/1lnjvTvUoQwl4bQzTVXHRwcEV5JpjAa/YjTRuwn0v+fqaHS8t979X7MLMWv0oTXy5oXpxvWmnC3rfqx0o5a9SDhdXNhS4Djkay/7rinx6cUq+o/J5dBFW5047fzuEoTTjbqbA82YcgK3fPzmjLTMSvojNja8rHc6rMYqssKE282U+e7Cuw7XZNZ3O4lWuUsszJz5h8LpS6Epef0b/4VZH8ajn6YKQ6Pf3rko/8ch7BKzzxmMrUsOobc+eZsMP9LXhU5W57FT/ouVM2lESnBOh3G1LzBxpNvKHtO1inasTpwirGbiN3MNPtlLCjt7x7lRAO8NU4I6no5Ojq8viV5zT41O8PS//cN8S155pXmi64ZSV+lXze3KuKsXXdfy8EazqUvCD012UtC/Mtmx3SMtlvwpezg11sKILgmTc7GvrbcbFxF+c3sxKQBta1/3dNI8Vvap+J+FXy6N8m8vmzDAbu0PCNyfwGS5v/1bziKH/MV/UbcrycdwL6Fr+mJ7+aNjkYrPHTQXx9dj15Qj1ZuvkapYGoeYee9TOJTo2uzpMnv2r93NR6UIkoMT77j8SkrJ9Ra1HwAhcEPvt2knfp8OuNxvitXG8QaeLNvvK+K9Kk2fEInf5jyrNPEonf8qZMREUIe6zXGCmKjo+smhK/1h158bdNSMfsb2ppXqRsOY2PIpbMPFckz6xY6gaVhm75qKGd6B/HJrhalWjt+yiKdcakXz9jElNnRRWFbXez4JVquT6DvtWE91sa2fCZu85eTr/rAeuYyqsJkd+isxs1ZiJ+lYUd7GPO5CgIu63WPBaNjjvakplyjyzhtUZ+0wfl0mEZAvkmoG/xa8zDOeWYSV4J29qzAzWGrzJZUtDmBpbMBRPK0GGNf0bppPSvZ2vGdG3ZvO2wuUdSJu1LCHu0ec7Qru3btGzRdtC0zU9C1TuwYm5uGtu+uVeHfnMvfUiQyejgR/sXTBrcvWPbDj5jtt8M0fDJKBN9fXRs1dyJA3p2bufdrtfopVffq9xpMmVCmWwkv9Kx57qVMCLTkq6ydWWCFBZsduCTFsp8e9/ixNkXCN/XQz4zJc85h+8Gmf0i6fGe0sSrPUsxd2OizHR4/FYef5JEXh5XmAlgBa3mP9P0gZm0f1CFlPwB70Uvf/vmkUrCHl46p/lxdndfBzMmk9ai8bITZ+XbXHwSpH6VK+760hZFy3U8FKCetBq72afWrIupkWjso7nlDfguwzK+z2rC3gHlKX4xxV6/rUK2N8hM/Jrw4T9nY4ogtGUbyxI/rqnNpO7y3aeo3sE128XCjhDICQE9i18l/iuayZNf+fXHXlEPLdPEonx7lU35+V576Bn1DyTFZvuGeIzd+yH6zQpHI2GdoSeen53dsmmfjRdex4hlSaEnupQ1snOe/krllzH95fhwzz57QhPezWhgWaBi/9VLujXpOPfym2ha8nVrv+rGVs0Oq15Xiv98aUrbypaF6k/dfOULM9Qs9umeIRXK+lxSGnYrCtnaMHvJr3S03/l927Zszv5jy9Y9xx7k8ZyFaf74P1cEvh3uJ+9uoWoNOK5l9HSunPfvPKgihVFg01WHcwfz9pOEjrvSq4whQfAcB57U9BaN39KNmfKJFJT79456rJmVNxr9eW7jgkykbNPlqtbMTvGz7T0rVPU5nn7z0vRTSJOfjHJx3xWU+gs/4c2KWka8yt33Z3hnEPHVGXUERnW3acjHTT9yjixlJn6VJr+c6FKAJKhqvQ5p0pZFXhlfhE+QgjLzrmeqwzhHSo6DZFsgKezZ/YCfmn74ZfuQHN1Rz+JXRfKr9fhjmm+Fl9IOkbemlpZ30wps2pzUkoSUELjevf70N8ky8fcdbmY8QYHC1T2nPU6/LBS/oVMRyqDaf0+VenAlH+c2rbfaL0kmi/hfS1uSoEp7Lvsgv7SUcht0XgGP/Up9mbFv97QtZ2Jawnvfi/QOV2ni7f6VqyxR+lwOPdwPya8c/QPSwWJFnB5mLb/8UMVnv8avKx2sE3eLTMce9LJguI1KDLivC4MvuUEpOj/RkUcSNs4z1GM8afLLCc4FCIK0cWI+n5UfdOyrLVN7d/IZd/Cp5lRO5Y2Z5d/Hr/TH0+OdnPqc/aj5byXh7ao65freSWtZaSJzqyrjUgPuZTBXGv1jbcdiherN+5j7l7UyE7/KZJInK1oYkYRl9UmvVD3lXJInSz34BGlZdbwOjXhmN/RftB6zu1+FKt32ae+Y46xF3MPdM3w6+szyfZweD2VYWL2KX+nYc12KM6l9lIn7Ac3Jr6kY0uQX453kU7SQBq3mPWGPE2W2ol+tadtuvj8tk0Vem1iUTwhsmu5Vvs8KHbzQ05o0qK4cvyZ92uDhNidQLGNuGF3OiDJ02vAiNbqVir+cXLt8781Pis8HqejlLDc7krIbvlc5xyvp+a6+ZSoNuZ/+8Sc6OaoajzT0Was5OSnD9sWLWRBI/vX1xeP7T18Hx+X+90oWipXTm0bfnpby461ki/WZnz0zp0vBteNJor4EPLr/+F1IjKZPg+yXVhS6pb4pk8Fjga//rCiKQg61LmZAGdb87wn7izj68fwqhhQlLDn5OGsyffHVWa4pYxssqox9pnJlTMu5JR9nNrBk8gcKar67xLd7i12sTcrVbtZC7dG8mYdbfefyhUwLN1qs9G0jebSqlTnfxHveQ21dsBH35lazKDXt9HctZcrJpyWRezyYe4BR1Xsf1hyAy88mib7Wp6IJKSi38CY7cpCKXk6uY0FSNoN3fP4buvRyUv8PjpXtLyNR2F4vu8LjNY9NF0UGv33y8On7kChFHPIHZczhXWMfzatkyFwXp4QVlPvvMjiNPsSvSb9+hIV8ef/y7o4pHvKeDoJn0Wjl1ZefQ8K+/YxV+5OT/Poe9ODY3Hr28kmECEJg6TTF99rrT+EJql9c0R/93jLJ6vL7XBP8RhOvKzc5HXO8rb1AYN3xotJ8eHRckN9L5r4IcS+WVDckrZ00dB4oGiP89HB7PiGw8lx+/Oy5s6cO7dqweNbozu41a7kPO/w8/T7U0uRXzMUdHZn5VVE73Vr49f7kpA4u5Ss6unt6uFYvZmJoUblhr9XnAjP4xNetCiqXNv7VspryG8jauc5TuhigvMlftZz04tjcVk4Vqji5eXo0qFLU3NiyTNM+C68HscOm7KHEv/5finYhlSgnewf7q/aSvDkwuKQRr3DtKUpXvWSSqHtj6liRlEXr2dfYoZYsaf/AlLxYQmDX/brWfACZVBIfER76NejNraOz6lrLB98LSg5Ydf7Z+08hoeGKwVixAb7NiqdMd5XB2AGqZr9jyh8UUnHQxt7VDITFei67EaX6nSKTiT9eXexWzK7dvFt/lveQ0TtBmhgdHhYWGvzptf/N7dO9CsonxzIt2ea/Y7dfffgcEhoW/i1KucDyY9HBl6dWNuNZ1xh5V2WIVsK1+U1MKQPnPntU7uKb0fnx2h8J/NmXkcR/VQur8sNZ82zScW+2T+1U17lh+15Dx44d3qVJRWu7Kp3Hbwvg0t2AI86OsGXeqwRBab2VMUtW5+NX+dQehhRfYGRiZmFpZWNnb29nY2VpYW5qbCDgCy089qVlJqXUPPLqhGJCvsDA2NTcoqC1rb29nZVlARMjIZ9n2mPtB7VgVyajvy1rYUfyS829qpL68/3cSHs+WbrlOpU/9lRd+t2mdgYkz3XURbWPCYW/6NCQShRBlWw4eMGiRYsXL121bsuBk9cDQlXOIpPJRMFb6pszM78+SrtEpTgEFnJEIOb5eo8qdWfuf5n2jSL5fGtF0xJGJM+2y9J7ORPF5EhBc+ggkoj/t3fecU0kXwDf3RR67whiQ7GLil1UEAERBAVBUcGGZ+/97O3s9Q5EFFGwwKmIIIoFxa4giKhYsSDFo/mjSEg2+X02ISEhCwYIQuDlD9js7sy8+c5m5u3Mm/dOWXHDsqt1WijRHJWUym2U2TAeHRrTdeDcy6/K3eCzWd9jdo01oKPy+lb+TyT0jV9dzQrurmpNmCqhbZ2P8x+w6u6Ha0IEGMnnFpnryel0HL3+73PRMVHBh1bamanLa3Wb6/tITHkl0hW+DHLtoa+q1Wmmb0LVfS+nOGV3TzUVNQ0tHV09fX0D7kdfT1dHW1NdRVHtj2Ce7ZnAS1c1yithOuv816tKAweblRm507O9hrxRD8c5q7f7nw6/fD7owLZVk+0667ccuCo4uV57lS9BHipKquoaWtpC1TMw0Nfj1lBDXUXVeNIdEuWe9eXuPof26i16Tfo7IjEzP//Lixt7/7DUVTQYs+7K9ya9JCX0yDXwYR0HI3bZi6UWmnbrn4q8NzHf73Hu4rjmWk7FDFzJ7W02KhjWYsCapIqV3gauO5uRenByby0VrT7uh97wHXpUL5PM66/VV6/uV/HCKFcjOk3X/YbIUmvxWR8zCqrkdYR0Tb/41JR2GKa5IKyypxUheQoPuegjCN3jwIdKfZ/QPcRheqi3WtPx/Mr4kpKc9YtHk5X18WNBld0l49uHz9LUA1hp2+07zjxR2WNOxtWFLekoptjjwNN6HWsqtfbv+Mr1MUeY2ci18LpLMoz9DhkaSRkl748M7eB88XOlp60gbHZXCoLq9l79SpI16Gorkx02RYUwH6D0nR1djUZVbR7N+iIj+1nwriXjnUYMsRw2auyUP/eHpWRXaq9Gyqcs71X4sT1rFk53c7AaMszOdfKC3SducDfpNlKBCbO5wk8xQVtnejhZD7G0GeU2+0/f26l51Y9QjbcyMidZnQejvNvLW6tbnnxfoagSL3XxmzvIYzRtG+H4Hay8c/ZaFARV9QnKlDlOAoFBfxWgID8ouL+mDR0zsTsk5A+Aw8q/7GpEp2o5RmQyORxWyon52yIrtouxS4lo0ZiydbU2uKUnvdtgqMIfIRUJySRgXJrXRWD8mn9v5+JDSWS3ycA5dunH4KVWWnTD1VcqzzGLSM9MXdVfXUm3s4PXyr+DL8bEPU55/Tr52cOYiyd2rp5h29O45aBN4ls6RHKoyRdGxrHBqhSqWrcdt0ReNtilT+d2U0YQ6tDld2qSnwzcy9tuTKzSKNuGN+uIpvgbv9FyKKbfc8kT0UmIgkfrTekoSmsvoRlW1a2Op5RH66U5/5UCekDVoOAKEGjuBOo8GBWc9G7fwSWg0ppwYeI2rr14l/1PK/aas0uuuBKh3Whjdr6W3X4J9NfqfzO82CS0kRufCU/Ip4dNUadg3SadK+Rw2GUpK4YNDxLa2sV1oYLqWKwVfQuqVBD+9pibCkZ33y8+/1qaGDJn9PwLRAJW2jpLDZTe9UBCKYfDuLJ02OJ/JdtXW6m0BvvKKsr7+vxu+P5Vk/q3UcdQBMEMVkZVp7/y7H2rWLFDtTtNjngvzWms0k9+A1UwBJWfcDhN5GeMZ+2w1UYQ1Mw9pMHg1VfBP/5xbUEsadM7731S0aPVV2mNN1885dAoORTBlK3OiHq1K990harOPVf96+Uv68a4vsqCa3+oMueMyAvSL1PCDUAACDQrAnUcjBiZIba6LZaS7NwqeRN3PjxOZDsHbxcESmu7LU6ay5m/ub1Af60WOP59n6MBYfwaK9zGPPNWusfBTziHEe/rYbfwqpAxFv4+wFUBpfSfd616PQsvfDi/t4auxaoXReWxWwhr15zkwBW2HS1mXOE9bHjGDjtdTNE6LJPJyr0y0dLnnogZQ7XCN/RF9s873u0UUIq8buseIyet3L3QinDNW1v9FaPpDJ16MFF4Glw6FSx5dn731oOX04RagQiZWJa8uJcqghBBqqRTTiPKhXFteS+uUqU2P6yO+lkjqlUtRMGLU0/v3Owf9brSVvHCxK0d5VGU1mazyA+/FiUUHfUw5r0q7GvWrwq1QAdJgEBzI1CXwYiVtH+kVoe5CRLtkymKWtqbilK7up/IFJ6ZkzXeoL9W12JE7HIzJeU2Mx4SkQUqPmW5t+b1NTAd5rPQ287G81CqyFVG1KIeVJrRKiGLgoqUokcln68stu/YtpfzgjUbNv65ZMrY4X362S/aG/VV6BHMebJ/WEudgR7zJtsMW/mvbHkwYfzIyS3m26plneaGfZJEf+2rZz111Wwv15HWlv0HDLVznrh0a0DcOylsphHFX923H/FbOspjKK3d1ttC7ybVpZClax+OuXJDcNFcdsjw4lG9EWfEEm6YUFWzeZINBlUKIjDVoKg7RTWmrb5VSgwXgAAQaGQEJBmM2GXJSyw07Svt3CKrCKv4S9SucUby6gO8fF+L2k2R3d6oz4H+Wn3z4Hkfk99mk6yxspl5qU/uPXmdJT7JyvxfWlJKhvj5KkrCC74k34u9Fffw2fts8q1CeFFG0oOHqVmVJomqyK+xnq6J/mq85GKFB7EGqBD+9W+31hgq13/mhYqAFQ0gR30VWfBoXTtiUzyl98zLEj+o9SVMY8u39PPpkS1omLzp6kuk0Z42hTLjAAAf1klEQVRrIC9eFOGiR/jpq8IzfA2ygluBABBojgQkG4xyY5e1Uh9SaeeWKC5mQuBsG8t+XVpp0igq/Tz3PPxMrm+IpmrU30B/bdTN05SEkx39lRHvO1aLSus61vfDL1wlyGr7sPJDic2nCGJguf0rf4JcVisjVbnZjDc7HU0odOMZR5PrrtnzXe1inSeclfmxQqqcITMgAAQkICDhYFQQ5G1qNqbyzq2q8i/8FLPSvo2ccjvv3bcLRLZ9VJWikZ4H/bWRNkzTE0tG9Ffmq7A57ZTVreacTa+z76TG24jMt2sGEmGHaDrjbsr4EpIUIbOZnwN9zJU0zNeEScfFRcY5L1XCeRZt7O43sjxMSJExZAUEgICEBCQdjBgZwSN0WywLr8Hebvx/sV4dFBFMw2NfkuxawIL+KuGTBLfVlcDv0V/ZLMbPn8XVf36WMqtQJpgvz/5hqt1h9pF44f16YjUvfh17xj8g5HpShvAvn80qSL1/+eRRX/8T524kfhNxwcfhMAs+PboZHuTvGxAckfilOg8MYsXVxwkiKDEmDRcEeNHHmNNHj5+OepklUuOy/I+3L4X4+/qdDL2SklnJAgfPTUuIuRh8xNf/zOVHGfXvg5b5v88Pr1886f/PkcDQuJQs4VYTwGUzPwVM66HX0T3kmbQcBTCuLDGnIAhKa7v1TrUPlEAIOAACQKCBCLCZeYnRxzavXDB9ytTZi1bvP3nzU0GlroL54/vXtykJ92KvhocGB4Xe51mXlX5/ERkScDQoPOkbuY0fs+jrg6izx3wP+wWG3n35nZcpIyfpxJZZTlYDBw7z+PtWhtiQJOFgRHgpStxnr1VjY31G5MLuGILIGY6/KbOm+aC/NtBvpfkVW2P9FS94cftf311r50zz9pm/6kBI3LfqFJ2SpPMbHXu3VOKGP6/C/Rb/NIopG7tfyaq0cM54cdqnY7sRh+9kCrqSn59jT0W8FGkrPO/iCuv+jovWr3Rto6I5YsklbhdW/Ozcars+fUZPXb5t76Fdf3p20lTr4bTxMddbApuRdnb9hMED7KYv3bDn4N5lrt2UFAzHbogRiy0pUk59f0k/56VKKLAKXv7fal0WsZHRsve4xesXu3RW1uq5OZLohYlYhSucLfrY+azYvO/Q3uXje6mrm3puuc6rb9GHyJXuwwbZe63YuOvA3rWOnTVUjIbtu5ktYF5rYUgTFn26sWnq8DZ6LYe6z9mwc+/W1bPdrM37OG54KNr6bMZH/ynmXe03P62Iksl6d/NkTGodjAhYnzYO00IRhK4/IRYmuUmbB04CgUZBgPnu2o5RnTRVjIes+ifiwdP7Uad3e/Y1VDbov+bMS0EXwMo/PZzoNMs/hFF76c+Ek7MH9Xdc8OeCocbydI3eu2JzhCvEZmZc3OphpqXacZj3mq3b1y2e0NNA3cxq3pGAjSMHO24MjP2QdtHNRF6z+/KXIq//kg1G3JIIVzm9Ne3Xx1fStfliFMX5zRvjMiPoUaXXctbzfXZU4u267l6u+UX99v+gv/525M21wBrpr9N3B0yz7us0c0tI1N3E5KTYsB3OnTXUWtn8FfGB7FdacG2LvRbhnQtFMQqVSqVSsPID4guViqEoxjsi/tJoyh2s1ieKeo1IDp7eq++MqPciL9CfgzxtV94VbrG8e3/2sd76vpTNwdO32Wgj1BYrIz5e2eAweNzuhAqViPlw61AqSuk941JB/pONLpZTdsXm8LVldtnL5X3VUKrRygiRnk64lN9wzMgIHKJGQRCsx5TzInWuQdml0csHue1IKuNwSr8cGaCMKph430yLW2bbb9q+uNyK+r5a2U8dpRqviszJfrTfYfAYvzsV+6KKknd0k8cUTbzv5Iu4+KiBFFXeWhJ/YlZnTZph75mXXlY4r8CL3h2d1r2nV6jAEx2b8cHPq5/1HyFfRSxG8g679dtdh/jB+I8LjrrEANHawbdJ7gKsEjxcAAKyRIDxLNDLRIFi2Hc5b8aBJzteGL9skC5Gb7XwjCA6I+N/uVlpKTc3OLXGiE2Zy25GLR/usie1kJ13fZEhFUEQ1HRsUMVSC+vz0cmdaaiCzdKK2YrC1AArfRpKa732GrHcj//vzty+rftODPjG7zA5HIaEgxFPTt7OrVMfRPRfAX68KNxJh+iF2rueFDXBZz3aNpS4INfbr0JFF6STjQPQX2WjnZqAlDXQX/uoK7d0OPZYRL1jZEV7milhCqaLz7wXnatjvQyebKBoMHLu4VsvMkqJa8y7G4aM2f6Cf1vpmRldZgRmV82QkXhiioXlwuiX3zIFn4z0tDdPtru09znxXShhQaB3r4Wh3DN4+lYbbQShtOjYfZh3cEZF70PcnnNlni4FUTBynDFuyJwTr0W7ltKQae0whNJrRoTgzV6oiN91yHy/drAmiiAqpnMSarVNjZV/ya3H+JgcQu8s/eI3QBlFKLqduvcVa6DSsz4dMITS2clnjOW4C29FtGV2SbS7ER2h/CoqW42p/Li501GHhur1WvRIyGcwI/uKd1cNFEFUzeYncbVVdum7vyf2c1gS+kak8T+9evyPXVuny7m116rzbi014o4PbnvB+LXG7QcJgMDvIfDfg03dVDFM0fzwM1EFj8P5L3Z5KzqqYOx5I4c/mHBl+hY8Xg5F1Du5utuOu8jdAFvy7ugQHSqKqThvE8yD4m9PeWpSEJqOS7RImENG9NKeFATV7rnqpcgLM6+6kg9GvPt5O7eOVfWGzP55b3oHRTm19nMDU0XqwMn9x80YRVD9fuuFgi/9HuRSKwX0V6mhhIyqJyCh/srB0w95O+y9kyuWGxGxTBlF6Lq2wnGcSz6csGlptuKCkGdcZuqqQd3+ulf+Gkwsr/TttvNB5b6Jnz8j4fhkY7mKVSH+6hDxH6V12CWUEP8R4dZ53DWutRD7Z+zkNvIIgmp1X5QgmMrjZ5oV6q1MTAfTBsyOFLtYctzTBEXQDm6nRFQ5ftrf9Z/5YMsQKorwo7vVuNjMCz69xwfzvOPmXV9kwNXVrJbdqph+KM+yOGA84cMfo7dbGy38MkBcxosujdalIqjynDNSjKSAp56bbkRHKaoWh5+KmBpn/zuVu6EKM3U68h+Lwy59c9CjIzFxT/ZR77w4mWSAkRBUeZAITK7P0dcN+Z4iobhwGxBojgSY7zYM00UR1MT2YLldqhAFdumjmZ2UEFR5sp9IdEZeN4LJKw+cEy34bRd9e5HwWuhdGf/61whdFEH0+m9ME53dyAqboooSZvFbblfqLGswGPHEZGScstFtsfxSNTu3WAm+rua2m55ULA8SSTPvruuihNE0+h28L8WOV4jdbzkE/fW3YIZCOBxJ9deqWTEyjluqUhCE0ndWFP93XxI2u/+EvQmCToTD4ZS8O9i39cQ7fPMARtaJYfojw0XegCvK+PnRbyCRJ/kHUx0ZwZ1f5CXIu7W0t7MfL7p0UdL2zvIoQjFYcqGyTsbhMO9vHkIldvc7hqeLdl1EZC/CfgBBqIMW3hCdl62Q6vccFfKqgNLH7qpFFAPGpfkW0wK+ct/pmfc2DqIiiLyR502xCUt+fbEOLgFCvXt5FYuSd3QlIl2Jd+W1Z1D6JdhOn4YglJ5Tz1cKPsH4Fr3AYcCQ0YujiGlgVjI3fix52yNoG6ejYu8ekkpFuBPvrYogqI7Fn9LxZSBpyXAfEAACkhIoeLC2LeEJmzZqy3PR6UleDoX+7sYognUaf5o/4hDny1+DMZ2l4RWGSZWKZJfen95BAUEQY5v9lfq9cmMDVM77qMiqYI0GI25xrIS9dlpm85/9YgGt8FHggkGduzl4L93lGxRyyn/LAqfWyvKt+00JfiI6sVypDo3+K+ivjb6JmoqAdddf2T/vTjUlegS64cTb/A0xZcXFohaxrIQ9tqaORwRbKnOvzjdqPe2hUEizWhNlZL+If8PrsPBXfzvKoQhNx+16gdgSM561e6QeiqCmLsfEFSBGesAgVQqCqc0OEVd8ay1abRKyy1KWWqihCGpsveebKEQJssO/JT/5yGsFPH37CB3xXp6XSbmhLarsE5Qlli3+2tdJDkWoWi5XxBRfsZslPFF4YW53CjGv3GnP46om3SXMqva3Fb/a00MBQ1C645YksnGx9jlDSiAABKREoHyiAUGVZoWQTmEyIhZ0wxBEb+CWL0ITETz9FZXrd+yt8MyJiFDsspfL+qghCGI4dKeInxoOJ+fybML7NlrX8N3ssqTFvTRHbkiQpPNmM/97fvvSKf8DWzdt3n0oKCb+a4Ou/omwqvUX0F9rjQ4S1oyAFPTXsmcLzFXFl/WF5eDO9mm57HjFVxpYT3dYa3VZ8qL2C8HC2QuOf/h7tCRWnewO8aZjBReINfHCyLGGdASlj9lJMq/55fREVRTBlK3PNXzkANbTndZ0FMEUBhyvuiMWrhrpMSv//CjCBkB+ku8XPvaKG7Mu+mhQEEy+/3GSech8v3FcjGSLdxVZ1OSIkXXaTocIeaUu/UaXXA5W4p4RBFilgYGya1wmeXXhTiAgkwRKT01pQ5iOoXKtew61Ivv0MTNUV1M3td1Nor8q2YZX99bNuL7Kgooiiq1mPBaZPcHfHHEm5j60nSIyhZTimgPMubXURGNo8MeGXcarudzSSwH6q/RYQk7VEpBIf8VzH4Qe3usb/k583pLYl5W6qr86sdqLaSy+QB5g9seTDe2VOuy8L1jtKT7uaaLda/Ubqf7G2T9vexHGr7RRm0lm1wrur2lDRwm/JEK2s3w2BQGerTAENRzSKAJf/UzzH6ROqeM0Yd6NxYZUBJXr5ZsiPhvBuLqsFwVB1DotErclZeVdHK1PQ1C5Whkw8ImK/i/3C4ZQ+s+LEZdG9N56+8Z8vaq/BoqgrWwPZkoyN1JvgkDGQAAIVE2g5IibIWH9Tm214aaIoXzVSYgr5fOvSqOu8K3USO9nZF5yb6eIUo2Wh1cstbHL3qwfpoNiamN3xNdtUCo44dWuw5jjVe3cIhWpiZ0E/bWJNWjjrY4E+isrxX+sGvE6jHV2P5UnPvAz36wewNNftZeGV7Js5FW85OLcrgotJguMXznMd38O0pB69PnCxK2duFabZH7pWQm7hlMJI4dJFWLwm6U+NDZ+3rX7n3vUsw2GoFo9V6fWsjdlxq0fSEUQlfbzSMywCC8HGlwvXRfEl6vSw6aoUxBMcWAgydRs7arDvL2uP9ePjfLMkxVjRu3yqnWqwvjNZvIYguktDGswGWotPCQEAs2GAOPfWR25A47u8gjSAYWchIT6K4fDfLB1hIGOmmILq+1h8ZkF/0tPid7o1kWebuCyNlp84Y68sCrOMr6dHK5jtKK6nVtVpGxCp0F/bUKN2birIoH+ygif25nnCEDHYu17MXWKXRo/r7sysdpD77L/aaWQTkTly76HjTKgt3XyF5ik8kxmFUykY//KB4ynHHKgowhN1+MmyTxxzkFnQxRB2wrZ4PITcj6dnkRobMqDg8qrh2cmxb3km/fj/3t+eKGHg73z7E3n07hLTiWZT49umOUxxslhpLPP6qMJGWJQBFnX9iAndpkJMVvcdtPNKvciVJc3/mXrcG0UwbpMPCuY9Bbcz/webKNJQVClqcfEoyTk+rmbYAhqNGxnuSUFnhsf90wQ1iEnMWj2uFEjnafuDn/FnUllfH4csuaPCS5ODg7OXpuO3f2PZPGNcZlrr4bKdTv4jOQJEQhWnwclF+Z0wbguup6JrBvWZ5mQNxAAAjUmgL/xd1EgHMXITfZLF7d9qio/CfVXVm74uEGzH+V9u3F8o7ezlYW5ed/BtpMX/BWZVPddU8Q2Dy2z+Ym/2LlVVQ2ayHnQX5tIQzb+akigvxIeleXklHVN+q0J+yDem+D/uzRalzBtVGo36ymJZsB6fmiUArGbvsLRHbvkqrsxHVO0OifmB6AOxPL9xhGe80xsD/I1z4rM8MIoNyM6gtKEbHD5V/HsA84tiIQj9pcvKzPfrbce9E8yT9PKP/PH8EWn3xek7uulQO8361LylfUOI7yPXHv9g8kpzbjk3k5Bz2INmctAfv61+898v9laD0WwDmOO1WJKgJUbNlKHMH6d+I+QCzO+JNmX/tCiIKQbHRiZp2y0KQiqOOFwuUPfktQDloPX8RR7Vl6017CJkWmF8TuH0+ht/ox8+e8al1FT99xOzWVxWO/P/2FEV7Rf/1BMnWfe32RJOPJSsAqt5JKXLxLvf2Fevlha0Ttq+42RHepgQENQZY8DJNbPtc0V0gEBICB9AqWfAiw1CZ82PadfqsrcCC9KOOZ/s0BoQJJQf82JmmNqVXnzlnTqgGfutG8xelui+CKldPKXkVxAf5WRhpJ9MSXQXzkFD9f2GrKlkrc8QdXz7qwwoaMIKme/Tlxx4RCzsz1UMHrnfU8qJt4E7vFXRtVgeUhQIukBu+TmxFaE8Svpxs+Cu6ta84xfK2xwy7NhZPMmI1W8j5arekUvdg0YuJa3eF7ywddq0JrUMg7ze9BQFQpNzbCbzer4CuOmYj+3Fphc1/qYVsyInGtIRTCFXr7lmjRpvclP5lxboE8Yv5r//bwCO/9WxpUl5jzj1+eV98/hHwLdlVCEomF7vvzVgvlw63DHjby9tKznB51GrX/C4nA+BIyho5hmi/Zj1976wR9C8MJ/7TUpWuarxG0ecq7OJ+RRGBLyiWR6lisY/vna6n4Dlovb4/LFrst/5qO/rORQRKnt9Pskc/N1yRnSAgEgIHUCRZcWmFNQRNHEO448BCD+0t+15/gQ4V+zhPprdqi3uu4oXoADqcvNKi4s4feHUs9cVjIE/VVWWkrm5fxyYhzhaA/TqsJ0lVtBVtp2+84+x9+S/DDxrCPj22IIqtFp1j2yXZ9fzk/XoSAq7ecKW2GWO6BGqAMX3Kjq9bqmZAvjN3eQRxGqyYYb4ib/rMfbh1ERRK7F5Dgx0/7CxK0d5VFMyZo/GZx3apqFz3GeLou/POzswvW1lBe7zIiK0HRGnBbeus4NV1tfy+LMd1ts9FGEYuETIW4DUC0fZuzaflQEUWo784n4jDjzDXe/HdZj6kUx41dm3PoBVARtyZ/DZuVGju85uryvx79uGznUj9gNxoiY3xVDsHb2B4X3//LC1ZLa7LJ/Pp1vropiuosvkDvESTm/1KKzfeCzWhlLVMuCw+Gw8q9OaCuPoKow+forVHAdCDQKAmV5d2b30kBR+eHLbopPcjAyL0/oPuDQU5F+MfPMJEUUQZXsIwRuGsmqUvLmgIUyxbDnxF3Hwy5FXL58OTIq+vrtuPtP4p+/TRe8jJOlhHOSEQD9VTJOcFetCBTnZ2dmZnz99DbhzrnF1kZcTyXUrs7boh4kf/j8LSMz63tBZcWm+HWAjWn3pcEpIhfwglu7nLQoqJKxw8kkca2Rw2G+32SliyLUwYtvieqpOQdGG6AIotiGTMGqTaXwVF8nwvhVeywvEJdoHrkHnQ1QBG03+qjABldwA/P7OXsdGl3f804xm8Nhppye3m/kboHzk4KPiW+I9XtuWCyEOmTZbeEFbvxHuLM+jabtGiOer6CAOhzk3PvTVA7DlPv4kUyjVp0vnrnDlvD82tG9PBCX8K2s/0JsNCgIqjw9MFP4PO/4Y7CnKoZ18/qXweGwmWn+Xv089yfzl8OK3yam5LM47LJXK/qqobT2O/jR1HhpM8KmqlOwjh4hIqMKv4ysuI3d1CmG/Vc/5zsJ5l5hpsefXeHe39xq0dX3Ig8XP13d/zMf7bCWR1Ft82XJ4tp83bOHHIAAEKgHAiVpl6b30cWoei7rIrMqVorwrKdH3Xp2nu6fzO+KS/OyMtLePD7s1YWIeUMx9PwrIvFN2reMrPxKXsh5QuJ5ofPM5UjD+6FUNcPuY+bsuvlWXGeuhxo20SxBf22iDdsoqlVwaEwLZVV1DS1tHV09fX2D8o++np6ujramhpqKkvkUQn2p9MlNPOba3bjbiKnrdh0JuxwZGrhrhp2ZIqbYxW7F9Y/kmgcrJ9hajYIp9fonqdIqNiv5sKMiiqr9OkhJJSmq+sqIWdmbgmCd3E6IW4uyyxIX9VRFMPVpx0iMQTmc0gd/jzNSNnZdum6ex9C+I1bEifv/w7OJ2AfU1ptuiajp36Pn6VPRNg7/iBdalaA1O4//FzTFDEOwDs7+WXwt8pc5sEufzOmqjGDas4NJdj8Uxm8xk0dp2g4XyDzdsn++2OHSXs3EbsW6xWMte49ZHSleNUZm4BA1inK72fEi6mDJOR8zCqrk7U/ibpYrM/713mG3nvr6HR3mr9uxb9+OtUumO1l27zHIY3vIU8H+sF/WrqY3ML6FjjKiozSTlRHgdqCm8OB+INCQBPCit2c2eXbTU9RoNWDctHnLls33HGnRsYfTzoh3ghGKlX9mhIacgpKKuoa2LjGcESOZtqaakryS52GSDr8sLzlw3ZjWyiqtuvQZSHwG9OvTu1fP7h3bGasrULhqLUrTsNh18zvJemNDwpCZskF/lZmmalaCskvTb536a9aE0cOHWFrbu0xb/Ne/99IE/QgZipLU22Hh9z/xX5QrbmEzMu5cCHvymVzxrbhP4qOynOTwsOj35HoQnpV85Vx4PInzr/L88f/exIUGBV28lSK8IUBQOF4Y5WpEp+m63xC2t+IUn+VqbF5HPtVfT1eafm6UkRxKa70m6j+BPL86wDOTos5ffVlIKhaeFx959sqzLNKLRM54QcqdC0FBoXdfkYf3zb40U4uCdfY8IzzPihfGTGglT1GzCSNTi4UELv2UcO1sUID/sVPnI2+/zpLaAyBUhNAhnhsyoxMFpXT3DBFXxIXug0MgAAQaKQE2My/lbuSZE0cDgkJjHn+sS/i+4vdnXDuq6XX3Pv+CxFSJzcxPiQ1aYN+OjqIq7Wc/LRIL4thICTUusUB/bVztAdI0cwLc2AeYid0hYc8GrPzLrkZ0qpYjN14LK+XE/G2RBfUAipVyYoI2BdU2X5IoZrxbD8X9Mktu7AMirJfI3Ma3iz7aFKy9yzFibxvr899zVtwVsRP4Zbb1ckPmzWVt5DAFY/fob1XtG6uXciFTIAAEGh0BPH2fkxFFwcLvRaX1QBFJ2WWpG6z0UKrh6isiq20iN8GXqgmA/lo1G7gCBH43ASLaLRWhjdz4THgNn+vnH+s26Vwhh8MuS1kxbHiQ8NYuKQqJZwR4tqegctbLbglPeUqxhBpkxY09gcqZ8/2L8ZL+OObZCsN0F53P4XA4Rc93WI7YKby1qwb5S+9WvPDh3J5qGL3V0n+/VjnZLL3iICcgAAQaMwG86MIobap616W/iltOBJqmIvQJZOYHjbmCjUQ20F8bSUOAGECAw8G/73M0IIxfY4W1R/ytv4scSvc4+AnnMOJ9PewWXq0/m39GdvSkTsqYfKdtsbkN2ySMrJNWGoTxa4Kwj27CPlgXUxhy+iuTzXi7a4zlpuuEItuQH/y/0DnmNFRh+HKS/csNKRiUDQSAQEMQYJfen2GmRNcfF0PmKkdIopLwuV0pWM2ifwklb+6HoL829ycA6t94CLBLH8wwU1JuM+Oh6PJ9We6teX0NTIf5LPS2s/E8lCp6Very5z7daaFJVWnreVWaQR9qLGZB3AoTOrXvrChRw1U8/fra7jotHGYumWg7dNY/8XWxUauxTCQJWK9Pe+lTsbYjdr8T1rNJ7oRTQAAINBMCrAS/MZpU+cGzL5KFCeRBwL/dXt9DnWo8eHNqhdODZsJHOtUE/VU6HCEXICANAnjex+S32SQmU2xmXuqTe09eZ1W7iU0aIhB5sF6enmpEx1oP3/m2AXUyvPB98qscsgqX5n54dPfhu2xRzVZata9JPnnP9g/Uoen2mHcvG8xeawIO7gUCTZsAnh97aLKpmmJHm0Wn736q1I0VZ8QfXzu2tYpiN6dND8W90DRtMtKrHeiv0mMJOQGBpkOg5MFBFx2aXN+pZ6B3rapVf34679ZeSaW168X3lYanqlLAeSAABJoRgeJvj/zXz7DpbdqybTdL29Hjxo93c3EYbN7epHU3+4nLA2/UlyfqZoIY9Ndm0tBQTSBQUwKFt/eM1qGpjFh2tWp3YDXNs+ncz8iK8emlpdpmbNirhjZhaDpQoSZAoGkSYJfmf3n3KjE+Pikl9WsuRH6VTiuD/iodjpALEGiKBIof+I03VtAYvfk+TDAKty9e9GhJf111U48LqQ1vwyAsGBwDASAABJoJAdBfm0lDQzWBQO0IsL4kxlx7/EXYn1ftMmpKqdiM9PvRMS+zwOa1KbUq1AUIAAFZIgD6qyy1FsgKBIAAEAACQAAIAAEgAPorPANAAAgAASAABIAAEAACskQA9FdZai2QFQgAASAABIAAEAACQAD0V3gGgAAQAAJAAAgAASAABGSJAOivstRaICsQAAJAAAgAASAABIAA6K/wDAABIAAEgAAQAAJAAAjIEgHQX2WptUBWIAAEgAAQAAJAAAgAAdBf4RkAAkAACAABIAAEgAAQkCUCoL/KUmuBrEAACAABIAAEgAAQAAKgv8IzAASAABAAAkAACAABICBLBEB/laXWAlmBABAAAkAACAABIAAEQH+FZwAIAAEgAASAABAAAkBAlgiA/ipLrQWyAgEgAASAABAAAkAACID+Cs8AEAACQAAIAAEgAASAgCwRAP1VlloLZAUCQAAIAAEgAASAABAA/RWeASAABIAAEAACQAAIAAFZIgD6qyy1FsgKBIAAEAACQAAIAAEgAPorPANAAAgAASAABIAAEAACskQA9FdZai2QFQgAASAABIAAEAACQAD0V3gGgAAQAAJAAAgAASAABGSJAOivstRaICsQAAJAAAgAASAABIAA6K/wDAABIAAEgAAQAAJAAAjIEgHQX2WptUBWIAAEgAAQAAJAAAgAgf8DLSoVrFLtzGYAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.433772787333139e-26\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from numpy import pi\n",
+    "\n",
+    "K_nrel = hbar**2 / (15 * pi**2 * m_n) * (3 * pi**2 / (m_n * c**2)) ** (5 / 3)  # Rule 1\n",
+    "print(K_nrel / (cm**2 / erg ** (2 / 3)))  # Rule 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def EOS_P(e):\n",
+    "    return K_nrel * e ** (5 / 3)  # Rule 4\n",
+    "\n",
+    "\n",
+    "e_min = 1e4 * g_cm_3  # Rule 1\n",
+    "e_max = 1e18 * g_cm_3  # Rule 1\n",
+    "e_grid = np.geomspace(e_min, e_max, 10000)\n",
+    "p_grid = EOS_P(e_grid)\n",
+    "\n",
+    "\n",
+    "def EofP(P, e_grid, p_grid):\n",
+    "    return np.interp(P, p_grid, e_grid)\n",
+    "\n",
+    "\n",
+    "def PofE(E, e_grid, p_grid):\n",
+    "    return np.interp(E, e_grid, p_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import ode\n",
+    "\n",
+    "\n",
+    "# working with rescaled quantities\n",
+    "def TOV1(r, y, grids):\n",
+    "    p, m = y\n",
+    "    e_grid = grids[0]\n",
+    "    p_grid = grids[1]\n",
+    "\n",
+    "    e = EofP(p, e_grid, p_grid)\n",
+    "\n",
+    "    dpdr = -(e + p) * (m + 4 * pi * r**3 * p)  # Rule 4\n",
+    "    dpdr = dpdr / (r * (r - 2 * m))  # Rule 4\n",
+    "    dmdr = 4 * pi * r**2 * e  # Rule 4\n",
+    "\n",
+    "    return np.array([dpdr, dmdr])\n",
+    "\n",
+    "\n",
+    "def solveTOV1(rho_c, e_grid, p_grid):\n",
+    "    # Notice that we only rescale quantities inside this function\n",
+    "    rho_c = rho_c * G / c**2\n",
+    "    e_grid = e_grid * G / c**2\n",
+    "    p_grid = p_grid * G / c**4\n",
+    "\n",
+    "    Pmin = p_grid[0]\n",
+    "\n",
+    "    r = 4.441e-16 * cm  # Rule 1\n",
+    "    dr = 10.0 * cm  # Rule 1\n",
+    "\n",
+    "    p_c = PofE(rho_c, e_grid, p_grid)\n",
+    "\n",
+    "    P0 = p_c - (4 * pi / 3) * (p_c + rho_c) * (3 * p_c + rho_c) * r**2  # Rule 4\n",
+    "    m0 = 4 / 3 * pi * rho_c * r**3  # Rule 4\n",
+    "\n",
+    "    param = (e_grid, p_grid)\n",
+    "\n",
+    "    stateTOV = np.array((P0, m0))\n",
+    "    sy = ode(TOV1).set_integrator(\"dop853\")\n",
+    "    sy.set_initial_value(stateTOV, r).set_f_params(param)\n",
+    "    while sy.successful() and stateTOV[0] > Pmin:\n",
+    "        stateTOV = sy.integrate(sy.t + dr)\n",
+    "        dpdr, dmdr = TOV1(sy.t + dr, stateTOV, param)\n",
+    "        dr = 0.46 / (dmdr / stateTOV[1] - dpdr / stateTOV[0])\n",
+    "    # at the end of this function, we rescale the quantities back\n",
+    "    return stateTOV[1] * c**2 / G, sy.t\n",
+    "\n",
+    "\n",
+    "def compute_tov_properties(rho_c, e_grid, p_grid):\n",
+    "    M = np.zeros_like(rho_c)\n",
+    "    R = np.zeros_like(rho_c)\n",
+    "    for i in range(len(rho_c)):\n",
+    "        M[i], R[i] = solveTOV1(rho_c[i], e_grid, p_grid)\n",
+    "    return M, R\n",
+    "\n",
+    "\n",
+    "def cal_MR():\n",
+    "    density = np.geomspace(1e14 * g_cm_3, e_max, 100)  # Rule 1\n",
+    "    return compute_tov_properties(density, e_grid, p_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2560x1920 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "M, R = cal_MR()\n",
+    "\n",
+    "plt.figure(dpi=400)\n",
+    "plt.plot(R / km, M / Msun, \"ro-\", ms=2)  # Rule 3\n",
+    "plt.xlabel(\"Radius [km]\")\n",
+    "plt.ylabel(\"Mass [Msun]\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/cluster.rst.txt b/_sources/cluster.rst.txt
new file mode 100644
index 0000000..11c2094
--- /dev/null
+++ b/_sources/cluster.rst.txt
@@ -0,0 +1,74 @@
+Computation Guide
+=================
+
+Computation Time Scaling
+------------------------
+
+Fully converged Bayesian inference requires numerous iterations to explore the entire prior parameter space. This process demands substantial computation time, which can be very expensive depending on the Equation of State (EOS) you are using.
+
+ **1. Using Integrated EOS Models**
+
+If you are utilizing the EOS models integrated into this package, here is an overview of the computational resources required:
+
+a. **Relativistic Mean Field (RMF) Model**
+
+   - **Optimization:** Please use the Numba-accelerated version of EOS computation.
+   - **Resources:** With one NICER observation constraint and utilizing **256 CPU cores**, the computation can be completed within approximately **1 hour**.
+   - **Scalability:** The computation time will increase as you include more observations.
+
+b. **Speed of Sound Model and Polytropic Model**
+
+   - **Nature:** These are meta-models with explicit mathematical formulas, allowing for analytical computations.
+   - **Resources:** Using **24 CPU cores**, the inference can be completed within **6 hours**.
+   - **Scalability:** The computation time scales with the number of observations included.
+
+c. **Strangeon Model and MIT Bag Model**
+
+   - **Nature:** Although these are physics-based models, the computations are relatively straightforward and fast.
+   - **Resources:** On a laptop with **24 cores**, the computation can be completed within **5-6 hours**.
+   - **Scalability:** The computation time increases with the addition of more observations.
+
+**2. Using Custom EOS Models**
+
+If you are defining your own EOS and wish to perform inference using this package:
+
+- **Performance Requirement:** Ensure that the computation time for your EOS is **less than 1 second** per evaluation. Otherwise, the overall computation will become prohibitively expensive due to the large number of samples required for Bayesian inference.
+
+Parallelization
+---------------
+
+Given the computational intensity of Bayesian inference, parallelization strategies are essential to optimize performance. Below are the steps to parallelize computations using **UltraNest** without modifying the package's code.
+
+**Requirements**
+
+- **OpenMPI:** Ensure that OpenMPI is installed and available on your system.
+- **Python Packages:** Install `h5py` and `mpi4py`.
+
+**Steps to Parallelize**
+
+1. **Set the Number of Threads**
+
+   Control the number of threads per process by setting the `OMP_NUM_THREADS` environment variable. For example, to set it to 1:
+
+   .. code-block:: bash
+
+       export OMP_NUM_THREADS=1
+
+2. **Run the Inference Script with MPI**
+
+   Use `mpiexec` to execute your inference script across multiple cores. Replace `inference.py` with the name of your inference script.
+
+   .. code-block:: bash
+
+       mpiexec -np 4 python inference.py
+
+   - **Explanation:** This command runs the inference using **4 CPU cores**. Adjust the `-np` value according to the number of available cores you wish to utilize.
+
+**Recommendations**
+
+- **Virtual Environment:** Regardless of where you are running the code, it is highly recommended to use a virtual environment to manage your installations. This practice helps maintain dependencies and avoids conflicts with other projects.
+
+**Additional Resources**
+
+For more detailed information on performance and parallelization strategies with **UltraNest**, refer to the `UltraNest Performance Page <https://johannesbuchner.github.io/UltraNest/performance.html>`_.
+
diff --git a/_sources/crust_EOS.rst.txt b/_sources/crust_EOS.rst.txt
new file mode 100644
index 0000000..a1c6749
--- /dev/null
+++ b/_sources/crust_EOS.rst.txt
@@ -0,0 +1,9 @@
+.. _Crust_EOS_conect:
+
+Crust Equation of state interpolation
+======================================
+
+Function to connect the crust EoS given by user with the core part by function calls
+
+.. automodule:: EOSgenerators.crust_EOS
+   :members:
diff --git a/_sources/fastRMF_EoS.rst.txt b/_sources/fastRMF_EoS.rst.txt
new file mode 100644
index 0000000..f094410
--- /dev/null
+++ b/_sources/fastRMF_EoS.rst.txt
@@ -0,0 +1,9 @@
+.. _Fast_RMF_EOS:
+
+RMF EOS solver (numba speed up version)
+========================================
+
+Functions to compute RMF Equation of state from given parameters. (numba speed up)
+
+.. automodule:: EOSgenerators.fastRMF_EoS
+   :members:
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
new file mode 100644
index 0000000..c201cd5
--- /dev/null
+++ b/_sources/index.rst.txt
@@ -0,0 +1,67 @@
+.. module:: CompactObject
+
+.. include:: intro.rst
+
+.. toctree::
+   :maxdepth: 2
+   :caption: User Guide
+
+   installation
+   publications
+   cluster
+   starthere
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Development
+   
+   Contributor
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   test_EOSgenerators
+   test_TOVsolver
+   test_Inference
+   test_Bayesian_inference_Strangeon_EOS
+   test_Bayesian_inference_MITbag_EOS
+   UnitConventionForDeveloper
+
+.. toctree::
+   :maxdepth: 2
+   :caption: EOS generators module
+
+   crust_EOS
+   EOS
+   fastRMF_EoS
+
+.. toctree::
+   :maxdepth: 2
+   :caption: TOV solver module
+
+   EoS_import
+   main
+   TOV_solver
+   speed_of_sound
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Bayesian Inference module
+
+   BayesianSampler
+   prior
+   Likelihood
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Postprocessing module
+
+   Cornerplot
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt
new file mode 100644
index 0000000..1f383c6
--- /dev/null
+++ b/_sources/installation.rst.txt
@@ -0,0 +1,133 @@
+Installation
+=========================
+
+CompactObject is an open-source tool designed for comprehensive neutron star equation inference. It is built to be easy to install and use. Follow the step-by-step installation guide below to get started.
+
+Using Anaconda (Recommended)
+---------------------------
+
+1. **Create a Virtual Environment**
+
+   We recommend creating a virtual environment for CompactObject using Anaconda:
+
+   .. code-block:: bash
+
+       conda create -n CompactObject
+
+   When prompted to proceed, type `y` and press Enter:
+
+   .. code-block:: none
+
+       Proceed ([y]/n)?
+
+2. **Activate the Environment**
+
+   Activate your newly created environment with the following command:
+
+   .. code-block:: bash
+
+       conda activate CompactObject
+
+   **Note:** Once you create this environment, you don't need to create it again. Simply activate it whenever you want to use CompactObject.
+
+3. **Install CompactObject**
+
+   Use `pip` to install the CompactObject package:
+
+   .. code-block:: bash
+
+       pip install CompactObject-TOV
+
+   To upgrade to the latest version, run:
+
+   .. code-block:: bash
+
+       pip install CompactObject-TOV --upgrade
+
+4. **You're Ready to Use CompactObject!**
+
+   You are now ready to use CompactObject. Whenever you want to use this package, remember to activate the environment first:
+
+   .. code-block:: bash
+
+       conda activate CompactObject
+
+   Our package automatically installs all necessary dependencies for you. The dependencies include:
+
+   - `corner`
+   - `csv`
+   - `itertools`
+   - `math`
+   - `matplotlib`
+   - `numba`
+   - `numbaminpack`
+   - `numpy`
+   - `os`
+   - `pandas`
+   - `scipy`
+   - `sys`
+   - `ultranest`
+
+Some of the dependencies like numbaminpack may hard to install if don't have a fortran complier, please
+refer to this page `NumbaMinpack documentation <https://pypi.org/project/NumbaMinpack/>`, and you can skip
+this NumbaMinpack if you are not using "fastRMF_EoS" and "pQCD"
+
+Optional: Using Python Virtual Environment
+------------------------------------------
+
+If you are not using Anaconda, you can create a virtual environment using Python's `venv` module:
+
+1. **Create a Virtual Environment**
+
+   Run the following command to create a virtual environment named `CompactObject`:
+
+   .. code-block:: bash
+
+       python3 -m venv CompactObject
+
+   *You can specify a different path by replacing `CompactObject` with your desired directory name.*
+
+2. **Activate the Environment**
+
+   Activate the virtual environment with:
+
+   .. code-block:: bash
+
+       source CompactObject/bin/activate
+
+3. **Install CompactObject**
+
+   Once the environment is activated, install CompactObject using `pip`:
+
+   .. code-block:: bash
+
+       pip install CompactObject-TOV
+
+   To upgrade to the latest version, run:
+
+   .. code-block:: bash
+
+       pip install CompactObject-TOV --upgrade
+
+4. **Using the Package**
+
+   You are now ready to use CompactObject. Each time you want to use the package, ensure you activate the environment:
+
+   .. code-block:: bash
+
+       source CompactObject/bin/activate
+
+Summary
+-------
+
+- **Using Anaconda:**
+  1. Create and activate the `CompactObject` environment.
+  2. Install CompactObject with `pip`.
+  3. Activate the environment whenever you use the package.
+
+- **Using Python Virtual Environment:**
+  1. Create and activate the `CompactObject` virtual environment.
+  2. Install CompactObject with `pip`.
+  3. Activate the environment whenever you use the package.
+
+If you encounter any issues or have questions, feel free to reach out for support. Happy computing!
diff --git a/_sources/intro.rst.txt b/_sources/intro.rst.txt
new file mode 100644
index 0000000..9db5df8
--- /dev/null
+++ b/_sources/intro.rst.txt
@@ -0,0 +1,184 @@
+.. _readme:
+
+***********************************
+CompactObject Package Tutorials
+***********************************
+
+**An Open-Source Package for Neutron Stars**
+**Comprehensive Workflow for Bayesian Inference Constraining**
+**Neutron Star EOS Package**
+
+**CompactObject** is an open-source package designed to perform Bayesian inference 
+on neutron star equation of state (EOS) constraints. It offers a comprehensive workflow
+that integrates astrophysical observations and nuclear measurements to explore the interior 
+composition of neutron stars. The package is built to be user-friendly, easily extendable, 
+and thoroughly documented, making it an essential tool for researchers in nuclear astrophysics.
+The package integrates three independent modules:
+
+1. **Tolman–Oppenheimer–Volkoff (TOV) Equation Solver**  
+   A user-friendly solver for determining neutron star structure based on a given EOS.
+
+2. **Neutron Star EOS Generator**  
+   Generates EOS across the full density range. Currently, it includes the relativistic mean 
+   field theory EOS solver and will support polytropes and additional EOS models in the future.
+
+3. **Bayesian Inference Workflow**  
+   A complete package for constraining the EOS of neutron stars. This includes defining the 
+   likelihood from observations (X-ray timing, gravitational waves, radio timing) and nuclear 
+   experiments (nuclear quantities, pQCD, chiral EFT), as well as from simulated astrophysical 
+   observations. It also involves defining priors for the parameters and running nested sampling 
+   of the posterior space.
+
+These three components are independent, allowing users to integrate them into their workflows 
+seamlessly. The package's functionality extends beyond inference studies, and we welcome 
+contributions of new features.
+
+For installation instructions, please refer to the `Installation Page <https://chunhuangphy.github.io/CompactObject/installation.html>`_.
+
+**Publications**
+
+Papers generated using this package include:
+
+.. [1] **Huang, C., Raaijmakers, G., Watts, A. L., Tolos, L., & Providência, C.** (2024).  
+   *Constraining fundamental nuclear physics parameters using neutron star mass-radius measurements I: Nucleonic models*.  
+   *Monthly Notices of the Royal Astronomical Society*, 529.  
+   `DOI:10.1093/mnras/stae844 <https://academic.oup.com/mnras/article/529/4/4650/7634362>`_
+
+.. [2] **Huang, C., Tolos, L., Providência, C., & Watts, A.** (2024).  
+   *Constraining a relativistic mean field model using neutron star mass-radius measurements II: Hyperonic models*.  
+   *arXiv preprint arXiv:2410.14572*.  
+   `https://arxiv.org/abs/2410.14572 <https://arxiv.org/abs/2410.14572>`_
+
+.. [3] **Huang, C., & Zheng, X.-P.** (2024).  
+   *Bayesian Insights into post-Glitch Dynamics: Model comparison and parameter constraint from decades-long observation data of the Crab pulsar*.  
+   *arXiv preprint arXiv:2409.18432*.  
+   `https://arxiv.org/abs/2409.18432 <https://arxiv.org/abs/2409.18432>`_
+
+If you use our software, please consider citing us with the following standard citation:
+
+The inference conducted here relies on the framework in the :math:`\textit{CompactObject}` package [CompactObject]_.  
+This is an open-source, full-scope package designed to implement Bayesian constraints on the neutron star EOS. Other work based on this package is ....
+
+.. _CompactObject: https://chunhuangphy.github.io/CompactObject/
+
+Concept
+=======
+
+Bayesian inference studies of the neutron star equation of state have become a trending
+field, particularly due to significant advancements such as the Neutron Star 
+Interior Composition Explorer (NICER) measuring neutron star masses and radii through X-ray timing, and the detection of neutron star merger events through gravitational wave observations by the LIGO detector.
+
+Below is the overall pipeline of this field:
+
+.. image:: workflow.png
+   :alt: Workflow Diagram
+
+As depicted in this diagram, fundamental physics provides the EOS for neutron stars. By inputting the EOS into the Tolman–Oppenheimer–Volkoff (TOV) equation, we can obtain parameters related to the neutron star structure, such as mass, radius, and tidal properties. Instruments like NICER and LIGO measure these properties, providing data for Bayesian inference. This process constrains the EOS by determining the region in the Mass-Radius space where neutron stars can exist, ultimately offering insights into the fundamental composition of their interiors.
+
+Equation of State
+=================
+
+The neutron star equation of state (EOS) plays a crucial role in determining the composition of a neutron star. It is closely connected to the microphysical properties of neutron stars. Here, we introduce an EOS derived from the Relativistic Mean Field (RMF) theory. The Lagrangian of this model is represented as follows:
+
+.. math::
+
+   \mathcal{L} = \sum_N \mathcal{L}_N + \mathcal{L}_{\mathcal{M}} + \sum_l \mathcal{L}_l
+
+Where:
+
+- :math:`\mathcal{L}_N` is the nucleonic Lagrangian
+- :math:`\mathcal{L}_{\mathcal{M}}` is the meson part of the Lagrangian
+- :math:`\mathcal{L}_l` is the lepton Lagrangian
+
+Details of the Lagrangian are illustrated below:
+
+.. image:: lagrangian.png
+   :alt: Lagrangian Diagram
+
+In the equation:
+
+- :math:`\Psi_{N}` and :math:`\psi_{l}` are the nucleon and lepton spinors
+- :math:`\bar{I}_{N}` is the nucleon isospin operator
+- :math:`g` denotes the strong interaction coupling of a meson to a nucleon
+- :math:`m` represents the masses of the nucleons, mesons, and leptons
+- The parameters :math:`\kappa`, :math:`\lambda_0`, :math:`\zeta`, and :math:`\Lambda_{\omega}` are coupling constants to be determined by the inference method
+
+These free parameters represent the degrees of freedom in the RMF model and can be determined through nuclear experiments. Additionally, astrophysical observations can help constrain these parameters. The complete list of parameters includes:
+
+.. image:: free_para.png
+   :alt: Free Parameters Diagram
+
+These parameters should be inputted to generate the EOS using our EOS generators module. Different EOS parameters will affect mass-radius relations, as demonstrated here. For more details on the physics, refer to `Glendenning (1996) <https://ui.adsabs.harvard.edu/abs/1996cost.book.....G/abstract>`_.
+
+Tolman–Oppenheimer–Volkoff (TOV) Equation
+==========================================
+
+The TOV equation is a general relativity equation that constrains the structure of a spherically symmetrical body under gravity. The original equation is:
+
+.. math::
+
+    \frac{d P}{d r} = -\frac{G m}{r^2} \rho \left(1 + \frac{P}{\rho c^2}\right) \left(1 + \frac{4 \pi r^3 P}{m c^2}\right) \left(1 - \frac{2 G m}{r c^2}\right)^{-1}
+
+To solve this equation, the essential ingredient is the EOS. The basic strategy is as follows: at a given central density, input it into the neutron star EOS to obtain the pressure. Then, integrate the density from the center to the boundary, repeating this process across the entire possible density range.
+
+In our code, we provide two different functions:
+
+1. **Default Solver:**  
+   Solves the TOV equation within a predefined density range (log(14.3, 15.6) on a scale of 10).
+
+2. **Point-by-Point Solver:**  
+   Allows solving the equation for any chosen central density range.
+
+For more information on the TOV equation, visit the `Wikipedia page <https://en.wikipedia.org/wiki/Tolman–Oppenheimer–Volkoff_equation>`_.
+
+Bayesian Inference
+==================
+
+Using Bayesian inference tools to explore constraints on the neutron star EOS is increasingly common. The foundational equation is Bayes' Theorem:
+
+.. math::
+    P(A \mid B) = \frac{P(B \mid A) P(A)}{P(B)}
+
+Where:
+
+- **Posterior Probability** (:math:`P(A \mid B)`): The probability of hypothesis A after observing B.
+- **Prior Probability** (:math:`P(A)`): The initial probability of hypothesis A before observing B.
+- **Likelihood** (:math:`P(B \mid A)`): The probability of observing B given that hypothesis A is true.
+- **Evidence** (:math:`P(B)`): The probability of observing B under all possible hypotheses.
+
+In this context:
+
+- **Posterior** is the updated probability after considering new observations or experiments.
+- **Prior** is the initial belief before new data is considered.
+- **Likelihood** is the influence of new data on the belief.
+
+The likelihood in our studies primarily comes from four sources:
+
+1. **Mass-Radius Measurements from X-ray Timing** (e.g., NICER)
+2. **Tidal Measurements from Gravitational Wave Detection**
+3. **Mass Measurements from Radio Timing**
+4. **Nuclear Physics Constraints from Experiments**
+
+NICER's mass-radius measurements are a significant achievement in neutron star physics, comparable to gravitational wave detections. For the connection between our EOS and nuclear quantities, refer to `Chen & Piekarewicz (2014a) <https://journals.aps.org/prc/abstract/10.1103/PhysRevC.90.044305>`_.
+
+The nuclear physics quantities we consider are:
+
+- **K**: Compressibility of nuclear matter
+- **J**: Symmetry energy at saturation density
+- **L**: Slope of symmetry energy at saturation density
+
+There are more on constraining the EOS by Pure neutron matter (PNM):
+
+- **chiral EFT constraint**
+- **pQCD constraint**
+
+These can be computed from posterior samples (we will add the nuclear properties computation code in the near future). They also serve as independent constraints on the neutron star EOS.
+
+When performing real astrophysical sampling, it is crucial to sample the neutron star central density corresponding to each measurement used. For instance, if investigating the constraining effect of two mass-radius measurements on the EOS, you need to define two additional free parameters—the central densities for each measurement. Otherwise, the inference would not represent a full-scope EOS analysis. Therefore, our likelihood functions include a parameter :math:`d_1`, representing the density parameter of each observation.
+
+Summary
+=======
+
+**CompactObject** provides a comprehensive suite of tools for Bayesian inference on neutron star EOS constraints. By integrating astrophysical observations with nuclear experiments, it facilitates a deeper understanding of neutron star interiors.
+
+For more information, tutorials, and support, please refer to our documentation or contact the development team.
diff --git a/_sources/main.rst.txt b/_sources/main.rst.txt
new file mode 100644
index 0000000..d7fc8bd
--- /dev/null
+++ b/_sources/main.rst.txt
@@ -0,0 +1,12 @@
+.. _main:
+
+Main functions
+=====================
+
+Controls most functionality of package by calling the following functions.
+* :ref:'EoS_importing'
+* :ref: 'TOV_solving'
+* :ref: '__s'
+
+.. automodule:: TOVsolver.main
+   :members:
diff --git a/_sources/prior.rst.txt b/_sources/prior.rst.txt
new file mode 100644
index 0000000..7ef8de9
--- /dev/null
+++ b/_sources/prior.rst.txt
@@ -0,0 +1,9 @@
+.. _Prior_definition:
+
+Prior Definition Module
+========================
+
+Functions to define prior distribution of parameters
+
+.. automodule:: InferenceWorkflow.prior
+   :members:
diff --git a/_sources/publications.rst.txt b/_sources/publications.rst.txt
new file mode 100644
index 0000000..2d0b83a
--- /dev/null
+++ b/_sources/publications.rst.txt
@@ -0,0 +1,94 @@
+Publications
+============
+
+**CompactObject** has been implemented in several studies and ongoing research projects. If your work is based on or has contributed to our package and you would like to be mentioned on this page, please send us an email at `chun.h@wustl.edu <mailto:chun.h@wustl.edu>`_. This allows us to promote your work within our documentation, thereby extending the impact of the **CompactObject** package. Please ensure you specifically cite us if you are using this package, even if you utilize only one functionality, so that we can acknowledge your contribution. We greatly appreciate feedback and contributions to enhance our functionalities.
+
+The initial papers based on or contributing to this package are listed below:
+
+**1. Definition of RMF Models**
+
+.. [1] **Huang, C., Raaijmakers, G., Watts, A. L., Tolos, L., & Providência, C.** (2024).  
+   *Constraining fundamental nuclear physics parameters using neutron star mass-radius measurements I: Nucleonic models*.  
+   *Monthly Notices of the Royal Astronomical Society*, 529.  
+   `DOI:10.1093/mnras/stae844 <https://academic.oup.com/mnras/article/529/4/4650/7634362>`_
+
+.. [2] **Huang, C., Tolos, L., Providência, C., & Watts, A.** (2024).  
+   *Constraining a relativistic mean field model using neutron star mass-radius measurements II: Hyperonic models*.  
+   *arXiv preprint arXiv:2410.14572*.  
+   `https://arxiv.org/abs/2410.14572 <https://arxiv.org/abs/2410.14572>`_
+
+**2. Application of Likelihood Functions and analysis glitch data**
+
+.. [3] **Huang, C., & Zheng, X.-P.** (2024).  
+   *Bayesian Insights into post-Glitch Dynamics: Model comparison and parameter constraint from decades-long observation data of the Crab pulsar*.  
+   *arXiv preprint arXiv:2409.18432*.  
+   `https://arxiv.org/abs/2409.18432 <https://arxiv.org/abs/2409.18432>`_
+
+**3. Strangeon Star EOS Inference**
+
+.. [4] **Yuan, W.-L., Huang, C., Zhang, C., Zhou, E.-P., & Xu, R.-X.** (2024).  
+   *Bayesian inference of strangeon matter equation of state using the new NICER mass-radius measurements J0437-4751 and GW190814 data*.  
+   *In preparation*.
+
+**4. pQCD Computation Implementation**
+
+.. _pQCD_publications:
+
+.. [5] **Gorda, T., Komoltsev, O., & Kurkela, A.**  
+   *Ab-initio QCD Calculations Impact the Inference of the Neutron-star-matter Equation of State*.  
+   e-Print: [2204.11877 [nucl-th]](https://arxiv.org/abs/2204.11877)  
+   DOI: [10.3847/1538-4357/acce3a](https://doi.org/10.3847/1538-4357/acce3a)  
+   Published in: *Astrophysical Journal*, 950 (2023) 2, 107
+
+**5. Chiral Effective Field Theory Constraint Implementation**
+
+.. _chiral_EFT_publications:
+
+.. [6] **Marquez, K.D., Malik, T., Pais, H., Menezes, D.P., & Providência, C.**  
+   *Nambu–Jona-Lasinio Description of Hadronic Matter from a Bayesian Approach*.  
+   e-Print: [2407.18452 [nucl-th]](https://arxiv.org/abs/2407.18452)  
+   DOI: [10.1103/PhysRevD.110.063040](https://doi.org/10.1103/PhysRevD.110.063040)  
+   Published in: *Physical Review D*, 110 (2024) 6, 063040
+
+**6. Density-dependent RMF Implementation**
+
+.. _density_function_publications:
+
+.. [7] **Malik, T., Ferreira, M., Agrawal, B.K., & Providência, C.** (2022).  
+   *Relativistic Description of Dense Matter Equation of State and Compatibility with Neutron Star Observables: A Bayesian Approach*.  
+   *Astrophysical Journal*, 930(1), 17.  
+   `https://doi.org/10.3847/1538-4357/ac5d3c <https://doi.org/10.3847/1538-4357/ac5d3c>`_
+
+
+.. [8] **Providência, C., Malik, T., Bastos Albino, M., & Ferreira, M.**  
+   *Neutron Star Equation of State: Identifying Hadronic Matter Characteristics*.  
+   e-Print: [2307.05086 [nucl-th]](https://arxiv.org/abs/2307.05086)  
+   DOI: [10.1201/9781003306580-5](https://doi.org/10.1201/9781003306580-5)
+
+
+
+
+If your work relates to pQCD computations, chiral effective field theory constraints, or density-function implementations within the **CompactObject** framework, please ensure to update the corresponding sections with your publications.
+
+Citing CompactObject
+--------------------
+
+.. code-block:: none
+
+    If you use our software, please consider citing us with the following standard citation:
+
+    The inference conducted here relies on the framework in the :math:`\textit{CompactObject}` package [CompactObject]_.  
+    This is an open-source, full-scope package designed to implement Bayesian constraints on the neutron star EOS. Other work based on this package is ....
+
+.. [CompactObject] https://chunhuangphy.github.io/CompactObject/
+
+Acknowledgements
+----------------
+
+We welcome feedback and contributions to expand the functionalities of **CompactObject**. Your support helps enhance the tool for the entire research community.
+
+Contact
+-------
+
+For inquiries, contributions, or to be featured in our publications list, please contact us at `chun.h@wustl.edu <mailto:chun.h@wustl.edu>`_.
+
diff --git a/_sources/speed_of_sound.rst.txt b/_sources/speed_of_sound.rst.txt
new file mode 100644
index 0000000..65db08a
--- /dev/null
+++ b/_sources/speed_of_sound.rst.txt
@@ -0,0 +1,9 @@
+.. _C_s:
+
+Calculate Speed of Sound
+=====================
+
+Calculates and returns speed of sound from an equaiton of state inputed by the user.
+
+.. automodule:: TOVsolver.speed_of_sound
+   :members:
diff --git a/_sources/starthere.rst.txt b/_sources/starthere.rst.txt
new file mode 100644
index 0000000..c511d53
--- /dev/null
+++ b/_sources/starthere.rst.txt
@@ -0,0 +1,96 @@
+Start Here!
+==========
+
+Welcome to **CompactObject**! This guide will help you get started after successfully installing the package and activating your virtual environment. Follow the steps below to achieve your scientific goals using the functionalities of the **CompactObject** package.
+
+Prerequisites
+-------------
+- **Package Installation:** Ensure you have installed **CompactObject** successfully.
+- **Virtual Environment:** Activate your virtual environment where **CompactObject** is installed.
+
+Depends on Your Scientific Goal
+-------------------------------
+
+ 1. **Understanding Basic Concepts**
+
+If you are new to **CompactObject** and wish to familiarize yourself with its basic concepts and introduction:
+
+- **Index Page:** Explore the `Index Page <https://chunhuangphy.github.io/CompactObject/index.html>`_ for an overview of the package.
+- **README:** Review the `README <https://github.com/ChunHuangPhy/CompactObject/blob/main/README.md>`_ on GitHub to understand the package’s purpose and features.
+
+ 2. **Generating Equations of State (EOS)**
+
+To use **CompactObject** for generating equations of state:
+
+- **EOS Generators Tutorial:** Start with the `EOS Generators Tutorial <https://chunhuangphy.github.io/CompactObject/test_EOSgenerators.html>`_ which showcases all currently integrated EOS computations in the package. The supported EOS models include:
+  - **Relativistic Mean Field (RMF) Model**
+  - **Strangeon Star Model**
+  - **Quark Star Model:** MIT Bag Model
+  - **Polytropic Model**
+  - **Speed of Sound Model**
+  - **Density-Dependent EOS Model**
+  - *...and more*
+
+ 3. **Using the Tolman–Oppenheimer–Volkoff (TOV) Solver**
+
+To utilize the TOV solver integrated within **CompactObject**:
+
+- **TOV Solver Tutorial:** Refer to the `TOV Solver Tutorial <https://chunhuangphy.github.io/CompactObject/test_TOVsolver.html>`_. This tutorial demonstrates how to perform TOV computations using table-based EOS.
+- **Inputting an Array:** If you prefer to input an array instead of a table, refer back to the `EOS Generators Tutorial <https://chunhuangphy.github.io/CompactObject/test_EOSgenerators.html>`_. The quantities currently available are:
+  - **Mass**
+  - **Radius**
+  - **Tidal Deformability**
+
+ 4. **Performing Bayesian Inference on the EOS**
+
+**CompactObject** offers robust tools for Bayesian inference related to neutron star EOS. There are two primary scenarios:
+
+ a. **Using Integrated EOS for Inference**
+
+If you wish to perform Bayesian inference using the EOS models already integrated into **CompactObject**, follow these examples:
+
+1. **RMF EOS Inference Pipeline:** Explore the `RMF EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Inference.html>`_.
+2. **Strangeon EOS Inference Pipeline:** Check out the `Strangeon EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_Strangeon_EOS.html>`_.
+3. *...and more examples*
+
+ b. **Using Your Own EOS for Inference**
+
+If you have developed your own EOS and wish to perform Bayesian inference:
+
+1. **Contributor Instructions:** Follow the `Instructions for Contributors <https://chunhuangphy.github.io/CompactObject/Contributor.html>`_ to define your EOS without conflicts and understand the necessary tests to ensure computational accuracy.
+2. **Constructing a New Pipeline:** Utilize the existing inference pipelines, such as the `RMF EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Inference.html>`_ and `Strangeon EOS Inference Pipeline <https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_Strangeon_EOS.html>`_, as reference points to build your own inference workflow based on **CompactObject**.
+3. **Performance Considerations:** Ensure that the computation time for a single EOS is within the sub-second range to handle millions of samples required for Bayesian inference efficiently.
+
+**Contribution Invitation:**  
+Consider contributing your EOS to our community if you are using our package! This will boost the influence of your work and make you a collaborator on this project. We can also promote your results on our page. Please check the `Instructions for Contributors <https://chunhuangphy.github.io/CompactObject/Contributor.html>`_ if you wish to contribute.
+
+ 5. **Performing Bayesian Inference in Other Fields**
+
+If you aim to use the inference modules of **CompactObject** for Bayesian inference outside of EOS studies, such as in high-energy physics or other astrophysical fields:
+
+- **Example Notebook:** Refer to the `Sample Glitch Bayesian Inference Notebook <https://github.com/ChunHuangPhy/CompactObject/blob/main/Test_Case/Sample_glitchBayesian.ipynb>`_ for an example of how to apply **CompactObject** to different inference scenarios.
+
+ 6. **Advanced Usage: Modifying Solvers or APIs**
+
+If the existing tutorials do not cover your specific needs, such as:
+
+- **Modifying the TOV Solver:** For example, changing to a double-fluid TOV solver.
+- **Investigating Additional EOS-Related Quantities:** Beyond pressure and density used in TOV solving.
+
+You can:
+
+- **API Documentation:** Consult the detailed API documentation in this documentation to identify and utilize the functions you need.
+- **GitHub Repository:** Visit our `GitHub Page <https://github.com/ChunHuangPhy/CompactObject/tree/main>`_ to explore the source code and understand the internal workings of **CompactObject**.
+
+**Note:**  
+The key functions, including the **TOVsolver module** code, form the foundation for all inference and computations. Modifying them may require substantial code restructuring. Therefore, if you clone the repository locally and need to make changes to the **TOVsolver**, proceed with caution to avoid extensive code rebuilding. Only original contributors have permission to modify the **TOVsolver** module code, and such commits must undergo detailed reviews by the original members of the project.
+
+Acknowledgements
+----------------
+
+We welcome feedback and contributions to expand the functionalities of **CompactObject**. Your support helps enhance the tool for the entire research community.
+
+Contact
+-------
+
+For inquiries, contributions, or to be featured in our publications list, please contact us at `chun.h@wustl.edu <mailto:chun.h@wustl.edu>`_.
diff --git a/_sources/test_Bayesian_inference_MITbag_EOS.ipynb.txt b/_sources/test_Bayesian_inference_MITbag_EOS.ipynb.txt
new file mode 100644
index 0000000..c733e26
--- /dev/null
+++ b/_sources/test_Bayesian_inference_MITbag_EOS.ipynb.txt
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "edb2cf2a",
+   "metadata": {},
+   "source": [
+    "## MIT bag EOS Inference pipline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc633d3",
+   "metadata": {},
+   "source": [
+    "The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state $p=\\frac{\\epsilon}{3}-\\frac{4B}{3}$. There is only one parameter, the \"bag constant\" $B$. This represents the vacuum energy density, which creates a \"bag\" in which quarks are confined. See [Chodos et al. (1974)](https://doi.org/10.1103/PhysRevD.9.3471).\n",
+    "\n",
+    "In this notebook, we use our Bayesian inference code to constrain the value of $B$ using observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acdcb64a",
+   "metadata": {},
+   "source": [
+    "### (a) Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6f20856",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import EOSgenerators.MITbag_EOS as MITbag\n",
+    "from TOVsolver.unit import MeV, fm, g_cm_3, dyn_cm_2, km, Msun\n",
+    "import TOVsolver.main as main"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edfa0f72",
+   "metadata": {},
+   "source": [
+    "### (b) Set up priors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38136542",
+   "metadata": {},
+   "source": [
+    "Next, we need to set up the priors. We first use a parameters array to specify the variable name. This process should be consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube is a set of random numbers from 0 to 1. This prior setting is the standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.\n",
+    "\n",
+    "We provided two options call from prior:\"normal_Prior\" and \"flat_prior\".\n",
+    "\n",
+    "We note that since we are doing Equation of state inference from mass-radius data of neutron star measurement. The central density of the star should be also sampled. Otherwise, this will be a partially-defined prior that did not span all of the parameter space, and proved to be different from full-scope inference.\n",
+    "\n",
+    "This request will randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of the compact star --- different equations of state will predict different upper bounds, so here we need to use the prior-setting EoS parameters computing the mass-radius curve for this equation of state, then find out the last stable point of this equation of state (first mass points that give the direvative to be negative). We can find that index with the len() function, then reset this max_d to be upper limit of this density range.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b87dd3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = ['B','d1']\n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = prior.flat_prior(20,100,cube[0])\n",
+    "\n",
+    "    B = params[0]\n",
+    "    \n",
+    "    epsilon,p = MITbag.MITbag_compute_EOS(B)\n",
+    "\n",
+    "    RFSU2R = [] \n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50) \n",
+    "    if all(x<y for x,y in zip(epsilon[:], epsilon[1:])) and all(x<y for x, y in zip(p[:], p[1:])):\n",
+    "        MR = main.OutputMR(\"\",epsilon,p).T  \n",
+    "        \n",
+    "    else:\n",
+    "        MR = []\n",
+    "    if len(MR) == False: \n",
+    "        params[1] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "   \n",
+    "        for i in range(len(MR[1])):\n",
+    "            RFSU2R.append(MR[1][i])\n",
+    "            MFSU2R.append(MR[0][i])   \n",
+    "            if i > 20 and MR[0][i] - MR[0][i-1]< 0:    \n",
+    "                break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[1] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[1] = 14.3 + (max_d - 14.3) * cube[1]\n",
+    "        #params[3] = 14.3 + (max_d - 14.3) * cube[3]\n",
+    "        #this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8e4822",
+   "metadata": {},
+   "source": [
+    "In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.\n",
+    "\n",
+    "Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement.\n",
+    "For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. \"d1\" for the centre density of this measurement, in the meantime add \"params[2]\" to this code.\n",
+    "\n",
+    "If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. \"d1\" and \"d2\" for the centre density of these two measurements, in the meantime add \"params[3]\" to the above code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ab6d43",
+   "metadata": {},
+   "source": [
+    "### (c) Set up likehood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfd12ff",
+   "metadata": {},
+   "source": [
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is  𝑀=1.4𝑀⊙\n",
+    "  and  𝑅=13\n",
+    "  km, With a 5% Mass radius measurement uncertainty, \n",
+    "  \n",
+    " so here\n",
+    " \n",
+    "      likelihood.MRlikihood_Gaussian\n",
+    "      \n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood. eg:\n",
+    "\n",
+    "1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into\n",
+    "\n",
+    "likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2.If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.\n",
+    "\n",
+    "3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Where x is sampled distribution from real measurements, the standard is\n",
+    "\n",
+    "kernel, chrip = x,\n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d3a8359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "      \n",
+    "    B, d1 = theta\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "\n",
+    "    epsilon,p = MITbag.MITbag_compute_EOS(B)\n",
+    "\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(epsilon,p,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3802c4",
+   "metadata": {},
+   "source": [
+    "### (d) Set up sampler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76bce7fb",
+   "metadata": {},
+   "source": [
+    "Here next, we define sampler, there is two different sampler we provided for. \n",
+    "\n",
+    "Considering where you need resume file:\n",
+    "\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f05849a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 400\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/test_Bayesian_inference_Strangeon_EOS.ipynb.txt b/_sources/test_Bayesian_inference_Strangeon_EOS.ipynb.txt
new file mode 100644
index 0000000..3d57d0d
--- /dev/null
+++ b/_sources/test_Bayesian_inference_Strangeon_EOS.ipynb.txt
@@ -0,0 +1,408 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "edb2cf2a",
+   "metadata": {},
+   "source": [
+    "## Strangeon EOS inference pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc633d3",
+   "metadata": {},
+   "source": [
+    "This is an example notebook about how to use our tools to analysis a observation constraint on strangeon star equation of state.\n",
+    "\n",
+    "Here in this notebook, we are using a strangeon matter EOS.\n",
+    "\n",
+    "The following is the package that we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f6f20856",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/a9/CompactOject/InferenceWorkflow/Likelihood.py:314: SyntaxWarning: invalid escape sequence '\\('\n",
+      "  \"\"\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "from TOVsolver.constant import oneoverfm_MeV, m_rho, m_w,G,c\n",
+    "import TOVsolver.main as main"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b494a39",
+   "metadata": {},
+   "source": [
+    "The following is the strangeon matter EOS function.\n",
+    "\n",
+    "We note that because the parameter Nq is an integer in the strangeon matter EOS.\n",
+    "\n",
+    "In the following, we use a possible value of this parameter, Nq=18, as an example to carry out the Bayesian analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c9d879d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nq=18\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edfa0f72",
+   "metadata": {},
+   "source": [
+    "### Set up prior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38136542",
+   "metadata": {},
+   "source": [
+    "Next step, we need to set up the prior, first use parameters array to specify the variable name, this process should consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.\n",
+    "\n",
+    " We provided two options call from prior:\"normal_Prior\" and \"flat_prior\".\n",
+    " \n",
+    "Then the Parameters prior should all set.\n",
+    "\n",
+    "We note that since we are doing Equation of state Inference from mass-radius data of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.\n",
+    "\n",
+    "This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of compact star --- different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by\n",
+    "\n",
+    "strangeon.compute_EOS\n",
+    "Compute out EOS, put into\n",
+    "\n",
+    "main.OutputMR\n",
+    "\n",
+    "find out Mass Radius of this equation of state, then find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)\n",
+    "\n",
+    "and find out that index by len() function, then reset this max_d to be upper limit of this density range.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b87dd3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = [ 'epsilon', 'ns', 'd1']\n",
+    "# for two or more MR measurements, define \n",
+    "# parameters = ['epsilon', 'ns', 'd1', 'd2'] \n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = prior.flat_prior(10, 170,cube[0])   #epsilon=10-170MeV\n",
+    "    params[1] = prior.flat_prior(0.17,0.36,cube[1]) #ns=0.17-0.36fm^-3    \n",
+    "    \n",
+    "    epsilon = params[0]\n",
+    "    ns = params[1]\n",
+    "\n",
+    "    theta1 = np.array([Nq, epsilon, ns])\n",
+    "    n_min = 3 * theta1[2] / theta1[0]  \n",
+    "    n_max = 0.16 * 8 * 3 / theta1[0]   \n",
+    "    n_values = np.linspace(n_min, n_max, 2000) \n",
+    "    energy_density, pressure = Strangeon.compute_EOS(n_values, theta1)\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "    eps_total=energy_density\n",
+    "    \n",
+    "    pres_total=pressure\n",
+    "    \n",
+    "    RFSU2R = [] \n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50) \n",
+    "    if all(x<y for x,y in zip(eps_total[:], eps_total[1:])) and all(x<y for x, y in zip(pres_total[:], pres_total[1:])):\n",
+    "        MR = main.OutputMR('',energy_density,pressure).T  \n",
+    "        \n",
+    "    else:\n",
+    "        MR = []\n",
+    "    if len(MR) == False: \n",
+    "        params[2] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "   \n",
+    "        for i in range(len(MR[0])):\n",
+    "            RFSU2R.append(MR[1][i])\n",
+    "            MFSU2R.append(MR[0][i])   \n",
+    "            if i > 20 and MR[0][i] - MR[0][i-1]< 0:    \n",
+    "                break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[2] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[2] = 14.3 + (max_d - 14.3) * cube[2]\n",
+    "        #params[3] = 14.3 + (max_d - 14.3) * cube[3]\n",
+    "        #this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8e4822",
+   "metadata": {},
+   "source": [
+    "In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.\n",
+    "\n",
+    "Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement.\n",
+    "For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. \"d1\" for the centre density of this measurement, in the meantime add \"params[2]\" to this code.\n",
+    "\n",
+    "If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. \"d1\" and \"d2\" for the centre density of these two measurements, in the meantime add \"params[3]\" to the above code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ab6d43",
+   "metadata": {},
+   "source": [
+    "### Set up likehood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfd12ff",
+   "metadata": {},
+   "source": [
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is  𝑀=1.4𝑀⊙\n",
+    "  and  𝑅=13\n",
+    "  km, With a 5% Mass radius measurement uncertainty, \n",
+    "  \n",
+    " so here\n",
+    " \n",
+    "      likelihood.MRlikihood_Gaussian\n",
+    "      \n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood. eg:\n",
+    "\n",
+    "1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into\n",
+    "\n",
+    "likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2.If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.\n",
+    "\n",
+    "3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Where x is sampled distribution from real measurements, the standard is\n",
+    "\n",
+    "kernel, chrip = x,\n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d3a8359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "      \n",
+    "    epsilon, ns, d1 = theta \n",
+    "    # comment this line if you need two measuremnts.\n",
+    "    #epsilon, ns, d1, d2 = theta\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "\n",
+    "\n",
+    "    theta1 = np.array([Nq, epsilon, ns])\n",
+    "    n_min = 3 * theta1[2] / theta1[0]  \n",
+    "    n_max = 0.16 * 8 * 3 / theta1[0]   \n",
+    "    n_values = np.linspace(n_min, n_max, 2000) \n",
+    "    energy_density, pressure = Strangeon.compute_EOS(n_values, theta1)\n",
+    "\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(energy_density,pressure,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96d26ce7",
+   "metadata": {},
+   "source": [
+    "In the following, we will show how to modify the likehood_transform function when considering more observations.\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "\n",
+    "    Nq, epsilon, ns, d1 = theta \n",
+    "    theta = np.array([epsilon, ns]) \n",
+    "    n_min = 3 * theta[1] / Nq      #n_min=3*ns/Nq\n",
+    "    n_max = 0.16 * 8 * 3 / Nq      #n_max=0.16*8*3/Nq\n",
+    "    n_values = np.linspace(n_min, n_max, 100)  # 100 points between n_min and n_max\n",
+    "    energy_density_total, pressure_total = Strangeon_compute_EOS(n_values, theta)\n",
+    "    \n",
+    "    #1. This line is to compute MR likelihood from a Simulated MR measurement:\n",
+    "    \n",
+    "    probMRgaussian = MRlikihood_Gaussian(energy_density_total,pressure_total,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    #probMRgaussian = likelihood.MRlikihood_Gaussian(energy_density_total,pressure_total,(2.08,13,0.08,0.65),d2)\n",
+    "    \n",
+    "    #2. This is  a block that constrain from given real MR measurement, say J0030:\n",
+    "    \n",
+    "    #J0030 = numpy.loadtxt('data/PST_equal_sampled_MR.txt', delimiter=' ')\n",
+    "    #J30R_list, J30M_list = zip(*J0030)\n",
+    "    #J30R_list = numpy.array(J30R_list).T    \n",
+    "    #J30M_list = numpy.array(J30M_list).T\n",
+    "    #Rmin = J30R_list.min()\n",
+    "    #Rmax = J30R_list.max()\n",
+    "    #Mmin = J30M_list.min()\n",
+    "    #Mmax = J30M_list.max()\n",
+    "    #X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]\n",
+    "    #positions = numpy.vstack([X3.ravel(), Y3.ravel()])\n",
+    "    #values = numpy.vstack([J30R_list, J30M_list])\n",
+    "    #kernel3 = stats.gaussian_kde(values)\n",
+    "    #probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)\n",
+    "    \n",
+    "    #3. This is to compute the constraint from experiment of nuclearmatter\n",
+    "    # 250<K<400, 25<J<38, 30<L<86:\n",
+    "    # hint: since this K,J,L sampling don't need to sample central density so this \n",
+    "    # theta should be redifined.\n",
+    "    #probK = likelihood.Kliklihood(theta,250,400)\n",
+    "    #probJ = likelihood.Jliklihood(theta,250,400)\n",
+    "    #probL = likelihood.Lliklihood(theta,250,400)\n",
+    "    \n",
+    "    #4. This is block to cosntrain from a GW event, say GW170817, here the file of this\n",
+    "    # event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:\n",
+    "    #GW170817 = np.load('GW170817_McQL1L2weights.npy')\n",
+    "    #chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])\n",
+    "    #kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])\n",
+    "    #probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)\n",
+    "    \n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    #prob =  probGW#+ probMRJ0030 + probK + probJ + probL + probGW\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3802c4",
+   "metadata": {},
+   "source": [
+    "### Set up sampler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76bce7fb",
+   "metadata": {},
+   "source": [
+    "Here next, we define sampler, there is two different sampler we provided for. \n",
+    "\n",
+    "Considering where you need resume file:\n",
+    "\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f05849a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 400\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "240dec67",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/test_EOSgenerators.ipynb.txt b/_sources/test_EOSgenerators.ipynb.txt
new file mode 100644
index 0000000..5faa8b4
--- /dev/null
+++ b/_sources/test_EOSgenerators.ipynb.txt
@@ -0,0 +1,996 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Equation of State Generators illustration notebook\n",
+    "\n",
+    "## RMF EOS \n",
+    "\n",
+    "First import all of the package that will be used here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import EOSgenerators.crust_EOS as crust\n",
+    "import EOSgenerators.RMF_EOS as RMF\n",
+    "import EOSgenerators.Polytrope_EOS as Polytrope\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import math\n",
+    "from TOVsolver.unit import g_cm_3, dyn_cm_2, km, Msun\n",
+    "from TOVsolver.maxium_central_density import maxium_central_density"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  (a) Load crust EOS and prepare interface EOS\n",
+    "\n",
+    "Define constants, and load the crust EOS into this notebook, since when we solve the equation of state to TOV equation, we need full scope of the equation of state. Here, crust eos file is 'Tolos_crust_out.txt', while we need to times g_cm_3 for energy density and dyn_cm_2 for pressure, that will be easier for treatment, no need worry about G and c.\n",
+    "\n",
+    "Then just connect the crust part with the interface part(the part between core equation of state and crust) by a PolyInterpolate function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example of RMF"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Defining the paramters\n",
+    "\n",
+    "Defining the free prameters that we need to compute the equation of state, you could check our intro part of documentation or just check the original paper of us about details. [Huang et al,2023](https://arxiv.org/abs/2303.17518)\n",
+    "\n",
+    "After defined the equation of state parameters, we could call the 'compute_EOS' function from RMF, to compute full core equation of state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m_sig = 495.0 / 197.33\n",
+    "m_w = 3.96544\n",
+    "m_rho = 3.86662\n",
+    "\n",
+    "g_sigma = math.sqrt(107.5751)\n",
+    "g_omega = math.sqrt(182.3949)\n",
+    "g_rho = math.sqrt(206.4260)\n",
+    "\n",
+    "kappa = 3.09114168 / 197.33\n",
+    "lambda_0 = -0.00168015405\n",
+    "zeta = 0.024\n",
+    "Lambda_w = 0.045\n",
+    "theta = np.array(\n",
+    "    [m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w]\n",
+    ")\n",
+    "eps, pres = RMF.compute_EOS(eps_com, pres_com, theta)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (c) Connecting Core and crust EOS\n",
+    "\n",
+    "These two line can easily connect the Equation of state we computed for core part by setting the parameters and the crust equation of state, to a full equation of state that prepared for next step output\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eps_total = np.array([*eps_com, *eps])\n",
+    "pres_total = np.array([*pres_com, *pres])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below is one of the possiblity, use our defined function, 'OutputMR' to compute out what the mass radius curve corresponding to this equation of state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR(\"\", eps_total, pres_total).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[1] / km, MR[0] / Msun, lw=2)\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Illustrating the power of Numba accelerated EoS Generation\n",
+    "\n",
+    "We shall now run the same code but with the Numba accelerated version, comparing the times for EoS Generation.\n",
+    "\n",
+    "Keep in mind all previous definitions are still valid, all we need to change is a single line of code.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import EOSgenerators.fastRMF_EoS as FastRMF\n",
+    "import time\n",
+    "\n",
+    "Tolos_crust_out = np.loadtxt(\"Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)\n",
+    "\n",
+    "m_sig = 495.0 / 197.33\n",
+    "m_w = 3.96544\n",
+    "m_rho = 3.86662\n",
+    "\n",
+    "g_sigma = math.sqrt(107.5751)\n",
+    "g_omega = math.sqrt(182.3949)\n",
+    "g_rho = math.sqrt(206.4260)\n",
+    "\n",
+    "kappa = 3.09114168 / 197.33\n",
+    "lambda_0 = -0.00168015405\n",
+    "zeta = 0.024\n",
+    "Lambda_w = 0.045\n",
+    "theta = np.array(\n",
+    "    [m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Comparing the time it takes to generate the EoS\n",
+    "\n",
+    "The reason why we test the time it takes to generate the EoS here is due to the fact that Numba is compiling the code, and as such the first run will take considerably longer to complete, but all other runs will be greatly faster.\n",
+    "\n",
+    "Warning: Do not rerun this cell, as the compilation time will be gone.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------------\n",
+      "|                                                                                    |\n",
+      "|                                Time Comparion Table                                |\n",
+      "|                                                                                    |\n",
+      "|                 Time for first run  with RMF-Normal  =    550 ms                   |\n",
+      "|                 Time for first run  with RMF-Fast    =   1027 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                 Time for   20  runs with RMF-Normal  =  10099 ms                   |\n",
+      "|                 Time for   20  runs with RMF-Fast    =    287 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                 Time for  all  runs with RMF-Normal  =  10649 ms                   |\n",
+      "|                 Time for  all  runs with RMF-Fast    =   1315 ms (Has Compilation) |\n",
+      "|                                                                                    |\n",
+      "|                 Time for   1   run  with RMF-Normal  =    505 ms                   |\n",
+      "|                 Time for   1   run  with RMF-Fast    =     14 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                                                                                    |\n",
+      "|                              Total Speed Up = 35.15 x                              |\n",
+      "--------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Normal RMF ####\n",
+    "\n",
+    "first_run_time_normal = time.time()\n",
+    "eps_fast, pres_fast = RMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "first_run_time_normal = time.time() - first_run_time_normal\n",
+    "\n",
+    "time_for_20_runs_normal = time.time()\n",
+    "for _ in range(20):\n",
+    "    eps, pres = RMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "time_for_20_runs_normal = time.time() - time_for_20_runs_normal\n",
+    "\n",
+    "\n",
+    "##### Fast RMF #####\n",
+    "\n",
+    "first_run_time = time.time()\n",
+    "eps_fast, pres_fast = FastRMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "first_run_time = time.time() - first_run_time\n",
+    "\n",
+    "time_for_20_runs = time.time()\n",
+    "for _ in range(20):\n",
+    "    eps_fast, pres_fast = FastRMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "time_for_20_runs = time.time() - time_for_20_runs\n",
+    "\n",
+    "\n",
+    "print(f\"--------------------------------------------------------------------------------------\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*32 + \"Time Comparion Table\" + \" \"*32 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for first run  with RMF-Normal  = {first_run_time_normal*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for first run  with RMF-Fast    = {first_run_time*1000       :6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   20  runs with RMF-Normal  = {time_for_20_runs_normal*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   20  runs with RMF-Fast    = {time_for_20_runs*1000       :6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for  all  runs with RMF-Normal  = {(first_run_time_normal + time_for_20_runs_normal)*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for  all  runs with RMF-Fast    = {(first_run_time        + time_for_20_runs       )*1000:6.0f} ms (Has Compilation) \" + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   1   run  with RMF-Normal  = {time_for_20_runs_normal*1000/20:6.0f} ms  \" + \" \"*17 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   1   run  with RMF-Fast    = {time_for_20_runs       *1000/20:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*30 + f\"Total Speed Up = {time_for_20_runs_normal/time_for_20_runs:.2f} x\" + \" \"*30 + \"|\" )\n",
+    "print(f\"--------------------------------------------------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eps_total_fast = np.array([*eps_com, *eps_fast])\n",
+    "pres_total_fast = np.array([*pres_com, *pres_fast])\n",
+    "\n",
+    "compare_fig1, ax = plt.subplots(1, 1, figsize=(9,6))\n",
+    "\n",
+    "ax.plot(eps_total_fast, pres_total_fast, lw=2, label=\"Fast Version\")\n",
+    "ax.plot(eps_total, pres_total, lw=6, label=\"Normal Version\", alpha=0.3)\n",
+    "ax.set_ylabel(r\"P $[fm^{-4}]$\", fontsize=16)\n",
+    "ax.set_xlabel(r\"$\\epsilon$ $[fm^{-4}]$\", fontsize=16)\n",
+    "ax.set_xlim(0, 6)\n",
+    "ax.set_ylim(0, 2)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "plt.legend()\n",
+    "compare_fig1.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MR_fast = main.OutputMR(\"\", eps_total_fast, pres_total_fast).T\n",
+    "\n",
+    "compare_fig2, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "\n",
+    "ax.plot(MR_fast[1] / km, MR_fast[0] / Msun, lw=2, label=\"Fast Version\")\n",
+    "ax.plot(MR[1] / km, MR[0] / Msun, lw=6, label=\"Normal Version\", alpha=0.3)\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "plt.legend()\n",
+    "compare_fig2.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polytrope EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.integrate import ode\n",
+    "import EOSgenerators.Polytrope_EOS as Polytrope\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "from TOVsolver.unit import  km, Msun, MeV,fm,g_cm_3,dyn_cm_2, G,c "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The polytropic equation of state (Polytrope) is the most commonly used EOS in astrophysics, its advantage is assumed to be capable to simulate all EOS shape with given parameters. Here, we provide one of the computation for polytrope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Connecting outer crust with polytropic part"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For polytrope, we first connecting it with crust as we did in RMF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define a set of parameters, first three in theta are the parameters control the slope of each polytrope, and the last two are transition point from one segment to another. Details see the documentation about polytrope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gammas = np.array([1.03, 2.62, 1.38])\n",
+    "rho_ts = np.array([8.087e13, 1.8078e15]) * g_cm_3\n",
+    "theta = np.append(gammas, rho_ts)\n",
+    "\n",
+    "eps_set = np.logspace(11.7, 15.6, 100, base=10) * g_cm_3\n",
+    "pres_out = Polytrope.compute_EOS(eps_set, theta, eps_crust_T_out[-1], pres_crust_T_out[-1])\n",
+    "\n",
+    "eps_total = np.hstack((eps_crust_T_out, eps_set))\n",
+    "pres_total = np.hstack((pres_crust_T_out, pres_out))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS\n",
+    "\n",
+    "Here below we use the strangeon matter EOS to compute the mass radius curve.\n",
+    "\n",
+    "The following code calculates the mass and radius of the strange stars for the given EOS of the polytrope using our main.OutputMR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR('',eps_total, pres_total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[:,1]/km , MR[:,0]/Msun, lw=2)\n",
+    "# ax.scatter(R /km, M / Msun, c='r')\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Strangeon matter EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.integrate import ode\n",
+    "from EOSgenerators import Strangeon_EOS as Strangeon\n",
+    "\n",
+    "from TOVsolver.unit import  km, Msun, MeV,fm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The strangeon matter EOS describes the strongly interacting matter in the solid state, and the compact star can be a self-bound quark star composed of strangeon matter. Therefore, in the following we present the bare quark star EOS without a crust.\n",
+    "\n",
+    "Note that the strangeon matter parameters we input are in the natural unit system. The quentity returned by the Strangeon_compute_EOS function is in the Geometric Unit System. Thus, here, the parameter epsilon and ns are in the units of $MeV/fm^{-3}$. So follow the unite conversion rule, if we are using this unit, we just time this unit to do compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the EOS parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the strangeon matter EOS that we will use to calculate the energy densities and pressures of the system.\n",
+    "The following is an example of how to define the theta and Nq.\n",
+    "For example, theta=[50, 0.24] means the example values for epsilon and ns:\n",
+    "epsilon in units of MeV, ns in units of $fm^-3$.\n",
+    "Nq is an integer, e.g. Nq=9, 12, 15, 18, 21, 24, 27\n",
+    "\n",
+    "For the definition of parameter n, the minimum value is 3*ns/Nq, the maximum value is about 0.16*8*3/Nq.\n",
+    "Then we can generate,for example, 1000 points for the input of the srangeon matter EOS. Special note: If you are using this EOS, need a very fine grid for the EOS. From our test, 1000 points for this EOS is the minimum requirement\n",
+    "\n",
+    "Once we define the EOS parameters already, we can use the Strangeon_compute_EOS function to calcute the energy_density and pressure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "n values: [0.04       0.04008671 0.04017342 ... 0.21315991 0.21324662 0.21333333]\n",
+      "Energy densities: [  204.61935484   205.0631334    205.50734538 ... 46616.1606005\n",
+      " 46711.98281964 46807.96367981]\n",
+      "Pressures: [0.00000000e+00 1.98864796e-01 4.00749542e-01 ... 1.88749504e+05\n",
+      " 1.89139331e+05 1.89529799e+05]\n"
+     ]
+    }
+   ],
+   "source": [
+    "Nq=18\n",
+    "epsilon=50\n",
+    "ns=0.24\n",
+    "theta = np.array([Nq, epsilon, ns])\n",
+    "n_min = 3 * theta[2] / theta[0]  \n",
+    "n_max = 0.16 * 8 * 3 / theta[0]   \n",
+    "n_values = np.linspace(n_min, n_max, 2000) \n",
+    "\n",
+    "energy_densities, pressures = Strangeon.compute_EOS(n_values, theta)\n",
+    "print(\"n values:\", n_values)\n",
+    "print(\"Energy densities:\", energy_densities)\n",
+    "print(\"Pressures:\", pressures) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below we use the strangeon matter EOS to compute the mass radius curve.\n",
+    "\n",
+    "The following code calculates the mass and radius of the strange stars for the given EOS of the strange matter EOS. Since our energy_density and pressure are all in same unit $MeV/fm^{-3}$ so all we need is to time this unit\n",
+    "\n",
+    "Input a given central pressure and central energy density, you will obtain the radius where the pressure is zero and the mass at that radius."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Solve the TOV equations using each central pressure and energy density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "MR= main.OutputMR('',energy_densities* MeV/fm**3   , pressures* MeV/fm**3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[:,1]/km , MR[:,0]/Msun, lw=2)\n",
+    "\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "# ax.set_xlim(8.0, 20.0)\n",
+    "# ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Speed of sound EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from EOSgenerators import SpeedofSound_EOS\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "from TOVsolver.unit import g_cm_3, dyn_cm_2, km, Msun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Speed of sound EOS describes the core EOS of a compact star, \n",
+    "so it should be connected with the crust EOS to form a full EOS.\n",
+    "See https://arxiv.org/abs/1812.08188 for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the EOS parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To construct the Speed of sound EOS, we need to specify the outer crust EOS and \n",
+    "the interface EOS, and then connect them with the core EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_last = eps_com[-1]\n",
+    "y_last = pres_com[-1]\n",
+    "dydx_last = (pres_com[-1] - pres_com[-2]) / (eps_com[-1] - eps_com[-2])\n",
+    "CS_EOS = SpeedofSound_EOS.compute_EOS(x_last, y_last, dydx_last)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to generate the parameters of the Speed of sound EOS, we need a list\n",
+    "of 5 uniform random numbers between 0 and 1.\n",
+    "\n",
+    "After generate the parameters, we use function `check_a` to check if the parameters\n",
+    "are valid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "cs_a = CS_EOS.gen_a((0.2, 0.2, 0.3, 0.4, 0.5))\n",
+    "print(CS_EOS.check_a(cs_a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below we use the speed of sound EOS to compute the mass radius curve.\n",
+    "\n",
+    "First we calculate the core EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_e_min = x_last\n",
+    "core_e_max = 2e16 * g_cm_3\n",
+    "n_core_e = 1000\n",
+    "core_e = np.geomspace(core_e_min, core_e_max, n_core_e)\n",
+    "core_p = CS_EOS.cal_core_p(core_e, cs_a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we concat the core EOS with the crust EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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V+fU3bNggSYqMjLyygPAa7t5Lp0+f1meffabatWvrrrvucl9QeLyK/r703nvvSZKGDx9+hQnhSSp6v8A6PG0vHT9+XJJKvNU9PJun7SXej6MklEMAnOzevVuS1KpVq0u++Wjbtq3Lc4rt2rWrxAu7rlu3TrNmzVJAQIAGDRrknsDwWO7YSxdavHix8vPzNWLECAUEBLgvKDyeu/bSuXPnXMaWLVumBQsWKCYmhu9LVYS7v/fAuszYS1u3bnW6k1WxM2fOaMqUKZL+e4F9eA8z9hLvx1Fe1M4AHHJzc5WWliZJaty48SXX1qpVSyEhIcrKytLRo0ed5pYuXapXXnlFffr0UWRkpAICArRz50599dVX8vHx0dtvv62mTZtW2NcB87lrL12IU8qsyZ176dprr1WTJk0UFRWlwMBA/fLLL1q9erVatGihjz76iLvfVQHu3C8JCQmO/7nfsWOHY6z4VI+BAwdq4MCBV7wens2svZSYmKiEhATFxcWpWbNmCgkJ0eHDh7Vy5UplZmbq7rvv1r333uumrxKVway9xPtxlBflEACH8+fPOx6HhoZedn3xD6+Lb7cZFxen3bt3a/Pmzfrhhx+Um5ur8PBwDRs2TJMmTVJsbKzbs8OzuGsvFfvll1+0c+dOxcbGqn379m7LCc/nzr00bNgwffLJJ/r5559VUFCg5s2b69lnn9VTTz2lsLAwt+aGOdy5X9atW6dFixY5jf3444/68ccfJf1+OsaFZU9518OzmbWXBg8erIyMDP38889as2aNsrOzVbt2bfXo0UOjRo3S8OHDZbPZ/sBXhspm1l7i/TjKi3IIgENubq7jcbVq1S67vvjUnpycHKfxXr16OZ0zDetx114qFhsbK8Mw3BMOXsWde2natGmaNm2a27LB87hzvyQmJioxMbHMn7u86+HZzNpLPXr0UI8ePcoWEl7BrL3E+3GUF9ccAuAQGBjoeJyfn3/Z9Xl5eZKkoKCgCssE78Regruwl1Ae7Be4C3sJ7sJegregHALgUL16dcfj0k7vuVBWVpaksh0iC2thL8Fd2EsoD/YL3IW9BHdhL8FbUA4BcAgMDFTdunUlSceOHbvk2rNnzzp+eDVp0qTCs8G7sJfgLuwllAf7Be7CXoK7sJfgLSiHADiJioqSJO3fv1+FhYWlrktOTnZ5DnAh9hLchb2E8mC/wF3YS3AX9hK8AeUQACfFF0HMysrSpk2bSl33ww8/OB537969wnPB+7CX4C7sJZQH+wXuwl6Cu7CX4A0ohwA4ufA2uwsXLixxjd1u17vvvitJqlmzpuLi4iojGrwMewnuwl5CebBf4C7sJbgLewnegHIIgJPY2Fj17NlTkjR//nytX7/eZc3rr7+u3bt3S5Iee+wx+fv7V2pGeAf2EtyFvYTyYL/AXdhLcBf2EryBzTAMw+wQANxn3bp12r9/v+PjtLQ0PfXUU5J+Pzz1wQcfdFofHx/v8hpbtmxR9+7dlZOTo9DQUE2ZMkVxcXHKycnRP//5T82bN0+S1KZNG23cuNHpLgyoOthLcBf2EsqD/QJ3YS/BXdhLsAQDQJVy//33G5LK/Kc0n3/+uREWFlbq89q0aWPs27evEr8yVDb2EtyFvYTyYL/AXdhLcBf2EqyA08oAlOj222/X9u3bNWnSJLVp00bBwcGqWbOmoqOjNXPmTG3ZskWtWrUyOya8AHsJ7sJeQnmwX+Au7CW4C3sJnozTygAAAAAAACyMI4cAAAAAAAAsjHIIAAAAAADAwiiHAAAAAAAALIxyCAAAAAAAwMIohwAAAAAAACyMcggAAAAAAMDCKIcAAAAAAAAsjHIIAAAAAADAwiiHAAAAAAAALIxyCAAAAAAAwMIohwAAAAAAACyMcggAAAAAAMDCKIcAAAAAAAAsjHIIAAAAAADAwiiHAAAAAAAALIxyCAAAAAAAwMIohwAAAAAAACyMcggAAAAAAMDCKIcAAAAAAAAsjHIIAAAAVUZ6eroeffRRXXfddYqIiFBAQIAaNWqk3r176+OPP5ZhGGZHBADA49gMfkICAACgiti/f786deqkbt26qVWrVqpdu7ZSU1P1xRdfKDU1VWPHjtW8efPMjgkAgEehHAIAAECVUVRUJMMw5Ofn5zR+/vx5devWTbt27dLOnTvVrl07kxICAOB5OK0MAADAg8THx8tms7n8OXTokNnRvIKvr69LMSRJ1atX1y233CLp96OLLrZ69eoS/96nTZtW0ZEBADAd5RAAAAAcUlJSHMVIUlKS2XHcJjc3V999951sNpuuvvpqs+MAAOBRXP9bBQAAAKZr2LCh/v3vfzs+btSoUaV83hUrVkiSIiIiFB0dXSmfsyKkp6dr9uzZstvtSk1N1ZdffqmjR4/queeeU+vWrV3Wx8TEaMeOHY6P27dvX5lxAQAwFeUQAACAB/L399c111xT6Z/3iy++kCQNGDBANput0j+/u6Snp+v55593fOzv769XX31VTzzxRInrQ0JCTPn7BgDAE3BaGQAAACRJ2dnZ+u677yRJt99+u8lp/pjIyEgZhqHCwkIdPHhQL7zwgv785z/r7rvvVmFhodnxAADwKJRDAAAAkCR98803ysnJUWBgoG666Saz47iFr6+vIiMj9b//+7968cUX9emnn+qdd94xOxYAAB6FcggAAFjO3r179cQTT6hLly6qUaOG/P39Va9ePbVr10733HOPZs2aZXbEcjEMQ++//75uueUW1atXTyEhIerYsaNmz56twsJC5eTkyMfHRzabTTNnziz1dYqvN9S7d28FBweXui41NVXTp09XXFycGjRooICAADVs2FDXX3+9pk2bpt27d7s8Z//+/Y4LXX/wwQeSpKVLl6pfv34KDw9XaGioOnfurAULFsgwDMfz8vLyNH/+fN1www2qW7eugoKCdO211+qzzz4r999T3759Jf1+ZzIAAPBfXHMIAABYyvTp0/WXv/xFRUVFTuNpaWlKS0vTrl27tGHDBj3++OMmJSyf1NRUDRw4UOvXr3ca3759uyZNmqSvvvpKL730kqNw6dixY4mvYxiGVq5cKenSp5S9+uqreu6555STk+M0fuLECZ04cULr16/XqlWr9PPPPzvNb9261fG4cePG6tevn9MFt4vXjBkzRseOHdPUqVO1d+9eDRs2zOm5kvTLL79o4MCBWrp0qYYMGVJq1osdP35ckkq81T0AAFbGT0YAAGAZc+fO1ZQpUyRJnTp10ujRo9WhQwdVr15dp06d0tGjR7VhwwbVqlXL5KRlk5mZqRtvvFG7d++WzWbTPffco+HDh6tRo0Y6ePCgXn75Zf3rX/9Sbm6u4zmdOnUq8bU2bdrkKE8GDBhQ4pqxY8cqISFBktSkSRONHTtW3bt3V61atfTbb79pzZo1SkxMVNeuXV2eu23bNsfjJ598Utu3b9fEiRN1++23Kzg42FFi5efn6+WXX9bgwYPVp08f5efn64UXXlDv3r1VVFSkDz/8UHPmzJEk/eUvf3Eph7Zu3armzZurRo0aTuNnzpxx/Nv379//Un+tAABYDuUQAACwjJdeekmSdPfdd2vp0qXy8XE9w/7BBx+s7FhXbPz48dq9e7f8/Pz0ySefOB3x06VLF/Xr109t27bV999/L0mqX7++IiIiSnyt4ruUderUSY0bN3aZf/nllx3F0MiRI/XOO+8oICDAaU2/fv307LPP6siRIy7Pv/Don/3792v9+vVORVW3bt2Ul5enl19+WXl5eerevbsiIiL0zTffqFGjRo51N9xwg06cOKFPP/1Ue/bsUVpamurWreuYT0xMVEJCguLi4tSsWTOFhITo8OHDWrlypTIzM3X33Xfr3nvvLe2vFAAAS+KaQwAAwBLOnTuno0ePSpJuueWWEoshb7J27Vq99957kqRp06aVeCpYSEiIHnvsMcfHpR01JP33ekMlvc6vv/6qqVOnSvq9AEpMTHQphooFBwerbdu2LuMXHjmUkJBQYpY+ffo4Hufm5mrp0qVOxVCxCy+WnZGR4TQ3ePBgDRkyRPv379fixYs1a9Ysff/99+rRo4fef/99ffTRR17/bw8AgLtx5BAAALCE0NBQ1alTR6dPn9YLL7yg2rVrq1evXqpTp45sNpvZ8crthRdekCQ1bdpUTz/9dKnr2rVr53hc2vWGUlJStHnzZkkll0OTJ09WUVGRQkNDtWjRonKXK2fOnHEq5u66664S1/n6+joe/+lPf1L79u1LXGe32x2PLz59rEePHurRo0e58gEAYHX8twkAALAEHx8fJSYmKjg4WMeOHdPgwYNVr149x128lixZYnbEMjtx4oS++eYbSdJDDz10yQssX1ielHbkUPFRQxEREYqOjnaaO3nypP71r39JkiZMmKD69euXO++FRw0NGzas1HX79u0r07r9+/dLksLCwpxOKQMAAFeGcggAAFhG37599fbbb5dYKHTo0MGERFemuKyRpFtvvfWSa8+cOeN4XNqRQ8XXG7rttttcjqJasWKF405n5bkz2IUuvN7QpfIWl0j16tVTTEzMZdd5078ZAACejNPKAACAJWzevFkjR47Url271KdPH91///1q3ry5atasKUmKiooyN2A5FJctAQEBpZ56VWznzp2SpMDAwBKvBZSTk6PvvvtOUsmnlO3YsUOS5O/v73JUUVkVlzkNGzZUeHh4qeu2bNki6dLXRrrw9S63DgAAlA3lEAAAqPIOHz6sPn36KD09XW+88YYefvhhsyP9ISdPnpT0+xE2l/P1119L+v3aQxde06fYN998o5ycHAUGBjpd6PnizxUeHn7F12YqLrM6d+5c6hrDMMpU+hw5ckRnz5697DoAAFB2nFYGAACqvOnTpys9PV3XXnut1xdDkpSXlyfp9zuwXUpycrLjNvalFSnFp5T17t1bISEhLvO5ubmSpPz8/CvKWlBQoN27d18yg/T7dYQyMzMlXbpEuvAUNcohAADcg3IIAABUeWvWrJEktWrVyuQk7lF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+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "full_e = np.concatenate((eps_com, core_e[1:]))\n",
+    "full_p = np.concatenate((pres_com, core_p[1:]))\n",
+    "plt.figure(dpi=200)\n",
+    "plt.plot(full_e / g_cm_3, full_p / dyn_cm_2)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(r\"$\\epsilon\\quad [g/cm^3]$\")\n",
+    "plt.ylabel(r\"$P\\quad [dyn/cm^2]$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we compute the mass radius curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MR = main.OutputMR(\"\", full_e, full_p)\n",
+    "plt.figure(dpi=200)\n",
+    "plt.plot(MR[:, 1] / km, MR[:, 0] / Msun)\n",
+    "plt.xlim(6, 17)\n",
+    "plt.ylim(0, 2)\n",
+    "plt.xlabel(\"Radius [km]\")\n",
+    "plt.ylabel(\"Mass [Msun]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MIT bag model EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state $p=\\frac{\\epsilon}{3}-\\frac{4B}{3}$. There is only one parameter, the \"bag constant\" $B$. This represents the vacuum energy density, which creates a \"bag\" in which quarks are confined. See [Chodos et al. (1974)](https://doi.org/10.1103/PhysRevD.9.3471)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from EOSgenerators import MITbag_EOS\n",
+    "from TOVsolver.unit import MeV, fm, g_cm_3, dyn_cm_2, km, Msun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the parameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A \"typical\" value of 57 $MeVfm^{-3}$ for the bag constant is taken from [Alcock (1986)](http://dx.doi.org/10.1086/164679)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy density range: 1.1554421785237932 to 2.888605446309483\n",
+      "pressure range: 0.0 to 0.5777210892618965\n"
+     ]
+    }
+   ],
+   "source": [
+    "B = 57\n",
+    "\n",
+    "epsilon,p = MITbag_EOS.MITbag_compute_EOS(B)\n",
+    "\n",
+    "print('energy density range:',epsilon[0],'to',epsilon[-1])\n",
+    "print('pressure range:',p[0],'to',p[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Computing the mass-radius relation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute a mass-radius relation for this EOS by solving the TOV equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import TOVsolver.main as main\n",
+    "import TOVsolver.EoS_import as EoS_import\n",
+    "from itertools import repeat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR('',epsilon,p).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MR[1]/km, MR[0]/Msun,lw=2,label=f'B = {B:.2f} MeV $fm^{-3}$')\n",
+    "ax.set_ylabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/test_Inference.ipynb.txt b/_sources/test_Inference.ipynb.txt
new file mode 100644
index 0000000..12b2412
--- /dev/null
+++ b/_sources/test_Inference.ipynb.txt
@@ -0,0 +1,425 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RMF EOS inference pipeline\n",
+    "\n",
+    "This is an example notebook about how to use our tools to analysis a observation/nuclear constraint on neutron star equation of stat. \n",
+    "\n",
+    "Here in this notebook, we are using a RMF EoS model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Package we need:\n",
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "\n",
+    "from TOVsolver.constant import oneoverfm_MeV, m_rho, m_w,G,c\n",
+    "import TOVsolver.main as main\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "import EOSgenerators.fastRMF_EoS as RMF"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to define the crust, this crust and interface is unversial for all the equation of state here, we just change the core part of equation of state and that is come from the RMF computation. Crust as below, \"Tolos_crust_out.txt\" is BPS crust model. Then as in README, since that file energy_density and presurssure all in MeV/fm3, so we need some conversion. then just call \n",
+    "\n",
+    "   ```sh\n",
+    "   crust.PolyInterpolate\n",
+    "   ```\n",
+    "\n",
+    "To finish the interface and make it ready to connect with core part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Tolos_crust_out = np.loadtxt('Tolos_crust_out.txt', delimiter='  ')\n",
+    "Tolos_crust_out = np.loadtxt('Tolos_crust_out.txt', delimiter=None, comments='#', usecols=(0, 1, 2, 3, 4))\n",
+    "eps_crust_out = Tolos_crust_out[:,3] * G / c**2\n",
+    "pres_crust_out = Tolos_crust_out[:,4] * G / c**4\n",
+    "\n",
+    "eps_crust, pres_crust = crust.PolyInterpolate(eps_crust_out, pres_crust_out)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up prior\n",
+    "\n",
+    "Next step, we need to set up the prior, first use parameters array to specify the variable name, should consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling. We provided \n",
+    "\n",
+    "\"normal_Prior\" and \"flat_prior\"\n",
+    "\n",
+    "two options call from prior. Here then the Parameters prior should all set\n",
+    "\n",
+    "------------------\n",
+    "\n",
+    "However, since we are doing Equation of state Inference from mass radius of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.\n",
+    "\n",
+    "This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of neutron star ---  different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by\n",
+    "\n",
+    "```sh\n",
+    "RMF.compute_EOS\n",
+    "```\n",
+    "\n",
+    "Compute out EOS, put into\n",
+    "\n",
+    "```sh\n",
+    "main.OutputMR\n",
+    "```\n",
+    "\n",
+    "find out Mass Radius of this equation of state, find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)\n",
+    "\n",
+    "found out that index by len() function, then reset this max_d to be upper limit of this density range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = ['g_sigma', 'g_omega','g_rho', 'kappa', 'lambda_0', 'zeta', 'Lambda_w','d1']\n",
+    "# for two or more MR measurements, define d2 or more depend on complexity.\n",
+    "# parameters = ['g_sigma', 'g_omega','g_rho', 'kappa', 'lambda_0', 'zeta', 'Lambda_w','d1', 'd2']\n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = math.sqrt(prior.normal_Prior(107.5, 7.5,cube[0]))\n",
+    "    params[2] = math.sqrt(prior.flat_prior(75,210,cube[2]))\n",
+    "    params[1] = math.sqrt(prior.flat_prior(150,210,cube[1]))\n",
+    "    params[3] = prior.normal_Prior(2.525/oneoverfm_MeV, 1.525/oneoverfm_MeV,cube[3])\n",
+    "    params[4] = prior.normal_Prior(0.0045, 0.0205,cube[4])\n",
+    "    params[5] = prior.flat_prior(0,0.04,cube[5])\n",
+    "    params[6] = prior.flat_prior(0,0.045,cube[6])\n",
+    "    \n",
+    "    g_sigma = params[0]\n",
+    "    g_omega = params[1]\n",
+    "    g_rho = params[2]\n",
+    "\n",
+    "    kappa = params[3]\n",
+    "    lambda_0 = params[4]\n",
+    "    zeta = params[5]\n",
+    "    Lambda_w = params[6]\n",
+    "    m_sig = 495 / oneoverfm_MeV\n",
+    "\n",
+    "    theta = np.array([m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa,\n",
+    "                         lambda_0, zeta, Lambda_w])\n",
+    "    \n",
+    "    ep, pr = RMF.compute_EOS(eps_crust, pres_crust, theta)\n",
+    "\n",
+    "    eps_total = np.hstack((eps_crust,ep))\n",
+    "    \n",
+    "    pres_total = np.hstack((pres_crust,pr))\n",
+    "    \n",
+    "    RFSU2R = []\n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50)\n",
+    "    if all(x<y for x,y in zip(eps_total[:], eps_total[1:])) and all(x<y for x, y in zip(pres_total[:], pres_total[1:])):\n",
+    "        MR = main.OutputMR('',eps_total,pres_total).T\n",
+    "        if len(MR[1]) == False: \n",
+    "            params[7] = 0\n",
+    "            #params[8] = 0\n",
+    "            # this line for showing how to add one more observation\n",
+    "        else:\n",
+    "   \n",
+    "            for i in range(len(MR[1])):\n",
+    "                RFSU2R.append(MR[0][i])\n",
+    "                MFSU2R.append(MR[1][i])   \n",
+    "                if i > 20 and MR[1][i] - MR[1][i-1]< 0:\n",
+    "                    break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[7] = 0\n",
+    "        # params[8] = 0\n",
+    "        # this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[7] = 14.3 + (max_d - 14.3) * cube[7]\n",
+    "        # params[8] = 14.3 + (max_d - 14.3) * cube[8]\n",
+    "        # this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up likelihood\n",
+    "\n",
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is $M = 1.4 M_{\\odot}$ and $R = 13$ km, With a 5% Mass radius measurement uncertainty, so here \n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.MRlikihood_Gaussian\n",
+    "   ```\n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/EoS_inference/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood.\n",
+    "eg:\n",
+    "1. If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into \n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2. If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement\n",
+    "\n",
+    "3. If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "   likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "   likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "   ```\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4. If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "Where x is sampled distribution from real measurements, the standard is \n",
+    "\n",
+    "kernel, chrip = x, \n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is  [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling.\n",
+    "\n",
+    "\n",
+    "\n",
+    "5. To calculate the log-likelihood for the pure neutron matter (PNM) equation of state (EoS) using chiral effective field theory (chiEFT) constraints, the function\n",
+    "   \n",
+    "     `likelihood.chiEFT_PNM(EoS_PNM, type=\"Gaussian\", contraint_quantity=\"e\", enlargement=0)`\n",
+    "\n",
+    "EoS_PNM is a 3D array of number density (rho), energy density (e) and pressure (p) with unit fm^-3, MeV.fm^-3 and MeV.fm^-3.  \n",
+    "\n",
+    "compares PNM EoS data with chiEFT-derived constraints for either energy per neutron \\( E/N \\) or pressure \\( p \\),\n",
+    "depending on the specified `contraint_quantity` parameter (\"e\" for energy or \"p\" for pressure). \n",
+    "\n",
+    "The likelihood model can be either \"Gaussian\" or \"Super Gaussian\" with an optional `enlargement` factor for the Super Gaussian to flatten its peak. e.g., 0.05 for 5% enlargement. \n",
+    "\n",
+    "The function calculates the sum of log-likelihoods over densities of 0.08, 0.12, and 0.16 fm\\(^-3\\), utilizing energy constraints from Huth et al. (Nature, 2022) and pressure constraints from Hebeler et al. (ApJ, 2013). This enables a statistical comparison of the PNM EoS data against chiEFT-derived reference values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "    \n",
+    "    # g_sigma, g_omega,g_rho, kappa, lambda_0, zeta, Lambda_w, d1, d2 = theta\n",
+    "    g_sigma, g_omega,g_rho, kappa, lambda_0, zeta, Lambda_w, d1 = theta # comment this line if you need two measuremnts.\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "    m_sig = 495 / oneoverfm_MeV\n",
+    "    m_w = 3.96544\n",
+    "    m_rho = 3.86662\n",
+    "    theta1 = np.array([m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w])\n",
+    "    ep, pr = RMF.compute_EOS(eps_crust, pres_crust, theta1)\n",
+    "\n",
+    "    eps_total = np.hstack((eps_crust,ep))\n",
+    "    pres_total = np.hstack((pres_crust,pr))\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    # probMRgaussian1 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(1.4,13,0.07,0.65),d1)\n",
+    "    # probMRgaussian2 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(2.0,12,0.1,0.6),d2)\n",
+    "    # probMR = probMRgaussian1 + probMRgaussian2\n",
+    "    \n",
+    "    # Same could be extended to more distributions. Notice the prior definition should be change accordingly\n",
+    "    # by define more density parameters like here d2.\n",
+    "    \n",
+    "    #1. This line is to compute MR likelihood from a Simulated MR measurement:\n",
+    "    \n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    #2. This is  a block that constrain from given real MR measurement, say J0030:\n",
+    "    #J0030 = numpy.loadtxt('data/PST_equal_sampled_MR.txt', delimiter=' ')\n",
+    "    #J30R_list, J30M_list = zip(*J0030)\n",
+    "    #J30R_list = numpy.array(J30R_list).T    \n",
+    "    #J30M_list = numpy.array(J30M_list).T\n",
+    "    #Rmin = J30R_list.min()\n",
+    "    #Rmax = J30R_list.max()\n",
+    "    #Mmin = J30M_list.min()\n",
+    "    #Mmax = J30M_list.max()\n",
+    "    #X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]\n",
+    "    #positions = numpy.vstack([X3.ravel(), Y3.ravel()])\n",
+    "    #values = numpy.vstack([J30R_list, J30M_list])\n",
+    "    #kernel3 = stats.gaussian_kde(values)\n",
+    "    #probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)\n",
+    "    \n",
+    "    #3. This is to compute the constraint from experiment of nuclearmatter\n",
+    "    # 250<K<400, 25<J<38, 30<L<86:\n",
+    "    # hint: since this K,J,L sampling don't need to sample central density so this \n",
+    "    # theta should be redifined.\n",
+    "    #probK = likelihood.Kliklihood(theta,250,400)\n",
+    "    #probJ = likelihood.Jliklihood(theta,250,400)\n",
+    "    #probL = likelihood.Lliklihood(theta,250,400)\n",
+    "    \n",
+    "    #4. This is block to cosntrain from a GW event, say GW170817, here the file of this\n",
+    "    # event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:\n",
+    "    #GW170817 = np.load('GW170817_McQL1L2weights.npy')\n",
+    "    #chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])\n",
+    "    #kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])\n",
+    "    #probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)\n",
+    "\n",
+    "\n",
+    "    #5. This block constraints pure neutron matter EOS\n",
+    "    ## For pure neutron matter alpha = 0\n",
+    "    #theta1    = np.append(theta, 0)\n",
+    "    #rho, e, p = RMF.get_eos_alpha(theta1)\n",
+    "    #EoS_PNM   = np.array([rho, e*oneoverfm_MeV, p*oneoverfm_MeV]) # energy density and pressure are converted from fm^-4 to MeV fm^-3\n",
+    " \n",
+    "    #probPNM = likelihood.chiEFT_PNM(EoS_PNM, type=\"Super Gaussian\", contraint_quantity=\"e\", enlargement=0.1) # 10% enlargement \n",
+    "\n",
+    "    prob =  probMRgaussian #+ probMRJ0030 + probK + probJ + probL + probGW + probPNM \n",
+    "    return prob"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up sampler\n",
+    "\n",
+    "Here next, we define sampler, there is two different sampler we provided for. Considering where you need resume file:\n",
+    "\n",
+    "```sh\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "```\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 2000\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Posterior plot\n",
+    "Since we only run our code at my labtob so here is the result corner plot(still have a lot flucatuation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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REZHc+Eok3VNRURG2bNkCwLy2GtWztLTE/fffDwA4deqUwt3cTa1Ww93dvUMGhABga2uLPn36AJB2y1EA6N69Ox555BEAwMsvv4zq6mpJ65kDGxsbHDp0CIcOHYKNjY3S7ZCCeC8QERERERERERGR3BgS0j1duHABdXV18PDwMIRF5kZ/HqB+W08SV69evQAAubm5kteaO3cuLC0tUV5ejqysLMnrERERERERERERERF1VQwJ6Z70QU3Pnj2hUqkU7qZ5R48eBQAEBQUp3EnnpN/6UBAEyWtpNBpDPXPb2paIiIiIiIiIiIiIqDNhSEj3pA8J3dzcFO6kefX19YiNjQUAhIaGKtxN56QPh+UICYHGcxyBrnEuW2VlJdzd3eHu7o7Kykql2yEF8V4gIiIiIiIiIiIiuWmVboDMm7mHhMnJyaisrISNjY1hW0wSl9whob5OVwgJAaCwsFDpFshM8F4gIiIiIiIiIiIiOXWNV+Gp3bKzswEArq6uCnfSvPj4eACAn5+f2W6H2llwkpCIiIiIiIiIiIiIqPPgq/B0T+Y+SagPCXv37q1sI52YPnzVh3dS62qThERERERERERERERESuCr8HRP+pDQ3d1d4U7uVlNTg8TERACNk4QkDZ5JSERERERERERERETU+fBVeGpRQ0ODWW83eunSJTQ0NMDBwcEs++ss5AwJ76zBkJCIiIiIiIiIiIiISDp8FZ5aVFBQgPr6eqhUKrMM4XgeoTzkDAnv3NKUISERERERERERERERkXS0SjdA5ks/Rejs7Ayt1vxuFZ5HKC85ziTsaiGhWq3GoEGDDO9T18V7gYiIiIiIiIiIiORmfskPmQ0rKysAjWf/CYJgVtN6giDg+vXrAABvb2+Fu+ncMjMzAQAeHh6S17p06RKAxmDawcFB8npKs7GxwenTp5Vug8wA7wUiIiIiIiIiIiKSG8cVqEWBgYGwsLBAVVUV8vPzlW6nicLCQty+fRsqlQouLi5Kt9Np1dXVITk5GQAwevRoyesdOnQIADB79myznF4lIiIiIiIiIiIiIuosGBJSiywtLREUFAQASE1NVbibpvTTbd26dYNGo1G4m87r2rVrqK2thYeHB8LCwiStpdPpcOTIEQDAggULJK1FRERERERERERERNTVMSSkewoPDwcApKWlKdvIr+hDQjc3N4U76dwuXrwIABg7dqzk56RduHABxcXFcHZ2xrhx4yStZS6qqqrQu3dv9O7dG1VVVUq3QwrivUBERERERERERERy435+EjC38/tMoZ8eM9eQ0NXVVeFOOq/bt2/jypUrABpDQqnptxqdM2cOLC0tJa9nDgRBQHp6uuF96rp4LxAREREREREREZHcGBKKIDs7G+fPn4cgCBgwYAB69uzZ5qCwpqYGNTU1ho/LysqkaLXN9JOE5rbd6M2bNwEwJJRScnIyGhoa4Ovri4CAAElr3bnV6P333y9pLSIiIiIiIiIiIiIiYkhosgsXLmDevHmwsLBARUUF+vbti6+++grdu3dv0zorVqzAn//8Z4m6bD/9JGF6ejoaGhok33LSWJwklN6dW41KPRmr32q0W7duXWarUSIiIiIiIiIiIiIiJZlH4tNBpaamYsKECbj//vtx4MABfPDBB0hPT0dRUZHhmoaGBqPWev3111FaWmp404dgSgsICIClpSVqamqQn5+vdDsAgNraWuTk5ADgmYRSqaysxI0bNwBAltBOv9Xo7Nmzu8xWo0RERERERERERERESmJIaILt27dj8ODBeO+99+Dh4YGFCxeiR48eOHfuHH744QekpKRArVYbFRRaWVnB0dGxyZs50Gg0hq0m9Vt8Ki0rKwuCIMDKygp2dnZKt9MpXbp0CYIgICQkBF5eXpLWunOr0QULFkhai4iIiIiIiIiIiIiIGnG7UROUl5fj/PnzyM/PR/fu3bFixQocPnwYZWVlqK+vx+XLlxEbG4uBAwcq3apJgoKCkJSUhIyMDAwaNEjpdpqcRyj1Nphd1YULFwA0bjUqtcTERG41SkREREREREREREQkM04SmmDQoEHw9vbG0KFDsXTpUrz55pvYunUrDh48iH379mHWrFl4+eWXUVlZqXSrJomKigIAxMfHK9xJIysrKwBAdXW1wp10Tjk5Obh58yZUKpUsIeHmzZsBNE4RWlhYSF7PnKhUKvTt2xd9+/Zl4N3F8V4gIiIiIiIiIiIiuXGS0ASTJ0+GVqtFamoqbty4gaeeegrTp08HANjb2yMwMBC5ubmwsbFRuFPTzJo1C2+//TZOnz6N27dvw9raWtF+wsLCoFKpUFxcjNLSUjg5OSnaT2cTFxcHABgzZgxcXV0lrZWeno7Y2FioVCq8+OKLktYyR7a2trh06ZLSbZAZ4L1AREREREREREREcuMkYTvpzxkcP348Hn/8cdjY2CAjI6PJNeXl5fD09ERNTY0SLYqmf//+6N27N2pqanDmzBml24GdnR2Cg4MBAGlpaco208lUVFQYthqdN2+e5PW+//57AI1BtP53SkRERERERERERERE0mNI2E5qddMfnY+PD7KysvDRRx9hz549eO211/DNN9/g7bff7vCThCqVCrNnzwYAHDt2TNlm/r+IiAgADAnFdubMGTQ0NCA0NBR9+/aVtFZRURH27t0LAHjllVckrUVERERERERERERERE0xJBTJww8/jKioKHz++ed46qmncPToURw8eBBhYWFKtyYKfUh44sQJ6HQ6ZZsBEBkZCYAhoZjq6+sNk6JyTBH++OOPqKurw4gRIzB8+HDJ65mjqqoqhIWFISwsDFVVVUq3QwrivUBERERERERERERy45mE95CamoqtW7fi5s2bGDx4MBYuXHjXNQ0NDYapwjVr1iApKQmWlpbo1q0bXFxc5G5ZMiNGjICbmxsKCwtx4cIFwySfUsLDw6FSqVBSUoKSkhI4Ozsr2k9ncOnSJVRWVsLNzQ0xMTGS1qqtrcX27dsBdO0pQkEQcPnyZcP71HXxXiAiIiIiIiIiIiK5cZKwBRcuXMCoUaOwa9cunDx5EkuWLMHf//73u67TB4SpqakAgNDQUAQEBHSqgBAAtFotZsyYAQA4evSowt0Atra2CA0NBcBpQjEIgoC4uDgAjecDarXS/vuBixcvoqKiAj169DDcV0REREREREREREREJB+GhM1IT0/H3LlzsWTJEuzZswfHjx/H6tWr8Y9//AMpKSl3Xf/hhx/iiSeewNmzZxXoVj76LUePHz9uFpMuPJdQPJmZmcjJyYFGo5EltDt9+jQAYOLEiXed70lERERERERERERERNLjq/O/0tDQgO+++w6BgYF44403oNFoAACDBw+GhYVFs+fxubm5oba2Fh4eHnK3K6sJEybA2toaeXl5zYalcmNIKB79FOGkSZPg5OQkeb07Q0IiIiIiIiIiIiIiIpIfQ8JfUavVGDZsGCIiIpqEJX379oVWq0VOTs5d3/PQQw/hp59+gre3t5ytys7GxgZTp04FYB5bjurPJSwtLUVxcbHS7XRY5eXlSEpKAgDMmzdP8nq3bt3C9evXATQGz0REREREREREREREJD+GhP/fnROCo0aNwooVKwDAsK2mSqWCSqVCXV2d4br9+/cjNzcXAODo6Chjt8qZNWsWACA2NlbhThpDy6CgIABoNrwl4+Tl5UEQBPj6+sLf31/yeleuXAEAhIWFwd3dXfJ6RERERERERERERER0N4aEAK5evYqPPvqoSdB0ZzhYX1+PqqoqqNVqQxj4xhtvYMKECWhoaFCkZ6VMnToVarUa169fNwSkSgoICAAAs+ilo6qsrATQuG2uHPRThPrtYrsylUoFX19f+Pr6QqVSKd0OKYj3AhEREREREREREclNq3QDSktJScGwYcNQXFyMoqIivPTSS3Bzc2vyIq1arYZGo4EgCNBqtXj33Xfx8ccfIy4uDj179lSwe/m5ublhxIgROHr0KE6cOIE5c+Yo2o8+JMzPz1e0j46soqICAODi4iJLPf15lgMGDJClnjmztbXlmZoEgPcCERERERERERERya9Lh4SVlZVYsWIFZs6ciUGDBuH5559HfX09Xn311SZTVWq1GtbW1nB0dMTTTz+NxMREHD9+HIMGDVKwe+XMmjULR48eRWxsrOIhoX57zLy8PEX76Mj0IWG3bt1kqXfjxg0ADAmJiIiIiIiIiIiIiJTUpUNCtVqNgQMHwtXVFQsXLoS7uzsWLVoEAE2CQp1Oh9LSUty4cQMVFRWIj49Hv379lGxdUTNnzsTvfvc7JCQkoKKiAvb29or1op8kLCkpQU1NDaysrBTrpaPSbzcqxyRhdXU1bt68CYAhIRERERERERERERGRkrp0SGhjY4OHHnoIdnZ2AIAFCxZAEAQsXrwYgiDg97//PVxdXSEIAgRBwMaNG+Ht7Y2wsDCFO1dWnz59EBISguTkZJw+fRpjxoxRrBcHBwc4OzujpKQExcXF8PT0VKyXjkrOScLU1FQIggAPDw94eHhIXs/cVVdXY9SoUQCAI0eOwMbGRuGOSCm8F4iIiIiIiIiIiEhuXTokBGAICHU6HdRqNRYuXAhBELBkyRKoVCr89re/xcqVK5GWlob169fD1tZW4Y7Nw8yZM5GcnIzY2FhFQ0IA6NmzJ0pKSnDr1i2GhO0g5yTh9evXAQARERGS1+oIGhoacObMGcP71HXxXiASx7lz57Bv3z6cPn0ap0+fRnp6OoDGf6TSu3dvo9c5dOiQUf9/M2bMGBw4cED0+kREREREREREcujyIaGeRqOBIAhoaGjAokWLoFKp8OCDD2L79u1ISUnBmTNnGBDeYebMmfjggw9w8uRJ1NfXQ6tV7lby8vLC5cuXcevWLcV66MjknCRMSUkBwK1GiYhIGu+88w62bdtm8jqenp546KGHWvz6li1bUFZWhpiYGEnqExERERERERHJgSHhHVQqFQBAEAQsXLgQq1atQkJCQpc/g7A5Q4cOhZubGwoLC3HhwgVERkYq1ouXlxcAMCRsh4aGBlRVVQGQJyTUTxIyJCQiIikMGzYM/fr1w6BBgzBo0CAMHDgQeXl5bV4nJCQEa9eubfZrmZmZ+Prrr6FSqbBs2TJJ6hMRERERERERyYEh4a+oVCrodDq88sorOHjwIBISEhgQNkOj0WD69OlYu3Yt9uzZo2hI2LNnTwAMCdvj9u3bhvednJwkrdXQ0IAbN24AYEhIRETG0W/RmZaWZtT1r732mnTN/H9ff/01GhoaMGrUKPj5+clen4iIiIiIiIhILGqlGzBXYWFhOHfuHPr37690K2ZLvw3XL7/8gp9++kmxPnx8fAAA+fn5EARBsT46orKyMgCNAaFGo5G01o0bN1BdXQ17e3sEBwdLWouIiEgqX3/9NQDccztSIiIiIiIiIqKOgCFhMzQaDZYvX46IiAilWzFro0ePxrvvvgsA+Ne//oX4+HhF+vDz84NWq0V1dTVKS0sV6aGj0k9f6rdslZL+/oiOjlb0DEsiovbS6XS4ePEi1q5di+effx7Dhg2Dra0tVCoVVCoVHn744TatV15ejs2bN+O5557D8OHD4e7uDgsLCzg6OiIkJATLli3Dnj17JPsHMNu2bcP8+fPh6+sLa2trdO/eHcOGDcMHH3xg1N+nSvevhLi4OCQnJ8PW1hb333+/0u0QEREREREREZmEr9S3QH8+Id3bm2++icuXL2PDhg14++238dlnn8Hb21vWHiwtLeHn54dr164hJycHzs7OstbvyOQMCRMSEgAAY8aMkbxWR+Lm5qZ0C2QmeC+YvwULFuDHH38UZa1//OMfePPNN5ts+6xXXl6OK1eu4MqVK/j6668RHR2N9evXGybnTVVeXo4lS5Zgx44dTT5fUFCAgoICnDx5Ep988gk2btyI4cOHm13/Slq3bh0AYO7cuXBwcFC4GyIiIiIiIiIi0zAkJJOoVCqsWbMGN27cQFxcHN566y3897//lX1SrE+fPoaQMDQ0VNbaHZlcIaFOp0NiYiIAhoR3srOzQ0FBgdJtkBngvdAx6HS6Jh+7uLjA1dUV165da/NaV69eNQRs3t7eGDduHAYNGgR3d3dUV1cjLi4O69evR0VFBY4ePYrRo0fj5MmT6N69u0mPob6+HvPmzcPevXsBAB4eHnj88cfRt29f3Lp1Cxs2bMDx48dx8+ZNTJs2DceOHUNYWJjZ9K+k2tpafPfddwC41SgRERERERERdQ4MCclkNjY22Lp1KwYMGID09HT8/PPPmDZtmqw99OnTBwCQk5Mja92Orri4GADQs2dPSeukpKSgsrISjo6OiIyMlLQWEZFUBg8ejNDQUAwcOBADBw6En58f1q5di0ceeaTNa6lUKkycOBG/+93vMG7cOKjVTXeAf/jhh/H73/8ekyZNwpUrV5Camorf//73+OKLL0x6DKtXrzYEhH379sWBAwfg4eFh+Pqzzz6L3/3ud/jwww9RUlKCJ598EseOHZO8/7/97W9ITk6+6/OFhYWG9X5t5MiReOyxx4x+7Kb66aefcOvWLXh7e2Ps2LGy1SUiIiIiIiIikgpDQhKFp6cnXnvtNbz88sv45ptvMGnSJFmnCYOCggA0hoSCIHC7WCPJNUmoP49w1KhR0Gg0ktYiIpLKG2+8Idpa7733HlxcXO55ja+vLzZu3Gg4I3njxo349NNPYWtr266aOp0O77zzjuHjr7/+uklAqPf+++9j//79SEhIwPHjx/Hzzz9j0qRJkva/Z88eHD58uMW1vvrqq2Y/L2dIqN9q9MEHH7wrFCUiIiIiIiIi6oj4CgeJ5sknn4S7uztycnKwb98+WWsHBARApVKhsrIS5eXlstbuqOrq6lBWVgZA+pCQ5xE2r7q6GqNHj8bo0aNRXV2tdDukICXvhZKSEqxYsQJDhw6Fi4sL7O3t0a9fP7z33nuGP08feughqFQqqFQqZGRkyNpfZ9VawKY3YMAAhISEAACqqqqQkpLS7pqHDx9Gbm4uACAmJgZRUVHNXqfRaPDCCy8YPt6wYcNd14jd/6FDhyAIwl1vvr6+8PX1bfZra9euNaoHMRQWFmL37t0AuNUoEREREREREXUenCQk0djZ2eF3v/sdXnvtNaxfvx4TJkyQbWrMysoKvXv3RmpqKnJycuDo6ChL3Y6spKQEQOPvTcqfl06nw4ULFwAwJPy1hoYGw+RMQ0ODwt2QkpS8FyIiIgxbD+tdvHgRb731Fr777jv88ssvhm0gXV1d4ePjI2t/BDg4OBjeNyVE3rNnj+H9qVOn3vPaO7+uD8faS6z+lfTtt9+irq4OQ4cORXBwsNLtEBERERERERGJgpOEJKpnnnkGrq6uyMrKwoEDB2StfeeWo9S6O7calXJ71itXrqCqqgrdunXDgAEDJKtDRG2jP5dOHxBOmTIFn3zyCTZu3IgPPvgAgYGBuHjxIp599llcuXIFAHimqAJqampw9epVw8e+vr7tXkv/DzYA4L777rvntR4eHujVqxcAID8/HwUFBe2qKWb/StJvd8opQiIiIiIiIiLqTDhJSKKyt7fHyy+/jDfeeANfffUVRo4cCRsbG1lq9+nTBz///DOys7NlqdfRyXUeoX6r0ZiYGJ7hRGQmzp8/j7///e8AGqcDN2/ejJiYmCbXPP300xg+fDi2bNli+FxL21NK6dixYygsLBRlraioqA43CblhwwaUlpYCaOzf09Oz3Wvpw14A8PPza/V6Pz8/ZGZmGr7X3d29zTXF7F9Ky5Ytw6lTp/Dcc8/hueeea/K1S5cu4dy5c7CyssLChQsV6pCIiIiIiIiISHwMCUl0zz77LD755BNkZWXhww8/xJtvvinppJpeeHg4ACA1NRV1dXWwsLCQvGZHVltbC6Bxu1EpxcfHA+BWo0TmQhAEPPjgg6ipqQEArFu37q6AEGj8Rx9//etfMWPGDMPnlAgJ33rrLcNWrKb68ssv8fDDD4uylhwKCgrw6quvGj5+6623TFpPv800ALi5ubV6vaura7Pfayyx+zfGzp078e677xo+1v+DmDlz5sDKygoAMG3aNPzhD39o8n0ZGRm4cuVKs4G0fopw5syZ6NatmyT1iYiIiIiIiIiUwJCQROfo6IiNGzdi9OjR2L9/P8LCwjBnzhzJ6wYFBcHDwwN5eXm4fv06QkJCJK/ZkWm1jU9/fVgohdraWsP2duPGjZOsDhEZ7/vvv8f58+cNH48cObLFa0eNGtXkYyVCwq6qtrYW8+bNM2zzOXv2bJP/Lq2oqDC8b21t3er1d+4EUF5e3qZaUvRvjIKCAsTFxd31ef1UO4A2/f+BTqfDN998A8C4rUbFrk9EREREREREJCWGhCSJ6OhorFy5Ei+99BI+++wzBAcHo2/fvpLWVKlUiI6Oxg8//IDk5GS+CNcKfUionyaSwuXLl1FTU4Pu3btL/vsnIuP84x//AABYWFigrq7untc6OjrCwcEB5eXlcHR0RGBgoBwtNnHo0CHZayqtoaEBy5cvx9GjRwEAAQEB+OKLLxTuynhi9p+Wltam6x9++OF2TYu2dJ9pNBpkZWVJXp+IiIiIiIiISAk8IIwk89vf/hbz589HfX09/vSnP7Vrq7K2io6OBtB4dpJOp5O8Xkem3461tZDAFPqtRseOHSvLlrMdka2tLWxtbZVug8yAHPdCbm4uTp8+DQCIjIw06nuqqqoAABEREXwey0AQBDz11FOG6TUfHx/s27ev1W0ujWFvb294//bt261eX11dbXjfwcHBqBpS9k9EREREREREROJiSEiSUalUWLNmDYKDg1FQUIB3331X8uAuLCwM3bp1w+3bt9s8fdDVyDFJqA8JudVo8+zs7FBZWYnKykrJz4Yk8ybXvbB//34IggDAuK1Ds7OzDX9uc6tR6QmCgGeeeQarV68GAHh7e+PAgQPo3bu3KOs7Ozsb3m/u7L1fKyoqavZ7WyJ1/0REREREREREJC5uN0qScnR0xObNmzFo0CCcO3cOa9euxaOPPipZPY1GgxEjRmDHjh1ISkpCQECAZLU6OqnPJKyursbly5cBNE4SEpHy0tPTDe97enq2ev2dZ6sZO3kotmPHjhkVaBkjKioKPj4+oqwlNkEQ8Oyzz+I///kPAMDLywsHDx4U9e+x4OBgpKamAgBSU1NbDe/01+q/917k6J+IiIiIiIiIiMTFkJAkFxYWhjVr1uCBBx7A+vXr0bdvXwwbNkyyetHR0dixYweSk5MxdepUqNUcmG2OfrtRqSYJL1y4AJ1OB19fX/j5+UlSg4jaJj8/3/C+MROLGzZsMLyv1CThW2+9hcOHD4uy1pdffmmW58XpA7bPP/8cANCzZ08cPHhQ9DMg+/Xrhz179gAATp8+jTFjxrR4bV5eHjIzMwEA3bt3h7u7e4vXytV/a+rq6nDw4EHs2LEDsbGxSEtLQ0VFBby9vTFp0iS8+uqr8PX1bde6H374Ib7++mvcuHED9vb2iI6Oxh/+8Id7hudXrlzB+++/jwMHDiAnJwdarRaBgYGYPXs2Xn75ZTg6OprycImIiIiIiIiITMb0hGSxZMkSPPfccwCAv/71r8jOzpasVmRkpGHrvps3b0pWp6OTervRc+fOAWjcapTnmDXv9u3bmDZtGqZNm2bU+WDUecl1L+if9wBQUFBwz2svX76MrVu3AgBsbGwQGhp6z+vLysrw4osvwsfHB5aWlrC1tcWwYcNw4MABk/vuzH4dsPXo0QMHDx5Enz59RK81efJkw/u7d+++57W7du0yvD916tQWr5Oz/9YcPnwYkyZNwieffILCwkJER0djypQpqK6uxmeffYb+/fvjxIkTbVqzrq4OkydPxuuvv47CwkLMmDEDISEh2LJlC4YOHYq9e/c2+31Hjx5FZGQkvvzyS1haWmLmzJmIiYlBWloa3nnnHQwePBi3bt0S42ETEREREREREbUbQ0KSzYcffoihQ4eioqICb7/9tmQvhFtYWGD48OEAgKSkJElqdAb6sKCurk6S9fUhIbcabZlOp8OuXbuwa9cuyc/rJPMm171w51RvYmJii9dVV1dj+fLlhj8f+vfvD41G0+L1Z8+eRUhICP71r3/Bx8cH8+fPh6+vL06ePInJkyfj9OnT7e750KFDEARBlDdznCJ87rnnDAGbp6cnDh48iKCgIElqxcTEGLaZPXTokOHP6V/T6XT4+OOPDR8vWrSoxTXl7L81arUaCxcuxKlTp5CWloYtW7Zgy5YtuH79Oh5++GGUlZVh8eLFbfp774MPPsCBAwdw33334dq1a/j+++9x9OhRfPvtt6itrcUDDzyAioqKu77v6aefRnV1Nf7whz/gypUr2LRpE3bt2oXU1FRERUXhypUr+OCDD8R8+EREREREREREbcaQkGRjaWmJTZs2wdnZGSkpKfjyyy8lqxUdHQ2gcasvap6U242Wl5cjJSUFAO65nR0RyWvcuHGG9/ft29fsNYWFhZg9e3aT8wjvtdXo9evXMXbsWNTV1eHo0aM4duwYvv32W1y6dMkQyLz33nviPYhO5Pnnn8dnn30G4H8BW2tn/7Vk9OjRUKlUUKlUWLt2bbPXaDQa/PGPfzR8vGzZsiZb0Or9/ve/R0JCAgBgxIgRmDRpkuT9i2Hs2LH47rvvcN999zX5vLW1NT7//HM4OTkhPT0dsbGxRq1XX1+Pf/7znwCAzz77rMn2oIsXL8bUqVNRUFCAL774osn3FRUV4dKlS7CwsMBbb73VZJrexcUFv/vd7wAAp06datfjJCIiIiIiIiISC88kJFl5e3vj73//Ox5//HFJA7yQkBAAQGlpqWQ1Ojr9WY1STC0VFxdDEAQ4OTmhZ8+eoq9PRO0TEhKCGTNm4KeffjI891999VWMGjUKdXV1iI+Px/r161FUVITBgwcbQoyBAwe2uObTTz+NsrIybNmyBSNGjDB8Xq1W4/XXX8eGDRtw7NgxaR+YTFJTU7FmzZomnzt//rzh/fj4eLz11ltNvh4VFYW5c+fetdZbb72FTz/9FACgUqnwm9/8BsnJyUhOTr5nD1FRUfDx8WnvQ8Djjz+OLVu2YO/evbh06RIGDBiAxx9/HH379sWtW7ea/L6cnJzwf//3f82uo1T/7WVtbY2goCCcPn3a6C3Pjx8/jqKiIvj5+WHQoEF3fX3hwoXYtWsXtm3bhhdeeMHweUtLSwBodattNze3NjwCIiIiIiIiIiLxMSQk2d35L/GlwjPwzAN/D0TmZ/Xq1Rg9erQhzPm///u/JkGQjY0N/va3v0Gj0RhCwpiYmGbXSkxMxN69ezFy5EjMnj37rq/rtzdtbjvGjig9Pf2eU5Hnz59vEhoCwEMPPdRsSHhncCoIAl5//XWjevjyyy9N2jZVq9Vi8+bNWLJkCXbs2IHc3Fy8++67d13n7e2NjRs3IiwsrNl1lOq/vXQ6HdLS0gDAsOVqa/Rb8rYUkusnbH+9da+DgwOGDh2KkydP4i9/+Qv+/Oc/G/4+vHXrFlauXAkAWL58eZsfBxERERERERGRmLjdKBERURfi4eGBuLg4LFu2DABgZWUFZ2dnRERE4I033kBSUhJee+017NmzBwDg6+uLwMDAZtfatm0bAGDevHnNfr24uBiA8aEMycPBwQE//fQTtm7dirlz56JXr16wsrKCm5sbhgwZgvfffx8XL140nO/bGXz99dcoKChA9+7djX5c6enpABoD0+boP19UVHRXEP7f//4X3t7eePfddxEcHIz7778f06ZNg5+fH3JycvDVV19h8uTJJjwiIiIiIiIiIiLTcZKQiIioi3F0dMSjjz6KdevWIT8//64J7wsXLuDAgQMAgEceeaTFdfbv3w8AGDlyZLNfP3r0KIB7b1fakYwePRqCIIiy1qFDh0RZx5T1Zs2ahVmzZslWTylpaWl4+eWXAQB//etfYWVlZdT3lZeXAwDs7Oya/bq9vX2Ta+/8OCwsDCdOnMD8+fMRFxeHa9euGb42bty4Fic0iYiIiIiIiIjkxJDQTOlfhCwrK1O4E/FVVVUBaNz6q7KyUtIagiCgpqZGkhodXW1tLYDGn5HYv4fq6mrD2lLcw/o1W3uxXv/1yspKs3wu3flzLysrk+R8SOoYlLgX9DV//TyqrKzEI488AkEQ4ODggCeffLLZ729oaEB8fDwAwNXVtdlrNm/eDACYOXOmWG1TJ7Vs2TLD9rbGmjNnDlasWHHPa0pLSzFr1izcunULCxYswKOPPmr0+vrnRnu2zj548CDmzZsHT09P7NmzB0OGDEFVVRV27NiBV199Fbt378bWrVsxadKkNq9NRERERERERCQWhoRmSv+v13v16qVwJ9K5ePEipk+fLmkNQRDwt7/9TdIaHV1paalkv4fS0lI4OTlJsjbQ+Dy51/r659HUqVMl60EsPXv2VLoFMhNy3wt3Po9SUlLw4IMP4uzZswCAv/zlLy1uFXr16lXDcyw9Pd1w/qDe8ePHsXXrVvTs2RMLFiyQ8BFQZ5CRkYErV6606XtycnLu+fXq6mrMnDkT58+fx9ixY7Fu3bo2re/g4ACg5TM17wz39dcCjdvszp8/HzU1NdizZw98fHwAAM7OznjiiSfg7OyMhQsX4umnn8a1a9eg0Wja1BcRERERERERkVgYEpqpnj17IjMzEw4ODkb/C/aysjL06tULmZmZd20dR03xZ9U25vbzEgQB5eXlrYYp7XkemRtz+9mbG/58Wtbcz2bRokWIjY3FxIkTERkZCScnJxw9ehS5ubk4fPgwdu7cifr6egDA448/jhdeeKHF9fVBIgC8//77GDFiBCwsLAA0ble6ZMkSCIKATz/9FDY2NhI+UuoMxN6+tLa2FvPmzcORI0cwbNgwbNu2zehtRvV8fX0BADdv3mz26/rPu7i4NNlqdOfOnbh16xbGjRtnCAjvNHfuXFhaWiI1NRU3btxAnz592tQXEREREREREZFYGBKaKbVaDW9v73Z9r6OjI18sNxJ/Vm1jTj8vYyYUTXkemRtz+tmbI/58WnbnzyYxMRGlpaXYtGkTNm3a1Oz1NjY2eO+99/Db3/72nuueOXMGAPDMM8/gP//5D0JCQjBkyBDk5ubiyJEjEAQB//rXvzBnzhxRHw9Ra3Q6HR544AHs3r0bERER2LVrV5MQz1gREREAgHPnzjX7df3nBwwY0OTzmZmZAFr+e0qr1cLOzg61tbW4detWm/siIiIiIiIiIhILQ0IiIqIuYt26dfjpp59w9OhR5Ofno6ioCIIgwNXVFeHh4Rg/fjweeeSRFs8YvJN+kvDpp5/GpEmT8M4772Dr1q2wtrbGtGnT8Prrr2Po0KFSPySiJgRBwKOPPooffvgBoaGh+OWXX+Ds7NyutUaMGAEXFxfcuHEDZ8+excCBA5t8/fvvvwcAzJo1q8nne/ToAaAxRKyvr4dW2/R/t69cuYLi4mIAQO/evdvVGxERERERERGRGFSCIAhKN0HiKCsrg5OTE0pLSzlR0wr+rNqGPy/l8Gd/b/z5tEzKn01DQwOcnZ1RX1+P8vJynqlGZuO3v/0t/vWvf8Hf3x9Hjx41+ozPZcuW4dSpU3juuefw3HPPGT7/7rvv4o9//COGDBmCvXv3Gs4e3LhxIxYtWgQ3NzfcuHGjyZmEeXl58PPzQ3V1NV566SW8//77hqCwoKAAc+fOxbFjxxATEyP6NqtERERERERERG3BScJOxMrKCm+//Xabz9zpivizahv+vJTDn/298efTMil/NlevXkV5eTmGDRvGgJDMxrZt2/Cvf/0LQON5gm+88Uaz182ePRuzZ89u8rmMjAxcuXIFhYWFTT7/2muv4cCBAzh06BACAwMRExOD3NxcHD16FBYWFvj666+bBIQA4OHhgY8//hhPPvkk/vGPf+CHH35AVFQUqqqqEBcXh9LSUnh4eGDVqlXiPXgiIiIiIiIionbgJCERERG1yTfffIOlS5fiueeewyeffKJ0O0QAgLVr1+KRRx5p9bq3334bf/rTn5p8bvTo0Th8+HCzX6utrcXKlSuxfv163LhxA/b29hg5ciT++Mc/IioqqsU6cXFx+Oijj3D8+HHk5uZCq9XCz88PU6dOxSuvvILu3bu352ESEREREREREYmGISERERG1yUsvvYR//vOf+OKLL4wKZYiIiIiIiIiIiMj8MCQkIiIiIiIiIiIiIiIi6mJ4JqGZamhoQHZ2NhwcHKBSqZRuh8isCIKA8vJy9OzZE2q1usXr+DwiahmfR0SmM/Z5RERERERERERkjhgSmqns7Gz06tVL6TaIzFpmZia8vb1b/DqfR0St4/OIyHStPY+IiIiIiIiIiMwRQ0Iz5eDgAKDxRSdHR0eFu7m33r17o7i4GJ9//rksLyS//fbbOHv2LKZNm4Z+/fq1e51//OMfqK2tFbEz+d1///0ICAiQrd6RI0cQGxuL6dOn46mnnmr3Oo899hhyc3Oxd+9eDB48uM3fX1ZWhl69ehmeJy3Rf/3FF1+ElZVVu3rtaM6cOYN9+/ZhxIgReP311+/6+s8//4xPPvkEAQEBuP/++41ac/Pmzbh27ZrJvQ0bNgwxMTEmrwMAOTk5+Oqrr0RZq6sz9nnUEf4+0ul0iI2NBQAMHz4cGo1G4Y5ICXLeB8b+fUREREREREREZI4YEpop/ZZujo6OZv+irH57LRsbG9jZ2UleT/9in4WFhUnBT2fYNs/Un0FbabVaQ11Tftf6e8bOzs6k+7u136H+61ZWVl0mJLSwsADQ+Ltq7nek/zloNBqjfyZivcCu1WpF+z1YWlqKsg4Z/zzqCH8fAcC0adOUboHMgNz3QWf4fwoiIiIiIiIi6np4eAoRERERERERERERERFRF8NJQjKZIAiy1mtoaJC1Hv2P2L9rue+druT27dvNfr6oqKjNa9XX15vaDgCgsLAQDQ0NhklSU/DPAWpOXV0dVq1aBQB44oknDJO11LXwPiAiIiIiIiIiMg4nCckkDQ0NKC8vBwBYW1tLXk+n0yElJQUATNr2rra2FjU1NWK1pRi5t9DU/66dnJxMWsfGxgZA41lOJK4ePXoAAOLi4nDixIkmX9uzZw/Wrl0LAEafH5qenm54zpkqKSkJ69evR2VlpclridUTdS61tbV47rnn8Nxzz3X4M2ep/XgfEBEREREREREZhyEhmaSoqAh1dXUAAFdXV8nrJScno6SkBFZWVvDx8Wn3OoWFhSJ2pRxnZ2dZ6+Xk5AAAAgICTFrHzc0NAJCVlWVyT9RUr169MGTIEADAn//8Z+Tn5wMAdu7ciffffx+CICAqKgojRoxoda3a2lps27ZN1P5SU1OxatUq3Lx5s91r6HQ6nD17VsSuiIiIiIiIiIiIiLoehoRkEn3I061bN2i10u9eGxsbCwDo06cPNBpNu9cpKCgQqyXFqNVqODg4yFZPp9MZfm6BgYEmraUPCU0JiqhlEyZMQM+ePVFTU4N33nkHmzdvxsqVKwEA9913H6ZPnw6VStXqOvv370dxcbHo/ZWVlWHNmjU4ffp0u7acvXLlCioqKkTvi4iIiIiIiIiIiKgrYUhIJsnOzgbwv9BHavqQMCgoyKR1OkNI6OzsbFTQI5aCggI0NDTAwcEBHh4eJq3l7u4OgJOEUtFoNJg/fz4sLCxw6dIlfPrppwCAoUOHYsqUKUbdN2lpaTh16pSkfe7atQtbt25t83aAp0+flqgjIiIiIiIiIiIioq5D+tEv6tT0IaEcW41mZWUhLS0NKpXK5Em2zhASmnouYFvl5uYCaJwiNDWclHu70atXr8LCwsLwsbOzMzw8PGQNWeXWrVs3zJ49G5s2bQIAjBw5EmPHjjXqMdfW1mL79u1StwgAOH/+PLKysjBhwgQEBQW12l9BQQHS0tJk6Y2IiIiIiIiIiIioM2NISCaRMyQ8ceIEAKB3796wsbExaa3OEBJ21PMIAflDwh9//PGuz7m7u2PAgAHo37+/rNu2yqlv376YP38+BEFAWFiY0aHovn37JNlmtCVFRUX47rvv0KtXL4wfP/6e541yipCIiIiIiIiIiIhIHAwJyST6kEe/faSUxNpqtK6uTtYARCpyh4R5eXkATD+PEJD/TMKQkBDDGZYNDQ24cuUKCgoKsG/fPuzfvx/+/v4YMGAAQkJCmkwcdgZhYWFtuj41NVWxIC4zMxNffvklgoODMW7cuLv+XKmtrWVISERERERERERERCQShoRkErkmCcvLy5GQkAAACA4ONmmtwsJCETpSnpwhoSAITbYbNZU+/CkqKkJRUZHk98/KlSthZ2dn+LiiogKHDh3Czz//jIsXL+L69eu4fv06LC0t0bdvX0RERMDHx6dTb0faHDm3Gb2XK1eu4MqVK4iIiMDo0aMNW+ueP39e4c7I3FlZWWHHjh2G96lr4n1ARERERERERGQchoRkErlCwri4OAiCAHd3d3Tr1s2ktTrDVqOAvCFhcXExampqYGFhcc+tII3l6OiIgIAAXL9+Hd999x2effZZEbo0nr29PaZPn47p06cjKysLv/zyC3755Rfk5uYiISEBCQkJcHZ2xrBhwzB48GBZe1PS3r17UVJSonQbBvrfxfDhwzFy5EhOEVKrtFotpk2bpnQbpDDeB0RERERERERExlEr3QB1XIIgGLaL1G8fKRX9eYSmThECnSck1E9XyUF/HqG/v79o23FOmTIFALB27VpR1msvLy8vPPLII/jmm2/w0UcfYerUqbCwsEBJSQl2796Nq1evKtqfXDIzM3HmzBml22hWbGwsPvjgA+Tn5yvdChEREREREREREVGnwZCQ2i0xMRH5+fmwsLCAl5eXpLUuXLgAAAgICDB5rc5wHqGVlRUcHBxkq6efGBUjpNUbM2YMAODs2bMoLS0Vbd32UqvVGDBgAF555RVs374dUVFRAP537mZnZ64BIVFb1NXVYe3atVi7di3q6uqUbocUwvuAiIiIiIiIiMg4DAlFcOvWLZOn02pqalBWVtbkzdxt2LABADBs2DDY2tpKVqeoqAgFBQVQqVTo2bOnyeuZ03aK7eXj4wO1Wr6nrxQhoYuLCzw9PSEIgmjbSIr1PLK2tkafPn0ANJ7T19nV1NTwvD/qFGpra/HII4/gkUce6RLPXWoe7wMiIiIiIiIiIuPwTEIT3bhxAxMmTMADDzyAp556qt0h1ooVK/DnP/9Z5O6k09DQYAgJx40bJ2mt5ORkAIC7uzssLS1NXs8cptZMJca5gMZqaGgwhIQhISGirt23b1/k5uYiLi4O48ePN3k9MZ9HNjY2ACDaFIr+hWox7mGxXbp0SekWiKgTycjIQGFhYavXubm5ifr3mb5udXW14XMJCQmGP8/bWteYx1FRUdG+ZomIiIiIiIiIzABDQhP98ssvSE1NxY4dO2BtbY3ly5fD09MTQOOZfQCgUqlaXef111/HSy+9ZPi4rKwMvXr1kqZpERw/fhyZmZmws7PD0KFDJa2lDwnFmCKsr6/vFC/oyRkSFhUVoba2FtbW1vD19RV17dDQUBw4cABxcXGirCfm80j/orIYUyg3b97EmjVrAADz589HWFiYyWuKKT4+XukWiKiTyMjIQGhoKKqqqlq91tbWFklJSaL8ndZS3ZEjR7arblseBxERERERERFRR8WQ0ETDhw/HsmXL0KdPH/z73/9GQ0MDnnvuOTg7OxsVDupZWVnByspKwk7FpZ8ijI6OlnwySh8SinHuYUfYxrU1Wq1WlMDUWPoz+YKCgqDRaERdOzQ0FAAQFxcHQRDa9JxpjpjPI2trawCmTxJev34d69evN3z8ww8/4MaNG5g8eTIsLCxMWlsMBQUFuHnzptJtEFEnUVhYiKqqKqxfv97wZ3xzkpKSsHTpUhQWFooSEt5Zt3fv3oZw8NixY00mCY2ta+zjOHv2LJ544gmT+yciIiIiIiIiUgJDQhMJgoDY2Fh8+eWX0Ol0WLVqFRwcHHD48GGEhobivffeU7pF0dXV1eH7778HAIwdO1bSWoIgiBoSdoatRr28vKDVyvfUleI8Qr0+ffpAq9UiPz8f6enp6N27t+g12ksfEpoySXj58mVs2rTprs+fO3cOmZmZmD9/Prp3797u9cWQkJCgaH0i6pxCQ0MRFRWlSN07/76KiIiAnZ2dSevd63F0ht0JiIiIiIiIiKjrUivdQEc3YMAA+Pn5IT09HX/84x/x/PPP480338SBAwcQExOjdHuS2Lt3L4qKitCtWzfJXwDMyspCRUUFtFqtKGFKSUmJ6U0pTOwtP1sj1XmEQOP5fAEBAQAg2pajYjF1kvDs2bPNBoR6BQUF+Pzzz3H27FnD1sRy0+l0SExMVKQ2ERERERERERERESmLIWEb6HS6Zj9fW1uLI0eOAACuXLkCjUYDGxsbJCYmGgKWzkS/1ejo0aNF337y15KSkgAAnp6eotTqDJOEcp5HqNPpkJubC0CakBBouuWoOTHlTMJjx45hx44dRl27Y8cO/PDDD7h9+3ab65gqJSUFlZWVstclIiIiIiIiIiIiIuVxu1EjXb16FT/99BOWLFmCHj16AGicMLKwsMCQIUOgVqvxwgsvYPfu3UhISMB3332HP/3pT9BoNPjNb34jeZgml6qqKvzwww8AgHHjxkleT7/VqFhn8HWGkLBXr16y1crLy4NOp4Ojo6PhvhdbaGgotm7dKmlI+J///KfJ+X8hISGYOHHiPb+nPduNCoKAffv2ITY2tk39Xb58GdnZ2Zg3bx68vb3b9L2m4Faj1NlYWVkZtsPuSOf8krh4HxARERERERERGYchoRFSUlIwbNgwFBcXo6ioCC+99BLc3NwMoUNwcDCWLVsGT09PbN++HX5+fnj99deh0WgwY8aMThMQAo1TT7dv34anpyf69u0reb0rV64AEOc8QqDjh4Q9e/aEpaWlbPWysrIANN7jKpVKkhr6ScJz586hpqZGkhd0m5vq8/b2vuc9rJ8kbMt2oydOnGhzQKhXUlKCNWvWYPny5bIEwdXV1YYQnqiz0Gq1uP/++5VugxTG+4CIiIiIiIiIyDgMCVtRWVmJFStWYObMmRg0aBCef/551NfX49VXX4WbmxsAYMSIEXjzzTcxb948REREoKGhAWq1Gq+++qrC3YtPf8bamDFjJAuN7qQ/q02sWh19okDOKTMAyMjIAABJA2Fvb2+4ubmhsLAQ+/fvx9SpU0Wv8dxzzxl+9x9++CGAxinJez2umpoaAGhTyH/t2jUTumwUHx8vS0hYX18veQ0iIiIiIiIiIiIiMl8MCVuhVqsxcOBAuLq6YuHChXB3d8eiRYsAwBAUBgUF4fXXX4etrS0A8QItc1NdXW2YyIqJiZGlZp8+fXD58mXk5OSgX79+Jq/n7+/foaenpNryszmCICA9PR0AMGDAAMnqqFQqREdHY8uWLdi0aZMkIeGnn3561+cCAgLu+T0FBQUAAEdHR6Pr3Lp1q22NNePatWsQBEHyP0fs7e1hY2OD6upqSesQyam+vh5btmwBAMyZMwdaLf83pyvifUBEREREREREZBy+atIKGxsbPPTQQ7CzswMALFiwAIIgYPHixRAEAa+++irc3d1hbW2N1NRU+Pn5ddqQ8Oeff8bt27fh4eGBoKAgWWrq6+Tk5IiyXmBgoCjrKMXT01O2WsXFxSgvL4darTZsCSqVmJgYbNmyBVu3bsX//d//Sb6lanBwcKtb2LY1JKyvr0dZWZnJvVVUVCA7O1u0LXZbolKp4OHhgbS0NEnrEMmppqYGCxYsAND4XGI41DXxPiAiIiIiIiIiMg5fNTGCPiDU6XRQq9VYuHAhBEHAkiVLoFKp8Nvf/hYrV65Eeno6vv76a8NEYWfz448/AgCio6NlC0LvDAnFmK7q1q0bXF1dUVRUJEZ7slKr1XB3d5etnn6KsG/fvrC2tpa0Vnh4OFxcXHDr1i3s378fU6ZMEXX9TZs2NXle2tjYtHovtTUkLCkpaXd/v3b16lXJQ0IADAmJiIiIiIiIiIiIujCGhG2g0WggCAIaGhqwaNEiqFQqPPjgg9i+fTuuX7+O06dPd9qAsLa2Ftu3bwcAjBo1Sra6vXv3hoWFBWpqalBcXAwXFxeT1wwMDOyQIWH37t3bdD6eqeTYalRPo9EgOjoa27Ztww8//CB6SGhjY9Pm52ZbQ0IxthrVu3r1KsaMGSPaei2RczKViMxXRkYGCgsLRVkrKSlJlHWk1lqfHeVxEBERERERERGZgiFhG+mnjwRBwMKFC7Fq1SokJCTg3LlzopyZZ64OHDiA0tJSdOvWDX379pWtrlarRUBAAJKTk5GTkyNaSBgXFydCd/KSO9DRh4T9+/eXpd7o0aOxbds2bN26Ff/5z39gYWEhS92WtDUkLC4uFq12bm4uqqurYWNjI9qazfHw8JB0fSIyfxkZGQgNDUVVVZVoa9ra2sLNzU209cTk5uYGW1tbLF26tNVrzflxEBERERERERGJgSFhO6hUKuh0Orzyyis4ePAgEhISOnVACDTdalTOaTYA6NOnjyEkDAsLM3k9X19fEbqSn5whYWlpKUpKSqBSqRAeHi5LzX79+qFbt264desWDhw4gEmTJslStyX6qRolJgkBIC8vD7179xZ1zV+Tc/taMXh7e2PEiBGor69v8qbT6QzvFxcX49KlS0q3StRhFBYWoqqqCuvXrxft/Fk3Nzf4+PiIspbYfHx8kJSUZNTkpDk/DiIiIiIiIiIiMTAkNEFYWBjOnTsn26SVUnQ6HbZu3QqgMSSU253nEorBwsICgYGBSElJEWU9ucgZEuqnCIODg2XbQle/5ej27dvxww8/KBoS6nS6NoeEYk4SAvKEhFqtFu7u7oapSXMXHh6OkJCQe14jCAIcHBxw8uRJmbrqOOzs7GBpaWn4WBAEUc/SpI4tNDQUUVFRSrchCx8fH4Z/RERERERERERgSNhuGo0Gy5cvN2w/2pkdO3YMBQUFcHBwQEREhOz19SFhdnY2BEEQ5WceEBDQ4UJCObeGTEtLAyDPeYR3iomJwfbt27FlyxZ89tlnim05WlhYCJ1OB7VaDXt7e6O+R+yQMDc3V9T1WuLh4dFhQsI+ffq0eo1KpcLEiRNRV1eHs2fPytBVx+Dn54fFixc3eU7V1NTgb3/7m4JdERnHmDMTeYYgEREREREREVHbMSQ0QVcICAFgy5YtAIARI0ZAq5X/lvHz84NWq8Xt27dRXFwsyrmEffr0wc8//yxCd/JwdnaGtbW1bPUyMjIAyB8SDhgwAE5OTigqKlJ0y1H91Kq1tTXUanWr1wuCIHpImJ+fL+p6LfHw8MDFixdlqWUKFxcXo5/7KpUK06ZNQ11dHc6fPy9xZ+YvICAACxcuVPycTzlYWlriyy+/NLxPHV9bzkzUnyHI+4CIiIiIiIiIyDgMCalV169fBwD07dtXkfoWFhYICwtDYmIiLl26JMqWpy4uLnB1dUVRUZEIHUqvtrZWtClKY2rpfy5y/841Gg3GjBmDrVu34j//+Y9iIaG7uztUKhWqqqpw6tQpDB48+J7Xq1QqdO/eXbQtcYHGrSHl0LNnT1nqmKqtZ2OqVCrMmjULN2/eFP28yI7E2dkZixYtUuQfeCjBwsICDz/8sNJtkIjacmbinWcI8j4gIiIiIiIiImpd6yMy1OVVV1cDAGxsbBTrYfLkyQCA+Ph4CIJg8noqlUqRrVPbq6qqSrbJMv3Wk926dYOTk5MsNe80e/ZsAMD27duRmpoqe30A8PLywuOPPw4A2LNnj1Fb0y5fvhyTJk0S7QzHYcOGibJOa7y8vGSpYwpXV1eMHDmyzd+nVqu7TDjWEhsbmy7/M6DOQX9m4r3eeM4gEREREREREVHbMCSkVulDQisrK8V6iImJgYWFBYqLiw3n5ZlK7q00TSVXYKYPI/38/GSp92u+vr4YNGgQGhoa8NlnnynSAwAsWrQIkydPhiAI+P7771sNabVaLYYOHYoXXngBY8aMaXddOzs7xMTEoHfv3u1eoy2srKzg6ekpS632mj17dru3yqytrRW5m45FjH9U0ZHU19dj586d2LlzJ+rr65VuhxTC+4CIiIiIiIiIyDgMCalV+nOAlAwJbWxsMGXKFACN04RicHBwQFBQkChryaGrhIQAMHfuXADAf//7X1RWVirSg0qlwksvvYT+/fujrq4OGzZsMKoXKysrjBo1Cq+88gqGDx/e6vX29vYICwvDtGnT8Oyzz+Lll1/G6NGjZT3ztFevXrLVaqvhw4fD29u73d9fV1cnYjdk7mpqajB9+nRMnz4dNTU1SrdDCuF9QERERERERERkHIaE1CpzmCQEgKlTpwIALl26ZOjJVJGRkaKsI4f09HQ0NDRIXke/3ahck2zNGTx4MHr27ImSkhJ88803ivVhYWGBd955B15eXigpKcF3331n9FSKra0tJkyYgBdffBEDBw40fN7e3h7h4eGGUPCll17C/PnzMWjQILi5uckaDuqZa0jo5uZm0lQmwJCwq00SEhERERERERERkfF4UBG1ylxCwqCgIAQEBOD69es4f/48hgwZYvKaffr0gZ2dnWLTam1RU1OD7Oxsk6aqjGEOk4QajQazZ8/GZ599hk8++QSPP/64IuEZADg5OeGvf/0rHn/8cdy8eRPbtm3D3Llzje7H0dER06dPx+jRo1FbW4tu3bop9lhaYq7neM2ePduk8/QEQejyISF1bRkZGSgsLLznNUlJSTJ1Q0RERERERERE5oaThNQqc9huFGjc/nHatGkAgHPnzokyIaPRaNC/f3+T15GL1FuOVldXo7y8HEDj2YBKmjJlCqytrXHx4kUcOnRI0V58fHywYsUKqFQqXLx4EUeOHGnzGvb29nBxcTG7gBBoDELvnHY0ByNGjICXl5dJa+h0ui4/SdfVH39XlpGRgdDQUAwcOPCeb0uXLoWtrS3c3NyUbpmIiIiIiIiIiGTGSUJqlblMEgLA+PHj8emnnyI/Px/Z2dkmhwgAEBERgRMnTojQnfRSU1MRHR0t2fr6rUa7d+8Oe3t7yeoYw97eHpMmTcK2bdvw2muvYfr06Yav3b59W/Z+oqKi8PLLL2PlypU4dOgQXF1dER4eLnsfUpk2bRrs7OzaFYCKzd3dHaNHjzZ5HU4RMiTsygoLC1FVVYX169cjNDT0nte6ubmZ7UQxERERERERERFJhyEh3ZMgCGYVEjo4OGDs2LHYt28fzp07J0pI2L17d3h5eSErK0uEDqWVmpqK+vp6k7ZgvBdz2Gr0TnPmzMG2bdtw+vRpnD59Wul2MG3aNGRkZOD777/Hli1b4OzsLPn2r3JRqVQYM2YMnJyc8NNPPynai6nbjOoxJCQCQkNDERUVpXQbRERERERERERkhrjdKN1TbW0tGhoaAACWlpYKd9NIv+XohQsXRAsBIiMjRVlHDmlpaZKtrZ8k7N27t2Q12sLX1xevv/46Zs6c2eRtypQpivX0xBNPYPjw4WhoaMC3334LnU6nWC9SiIqKwgMPPKBY/ejoaPTs2VOUtWpra0VZpyOrra3lNCERERERERERERE1i5OEdE8WFhZwcXHBrVu3kJqaahbn9w0YMABubm4oLCxEZmYm/P39TV4zLCwMO3bsEKE76R05cgQBAQGSnG2nDxPM6dy8iRMnYuLEiU0+V1lZid27dyvSj0ajwVtvvYWZM2eiuroaZWVl6NatmyK9SCUwMBBPPPEENmzYYDijUi4hISGirSVloN5RlJaW4sSJExg+fLjSrcjC0tISn376qeF96pp4HxARERERERERGYeThHRParXaMLUVGxurcDeNVCqVYeu01NRUUda0trZGnz59RFlLapmZmUhOTpZkbTc3NwBARkaGJOt3FjY2Nob31erO+cdojx498OSTTyI4OFjWuklJSaKtdeHCBdHW6sj27t2L7OxspduQhYWFBZ599lk8++yzsLCwULodUgjvAyIiIiIiIiIi43TOV7dJVDNmzAAAnDhxQuFO/ke/PaiYk0KhoaGirSW1/fv3G7aBFZM+JMzMzBR97c5G//PvrCEhANjZ2WHhwoWGLX7lcPnyZVG2xywtLWXYfYcffvgBNTU1SrdBREREREREREREZoTbjVKrJk+eDI1Gg4yMDGRlZcHLy0vplgwhYVZWFmpqamBlZWXymnJPTJmiqKgI165dE71nfUiYlZWFuro6TmC0QBCELhESAo2Tu4MGDULv3r2xefNm5ObmSlrv1q1byMvLg6enp0nrXLx4UaSOOofi4mKsW7cOLi4uhs91tvM0gcbHdPToUQCN51tqNBqFOyIl8D4gIiIiIiIiIjIOQ0JqlZOTE2JiYnDgwAHExsbi/vvvV7oleHh4wMvLC1lZWUhPT0dQUJDJa9ra2sLf3x83btwQoUPpnT59WvSQ0MHBAZaWlqitrUVWVhZ69+4t6vpKefLJJ5uEeeHh4XjllVfaffbinVOc5nR+Y3Nqa2uhUqlMDnzd3Nzw2GOP4YcffpBsu1u9S5cuMSSUQHZ2dqffdvT27dsYM2YMAKCiogJ2dnYKd0RK4H1ARERERERERGQchoRklBkzZuDAgQM4ceKEWYSEQOM0YVZWFlJTU0UJCQEgJCSkw4SE169fR1FREVxdXUVbU6VSwc3NDdnZ2cjIyDCLkHD16tU4d+5ck8+1dQIqKyuryceZmZmYPHky+vfv366e7qxvrpOEJSUlOHnyJOLi4gAACxcuREhIiElrajQazJgxA5mZmaisrBSjzWZdvnwZY8eObXcAW1BQIPnEI3VuGRkZKCwsbPU6Nzc3+Pj4yNARERERERERERGR+BgSklFmzJiBF198EefPn0dFRQXs7e2VbgmRkZHYsWOH6OcS7tq1S7T1pHb69GlMnjxZ1DXvDAmVVlVVhW+//dbkdfbs2WOYJPn444+xadMm7Nixo90h4Z2ThOYWEubl5SE2Nhbnz59v8vmNGzciKioKkyZNgqWlZbvXt7W1xYwZM/Ddd9+Z2mqLTN1y9MKFCyJ3RF1JRkYGQkNDUVVV1eq1tra2SEpKYlBIREREREREREQdEkNCMkpAQABCQ0ORlJSEU6dOYezYsUq3hIiICABAbm4uqqqqYGtra/Ka9vb28PT07DBTSHFxcRg7dqxJoc+v6c8lNIeQUD+tptFosG3bNsPnq6qqsGDBAqPXGTZsGBwdHQEAlpaW2LRpEw4fPoznn38eDg4Obe7L3EJCQRCQnp6O48ePIyUlpcXrzp07h/T0dMybNw89evRod73g4GBEREQgISGh3Wu0pr1bjgqCwK1GySSFhYWoqqrC+vXrERoa2uJ1SUlJWLp0KQoLCxkSEhERERERERFRh8SQkIw2Y8YMJCUlITY21ixCQhcXF/Tu3RtpaWlIS0tD3759RVnXz8+vw4SEAHD+/HkMGjRItPX0IWF6erpoa7ZXdXU1gMazEqdNm2b4fFlZWbvXvO+++9C/f3+cP38ee/fuxdy5c9u8xp3bjSp5JmFDQwOSk5Nx/Phxo8+aKyoqwqpVqzBhwgQMGzas3f1PmjQJN27cMOl3cS/t3XI0OzsbxcXFkvREXUtoaCiioqJavS4pKanVa7gtKRERERERERERmSPlR2Cow5gxYwaAxum1+vp6hbtpFBkZCQCibjnq7+8v2lpyOHv2rKjrubu7A2icJBQEQdS120q/3V97pv1aolKp8NhjjwEA9u7d26416urqDO8rMUkoCAKSk5Px+eefY9OmTUYHhHfau3cvfv7553b3YG1tjVmzZrX7+1tz69YtfP/997h8+XKb/ry5fPmyZD0R3cnNzQ22trZYunQpBg4ceM+30NBQs5jOJiIiIiIiIiIiuhMnCclow4YNg5ubGwoLC5GYmIiBAwcq3ZJhokvMaS5TtmFUQl5eHhoaGkQLq7p16wYAuH37NsrKyuDk5CTKuu2RmZkJAPD29hZ13fz8fACAVtu+PwL151a6urrKOkkoCAJSUlJw8OBB5OTkmLye/ufQXv7+/ujRo4covTQnOTkZycnJsLS0RGhoKIYMGdLq89PKykqSXoh+zcfHB0lJSSgsLLznddyWlIiIiIiIiIiIzBVDQjKaRqPBzJkz8cUXX+DYsWOKh4SCIODUqVMAGs9MFIuNjY1oa8lBEARUVVXB3t5elPU0Gg3s7OxQWVmJgoICRUNC/TZ+gwcPFm3N4uJi/POf/wSANp1rqFdWVoZ169YBAGJiYkTr614EQcCNGzdw6NAh3Lx5U7R1xTjHUz/tKaXa2lokJiYiMTERL774ouF8yebcd999OHjwoOQ9kXmysLDABx98YHhfaj4+PkYHf9yWVD5y3wdERERERERERB0VQ0Jqk7lz5xpCwueff16RrRb1MjMzkZubC7Vajd69e4u2rlqtho2NjeE8vI6goqJCtJAQaNzeUx8SBgYGirZuWyUnJwMAhgwZYtI658+fN/x81q1bh8rKSvj7+2PEiBFtXuu7775DXV0dunfvjvDwcJP6MkZqaioOHTokyVaFdnZ2Jn2/IAgoLy8XqRvjxMbGYvLkyS1+3cbGBpMnT8aePXtk7IrMhaWlJV555RWl22jizm1JW2Nra4ukpCQGhSYyx/uAiIiIiIiIiMgcMSSkNhk3bhzs7e1RWFiI5ORk9O3bV7Fe9FOEvXv3hqWlpahr29radqiQsLy8HJ6enqKt5+joiNzcXBQUFIi2ZlvV1tYiJSUFgOmThNHR0Xd97sEHH2xzyF1UVITvv/8eADB27FhJtxrNyMjAwYMHRT1v89dMnSSsrKxEQ0ODSN0YJy4uDtHR0fcMOAcNGoRTp07h1q1bMnZG1DxuS3pvrU1YGjOBSURERERERERE7cOQkNrE2toa06ZNw8aNG3Hs2DFFQ8LTp08DEHerUb2OtuVoRUWFqOs5ODgAQKsvakvpxo0bqKurg6urK/z9/U1ay8XFpUkgGBYWhlGjRrV5na+//ho6nQ7e3t4ICgoyqaeWZGdnY//+/bhx44Yk69/J1EnCsrIykTppmxMnTmD8+PEtfl2j0WDChAnYuHGjjF2ROdDpdDh37hwAICoqChqNRuGOGrVlW9Kuoq0Tlm5ubkavba73ARERERERERGRuWFISG02d+5cbNy4EUePHsXjjz8u6TRVS2pqanD27FkAkGQ7TDHOapOT2CGh/sw3JUPCO88jNPUeW7duncmBWHZ2NrZv3w6gcaJWivv+4sWL2Lx5s+jrtsTU+1ypkPD48eMYMWLEPcP84OBg+Pr6Ij09XcbOSGm3b982TB5XVFSY/Lwn6Rg7YQm0/axG3gdERERERERERMZhSEhtNmXKFFhYWODmzZtIS0uDn5+f7D0kJiZCp9PB0dER7u7uoq/f1UNC/SShktuN3hkSmoOvvvoKgiDA399f1DMw9c6cOYOdO3eKvu69dNRJQqBxu+GYmJgWv65SqTBx4kSsXr1axq6IqC04YUlEREREREREpCyGhNRmDg4OmDRpEnbs2IGjR48qEhLqzyMMDAyUZKKrq283ag6ThMnJyQCAIUOGKNaDXmpqKn755RcAjVOEYhIEAcePH8f+/ftFXdcYHTkkjIuLw9ChQ2FlZdXiNT179kT//v1x/vx5GTsjc5aRkWHU2YBERERERERERERdAUNCapc5c+Zgx44dOHbsGJYtWyZ7/TtDQil09UlCfUio1CRheXk5MjMzAQD33XefIj3c6csvvwQAhIaGomfPnqKtKwgC9u3bh9jYWNHWbAtT7/Py8nKROmm76upqnDlzBiNGjLjndWPHjmVISAAaA8LQ0FBUVVW1em1bz8AjIiIiIiIiIiLqiBgSUrvMnDkTarUa165dQ35+Prp37y5b7aysLGRmZkKlUkk2xdjRQkKxwxr9dqOVlZWorq6WfbJSH+r4+/sr/kJ9RkYGjh49CpVKhTFjxoi6dmxsrGIBIQAUFxe3+14vKSkxBLlKOXHiBIYNGwa1Wt3iNU5OToiOjsbRo0dl7IzMUWFhIaqqqrB+/XqEhobe89q2noFHRERERERERETUETEkpHZxdnaGlZUVqqurUVNTI2vtzZs3A2gMkKytrSWpoeRZfO0hdqhZW1sLoPFcN41GI+raxtRetWoVAGD69Omy1m6O/mehVqsNE5ZiuXTpkqjrtdV///tfjBw5EjExMdBqjfvrQBAEJCQkYPv27RJ317rKykqUl5fDycnpnteNGDEC58+fR2lpqUydkTkLDQ1FVFSU0m20yJjtThliEhERERERERGRGBgSUrtcvnwZ1dXVsLW1hZeXl2x1i4uLDeHE8OHDJakhCAKuXLkiydpSCQoKEnW93NxcAICvry8sLS1FXbs169evR0ZGBjw9PfHnP/9Z1trNCQgIQK9evZCZmYmkpCRERESIsm5dXR1ycnJEWcsUx44dw9WrVzFr1qwmW6nevn0bpaWld70VFhYa7g9zUFlZ2WpIaGVlhQcffBCffvqpTF0RtZ2bmxtsbW2xdOnSVq+1tbVFUlISg0IiIiIiIiIiIjIJQ0Jql9OnTwNoDKfutdWf2H744QfodDp4eXlJttVoQUEBiouLJVlbKiEhIaKul5eXB6AxIJNTamoqNmzYAAD45JNP4OzsLGv95qhUKkycOBFr1qxBYmKiaCFhdna2KOuIIT8/H6tXr4a/vz8qKipQWloq+4Rwe1VWVhp1naurK5566in85z//kbgjUpKFhQXefvttw/sdiY+PD5KSklBYWHjP65KSkrB06VIUFhYyJGxBR74PiIiIiIiIiIjkxJCQ2uXMmTMAgODgYNlqVlRUYNOmTQCAkSNHQqVSSVInOTlZknWl4uLiAnd3d1HX1E+KBQYGirruveh0OqxcuRL19fWYOXMm5s2bJ1vt1owfPx5r1qxBWloaSkpKRAkvs7KyTG9MZDdu3FC6hTarqKgw+loPDw88+uijWLNmjYQdkZIsLS3xpz/9Sek22s3Hx8fo4K+1bUmN2ba0s+ro9wERERERERERkVwYElK7KBESbt++HXV1dXB3d5e0bkcLCYODg0UPTJWYJNy+fTsuX74MW1tb/Pvf/5YsBG4PT09PREZGIj4+HhcuXEB0dLTJa968eVOEzsjYSUI9b29vPPTQQ/jqq68k6ohIWm3dltTNzU2GroiIiIiIiIiIqCNiSEhtVlNTg8TERADib3N5r5o//PADAGDEiBGSBUjZ2dlmcU5cW4j9O6itrUVRUREA+UJC/XaXAPD3v/8d3t7estRti4kTJyI+Ph6JiYmiTLIyJBRHWyYJ9Xr37o1nnnkGp0+fxoULF3D79m0JOiMlNDQ0GCboQkNDZd0OWy7GbksKNAaKXXFL0q5wHxARERERERERiYEhIbXZhQsXUFdXB0dHR3h6espSc9euXSguLoaTkxPCw8Mlq3Pw4EHJ1paCnZ2d6IFafn4+gMYz3FxcXERduzmCIOCjjz5CdXU1hg8fjqeeekrymu0xatQorFy5EkVFRcjKyjLp515WVoby8nIRu+u62jpJqOfu7o6pU6di4sSJSE5ORnx8fIfcbrWzy8jIaFMYVl1dbfg7oqKiAnZ2dlK3qIi2bEvaFXWV+4CIiIiIiIiIyFQMCanNTp8+DQAICgqSZUvI+vp6bNy4EQAwfPhwaDQaSepkZGQgJSVFkrWlEhQUJPqEhP48QrmmCA8dOoQTJ05Aq9Vi9erVZjvxYWtrizFjxmDfvn1ITEw0KSTkFKF42hsS6mm1WoSHhyM8PBwlJSWIj49HQkICysrKROqQ2isjIwOhoaGoqqpq9VpbW1skJSXB1dVVhs6IiIiIiIiIiIg6B4aE1Gb68wjl2mp0//79yMvLg52dHSIjIyWpIQgCDhw4IMnaUpLid6APCQMDA0Vf+9fKysrwySefAADefPNN9O3bV/Kappg4cSL27duHS5cuYdKkSdBq2/dHKENC8bRnu9GWODs7Y8yYMYiJiUFqairi4+Nx6dIl0dantiksLERVVRXWr1+P0NDQFq9LSkrC0qVLUVhYyJCQiIiIiIiIiIioDRgSUpvpQ8Lg4GBZ6n3//fcAgKFDh8LCwkKSGpmZmUhPT5dkbalYWFjA399f9HXz8vIAyDNJ+J///AfFxcUIDQ3F66+/Lnk9U0VFRcHNzQ2FhYW4ceMGgoKC2rVOdna2yJ11XaZOEjZHrVYjICAAAQEBmD59OkpKSlBTU3PXW21t7V0f3/lWV1dneJ/aLzQ0FFFRUUq3QURERERERERE1OkwJKQ2qa2txeXLlwHIM2lWXV1tOCdMyheJb926JdnaUgkPD2/3JFtL6uvrkZOTAwDtDsCMlZCQgN27dwMAVq1aBSsrK0nriaG4uBjFxcUAYFJg7eTkJFZLXZ6lpaWk61tbW5t89qogCIbAsKysDFu3bkVBQYFIHRIRERERERERERG1j3ke/kVm69q1a6ivr4eNjQ08PDwkr6efuLKxsYGtra1kdaQOGsTm5OSECRMmiL5ubm4udDodnJyc4OXlJfr6enV1dfjnP/8JAHjyyScxcuRIyWqJad26ddDpdOjVqxd69+7d7nWk+N11VaacDSkXlUoFS0tL2Nvbo2fPnnjssccwaNAgpdsiIiIiIiIiIiKiLo4hIbWJ/nwuX19fqFQqyevpp9q6desmaZ2OFBI6ODhg2bJlsLGxEX3tzMxMAEBYWJikv9/t27cjIyMD3bt3x9/+9jfJ6ogpIyMDO3bsAACMHz/epJ+Pvb09lixZIlZrXVpHCAl/zdLSEtOmTcOSJUtgZ2endDtERERERERERETURXG7UWoTfUjo5+cnSz39JKHUIWFH2OoSaAwIH374Ybi4uEiyvj4kDA8Pl2R9ACgvL8e6desAAO+++y6cnZ0lqyWmL774AoIgICgoCD4+Piav16dPHwwZMgRxcXEidNd1dcSQUK9Pnz54+umnsWPHDiQnJyvdTqdgYWGB3/3ud4b3qWvifUBEREREREREZByGhNQm+pDQlK0W2yIrKwsAJwkB6QNCQRCaTBJKZf369SgrK0NYWBiWL18uWR0xJSUl4fDhwwCAsWPHirbu+PHjkZaWhry8PNHWNCfDhg2DVqtFeXk5KioqUF5ejvLyclRVVYmyvkajMfm8QKXZ2dlhwYIFSEhIwPbt25Vup8OztLTE3//+d6XbIIXxPiAiIiIiIiIiMg5DQmoTuUNCuSYJzT0klDogBIDS0lJUVFRApVIhODhYkhrZ2dnYsmULAGDlypXQas3/jyBBELB69WoAwIABA0Q9i1Or1WLevHn47LPPRFvTXAwZMgQTJ05s9mv19fWG0PDWrVs4dOgQSkpK2lyjR48e0Gg0JnaqPJVKhcjISPTu3RtbtmwxhPVEREREREREREREUjL/V+jJbNTU1ODatWsA5NtuVH8moZThGGDeIaEcASHwv61Gg4ODJdt+dfXq1airq8OECRMwadIkSWo058cff2zyO3Zzc8OgQYPg5OTU6veePn0a8fHxUKvVGD16tOi9ubu7Y/r06YbzDjsDf3//FgNCoDEcdXZ2hrOzM3r16oXQ0FDs378fp06dalMdLy8vU1s1K926dcPDDz+MY8eO4eDBg0q3c5eEhATY29vf8xo3NzdRtuNtr4aGBmRkZAAAfHx8oFbz6OWuiPcBEREREREREZFxGBKS0a5evQqdTgc7Ozu4ublJXk+n0yE3NxdA150kdHBwwEMPPSR5QAhIfx7hpUuXcOjQIahUKqxcuRIqlUqSOs354osvmv18SEgIhg4disGDByM4OPiuF5IbGhoMU4SDBw+W7PzEqKgoXL9+HUlJSZKsLycXFxfMnz+/TS/KW1paYsqUKQgLC8O2bdtw69Yto76vI59H2BK1Wo1Ro0YhKCgIO3fuxM2bN5VuySAmJqbVa2xtbZGUlKRYUFhdXW34RywVFRWws7NTpA9SFu8DIiIiIiIiIiLjMCQko9251agcAU9+fj50Oh00Gg0cHR0lraXVaqFSqSAIgqR12kIfELq6uspST8rzCAVBMGypuXz5cvTv31/0GvcSHh5u2JZSEATk5eUhLy8PycnJSE5Oxtq1a+Hk5IT77rsPgwcPxuDBg+Hk5ISDBw8iJSUFFhYWiI6Olqw/lUqFGTNmICsrC2VlZZLVkcOiRYtgY2PTru/18fHBU089hYMHD+LEiROtXt8ZQ0I9T09PLF++HPHx8fjpp5+UbgcAsGrVKgwcOLDFryclJWHp0qUoLCxUdJqQiIiIiIiIiIiIjMOQkIx2+fJlAPKfR+jk5CR5KKlSqWBpaYmamhpJ6xhL7oCwtrYWeXl5AKQJCU+dOoXLly/D2toa7777rujrt2b69Ol3baFaVlaGlJQUpKSk4MaNGygtLcW+ffuwb98+qFQqTJgwAfHx8QCAkSNHwtbWVtIebWxsMHfuXKxdu1bSOlJavHgx3N3dTVrDwsICEydORN++fbF9+3YUFBQ0e529vb1R28V2ZCqVClFRUQgJCcH+/ftx7tw5RfsJDg5GVFSUoj0QERERERERERGReBgSmig1NRVbt27FzZs3MXjwYCxcuLBd69TU1DQJqMxxmki/faBOp5Olnn6LzeLiYpSWlkoeCLi7u5vN1n5yBoQAUFRUBEEQ4OzsbHLI0xz99pFDhgxBjx49RF9fry3PI0dHR0RFRSEqKgo6nQ43b97EtWvXkJKSgry8PPzyyy8AGre6HTp0qGQ938nX1xePPPIIkpOTUVRUhKKiIty6dcusJlxbMn78eAQFBYm2nre3N5544gkcPnwYx44du+vr4eHhsm5ZqyRbW1vMmDEDkZGR2Llzp2EbZiIiIiIiIiIiIiJTyBISnj17Fu+99x6OHTuGiooKeHp6YuDAgZg1axbuv//+uyZ8OooLFy5g6tSpCAkJQVVVFT766CNkZGTglVdeafNaK1aswJ///GcJuhRPZGQkgMazCeXg5+eHyMhIxMfHIy4uDhMnTpS03uDBg80iJPTw8JA1IASAkpISAI1bHEohICAAAHD+/HkIgiBZuNPe55FGo4Gvry98fX0xfvx4ZGVl4ciRI9BoNJg2bZqsZ1b6+Pg02aqxvr4eWVlZSE1NRXp6OtLS0mTrxVhTp07FfffdJ/q6Wq0W48aNQ3h4OA4cOIDq6mr06dMHISEhspyLam68vb3x6KOPYteuXYYpVyIiIiIiIiIiIqL2kjwkPHjwIKZMmYK6ujrDNExaWhrS0tLw448/4o033sCnn36KmTNnSt2KqNLT0zF37lwsWbIEf/3rX6HRaPDFF1/gzTffxJw5cxAYGNim9V5//XW89NJLho/LysrQq1cvsds2iX6bubS0NNTW1soSnCxYsADx8fE4c+YMRo0aBWtra8lq9e3bF3v37kV5eblkNYzRs2dP2WsWFxcDgGRTfr1794ZGo0FxcTEyMjLg6+srSR2xnkdeXl5YvHixmK21m1arNQSYAFBXV4ebN28iMTERiYmJCnfXuMWomBOEzfHw8DCb34fStFotZsyYAU9PT+zevVvpdoiIiIiIiIiIiKgDU0td4M0330RtbS0WLVqE8+fPo7y8HBkZGfjmm28we/ZsZGdnY+7cufjss8+kbkU0DQ0N+O677xAYGIg33ngDGo0GQOMkmoWFRbu247SysoKjo2OTN3Pj7e0NNzc3NDQ0IDU1VZaagwcPhq+vL+rq6iQ/j0uj0WDw4MGS1jCGl5eX7DX1k4RShYSWlpaGsyylnIDqCM8jU1lYWMDPzw+zZs0yTPcq5bHHHpM8IKS7qVQqDB48GMuWLVO6FSIiIiIiIiIiIurAJA8JExMTERoaim+++Qbh4eGws7ODt7c3Fi9ejM2bN+Pw4cPw8vLCb37zG5w+fVrqdkShVqsxbNgwRERENDknr2/fvtBqtcjJyVGwO+moVCrDNKFcW46q1WosWLAAABAXFyf5eYgDBw6UdH1jKBkSSrXdKAD06dMHgLQhYVeiUqkwffp09O3bV/babm5ueOGFFxS5V+l//Pz88Nprr2HevHno378/bG1tlW5JcVqtFs888wyeeeYZaLU8drmr4n1ARERERERERGQcyV85qa+vv+e0y4gRI7Bjxw5ERUVhxYoV+PHHH6Vuqd10Op1hanDUqFEYNWoUABjOWNO/1dXVGb5n//796N+/P9zd3RXpWWyRkZH45ZdfcO3aNdlqjh8/Hv/9739RXFyMS5cuoX///pLVsrGxQUxMDA4fPixZjXvRarWK3CtSbzcKNIaEe/bsYUgoIrVajblz56K2thYpKSmy1IyOjsaoUaP4wruZsLa2Rnh4OMLDw9HQ0IDs7Gxcu3YN165d67T/YOVerKys8O9//1vpNkhhvA+IiIiIiIiIiIwj+au8np6eyMjIuOc1/fr1w6hRo3DkyBGp22m3q1ev4qeffsKSJUsMQcqd4WB9fT1qamqgVqsNWxy+8cYb+Nvf/oabN28q2bqo9JOEcoaElpaWmDt3LtasWYMTJ06gX79+UKlUktUbPnw4zp49i4qKCslqtKRHjx6GIFougiBIvt0o8L9JQqm3je1qNBoNFixYgPXr17f6Z60pvLy8MGPGDHh4eEhWg0yjVqvh7e0Nb29vjBkzBuXl5UhJScG1a9dw/fp11NbWKt2ipJKSkkz6OhERERERERERUVcjeUg4d+5cfPLJJzh58iSGDh3a4nWenp6Ii4uTup12SUlJwbBhw1BcXIyioiK89NJLcHNzaxJUqdVqaDQaCIIArVaLd999Fx9//DHi4uLQs2dPBbsXlz4kvH79Ourr62WbJpoxYwa++uor5ObmIjU1Ff7+/pLVsrS0xOjRo7Fjxw7JarREie0bKysrUV9fD5VKhe7du0tWJyAgACqVCllZWSgoKOg007XmwMLCAosXLzY8R8Q2efJk3HfffVCrJd+hmkTk4OCAyMhIREZGoqGh4a6QsKamBh999JEyzYnIzc0Ntra2WLp0aavX2traws3NTYauSEmCIKCwsBAA7vr/NSIiIiIiIiIi+h/JE5533nkHGzduxKxZs/Df//4XM2bMuOua+vp6xMXFKXK2VmsqKyuxYsUKzJw5E4MGDcLzzz+P+vp6vPrqq01eaFSr1bC2toajoyOefvppJCYm4vjx4xg0aJCC3YvP398fjo6OKCsrQ0ZGhqRh3Z2cnJwwY8YMbNmyBSdOnJC8bmRkJE6ePGl4kVEuSoSE+q1G3d3dYWFhIVkdW1tbeHl54ebNm4iPj8fEiRMlq/VrJ06caBJo9+jRAwEBAbLVl4O1tTWWLl2KL7/8EkVFRaKs2adPH0ydOhXOzs6irEfGKSwsxPbt2wEAM2fOFCXU0v8ddafOEpz4+PggKSkJhYWFqK6uxsiRIwEAx44dg42NTZNr3dzc4OPjo0SbJKOqqirDP3qpqKiAnZ2dwh0REREREREREZknyUNCBwcH/PTTTxg7dixmz56N6OhozJ07FyNHjoSrqyvS09Px/vvvIz093SwnGtRqNQYOHAhXV1csXLgQ7u7uWLRoEQA0CQp1Oh1KS0tx48YNVFRUID4+Hv369VOydUmo1WpERkbi8OHDuHz5smwhIQDMmzcPW7ZsQUpKCgoLCyWdBlGr1ZgwYQI2bNggWY3mSLndZ0v0W416enpKXiswMFCRkLC5MyZnzZqFiIgI2XqQg52dHR588EF8+eWXKC0tbfc6tra2mDx5MsLDwztNkNRRlJeXY9WqVYazbVetWoUXXngB9vb2Cndm3nx8fODj44PKykrD5yIiIhgOERERERERERER3YMse0UOHDgQ8fHxeOqpp7Bv3z4cPXr0rmueeeYZ+Pv7G875Mxc2NjZ46KGHDC80LliwAIIgYPHixRAEAb///e/h6uoKQRAgCAI2btwIb29vhIWFKdy5dCZMmIDDhw/j+++/x5QpU2Q7Q8/LywsDBw7E2bNnkZaWJvmWcX369EGPHj2Qk5MjaZ07nT59GpMnT5atHvC/SUI5Akp9EJmXlyd5rdY0NDQo3YIknJyc8OCDD+LTTz9t1/eHh4djypQpsLW1FbkzMoZKpWpybzY0NJjV34lERERERERERETUech2wJS/vz9++eUXJCQk4O2330Z0dDTs7e0N4dpnn32Gfv36wcHBASNHjsRvfvMbrFu3Tq727kkfEOp0OgiCgIULF+Lbb7/Fhx9+iPfffx/Z2dl49dVX8fjjjyM6OrpTB4QA8Pzzz8PNzQ2ZmZnYs2ePrLVDQkIAANnZ2ZLXUqlU6NOnj+R17hQXF9dkEkYO+pBQjrMz9duZ6qek5OLv748+ffoY3saPH4/IyEhZe5CTq6srnn/+eXTr1s2o67VaLVxdXTFs2DDMnTuXAaGC7O3tsXjxYqjVaqjVaixZsoTTcERERERERERERCQJWSYJ79S/f3/0798ff/zjHyEIAq5evYqzZ8/izJkzOHv2LBISEhAbG4vY2FioVCosW7ZM7hZbpNFoIAgCGhoasGjRIqhUKjz44IPYvn07UlJScObMmS7x4rqjoyPefPNNvPjii1i7di3Gjx8PKysrWWoHBQUBgGzTff7+/jhy5IgstfSuXbsm6zaYcoaE+qlTuUPCBQsWyHaPmgsXFxcsX74cW7duRV5eHpycnODk5ARHR0fD+/o3W1tbTquZkYCAADz//PMAwPMgiYiIiIiIiIiISDKyh4R3UqlUCA4ORnBwMJYsWQIAhuDwzJkzOHfunJLtNUv/Qrp+onDVqlVISEjotGcQtuSpp57CP//5T2RkZODHH3/E4sWLZamrDwnz8/NRX18PrVbaW9jb2xsWFhayhlqdOSTUTxLW1tZKXosap9KWLl2qdBvUDp09HMzIyEBhYeE9r0lKSpKpGyIiIiIiIiIioq6p3QnLzZs34e3tLWYvAJoGhw888IDo64tBpVJBp9PhlVdewcGDB5GQkNClAkIAsLa2xjvvvIOHH34Y3377LaZPnw4HBwfJ63p4eMDR0RFlZWXIz8+XPNjSaDTo3bs3rl27JmmdO6WkpECn08ly1mNdXR3KysoAyBMS6kNduScJich8ZGRkIDQ0FFVVVa1ea2trK/n5s0RERERERERERF1Vu0NCX19fuLq6IjIyElFRUYa3gIAAMfsza2FhYTh37hz69++vdCuKWLp0KVauXImLFy9iw4YNeOKJJySvqVKpEBQUhDNnziAnJ0eWYMvf31/WkLC2thbp6enw9/eXvFZubi6Axq0pnZycJK/HkJCICgsLUVVVhfXr1yM0NPSe17q5ucHHx8fotbVaLR566CHD+9Q18T4gIiIiIiIiIjJOu185EQQBhYWF2LdvH/bt22f4vKOjIyIiIhAVFWUIEENDQzvdeVcajQbLly/vdI+rLTQaDf76179i5syZ2Lx5M+bMmQN3d3fJ6/bp08cQEspBjrDu165evSpL3aysLABASEiILPey/sVabjdKRKGhoYiKihJ1TSsrK6xdu1bUNanj4X1ARERERERERGQcdXu/8cEHH8TgwYPh6OgIQRAMb6WlpThy5Ag++ugjPPTQQ+jXrx8cHBwwfPhwPPfcc/jyyy9x48YNMR+DYrpyQKg3ffp0jBgxArW1tVi3bp0sNfXnEsoVErq7u8Pe3l6WWnpyTS5mZ2cDAIKDg2Wpp99ClZOERERERERERERERETKandI+NVXX2Hr1q0YO3Zsk887ODhArVY3CQ6rqqoQFxeHzz//HI899hj69OkDf39//OUvf0F+fr7JD4KUo1Kp8Le//Q0AsGvXLmRkZEheUx8S5uXlQafTSV5PpVLJPk1469YtFBUVSV7nzklCOVhYWABgSEhE0hAEAZWVlaisrIQgCEq3QwrhfUBEREREREREZJx2h4QVFRWYNGkStm7din79+uGnn35CaWkpSktLUVdXh6ysLPz444+4//77YWFh0SQ0FAQBaWlpePvttxEUFIR///vfYj4mktnIkSMxffp0NDQ0YP369ZLX69GjB+zt7aHT6VBQUCB5PQDw8/OTpc6drl69Kun61dXVuHXrFgD5Jgm53SgRSamqqgr29vawt7dHVVWV0u2QQngfEBEREREREREZp90h4ccff4wLFy4gMDAQJ06cwLRp0+Dg4GD4eo8ePTB79mxs3LgR8fHxGDx4MABgyJAh+Pvf/26YQCwrK8MLL7yAp59+2sSHQkqqr68HIM8WrCqVCm5ubgAagy45yHHW4q9JPWVbU1MDoHG6z8nJSdJaes7OzgAgy8QpERERERERERERERG1rN0h4XfffQeVSoUnnngCtra297y2b9++OHToECZMmIBTp05BEATs27cPCQkJiIqKgiAIWLVqFT7++OP2tkMK2rdvH/bs2QOtVotly5bJUlO/zaj+jDupNTQ0yFLnTmVlZZKub2lpCaBx6099yCu1gIAAAMD169dRUVEhS00iIiIiIiIiIiIiIrpbu0PC1NRUAICvr69R11tbW+Pbb7+Fi4sL/vSnPyEzMxP9+vXDkSNHMGTIEAiCgDfeeAPFxcXtbYkU0NDQgFdffRUAMHPmTHh5eclSV3+mnVrd7lu4TeQ4+/DXSktLJV3fysrK8L5cE5lOTk6GKdALFy7IUpOIiIiIiIiIiIiIiO7W7oRFP4XUli0RXV1d8cADD6CqqgqrV68GANja2uLrr7+GVqtFdXU1vvzyy/a2RArYsGED4uPjYWdnhwcffFC2uvrJt84+SSgIgmTrazQaw89PzjOb/P39AQDnz5+XraaYBEFAfn4+Tp06hU2bNmHlypX48MMP8eOPPyIhIQHl5eVKt0hERERERERERERE1Cpte78xKCgIp06dwq5du/DMM88Y/X1RUVEAgJ07d+Kdd94BAAQGBmLatGnYtm0b9u3bh5deeqm9bZGMbt++jTfffBMAsHjxYsN5c3LoCtuN1tXVobq6utXtfE1haWmJ6upq2SYJgcaQ8NSpUx0mJBQEAQUFBUhLS0N6ejrS0tKaDVUvXLhgmI50d3eHv78//P390bt3b8M/qiAiIiIiIiIiIiIiMhftDglnzpyJuLg47N69G8eOHcPIkSON+j4LCwsAQFpaWpPPx8TEYNu2bbh48WJ7WyKZ/fvf/0Z6ejrc3Nwwb948WWt3he1GgcZpQjlCQiUmCRMTE2Wr2Vb19fW4ePEirl271mwoaGVlhfDwcAwYMAADBgxAQ0MDzpw5g7NnzyI5ORkFBQUoKChAXFwc1Go1evXqBX9/f4SFhcHV1VWhR0VERERERERERERE9D/tDgmfeeYZfPjhhyguLsa8efOwefNmo4LCkydPArj7DLSePXsCAIqKitrbEsno1q1b+NOf/gQAWL58OaytrWWt3xW2GwUazyX09PSUbH39hJuck4SBgYEAGrcbFQQBKpVKttqtqampwZkzZxAXF9dk21B9KBgREYEBAwYgJCTE8A8e9CIiIvDYY4+hrKwM8fHxhtAwJycH6enpSE9Px+HDh7F06VL4+fnJ/dCIugSNRoP58+cb3qeuifcBEREREREREZFx2h0SOjk5YfXq1bj//vtRWFiIMWPG4IUXXsArr7zSYqhx5swZrFmzBiqVqsUXyc0pMKCWrVixAhUVFfDz88PEiRNlr98VthsFGkNCKVlZWQGQNyTs1asXtFotysvLkZ6ejt69e8tWuyXl5eWIi4vDqVOnDFOqbm5umD59OqKiopoNBVvi6OiImJgYxMTEAACysrJw9uxZ7N27FxcvXsS3336LZcuWoVevXpI9HnN0+/ZtlJSUoKSkBMXFxSgpKUFpaSlKSkrQvXt3zJ49W7bJYCVlZmaipqYG/v7+XeLxys3a2hqbNm1Sug1SGO8DIiIiIiIiIiLjtDskBIA5c+bgq6++whNPPIHq6mp89NFH+OyzzzB27FiMHTsWvXv3hqOjI27evIkjR47gm2++QX19PVQqleFfeOvl5OQAALfi6wDS0tLwr3/9CwDw5JNPyv6v9AVBkH27UaVCwrKyMknX108SyrndqFarha+vL65fv47ExERFQ8KioiLExsYiISHB8Dv28fHBokWLMH78eKODwXvx8vKCl5cXJk+ejDfffBNnzpzB119/jYcfftgwQd3Z6HQ6nDp1ChkZGYZQsKampsXr8/Ly4Ofnh8jISBm7lN/FixexefNmAICDgwMiIyMRFRUFJycnhTsjIiIiIiIiIiKirsikkBAAHnjgAfTr1w8PPvggLly4gJqaGuzZswd79uy561pBEAAAISEheOWVV5p87fTp0wCA7t27m9oSSWzFihWoq6tDZGQkBg8eLHv9OwM7uQJKpc4klHqSUImQEAACAgIMIeGsWbNkra13/fp1fPPNN4Y/l8LCwrB48WIMGzZMkvDZ0tIS7777Ll577TWcP38ea9euxfDhwzFkyBDY2NiIXk8pDQ0NWL9+/V3nzrbmwIED6NevH7Rak/9aMku5ubmGgBBonF49cuQIjhw5gtGjR2PUqFGcpCciIiIiIiIiIiJZifJKeP/+/ZGYmIgNGzYYQiNBEO56A4AZM2bg4MGDsLe3N3x/ZWUldu7cCZVKhejoaDFaIgmlpqYCAMaNG6fIi9p3BnZy1VfqrEypQ1AHBwcAQHZ2tqR1fq1Pnz4AgFOnTsla9053/rmk0WgwZcoUyQJCPWtra6xYsQLh4eGoq6vD4cOH8c9//hP79+9HZWWlZHXldPr06TYHhABQUVGB5ORk8RsyE+fPn2/xa4cOHcLmzZsNE9LUfpWVlVCpVFCpVJ3mOUVtx/uAiIiIiIiIiMg4oo5sLFy4EAsXLkReXh7279+P9PR05OfnQ6vVws/PD+PGjUNwcPBd33ft2jVER0fj3LlzmDFjhpgtkQQ8PDwASL8VpjHkCglv3rwpS51fc3Nzk3T9Hj16AGh8DsopPDwcABAbGwudTif7lrUAEBgYiEcffRQ7duxAXl4eVq5ciZ9//hkvvvhii2emisHW1hYfffQRjhw5gvXr1+PGjRs4duwYTp48iUGDBmH48OGG8LajKS0tbXaK3FgJCQmGe6OzuXr16j2/funSJdy6dQuLFi2Co6OjTF0RERERERERERFRVybJvm4eHh5YsmSJ0ddHRERg+/btUrRCEvD09AQAFBcXK1JfP/0ll4aGBmRlZclaU0/OkLChoUG2Mx4DAwNhb2+P4uJi7N+/HxMnTpSl7q95e3vjiSeewMmTJ3Hw4EFcuHABjz76KBYvXowHH3wQ1tbWktTVaDQYM2YMYmJiEBsbi/Xr1+PKlSs4efIkTp06hSFDhmD8+PGy/T7EIAgCdu7cadIa169fR0lJCZydncVpykwUFRUZNY2ck5ODVatWYdGiRfD29pahMyIiIiIiIiIiIurKOufhTyQpfUh469YthTuRZ5IwLy9PsW0ApQ4J3d3dodVqUVlZifT0dEkn6O6k1Woxfvx4bN26FV988YXkIeFXX33VJHDr1asXJk6cCJVKBbVajeHDhyMsLAy7d+/GlStX8O233+LAgQOYM2cOLCws7to2+dfvBwcHIyIios19qdVqjBw5EiNGjMDp06exbt06XLp0CSdOnEBRURHmz58PCwsLUX4GUrt8+bIoE6mJiYmIiYkRoSPzceXKFaOvraysxJo1azB79mwMGDCg1ev19yHPMyQiIiIiIiIiIqK2YkhIbabfbrSrTBJmZmbKWk9PrVbDxcVF0hoajQbe3t5IS0vDhQsXZAsJAWDKlCnYunUrfvzxRxQVFcHV1VWyWjk5OU0+zsrKQkBAAAIDAw2fc3JywqJFi5CcnIzdu3cjNzcXn3/+uVHrq1QqrF69GgEBAe3qT6VSYfDgwbjvvvtw5MgRvPvuu7h69SrWrVuHJUuWwMbGpl3ryqW6uhq7d+8WZa2EhASMGjWqU4Ve7QlPt27digsXLkCtVqO+vh51dXXNvul0Ojg7O2PkyJGIiIhQZOteIiIiIiIiIiIi6pgYElKbKT1JKHdIqNR5hK6urrJsN+nr64u0tDQkJiZi5syZktfTCwoKQmBgIFJSUvDtt9/i+eefl6zWH/7wB8PWoYcOHcLevXtx+PBhBAQE3BVGhYSEwN/fH7GxscjPz4dKpTJc09x/8/LykJeXh48//hgfffSRSeGWSqVCTEwMXFxc8Lvf/Q43b97EF198gaVLl8LJyand60pt3759qKysFGWtkpISpKamwt/fX5T1lFZdXY20tLR2fe/169eNuq6kpAQ7duzA0aNHMXLkSERGRjIsJCIiIiIiIiIiolYxJKQ2008SdpXtRpWaJJR6q1E9Hx8fAMD58+chCIKsE1xTpkzBJ598gjVr1uC5556TrPaQIUNgZ2cHAAgODsaBAwdw8/+xd99hUZ3p+8DvmWHovRfpqBRBxYZdTFTsvcWWmDUmm2Q1JsYYTYzJNzGaZLMpm240zZaiscVeIhYEAREBC0gTUIr0PjO/P/jNWbFSZuYMcn+uay6nnuc94xzQc8/zvtnZ9w2jDA0NMWTIkCZtu6SkBJ999hkSEhJw6NAhDBs2rNXjDQ4OxldffYVXX30VBQUF+P777zFr1iw4Ojq2etualpGRgdjYWI1uMy4u7pEJCa9evaqzWiUlJdizZw9OnDiBgQMHolu3bjAw4K95IiIiIiIiIiIiujfttynRI0fdSVhaWor6+nqRR6NdZWVlKC4uFqW2rkLCDh06QCKRoKCgAHl5eTqpMet7jgABAABJREFUqfb4449DLpfj/PnziIuL00lNOzs7jB8/HgDw999/t3p7VlZWGDRoEADgq6++0lhHnbe3Nz7//HN4enqitLQU69evFy2wvp/6+nrs2rVL49tNTExEVVWVxrcrhsuXL+u8ZmlpKfbs2YPPPvsM0dHRj/zP6dvJZDKMGjUKo0aNYjdlO8bPARERERERERFR0zAkpGazs7MTTrqJsS6hLqcbFWuqUQBwcHDQSR1DQ0O4ubkBaOgm1CVLS0sMHDgQALB+/Xqd1Z0xYwakUikyMjJaPBXk7fr16wdbW1sUFRXhxx9/bP0A/z8nJyd88sknCAwMRG1tLX744QdcunRJY9tvrcjISBQWFmpl2xcuXNDKdnVJoVDotJPwTqWlpdi7d2+7CguNjY2xZ88e7NmzR5himNoffg6IiIiIiIiIiJqGISE1m1QqFaY9LCgo0Hn920NCbU+NKWZIqKtOQqDxlKO6NnLkSADAL7/8gtraWp3UtLKyQlBQEADg9OnTrd6egYEBIiIiAAC//vqrRrvHrKys8NFHHyEsLAwKhQJbt27FiRMnoFQqNVajJSorK3H8+HGtbV+Mz6KmXbp0CdXV1WIPQwgLN27cKPZQiIiIiIiIiIiISI8wJKQWCQ0NBQAcOnRI57Xr6uqE61Kpdj/CuuxavJORkZHOaqlD35s3b+qsplpoaCiMjY1RUlKitek0f/31V2zatAmbNm3CDz/8gHnz5gmdaiYmJhqp0bFjRwQEBEClUmH16tUoLy/XyHaBhq6Yd955BxEREVCpVDhy5Ah+/PFH0abCBRofh9qgq8BYW3Jzc/Hrr7+KPYxGtP3zkoiIiIiIiIiIiNoWnjGkFlm0aBEAYO/evSgrK9Np7crKSgCAXC7X+knvDh06aHX7D6LLDiT1dGyaDLaaSiqVws7ODgCQk5OjlRo//PADvv32W3z77bfYuHEj8vLyYGFhgfHjx2PcuHEaqzN27FhYW1sjJycH77//vkZDZgMDA7z66qtYtmwZDAwMkJGRgS+//BKpqakaq9EcVlZWWj0+XFxctLZtbSsuLsYvv/wi9jAANPwMGzx4MObPn48nn3xS7OFoXUVFBczMzGBmZqax9UGp7eHngIiIiIiIiIioaQzEHgC1TY8//jhCQkKQkJCAXbt24YknntBZbXVIaGhoqPVa7SUkVHctqt9bXbOzs8P169e1FhIGBwcL62gCDes99urVC3K5XKN1TExMMHXqVKxfvx4nT57Etm3bMH36dI1tXyKRICIiAsHBwfi///s/pKSk4JdffsHjjz+Ovn37an363Tt17dpVa1Pyurq6amW72lZVVYWff/5ZtGDCwsICfn5+8PX1hY+Pj8Y6ZdsSsX6OkX7h54CIiIiIiIiI6OEYElKLSCQSvPLKK5g7dy7++OMPTJkyRSehHdBwEh7QzXSclpaWsLS0RGlpqdZr3ak9hYTq9Re1FRKOHj1aZ9O3urq6YuTIkdizZw++/vprBAYGIjg4WKM13Nzc8Mknn+Djjz/Gvn37cPDgQdy4cQNjxozRePD5IEFBQdizZ49Wtt0WQ8K6ujps3rwZhYWFOq3r5eWFTp06wdfXFw4ODjoPi4mIiIiIiIiIiKht4nSj1GLTp0+Hq6srCgsLceTIEZ3V1WUnISBeN6E6DNUFdYAmVveTtqcb1bUePXogODhYWJ+wqKhI4zUMDQ3x6quv4sUXX4REIkFCQgI2bNiAkpISjde6HxMTEwQGBmpl287OzlrZrrYolUps375da+tq3ouvry/+8Y9/YN68eejbty8cHR0ZEBIREREREREREVGTsZOQWszQ0BCLFi3CsmXLsHXrVowYMUInJ6h12UkINISESUlJOql1OzE6CauqqqBQKBpNzakL2u4k1DWJRIIxY8YgNzcXBQUF+L//+z988MEHGn9fJRIJJk2aBC8vL7z++uvIzc3FV199hZkzZ8LDw0Ojte6na9euGj8+nJycdNoR2RQqlQrV1dWoqqoSLpWVlcL13NxcXL58WSdj8fLyQnh4uM7+jpsrOTm5VY8TERERERERERGRbjAkpFZ55pln8NZbbyE9PR3R0dHo3bu31mvqupPQ3d1dJ3XuJEYnobquubm5zmoDgK2tLYBHJyQEGj6f06ZNwzfffIO4uDj88MMPmD9/vlZqhYaGYsOGDVi5ciXS0tLwww8/YNSoUejRo4dW6t3Oz88PZmZmGu1CFXOq0YqKCty4caPRpbS0FFVVVVCpVKKNC2j4WRQeHg5vb29Rx3E/9vb2MDU1xezZsx/6XFNTU+HLAURERERERERERCQOhoTUKtbW1nj22Wfx8ccfY+vWrY9kSOjs7AyZTAaFQqGTemq67CQ0MDAQ9rGyslLnIeGj1kmo5uDggHHjxuGPP/7ATz/9hKCgIPTp00crtVxcXPD5559j7dq1OH78OHbv3o28vDxERERotTNUKpUiODgYZ86c0dg2dRESKpVKFBQUNAoD8/LyUF5ervXazeXq6orw8HD4+vrq9XSiHh4eSE5ORkFBwUOfa29vr7edkERERERERERERO0FQ0JqtUWLFuGTTz5BbGwsrl69Cj8/P63WU3fY6SokNDAwgIuLC7Kzs3VST02XISHQ0E1YWVkpyrqE6jUJc3NzdV5b24KDg5GZmYmYmBisWrUK27dvh4mJiVZqmZiYYNWqVfjll1+wfv16xMTEID8/H1OnToWZmZlWagINU462pZDw2rVr2LlzJ4qLi7Vap7VMTU0xbtw4dOrUSa/Dwdt5eHiIGv5JpVIMHjxYuE7tEz8HRERERERERERNwzMn1Gqenp6YNm0aAGDLli1ar6fuJNTVmoRAw7qEuiZGSAhAlJBQ3UlYVlaGsrIyndfXthEjRsDGxgY1NTU4fPiwVmtJJBLMnj0b7733HuRyOTIyMrB+/XrhuNEGZ2dnODk5aWRbMplMY9u6l0uXLuHHH3/U+4AQALp164bOnTu3mYBQH5iYmODYsWM4duyY1sJ40n/8HBARERERERERNQ1DQtKIpUuXAgCOHj2q1TAC+F+YVVtbq9U6t/P19dVZLTULCwud1lO/n3K5XKd1AQiBjYGBgSj1tc3AwADdu3cHAJw9e1YnNfv27YvvvvsO9vb2uHXrFg4ePKjVegMHDtTIdgICArQ6PWpbCAfVQkNDxR4CERERERERERERPcIYEpJGhIaGwtnZGUqlEhkZGVqt5eLiAgC4deuWVuvcztfXFzY2NjqrBzRMU6krdXV1QgehLtaDu9PFixcBNHyOjI2NdV5fF7y8vAAAFy5cgEql0klNDw8PrFq1CgAQHx+Pa9euaa1WUFAQnnvuOYSEhDT7tWZmZujduzeefvppTJo0SQuj+x8xPt/NZW5ujgULFgjT8BIRERERERERERFpA9ckJI0JCgpCXl4eMjIyEBAQoLU66pP8ugwJJRIJevfujf379+uknqWlpRAq6YL6vTQzM9N5ByMAJCUlAQDCwsJ0XltXXF1dYWBggOLiYmRlZels3bYuXbpg3Lhx2LlzJ3bv3o1nn31Wa92ajo6OmDhxIoYMGYKoqCiUlJTA2NgYJiYmwp/qy+23jY2NdTalpvpLBvrK0dERTzzxBKysrMQeSptUUVEh/OxMT0/X6lqcpL/4OSAiIiIiIiIiahqGhKQxgYGBOHz4MNLT07VaRx0SlpSUQKFQaHVqwtt1795dZyFhcHAwpFLdNfqqp2AUq8tK3UnYt29fUerrgkwmQ4cOHZCeno7z58/rLCQEgAULFuDkyZMoLCzEiRMnMHToUK3Ws7GxQUREhFZrtJSBgQFcXFyQm5sr9lDu4uvri6lTp+p0vdVHUUFBgdhDID3AzwERERERERER0cNxulHSmMDAQADQ+nSjtra2MDIygkqlQklJiVZr3c7IyAi9e/fWSa1u3brppI6aupPQ2dlZp3UBoLq6GqmpqQAe7ZAQgBAMXrhwQad1zc3N8a9//QsAEBkZiZs3b+q0vr5xc3MTewh36dGjB5544gkGhERERERERERERKQzDAlJY9QhobY7CSUSiTBlYFFRkVZr3UkXIaGbmxvs7e21Xud26pBQjE7CS5cuQaFQwMXFRafddWJQ719CQoLOaw8cOBD9+/eHSqXCrl27dLYuoj7St5Bw2LBhGD16tE67h4mIiIiIiIiIiIh4RpI0Rh0S5uXloaqqSqu1xFiXEADs7OzQsWNHrdYICQnR6vbvRT3dqBidhOr1CPv27auzdenE4u7uDolEghs3buDGjRs6rS2RSLBo0SIYGBggOzsbMTExOq2vTzp06CD2EATTpk1Dv379HvnPPhEREREREREREekfhoSkMfb29nBwcAAAZGZmarWWWCEhoP1uwi5dumh1+/ciZifh7SHho87Q0FDoghWjm9DBwQH//Oc/AQAHDx5EaWmpzsegD+zs7PSia+8f//gHAgICxB4GERERERERERERtVPinyWlR4qu1iUUMyT09fWFnZ2dVrY9depUmJqaamXb96NSqUTrJFSpVLh48SKA9hESAoCnpyeA1oWECoUCa9euxdq1a6FQKJr12nHjxiEgIAB1dXXYt29fi8fQlkkkElhYWIhW397eHosWLdK7aU+JiIiIiIiIiIiofWFISBoVFBQEQPvrEooZEkokEvTp00fj2506daoQsupSZWUlamtrAeg+JLx06RJu3boFAwMD9OjRQ6e1xaJelzAxMbHF2zh06BD27duHffv24eDBg816rUwmw8svvwyJRILk5GStH6v6ytLSUpS6NjY2mDt3LqytrUWp/6iTSqXo2bMnevbsqRfdoiQOfg6IiIiIiIiIiJqGZ05Io/z9/QEAWVlZWq2jDrPUHXC61rVrV41ub8qUKaIEhABQVlYGoCG8MDQ01Fndmzdv4o033gAAjBkzBsbGxjqrLaaKigoAaNV7bWRkdM/rTeXr6ytMa1tSUtLicbRlnTp10nlNKysrzJkzR9QuxkediYkJoqOjER0dDRMTE7GHQyLh54CIiIiIiIiIqGkMxB4APVrUJ961HRI6OjoCAGpqalBTU9OioKQ1DA0N8dJLLyEmJgZxcXEoLy9v0Xb8/f0xbNgw2NraaniETaceu42Njc5q1tfXY/ny5SgoKEBQUBA2bNigs9piU3cQDhkypMXbGDJkiBA0tbQDMzc3FwC0NnWuvuvfvz+MjY2xZ88endQbNGgQ+vfvr9MgnoiIiIiIiIiIiOhBGBKSRnXu3BkAkJOTA4VCAZlMppU6JiYmsLCwQFlZGUpKSoTQUJcsLS0xdOhQDB48GJcvX0ZsbCyuXr3apNc6OTlhxIgR8Pb21vIoH04dEuoyqNy3bx/S0tJgb2+PvXv3tpupF0tLS4XpPYcOHdqqbbVmetaKigoUFBQAaFgfTxuuXr2Kqqoq+Pv7Qy6Xa6VGa0gkEvTs2RMeHh74/fffcfPmTa3UCQkJwdChQ2FlZaWV7RMRERERERERERG1FENC0ih3d3cYGRmhpqYGN2/ehIuLi9ZqOTg4iBoSqslkMgQEBCAgIADFxcWIjY29b3ehmZkZhg4dim7duunNOknq6S911UlYW1uLH3/8EQCwcuVKYY2+9kDdRRgSEgInJyfRxqHu9DU3N9fKNK+XL1/G5s2bATRMhxocHIzQ0FCt/jxoKUdHR/zjH//AwYMHER0drbHtenh4YMSIEcL6qaQblZWVwtTNSUlJMDU1FXlEJAZ+DoiIiIiIiIiImoYhIWmUTCaDn58fLl68iKysLK2GAk5OTkhLS0NpaanWajSXtbX1fbsL+/fvj4EDB+p8atSH0XUn4e7du5Gfnw83NzcsXLhQJzX1hTokbG0XYWupQ0JtTDVaVFSE3377DUBDKF5RUYGYmBjExMTAxcUFPXr0QJcuXfTqOJDL5Rg1ahR8fHywdevWVm3LxsYGjz/+OAICAiCRSDQ0QmoqlUqFjIwM4Tq1T/wcEBERERERERE1DUNC0rhOnTrh4sWLyM7ORu/evbVWx8HBAQBQUlKitRotdXt3YXl5OWQyGUxMTMQe1j3pspOwuroaP//8MwDgjTfe0EoXm74qKChAbm4uJBJJq9Yj1ITMzEwAmp9qtLa2Flu3bkVdXR2CgoLw73//GxcuXMDu3bvx999/Izc3F7t378a+ffuE7kI3Nze9CdP8/f3x0ksvYfv27cK0sE0hl8vh7u6OTp06oUePHjAw4K9WIiIiIiIiIiIi0n88k0ka16lTJwBAdna2VuuopxjVp07CezE3Nxd7CA+k7iTURUi4Y8cO3Lp1C97e3njqqae0Xk+fXLhwAQDQu3dv0den00ZIqFKpsGvXLty8eRO2trZ46623YGhoiB49eqBHjx4oLi7GgQMHsHv3bmRlZSEuLg5xcXFwdHREv3790LVrV42NpTUsLS0xZ84cREZG4ujRo/d8jqGhIdzd3eHp6QkvLy+4urpqbf1VTaivr8euXbvuCj7ZYUVERERERERERNS+MSQkjVOHhOopDbVFHRLqYydhW6KrTsKKigphnbpVq1bB0NBQq/X0hVKpxNWrVxEXFwcAeOyxx0Qe0f9CQk1ONxoVFYXExERIJBK89dZbdwWQ1tbWmDZtGqZOnSp0Fx45cgQ3b97Ejh07UFFRgX79+mlsPK0hlUoxaNAgeHt7Y9euXSgtLYWHh4cQCrq4uOjNmqJNsW/fPiQkJIg9DCIiIiIiIiIiItIzDAm1RKVSNWsKvZqaGtTU1Ai39b077kEYErYtuuok/P3331FaWorOnTtj1qxZWqmhyeMoPj4eVVVV6NWrV4umjywvL0dsbCxiY2OFz6iJiQkGDBjQ4jFpgkKhwPXr1wForpMwPT0dBw4cAAC88MILCA4Ovu9zJRIJQkJCEBISghdffBGbNm3Cli1bcPDgQchkMvTp00cjY9IEd3d3/POf/2z2z3N9Eh8fj3PnzgEAli9fDi8vL+GxqqoqLF68WJyBERERERERERERkegYErbSpUuX8PXXXyMnJwfdunXD8OHDERoaColE0qwTy2vWrMHq1au1PFrdUIeEN2/eRE1NDYyMjLRSx8nJCUBDENSWT+KLSalUorKyEgBga2urtTq1tbXYtm0bAGD16tVaW7NNU8dRUVER/vzzTwBARkYGZsyY0azXZ2Rk4KeffoJCoQDQMIVlREQEJk6cKPralDdu3EBdXR2kUqlGpj2trq7G1q1boVKpMGzYMEycOLHJr7WwsMDChQshl8vx008/Yd++fXBxcYGHh0erx6VJbfVnS1lZGXbv3g0AePLJJzF8+PBGj6u7iImIiIiIiIiIiKh9ajvzpemhpKQk9OnTB1euXIFcLscnn3yCxYsX49///jcACEFhUyxfvhwlJSXCRdtdeNrk4OAAIyMjqFQq3Lp1S6t15HI5FAoFioqKtFbnUVZfXy9c12Z4VV5eLgQSzQmRmktTx1Ftba1wXR30NZVSqcSePXugUCjQsWNHvPbaa9i2bRuee+45ODs7t2g8mmRhYQGpVAqlUolr1661envnzp1DdXU13N3dsWTJkhYFak899RSCgoIANHy5gDSjoKAACoUCTk5OmDNnjtjD0QmJRILAwEAEBga22XCXWo+fAyIiIiIiIiKipmEnYQvV1dVh7dq1mDJlCr777jsADet8rVmzBr/88guqqqqwYsWKJncUGhkZaa3jTtckEglsbGyQl5eHsrIyrQUjMpkMfn5+SE5ORm5urkbXV2svbl9XTalUaq2OlZWVEEwVFhbCxcVFK3U0dRw5OjrC3t4e9fX1GDt2bLNeGxcXh/z8fBgZGeGjjz6ChYVFq8ejSRYWFpg0aRJ+++03HDlyBD4+Pi0+ia5QKBAVFQUAmDlzJoyNjVu0HYlEguLiYgCaXSexvZPJZAAAQ0PDNrWGYmuYmpri4sWLYg+DRMbPARERERERERFR07SPs4ZaIJfLkZubK3QKqlQqeHh44M0338SgQYOwe/du/PLLLwDa7lR1raGeurKsrEyrddRTm+bk5Gi1zqPq9s9mU7teW0Imk8Ha2hpAw3SX+k4qleK5557DCy+8AEtLyya/rqamBkePHgUALFiwQO8CQrUnnngCBgYGyMnJweXLl1u8nQsXLqCsrAx2dnZ47LHHWryd2tpa4RjW1DqJ9L8vATS3G5aIiIiIiIiIiIjaB4aELaBQKFBXV4cOHTrg1q1bqK6uBtDQieXi4oKXXnoJNjY2whps7ZGNjQ0A7YeEnTt3BgDk5uZqtc6j6vaQUJudhMD/guO8vDyt1tEUqVQqdGI1VWRkJCoqKtChQweMGzdOSyNrPRsbG0ybNg0AcPTo0RYFxCqVCqdPnwYATJ48GYaGhi0ez/Xr16FSqWBkZARzc/MWb4caY0hIRERERERERERED8KQsBnUJ1plMhnkcjnmzZuHnTt34ptvvoFEIhGmU/Tw8MDq1auxa9cuxMfHiztokYjRSajNTrhHla46CYG2FxI2V0lJiRCaLVy4EHK5XOQRPdj06dMhl8tx48YNJCUlNfv1V69exc2bN2FgYNDsKVnvlJmZCaChi7A9dl5rS3sMCSsrKxEUFISgoCBUVlaKPRwSCT8HRERERERERERNw5CwiS5fvoz//Oc/jTrWBg8ejLVr1+Kll14S1iVUn5Q1NzdHYGAgTE1NRRmv2HTVSejl5QUDAwPU1taitLRUq7UeRbcHMtoOEh71kPDw4cNQKBTo1q0b+vfvL/ZwHsrS0hKzZs0CABw7dqzZnaSnTp0CAEycOLHV3X9ZWVkAONWoprXHkFClUiEpKQlJSUn84kg7xs8BEREREREREVHTGIg9gLbg6tWr6Nu3L27duoXCwkIsWbJEOJn93HPPoaKiAs888wzS09MxceJEeHp64scff0RVVRWsrKxEHr04dNVJKJPJ4OLigqysLBQVFbXb97s1JBIJVCoVOwlb4fr167hw4QKAhp8JbaUbbsqUKfjjjz9QUFCACxcuoGvXrk16XU5ODtLT0yGRSDBlypRWj0PdSWhnZ9fqbTWFSqXCtWvXcOvWLRgbG8PY2BgmJibCdWNjYyFga8vaY0hIRERERERERERETceQ8CEqKiqwZs0ajBs3Dj179sSLL76I+vp6LF26FA4ODjA1NcXKlSvh7e2NV199FRs2bIClpSXKysqwa9cuODk5ib0LolB3Euqiu8/V1RVZWVkoLCyEt7e31us9atQhobbXJFR/Jh61kFClUmH//v0AgBEjRghT4LYFZmZmmDFjBr755hscP34cXbp0adI6jOouwscffxyOjo6tHsft041qW05ODg4cOICMjIwHPs/Q0FAID21sbODt7Q1fX1/Y2tq2mRCYISERERERERERERE9CEPCh5BKpejRowfs7Owwffp0ODg4YMaMGQAgBIVSqRRz5szBwIEDkZmZiaqqKnTp0gVubm4ij1486q6x8vJyrdfq0KEDoqKiUFRUpPVajyJ14MFOwpZJTk5GVlYWZDIZnn76abGH02wTJkzAr7/+ilu3biEuLg6hoaGQSCT3DcJu3bolrGE4bdq0VtdXqVQ6CQmLi4tx5MgRoeNTKpWiV69eqKqqQnl5OcrKylBeXo6qqioAQG1trTCN8Y0bN5CSkgKgYZpWHx8f4WJmZqa1MbeWOiSsr68XeSRERERERERERESkjxgSPoSJiQnmzZsnnAieNm0aVCoVZs6cCZVKhWXLlsHe3h719fWQSqUYNGiQyCPWD7rsJFSHsYWFhVqv9ai5fZpRbXcSqkPC29f11GdKpRIVFRUoKysTAqQ7r6v/BIDZs2fDwcFB5FE3n4mJCWbNmoXPP/8ce/bswZ49ewA0TOUrlUrv+rOsrAwqlQo9e/aEn59fq+unpqaiqqoKEolE+Lmhafn5+fjmm2+EsGzYsGF4+umn79npXV9fj/Ly8kZ/31euXMG5c+dw/vx5lJaWIj4+HvHx8QAAf39/DBgwQC+/FKIOCevq6qBQKJrUJUpERERERERERETtB0PCJlAHhAqFAlKpFNOnT4dKpcITTzwBiUSCxYsX48MPP0RGRgZ+/PFHmJqatpnp6LRFPeViSkoKamtrYWhoqLVa6qAiKysLSqXykVhLTFeuXLkCpVIJAwMDra/nqA5Rrl69isrKSpiammq1XnPV1tbi+vXryMzMRGZmJrKzs1FbW9uk1/r5+WH69OlaHqH2jB07Fnv37kVaWppwn0KhgEKhQF1d3V3Pl0qleOqpp1pdt6SkBKtWrQIAeHt7w8BAO7+Szp8/j/r6evj6+uLVV1994JSwBgYGsLa2hrW1tXBf7969MWvWLFRXV+PChQs4d+4czp07h6tXryIlJQUpKSnw9vbG4MGD4enpqZV9aAkzMzOYmJigqqoKBw8eREREhNhDIiIiIiIiIiIiIj3CkLAZZDKZsHbbjBkzIJFIMGfOHOzcuROpqamIjo7W66nndKlHjx5wdXVFTk4O4uLi0KdPH63VCggIgJmZGSoqKpCXlwdXV1et1XqUqFQqnDhxAgAwefJkmJiYaLWeg4MD7O3tUVBQgNjYWAwYMECr9R6mvLxcCASzsrKQm5t715SrUqkUtra2sLW1hb29PWxtbWFnZ3fXxcbGpk13aRkaGuLbb79FVVUV6uvroVAoUF9fL1zuvO3q6go7O7tW1ayvr8fq1auRk5MDGxsbTJo0SUN7c7dr164BAKZOndqqNSONjY3Rq1cv9OrVCwCQkZGBLVu2YP/+/bh27RrS09MxduxYdO/eXSPjbi2ZTIb+/fvj0KFD+PHHH/HYY49BLpeLPSytkkgkQlDb3r+s057xc0BERERERERE1DQMCZvp9vXbpk+fjm+++Qbx8fGIjY1FcHCwyKPTH1KpFOPHj8eXX36JyMhIrYaEBgYG6N69OyIjI3H16lWGhE2Unp6O7OxsSKVSTJ06VSc1AwICcOLECZw5c0anIeG2bduEEE+lUqGoqOiea1g6ODggODgYXbp0QXBwMLy9vdt0+NccUqlUp19y+O9//4u4uDgYGBhgxowZWqtdXV0tTHEbGhqq0W17enpi2bJlePLJJ/Htt9/i8OHD2LlzJ2pqahAWFqbRWi3Vq1cvnD59Grm5udi9e3ejbsLq6moRR6YdpqamSE9PF3sYJDJ+DoiIiIiIiIiImoYhYQtIJBIoFAosXboUR48eRXx8PAPCe5gwYQK+/PJLnDx5Ei+99JJWpwHt1asXIiMjkZqaynUhm0jdRTh27NhWd4U1lTokjIqK0kk9tdun0bydj49Po1DwXmvUkebt3LkTO3bsAABMmTIFjo6OWquVnp4OlUoFd3d3ra0Z6eTkhBUrVsDOzg7btm3D/v37UV1djcGDB4vexWRoaIiBAwdi3759+PTTT/Hpp5+KOh4iIiIiIiIiIiLSHwwJWyEoKAixsbEICQkReyh6aciQIbC0tMStW7eQnJyMoKAgrdVST/+XlZWFmpoaGBkZaa3WoyA7OxvXrl2DRCLBjBkzdFY3ICAAAHQeEi5evLjRZ8LGxgZBQUEwNzfX6TioYX3A//znPwCAoUOHonPnzlqtpw6INd1FeCeJRIJnn30WFhYWWL9+PY4fP47q6mqMGDFC9KCwR48eiIuLw40bN0QdBxEREREREREREekXhoQtJJPJMH/+fNFP/uozQ0NDjB49Gps3b0ZkZKRWQ0IXFxd06NBBCL/8/f21VutRoO4iHDFiBJydnXVWt3PnzpBKpcIagC4uLjqp+/jjj3O9UD2Ql5eHZcuWQaVSoUuXLjqZcla9HqG2Q0KgISicPXs2zMzM8OmnnyIqKgo1NTUYO3asVjupH8bAwAALFy5EfX19o/tramrw0UcfiTQq7aiqqhK6yf/++2+tr7VK+omfAyIiIiIiIiKiphHvrOUjgAHhw02cOBEAEBkZCZVKpdVa6m7Cq1evarVOW5eXl4fLly8DAGbOnKnT2iYmJvDy8gKg+25CEldVVRVWrFiBmpoauLi4YNy4cVr/GVpWVoaCggIAQLdu3bRa63YTJ07Ea6+9BolEgvj4ePz22293BXS6JpFIIJfL77o8apRKJWJiYhATEwOlUin2cEgk/BwQERERERERETUNQ0LSqoiICMjlcmRnZyMzM1OrtXr27Ang/uvPUYPIyEgAQHh4ODw8PHReX6wpR0k8SqUS77//PtLS0mBmZobp06frJKBSdxF26tQJlpaWWq93uxEjRuDtt9+GVCpFcnIyNm/ejNraWp2OgYiI2qe6ujq8//77CAoKgomJCRwcHDBp0iTExcXpbHuxsbFYt24dpk6dCi8vL0gkEkgkEqSnp7dwr4iIiIiIiEgbGBKSVllYWGDYsGEA/hdOaUv37t0hlUpx69YtFBUVabVWW1VQUICLFy8CAGbNmiXKGBgStj8//fQT/v77b0ilUkyfPh1WVlY6qavLqUbvZcCAAVi3bh0MDAyQlpaGn3/+GdXV1aKMhYiI2oe6ujpERERg+fLlKCgowNixY+Hv74/t27cjLCwMBw8e1Mn23n77bSxbtgy//fYbMjIyNLFrREREREREpAUMCUnrJkyYAED7IaGJiQlCQkIAAKmpqVqt1VadOnUKANCvXz/4+vqKMgZ1SBgdHc1p4NqBhIQEbNy4EQAwZswYuLu766y2uluhe/fuOqt5px49euCTTz6BoaEhsrKysGHDBhQWFoo2HiIierStW7cOR44cQa9evXDlyhVs27YNJ06cwKZNm1BbW4tZs2ahvLxc69vr27cvVq5ciR07diA7OxtOTk6a3E0iIiIiIiLSEIaEpHWDBg0CAK1PNwpACL5KS0u1XqutUSqVSElJAQBMmTJFtHFUVVUBAAwMDLiuZzug7hgNCgrSeVgnk8kAACUlJTqte6fAwEB8+eWXsLOzw82bN/H5559jw4YNOHXqFG7duiXq2IiISL95eXkJ6zk/TH19PT7++GMAwBdffNFoqu2ZM2di1KhRyM/Px/fff6/17S1btgzvvPMOxo8fDzc3tybVIyIiIiIiIt1jSEha5+zsDACorKxETU2NVmsZGBgAABQKhVbrtEU5OTmoqqqCmZkZgoODRRvH2bNnAQDDhg1jSNgOqNcI9fT01HntLl26AAAOHDig89p38vHxwVdffYUePXoAaPjSxMGDB/Hpp5/iyy+/xNGjR5GbmwuVSiXySImIqK06efIkCgsL4e3tLazVfbvp06cDAP78809RtkdERERERET6hyEhaZ2lpSWMjIwAQOtdM+rOIU5jeberV68CaJj+UB2mikEdEkZERIg2BtId9bqAjo6OOq+tnn743LlzKCgo0Hn9O9nb2+PDDz/Eli1b8OKLL6J79+6QSCS4efMm/v77b3zzzTf45JNPsG/fPqSnp/PnWAvZ29vD3t5e7GGQyPg5aHv+/PNPTJkyBZ6enjA2NoajoyP69u2LdevWaa0jvLU1FQoFEhMTsXHjRrz44ovo27cvTE1NIZFIIJFI8OSTT2pl3Pdz/vx5ABC+kHIn9Rq96ufpentERERERESkf8RLCqjdkEgkcHR0RFZWFoqKioTOQm1gJ+H9qUPC3r17izaGkpISXLp0CQAwYsQI0cZBulFeXo4bN24AECcktLW1hbu7O7KysnD48GGh40FsTk5OmDRpEiZNmoTS0lKcOXMGkZGROHXqFEpKShAVFYWoqCgYGxvDy8sLPj4+8PHxga2tLbtvH8LMzAz5+fliD4NExs9B21JWVoYnnngCu3fvbnR/fn4+8vPzcebMGXz22WfYunUr+vXrp1c1p02bhj/++EMjY9KEjIwMAECHDh3u+bj6/sLCQpSXl8Pc3Fyn2yMiIiIiIiL9w5CQdMLJyQlZWVla7yRUh4TswGmssrIS169fByBuSHju3DmoVCoEBwdzfZp2QN1FaGlpCRMTE1HG0LVrV2RlZWH//v2YNm2a3oVslpaWGD58OIYPH47q6mqcO3dOCAxLS0uRkpIirCVqaWkpBIbe3t48GUtEbV59fT0mT56MgwcPAmj49+KCBQsQGBiIoqIibN68GSdPnkR2djZGjx6NyMhIBAUF6U3NO7+UZmtrCzs7O1y5cqVVY2ypsrIyAA1B+b3c/nujrKzsob9HNL09IiIiIiIi0j8MCUknnJycAHC6UbGkpqYCaFgXzcHBQbRxPGpTjaanp+PYsWOoqKiAu7s7PDw84OHhARsbG70Lo8SgXo9QjC5CtcDAQOzduxfXrl1Damoq/Pz8RBvLwxgbG6N///7o378/FAoFrly5gnPnzuHcuXM4f/48SktLER8fj/j4eAAN76uPjw/8/Pzg5eUl/PwjImorvv32WyGsCwwMxJEjR4R/MwLA888/j1deeQUfffQRiouLsXDhQkRGRupNzd69eyMgIAA9evRAjx494O3tjY0bN+Kpp55q9rjef/994Usht1NPl32vqUsHDBiAf/zjH8Jt9bq2mvo3iKa3R0RERERERPqHISHphK5CQnYS3pt6qtFevXqJNgalUvlIhIT5+fk4fPgwDh8+LLyvdzI3NxcCQw8PDyFAdHNza1dBjjokvP3kq66ZmJjA398fSUlJOHDggF6HhLeTyWTw9/eHv78/Zs2aherqaly4cAHnzp1DbGwsrly5gps3b+LmzZs4c+YMTExMEBAQgKCgIHh5eUEqbZ9LDldVVWHkyJEAgL/++ku0DlYSFz8HbYNCocDbb78t3P7pp5/u+fti7dq1OHz4MOLj43Hy5Ens37+/xVOWa7rm66+/3qJx3Mu+fftw/Pjx+z7+ww8/3PP+20NCCwsLAA3Tfd9LRUXFXc99EE1vj4iIiIiIiPQPQ0LSCV13EnJNwv9RqVRCmNWnTx/RxpGWloZbt27BzMwM/fv3F20cLVFaWoq///4bhw4dQkJCgvDNeplMhlGjRqFjx464dOkSUlJScO3aNZSXlyMpKQlJSUmNtuPj44P//Oc/enUirbq6GlFRUfj777/h4OCAhQsXaqxjQD3dqJidhAAQEhKCpKQkHDp0CAsXLmyTQa2xsTF69eolBP3FxcWIi4tDTEwMTp06heLiYsTGxiI2NhZmZmZCYOjh4dGuAkOlUimcZOeXRdqvtvg5ePrpp/H9998DAKKiou45Nfi5c+cwfvx4XL9+HRYWFvjll18wduxYXQ9VY44fP468vDwAwODBgxEaGnrP58lkMvzrX//C/PnzAQCbN29ucUgoRs2mOnbs2D3v9/LyAtAwe8HDeHp6AgCys7Pv+bj6fltb2yZNDarp7REREREREZH+YUhIOqEOCYuKirRah52Ed8vNzUVlZSVMTEzQpUsX0cah7iIcOnQojIyMdFr777//blRTIpFALpc3uhgYGNx1++LFizh8+DCioqJQX18vvH7QoEGYNWsWJk+eDDs7u0a1qqurcfXqVWEtuZSUFCQnJyMpKQlpaWlYu3Yt3nnnHVGn7qqtrcXZs2dx9OhRnDp1CtXV1cJjrq6uGDduXKtrqFQqvegkBAA/Pz+Ympri1q1biImJETUs1xRra2uEh4cjPDwcCoUC8fHxOHr0KE6cOIHS0lLExMQgJiYG5ubmCAgIQJcuXeDu7s4p44j0VFxcHICGcCo4OPiux7dt24Ynn3wSVVVV8PHxwc6dO1u9Np/Y9u3bJ1wfNWrUA597++N//fVXm6qpS926dQMAxMbG3vNx9f1du3YVZXtERERERESkfxgSkk6oO4m03Umo7phhJ+H/qNcj7N69O+RyuWjjOHPmDABxphpdt25dq7fRtWtXPPHEE5g5cybc3d3v+zxjY2N06dLlrkA2NjYWYWFhOHnyJI4ePYqhQ4e2ekwtUVNTgwULFiArK+uej3/11VcYPnw4jI2NW1UnPz8fFRUVkEqlsLe3b9W2Wksmk6FLly44e/Ysfv/9d/Tq1euR6q6TyWTCeliLFy9GbGwsjh49isjISJSXlyM6OhrR0dEIDAzE1KlTxR4uEd2hrq4OFy9eBAD4+/s3mh5VpVJh1apVeOeddwAA4eHh+O2332BrayvKWDXpwoULwvWHTYfu5OQEd3d3ZGVl4ebNm8jPz2/RGsti1NSl/v37w9bWFmlpaTh37hx69OjR6PFt27YBAMaPHy/K9oiIiIiIiEj/MCQknYiPjwcA2NjYaLWOOhDTp+kcxabuoMvOzoZSqRQlHLl8+TIuXLgAqVQqytRoAwcOFLpMgYYQuba2FjU1NaitrRUut9+uqamBu7s7ZsyYgSeeeKLVHRuhoaGYPHkytmzZct9pu3ThYVNtWlhYaCRkV69TZGxsrBfTe/bo0QMxMTGIjo7Ghg0b8PTTT4s9JK0wMDBA79690bt3byxZsgQxMTHYunUrzp8/j8LCQrGHR0T3cPHiRdTW1gJo+EKPWkVFBebMmYPt27cDAP75z3/ik08+afT7rLkiIyNRUFDQugH/f6GhofDw8Gjx6y9duiRc9/b2fujzvb29hS+4XLp0qUWBnRg1dcnAwACLFy/Gm2++ieeffx4HDx4U/k28detW7N69G/b29sI0qmpz587F2bNn8cILL+CFF15o9faIiIiIiIio7WBISFqnVCqxefNmAA3fgNcWlUqFU6dOAQA6deqktTptTdeuXXHo0CFkZmbi5MmTGDhwoM7HsGXLFgB4aBeetuzevRuWlpY6r3unzMxMAECHDh1EG4OBgQE2bNiAgoICoTPi5s2bKC8vR7du3eDv7w8zM7NW11GH03V1da3eliY4Ojpi7Nix+PPPP/Hzzz+jQ4cOWl9fSmxyuRx9+/bF0aNHAQC+vr4ij4iI7kU91SgAYY28jIwMjBs3DgkJCZDL5fjss8+wcOHCVtdauXKlsF5ja23YsAFPPvlki19fXFwsXG9Kx/nt03vf/lp9r6lry5Ytw5EjR3Ds2DH4+flh8ODByMvLw4kTJyCXy/HTTz/d9WW6zMxMXLp06Z4Bcku2BwB79uwROmCB/y05MHHiROHfCKNHj8Ybb7yhyd0nIiIiIiKiZmJISFp38uRJZGVlwczMDGFhYVqrc+XKFeTn50Mulzfp2+HthZGREfr06YPIyEhs2rQJAwYM0Om6ZNevXxdOSL766qs6q6uP1B0Mrem80ASZTAYnJyetrhV4e0ioUqn0Yi28bt26obCwEJGRkVi3bh2cnZ0f+XWUFAqFsB5ox44dRR4NEd3L7eu9de/eHZGRkZg0aRLy8/Nhb2+P3377DYMHDxZxhNpRXl4uXG/KFNe3T8NaVlbWZmrqmqGhIfbv348PP/wQP//8M3bu3Alzc3OMHz8eb775phBEa3t7+fn5iIqKuut+9ewiQMP0ukRERERERCQuhoR6rnPnzo2mh+zbty++//57veiKaqpNmzYBaJjyUR0caIO6i9DX11fUtff0UZ8+fXD69GmkpKQgPj6+0XRm2rZt2zYolUqMHDkSISEhOqurbwoLC4XpHt3c3EQejfbdfqwrFIpWTY+nSUOHDkVRURGSkpKwZMkSjBo1CtOnTxe1u1ObLl26hJKSEhgZGYnSxSsGU1NTsYdAeqAtfQ5u7ySMiYnBihUrhOlHt23bptGA8NixYxrbFulGenp6s19jaGiI119/Ha+//nqTnv+wz0VztwcATz75ZKs6TYmIiIiIiEg3dL84GTVLXl4ecnJyhMvvv/+OESNGoKSkROyhNUltbS22bdsGAHjssce0WksdEnbu3Fmrddoic3NzIRhUh7a6cOvWLezbtw9Aw3RV7Zm6i9DBwaFRZ8Kj6vaQsL6+XsSRNCaRSDBhwgT4+vpCqVRi9+7dmDNnDlatWoWUlBSxh6dxZ86cAdDw5Ql9WBtS28zMzFBRUYGKigqNTJtLbVNb+hyoVCqcP39euL106VIhIASA77//Xoxh6YS5ublwvbq6+qHPr6qqEq63dO1pMWoSERERERER6TOGhHru008/xbfffotvv/0Wa9euhYWFBc6cOdNmgsIDBw6gqKgINjY2Wu1eu3nzJq5cuQKAU+rdT79+/SCRSBATE4PLly/rpOYff/yB2tpa9OnTB4MGDdJJTX2lfs/bSzeXgYGB0AXd2nUJ6+rqsGPHDvzwww/Yv38/EhISkJ+fD6VS2aLtyeVyzJo1C08++aSwfunff/+N5557DkuWLMHZs2ehUqlaNWZ9oQ4J+XORSD9duXKl0RSYUqkUr732mvBFi02bNiExMVGs4WmVtbW1cP1ea+HdSd2Nf+dr9b0mERERERERkT7Tj/nf6L58fHwafQv+o48+wiuvvIKoqCgMHz4c+/fv1+uTFuqutaFDh2q1i+XkyZMAGgIYfe8aEIuNjQ26dOmCCxcuYPPmzVi1apVW61VWVmLHjh0AGroI9WFNOjGpOwnbS0gokUhgZGSEqqqqVoWEdXV12LJlC9LS0gA0nnZNLpfDyckJzs7OcHFxgYuLCxwdHZv0s0YikcDT0xOenp64efMmTp06hYSEBMTFxSEuLg6+vr6YMWMGwsPD22wHXmFhofDlCT8/P5FHQ0T3cvtUozY2Nti8eTNGjBiBiooKfPbZZ1AqlVi5cqXw+7S1IiMjmxSONUVoaGir1tjt3Lkzrl27BgC4du0avLy8Hvh89XPVr20rNYmIiIiIiIj0GUPCNqZjx4746KOP8PLLL+Ps2bMYPnw4Dhw4oJdBYXl5ObZv3w6AU43qi/79++PChQs4fvw4srOztboO2+7du1FeXo7OnTtj/PjxWqvTVqhDQk2957W1taivr9frdbdaGxLeGRDe6/Hs7GxkZ2cL90mlUvj7+2PChAlNXpvU0dEREyZMQHh4OM6cOYOYmBikpqbi3Xffxfr16zF9+nSMHj26za11GhUVBQBwdXVtNMXeo6y6uhqTJ08GAPz+++8wNjYWeUQkhrb0Obg9JFy/fj1GjBgBAHj99dfx3XffoaqqCn/++SfOnDmDsLCwVtdbuXIljh8/3urtAMCGDRtateZccHCwMCV5dHQ0wsPD7/vcGzduICsrC0DDz2wHB4c2U1MMdXV1+Oijj/DTTz8hLS0N5ubmGDhwIN54440WzezRku3Fxsbi0KFDiI6ORnR0NDIyMgA0LZwlIiIiIiIi3eF0o22Qn58fPvroI1haWiI6OhrDhw9HcXGx2MO6y59//onq6mq4ubnB399fa3UqKioQGxsLgCHhwzg5OaFjx45QqVTYunWr1urU1dXh119/BdCwvpJ62sn2TB0StqbrAmg4+f3zzz9jzJgxGDNmDF588UX8/PPPuHLlit5NkameLq8laxI+LCC8H6VSiaSkJGzfvr3Z74eVlRVGjBiBJUuWIDw8HKampsjLy8Mnn3yCefPm4eDBgy2e4lQM7XGqUYVCgb1792Lv3r1QKBRiD4dE0pY+B+p/vwBA7969hevOzs54/vnnhduvv/66TselCxEREcL1v/7664HP3bt3r3B91KhRbaqmrtXV1SEiIgLLly9HQUEBxo4dC39/f2zfvh1hYWE4ePCgTrb39ttvY9myZfjtt9+EgJCIiIiIiIj0D8/ct1F3BoVz584Ve0h3UYdQjz32mFanmoyJiYFSqYSdnR3s7e21VudRMWDAAAANJ7+0ta7l8ePHUVBQAFdXV8yePVsrNdoShUKBq1evAmjddKNKpRLPPfcc1q9fj7q6OqhUKiQmJmL9+vV45plnsHr1ak0NWSPUIWFLOgn/+uuvZgeEt0tOThY66ZrLxMQEgwYNwuLFizFy5EiYm5sjNzcX7733Ht57770Wj0mXFAoFzp07B6B9hYREbU18fDwAwMHBAW5ubo0eW7ZsmdAFfPTo0WaHO/dy7NgxqFQqjVxa00UIAIMHD4azs7MwrtsD09spFAp8+umnwu0ZM2a0qZq6tm7dOhw5cgS9evXClStXsG3bNpw4cQKbNm1CbW0tZs2a1WgdTG1tr2/fvsJUudnZ2XByctLkbhIREREREZGGMCRsw/z8/IR15dTTbeoT9dphXbp00Wqd/Px8AICLi4tW6zwqPDw8YG5uDqVSiRs3bmilhnr6xzFjxghBUXtWXV2N2tpaAGjV1MBVVVXCcTVhwgQsWrQIo0ePho+PDwAgISGhtUPVKAODhhmtW9J9d/sUoi2Vk5PTqtfL5XL07t0bL774Ivr27QsASEpKavW4dKGqqgqVlZUAIJwQJyL9kp2dLfwbplu3bnc9bm9vj0WLFgm3V6xYoauh6YRMJsObb74p3J47dy5u3rx51/Nee+01IUzt37+/MCXrnYYMGQKJRAKJRIKNGzfqpKa+qa+vx8cffwwA+OKLL2BpaSk8NnPmTIwaNQr5+fn4/vvvtb69ZcuW4Z133sH48ePvCsCJiIiIiIhIf3BNwjbOzs5O7CE8FKea1D+6+jtRh0SkeUFBQTAwMEDPnj3h7u6Or776SuwhPbIMDQ0RGBiI06dPiz0UInqE3L4e4f3WdXv55Zfx+eefo6SkBNHR0fjjjz8wadIkXQ1R6xYsWIDt27fj4MGDuHjxIrp27YoFCxYgMDAQRUVF2Lx5MyIjIwE0TAn99ddf61XNa9euYf369Y3uu/0LO3FxcVi5cmWjx0NDQ7X2d3jy5EkUFhbC29sbPXv2vOvx6dOnY+/evfjzzz/xr3/9S+fbIyIiIiIiIv3DM/hERERERDrWlJDQxsYGL7/8stD9tnLlSowfPx4ymUwnY9Q2AwMD/P7773jiiSewe/du5OXl4Z133rnreR06dMDWrVsRFBSkVzUzMjLw7rvv3vfxhISEu7r8582bp7WQ8Pz58wCAHj163PPx0NDQRs/T9faIiIiIiIhI/7DFi4iIiIhIx24PCe813aja4sWLhZkjkpOT8dNPP2l7aDplYWGBXbt2YceOHZg0aRLc3d1hZGQEe3t79OnTB2vXrkViYiL69evXpmvqQkZGBoCGgPNe1PcXFhY2aV1CTW+PiIiIiIiI9A87CfWUSqUCAGFNqfupqqoC0LDmV2lpqdbH1RwKhQJAw3psFRUVWqujXutNoVCgpqZGa3UeJerPV1VVlVb+btR/J7W1tVr5XKq3qd6P+1E/Lvaxcft7XFlZ2aI1+tSvVaupqRGOMfX7rVKptHqsNZd6P2tra5t9bLb0PbqdJn8mqN9jpVKpV+/x/dz5WblX15H6vWnqcVRRUSH6sfQwt//dlJaWCscItS+6/Byoaz3sOLqX7du3N+l5FhYWKCgoaPb225rx48dj/PjxLX79sWPHdF5zyJAhLfq715aysjIAgJmZ2T0fNzc3b/Tc22/rYntERERERESkfyQqffqfLQmys7Ph7u4u9jCI9FpWVtZ9v90O8DgiagoeR0St97DjiOhh5s6di7NnzzbrNRMnTsSaNWuE2wsWLMB3332HlStX3nMK1fr6esjlcgBATk4OXFxcHrh9TW7P2dkZN27cwLVr1+Dl5dWU3SMiIiIiIiIdYCehnnJ1dUVWVhYsLCwgkUia9JrS0lK4u7sjKysLlpaWWh5h28b3qnn07f1SqVQoKyuDq6vrA5/XkuNI3+jbe69v+P7c38Pem0f9OOJnozG+H3fTxHvS1OOI6GEyMzNx6dKlZr0mNze30W0LCwsAuO/Un7d32aqf+yCa3h4RERERERHpH4aEekoqlbb4G+mWlpY8AdhEfK+aR5/eLysrq4c+pzXHkb7Rp/deH/H9ub8HvTft4TjiZ6Mxvh93a+170pTjiOhhWjJd6p08PT0BNHSA34v6fltb2yZNDarp7REREREREZH+kYo9ACIiIiIiImqdbt26AQBiY2Pv+bj6/q5du4qyPSIiIiIiItI/DAmJiIiIiIjauP79+8PW1hZpaWk4d+7cXY9v27YNADB+/HhRtkdERERERET6hyHhI8TIyAirVq2CkZGR2EPRe3yvmofvl3j43j8Y35/7a+/vTXvf/zvx/bgb3xN61BgYGGDx4sUAgOeffx5lZWXCY1u3bsXu3bthb2+P+fPnN3rd3Llz4e/vj88//1wj2yMiIiIiIqK2Q6JSqVRiD4KIiIiIiIhap7a2FiNGjMCxY8fg6OiIwYMHIy8vDydOnIBcLsfOnTsRERHR6DVDhgzB8ePHsWrVKrz11lut3h4A7NmzB++8845wOzY2FnV1dejWrZsQzI8ePRpvvPGG5t8EIiIiIiIiajIDsQdARERERERErWdoaIj9+/fjww8/xM8//4ydO3fC3Nwc48ePx5tvvonQ0FCdbC8/Px9RUVF33R8fHy9c9/f3b9ZYiIiIiIiISPPYSUhERERERERERERERETUzrCTUE8plUrk5OTAwsICEolE7OEQ6RWVSoWysjK4urpCKr3/0qo8jojuj8cRUevxOCJqvaYeR0RERERERKR5DAn1VE5ODtzd3cUeBpFey8rKQocOHe77OI8joofjcUTUejyOiFrvYccRERERERERaR5DQj1lYWEBoOE/y5aWliKPhki/lJaWwt3dXThO7ofHEdH98Tgiaj0eRy2nUChw6tQpAEC/fv0gk8lEHpFutNf9fpCmHkdERERERESkeQwJ9ZR6KipLS0ueTCK6j4dN2cbjiOjheBwRtR6Po5YZPXq02EMQRXvd74fhVLxERERERES6x0UfiIiIiIiIiIiIiIiIiNoZdhISEREREZFO1dXV4ZtvvgEAPPPMM5DL5SKPSDfa634TERERERGRfmJISEREREREOlVbW4sXXngBAPDkk0+2m7Csve43ERERERER6SeGhET/X0VFBWJjYxEdHY2bN29CoVBAqVRCoVA0un77fZaWlvD29oaPjw98fHzg7e0Nc3NzsXeFiIiIiIiIiIiIiIjogRgSUrtUV1eHixcv4uzZszh79iyio6ORmJgIpVLZ6m07ODg0Cg19fHxgb28PmUwGmUwGqVQqXL/zdl1dHcrLy1FWVoby8vL7Xjc0NISLiwtcXV0bXVxcXGBqaqqBd4iIiIiIiIiIiIiIiB5lDAmp3Thy5Ah27dqFs2fPIjY2FtXV1Xc9x87ODv7+/nBxcYFMJoNEIhFCvHtdLysrQ25urnApLS1Ffn4+8vPzERUVJcJeAlZWVkJo2LlzZwwZMgRDhgyBg4ODKOMhIiIiIiIiIiIiIiL9w5CQHnnl5eV46aWX8N133zW638zMDJ07d4a/v79waW2QVl5ejry8POTk5CA3N1f4s7y8HEql8qEXmUwGU1NTmJiYwNTUFMbGxsJt9X0mJiaoq6tDQUEBCgsLG/1ZU1ODkpISlJSUIDk5GYcPH8YXX3wBAOjSpQvCw8MRHh6OwYMHw9bWtlX7SqQLSqUSSUlJiIyMRHV1tXCsenh4QCqVij08IiIiIiIiIiIiojaLISE90s6ePYtZs2bh6tWrkEgkGDFiBLp37w5/f3906NBB4yGDubk5/Pz84Ofnp9HtNoVKpUJFRQUKCwtRWFiI/Px8XL58GXFxcbh27RoSExORmJiIzz77DBKJBN26dRNCwyFDhnAtRdIL9fX1iIuLw99//42///4bkZGRKCoquut5JiYm6NSpU6OQ39/fH506deKUu7fJzc3Fxo0bsWHDBqSlpTV6TKVSiTQqauuKi4uxf/9+7N27FxkZGaitrb3vpaamBrW1tfD09MSsWbMwZ84ceHl5ib0LREREREREREQEQKLiWUK9VFpaCisrK5SUlMDS0lLs4bQ59fX1WLNmDd566y0olUo4ODhg+fLl6N69u9hDE0VxcTHi4+MRFxeH+Ph4ZGZmNnrcxsYG+/btQ+/evUUaYfM09fjgcaT/qqurcfbsWSEUPHXqFCoqKho9x9jYGEFBQTA1NUVWVhauX7+Ourq6+26zT58+WLNmDcLDw7U9fL2kUCiwb98+fPfdd9i5c+dD11rlcUQPo1KpkJKSgt27d2PPnj04ceJEq9bwHTx4MObNm4cpU6bAwsJCgyPVPf4+armKigrhC0rl5eUwMzMTeUS60V73+0F4fBAREREREYmHnYT0yElLS8OcOXNw6tQpAMDQoUOxePHiNn8isjWsra2FtQkBoLCwUAgNz549i/z8fAwaNAibN2/GxIkTxR0stQvV1dX48ssv8d5776GgoKDRY+bm5ggJCREuHTt2hIHB/35dKRQK5OXlITMz865LaWkpoqKiMHToUIwbNw7r1q1D586ddb17osjMzMT69evx/fffIzs7W7g/KCgIY8aMQY8ePSCRSIT7KysrMW/ePDGGSm1AdXU1jh8/jj179mD37t24du1ao8c9PT0RFhaGTp06wdDQEHK5HAYGBvf8UyaT4cKFC9i/fz/i4uJw/PhxHD9+HM899xymTJmCefPmYejQoZDJZCLtLYnByMgIu3fvFq63F+11v4mIiIiIiEg/sZNQT/Ebtc2nUqnw448/4p///CcqKythZmaGRYsW4fHHH290Ypwaq6ysxNtvv42oqChIJBJ89NFHWLx4sV6/Z+zcaLvq6+uxYcMGvP3220KQZWNjg65duyIkJARdu3aFl5dXi6cCzs/Px6ZNm4QOOplMhueffx5vvvkm7OzsNLkreqGurg67du3Ct99+i/379wtTiFpaWmL48OEYNWoUvL297/naiooKjBkzhscRNVJZWYlFixZh8+bNjbp65XI5unXrhr59+yIsLAwuLi4t2v7Nmzdx8OBB7N+/H1lZWcL9bm5umD17NubPn49OnTq1ej90hb+PiFqPxwcREREREZF4GBLqKf5nuXkUCgWee+45fPvttwCAkJAQLF++HM7OziKPrG1QKBT49NNPsXPnTgDAu+++i9dff13kUd0fT8q2TVFRUZg/fz6SkpIAAI6Ojpg7dy4iIiI03kGUkZGBr7/+GqdPnwYAmJmZ4eWXX8bixYthY2Oj0Vq6plQqERkZiS1btuDXX39t1InZvXt3jBkzBgMGDIChoeEDt8OQsGnS0tLw008/4cqVK6ipqWl0qa6uvus+Ozs79OnTB2FhYQgLC4O/v7/G17/VloqKCgwfPlzoxLezsxP2o0ePHjAxMdFYLZVKheTkZOzfvx9Hjx5FWVkZAEAikSA8PBxPPPEEJk+eDGtra43V1Ab+PiJqPR4fRERERERE4mFIqKf4n+Wmq6+vx7x587Bp0yZIpVI89dRTmDlzJqctayaVSoUtW7bgm2++gVQqxfHjxzFgwACxh3VPPCnbtlRWVuLNN9/Exx9/DKVSCSsrK8yePRvjxo17aJDVWufOncOXX36J1NRUAICpqSleeuklvPTSS22qs1ClUiE6OhpbtmzBtm3bcP36deExGxsbjBw5EqNGjYKbm1uTt8mQ8P4qKirw22+/YcOGDTh+/HirtmVpaYk+ffoIwWGfPn1gb2+voZFqTk1NDcaOHYuDBw/C3Nwcq1atumuKWm2pra3F6dOn8ddffyEqKkq4Xy6XY8yYMZg1axZGjx4NY2NjrY+lufj7qOXq6urwyy+/AABmzZoFuVwu8oh0o73u94Pw+CAiIiIiIhIPQ0I9xf8sN41SqcTMmTOxbds2yGQyrFy5Ulh3j1pmzZo1OHDgADw8PBAfH6+XXVc8Kdt2/P3333j66adx9epVAMCwYcPw/PPPw8rKSmdjUCqVOHHiBH788UekpaUBAExMTDB//nw8/vjjGDRoEGxtbXU2nqZSqVRITEzEli1bsGXLFmHsQENn5KBBgzB06FB07969RV+KYEjYmEqlwsmTJ7FhwwZs3rwZVVVVABo623r06IEePXrAyMhIWH9PfTE0NGx0382bN5GUlISkpCRcunQJ1dXVd9Xy8/PD9OnTsXr1ar34QotCocCMGTPw22+/wdjYGB999BECAwNFGUteXh6OHDmCQ4cONVoH0dLSEpMmTcKsWbMQHh6uF+8bwN9HrVFRUQFzc3MAQHl5OczMzEQekW601/1+EB4fRERERERE4mFIqKf4n+Wm+fDDD7F06VLI5XKsWrUK/fv3F3tIbV5lZSUWLFiAnJwcTJ06FVu3btW79Ql5Ulb/lZeX47XXXsN///tfAIC9vT2WLFmCvn37ijYmpVKJkydP4scffxRCS6AhBAoJCcGQIUMwZMgQ0ULDuro6XL58GQkJCTh//jx27dolTM0KAMbGxujXrx+GDh2KXr16tboLkyFhg+zsbPz444/YuHEjrly5Itzv5uaGiIgIDB8+HI6Oji3atkKhwLVr14TQMCkpqdE6fM899xz++9//ivozVqVSYcGCBVi/fj3kcjnWrFmDHj16iDae26WlpeHQoUM4cuQIbty4Idzv7OyMuXPn4uWXX27x342m8PdRy4kdlmVmZjaarvl+7O3t4eHhobG6Yu+3PuLxQUREREREJB6GhHqK/1l+uOjoaPTt2xcKhQJLlizB2LFjxR7SIyMlJQUvvPACFAoFvvvuOzz99NNiD6kRnpTVb3FxcZgyZYrQ+TZ69Gg8++yzwklRsalUKkRFReH06dM4f/48MjIyGj0ukUgQHBwshIYDBw7U6PSQKpUKeXl5SEhIwIULF5CQkICEhAQkJyejtra20XPlcjn69OmD8PBw9O3bV6NrwrXnkFClUuHIkSP497//jX379kGpVAJoCGKHDBmCkSNHIjg4WCvhXWlpKY4dO4b//Oc/UKlUWLFiBf7v//5P43WaQqVS4dVXX8WHH34IqVSKVatWYdCgQaKM5UGUSiUuXryIQ4cO4dixYygtLQXQ8Pf1r3/9C0uXLhVtClf+Pmo5McOyzMxMBAQEoLKy8qHPNTU1RXJyssaCQoaEd+PxQUREREREJB6GhHqK/1l+sNLSUnTv3h1paWkYPHgwVq1apXfdbm3d5s2b8c0338DY2BhxcXHw9/cXe0gCnpTVXz/88AOeeeYZ1NbWwsnJCUuXLtWbrqT7KSoqwvnz5xEfH3/P0BAAOnXqhH79+gmXgIAASKXSh25boVDg0qVLiIuLQ1xcnFDjft0rJiYm8PHxgY+PDwIDAzFgwACthavtMSSsq6vD1q1b8dFHHyE+Pl64PyQkBCNHjsTgwYM1GsQ+yM6dO/Hxxx8DaOiKf/nll3VS93Zr1qzB66+/DgB49dVXMXLkSJ2Pobnq6uoQFRWFX375BSkpKQAawsLFixfjlVde0flao/x91HJihmWxsbHo0aMHfv75ZwQEBNz3ecnJyZg9ezbOnTuH0NBQjdRmSHg3Hh9ERERERETiMRB7AETNpVKp8OyzzyItLQ1OTk545ZVXGBBqwfTp0xETE4PY2FjMnDkTZ86cgZGRkdjDIj1VU1ODxYsX46uvvgIA9OnTBytWrICFhYXII3s4W1tbhIeHIzw8HEBDaJiQkID4+HjEx8cjIyMDly9fxuXLl7Fx40YAgJWVFcLCwoTQsHfv3pDL5UhMTBQCwbi4OCQkJAhr291OKpWiQ4cOQiCovjg5OTUpfKTmKSkpwTfffINPP/0U2dnZABqCpYiICEyePBkdOnTQ+ZjGjRuH8vJyfPvtt3jllVdgbW2t067tr776SggI//nPf7aJgBBo6K4dMGAA+vfvjzNnzmDjxo24fPky3n//fXzyySdYsmQJlixZopfrjJL+CQgI0Fj4R0RERERERNQWMSSkNmfjxo3YvHkzpFIp3njjDb2ZwvBRI5VKsXz5cjz99NOIj4/H8uXL8e9//1vsYZEeysrKwpQpU3D27FlIJBLMmzcPc+bMabNhl62trTDVKNDQ4ZCUlISLFy/i4sWLSE5ORklJCfbv34/9+/cDaJiiVCKRCNNW3s7Y2Bh+fn7o2LEjfH194efnBy8vL4buOpCRkYFPPvkEX3/9tTCtoI2NDSZNmoSxY8fCyspK1PHNnDkTpaWl2Lp1K5555hlYW1tj8uTJWq+7efNm/POf/wQAzJkzB1OnTtV6TU2TSCTo27cvwsLCcOrUKWzcuBFXr17Fu+++i//85z9YsmQJXnrpJdjY2Gik3q1bt3D58mWUlJQ0ur+iokIj2yciIiIiIiIiEgNDQmpTUlJShBObTz31FIKCgkQe0aPN3t4ey5Ytw4oVK/Dxxx9j+PDhiIiIEHtYpEcOHz6MyZMno6SkBBYWFlixYgX69Okj9rA0ytLSEmFhYQgLCwPQMIVoWloaEhMTcfHiRSQlJSE3NxcqlQrW1tZCIKj+083Nrc0GpvpCoVDgxIkT2LFjB65fvw5TU1OYmJgIlztvGxkZYffu3di6dasQ3Hp6emLatGl4/PHHYWhoKPIeNZBIJFi4cCHKy8uxZ88ezJw5E3v27MGwYcO0VnPv3r2YM2cOVCoVJkyYgKeeekprtXRBIpGgf//+6NevHyIjI7Fx40akpaXhnXfewb///W/MnDkTLi4ucHBwuOtib28PuVwubKuiogJXrlzBlStXhO5h9fXCwkIR95KIiIiIiIiISDsYElKbUV1djRkzZqC6uhqhoaGYOXOm2ENqF/r164cJEyZgx44dmDdvHhITE+Hg4CD2sEhkKpUKa9euxYoVK6BUKtGxY0esXr0aLi4uYg9N62QyGTp27IiOHTti4sSJABqmKFUqlbCzs+P0xxpSW1uLo0eP4vfff8eOHTuQn5/fou2EhoZi2rRp6N27t17+3UgkErz00ksoLy/H8ePHMW7cOBw5cgR9+/bVeK0TJ05g4sSJUCgUeOyxx/Diiy/q5XvSEhKJBAMHDkT//v1x4sQJbNy4Eenp6fjuu+8e+Dpra2vY29ujsrISOTk5D3yuvb09rKysGr1nSqUSaWlpGtmH9sbIyAjbtm0TrrcX7XW/iYiIiIiISD8xJKQ2QaVS4cUXX8T58+dhbW2N119/HTKZTOxhtRvPPvss4uPjkZ6ejoULF+L3339/ZE4sU/PFxMRg6dKlOHbsGAAgIiICixcvbtcnO7n+mWbk5uZi37592Lt3Lw4cOIDS0lLhMUtLS/Tr1w8dO3ZEbW0tampqUFtbi+rq6kbX1X+6uLhg0qRJ6Nixo4h71DQymQwrVqxARUUFYmJiMHz4cOzbtw/9+/fXyPZramrw+eefY+XKlaitrUXfvn3x2muvPZIdrlKpFIMHD8bAgQNx6tQpXLlyBcXFxSgpKUFxcbFwvbS0FEqlUrhPzcrKCh06dLjr4ubmBhMTk7vqVVRUYMyYMTrcw0eHgYFBm5zqtrXa634TERERERGRfmJISG3Cu+++i++++w4SiQSvvfYa7OzsxB5Su2JkZITly5fj+eefx/bt2/Hll18K077Soy8vLw8xMTE4d+4cTp8+LazDJ5fL8eKLL2LMmDEMjanF6uvr8dlnn+Hnn39GbGxso8dsbGwwcOBADBo0CF27doWBwaP7zxa5XI63334br776KhITEzF06FBs3rwZkyZNavE2VSoVtm3bhtdeew3p6ekAgO7du2PVqlWP9HsJNISFAwYMwIABA+75uFKpRFlZmRAeGhgYoEOHDrC0tNTxSImIiIiIiIiIxPNonyGiR8KGDRvwxhtvAABefPHFR269s7aiU6dOWLhwIf773/9i8eLF6Nu3L7p37y72sEjDbty4gXPnzgmhYExMzF1T8EkkEgwbNgzz58+Hk5OTSCOlR0FmZiZmz56NEydOCPd17twZYWFh6NOnDzp37vxIdrvdj4mJCT744AO88847OHXqFKZMmYJVq1Zh3rx58PLyata2Tp48iZdffhlRUVEAGqbKnD9/PoYPH85OfDSEiFZWVrCysoKHh4fYw2mX6uvrsX37dgDAxIkTH/ngWq297jcRERERERHpJ/6vlPTaX3/9hX/84x8AgCeeeEJY/4vEMXnyZMTFxeHUqVOYNm0aYmNjYWFhIfawHqp3796NTorb2toiJCQEISEh6Nq1K7p06QJzc3MRR6hbKpUKOTk5uHz5snC5dOkSzp8/j+zs7LueL5FI4OHhgU6dOqFz587o0aNHswMLojv9/vvvePLJJ1FeXg4TExMsWLAAgwcPbvdTtxobG+Ptt9/GJ598gl27duGtt97CW2+9haCgIIwePRpjxoxB37597xssXL16Fa+99hp+//13YXszZ87E1KlT7zldJpFYampqMG3aNABAeXl5uwnL2ut+ExERERERkX7i/0pJb0VHR2PSpElQKpUYPny4EBaSeCQSCZYtW4YFCxbg6tWrWLhwIX755Re9n2ry0qVLd90XGRnZ6Lavr68QHKovrq6uqK+vR319PRQKhXD9zts2NjZwdXXVy/chKSkJ586daxQIXr58GZWVlfd8/u2BoDoU9PPzY7hAGlNZWYklS5bg66+/BgD4+/tj5cqVcHNzE3lk+kMmk+Gll16Cv78/9u/fj8TERFy8eBEXL17EunXrYGNjg4iICIwePRoRERGws7NDUVER3nnnHXz++eeor6+HVCrFyJEjMX/+/HYfvBIRERERERER0b0xJCS9dPbsWQwfPhzV1dXo2bMnli5dqpcBTHtkaWmJN954A4sWLcLmzZsxdOhQvQ9w16xZA2NjYwANXXQFBQVIS0sTLgUFBUhNTUVqaqowBVhzWVlZoUuXLggKCkKXLl2E646OjprclSZJSkrCtm3bsG3bNiQnJ9/zOTKZDK6urujQoYNw8fLygp+fH0xNTXU8YmovLly4gBkzZiApKQkAMHPmTDz11FOQy+Uij0z/SCQSjBo1CqNGjUJZWRmio6Nx+vRpnD17Frdu3cLmzZuxefNmSKVShIWFISEhAeXl5QCAPn36YOHChfD29hZ5L4iIiIiIiIiISJ8xJCS989dff2HSpEmorq6Gv78/Vq9ezamY9EyXLl3wj3/8A9988w2ef/559OnTB8HBwWIP676Cg4NhZmZ238eLi4uFwDA1NRVpaWlIT09HbW1to+dJpVLIZDLhIpVKIZVKUVZWhpKSEpw8eRInT55s9BoHBwchOAwNDcXQoUPh6emp8X1MTk7Gtm3b8Ouvv+LixYvC/XK5HIGBgfDw8ECHDh3g7u6ODh06wMXFhccV6YxKpcIXX3yBl156CXV1dbC1tcXrr7+OHj16iD20NsHCwgJDhw7F0KFDoVAokJSUhDNnzuDMmTNIS0vDqVOnAAA+Pj547rnn0LNnT5FHTEREREREREREbQHPEJNe2bhxI55++mkolUr06tULq1ev5jSHemr69OmIj4/H2bNnMW3aNMTExDwwiNNn1tbWCA0NRWhoqHCfQqFAbW1to0Dwft2stbW1yMrKQnp6Oq5duyb8mZubi/z8fBw7dgzHjh0Tnu/t7S2c8A8PD4eLi0uLxp2SkoJff/0V27ZtQ2JionC/gYEBevXqhSFDhqBfv37tar1F0p7u3btDKpUKt62trRtNS6u+fufnrbCwEPPnz8fOnTsBNHS5vfbaa7C2ttbl8B8ZMpkMwcHBCA4OxoIFC3Djxg3ExMTA3NwcAwYMaLT+KhERERERERER0YMwJCS9oFKpsGbNGqxYsQIAMGzYMLz66qvsdNJjUqkUy5cvx4IFC5CSkoIXXngBGzZsEHtYGiOTyZocUBsaGsLX1xe+vr6N7q+urkZGRoYQHCYmJiI5ORnXrl3D+vXrsX79egANa7KpQ8MhQ4bA1tYWxcXFyMnJES7Xr19vdDs7OxvXr18XahkYGKBnz54YMmQI+vfvz2CQNC4tLe2u+2JiYu66z9XVVQgOPTw88MUXX+D69euQy+V45plnMHnyZE4frUFOTk4YPXq02MMgIiIiIiIiIqI2iAkMiU6hUOBf//oXvvjiCwANa1QtWLCAJ5HbAGtra6xYsQIvv/wyNm7ciPDwcMydO1fsYekNY2NjdO7cGZ07dxbuq6ysREJCAuLj4xEbG4urV68iJSUFKSkpwjFgbGyM6urqh25fJpM1CgYtLCy0ti9E69atE4JzlUqFkpISZGVlCZfs7OxG4fbt3bPu7u5444030LFjR5FGT0RERERERERERHdiSEiiqqysxOzZs7F9+3ZIJBI8//zzmDx5stjDuq/S0lKkpqYKF4lEAn9/f/j7+8PHx6dddj5269YN8+bNw4YNG7Bw4UIEBASgV69eYg9Lb5mamiIsLAxhYWEAGj5T58+fR1xcHOLi4pCeni4EhBYWFrC3t4ednR3s7e0bXbezs4O7uzs7BklnAgMDHzqlcFlZWaPQMCsrC25ubpg9ezanjiaiRgwNDYUZCAwNDUUeje601/0mIiIiIiIi/dT+Eg3SG/n5+Rg7diyioqIgl8uxfPlyhIeHiz0sAIBSqURubi6uXr0qBIJXr17FzZs373ruX3/9BaDhRE/Hjh2F0DAgIACurq7toiNy1qxZuHDhAmJiYvDYY4/hxIkT6Nq1q9jDahMsLS0xcOBADBw4EABw69YtVFZWwt7eHkZGRiKPjqh5LCwsEBgYiMDAQLGHQkR6Ti6X48knnxR7GDrXXvebiIiIiIiI9BNDQhLF5cuXMWrUKKSmpsLCwgLvvPOO6KFSVVUVTpw4gUOHDiExMRFVVVX3fJ61tTWcnZ3h5OQEpVIprBdXXV2Nixcv4uLFi8JzLS0t4e/vj86dO8PX1xd+fn5wcXGBVCpt0RhramqQnZ0NqVSKDh06QC6Xt2g7miaTybB69WosXboUSUlJCA8PR3x8PDw8PMQeWptjY2MDGxsbsYdBRERERERERERERI84hoSkcydPnsSoUaNQWloKZ2dnrF27VrQwSaFQ4Ny5czh48CCOHTuG+vp64TGZTAZHR0c4OTnB2dlZCAaNjY3v2o5KpUJRURGuX78uXPLy8lBaWoqzZ8/i7NmzwnPNzMzg6+sLX19fdOzYEX5+fvD09BSmnFKpVCgsLERWVhYyMzMb/ZmXl9dofB06dICXlxe8vb3h7e0NLy8vuLm5QSaTafFduzdTU1OsXbsWixYtQlpaGiZMmIDIyEiYmprqfCxERESk3+rr67F//34AwIgRIx46ZXtmZiYKCgoeul17e3u9/pJSc/ebiIiIiIiISJv4v1LSqfXr1+O5555DXV0d/P398e6778LW1lanY1CpVLh69SoOHDiAI0eOoKioSHjM1tYWISEh8Pf3h729fZPDNolEAjs7O9jZ2SEkJARAQwB548YNXL9+Hbm5ucjLy8PNmzdRUVGBhIQEJCQkCK+XyWTw9PSEXC5HVlYWKisr71vL2NgYKpUKNTU1yMjIQEZGBo4fPy48LpfL4eHhAW9vb/Tu3RuPP/64zqY8NTc3x7vvvovnnnsOcXFxeOqpp7Bly5Z2MeUqERERNV1NTQ3GjBkDACgvL39gWJaZmYmAgIAH/vtIzdTUFMnJyXobFDZnv4mIiIiIiIi0jf8rJZ2oqanBokWL8PXXXwMABg4ciOXLl8PExERnY8jPz8fBgwdx4MABZGRkCPebmJigS5cuCAkJgZubm8YCLZlMBldXV7i6ugr3KRQKFBQUIC8vr9GluroaaWlpwvMkEglsbGxgb28POzu7Rn+qO/PKyspw8+ZN3Lx5E/n5+cL1uro6YR3FQ4cO4eTJk1i6dCnMzMw0sl8P4+zsjNWrV2PJkiXYtm0bQkJCsGLFCp3UJiIiokdPQUEBKisr8fPPPyMgIOC+z0tOTsbs2bNRUFCgtyEhERERERERkT5hSEhal5OTgylTpuD06dOQSCSYP38+nnjiiRavy9dcSqUSf/75J/773/9CoVAAAKRSKfz9/RESEgI/Pz+dTc8pk8ng5OQEJycnYQ1GlUqF0tJS5OXlQalUwt7eHjY2Ng/9ZrmlpSUsLS3h5+cn3KdSqVBcXIybN28iKysLp06dwvHjx5GamorVq1fDx8dHq/unFhISgkWLFuHf//43Vq5cieDgYIwbN04ntYmIiOjRFBAQgNDQULGHQURERERERPTIYEhIWnXy5ElMmTIFeXl5MDMzw8qVKxEWFqaz+nl5eVi3bh3i4uIAAG5ubggNDUVgYOA91xYUg0QigZWVFaysrDSyLRsbG9jY2KBz587o3LkzfvvtN2RnZ+OZZ57BK6+8goiICA2M+uHGjh2L1NRU/Pnnn5gxYwaio6MRFBSkk9pERERERERERERERPRgumnlonZHpVLhq6++wpAhQ5CXlwcvLy98/fXXOgsIVSoV9uzZg7lz5yIuLg4GBgYYOXIknn76aYSGhupNQKht7u7uWLhwIXx9faFQKLB27Vp88MEHqKmp0Un9F154Ad26dUNVVRXGjRuHwsJCndQlIiIiIiIiIiIiIqIHY0hIGlddXY0FCxbgueeeQ319PQYPHowvvvgCbm5uOqlfUFCA5cuX48MPP0RdXR3c3d3x7LPPonfv3hpbb7AtMTU1xaxZszBkyBAAwN69e/H8888jOztb67UNDAzw1ltvwdnZGWlpaZg+fTrq6+u1XpeIiIiIiIiIiIiIiB6M042SRtXU1GDkyJE4duwYpFIp/vGPf2DGjBk6C+cSExPxyiuvoKamBlKpFI899hjCwsJ0tv6hvpJIJBg8eDDc3d3x66+/IjU1FfPnz8eHH36IkJAQrda2srLCu+++i+effx6HDx/Gu+++i1WrVmm1JhFRU6jXhL116xaKi4vv+vP26zY2NnjssccQHh4OS0tLsYeuUQqFAiUlJSgqKsKtW7dQVFSEsrIyVFRUoLKystGf6uvq24aGhnB2doazszNcXFzg4uIiXHd0dHzo+rpERERERERERCQenrkhjVGpVHjmmWdw7NgxmJmZYdWqVejVq5fO6kdFRWHFihVQKBRwdnbGxIkT4ejoqLP6bYGPjw+ee+45/Pbbb8jKysLLL7+MDz74AN26ddN63UWLFmHt2rX4/vvv8eabb7bLrk5qW1QqFaqrq1FVVSVc1Lerq6sbXa+qqoJMJoOlpaWwxqj6YmlpyaBEDxUVFeG1117DlStXmvT87OxsXLhwAZ9++in69++P4cOHo0+fPpDL5VoeqWbU1tbixIkTuHLlihAEqv8sKSmBUqls8baLioqQlJR01/1SqRQODg6NQkMnJyfh4ujoCENDw9bslk7V19ejsrKy0X133qamMzQ0xOeffy5cby/a634TERERERGRfuJZS9KYNWvW4Mcff4RUKsVbb72Fnj176qy2ukNNpVLBz88PU6dO5YmX+7C0tMScOXOwZcsWpKWl4ZVXXsHatWvRo0cPrdYNDw/HJ598gszMTMTGxmq9HgElJSX4+++/kZeXB4VCAaVSKVzuddvb2xsTJ058JNbsVCqVqKqqQnl5OSoqKhr9qb6u7ogqLy9v1CV1e9dUa4KT25mZmQmBoZeXF0aMGIGQkJB23+UslrKyMixduhRpaWkAABMTE5iZmQkXU1PTRrfNzMyQk5ODhIQE5OXl4cSJEzhx4gQsLS3x2GOPYfjw4ejcubNefvmhuLgYO3fuxJ9//omioqIHPle9r+bm5jA1NYWRkdFDL7W1tXd1Xaov9fX1uHHjBm7cuIHz58/fs6atra0QHjo7O8PKygpyuRxyuRwGBgYwNDQUbsvlchgaGsLAwABWVlZwdnbWakirUqmQlZWFmJgYxMTEIC4uDtXV1Vqr197I5XI8//zzYg9D59rrfhMREREREZF+YkhIGvHLL79gxYoVAIBFixbpNCDcsWMHPvnkEwBAly5dMGHCBMhkMp3Vb4vkcjlmzpyJbdu24cqVK3j11Vfx7rvvIiwsTGs1jYyM0KdPHxw/fhx//PEHQ0Itqa6uxqlTp3Do0CGcOXMGKpWqWa/fsWMHFi5ciPDwcL0JPFQqFbKzs5GXl4eysjKUl5ejrKzsntdLS0uF0K+5+/4g6qDC0NBQuNx+Wy6XQ6lUorKyElVVVcJ0jOouI3X4mJOTg5SUFOzbtw8uLi4YMWIERowYAWdnZ42NlR6sqqoKy5cvR1paGszNzfHUU0/B1tb2oa/z9PRE3759hcDrwoULKC0txfbt27F9+3Z4eHhg+PDhGD58OBwcHHSwJw927do1/P7779i3bx8UCgUAwMLCAgEBAbCwsIC5uXmji6mpaYtDa1dX17vuU6lUKC8vF8LDkpISFBcXo6SkRLjU1dWhqKgIRUVFSElJaXZdqVQKZ2dnuLq6okOHDnBzc4Obmxs6dOgAFxeXFnXwlpaWIjY2VggGb9y40extEBERERERERG1FQwJqdUOHTqEJ598EgAwbdo0jBs3Tme14+PjhYCwV69eGDlypN4EG/rOwMAA06ZNw2+//YZLly5hxYoVWL16NQYMGKC1mgMHDhRCwnfffVdrdXSlrq4OycnJAICAgADRph1UKBSIjY3FwYMHcfToUdTX1wuPubi4wMPDA1KpFFKpFBKJBBKJRLiu/lOpVCI2NhY3b97EO++8gz///BMvvvgi/Pz8dL4/5eXlSElJQVJSEpKSkpCcnIzS0tIWbUsmk8HY2BhGRkYwNjZudP32bqgH3ZbL5S3+uaJUKlFdXS2Eh+Xl5bh69SouXLiA3NxcbNy4ERs3bkT37t0RERGBgQMHwsTEpEW16OHq6uqwatUqXLx4EYaGhpg9e3aTAsLbOTk5Yfjw4Xj88ceRlpaGhIQEJCUlITMzE9999x3Wr1+P/v37Y8KECQgNDdXp7ySVSoXo6Gj8+uuviImJEe53cXFBWFgYgoKCdPYlGolEAgsLC1hYWMDd3f2eY62qqmoUHhYXF6O6uhoKhQIKhQL19fWN/rz9enl5Oerq6pCTk4OcnJxG+wv8L0C0t7cXjn1jY2OYmJjc8091x+CdYaVUKoWXlxd8fHzg6+t71zTmNTU1WLdunebfwHZAoVDgxIkTABr+fdBevuDVXvebiIiIiIiI9BNDQmqV+Ph4jB8/HvX19Rg6dCgWLlyos9oVFRV4//33AQDdunVjQNgCBgYGmDp1Kv744w8kJSXhzTffxBtvvIHw8HCt1AsLC4NcLkdKSgqSk5MREBCglTraou5oi46OvmvqOWNjY3Tv3h29evVCr1694ObmptXPo0qlwqVLl3Do0CEcOXIEt27dEh6ztrZGcHAwgoODm9XR1LdvX5w6dQonTpxAQkICnnnmGYwdOxbz58+HlZWVNnYDSqUSmZmZuHjxIpKTk3Hx4kWkp6ff9TwDAwPY2tre9wT/nX+qL2KvBSiVSmFqagpTU1PhvoCAAERERCA5ORnx8fG4du0a4uLiEBcXhw8//BDDhg1DREQEunTpwp9pGqRQKPDuu+8iOjoaBgYGmDNnDpycnFq8PalUCj8/P/j5+WH06NFISkrC+fPnkZGRgcjISERGRsLDwwPjx4/HiBEjYGZmpsG9aaympgYHDhzA77//joyMDOF+f39/hIWFwcPDQ+8+SxKJRDg2XFxcmv16dadiUVERCgsLhY5E9eX2ALG5HB0dhVDQ09PzgV8A4ZTBLVddXS38e6O8vFyrx4g+aa/7TURERERERPqJISG1WHp6OkaOHInKykp0794dy5Yt0+nJsv/+97+4ceMGrK2tERERoXcnQNsKmUyGyZMnQyaT4cKFC3jnnXdQX1+PYcOGabyWmZkZQkNDERUVhe3bt+ssJHzjjTcafVPfxMQEjo6OcHR0hIODg3Dd1tb2rm/0l5eX49y5c4iJiUF0dPRdU8+ZmZlBpVKhsrISp0+fxunTpwEAzs7OQmDYvXt3mJubt2ofampqcPnyZSQnJwvddTdv3my0T0FBQQgJCUGHDh1adDzI5XIMHjwY3bp1w8GDB3Hx4kXs3LkT+/btw8KFCzF+/PhWdTyoVCrcuHEDKSkpSElJweXLl3H58mVUVFTc9Vxra2u4u7sLUwc6Ozs/Ut0WcrkcISEhCAkJQXFxMc6fP4/z58/j1q1b2Lt3L/bu3QsHBwd069YNXbt2RdeuXbUePD/KVCoVPv74Yxw/fhxSqRQzZsxAhw4dNLZ9IyMjdO/eHd27d0d+fj6io6MRFxeHzMxMfPbZZ/jyyy8xatQoTJgwAd7e3hqpWV5ejrNnz+LEiROIiopCVVUVgIbPVmhoKPr06QMbGxuN1NJHt3cqenp6Nnrs9gCxoqICtbW1qK2tRV1dXaPrt982MzODj48PfHx8YGlpKdJeERERERERERHpFkNCapHCwkJEREQgLy8PPj4+ePvtt2FoaKiz+idPnsRff/0FAJgwYQKMjIx0VvtRJJVKMWHCBBgYGCAuLg7vvfce6urqMGrUKI3XGjhwIKKiovDHH3/g9ddf1/j27yUuLq5Jz5NKpbCzsxPCw/z8fCQlJTVa204qlcLT0xM+Pj7w8/MTOpHy8vKQmpqK1NRUZGZmIi8vD7t27cKuXbsgkUgQFBQEf39/WFtbw8rKSvhTfTE3NxdCMHXH4u2B4JUrV+5aY08mkyEgIADBwcHw9fXVWIhmZWWFKVOmoFevXvjrr79w48YNfPbZZ9i1axdeeOGFJq8nqV5nLCUlBZcuXcKlS5dQUlJy1/PkcnmjtcQ6dOjQ6lC1LbG2tsbgwYMxaNAgZGZmIj4+HomJicjPz8fBgwdx8OBBAIC9vT1CQkKE0FAfu8P01TfffIM9e/ZAIpFg8uTJ8PX11VotBwcHjBo1Co899hjOnz+P6OhoFBQUYOfOndi5cydCQkIwYcIEDBw4sNmdroWFhTh58iQiIyNx7tw5KJVK4TFra2v07t0b3bt3h7GxsaZ3q025PUAkIiIiIiIiIqL7Y0hIzVZVVYVx48bh0qVLcHR0xPvvv6/TE/rFxcX46KOPAAD9+vW7q4OAWkYqlWLs2LGQyWSIiYnBBx98gLq6OowfP16jdfr16wepVIpz584hIyNDJ39/o0ePbjRdXHV1NUpLS1FaWoqSkhLhulKpRH5+PvLz8xu93t7eXggFPT097xmIu7i4wMXFBQMGDEBtbS3S09OF0LCwsBCJiYlITEy87xilUiksLCxgZWWF4uLie67BZ2Zmhg4dOghhmpubm1bDeU9PTzzzzDOIjY3FoUOHkJ6ejldeeUUIp5RKpbBO2L3WEKutrb3nfjo5OcHV1RVubm5wdXWFg4MDp+xDQ7Dh6ekJT09PjBo1CllZWUhPT0dGRgays7NRUFCAI0eO4MiRIwAAGxsbhISECGtO3mutydvXobwzULzX38+jaNOmTdiyZQsAYOzYsQgMDNRJXSMjI/Tu3Ru9evVCeno6oqOjkZKSgoSEBCQkJEAmk8He3h729vZwcHC458XOzg45OTnC9KVJSUmNatjb28Pf3x/+/v5wdXVlaExERERERERERM3CkJCaRaFQ4IknnsCpU6dgbm6OtWvXNmvNs9ZSqVT497//jVu3bsHR0VFra+e1VxKJBKNGjYKBgQHOnDmD//znP6itrcXUqVM1VsPGxgbBwcE4f/48tm/fjsWLF2ts2/cTHBz80G5TpVKJiooKITQsKSmBkZERfH19m70en6GhITp16oROnToBaAi2U1NTUVRUhMrKyrsu1dXVUCqVKCkpETrtZDKZEKSpQ0ErKyudhwBSqRQ9e/ZEUFAQjh07hujoaGRmZjbptRKJBPb29kIY6OrqCicnJ9HXCWwL5HK5MPUhANTV1SE7OxsZGRlCaHjr1i0cP35cZ2PKycmBiYmJcNvS0lLvp2XcuXMnvv32WwDAsGHD0L17d52PQSKRwNvbG97e3igtLcW5c+dw7tw5VFRU4MaNG3dNYXzna+/sIHZzcxOCQXt7e20Pn4iIiIiIiIiIHmE8U0vN8t5772HHjh2Qy+V499134eXlpdP6kZGROHHiBKRSKSZOnMiwQQskEgmGDx8OAwMDREZG4osvvoCVlRWGDx+usRoDBw7UaUjYFOpOPm1MT2dtbf3AKToVCgWqqqpQWVmJiooKGBkZwcnJSa/W4DMxMcHIkSMRFhaGoqIiSKVS4SKTyRrdVl9MTU11Og3xo0wulwtBEwDU19fj+vXryMjIQFlZGYCGL1Hcfrn9vtunpVRTKBRITk5u8hieeeaZu+7z9vZGaGgounfvjq5du+rVNLGHDx/Gxx9/DKDhZ06/fv1EHlFDsBoeHo7BgwejvLxc6GK+16WsrAxKpRISiQQ+Pj7o3Lkz/P39OYUmERERERERERFpDBMWapZNmzYBAP71r38hJCRE5/XVXTO9e/eGs7Ozzuu3FxKJBEOHDoVKpcLJkyexYcMGDB06VGOhbP/+/fH555/j77//xr59+xAREaGR7bZVMpkM5ubmehWw3I+NjQ1sbGzEHka7Z2BgIExN2lI1NTXNCgkNDQ0bdbLW1NTg2rVruHbtGn7//XdIJBJ07twZoaGhCA0NRZcuXRp18KpUKhQXF+P69ev3vEgkEnTo0AHu7u7CxcPDo8nT6lZVVeHWrVsoLi7GpUuX8NlnnwEAevbsqXdd51Kp9KGdmCqVChUVFZDL5Vx3l4iIiIiIiIiItIIhITXZ9evXkZKSAqlUisGDB+u8vkqlwvnz5wFAmMaRtEcikWDw4MGIj49HXl4eDh06pLEwz9nZGcOGDcPBgwcxYcIEHDp0CAMGDNDItolIO5YsWdIorKqsrER6ejrS0tKQnp6OwsJCpKSkICUlBZs2bYJUKkVISAisrKyQk5OD69evo7Ky8oE1kpOT7xlcuri4CAGisbExiouLUVxcjKKiIuF6dXX1Xa/r2rUrRo0a1SbX6pNIJG3iiwNELSWXy7Fu3TrhuiY97AsQzfmCRFOfb29vDw8Pj4c+T5v7TURERERERNRcDAmpyY4ePQoA6NixoyjTnWVnZ6OgoABSqRTu7u46r98eyeVyhIWF4fDhw9i0aROGDRumsSkwly5ditLSUkRFRSEiIgKRkZHo1q2bRrZNRNpnamqKwMBABAYGAgBKSkqQnp6Oa9euIS0tDWVlZYiPj7/rdVZWVrC1tb3rolKpUFhYiIKCAhQWFgrXa2pqkJubi9zcXERHRz9wTAYGBjAzM4OZmRl8fHwQHh7eJgNCovbA0NAQS5cu1eg27e3tYWpqitmzZz/0uaampg9d17O520tOTn5oUKiN/SYiIiIiIiJqKYaE1GSHDx8GAISGhopSPy4uDgDg7u7OtQh1qFevXjhx4gSysrJw4sQJDBkyRCPblcvleOutt7Bs2TIkJCQgPDwcUVFR7BIlaqOsrKzQtWtXdO3aFSqVCkVFRbh27Rrq6upga2sLOzs7WFtbP/Dnt5OTU6Pb6ik3bw8PFQqFEASqL+bm5jAzM4NcLmcoSNSOeXh4IDk5GQUFBQ99blM6/5q6veTkZMyePRsFBQVN6iYkIiIiIiIi0hdMWqhJVCoVjhw5AgDo3r27KGNQTzXq5eUlSv32ysjICH379sXx48fx888/Y/DgwRo7CW9sbIx3330XS5YswZUrV/D4448jMjKSJ9iI2jiJRAI7OzvY2dm1ejvq9Tpbs/4iEekfhUKB2NhYAA1fQNPUTAUeHh4a/XeEprenrf0mIiIiIiIiagmp2AOgtiE1NRWZmZkwMDBAcHCwzuurVCqhk9Db21vn9du7Pn36QCaTITU1FZcvX9bots3NzbFu3Tq4u7sjKysLw4YNw82bNzVag4iIiPRLdXU1evfujd69e99zTdFHVXvdbyIiIiIiItJPDAmpSdRdhIGBgTA2NtZ5/czMTNy6dQsGBgZwc3PTef32zsTEBP7+/gCAQ4cOaXz71tbW+PDDD+Hk5ITLly8jIiICJSUlGq9DREREREREREREREQNON0oNQnXI6QuXbrg4sWLOHbsGJ599lmNT4/l6OiIDz74AIsWLUJcXBzGjBmD/fv3w9TUVKN1iIiISL/Ex8fDxMTkvo8nJyfrcDRERERERERE7QfTFnoopVKJo0ePAhAvJIyPjwfA9QjF5OfnB2NjYxQUFODChQvo1q2bxmu4u7tj3bp1WLx4MSIjIzFlyhTs2LEDhoaGGq9FRERE+mHAgAEPfY6pqSns7e11MBoiIiIiIiKi9oMhIT1UfHw88vPzYWxsLEw5qUu1tbVtMiSsr69HWVkZSktLYW5uDjs7O7GH1CoGBgYICAhAXFwcDh06pJWQEGgII9esWYOlS5fir7/+wvLly/HRRx9ppRYRERGJ77vvvkP37t0f+Bx7e3t4eHjoaERERERERERE7QNDQnoodUDTp08fyOVyndfftGkTSkpKYGFhoVfrEdbU1ODGjRsoLS1FSUkJSktLG12vqKho9HwfHx+EhYXBz88PEolEpFG3TpcuXRAXF4dTp05BoVBofMpRteDgYLz88st47733sHv3boaEREREjzB/f3/RZqvQpKZMi8pp1ImIiIiIiEifMCSkB0pMTMTmzZsBALNmzdJ5/ezsbPz0008AgBEjRmgtlGqutLQ0YVzNeU1aWhrs7OwQFhaGrl27ihK6toanpyeMjY1x69YtJCUlITg4WGu11B0FV69eRXV1NYyNjbVWi4iIiKil7O3tYWpqitmzZz/0uQ9ae5GIiIiIiIhI1xgS0gOtXLkSKpUKgwYNQseOHXVaW6VS4ZNPPoFSqYSvry8CAwN1Wv9+Yzpz5gwOHDjQ4m0UFhZiz549OHLkCHr06IFevXrB0tJSg6PUHplMhk6dOiEhIQGRkZFaDQnt7Oxgbm6O8vJyXLp0CV27dtVaLSIiItItuVyOZ555Bt988w0MDNr2f0k8PDyQnJyMgoKCBz4vOTkZs2fPxjPPPAMXF5c292UxIiIiIiIievS07f+Rk1adPn0af/75J6RSKZ5++mmd1z927BhiYmIglUoxatQo0aforK+vx+7du3H+/HmNbK+qqgqRkZFC2BYWFgZXV1eNbFubOnfuLISEzz77rNb+XiQSCby8vJCYmIiLFy8yJCQiInqEGBoaYuHChfjmm28eibDMw8OjyWsmLly48JGYXpWIiIiIiIjaPoaEdE8qlQrLly8HAERERDT5pIemFBYW4vPPPwcADBw4ELa2tjqtf6fS0lJs27YN169f18r2L1y4gAsXLsDPzw+TJ0/W66k1/fz8IJPJkJOTg2vXrsHHx0drtW4PCYmIiJqitLQUqampSEtLg0qlgpeXF7y8vGBnZyf6F46IiIiIiIiIiPQJQ0K6pwMHDuD48eOQy+WYN2+eTmvX1NRg5cqVKCoqgoODAwYMGKDT+nfKzs7G1q1bUV5ervVaV69exY8//ohZs2bBzMxM6/VawtDQEL6+vrh8+TIiIyO1HhICYEhIRET3VVtbi4yMDKSmpiI1NfWuKR/Vv0MsLCzg5eUFb29veHl5wcbGRozh0v+nVCqRmpoqXG9PUlNTYWRkhICAAEilUrGHQ0RERERERO0YQ0K6i1KpFLoIJ0yYAEdHR53VVqlU+OCDD5CSkgIjIyPMmDFD1HVq4uLisHPnTp3WzM3NxcaNGzFnzhy9XavQ399fCAnnzp2rtTrqkPDcuXPYunWrcH9lZaXWahIRkX5TqVTIy8sTugWvXbvWpNeVlZUJnfsAYGVlJQSGPj4+sLCw0Oaw6Q5VVVWYNm0agIYviLUn6v0uLy/X2y+FERERERERUfvAkJDu8uuvvyIuLg6mpqaYNWuWTmtv2rQJhw8fhkQiwfTp00WbZlSpVOLAgQOIiooSpX5BQQE2bNiAuXPn6mWnQ6dOnSCRSHDlyhXk5eXB2dlZK3W8vb0BNHRzzpgxQys1iIioeerq6pCSkoKEhASUlZXB1NT0oRdzc3ONdEwVFBRg27ZtyM/Pb/W2SkpKEB8fj/j4eAANa8oFBgYiICBAb7+kQ0RERERERESkSQwJqZG6ujq88cYbAIDp06fDyspKZ7UjIyPx3XffAQBGjRolBES6VllZid9++63JnQnaUlxcjO+//x5z5szRaTdnU5iZmcHDwwMZGRk4efIkJk+erJU6tra2eOqpp4QTuGoKhQIJCQlaqUlERHdTqVTIyclBXFwcEhMTm935ZWVlhTFjxsDPz6/FY7hy5Qo2bdrU4tc/TGZmJjIzM7Fv3z64u7sLgaEu/y1ERERERERERKRLDAmpkZ9//hlXrlyBtbU1pkyZorO6mZmZWL16NQCgV69e6Nmzp85q366urg7ff/89CgsLRal/p/Lycnz55Zd45ZVX9G46Kn9/f2RkZODEiRNaCwkBYO7cuXdNaVpRUYExY8Zoraa21dTUoKSk5L4XQ0NDeHh4wMvLC56enpwCj4hEU1RUhAsXLiAxMfGutf6ao6SkBL/88gsiIiLQp0+fZr8+NTVVqwHhnbKyspCVlYX9+/fD3d0dXbp0QWBgIMzNzXU2BiIiIiIiIiIibWNI2EqpqanYunUrUlJS8Pjjj+Oxxx6Dm5tbs7dTU1PT6Fv5paWlmhxmk23ZsgUAMGXKFJiamuqkZn19Pd577z3U19fD29sbEREROql7L6dPn9abgPB2p06dwrBhw8QeRiP+/v7Yv38/zp8/j4KCAtjb24s9JL05ju6kUChw+PBhpKamoqSkpEkdOPn5+Th37hyAho5KT09P4WJtbd2kuvX19aiuroZcLoeRkVFrdkFramtrUVxcjKqqKlRXVwuXO29XV1fD1NQUPj4+6NixIzt7tEhfjyNqPYVCgerqaiiVSqhUqgf+mZGRgcTERFy/fl2jY9i3bx98fHzg4ODQrNcdP35co+NoDnVg+Ndff8HOzg4SiUR4TKlUijYuIiIiIiIiIqLWYkjYComJiRg+fDhCQ0NRU1OD559/Hi+88ALWrFkDpVLZrLV31qxZI3TSiaW0tBRHjhwBAAwaNEhndX/66SdcunQJhoaGmDBhgkbWLGqJsrIyHD16VJTaD3Pq1Cn0799fZ8FtU1hbW8Pd3R1ZWVk4duyY1jpPz5w5gwsXLjS6r66u7p7P1Yfj6E5KpRI7duxAYmJii7dRVFSEoqIixMXFAWiYts/LywuWlpZ3BWm3B2z19fXCNqysrODo6NjoYm9vDwMDcX4N3Lp1C2fOnMHZs2eb9brk5GQAgIODA/z8/ODn5wcPDw/R9uNRpI/HEbVcXV0drl69iuTkZFy6dAm1tbViDwk7duzA008/3eTf99evX0dWVpaWR9U0+vhFIiIiIiIiIiKiluJZ1RbKysrCtGnTMHfuXLz//vsAgK1bt2L+/Pl4+umnm73mzvLly7FkyRLhdmlpKdzd3TU65oc5cOAA6uvr4e7urrPaSUlJ+OmnnwAAY8eOhaWlpU7q3os6INVXp0+fxmOPPSb2MBrp0qULsrKycPjwYa2EhDU1NXjzzTfvGwreSR+Oo9upVCrs3LmzVQHhvZSUlOD8+fPNfk1JSQmuXLnS6H57e3s4OjrCwcEBPj4+8PDw0ORQ75KdnY3Tp08jKSmpVdvJz89Hfn4+Tp8+DblcDm9vbyE0tLGx0dBo2yd9O46o+Wpra3HlyhUkJyfj4sWLYg/nLjk5OTh16hQGDBjQpOefOXNGyyMiIiIiIiIiImqfGBK2gFKpxL59+xAYGIgXX3wRKpUKKpUKI0aMgKenJwoLC5sdEhoZGYk+HeCuXbsAAH379tVJvaqqKqxZswYqlQrBwcHo0qWLTureS05ODuLj40Wr3xSRkZHo168fTExMxB6KIDAwEPv27UNKSgquX7/eoql2H6S0tBR1dXWQSqX417/+JdxfU1ODL7/88q7n68NxpKZSqbBr165mh3m6VlBQIKwzdvz4cfj6+uLxxx+Hs7OzxmoolUpcvnwZp0+fRmZmpsa2q1ZXV4fLly/j8uXLAABLS0vY29s3utjZ2cHCwqLRNIGPAqVSiZqaGshkMkilUshkslbvoz4dR/qgpKQEhw8fRmVlJXx9fdGpUyfY2dmJPay71NTU4PLly0hKSkJKSorYw3mow4cPo1OnTnB0dHzg80pKSjT+RQvSD3K5HHPmzMFPP/3U7rrB58yZAycnJ8jlcrGHQkRERERERO1c+/ofuYZIpVJ4e3uj2/9j777DojjXv4F/l6V3adIERIogKKBg72KNsfcWNcYkGmPUE02OaaZYUoxJPInGqCeWHDWJsWPDjgVFURFERYogICC97+77h7+dV2wg7O4s8P1c11wss7Pz3LPuAM499/0EBAhJEYlEAjMzM5SXl+PevXto3769yFG+HJlMhn379gEAOnXqpJEx16xZg3v37sHMzAwDBgzQyJjPolAocPDgQdHGfxnnzp1Dz549xQ5DYGpqCnd3d9y5cwdHjx7F5MmTVbr/wsJCAI/m5Fu5cqWwPj8//5lJQm2hUCiwf/9+oT1ofXLnzh3cuXMHbdq0Qc+ePes0919FRQWuXLmCc+fOIScnR4VRvlh+fj7y8/ORkJBQZb2+vr6QMLSxsUGLFi1UntjWBIVCgZSUFERHR+PGjRsoLS2t8rwyWaj8qlwkEkmVBKJCodB06PWKQqFAVFQU9u7dK6y7c+cODh06BGtra3h5ecHb2xvNmjUTrU028KgVZ0RERJ2rc8WgbDsqlUqfu83LtiSm+kNfXx9z587Fpk2bGl2ybO7cuQgKChI7DCIiIiIiIiImCWurT58+6NWrF4BHFxKVF14NDQ2rXCzcuXMn3NzcEBgYKEqcNXX27FlkZ2fDzMxMIxV9Fy5cwK5duwAAQ4cOFbU6LjY2Vi3VTepw8uRJdOzYEYaGhmKHIvDz88OdO3cQHh6OSZMmqbRSS5kktLS0VNk+1U2ZdL548aLYodRJdHQ0oqOj0alTJ3Tp0qXG52hlZSWSk5Nx+/ZtXLlyBSUlJWqOtObKy8uRlpaGtLQ0AMCxY8cwYMAAhISEiBxZzeTk5CA6OhrXrl3Dw4cPn7udXC6HXC7XYGQNT15eHnbv3v1UolkpOzsbZ8+exdmzZ2FkZAQPDw94eXnBw8NDIz+fFQoFEhMTcfr06efGWB/cv38fZ86cee48yOXl5YiIiNBwVEREREREREREjQeThDV09+5dHD16FIWFhWjZsiX69+8PHR0dVFZWQldXF3K5XKgmVM6r98EHH2D16tW4evWqyNFXb/fu3QCADh06vPCOflXIy8vDihUrAAAhISFwd3dX63gvUllZicOHD4s2fm2cP38e3bt3FzsMQcuWLbFnzx4kJSXhzp07L91q90XqW5JQoVDg6NGjOH/+vNihqExERAQiIiIQGhqKkJCQp1rCKRQKpKenIyEhQVjqkwMHDiA3NxehoaFa2Yq0pKQE169fx9WrV3Hv3j2xw2nwnlU9WJ2SkhJcu3YN165dAwA0b94cnp6eamlLqlAoEB8fj9OnTzeYz8OxY8fg5eX1zBbH9bEam2pOLpcLN200thsb0tLSYGVlBRcXF1ErkYmIiIiIiIiYJKyB69evo3v37ggMDERsbCwsLS2xYsUK7NmzByYmJgAgJAyLiopQXl6Ozz77DD/88ANOnDgBNzc3cQ+gBjQ5H+H333+P7Oxs2NjYoE+fPmof70XOnz+P3NxcUWN4WcePH0eHDh20Zs4wQ0NDeHt7IzY2FkePHm3UScLjx4/jzJkzYoehFocPH8aFCxfQq1cvuLi44O7du0JSsLi4WOzw6uTs2bPIy8vDsGHDtGZerDt37uDixYv1Ym65hqK66sGaunv3Lu7evYtDhw7ByspKSBi6urrW+iYcuVyOmJgYnD59GpmZmXWKTxvt2rULr7/+epX3Ry6XN6gbLuhpJSUlGDx4MIBHc2o2JsrjLiwsFP4vQURERERERCQG7bgaqsWKi4vx5ptvYsyYMfjPf/6D3NxcnDt3Dv/617/Qvn17HD16FE2bNhXugDYxMcH8+fNx9+5dnD59Gm3bthX5CKp369YtxMXFQVdXF8HBwWod6/Tp0zh+/DgkEgmGDRsm6hw0hYWFOHLkiGjj18WFCxfQtWtXscMQ+Pn5ITY2FseOHcOMGTNUdld8fUoSnjp1CidPnhQ7DLXKy8vDzp07xQ5DLW7cuIGCggKMHTsWxsbGosVRXl6OsLAwVlBpUG2qB2sqJycH58+fx/nz56Gvr48WLVrAy8sLnp6e1SYGFAoFKioqcO3aNZw5c+aFLWbru/T0dJw6dQo9evQQ1sXHxzfoYyYiIiIiIiIi0gZMElajrKwM+fn5CA0NBfAoWdGvXz+0aNECY8eORb9+/XDlyhXo6OigsLAQRUVFuH//Pi5cuAB/f3+Ro6+ZdevWAQDatGkDU1NTtY0jl8vx448/AgA6d+4MR0dHtY1VEydOnBB1/Lo4e/YsOnbsqDVVT56entDX10dGRgZu3Lihsnkt60uS8NatWwgPDxc7DKqjlJQUrF+/HpMmTYKFhYXGx4+Pjxfan5LmHDp0COfOnVP7OOXl5YiNjUVsbCwACK1IZTLZMxeFQqH2mLTJiRMn4O7uDhcXF6SlpdW7VuBERERERERERPURJ8Gohrm5OeRyOY4dOyask0gk8PT0xIYNG1BSUoLZs2cL23788cc4c+ZMvUkQrlmzRpgfcMCAAWodq7i4WGiT1qVLF7WOVZ3S0lJcvHhR1BjqoqSkRKtazunp6cHV1RUAkJSUpLL9Ki+Sa/t8Penp6WKHQCqSnZ2NDRs2IDs7W2Nj5ubm4n//+x/++OMPJgg1rKSkRCMJwmfJzs5GdnY2cnNzUVBQgOLiYpSVlaGysrLRJQiVNmzYgL179+LXX39FTk6O2OEQERERERERETV42n3lXWQKhQJSqRSjRo3CxYsXsX///irP+/v7Y9y4cbh69SoKCgoAACNHjoSvr68Y4b60zZs346233gIATJw4Eb1791breI+3FpVIJGodqzpXr14VdXxV0LbElLJ1nirbwxkZGQEAioqKVLZPdTAzMxM7BFKhvLw8/PTTT8jIyFDrODKZDKdOncKqVatw8+ZNtY5Fz3bt2jWxQ6AnXLp0SewQiIiIiIiIiIgaDSYJX0CZyJo0aRIUCgVWr16N48ePV3ne19cXaWlpKC0tFSnKl6NQKHDkyBF0795dOK4hQ4Zg2rRpah/78daYMplM7eM9j0KhqNdVhEramiRUZSWUMkmobDuqrczNzcUOgdTgl19+wb1799Sy74SEBPz8889sUysi5VyEREREREREREREjRWThNVQKBRwd3fH2rVrkZycjBUrVmDjxo0AHs1XeOHCBTg6OgrJDG2lUCiwf/9+dOrUCaGhoTh58iT09PQwZswYzJkzRyOVfTo6OsI4YiYJk5OT8eDBA9HGVxV1Vzm9LHVWEjJJSGL57bffcPv2bZXtr6CgAH/99Rc2bdqk0Zam9LS7d+9q3c9RIiIiIiIiIiIiTdKtfpPGQTkH0OMtMeVyOXR0dCCXy+Hv749t27Zh8eLF+PLLL7F48WJ4eHjg6tWrCA8Ph6mpqYjRP59cLsfu3bvxxRdfCC289PX18corr2Ds2LGwtbXVWCwSiQS6urqoqKgQNUnYEKoIgUeVhAqFQvTWrUqNuZKQ7UYbti1btsDBwQFBQUHw9/eHgYHBS71eLpfjzp07uHz5MmJjY9UUJb2sU6dOiR0CUaOmq6uLUaNGYceOHZBKpWKHo1GjRo2Cra1tlS4bRERERERERGLg/0wB3LhxA5999hnS0tLg4eGBvn37Yty4cdDR0YFMJoNUKoVcLoevry/Wrl2LxMREHDhwAM7OzujatSs8PDzUFtuoUaOqJC7NzMzg6uoKNzc3uLq6CosyQaMkl8vx119/4YsvvhDm3zM0NMSQIUMwevRoWFlZqS3mFxE7SVhUVITr16+LMraqlZeXIzc3F02aNBE7FACNO0loYGAAAwMDlJWViR0Kqcn9+/exb98+7Nu3DwEBAWjbti2cnJxemKTPzs7G5cuXER0drfWf4cYmOTkZiYmJYodB1KgZGBhg0aJF2LFjB/T19cUOR6MWLVqEoKAgscMgIiIiIiIiYpIwPj4enTp1wuDBgxEaGoqjR4/i66+/xqFDh7BhwwZIpVKUl5cLFy9sbGxgY2ODdu3aaSS+Q4cO1Wg7GxsbIXnYrFkzHDx4UKhYMTY2xrBhwzBq1ChYWFioM9xq6enpoaSkBHK5XJTxL1++LMq46pKens4koZYwNzdvEG1sqXpXrlzBlStXYGdnh6CgILRu3Vr4rJaVlSEmJgZXrlxBSkqKyJHS87CKkIiIiIiIiIiIqJEnCRUKBX7//XeEhoZi06ZNAIAFCxZgw4YNWLNmDcaMGYNt27YJCcINGzagT58+aNasmcZifPfdd4XWdgqFAoWFhUhPT0dGRobwtaioCFlZWcjKyhJaigKAqakpRo4cieHDh2tNO0RlOykxKgkVCkWV96chSE9Ph4+Pj9hhAKiaJFS26q2r+pQkNDMzY5KwkcnMzERYWBjCwsLQunVrSCQSREdHix0WVSMtLU2l80wSUe0oFAphHmOFQiFyNJr18OFDPHjwADY2NlrTNp6IiIiIiIgaJ5UmCfft24dBgwapcpdqJZFIkJqaivT0dGGdsbExpk2bBkNDQ6xevRoffPABli5dioiICHz11VcIDw/Hxo0bNTZ3Smho6FOtRJ/0rMShra0tBg4cWO1rNU3ZOlWMJOHt27dVWuWmDTIyMsQOQWBsbAzgUavbgoIClVSt1qckobm5udghkIiUbZ1J+50+fVrsEIgIQHFxMfr06QMAKC0tFTkazVIed2Fhodb9rU5ERERERESNi0qThBMmTMC5c+fQsmVLVe5WLRQKBSQSCYKCgnDz5k3ExcUJcRsZGWHUqFGIj4/HsWPH8PDhQ3Tq1Anvv/8++vTpo7EEYU2ZmprCw8NDrXMjqoqu7qOPXGVlpcbHbmitRgFUSXCLTSqVCvPy5ebmqjRJWFZWhoqKiirzc2obbanWfVJwcDD69u2L4uJiPHz48JlLUVGR2GESacTDhw+FVtxERERERERERESNXd37AT4mPz8fr776aq2qtZStPzVF2dpn4MCBuHXrFlasWIGCggLheXNzc8ydOxeRkZEIDw8HAMyYMQPNmzfXWIwNkbJVa1xcnMbHbmhVhACQl5cn2vyOT8rNzUVZWRkA1VXVVVRUAHh0vmp7Oy5t/NkwZMgQDBw4ELq6ujA3N4erqysCAgLQs2dPDB8+HNOnT8eCBQvwwQcfYOrUqSppEUukzfgZJyIiIiIiIiIi+v9UerXM0dERd+7cwahRo14qcfHPP//A398fU6dOVWU4NdKiRQts374dW7duxQcffICsrCzhOX19fQQGBsLa2lrjcTVUQ4YMAQBcvHgR5eXlGh1bWcXY0BQXF4sdAgAgJiYGABAQEIAmTZqoZJ/KxK6NjY3W//s1b94cb731Ftzd3cUOBQDQv39/BAQE1GhbfX19uLi4CPOvEjVUFhYWmD17NhwcHMQOhYiIiIiIiIiISHQqTRLu2rULhoaGCA8Px9y5c6vd/ujRo+jQoQNGjBiBGzduqDKUl9KzZ0/s2LED69atwxtvvIE//vgDMTEx+Prrr3Hv3j20aNFCtNgamvbt28PZ2RkVFRWIjo7W6NjanmSqLW2Zr0+ZJOzZs6fK9vnw4UMAgJ2dncr2qU52dnaYOHEixowZA0tLS9HiaNOmDUJCQl76ddrWSplIHaytrTF9+nR07txZ7FCIiIiIiIiIiIhEpdKsSdu2bbFhwwaMHTsWq1evRuvWrfH6668/td2FCxfw4Ycf4tixYwAetRoFIGrF3uDBgxEREYF58+Zh0aJF0NXVhZ6eHg4cOCC0yBTDhx9+WOXCfYsWLTB58mStnf+sOjo6OhgyZAhWr16NyMhItGvXTmNtJJkkVJ+cnBzcv38fEokE3bp1U9l+61uSEHjUGrVly5bw8PBARESE8HNOUxwcHDBo0KBanVcN9RwhepJUKkWfPn3g5eWFgwcPIi0tTeyQiIiIiIiIiIiINE7lk/OMHj0aixcvhkKhwOzZs3H69GnhuRs3bmD48OHo2LEjjh07BoVCAYVCAQsLCyxZsgQJCQmqDuelBAUFYffu3Thx4gR27dqF06dPIzAwUNSYrl69isuXLwvLn3/+ialTp+LMmTOixlUX/fv3h66uLh48eIDExESNjdtQq6S0IUl4/fp1AI9uFFBlBZ2y3Wh9ShIq6erqolu3bnjvvffg7++vsXFHjx4NPT29Wr2WSUJqbFxcXPD6669jxIgRolb/EhERERERERERiUEtV4SXLFmC69ev459//sHIkSOxbds2rF+/Hlu3boVcLhcqB01NTfHuu+9i/vz5WnNxztzcHObm5mKHIfjtt99gbGwMACgpKcGyZcsQHx+PxYsXo1evXpgzZw4sLCxEjvLlmJqaYtCgQdi1axcuXLiA5s2ba2TchpoA0YYkoTpajQL/v5KwadOmKt2vJpmbm2P48OEICAjApk2b1DrW5MmT6/SztKGeI0QvIpFI4Ofnh5YtWyIyMhKHDh0SOySiRkFXVxevvPIK9u7d22Bv5HqeV155BdbW1vy9S0RERERERKJTeSWh0ubNm+Hv74/MzEz06tULmzdvhkwmg0KhgJGRERYsWIC7d+/i888/15oEoTYaOXIkxo4di7Fjx2Lq1Km4cuUK3n//fejo6CA8PByvvfaaUJVZnwwdOhQAcPPmTaFaTN0a6oWYoqIiUcd/8OABMjMzoaOjg65du6p03/W5kvBJ7u7umDt3LhwdHdWy/759+9Y54d5QzxGimtDV1UXHjh3x/vvvo2PHjmKHQ9TgGRgY4LPPPgMA6OvrixyNZn322WfYuHEjDAwMxA6FiIiIiIiIGjm1JQmNjY2xe/du2NraCm1FDQwM8M477yAhIQErVqwQdQ7C+srIyAjLly/HuXPn4Ofnh9zcXCxZsgSffPIJcnJyxA6vxtzc3BAUFASFQoHIyEiNjNlQ71IXu5JQ2Wq0ffv2Kp8rsyElCQHAwsICU6dORVBQkEr36+fnhw4dOtR5Pw31HCF6GUZGRujbty/mzJkDPz8/scMhIiIiIiIiIiJSG5UmCS9evIiysjLhe1dXV/z111/Q19eHRCLBvHnzsGrVqnrdOlBbBAcH49KlS/jkk08glUpx6tQpvPbaazh48GC9qSocNmwYAODy5cuoqKhQ+3gNtUpKzCShQqFQW6tRAELiu6EkCYFHn8PBgwdj8ODBKtlf06ZN8eqrr0IikdR5Xw31HCGqjSZNmmDEiBGYMWMGXFxcxA6HqMFRKBQoKSkRHjcmJSUlKCoqanTHTURERERERNpHpUnCkJAQmJmZoXXr1pgyZQpWrVoFuVyOL7/8EgqFAitWrMCRI0dUOWSjpq+vj08//RSXLl1CUFAQCgoKsGzZMsydO1eo7tJmHTt2hL29PUpKSoREkzo11ASImEnCjIwMZGdnQ0dHB506dVL5/pVzEjakJKFSUFAQZsyYUec5RceMGQM9PT2VxNRQzxGiunB0dMRrr72GV199VexQiBqU4uJidOnSBQBQWloqcjSa1aVLF5iamqK4uFjsUIiIiIiIiKiRU3m70crKSsTExGDz5s2YN28eevbsiffffx8SiQQymQxjx47F7t27cf/+fVUP3Wi1adMG58+fx9KlS6Gnp4erV6/i3XffxbZt27T6DmWpVIrQ0FAAQGJiotrHMzIyUvsYYsjKyqpSwatJ165dAwB07twZJiYmKt13cXExMjMzAaDOc+1pK0dHR7zxxhu1Or6QkBDMmzcPTZo0UVk8yqQsEVUlkUgQGBiIBQsWwN/fX+xwiIiIiIiIiIiIVEKlScKpU6ciMDAQenp6wjyEygV4dJHt4cOHGDZsGJydnWFvb4/+/fvjgw8+wPbt2xEfH6/KcBoVXV1dLFq0CAkJCZgwYQLkcjl++eUXfPXVV6IlkGrC29sbADSSNFZlMkXbaCLJ+iS5XC4kCfv27avy/cfHx0OhUMDFxQX29vYq37+2MDY2xoQJExAQEPDC7UxMTBAQEIDRo0dj0aJFGDBggErngCwpKcGDBw9Utj+ihsjExATDhw9Xy888IiIiIiIiIiIiTVNpb7nffvsNwP+vJrx8+TKioqJw+fJlREdHP9UWMTMzE4cPH8bhw4cBPEoiVlZWqjKkRsfZ2RmbNm1Chw4d8O677+LIkSNISkrC559/rpVzQXp5eQEAHjx4gIqKCpW1TXyWhpwkvHv3rpBw1ZTExEQUFBTAzMwMISEhKt9/bGwsAKhl39pGKpXi1Vdfhbm5OU6ePCmsd3R0hKenJ7y8vODg4KCSeQefJyUlRW37JmpoOnbsCAsLC+zYsUPsUIi0VnJyMrKysp77vHI+QiIiIiIiIiISj1omoNLV1UWbNm3Qpk0bvPbaa8L6W7du4fLly0Ly8MqVK6xcUQOJRILZs2fDz88PQ4cOxa1bt/DWW2/h008/RevWrcUOrwobGxtYWloiNzcXmZmZcHJyUttYDT1JqGnKKsIePXpAX19f5ftXJgnbt2+v8n1rI4lEgp49e8LJyQnFxcVo0aKFSisFq5OcnKyxsYgaAl9fX0ydOhUbNmwQOxQirZOcnAwfH58az7lnbW2t5oiIiIiIiIiI6FnUkiR8Hk9PT3h6emL06NHCutTUVCFxePnyZU2G0+D16NEDV65cwdChQxEdHY158+Zhzpw5ePXVV8UOTSCRSODp6YnIyEjcv39frUlCQ0NDGBkZNcg71zMzM1FUVKTyeQGfp6KiAjExMQAgzCupanFxcQAaRyXh45TVtZrGSkKil+fi4oJZs2Zhy5YtyM3NFTscIq2RlZWF4uJibN68GT4+Ps/cpqSkBF26dAEANGvWTJPhEREREREREdH/0WiS8FmcnJzg5OSEV155RexQGiQ3NzecOXMG06dPx7Zt27By5Urcvn0b77zzjlpbe74MLy8vIUmobk2aNGmQSULgUTWhn5+fRsa6efMmKioqYG9vr5Yxs7Ky8ODBA+jo6CAoKEjl+6eqKioqWElIVEs2NjaYPn06tm7dqpHfY0T1iY+Pz3N/jxcVFWk4GiIiIiIiIiJ6ko7YAZD6mZiY4I8//sDSpUshkUiwZ88ezJ8/Hzk5OWKHBuBRhSkAjSUJGypNthxVthoNDQ1Vyzx5yirCVq1awdTUVOX7p6oSExPFDoGoXjM1NcVrr7323IopInqaVCrFyJEjMXLkSEilUrHD0ajevXs3yuMmIiIiIiIi7cMkYSMhkUiwaNEi7N27FyYmJrh27RpmzpwpJGPEpGyvmJmZCZlMptaxmCSsu6KiIty6dQsA0KdPH7WM0djmIxRbfHy82CFUy9HREQMGDBA7DKLn0tfXx6hRozBixAixQyGqFwwNDbFjxw7s2LEDhoaGYoejUStWrGiUx01ERERERETaR/R2o6RZAwcOxKVLlzB06FDExcVh1qxZGDJkCKZMmQILCwtRYrK3t4epqSkKCwvx4MED2Nvbq22shpwkfPjwIXJzc2FpaanWcWJiYqBQKODt7Q0XFxe1jNFY5yMUg0KhEJK+2srMzAxjx46FmZkZHBwcsH37dhQWFoodFtFTJBIJ/Pz80Lx5c4SFheH69etqH9PKygodO3ZEixYtUFRUhIKCAhQWFgpfH39cVFQEhUKh9piIiIiIiIiIiKh+YJKwEfL29sa5c+cwY8YM7NixAzt37sSRI0cwY8YMDBo0CDo6mi0wlUgk8PT0xOXLl3H//n0mCesgKSlJ7UnCx1uNqoNCocDNmzcBAMHBwWoZg/6/rKws5OXliR3GCykThADQrFkzvPHGG9i+fTvu3bsncmREz2ZiYoIRI0bAz88Pe/fuVUtS28nJCZ07d4a3t7fwe7u633GlpaXYvXu3UK1NROKoyTloY2OjtpuxiIiIiIiIiJSYJGykLCwssH37doSHh+O9997D1atX8d133+HgwYOYO3cuPDw8NBqPjY0NAKC4uFit46gzAakNDAwM1D7GgwcPAABt27ZVy/4lEgkMDQ1RVFSElJQUBAQEqGUcekQTn5m6GDFiBBwdHausMzMzw5QpUxAWFoZLly6JFBlR9by9veHq6oqDBw/iypUrKtmnp6cnOnfuDBcXl5eeE9bQ0BCjRo3CqVOncOzYMZXEQ1RbRUVFwrzDhYWFMDExETki9bOxsYGRkREmTpxY7bbGxsaIjY1lopCIiIiIiIjUiknCRq5Xr164dOkSVq9ejQ8++AAxMTGYOXMmhg8fjqlTp8LY2FgjcVRWVgIAdHXV+5E0MjLCoEGDsG/fPrWOIwYLCwthfkd1kcvlKCsrE8ZTl549e+LPP//Epk2bMHjwYLWNQ4C5uTlmz56N//3vf8jKyhI7nCp69uwJPz+/Zz6nq6uLV155BQEBATh27BgSEhI0HF39M3HiRDRr1kz4vqysDN99952IETUOhoaGGDJkCHr16oVbt27h1q1buHPnDioqKmq8Dy8vL3h5ecHT0xPm5uZ1ikcikaB9+/ZMEhKJwMXFBVFRUfDx8QEAnD59GkZGRk9tFxsbi4kTJyIrK4tJQiIiIiIiIlIrJgkJurq6ePfddzFy5Ei899572LFjB/78808cP34cs2fPRrdu3V66WuFlKZOEUqlUreMAjyrgYmJikJiYqPaxNKljx45qbxVbWloqPFa2f1SHvn374s8//8Q///yDvLw80ebLbCysra0xceJE/PrrrygqKhI7HABA69at0bVr12q3c3Z2xqRJk5CYmIjw8HCkpKRoILr6p3379mjRokWVdZybTrPMzMwQFBSEoKAgyGQy4efp4/8Oz3psZGQEPT09lcZiYGAAR0dHpKWlqXS/RFS9x2/WCAgIaBQVlERERERERKS9NDv5HGk1JycnbN++HQcOHIC7uzuysrLw6aef4oMPPsD9+/fVOrayokITSUKJRNLgqtMMDQ0RGBio9nGUF7WNjIzUWvXp4eEBV1dXVFRU4M8//1TbOPT/WVhYYNy4cWKHAQBwdXXF4MGDX+rmBDc3N0ydOhW9evVSY2T1k5WVFXr37i12GPQYqVQKExMTmJiYwNTUVFjMzMyExdzcHObm5ipPECqxOomIiIiIiIiIiJgkpKf0798f169fx0cffQRdXV2cP38eb731FnJzc9U2piYrCYFHF8379eunkbE0ITg4GPr6+mofp6SkBIB6qwiBR4ncvn37AgA2bdqk1rHo/3NycsLIkSNFjcHKygqjR4+uVRJaIpGgS5cu8PT0VENk9dfQoUPVlmii+svV1VXsEIiIiIiIiIiISGRMEtIzGRkZYcmSJbh+/Tp8fHyQl5eHDRs2qG08TScJASAkJATOzs4aG0+dQkJCNDKOppKEANCnTx8AwIkTJ5CcnKz28eiRVq1aoWfPnqKNP378+DrNhSqRSDB06NA6z9vWUHTq1KlKazsiJVYSEhERERERERERk4T0Qt7e3vjll18AAHv37sWdO3fUMo6y3ai659R7nI6ODl599VWNjacuQUFBMDU11chYyiShJhIwdnZ2CAgIAABs2bJF7ePR/9e1a1e0bt1a4+NOmTIF1tbWdd6PsbGx6BWR2sDW1lbUhC9pN2NjY9jZ2YkdBhERERERERERiYhJQqpWt27dMGrUKMjlcqxevRoKhULlY4hRSQg8uohe3+cw69Spk8bGUiYJNZWUDA0NBQAsX74cISEhwsLEh3op5+3UZAXakCFD4ObmprL9NWvWTPj8NFZDhw5V69yhVP+xmpDEJJVKMXDgQAwcOFDjf/+JqbEeNxEREREREWknJgmpRlasWAE9PT1cvnwZp06dUvn+lZWEYlws6dSpExwcHDQ+riq0bNlSJZVXNVVaWgpAM5WEwKMEtampKfLy8hAZGSksUVFRGhm/MdPV1cWYMWNgaWmp9rH69+8vVI2qUseOHeHl5aXy/dYH3bp1g6Ojo9hhkJbjvIQkJkNDQ+zbtw/79u2DoaGh2OFoTGM9biIiIiIiItJOTBJSjbi5uWHhwoUAgF9++QUymUyl+xerklA5Zn1tO9q5c2eNjqfpSkJTU1P8+uuv+Oqrr6osH3/8sUbGb+xMTEwwfvx4tY4xadIktG/fXi37lkgkGDJkSKObn9De3h7dunUTOwyqB+pbktDU1BTOzs7w9fXV2O8hIiIiIiIiIqKGjH3IqMYWLVqEn3/+Gffv38eFCxfQsWNHle3bzMwMAJCfn6+yfb4Me3t79OrVC+Hh4aKMXxuenp5wdnbW6JiZmZkAHr1fmmJvb//UeEVFRRobv7GztbXFxIkTsXnzZpXu197eHqNHj0aTJk1Uut8nGRsbY9SoUfjtt9/UOo42GTp0KFvYUY2YmZnBw8MDt2/fFjsUgaurKywtLWFhYVHlq7m5eZX2uZWVlbh+/ToiIiLw4MEDESMmIiIiIiIiIqq/mCSkGjMxMcHkyZOxcuVK7N+/X6VJQi8vL8TExOD+/fto3bq1yvb7Mrp27YomTZrgr7/+EmX8l+Hv74/BgwdrdEy5XI579+4J41Pj0aJFC8yYMQM7duxAbm5urfZhaGgIV1dXuLm5oXnz5rCzs4NEIlFtoM/h7OyMuXPn4siRI7h+/bpGxhTLq6++iqZNm4odBtUjY8aMwenTp3HixAnRYvD19YWPjw88PT1hYGBQo9fo6uoiICAAbdq0QVpaGmJiYhATEyPazUb08oqKimBnZwfg0U1IJiYmIkekGY31uImIiIiIiEg7MUlIL2X69OlYuXIlIiIikJOTAysrK5XsVzlv2P3791Wyv9ry8/ODu7s7rl69iqioKK2rTmjatCn69+8PNzc3jY+dkZGB8vJymJiYiDI+icvR0REzZ87EoUOHkJ6eXu32CoUCZmZmcHNzg5ubG+zt7aGjI16HawsLC4wYMQIhISEICwtDWlqaaLGoS1BQEAIDA8UOg+oZXV1d9OjRA76+vtizZ49wM4g66evrw9vbGz4+PvDw8ICenl6t9yWRSODk5AQnJyeEhoYiJSUF169fx40bN1h1Xg8UFxeLHYIoGutxExERERERkfZhkpBeSqtWrdCxY0ecPXsWBw8exLhx41SyX29vbwCPkoQKhUJjFUbPYmxsjA4dOqB9+/ZITU1FVFQULl++LFo8SoMGDUJQUJBoiZbk5GQAjz4DbGXYOBkaGtbb+TuVmjVrhtdff12oOkpISEB5ebnYYdWZo6MjBgwYIHYYVI/Z2dlh6tSpiIyMRFhYmMr3b2hoCG9vb/j6+sLd3b1K61BVkUgkcHFxgYuLC/r374+kpCQkJSUhLS0NaWlpKkkaPvn3iUKhqPM+iZ4nNja22m1sbGzg4uKigWiIiIiIiIioIWKSkF7a66+/jrNnz2Lfvn0YO3asShJ6Li4uMDAwQFlZGbKzs2FjY6OCSOtGIpHA2dkZzs7O6N+/P2JiYhAVFaWRKovHhYSEoEePHjAyMtLouE9KSUkBwFajVP9JJBL4+fnBz88PMpkMKSkpuHXrFu7cuYOMjAyxw6uVUaNGqSXpQo2Ljo4O2rdvDz8/P8TFxSE+Ph7x8fG13p+ZmZlQMejq6qrRG0x0dHTQvHlzNG/eHMCjZF5+fj5SU1OFpGFaWhrKysqeeq2BgQGsra1hZWUFKysrWFtbC98/+bu4rKwMy5Yt08gxUeNhY2MDY2NjTJw4sdptjY2NERsby0QhERERERER1QqvKNJLGz16NGbPno3U1FRER0cjICCgzvuUSqXw8PBATEwM0tLStCJJ+Dh9fX0EBgYiMDAQDx48wOXLlxEdHa3WdlHu7u7o16+fMG+NmBQKhVBJ6OfnJ3I0RKojlUqFlqihoaHIz8/H7du3cfv2bSQkJDwzgSBGjGZmZjAzM4O5uTlMTU1hbm4ufG9vb1/jedyIasLExARt27ZF27ZtUVFRgbt37woJw4KCghe+1sbGBi1btkTLli3h6OgoameAx0kkElhYWMDCwgK+vr4AHv1uy8nJQVpaGmQymZAQNDY21pq4qXFycXFBbGwssrKyXrhdbGwsJk6ciKysLCYJiYiIiIiIqFaYJKSXZmpqiokTJ+LXX3/F/v37VZIkBB7NSxgTE4P79++jdevWKtmnOtja2qJv377o06cPMjMzkZqaKiyZmZl13n+TJk3Qt29feHt7a81Fyvz8fBQUFEAikaBly5Zih0OkNubm5ggKCkJQUBBkMhnu3buHhw8forS0tNpFoVDAwMAA+vr60NfXh56envD48UVXVxe6urqQSqVPfX38saGhIczNzWFkZKQ1Pwuo8dHT04OXlxe8vLygUCiQnp6OmzdvIj4+XphH2NnZGd7e3mjZsqXW3eTzIhKJRKgSJNI2yta5REREREREROrEJCHVyowZM/Drr7/ixIkTeOedd2BmZlbnfXp5eQGAcNFR2+no6MDe3h729vZo27YtAKC8vBxpaWlVEof5+flPvc7Q0BAGBgYwMDAQHhsaGsLe3h7t2rXTuraByharnp6eMDQ0FDkaIs2QSqVwdXWFq6ur2KEQaQWJRAIHBwc4ODigR48eKCwshEQigYmJidihERERERERERFRLWhXJoLqjXbt2qF169a4evUqjhw5gmHDhtV5n97e3gAeJQkVCkW9rJzR19cXWhcqFRYWorS0VEgG6urq1rtjUyYJlS3aiIiITE1NxQ6B6jEdHR10795deNxYNNbjJiIiIiIiIu3E/5lSrUgkErz++usAgIMHD6pkny4uLjAwMEB5eTkePHigkn1qA1NTU9jY2MDU1BR6enr1LkFYUFCA+Ph4AEwSEhERkWoYGRnh+PHjOH78OIyMjMQOR2Ma63ETERERERGRdmKSkGqtffv2AICcnByV7E8qlQrzG0ZFRalkn1Q3KSkpWLt2LXJycmBgYIB27dqJHRIREREREREREREREakA241SrWVmZgIAmjRporJ9jhgxAufPn8elS5fQvXt33mEtEoVCgUuXLuHAgQOQy+VwdXXFF198odJ/a6o5mUyG4uJiFBUVVflqZGSEFi1acD4w0jpyuRzZ2dlIS0uDgYEBPDw8tG6uVSIiIiIiIiIiosaOV+yo1tLT0wGoNknYrl07uLu7IyEhAZcuXUKXLl1Utm+qmcrKSuzfvx+XL18GAHTv3h0LFy5kwlaNysvLkZmZiYyMDGRkZCAvL09IBBYXF6OsrOyFr3dycoKHhwc8PT3h6OhY71raUv2mUCjw8OFDpKamIi0tDffv38f9+/dRXl4ubGNkZITWrVsjMDAQTZs2FTFaItIWRUVFwhzOiYmJjeaGl8Z63ERERERERKSdmCSkWsvIyAAAWFlZqWyfEokEo0ePxrJly3D+/Hl06NCB1ScalJ+fj+3btyM1NRUSiQQzZszA2LFjmXRSEYVCgby8PGRkZCA9PR2ZmZlIT0+vUcteHR0dWFhYwMLCApaWlrCwsEBaWhpu3bqF1NRUpKam4sSJEzA2NhYShi1atGByl1QmKioKenp6wve5ublCUrC0tPSp7Q0NDeHh4YH09HRkZWXh/PnzOH/+PBwdHREYGAg/Pz8YGhpq8hCISMtkZWWJHYIoGutxExERERERkfZh9oVqTZkkVHULyl69emHdunXIysrCtWvXEBgYqNL907MlJSXhf//7H0pLS6Gvr48vvvgCwcHBYoclOoVCgZKSEqGq71lLRUUFZDIZ5HJ5leXJdXl5ec+tCrSysoK7uztatGgBJycnWFpaCslAS0tLmJqaQkfn6Wlks7KycOHCBZw/fx4XL15EcXExrl69iqtXr0IikcDZ2Rm+vr5o27ZtlQQP0cs6dOjQc5/T09ODh4cHvL29hcXFxQVSqRQymQyRkZHYv38/Tp8+jbS0NKSlpSEsLAytWrVCQEAA3NzceDMCERERERERERGRhjFJSLWmbDeqykpC4NHF5hEjRmDNmjU4e/Ys2rRp88zkCKmGQqFAZGQkwsLCoFAo4O7ujs8//xyOjo5ih6ZRFRUVyMjIwP3795Geno779+8jLy8PJSUlUCgUKhtHV1cXrq6ucHd3h4eHh5AYrG2y3cbGBgMHDsTAgQNRUVGBmJgYnDt3DufPn0diYiJSUlKQkpKCM2fOoEuXLmjbti2rc6lWnqzstrS0FBKCbm5uz01CS6VSdOjQAR06dEBubi4OHz6M/fv3IzExUUhoN2nSBG3atIG/v7/Kf6cQERERERERERHRs/FKMdWauioJAeCVV17B+vXr8eDBA1y4cAEdOnRQ+Rj0KDG2b98+REdHA3hUxblgwYIG36KytLRUSAQqv2ZlZb0wGWhiYiK0+3xyMTIygq6uLqRSqfD1ycdSqRSWlpZwcXFRW0Wfnp4eAgICEBAQgDfffBMZGRmIiIjA9u3bkZ6ejrCwMERERKBr164IDAyEVCpVSxzUMC1evLjOc2dZWlpi1KhRGDlyJOLi4nDgwAGEhYXh4cOHOH78OI4fPw4nJyf4+/ujVatWMDU1VVH0RERERERERERE9CQmCanW1FVJCACmpqaYPXs2Vq5ciaNHj6Jly5awtLRU+TiNWXZ2NrZv347MzExIJBK89dZbGDlypFa1/JPL5fj1118RFxdXZb1MJnup/Xz33XdVjut5LT+bNGkCLy8vYU6/Zs2awdLSEmZmZvWyVWfTpk0xbNgwvPLKKzhw4AA2bdqErKws7Nu3D2fOnEH37t3RunVrra3UlcvlyMnJQUZGBjIyMoRWuPr6+tDT06vy9fHH5ubmDT7RXd9JJBL4+PjAx8cHb7/9Nk6ePInDhw/j0qVLwhybBw8ehLu7O/z9/dGyZUsYGBiIHTYREREREREREVGDwiQh1ZqyklBdreFeeeUVHDlyBNeuXcP+/fsxbtw4rUpg1WcxMTHYtWsXKioq0KRJE3z88ccICAgQO6yn/P333/jf//5X5/2Ul5c/tc7e3h6enp7w9PSEh4cHvLy8YG1tXeextJGenh5effVV9O/fH7t378bWrVvx8OFD7Nq1C6dPn0b37t3RqlUrUZOFZWVlQjIwPT0dGRkZyMzMREVFRa32Z2Njg2bNmqFZs2ZwcXGBlZUVf35oKUNDQ/Tt2xd9+/ZFTk4Ojh07hqNHjyI2NhZ37tzBnTt3IJVK4e3tDX9/f3h4eLBlbi0oFArk5eVBLpfDwsKClcRUa8nJycjKynrhNrGxsRqKhoiIiIiIiIjqglfZqFbKysqQl5cHAHVuP/c8Ojo6mD9/PqZNm4Zbt27h7Nmz6NSpk1rGaiwUCgWOHj2KM2fOAADatGmDjz76SOuSY3K5HLt27cKaNWsAAAsXLkRgYKDwfHFxMaZNm1bj/a1du7ZKZZm5uTnMzc1VF3A9oa+vj5EjR2LQoEHYtWsX/vjjD2RnZ+Pvv/9GREQEBg4ciGbNmmkkFrlcjtTUVNy+fRt37txBWlraM9u9GhgYoHnz5sK8jaWlpSgpKanyVbmUlJSgpKQEubm5yMrKQlZWFi5fvgwAsLCwgK+vL1q1agVHR0cmDLWUlZUVRowYgREjRiA1NRVHjx7FkSNHkJKSghs3buDGjRvQ09NDs2bN4ObmBjc3Nzg6OjLh9QyVlZVIS0tDcnKyMDdpSUkJgEeVnNbW1rC1ta2yWFtbMwFLL5ScnAwfHx8UFxdXu62xsTFsbGye+7yOjg7atWsnPG4sGutxExERERERkXaSKF40CReJJj8/HxYWFsjLy9PKZIZCoYC7uzsSExPxyiuvYP78+Woba+vWrfj1118BAJ07d0bv3r15gb8WKisrsWvXLly/fh0AMH78eEybNk3rLq6npKTg66+/xrVr1wAAY8aMwR9//FHl37ym54dyu71796otmV2fFRcX46+//sKmTZuEij0XFxd07NgRXl5eKr94WVhYiNu3bwuJwdLS0irP29jYoEWLFsLi4eEBJyenl/6M5uXlISYmBtevX8f169cRExMDuVwuPK9MGHp7e8PR0bFetpJVhbKyMixbtkzrzyOFQoFbt27hyJEjOHbs2FMVTMqkoaurK9zc3Gr8mVEoFJDJZJDL5dDR0YGOjg4kEkm9/f1SVFQkJANTUlKQmppa5XMPALq6utDR0XlmdTXwKHloZWUFW1tbODk5oVWrVmqZd7ghednzSFv/rqupqKgotG3bFps3b4aPj88Lt7WxsYGLi4uGImuclP8ely5dQlBQkNjh1FpDOT+IiIiIiIjqI94uTrUikUjw22+/oXfv3ti7dy86duyotiq/cePGQaFQYN26dThz5gwKCwsxePBgrUtuabOSkhJs27YNSUlJkEgkWLRoEfr27St2WFXIZDJs374dGzZsQEVFBYyMjPDtt99i5syZ9faivbYzNjbGpEmT8Morr+C3337DgQMHkJycjOTkZFhZWaFDhw4ICAiodRJNJpPh3r17QmJQOY+pkqmpKdq2bYvg4GAEBwfDzs5OFYcFCwsLdOrUSfiZVFZWhgsXLuD48eM4efIk8vLycPbsWZw9exYSiQRNmzaFk5OTsNjY2LC6Q4tIJBJ4eXnBy8sLb775JpKSknDlyhVER0fjypUryMvLQ0JCAhISEgA8SoS5uLjA0tIS5eXlL1yeTKIpx1MmDR9PHhobG8PW1hY2NjZVKu9e9vxQKBTPrIQtKyt75vcVFRWQyWRCQlP5+Ml1yirBxzVp0gR+fn7C4unpCV1dXTx48ACJiYlISkpCUlISEhMTkZiYiKKiImRnZyM7OxtxcXE4evQoHB0d4efnh1atWvHiPQl8fHzqdVKKiIiIiIiIiB5hJaGWqi931C5YsADffvstLC0t8dtvv6ltfkIAOHDgAL7++msoFAp4enpi5MiR0NfXV9t4DcXDhw+xdetWZGVlQU9PD0uXLkXbtm3FDquKhIQErFixAjdv3gQA9OvXD2vWrIGrq+szt2cloXpkZWVh586d2LNnDwoKCgAARkZGaNeuHUJCQmBqavrc1yrnO0tPT8f9+/dx//59JCcno6ysrMp23t7eCAkJQXBwMHx9fTWe7C8tLcX58+dx8uRJREdHIzs7+6lt9PX14ejoCCcnJzg7O8Pe3h4WFhYNLlldXyoJX0Qulz8zaahJTZo0qZI81NfXR1FREYqLi4WvysfK79X1p5erq6uQEPT393+p1roKhQLZ2dlISkrC3bt3ce7cOURFRVWJ1cXFBa1atYKvr+8Lfx7URnl5OVJTU2FiYqKyGwY0obFWEtb3yrWGoqH8ezSU84OIiIiIiKg+YpJQS9WX/yyXlZUhODgY165dQ8eOHfHll1+q9UL62bNn8dFHH0Emk8HJyQnjx4+HsbGx2sar79LS0rBp0yaUlpbC1tYWy5cvR/PmzcUOS1BRUYEtW7Zgy5YtqKyshKmpKX788UdMmTLlhZ8jJgnVq6SkBGFhYfjzzz+RlpYG4NG8Sa1bt0bHjh1hY2ODnJwcIRmYnp6O9PT0Z1YyWVhYIDg4GCEhIWjXrp3WtS588OABbty4gbi4OMTGxuLmzZtPtUEFHlWnWVtbC4uNjY3w1cDAQITI664hJAmfpFAokJiYiOjoaOTn58PY2BhGRkZPLY+vl0qlL6zQk8lkqKysFBJoiYmJSE5ORlJSEvLz82sdq6GhIczMzGBiYgJTU1OYmpo+87GhoSF0dXWhp6cHqVQKPT096OrqPrVYWVmp/O+FnJwcnDx5EseOHcPVq1eF9RKJBG5ubmjVqhU8PDxgZmb20tW3JSUlwvuYnJxcZV7S1q1bo0+fPjAzM1Pp8agDk4S1V1xcDF9fXwDAjRs3Gs3fc6o8biYJiYiIiIiIqK6YJNRS9ek/y9euXUPbtm1RUVGBefPmYfDgwWodLyYmBvPnz0dZWRmsra0xceJEWFpaqnXM+ig+Ph47duxAZWUlWrRogaVLl8LW1lbssAQ3btzAt99+K7QIHDJkCH7++Wc4ODhU+1omCTVDJpPhzJkz2L59O2JiYoT1enp6whyGj5NIJMJcgl5eXvDx8VHL3IbqJJPJkJiYKCQNY2NjkZycjMrKyue+xtTUFNbW1nB2dkabNm206jx7kYaYJNQkhUKB3NzcKonDxMRElJeXw9LSsspiYWEBS0tLNGnSBBYWFrCwsKh3lfAPHjzA8ePHER4ejri4uCrP6ejowNzcXDhO5TEqj9/c3BzFxcVVkoKZmZlPjWFjYyPMOamnp4cePXqgffv2Wt1enEnC2isqKhIqUgsLCxvNzxdVHjeThERERERERFRXnJOQ6szf3x/Lli3D/Pnz8Z///AfBwcGwt7dX23itWrXCmjVr8P777yMzMxO//fYbxo8fX6PkUmMRGRmJAwcOQKFQIDg4GJ9++qlW3KFfWVmJkydP4u+//xaSThYWFlizZg1Gjx7d4No51ndSqRTdunVDt27dcOPGDWzfvh0nT55ERUUFDA0NhYSgp6cnPD094ebmVu8SH0+SSqVo0aIFWrRogUGDBgF4lDi8f/8+UlJSkJKSguTkZOHxw4cPUVhYiMLCQiQlJeHMmTNwdnZGQEAAWrVqBUNDQ1GPR6FQICUlBXFxcU9VvclkMpGiahgkEgmaNGmCJk2aICAgQOxw1M7W1hajRo3CqFGjkJaWhuPHj+P48eNISEiATCZDbm6ukDStKRcXF7Ru3RqtW7eGv78/7O3tERcXh1WrViEuLg6HDx/G5cuXMWDAALi7u6vx6IiIiIiIiIiIGidWEmqp+nZHrVwuR8+ePXHy5Em0b98eS5cuVXvC58GDB1i4cCHu3r0LALCyskLz5s3h7u4ONzc3rUiKaZpCocDhw4dx9uxZAMCAAQMwb9486OqKez9ATk4O9uzZgz179ghzwEmlUkyaNAkrVqx46corVhKKJysrC8XFxXByctLq6h5NKSwsREpKCpKSknD69GlEREQILROlUilatWqFgIAAuLm5aSwJLpPJkJSUhNjYWMTFxaGwsPCF2/M8orqQyWTIyclBeno6MjIynrmUlpZCIpHA09NTSAj6+/s/t/2wXC5HWFgYfv31V+Tm5gIAfHx80LdvX5V1DlAoFEhLS8Pt27dx584dyGQyNG3aFPb29nBwcEDTpk1rdNMDKwlrj5WErCRUaijnBxERERERUX3ESkJSCR0dHaxduxb+/v44f/48jh07hl69eql1TFtbW6xatQrLli3D2bNnkZOTg5ycHFy6dAkAYG9vj+bNm6N58+ZwdXWt9xVO1amoqMA///yDGzduAACmT5+OCRMmiFadp1AocOPGDfz99984efKk0K7R3t4eM2fOxMyZM1n9WQ/Z2NiIHYJWMTU1hY+PD3x8fNC/f3/k5OTg8OHDOHDgAJKSknD16lVcvXoVlpaWCAgIQJs2bdTSHrmyshJ37txBXFwcbt68WWV+SBMTE3Tq1Omp1q/l5eVYs2aNymOhxkUqlcLW1ha2trbw9/d/6nmFQoH8/Hzo6enV+OYdHR0dDBw4EN26dcOGDRuwc+dOxMbGIj4+Ht26dUOnTp1qdfNLUVER7ty5IyQGi4uLqzyvnINVydraGvb29kLi0N7evtEksoiIiIiIiIiocWAloZaqr3fULlmyBJ988gmaNGmCjRs3aiz2wsJCXL16FVFRUYiKihKqC5UkEgmcnZ3h4eEBPz8/WFlZaSQuTSkqKsL//vc/3Lt3Dzo6Oli0aBFCQ0NFiaW8vBzh4eHYuXMn4uPjhfUdO3bEO++8gxEjRtQ5YctKQtJ2CoUCsbGxCAsLQ3h4OIqKioTn7OzsYGJiAhMTExgbGwvLk9/r6uqivLwcFRUVz13Ky8tx7949xMfHo7y8XBjDwsICXbp0Qbdu3RAYGAg9Pb2nYiwqKsIrr7zC84i03p07d/DDDz/g6tWrAIAmTZogJCQEhoaG0NPTe+6iq6uLrKws3L59G7dv334qCWhsbIy2bdsiJCQEZmZmuHXrlrCtsur9SU9WUCsUCsjlclYS1gIrCVlJqNRQzg8iIiIiIqL6iJWEpFILFy7E//73P8TGxmLFihX45JNPnnlxWtVMTU3RqVMndOrUCcCj9pZXrlxBVFQULl++jLS0NGEOsWPHjqFDhw7o27dvg5gDr7i4GBs2bEB2djb09fWxfPly0ebHkslkmDlzJhITEwEAenp6mDBhAmbPno22bduKEhORGCQSCXx9feHr64u3334bp06dwoEDB3D58mVkZmaqZUwbGxt07doV3bp1g7+/P9vBUoPRokULfP/99wgPD8cvv/yCrKwsHDx4sFb78vDwQEhICIKDg+Hn51elIrF79+7C45ycHCFheOvWLdy5cwcpKSmcy5OIiIiIiIiIGhQmCUmlDAwMsG7dOnTv3h1nzpzBRx99hM8++wwGBgYajcPKygq9evUSWp6mp6fj0qVLOH78OC5evIhz586hoKAAQ4cOFX2+vrqQyWTYsWMHsrOzYWdnhxUrVsDV1VW0eFJSUoQE4dKlSzF9+vSXnm+QqKExNDREaGgoQkNDkZGRgaSkJOTl5SE3Nxe5ubnIy8sTFuX3+fn5AB61XTQyMoKBgQEMDQ1haGgoPDYwMICBgQEcHR3RpUsXtGzZsko7UaKGRCKRoHfv3ujYsSN27NiBO3fuoLS0FGVlZSgrK0NpaSnKy8uFdaWlpZDL5TA1NUW7du3Qvn17BAcHw9raukbjWVlZISQkBCEhIcK6kpISFBQUVNmuuLgYU6dOVemxNhbKmymUjxuLxnrcREREREREpJ3qb3aEtFanTp2wZ88eDB06FOfPn8fChQvx1Vdf1XguInWwt7fHoEGDMGjQIISHh+PLL79ETEwMiouLMWbMGI0nMVVBoVBg//79SExMhK6uLpYtWyZqghAA7t27BwBo27YtFi1aJGosRNqoadOmaNq0abXbyWQyyOVyjVRiE9UnxsbGmDJlSo22raiogFQqVVny3MjICEZGRlXWPd5KmF6OsbExYmJixA5D4xrrcRMREREREZF2YskBqUX//v1x+PBhGBsbIzo6GvPnzxcqY8TWq1cvrFixArq6urh79y42btyIwsJCscN6aefPn0dUVBQkEgmWLFmC5s2bix0SUlJSAADe3t4iR0JUv0mlUiYIiepIT0+P1bVERERERERERC/AKyekNl27dsWJEydgbm6OuLg4zJ07Fzk5OWKHBeBRpdtPP/0EQ0NDpKen47fffkN2drbYYdXYrVu3cOjQIQDAW2+9hY4dO4oc0SPKJKGXl5fIkRARERERERERERER0YswSagCcrkcMpmsyjqFQiFSNNqlXbt2iIiIgIODA+7evYs5c+YgPT1d7LAAPKp2W7duHRwdHZGbm4t169YhLS1N7LCqlZmZiR07dkChUGDQoEEYOXKk2CEJWElIRERENVFcXIxWrVqhVatWKC4uFjscjWmsx01ERERERETaiXMS1lFsbCxWrVqF27dvo2PHjmjXrh2GDBkCiUQChUIBiURSo/2UlZWhrKxM+F5bWnOqQqtWrXD69Gn07t0biYmJePfdd/HNN9+gWbNmYocGJycn/Pjjj1i0aBFu3bqFDRs2YOzYsWjRooXYoVVRWlqK27dvIz4+HvHx8aioqECbNm3w7rvv1vgzpgnKOQnFqiRsyOcRkabwPCIiTVAoFLhx44bwuLFQx3HHxsZWu42NjQ1cXFxUMh4RERERERE1HEwS1kFcXBw6deqEQYMGoXnz5jhz5gy2bNmCixcv4vPPP3+pROHSpUvx2WefaSBqcbi7u+P06dPo06cP4uLi8O6772LVqlVakSi0srLC999/j48//hiXLl3Cli1bMHjwYAQEBIiagHv48CFu3ryJ+Ph4JCYmVrmQ5O7ujs8++0yr5iwrKChAbm4uAPGShA39PCLSBJ5HRET1g42NDYyNjTFx4sRqtzU2NkZsbCwThURERERERFQFk4S1pFAosHbtWvTt2xebN28G8KiK6s8//8SHH36IsrIyrFixosZJpg8++ADz5s0Tvs/Pz9eKBJoqOTk54eTJkwgNDUV0dDTmz5+PH374Afb29mKHBmNjYyxduhTLli1DeHg4du/ejXPnzqFLly5o1aoVdHTU35lXoVAgNTUVN2/exM2bN/HgwYMqz7u6uqJTp07o2LEjfH19IZVK1R7Ty1C2GnVycoKpqakoMTSG84hI3XgeERHVDy4uLoiNjUVWVtYLt4uNjcXEiRORlZXFJCERERERERFVwSRhLUkkEty+fbtKJZezszOmT58OfX19fPTRR3BwcMB7771Xo/0ZGBjAwMBAXeFqDVtbWxw+fBjdu3dHbGws5s+fj++//x62trZihwY9PT38+9//RvPmzfH7778jMzMTf//9N44fP44uXbqgdevWtUrMyeVyFBUVobCwEAUFBcJX5ePH18vlcuF1EokEbdq0QadOndCpUyc4OTmp8nBVTpkkFKuKEGg85xGROvE8anjy8/Nx+/Zt3LlzB6WlpbCwsHjmom03nxBR9VxcXJj4IyIiIiIiolpjkrAWlC1Eu3Xrhp07dyI+Pl5IjJiZmWH06NFITEzEP//8g3HjxmlFpZw2sbW1xZEjR9C1a1ckJCTg/fffx88//wxDQ0OxQ4OOjg4mTpyIoUOHYufOnfjzzz+Rk5OD3bt349ChQ3B1dYWbmxuaN28OOzs7oaVsUVERHj58iNzcXOTm5lZ5nJeXVyX59yImJiYICQlBp06d0L59e5iZman5iFUnOTkZAODt7S1yJEREpHTixAksWbKk2t9DlpaWGDZsGIYMGQILCwsNRUdERERERERERGJikrAWlC1E27Rpg9WrV2PLli149913YWVlBeDR/CBDhgzBqlWrcPfuXSYJn8HR0RFHjx5Fx44dkZiYiHXr1mH27NlihyUwNTXFpEmTMHLkSOzevRvbt29HTk6O0AoUeNSi1NjYGLm5uaisrHzh/nR0dNCkSRNYWVnB2toa1tbWz3xsY2NTLys5FAoFTpw4AQAICQkRORoiIlJKS0uDXC6HgYEBXnnlFVhaWiI7OxtZWVnCkp2djdzcXGzYsAF//PEHBg0ahDFjxmhFlT8REREREREREakPk4Q1dPfuXRw9ehSFhYXw8fFBv379EBoainfffRcLFiyAgYEBXnvtNTg6OgIAPD094ePjI3LU2s3NzQ0bNmzAgAED8Ndff6FTp04ICgoSO6wqjIyMMGbMGIwcORLx8fG4cuUKoqKicP36dRQXF6O4uBjAo8Sxra0t7O3tYW9vDwcHB+Grg4MDrK2t62Xyr6YuX76M1NRUGBsbY9SoUWKHQ0RE/6dz585Yu3YtKisr8euvv6JJkyZPbVNRUYG//voLy5cvx5UrV/DXX3/h0KFDWL16NeejJLWRSCRwdXUVHjcWjfW4iYiIiIiISDsxSVgD169fR/fu3REYGIjY2FhYWlrCxsYGBw4cwNy5c1FZWYklS5YgKSkJr776Kvz8/PDTTz8hKysLbm5uYoev1fr374+ZM2dizZo1WLFiBdatWwdTU1Oxw3qKVCqFj48PfHx8MG7cOFRUVODmzZsoLS2Fg4MD7OzsqsxP2djs3bsXADB58mSt/PcjImqsXFxc4O7ujoSEBPzzzz+YOnXqU9vo6elh7NixGDNmDA4fPoz3338f0dHR+Oijj7B69WqYmJiIEDk1dMbGxkhMTBQ7DI1rrMdNRERERERE2klH7AC0XXFxMd58802MGTMGR44cQUxMDL799lvk5OSgbdu2yMjIwIIFC7Bq1SokJCRg9OjRGDhwIHbs2IE9e/bAwcFB7EPQet988w3c3d2RkZGB1atXix1Ojejp6cHPzw/t2rWDk5NTo04Q5ubm4tSpUwCAN954Q+RoiIjoST169AAA7Nix44XbSSQS9O3bF2FhYXByckJSUhK++uqrGs+rS0RERERERERE9QuThNUoKytDfn4+QkNDAQCWlpbo168f/v77bxgbG6NPnz4AgKlTp2LLli04f/48Nm/ejPPnzyMwMFDM0OsNU1NT/Pe//4VEIkFYWBhOnz4tdkj0Eg4ePIjKykq0a9eOn3kiIi3UvXt3AMChQ4fw8OHDare3t7fHzp07oaenh4iICGzcuFHNERIRERERERERkRiYJKyGubk55HI5jh07JqyTSCTw9PTEhg0bUF5ejrfeegsAYGdnBz8/PwQGBqJp06ZihVwvdenSBQsWLAAAfPfdd8jNzRU3IKoRhUIhtBqdOXOmyNEQEdGzKFuOymQy/PPPPzV6TXBwMNatWwcA2LRpE06ePKnGCKkxKikpQXBwMIKDg1FSUiJ2OBrTWI+biIiIiIiItBOThC+gUCgglUoxatQoXLx4Efv376/yvL+/P8aNG4eYmBgUFRWJFGXDsWTJEvj5+eHhw4f47rvvoFAoxA6JqhEdHY179+7ByMgIY8eOFTscIiJ6jpq2HH3c5MmTMXfuXADA0qVLkZCQoIbIqLGSy+W4ePEiLl682Kha2jbW4yYiIiIiIiLtxCThC0gkEgDApEmToFAosHr1ahw/frzK876+vkhLS0NxcbFIUTYchoaG+P333yGVSnHq1CkcPnxY7JCoGnv27AHw6EKyqampyNEQEdHzPN5yNDMzs8av+/rrr9G7d2+Ulpbio48+Qn5+vrpCJCIiqjcqKiqwbNkytGrVCkZGRrC1tcXw4cNx+fJljeyvoqIChw4dwpw5c9CuXTvY2NjA0NAQHh4emDVrFpKSkupyeERERETUiOiKHYC2UygUcHd3x9q1azF+/HisWLECiYmJeO2111BWVoYLFy7A0dERRkZGYofaIAQGBuKzzz7D4sWL8cMPPyAgIAB2dnZih0XPkJeXh1OnTgEA3njjDZGjaVgqKiqQk5OD7OxsPHjwAFlZWc9cbGxs8Oqrr2LAgAFM0taRQqFAVlYWUlNTce/evSqLqakpOnfujG7dusHJyUnsUIlqxcXFBT4+PoiNjcX69euxaNGiGr1OV1cX27ZtQ3BwMO7evYslS5Zg+fLlkEqlao6YVCU5ORlZWVnVbmdjYwMXFxcNREREVL9VVFSgf//+CA8Ph52dHQYPHoz79+9j586d2LdvH/bu3YvQ0FC17u/EiRPo168fAMDV1RVdu3YFAFy4cAH/+c9/sHnzZoSFhaFjx46qO3AiIiIiapCYJPw/lZWVUCgU0NPTE9bJ5XLo6OhALpfD398f27Ztw+LFi/Hll19i8eLF8PDwwNWrVxEeHs4L9Cq0cOFC7NmzB+fPn8cnn3yCxYsX88K8Ftq3bx8qKirQtm1bBAUFiR1OvZeeno6DBw/i0KFDSEtLq9Fr7t27h//85z9Yt24dgoKC0KFDB3To0EEr5kQtKCiArq6u1t5AUV5ejvj4eFy7dg3Xr19HTEwM8vLynrt9TEwM1q5dizZt2mDChAlo166dUG1OpK0qKytx//59JCcnIyUlBbGxsQCADRs21DhJCADW1tb4559/0L59e1y6dAmzZs3CoEGD0LNnT/79o+WSk5Ph4+NTo44XxsbGiI2NZaKQiKgaK1asQHh4OIKDg3HkyBGYm5sDAP744w+MHz8eEyZMQEJCQo1/R9Zmfzo6OhgzZgzmz5+P4OBgYX1paSneeustbNy4EePGjcOtW7eqXOMgIiIiInoSk4QAbty4gc8++wxpaWnw8PBA3759MW7cOOjo6EAmk0EqlUIul8PX1xdr165FYmIiDhw4AGdnZ3Tt2hUeHh5iH0KDoquri//+979o27Yt4uLiMG3aNEyePBmjR4/mf3C0RHZ2NrZs2QIAeOedd0SOpv4qKyvDyZMnERYWhsuXL1eZh1NXVxeOjo5wcnISlse/d3BwwIkTJ7Bq1SrcuHED586dw7lz5wAA7u7uQsLQ19dXIxU/MpkMcXFxuHDhAi5cuICbN29CT08P/fv3x+jRo0VP9CsUCiQlJQnzQF25cgVlZWVVtpFKpWjevDk8PT3h6ekJLy8veHh4IDExEX///TeOHj2K6OhoREdHw8vLCxMmTECXLl2go8PO3SSukpISJCQkICUlRUgIJicnIzU1FTKZ7KntmzVr9tJjtG7dGlu3bsWYMWNw8+ZN3Lx5E6tXr0afPn0wdepUWFtbq+JQSMWysrJQXFyMzZs3w8fH57nbxcbGYuLEiTh16lS12xERNTRubm4AgMTExGq3raysxMqVKwEA//nPf4SEHgCMGzcOmzdvxv79+7F+/XrMmTNHbfvr1asXevXq9dT+DA0N8fPPP2Pnzp1ISkpCRESE0HKciIiIiOhZGn2SMD4+Hp06dcLgwYMRGhqKo0eP4uuvv8ahQ4ewYcMGSKVSlJeXQ19fH8CjVkw2NjZo166dyJE3bN7e3rhy5QrefPNNHD16FOvWrcPRo0cxb948+Pn5iR1eo/fLL7+guLgYISEhmDRpktjh1CsKhQKxsbEICwtDeHg4ioqKhOd69eqFqVOnol+/frC2tq42+eTt7Y0ZM2bg+vXr2Lt3L/bt24ezZ88iISEBCQkJ2Lp1K8zNzREYGAgvLy8h+WVpaamSY8nJyRGSghcvXkRBQUGV58vLy7F7927s3bsXXbt2xbhx4+Dt7a2SsWsiLy8Ply5dEhKDDx48qPK8nZ0dOnfuLCyBgYEwMDB45r5mzpyJe/fu4dtvv8XPP/+M+Ph4fPLJJ3BxccG4cePQp08f6Oo27F+p9+7dq1IZamRkBCsrK7adFInyZ8mePXtw/PhxlJaWPnM7Y2NjeHl5oWXLlvD29kb79u1fqgXa44YNG4aUlBRs3rwZv/32G2JjY7Fv3z4cO3YMkyZNwogRI3gzj5by8fF5YdW/jY0NjI2NMXHixGr3ZWxsDBsbG1WGR/RSvvvuO8yfPx96enpwcHDAoEGD8Pnnn/NmBdKIM2fOIDs7G82bN3/mNYExY8Zg//792LVrV42ShKreH/AoUejl5YXIyMgadyghIiIiosarYV/RrIZCocDvv/+O0NBQbNq0CQCwYMECbNiwAWvWrMGYMWOwbds2IUG4YcMG9OnTp1Z34NPL8/DwwOHDh7FlyxbMnj0bd+/exZw5czB48GDMmDGDLc5EEh0djSNHjkAikWD16tWsoqqhnJwcHD58GAcOHEBSUpKw3s3NDa+99hqmTJki3MX8MiQSCfz9/eHv748PPvgA2dnZCAsLw969exEWFobc3FycOHECJ06cEF5ja2tbpVLOy8sLNjY2VdpnKhQKlJaWori4GEVFRSgpKUFRURGKiooQFxeHyMhI3Lp1q0oslpaWCA0NxYABA9CvXz/Ex8djxYoVOHDggBBDYGAgxo4di+DgYJW365TJZIiJiUFkZCQuXryImzdvVqnO1NfXR48ePdC3b1/069cPrVq1eqkYnJ2dsXLlSnz44Yf44YcfsHLlSiQnJ2P58uXYuHEjRo8ejf79+8PY2Filx6Ut3nzzzafW6ejowNraGra2ts9dbGxsmEhUocLCQhw+fBh79+5FQkKCsN7R0RG+vr7w9vYWEoLe3t5wdnZW6c/ppk2bYv78+Zg3bx5Onz6N+fPnIzIyEmvWrMH+/fsxe/ZshISEqGw80gwXFxfExsZqfO7CxppsbKzHrSpRUVEAHs3jlpycjJ9//hlXrlxBRESEyJFpt127dmHTpk2IjIxERkYGzM3N0aJFCwwbNgwzZ86EhYWFVo4rk8kQGxuLixcvCjd/RUdHo6SkBAAwZcoUbNy4US2xP0t0dDQAoG3bts98XnlDhnI7Te8PePSeKasi7e3ta/w6IiIiImqcGnWSUCKRIDU1Fenp6cI6Y2NjTJs2DYaGhli9ejU++OADLF26FBEREfjqq68QHh6OjRs38oKnhkgkEkycOBEDBgzAv/71L2zYsAG7d+/G6dOnMXv2bPTo0YPzgmlQZWUlVq1aBeBRZRUral+svLwcZ86cwcGDBxEZGQm5XA4AMDAwwKhRozB16lT06NFDpRfwra2tMWHCBEyYMAGVlZU4e/Yszp07h6ioKERFRSE+Ph4PHjzAgwcPqlxMs7S0hKWlpZAMLC4uFuJ9kbZt22LAgAHo378/2rdvX6WaztHRET169MDVq1fxzTffYOvWrbh8+TIuX74Md3d3jB07Fj179qxTBV5ubi4uXLiAc+fOITIyEoWFhVWe9/f3R9++fdG3b1907dpVJXMk2tra4vPPP8e//vUv/Pzzz/juu++QkZGBH3/8EevXr8fAgQMxbNgwODg41HksbdKkSRPh561CoUBBQQEqKyuFz9PzNG3aFG+99Ra6deumdT+v8/LyUFZWBmtra63+va5QKISK4ePHj6O8vBzAo8T3uHHj8MYbb6Bjx44afX8lEgm6du2Kc+fO4b///S8WLVqElJQULFy4EJ06dcLbb78tepthejkuLi4anY/QxMTkhT87GqrGetyqNHPmTAwfPhxnzpzBd999BwA4e/YsoqOj0aZNG5Gj0z4FBQUYP3489u7dW2W98vf3uXPn8OOPP2Lbtm3o1KmT1o07evRo/P333yqLq66UN/s5Ozs/83nl+uzsbBQWFlZ7Y6uq9wcAmzZtwoMHD2BnZ6fSf1MiIiIiapgabZJQoVBAIpEgKCgIN2/eRFxcHFq2bAngUQu1UaNGIT4+HseOHcPDhw/RqVMnvP/+++jTp49WX0hsqKytrbF+/XpMnjwZM2fORHx8PJYsWYKDBw9i7ty5vENSQ/755x/cvXsX5ubm+PLLL8UORyspFArExMTg4MGDOHbsWJV2oh06dMC0adMwevRotd2t/ThdXV107doVXbt2Fdbl5+cjOjoaly9fFhKHMTExyM3NRW5u7lP70NHRgZmZGczNzYWlefPm6N+/P/r16wc7O7tq42jdujV+//13fPHFF1i5ciV++eUXJCQk4KuvvsJvv/2G3r17w97eHk2bNoWtrS3s7OxgYmLyzH3J5XLcvn1bmIMxLi6uSrWglZUV+vXrh379+iE0NBSOjo4v/8bVkLm5ORYuXIg5c+Zgw4YN+P7773Hr1i3s2LEDf/31Fzp37ozhw4ejTZs2Wpccq43ExMQq8+TIZDJkZmbi3r17uHfvHlJSUp56nJqaioyMDHz66ado164d3nnnHY0mIZ5UVlaGa9eu4eLFi4iKihKqYfX09GBvbw97e3s4ODjA0dGxylfl51FZYZuXlycs+fn5VR7L5XIYGhrCyMhIWB7/XvlYT0+v2s+FTCbDpUuXsHfv3ioVyP7+/njjjTcwYcIENGnSRH1vWA3o6Ohg6tSpGD58OJYsWYJVq1YhIiICkZGRGDVqFCZOnKiS5DwRkZLy75rhw4cjJSUFO3bsAABERkYySfiEyspKjBgxAocPHwbw6MadGTNmwNfXFzk5Ofjjjz9w5swZ3Lt3D4MGDcLp06fRqlUrrRr3ybl1raysYG1t/VRHC01Rttd/3t+qjyfxCgoKqk3qqXp/iYmJmD9/PgDgq6++em4rfSIiIiIipUabJFRemBs4cCCWLFmCFStWYNWqVTAzMwPw6OLv3Llz8c033yA8PBwjRozAjBkzxAyZAKEqaenSpfjqq69w/vx5TJ06FZMnT8aIESOE1rCketnZ2diwYQMA4Ntvv4WVlZXIEWmX9PR0HDp0CIcOHUJqaqqwvlmzZpg0aRImT56s0fn4nsfc3PypxGFJSQmuXbuGgoKCKslAc3NzGBsbqyzB5eLigpUrV+Kjjz7Czz//jFWrViEjIwNbt259alsTExMhYaj8mp6ejvPnzyMnJ6fKtgEBARg4cCAGDRqE9u3ba/xGDiMjI7z99tt48803ERYWhu+//x6HDx/GqVOncOrUKXh4eGDEiBHo1atXg/oZJZVK4eDgAAcHBwQHBz9zm5KSEixbtgxLly7FxYsXMX36dIwePVpjiSO5XI5bt27h0qVLuHTpEq5du4aKiooq2+jq6qKiogIpKSlISUl55n7Mzc2hr6+PvLy8p16vKYaGhhg/fjzeeOMNhISEaF3i2cLCAt9++y1ef/11zJ07F4cOHcLWrVtx6NAhzJw5E71799a6mImo/hswYICQJHyZdoyNxa+//iok6nx9fREeHo6mTZsKz8+aNQsLFizAt99+i9zcXMycOROnT5/WqnFDQkLg4+ODtm3bom3btmjevDk2btyIqVOnvnRcy5YtQ1xc3FPrla2WX3vttaee69KlC15//XXhe+XNaar6nabK/eXl5WHIkCHIycnB6NGjMX369Drvk4iIiIgavkabJFRq0aIFtm/fjgEDBsDY2BiffvqpME+Ivr4+AgMDYW1tLXKU9DgDAwN8+umnGDt2LN58802cOHECa9euxT///IOpU6ciNDSU1Z5q8Msvv6C4uBghISGYNm2a2OFohaysLJw7dw5Hjx7FlStXhPWGhoYYPXo0pkyZovJ2oupgZGSk0TnErKys8O9//xvz5s3D1q1bcfHiRSFBk5KSgocPHwrzHyrnU3mciYkJQkNDMXDgQAwcOFBrWhrq6OgIMcXExOCHH37Af//7X9y+fRvLly/H2rVrMXjwYHTp0gX6+vrQ1dWFVCqFnp7eU491dHQaRELFyMgIn332GSZNmoQ5c+bgwIED2Lp1K44cOYJZs2aha9euKj9OhUKBU6dOITw8HJcvX0Z+fn6V552cnBAaGorQ0FD07t0b1tbWSE1NRUJCwlPL3bt38eDBg6f2oa+vD1tbW1hbW8PGxgY2NjbCY11dXRQVFaGwsBCFhYVVHiu/LygoqFGyUaFQwN3dHdOnT8f48eM1UoFcVz4+PggLC8Pu3bvx3nvv4e7du/jyyy+xadMm+Pn5CXMlNm/evEElzenllZSUYMCAAQCAAwcONJqK08Z63OrSunVr4fHVq1dFjET7yGQyLFmyRPh+06ZNVRJ1SsuXLxf+llW2ye/Xr5/WjPvhhx/WOpYnhYWFVZmn+0n//e9/n7n+8SSh8qbiJ1vcKz3eRUS57Yuoan8lJSV49dVXcfXqVfTq1Qu///57tWMTEREREQFMEgIAevbsiR07dmDUqFFIS0vDqFGj0Lp1a2zatAn37t1DixYtxA6RnqFly5Y4duwYfv/9dyxevBj37t3D8uXLsW3bNsyYMUPj8zM1ZLGxsThy5AgkEglWr16t9UkvdZHJZLh586bQ6vLxNkcSiQQ9e/bElClTMHz48BrNF9LYGRkZYfr06U/d5VxYWIh79+4hOTm5SvLQwsICAwYMQLdu3bS+dVKrVq2wZs0afPXVV1i3bh1++ukn3Lt3D7///nuNL9oYGRnB3NwcZmZmVRblOuVXNzc3UVt41oSHhwf27duH3bt3491330VSUhI++eQTBAcHY86cOc+dh+dl5eXlYeXKlVUuAJqZmaFnz57o06cPQkND4e3t/dTvBldXV7i6uqJnz55P7bOgoAAJCQmoqKgQEoImJib8/fICEokEQ4YMQb9+/fDtt9/iiy++QHJyMpKTk7F//34Ajyo43d3dhaSht7c33Nzc6jRH6ctSKBTIzc1Famqq0O5NLpdDoVBAoVAI87I+vu5JZWVlGou3oZHL5cK5WpM5cBuKxnrc6tKqVStIpVLIZDJcu3ZN7HC0yokTJ5Ceng4A6N69O4KCgp65nVQqxZw5c4SbAP/44486JQnFGrcmjh8//sz1bm5uAPDMm9Oe5OrqCgC4d+/eM59XrreysqrR/wdUsb/y8nKMGDECJ0+eRMeOHbFr1y6t/1uZiIiIiLQHk4T/Z/DgwYiIiMC8efOwaNEi6OrqQk9PDwcOHECzZs3EDo+eQyKRYMqUKRg9ejRWr16NJUuWIDExEf/+97/h5+eHN954A/7+/mKHWa8pFAqsXbsWADB58mS0a9dO5Ig0q7CwEBcuXMC5c+dw4cIF5OXlCc9JJBIEBwdjyJAhmDhxotYnauoLU1NTtGzZUpgntj6ztrbGwoULMW/ePOzcuROrV6/GzZs3UVFRgYqKCpSXl6OiouKZF4pLSkpQUlKCjIyMasfx8/PDoEGD0KNHDxgaGqrjUOpMmTgKDQ3F0qVLsXz5ckRGRmLatGkYM2YMJkyYUKfYIyMjsXz5cmRnZ0MqleK9997D0KFDERISAj09vVrv18zMjHNc1ZKhoSH+/e9/C23kLl68KCzZ2dmIj49HfHw89uzZA+DR3JA+Pj7o3LkzOnfurLIq4fz8fGGOzJSUFKSmpgrfP16hQUT1z82bN4WbNh4+fIiUlBT+3+3/hIWFCY8HDhz4wm0ff/7AgQP1clxNCQgIAABERUU983nl+pr+7VDX/clkMkyYMAEHDhxAQEAA9u/fz5sViYiIiOilMEn4mKCgIOzevRs5OTkoLCyEvb290HqUtJuRkREWLFiA6dOnY/ny5Vi5ciWuX7+OOXPmoHPnznjjjTeYwKmlyMhIXLlyBXp6elVaBzV0ubm52Lx5M3bv3l2lLaC5uTn69euHQYMGYcCAAbCzsxMxSqov9PT0MHr0aIwePfqZz8tkMiFxqEweFhUVIScn54XLgwcPEBkZievXr+P69etYvXo1hg4diuHDh6NJkyYaPsqaMTY2xueff47Jkydjzpw5CAsLw+bNm3Hw4EFMmTIF/fv3f6mW0TKZDOvXrxfmtvT29saWLVvQtm1bdR0CvSQbGxsMHToUQ4cOBfDo5pOkpCRcvHgRly5dEhKHubm5uHr1Kq5evYqff/4Z/v7+GDFiBLp06fLSn4n4+HhERkbi9OnTVaq+nySRSNCsWTPY2tpCR0dHaPX7vK9PVpBWVlbi5MmTtXpf6io5OVmYR+t5YmNjNRQN1Qc1+TzY2NjUm7+ZKysrMW3aNFRWVgrroqOjmST8P49XVj5v7mClpk2bolmzZkhJSUFmZiYePHgAW1vbejWupnTu3BlWVlZISEjApUuXnvp7Y/v27QCAIUOGqH1/CoUC06dPx59//gkfHx8cOnQIlpaWtTgqIiIiImrMmCR8grm5OczNzcUOg2qpSZMmWLZsGWbPno3PPvsM69evx5kzZ3D27FkMHDgQkydP1vr/eGoThUIhtEacNWtWvbloVBdyuRx79+7F2rVrhQoTHx8fDBo0CIMGDULnzp3rVJVE9CxSqRRSqfSpSrqatLu+f/8+NmzYgHXr1uHu3bvYvHkztm/fjv79+2PMmDFwdHRUV9h14unpif3792PXrl2YO3cukpKS8M0332Dbtm2YNm0aunXrVm1r48LCQnz55Zc4d+4cAOCtt97CN998A2NjY00cAtWSRCKBm5sb3NzcMHLkSACPft/cvn0bBw8exM6dO3H8+HFcu3YN165dQ9OmTTFs2DAMHDjwufMxZWZmIjIyEpGRkYiKihLahyo5OjrC09OzyuLl5QV3d/c6zQmXn58vylyRycnJ8PHxQXFxcbXbGhsb86a3Rs7GxgbGxsaYOHFitdsaGxsjNja2XvzNt2LFiqeqr65evYpXXnlFpIgeOX36dLUJ/JoKCgqq9b/FzZs3hcfNmzevdvvmzZsjJSVFeG1t/88k1riaoquri7lz5+Ljjz/GrFmzcPjwYeF307Zt27B3717Y2Ng8NYf75MmTceHCBcyePRuzZ8+u8/4A4L333sN///tfuLu748iRI1r/3hERERGRdmKSkBokZ2dn/Prrr3jvvffwwQcfYPfu3di7dy/27t0LLy8vtG/fHsHBwfD19X2p6oTG5vLly4iJiYGenh7ef/99scNRu4SEBHz33XeIiYkB8Kj9z9dff40+ffqIHBnR8zk4OODDDz/EwoULsWvXLixfvhwXLlwQfu51794dY8eOhZeXl9ihPkUikWDo0KHo378/fv75Z3z11VdISUnBZ599Bk9PT7z++usIDg5+5vx/ycnJWLx4MVJSUqCvr4/169djwoQJIhwFqYJEIhGSd7Nnz0Zqaip+/vlnrFmzBhkZGfjll1+wceNG9OvXD8OHD4etrS2io6Nx8eJFREZGIjk5ucr+LCws0Lt3bwwYMABDhgxpcBdOs7KyUFxcjM2bN8PHx+eF29anyjBSDxcXF8TGxtao8nTixIk4deqU1n+uYmNjhQ4X+vr6KC8vB/AoSSi2xYsXV5kbty42bNiA1157rVavzc3NFR7X5EYBa2vrZ762voyrSQsXLkR4eDiOHz8ODw8PdO/eHenp6Th16hT09PSwadOmp25qSU5Oxs2bN595HtZmf7t27cKqVasAPJrX8MMPP3xmrI9X8hMRERERPQuThNSg+fr6YteuXTh16hT+/e9/49SpU8IcSJs2bYKpqSnatm2LkJAQBAcHN7iLiHW1adMmAMDMmTPh4OAgcjTqU1JSgt9//x3bt2+HXC6HkZERli5dilmzZkFXlz8mqX6QSqUYPnw4hg0bhhMnTmD58uUICwvDsWPHcOzYMTg4OFS5KeJZ8yCKxdDQEO+99x6mT5+O7777DitWrMCtW7ewcOFCBAQEYMaMGfD19RW2P3fuHL744gsUFRXB2dkZ//zzD9uLNjBOTk744osv8O9//xtbt27FqlWrcO3aNezatQu7du2Crq5ulRaDOjo6aN++Pfr27Yu+ffsiJCSkUfz89vHxQVBQkNhhUD3g4uJSbVKvvlQcyuVyTJs2DWVlZZBIJPj1118xZcoUAI/ajdIjhYWFwuOazPn7eFX1k9XY9WFcTdLX18fBgwfxzTffCNMTmJqaYsiQIfj4449f+udybfb38OFD4fGxY8eeu283NzcmCYmIiIjohRr+1RMiAF27dsXJkyeRnp6OgwcPIiwsDIcOHUJOTg5OnDgh3O3r7u6OkJAQtGrVCra2trC2tkaTJk0aZbXhtWvXcOXKFejq6jboKsJz587h+++/R0ZGBgBg+PDhWLVqFZydnUWOjKh2JBIJevTogR49eiA6OhorVqzA//73P9y/f1/s0Kplbm6OTz/9FLNmzcLSpUvx008/4cqVK5g1axY6deqE6dOn49y5c1i3bh0UCgU6d+6Mv/76C02bNhU7dFITIyMjTJ8+HdOmTcOxY8ewatUq7NmzB5WVlXB1dUW/fv3Qt29f9OrVS2vn4aTna6ytgbX1uF+24jArK0uUJOHKlSuFNtNz5swR5rjNy8vDrVu3UFpaWqPklLocP35ctLGpdhITE1/6Nfr6+vjwww+fW8H3pOo+Fy+7v9dee63WVaZERERERI9jkpAaFXt7e0yZMgVTpkyBTCZDZGQkwsLCEBYWhgsXLiAhIQEJCQlVXqOjowMrKytYW1vD2toaNjY2sLGxqfLYxsYGZmZmz2yJV18p5yKcOnUqmjVrJnI0qpeVlYWffvpJSBC7uLjgp59+wuDBg0WOjEh12rRpgy1btuDrr79+6mdbUVER+vfvL1JkL2Zra4vvvvsOc+fOxWeffYaNGzciIiICERERwjZvvPEGfvzxR+jr64sYKWmKRCJBr1690KtXL6SmpqK0tBTu7u4N6vduY2NiYiLM/duYaPtx16TiUEy3b9/GRx99BADw8PDAV199BeDR77uTJ09CJpMhJibmhdXlFRUV+Pbbb7Fp0yYkJCTA1NQUXbt2xUcffYTAwECNHIcmmJqaCtVmpaWlMDU1feH2JSUlwuPnzf+qzeMSEREREVHtMElIjZZUKkWHDh3QoUMHfPrpp8jOzsahQ4cQFhaG2NhYpKWlIT09HTKZDFlZWdXeVa2vr//M5KGydZNCoRC2VSgUwvePrzc2NoaTkxNsbW2ho6OjngOvgdjYWFy8eBE6OjpYtGiRaHGog0wmw+7du7Fu3ToUFxdDR0cH8+bNwyeffFLtRQyi+srR0RGOjo5V1uXn54sUTc25uLjgt99+w7/+9S989NFH+PPPPyGVSvHTTz/hzTffFDs8EomTk5PYIRCRCBQKBaZPn46SkhJIJBKsX79eqMpUJgmBRy1Hn5ckrKioQP/+/REeHg47OzsMHjwY9+/fx86dO7Fv3z7s3bsXoaGhGjsmdbK0tBSSdVlZWdX+nZudnV3ltfVtXCIiIiIiqh0mCYn+j7W1NcaNG4dx48YJ62QyGTIzM5GWlobU1NRnfk1LS0N2djbKy8uF7+tKT08Pjo6OcHJyEr4ql6ZNm6q9/enmzZsBAJMmTYK7u7tax9KEcePGCdUmMplMuIO/ffv2WLNmDdq0aSNmeERUjZYtW2LHjh24ceMGpFIpvL29xQ6JiIg07D//+Y+QCJwzZw66du0qPBcQECA8vnr16nP3sWLFCoSHhyM4OBhHjhyBubk5AOCPP/7A+PHjMWHCBKG6sLZOnz5d7c2FNRUUFFTryk5vb2/cvXsXAHD37l24ubm9cHvltsrX1pZY4xIRERERUe0wSUj0AlKpFA4ODnBwcHhh26LS0lLcv3//qeRhamoqUlNTUVhYCIlEIiwAnnoMPLqT9u7du6ioqEBSUhKSkpKeGZOrqytat26NNm3aoHXr1rCyslLZMd++fRsRERHQ0dGp8ZwY2q6goKDK9yYmJvj666/xxhtvNMr5JonqK19fX7FDICIVKS0txYgRIwAAf/31l6hzyGlSYz3uukpKShK6WzzeZlSpJknCyspKrFy5EsCjhKMyQQg8uqFs8+bN2L9/P9avX485c+bUOtbFixcL7ezrasOGDbWed87f3x9hYWEAgMjISPTs2fO522ZkZCAlJQUAYGdnB1tb21qNKea4mqbqtrW12V9UVBSOHDmCyMhIREZGCv93rElyloiIiIhIiUlCIhUwNDRE8+bN0bx58zrvq7KyEikpKbh9+zbu3LmD27dvC8udO3dQWloqzJ34zz//AHjUkq9NmzbCYmNjU+vxN23aBAAYM2YMvLy86nw82uDChQtV7ghv1qwZW4sSERGJSCaTYf/+/cLjxqKxHnddzZgxA4WFhdDR0anSZlSpVatW0NXVRWVl5XOThGfOnEF2djaaN2+Odu3aPfX8mDFjsH//fuzatatOSUJt0b9/f3z99dcAgAMHDuD9999/7rbKzyQADBw4sF6Oq0mqbltb2/0tWbIEu3btUuWhEREREVEjxCQhkZbR1dUVEo5P/mdQLpcjNTUVFy5cwIkTJ3DixAlcvXoVycnJSE5Oxp49ewAA7733Hl599dWXHjs1NVVo49RQqgiBR62LHr9bnIiIiIjqh3Xr1uHw4cMAgHfeeadKm1ElAwMDtGzZEtevX0d2djZSU1Ofmr80OjoaAJ7bHSQoKKjKdrV1/PjxOr1eVbp37w57e3ukp6fj+PHjiIqKEo7xcTKZDD/88IPw/dixY+vluJqk6ra1td1fx44d4e/vj3bt2qFdu3Zo27YtMjIyVHuwRERERNTg6YgdABHVnI6ODpo1a4YRI0bghx9+QHR0NLKzs/HPP//gvffeQ4sWLQDU/uJGamoqAKB169bw8/NTWdxERERE1LDExsYiKirqhUtycnKdxkhNTcWCBQsAPLvN6OOqazmqbMXo7Oz8zNcr12dnZ6OwsLC2IWsNqVSKjz/+WPh+8uTJyMzMfGq7RYsW4cqVKwCAzp07o1+/fs/dZ48ePYQpEzZu3KixcbVJdW1rBw4ciAcPHmD9+vVq39/ChQvx+eefY8iQIU8lxYmIiIiIaoqVhET1nJWVFYYMGYIhQ4bAxcUF7733Xp33qavLHw1ERERiSE5ORlZW1gu3iY2N1VA0RE+zsbGBsbExJk6cWO22xsbGiI2NhYuLS63GevPNN5GXl/fcNqOPa9OmDTZv3gzg0Q1zAwYMqPK8co5qExOTZ77+8SqtgoKCBtGafsaMGdi5cycOHz6MmJgYtGnTBjNmzICvry9ycnLwxx9/4PTp0wAACwsLrFmzRuvGvXv3Ln777bcq6x5PAl++fBmLFy+u8nxQUBCGDx+ukmN5kqrb1ja2NrhEREREpH2YCSAiIiIi0gLJycnw8fFBcXFxtdsaGxvXaQ5iotpycXFBbGxsjZLZEydORFZWVq2ShJs3b8bevXsBAHPmzHlmm9HHVVdJqFAoAAASieSlY6mvdHV18ddff2H8+PHYu3cv0tPT8fnnnz+1nbOzM7Zt24ZWrVpp3bhJSUn48ssvn/v81atXn/r3njJlitqShKpuW6upNrhERERERM/DJCERERERkQZcuXLlhdVJsbGxKC4uxubNm+Hj4/PCfdnY2NS6OouorlxcXGr8+auu8vVZrT0zMzMxd+5cAI/ajL4oSaRUXZLQzMzsueMBQFFR0VPbNgRmZmbYs2cPdu3ahd9//x2RkZHIzMyEmZkZWrRogeHDh2PmzJmwsLBoEOOq28u2ra2uIlXV+yMiIiIiellMEmop5Z2u+fn5IkdC9UlpaSmAR3NbPH6h42VfL5PJtPqzp4xNeZ48D88joufjeURUdy97HnXv3r3afRoZGSEgIADNmjWr8fj10eN/p+Tn50Mmk4kYjeY0puM2MDCAkZFRjdqSAlXPIzs7u2orFZ9kY2PzwnPR1dUVAHDv3r1nPq9cb2Vl1SATMcrpCeri+PHjGh+3R48e1f6M1SRVt61tjG1wiYiIiEi7MEmopZT/WajJBSKiJ508eRInT56s9eujo6PrxV29BQUFL4yT5xFR9XgeEdVdTc+jmigpKYGfn58qwqo3HB0dxQ5BFI31uJ+nuvOorpSVhlFRUc98Xrm+TZs2aouBxDV58mRcuHDhpV4zbNgwLF26VPhe1W1rG2MbXCIiIiLSLkwSailHR0ekpKTAzMysxv9hyM/PR7NmzZCSkgJzc3M1R1i/8b16Odr2fikUChQUFFR7ca0255G20bb3Xtvw/Xm+6t6bhn4e8bNRFd+Pp6niPWkI5xE/G6rH9/Tl1PQ8qqvOnTvDysoKCQkJuHTp0lNzwG3fvh0A6lxtR9orOTkZN2/efKnX3L9/v8r3qm5b21jb4BIRERGR9mCSUEvp6Og8d16C6pibm/OCRA3xvXo52vR+1eRO87qcR9pGm957bcT35/le9N40hvOIn42q+H48ra7vSUM5j/jZUD2+pzWniQ4Wurq6mDt3Lj7++GPMmjULhw8fFpIu27Ztw969e2FjY4Np06apPRYSR23apT5J1W1rG3sbXCIiIiISH5OERERERERE1OAtXLgQ4eHhOH78ODw8PNC9e3ekp6fj1KlT0NPTw6ZNm1itRS+k6ra1bINLRERERGLTETsAIiIiIiIiInXT19fHwYMH8eWXX8La2hq7d+/GjRs3MGTIEJw7dw79+/cXO0TSck+2rX3Sy7atVfX+iIiIiIheFpOEDYiBgQE++eQTGBgYiB2K1uN79XL4fomH7/2L8f15vsb+3jT2438S34+n8T15hO+D6vE91W76+vr48MMPcePGDZSWliIrKwv//PMPgoKCxA6N6gFl21oAmDVrFgoKCoTnXtS2dvLkyWjZsiV++uknleyPiIiIiEhVJAqFQiF2EERERERERERE2q68vBz9+vXD8ePHYWdn91Tb2t27dz9VldqjRw+cOHECn3zyCT799NM67w8A9u3bh88//1z4PioqChUVFQgICBBuUhg0aBA++ugj1b8JRERERNRgcE5CIiIiIiIiIqIaULat/eabb7B582bs3r0bpqamGDJkCD7++OOXrkqt7f4ePHiA8+fPP7X+ypUrwuOWLVu+VCxERERE1PiwkpCIiIiIiIiIiIiIiIiokWEloZaSy+VIS0uDmZkZJBKJ2OEQaRWFQoGCggI4OjpCR+f5U6vyPCJ6Pp5HRHXH84io7ngeEdVdTc8jIiIiIqInMUmopdLS0tCsWTOxwyDSaikpKXB2dn7u8zyPiKrH84io7ngeEdUdzyOiuqvuPCIiIiIiehKThFrKzMwMwKM/8s3NzUWORjv88ssvWLhwIbp164b333//hdsuWbIEFy5cwA8//IApU6ZoKELSlPz8fDRr1kw4T56H51H9UlZWBjs7OwDA9u3bYWxs/MLtCwsLMXbsWABAdnY2dHX5K+1l8Dyiffv2Yfz48fD29sa33377wm0XLFiAuLg4bN26FYMGDdJQhNqP51FV//73v/HTTz9hxIgRmDp1ao1e88cff2DLli2YOnUqvv/+e/UGSFqJ55F6yWQyREREAAA6deoEqVQqckTPV59i1TY1PY+IiIiIiJ7EK6paStlCx9zcnP8J/j+GhoYAAF1dXZiYmLxwW2WywMjIiO9fA1ZdqxjJUaEAAQAASURBVCmeR/VLWVmZ8NjY2Lja81wulwuPzc3NmSSsJZ5HjZcyES+VSqs935QXao2Njfk5eAaeR48YGBgAAPT09Kr9TCnp6+sLXxvye0PV43mkPvXp5o76FKs2YiteIiIiInpZbFZPRERERERERERERERE1MgwSUj1TlpaWpWKoyeVlJQgPT1dgxERkaolJSWpZBsiql52djby8vKe+3xeXh6ys7M1GBHVd6mpqZDJZNVuJ5fLkZKSooGIiBqviooKrF69GqtXr0ZFRYXY4bxQfYqViIiIiKihYJKQ6o2uXbtCR0cHcXFxmD17Nu7fv//UNvfu3cPbb7+NhIQESKVSdO7cWYRIiag29PX10aFDBwDAvHnzcPjw4edue+jQIcyfPx8A0KVLF85ZQ1QLISEhMDQ0REZGBmbOnImbN28+tc3Nmzcxc+ZMpKenw9DQECEhISJESvVF9+7dAQCnTp3C4sWLUVhY+Nxti4uL8emnn+LIkSMAgB49emgiRKJGp7y8HLNnz8bs2bNRXl4udjgvVJ9iJSIiIiJqKJgkpHojMDAQR44cgaWlJW7fvo2ZM2fiwoULwvNnz57Fm2++icTERNjb2+PEiRPw8fERMWIiehkSiQT79+9H//79UVZWhq+++gqrV6+uUo1SWVmJn376CUuXLkV5eTkGDRqEPXv2cP4VolpwcHDA+fPn4eHhgYyMDLzzzjvYt2+f8Py+ffvwzjvvICMjAx4eHjh//jwcHBxEjJi03eDBg7Flyxbo6+vj3LlzePvtt59ZKZiamopZs2bh1KlT0NPTw/r16zF27FgRIiYiIiIiIiJq3JgkpHqlZ8+euHr1KkJCQlBQUIBFixbh999/x8aNG/Hhhx+iqKgInTt3RlRUFKsIieqhJk2aYO/evfjwww8BAH/++ScWLFiA3NxcPHz4EP/617/w119/AQA++ugj7N69G5aWliJGTFS/tW7dGpGRkRgyZAgqKirwzTffYMWKFVixYgW++eYbVFRUYMiQIYiMjETr1q3FDpfqgfHjxyMiIgLOzs5ISUnBW2+9hfPnzwvPR0ZGCjd1OTg44OTJk5g6daqIERMRERERERE1XhKFQqEQOwh6Wn5+PiwsLJCXlwdzc3Oxw9E6ZWVlePfdd7FmzZoq62fNmoXvvvsO+vr6IkVGmlDT84PnUf32999/Y+LEiSgpKYGdnR0AIDMzE8bGxtiyZQuGDh0qboD1HM8jepxcLsfy5cuxePFiyOVyAICOjg6++OILLFy4EDo6vK/sWXgePV96ejpGjBiBiIgISCQSzJgxAzo6Oli7di3kcjk6dOiAv//+m9WpxPNIzYqKimBqagoAKCwshImJicgRPV99ilXb8PwgIiIiotriFR+qlwwMDPDLL79g/fr10NPTg76+PjZu3IiffvqJCUKiBmL48OG4ePEivLy8kJmZiczMTHh7e+PixYtMEBKpmI6ODj744AMcOnQIlpaWaNKkCQ4dOoQPPviACUKqFXt7e4SHh+P111+HQqHA2rVr8csvv0Aul2P69Ok4fvw4E4REREREREREItMVOwCiupg6dSpCQ0MhkUjg5OQkdjhEpGK+vr64cOEC5s+fD4lEgm+++QYWFhZih0XUYPXu3Rv379+HRCKBgYGB2OFQPWdgYIC1a9ciMDAQc+bMAQCsWrUKb7/9NueSJSIiIiIiItICTBJSvefs7Cx2CESkRhYWFli3bp3YYRA1GoaGhmKHQA2IRCLB22+/jd69ewMAvL29RY6IiIiIiIiIiJSYJCQiIiIiIrVicpBIHAYGBti7d6/wWJvVp1iJiIiIiBoKJgmJiIiIiIiIGiBdXV0MGjRI7DBqpD7FSkRERETUUOiIHQARERERERERERERERERaRYrCYmIiIiIiIgaoIqKCmzZsgUAMGHCBOjp6Ykc0fPVp1iJiIiIiBoKJgmJiIiIiIiIGqDy8nJMnToVADBq1CitTrzVp1iJiIiIiBoKJgmJiIiIiIiICACQnJyMrKysarezsbGBi4uLBiIiIiIiIiJ1YZKQiIiIiIiIiJCcnAwfHx8UFxdXu62xsTFiY2OZKCQiIiIiqseYJCQiIiIiIiIiZGVlobi4GJs3b4aPj89zt4uNjcXEiRORlZXFJCERERERUT3GJCERERERERERCXx8fBAUFCR2GEREREREpGY6YgdARERERFQTWVlZmDNnDt58801cu3ZN7HCINObhw4fIzc0VOwwiIiIiIiJqYJgkJCIiIiKtJpfL8euvv6JFixb48ccfsWbNGrRu3RqhoaHYv38/5HK52CESqUVqaireeecdNG3aFC4uLjh//rzYIREREREREVEDwnajRERERKS1rly5grfeegvnzp0DALi7u6NZs2Y4deoUjhw5giNHjsDb2xtz587F5MmTYWxsLHLERHWXmpqKZcuWYc2aNaioqAAAVFRUoGfPnjhw4AC6d+8ucoRUXxgYGGD79u3CY21Wn2IlIiIiImoomCQkEoFMJsPDhw+RlZVVZcnLy0NRUVG1i729PQYOHIjBgwfD29tb7MOhGqisrIRCoYCenp7YoRARabXKykpERkYiLCwMBw8exIULF6BQKGBkZISpU6di+PDhkEqlSE9Px99//439+/fj5s2beOutt7BgwQJ07doVvXv3Rq9evRAQEAAdHTbO0GYymQw3b97EpUuXcOnSJURFReHBgwfC8wqF4pmPAcDY2Bh2dnaws7ND06ZNq3xVPra1ta1XyYb09HQsW7YM//nPf4TkoL+/P8aPH48dO3YgKioKffv2xY0bN9CiRQuRo6X6QFdXF6NGjRI7jBqpT7ESERERETUUTBISPYdCoUBpaSmKi4urLEVFRcLjkpISlJaWVlmetS4vLw9ZWVnIzs5GVlYWcnJynrrQ9TJiY2Nx7Ngx/Otf/4KnpycGDx6MV155BV26dGESSoskJSXhwIEDOHDgAI4ePYry8nK0atUKgYGBCAgIQGBgINq0aQNzc3OxQyUiElVubi527dqFvXv34siRI0/Nvda9e3fMmjULtra2wjp7e3u8/fbbmDJlCg4cOIC///4b9+/fR1hYGMLCwoRtxo8fj8mTJ6NNmzaaPCR6hicTgpcuXcLly5dRVFSk1nH19PQgkUiq3c7V1RXDhw/HyJEj0bZt2xq9RlWys7Px9ddfY9WqVSgtLQXwKDn42muvITAwEBKJBEFBQXj33XcRFxeHPXv2YO7cuRqLj4iIiIiIiBomJgmp0ZPL5bhx4wbOnj2LiIgInD17Fvfu3UNxcXGdEnk1YWpqCgsLC2ExNTWFoaHhcxcjIyMYGBjg7t27OHfuHK5cuYJbt27hu+++w3fffQdLS0v0798fgwcPxoABA9CkSRO1xk9VlZWV4eTJk0JiMC4u7qltrly5gitXrlRZ5+HhUSVx2KlTJ1hYWGgoanqekpISpKeno6CgAHK5HDKZ7IVLq1at0KxZM7HDJpFUVFQgMTERlpaWVRJZ9HyFhYXYvXs3tm3bhgMHDghVUwBgZmaGoKAghISEIDg4+IXvqYmJCUaOHInhw4fj7t27iIqKQlRUFKKjo5Geni78jmzdujUmTZqE8ePHw9HRUROHSP8nMjISn3/+OcLDw5+ZEDQ0NISHhwc8PT3h5eUFe3t74bnHE3XKx8qvJSUlePjwIR4+fIjc3Fzk5OQgNzcXubm5wnqZTFbls/Uit27dwvLly7F8+XK4urpi5MiRGDlyJEJCQtRWkZqXl4eVK1fi66+/RnFxMQCgZcuWmD59+lOJSn19fXTt2hVxcXE4ffo0k4RUI5WVldi5cycAYNiwYdDV1d5LAPUpViIiIiKihkKiUHcWhGolPz8fFhYWyMvLY5WRiuXn5+PChQuIiIhAREQEzp07h7y8vBe+Rk9PDwYGBkKyTvlYX1+/ymJgYPDUOn19fRgZGVVJBlpYWMDc3LzO//EtKirCxYsXERERgfPnz1c5Dh0dHfTq1QszZ87EkCFDGlSFYU3PD02cR2lpadi1a5dQLai8wAc8+jf4f+zddVhU+fv/8efQDdKtEgpY2N3Y3bqru67bXW6H2+u6brduuXatH7uxURQxEOkwSBGUlJr5/eGP+crayswZ9H5c17kYmOG871HOMJzXed/vkJAQOnbsSIcOHbCzsyMpKYnk5GSSk5NJSkqq1VKthp2dHStXriQsLEwnNQvIysoiNjaWrKwssrOzr/nxZq8L/2VnZ0dWVla9WQ/tdo+jPn361HrNCggI4IknnrjvZmfl5+eTkJBAfHx8rY/JyclUVVVhbGzMlClTeP311wkODla6XINTVlbGhg0bWLJkCWvXrqW8vFx7X8OGDenZsycdO3akadOmGBsb39VYlZWVHDx4kC1btrB//35tUGRkZES/fv2YMmUKI0eOxNra+o7HMKTfR4bo6NGjvP/++6xdu1b7NQsLC20YWLP5+Pjc9f/3tWg0GoqKimr9nNV8/b/UajVxcXHs2rWLyMhI7Ww+AG9vb8aMGcPYsWPp0qVLnQSGarWaf/75h9dee428vDwA/P39efTRR+nUqdN1ZzEeP36cF198EXd3dzIzM/U621FX5DjSrZKSEmxsbIDLF2fc7DUvOjqatm3bcvjwYdq0aXPXj9NlreL/yPEhhBBCCCHulISEBkre5N+aoqIijh8/TmZmJmVlZZSVlWnbgF7rdkJCAjExMajV6lr7sbCwIDg4mGbNmmlnA1laWmoDQV2cuKpr1dXVxMXFaWdEpqena+9zd3fn0Ucf5fHHH6dhw4bKFVlHDOFkkkaj4bfffuOll16qdfLRycmJ9u3b07FjR9q2bYutre0N93Px4kVtYJiUlERsbCw5OTkYGRnx9ddf88ILL9wTJwCvVF1dzcmTJzl48CCHDh3Czs6Ohx9+mGbNmul03MrKStavX8/vv//Oxo0br3oduBZTU1NsbGwwMjLSbsbGxhgZGaFSqbSfZ2RkUF5ezpYtW+jXr59On0ddud3j6Hq6dOnCs88+y5gxY+rVul+3qqSkhFmzZhEeHk5CQsI1g/0aZmZmVFRUaD8fOXIkb7zxBp06ddJHqQar5thYunQpq1evpri4WHufl5cXffr0oXfv3jRu3FhnNRQWFrJz5062bNlCbGys9uuWlpaMHz+esWPH0qlTJ5ydnW97v0r/PjJEJ0+eZMaMGaxYsQL4v2B23LhxNGrUyODfV126dImDBw+ya9cu9u/fT1lZmfY+Dw8PJk2axEsvvXTHs8djYmJ45pln2Lt3LwA+Pj5MmzaNHj163DSALC8vZ+jQoVRVVZGSkoKfn98d1WBI5DjSLQkJ7w9yfAghhBBCiDslIaGBkjf5V8vPz+fIkSNER0drPyYmJt5RS1APDw9CQkJo1qwZzZs3x8/Pz+BPWN2ujIwMNm3axPr16ykoKAAut+caPHgwTz/9NAMHDqy3z1npk0m5ubk8+uijrFu3DoAmTZrQo0cPOnbsiL+//12FehUVFXz99dds3rwZgKlTp/Lrr7/W2/BFo9Fw+vRpDh48qN0OHz58zXZznTp14vHHH2f8+PHaE0R1ITk5mT/++IO///6b7Oxs7dcbNmyIs7Mzjo6O2s3JyanWR2tr61v6/5w5cyabN2/mjTfeYObMmXVW+40UFBTw8ccfU1lZyYsvvkhAQMBtff/tHkevvPKK9udQrVZz8OBBdu/eTXV1NQCurq489thjPPnkk/j6+t75EzMgmzdv5qmnnqp10QVcfq4+Pj74+vrW+ujs7Ex8fDxLlixh79692t9PPXv25M0332TAgAH3XOh/I1VVVfz111/MmDGDrKws7dfd3Nzo1asXffr0ITAwUO//JhkZGWzZsoWtW7fWqgsuz+Tq2LEjnTp1omPHjrRq1eqGr79K/z4yNElJSXz44YcsWrQIjUaDSqWid+/ePPzww/X2daGiooJDhw6xa9cuIiIitL+/jI2NmTx5Mq+//johISG3tK+ioiI+/PBDvvnmG9RqNRYWFjz88MOMHTv2trpLPPfcc8TGxvLPP/8wZcqUO3pehkSOI92SkPD+IMeHEEIIIYS4UxISGqj7/U2+RqNh//797NixQ7u20H9P0tZwdnbGw8OjVgvQ6912cXGhWbNmODk56fcJKaiqqop9+/axZs0aoqOjtV/39fXliSeeYNq0aXh4eChY4e1T8mTS+vXrmTZtGrm5uZiamvL4448zZsyYOl2rSKPRsHLlSn755RfUajWdOnXi33//rTf/T2fPnmXp0qXs2rWLgwcPkpOTc9VjLC0tCQoKomnTpmRkZBAREaENmywtLZk8eTKPPfYY7du3v6MA4dKlS6xatYq5c+eyY8cO7dcbNGjAgAEDGDx4cJ2uH7h161Y+++wz2rZtS1RUVJ3t91o0Gg2LFy/m5ZdfJjc3F7g8S2fMmDG88cYbtG3b9pb2c7vH0bp16646WXf+/HnWr1/P2rVrte3yjIyMGDZsGM888wxhYWE6W8dLl3Jzc3n55ZdZtGgRcDnUeuihhwgMDMTb2xtLS8ub7uP06dMsWbKErVu3UlVVBUCrVq144403GDdu3D29zpJGo2H9+vW88cYbnDx5EgBHR0d69+5N7969CQkJMYiwVKPRcOLECbZu3crRo0c5c+bMVY8xMzOjdevWdOzYkY4dO+Ln51er9pKSEvr27Xvfhxvp6el89NFHzJs3TztLu3v37jzyyCM6nSGqbxUVFURFRbFixQqOHDmi/frw4cN588036dy58zW/r+b3+ksvvURGRgZw+d/n2Wefxc3N7bbr+PXXX1m6dClPPPEEv/322509GQMiIaFuSUh4f5DjQwghhBBC3CkJCQ3U/fomv7KykhUrVvDVV19x+PDhq+739PQkMDBQuwUEBODo6KhApfXTmTNnWLduHZs2baKwsBC4fCX8oEGDGDhwIAMGDLjrmXD6oMTJpNLSUqZPn84vv/wCQOPGjXnnnXfw9/e/q/3eSFRUFB999BFFRUV4enqyatUqOnTooLPx7kZBQQErVqxg4cKF7N69u9YMX2NjY/z9/QkKCiI4OJigoKCr1p/Kz89n8+bNbNiwgbNnz2q/3rJlSx577DEefPDBq451tVpNcXExRUVFFBYWUlRUxIULF9iwYQPz588nPz8fuDyDtkOHDgwePJguXbroJJzJy8tj3LhxqFQq8vLydPa6lJKSwtNPP83WrVuBy7Mh3d3diYyM1D6mX79+vPnmm/Tu3fuGx3JdhIQ1ai5GWL16da0T54GBgbzxxhtMmzbN4F9X4PKJ/Hnz5vHiiy9SWFiIkZERo0ePZtq0abcUDF7LuXPnWL58OWvXrtWucda4cWPeeustpk2bVm9ndF/PoUOHeO2119i1axdwea3OKVOmMHz4cMzMzBSu7saKioqIi4urtdX8rryZ+zXciI+P5+uvv+avv/7ShuGdO3fmkUceITAwUOHqdCsuLo7FixfXmjXco0cP3njjDQYNGqR9zUtOTub5559n06ZNwOX3ss8///xdtSHeu3cv7733Hs2aNePEiRN3/2QUJiGhbklIeH+Q40MIIYQQQtwpCQkN1P32Jv/ChQv8/vvvfP/999or+c3MzOjatStBQUE0adKEgICAOm1BeD+rqKhg165drFmz5qqTS40bN6Z///4MGDCAPn363HAtMqXo+2TS4cOHefDBB0lISABg7NixPP7443o54Z2RkcE777zDqVOnMDU15ffff+ehhx7S+bi34tKlS6xbt46FCxeyfv16Kisrtfe1bNmSbt26ERISQmBg4C3/W2k0Go4dO8aGDRvYtWuXdn03U1NTWrZsWSsUvHJds2txdXVl8ODBDBw48I5matyuqVOncurUKVauXMno0aPrdN8VFRXMnj2bDz/8kIqKCkxNTZkyZQoTJ07E1NSUlJQUlixZQnh4uHYWT/v27XnzzTcZMWLENYOougwJr3Tq1ClWr17Nli1btG35Xn/9dWbOnGnQQWFycjJPPvkk4eHhwOW2k9OnTycoKKhO9l9YWMjq1atZuXIlFy9eBKBt27b88ssvtG/fvk7GUFJqairvvPMOS5YsAS4fs2PHjuWBBx6ot7+7NRoNmZmZtULDCxcu1HqMWq0mJyfnvgo3NBoNO3fu5Ouvv9a23QZo164djzzyyC233rxXnD59mqVLl7JlyxZtUNqyZUtef/11kpOT+fTTT6msrMTU1JRJkybxwAMP3HUL8YKCAu3vmfz8fBo0aHDXz0NJEhLqloSE9wc5PoQQQgghxJ2SkNBA3S9v8tPS0vjuu++YM2cOZWVlwOV2gKNGjWL48OEGGVDda9LS0ti/fz+HDh3ixIkT2hNccLltYKdOnbShYbt27QyiRZ6+TiZVV1cza9Ys3nvvPaqrq3FycuLNN9+kXbt2d1P+bSspKeGzzz4jIiICgFdeeYUvvvhCkf+L6upqdu7cycKFC1m5cmWtWTZ+fn6EhYXRu3dv3N3d73qsoqIitm3bxrp160hNTb3u44yMjLC2tsbKygorKyt8fX0ZPHgwbdu21essre+//55Vq1bxzDPP8NNPP9XZfvfu3cuTTz6pbdvYpk0bXn75Zby9va96bHZ2NsuWLWP9+vXagLVJkya8/vrrV615VVhYiKenZ52HhDXKyspYtmwZf//9NwBPPfUUP/30k8G1H62srOSrr75ixowZVFRUYG5uztSpU297jbBbdenSJdauXcu8efMoKSlBpVLx1FNP8emnn9bLE/3nz5/nk08+4ccff6SqqgqVSkW/fv2YNm2aXsJ5pZWUlDB06ND7ItyoqKhg2bJlfP3119oZwyqVii5dujBhwgRatGihcIXKOnfuHCtWrGDt2rXa97Q12rZty4svvlinba6nTJnC2bNnWb9+PYMHD66z/SpBQkLd0nVIuGDBAoKDg2+4T2dn51tal1RCwjsnx4cQQgghhLhTEhIaqHv9TX55eTnvv/8+s2fP1s56ady4MePGjaNv374G35LsXlVWVsbRo0c5dOgQUVFRV63P5OnpyUsvvcSTTz6p6M+lrk8m5eXlsWXLFmbPnq09Edq9e3deffVVxYJrtVrN33//zfz58wHo06cP8+fPx9PTs072r9FouHDhAhkZGWRmZtbarvxadnZ2rSDZ1dWVvn37EhYWhp+fX53Ucq3akpOTOXfunDYItLa2xtLSEmtra8zMzAxihlpN+zcbGxsmTJjA8OHDCQsLw8rK6rb3dfHiRVavXs3ff/+tXVPRwcFBu87fzZ5vQUEBq1atYtWqVTedcamrkLDG2rVr+eabb9BoNAwdOpTff/9d8fBIo9EQExPDsmXLWLBgAadOnQIun8h/+eWX8fLy0nkN+fn5/Prrr9rWsVZWVvTo0YOwsDD69u1Ly5YtDSZQ1Wg05OTkkJaWRlpaGunp6aSlpZGamkpERIS2jWq7du148sknCQgIULhi/amPIaFGo0Gj0dzSz5dGo+HQoUMsXLiQJUuWaNdBNTc3Z+DAgYwZM6ZOg697QVFRkXbWsImJCc888wy9evWq899TX3zxBZs2bWLMmDEsXrwYU1PTOt2/PklIqFuVlZUsXLgQgAcffPCmPyu3GhKePn2a4OBgSktLb1qDlZUVcXFxNw0Kb7dW8X/k+BBCCCGEEHdKQkIDdS+/yT9x4gSTJ0/m2LFjwOWTiuPHj6ddu3YGcaJf/J/s7GwOHz7MoUOHOHz4sDZssLa25vnnn+fFF1+skxljt6uuTyYVFxezZ88etm/fzvbt2zl69Kj2PktLS55//nkGDhxoED+fO3fu5IsvvuDSpUuoVCqCg4Np3769dmvVqtVN25ip1WqSk5OJjo6utRUUFNxSDba2tvTs2ZOwsDBatGhhMEGG0kpLS5k2bRo5OTnar5mZmdGvXz+GDRvG0KFDa4VPVVVVpKenk5CQoN0SExNJSEggKyur1r4HDx58R+F8aWkp69atY/ny5eTl5V3zMboOCQG2b9/OzJkzqaqqwt7enj/++IMxY8bc0b7uVE0wuHz5cpYtW0ZiYqL2Pnt7e5555hn69eun9+P86NGjfPfdd6Snp9f6urOzM3369CE0NBQ7OztsbW21H/9728bGBpVKRWlpKSUlJdqtuLj4qs8rKiqorq5GrVbf8GNeXp42DExPT9cGgdfi7+/Pk08+eU+0Tb1d9SkkrKio4O+//+aLL74gNTUVS0tL7UUXNbOx//vxwIEDJCUlaffh6OjIqFGjGDZsmHR7uInq6moAnc1oj4qK4vXXX0ej0dCrVy+WL1+Os7OzTsbSNQkJDcvttBE9ffr0dd9f1IiLi2Py5Ml12pZUXE2ODyGEEEIIcackJDRQ9+qb/PT0dJo3b05JSQn29vZMnz6dbt26KV2WuAUVFRVs27aNpUuXcvr0aeDymlOPPPII06dPJzAwUG+13O3JpMrKSg4ePMi2bdvYvn07+/fvrzU7Di63zuzatStjxowxuBOhp0+f5vPPPyc+Pv6q+0xMTGjVqlWt4NDIyKhWGHjkyBGKioquuW87OzucnJxwcnLC2dkZZ2dn7e2aj46Ojnpt41mfVFZWcuzYMSIiIti/fz/Z2dm17m/Tpg0+Pj4kJCSQkpJSax3H//L29qZfv37079//rsN4tVpNeXl5ra+VlpYyduxYvYSEACkpKXz22Wfa1rEPPvggP/zwg05bbGo0Gk6cOMGyZctYvny5dl1RuPz61bFjR3r37k3Xrl3veo2wu6FWq0lNTdUeo8eOHbthKHctKpUKXb6lMzIywsXFBTc3Nzw8PPDw8MDNzY2AgAD8/f0N4iIKJdSHkLCiooK//vqLzz77TPv7+3aYm5vTtWtX+vXrZzBtx8Vl+/fv55NPPqG0tJRGjRqxevVqWrZsqXRZt01CQsNS12sN6mLtQnE1OT6EEEIIIcSdkpDQQN2Lb/LVajV9+/Zl586dBAUF8emnn+Lo6Kh0WeI2qdVqIiIiWLJkCbGxscDlk9NjxozhjTfe0Mt6fbd7Mik0NFQbamk0GhITE69qwejm5kbbtm1p06YNrVu3rhc/m/n5+SQkJBAfH6/9ePHixVv6XjMzM/z9/QkMDKRJkyY0adIEX19fRYOSe41GoyEtLU0bGMbFxV0V4pibm+Pl5YWPjw++vr54e3vj4+ODj4+Pdk0eXbndcONuQ0K4HKLOmzePxYsXo1ar8fLy4o8//mDAgAF3td8rqdVqjh07xqpVq1i2bNk1g8GePXvSuXNng13rqLKykvj4eKKjo8nOzqa0tLTWVlZWpp01WNOy+0oWFhbazdLSstZtU1NTjIyMUKlUGBsbo1KpMDIyumqztrbGw8MDd3d33N3dcXV1lbZv12DIIWF5eTl//fUXn3/+uTYcdHJyYuLEifTq1YuqqirKysooLy/n0qVLtW7XbM7OznTp0uWO2iYL/UhPT+edd94hMzMTCwsLFi1axKhRo5Qu67ZISKhbVVVVbN68GYABAwbcNOhXMiS83VrF/5HjQwghhBBC3CkJCQ3Uvfgm/7vvvuOll17CwsKC33//XS9rPgndqWndt3jxYg4cOKD9ep8+ffjggw/o3r27zsa+3ZNJ12JnZ0fr1q1p06YNbdu2xdPTs97PhKlZNyw+Pl67JSYmotFoCAwM1G5NmjShYcOGMhtQz/Lz8zl48CBlZWXaINDFxUWxdq1KhIQ1Tp48yeeff87Zs2cBeOqpp/jyyy/vOBhNS0tj27ZtbNu2jfDw8Fqtz0xNTenQoQO9evUy6GDwTmg0GioqKigtLUWj0WBpaYm5ubm0ANYjQwwJy8vL+fPPP/n888+1aws7OTkxadIkhg4dKheD3IMKCwv56KOPOHz4MABvvvkmr7/+uk5natclCQl1q6SkRPv7tbi4+Ka/B5UMCW+3VvF/5PgQQgghhBB3Si7NE3qRkJDA66+/DsDTTz8tAeE9QKVS0bJlS1q2bElqaipLliwhPDyc8PBwdu7cyYoVKwzmSvYZM2ZgYWGh/dzZ2Rk/P7977kS6SqXSzvrp1asXcDlE0Gg099xzrY8cHR0ZOHCg0mUYhJCQEObOncucOXNYtWoVv/76K3///Tf+/v74+/trW1jWfGzYsGGt2QR5eXmEh4drg8G0tLRa+7e0tKRNmzb07NmTLl263LMnGVUqFebm5hL6COByOPjHH3/UCuCdnZ214aCZmZnCFQpdsbOz44svvuCXX35h5cqVzJw5k2+//ZYnn3ySl156iUaNGildohBCCCGEEEIIAyUhodC5qqoqHnroISoqKmjXrh3Dhg1TuiRRx/z8/Hj77bd59NFH+eWXX9i1axfjx49n7dq1BhGKtG/f/p4NCW5GpVLV+xmS4t5kYWHBCy+8QNeuXfnyyy/JyckhNjZW28b4SiYmJjRs2BB/f39yc3M5evRorfuNjY0JCQnRtgwODg6WFmXivpKRkUGfPn1ITEwELoeDDzzwAEOGDJFw8D5hbGzMc889R4sWLfjnn39ITU3lu+++44cffmD8+PFMnz6dtm3bKl2mEEIIIYQQQggDI2fQhM7NmjWLgwcPYm1tzeuvvy6BxT3Mzc2N9957D4Bdu3YxYsQItm7dSo8ePRSuTAhhqNq2bcvChQvJzs4mIyODzMzMWh+zsrKoqKggJSWFlJQU7ff5+flp2wW3atUKS0tLBZ+FEMrJzs7WBoSOjo5MnjxZwsH7WM+ePenRowdRUVEsW7aMqKgolixZwpIlS+jduzfTp09n4MCB0mHgPnXmzBlKS0tv+Ji4uDg9VSOEEEIIIYQwBBISCp06duwYM2bMAOCFF17AxcVF4YqErhkbG/POO+9QXl7OgQMHGDRoEDt27KBDhw5KlyaEMFDGxsZ4eXldsxW1Wq3m/Pnz2uDQwsKC0NBQHB0dFahUCMOSl5dHWFgYiYmJuLm58e233+Lu7q50WUJhKpWK9u3b0759e5KTk1m2bBnh4eHs2LGDHTt2EBISwvTp03nggQekXfF9pk2bNpSVld30cVZWVjg7O+uhIiGEEEIIIYTSJCQUOlNeXs5DDz1EVVUV3bt3p1+/fkqXJPTE1NSUDz74gLfeeosjR44QFhbG3r17admypdKlCSHqGSMjI1xcXHBxcSE0NFTpcoQwGAUFBfTv35/Y2FicnZ356quvJCAUVwkICODtt9/mscceY+XKlaxbt46TJ08ybdo0nnjiCXx9ffHz88PPz4/GjRvXuu3o6CgdQO4xZWVlLFiwgODg4Bs+ztnZGV9fXz1VJYQQQgghhFCShIRCZ2bPns3x48dxcHDg5ZdflpMM9xlzc3M+/fRTXnvtNWJjY+nVqxcpKSk0aNBA6dKEEEKIek2j0TB69GiOHDlCgwYN+Oqrr645E1eIGq6urjz99NNMmTKF9evXs3LlSs6dO0dqaiqpqanX/B4LCws8PT0ZMGAA3377rbSwvUcEBwfTpk0bpcsQQgghhBBCGAgJCeuQWq2W9T2u8M8//wDw5JNPSjB0n7K0tGTmzJk888wznDlzhh9//FG7ZqEQQggh7syGDRvYuXMnFhYWzJ49W2b8iFtmY2PDhAkTGDduHOfPnyczM5OsrCztlpmZSXZ2NufPn+fSpUukpqbyyy+/UFRUxLx58+RvnXrIzMyMH3/8kTNnzvDFF18oXc4N1dRac1sIIYQQQgihexIS1oGYmBiqqqpo3bq10qUYjISEBBITEzExMaF79+5KlyMUZGNjw9SpU/n444/59ttvefnll7GxsVG6LCGEEKJe0mg0vP/++wCMHDkSPz8/hSsS9dGVrZxbtWp11f3l5eWcP3+eEydOMGvWLBYsWICbmxuzZ89WoFpxN0xNTXn22WeJjo42+JCwplYhhBBCCCGE/siloHfp+PHjtGrVirVr197VfsrLyyksLKy11Wdr1qwBIDQ0FGtra4WrEUrr2bMnXl5e5OfnM3fuXJ2Nc68dR0IoQY4jIe6eLo+jNWvWEB0djaWlJRMnTqyz/QpxJXNzczw9Penfvz+vvfYaAF999ZWEhEIIIYQQQghxj5GQ8C4cPXqUTp068dprr2mv6L5Tn3/+Ofb29trNx8enjqpUxurVqwHo0qWLwpUIQ2BsbMykSZOAy2tVlpeX62Sce+04EkIJchwJcfd0dRyp1WpmzJgBwOjRo7G3t6+T/QpxIwMGDODJJ58E4LXXXmP+/PkKVyRuR3V1NTt37iQqKkrpUm6qptadO3dSXV2tdDlCCCGEEELcF1QajUajdBH1UXJyMk2bNuXjjz/m7bffprKykpUrVxIfH4+/vz/BwcG0a9fulvdXXl5eKzgpLCzEx8eHixcvYmdnp4unoDPnzp3Dzc0NjUbD0qVLcXV1VbqkOlVRUUF+fj75+fmo1WpsbW2xtbXFxsZG1s64gYqKCh588EHy8vKYM2cOjz/++B3vq7CwEHt7+6uOj+sdR+vWrZMZrUL8R0lJCUOHDpXjSIi7cLvH0d2+r1u5ciVjx47FysqKxYsX17v3iEqoqqqirKyMS5cuUVZWpt0uXbqk/VrNR1dXV7p164aFhYXSZRscjUbDL7/8wvLlyzE2NmbdunUMHDiwTvZ9vfd1d/o4UVtJSUmtVv+HDx+mTZs2ehs/Ojqatm3b3tK4V9ZaXFws7ztugxwfQgghhBDiTsmahHegurqaTZs2odFo8PT0BGDQoEHk5eVRWVlJYWEhvr6+PPPMMzz44IO3tE9zc3PMzc11WbberF+/Ho1GQ2BgYL0MCNPT00lMTCQ/P5/z58+Tn59PQUGBNhgsKiq67veam5trA8Mrw0Nzc3PUajUajeaGH11cXOjUqRMtW7bE1NRUj89a98zMzJgwYQI//fQTM2fO5JFHHsHEpG5fgu6l40gIpchxJMTd08VxpFar+eCDDwAYM2bMPXcSuLq6mr179/Lvv/+SnZ2NhYUF5ubmWFpaam9bWFhgaWmpva3RaCgpKaG0tJSSkhLt7dLSUoqLiyktLb3t7gXW1tb07duXIUOG0KRJEx092/pHpVLx1FNPUVBQwLZt2xg1ahS7du2iQ4cOSpcmhBBCCCGEEOIuSEh4B4yNjRk9ejQlJSW8+OKLvPHGG3Tr1o1ff/2VgIAAjh8/zueff86ff/5JWFgYbm5uSpesV/W11ejp06f5888/2bVr100fa2xsjI2NDUZGRtor0OH/Zg7k5eXdcR0rVqzAysqKdu3a0blzZzp06ICjo+Md78+QDBkyhPnz55OamsqyZct44IEHlC5JCCGEqBdWrFjBiRMnsLa2Zty4cUqXU2fKysrYtGkTK1asIDMzU2fjGBkZYWZmhpmZGaamptrbNZ+bmppy6tQpLly4wJo1a1izZg2BgYEMGTKEvn371pqJdb8yMjLi9ddf58KFC0RFRdG/f38iIyNp2rSp0qUJIYQQQgghhLhDEhLeIU9PTx566CGqqqrYu3cv77//PgEBAQC0bNmSadOmMWDAAE6fPn1fhYQ1J3oAunbtqnA1tyY7O5t58+axefNmarrvNmzYEHt7e2xsbK65WVhYoFKptPvQaDTXbFtV87G6uhqVSnXDDSAnJ4ekpCSKi4vZvXs3u3fvBiAoKIjOnTvTuXNnAgICao1dn1haWjJ27Fj+/PNPPv/8cyZOnIiRkSyNKoQQQtxIdXW1dhbhuHHjsLW1VbagOnD+/HlWrVrFmjVrtF0aLC0tad++PYGBgVRVVVFZWUllZSUVFRXXvA1oZxleb7OwsMDMzAxjY+Ob1qTRaEhLS+PIkSOcPHmSpKQkvv32W3744Qf69u3L0KFDad68eb19H1YXTE1N+eijj3jllVeIj49nwIABREREaLurCCGEEEIIIYSoXyQkvEVpaWls376d4uJigoKCGDhwIB4eHkydOpX+/fsTEhICXG4FZWRkhK2tLcHBwTg5OSlcuX6Fh4dz6dIlXFxctKGpocrPz2fBggWsXr0atVoNQNOmTendu/dtB7sqlQpLS0ssLS1p0KDBXdWl0WjIysoiMTGRxMREsrKyiI+PJz4+nr/++gtnZ2d69OjBuHHjcHd3v6uxlDBy5EiWLFnCiRMnWLduHcOHD1e6JCGEEMKgLV26lLi4OGxtbRkzZozS5dyVtLQ0li9fzubNm7XvvxwdHenUqROhoaGKtltXqVT4+fnh5+fHoEGDOH78ONHR0Zw7d44tW7awZcsWfHx8GD58OEOHDr1v1y60tLTk888/5/nnn+fUqVMMGjSIXbt24eDgoHRpQgghhBBCCCFuk4SEt+DEiRP07NmT1q1bExcXh4ODAzNnzmT9+vV4eXnh7u6uvTq5ZlbU//73P+zs7O67P5bXrFkDXG41aqhXWZeWlrJkyRIWLVpEdXU1AH5+fvTu3Rtvb2+Fq7t8gsrT0xNPT0969epFUVERSUlJJCUlkZKSQl5eHv/++y+rVq0iLCyMiRMn4ufnp3TZt8zW1pYRI0awePFivvjiCwkJhRBCiBuorKzUziIcP358vW17eezYMRYuXMihQ4e0X/Px8aFz5840bdrU4DoLWFlZ0alTJzp27EhGRgbR0dHExMRw5swZfvrpJxYsWMCQIUMYNmxYvbxo6245ODgwa9YsnnvuOY4fP87zzz/P/PnzlS5LCCGEEEIIIcRtkpDwJkpLS3nqqaeYMGECP//8MxcuXODAgQO89tprdOjQgfDwcNzc3NBoNKhUKuLj4/njjz+YO3cuu3fvvmfWkrsVGo2G9evXA9C5c2eFq7ladXU169ev5++//6agoAAAb29v+vTpQ+PGjRWu7vpsbW1p06YNbdq0oaqqitTUVCIjI0lNTWXr1q1s3bqVjh07MnHiRFq1amWw4eyVRo0axeLFi4mIiCAuLo7g4GClSxJCCCEM0k8//URSUhIODg6MHj1a6XLuyPr165k9e7b28+DgYDp37oyPj4+CVd0alUqFt7c33t7eDBgwgJiYGPbt28eFCxdYtGgRixcvpnPnzowYMYJ27doZXNipSx4eHjz99NN8+umnxMTEKF2OEEIIIYQQQog7ICHhTZSXl1NYWEi/fv2Ay1fNDhgwAH9/fyZOnMjAgQM5cuSINiD86KOPiIuLY+fOnbRs2VLh6vXrxIkTZGRkYG5uTmhoqNLl1FJQUMAbb7xBUlIScLmtVVhYGEFBQfUiVKthYmJCkyZNaNKkCZmZmURERHDy5EkiIyOJjIwkKCiIiRMn0q1bt1tae0cpLi4udOvWjb179/LBBx+wdOlSpUsSOlZdXU1JSQmFhYUUFhZSVFSkve3m5kbnzp0N+mdWCCGUkJyczLvvvgvAo48+ipWVlcIV3b5ly5bxyy+/ANCiRQt69+59163ZlWJubk67du1o06YNCQkJREVFkZqaSkREBBEREXh5eTF8+HAGDRp0T6wbeSsKCwsB6lVXi/uNqakps2bNIiMjg++++07pcm6optaa20IIIYQQQgjdk5DwJuzs7FCr1ezYsYNRo0YBl68oDgwM5K+//mLcuHE899xz/PjjjwQFBfHmm2/i4uKCh4eHwpXr34YNGwAIDQ3F3Nxc4Wr+T35+Pq+88gqnTp3C3Nyc3r17065du3ofSHh6ejJ27Fjy8/PZv38/R44cIT4+ng8++ABvb2/Gjx/PgAEDMDMzU7rUa5o6dSp79+5l2bJlvP3227Rq1UrpkkQdKCws5ODBg0RGRnL27FltGFhcXIxGo7nu93l6ejJhwgQGDBhgUK8fQgihlKKiIkaOHElJSQnNmjVj0KBBSpd0WzQaDX/99Ze2BWXXrl3p27dvvbo463qMjIwIDg4mODiYvLw8oqKiiI6OJiMjg19++YU5c+bQv39/Ro0aRWBgoNLl6lRWVhYgIaEhMzMz47XXXiM6OtrgQ8KaWoUQQgghhBD6IyHhDWg0GoyNjRk3bhybN29mw4YNDB48WHt/ixYtmDRpEuHh4RQXF2NjY3PfzR680saNGwHo2LGjwpX8n7y8PF555RXOnDmDnZ0dDz/88D3XAtbR0ZEhQ4bQq1cvIiMjOXToEGfPnuXrr79mwYIFvPTSSwbZ/tXf35/evXuzY8cOZsyYwf/+9z+lSxJ3QKPRkJqayoEDBzhw4ACxsbE3DAPNzMywtLTUbhYWFqSnp5OZmck333zD33//zejRoxkxYsR9MwtDCFG3Ll26RHx8PCdOnCAvL4/KykrtVlVVVevzms3MzAxHR8frbg0aNNDrBQxqtZqHH36Y2NhYnJyc+OCDD+rVxU1qtZoff/yRVatWAdC3b1+6deumcFW64ezszMCBA+nTpw8xMTEcOnSInJwcNm7cyMaNG2ndujUTJkygQ4cO90RA+l+ZmZnA5fd1QgghhBBCCCHqHwkJb6DmD/kpU6awadMmfvrpJ6ysrOjVq5f2/pCQEBYsWEBZWRk2NjYKVqusmJgY9uzZAxhOSHju3DlefvllMjIysLe35+GHH6637a1uhbW1NX369KFbt25ER0ezf/9+cnNzefvtt+nduzfPPfecwQWkU6dOZdeuXaxevZqoqCjatWundEniFly6dIno6GgOHDhAZGQkubm5te53dXUlMDAQb29vrKysaoWC1zrJXVFRwZEjR9i/fz8FBQX88ccfzJs3j1GjRjFu3DhcXFz09dSEEPVQfn4+MTExnDhxghMnTpCQkHDDixXulLW1NZaWlrWCHrVaXefjAHz66aesWrUKU1NTPvroI5ydnXUyji5UV1fzxRdfsHXrVgAGDx5M+/btFa5K98zMzGjbti1t2rTh7NmzHDx4kNjYWI4cOcKRI0do3Lgx48ePp2/fvvdUG8WakFBmEhqu6upqoqOjiY+PV7qUm6qpFaBNmzb16uIIIYQQQggh6isJCW9Co9Hg5+fHnDlzeOCBB5g1axbp6elMnTqV8vJyDh48iKenJ5aWlkqXqpiKigqmTJmCWq2mS5cueHp6Kl0S2dnZvPrqq2RmZuLg4MDDDz+Mg4OD0mXphZmZGZ06daJt27bs3LmT/fv3s2PHDvbt28eLL77IoEGDDOZKdl9fX8LCwtiyZQvvvfeedjaqMDwXL15k37597Nmzh4MHD9Y6MW5sbIy/vz+BgYEEBgZib29/W/s2MzOjY8eOtGvXjtjYWPbt20dubi7Lly9n5cqV9O/fnwkTJtCoUaM6flb6UVZWRnJyMvHx8SQkJHDhwgU0Gg0ajQa1Wl3rNqD9mrGxsTZkvTJs/e/XfH19ady4scLPUgjdKy8vp6CggIKCAlJSUrTBYE1IcSVbW1t8fX1xcnLC2NgYExMTjI2Ntdt/P6+qqqK4uPi6W826qiUlJTp/nmvWrOH9998H4MUXXyQkJETnY9aViooKPvroI/bt24dKpWLkyJH3XZcNlUqFj48PPj4+hIWFceDAAaKiokhLS+OLL77gjz/+YPTo0QwbNqzeX2Co0Wi07UZlJqHhunTpEh06dFC6jFtyZa3FxcVYW1srXJEQQgghhBD3PgkJ/7+qqio0Gk2tK3vVajVGRkao1WpatGjB0qVLeffdd/n000959913CQgI4Pjx44SHh9f7P/LvxocffsixY8ewt7fn1VdfVbocsrKyePnll8nJyaFBgwY8/PDDtx1a3AtMTU3p168fzZs3Z82aNWRnZ/Pll1+ybds2Xn31Vby8vJQuEYCHHnqIbdu2sWnTJvbt20fXrl2VLkn8f/n5+ezZs4fdu3dz5MiRWjNzHBwctKFgo0aN6mRWhLGxMS1btqRFixYkJycTERFBeno6mzZtYtOmTXTv3p3nn3/eoGcWVlRUkJKSQkJCgnZLT0/XyaymKzVt2pTBgwfTt29fOaEm6gWNRkNJSQnFxcUUFhZSVFREUVERFy9e1AaB/91KS0uvuz83NzdtMOPr64u9vX2dXRCj0WgoLy+nqKiIysrKWvdVVFQwb968OhkHID4+nkmTJgEwcuRIhgwZUmf71rXS0lLeffddjhw5gpGREePHj6dp06ZKl6Uoe3t7BgwYQM+ePTl8+DCRkZHk5eUxZ84c/vzzT0aOHMnYsWNxc3NTutQ7kp+fT3l5OUZGRvj6+ipdjhBCCCGEEEKIOyAhIXDy5Ek+/PBDMjMzCQgIoH///kyaNAkjIyOqq6sxNjZGrVYTEhLCnDlzSE9PZ+PGjXh7e9O9e3cCAgKUfgqK2b9/PzNnzgTg5ZdfVrydZUZGBq+88gq5ubk4Ojry8MMPY2dnp2hNSvPw8ODxxx/nwIEDhIeHc+TIER5++GGmTZvG+PHjMTFR9mXAy8uLQYMGsX79et577z3Cw8MVred+l5uby+7du9m9ezcxMTG17nN3dyc4OJigoCBcXFx0NiNVpVJpA8izZ88SERFBXFwce/bs4cCBAzz33HMMHToUIyMjnYx/q4qLi0lNTSUlJYWUlBQSExNJSUm5ZvtBW1tbPD098fT0xMHBAZVKpd2AWp/XfE2tVlNeXk5FRUWtrby8nMrKSsrLyykvLycjI0MbSP7www+EhYUxePBgmjdvbjCzhsWdq66uprS0VDuL7XqbtbU1TZs2pUmTJoqu51lRUUFOTs5VW0FBgTYILCoqori4+I5adRobG2NjY0ODBg20gaC3tzcWFhY6eDaXqVQqLCwsrjlGeXl5nY1z4cIFRowYQWlpKS1btuTZZ5+ts33rWmFhIW+++SZxcXGYmJjwwAMPyAznK1hYWNC1a1c6depETEyMtiX8ihUrWLlyJWFhYUydOtUgunHcjppZvL6+vpiZmSlcjRBCCCGEEEKIO3Hfh4SJiYl06dKFYcOG0a9fP7Zv386XX37Jli1b+OuvvzA2NqaiokL7h6+zszPOzs6ydhpQUlLCQw89hFqtpl+/fvTs2VPRes6ePcvLL79MXl4eTk5OPPzww4qeKDUkRkZGdOnSheDgYNatW0dqaipz584lPDyc6dOnExQUpGh9U6ZMYcuWLezYsYMdO3bQu3dvReu5H5SUlJCbm0tubq72JP611qvx8vIiODiY4OBgRS4C8Pb2Zvz48eTm5rJ27VrOnj3LN998w/bt25k+fTo+Pj46r6G6uprMzMxagWBKSgo5OTnXfLylpSVeXl54eHjg5eWFp6enTl+LSkpKOH78ONHR0eTl5WlnXvr6+jJkyBD69+9/37Rbru+qq6tJSUnRrq0XGxt71Zqft8LLy4umTZtqQ8MmTZpgZWVVJzWWlZWRk5NDdnY2WVlZ2ttXhoG3w8TEBAsLi1rtdK2trbWbjY1NrY/m5ub3ZPhdXV3Ngw8+SGJiIq6urnzwwQeKX8Rzq86fP89rr71GWloa5ubmTJkyxWC6FRgaY2NjQkNDadWqFSkpKURERJCWlsbWrVvZvn07I0aMYPLkyYpfdHerZD1CIYQQQgghhKj/6sfZBx3RaDT8888/9OvXj/nz5wMwffp0/vrrL3777TcmTJjA0qVLtQHhX3/9RVhYmF5OStcHH330EcnJybi4uPDCCy8oWkt+fr42IHRxceGhhx66r1vAXk+DBg2YPHkyx48fZ+PGjaSkpPDMM88wffp0Bg8erFhdbm5uDB06lFWrVvHWW28RERGh+Cyx+kqj0VBcXExOTg7nzp3TBoF5eXna7dy5c5SVlV13H76+vtpg0FBa9bq6uvLII49w6NAhtm3bxvHjx5k6dSqjR49m0qRJdX5CNSMjg4iICPbv309cXByXLl265uPs7Oxwd3fH1dUVd3d3vLy86rTN4a2wtramc+fOdOrUibNnzxIdHc2JEyc4ffo0v/zyC7/99pu2beGVocONfgaE/mRnZ3Pw4EGioqKIjo6+7pp3JiYmmJubY25ujoWFRa3bZmZmlJSUkJmZSUFBARkZGWRkZGhnZqtUKvz9/WnevDleXl5XrYl5rY9lZWVcuHCBixcv1vp4KzPnTE1Nsbe3x8HBAXt7e+zt7bG1tdUGgTWhoIWFRZ20Kr4XzJo1iw0bNmBmZsbHH39MgwYNlC7plqSmpvLuu++SlZWFjY0NU6ZMwdXVVemyDJ5KpSIgIICAgAAyMzMJDw8nJSWFVatWsXbtWqZOncq4ceMMfnZezXqEEhIKIYQQQgghRP11X4eEKpWKjIwMsrOztV+zsrJi2rRpWFhY8NNPP/HWW2/x+eefExERwWeffUZ4eDh///03xsbGClZuGFatWgXA008/rWggp9FomDVrFnl5eTg7O/Pwww/Lmlw3oFKpaNWqFQEBAWzcuJHY2Fhmz55Nq1atFL3y/8EHH2TTpk1ERkby66+/8swzzyhWi6G7ePEi6enpZGZmakPAK7frBVr/ZWlpiZ2dnfYkvqurK0FBQQYbsBsZGdGxY0eaNm3KunXrSElJYcWKFfzvf/9j7NixTJw48Y5DzerqauLj49m3bx8RERGcOnWq1v0mJia4urri5uZWa7O0tKyLp1YnVCqVdk22gQMHcuLECaKjo8nMzOTff//l33//VbrEe15xcTEXLlygsrKy1lZRUXHV5ykpKRw6dIizZ8/W2oe5uXmttfVcXFywsLC45fcdZWVlZGZm1toKCwtJTk4mOTm5Tp6nhYVFrQDQwcFB+7mDgwOWlpb35Gw/XcnNzeXjjz8G4KWXXqJJkyYKV3RrwsPD+fzzz6mqqqJBgwZMmTKl3oSbhsTT05PJkyeTlpbGtm3byMzM5Pfff2fDhg08/fTTdO3a1WCPp6KiIgCDXitYCCGEEEIIIcSN3bchoUajQaVS0aZNGxISEoiPj9e2XLS0tGTcuHEkJiayY8cOCgoK6NKlC6+//jphYWESEAI5OTkkJSWhUqkUb726atUqIiMjMTIyYty4cRIQ3iJra2vGjBnDpUuXSElJ4a+//uLdd99VrB4nJycef/xxvv/+e6ZPn86wYcMMctZuSUkJ8fHxxMXFabfy8vKrwqMrN0dHxzuaGVlSUkJ6ejrp6emkpaWRlpZGeno6+fn5N/1eKysr7O3tsbOz0262tra1bhv6DIXrcXBw4MEHHyQ1NZUdO3aQkZHBkiVLWLFiBRMnTmTQoEFYWVlhZmaGmZnZdVv2lZWVcfjwYSIiIjhw4ECtNokqlYpGjRrRtGlT/Pz8cHJyqlezW83NzWnbti1t27YlNTWVnTt3kpeXV+sxGo3mlgNlUVtJSQmnTp3SHpM123//jW9FTbjr5+dHQEAAHh4ed/WzZmlpib+/P/7+/tqvFRUVcfr0ac6cOUNJSclVa2Bea01MU1NTrK2tte0/r/xobm5+x/WJq33yySeUlZURFBTEwIEDlS7npqqrq5kzZw7Lli0DwN/fn9GjR9dZS9v7VePGjXnssceIiYnRhoXvvfcebdu25dlnnzXINR5r1hWtL61xhRBCCCGEEEJc7b79i67mJNjgwYP56KOPmDVrFt9995123Sg7OzteeuklZs+eTXh4OGPGjOHxxx9XsmSDsm/fPuDyCQ0l1/1LTEzkp59+AmDAgAHS4uo2qVQq+vbtS0pKCtu3b2fixIkEBAQoVs+IESPYvn07sbGxPP3006xdu1bRq+erq6s5ffo0J0+eJC4ujpMnT5KWlnbNx9aEeNdiamqKi4sL9vb2mJiYYGxsrP145e2ajwUFBaSlpd1wLTIHBwecnJxqzQSsuW1nZ3fPt/CraZ/o5+dHUlISO3fuJCsriwULFrBgwYJajzUyMtIGhubm5piZmWFqasqZM2eorq7WPs7c3JzAwECaNm1KQEAAFhYW+n5aOuHn53fNVnDl5eXMnDlTgYruTHJyMj///DNZWVm4ubnh7u5+1ebi4lLnF/JcunSJyMhI4uLitGHg9dajBLTB9I2OcWNjY+zt7fHz86Nx48Y6/1mztbWlWbNmNGvWTKfjiNuXmprKL7/8AsDjjz9usDPGaly4cIGPPvqII0eOANC1a1f69OlTry6iMGQqlYqWLVsSFBTEnj17iIiI4PDhwzz66KOMHDmSRx55xKDW25aQsH4wNTVlxowZZGVlMWfOHKXLuaGaWmtuCyGEEEIIIXTvvv+Lzt/fn2XLlmlnnnzwwQc4OzsDl0/0tW7dGicnJ4WrNDx79+4FoHnz5orVUFRUxAcffIBaraZp06a0b99esVrqMw8PD5o1a0ZsbCx//PEHn3/+uWK1GBkZ8dprr/H444+zfv16lixZwqRJk/RaQ0VFBfv27WPr1q0cO3aM0tLSqx7j4OCAt7c3Xl5eeHt7Y2FhUWvdritvFxYWUllZqW37d7tsbW1xdXXFxcUFV1dX7e36OguwrqlUKpo0aUJgYCDx8fHs2bOH3NzcWuGfWq3m0qVL15w15+DgQNOmTWnatCm+vr4yU9wAlZeX888//7B48WI0Gg1weR2/Y8eOXfVYIyMjXFxccHd3p3HjxrRt25bQ0NDbbqFbWVlJVFQU4eHh7Ny5k6qqqqseY2NjU+vYdHFx0bYGFeJWvffee1RVVdG+fXvatGmjdDk3lJCQwPvvv09ubi4mJiaMGjWKkJAQpcu6J5mZmdG3b19at27N1q1biY+PZ9WqVezZs4dvvvkGb29vpUsE0P6uld+dhs3MzIwPPviA6Ohogw8Ja2oVQgghhBBC6M99HxIC9O7dm+XLlzNu3DgyMzMZN24cLVu2ZP78+Zw9e7ZWyy5x2Z49ewBo0aKFIuOr1WpmzpxJVlYWDg4OjBw50uCvvjdkvXv35uTJkxw4cICYmBjF/l8BGjZsyOTJk/nrr794+umn6devnza41xWNRsOJEyfYsmULO3bsoKSkRHufqampNgys+XitwOF6NarVagoLC7l48SKXLl2iuroatVqNWq2udfvKzy0sLLShgyGteWfIVCoVwcHBBAcHA5f/T6uqqq67VVZWYmNjg4uLi7x2GLCjR4/y1VdfadftCw4OpkOHDhQVFXHhwoVa28WLF6muriYnJ4ecnByOHTvG//73P+3PRk371ZCQkGvOTqiurubo0aOEh4eze/duiouLtfc5ODgQGBhYK6iXY1PcrSNHjrBo0SIAg+9WsXHjRmbPno1arcbR0ZGJEyfKOnR64OjoyIQJE0hNTWX9+vXk5eXx8ssv8+233yq6jnQNCQmFEEIIIYQQov6TkPD/GzZsGBEREbzyyiu8+eabmJiYYGpqysaNGw1yXTQlFRcXEx0dDUDLli0VqWHp0qVERERgZGTE+PHjZebGXXJycqJ169ZER0czd+5cvvvuO0WDk0mTJrFz507S0tJ4+eWXmT9/vk7GycrKYsuWLWzZsqXWLD87OztatmxJs2bNcHV1vas2akZGRjg4OODg4FAHFYtbpVKpMDU1lVZV9VRxcTG//vor69evBy7PqB08eLB27eBr0Wg0FBcXc+HCBQoKCjh79iypqamcP3+ekydPcvLkSebPn4+FhQWhoaHa0LC4uJgdO3awc+fOWutS2tjY0KxZM5o3b46Xl5eEyaJOTJ48Wfu6FBMTA0Dfvn0JDAxUsqzrqqys5Mcff2TNmjUANGnShFGjRsn7Lj3z8/Nj2rRpzJs3j3PnzvHKK6/w7bff4uHhoWhdNSGhtBs1bGq1mri4OFJSUpQu5aZqaoXLFwZJK2MhhBBCCCF0T/6iu0KbNm1Ys2YN+fn5FBcX4+7urvMZTPVRZGQkarUaNzc3RdYAPHr0KHPnzgUurymp9AmSe0XPnj05duwYMTExREZG0qlTJ8VqMTU15bXXXuO5555jwYIFjBs3jl69emnvLywsvK39ZWdnY2VlBVw++XD06FG2bNnC8ePHtY8xMTGhWbNmtGrVikaNGkkgIIRCdu/ezffff8/58+cBaNu2LWFhYTcNJVQqFba2ttja2uLj46O9iOXixYukpqZqt9LSUg4cOMCBAweu2oelpSXBwcG0aNECX19fOTkp6tzatWtrfW5qasq0adMUqubGzp8/z4wZM4iNjQWgV69e9OjRQ34/KsTa2pqHHnqIv//+m9zcXF555RW++eYb3N3dFaupZk1CmUlo2MrKyhRdIuJ2XFlrcXEx1tbWClckhBBCCCHEvU9Cwv+ws7PDzs5O6TIMmpLrEZ4/f56PPvoIjUZDq1atDH79nvrEzs6ODh06sH//fn7//Xc6dOig6Any4OBgRo8ezYoVKxgxYsRd7euxxx677n1+fn60bNmS4OBgWedPiBt44YUXap0I9vLyYuzYsTRr1qxO9p+Xl8f333+vbWft5OTEsGHDaNiw4V3t197entatW9O6dWs0Gg05OTnawDA9PR1jY2OCg4Np1qwZfn5+crJb6NQzzzyDubm59vPAwEA8PT0VrOjaYmJi+OCDD8jPz8fU1JSxY8fSpEkTpcu679nY2PDwww/z999/k52drZ1RqMRFe/B/IaHMJBRCCCGEEEKI+kv+ohO3rSYk1Pe6ddXV1Xz88ccUFBTg6urKkCFD5Gr2OtatWzeioqJISUlhx44d9O3bV9F6pk2bxpEjR+66PZKJiUmtnxUHBwdatmxJixYtsLe3v9syhbgvpKam1vo8KSmJnTt30rJlSyZOnEjHjh3v6MKC0tJSNm7cyG+//UZlZSUqlYpu3brRo0ePOj/xrFKpcHd3x93dnS5dulBdXY1KpZIZg0JvBg8ebNAzYzQaDStXruTnn39Go9Hg4uLChAkTcHJyUro08f/Z2tpqg8KsrCztGoVKrBEpaxIKIYQQQgghRP0nIaG4LZWVlezfvx/Qf0i4YMECjh07hqmpKePHj5e1xnTAysqKbt26sWPHDv7880969Oih6L+zpaUlc+fOpbKystbXS0pKGD169C3vZ/r06bVmbgghbt+Vr7s1awYdP35cuzVu3JgHHniA3r173/SEcXl5OZGRkezcuZM9e/ZQVVUFXJ6dOGzYMNzc3HT+fEBObAvxX3PnzmXx4sXA5Y4Rw4YNk1n2BsjOzk4bFGZmZvLaa6/x22+/6f29Ts1MwprXcCGEEEIIIYQQ9Y9cOi9uy/79+ykpKcHOzo5GjRrpbdwLFy6wYMECAIYOHSpXtOtQp06dsLa2JjMzk5UrVypdDiqVCjMzs6s2IYR++fn5ERAQQEBAAE2aNGHEiBG89NJLdOnSBVNTU9LS0vj000956KGHWL9+/VXhfkVFBXv37uXjjz9m6NChzJgxgx07dlBVVYWTkxODBw9m2rRpegsIhRC1HT9+XBsQDhgwgNGjR8vvWwNmb2/Pww8/jI2NDadOndL+3+lTzd8C33//PWVlZXofXwghhBBCCCHE3ZOZhOK2bNiwAeCO28rdqUWLFlFVVYWHh4feZzDeb8zMzAgLC2P16tX88ccf9OvXT0JZIcQ12dra0q9fP7p3787Bgwc5cOAAmZmZzJ49m3nz5jFhwgQ8PDzYuXMn+/bto7S0VPu99vb2NGvWjGbNmuHh4SHto4VQUFlZGV988QUArVu3plOnTgpXJG6Fg4MDAwcOZMWKFcyfP5+wsDC8vb31Nv7EiRPZtGkTKSkpfPLJJ3z66ad6G1sIIYQQQgghRN2QmYTitqxfvx64HBLqy7lz5/j3338B6NOnj5xI1oNWrVrh5eVFVVUVc+fOVbocIYSBs7CwoEePHrz00kv0798fGxsbzp07x48//sg777zD1q1bKS0txc7Ojk6dOvHoo4/y4osv0q9fPzw9PeV1XQiF/f7772RmZmJnZ0f//v2VLkfchpCQEPz9/VGr1Xz33XdoNBq9jW1tbc0LL7wAwBdffEFsbKzexhZCCCGEEEIIUTdkJqG4ZadPn+bEiRMYGRnRrl07vY27YMECqqur8fX1xd/fX2/j3gmNRkNhYSE5OTnaLTc3FxMTE22LPi8vL4M/Ia5SqRg4cCB//PEHmzdvZvjw4YSEhChdlhDCwJmZmdG5c2fat2/P0aNHOXDgAFVVVQQFBdGsWTO8vb0N/vVP3B8qKys5e/Ys6enpnDp1ioqKCuzt7bG3t8fBwaHWR0tLy3v65/bYsWPai7GGDRuGhYWFwhWJ26FSqRg8eDA//fQTUVFR7Ny5k969e+tt/O7du9O1a1f27dtH//79CQkJueoY+u9HCwuLWsdUcXGx3uq9H5mamjJ9+nRycnKYP3++0uXcUE2tNbeFEEIIIYQQuichobhlGzduBCA4OBh7e3u9jJmVlcW6desA6N27t0GdpKusrCQ7O7tWGJiTk0N5efk1H5+VlcWePXuwtrbWBob+/v6Ym5vrufJb4+3tTWhoKEePHuWHH37gp59+0muLWSFE/WViYkK7du30ekGJEDdyZSiYnp7O6dOnr3pMVlbWNb/X1NRUG3D8N9yorq7WWc36UFZWxqxZs4DLbUYDAgIUrkjcCUdHR7p3786uXbv46aef6NChA9bW1nob/4UXXuDo0aNkZmaSmZmpt3HFrTEzM+PLL78kOjra4EPCmlqFEEIIIYQQ+iMhobhlNesR6nOdmn/++Qe1Wo2fnx+NGjXS27g3otFoOHr0KGvWrLmj7y8pKeHYsWMcO3YMgMaNGxMYGEiTJk0Mbu2/vn37EhsbS3x8PFu2bGHgwIFKlySEEELckszMTOLj4zl16tQ1Q8FbVVlZSV5eHnl5eXVYnWGYO3eutBm9R3Tr1o2YmBjOnz/P33//zbPPPqu3sV1dXfnjjz9ITU2luLiY4uJiSkpKtLev/LykpISKiopa369Wqzl37pze6hVCCCGEEEII8X8kJBS3pLy8nG3btgH6CwlPnz7N5s2bgctrERqC3Nxc1q9ff1cnG/8rLS2NtLQ0tmzZgqenJyNHjsTFxaXO9n83bGxs6NWrF1u3bmXOnDl0795dr1emCyGEELfr0qVLbNu2jcOHDytdikE7evQoq1atAu7tNqPV1dUUFRVRUlJy083GxobQ0FBatmyJpaWl0qXfFhMTEwYPHsyCBQtYuXIlQ4YM0esFdm5ubri5ud3R95aUlDB06NA6rkjUUKvVnD59ul7M8qypFcDX11e6mAghhBBCCKEHEhKKW7J7925KS0txcnLS27qA8+bNQ6PR0LRpU7y8vPQy5vVUVFSwa9cuIiIidDpOZmYmP//8M4MGDaJ9+/YG0V61Y8eOREdHc/78eebPn89TTz2ldElCCCEUdvHiRWJiYqioqMDHxwcfHx+DCJni4+PZsGEDRUVFSpdi0MrKyrQt/e6lNqNqtZrc3Fxt28usrCxycnJuuS1scXExmzZtYtOmTbRo0YLWrVvTqFEjg3g/div8/f0JCgoiPj6eX3/9lZkzZypdkjAAZWVlNG7cWOkybsmVtRYXF8vFiUIIIYQQQuiBhITiltS0Gu3YsaNeTpQkJCQQHh4OQK9evXQ+3vVoNBoSEhLYuHEjhYWFeht348aNJCUlMWLECGxsbPQ27rUYGxszYMAAFi1axLJly2jevDndunVTtCYhhBD6V1VVRXx8PEeOHCE1NfWq+93c3PDx8cHX1xdfX1+9rV8Ml08mb9y4kZMnT+ptzPrsyjajAwYMULqcO6JWqzl//rw2EMzMzCQ7O5uqqqo62X9MTAwxMTE0aNCA1q1bExoaiq2tbZ3sW5fCwsJISEggMjKSw4cP07ZtW6VLEnpw+vTp67ZELisr03M1QgghhBBCiPpEQkJxU2q1mrVr1wKXQ0J9+OWXXwBo2bIl7u7uehnzv4qLi1m/fj3x8fGKjJ+cnMxXX33Fo48+ire3tyI11AgMDKR169YcOXKEGTNm8O2339KiRQtFaxL6p9FoOHLkCOfOncPR0RFnZ2ecnJywtbWtN7MshBC3r7y8nEOHDrF//35KS0uv+7icnBxycnKIiooCwNHRkYCAAAICAmjUqBGmpqY6qS8xMZHFixfrZN/3opiYGG2b0eHDh2Nubq5wRbdGrVaTlZVFenq6dp3J8vJynY9bUFBAeHg44eHhNG/enM6dO+Pp6anzce+Uk5MTHTp0IDIykt9++405c+YoXZLQsdOnTxMcHHzD1+calpaWODs766EqIYQQQgghRH0hIaG4qVWrVpGSkoK1tTXt2rXT+XgpKSkcO3YMlUpF3759dT7ef2k0GmJjY1m5cqXex76WP/74g/HjxxMcHKxoHUOHDqWsrIz4+Hhef/115s2bh6urq6I1Cf2prq5m9erVxMTEXHWfqakpTk5OODs71woPXVxcdBYK1FfV1dVcunQJlUp1y5sQSikvL+fgwYPs37//jmai5Ofnc/DgQQ4ePIilpSW9e/embdu2dbbGlFqtZvv27TpvBX4vqa6u5rvvvgMutxnVVwv5O1FdXU1GRganTp3i1KlTnDlzhoqKCkVrOnHiBCdOnMDf35+RI0cq3u3henr06MGhQ4dISkoiIyND8bb9Qrfy8vIoLS1lwYIF1/x7oaysTNsFJDo6Gl9fX32XKIQQQgghhDBgioSEpaWl2rVibG1tsbKyUqIMcQt2797Nm2++CcDo0aP18n+1fv16AIKCgrCzs9P5eFe6URCipGXLltG/f386deqkWGhgZGTEqFGj+PPPP8nJyeHdd9/l+++/N4g1qIRuXbp0iWXLlpGWlnbN+ysrK8nOziY7O/uq+3r16kXHjh3v65+T8vJykpKSiI+PJykp6ZZPcltbWxMQEIC/vz/+/v7yu1LoTXl5OZGRkezYsaPO9llWVsaGDRs4dOgQ/fv3v+s18IqKilixYgWnT5+uowrvDzt27CAlJQVTU1PCwsKULucqGo2Gw4cPc/LkSc6cOVNnrUPrWkpKCnPnzmXChAkGOavQysqKhg0bkpaWRmRkJKNHj1a6JKEHwcHBtGnT5qqvl5SUaG/7+PjosyQhhBBCCCFEPaCXkLC0tJQ///yT1atXc+zYMc6fP1/rfkdHR1q1asXIkSN55JFHZIHyK0RFRdW6Srlx48a4uLjodEyNRsPmzZv59NNP2bt3LwD29vaMHTtWp+PC5TBiy5YtAHpfQ0Wj0bB27VqDCwhrbNmyhYKCAgYOHFhnszBul5mZGRMnTuTXX38lKSmJL7/8knfffVdmO93DCgsLWbRoETk5OXf0/Tt37mTnzp307NmTTp06GUxYWFVVhUqlwtjYWCf7Ly0tJSEhgbi4OJKSku5oHyUlJRw7doxjx44B4OnpqQ0Nvb29FXsdEPcujUZDTEwMmzdvvqW2dXfi3LlzLFy4kICAAPr163fbM9Krq6tJTExk2bJlOqlP1x599NFax26bNm149dVX9fJ7tLq6mnnz5gHQrVs3g7vwoKqqijVr1hjs+7D/KiwsZO7cuYwaNYqWLVsqXc5VAgICSEtL48CBAxISCiGEEEIIIYS4Lp2HhNu3b+fBBx/k3LlzaDSaaz7m/PnzhIeHs2PHDj755BPmz59Pv379dF1avfDfdpumpqY899xzvP3223W+noRarWbVqlV89tlnREdHa8cbNGgQkyZN0susvp07d1JSUoKDgwN+fn46H+9K4eHh2pPxhurQoUNcvHiRMWPGYGZmpkgNDg4OTJw4kX/++Yfw8HACAgKYNGmSIrUI3crNzWXhwoUUFhbe9b527drFrl276NGjB506dcLS0rIOKry+qqoqLl68yIULF7TblZ8XFRVhZGSEs7Mzrq6uuLq64ubmhpubG3Z2dnd0wr6wsJD4+Hji4uJIT0+v8+eUmZlJZmYmu3fvxtzcXDvDMDAwEFtb2zofT9xf8vPzWb9+PampqXoZLzk5meTkZNq1a0evXr1ueIFYUVERSUlJJCUlkZqaqnjLybvx3wsu1q9fz7Rp03B0dNT52Fu3buXs2bNYWlrqbY3pW1VcXMzSpUs5e/as0qXctlWrVpGVlUW/fv0M6uKNwMBAtm7dyuHDhykrK9P5710hhBBCCCGEEPWTTkPCo0ePMmTIECoqKhg8eDATJkygTZs2eHt7a2fHlZSUcPbsWaKjo1m6dCnr169n2LBhHDhwgNDQUF2WVy+4urpqTzhUVVWRl5fHN998w2+//cabb77Jyy+/fNfroVRVVbF48WI+//xz4uLiALCwsGDYsGGMHz9er4vbr1u3Drh8Zb0+Z6dFRkZqZ00ausTERObNm8eUKVMUm5XVqFEjBg0axIYNG5gzZw6NGzemU6dOitRyr6iqquLs2bOkp6djbGxMcHCwXo+9/0pPT9fOOKlLu3fvZvfu3XTv3p1OnTrV2UyWwsJCEhISSExMJCcnR9vS+kbUajW5ubnk5ubW+rq5uXmt4NDS0pLy8nIuXbpEeXm5dqv5/NKlS1y6dIkLFy7UyXO5FeXl5Zw8eZKTJ08Cl9szt2vXDj8/P5nZK25LdXU1ERERhIeHKzJ+VFQUUVFR9OvXjw4dOmBiYoJarSYjI0MbDF6rlXF9tW3bNm0gOnr0aLKyssjOztZ5SFhVVcU///wDQNeuXTE3N9fpeLcjJyeHxYsXc/HiRaVLuWMHDhwgNzeXMWPGGMwMTWdnZxwcHLhw4QJHjx6lc+fOSpckFGJiYsIzzzyjvW3I6lOtQgghhBBC3Ct0+s77448/prKyksWLFzNhwoRrPsbOzo6QkBBCQkKYPHkyS5cuZdKkSXz88cesXLlSl+XVC3/++af2ZJJGo+HQoUP8/vvvJCUl8f777/Pjjz/y3nvv8cQTT9z2zLLy8nL+/vtvvvjiC+1aY9bW1owePZoxY8Zgb29f58/nRtLS0oiNjUWlUtG6dWu9jRsbG8umTZv0Nl5dyMzMZMmSJTz44IOYmpoqUkO7du3Izs4mOjqa999/n99//x1fX19FaqmP1Go1WVlZpKamkp6ezunTp2utvRQeHo6LiwshISE0a9ZM522Gr3TixAmdv/7u2bOHPXv20K1bN9q3b4+tre1thVsajYbc3FwSEhJISEggMzOzzmorLy/nzJkznDlzps72qWvx8fHEx8fToEED2rZtS2hoqLTuFjd15swZ1q1bd1VQroStW7cSFRWFt7c3ycnJlJWVKV2STrRv317bmSEgIEAbEoaEhOh03I0bN5KVlYW1tTUdOnTQ6Vi3IzExkcWLFytdRp1ITU1l7ty5TJw4ETc3N6XLQaVSERAQQFRUFAcOHJCQ8D5mbm7OTz/9pHQZ2otRb8TZ2dkgahVCCCGEEOJ+otOQsOYE8PUCwmuZMGECP//8M3v27NFhZfWTSqWiQ4cOtGvXjp07d/Lnn3+SkZHB888/z9dff81HH33EAw88cN1WR5mZmRw4cIDIyEgiIyOJiorSLmTv4ODA2LFjGTFixF3PTLxTa9euBS7PiNFXDWlpaaxYsUIvY9W1U6dOsWLFCsaPH6+zddVuRKVSMXjwYPLy8jh9+jTvvvsuP//8s2I/P4auJtRKS0sjLS2NU6dOUV5efsPvOXfunLZNZ01gGBISgouLi85mi+3fv1+7Lqg+7N27l71792JmZoajoyOOjo40aNAAR0dHnJyccHR0xMbGBpVKhVqt5vTp08THx5OQkKDXmXv1RUFBAdu2bWPbtm20aNGCtm3b4uvrK7MLRS2XLl1i+/btREVFKV1KLQUFBRQUFChdht40atSIPXv26HymZEVFBfPnzwege/fuil1cdCWNRsOBAwf0+vtGHy5cuMCvv/7KuHHjdB783orAwECioqKIjIxEo9HI7wKhCGdnZ6ysrJg8efJNH2tlZUVcXJxceCiEEEIIIYQe6TQkLC4uxsPD47a/z93dnUOHDumgonuDkZERffr0oUePHmzYsIF58+aRlpbGlClT+PLLL/nss8/o1asX0dHRtULBa63z4uLiwvjx4xk6dKhirSvh8gnLrVu3AtC2bVu9jJmdna1tvVVfJSYmsmbNGkaOHKnIiR9jY2PGjRvH3LlzOXPmDJ988gmffvqpIqGloamqqiIzM5PTp09rZ6XdzcyYKwNDZ2dnQkJCaNGiRZ21JNVoNGzevJnIyMg62d/tqqioIDs7+5ony01NTWnQoAFFRUX37OwiXYiJiSEmJgZnZ2fatWuHv79/rYtI6vO6bvVJeXk5eXl5nD9/Xvvx/PnzGBkZ4e7ujoeHBx4eHri5uek8vNFoNMTFxbFx40aKi4t1Opa4uUaNGgGQlZWl03HWrVvHuXPnsLOz09t7rBuprq5m/fr1HDlyROlSdGb58uV0796d3r17KxrMNW7cGCMjI3JyckhPT6dx48aK1SKUo9FoyMvLAy4Hdvr+mfT19SUuLk5bw/XExcUxefJkkpKSsLS0VKRWIYQQQggh7kc6DQn9/PzYtWsXJSUlt9z2rLi4mF27duHn56fL0u4JJiYmDB8+nH79+rFq1SoWLVrE8ePHGTp0KCqVCo1GU+vxRkZGNGrUiODgYIKDgwkJCcHX19cgAp1du3ZRXFyMg4ODXv7vL1y4wMKFC3U+jj4cP34cS0tLBgwYoMgf0jY2NkycOJE//viDyMhI/vjjD5544gm916G00tJSzpw5UysU1JW8vDzt2n5BQUF069YNLy+vO9qXRqPh7Nmz7N69m+Tk5DqutG5UVlYaRDvE+iovL6/etVSuj6qqqjhz5gzZ2dm1wsAbhXFZWVm1ghIXFxc8PDy04aG7u/tdX8CjVqvJzs4mNTWVpKQkTp8+fVf7E3WnJrDR5UzCS5cuad/vdO/eXfE1vsrKyli2bBnp6emK1qEPe/bs4dy5c4wdO1ax99qmpqb4+fmRnJxMZGSkhIT3qdLSUlxdXYHLf2sr0Y7c19f3lmcHhoWFAcrVKoQQQgghxP1Gp2cKJk+ezNtvv01YWBg///zzTdeZO3LkCM8++yznzp3jpZde0mVp9xRLS0seeOABhg4dyuLFi/n333+pqKjA2dlZGwgGBwfTtGlTLC0tlS73mtavXw9AmzZtdB50qdVqFi1adE/NooiMjMTV1ZU2bdooMr6HhwcjRozg33//ZfHixbRr106xWpSQlJTEokWLFBm7Zi26gIAA+vXrpz0JdDMajYakpCR2795NRkaGjqsU4t6VnZ3N4cOHOX78+F3Pzjx37hznzp3j+PHj2q916dKFjh07atexu1UajYaEhAQ2b94srXkNVM1MQl2GhFu2bCE/Px8HBwe9rvd8LRqNhoULF95Xv3Pi4+MJDw+nX79+itUQEBCgDQknTpyoWB1CCCGEEEIIIQyTTkPC6dOns3XrVnbs2EG7du1o3Lgxbdu2xdvbG2tra1QqFcXFxZw9e5bo6GhSU1PRaDT07t2b6dOn67K0e5KdnR1PPvkkDz74IJcuXaqzNoS6lpubS0xMDACtWrXS+XinT5/m3LlzOh9H344ePapoMNeiRQsSEhKIjY0lJibmvgoJ/ztrVwnJyckkJyfTunVrevfuja2t7TUfVxMc7N69W+ct7oS4V1VUVBAbG8vhw4d1HnhEREQQERGBn58foaGhBAUF3bQt6fnz59m0aZPBzg4WlwUGBgKXZ5RWVFRgZmZW52PUrPFoCJ0jysvL76uAsEZERATdu3dXrK1/zezBxMREWZdQCCGEEEIIIcRVdBoSmpiYsHnzZr744gu+/fZbUlNTSU1NBdD+gXrlyXVHR0deeukl3njjDcXbIdVnNjY22NjYKF3GLdu9ezdw+QTW7c6UuBM1geS95syZM7fV2lcXHBwcACgqKlKsBiU0adKERx55RLvuk5KOHDnCkSNH6NGjB126dMHc3By4/Fp78uRJ9uzZQ05OjqI1ClFf5eTkEBUVRVRUlN7HrnkPZWZmRrNmzWjVqhW+vr61TvhXVFSwe/du9u3bp/f6xO3z8vLC3t6eixcvcvbsWZ20W78yiFRaSUmJ0iUoJjo6mi5duigytpOTE0ZGRpSWlpKTk4O7u7sidQghhBBCCCGEMEw6T+JMTEx45513ePPNN9m3bx/Hjh3j1KlTFBUVodFosLW1pWHDhoSGhtK1a1fFr3K+F5SVlWFmZlZv/i137doFQEhIiM7Hqq6uJi4uTufjKCUxMVHRdmI17WwLCwsVq0Epvr6+PPnkk0RERBAeHq50OezevZvDhw/Tq1cvzM3NtWsjCSFuT2VlpXbW4NmzZ5Uuh4qKCu3FAA0aNKBly5a0atWKjIwMtmzZct9dpFGfqVQqmjdvzr59+0hLS9NJSNikSRPg8tqkupqteKvu55Dw0KFDdOrUCSMjI72PbWxsjLOzM7m5uaSlpUlIKIQQQgghhBCiFr1N1zM2NqZHjx706NFDX0PeNzQaDWfOnOHAgQPs37+fmJgYPD09+euvvww+KDx37hwnTpwAIDg4WOfjpaSkUFZWpvNxlKJ0SGhlZQXcnyEhXH6d6969O82aNWPDhg2kpKQoWk9JSYl2vU8hxO3Jz88nKiqKo0ePGuzvjYKCAnbt2qW92EbUP82aNdOGhLrg7OyMo6Mj+fn55OTk4OPjo5NxbsX9HBJeuHCBhIQEvbzXvRZXV1dyc3NJT0+nc+fOitQghBBCCCGEEMIwSU/PeqqiooLjx4+zf/9+Dhw4QGZmZq37z5w5w6lTp3RyVXpd2rNnDwA+Pj56aTVaE0jeq+Lj46msrLzpelW6cj/PJLySo6MjDz74ICdOnGDz5s339YlRIeoTtVpNUlISUVFRsp6f0IvmzZsD6CwkhMuzCWveK0pIqJyDBw8qGhKCbn/OhBBCCCGEEELUTxISGriUlBRt8KLRaEhKSiIyMpJDhw5x6dIl7eNMTU3p3bs3Q4YM4e+//+bIkSMkJSUZfEioz1ajlZWV9+x6hFdKS0vTthfTNwkJ/49KpaJFixYEBASwbds2oqOjlS5JCHEdJSUlREdHc/jwYS5evKh0OeI+UhMSpqen62yMmpBQ6XUJ7/eQMD09nZycHNzc3PQ+toSE9zcTExMefvhh7W1DN3ToUJycnOpFrUIIIYQQQtwL5J23gXvxxReve5+HhwdDhgxhyJAhhIWFYWNjA1wOFo8cOUJycjIDBgzQV6m37fz58xw/fhzQT6vRxMREnY9hCBISEhQPCWVNrP9jaWnJsGHD8PPzY8WKFUqXI4S4QmZmJgcOHLgvLiARhqkmJMzKyqKsrEz7e7Qu1bwnkJBQeQcOHGDEiBF6H7cmJDx16hTV1dUGvxyBqFvm5ub8/fffSpdxyz788EPatGmjdBlCCCGEEELcNxQJCbOysjh+/DjHjh3j+PHjxMTEcOzYMSVKMXgeHh4YGRlpP/f29mbw4MEMHTqU0NDQWvfVqFmTLikpSW913ondu3cD4OXlhb29vc7Hu9dbjdZITExEo9GgUqn0PnbNmoRFRUVyEuo/mjVrRmlpKRs2bFC6FCHue5WVlYSHh3PgwAGlSxH3ORcXF+16cadPn6Zp06Z1PkZNSHju3DlFW5JLSAhHjx4lLCwMa2trvY7r4OCAqakplZWVZGRk4Ovrq9fxhRBCCCGEEEIYLp2GhBUVFcTGxmrDwJrt/Pnzuhz2nhIfH3/ba/XVhITJycmKhUW3Qp+tRisqKoiPj9f5OIaguLiYzMxMvLy89D72la1xL168iKOjo95rMGTt27enuLhYG5ALIfTv7Nmz/O9//5P3IsJgNG/enPDwcNLS0nQSEjo7O9OgQQMKCgrIzs5WbF1CCQkvO3z4MD169NDrmCqVCldXVzIyMkhLS5OQ8D6j0WgoLS0FLl/QZ6h/G9YoKyujpKSkXtQqhBBCCCHEvaDOQsKMjIxaYeCxY8dISkqiurq61uM0Gs1V3ytv/utWSEgIZmZmlJSUkJSUpFjryRspKCjQzh7VR0h46tQpnY9hSBITExUJCY2NjbG0tKSsrIzz589LSHgNvXr1oqSkhMOHDytdihD3nbS0NP755x+lyxCilpqQMCEhgYEDB9b5/lUqFU2aNCEyMpLk5GQJCRUWFRVF9+7d9f73T01ImJqaSs+ePfU6tlBWaWmpdlmK4uJivc9kvV3dunUD6ketQgghhBBC3AvuKCSMiYnh0KFDtdqFFhQUXPW4awWCJiYmBAUFERoaSqtWrQgNDSU0NPROyhDXYWpqyqBBg1i9ejUfffQRv/zyC7a2tkqXVUvNrD4XFxccHBx0Pt5/w+p7nS7WNLoVZWVllJWVAeilhWx9pFKpGDx4MOXl5fdNC1whDEVOTo7SJQhxlb59+/L999+zYcMGxo8fj4eHR52P0b17dyIjI9m9ezdubm56uUDrv5ycnMjLy9P7uIamqKiI8vJyLCws9DpuTUgk60YLIYQQQgghhLjSbYWE+fn5DB8+nP3799f6+rXCQLgcErRq1apWGNisWTPMzMzuvGJxS+bOncvRo0c5deoUH330ETNnzjSo9eFSUlIAcHd318t4+hrHEDRq1IgOHTooMnZGRgZweZ1JV1dXRWqoD4yMjBg9ejStW7dm7969pKWlKV2SEPcFef8hDNGwYcPo06cP4eHh/PTTT3zyySd1PsbgwYNJSEhg7dq1rFy5EisrKxo1alTn49xIv379SEhI0OuYhkqJkFCtVgMY1N8DQgghhBBCCCGUZ3Q7D37vvfeIiIhAo9HU2oyNjTExMUGlUqFSqTA3N+eff/6hoKCAnTt38t133/HII4/QunVrOUGnJy4uLqxevRoLCwuioqL49ddflS6pluTkZEB/4Z29vb32Cup7mY2NDWPGjMHI6LYO7Tpz5swZQD8tZOs7lUqFn58fDz30EI899hhBQUFKlyTEPc/c3FzpEoS4ikql4ocffsDY2Jh9+/YRGRmpkzFefPFFunXrhlqtZtGiRWRnZ9f5ODfi5ORE37599TqmoSovL9f7mDUXdSr1HlEIIYQQQgghhGG6rb8S169fr709btw4/vzzT6KioiguLubQoUOEhoai0WioqKhg6tSpvPrqq1y6dKnOixa3plWrVixYsACAFStWsGnTJoUr+j/6nklYE8jc68aMGaNoGFozk1BCwtvj5eXFhAkTeOaZZ2jdurVeWvAKcT+SC5WEoQoJCeGll14C4IcffqCioqLOxzA2Nubdd9+lZcuWVFZWsnDhwmsuF6BLXbp0ua+6O1yPEiFhzUxCCQmFEEIIIYQQQlzptv5KzMrKQqVS8eqrr7J06VKmTp1KmzZtMDc3p1WrVhw8eJBPPvkEMzMz1Go13377LaGhoUREROiqfnETY8aM4f333wfg66+/5uTJkwpXBKWlpZw9exYANzc3vY17r4eEffr00XvrsCtpNBrt/2uzZs0Uq6M+c3FxYfjw4bz44ou8/vrrTJkyhbCwMJo1a4ajo6PS5QlR78lMQmHIZsyYgYeHBxkZGSxbtkwnY5ibm/Ppp5/i5+dHcXEx8+fPp7i4WCdjXYuRkRHDhw/X23iGSmYSCiGEEEIIIYQwFLf1V2JlZSUAXbt2veb9xsbGvP3220RHR9OxY0c0Gg1JSUn06NFDZhUqaMaMGYwaNYrKykree+89zp07p2g9qampANja2mJtba23ce/lkDAgIIBu3bopWkNeXp52jZ17+d9aXywtLfHz86Nr166MHTuW559/njfffJOHH36Y/v37SxArxB2QmYTCkNna2jJ79mwAFixYoLN2oDY2NsyaNQt3d3cKCgpYuHChXkMrDw8PunTporfxDJGEhEIIIYQQQgghDMVt/ZU4e/ZsHnjgAVxcXG74uODgYCIiIvjyyy+xsLCQWYUKMzIy4p9//qF58+bk5+fz3nvvKXJyokZNq1F9ziKEyyffXF1d9TqmPtjZ2TFq1ChUKpWiddSsR9i0aVOMjY0VreVeZW5uTqNGjejcuTNjx47lueeeo3nz5kqXJUS9Ud9mEvr6+ipdgtCzSZMm0bNnT8rLy/n55591No6Tk5P2fXp2djZLly6lqqpKZ+P9V69evWjQoIHexjM00m5U6JOxsTFjx45l7Nix9eI9et++fetNrUIIIYQQQtwLbuuvxFdeeYX58+dfdybhlWrakh47dozu3btrZxX27NlTZhUqwMbGhtWrV2NnZ0dCQgKzZ8/WXlGsb8nJyYD+1iO80r04w23s2LFYWVkpXYa0GlWAk5MTY8aM4YknnsDf31/pcoQwePUlJPT09GTy5MlMnTqVF154Qe8X1QjlqFQqfvzxR4yMjNizZw8HDx7U2Vje3t58++23mJiYkJaWxqpVq7RBkq6ZmpoybNgwvYxliGQmodAnCwsLli9fzvLly7GwsFC6nJuaNWtWvalVCCGEEEKIe4HO/0oMCAhg165d/PDDD1hbW1NdXS2zChXi5+fHqlWrMDIyYtu2bSxdulSROmpmEkpIePf69++Pj4+P0mUA/xcShoSEKFzJ/cfDw4PJkyfz0EMP4eXlpdOxzMzMDCKUFuJOGHq7URcXF8aPH89jjz2Gv78/KpWKBg0aMG3aNLkA4z7SvHlzXnjhBQB++OEHKioqdDZW06ZNmTlzJkZGRpw8eZKNGzfq7SKyxo0b07p1a72MZWhkJqEQQgghhBBCCEOht78Sn332WWJiYggLC6s1q3D69Okyq1CP+vTpw3fffQfAnDlziImJ0ev4arVauyahEjMjGjZsqPcxdaVp06Z06tRJ6TIAuHDhgnatSwkJldO4cWMeffRRxo8fj7Ozc53t19zcnJYtWzJx4kRee+01pk+fzjPPPMPgwYNp1qyZXtcWFeJumJiYGOQJ8gYNGjBy5EieeuopgoODr2ofbWZmxpgxYwgLC1OoQqFvH3zwAe7u7pw9e5Z58+bpdKy2bdvy7rvvAhAVFcXatWu165DrWr9+/bCxsdHLWIZEZhIKIYQQQgghhDAUJvocrGHDhmzZsoXff/+d1157jYsXL/LNN9+wbt064uPj9VnKfe3ZZ58lMjKSBQsWMG/ePGbPnq23sVUqFZaWlpSXl3Px4sU6DTJuhZmZGS1btuT48eN6HbeuOTo6MmLECMXXIQSorKzUzkpt1qzZfb3GkCFQqVQEBwfTtGlTjh07xq5du7h48eJt78fCwoKgoCCCg4Px8/PDxKT2rwsXFxdcXFxo3749Go2G8+fPk56ezqlTp0hPT6e4uLiunpIQdapRo0bai1WUZmtrS48ePWjduvVN115SqVR07doVd3d3FixYoKcKhVLs7e359ttvmThxIosWLcLDw4OhQ4fqbLzevXtTWFjIt99+y5EjRzhz5gxjxozRedcHS0tLhg4dypIlS3Q6jqFRco1sQ3jvKPSrpKREG8YXFxcb/MVdbdu2BWDv3r1YWlpe8zHOzs6ybq8QQgghhBB1RK8hYY3HHnuMwYMH8+STT7J+/XqSkpKUKKPOqNVqNBpNrRN8Go3GYP8IV6lUfPzxxyxcuJDDhw9TUFCgt2BHpVLRo0cP1qxZw4kTJxRZR2348OGYm5tz6NAhvY9dF3x9fZkwYcJ1/2jWJ41Gw5o1a8jOzsbc3Fw7E0Eoz8jIiNatWxMaGkpJSQlFRUUUFxdTVFSkvf3fzy0tLbXBYKNGjW4aWtRQqVQ4Ozvj7OxMu3bt0Gg0FBQUEBkZqdP1tIS4Ew888ADR0dHs3LmT0tJSvY1rbW2Nm5sbbm5uuLu74+7ujrOz823P6vH39+ett97ixIkTHD58mMzMTB1VfO+wtLTEzs4OW1tbbGxstLev3HJzc4mNjSUuLs5gOlxMmDCB2NhYPv74Y7755hvc3Nxo3769zsYbMWIE3t7efP755+Tl5TF37lz69+9Phw4ddPqetmnTpkycOPG+CQr9/f0JDQ1VugwhDI6zszOWlpaUlZUB0K1bt+s+1srKiri4OAkKhRBCCCGEqAOKhIQAnp6erF27lgULFvDyyy8rVcZdi4uL47vvviM5OZnOnTvTrl077Qyv2wkKy8vLa7UeKiws1FXJwOWZFE2bNiU+Pp6EhAS9tq3s3bs3a9asIT4+nqFDh95yEFFXjI2NGTx4MB4eHqxZs0avY9+t0NBQhgwZctWsLqVERERw4sQJVCoVM2fOVGSdySvp+ziqD1QqFTY2Njdt51bThqwuTgSrVCocHR0ZNGgQ3bt3Z//+/bIGbT1yrx9HxsbGtG/fnpYtW7J//37S0tLIycmps/Z/RkZGuLi4aAPBmq0uWyqamZnRpk0b2rRpQ1ZWFgcPHuTo0aN1tv/6wMjISPva9t+tJgys2W7ld6atrS3+/v4MGTKE1NRUbWB4p+sB1tVx9OGHH3Lq1Cn++ecfPvzwQ3744QcaN258R/u6FW3btuX333/nyy+/JCIigk2bNpGRkcGwYcMwNTXV2bhNmzblySefZNGiRRQVFelsHKVZW1szcuRIg72QUAgl+fr6Eh0dTXBwMHD9mYRxcXFMnjyZvLw8CQmFEEIIIYSoA4onDZMnT2bAgAFKl3FH4uPj6dKlC0OGDKFx48bs27ePhQsXEhUVxccff3xbQeHnn3/Ohx9+qIeq/0+7du0UCQlbtGiBs7MzeXl5pKSk0KRJE72NfaXWrVvj6urK0qVL68UJqbCwMLp06WIwJ5aSk5PZtm0bAC+88IJBXBWvxHF0r9DVz5WNjQ39+vWja9euHDhwgD179uhkHFF37pfjyNzcnF69etGrVy80Gg2FhYVkZ2eTk5Oj3c6fP3/N7zUyMqJBgwY4Ojpe86M+L37x8PBgxIgRdOrUic2bN5OWlqa3se+Uubk5Dg4ONGjQAHt7exo0aKD93NLSEiMjI+1rkkqlumqDy+tL6uJ1y9jYmMDAQAIDAxk6dCjJycnExsZy4sSJ29pPXR1HKpWKOXPmkJ6ezu7du3nrrbf4+eefcXR0vOt9X4+DgwOffPIJK1eu5OeffyYmJoZz584xYcIEHBwcdDauu7s7jz/+OEuXLiUjI0Nn4yhp1KhR9+UajELcKh8fH+3t0NBQg2+NKoQQQgghxL1A8ZAQLq9tVd9oNBrmzJlD//79tWsDnT17lhUrVvD2229TXl7OrFmzbvkE1ltvvcUrr7yi/bywsLDWH0m60L59exYsWKD39SCNjY3p1asXK1as4MSJE4qFhABeXl488cQTLF++nNOnTytWx82MHz9ee1WtITh//jzLli0DYMiQIYwYMUJnY+3evZvY2NhaX6usrLzmY5U4jsStsbKyok+fPnTu3JmDBw+yc+dOpUsS13E/HkcqlQp7e3vs7e1p2rSp9uuVlZXk5uaSk5ODWq3G0dERR0dH7OzsbrtNqK65ubkxZcoUEhIS2LJlCwUFBXqvwczMDGtr62tutra22iDQwsLCYC54uRETExOCgoIICgpi+PDhJCUlXbXGa1VVFeHh4Vd9b10eR+bm5vz777906tSJ5ORk3nvvPb7++mvMzc3vaH+3QqVSMXbsWAICAnjzzTfJzs7m119/ZcKECTqdyWhra8ujjz7KiRMnCA8P58KFCzobS9+6dOmiSJt9Q3PixAkSEhJqfe1OZ+wKIYQQQgghhLh7BhES1kcqlYrk5ORarZe8vb159NFHMTMz47333sPDw+OWW6mam5vr9GTPtdSsa5OQkKD3NRR79+7NihUriIuLo7KyUqctrG7GxsaGhx56iE2bNhEVFaVYHddia2vLpEmT8PDwULoUrfLycpYsWUJlZSXNmjXjxRdf1NnPzoIFC/jjjz9u+fFKHEfi9lhaWtKzZ086derEoUOH2L9/v17XhRM3J8fR/zE1NcXLywsvLy+lS7klKpWKoKAgAgICiIyM1M72rmsODg74+vri6+uLu7s7NjY2WFlZKfq7XNdMTU0JCQm56uvl5eXXDAnr+jhycnJi/fr1tGvXjpMnT/LFF1/w7rvv6jysDg0N5e+//+b9998nKSmJ+fPn079/fzp27Kiz3/0qlYoWLVoQHBxMVFQUmzdv1sk4+uTp6UmfPn2ULkNx+fn5vPLKK9e92EsIIYQQQgghhP5JSHgHagK1Hj16sGrVKhITE7Wz4WxtbRk/fjzp6en873//Y9KkSYqv03Y9oaGhGBkZUVBQwLlz53B1ddXb2MHBwbi5uZGTk0NSUtI1T7zpk7GxMUOGDMHDw4O1a9cqWksNT09PJk6ciK2trdKlaGk0Gv7991/y8vJwdnbmo48+0tlJ4UWLFmkDwsmTJ9cKSsvLy/n+++91Mq7QD3Nzc7p160bnzp3Jy8sjLy+Pc+fOabfz58+jVqtve7+mpqZUV1ff0ffqg5WVlYSiQudMTEzo2rUrrVq1Ijw8nCNHjtzV/lxcXPD19aVhw4b4+vpib29fR5WK29GkSRPWrl1LWFgYO3bswNvbm2nTpul8XHd3d3744Qe++uortm7dyubNm8nKymLo0KE6DYZNTEzo1KkToaGh7Nu3j7179+psLF0bM2aM3tfgNkRbt26lsrISHx8funTpov16ZWUl//77r4KVCSGEEEIIIcT9S0LCO1Bz5XSrVq346aefWLhwIS+++KJ2fRhnZ2dGjBjBd999R1pamsGGhJaWlrRo0YJjx44RHx+v15BQpVLRp08fFi9eTGxsrOIhYY02bdrg7e3Nrl27OHnypGJ1hISEMHLkSIOblbFjxw4SExMxNjbmk08+0dmaSIsXL2bu3LkAfPrpp7z99tu17i8sLJSQ8B5hbGyMm5sbbm5utb5eXV2tvYChZquqqsLKygpra2usrKy025Wfm5qaolarKSwspKCg4JpbWVmZTp6LmZmZtmWlnZ1drY81t01MTCgtLSUxMZGEhAS9t3sW9xcbGxuGDx9O+/bt2bRp0w3baltYWGiPJWtraxwcHLShoJWVlR6rFjfSs2dP5s6dyyOPPML8+fPx8vLSy9re5ubmvPXWWzRp0oSff/6Z48ePa9cp1HVobGFhQd++fWnfvj07d+6869Bb30aPHq3TNSTrC41Gw8aNGwF49913eeKJJ7T3FRYWSkioQ8bGxgwePFh725DVp1qFEEIIIYS4V0hIeIvS0tLYvn07xcXFBAcHM2DAAPr168eLL77I9OnTMTc3Z+rUqXh6egIQGBhoUGvIXU/79u05duwYCQkJ9OjRQ69j9+7dm8WLF5OQkEB5ebnBtLdzdXVl3Lhx5ObmXnM9PF3r3r07vXv3Nrj1mk6ePMmePXsAeOONN2qt21WXli5dypw5cwD4+OOPrwoIxf3B2NgYZ2dnnJ2db/u11MjICAcHBxwcHK65dtalS5fIz8/n/Pnz5Obmkpuby7lz525rDTcbGxs8PT1xd3fHw8MDDw8P7Ozsbum4tbKyIjQ0lNDQUCorK0lJSSE+Pp7ExESdBZji/ubh4cHUqVNJTk4mPz+/VhhYE7DLydj6Y+rUqSQlJfHZZ58xe/Zs3N3dadWqlc7HrVmn0N/fn7feeousrCztOoWNGjXS+fh2dnYMHz6czp07s3379qvWtTNEYWFhtGjRQukyDEJCQgKnTp3CzMyMCRMmKF3OfcXCwoL169crXcYtqU+1CiGEEEIIca+QkPAWnDhxgp49e9K6dWvi4uJwcHDA2dmZjRs38tJLL1FVVcVHH33EqVOnGD58OM2bN+fHH38kLy9PLydN7kb79u35/fffFZk1FxAQgI+PD2fOnOHgwYN0795d7zXciKurK2PHjqVnz57s3r2bEydO6HQ8Hx8funXrpm1dayg0Gg2RkZFs3boVgPHjx9OvXz+djLNgwQL+/PNPAD788EPefffdOtl3dHR0rVmZKpUKExMTTExMMDY2vu5tW1tbg5vNKe6ehYUFnp6e2os6alRUVJCXl1crOMzNzUWlUmmDQA8PD9zd3eusDbCpqSlBQUEEBQWhVqs5deoU8fHxxMfHU1hYWCdjCAGXX/cCAwOVLkPUkY8//pikpCSWL1/Oe++9x5dffqmzi3f+q3Xr1rXWKfznn3/o06cPXbp00fkaiXC5/e3EiRPJyMggPT291oxzQ1nrLiQkhH79+uHg4KB0KVoajUbR8Wva+Y8dO1ZaFgshhBBCCCGEAZGQ8CZKS0t56qmnmDBhAj///DMXLlzgwIEDvPbaa7Rt25adO3cyffp0nJycWLRoEePHj6dRo0aUlJSwdu3aWuuoGaI+ffoAcOzYMU6fPo2vr6/exlapVIwYMYIff/yR8PBwjIyM6Nq1q97Gv1UuLi6MGTOGHj16sGfPHmJiYup0/y1atKBjx454eXnV6X7rQmlpKatXryYxMRG43ObsyvZQdaWqqopvv/1We+XwjBkzeP/99+ts/1u2bLmj7zM2NiYoKIhmzZoRGBiIiYm8ZN7LzMzMrhke6ouRkRGNGzemcePGDBw4kLNnzxITE0NsbOxdr2NobW2Nvb19rZmOarWarKysuy1bCKEAIyMj5s2bx9mzZ9m/fz+vvPIKn332mV5mFMLV6xTWzOwbOXIkTk5OeqnBy8ur1nsnjUbDhQsXtBd51FzokZeXR1VVlV5qcnV1ZeDAgdecxa4kjUbDqVOnABRZBiE5OZlNmzYB8NRTT+l9fCGEEEIIIYQQ1ydnvG+ivLycwsJC7cwpBwcHBgwYgL+/PxMnTiQsLIyYmBgeeeQRhgwZQm5uLpWVlXh6el61xpYhCggIYPjw4axZs4bly5fz6quv6nX80aNHc/HiRebPn8+2bduoqKigV69eBtdqEy6HhaNHj6Zfv36cPHmSkydPkpeXR1lZ2W1dne3j44Ovry8NGzbEx8cHCwsLHVZ959LT0/n3338pKirCyMiI5557jpEjR9b5/01ZWRkffPABBw8exMjIiB9++IFnnnmmTsfo3LlzrYCvurqayspKKisrqaiouOpjzXbp0iViY2OJjY3F3NycoKAgmjdvjp+fn15mS4j7l0qlwsfHBx8fHwYOHMj58+fJyMggIyODzMxMcnJyqK6uvur7LC0tcXFxqbW5urpibW191bFbXl7OzJkz9fWUhBB1zNLSks2bNzN8+HB27tzJ66+/zowZM+jSpYtexq9Zp7B169Z88803nD17ll9++YWwsDA6duyo9/dyKpWKBg0a0KBBg1qzKtVqNcXFxVy4cIELFy5w8eLFqz7eaYhoZWVFo0aNaNiwIY0aNcLFxcUg38OeOnWKCxcuYGpqqrefjxoajYYffvgBtVrNuHHjDK5zyP2gpKREu/Z8bm4u1tbWCld0ffWpViGEEEIIIe4VEhLehJ2dHWq1mh07djBq1Cjg/1p2/fXXX4wbN46nn36aX375BVdXV+0fNfXJ9OnTWbNmDZs3b2batGk0aNBAb2OrVCqmTZuGhYUFc+fOZffu3VRUVNC/f3+DPMkCYGtrS8eOHenYsSNw+eRHeXk5ZWVlV22lpaWUlZVhYWFBw4YN8fb2Nvj2lWq1ml27drF7927gcqj5/vvvExAQUOdj5efn8+abb5KUlIS5uTnLli1j+PDhdT7OO++8c9snGTQaDYmJiYSHh7Njxw7OnTvHsWPHOHbsGFZWVoSEhNC8eXN8fX0N9mdV3BuMjIy0gV9oaChwefZtbm4uGRkZqNVqXF1dcXFxuWYYWFeOHz+uff3SaDSo1Wpt2F6zVVVVXXXb0tISLy8vvL298fDwMPjXQCHqE1tbWzZs2MCECRNYu3Yt77zzDn379uXJJ5/ExcVF5+OrVCoGDRpEmzZt+PLLLzl8+DCbN28mLi6OESNG4OjoqPMabsbIyAg7Ozvs7Oyu2TFDo9FQUlLCxYsXKSsro7q6WrtVVVXV+rzma9bW1gYdCv7XsWPHAOjfv7/eL07bvn07x48fx9zcnNmzZ+t1bPF/7rYjgT7Vp1qFEEIIIYS4F0hIeAMajQZjY2PGjRvH5s2b2bBhA4MHD9be36JFCyZNmkR4eDglJSX19krHbt260aFDBw4ePMiqVauYNm2a3mt44IEHsLCw4IcffuDAgQNUVlYyZMiQenHiRaVSYWFhgYWFhV4DVl24ePEi//77L6dPnwZg0KBBPP/881haWtb5WKdOneKNN94gJycHBwcHNm/eTIcOHep8nDulUqlo2rQpTZs25cknn+TEiROEh4eza9cuLly4QFRUFFFRUdja2tKsWTOCg4Px8fGpFz+zov4zMTHRe2vUDRs23PH31qx7q1KpcHd3x9vbG29vb7y8vHB0dJTjRoi7YGlpycqVK3n55Zf5+eef2b59O/v27ePBBx9k/PjxmJmZ6bwGNzc3vvzyS9auXcsPP/zA6dOn+eWXX+jfvz/t2rUz6GNcpVJhY2ODjY2N0qXoREVFhXZd7YEDB+p17NLSUn777TcA3nvvPb0uayCEEEIIIYQQ4tZISHgDNSc0pkyZwqZNm/jpp5+wsrKiV69e2vtDQkJYsGABpaWl9TYkVKlUvPbaa4wbN47Vq1drAzt9Gz16NBYWFtor0SsrKxkxYoS0ddST+Ph4Vq1aRUVFBaamprzxxhv07dtXJ2Pl5+fz0ksvceHCBQIDA9m4cSP+/v46GasuGBkZ0bJlS1q2bMnzzz9PdHQ04eHh7Nmzh6KiIg4cOMCBAwewsbEhKCiIkJAQGjZsKD+74p7Stm3bWm17jY2NsbCwwNzcHAsLC8zMzGp9bm5ujrm5Obm5ucTFxREbG0t+fj5ZWVlkZWVx6NAh4HLAURMY+vj44OXlhbm5uVJPU4h6ydTUlB9//JFHHnmEF154gYiICP744w82bNjA008/Tbdu3XQe1KlUKoYPH0779u2ZNWsWR48eZcOGDcTFxTF8+HAcHBx0Or64tpMnT1JVVYW3tzfNmjXT69jz588nLy8PPz8/vS9pIIQQQgghhBDi1khIeBMajQY/Pz/mzJnDAw88wKxZs0hPT2fq1KmUl5dz8OBBPD09dTLTSp9GjRpF48aNSUtLY9OmTYwcOVKROgYPHoyFhQWffPIJx48fp7y8nOHDh2NlZaVIPfeDqqoqtmzZoj1h37RpU9577z28vLx0NuZ3333HhQsXaN68OTt27MDZ2VlnY9U1Y2Nj2rdvT/v27Xn55Zc5ePAgu3btYv/+/RQXF2tnGFpZWdG0aVOCg4Px8/PD2NhY6dKFuCsffvjhXV0Mo9FoyM3N1a7pGhcXR2JiImVlZSQlJZGUlKR9rKurK97e3vj4+ODt7Y2Tk5NBz0QSwlC0bduWvXv3snjxYl5//XUyMjJ4//33adOmDc899xyNGzfWeQ0eHh589dVXrF69mp9//pm0tDR++uknBg4cSJs2beRY1rOaVqMDBgzQ67/96dOnWbFiBQDffvutwa7BLYQQQgghhBD3OwkJ/7+qqio0Gk2ttZLUajVGRkao1WpatGjB0qVLeffdd/n000959913CQgI4Pjx44SHh9f7FkXGxsa88sorPP/88yxfvpxhw4YpFmr06dMHMzMzZsyYQUJCAj///DODBw8mJCREkXruVRqNhoSEBMLDwzl37hwA48eP57HHHtPpmmE7d+5k9+7dGBsbM3/+/HoVEP6XmZkZ3bp1o1u3blRUVBAdHc3u3bvZt28fhYWFHDlyhCNHjmBubk7Tpk1p2LAh1tbWWFlZYW1tjbW1NWZmZrd10k6j0VBRUUF5ebl2ppYQ9YFKpcLNzQ03Nzd69+4NQGVlJSkpKcTGxmrDw+zsbHJzc8nNzSU6OhoACwsLbYtSX1/ferG+qxBKUalUPPDAAwwfPpyZM2cya9YsoqOjeeyxxxg5ciRTp07F1tZWpzUYGRkxatQoOnTowBdffEFMTAzr1q0jNjaW/v374+7urtPxxWUFBQWkp6cDl9cj1BeNRsOPP/5IVVUVgwcPZujQoXobWwghhBBCCCHE7ZGQkMtteD788EMyMzMJCAigf//+TJo0CSMjI6qrqzE2NkatVhMSEsKcOXNIT09n48aNeHt70717dwICApR+CnXikUceYcaMGWRmZrJ371569uypWC3dunXjxx9/5IsvvuDUqVMsX76ckJAQBg8eXG/buhqS1NRUwsPDycjIAC6fgP/ggw/o2LGjTse9ePEi3333HQBvv/02oaGhOh1Pn8zMzOjUqROdOnWiurqao0ePsmfPHvbs2UN+fj7Hjx/n+PHjV32fsbGxNjSs+WhkZER5ebk2DLxyq6ioqPX9V8648vHxkfXdRL1iampKUFAQQUFBjBkzBoDz589z8uRJbXCYkJDApUuXSE5OJjk5GbgcQHh5edGwYUMaNmyIj4+PwQfmlZWVFBcXU1RURFFRERqNptaxb2VlJTOORZ2ysbHhk08+Ydq0abz66qv873//499//2Xbtm089NBDDBo0SOedGry8vPjmm2/4999/+e2330hLS+O3336jZcuW9O7dW1qQ6ljNLMJ27drh6uqqt3H37dvHoUOHMDU15dtvv5X3JUIIIYQQQghhwO77kDAxMZEuXbowbNgw+vXrx/bt2/nyyy/ZsmULf/31F8bGxlRUVGBmZgaAs7Mzzs7OtGvXTuHK6561tTXPPPMMn3zyCUuXLqVHjx6K/lEfHBzMnDlzmD9/PgsXLuTkyZOkpKQwZMgQmjdvLicc7sCZM2cIDw/XXlVuYmLChAkTmDBhgs5nFQB8//332jaj7777rs7HU4qxsTFt27albdu2vPDCC8TGxrJnzx5Onz7NxYsXKSgo4OLFi1y6dInq/8fefcc1cf9/AH8lEEbYyJIpoAJuRdx7I+5Z96paq9VarVpHba2jtda6WlfdW2uruEVxWyciCogoG2TvHZL7/cEv9wVB5iUX4P18PO5BSC6fzzvhLlzufZ/3RyplkwaVIR/l/PGIK/n8bvLEoZWVFfv5VR2JiYl48+YNgoKCkJaWBqBwpEBZi66uLmxtbdlEjpGREaf7rVQqRXJyMhITE4stUqmUTbp8nIQpelssFtO8kSqoXr166Nq1K7p27Qqg5GhDPz8/JCYmIjIyEpGRkbh//z4EAgHq16/Pbmt2dnZKLW0nk8mQkpKChIQEpKWlISMjo1hCMCMjA7m5ueW2o6mpWWLb1dPTY489TExMONmfa7OsrCxER0cjOjoaMTExyMvLg1gshra2drGfpd2urZ8HDg4O+Pfff3Hjxg18/fXX8Pf3x44dO7B//370798fQ4cOhZ2dncL6V1NTw+jRo9G5c2fs27cP3t7e8PPzw+vXr9GuXTt07dqVysorAMMwxUqNKkteXh7+/PNPAMDixYvRqFEjpfVNSicUCtmLP1X9c64mxUoIIYQQQkhtIWAYhuE7CL4wDINVq1YhKCgIZ86cAQBkZ2fjwIED2L17N1xcXHDq1Cl2/QMHDqBPnz6wsbFReGzp6ekwMDBAWloa9PX1Fd6fXFxcHGxsbCCRSLB+/Xp07NhRaX2X5e3bt/jll18QEhICoHDePA8PD6UktmqDlJQUeHl5ITAwEEDhl+5hw4ZhwoQJMDY2VkoMd+/exerVqyEUCvH48eNqJdorun/I17t48aJKjkDNzc1Famoq0tLSkJqayt4uLcmlra1dLMGlqamJ5ORkNnHi7++PoKAgSCSSYn0IBAJYW1vDysoKVlZWsLa2hoGBQbnJOoZhEBsbi8DAQAQGBiIxMbHar9fAwACNGjVCo0aNYG9vX+lykQUFBYiOjkZYWBjCwsIQGRkJqVRa5Xg0NTVhb28Pe3t7ODg41Ll57/Ly8vDzzz/XuP2IYRjExMTAz88PL1++xMuXLxEbG1tsHYFAAFtbWzRo0AANGjSAtbU11NW5uy6KYRgkJSXh/fv3CAkJQVhYWIlRvqXR0NCAiYkJjI2NIRQKkZaWhvT0dKSlpUEmk1WobyMjI5ibm8PMzIwt32pkZMTLyVR5zHyeyC0oKEBERAQ70lRePruytLS04ODggIYNG6Jhw4YVPr6o7H6k7OO6jxUUFGDv3r34/fffi80DOmjQIMydO1cpyfWgoCDs3r0bL168AFC4XwwcOBAtWrSoU5/BivbmzRucOnUKIpEInp6eSrtw4uDBgzh06BCsrKzw5s2bCk3JUNnjOr73I1Xj4+MDV1dXPH/+HG3atOE7HIWra6+3omj/IIQQQgghVVWnk4RAYYnNd+/e4d69e+x9OTk5OH78OP744w/0798fGzZswMOHDzFlyhR06NABBw8eVHhJMD4P8r/++mts3boVOjo6+PPPP2Fra6vU/j9FIpHg+PHjOHz4MGQyGUQiEdzd3dGqVSs6qfQJ+fn5uH//Ph48eACZTAaBQIABAwZg8uTJSp0PKDY2FjNnzkRmZiaWLVuGDRs2VKu92pIk5JpEIkFwcDCbNPT39y/1hLmOjk6xxKGVlRU0NTXBMAwiIyPZxKB8xCBQmARo27YtOnfuDCcnJ/YzUCgUQiAQsD/ltwEgOjoaL1++xKtXr+Dv718sCSIUCtkT8iYmJsjLy0Nubm6xn/Lb8t8TEhJQUFBQ7LWIxWLY2trCxsYGtra2sLW1hZaWFtLS0tjka9Hb8t/l5R6L0tPTg4ODA5s4rO0nWGpqkrA0cXFxbNLQz88PkZGRxR4XCoWwsbGBnZ0dmzSsbJI6KysLISEh7JKenl7scS0tLdjY2MDS0hL16tWDiYkJ6tWrV2zR1dUt9f+VTCZDZmZmse1Vvs3GxsYiIiICkZGRSElJKTU2kUgEMzMzdpEnD6s7Ois/P79YPB/HJn8P9PT0oK+vDwMDg2K39fX1oa+vD11dXc4SifKLK+SJwbCwsBIXR9jZ2cHFxQXOzs4wNDREeno6m4wt7WdpI7nNzc3ZhKGNjc0nj/tqWpJQTiaT4caNG/jzzz/h6ekJhmHQoEEDfP/997C3t1d4/wzD4OnTp9i9e3exC8AGDRpU4+f5VgUJCQnYu3cvJBIJRo8ejS+//FIp/YaEhGD27NkoKCjAqVOnMGbMmAo9j5KE1VPXkmZ17fVWFO0fhBBCCCGkqupskpBhGAgEAmzfvh0nTpzA/v374ezszD6enp6OdevW4c6dO7hy5QqMjIywd+9e9OnTRyknT/g8yM/Ly0Pv3r3x4MEDWFpa4s8//4SBgYFSYyhLSEgINm7ciKCgIACFZbQGDx5M89oUIZPJ8OrVK9y8eZM9+dm6dWvMmzcPDg4OSo1FKpViwYIF8Pf3R/v27XHv3r1Kn5z/GCUJKy4mJgavX79GYGAgAgICEBwcXCI5BgCmpqbIyclBZmYme5+6ujo6deqErl27okOHDtU6cZubm4sXL17g0aNHePz4MeLi4qrUjqGhIVq1aoXWrVujVatWsLGxqdJFAlKpFMHBwXj+/Dl8fHzg6+tbYiSXiYkJ7O3tYWNjg3r16sHY2FipJSwVrTYlCT/24cMHPH/+HL6+vvD19UVSUlKxx4VCIaytrdGgQQMYGhqWmPvz4yU3N7dEUlAoFKJVq1Zo27Yt2rZtC0dHR4WPqEtLS0NISAg7gjEkJAShoaGfHMWoq6vLJg3lP7W0tJCdnY3s7Gzk5OSwtz/+PSMjAzk5OZzELRAIoKurW2zR0dEpcZ+uri5kMhmb0C+a2JffzsvLK9F+vXr14Obmhnbt2sHV1bXSx00FBQUICgrC48eP8fTpU7x586bY4xoaGuxFDebm5sU+c/Lz83H48OEandy4ceMGJk2ahNjYWGhoaOCrr76Ch4eHUi7AkkqlOHHiBA4cOACZTAZNTU0MGjQIzZo1U3jftVV2djb++usvpKSkoHnz5ti0aZNSyhRLpVLMnTsXQUFBGDp0KP79998Kb0OUJKyeupY0q2uvt6Jo/yCEEEIIIVVVZ5OEcu/fv0eHDh0wePBgbN26tVh5qQ8fPsDa2hqnT5/GyJEjlRoX3wf5CQkJaNeuHcLCwtCqVSts3Lix2okdLkmlUpw5cwZ//fUXpFIpBAIBXFxc4ObmBjs7uzo7slAqlcLPzw/3799HcnIyAKB+/fqYM2cOunTpwsv7sm/fPhw9ehQ6Ojp49eoVJ0l2ShJWXV5eHoKDg9nRgoGBgcVKNero6KBTp07o1q0b2rZtq5CkGMMwCA8Px6NHj/DkyROkpaVBR0en2CJPIhT93cLCQmH7d15eHl6/fg0fHx/4+PiUSBLIicViNmFobGzMjhAzNjaucXPF1eYkYVEMwyA6OppNGL58+bLK5XMdHR3Rtm1buLq6okWLFtDU1OQ42sqTSqWIjo4uljQMCQlBTEwMJ+3r6OiUKG9a9HeBQICEhATEx8cjISEBiYmJ7G3579UpC1waAwMD2Nvbo127dnBzc4OjoyOnnwupqal4+vQpu6Smppb7nJqe3IiPj8fkyZNx7do1AECPHj2waNEipY3qe//+PX7++We8e/cOANCoUSO4urqiYcOGCq/eUZskJSXh3LlziIqKgoWFBXbu3Km0i+hOnjyJ3bt3Q0dHB2/fvoWlpWWFn0tJwuopL2mWlZWFBg0aAADCwsJU+n95RWKlJGHpaP8ghBBCCCFVVeeThABw69YtuLu74/PPP8cPP/wAExMTAIVftPv3749NmzahR48eSo1JFQ7yX79+jXbt2iEnJwfu7u5YvHixyk0gHxkZid9//52d1wYoHP3Ttm1btGzZslaN+ilLQUEBfH19cf/+fbZEpL6+PsaOHYtRo0bxlrzw8fHB4sWLwTAMTp48ibFjx3LSLiUJuZWcnIygoCBoaGigRYsWKnVBAF8yMjLg6+uL58+fIyQkBFFRUZ8s9Sino6PDlleUl1wsWnZRT09Ppd7bupIk/Jh8TkN5wjA9PZ2d77Poz4/vs7CwgJGREd/hV1hOTg6bMCyaPMzLy4OBgQG7XZb208DAgJ37sLpJIqlUipSUFCQlJSE5ORnJyclISUlhbxdd5CMXjYyMYGFhAXNzc1hYWBS7bW5uDm1tbS7eogqRyWQIDg5mRxl+nGCWyWSIj4+vFckNmUyGzZs3Y9myZZBKpbCwsMDy5cvRvHlzpfQvkUhw7NgxHD58mB3xLhaL0axZM7Rs2RL169evsxeBlScvLw/37t3Df//9x5bk3717t1KqnwCFFxdOnDgR+fn52L9/P6ZNm1ap51OSsHoqkiSUf5ZnZmaq9P/yisRKScLS0f5BCCGEEEKqipKE/+/ChQsYPXo0Bg4ciNGjR6NFixY4cuQIDh48iKdPn8LGxkap8ajKQf6lS5cwePBgMAyDLl26YPny5Uo9OVdR7969g6enJ65cucLOWaauro4WLVqgbdu2qF+/Ps8RKoZEIoGPjw8ePHjAlhU1MjLC2LFjMWTIEF7/VikpKfj888+RnJyMmTNnYs+ePZy1TUlCwoesrCxER0cjKiqqxM+i8zeWRVtbm03CGBgYwNDQkP1paGgIbW1tpZ0Er6tJQqK6cnJyIBQKVWKEZkVlZWVh0KBBtSq58eTJE3z22WcIDQ0FALi4uGDIkCHo2bOnUv424eHhuHz5Mry8vIpdnGFiYoKWLVuiefPmKlUGn08Mw8DPzw83btxgy4W3a9cO8+bNU+p3l82bN+PChQvo0qUL7t69W+n/Y5QkrB5KEhKA9g9CCCGEEFJ1lCQswsfHB9988w1CQ0Ohrq4OkUiEEydOoHXr1kqPRZUO8k+cOIEpU6ZAIpGgUaNGWLduHUxNTXmN6VOysrJw/fp1eHp6IiwsjL3fysoKbm5uaNKkiUqN5Kmq/Px8PH36FP/99x+ysrIAFJ48++yzzzBo0CDeT7DKZDIsX74cjx8/RpMmTfD06VOIxWLO2qckIVE1GRkZiIuLY0sslrbk5uaW245IJGKThvr6+tDV1YWenl6xn7q6umWW35PJZMjNzWXnmJPPMye/gEJOIpHg+vXrtB8RUg21MUkIFJZP/eabb3D48GH2s0NXVxcDBgzA4MGDYWtrq/AYpFIpnj17huvXr+POnTvFytba29ujZcuWaNiwIcRicZ0cYRgTE4MrV64gKioKQOGx7ty5c9GhQwelvh/R0dGYMmUKpFIp7t27hy5dulS6DUoSVg8lCQlA+wchhBBCCKk6db4DUCVt2rSBp6cnkpOTkZmZCQsLC7b0aF02btw42NnZwcPDA8HBwfjyyy+xdu1aODk58R1aCTo6Ohg+fDiGDRsGPz8/eHp64vbt24iOjkZ0dDQuXLgACwsLWFlZsYuxsbHKn1xiGAbJycmIjY1l59aSl2WzsLDAuHHjMGDAAJWZE+3vv//G48ePoaGhgVOnTnGaICREFenp6UFPTw8NGzYs9XGGYZCVlYX4+HjEx8cjLi4OsbGxiIuLY5ekpCRIJBIkJiaWO2eeWCxmE4dCoZBNCGZnZ1coGUkIIWUxMDDAvn37sGHDBuzfvx+7d+9GWFgY/v77b/z9999o3bo1hgwZgs6dOyvs4is1NTW0b98e7du3x8KFC3Hnzh14eXnh5cuXCA0NZUc6CoXCEhdSyBf5fTo6OtDW1oaGhobKH/OVJzMzEzdv3oSvry+AwsoZ06dPx8iRI3k5Djxw4ACkUikGDhxYpQQhIYQQQgghhBB+UZLwI/I5pEhxnTp1go+PDwYNGoSAgAAsWLAAy5cvR7du3fgOrVQCgQAtW7ZEy5YtMXfuXFy+fBkXL15EXFwcmzCU09LSgqWlJZs0tLS0hJ6eHm+xy2QyJCQkIDY2Fh8+fGB/5ufnF1vP2toaEyZMQJ8+faCurjq78ps3b9jSotu2bUOzZs14jogQ/gkEAvaktYODQ6nr5OfnF0sgJiYmIjk5mf0pn9NNKpUiOzsb2dnZiI+P/2SfRedI1NfXLzFHa0FBAf777z9OXychpHYxMzPDsmXLsGTJEly7dg27du3CxYsX8eLFC7x48QJGRkYYOHAgBg4cCEtLS4XFoaurCw8PD3h4eCA2NhZeXl7w8vJCZGQkZDIZ0tPTkZ6eXm47QqEQ2traEIvF0NbWLraIxWJoaWlBW1sbWlpaJW7zPS+3VCrFkydPcOvWLUgkEgBA3759MWvWLN4uanz//j28vb0BAGvXruUlBkIIIYQQQggh1aM6mQWi8uzt7fHw4UN89tlnuHr1KlavXo1Zs2bhs88+U+mrso2NjTFx4kRMmDABHz58wJs3bxAYGIjAwEAEBwcjNzcXISEhCAkJYZ+joaEBkUj0yZ/y22pqapBX7C1auffj+xiGYReZTPbJ31NTUxEfH1+iLCBQWIbQ0dERjRo1QuvWrdGtW7cySw7yITMzEz/99BOkUilGjRqFWbNm8R0SITWGhoYGrK2tYW1t/cl15CfDk5KS2KShTCaDvr4+DAwM2ISgnp5euRcPyMskEkJIeYRCIdzd3eHu7o6IiAj89ddf2Lt3L2JjY3Hs2DEcO3YMDg4O6Ny5Mzp37ozGjRsr7NjQwsICkyZNwqRJk5Cfn4+UlBT281C+FP09KSkJqampkEgkkMlkyMrKYku1V4aGhgabNCwtwfhx8lEsFrOjLEs7Riz6e35+PjIzM5GZmYmsrKxSb2dkZLDJwcaNG2P+/Plo2rRpNd7J6tu/fz8YhsGYMWN4mZ6BEEIIIYQQQkj1UZKQVIqBgQEuXLiAb775Btu3b8eePXsQGhqKRYsW8T4PXnkEAgEsLS1haWmJXr16ASgcSRMaGorAwEC8efMGb968QWhoKPLz85Gfn1+lk0hcEIvFaNiwIRo1aoSGDRuicePGsLW1VakRgx+TSCT46aefEBMTgwYNGmDv3r0qnTwmpCYSCoUwNDSEoaEhHB0d+Q6HEFIH2draYs2aNVi1ahU8PT2xa9cueHt7sxdcHTlyBM7Ozli1apVCRxcChYk7c3NzmJubl7kewzDIy8tjRxx+vGRkZLA/MzIyiiXm5OXd5ceGaWlpCn1NZTE0NMTnn38Od3d33kc23rt3Dw8fPoRQKMSaNWt4jYWUTSgUom3btuxtVVaTYiWEEEIIIaS2UN2MA1FZ6urq2LZtG5ycnDB//nx4eXkhLCwMa9asgYWFBd/hVYq6ujoaNWqERo0aYciQIQCAnJwcJCcnIycnB7m5uezy8e+5ubmQSqVsWwKBgF1Ku08oFBZbBAIB1NTU2NtCoRAGBgZo3LgxLC0ta9QXY6lUinXr1uHJkyfQ1NTE33//DUNDQ77DIoQQQoiCiEQijBw5EiNHjkRSUhIuX76M8+fP49KlS3jz5g1mzZqFb7/9Ft27d+c7VAgEArZsqJmZWaWeW1BQUCxpKF8+lXCUL5mZmRXuQ11dHcbGxjAyMir1p/y2hYWFwuaArIy7d++yicE5c+ao5Dzl5H+0tbXx9OlTvsOokMrEGhgYWO46JiYmsLW1rW5YhBBCCCGE1GqUJCRVNnfuXDRp0gTDhw9HcHAwvvjiC6xYsQJubm58h1Yt2trasLKy4juMGkMmk2Hjxo24c+cORCIRzp8/D1dXV77DIoQQQoiS1KtXjy0BGhkZiXHjxuHBgwf44YcfMGTIEMydOxcaGhp8h1kl6urq7AjuypBKpWx5UPkFZB9fSCb/qaamVmOqL9y5cwdr1qyBTCbDuHHjsGXLFr5DInWMiYkJxGIxJk6cWO66YrEYgYGBlCgkhBBCCCGkDJQkJNXSs2dPvHz5EiNGjICPjw+WLFmCpk2bYuzYsejUqZPKzZlHuMUwDLZu3Yrr169DKBTi9OnT6N+/P99hEUIIIYQnNjY2uHXrFlavXo0NGzbA09MT/v7+WL16NWxsbPgOT2nU1NRq3XGwt7c31q1bB5lMhokTJ+LgwYO17jUS1Wdra4vAwEAkJiaWuV5gYCAmTpyIxMREShISQgghhBBSBkoSkmqzs7PD/fv38e2332LPnj3w9/fH999/DysrK4wePRr9+/eHlpYW32ESjjEMg927d8PT0xMCgQBHjhzBsGHD+A6LEEIIITwTiURYv349unfvjs8++wzv37/HrFmzMG/ePPTr108lSmaSyrlx4wY2bNgAmUyGKVOmYN++fZQgrCGys7PRpEkTAEBAQADEYjHPEX1aRWO1tbWlxB8hhBBCCCEcqTmTnhGVpq2tjR07diA8PBzLly+HkZERoqOjsWXLFnz22Wc4ePAgUlJS+A6TcOjw4cM4deoUAGDPnj0YP348zxERQgghRJX0798f/v7+6NGjB3Jzc7Fp0yaMGzcOR44coePCGuT69etsgnD69OnYv38/JQhrEIZhEB4ejvDwcDAMw3c4ZapJsRJCCCGEEFJbUJKQcKp+/fpYt24dIiIisG3bNjRo0ABpaWk4dOgQPvvsM2zevBmRkZF8h0mq6fTp0zh48CAA4Pfff8fnn3/Ob0CEEEIIUUmWlpa4ceMGNm7cCAsLCyQlJWH//v0YO3Ysfv75ZwQHB/MdIinDlStX8PPPP0Mmk2HmzJnYu3cvhEL6CkkIIYQQQgghtQV9wyMKoauri6+++grBwcE4ffo03NzckJ+fjwsXLmDKlClYuXIlAgIC+A6TVIGnpyd27twJAFi7di2+/vprfgMihBBCiEpTU1PDt99+i/DwcBw7dgzt2rWDRCLBtWvXMGvWLMyfPx+3b9+GVCrlO1Ty/yQSCXbs2IGNGzeCYRjMmTMHu3btogQhIYQQQgghhNQyNCchUSh1dXWMHj0ao0aNwv379/Hrr7/iwoULePDgAR48eIBWrVph3LhxcHNzg0Ag4DtcUo7r169jy5YtAIBly5Zh+fLl/AZESCVJpVJIpVJIJBKIRCJoaGjwHVKtlpOTg4SEBOTm5n5yycvLQ25uLvT19dGkSRM4OTnVmb8LwzDIy8tDdnY2cnJykJOTg+zsbPb37OxsCIVC6OrqQldXF3p6euxtsVhMJ+tJjaOhoYHx48dj/PjxePz4MbZu3YrTp0/j1atXePXqFUxNTTFs2DD06dMHpqamdGzIk7i4OPz4448IDAwEUHjMt379evp7EEIIIYQQQkgtRElCohQCgQBdu3ZF165d8ebNG2zcuBGHDx+Gr68vfH190ahRI4wbNw7dunWjOU5UkEwmw/Xr1/Hrr7+CYRjMmzePThZVglQqRV5eHjQ0NKCurryP3aysLCQnJyM7OxtSqRQFBQXs8vHvBQUFyMvLYxMV8gRF0Z/y2wUFBRAKhRAIBJ/8KRAIIBaLYWJiwi6mpqbFfq/seyGVSpGamoqUlBR2SU1NRXJyMns7JSWFjbGgoAASiaTYa5TJZMXa1NHRgYGBAYyMjD7508zMDFZWVvTZVAEymQzBwcF4+vQpnj59Cn9//0qPDFJXV0fjxo3RtGlTNG3aFM2aNUO9evUUFLHyMAyDsLAw3Lt3D/fv30dMTAxycnJKbJMVJRQKoaOjUyyBqK+vzy4f/y5fRCJRsX3jUz8FAgG0tLSgra0NLS0taGpqQiwWQ0tLi+N3htRV7du3x/Hjx7Fp0ybs2rULu3btQkJCAvbu3Yu9e/dCS0sLlpaWsLKyKrFQAlFx/vvvP/z8889IT0+Hrq4ujh07hiFDhvAdFiGEEEIIIYQQBaEkIVE6Z2dn7N+/Hz/++CM2b96MXbt2ITg4GGvWrIGDgwOWLFkCJycnvsMk/49hGCxfvhyPHz8GAEybNg1bt26lk3MofG9u3ryJgIAAdvRPVlZWiZ+5ubkAAJFIhIYNG8LZ2ZldrK2tOR0NFBERgbt37+Lu3bsqPc+TlpYWOnTogB49esDR0REZGRnskp6ejszMTKSnpxe7LyQkBNnZ2ZzGkZWVhaysLMTExJS5nrm5OWbNmoWePXvStl+K/Px8XLlyBSdPnkRsbGyxx/T19aGrqwsdHR2IxWLo6OgUuy0WiyEWixEZGYmHDx8iLi4OAQEBCAgIwJkzZwAAvXr1wsKFC6Grq8vHy6u2yMhI/P7773jx4sUn15En+uSjBeW3ZTJZsSR4amoqcnNzIZPJ2P1DmUxMTNCgQQPY29vD3t4ezs7OaNCgQa3dLxiGQXh4OJ48eYKnT58iISGh2ONVTfKS/7G0tMSaNWuwfPlynDp1Cn/88QeeP3+O3NxchISEICQkpMRzbG1tMW/ePLi5ufEQce1UUFCA/fv348SJEwAANzc3nD59Gg0aNOA3MEIIIYQQQgghCkVJQsIbGxsb/P7771i5ciV27NiB3377DSEhIfjyyy8xYcIETJw4sc6UnFNljx49YhOEixYtwi+//EIl7gBkZ2fj119/xe3btyv8HIlEgsDAQLZ8F1A4ks3JyQnOzs5wcXGBs7MzTExMKtymfHTSnTt3cOfOHYSFhRV7XE9PDwYGBhCJROUu2traJRIURUscyu8TiURgGAYymeyTi1QqRVpaGqKjo9klKioK0dHRiImJQW5uLm7fvl2p9w8oHD1lYmICc3NzmJubw8zMjL0t/93AwAAaGhrQ0NBgS4rKf8pvi0Qi5ObmIj4+HgkJCUhISGBvF/0ZHx+Pd+/eIS4uDj/99BPOnTuHefPmoXHjxpWKu7bKzs6Gp6cnzpw5g+TkZACF21zv3r3Rv39/9OvXDw4ODhVuj2EYhIaG4uHDh+zi5+cHb29v+Pv747vvvkPLli0V9XI4J5FIcOrUKRw+fJgtcduvXz+MGDECnTt3Zkf8VbZ0aG5ubrGkoXxkbVJSEpKTkz/5MzU1lW1DTU2N3Sc+XkQiEWQyGZtEl18EAQCJiYlITEzEs2fP2LaMjY3h6urKLpX5DFNFOTk5ePHiBR4/fozHjx8jLi6O75DqBC0tLUyZMgVTpkxBfn4+wsPD8e7dOwQHB+Pdu3fsEhoaioiICCxZsgQdOnTA0KFD4ebmRqO9q0H+P87f3x8AMG/ePGzatAmampo8R0a4IBAI0KRJE/a2KqtJsRJCCCGEEFJbCBiGYfgOgpSUnp4OAwMDpKWlQV9fn+9wlCIhIQHz5s3D6dOnAQD29vZYvHgx+0WRKB/DMJg7dy4CAwPx7bffYuPGjXyHBKDi+4d8vYsXL0JHR4ez/sPCwrB69WpERERATU0NX375Jaytrcss9ScWi/Hhwwc8ffqUHZEiHynxMbFYzCbk5Ak6+WisoveFhITg7t27iIyMZJ+rrq6Ovn37YuTIkRgyZAhMTU05e91ckclkeP78Oc6cOYOzZ88iPj4eRkZGMDY2hrGxMXv74/scHR3RokULpZZsBQoTYb/99hvWrVuHvLw8CAQCuLu7Y8aMGTA2NlZqLFzKysrCoEGDKrwf7dmzB2KxGEBh6ddbt27h7Nmz7Eg2GxsbLFmyBNOnT2fX48KTJ08wfvx4vH//HkKhEEOHDkX//v3RuHFjlT6B6O/vj99++w2hoaEAgH79+mHXrl2wt7fnLSZ5OVENDY1KJ1RkMhnS09MRGBiI169f4/Xr1/Dz88Pjx4+Rk5NTbN0GDRqgbdu2cHV1RcuWLaGtrc3ly+AcwzCIjIzEkydP8PjxY7x8+RISiYR9XCQSoWfPnhg4cCBatGhRbLur7H5UleM6mUzGlnEmQGpqKlavXo0dO3awIzlNTEzg7u4Od3d31K9fn+cIa5b79+9j48aNyMjIgI6ODg4dOoSRI0cqNYbKHtfVpe9HERERSExMLHOdwMBATJw4Ec+fP0ebNm2UFJnq8/Hxgaura515X+ri/kEIIYQQQrhBSUIVVZcP8v/++298+eWXbEkvFxcXDB48GD179qS5kJTs2bNn+Pbbb6GpqYnw8HCYm5vzHRIAfpOEN2/exKZNm5CbmwsrKyucPn0anTp1qlJbEokE/v7+bOLwyZMneP36daXL14lEIgwYMACjRo3C4MGDYWRkVKV4SNkiIyOxbNkyHD9+HEBhMnfSpEkYMWJEjRz1XNnkxqc0btwYy5Ytw4QJExT2PmRkZGD+/Pk4ePAge5+5uTm6du2Kbt26oUmTJioziigrKwt//fUXzp8/D4ZhYGBggD/++APjx4+vlUmevLw8PHz4EF5eXvDy8sLz589R9NBSXV0dTZs2RceOHdGnTx+Vm18yKCgIv/32W4nyzA0aNMDAgQPh7u6Onj17fvJ/iKKTG69fv8bIkSPZUpDdu3ev8HNruzdv3mD37t04fPgwO5IZANq0aYOBAweia9euNfKzWVny8/OxZ88enD17FgDQrl07nDx5kpcLGShJWLqIiAi4uLhUqNS6WCxGYGAgbG1tlRBZzUBJQkIIIYQQQiqGkoQqqq4f5CckJODbb7/FsWPHUFBQAKCwLGO/fv0wePBgXkdi1CULFiyAn58fFixYgC1btvAdDouPJKFEIsHOnTvx77//AiicI+3EiRMwMzOrVrsfy8rKQnR0NFs+MDk5udTbKSkpMDMzw4gRI+Dh4VEnPyf48vDhQyxYsIAtt2hpaYk5c+agc+fONSoJVNkkoZ6eXrHX17hxY3z77bcYOXKk0hJ0ly9fxoEDB3Dx4sVio3CNjY3RpUsXdOvWDS1btlT6aFOpVIqoqCi8fv0aBw8eZEd9TJ06FZs2bVK5xJgiJSUlwdvbm00aFi2BLBQK0a5dO7i7u6Njx44QiUS8xZmfn49Dhw7h5MmTkMlkUFdXR/fu3dnEoLOzc4X2Z0UmN65evYqRI0eyCQKBQIBvv/0Wa9asoTKQReTl5eH8+fPYt28fvLy82CS1np4e+vTpg4EDB6Jhw4Y8R6k6srOzcePGDfzzzz8IDw8HUFhOfv369bwlVSlJWDp5kuvo0aNwcXEpc10TExNKEH6EkoSEEEIIIYRUDCUJVRQd5BeKj4/HgQMHsGfPHoSEhLD3N23aFIMHD0aPHj3oRJmC+Pr6YuHChRCJRAgNDYWVlRXfIbGUnSRMSEjADz/8gICAAADA8uXLsWbNGpUZuUSUTyaT4ciRI1i2bBliY2MBFI48srW1Rf369WFhYYH69euzt1VxNIsyyiQqSk5ODq5du4Z//vkHnp6eSEtLYx/T19dHp06d0Lx5cxgYGBQrASyfU7M6pFIpIiMj8fbtW3YJDg4ulrR0dHTE7t270bt372r1VdMxDIP379/j+vXrOHbsGB4+fMg+ZmBggD59+mDAgAFKT+AEBgbil19+YRMkY8aMwfbt26t00Yeikht//PEH5s+fD5lMhpYtW8LS0hJXrlwBALRq1QpHjx5F06ZNKx1vbRceHo4DBw5g//79xUpxW1pawsbGBtbW1sUWU1PTOvO//P379/D09ISXlxdbIlhfXx/Hjh3DoEGDeI2NkoSl4yrJlZ2dDTc3NwDA06dPOS0JzjUuY6UkISGEEEIIIRVDSUIVRQf5xclkMty4cQO7d+/GuXPn2HKMenp66N+/P7p16wYLCwvUq1cPQqGQ52hrh8WLF+P58+eYM2cO/vzzT77DKUaZScLnz5/jp59+QlpaGnR0dHDixAkMHjy4qqGTWiYjIwMbNmzApk2bis1h9jETExM2cWhsbAwtLS1oampCS0urxCK/X0dHBwYGBgors1yTk4RF5efns/Mjnjt3ji1V/Sna2trFEodisRgikQjq6uoQiUTFlqL3JSQk4O3bt3j37l2pc4lqa2ujVatWcHd3x+LFi1V+Hj4+BAUF4eDBgzh06BA+fPjA3t+oUSO4u7ujd+/eCt3G8vLycODAAZw5cwYymQxmZmbYuXMnRowYUeU2uU5uFBQU4JtvvsH27dsBAAMGDMA333wDkUiE+/fv49dff0V6ejpEIhE2bdqEefPm0XFPKaRSKW7cuIG//voL586dY6tSfEwkEsHS0pJNGpqYmEBbW7vcpaYkFvPz83H79m14enrC39+fvd/JyQlffPEFpkyZohIlyilJWDquklxZWVnQ1dUFAGRmZnI6TzfXuIyVkoSEEEIIIYRUDCUJVRQd5H/ahw8fsH//fuzdu5cdBSCnrq4OU1NTmJubw8zMDObm5uxiZmYGY2NjTk7uSCQSZGdnIysrC5mZmcjKymJvy38ver9IJIKZmRkbk/y2rq6uSpYnDAgIwNy5c6Gmpob379/Dzs6O75CKUUaSUCaT4fjx49i/fz8YhkHr1q3x999/w8HBobrhk1row4cPePbsGUJDQxEaGoqQkBD2dmZmZrXa1tTUhIGBAbvo6+uX+F1PT6/YT7FYXG7ioLYkCYuSSqV48OAB/vnnH7x9+xZJSUlITk5mS/VydcgjFovRunVruLq6souTk5PSy5zWVAUFBbh+/ToOHDhQLIEjEonQu3dvfPHFF2XOg1kVr1+/xsaNG9nRZRMmTMDWrVurXQqWy+RGeno6PvvsM3bE4MyZMzFu3LhixwlJSUnYuHEjnjx5AgDo27cvDh48CEtLy2q9jtosOTkZvr6+CA4OLra8f/8e+fn5VWpTX1+fTSza2NiwoxStrKxUYv7sqKgoXLhwAVevXkV6ejoAQE1NDSNGjMCcOXPQo0cPlTr+pCRh6ShJSEnCyqhr+wchhBBCCOEOJQlVFB3kl08qleLatWv466+/4OPjg6ioKEil0go9V0tLC9ra2hCLxcUWbW1t9stoVlYWsrOziy3y+8oaMVQZ2trabMJQvpiamsLU1JS9zcdolGXLluHx48eYPn069u3bp/T+y6PoJCHDMFi7di28vb0BADNmzMD27dtpZBCpNIZhkJSUVCxxmJSUxH6WFP2cKXpfVlYW0tLSqnwCWygUQldXly2xqaenB01NzWInhQsKCvDff//VmZOyUqkUaWlpSE5OZpOH8r9FXl4e8vPzkZ+fz97++D4jI6NiCcGaMpJI1SUmJuL48eM4cOAAfH19ARTOMbl48WJ07Nix2u3n5uZi3759OHv2LBiGQf369bFr1y4MGTKk2m0D3CU3wsPDMWjQILx+/Rqampr47rvv0L1791LbYhgG58+fx86dO5Gfnw99fX3s378fI0eO5OQ11RVSqRQRERHFEofx8fHsBV/yJSMjg/1ZkeNMc3NzNoFoZmYGsVgMHR0d6OjosLfFYjF0dXXZkcwVwTAMcnJykJqayi4pKSnFfk9NTUVycnKxEv22traYNWsWpk+fjvr161f5/VIkShKWjpKElCSsjLq2fxBCCCGEEO5QklBF0UF+5RUUFODDhw8IDw9HREQEwsPDS9zOysritE95OUADAwMYGhoWG91T9Pfc3FxERESwS2RkZLkl8Yr2IU8cyhcrKyu4uLjAysqK8yvBg4KC8MUXX0AoFCIoKEjpc0VVhKKThB8+fMD48eMhFAqxZ88ezJgxg4uwCakUhmGQmZmJxMTETy4JCQnFRsolJycjOzu7Uv3QSVmiKh4+fIjPP/8cgYGBAAB3d3fMnTu3yieJX7x4gU2bNiEmJgYAMHXqVGzevJnT8opcJDcePXqEgQMHIiUlBcbGxli/fj2cnJzK7TsiIgJr165FcHAwgMLXt3XrVtpPFYRhGOTn5yMjIwPR0dHsnKRBQUHsz9TU1Eq3KxKJoKGhAZlMBoZhwDBMsdtFl4oSCARwd3fHnDlz4O7urvIXNVCSsHSUJKQkYWXUtf2DEEIIIYRwh+pikVpDXV2dLflUGoZhkJeXh4yMjBJLenp6sd8BsHNWFS3hV/S2rq5utUrLZWdnIyoqik0ahoeHIyoqqtiSlpbGjioKCwsr0caAAQOwdOnSKsdQmhMnTgAAxo8fr5IJQmV4+vQpAMDV1ZUShIQ3AoGAHQVob29f4efl5uYiJSWFTRrKl4/n0cvJycE333zDddiEVFmnTp3g4+ODVatW4bfffsOVK1fw6tUr7Nq1q1InimUyGbZv345z584BAKytrbFnzx64u7srKPKqCwsLQ/fu3ZGfnw9HR0esX78eZmZmFXqura0t/vjjDxw6dAgnTpzAwYMH4e/vj8ePH6tUKcnaQiAQQFNTE5qamjAxMUHLli2LPc4wDBITE4slD+Pj45GWlob09PQSi7wUtUQiqVSFio+rUBStPiG/r2nTpp88HiaEEEIIIYQQQoqiJCGpMwQCAbS0tKClpQVTU1O+w4FYLEbjxo3RuHHjT66TkZEBf39/XL58GZcvX8bz58+LPV7VUoSfIpVK2QTZggULOG27poiOjsaePXsAAEOHDuU5GkIqT0tLC/Xr1y+3rFx6ejolCYnK0dLSwq+//oqhQ4fis88+Q1RUFHbu3InFixdX6PlSqRS//PILvLy8IBAI8MUXX2DDhg2cz3HIFYZh2BKWXbt2rXCCUE4kEmHYsGG4ceMG4uLi2PkdifIJBAK24kPnzp3LXV8qlSIjIwNpaWmQSCQQCoUQCATFfn58n7xsKSGEEEIIIYQQwhVKEhKiImQyGd6/f4+XL1/Cz8+P/VnaCEINDQ1MnToVo0eP5jSG9+/fIzs7G/r6+mjdujWnbdcE+fn5+PHHH5GVlYVOnTphyZIlfIdECCF1UpcuXXD8+HF0794dly5dQvfu3eHm5lbmcyQSCdauXYu7d+9CKBTi6NGjGDdunJIirhp7e3v8+eefmD17Ng4ePAhHR0d06dKlws9PTk7GokWLEBcXBzs7O/z77780irCGUFNTg6GhIQwNDfkOhdRyAoEAdnZ27G1VVpNiJYQQQgghpLagJCEhKuDVq1cYPXo0goKCSn3czMwMjo6OcHR0hIODA5o3bw4TExPO4/Dz8wMAdO7cWeXnr1GEP/74A8HBwdDX18epU6cgEon4DokQQuqsbt26Yf78+di2bRt+/fVX7N+/n52r6mN5eXlYvXo1Hj9+DJFIhDNnztSY0eCzZs3Cq1evsGPHDqxfvx47duyAg4NDuc9LTU3FokWLEBERAWtra9y6dYs9uU4IIXJisbjUiw5VUU2KlRBCCCGEkNqCkoSE8OzMmTOYPHkycnNzoaGhAXt7+2IJQUdHR+jp6SklllevXgEoLHlW19y8eROenp4QCAQ4deoUrK2t+Q6JEELqvPXr1+PSpUt4//49du7ciW+//bbEOtnZ2Vi5ciVevHgBTU1NeHp6ol+/fjxEW3WbN29GQEAAvL29sWLFCuzatavMEqlpaWlYtGgRwsLCYGlpiVu3blVq7lJCCCGEEEIIIYQQgJKEhPBGKpVi5cqV+PnnnwEArq6uWLVqFW/zJjEMU2eThBEREdi0aRMAYMWKFRgwYADPERFCCAEAHR0dHDhwAN27d8fly5fRvXt3tGvXjn08MzMTS5cuRUBAAMRiMa5evVoj/4eJRCKcPn0a7du3x/v377F69Wps2rQJ6uolD9UzMjLw7bffIiQkBBYWFvD29kbDhg15iJoQQgghhBBCCCE1nZDvAAipi5KTk+Hh4cEmCMeOHYtffvmFtwQhAERFRSElJQWamprlzvtUm+Tm5uKHH35Abm4uevbsiR9++IHvkAghhBTRtWtXfPXVVwCATZs2ITMzE0Bhuc2FCxciICAAenp6uH37do1MEMrVq1cPnp6eEIvFePnyJbZv315inczMTCxZsgTBwcEwNDTEzZs34eTkxEO0hJCaIicnB25ubnBzc0NOTg7f4ZSpJsVKCCGEEEJIbUFJQkKU7NWrV3Bzc8O1a9egqamJVatW4YsvvuB9DkD5fITt2rWDpqYmr7Eo09atWxEaGgpzc3McP36c978DIYSQktavXw9HR0ckJCTgzz//RGJiIr7++mu8e/cORkZGuH//fq24wKVJkyY4deoUBAIBPD09cf78efax7OxsLF26FG/evIG+vj7u3r2LJk2a8BgtIaQmkMlkePbsGZ49ewaZTMZ3OGWqSbESQgghhBBSW1CSkBAl+vvvv9GuXTu2RNiOHTvQq1cvvsMCUDfnI7x69SquXr0KoVCIEydOwMLCgu+QCCGElEJedlQgEODKlSuYPXs2wsPDYW1tjUePHqFFixZ8h8iZQYMGYcOGDQCA7du348WLF8jJycGyZcvYUZN37txB8+bNeY6UEEIIIaR0EokEP//8M5o2bQptbW2YmppixIgRePHihdLa8/HxwcaNGzF69Gg0aNAAAoEAAoEAYWFhVXxVhBBCSO1EcxISogRSqRSrVq1iT/q1adMG33//Pa/lRT9W15KEoaGh2LJlCwBgzZo16NmzJ78BEUIIKVPXrl0xf/58bN26FcnJyXBwcMDNmzfRoEEDvkPj3JIlS/Dq1SscO3YMy5Ytg5GREeLi4qCjo4Nbt26hVatWfIdYIwQFBeHHH39EeHg4GjRoUGKxsbGBlpYW32ESQhQoMDCw3HVMTExga2urhGgIqRskEgkGDBgAb29vmJmZYfDgwfjw4QP+/fdfXLp0CRcvXkTfvn0V3t6aNWuKVWUghBBCSOkoSUiIgjEMg2nTpuHIkSMACucfnDlzpkqVtUxKSkJMTAyEQiE6derEdzgKFxAQgJ9++gl5eXno378/vvvuO75DIioqPz8fDMPUqRK8hKiy9evX4+XLlygoKMDJkydhZWXFd0gKIRAIsHfvXrx9+xZPnz5FXFwcxGIxbt68CVdXV77DU3kMw2Dbtm1YtGgRpFIpAODhw4elrmtgYAAzMzOYmZnBzc0N3333HczMzJQZLiFEAUxMTCAWizFx4sRy1xWLxQgMDKREISEc2bhxI7y9veHm5oYbN25AX18fAHDixAmMHz8eEyZMQEhICHR1dRXaXseOHdG8eXO0bdsWbdu2haurK+Li4rh9sYQQQkgtQElCQhTs2LFjOHLkCIRCIZYvX47evXvzHVIJUVFRAAAHBwf2gLs22bJlC9TVCz/ucnNzcffuXchkMtjb27N/G0I+9vTpUwwfPhzx8fHo3bs3Bg4ciIEDB8LR0ZHv0Aips8RiMW7dusV3GEqhra2Nc+fOoWvXrkhKSsKlS5fQvn17vsNSeXl5eZg7dy727dsHAOjQoQP69OmDxMRExMbGFltyc3ORlpaGtLQ0BAcH48GDB9i7dy9Wr16N+fPn0wUihNRgtra2CAwMRGJiYpnrBQYGYuLEiUhMTKQkISGfIK/aUJEynQUFBfj9998BAH/++Wex8wvjxo3D0aNHcfnyZezfvx/z589XaHtLly4tt31CCCGEUJKQEIWKjY3FF198AQCYOnWqSiYIASAlJQUAau2cfDdu3Chx34QJE7Bjxw4YGhoqPyCi8s6cOYOJEyciPz8fwP/mr5w/fz4aN27MJgy7detGJ5EJIQpjaWmJd+/eoaCgACKRiO9wVF58fDxGjBiBBw8eQCgUYvbs2Rg9ejQEAkGJdRmGQWZmJlJSUpCamor4+HicPn0awcHBWLJkCXbt2oVff/0Vw4cPL/X5hBDVZ2trS4m/SkpNTYWxsTEYhqnwc4yNjZGUlKTAqEhN8uDBAyQlJcHe3h5t27Yt8fjYsWNx+fJlnD9/vkJJQq7bI4QQQkhJlCQkRIGWLl2KrKwsODk5Yfz48XyH80nJyckAam+S8Mcffyw251CrVq3Qr18/HiMiqophGKxduxbff/89AKB9+/aYNm0afH198fjxY/j5+eHt27d4+/YttmzZAh0dHXaU4fDhw6lEHSGEcwKBgBKEFfDy5UsMGTIEERER0NHRwapVq8oceSkQCKCnpwc9PT02idCrVy9cv34de/fuRUhICEaOHInu3btj8+bNaNOmjbJeCiGcMzEx4TuECqtJsdZGDx48qFSCEECt+Xw8f/48jhw5wpb51tfXh6OjI4YPH47Zs2fDwMBAJfvMyMjA9evXcevWLfj4+CA4OBipqanQ1taGpaUl2rVrh/Hjx6N///5Kuejl5cuXAPDJ8ujy7UW+nrLbI4QQQkhJlCQkREEePHiAw4cPQyAQYMGCBSo1B+HH5CMJzc3NeY5EMb7++utaWUaVcCs3NxczZszA8ePHAQCjRo3CF198ATU1NTg5OWHs2LHIysrC8+fP8fjxYzx+/BhJSUnw9PSEp6cnlixZAh8fHypHSgghSvbPP/9gwoQJyM3NhbW1NdatW1el0UNCoRADBgxA9+7dceLECZw6dQp37txB27ZtMXXqVKxbtw7169dXwCsgRHF0dHSQkJDAdxgVUpNira2cnZ3x77//lvpYUFAQNmzYgLS0NPY+oVAIDw8PZYWnEBkZGRg/fjwuXrxY7P6EhAQkJCTg0aNH2L59O06dOoVOnTqpVJ+bN2/GihUrkJubW2ofQUFBCAoKwpEjR9C1a1ccPXpU4aNrw8PDAQDW1talPi6/PykpCZmZmeXOS8h1e4QQQggpiZKEhCiAVCrFvHnzAAADBw6Ei4sLzxGVTT6SsLYmCQkpT1xcHIYNG4ZHjx5BTU0N8+fPx5AhQ0qsp6Ojg27duqFbt25gGAbv37/Ho0ePcO3aNURFRWHkyJF4+PAhxGIxD6+CEELqlo9Hf7u6umL16tXQ09OrVrva2tqYPn06PDw8sGfPHnh7e+PAgQM4ceIEVq5ciW+++Qba2tpcvARCCFEpjo6OJS54y87OxqpVq7B9+3ZIJBIAgLq6OsaPH4/vvvsOzs7OfITKiYKCAowcORJeXl4ACr8Pz5w5E02aNEFycjJOnDiBBw8eICoqCh4eHrh//z6aNm2qMn2+ffuWTRBaW1ujd+/eaNu2LUxNTZGTk4PHjx/j6NGjyMzMxL1799CjRw88evRIodVPMjIyABR+bypN0SReRkZGuUk9rtsjhBBCSEmUJCREAXbv3g1fX1/o6uri888/5zucctX2OQkJKYufnx8GDx6MiIgI6Orq4scff6xQ2SSBQICGDRuiYcOG6N+/P2bPno2XL1/iiy++wKFDh2gOK1KquLg4zJs3D7m5uVi0aBG6d+9O2wrP5KPFpFIpCgoKUFBQ8MnbH5dgKygo4Clqkp2djalTp+LMmTMAgBEjRuDLL7/ktHKDubk5Vq1ahZEjR2LHjh0IDAzEypUrsX//fpw6darUuZEIIaQ2efToESZPnozg4GD2viFDhmDTpk1o1KgRj5FxY+/evWyyrkmTJvD29i524ezcuXOxePFi/Pbbb0hNTcXs2bNx//59lelTIBCgX79+WLx4MXr37g2hUFjs8alTp2LZsmXo378/goKCEBoaimXLlmH//v2ltvfzzz/jzZs3Je5PTExk2/tYly5dip3zkB8rcXV8y3V7hBBCCCmJkoSEcCwhIQFLly4FAMyYMQOGhob8BlQBNJKQ1FUXL17EmDFjkJOTA2tra6xfvx42NjaVbsfU1BTff/89Fi1ahCNHjqB9+/aYO3euAiImNdmtW7cwfvx4xMbGAijc/jp16oQVK1bA3d2dTn7wYPPmzVi0aBHfYZBKCA4OxsmTJ3Ho0CG8f/8e6urq+PrrrxVa7q5Jkyb4448/cPPmTezZswchISHo2LEjNm/ejHnz5tG+S1RaTk4O3N3dAQBXrlxR6VGwNSnWumDHjh1YuHAhe0FMo0aNsGfPHvTo0YPfwDgilUqxZs0a9vcjR46U+n34l19+wc2bN+Hr64sHDx7g2rVr6N+/v0r0uW7dOhgbG5fZp52dHU6dOoVWrVoBKLw4aseOHaVWPrl69Sru3LnzybYOHTpU6v1Fk4Ty0fyZmZmlrpuVlVVi3bJw3R4hhBBCSqIkISEcW758OTIzM9GwYUMMHjyY73AqhEYSkrqGYRj8/vvvWLx4MRiGQevWrfHDDz9Ua+7KVq1aYfbs2di5cycWLFiA1q1bczZvCanZpFIp1q9fjx9++AEymQx2dnZo0aIFrl69iocPH8LDwwOtWrXC8uXLMWLECJWew7Y2OXz4MJsgHDBgAOrXrw81NTV2EQqFJX7/+Ar9vLw8bN68mY/w65SIiAicPn0aJ06cgI+PD3u/oaEhfvzxR7Ro0ULhMQgEAvTp0wcdOnTAxo0bce/ePcyfPx937tzBvn37YGBgoPAYCKkKmUzGnvSXyWQ8R1O2mhRrbcYwDL766iv88ccfAAo//+bPn48NGzbUqsTtnTt32Au3unfv/slKIvKpCKZPnw4AOHHiRJWThFz3WV6CUK5ly5ZwdnbGmzdvkJ2djXfv3pX6v/P27dulPr9BgwYAgLCwsHL7srOzAwBERUWV+rj8fmNj4wqVBuW6PUIIIYSURElCQjj05MkT7Nu3DwAwf/78GnGil2EYNklIIwlJXSCVSvHll19iz549AAAPDw98/fXXUFev/r/E0aNHIyAgAHfu3MHo0aPx/PlzSr7XcfHx8ZgwYQJu3LgBoDAZNX/+fGhra2PKlCk4c+YMzp8/D19fX4wZMwZOTk5YtmwZJkyYAJFIxHP0tdeFCxcwbdo0AMCYMWPwxRdfVGk0WFZWFiUJFSQuLg5nzpzByZMn8eDBA/Z+oVCItm3bomfPnujatesn5yhSFHlZ6rNnz2L37t04e/YsXrx4gdOnT8PV1VWpsRBCiCLMnTsXO3fuBFA4D9yhQ4cwcuRInqPi3tWrV9nbAwcOLHPdoo9fuXKlRvUpV3SUXU5OTrXb+xT5iMWiF/UUJb+/ZcuWvLRHCCGEkJKE5a9CCKmoJUuWgGEY9OvXD82bN+c7nApJTk5mJ6CnJCGpC3788Uc2QTh37lwsWrSIkwQhUHil9ZIlS2BnZ4eYmBjMmTOHk3ZJzSSRSNC1a1fcuHEDWlpaWLp0KZYuXcpehV+vXj188cUXOHnyJKZMmQI9PT0EBQVh2rRp5Z44IlUXFRWFUaNGQSaToX///lVOEBLFefjwIWxsbPDVV1/hwYMHEAgEaNmyJRYuXIh//vkHv/zyCwYMGKD0BKGcQCDAqFGjsG3bNlhYWLDlR+X/WwghpKb6+eef2QRhvXr1cO/evVqZIASAV69esbfd3NzKXNfc3JydkiA+Ph4JCQk1pk+gsPLB27dv2d/lo/MUoXPnzjA2NkZISAieP39e4vHTp08DAIYOHcpLe4QQQggpiUYScoxhGDrRRGqUAwcOACi88k5LS4vnaAhRPPm8KkDhKEKuP7PFYjG+/vprLFy4EE+fPuW0bVKzhIeH4+3btxCJRNi5cydbquljBgYGmDp1KkaPHo1Zs2YhJiYGGRkZyg22DqNjN9XEMAx7W0tLC/b29mjSpIlKlfV0cXHBnj17sHHjRty/fx+zZ8/GkydPsGPHDjqmImWKiIhAYmJimesEBgYqKRpCCt25cwcrV64EUHhscv36dbRu3Zrzfu7fv1/u9l9Rbdq0ga2tbZWeGxQUxN62t7cvd317e3tERkayzzU1Na0RfQKF5UrT0tIAFL5niqx0Ip8r+Pvvv8fcuXPh5eXFjmI8deoULl68CBMTE7aUqtzkyZPx5MkTzJs3D/Pmzat2e4QQQgipOEoSciQlJQVGRkZ0kqmO27hxI9q3b4/r169j8ODBaNasGd8hlSkyMhKXL18GAGzfvp3naAhRjpUrV+LkyZMIDQ2Fp6cnxo4dy3kf8pPYeXl5nLdNag55ok9fX/+TCcKibt++jZiYGKirq7NX8RPuWVtb4++//8awYcNw7do1GBgY0GhCFdOpUyeEhYXh4MGD+OuvvxAWFoZz587h3LlzcHJygoeHB3r16sXbSMKi9PT0sGbNGpw8eRJ//fUX9u3bh5cvX+Ls2bNVPnFNareIiAi4uLggOzu73HXFYjFMTEyUEBWp6yQSCebMmQOpVAqg8ELST82XV10rV65k556srgMHDmDq1KlVem5qaip7uyL7Wb169Up9rqr3mZCQgCVLlrC/yxPBirR06VJ4e3vj9u3baNiwIbp3747Y2Fjcu3cPIpEIR44cKVb+FCj8bAwKCio1gVyV9gDg0qVL+Omnn9jfk5OTAQDDhw+HpqYmgMKLRletWsXlyyeEEEJqHEoScuDly5f4/PPPsXfvXrZeemXl5eUVO5mcnp7OUXREmdq1a4cZM2Zg37592LZtG3bu3KnS8xL+/fffYBgGgwcPRteuXfkOp9poPyIVIRaLsXLlSsyYMQOnTp3C0KFDOR/xIZ9LLj8/n9N2lYH2I+5kZmYCAFtetCyRkZHYsWMHAGDDhg0KuXKfS4GBgZg/fz40NDRw4sQJ6Ovr8x1SpQwePBgHDhzAlClTcPr0aeTk5MDJyQnGxsYwMjJiFw0NjSq1T/tR9VlZWWHFihX47rvvcPPmTezduxf//vsvgoKCEBQUhD///BPDhw/HzJkzeU/wCgQCjBs3Do0aNcJPP/2EZ8+eoUWLFjh79ix69+7Na2xE9SQmJiI7OxtHjx6Fi4tLmeuamJhQsllJKjJyszb/Pfbv38++BzNmzMDw4cN5jkjx5MdpACr0XaDo8VxVKz4ou8/8/HyMHDmSLVU6bNgwpfxtNTQ0cO3aNWzatAlHjx6Fp6cndHV1MXToUHz//feVTkBXtb2EhAQ8fvy4xP2+vr7sbWdn50rFQgghhNRGlCSsppcvX6Jdu3ZYuHAhmyCUl62qTPmqDRs24Mcff1RgpERZNmzYgFOnTiE4OBiXLl3CkCFD+A6pVKmpqezE6YsWLeI5Gm7QfkQqatKkSVi7di07mnDMmDGcti9PEtbEkYS0H3GnoknCgoICrFu3Drm5uejVqxe++eYbZYRXJVKpFFu3bsWyZcvY+WyHDBmCK1euVCgZqkomT56MxMRELFq0CBcuXMCFCxdKrKOjo1MsaaitrV3s2K5o+eKiaD/ijlAoRN++fdG3b18kJCTg8OHD2Lt3L4KCgnDixAlYWFiozLFW27ZtsXv3bnz//fcIDg6Gu7s7bt++jU6dOvEdGlFBLi4uChup9TGxWKyUfrig7FhNTEwgFosxceLEctcVi8UIDAyslYlCeVUZTU1N/PDDDwrt6/bt2wptnxSSyWSYPn067t27BwBwdHTE/v37q9RWWFhYpZ+joaGB5cuXY/ny5RVav7ztorLtAcDUqVOrPNKUEEIIqUuEfAdQk71+/RodO3bE0qVL8fPPPwMoLDsaEhICAJW6qvm7775DWloau8jrzpOax9TUFOvXrwcA7Nu3j639r2rOnz+P/Px8uLq6olu3bnyHwwll70c5OTnsF5WcnByF9kW4JRKJ2C+YJ0+e5DyZVzRJWHRerZqA/h9xp6JJwoMHDyIoKAh6eno4dOgQhELVPDwLCQlBz549sWjRIkgkErRu3RpisRh37tzB2LFj2aRhTfLNN9/g2LFjmDZtGgYOHAhXV1dYW1uz+3BWVhaioqLw6tUr3L17F9euXcPVq1fZ5caNG6W2S/uRYpiammLRokUIDAxkj73//PNPRERE8BzZ/1hYWGD79u3o1KkTJBIJBg0apFLxkbpHR0cHWVlZyMrKUokSvWXhI1ZbW1sEBgbi+fPnZS5Hjx5FdnY2Z3PpqZKAgAD4+/sDKCzDaG1tzXNEyqGrq8vezs3NLXf9ot/3SittqUp9MgyDL774AseOHQNQuJ3fuHEDRkZGlYiWEEIIIXUFjSSsosTERAwZMgRNmzbFmjVrAADTp0+Hn58fwsPD0bRpU6xfvx7t2rWDunr5b7OmpiZbE53UfHPmzMHevXvx6tUr7N+/HwsXLuQ7pGLy8/Nx/vx5AMDixYt5L9PFFWXuR6GhoRg5ciRevHgBALh27RrOnj1boXnHiGqYPHky1q5di/DwcFy4cAGjRo3irG15ggEoHGlU9HdVR/+PuCMvC1VWkvDly5c4fvw4gMJ5dVTxxBzDMNizZw++/vpr5ObmQltbG19++SU8PDzg5+eHJUuW4MKFC5g+fbpKJzk/Zfz48Rg/fnyx+xiGQVpaGuLi4ootH88hlpubi9WrV5dok/YjxRIIBPj2229x48YN3LhxA2vXrsUff/yhMp+1mpqaWLlyJb766iu8f/8eQ4cOxf3791U+QUNIXWVra1srRwdW1M2bN9nbgwYN4jES5TI0NERKSgqAwvM7RRN4pUlKSir2XFXtk2EYfPnll9i7dy+AwrmYvb296XsqIYQQQj6JkoRVZGxsjJ49e+Ldu3fYsGEDzp07B2NjY8ycORO2trZYs2YNJk2ahHPnzqF58+aVKj1Kaj51dXXs2LED3bt3x4ULF+Dh4YHGjRvzHRbLy8sLKSkpsLW15TQxUldcuXIFY8eORUZGBjsPl4+PD1q2bIkzZ86gX79+PEdIKkJesmb27Nk4efIkhgwZUuX5xz5W9ER1Xl6eypy4JspV3kjCzMxMrF+/HgzDYPr06Rg5cqQyw6uQqKgozJgxA9evXwcAtGzZEkuXLkX9+vXZ33/44QesWrUKR48ehaGhIbZt21bjj3kEAgEMDQ1haGgIJyenT66Xnp5eapKQKJ5QKMTBgwfh4uKC4OBgHDx4EDNnzuQ7LJa2tjbWrl2LOXPmwNfXl53/sqYl0QkhtV/R+Rhbtmyp8P7u37/P2YjMNm3aVDnB6+TkhNDQUACFF4CWl0STryt/rir2yTAM5s6di127dgEonN/31q1bcHR0rFK8hBBCCKkb6FtqFchkMgiFQuzZswdNmzbFli1bYG5ujkOHDmH27Nlwd3fHf//9B01NTWzatAlA5UqPktqhW7duGD9+PBiGwbZt21Sm5KBMJsPp06cBAAsWLKjQSFdSSCaTYc2aNfDw8EBGRgacnZ2xZ88e7NmzB05OTkhPT8eAAQOwfv16yGQyvsMlFTB16lTY2toiKSkJFy9e5KzdoknB/Px8ztolNYs8SVja/EoMw+D3339HfHw8GjZsiK1btyo7vDIxDIMjR47A2dkZ169fh4aGBubOnYvNmzezCUK5jh07YtmyZRAIBNixYwclzYjSWFlZsfMrnThxAn5+fjxHVJyFhQXWrFkDdXV1nD17lq0+Qogy5ebmwsPDAx4eHhUqb8inmhRrbRIVFcXeNjY2Vnh/K1euxPDhwzlZvL29qxxH8+bN2dtPnz4tc924uDi2dLiZmRlMTU1Vrk95gnDnzp0AAEtLS9y6dQsNGzasUqzVJZFI8PPPP6Np06bQ1taGqakpRowYwVbiUUZ7Pj4+2LhxI0aPHo0GDRpAIBBAIBBUaY5FQgghpDajJGElZGVlITMzkz3hq6amhu3bt+Orr77CjBkzYGZmBqCwtBwAtGjRAunp6bzFS/i3ceNGaGpqwt/fn53ngW+PHz9GREQEdHR08Pnnn/MdTo2RkpKCwYMHY/Xq1WAYBkOGDMHWrVthbm4Oc3NzbNu2DR4eHmAYBitWrMCIESNUdj5K8j8aGhr47rvvABSeYOYqoaempsaOFuF6vkNSc5RVbvTGjRvw9vaGUCjE0aNHyy03pUzx8fEYOXIkJk+ejKysLDg7O2Pv3r0YNWrUJ0dB9enTBwsWLAAA/PTTT/j999+VGTKpw0aNGoWpU6eCYRisX7+eTc6riubNm7Nl53/88UecOXOG54hIXSOVSnH58mVcvnwZUqmU73DKVJNirU2KXjQaGxvLYyTKNWDAAPb2lStXylz38uXL7O2BAweqXJ8fJwjr16+PW7duoVGjRlWOtTokEgkGDBiA7777DomJiRg8eDCcnZ3x77//okOHDvDy8lJKe2vWrMHSpUvx999/Izw8nIuXRgghhNRKlCSsoNevX6NPnz7o2rUrGjZsiA0bNsDHxwdqampYsWIF+vfvz66rrq4OhmFQUFCAZs2aAYDKjCIjymVlZYVx48YBKH6Qz6d//vkHQOG8ifJSmaRsDMPA3d0dly9fhoaGBpYuXYqFCxcWK02poaGBxYsXY/HixRCJRDh//jyGDBlC+34NMG3aNFhbWyMxMbHSX1jLIh9NSEnCuutT5UYzMzOxZcsWAIVJg/bt2ys7tE8qKChA165d8e+//0JdXR0zZszAjh07KlTKa+jQoZgxYwYA4JtvvsHdu3cVHS4hAICtW7fC3t4ecXFx7MhCVTJw4EC2vPukSZMQFxfHc0SEEPI/NjY27G1lXMhw+/ZtMAzDyTJ16tQqx9G9e3dYWFiwMfn4+JS6nlQqxbZt29jfP/vsM5Xrc968eWyC0MLCArdu3eJ1upONGzfC29sbbm5uCA4OxunTp3Hv3j0cP34c+fn5mDBhQqUu6qlqex07dsTKlStx7tw5REVFwdzcnMuXSQghhNQalCSsgLCwMPTo0QOurq5YvXo1pk2bhlOnTmH58uW4ceMGBAIBtLS02PWlUilWrVqFhw8fYsqUKQCo3Ghd1rdvXwDAhw8feI6kUHR0NABg2LBh/AZSg+Tk5ODx48cAgC1bthS7AvRjHh4e+PXXXwEAd+/epSRhDaCpqYnRo0cDACIiIjhrVz7iiq6Er7skEgkAlCjr/OHDB2RnZ6NevXrsSFZVkZWVhbdv3wIAtm/fjokTJ0JNTa3Cz58wYQKb9JR/bhKiaPr6+vjhhx8AAO/fv+c3mE/44osvYG5ujry8PAQEBPAdDiGEsAYNGsTe3rx5Mzs1RW2npqaG77//nv198uTJiI+PL7HesmXL4OvrCwDo3LlzsQvEP9ajRw+2pOXBgweV0udXX32FP//8E8D/EoRVnTORCwUFBWxFiT///LPYhcnjxo3DwIEDkZCQUOGLeqrT3tKlS/HTTz9h6NChsLKyqs7LIoQQQmo1moysAq5evQoXFxfs2LEDQGFypUOHDti9ezd++uknaGlpoUuXLuy6O3fuxJMnT3D58mXe6r8T1VGZk6vKpKpxqbryJpev6DpEtShibk66OISUR1NTU6U/ix0dHSv9HIFAAAMDAwVEQ0jZSpv7U5WoqakVu6iQKFZERAQSExPLXc/ExKRCI6UJkQsMDCx3nZq2XfXt2xfu7u64cuUK8vPzMXbsWGzevBnu7u5wcHCAoaEh7Ozs0Lx581p3fDtz5kz8+++/8PLygr+/P1q2bImZM2eiSZMmSE5OxokTJ3D//n0AgIGBAXbv3q1Sfa5cuZI9TyUQCLBgwQK8efMGb968KTOGNm3aKGwbffDgAZKSkmBvb4+2bduWeHzs2LG4fPkyzp8/j/nz5yu9PUIIIYSUREnCCmAYBlFRUYiPj2fnHfTw8ICmpiZ+++037NmzB40bN4aJiQns7Ozg6OiIn3/+GS4uLjxHTgghhBBCCCF1R0REBFxcXJCdnV3uumKxGIGBgTUqoUP4YWJiArFYjIkTJ5a7bk3crk6dOoXJkyfj3LlzAAqrAXxcEaB169Y4f/58sfKkNZ26ujrOnj2L8ePH4+LFi4iNjcVPP/1UYj1ra2ucOnUKTZs2Vak+5clEoPC8VUUrVBw4cKBapVrL8vLlSwCAq6trqY+3adOm2HrKbo8QQgghJVGSsAKsra2RlZWF169fo1evXpBKpVBTU0OfPn0QHx+PuXPnYt68eTAzM4OLiwt+/fVXlR4ZQAghhBBCCCE1ka+vL3R1dT/5eGBgILKzs3H06NEyL9oMDAzExIkTce/ePc4u7qxpI8hIxdna2iIwMLDcEaqV2a4qur1UZGRsZeZ3K42enh7+/fdfPH78GEePHsWjR48QHh6OtLQ05OfnAwBevHiB6dOnczqHtyrQ09PDhQsXcP78eRw+fBhPnz5FfHw89PT04OjoiBEjRmD27NmcVkrgo09lCQ8PB1B4Hq008vuTkpKQmZlZ5ue5ItojhBBCSEmUJKyAwYMHo3v37pg6dSru3bsHOzs7FBQUQF1dHePHj8fatWtx8eJFtGvXDgA3ZRzl85ilp6dXuy3CL/lVzFKpFFlZWTxHA8hkMgCF807V1O1LHnd58/1xtR8VvRI9OzubfQ8rsn56ejo7Nx1RXXl5eQAK55Djaj+Vb38ZGRkqua8pez+qi+Qn1fLz84ttVzk5OQAKP49V7X0tGk9WVhZEIlGl2ygoKAAA5Obmqtzr4xrtR6pD1Y63SlMbjsEUobL7Uffu3cttU1tbG61atSpzxJOmpia0tbUrNDKsorS1tXH06FGYmJh8cp2goCAAhUkdZWwHRfeH9PR0lZ4rWdVjNTQ0hKGhYZnrVGa7qsj2kpiYiIkTJ7LHDuWp7nzo7du3Z+cWlgsICECPHj2QkJCAGzduIDs7W+VLPFfF0KFDMXTo0Gq1cfv2baX2Wdn+lCEjIwMAoKOjU+rjRZN4GRkZ5Sb1uG6PEEIIISUJmOoeRdZyMpkMQqEQCQkJGDFiBGJiYnDx4kX2qsC8vDz07t0bkydPxqxZszjrNyoqqlaV8SBEESIjIz95RSFA+xEhFUH7ESHVR/sRIdVH+xEh1VfeflRVn332GU6dOgWhUIjs7Gxoampy3gfh3+TJk/HkyZNKPWf48OHYsGED+/vMmTPx119/YeXKlaWWUS0oKGAvQouJiUH9+vXLbJ/L9iwsLBAXF4fQ0FA0aNCgIi+PEEIIqRNoJGEpGIZhJ+SWjwAyNTXF4cOHMXPmTHTr1g2LFi2Cubk5AgICEBAQgJ49e3Iag6WlJSIjI6Gnp1fhycHT09NhY2ODyMhI6OvrcxpPbUPvVeWo2vvFMAwyMjJgaWlZ5npV2Y9Ujaq996qG3p9PK++9qe37EW0bxdH7URIX70lt2I9o2+AevaeVo6j9iP4OpaP3pXQ1/X2p6H5UFfn5+ex8b3369KEEYS0WERHBjnauqA8fPhT7XU9PD8CnS+AWHTEsX7csXLdHCCGEkJIoSViKT33ptLe3x40bN7BkyRJ4enoiKSkJ9evXh7e3Nxo1asRpDEKhsMpXAOrr69fILzZ8oPeqclTp/arI/AzV2Y9UjSq996qI3p9PK+u9qQv7EW0bxdH7UVJ135Pash/RtsE9ek8rTpH7Ef0dSkfvS+lq8vvC9fx12dnZ8PT0xB9//IE3b95AKBRi8eLFnPZBVAsX5Uvt7OwAFI7+Lo38fmNj4wqVBuW6PUIIIYSURElCAMHBwfjrr7+Qm5uL+vXrY968edDR0YFAIGBHFcrLjgLAxo0bkZSUBJFIBIFAQFcrEUIIIYQQQgghpNZ48OABxo0bx/7+yy+/oG/fvjxGRGqCVq1aAQB8fHxKfVx+f8uWLXlpjxBCCCElCfkOgG8BAQFo27YtXr9+jffv3+O3335Dr169cO7cOeTn55dIECYkJAAA6tWrB319fUoQEkIIIYQQQgghpFZ59eoVzMzMMHToUHh7e9MoQlIhnTt3hrGxMUJCQvD8+fMSj58+fRoAMHToUF7aI4QQQkhJdTpJmJ+fj5UrV2LMmDG4dOkSLl68iPfv30NPTw/r1q3DmTNnUFBQwCYIFy1ahC+//BLh4eE8R146TU1NrF69muYIqAB6ryqH3i/+0HtfNnp/Pq2uvzd1/fV/jN6Pkug9KUTvA/foPVUN9HcoHb0vpaP3paRvvvkGcXFxOHfuHHr27Ml3OKSGUFdXx9dffw0AmDt3LjIyMtjHTp06hYsXL8LExATTp08v9rzJkyfD2dkZO3bs4KQ9QgghhFScgGEYhu8g+NSnTx+0b98e69atg0QigUgkQk5ODsaMGYPo6Gjs3r0bbm5uAIB//vkHM2fOxKtXrxQyITghhBBCCCGEEEIIITVVfn4++vfvj9u3b8PMzAzdu3dHbGws7t27B5FIBE9PTwwYMKDYc3r06IE7d+5g9erV+OGHH6rdHgBcunQJP/30E/u7j48PJBIJWrVqxV4Q4OHhgVWrVnH/JhBCCCE1SJ0eSSiTycAwDIKCggAAIpEI+fn50NbWxtmzZyGRSLB27Vp2/REjRiA8PJwShIQQQgghhBBCCCGEfERDQwPXrl3DunXrUK9ePXh6eiIgIABDhw7Fo0ePSk3oKaK9hIQEPH78mF0kEgkAwNfXl73v/fv31X69hBBCSE1XZ0cSMgwDgUCA27dvw8PDA2vXrsXChQsBADk5OdDW1sbdu3cxatQoeHt7o0mTJhAKhezzCCGEEEIIIYQQQgghhBBCCKmp1PkOgC/yRJ+rqysWLlyIbdu2QSQSYd68edDW1gYACIVCGBgYQE9Pj52XUFkJQplMhpiYGOjp6VFSkpCPMAyDjIwMWFpasvtmaWg/IuTTaD8ipPpoPyKk+mg/IqT6aD8ipPoquh8RQgghtU2dTRICQEFBAfT09DB16lTk5uZi3bp1iI2NxeLFi1FQUIDr169DJBJBLBYrPbaYmBjY2NgovV9CapLIyEhYW1t/8nHajwgpH+1HhFQf7UeEVB/tR4RUH+1HhFRfefsRIYQQUtvU2SShVCqFuro6wsLC8PDhQ8yZMwf29vZYsWIFDh48CH19faSmpuLixYswNTVVenx6enoACg9O9PX1ld5/ddy5cwdDhgyBnZ0d/vjjD4X1M2vWLMTExGDChAkK+aITHByMs2fPct7ux/r374/WrVsrvJ/qiIiIwPHjx2FtbY1du3bxHQ6ys7MxZswYdj/5FPnjCxcuZCcmVxVPnjyBt7d3meu4ubmhd+/eSoqo+ry9vfHkyROMGDECUVFRePLkCQYMGIBWrVpVu+24uDgcOHCg+kFWgrGxMWbNmqXwfo4dO4bIyEjO2qvoe56Xl4fff/+9wvvRp/4f7d69G0uWLEGXLl2wbNmyKsUMAJcuXcLOnTvRuHFjjBgxotR1vLy88Pz58yr3oeqaN28ODw8PvsMoV3x8PPbv3w9DQ0McPXq0Wm1FRUXhiy++gKamJlv2/WPnz59HYGBgtfqprIp+/nK1H3ElPDwcLVq0gIaGBv755x+F9SO3cuVK+Pr6YvDgwWjatKnC+/uYj48Prl+/rvR+q2v27NkwMjKq8vPlx6iNGzfG5s2bOYys4i5cuIDdu3dj+PDhOHjwYLXaSk9Ph42NjcrsR4r0/Plz9OrVC6amplU+rrl27Rq2b98OR0dHjB49ukpt7Ny5E2lpaVV6blWoqanh22+/VVp/ALB9+3ZkZWVh27ZtcHBwUGhfO3bswNWrV7F8+XIsXbpUoX19iqruR1KpFA8fPgQAdOrUCWpqagrvk9QsqrSNVHQ/IoQQQmqbOpkkLCgoYBOEjRs3xrhx4zB58mTMnTsXI0aMwJ07d6Cvr49mzZrB1taWlxjlpT/09fVr3JdgHR0dAIXlWuW3FUFe/kFDQ0MhCSCRSMR5m5/qR9USWB/T0NAAoPi/aWWVVyJH/rimpqbKvcfq6uV//Kqrq6tc3GWRvyaRSFTsNhevQb4NKpNQKFTK+891KZvKvucV3Y8+9f9IXqJbXV29Wp8P8pjV1NQ+GX9F9puarKzXrkrk+6NAIKj2/wT59iMQCD752vk4WVTZz9/q7kdckZ/U4uJvUxHyvw1fxzLKOlbjWnWPS+SvW01NjbfjMnn8IpGIs21aVfYjRdLV1QVQvWPqivy/LI+yy0yW9RmvyD6Bwv8zit5P5PuklpYW79umKu5HNeECKMIvVdtGqBQvIYSQuqZ2n20rRdEEYZs2bTBp0iR2ZJRMJkP9+vXx2Wef8RwlIYQQQgghhBBCCCGEEEIIIYpTp5KEHycIhwwZgt27d7MjE2hiYm4wDMN3CIRjUqmU7xBIDZKcnMx3CDVKQUEBp+3JZDJO2ytPVFQUp+1JJBJO2yOKI5VKwTBMta62pmMGbvH1fir7c4fwj+v/XYTUBvSdqSSJRII9e/YAKJwupKaOACeKQ9sIIYQQwr86kxUrOgehPEH4119/1frSZXwICgoCANSrV0+h/chLamVkZCikfWWVxDE0NFRKP9UhnwPMzs5O6X1/+PABfn5+xRZ/f3+lx8G1ikyEbmVlpYRIuCN/TV5eXvDx8QHA3X5kbGzMSTuVUb9+fYX3ERwcjOjoaE7bfPXqldISBVu2bMEvv/wCAGjWrFm12nJ0dAQAvHv3DmFhYaWuw1cJcGVR9P9NrhgYGEBdXR3p6em4d+9etdqSJxrKulCLj/dFGfu/IsjnblTWe2ZpaQmg8LOMD/JytTVNdUsfyvcbvk6kymQyeHl5AQCcnZ15iaGmkl9UUZ3/0/LtPjc3t8ptVGdOzKoQi8VK7Y9hGPb9UUbf8s/Ca9euKbyvmiY/Px/z5s3DvHnzkJ+fz3c4RAXRNkIIIYTwr84kCdXU1BAeHo6mTZti2LBh2LdvHyUIFeTOnTsAgBYtWii0H/kE9LGxsQppXxlJCV1dXdjb2yu8n+qIjo5GQEAAAGDKlCkK76+goAC+vr7YtWsXpkyZgvHjx2PBggXFlqVLlyo8DkWzsbHBsmXLMG3aNAwYMACtWrWCubk5gMIT8BMnTkTTpk15jrJyXFxc0LNnTwD/O3HF1X6koaGBL7/8UmlzpxgaGmLAgAEK7SMvLw8XL17kvN2IiIhPJtm4wjAMfvrpJyxcuBAAMGbMGIwYMaJabTZr1oydj+TcuXOlnvxs0qQJevfuXa1+VFnbtm35DqFCtLS00LFjRwDA3r17kZOTU+W28vLyAJQ976ibm1uV268KJyenaie9+eLt7Q0AaN26tVL6GzJkCAAgICAAHz58UEqfRZmZmSm9z+pydnau9jy78s9H+QVzynbv3j0EBwdDLBZj/vz5vMRQU8nn8azO6FtTU1MAQHp6epXbaNmyZZWfWxXK3lezs7NRUFAAgUDAvl+K1K9fP6irq+PRo0fw9fVVeH+EEEIIIYRwqc5kyaRSKX755ReMGzcOu3btYr+gEW4xDIO7d+8CUHySUD7qJD4+XiHt6+npQU1NTaFlY5o3b67SZW4ZhmGvFO/fvz/7nnMtLS0NT548waNHj/DkyRNkZmayj6mpqcHBwaFYOTupVIr3798rJBZl0tTUhK2tbbHRUVKpFEKhsMZOlt6tWzcwDIPbt29DIBBwmmw3NTXF9OnTcfToUSQmJnLWbmnGjBmj8CvPvby8qnWCryy3b99GgwYNON2O5syZw57YTkhIwKVLlwAA06ZNw6RJkzjpa+7cuXjx4gViYmJw9epVDBs2rMQ6Xbp0gZqaGq5fv17t/lRJjx49oKWlxXcYFda5c2e8ePECUVFR+Pnnn7F69eoq/T+TJzvKGhGlo6ODdu3a4cmTJ1WOt6L09fUxZMiQGvsZrOwkYcOGDdGjRw/cvn0b586dw8yZM5V6EZ6xsbHCj9W4xkUCWp6Y19XVrXZblSWVSnHgwAEAwKJFi2rMCGhVId8/qrPNyhNu6enpVS753KRJE1y5coW9UEPRlJ0kTEtLA1A4qloZI26NjY3RtWtX3Lp1Czt37sTu3bsV3ichhBBCCCFcqTNJQjU1Naxfvx76+voqnZSp6YKDgxEbGwuRSAQXFxeF9iVPWMXFxSmkfYFAACMjI4UmI5R9FW9lBQcHIzw8HEKhENOmTeO07YSEBNy4cQOPHj3C69evi11RXa9ePQwcOBCDBg1Cv379SpRkTU9Ph4GBAafxqIracAFD9+7dUa9ePaipqXFettfAwADTp0/H8ePHOZ8LT27o0KEKLzUYGhrKlvFVBPloQi5HKh8/frzEfXPnzsWoUaM460NbWxvLli3DggUL8PLlSzg5OZX6v6Rjx45QV1fH5cuXOeubbx06dOA7hErR1NTEmDFjcPDgQdy9exdHjhyp0mhz+Qnq8k7idurUSSlJwhEjRii9LB5XkpOT2REsykoSAsCCBQvw6NEjxMfH4+7du+jVq5fS+lZTU4OJiYnCjgUVoVGjRtVuQ55c5yNJeOvWLYSHh0NXVxfffPON0vuv6eTHedVJEpqYmEAoFEImkyEzM7NKI0pFIhGaN2+OZ8+eVTmOypBXy1CW1NRUpfc7ZMgQ3Lp1C4cPH8avv/6qtOoXhBBCCCGEVFedypYZGhpSglDB5KVGmzRpUu1SSuWRlxtNS0urVqmzsiiy5KiFhYXSvzBXhkwmw40bNwAUjqriMtbMzEzMnj0be/bsgZ+fH2QyGZo1a4Zly5bhwYMHiIuLw+HDhzFmzJgaMWcjKalZs2YKu1BAW1sbkydP5uRE68fatm2LVq1acd5uUfn5+bhw4YJC+wAKRxNyOTfh1KlTMWvWLHb5/fffOU0QyjVv3hzjxo0DAJw/f77YyOKi3NzcMHjwYM7750PPnj2VNg8ul2xsbDBo0CAAYJOFlVWRkYRA4QUCit43u3fvzsvcu1y5c+cOGIaBnZ2dUudxNTQ0xLJlywAA9+/f53ye1fKo8rHUx5o1a8bJ8TFf5UalUikOHToEAFi2bBkdo1UBF+VG1dTU2H28OhUJ2rRpU+XnVpay91P5SEJljmBs2bIl7OzskJubi6NHjyqtX0IIIYQQQqqLMmaEU8oqNQoUXj0t/8KpqJKjRkZGCmkXUP1RhC9fvkRCQgI0NDQwfvx4Ttu+efMmUlJSYG1tjR07diA0NBSvXr3Chg0b0KlTp1oxmk4VSKVSvHr1CoGBgZwmi1SBSCTC2LFjOd2PrK2tFT4PIVA4CiMlJUXh/XA9N+GoUaMwbtw4dlFkwmbq1KlwdHREXl4ePD09P7n9tmnTBsOHD1dYHMrSvn17vkOostatW7Px//TTT5UuBV3RkYRAYalZRWnYsCG6deumsPaVQdmlRovq3r07evXqBYZhcO7cORQUFCit75o0LyFXcw3L9xsdHR1O2quo69evIyoqCiYmJjQXYRVxUW4U+N92L0+GVUX9+vUVcsHVx4RCIUxMTBTeT1F8JAkFAgE7T+vOnTtr3bE3IYQQQgipvShJSDjDMAw7klBZCTB5ydHY2FiFtK/IK/G5mJNGUSQSCW7dugUAmDFjBudXqsvnMlu0aBHmzp2LBg0acNp+VaWmpuLFixf4559/sHnzZvz66684dOgQrly5Ah8fH0RFRSE/P5/vMMtVUFCAZ8+eYfv27fjnn39w+vRpnDx58pMjsmoqNTU1DB06FJ06dap2Wzo6Ohg9erTCE9SRkZF49OiRQvsoSj6yqKYRiURYsWIFhEIhgoOD4ePj88l1W7RooZARjcrSq1evGjmKsKh+/frBwcEBBQUFWLFiBVvmrSIqOpIQKCxFzVWSpShnZ2eMHTu2xleb4DNJCADz589ny7TLjyGUoaYkCTU1NdGwYUNO2uJjTkKJRMKOIly6dKnSRzHWFlyMJAQK52kGqjeSEAA7GlyRTExMlH4BoDxJqOwRjP369YOWlhZev36NBw8eKLVvQgghhBBCqqrOzElIFC8sLAyRkZFQV1dXyEm80jg4OODhw4cKm4tGUSMJGzduzMs8MhX16NEjZGRkwNzcHMOGDeO07aCgIAQHB0MkEmHSpEmctl1ZWVlZCAsLQ0hICEJDQ0sd3RUWFlZiNJaRkRHMzc1hZmYGc3NzWFpaqkTJLYlEgufPn+Phw4fIyMgAUFgGLj09HW/fvsWOHTswZMgQNGnShOdIuSMQCNC3b1/o6OjAy8uryu2MHj1a4XPHFBQUwNPTU6F9fCw8PJzzuQmVxd7eHrNmzcKuXbtw9epV2Nvbf/LCjaZNm0JNTQ2nTp1ScpTVo62tjXbt2vEdRrUJhUKMGjUKe/fuRVxcHH744Qds2rSJHTFTFvmIqIqWYOzSpQv8/f2rFW9RzZs3x9ChQ2v8CPagoCAdbLhzAAEAAElEQVQEBARAIBDwVqnAwMAA33zzDVatWoX//vsPzs7OsLGxUXi/NaXcqLOzc4X2iYrgY07CK1euIC4uDhYWFvjyyy+V1m9tI98GqpsklCfHq5sk1NfXx7Bhw3Du3LlqtVMWPhL5fCUJdXV10atXL1y+fBk7d+5U6Aj4mkJTUxMXL15kbxPyMdpGCCGEEP5RkpBwRl5q1MnJCVpaWkrpUz6SUFHlRhU1klAZ5VirKjs7G/fu3QNQOIqQ67kl5V8ARo8ejXr16nHa9qf4+voWG6WSkJCA0NDQEsllgUAAFxcXtGnTBm3atIGOjg5CQkLYJOL79++RkpLCLm/evGGfa2BggAYNGsDOzg4NGjSAoaEhBAKBUl5fXl4enj17hv/++w9ZWVkACq/a/uyzz+Dh4YGYmBisX78e79+/x5kzZ9C8eXO4u7tDW1tbKfEpQ6dOnaCjo1Olk1z9+/dXyjxkN2/eRGJiosL7+didO3fQoEEDpW2PXBo1ahQePnwIPz8/nDt3DlOnTv3kaC9nZ2eMHz8ex48fV3KUVdexY8daczJEW1sb48aNw969e/Hy5Uts374dCxcuLPd58mRHRZMnFhYWGDx4MLy9vdnPu6pq3bo1Bg0aVONHEALArl27AAAdOnSAgYEBb3F06dIFffv2hZeXF86fP4/Zs2dXaJRodejp6UFTU5NNOKsqLi+gU3aSMC8vD0eOHAEArFixAmKxWCn91kbyCxK4Kjda3SQhUPi9JDAwEEFBQdVuqzR8Jgn56HvIkCG4fPkyzpw5gy1btrCjPusqdXV1eHh48B0GUWG0jRBCCCH8oyQh4Yw8SajMK9gdHBwAAHFxcZDJZJyf6JMnergs16elpQUnJyfO2uPa/fv3IZFI0LBhQ/Tu3ZvTtvPy8nDz5k0AwMyZMzltuyxXr1795GMODg5sUrBFixYl5vdp3Lhxsd9TUlKKJQ1DQkIQHByMtLQ0vHz5Ei9fvgRQmKRr0qQJmjZtClNTU84TNBkZGXj79i2Cg4MREhICiUQCoPAE+rhx4zBgwAA2wevg4ICdO3fi8OHDOHbsGF69eoWIiAiFlJLlU8uWLWFhYYGQkBAkJCQgMTERiYmJbFm20jRr1kwp88G9evVKqWVGiwoPD0dUVJRSRvRwTU1NDd999x0mT56MyMhI/Pfff+jcufMn12/UqBEmTZrEnsxWZXp6erViFGFRpqamGDlyJE6ePAlPT0/286g0DMPg5cuXbDm2yiSS5J/Xvr6+ePjwYZXm+Gzfvj369+9fI5PnH8vMzMTBgwcBAEOHDuU3GABfffUVfHx8kJSUhMePHyt8JI1AIIC5uTkiIiIU2k91aGtrs8esXFB2kvDy5ctITEyEjY2NUo/faqOi5UYZhqnyZ5A88VSdOQnlBAIBBg0apLAkobJH80kkEmRnZwMoPC5WNicnJzg7O+PNmzc4dOgQFi9erPQYCCGEEEIIqQxKEhLOyOcFtLa2VlqfVlZW0NLSQm5uLhITEzm/WlRNTQ12dnYlyk1Wh4uLC2flprgmlUrh6+sLAJg2bRrnSdewsDDk5OTA1NQU3bt357Ttsri5uRV7z+vVq4fWrVujdevWlS4pa2RkBFdXV7i6urL35eTkwN/fH76+vnj58iUCAgKQmJiIu3fv4u7du2zC0MXFBebm5lU+IZSfn4/AwED4+fkhJCSk2GM2NjYYP348+vTpU+r2JRKJMGPGDHTs2BHr1q1DTEwMzpw5gylTptT4MntFmZubFzsZxTAMsrOz2aRh0eRhvXr1MHjwYIUnCR49eoRr164ptI/y1OSRUhYWFvj666/x66+/4tatW2jatGmZ5X0dHBwwf/58vH79GkFBQYiOjlZesBXUuHFjDBo0qNaMIizKyckJvXv3xs2bN7Fnzx7k5eVhypQp7H5WUFCAO3fu4PTp03j79i37PFtb20r1o66ujrZt26JNmzYICAjAvXv3KlxVoHPnzujdu3etSBACwI4dO5Camgpra2u4ubnxHQ709PTQpUsXnD9/ni19rWhmZmYqnSRs3rw5Z/9rU1JSkJWVBYFAoLQEyPXr1wEAixcvrpWfW8pUdDuQyWRV3i7kf/vKzAFbFl1dXQwfPhz//vsvJ+0VZWJiwnmbZZGPrtTS0ipx8Z+ydO7cGW/evEFAQAAv/asSiUSCY8eOAQAmTJig8NHlpOahbYQQQgjhn2pmKkiNJD8JzeWou/KoqamhcePG8PPzQ0xMjEJKygwbNgxbtmzhrD1VHkX47t075OTkwMjISCGjq+Qn8Jo0aaLUk7OrV69W6EkCbW1ttG3bFm3btgVQONfhw4cPcfv2bTx69KhYwlBHRweOjo5wcHCAg4NDuSP5ZDIZQkND4efnh4CAABQUFLCPubi4oGPHjmjfvj0aNmxYoURQkyZN8Msvv2D69OmIjIzE1atXa3V5F4FAAB0dHejo6KBBgwZK7ZthGNy4cQMPHz5Uar8fa9WqFaysrHiNobrc3d1x/fp1vHz5EpcuXcK4cePK3N6NjIzQtWtXdO3aFSkpKfDx8cGLFy+qXZ6yuoyMjNC/f380bty41iSoStOlSxcwDANvb28cOnQIubm5mDhxIi5fvoyzZ8+yyTw1NTW0bt0aHTp0qHL5aaFQiGbNmqFx48Y4fPhwuUnhli1b1qoEYXp6On799VcAwOTJk1XmggD5iXFljWDmY7RQZXTt2pWztt69ewegMPGojJGEsbGxePPmDQQCAcaMGaPw/mq7oknB6nxnkv9fz8zMRH5+PifTAzRv3lwhc70res7nj8mThGZmZrx91stfM1dJ3JosPz8f06ZNA1A43QQlgMjHaBshhBBC+EdJQsIZPpKEQOE8VH5+foiOjkarVq04b9/AwAATJkxgr26rLi7LTXFNXiqzT58+ChldFhkZCaDwb1ab6ejooG/fvujbty8yMzPx33//4fbt2/Dx8UFWVhb8/Pzg5+cHoPAEhoODAxwdHWFnZ8d+KYqLi8PLly/x+vXrYiMxrKys2LYtLS2rFJ+1tTV+/PFHLF++HM+ePYOlpSVat25d/RdOWFKpFJ6enuzfmS86Ojro168frzFwQSAQYOHChZg+fTrevXuHs2fPYvjw4RUalW1kZITevXujR48eCAoKgo+PD96/f6+EqIvr3bs3OnTooLIjybnWtWtXiEQiXLt2DadOncKZM2cgk8kAFG6Xbm5ucHNz42xuMw0NDYwfPx779+9HUlJSqes0aNBAKaOHlWn79u1ITk6GjY0NevXqxXc4AAovlJEnsio7QrSqlF3OsDK6devGaTIvODgYAJRSKhsonNcWKHwdqp6MrQmKJvLln4lVoaurC319faSnpyM5OZmTv41AIEDPnj1x8uTJarclp6urq/T/e/IkIZ9zAcr3eUoSkrokIiKiQvOvm5iYKO34gBBCCCEVUzfOVBGlkH/prc4X3qqQJ5xiYmIU1kfDhg3Ro0cP3L59u1rtNG7cWGWvjMvJyWHnIlFUUkE+krC2JwmL0tXVZZN6+fn5CAgIwNOnT/H8+XMEBQUhPj4e8fHxePToEYRCIezs7JCdnV3sKm59fX306NED/fv3h4uLCycnuDt27IipU6fi4MGDuHjxIszMzGr8aDNVkZ+fj9OnT/OSiPrYwIEDoa2tzXcYnLCzs8P333+PNWvWICAgALm5uRg7dmyFR0+oqamhSZMmaNKkiVJHF8pHrtWm+T8rqkOHDtDQ0MCFCxcgk8lgYmKCjh07okWLFgo5aSwWizFhwgTs378fmZmZxR6rV68exowZU6vKK6elpeHnn38GAJUqHe3v7w+GYWBkZKS0EUSKqCTBBR0dHXTq1Imz9goKChAaGgpAeUlC+bEvjSLkBldJQqDwwrH09HSkpKRwlsBt3LgxLC0tOfteVVZ5cEWRJwmVXea0KEoSkromIiICLi4u7HygZRGLxQgMDKREISGEEKJCKElIOMPnSEKgsBxSQUGBwq5W7datG6Kiotir46tClUuN+vv7QyaTwcHBAQ0bNlRIH3UxSViUhoYGWrVqhVatWmHmzJlIS0vD8+fP8fz5czx79gzx8fHsyT+hUIjOnTujX79+aN++vUKSy5MmTcLbt2/x8OFDnD59GrNmzeJt7pbaIisrC8ePH1foRQsV5eTkBBcXF77D4FT37t2xceNGLFu2DCEhITh8+DDGjx9f6dFo8tGFPXv2REJCAqKjoxEdHY2YmBh2ft3qsrKywoABA5Q6T68qatOmDUxNTSGRSGBvb6/wUXxGRkaYMGECdu/eXez+8ePH15qEudyWLVuQmZkJOzs79OjRg+9wWPIR1Mo8+aehoQFjY2MkJycrrc+K8PDw4HQOv7CwMBQUFMDExEQplSnS09Px5s0bAMCIESMU3l9dwFW5UaDw/0xgYCCn2718NCFXFVQMDAw4aacyaCQhIcqXmJiI7OxsHD16tMzvH4GBgZg4cSISExMpSUgIIYSoEEoSEs7wNZLQwsKCLbcTFxensNFQAoEAw4cPx549e5CWllalNho1asRxVNyRn9RT1ChCqVSKqKgoAHU3SfgxAwMD9OrVC7169QLDMIiMjISPjw9EIhG6du2q8BEYQqEQ3333Hb788ktERkbi77//xuTJk2tVKT5lSklJwdGjR1XmJPXAgQNr5d/S1dUVW7duxTfffIPo6GgcPHgQEydOrNL+IhQKYW5uDnNzc7Rp0wZA4UjQ2NhYNnEYHR1dqZN8urq66N27N1q2bFkr3/+qUNa8dHIWFhaYPHkyDh8+DACYNm0ajI2NlRqDoqWkpLBzEU6dOlVlRhEC/CQJgcK/u6p8/gJA06ZNOb9Qo2ipUWV8voSEhAAA7O3tqdQoR7gcSSgvO8/1du/o6AhbW1v24r7q4CNJKC/TT0lCQpTPxcWFPaYmhBBCSM1BSULCGb5GEgoEAri4uODx48eIjo5WaMlEsViMMWPGYO/evZV+rpWVlcqWm0tOTkZkZCQEAgF69+6tkD5iY2MhkUigpaVFVw2WQiAQwNbWVunvja6uLtauXYtp06YhLCwMcXFxdCKwCj58+IA9e/bwHQbLw8NDaWX++NCkSRPs3LkTS5YsQUJCAg4cOICJEyeiXr161W5bQ0OjxL6YlZWF7OxsCASCEotQKCz2u7a2drGTwIQf9vb2mD9/PoDC0YW1zebNm5GVlQUHBwd069aN73BY+fn58Pf3B1BYIliZzM3NERAQoNQ+y+Lu7s55m/JqFh06dOC87dLIy2a3aNFCKf3VBVyXGwW4TxLKRxMeOnSo2m3V9ZGEaWlpkMlkdFxACCGEEEJUGh2tEs7Iv/xIpVKl9y0v46mMEn+WlpYYNGhQpZ/XuHFjBUTDDflV/66urgqbv0N+NbKTkxN9UVYxtra27JxJgYGBSutXIpEgLi6OPZlTE0mlUty7d0+lEoS2trZwdXXlOwyFs7e3x/bt22FlZYXU1FT89ddfnJUK/ZiOjg5MTU1hYmKCevXqwdjYGEZGRjA0NIS+vj709PSgq6sLHR0d+nxTIUZGRrUyQZiUlITNmzcDKBxFqErb3Js3byCTyaCjo6P00Zvm5uZK7a8so0aN4rx8d1JSEpKTkyEUCpX2GS8fSdiyZUul9FcXFB31y1WSMCUlpVrtlKZBgwawt7evdjt1fU5CmUxWYo5cQgghhBBCVA2NJCSc4WskIfC/8pXR0dFK6a9NmzZgGAZv375Famoq0tLSkJ+fX+ZzVHU+QoZhFF5qFAAiIyMBUKlRVdWtWzfcv38fgYGB6NmzJ2ftFhQUICUlhT25WfSnvBwUADg4OKBNmzZwcnJS2LyiXIuMjMTFixcRHx/PdyjFDB48uM6UubSwsMC2bduwdOlSvHv3Dvv378eECROUPoKJEGX67bffkJ2djYYNG6JLly58h1OM/HjCzs5O6Z9DqjIKvkmTJmjatCnn7cpLjbZu3Vpp82vSSELuFd0vuJiTECgcraaIedl79uzJzpVdVcoeSSiVSpGVlQWA35GEGhoaEIlEkEgkSE1NrdXVHcqjqamJ06dPs7cJ+RhtI4QQQgj/asaZWFIj8DUnIfC/xFNiYiLy8vIUfnApEAjQtm1btG3bFkDhl/zc3Fw2YSj/GR8fj7i4OGhrayMsLAw5OTkwMzODWCxWaHyV8eHDB6SkpEBdXV2hJxvlIwlVeURlXdaxY0cIhUIkJCQgOTm5WiNAUlJScPnyZURGRiIvL69CzwkJCUFISAiMjIwwdepUlT+ZEhQUhJMnT/IdRgn29vbs1et1hbGxMX7//XesWLECfn5+OHLkCObMmcNJ6VFCVE18fDx+//13AIWjCFXpggCGYfDkyRMAyp+PEAD09fWhpaWF3Nxcpfdd1MCBAxXSrjxh165dO4W0/zGZTMYmiJo3b66UPusCeWlqhmGq/Z3JwMAAYrEY2dnZSElJ4TwpZmNjg0aNGrEJ6qpQdpJQfgGaSCTipdRpUbq6ukhJSUFqamqdnmpBXV0do0eP5jsMosJoGyGEEEL4R0lCwhktLS0AKHdEnSIUTQrykaSUz0Olra2NevXqwd/fH9HR0ezouaysLFy9epVdX1dXF2ZmZsUWU1NTaGhoKD32ohR5slGeuAgKClJYH6Tq8vPz2Svaq1q6jmEYvHjxAhcuXKhyHCkpKThx4gSmTZvG+/5QlqioKL5DKFVoaCh++eUXuLm5oX379nUmUaarq4uNGzdi0aJF8Pf3x6tXr9CjRw++wyI1QEpKCgIDA5GdnQ09Pb1ii66ursqNbF63bh1yc3Ph5OTElolWFRcvXsSrV68gFAp5uSAoLS2N9wShq6sr52VG5eRlKouOwlckgUAAHR0d5Ofn4/3793SRF0eSkpLY463qXjQoEAigqamJ7OxshU33UN3vVZmZmex3RGWQx6umpsb7RRQFBQUAoHL/RwghhBBCCPkYHbESzsiv1pSXeFEmeTJOLBYrrQRTUQzDICYmBj4+PvDx8Sl3/czMTGRmZrJzvcg1atQI48aNU+qX2vr168PQ0BCpqal4+PAhevXqpZB+evXqhVOnTuHff/9FamoqL3OUkE/z9vYGwzCwsrKq0t8mMzMTFy5cwNu3b6sdS2xsLP755x+MGTNGpebaKkp+4kdVPX36FE+fPkWjRo3Qvn17ODg48H6yTNE0NTUxcOBA+Pv7Izg4mJKE5JOSk5Ph7++PwMBAfPjwocx1tbW1iyUNP65UoMx5mENDQ/Hnn38CAGbOnKlS+3RkZCS2bdsGAOjduzcvc0G+ePFC6X1+LDk5WWFtN2/eHEFBQbh27RqmTp1abG47RRAIBOjZsyf++ecfHD58GO7u7grtr66Qj8ozNTWtdvKMYRg2aayI7z9hYWHsCNaqCg0NVercgPLEa25uLvLz83m74CwvL4/921haWvISg6ooKCjAv//+CwAYPnw4JU1JCbSNEEIIIfyj/76EM/LygHwmCZU9QX1OTg78/Pzg4+PDybxkwcHBiImJYecYUQaBQIAWLVrg7t278PLyUliSsFGjRrCzs0N4eDj+/vtvfP755wrph1TN9evXAVRt3qE3b97g1KlTnMYTFBQELy8v9O/fn9N2uaLMxEB1BAcHIzg4GKampmjcuHGxpIKqJzqrokOHDgCAmJgYZGRkQE9Pj+eIiKpITExEQEAAAgICEBcXV+Hn5eTkICcnRyXmHl29ejUKCgrg6uoKV1dXvsNhFRQUYN26dSgoKIC9vT06duyo9BhkMplKJAkVuZ04OTlBS0sLCQkJ8PX1Vco20K9fP/zzzz/4559/EBERUadLJnJFfjGVtbV1tdvKyclh/5dzPZUBwzC4efNmtdsJDQ2Fm5sbBxFVjKamJlvONSMjg7eKCvILBrS0tHgve8q3vLw8jBkzBkDhRYWUACIfo22EEEII4R/99yWcUYWRhMr6IsgwDG7cuIGHDx9y3vabN2+UmiQECq9Ov3v3Lh4/fqywUX4CgQD9+vXD3r17ceTIEUoSqpDnz58jODgYQqEQzZo1q/Dz8vLycPXqVfj6+iokrkePHsHY2FipJ5cqqjoJtr59++LDhw949+6d0krjJSQkICEhQSl98cnY2BjOzs548+YNgoOD0aZNG75DIjxKS0uDr68vAgICVCLJVx1+fn44evQogMJRhKrk4MGDCAoKgoaGBoYNG8bLCMe3b98qrQxnWbKyspCdna2QuafV1dXRrFkzPHv2DFevXlVKkrBx48Zo0aIF/Pz8sGDBAnakB6k6+UhCLpKE6enpAApLa3J9Uv3t27eclFYPDQ0FwzBK+1yQTwGRnZ2N9PR03pKEiYmJAAortqjSqO+ifH19y53H2sTEhC4OIIQQQgipA1SzjhupkeQjCbOzs5Xet7KThBEREQpJEAKFSUJlMzExgaWlJRiGwa1btxTWT58+fSAQCHD37l2EhYUprB9ScQzDYO/evQCAtm3bVvjEZnh4OHbt2qWwBKHc5cuX8e7dO4X2URVVHUk4atQodOrUCSNHjsS3336L6dOno2vXrrCwsOA4wrpLPoqJi9K3pObKzs7Gli1bcPv27RqfIASA5cuXg2EYdO/eHU5OTnyHw0pISMDx48cBAEOHDmWPBZXt+fPnvPRbGkVub61atQIA3Lp1C5mZmQrrR04gEODrr7+Gmpoazp07B09PT4X3WdtxOZIwLS0NQOEoQi4TUQzDwNvbm5O2cnNzERsby0lbFSUvvSpPovJBPpJQlUuNdu/enR2Z/qnFxcUFERERfIdKCCGEEEIUjJKEhDPykYTKOGnxMfmVrsoqN/r48WOFtZ2YmMhefapM8jKTXl5eCuvDzMyMPcF17NgxhfVDKu7u3bsICgqCuro6unXrVu76BQUF8PLywsGDB5Gamqr4AFG4rajaSf6qJAn79OmDpk2bsr8LhULY2NigV69emD17NhYuXIjBgwfDxcUFIpGIy3A5ERMTg+joaHZJTk5WybKr8iRhSEhIrSypSipGURfy8OHevXu4dOkShEIhZsyYwXc4xVy9ehUMw8DOzg5NmjThJYbU1FSVuphEkf+vLC0tYWJiAqlUijt37iisn6Ls7e3ZMnBfffUVLxVDahMuk4TyJBjX8xG+evWK0+04NDSUs7YqQhWShEVHEqqqPXv24Pnz559cjh49iuzsbF6+lxJCCCGEEOWicqOEM3yNJGQYRqkjCVNSUhAYGKjQPt68eYMuXbootI+PNW3aFNeuXUNgYCCioqI4OXlRmr59++LFixc4cuQIli9frpQSPJcvX4ampib7u4GBAZydnWFmZqayJYCUQSqVYt++fQCATp06QUdHp8z1ZTIZDh06xEn5qco6fvw4Pv/883LLIilLZZNjrq6u6NSpU5nr6Ovro02bNmjTpg2io6Px119/VSdEzs2aNavEfUKhEMbGxjAxMYGJiQnq1atX7KeJiQk7OpVhmGLPlf8u/2loaMhJib6GDRvC0NAQqamp+PDhA2xsbKrdJqlZsrOz8eDBA77D4IREIsGSJUsAAB4eHiq1PctkMly+fBkAeC3tq0qjCAEotLSzQCBAq1atcOPGDVy9ehUeHh4K66uoSZMmwdvbGxEREVizZg1++eUXpfRb2zAMw2m5UXmJXS6ThFKpFLdv3+asPaAwSVjeMRCX5McSNJKwbE5OTlSWnRBCCCGEAKAkIeEQX3MSJiYmIjc3F0KhEEZGRgrv78mTJwrvIygoSOlJQl1dXTg6OuLdu3e4ceMGpk6dqpB+unXrhi1btiAoKAjPnj1Tynxzf/75Z6n3GxkZwdnZmV2cnJzY7bguuHr1KiIjI6GtrV2hkzd+fn68JAiBwpJap0+fxrRp01QisVuZEWoNGzbEwIEDKxW3lZUV3N3dceXKlaqEpxAikajYa5BIJJDJZJyNfhYIBGjZsiU6dOiADh06wNbWtkp/a4FAgGbNmuH+/fuIiIhQqaQKUY7//vuP7xA4IZVKMWnSJDx69AhaWlqYPHky3yEV4+Pjg9jYWGhqasLFxYWXGKRSqcLLXleWoke+t2jRAjdv3sTr168VelFXUdra2pg/fz5WrFiB3377DZMmTarUHMak0IcPH5CVlQWhUMjJCLOi5Ua54uPjg5SUFM7aAwpL1EulUqipqXHa7qfQSEJCCCGEEEIqh5KEKi46OrrYFxwLCwvOJ6bnCl9JQvk8CUZGRgr/8pmXl4dHjx4ptA+gsHxqRkYG9PT0FN5XUc2bN8e7d+/g5eWFKVOmKCQZo6Ojgy5dusDb2xuHDx9WSpKwUaNGxbaN1NRUxMXFISUlBf/991+xE8qWlpZswtDNzQ329vYKj48PeXl5OHToEACga9euxUZalkYmk+HevXvKCO2TIiMjkZycrLS5R8tS0ZGE5ubmGDVqFITCylf3dnNzQ0REBPz9/Sv9XEVYtGhRse1EJpMhKysL6enpyMjI+OSSn59f7LPk488VgUAAhmEgkUjg6+sLX19f7Nq1CxYWFmzCsFWrVuVuo0U1b94c9+/fZ0eZE+4xDIOwsDAIhULY2dnxHQ4rOzsb9+/f5zuMapPJZJg+fTpOnToFdXV1fP/990orqV5Rly5dAlCYtOKrRPLbt295KXNfloSEBDAMo7ALWvT09NiLuq5evYrPP/9cIf18rFOnTujSpQvu37+POXPm4M6dO1X631aXyUuNWlhYcLLPcF1uVCKR4O7du5y09XG70dHRsLW15bzt0qhCklA+kpCShIQQQgghpCZQzWwTYX08v0vTpk3h6+urkonCouVGZTKZ0k4cyMv2KCNxoIwEodzbt2/h6uqqtP4AwNnZGerq6oiJiUFAQECx+dO41LdvX3h7e+PYsWNYsWIFLCwsFNKP3MiRI0skGCQSCWJjYxEdHc3OtZacnIyYmBjExMTA29sbO3fuxMKFCzFkyBCFxqds+fn52Lp1KxISEqCvr1+hRG1AQAB7woNPoaGhKpEkrMhIQj09PYwfP75Sya2iBAIBBg8ejNjYWCQlJVWpDUUSCoXQ09Pj7GKG5ORkBAcHIzg4GKGhoYiNjcW5c+dw7tw5ds7M3r17o02bNtDS0iqzLfn/TkoSKoZMJsOZM2fw5s0bAIUJbXd3d95H+TIMg5s3b/IaA1d+//13HD58GEKhEKtWrWLn2lQV6enpbDKBr3J1iYmJuHXrFi99lyUnJwepqakKrW7RqlUrvHv3DtevX8e0adOUNkJr3rx5ePbsGe7fv49Tp05h3LhxSum3tpB/Z+FqhDvXScL//vtPYUn3kJCQOpUklI8kVOVyo8qioaGBAwcOsLcJ+RhtI4QQQgj/VC/TRGos+YlihmGQl5fH6fwYn5KamoqDBw8CABo0aKDQvt69e8f5HB1l4SMpoKGhgSZNmsDPzw9eXl4KSxK6ubnBxsYGkZGRGDx4MG7fvl3ufHhcE4lEsLGxKXaiJicnh00ShoaGIjQ0FFu2bIGlpSXatm2r1PgUJTY2Fj/++CN7cr9v374VuuhAVZItly5dQqNGjXgvC2thYVHuezJ+/Hj24omq0tTURJs2beDl9X/snXdUVFfXxp+ZgaGKgBQBQQQBQYoVULFi770bE02iJpqiKSam+cY0k5jEaIrGxN47wYaiCAJBQASkCNJ7B+kwc78/+O4NGsVh5pYBz2+tWSLMPWcz3HqevZ8doNI4HQFjY2N4eXnBy8sLjY2NSE9PZ0TDqqoqBAYGIjAwEACgra0NQ0NDdO3a9Ymv5ORkAOxasBFaoCgK/v7+zDkEAG7fvg0AgguF169fR3R0tGDzswn9OcrlchQUFAgczX9JTU2FXC6HkZER54k+j0NRFCIiInDp0iVe520PN27cwKxZszgb38nJCTo6OiguLkZYWBhvFvXm5uaYM2cODh06hPPnzxORsJ3QlYRWVlasjFdYWAgArCTrlJaWciq637t3DyNHjuTlGkE/g9I9G4WA7k1KRMKWZy6u2lgQOgdkHyEQCAQCQXiIR4yak5CQgOzsbOalrlWEwKNZrA0NDZzPR1EUfvjhBzQ0NMDMzAyenp6czVVaWopDhw5xNv6TqK+v53U+Gnd3dwAti61NTU2czCGRSPDVV1/BwMAAkZGRWLRokcL2jVyio6MDe3t7DB8+HMuWLYO7uzsoisKmTZvURiRThdDQUCxfvhxJSUnQ0tLC4sWLFe4ppE6VbGfOnIFcLhc0Bl9f3zat/yZMmMDawnlaWhor43QkpFIpnJycMHXqVLz11lt4+eWX4eXlBX19fQAt58eCggIkJycjIiICAQEBOHnyJPbs2YNt27YxNoijRo0S8LfonISEhDxRiLt9+zYuXrwIiqIEiKrl/Ca0JTKbvP3229iwYQMA4Ndff8W2bdtw7949tbhWAmD60/Jtgfrw4UMcOnRIrQVCoKWHL22HzwUaGhpMBefZs2c5m+dJ0ElTwcHBgh3vHZXU1FQAYK2PJL2PmZqaqjQORVH4+++/2QjpqZSUlCA/P5/TOWhoBwe+W2DQVFdXM3PzVT1JIBAIBAKBQCCognqqTQQGKysrlStR+EIsFkNLSwsNDQ28iITXr1/HzZs3IRKJMHPmTM6slhoaGnD06FFOxn7WvELQq1cv6Ovro6qqChERERg2bBgn81hZWeGLL77A+vXr4efnh7feegvbt28X3K6OhrZ6LCsrQ05ODjZt2oSdO3dy2idSJpPhwoULSE9Ph1gshkQigVgsZr6mX/T3unTpAgsLC1hYWMDMzOypx0BzczP27NnD7MdWVlaYN29eu6rx1EkkzMzMREhICEaMGCFYDFpaWpg/fz5++eWX//zM2tqataSFxsZGPHjwgJWxOioikQhWVlawsrLChAkT0NDQgNra2v+8ampqUFtbi7q6OtTU1MDCwuI/lt0E1bh79y5Tzfkkbt++DZFIhIkTJ/J6Lo+Kiup01bYikQjffvstTExM8MEHH8DPzw9+fn7o0qULBg4cCE9PTwwePFiwPoW0SMin/XNiYiKOHz/O23yqcvHiRbzyyiuc2e8PGjQIoaGhiIqKQmZmJm+9QWlr+tzcXGRmZnLu5NGZoO1G2agkpPv8AaqL9Xfu3EFGRobKMT2Lu3fv8lJZR1uSC9WvlK4iNDIy4r2/vFBkZWUxFquP09zczPR/nzZtGuzs7PgMjdABaG5uxuXLlwG0JFqqa1I8gUAgEAidGXL1JbCKjo4OLyJhWVkZtm7dCgAYMWIEZ03hKYrC6dOnn/rQwyVCVRKKxWK4uroiPDwcAQEBnImEAODq6opNmzbhs88+w44dO2BnZ4e3336bs/nai4aGBhYsWIDdu3cjOzsbmzdvxtdff83Jg0t6ejq2bt36iIVfexCLxejevTsjGlpaWqJ79+4wNjbGnj17EBcXBwDw8vLCuHHj2iWqNzc3o6KiQqm4uOL69evo1asXa319lMHU1BRz5szBqVOnHvn+9OnTWVsUfh6rCNtCJBJBW1sb2traMDY2Fjqc54q0tDSFKpYiIiIgEokwYcIEXoTCuLg4zitghEIkEmHjxo1wdXXFgQMHcOXKFVRUVODGjRuM/bmdnR08PT3h6ekJV1dXaGpq8hIbLU7wcRw2NDTg0qVLiImJ4XwuNikoKEBUVJRCfX+VwdDQEI6OjkhOTsa5c+fwxhtvcDLP42hra8PR0REJCQkIDg4mIqGCyOVyJumHDZEwLy8PcrkcUqlUJSHq4cOH8PPzUzkeRYiPj8f48eM576FJi4RCVRLSNrBcVRE2NTVh/fr1/2mDIVSleVZWFpydnVFbW/vM937wwQdISkoiFZaER2hoaMDUqVMBtIj7RCQkEAgEAoF/yNWXwCq6urqoqKjgVCSkKArff/89Ghoa0L17dwwfPpyzua5fv870D+EboURCAPDw8EB4eDiCg4NRXV3NWPxxwciRI7F69Wr89ttv2LBhA2xsbDBnzhzO5msv+vr6WLRoEfbs2YOoqCjs3LkTb775JmvjNzc348iRI9i7dy/kcjk0NTUxaNAgiMViUBQFuVwOuVz+xK9ra2tRXl6OiooKyGQypp/ik9DU1MTMmTOVqqwqLy9X9dfkhNOnT2PVqlXMYpAQuLq6IicnB//88w+Alh6PbFb2CHX+IRBaU1hYiAMHDij8/n/++QcikQjjx4/nVChMTk7G6dOnORtfXZg6dSqmTp2K5uZmxtb10qVLiIyMRFpaGtLS0nD06FHo6Ohg3LhxWLduHecLbLQFN9eVhFlZWThz5ozaJaooyoULF+Du7s7YH7KNp6cnkpOT4efnh5UrV/LW39nNzQ0JCQkICQnBsmXLeJmzo5Obm4v6+npIJBJW7Mhpq1ETExOVzrN8WvfW1tbiwYMHcHR05HQeoSsJi4qKAHAnEr799tvYuXMnJ2MrQ0lJCWpra3Hw4EE4Ozv/5+d1dXVM39S6ujqUlJQQkZBAIBAIBAJBzSAiIYFV6L6EXApcV65cQWhoKMRiMac2o/fu3RO0v5GQIqG5uTlMTU1RXFyMGzduMJl9XDF//nzk5+fj3LlzWLx4MW7cuIEhQ4ZwOmd76N69O+bMmYNjx47h7NmzsLW1xYwZM1QeNzU1FVu3bmXspxwdHTFlypR2WwxTFIWHDx+ivLyceVVUVDBfm5iYYNq0aUpXfKiT1WhrKioq4O/vj9mzZwtqUztu3DjU1dVBJBLB29ubtXEpimL2DQJBKCorK5XqyRseHg6RSIRx48Zxcnymp6cLYgUuJBoaGhgyZAiGDBmC//3vfyguLkZAQAAuXbqEy5cvo6ioCOfPn0dzczPeeecdzs6LMpmM6S3GlUgok8kQFBTUKfpMRkZGcubK0KtXL3Tr1g2lpaW4cuUKZs2axck8j+Pm5oZjx451ir8PX9D9CC0sLFh5dmktEipLaWkpEhISVI6lPcTGxvIqElIUxfs9IpeVhL///jt27twJkUiEt95665H+lvX19di0aRPrcyqKs7Mz0yu1NUJVdBIIBAKBQCAQFIeIhARW0dXVBdDSR4sLiouL8f333wMARo0aBXNzc07mKSgowMmTJzkZW1GEFAlFIhHc3d1x7do1BAQEcC4SikQirFu3DkVFRQgLC8OkSZMQGRmJ3r17czpve+jTpw/GjBmDwMBA/PTTT+jRowcGDhyo1FhNTU04cOAADh48CIqiIJVKMWXKFLi5uSm1kCESiWBgYAADAwNOehKpq0gItFhX2dvbo1+/foLFIJFIOFmYLSgoECwLnkAAWq5Dhw8fxsOHD5XaPiwsDCKRCGPHjmV1kTYnJwf79+9nbbyOiqmpKRYvXozFixdDLpfjxIkTWLx4MS5cuAAzMzMsX76ck3kLCgrQ3NwMDQ0NTvpml5SU4MyZM0+tjO9ohIeHw8vLi5PqTpFIBE9PT1y8eBFnz57FzJkzeRFEXF1dAbT0iSwpKRGsN2ZHgs1+hMC/IqEqQn18fDwrsbSHe/fuYerUqZy6QNCVuzKZDA0NDbw7TnBVSXjz5k28/vrrAICVK1di+vTpj/yciHEEAoFAIBAIBGVhp2kSgfD/cFlJSFEUvvvuOzQ1NcHKyoqzrOza2lq1qE5oaGgARVGCze/m5gagJeO3oKCA8/kkEgk+/vhjODg4oLKyEpMnT1Y7ccrHxwfu7u6gKAoffvghY7fWHpKTk7Fq1SocOHAAFEWhT58+WLduHdzd3QWthmsLdfs7PM65c+dQVlYmdBisQ6xGCUIik8lw7NgxZrFTWUJDQ3Ht2jVWrmeNjY24fv069uzZo/JYnQ2xWIwFCxZgx44dAIC9e/fiwoULnMzVuh8h29etoqIi7Ny5s9MIhEBLNVNsbCxn43t4eEBTUxNZWVmIjo7mbJ7WdO3alUlKunXrFi9zdnS4EgmVFWgpimL6VfMN19WLUqmUOTcJkWzFhUiYkZGB6dOnQyaTYcyYMVi8eDFrYxMIBAKBQCAQCEQkJLAKXUnIRU/CCxcuICIiAmKxGDNmzIBYzP7uS1EUTp48icrKStbHbi8ymQzNzc2Czd+1a1fY2toCAK5evcrLnDo6Ovjqq69gbm6OlJQUzJgxQ9CKyscRiUSYNm0aevTogcbGRmzatEnhCpvGxkbs2rULa9asQXp6OrS0tDB37lzMnz+f056PbNARBLhTp05BJpMJHQarEJGQIBQUReHcuXPIyMhgZbxbt27hypUrSl/TKIpCTEwMduzYgZs3b7ISU2dlzZo1+OCDDwAA33//PdMvlU1ycnIAQGkL66dRXV2Nw4cPszqmuhAaGgq5XM7J2FpaWkw1/dmzZzmZ40nQyWQhISG8zdmRoe1GW9tDKgtFUYxIaGpqqtQYBQUFgiWBcSmaAy3363Q1oRDVdWzbjVZXV2PGjBmorKyEg4MD3n33XbVNLCQQCAQCgUAgdEyISEhgFa4qCevr6/HTTz8BAHx9fZV+IH4W6enpSE9P52RsZRBaIHN3dwcABAYG8jZnt27d8NVXX0FPTw+3bt3Cxo0beZtbETQ0NDBv3jxoamoiOzsbv/zyi0Lbbdq0CUeOHAFFUejbty/WrVuHvn37doiH/I4gEubl5fEmZvNBbW1tp6qkIXQsIiMjWa8wCQ8Px5EjR5Ta9tKlSzh37pzStqfPG1988QWWLVsGuVyOzz77DMXFxayOz5VIeOHCBbVI0uKC0tJSpKWlcTa+p6cngBZBvqSkhLN5WkPfI5K+hIrBZiVhaWkpamtrIRKJYGRkpNQY9+7dUzkOZcnMzOT8fN66LyGf1NfXM+dctkTCjRs3IjY2FkZGRtiyZQvv9qkEAoFAIBAIhM4PEQkJrGJmZgaAfXvC6upqNDU1AQAGDRrE6tityc/P52xsZRD6IVBPTw8AmM+eL3r16oW3334bABAQEMDr3M8iOzsbhw8fZj4TqVSq0Ha04GtnZ4e5c+cyn21HoEuXLkKHoBDh4eG4e/eu0GGwQmeriiR0LDQ1NTkZV9lKKnI8tA+RSIQ//vgD/fv3R319PeuVXnRFKNs99tTd2lpVuNyPTUxMoK+vD4qiePsc7e3tAQAPHjzgZb6OTGVlJRITEwG03OOqCp28pa+vr/RxWFVVpXIcqsBlkhxFUaitrQXwr8sNXxw6dAhyuRy2trawtLTkdW51RSqVYseOHXj//feFDoWgptD7yI4dOxR+tiYQCAQCgcAuRCQksIqjoyMAKNWrrS26devGZMrSFi5cIETfiqfh7u7O2UKtotBZ77SNFZ84OTkBaOnBIWRvRpra2lqcO3cOf/75JwoLCyGVSrF+/Xq8+eabCm2/evVqAC2fKVsWfnwxfPhwoUNQmLNnzzL9sjoyXbp06TDiLKHz0a9fP6xfvx4zZ86Eu7s7K0kNlpaWmDNnjlLbTpgwgREkCIohlUoxf/58AC2VoWzSrVs3AOzfM5mbm7M6njqhr6+P3r17czY+RVGoq6sDABgaGnI2T2vo+/LS0lJB7fE7ArTdrKWlJStuKLQARltqKgMXbRsUxdHRkVOr/dLSUjQ2NkJLS4vVvoDPIjs7G8eOHQMA/PDDD6x9xt988w08PDxQXl6Ojz76SHCnmfaiqamJ119/nbkmEQiPQ+8jr7/+uuDrHwQCgUAgPK8QkZBl1EHMEBJa2GFbJBSJRMzYXFb70Q/d6oAQwtzj0NarAwcO5H1uMzMziEQi1NbWsm6V1h4oikJUVBS2b9+OmJgYAMCkSZNw7NgxTJs2TeEFgL59+2L69OkAgL///rtDLag5OTmxYo/FF3/88QfOnj2L9PT0Dn1OZqNvEYGgLF26dIGHhwdmzZqFDRs2YPXq1Rg3bhzs7e3brFzR19eHra0tBg8ejMmTJ2P58uV455138PLLLyu9KKypqYmFCxfCwcFB2V/nuWTixIkAgOjoaFYdAWhxiO1eX927d2d1PHWif//+kEgknI3f2NjIVCp27dqVs3laY2BgALFYDIqiBL1P6wgEBQUB+NeiVVXo5xVVKm6EFAkHDx7M6fj0s6K9vT2nx11rKIrC9u3b0dTUhEmTJmHGjBmsja2np4dz587B0NAQKSkp2Lp1a4e+vyUQCAQCgUAgqB/s+gQ9pzx8+BANDQ3Q1dXl3dJE3ejTpw+AFpGQoihWrWQcHR0RHh7OaZ8udakkNDQ0hK2traAxVFdXo6ioCEDL4hbfSKVSdOvWDSUlJUhPT2esbPkkLy8PFy5cYCrT7Ozs8Pbbb8PV1VWp8V555RUEBwejtLQUt27dwsiRI9kMlzNEIhF8fX2xf/9+oUNRmLt37+Lu3bvo2rUrPDw84OHhwXr/LK6xtLRk7MkIBCERiUQwNzeHubk5hg4diubmZmRnZyMtLQ1NTU0wNTVlXnRvYrbR0NDAggULcPLkSSQlJXEyR2fD3d0d5ubmKCwsRHx8PGvXcvpcynZPMQsLC1bHUycGDBjA6fitK8v4sqqXSCTo2rUrysvLUVRU1Kn/fqpCi4QeHh6sjEcL9B2xktDY2JjzynBaJOQzsSQ4OBiRkZHQ1NTE9u3bWbdT7dmzJ86fP4/Ro0fj+vXrsLOzw9KlS1mdgytkMhmCg4Nx//59oUMhqCn0PgK0ONjwJe4TCAQCgUD4FyISqkhcXBxeeuklNDQ0oLKyEgsWLMDixYvRv3//dolkDQ0NaGhoYP4vdJ8IZenduzfEYjFqampQXl7O6qI8XUnIpUjIdla8snh4eHDaq0MR6CpCBwcH3rLSH8fCwoIRCb28vJ75fjaOo/r6eqSlpSE5ORmxsbEAWqpYXn31VcyaNUulhxZ9fX2sXbsWn3/+OW7evAlXV1fGtk3d6dWrF+zs7BgL2o5CZWUlbt68iZs3b8La2hr9+vWDi4uL4P0+FUGoSsLOcj0icIeGhgZ69erFSm+t9iCRSDB37lzs2bNH7XoIP446HEdisRjjx4/HgQMHcPv2bdZEQq7sRjtrJaGjoyPnFqC0SMiX1SiNkZERysvLOWsFoA7HkarU1NTg9u3bANgTCWlr2Y4oEg4aNIjzZxz6+kC3weCauro67Ny5EwCwceNGzqyFhw8fjl9//RWvvvoq9uzZA319fVhbWzM/V1cb0vr6eowePVroMAhqTOt9pLq6mhWbewKBQCAQCO2DiIQqkJmZiTFjxmDhwoWYOXMmbt26hevXr+Pq1avYtm0bRo8erbBQ+NVXX2Hz5s08RM0tWlpasLW1RVpaGrKyslgVCekHvZKSEjQ2NnLS1FqdREKhocUgrrPf26J79+6Ii4tjBMtnocxxJJfLkZeXhwcPHuDBgwfIycl5xMLH19cXa9asYU3MGz16NC5evIjIyEj4+/tj2bJlggvCijJmzJgOJxK2Jjs7G9nZ2fDz84OrqytsbW0hkUggEokgFoshEoke+Zr+V0dHB926deOsQuppWFpa8jofjRDXo4cPH0JTU5NX8ZaiKOTl5UEsFqN79+4d5jh83pFIJBgyZAhOnz4tdChtoi73dRMmTGBEwldffZWVMel7u+rqalZdI3R0dNC1a1dUVlayMp66wIdlOy0SGhgYcD5Xa7juF64ux5EqhIWFQSaTwczMjDUhvCP3JOS6nQJFUbyLhAcPHkRRURFsbW2xceNGTud65ZVXEBsbix07duCnn37idC4CgUAgEAgEwvMDEQlVIDg4GE5OTvjhhx+goaEBX19fjBo1Cr/88gtWrVqFP//8Ez4+PgqN9cEHH2D9+vXM/6uqqh7JDOxIODk5IS0tDdnZ2aw+CJqYmKBbt24oLS1FQUEB643oKYpSC5HQ1taWWXQRCoqiGGFOSJGQtq7KyMhQ6P2KHkcPHz5EamoqHjx4gLS0NCYjm8bGxgaDBw/GqFGjlLYWfRoikQhvvfUWli9fjvT0dMTFxbHWo4ZrrKys4OTkhOTkZKFDUZn4+HjEx8e3axtaLKRfxsbGzL9cJC1IpVKYmZkxtr98wfX1SCaTobCwkBFtc3JyUFlZCbFYjP79+2Po0KGcWsMWFRUhLi4OcXFxjBhhYWGBwYMHw9XVFZqampzNTWAHFxcXBAQEsG53ySbqcl83btw4AEBqairKyspYObboexS5XI66ujpWrfa7d+/eqURCQ0NDzqqKWkOLRnw7P3AtEqrLcaQKN2/eBNBi/8uWoN5RexL269eP84Sr8vJyNDQ0QFNTk5fWDVlZWTh+/DgA4KeffuKl9ci2bdsgFotx48aNR74vk8lw7949zucnEAgEAoFAIHQ+iEioAg0NDUhKSkJRURFT8TFixAhoaWnhu+++w5YtW7Br1y6FxCwtLS2VskHVCScnJ1y8eBE5OTmcjB0aGoq8vDzWRcK6ujq1aALPdYatIpSXlzOL9m5uboLFQYuEilYSPu04+umnn5iFGYqi/iMK6unpYcCAAfD09MTgwYNhbm6uYuRtY2VlhRdffBF79uzB5cuX4eDgwNmiSW1tLbS1tVlbDBoxYkSnEAmVoa6uDjk5OU88txkYGMDJyQmTJk1itSLNysqKd5GQ7etRbW0tIwZmZ2cjNzcXzc3N/3mfXC5HVFQUoqOj0bdvX/j4+LB2LFZVVTHCYOvFbB0dHTQ0NCA/Px/nz59HQEAA+vfvj0GDBgmerKEqFEWhqqoKeXl5yMvLQ35+PvLz82Fubt6hKpifhEQigaenJ65duyZ0KE9FXe7rzMzMMGDAAERHR+P27duYMGGCymNKpVIYGBigqqoK1dXVrIuEneka4+XlxYsYI5RISNubcnWdUpfjSBXY7kcIdFyR0NPTk/M56CpCOzs7aGhwv9Tx888/o7m5GVOmTMG0adM4nw9oaYPwpCrCqqoqwVpEEAgdgaysLJSUlLT5Hrat1AkEAoFA6CgQkVAJaGsle3t7dOvWDbdu3cKcOXOYhy0vLy8sWbIEGzZsQFpaGutilrpD9w7Mzs5mfWxHR0eEhoZy0otIHaoIAcDZ2VnoEBhLSVdXV94tFltD2zIpKhI+jcdFQQDo06cPBg8ejMGDB8PFxYX3BukLFizA1atXkZmZiatXr6q8sNDY2Iji4mIUFhaiqKiIedXU1EBbWxvz589npX+YpaUlHB0dcf/+fZXH6kxUVVXh9u3b6NevH6s2oVZWVrhz5w5r4/FJaWkpzp8/j6ysrP/8TF9fH3379mVeffr0QUpKCg4fPoyIiAim0tPBwQE+Pj5KXUfr6+uRkJCAuLi4R6qRxWIxvL29MXbsWAwdOhT19fW4cOECzp07h8LCQoSGhiI0NBSOjo7w9PSEnZ1dhxDUqqurkZubywiCeXl5T7yupaeno6ioiPNkCK4ZOHCgWouE6sSECRNYFQmBFstRWiQ0MzNjZUzg3+SgzgJbfSCfhVAiIV2ZylUlYUensLAQYWFhANgVCelze0eyG7WysuLl+KafER0cHDifKysrC5GRkZBIJI8kJBIIBPUjKysLzs7OzPWSQCAQCATCoxCRsB00NTVBJBIxYsKoUaPg7u6Ot99+G/b29o/YMs6cORObNm2Cv78/Ro0aJVDEwsC1SAgAubm5rI+tDiLhhAkTOLEtbC+0SMhHH522oJuWZ2ZmQi6XK72gsXPnzkfETmNjY8EzbTU1NbF+/Xq8+eabiI6OhkQiwfDhw9GlS5cnvp+iKDx8+BBlZWUoLy9HWVkZKioqUF5ejoqKijb33/r6ehw4cABz586Fi4uLyrGPHDmSiIRPISEhgXWRkC00NDQeOYYoikJTUxNr47cmLy8Pf/31F1MxaGNj84goaGNj85/j2cPDAx4eHkhNTcXhw4dx48YNpKSkICUlBaNGjcLIkSMVnr+kpAS7du165Pdzd3eHr68vRo4c+cjxr6WlhUWLFmH+/PkIDw/H2bNnERkZifv37+P+/ftwdHTEwoUL1XrxLywsDFeuXFH4/ZmZmR1eJNTR0cGgQYMQGRkpdChqz4QJE/DVV18hOjqatTGNjY2RkZHBuuUrWz3b1AFvb2/equDoewC6so8vuLYb7eh89tlnaGpqgpOTE3r06MHauHTyW0cSCQcPHszLPHz2I6QFYF9fX9jb23M+H0F5FKkgA1ramzxvCd7PCyUlJaitrcXBgwfbTMqOiopirYczgUAgEAgdCSISKkhSUhK+//57JCYmwtvbG3PmzMGQIUNw4sQJjBw5EvPmzcP+/fvh6ekJTU1NNDc3w9LS8rm8yaQzN/Pz8yGTyVit0HJxcYFIJEJpaSkqKipYXQwRsmKOhs0sY2V5+PAhY/U1ZMgQweIoKyvD559/DqBlcV+VxYyePXsygqM64e7ujrlz5+LkyZO4ffs2oqKiMGjQIDg4OKC8vJwRA2lh8EkWja0xMjJCr169YGdnx/xrYWGB7du3IzAwEKdOnYK+vr7K5yVLS0u4urq2u6ff84A6nEeexPTp0+Hh4fHIcdTQ0ICvv/6a9bmqq6tx7NgxNDc3w9XVFZs2bWrXwn/v3r3xySefYOXKlTh48CAuXbqEkJCQdi24FxQUoKmpCQYGBliwYAHGjBnzzBgkEgmGDRuGYcOGISsrC+fOncPZs2dx//59xMTE8FYRxAdPS0boaAwZMoSIhApAn6vZtAWlx2I70cDAwAD29vZ48OABq+MKgZeXFy/zUBSFzMxMAOC9Xx+dbJCamsrrvB2Buro67Nu3DwCwatUqVhNNysvLAah2TPN5v2JgYIC+fftyPk9zczOTpMpGUtyzoAVJvo51gnK0p4JMV1cXp0+fhqmpaZvvI2Jix8XZ2fmR5P7HIXajBAKBQHheISKhAsTFxWH06NGYPn06Bg0ahMuXL0MsFjN9Rvz9/TFjxgzMmzcPixcvhoODAxITExEZGYmdO3cKHT7vWFpaQktLCw0NDSgsLGS1qqZr165wc3NDbGwskpKS4O3tzdrYZmZmTI8doaipqRFcZIiMjIRcLoerqysvVj1PoqKiAuvXr0d2djZsbGxw6tQpQeLgg9deew3e3t7Yu3cv4uPjERERgYiIiCe+VywWo3v37rCysmJsm7p37868DAwMnrjdhx9+iPr6eoSGhuLQoUN4+eWXn/nw+yxmzpwJXV3dp8b6PKKnp8d6vx02qqb79OnDm8Alk8lw4sQJVFVVwcbGBl9//bXSAr2VlRXee+89JCQkICsrC7GxsQpXItD9Ze3t7bF48eJ2z21jY4N169bB3Nwcv/76Ky5dugRHR0e1TDYAWsSygQMHoqGhgXk1NjY+8f9WVlbo06eP0CGzgrGxMSZOnIhLly4JHYra0tTUhK1btwIA5s6dy9q4crkcAFivsBWJRFi8eDFiY2Nx48YNVFZWsjo+X0ycOJG3qr7i4mKUlZVBLBbz0vOtNbSNeVpaGmpqatT2HCkEf//9N+rq6tC9e3fW+43TPSCfdt+nCLRVLB/MmjWLl/6AWVlZaGpqgrGxMezs7Difr76+HkCLhTrh6WhqamLr1q3Izc19Yi9FrlG0gqy4uBizZ8/GxIkTnzmmrq4uEhMTiVDIEvQ+Qn9NIBAIBAKBf4hI+AwyMzMxY8YMvPrqq/jyyy8BAN988w3i4uJQV1cHLS0t6Ovr49q1a/jwww9x584d/P3337CxscH169d5sTpRN8RiMezs7JCYmIi8vDxWRUIA8PHxQWxsLJKTk1kVCUUiERwcHBAVFcXamO2lqqoKJiYmgs3f3NzM/P5z5swRJIaqqiq8++67yMzMhJWVFQIDA9GzZ09BYuEDkUiEgQMHYsCAAYiOjsbhw4dRWloKKysrWFpawtLSkhEFzc3NlVpkkUgk+Pjjj7FhwwYkJCTg0KFDWLlypUrVRBKJBJMmTYKFhQXOnTun9DidCTc3N9YfbNkSCfni0qVLyMrKgqamJj7//HOVF4xFIhFmzJiBn3/+Gbdv38agQYPaJUqoaqc2Z84cBAQEIDU1FZcvX8bs2bNVGo9LpFIppFJpp6kSVBRPT0+kpqaSSqancPDgQWRlZcHIyAiTJ08WOhyFEIvF6NevH1xdXREdHY3g4OAOldlvZ2fHq1iXlJQEoOVYYLNaVBEMDQ1hZGSE8vJyJCQk8GYp2RE4fPgwAGDMmDGsiulyuZyxTVTFOr9bt25shdQmPj4+sLW15WUuugJ58ODBvFiE0yIhEcfbRiqV4t1330V0dLQgIiHNsyrIACAxMfGZtqSJiYlYunQpSkpKiEjIEvQ+QiAQCAQCQTiISNgGcrkcISEhmDp1Kt566y3m+/n5+UhKSsLAgQPRr18/+Pj4YO3atfjyyy9RV1eHxsZGaGpq8v6grk7Y29szIiHbDBs2DL/88gsyMzNRW1vL6ufs6OgoqEjIdm+f9hIfH4+amhqYmZlh+PDhvM9fXV2N9957D6mpqTAyMsK1a9eemx4ftFjIVR9IbW1tfPnll1i7di1ycnJw+PBhvPjiiyr3S+rXrx9MTU1x/PhxQatw1QE3NzfWx2RDJOQrWSUqKoqxfty8eTNrCyfjx4/Hr7/+iuLiYmRmZiq02EhXEqq6SCiRSLBhwwasWbMGcXFxGDt2rEqVGwT2EYlEmDlzJn799Ve16C2sTshkMsZSeP78+az2PGbrGGsLDQ0NeHp6on///oiIiMDVq1c5m4tNZsyYwWsPU1ok9PHx4W3O1vTq1Qvl5eWIi4sjIuH/U15eDn9/fwDA2LFjWR27oqKCsZ9XpYLN0NAQYrGYqQrmAktLS4waNYqz8R+ntUjIB7RI+Dw/8wuNIr0GExMTFR7PxsaGCH8EAoFAIBCeS4hI2AZisRgjR46Eh4cHzMzMAACffPIJdu3ahS+//BLa2tqIjY3F7t27MWjQIHh7e0NHR0dwu0h1gBZ2uBAJLS0tYWdnh7S0NKSkpLDax4+2TRIKIUVCiqLwzz//AGixkmSzl6Qi1NbWYuPGjUhOTkbXrl0RHBwMJycnXmPo7HTt2hXffPMNVq5ciYKCApw4cQKLFi1S+W9tZWWFV155BSdOnEBWVhZL0XYsunXrBgsLC1bHbGhoYCzFlKVXr168XJOys7Nx4cIFAMDKlStZ7Weqr6+PSZMmwc/PD5GRkbyKhEBLJaazszMSExORmpr6zCx0Av/o6elh5syZOHTokNChqBWnTp3C/fv30aVLF0yfPp3VsfkQCWk0NTUxbNgwDBw4EOHh4QgKCuJ8TmWZM2cOr4kElZWVyM/Ph0gkwrBhw3ibtzW9evVCdHQ04uLiBJlfHTl16hSampqYHtFsQt8XdOnSRaX7N7FYDENDQ5SVlbEV2n+YPXs2b88TDx8+RGFhIQBg0KBBvMzZ0NAAgIiEz0ImkyE6OppJaGCL9vYaFNKthy0UEUUBxXsmsj2estD7CAAMGDCA93UIAoFAIBAIRCR8Jj169HjELrO+vh7Hjx/H1KlTAbT0Kzx06BCysrJYtb7s6HApEgIt2dJpaWlISkpiVSTU1NTEokWLcOTIEdbGbA9CVmJlZWWhoKAAEokEU6ZM4XXu+vp6fPjhh7h37x709fVx48YN9O3bl9cYnhcsLS3xww8/YN26dXjw4AH8/PxYqXrQ19fHCy+8gMuXL+P27dssRdtxcHNzY32xPD8/X+Ux+LAaraqqwvHjxyGXyzFixAgsWbKE9TlmzJgBPz8/JCQk4OHDh8+01GRbwPDy8iIioZrTu3dveHt7Izw8XOhQ1AKKohib/Dlz5rC+iE0fY3yira2NUaNGwdPTEyEhIQgLC+M9hrZwdXWFq6srr3PSi+5ubm689UB8HFoEi4+PF2R+dYS2GmW7ihBgpx8hTbdu3TgTCadNm8abpSnwbxWhk5OTSjas7YHYjSpGfX09JxbMivYaBLgXufigvaLos3omsj2eKrTeR6qrq8kxRSAQCASCABCR8DHS09Nx7do1VFdXw9nZGRMmTIBYLEZzczM0NDSYhspyuRxisRhGRkZwcXFB9+7dBY5cvaBFQjas8p7EsGHDsH//fqSmpqKpqYnVPmCOjo4YMWIEbt68ydqYiiJkJSFdRThp0iRes+AbGxvx8ccf4+7du9DV1UVgYCD69evH2/zPI3369MHnn3+ODz/8EHfv3oWBgQHGjBmj8rgSiQSTJ0+GhYUFzp8/z0KkHQcurEZzcnJUHoNrkbC5uRnHjx9HdXU1evXqhY0bN3JSWWRvbw83NzfExcUhOjoaI0eOVGg7NkXCvXv3Ii0tDTKZjGQ4qym+vr7IyMhAQUGB0KEIzoULF3D37l3o6Ohg1qxZnM3Dp60mja6uLsaPH49hw4YhPj4e8fHxrJwvVaFLly6C9HwU2moU+FckJJWELeTm5uLGjRsAgNGjR7M+fnFxMQDV+hHSGBsbqzzGkxg0aBD69+/PydhPg2+rUYDYjaoLivQa7AwoKooq2jOR7fEIBAKBQCB0bIhI2Ir4+HiMHDkS/fv3R2JiIgwNDWFubg4/Pz/o6ek9UpkgFosBAL/++itqa2t56/nUUWhdSUhRFOuLSA4ODjAzM0NRURHS0tJYt6UcOXIk8vLykJqayuq4z0IokbCiooJZaJo9ezZv8zY1NeHTTz9FZGQktLW1ceXKFdLPhie8vb2xYcMGfPfddwgODoaBgQFr9kz9+/eHtbU1oqOjERsb2+n7hPXo0YOThTZVK7GtrKw4FfwpioK/vz9yc3MhlUrx+eefc2ptOmPGDMTFxSEsLAwURWHAgAFP/f3YriR0dHSEoaEhKioqkJ2drZDlKYF/NDQ0MGfOHOzcuVPoUASFoih88cUXAIDp06dzch4QopLwcfT09ODl5QUvLy+mJ158fDwjovDJzJkzeW83UFtbi8zMTAAQpI80DS0SFhYWori4GKampoLFog4cO3YMFEXBzc2NkyRSev9m47jm4t5lwoQJ8PLy4jWBQC6XCyoSkqonAp+wLYo+LyIrgUAgEAiEtiEi4f9TW1uL1atXY8GCBfjll19QUVGB8PBwvPvuu/Dy8sK1a9dgbm7OvD85ORl//vkndu3ahRs3bpBKwsewtbWFSCRCfX09ysvLWX8IpXuvnDlzBklJSayLhGKxGLNnz8auXbtQUVHB6thtIZTdaEREBCiKwsCBA3nry9jc3IzPP/8c4eHhkEqluHjxomD9dFRBLpejvr4ecrkcMpkMcrmceT3+/27duqnVQsKUKVNQXFyMffv24cKFCzAwMGAt4cHExATjx4+Hr68vHjx4gJiYGCQmJrIytrrBRRUhoHolIddVhLdv30ZMTAxEIhG2bNkCKysrTucbMWIEevfujdTUVAQFBeHmzZtwdHTEoEGDYG9v/8iCJNsioVgsxuDBgxEQEICUlBQiEqoxJiYmmDZtGvz8/IQORTBu3LiBsLAwaGpqYv78+ZzMwWdPQkUwMjLCiBEjMHz4cBQVFTGCYWVlJedze3p6ws7OjvN5Hic5ORkURaF3796CPofo6OjAwsIC+fn5iI+P56R6riNB90b19fXlZHy27UbZZPHixXBwcGB1TEXIyclBXV0d9PT04OLiwtu8pCch4VmoS88/AoFAIBAIhGdBRML/p6GhAVVVVRg3bhwAwNDQEBMmTIC9vT0WLlyICRMmICYmBgBw//59bN26Fffu3cONGzdY7YnXWdDS0oK1tTWysrKQl5fHSaaqj48Pzpw5g/v373NSraijo4P58+dj165drI7bFtXV1YyVLV80Njbizp07AFr6FvHFH3/8geDgYGhqasLPzw+jRo3ibW42yMrKwoULF3DlyhWUl5crtI2+vj527typVg+By5cvR1paGoKDg3HixAmsX7+e1WoIiUQCR0dHODo6ora2FvHx8YiJiWGl3566wEX/zLKyMpUri9kWCWtrayGTyQAABQUFuHTpEgBg1apVvGTua2pqYvHixfj5559RXl4OiqKQnJyM5ORk9O3bF3Pnzv3PNmxeF7y8vBAQEIAHDx4w9woE9aR///548OABEhIShA6FdyiKwubNmwG0JIJwZSeoDpWET0IkEsHc3Bzm5ubw9fVFdnY2bt++zVm/PBMTE076zimCOliN0vTq1Qv5+fmIi4t7rkXCtLQ0REdHQyKRcHZfy6ZIyNb5wcjICIsWLRKsijQ5ORkAMGTIEGho8Le8UVdXB6Dzi4TPSvLrrEmAqqJOPf8IBAKBQCAQngURCf8fAwMDyOVyXL9+nendIhKJ4ODggL/++gvz5s3D2rVrsWPHDjg6OuKdd96BsbHxI9WFhH+pra1FYWEhAO4sWKytrQH8a/XCBRYWFpg5cybOnj3L2Ryt6datG+9Z+Xfu3EF9fT2srKzg5eXF27z0Isf69esxfvx43uZVhbq6OgQFBcHf37/NBUeRSASJRPLI6+HDh6iursa5c+ewbt06HqNum+zsbMaiiet9T1dXF56envD09ERhYSHu3r3b4e1Ie/Towfo5jqIoXLx4UaUxtLW1YWJiwlJELWzfvv0/3xszZgxnlUqPExAQgC+//PKJP3u8RyC9YEefZ9jA3d2dGZPvZA5C+xCJRJg6dSrS09OZRdznhYMHDyIoKAhSqRQLFizgbB66ci0zM1Ntk+VEIhFsbGxgbW2NHj16MIkNbDJz5kxW+2IrSnV1NVJSUgBA4R6tXGJnZ4fQ0NDnvi8hfR9FURSTVMM2dAIRG/cehoaGTBWosvTo0QOLFi0SVCjLysoC0FLVyydaWlqor69HdXU1r/PyhYmJCXR1dbF06dJnvldXV5f1+86ODun5RyAQCAQCoSNBREK0PMhJJBLMmzcPly9fxoULFzB58mTm525ubli0aBECAwNRXV0NfX39Nm/0CMC1a9fQ0NAAc3NzzmzZysrKALQ8JHMpbnh4eEAkEuHMmTOczUEzY8YMXkXCpqYmhIWFAQDmz5/P66J37969cf36dWRkZPA2pzJQFIWkpCT4+/vj+vXrTDaoWCzGlClTsHLlSowdOxaampqQSCQQi8VP/Bv6+/tj6tSpuH79Ol577bX/iBpC8M8//+Djjz9GU1MTDAwMsHDhQt56Kpmbm2P8+PEYM2YMIiIiEBAQwMu8bMO2VRcA3L17V+V+qHxk0js4OGDDhg28nLMSExPx9ddfAwBcXV1ha2uLLl26wMDAAF26dPnP4qSFhQUA4MGDB2hsbIRUKlU5hm7dukFTUxNNTU2oqqqCoaGhymMSuENHRweDBg1CcHCw0KHwRklJCZOEsnz5ck4tKH19ffH3338jISEBkydP5rV6p72IRCJ4eXlBX18fJ0+eZG3c3r17c26z/DRiY2NBURScnZ3Vwv6Ytlt93kXCXr16wdvbG+Hh4bh69SonSTR0ciQb4rRIJMILL7wAPz8/pSuv58yZI6hAKJPJGJGTT6tRoOVetrKyUq2TJVTBxsYGiYmJgthlKmLT2VEqGEnPPwKBQCAQCB0B9X2i5xF6gXPZsmW4dOkSdu7cCV1dXcYmRiQSwcXFBQcPHkRdXR309fUFjLZjcP78eQDA0KFDOVtALi0tBQBe/h7u7u7Q09PDwYMHOZtj9OjR6NGjB2fjP4nAwEBUVlaiW7dumDBhAq9z9+7dGwAYG191o7KyEgEBAbhw4QLS09OZ79vb22PlypVYvnw5LC0tFR5v/PjxMDExQUlJCaKionjPdm4NRVE4fvw4fv/9d1AUBWtra8yfP1+Qc5uGhgaGDh2KAQMG4NatWwgJCeE9BlUwMjJidbyHDx/i3LlzKo/DhQh97ty5RyoXniaIs01xcTE+/vhjyOVyODo6Yvbs2c+c18DAADo6Oqirq0N6ejorfWvFYjG6d++O7OxslJeXE5GwAzB48ODnSiR89913UVlZCTs7O84rfN3d3WFmZoaioiLcv3+f98V5Zejbty+kUikOHz7MynhC9VGmKIq5d5o0aZIgMTxOa5FQJpOpRSKUULz44osIDw/HpUuXMG/ePNavk7RIyEbyC9DiPDB37lxERkbiwoUL7dp24sSJgl8Li4qKIJPJ0KVLl3bdl7OBubk57t+/r/YJj6pgY2OjsvinqamJTz/9FPn5+Qq18WivTSepYOz40PsI/TWBQCAQCAT+IV5Z/w9FUbCzs8OuXbuQlZWFrVu3Yu/evQBa+hVGRETA0tKStyqbjoxcLoefnx+AFpGQK+hKwi5dunA2R2vs7e3xyiuvcDK2ra0t7z1lMjMzER4eDgDYsGEDtLS0eJ2fFgnv37+vVjY9dXV12LdvHxYtWoSdO3ciPT0dUqkUS5cuxY0bN5CSkoIPPvig3QsRmpqajPXb1atXuQhdIRobG/HVV1/ht99+A0VR6N+/P5YvXy548oO2tjZ8fX2xfv16DBw4UNBY2gObIiFFUfD392dlLC6qeh630OVDIGxoaMDHH3+M0tJSmJmZKSQQAi3JPXQ14f3791mLh67MqqioYG1Mwr/IZDJWe9116dIFrq6urI2nzpw+fRp79+6FSCTChg0bOK/sE4vFTC++2NhYTudiEwcHByxfvlzlcTw9PdGzZ08WImo/ubm5KC4uhkQiUZv+f7T1dm1tLaKjo4UOR1AWLFgATU1NpKens3r9oWGzkpBGJBJh8ODBWLVqlcIOCdbW1rz0I34Wubm5AAAnJyfeWza0tl0mPB2pVIrPPvsMq1atUuj9rW06o6Ki2nyRPn6dA3of+eyzz1hLgCAQCAQCgdA+njuRsLm5GU1NTY98Ty6XQyQSQS6Xw83NDceOHYO2tja++OIL9OjRAxMmTMCff/6J7du3C76Q3hG4ffs2CgsLoaury6n1Cp+VhDSWlpZYt24d69VDs2bN4tXqs7GxkalWmjRpEoYMGcLb3DTGxsbo1q0bKIpSiwVGmUwGf39/LFu2DHv37kVdXR08PDzwyy+/oLCwEAcOHMDIkSNVWoBYsmQJACA4OFiQPlklJSV48803ERAQAJFIhEmTJmHatGlqlfHfpUsXTJ06Fa+//nqHqExh81xw7949JCcnszKWOv1NlYWiKGzduhXJycnQ0tLCwoUL25XMQIuEdN8uNqDHLC8vZ21MQgvV1dX45ZdfsG3bNlYtxLy9vVkbS13JyMhghK8FCxbwdu4cN24cgJZjTJGKD3XB1tZW6aQvFxcXrFmzBpMmTeJdkKC5c+cOgJaesOryXCKRSBg7PS56P3YkDA0NMXv2bADA5cuXWR1bJpOhsbERAHuVhK3p3r07XnnlFbi5uT3zvdOmTVOL3ry0SNinTx/e5zY3NwdAREKuoG0623oRgZBAIBAIBAKBHYS/s+eRhIQELFmyBGPGjMFLL72EI0eOAGjJhpbJZBCLxZDL5XBxccGuXbtw5MgRrFq1CsuXL0dERAT69esn7C/QQaCrCD09PTm1i6ArCfleIDE2NsaKFStY60OzYMECGBgYsDKWogQEBKC8vBxmZmZ47bXXeJ27NepgOUpRFMLCwrBy5Up89913KC0tRa9evXD06FHcuXMHa9asYc1KydvbG3Z2dqivr0doaCgrYypKYmIiVq9ejaSkJGhpaWHZsmXw9PQUbJHzWZiYmGDevHl4+eWX1aLf0tMwNjZmZZyamhqcOnWKlbEAfnoScs2hQ4cQGBgIkUiEhQsXtluQJZWEHQeZTIbjx4+jrKwM1dXVOH78OE6dOsWK8GRlZcW7lTefNDU1YdGiRaiuroaLiwtWrlzJ29y2trZwcHCAXC7HvXv3eJuXDSwtLfHaa68pfA/p6OiIV199FfPmzYOZmRnH0T2dxsZGpu9f6/7p6gBto/68i4RAi+Uo0NKjnRb12ICuIgS4s+TT0tLCrFmzMH369Ke+Z/To0TA1NeVk/vaSl5cHQFiRsDPbjbIBfY148OCB0KEQ1BR6H7l37x7kcrnQ4RAIBAKB8FzS8VcRFeT+/fsYOnQopk2bhnHjxuHatWv49ttvceXKFfz111+QSCRobGxksjJNTExgYmKCQYMGCRx5x6N1P0IuEaKSkEZfXx8vvfQSbty4oVIPtYEDB/L+UJuWlobIyEgAwHvvvSdoFrqDgwP++ecfJiueb5KTk/Hbb78xIqWBgQH+97//YfXq1ZzYr4pEIixevBhbtmzB1atX4evry/ocT+LKlSv45ptvIJfLYWpqioULF7ImbnGNlZUVXnjhBWRmZiI/Px+1tbWora1FXV0d8zX9YtOmUBGkUil0dXVZGYvtRdWOXkkYEhKCPXv2AGhZCFdGKKZFwrS0NDQ3N7MinNJjEpGQXS5duoTs7Gxoampi6tSpOHv2LOLj45Geno6pU6eqfJ308vJCTk4OS9GqFx9//DHCw8Ohr6+Pjz/+mPcEgXHjxiElJQWxsbFqYT3YHkxNTbFixQrs37//qce0nZ2dID2jn0ZiYiKamppgaWkJd3d3ocN5BPrvHx4ejvLyctZdNzoS48aNg6WlJfLy8hAeHo4RI0awMi6dOCEWizk91kUiEfr3748ePXrgxIkTKC4uZn5mbm4uWD/Ox2lsbGRiE0IkJHajilFXV/fcWH8TlKP1PlJdXf1ID3RFyMrKQklJSZvvYdOlgkAgEAiEzshzIRJSFIX9+/dj3LhxOHDgAADgnXfewV9//YXff/8dCxYswLFjxxiB8K+//sLYsWNhbW0tZNgdkvT0dMTFxUEsFsPLy4vTufjuSfg4EokEvr6+sLe3x5kzZ1BVVdWu7U1NTTFhwgSOonsy9fX1jM3ojBkzBO/9RlcS8i0S5ufn448//kBgYCCAlmzs9evXY+PGjaxVDT6NJUuWYMuWLYiIiEBFRQVn88lkMiQkJODy5ctMnzsnJyfMmjWL9/6TqiISiWBra9umUERRFBoaGlBdXY34+HhERERwbulqZGTESiVmUlIS4uPjWYjoXzpyJeGDBw+wefNmAC2Lzsom6xgZGUFLSwsNDQ3IyMhgzjeqoG52o7W1tYiPj8e9e/egpaUFX19fprKhoxAdHc0krmzevBlDhgzBuHHj8M033yAzMxPHjh2Dm5sbJk6cqLQo7+zsjC5duuDhw4dshi44dAII0HJfSy9Y84mvry9+/fVX5OTkoLKyEl27duU9BlUwMjLCSy+9hIMHDz4ihFhbW2PMmDFqV8lO3y9NnDhRLaweW2Nubo6ePXsiMzMT165dw9y5c4UOSTAkEgmWLVuGb775BpcvX2ZNJKR7eGtpafHiBGFqaopXXnkFFy9eZPa96dOnq00iUn5+PiiKgqmpqcK9FNmEPueWlJQgNzeXNZcZAoGgOFlZWXB2dlbIfUJXVxcmJiY8REUgEAgEQsej464itgORSITc3FwUFBQw39PV1cWKFSugra2NnTt34oMPPsBXX32F0NBQfPnllwgMDMTevXvV5iGoo0Bbjbq5uXFuoSmU3ejj2NraYvXq1fD392+X3dacOXM4tWN9EpcvX0ZVVRUsLS0Vbh7PJfSifXx8PJqamjj7PL744gtGNGlubkZkZCSampogEomwbNkyfP7557z1tOjTpw8GDhyIqKgoXL9+HbNmzWJt7Pr6ekRFRSEkJATh4eGPVEYMHz4co0ePVlt7UVURiUTQ1taGtrY2Ro0ahWHDhiEmJgZhYWGcCTpsVEnU1dUxIi6bdNRrV0VFBTZt2oTm5mb06tULEydOVHoskUgECwsLZGRk4P79+6yIhPSCYHV1NafnrLaQyWRITU3F3bt3kZyc/IgtU2pqKoYNG4YRI0YIElt7ycnJYfb/FStWMP1xnZ2dsWvXLuzbtw9HjhxBXFwc0tLSlK4qlEgkGDx4MJMY0hkoKCjAwoULAbQs2o8cOVKQOIyNjeHi4oJ79+4hJSWlQzpwGBgY4MUXX8Tp06fR1NSE4cOHw97eXu2ul2VlZUzFEt9JZooyePBgZGZm4tKlS8+1SAgAy5cvxzfffIPw8HCUlZWx4uBQU1MDANDW1lZ5LEXR1NTE9OnT4e7uDrFYDEtLS97mfhZC9iMEWp5BXV1dER8fjwMHDmDjxo2CxEEgPM+UlJSgtrYWBw8ehLOzc5vvNTExIX0sCQQCgUB4Cp1eJKQoCiKRCAMGDEBycjKSkpKYBwkdHR3MmzcP9+/fx/Xr11FeXo6hQ4fivffew9ixYzvsIquQnD17FgD3VqNyuVxtREKgZV+aM2cOHBwcmM+gLSZOnMh7tUdWVhZjq/n+++9DR0eH1/mfhIWFBfT09FBTU4OkpCS4ublxMk9YWNh/vjd27Fhs3boV/fv352TOtliyZAmioqIQFBSkkkgol8uRlpaG27dvIyoqCjExMZDJZMzPtbW14eDgAA8PD9jb27MReodBU1MTgwcPxsCBA5GcnIzQ0FDW7QZLS0uRlJQER0dHpSo6SktLcf78eaYygE066vXru+++Q2FhIYyNjTFv3jyVK2VokTAqKoqV/l0SiQQSiQQymQwVFRWC9GTat28fsrOzmf/37t0b48aNQ3x8PIKDgxESEoLk5GQsX7683XZNfEJRFE6dOgW5XI6ePXsygheNVCrF8uXLoa+vj127dqGmpgbHjh3D9OnTlTpvDxgwoFOJhOvWrUN5eTns7Ozw+uuvCxqLt7d3hxYJgZbkwaVLlwodRpvQvQgHDx4saF/Ethg8eDBOnjyJS5cuMc9gzyvOzs7w9PREREQErl69ivnz56s8Ji0SsukIcefOHeTn58PMzIx5PUmEVLeqWkDYfoQ0kydPRnx8PPbv309EQgJBQJydnTFgwAChwyAQCAQCocPS6UVC+uF08uTJ+N///oetW7fip59+YiwqDQwM8NZbb+G7775DYGAg5syZg1deeUXIkDssSUlJuH79OkQiEWu2Ok8jJycHTU1N0NDQEMxu9HFEIhE8PDxgY2ODgwcPMiLm4/To0QOenp48RwcUFhYCaOnNpC59bMRiMXr06IHk5GRkZmZyJhJu27btEVHUxcWF8320LVJTUwEonwleW1uLP/74A9evX/9PH6WuXbvCyckJffr0gY2NTYcVi9hCLBbD2dkZzs7OyMrKQmhoKJKTk1kZu7i4GMeOHYO+vj769esHJycn6OvrQ09Pr80qrubmZoSEhCAoKIiVOJ6EOiQBKMPdu3cBADNnzmTld3B0dERYWBgCAwPRv39/TJ06VemxysrK8N5770Emk7Haj7K90NbWAwcOxOrVq5kKyfnz5yMkJAQ//PADiouLcejQIbzwwgu8Vpy0B4qimIXuzMxMLF++HEuXLoWjoyNjQRobG4uGhoZHtlNWOL5165bKMasToaGhAIA33niDscsXCvra/ax+QATVoKuGhTr3KAJdbZabm4u8vLzn3n7xpZdeQkREBP7++2/MmzdPZdGU3gfYspqtqalhesm3xsDAAObm5o8IhyYmJmpnZU47RfTs2VOwGOh+pe1tO0EgEAgEAoFAIKgT6nWnzyH29vY4fvw4Jk2aBF1dXXz22WeMH7lUKkX//v0F6WXQmfj5558BAEOGDOG8Lw7dv8vKykrtRBAjIyO4urri5s2bT/y50BZWQi8mPg7dk691LyC2WblyJef2t4qSmZmJXbt2AcB/KmcUITExEVu2bGGypzU1NWFraws7OzvY29vDxMTkuc7cbwsbGxvY2NggLS0NZ86cYa2Cr7q6GiEhIQgJCWG+J5VKoaen95+XtrY2oqOjUVpaysrcT4MNWzO+qa+vZ/4mbFXo2draYtSoUbhx4wa2bdsGKysrparQCgoK8M477yA3Nxd6enpYunSpYFV6AwcORGBgICorK/9TIezj4wMbGxusWrUK+fn5OHLkCJYuXaqW1qNisRgrV65EZGQkbt26hfz8fHz77bf/eZ++vj7s7OzQq1cv2NnZKXUuDw0NfWJFeUeGTkRShx6U9P0zF1XRhH9xcnLCzZs3cevWLTQ0NKhlf2FtbW307NkT6enpiI6Ofu5FwsWLF2PNmjXIzs5GVVWVyj076XN5c3MzG+E90oqjNVVVVaiqqkJKSgrzPZFIBEdHR8yfP19t+mHS5xwhn+HpxL9+/foJFgOBQCAQCAQCgaAqz41ICACjR4/GiRMnMG/ePOTl5WHevHlwd3fHgQMHkJOT89zZ8bFJYGAg/vjjDwAtvfa4JiEhAcC/2ZvqRlsZrUL54NPCEUVRgsz/NOgFk6KiIoEj4YfPP/8czc3NGDhwYLsWFGQyGY4cOYI///wTFEWha9eumDx5Muzt7dVOKFd37OzssGTJEvz++++czdHY2IjGxkbO+iE+C1p870jQwqmmpiari98jRoxASUkJ4uPjsXHjRuzZs0fha4dcLsfFixexe/duVFZWwtDQEMuWLRNUhB00aBCCgoKYvoSPn0dsbGywfft2vP7668jKysLx48excOFCtTxPaGpqYsiQIRg0aBBu376N0NBQNDY2omfPnrCzs4OdnR3MzMxUSny4e/cuAgICWIxaeOrq6lBfXw8AapEAQx8P9HlP3ZKROgsWFhbo2rUrKisrcfv2bfj4+Agd0hNxcHBgRMJp06YJHY6gGBgYwNTUFMXFxSguLlZZJKSPrdbW8qrwNJHwSVAUheTkZCQkJMDV1ZWV+VWBoihGJBTymvzgwQMARCQkEAgEAoFAIHRs1CMNkEemTZuG0NBQlJWVYePGjZg+fTrOnj2LixcvwtraWujwOiSXLl3CpEmT0NjYCG9vb156vNGVhOr6N2trAVrorGp1Ewn5qCRUF1JSUvDXX38BAFasWKHwdkVFRdiwYQP27NkDiqLQt29frF69Go6Ojmq58N8R6N69OxYsWCB0GJxhZGQkdAjtpnWfWTarYUUiEaZPnw4rKys0Njbiww8/VKjiKSEhAa+99hq+++47VFZWwtzcHC+99JLgVZo6OjrMYuSJEyee+B4HBwd8//33kEgkSE1NxenTpxmbOnVEU1MTQ4cOxYYNG7Bx40YsWbIEQ4YMgbm5uUr7QmpqqkJ9gjsa9LEikUjUwlpYR0eHsbUl1YTcIRKJmN5rwcHBAkfzdBwcHAC09Loj/Hvfz4aDAF+VhG1x8+ZNtXiWqK+vZ65rQiZGkUrCZ6OpqYl33nkHy5YtEzoUgppC7yPvvPOOWrpfEAgEAoHwPPBcVRLSDBgwAOfPn0dZWRmqq6vRvXt3xnqU0D7OnTuHefPmoampCUOHDsWnn37KudVhdXU1MjMzAahvJaFUKkWPHj2Qk5PzyPctLCwEs4dSVwvK50kk3Lx5M+RyOby9veHi4qLQNkFBQfjyyy/R2NgIDQ0NTJkyBR4eHmr79+xI9OnTB2PGjEFgYKDQobBORxQJ6X5mXPSZ1dTUxIIFC/DHH38gOzsbmzdvxtdff/1Ekb2srAy7d+/GpUuXmG1Hjx4NT09PtRHlvb29ERUVhdDQUOTm5j4x+cTNzQ1ffvklPvjgAyQkJEBLSwvTpk1T63OHSCRiJT6KonDnzh34+fmxEJX6QYuEBgYGavH3FIlEMDY2Rl5eHqqrqwUX0jszzs7O+OeffxAaGoqmpia1XEylRcLo6GiBI1EPrKysEBMTw0rPTrqSUEiRsLi4GAkJCejbty8rMSgLnZDQpUsXwaqXZTIZ0tLSAICXJNmOilQqxbfffovo6GgcOHAAiYmJbb7/WT8nCAOXfzd6HyEQCAQCgSAcz6VICLQsrKiDRVNH5sSJE1i0aBFkMhlGjhyJTZs28bJYQVuNGhsbC9YTShF69uz5H5FQHSof1SH7tzW09VJnFwkTEhJw+PBhAMBLL730zPfX1dVh586d8Pf3BwBYWlpi9uzZpHcqy/j4+KCkpASxsbFCh8IaUqkUurq6QofRbugqCy5EQnrchQsX4s8//0RkZCR27tyJN954g/l5c3MzTp8+jT/++ANNTU0AWioDfH19oa+vz0lMymJiYoLevXsjNTUVp06deuT3aI2npyc++eQTbN68GXfu3IGWlhbGjx+vFsISVzQ1NeHChQuIiYkROhTOoEVCro4VZTAyMmJEQgJ3WFtbQ09PD9XV1YiJicHgwYOFDuk/9O7dGwCQnZ2NkpKS5z4R09LSEgBYEQnZrCRsbGxUOqabN2/CxcVF0GuJOliNVlRUoKmpCWKxGLa2toLF0VEwMTGBrq4uli5d+sz36urqPvfnDnWB/N0IBAKBQHg+eG5FQoJqHDhwAC+++CLkcjnGjRuH999/n7cKi3v37gFQD8GtLXr27Ilbt2498j2h+hECpJJQaD799FNQFIXhw4fD0dGxzffev38fW7ZsQXZ2NgBg2LBhGD16tNpUMXUmRCIRpk2bhrKysv+I+h0VIyMjtT3e24IWCbkU5CwsLDB79mwcP34cZ86cgY2NDWbOnInIyEjs2LGDqVK3tLTEpEmT1LZaHWipJkxNTYWfnx9WrFjx1M9t5MiRePfdd7F161aEh4dDW1sbI0eO5DlafigrK8Px48dRWFgodCicwrWgrgz0Qj0RCblFLBajT58+iIqKws2bN9VSJNTT04OVlRVyc3Nx584djBs3TuiQBIWu9GazkpCNnoSq9AIvKipCUlISnJ2dVY5DWdRBJKT7TpuampJ79DaQy+XIysoC0PIcTye6tIWJiYmgz82Ef7GxsUFiYqJC5zBl/26t9xEbGxuIxc9dVyQCgUAgEASHiISEdrN7926sWrUKFEVh8uTJWL9+Pa8PRrRIqM6Lt8CTBUF1EDbVrZLweRAJDx8+jJMnT0IkEj2zivDMmTPYsWMH5HI5unTpglmzZqFXr148Rfp8oqGhgQULFmD37t2oqqoSOhyV6YhWowB/woezszNjM7t9+3bcunULkZGRAFoyoH19fdG/f3+1F1rt7OxgZmaGoqIi+Pv7t9ljc9KkSaipqcHOnTtx48YNGBgYdDprtOTkZBw9elToMHihtd2oukBEQv5wdnZGVFQUbt26hbfeekstxQkHBwfk5uYiOjr6uRcJ2awkbG03SlGUStcpZaxGWxMUFIQ+ffoIdq2kzzVC3vPQIqGZmZlgMXQE6urqmGeZ6upqUnXZAbGxsWFdtG1tT1pXVwcfHx8AQEhICNNvmYjFBAKBQCDwBxEJCe1ix44dWLduHQBgxowZeOONN3jN9JLJZIzdqDoIbm2hpaUFCwsL5OfnA2gRw4Rc0KMf4tVNJOzsdqMxMTFYsWIFAGDRokVtCn4xMTHYvn07gJZ+edOmTeuQtpFCQFEUsrOzERsbCy0tLYwaNapd9sf6+vpYtGgRfv/9dw6j5AdaeGebOXPmPLIYaGhoCBcXF7i4uKBv376wt7dXyXKay56Ej9PaZjYyMhIikQienp4YNWoUtLW1OZ+fDUQiEby8vODn54czZ85g7ty5bYoFc+fORUVFBQ4dOgQ/Pz8YGBjA3t6ex4i5QS6X4/r16wgJCRE6FN5QR7tR2gqbiITcY2trC21tbZSXl+PevXtwd3cXOqT/4OjoiBs3buDOnTtChyI4bFYStr7GyuVylQRi+vlEWQoLC5GcnIw+ffqoNI6y1NTUABDebhQAzM3NBYuBIBykt6JyPMu+lBYLgZbkvcTERCIUEggEAoHAA0QkJCjMpUuXGIFw/vz5WL16Ne/Zo5mZmairq4NUKoWpqSmvcytDz549mYdwfX19yGQywTO+1U0kpDOAa2trUVtb22lEsYaGBuzZswebN29GQ0MDvLy8GLHwSchkMvz4448AgP79+2PatGlqX8mkTvj5+T2yGFlQUIBly5a1a4zu3btj/vz5OH78ONvh8QpXdp0NDQ2P/L+goAAFBQUIDAwE0CJYzJgxA7NmzVJq0Y6uJOTjHEXbzMrlcjQ1NWHMmDEdshLA3d0d165dQ2FhIU6cOIGFCxe2+f6VK1eioKAA165dw7Fjx/Duu+/y0kuYK+rq6nDy5EmkpaUJHQqv0CKhOvVlJpWE/CGRSODk5IS7d+/i6NGj6NOnD1Nhpi7QfQmJSMidSNjc3KzSMwUbtsw3b96Ek5OTIPer6mQ32hHvHwjKQ3r0qcaT7EufVEmYmJiIpUuXoqSkhIiEBAKBQCDwABEJCQpDZ+mPGDFCEIEQaMmaBVoWeDuCgEJbZQBATk4O9uzZgzlz5jAZ90LEokgfCKFQNwFTGRobG/HXX3/hiy++YHoK9uzZE5s2bWpzMcff3x+ZmZnQ1dXFuHHjOsT+rU48vu/Q54r24uzsjL59+zK2xh2RsLAwuLu7s15ldPfuXWZMiqKQnp6OsLAwhIWFITw8HGVlZTh48CCOHTuGSZMmYf78+cziqCL07NkTWVlZOH/+PDQ1NeHi4qJSvM+yYtPQ0MCcOXNUmkNoNDQ0MGzYMAQEBOD3338HRVFYtGjRU98vEonw9ttv49q1a2hqakJNTQ1nladcU1xcjKNHj6r1NY0r7OzsAADBwcFYtWoVtLS0BI6I3V5phGczYMAAxMbGIiwsDG+//TY+//xzQcWSx6Hvf9TJElco6MSdxxNtlEEqlUIkEoGiKDQ1NQl+7Ofn5yM3N1eQFhDNzc0AIKhATp/vOnKyDaH98NGjr7PzuH0pXRkMAP369VOrJCgCgUAgEJ4XiEhIUBj6QcjMzEwwAcPW1haamppoaGhAeXm5Wi2IPAm6MoYmPz8fO3bswPTp09GvXz9eP0c6yzUjI0MtKhppaKseHR2dDv1A0NTUhH379mHLli3IzMwE0PJguGTJEkyePLnNRYy6ujrs27cPQIsI31pcJijGtGnTYGVlhatXr0JHRwczZ85Ueiw+LZS5oKamBidPnsQLL7zA6nFua2v7yIKvnZ0dfH19AbRcH86fP49vvvkG//zzD86fPw8/Pz+MGDECL7zwAiNqtMWmTZuwZcsWhISE4MSJE5g4cSK8vLzaFSNFUUhMTMS1a9cglUrxwgsvdPrjaciQIairq0NISAh27dqF2tparFix4qnXl4yMDAAt2e203XNHIyUlBYcPHxY6DMFYvnw5vvzyS2RmZuL06dNtCsN8QQsgGhrk0YIPbGxssHTpUhw7dgwJCQlYvXo1tmzZAkdHR6FDA0VR8Pf3B9Cyrz7vNDU1AWDn2BCLxejSpQuqqqpQV1enknNA9+7dkZubq3JMUVFRgoiE9OdJi4VCQNuTtxY4CM8HXPToIzwZYutKIBAIBAI/dOyVUAKv0CKhkOKShoYGY2GUl5cnWByK8rhISHP+/HmcOnUKdXV1vMViaGgIDQ0NNDU1qdVnR4uEHcE+9kk0Nzfjr7/+gpOTE1555RVkZmbC2NgY69atw6FDhzBz5sxnZjmfPHkSZWVlMDIywqBBg3iKvHMhFosxaNAgvPfee3j99ddVEj/oBb2OTFZWFq5du8bbfBKJBLNmzUJYWBhu3ryJqVOngqIoBAUFYe3atQpVe2lpaeGzzz7DjBkzALRYXAcEBChcYVxYWIj9+/fjxIkTKCsrQ0FBAc6cOdMpKpTbQiQSwdfXlxFsDx48iJ9//vmp1bR0X98ePXp0yIrlrKys51ogBFqOlf/9738AgMOHD+Phw4cCR/SvSEgqavjDzs4Or776Krp164bi4mK89tpruHHjhtBh4f79+0hLS4OmpiaWLFkidDiCw6ZICPzbi1TVZ4ju3buzEQ5iYmJ4fZ6hoRO6hLxno5OQiM0ygcA+rW1dBw4c+NTX0qVLia0rgUAgEAgsQERCgsLQmZpCV9k4OTkBUH+RkKKoNm1I7t27h99//52pOuMasVjMCHF0JYk6UFlZCaDjiYRyuRxHjhyBs7MzVqxYgfT0dBgZGeH111/H4cOHMXv2bIUskCoqKnDgwAEAwJgxY9SmwrOjIhaLVV6I6wwiIdBiO8p3dq1IJMLw4cPh5+eH+Ph4uLu7o66uDqdOnVJoe4lEgjfffBMvv/wyACA0NBSnT59us1Kgrq4OFy5cwO+//46MjAyIxWLMmjULEokEKSkpCAoKYuV3U3d8fHwwefJkAMCZM2fw7bffPtH6kd4nhKj8UJWqqqoO3zOULZYsWQJXV1dUV1fjyJEjQoeDxsZGAKSSkG+6deuGl19+Gb1794ZMJsPmzZvx119/KW25zQZ0FeG8efOYvtPPM/Q9BVv3d3RFf21trUrjsCUSAkBsbCxrYykK/XkSkZBA6JzQtq5RUVHPfCUmJpLKTgKBQCAQVIQ8yRMURh0qCYGOIxLW1tY+s/9IZWUl9u7dixEjRmDkyJGcC7BmZmbIz89Heno6hg8fzulcikKLhB0l+4+20dq0aROzKGJoaIhFixZh+vTpjPWQohw4cABNTU2wsLBA3759uQiZ0E46i0gIAMePH8fatWsF6YPat29fbNmyBdOnT8fZs2exaNEihazRRCIRlixZAlNTU3z99deIj49HTU0N5s+f/8jxJZfLER0djatXrzLn2uHDh2PNmjWwsLCAo6MjvvnmGwQFBcHS0lItbPi4ZvDgwZBKpTh37hwuXbqEuro6bNq06ZHqLrqSsD39ItWB5uZmHD9+nNi6/T8SiQRfffUVpk2bhlOnTmHWrFmCJtsQu1Hh0NbWxqJFixAQEIDw8HDs378fOTk5+Oijj3ivFq6rq2Oq2FeuXMnr3OoK25WEtEioavWeubk5G+EAACIjI+Hp6cnr/kY/jwppN0pEQsXQ0NDAa6+9xnxNeDLPs63m0/YRYutKIBAIBAJ/kLs0gsKoWyVhfn4+KIpSW7s0RZqZ09y8eRNpaWmYPXs2p1nX6lhJ2JHsRm/evIkPP/wQt27dAgDo6elh4cKFmDNnjlJ9z/Ly8nD27FkAwNixY9V2X37eoCtiOgsnTpzAypUrBbEBnDJlCtzc3BAXF4ezZ89i6dKlCm87fvx4GBsb44MPPkB6ejr27t2LxYsXw8DAAJmZmbh06RIKCgoAtPRLXLt2LQYOHMhsP3HiRCQmJjL2zqtWrVKLPrbNzc3IyMiAjo4OJ0Kdh4cHpFIpTp48iaCgINTV1WHz5s3Q1tZGWVkZCgsLAbAjEkZGRqKhoQGenp6c7l8UReHChQus9M/qTEyZMgU+Pj4ICQnBvn378M477wgWC7EbFRaxWIwJEybAzMwMf//9NwIDAzF69Gj4+PjwGkdQUBBqa2thZ2eHUaNG8Tq3usJ2JSFbdqOampowMTFp1/PK0ygpKUFWVhZ69uyp8liKQgsJQiZ26erqAiAi4bPQ0tLCzp07hQ5DbWltq/ksOqutJtlHCAQCgUAQHiISEhSGriQUWiS0sbGBtrY26uvrUVpaqrY3yk/rR/g0cnJysH37drz99ttMljDbmJmZAQDS09M5GV8ZOoLd6J07d/Dhhx/i0qVLAACpVIrZs2dj0aJFKv2t9uzZA7lcDnt7e9jZ2bEVLkFFOlMlIdDSq+/ChQuYPn0670K0WCzGxo0bsWTJEpw8ebLdgvqgQYPwyy+/YOPGjSgsLMSePXvQo0cPphpOKpVi1apVmDFjxhMXYNeuXYvU1FQkJCTg+PHjgomlDQ0NSElJQVJSElJSUhgh2sXFBRMnTmQWfdnC2dkZixcvxpEjRxAREYH3338fX3zxBfO5mZqaQktLS6U5MjIyGFvB6OhoTJs2Dba2tqqG/kQiIyNx584dTsbuyIhEInz99dfw8fHBxYsXMX/+fMEy7kkloXrQv39/lJeXIzg4GLt27cKQIUN4dQC5cOECAGDFihWCPy+oC+paSQi0WI6yIRICQFRUFK8iIbEbJXQWaFtNRY5FExMTUllHIBAIBAKBE8iTPEFh1MVuVCKRoHfv3oiPj0deXl6nEQlpTp8+jRdeeIGTxRVaJMzOzkZTU5NaZPzTlYTq+HdMTk7GJ598wvTAkkgkmDJlCpYtW6ZyvMnJyQgMDATQUkXYEcjLy0NwcDDKy8shlUqZl6am5iP/p182NjZqLf4+jc4mEgJATEwMunTpwpwDAP5+z/nz5+Pjjz9GWloa/P39MXfu3HZt7+DggJ07d+K9995DdnY2I3RNmzYNK1asgKGh4VO31dTUxGeffYYXXngBhYWF8PPzw6xZs3gRS2tra5GcnIykpCSkpqY+0iOsW7duKCsrQ0JCAlJSUuDr64vBgwezet63t7fHCy+8gIMHDyI2NhYbNmxgLFdV7UdIURQCAgKY/5eVlWHfvn3o378/xo8f327b5bbIzMxkhAfCfxk2bBimTZsGPz8/7NmzB5s3bxYkDlr4Vof7iuedYcOGISoqCtnZ2fD398f06dN5mTcrKwtxcXEQi8V48cUXeZmzI8BVT0K2RML4+HiVxwGAuLg4TJw4kamu4xoiEnYcKIpiBDATExPinPIEnndbTbKPEAgEAoEgPO0SCU1NTTFgwABMnToV69at4yomgppC240KLRICLZajtEjo7u4udDhPRFmRMDMzE8HBwRg5ciTLEbUsLEilUjQ2NiInJwe9evVifY72oo6VhCUlJdi0aRP++OMPyOVyiEQi+Pr64sUXX2TNHnD37t0AAHd3d3Tv3p2VMbmisLAQN27cQFJSUru2E4lEWLx4MXr37s1RZNzQGUVCAAgODhZkXg0NDbz//vtYtWoVjh8/jhkzZrRbSOjevTt+/vlnbN26Fc3NzXj55Zfh4OCg0Lampqb46quvsH79esTFxcHKygpeXl7K/CrPpKamBvfu3UNSUhIyMjJAURTzMysrK4wYMQLDhw+Hk5MTHjx4gG3btiEpKQmXLl3C3bt3MXXqVFhaWrIWj42NDV566SXs27cP9+/fx/379wGoLhImJCQ8sS/wnTt3kJKSgsmTJ8PZ2VmlOQCgqqoKJ06cUHmczs6XX36Jv//+Gzdv3kRiYiIrn317IZWE6oOWlhZGjBiBS5cuYe/evRg3bpxSlujthRbzJ02a1OF6nnIJ25WEbNmNAmD9/jMmJgZDhw5ldcynoW49CWtqaqCnpydYLOpMbW0tk6RWXV1NPifCfyD7CIFAIBAIwtOup5XS0lJcvXoVgYGBmDBhApORTng+oCsJWy96CgXdl/BJi5TqgrIiIQDcuHED5ubm6NOnD4sRtYg2ZmZmyMnJQXp6ulqJhG1VA/FJYGAgZs+ezcQ1dOhQrFy5khU70IcPHyIkJASBgYGIioqCWCzG6NGjVR6XK+rq6nD16lVER0cz3xs3bhx8fX3R2NiIuro65lVfX//I/7Ozs5GcnIxjx45h5cqVai+EAi3ntsTERNTX1wsdSqdj+fLl+Oyzz5Cfn4+zZ89i3rx57R6ja9eu+OKLL5Sav1+/fli9ejV+/fVXXL58Gfb29qxXLzc0NGDHjh2P7D/29vYYPnw4RowYAVtb20cyox0cHLBjxw74+/tjx44dyM/Px+7du7F06VLY29uzFpeFhQVWrlyJAwcO4OHDhwBU60fY3NyMa9euPfXn1dXVOH78OCwtLdGvXz+4uroqJVDI5XKcOHECNTU1Ssf6vODq6ooXXngB+/btw59//olvv/2W9xhIT0L1YtCgQfjnn39QXl4OPz8/zJ8/n9P5amtrcfnyZQDAyy+/zOlcHQ2u7EZra2tVHsvCwkLlMVoTGRmJIUOG8FIFpA6VhHp6etDU1ERTUxPGjBmDsLAwYrNLIBAIBAKBQOiQKPW0IpfL8fXXX+PPP/9kOx6CGkOLwteuXcPChQsFfQiiK5OKiopAUZTaWVLU1taq3OPj2LFjmDlzJjw8PFiKqoWuXbsiJycH5eXlrI6rLJaWlkhISMCePXswd+5cwf6WFEXhp59+woYNGyCXy2FnZ4e33noLbm5uSo338OFDZGRkID09Henp6UhLS0NcXNwjIvuoUaPURhxtDUVRiImJweXLl5mF3xEjRuCll15SuOdYU1MTPvzwQ0RGRuL48eN49dVXWbUgZJucnBxcuXIF2dnZQofSKdHS0sLHH3+M1157Db///jscHBzQr18/XmOYN28ebt26hdjYWKSnp7MuEhYVFTEC4Zo1a+Dj4/PMqkCJRILp06fDx8cH33zzDSIiIhATE8OqSAi0VFO+9NJLOHr0KDQ1NVWq3KaFh2eRl5eHvLw8XLhwAS4uLujfvz/s7e0VPseHhYUhJydH6TifNz766CPs27cP0dHRqKur46VyrDW0OEgnlHVm6Ou4ut17tkYikaBnz54oLy9nkp645JdffkFFRQV69eqFKVOmcD5fR4Ltnu60OMZG0qauri7s7OyQlpam8lgAUF5ejpSUFF4Siem+ukImkkilUvzvf//DRx99hIiICOTm5sLa2lqweAgEAoFAIBAIBGVRSiSkKAqHDh3Cpk2bWF/IIqgvr732Gr7++mukpaVxZoepKHQVRENDA2pqaqCvry9YLE8iIyODlXHOnj2L+vp6Vq3x6IU8WvwRmhdffBFBQUG4fPkyLly4IMjiUn19PVavXo19+/YBaKmW27BhA7MA0RaNjY2MEJiRkYG0tDSkp6c/VSQ2NzeHi4sL+vbti27durH6e7BBUVER/P39kZWVBQCwtbXFW2+91W6xWlNTEx999BFWrVqFwsJCnDt3DvPnz1e7RdXy8nJcu3YN9+7dEzqUR5BIJJ1usX316tW4desWDh06hE8//RS//vorq9aaz0IkEqFPnz6IjY1VOYnjSVRVVQEA3Nzc2l21Y2xsjMWLFyMiIgLp6emcJL8YGRlh9erVAJQXN6qrq3H16tV2b5eQkICEhASYmZlh2LBh6Nu3b5vW5SUlJUrN8zzTu3dvmJmZoaioCOnp6XBxceF1/q5duwJgp7pJnSksLMSJEydQW1uL3r17w9HREfb29ryLsopAi+x9+/bldJ6IiAj4+/sDAP78809STfoYdD9atkRC+v5dKpWyMl6/fv1YEwkBIDw8nBeRkO59SPc2Fwpvb290794dubm5SEtLIyIhgUAgEAgEAqFDorTvSXNzM959912cPn2alUDq6+shkUjIg6UaY2xsjPXr1+Pzzz/Hvn37MHz4cMGqCaVSKSwtLZGXl4eSkhK1EwnT09NZG+vSpUtoaGjA8OHDWVk0po8xdbFUtLKywty5c3HkyBG8/fbbGDduHGsLH4qQk5OD2bNn4/bt2xCLxVizZg3mzJmj0Gd9+/ZtfPPNN0+1lu3atSvMzMxgamoKMzMz9OjRQy2FQaBF7Lxx4wbCw8NBURQ0NDSwcuVKzJ07V2mLrK5du2Lz5s147bXXkJSUhNDQUAwbNozlyJWjrq4OwcHBCAsLEzoUBhMTEzg6OsLR0RFWVlbw9/dHTEyM0GGxhkgkwu7du3H//n3cvn0bH330EXbs2MEs9PGBjY0NANXsoJ8GLRIqW6Xn7OwMLS0t1NTUoLi4mOnNwiaqXkPashlVhKKiIpw5cwaBgYHw9vbGgAED/nO+l8vlOH/+vErzPK94eHggICAAaWlpRCTkgPj4eJw6dYr5f1xcHOLi4gAAPXv2hIODAxwdHWFiYiJ4QkxrRwsuRcLq6mrG3vaNN97AqFGjOJuro8K2SEj3ImTrmblPnz5Mv3I2SE9PR0FBAec283TPMqFFQqDFFSU3NxcPHjwQNImWQCAQCAQCgUBQFqVWfrt27YrKykqcO3cOwcHBGD58uNIB5ObmYsWKFbh69SrEYjHGjRuHd955B2PGjFF6TAJ3vP3229i2bRvS09Nx8+ZNQRcDrK2tkZeXh9LSUoUtEPmCrUpCmuvXr6O+vh7jxo1TeeFJ3URCAFi6dCkuXbqElJQU/Pzzz9iwYQMv8966dQtz5sxBYWEhDAwM8Mknn2DgwIHP3K6xsRG7d+/GyZMnAQA6OjowNzeHmZkZ8zI1NVVre02aqqoqREZGIjo6mrFsGj58ONauXcuKSOHk5IQ333wTP/zwA65duwYrKytBjtfq6mpkZ2cjMzMT2dnZatPP1N7enllYNjIyeuRn06dPh6amJm7fvi1QdOyjo6ODM2fOYPDgwUhPT8cXX3yBzz//nLeEEzrDn8tKQmVFQqlUCjc3N0RGRiItLY0TkVAV8vLyWBOtKysrcfnyZdy8eRODBw+Gp6cns+AbERFBbH+VhBYJHzx4wPvcbPZJUzfkcjmuXbuG0NDQp74nMzMTmZmZuHr1KgwNDeHo6AgHBwfY2tqy1ouuPdDHkI2NDSPgcsHPP/+MkpISODg44KuvvuJsno4MLRKyJRyz3f9TU1MTffv2xZ07d1gZD2ixpZ4xYwZr4z0JdRIJ6d6ObFZkEggEAoFAIBAIfKLUU+sHH3yAjz/+GE1NTXj11VcRExOjkC3fk5g+fTpiYmJAURRkMhkuXbqEgIAAbN26FW+//bZSYxK4w8jICO+88w42b96Mffv2YcSIEYJVE1pbW+Off/7hZLFXFaqqqjiJKSwsDPX19Zg6dapKn7m62Y0CLZZBr7zyCrZu3YpPP/0Uy5Yt43yBfNeuXXj99dfR3NwMOzs7bNmyhXnIb4uMjAxs2bKFWYQdPHgwxo0b16GqoCmKQnZ2NiIiIpCQkMD0tbG0tMS6devg7e3N6nzTpk1DfHw8AgICcPLkSaxatQpdunRhdY7WUBSF8vJyZGVlMaIgF5VjytCtWzdYW1vD0dERdnZ2bV47RSIRJk2aBC0tLYSEhPAYJbdYWVnh7Nmz8PHxQWhoKP7880+8/PLLvMxNVxJWVlaiqamJ1eNWVZEQAAYMGIDIyEhkZGSwfhyqAkVRuHjxIuvj1tXV4ebNm4xY2KdPH1y+fJn1eZ4X3N3dAQizUN1ZKwnr6upw8uTJdn2mFRUViIiIQEREBMzMzPDyyy/zfo9Ai4Surq6czRESEoIrV65ALBZj7969vFaFdyTYriSkk/zY3Kc8PDxYFQljYmIwYcIETpPl6P2toaFBkD6sraGt04VI0CAQCAQCgUAgENhAKZGwT58+WLt2LX744Qfcv38fH374Ib7//vt2jxMXF4c7d+5AJBLB1NQUY8aMwd27d5GUlIR33nkHAwcOxIgRI5QJkcAhb731Fr777jtkZGTgxo0bglV90hUh6rL4T8N2FWFr7ty5g4aGBsyePbvNfk5toY6VhAAwYcIEnD17Fvfv38dHH32EXbt2cTJPY2Mj3nzzTfz2228AgJEjR+L9999/5uICRVE4f/48fv75Z8hkMmhpaWHWrFlwcnLiJE4uaGpqQnx8PCIiIlBQUMB8393dHbNmzYKPjw8nFQ8ikQjr16/HgwcPkJaWhpMnT+KFF15Qeh9+GrW1tQgMDERycjKqq6tZHVsZDAwMYGlpCSsrK1haWsLS0rLdC2YikQi+vr6QSqUIDAzkKFL+8fT0xF9//YWlS5fi0KFD6NWrF3x9fTmft2vXrjAwMEBVVRVKS0tZtUOjRUJVEhwGDBgAoOU6IpfLBUvCeZz4+HimvxlX3L59u1NVzQoB3Tv2wYMHnPS1bIvOKBIWFhbi6NGjKlUqFRUV4Z9//oGPjw97gSkA1yJhZWUltm3bBgDYsGEDhg4dysk8nQG2Kwm5EAltbGxgaGjIalUe17bHUqmU6d9cUVGhFiIhqSR8MhoaGli+fDnzNYHwOGQfIRAIBAJBeJS+Am/ZsgV+fn5ITU3FTz/9hOHDh2PmzJntGiMzM5P5OigoCH369EFTUxN8fX1x69Yt/PTTT0QkVEMMDQ3x3nvv4dNPP8X+/fsxcuRI1hf7FYGuCFG3SkI2+xE+iYSEBMhkMixYsECpBQd1rCQEWjKs165dizfeeAN//PEH1qxZg/79+7MytrOzM7PYXldXh9LSUohEIqxcuRKLFy9+5udYWVmJrVu3MlZj9vb2mDFjBqfVcGxSUVGB27dv486dO0wvG4lEgokTJ2LmzJno3bs35zFoa2vjs88+w8qVK5GVlYVr165h/PjxrIxNURTu3buHixcvCrpA3bNnT9jY2DCiIJv7x/Dhw6GlpcVJNZdQLFmyBHFxcfjmm2+wdetWmJmZwcXFhdPriUgkgrW1Ne7du4eSkhJWRcLKykoAqlUS9u7dG/r6+qiurkZeXh569OjBVnhK09jYiICAAKHDIChAnz59oKmpiZqaGhQWFnLeE6w1nU0kfLz/oCpcu3YNAwcO5E3EaG5uRm5uLgDuRMIff/wR5eXlcHFxwf/+9z9O5ugs0CIhW9c2LkRCkUgEDw8PBAUFsTbm/fv3ORUJRSIR9PT0UFVVhYqKCoXcQLiCnptUEj4ZLS0t7N27V+gwCGoM2UcIBAKBQBAepUVCHR0d7N+/HyNGjEBzczNeeOEFhISEMFZHikBnK0qlUvTp0wdAywPPa6+9hpCQkDZ7fxCE5c0338S3336LzMxM3Lhxg5cKkMehRcKKigo0NzerRdYZRVGci4QAkJycjPz8fCZztT2oayUhALi5uWHMmDEIDAzEm2++iaCgIFYyrx/vP6enp4dNmzZhyJAhz9w2MjISX331FcrKyiAWizF27Fh4e3tzVqGRlpaG7OxsNDU1obm5+ZFX6+81NTUxNqFtQVEUioqKmPd2794dM2bMwOTJk5keUnxhbW2Njz76CJ9++inCwsJgbW0NZ2dnlcasqqqCv78/7t+/z1KU7cfQ0BCTJk2Co6Mjp/N4enpCKpXi3LlznM7DJ1988QXi4+Ph7++PN954AyKRCPr6+jAwMECXLl2Yyr8uXbrAwMAABgYGsLKygru7u9I2Zq1FQraQy+VM9aoqIqFEIkH//v0RHByM9PR0tRAJQ0JC8PDhQ6HDICiAVCqFi4sL7t69iwcPHvAqEhoaGgJoEQn5rmJkE0X6DypDcHAwa4kxzyI/Px8ymQyGhoacnEOuX7+OGzduQCwWY//+/R2i/7KQqHtPQhq2RcKUlBTOzwWtRUIhoZ/HSktLUVlZyWkfUAKBQCAQCAQCgQtUUlW8vb2xbds2vPHGG6iursbYsWMREBDA2B09C9qS4/Eb6RkzZuDKlSuIi4tTJTwCh3Tt2hXvvfcePvnkE+zfvx+jRo3ivZrQyMgIenp6qKmpQVlZGec97BShvLycqSbhmvj4+E4nEgLAqlWrcOvWLQQHB+PEiROYP3++ymP+8MMPj2TwW1lZQV9fv81tGhsbsWfPHhw/fhwAYGJigjlz5nC66FpbW4tDhw4xC0psMnDgQMycORNDhgwRpPKXZsSIEZg/fz6OHz+OM2fOwMzMDN26dWv3OBRFISoqCv7+/hxEqTg+Pj4YMWIEb/2m+vXrB6lUihMnTvAyX2uWLl36yO/Zv39/vPvuu0r3JAZaRLHDhw9j5syZuH79OiiKwsOHD58pSmlqasLV1RUDBw7EoEGD4ODgoLA1J51gwqZVdXV1NSiKgkQigZGRkUpjtRYJhw8fzlKEylFRUYHg4GBBYyC0D3d3d0YkHDZsGG/z0kknFEWhoaGhQwpHyvQfVJSwsDB4eXnxIh7QVqN9+/ZlXaApKyvDjz/+CADYtGkTBg4cyOr4nRG2exLSjhBSqZSV8WiMjIxgY2ODrKwsVsarra1Fbm4up8kudF9CoUVCXV1dxq41PT0d/fr1EzQedYOiKKbKXFdXt8MmkRC4g+wjBAKBQCAIj8qlV2vXrkVSUhJ++eUXlJSUYPTo0di7dy+mT5/+zG1p27SePXs+8n0dHR2MHTsWY8eOVTU8Aoe8+eab+OGHH5CVlYXg4GCMGjWK1/lp27ikpCSUlJSohUjIZzVTQkKCUlnp9KKCuoqEZmZmWLRoEfbu3YsPP/yQFZHQwcEBenp6Cr9fLpdj48aNuHPnDoAWgW3ChAmcC0Hp6emQy+UwMTHBqFGjIJVKIZVKoaWl9ci/9Netxb6nVRVSFAVLS0umh6c68MorryApKQmxsbE4c+YMXn755XaPERAQgLCwMA6iUwxbW1tMnjxZpaoxZXFxccHixYtx+PBhXuf18/N75P+nT5/GnTt3VLbkMzAwQGBgIJqamlBeXo7S0lKUlpairKzsP1+XlJQgMjISWVlZuHPnDu7cuYM//vgDXl5e+PrrrxWajz4W2KwkpPsR6unpQSaTqbQYTDsyZGVlQSaTCSrq37hxQ7C5CcpB2/vx3RtLKpVCV1cXtbW1qKmp6XAiIUVR2LdvHwoLCzmb49atW5g8eTJn49Nw2Y/w999/R1VVFfr164ePPvqI9fE7IzKZDID6VxICLdWEbImEQEs1IZciIX1vL7RICLRYjlZUVCA1NZWIhI9RW1vLJGdWV1e365mM8HxA9hECgUAgEISHFX/GHTt2QC6X47fffkNlZSVmzZqFl19+GZ9++ulTK51OnDiBf/75ByKRCN7e3myEQeAZAwMDLF68GDt37kRycjLvIiHQIiglJSUxNm9CUlVVhcuXL/M6nzI0NjYCgErVP1wzYsQI7N27F/n5+YLYll24cAF37tyBhoYG5syZw9ghcw3dQ2jYsGF4/fXXeZlTCDQ0NDBy5EjExsYy+2N7SUhIYDkqxZkwYQK8vLwEzXJ1cHDA8uXLsW/fPt7mfO2115jzxpkzZ5CRkfFIb2FV0dTUhJmZ2TMTPiiKQkpKCgICAvDHH38gJibmP5bCbUEvoLPZH8zY2BgSiQRVVVX44osv8NFHHyltgU0nUOnr67NWeaIsXAomBPapr69nzgl8J07V1dUxFU7qfH/xNEQikaCCPFtQFMWcl93c3FgfnxaovLy8WK9k66zQ9+tsCef0Mw8Xx9njibuqwrW7Ci0klJeXczqPItjb2yMxMRHBwcGYO3eu0OEQCEhMTFTp5wQCgUAgEJ4v2rWCVV5ejlu3bj2xh9Qvv/wCGxsbfPzxx5DJZPjjjz9w4MABzJo1C1OnToWHhweMjY1RUFCAEydOYNu2bcy2S5cuVf03IQgCF7Zt7YG2maEXLYSCoqj/VNlwjbIZxPQDuzpUXj4NeoHL1dWVdyGmsrISO3bsAAD4+vryJhACQFFREYCWhYbODEVROH/+PAAoZVXW3NzMm63vk7CwsFALGxxbW1ts2LABsbGxuHv3LrP/cMXkyZOZBbnU1FRkZGRgxIgRnM75JEQiERwdHeHo6IgzZ84AQLv64l69ehVAixUfW+jq6mLhwoU4cuQIgoKC0NjYiE8//bTdi7hRUVFMZebUqVMF38+4sD4mcMfnn3+OpKQkGBsbY9myZbzOnZycDIqiYGBg8Ew7b3VlxYoVSE1NRUxMDJKSklgfn4/zZWlpKerq6qClpQUnJyfWx58+fTqCgoKwb98+fP3110wvSsLToZMtlLFWfxL0/Q8XlTaK9LpuD3QVJVfQ5xp1EAk9PT3x999/4+LFi/jpp5+EDofwHGNiYgJdXV2F1th0dXVhYmLCQ1QEAoFAIBDUnXaJhF27dm3TJmfjxo0YMmQI1qxZg6SkJNTX1+Po0aM4evTof95LVwfNnDkTgwcPbn/kBLXAwsICgPAiobLVSGwRExOD1NRUXudUtkqlI4iEtE0abbvHJ7t370ZDQwPMzc3h6enJ69y0yGNnZ8frvHxz9+5dZGZmQkNDQ+Eetq0R2lZKnax69fX1MXToUAwZMgSFhYW4e/cu4uLiUFNTw/pcP/74I3PeCQ8PBwBBKshpMjMzERgYCKClulMRUlNTkZaWBrFYzKpICAC9e/fGwoULcezYMYSFheH999/HF198ofBCbnV1Nb755hsAwKBBg9C7d29W41MGtheMCdwRHx/P7D9vvvkmunTpwuv8dEUCl9aCXCORSODk5AQnJyfU1NQgLi4OMTExrFTUjh07lhfxlHYkcHBwUPo+sS369+8PW1tbZGRk4K+//sLbb7/N+hydjYKCAgBQuV8tDX0PRD8DsUlHFQnLyso4nUcRBgwYAIlEgpSUFDx48KDTJ/wR1BcbGxskJiYqZKtvYmLCJH0TCAQCgUB4vmH96ZG2kNuzZw927NiBe/fuPfW9EydOxP79+9kOgcAjtJ2s0CKhkJWElZWVTFUUnyi7+EPbHpmbm7MZDqs8ePAAAP8i4b179+Dv7w+gpWpKEas/ekFF1Yqfuro6PHz4EADQq1cvlcZSd86ePQugpfeNMvZbQmeM05Z66oRIJEL37t3RvXt3jBs3Dg8ePEBsbCzi4+NZm4OuwKPR1NTE8OHDWRu/vezbtw8URaF///7o3r27QtvQv4OTkxMnPdMcHBywbNkyHDp0CHfv3sX69euxdetWdO3a9Znbbt++HcXFxTA2Nsa4ceNYj00ZSCVhx0Aul2PNmjWQyWTw8fERpMKXFgmtrKx4n5sL9PT04O3tDS8vLxQUFODOnTuIi4tTKknEyMgIXl5eHET5X2iRkCsXBJFIhNmzZ2Pbtm3YsWMH3njjjU5h08oltEhobGys8lgymYy5VyQioXqJhHp6enBzc0NMTAwuXryItWvXCh0S4TnGxsaGiH8EAoFAIBDaBfsppmgRL1atWoVVq1YhMTER165dQ2JiInJycqChoQFra2tMnToVY8eO5WJ6Ao8875WErW0T+YZUErKLTCbDjz/+CADo16+fQg9WDQ0N2LdvH2QyGRYtWqSS7RZdqdC9e3dOFn7UheLiYty8eRMAlK4iF3oxSJ0qCZ+EWCyGg4MDHBwcMGXKFCQkJCAuLu4/FZgURbXLtvWzzz57RFjz8vJirTKivcjlcuzduxcAMGnSJIW2kclkuHbtGgBuzy09e/bESy+9hH379uH+/ft488038e2338LU1PSp2wQFBSEgIIBxWFCXXl9EJOwY7N+/HyEhIdDW1hZsYbqziYQ0IpEIFhYWsLCwwPjx45GcnIy7d++isLAQNTU1Cokg48eP56Sq70nQ/Vmf1BqCLcaOHYvdu3cjLS0NFy9exNSpUzmbqzPAZiUhff8jEolY7atLw/Y5n2uRkK7UF/q+kMbT05OIhAQCgUAgEAiEDgnnT6zOzs6cPqgShIWuJKyurkZDQ0O7+y+pitCVhNHR0YygxTeq9iRU10rC6upqZkHFzc2Nt3nPnj2L1NRUSKVShRMYAgICkJ+fD6ClqunFF19UqGLoSdAiId9Wo2VlZbh8+TKuXLkCIyMjfPXVV5wex35+fqAoCj179lR6HxR6MUgdKwmfhra2NgYMGIABAwb852cNDQ34+uuvFR7r7bffhoGBAZvhKU1wcDDS09Ohq6urcDVjTEwMSkpKoKOjAwcHh3bNJ5PJQFGUwgv9FhYWePnll3HgwAFkZmbijTfewHffffdEAaW0tBRfffUVAGDYsGGwtrZuV2xcQkRC9ae0tBRvvfUWAOCFF14Q5NpeXFyMkpISRlDrrGhoaKBv376MVTFFUWhoaEB1dTVqamr+829NTQ3Mzc056Q34JJqbm5n7Jy77Kevo6GDy5Mk4duwYtm/fLohIaGRk9IiDg4+PD65evcqbGNse6Ps7NioJi4uLAQBdunRRyO2ivXTUSsKqqio0NzcL/vf38vLCrl27cPXqVdTV1XEi5BIIBAKBQCAQCFygfk9ShA5F165doa2tjfr6epSWljKiIV8IWUlYUVGBv//+m/d5aZR5EG5sbGQEjraqWoQkPT0dQEtfIzYWVBShtLQUv//+OwBg3LhxCvUQy8jIQFRUFPP/iooKRihURkih+xHyYTUqk8kQGRkJf39/hISEMItCGRkZ2LVrF9atW8fJvE1NTcwxo0ovWmI3Svjrr78AAKNHj1bYNpS2GnVxcWmXPV5hYSF+++03AMDatWvRrVs3hbYzMTHBSy+9hAMHDqCgoADr1q3Dd99990giAEVR+Pbbb9HQ0IDu3bsL2uPxSTwPIqG7uzt8fHxw5swZJumjI/Hhhx+isrISPXv2xLx58wSJga4iNDMzU5sqWD4QiUTQ1taGtrY2TExMhA4HhYWFkMlkMDAw4FysnTlzJk6cOIGAgAAkJCTAxcWF0/ke5/FzU1BQEC5duqR2VY2NjY1MXzA2RUKuEnY6WiWhrq4uRCIRKIpCeXm54M82vXr1gomJCUpKShAUFISJEycKGo+6IJFIMHfuXOZrAuFxyD5CIBAIBILwsJ+CSHiuEIlEgvYlpDM0+a4kFNJmlEYZkZCuItTT02Oyb9UNIaxGf/31VzQ1NcHKyuqJFVeP09TU9MS/f3l5Ofbt28f0fWwPtEjIZSVhYWEh9u7di8WLF2Pjxo0IDg4GRVHo0aMHfHx8AACnT59GZGQkJ/MHBwejvLwc+vr6KlU5CC0SqrvdaGenuroax44dAwCFF+Dq6+sZkbA955bc3FxGIASAAwcOtOv4NjQ0xEsvvQRzc3OUl5djzZo1SEhIYH7u7++Pf/75B2KxGLNmzVK7hZHOLBI6ODhg9erVmDVrFkxNTbFw4UK1vS4+jfDwcOzevRtAS6WvUFU0ndVqtKPRuh+hqn2Sn0X37t0xbNgwAMCOHTs4netJ7N+/HydPnsTJkycxa9YsAMCuXbt4j+NZ3Lp1CxRFwdDQUGmnidbQgiNXImFHqyQUiURq1ZdQJBIx/UcvXrwocDTqg7a2Nk6cOIETJ05w0g+a0PEh+wiBQCAQCMJDREKCygjZl5Cu+OJbJLx//z5T8SYUyiwG0ovbpB/hv9y5c4fpUzZ58mSF7JsCAwOfKlSVlZVh//79ePjwocIxUBTFmUjY3NyMmzdv4v3338fChQuxb98+FBUVQVtbG15eXli9ejVWrlwJX19fprrvm2++UUrofBZnz54FAAwcOFBpMUQulxORsBNCURSam5sVeu+JEydQX18Pa2trxvbvWYSGhqK5uRmGhoYK23lmZGTgjz/+eOR7lZWVOHDgAGpqahQaA2ixQ1u+fDl69OiBxsZGvPnmm4iKikJubi5++uknAICvr69anpc7o0jYo0cPvPjii1i8ePEj1pwGBgZYsGCBgJG1j+bmZqxZswYURWHChAnw8PAQLJakpCQARCQUGrofIZdWo62ZPXs2gJbK7sd73nKNsbExunXrhm7dumHmzJkAWpIucnJyeI3jWdDuCd7e3qzYg7a2G+WCjiYSAlArkRBo6UsIEJGQQCAQCAQCgdCxIHajBJWhF9no7FY+EcJutLm5mRGVhESZnoR0JaE6LkYDLZ9tXFwcAH5EQoqisH37dgDAoEGDFLLLLSkpQXh4eJvvKS0txeHDh7Fq1SqF4qioqEBjYyM0NTXRo0cPhbZRlLVr1yI5OZn5v62tLQYMGABnZ+f/CM3jxo1DWloaSkpKsGvXLrzzzjusxRESEoK4uDiIRCIMHDhQ6XEqKip4WXRqC2I3yh6VlZX4/fffsX37duTm5kJHRwcGBgbo2rXrI//SX+vp6eHQoUMAgAkTJihcLXPp0iUALecVRbbJzMzEvn37nvizkpISHDhwAEuXLlW48kxHRwfLli3DsWPHkJaW9six1bNnTwwZMkShcfhG6GONTUxMTODr6wsnJ6en7gM9evTAzJkzmYQGdea3335DTEwMunTpovC1hguam5uZawzb169nUVdXh5KSEtTV1aG+vh51dXWPvFp/z8rKClOmTOnUdqitKwn5wMPDA7169UJ6ejr27t3L9MbkGxsbG3h4eODu3bv4888/8cknnwgSx+O0dh3x9vZmZUyu7UabmppYHU/R5B9VoBNG1UUkpBPhUlJS8ODBA9jb2wsdEoFAIBAIBAKB8EyISEhQicrKSly5cgUAHsnI54vs7GwA/4qFfHD9+nXmIV1IlMkiphd8lREYuYaiKPz0009IT0+HtrY2Ro4cyfmc9+/fR0ZGBjQ0NDBmzBiFtlG0YrY9lYR0VZKxsTGrdnGPV2cNGzYMvr6+T10g19TURO/evVFaWsqqEObv74/vv/8eANC/f3+VMuDDwsLYCktpioqKIJfLWakK6EiMHTv2kf3TxsYGs2fPxpQpUxTq49manJwc/Pjjj/j1119RW1vLfJ9e0C8sLGxzexMTE0yaNEmhuRISEnD79m2IRCKFqq2Ki4uxd+/eNt9TWFiIP//8E0uXLlW4z5RUKsWiRYtw6tQppvJKW1sbM2fO5NwaUBno5IXOQklJCfLy8mBtbd3m/qouC81tUVlZic8++wwAsGLFChgZGQkWS1RUFOrq6qCrq8tbXz65XI6IiAhcvnxZ4W1KS0tRVVWFxYsXq+U9EBtUV1cDAG/9wUUiESZPnoydO3fi8uXLgomEQIsTxN27d3H48GF8/PHHanFODQwMRGpqKnR0dFTqw9yarKwsAOz0N3wSz0qCay+tr+9cQdu40pW0QqOnpwc3NzfExMTg77//xptvvil0SIJTU1PDJFVVV1e3+56R0Pkh+wiBQCAQCMJDREKCSvz444+orq5Gz549MXToUN7np3uncdnHrTUZGRkIDQ3lZa5nYWtr2+5taDGVCytJVTlx4gT+/vtviEQiHDt2jJdFrps3bwIAHB0dmf6Wz0LRKgQbGxuF46DFW7YrHEQiEX744Qd8/fXXCA0Nxa1bt1BTU4PJkyc/cZFULpcz/dJGjx6t8vwUReHQoUPYs2cPAKBfv36YMmWK0uOVlZVx1i+xPTQ2NqKwsJCxWn5euH379iP/DwsLw7Fjx6ClpYWpU6di3rx5mDJlSpvVdXFxcfj2229x+PBhZr+3tbXFggUL4O3tjfr6elRXV6O2thY1NTWoqal55OuamhpYWFhg8uTJClfx0fufh4fHMxdWHz58yFQqPovy8nL8/PPPeOWVVxQ+X2loaGDu3Lm4cOEC5HI5fH191bYPHm393JkIDg5GcHAwBg8ejCFDhvxHXAsLC2OuC+rM1q1bUVpaCmtra0ybNk3QWAICAgAArq6uvCRO5Ofn4++//1ZKEMjIyMCRI0ewaNGiTikU0laRfPampBMvQkNDIZPJBOurOmzYMGhqaiI5ORmxsbGC2u/S0HbSEydOZCWZsampCRkZGQBaekKyTUpKClJSUlgds66uDg0NDdDS0mJ13NaYmpoC+FdAVQeGDh2KmJgYnD9/noiEBAKBQCAQCIQOAREJCUpTUVGBb7/9FgCwfPly3hcGKIpiBAM+rFzq6+vVyoKsZ8+e7d6GFsLUTSQMCQnBb7/9BgDYtm0bpk+fzvmcFEUxi8HOzs4Kb6foQkd7REK67xcXx1CXLl3w+eef48iRI9izZw9iYmKQn5+P+fPn/0cwyczMxMOHD9GlSxd4eXmpNK9MJsOOHTuYY8bHxwdjxoxRKbv/+vXrKsXEJpmZmc+dSLhp0yZoa2sDaNlnk5KSEBQUhLy8PJw6dQqnTp2CVCrFlClTMG/ePEydOhVdunQBRVG4fv06vv32W8b2E2gRjRcsWAAvLy/Oqj6io6MRHR0NsVj8zOrkhoYGHDp0iLFlVpTdu3dj6dKlCl+HJBKJ4MKOIqiTSGhqagorKyvmlZSUpJKYd/v2bdy+fRtubm4YNmwYzM3NER0dzTgjqDM5OTn47rvvAACvvvqqYKIM0CIABAUFAQDc3Nw4nauxsRE3btxQuZo8PT0dx44dw8KFC3kV0/iAvpfgs8rdzs4Oenp6qKqqQmxsLPr378/b3K3R09ODt7c3goODcfToUcFFwgcPHjD9CGfNmsXKmFlZWWhuboaWlhZTPccWMpmsXZW57aGiooJTtxm6gjkzM5OzOdrL0KFD8csvvyAoKAgVFRUwNDQUOiQCgUAgEAgEAqFNOtfTMYFXfvjhB9TU1KBXr168WEM+Tnp6OkpLSyGRSNolyCjLhQsX2r14zBV9+vRRyoZDHUXC5ORkbNmyBRRFYc2aNbxl3KanpyMnJwdisRgODg4Kb8dlJSFXlQ1isRhLliyBi4sLPvzwQxQWFuK3337D7NmzH+ldFBsbCwAYOXKkSrGUl5dj27ZtCAkJAdCSRa+q6Jifn4/4+HiVxmCTrKws1noMdRSGDBnyyHlnxIgReOWVV5CSkoKgoCAEBQUhNzcXZ86cwZkzZ6CpqYnJkycjOzsb0dHRAFr2xREjRmDhwoVwcnLiNF6KopgqwkGDBrW5SCeTyXDs2LFn2pw+jYMHD2LWrFm89FLlA4qikJ6eLsjcWlpa6NWrFyMIWlpa/ic5Q0dHh5WKv7i4OMTFxaFnz55qtcDcFp9++ikaGxvh6uqKYcOGCRpLSEgImpubYWRkBCsrK87mSUlJgb+/P2v3YA8ePMDx48cxf/78TiUUcplw9DQkEgn69u2LiIgIBAcHCyYSAi0OCMHBwTh27Bi+/PJLQS1Hd+zYAYqi4OXlBWtra1bGTE1NBdBSRcj27/bPP/8obKffXrgWCelKwtzcXDQ1NalFlbCVlRVzXbl06RIWLlwodEgEAoFAIBAIBEKbPF8NlQisUVZWxvQYW758uSC9uegqwl69enG+yBMfH4+4uDhO52gPPj4+Sm1H2x09fPiQsaUSkqKiImzatAkNDQ2YOHEitm/fztuiDr3A3Lt373bZICkiEkql0nZZQdEiIdf7cf/+/bF//364urqiqakJx44dQ0BAAORyOZqbm5GYmAigpfecMsjlcvj5+WHhwoUICQmBWCzG3LlzWalKpC3t1IXMzExOj6H6+npW+0JyhUgkgqOjI1555RUcOHCAqaqztrZGU1MTzp07h+joaGhpaWHmzJk4cOAAPv30U84FQqDFOjIhIQESiQTDhw9/6vsoisK5c+dUFsXOnDmjNnbUqlJQUMBLL6knMWPGDCxYsAA+Pj7o1avXE8/PhoaGSlluP42OIhDGx8cz/TJXr14teN+1a9euAQDc3d05iaW6uhonT57E4cOHWU/SSklJwcmTJ5nrb2dACJEQ+LeKNDg4mNd5H8fb2xva2tpIT08X1Jr84cOH2L17NwBg9uzZrI1Li4RsC27V1dWc3mNVVFRwNjbQ4pghlUohl8uRk5PD6VztYciQIQAAPz8/gSMhEAgEAoFAIBCeTedJnyXwyrZt21BTUwM7O7s2F1+5hO6PxbXVaFVVFU6dOsXpHO2BrrBQBrqSsKmpCfX19Qr34eOC2tpafPDBBygtLYWrqyuOHTvGa0a/MlajgGJ2o9bW1u0SzrmuJGyNqakpfvjhB+zatQsnTpxAaGgocnNz4eLigoaGBpiZmSllG5eWloZt27bh3r17AFoy3adNm6Zyb8na2lqcPHlSsKqmp1FbW4vS0lLG5opNcnJymAo4BwcHeHh4wMnJSe0rXkQiEXr37o3evXtjxYoVSEtLQ0hICLS0tDBp0iTW7dHaQi6XM5+ht7d3m33/rl27xloSSEBAAKqrqzFu3DjBBRxVEMpq1MbG5pHq5rbo168f05/reWHjxo2Qy+UYMWIE+vbtK2gs5eXliIiIAMC+1ShFUYiOjmbsGrkiOTkZp06dwpw5cwS1bWUDiqKYxBW+E/dai4QURQl27tPR0cGQIUNw/fp1HD16FIMHDxYkjn379qGmpgbW1tYYNGgQa+M+ePAAAPv9CGmxnyu4FglFIhFMTU2Rm5uLrKws9OrVi9P5FGXo0KE4evQoLly4oDYVjgQCgUAgEAgEwtNQ7xVHglpSWlqKbdu2AQBefPFFQaoIGxoacOfOHQDcioQURalVH0IAKomympqakEgkkMlkqKysFEwklMvl+Pzzz5GWlgZzc3P8/fffMDAw4G3+7OxspKenQyQStbuiSZFKwvba3/JVSUijoaGB1157DX379sUXX3yBzMxMppLG19e3Xcd0XV0d9u/fj2PHjoGiKGhoaMDX1xeenp4qnxuKiopw9OhRlJeXqzQOV2RmZrIuEqanp2P//v3M/1NSUpCSkgItLS307dsXHh4esLa2VnsBSiQSwd7enpd+sU/ixo0bSEtLg6amZpuWjBEREbh16xarc4eFhUFDQwNjxoxhdVw+EUokHD9+vML7trOzs9pdn7nkxo0b8Pf3h0Qiwcsvvyx0OLh+/TooioKlpSW6devG2rgUReHIkSNISUlhbcy2SExMxJkzZzB79mxB7mfZonVlO9+Cp7OzMzQ1NVFYWIjU1NR2WbizzZgxY3D9+nUcP34c3377Le9/U7lcju3btwNo6UXI1vwURXEiElZXVyMmJoa18Z4E1yIhAEYkVKeqcBcXF3Tt2hUVFRW4desWRo0aJXRIBAKBQCAQCATCUyEiIcsImUHLF99//z3q6urQu3dvpW0vVSU2NhYymQwGBgacVPLQhIeHq1UFk5WVlUoWayKRCDo6OqiursbDhw9Zz0ZWlKNHjyI8PBxSqRTnz59Hz549eZ2friK0s7Nrt1AqEomgqamJpqamp76nvb8Pn5WErRk5ciTs7Ozw6aefMvt5e6xGw8LC8NNPPzF93JydnTFx4kRWBN/k5GQcPXpU5XG4JCsrCwMHDmRtvPv37+PIkSNP/FlDQwOio6MRHR0NIyMjuLu7w8PDA0ZGRqzN39GRyWSIj49HWFgYLl++DKDFmvlpx3h5eTkuXrzISSzBwcFMVUtHo7GxURCR0NXVtV1V8lKpFP369eN8gVsdkMvlePfddwEA06ZNY63HmSpcvXoVAPtVhOnp6bwJhDT37t2DWCzGzJkzO6xQSFuNAvyLhFKpFE5OToiPj0dISIigIqGnpyf09PSQk5ODsLAw3vt2Xr58GSkpKdDT08OECRNYG7e4uBhVVVVM1Rxb0BamXMKHSEg/C6qTSCiRSODt7Y3Lly/Dz8/vuRYJJRIJJk+ezHxNIDwO2UcIBAKBQBAeIhKywJ07d7B161YcOXKk0wuExcXF+OGHHwC09CIU6vele43Y2dlxFkNRURGuXLnCydjK4uPjo/Lvq6uri+rqalRVVbEUVfuIi4tjbAB//fVXeHp68h6DslajNFpaWm2KhO21g+W7krA11tbW2LlzJ/766y/o6OjAzs7umdsUFxdjx44dzOfYtWtXTJ48GY6OjirHQ1EUQkJCEBgYqPJYXMPmYlR8fLzCtsbl5eUICgpCUFAQbGxs4O3trfS+3NF5+PAhbt++jdDQUERERODhw4fMz4yNjdvsh8l2BeHjXLlyBdra2uj/f+zdd1hU1/Y38O8ZOkhVpEjHBqIoYEFEsGONGjWW2KImMRqT3OTeNG96bhIT80tiNMXYYu8de0FsiKiAIKhIk957mXLeP3jPCShlyikzsD/P4xMNw96bYcqZvfZaa8AAXufh2sOHD0WZd9SoUSp/j6+vb4cIEu7fvx+3b9+GiYkJFixYIPZykJWVhQcPHoCiKPj4+HA69p07dzgdT1nx8fHQ09PDlClTdPJaXswgIdDQl/L+/fuIjIzE4sWLBZ+fYWhoiKFDh+LcuXPYu3ev4EHCn3/+GQAwfvx4tg84F5hgnq2tLafXiiRIyK+hQ4fizJkzOHbsGH744QedfG3hgrGxMU6ePCn2MggtRh4jBEEQBCE+EiTUUGxsLIYNG4bly5c3+f+qZhTW1dWhrq6O/bdYAZy2bNiwAbW1tejRo4fgH7wbY4KEfJWy08Yyo7a2tiqXxmwOk1VTXFys8Viqomka3377LRQKBebOncv5RpIyz6Pc3Fw8fPgQFEUp3fvqWa2VHHV2dlZ5A4cJEspkMrXWoykTExO88cYbSt328ePHeOONNyCVSkFRFIYMGYLQ0FClyrC2haZphIeHs89vbVdWVoby8nKNMidlMhmuXLmCyMhItb4/IyMDGRkZ8Pb2xqBBg+Di4qLxJpS2vx9VVVXh6tWruHTpEm7dutWkzJ6JiQl69OiBnj17okePHi0+LmtraxETE8P7Wo8dOwYLCwvRyq6qSqFQiPL8CwoKgpWVlcrf5+rqCisrK0E2oVXF1fNIJpPh448/BgDMnj1bK7KHL126BKDhoFZr/T5VVV1dzfa1FcO9e/dgZmamUka9tmgcJBQjG9Lb2xsAcP36dU7HVed5NHz4cJw7dw4HDhxgS38KISUlBWfOnAFFUZg2bRrnYwPclhpVKBTsuHyqra1FbW0tjI2NeZuDya7MzMyEXC7XmkykgQMHwsDAAI8fPxa9FC9BEARBEARBtIYECTUQGxuLoKAgvPHGG/j++++bfE3VTdpvvvkGn3/+OZfL40VOTg6Ahg89Yp2GLC8vZ0uh8dWc/tGjR+zPqi2Cg4M5uc/t7e2Rnp6O06dPY8yYMRysTHlyuRy5ubkAgK+++orzx5AyzyOmvKCbmxvMzMzUmsfGxqbFIKs65WDt7OwAAFFRUYiOjsbAgQPVWhff6urq8Mknn0AqlcLBwQFTpkzhdMMqOjpaZwKEDE02vXJycnDkyBHk5+drvI7ExEQkJiaia9euGDhwIPr166d24Fbo9yOpVIpff/0VsbGxABreP5v7w0hJSWmyIW5ra4sePXqgV69ecHJyUmqDvHFgkW9HjhzB66+/rvbrjZDOnj0rSiaGuqXLKYpCv3792KxmbcLV8+jgwYNISUmBhYUFZs6cycHKNJeRkQGA+2swIQIWbbl27RoCAwN14vnaWGFhIQDAwsJC8NLlABAREQHgn+sZrqj6PJLL5Th27BgAwMrKStA2EMx7WK9eveDo6Mjp2JmZmQDAaYuF3Nxc1NbWcjZea6qrq3kNElpZWYGiKNTX16O0tJTTPqmakMvl7PVGR80iJAiCIAiCIHSDbjbe0AJZWVkIDQ3FpEmT8P3330MqleKDDz7AjBkzEBgYiD///FOlXnYffvghysrK2D/Mh0Ftw2Reibm+xMREAEDnzp1528Th+iS0phwdHTkr6TVkyBBQFIU7d+4IfmJfX1+fPe3LBAu51NLzaOrUqRg7dizGjh2L7du3AwD8/PzUnqe1jE51Tgm7uroiICAAQMOGmBhZnso4cOAAcnJyYGFhgfnz53MaIExJSeGtPxxfXnzxRbUCcXK5HJcuXcKff/7JSYCwsfz8fJw8eRLffPMNwsPDUVBQoPIYQr4f0TSNtWvX4tixY0hPT0d6ejrS0tKQmpqKJ0+eICUlBY8fP8ajR4/YPwqFAl26dEFISAhWrFiBN954A2PGjIGLi4vSGTQmJiaclMdVRmVlJY4dOyZoYFId0dHRiIqKEnxeFxcXjTaPuS53yRUunkdM9j0ATJ8+XeUeunyprKwEAM7XI0YvzOYkJSWJvQSVMcH9vn37Ch6MuHv3Ls6dOweKorBmzRpOx1b1ebRt2zZER0fDyMgIe/fuFfS+YK5ruewZyGB6P3OZScwE+4XAd6UMiUTCZjUzAXNtEBUVBZlMht69e6N79+5iL0c0VVVVMDMzg5mZGaqqqsReDqGFyGOEIAiCIMRHMgnV9OTJE/j6+iIvLw+3b9/Gf//7X9TU1MDb2xv6+vr4+eefERMTg88++wwODg5tjmdkZAQjIyMBVq4Zpu+VmD0f7t+/D6ChrCMfnj59qnU9LcaOHcvZRoeVlRXbx2n79u3sBqRQ7O3tkZeXhydPniAwMJDTsVt6Hj27OWFtba12qVGgIUjYXN8EExMTlfsRMsaOHYv09HQUFBTgv//9LxYvXgw/Pz9RyoY1p7S0FNu2bQPQ0DuMy83hoqIi7Nixg7PxhBAQEKBWcCI3NxdHjhxhN/z4FB0djejoaLi5uWHgwIHo0aNHk9eRljbthHw/2rlzJ1uebfLkyWzJSZqm2aDas3+3srKCra2txq+JAwYMEKz/3sOHD3H79m2tzRJ+/PgxwsPDRZlbnezrxmxtbWFvb8/LwRNNcPE8OnfuHO7duwdjY2NMnTqVm4VxgAkScpkZRNO0VmQSAkBCQgL8/f3FXoZKmIBPv379BJ1XKpXip59+AgC89tprrfaBVYcqz6MbN26wB8E2bdqEvn37crqWtjCvQXyUBGbGVqcsc0uE/KzTWh9vrpibm6OiogKFhYWctGfgAlNKnuvys7qourpa7CUQWo48RgiCIAhCXCRIqKbg4GB88skn+OmnnzB06FCMHDkSBw8eZMubbNiwAV999RXmzZunVJBQVzBBwqysLMhkMpV7r3GByX7jK0iobVmEXl5ecHV15XTM4OBgxMbGIioqCsnJyYJ+mHZwcEBsbKxKmbaaWr58eZNNpk6dOmnUr8Tc3BzdunVDVlZWk//v6empdlDPwMAAM2bMwJ9//onExET8+9//RteuXTFu3DiEhYVxXrpKVdu3b4dUKoW9vT2nG281NTXYvXs3Z+MJwd7eHuPGjVP5+3Jzc/HHH3/wsKLWpaWlIS0tTfB523LhwgVs2rQJADBhwgQMGDBA0Pl79OghaD+78PBwuLm58ZJloom8vDzs3LlTtPm5KFnp4+OjdUFCLjCHeCZOnAhLS0uRV/MPPoKEhYWFqKio4Gw8TaSmprJZDbqApmk2SOjr6yvo3Hv37kVGRga6du2K//3vf4LO3VhWVhY7/8qVKzFv3jzB18C8BtnY2HA6rlQqZbPjuAoSNn7MCEGIIKGFhQWys7O1JpOwrq6Ozc6fPn26yKshCIIgCIIgiNZpR4qKjmGyL0aOHIk333wTy5cvx+rVq9G5c2e2T9Ibb7wBhUKBixcvirlUzjk7O8PMzAxyuRzZ2dmCzy+TyfDgwQN2LVwrKSlhx9cWo0eP5nxMGxsbNtAjdAYXU6JSyCChpaVlkz+aBAgZzQVW1Sk12ljXrl3x2muvISAgAMbGxsjPz8f27dsxb948vP322zhz5gxqamo0mkMdWVlZOHz4MABgzJgxnGW1ymQyHDhwAEVFRZyMJ5SZM2eqfECiuroae/fu5WlFuuf+/fvshm5gYCBbbldIenp6mDx5sqBzHjx4kJeyazk5OWq9J1dUVGDXrl2cr0cVTk5OGo+hrSVHNREdHY1Lly5BT09Pa3oRMphSYFwGCbUli5ChbdeCrcnPz0dtbS1MTEwELWmYnZ3NZu6tXbuWlww6ZdTW1uLTTz9FZWUlAgMDsXbtWlHWwfQy57ofXkFBARQKBfT19TkLXBcWFgqatSNEkFDbyo3GxMSgtrYWzs7OOpeZTBAEQRAEQXQ8JJNQScwGnL+/P/T19SGXy6Gnp4cxY8bAzc2NzfSSSCRQKBTIz8+Hq6ur4Cd6+UZRFHr37o2YmBikp6fDxcVF0PlTUlJQV1cHY2NjdOnShfPxb926xfmYmhgyZAjnJ5IZw4YNQ1xcHK5evYqUlBR4enryMs+zmIw4IYOEfOjdu/dzhwC4uA9tbW0xceJEjBs3DklJSbh37x5SUlIQGxuL2NhYrF27FmPGjMH48ePRp08fQfrtbNy4ETRNo3v37vDw8OBkzPr6euzZs0fnHgczZ85U+TmpUChw4MABwTLWtF1WVhbee+89KBQK9O7dm5eDEMry8PBA//79ce/ePUHmy8vLw8WLFzF27FjOxiwvL8eff/4JAPj444+VDmBLpVLs2bMH5eXlnK1FVW5ubpxUJLC0tISrq6vWlQrXxHfffQegobyznZ2dyKtpiskk5LIssbb0I2QkJCSIcnhBHczj3sfHh5NDUMqgaRq//PIL6uvrMWLECMEz99asWcO+duTk5CAlJQXW1tbYt2+fWr2CucBXJmHjUqNcXfMJ/VrJd09CoCGTENCeICFTanTq1KmC9wklCIIgCIIgCFWRIKESkpKSMHToUAwZMgSfffYZBg0aBD09PTZQ+Gz2kEQiwW+//Yby8vJ2eXLQy8uLDRIGBwcLOnd8fDyAhixCrj9w1dfX4+bNm5yOqanhw4fzNratrS28vb2RmJiIHTt24NNPP+VtrsbEyCTkQ5cuXdC5c2c2C65bt26clibT19eHj48PfHx8UFZWhtjYWNy7dw8lJSUIDw9HeHg4vL298c0337AbI3xITExEREQEAO6yWmtra7Fr1y5kZmZyMp5QBg0aBG9vb5W/79y5czr/eAfw3Oaro6Mjhg0bBgMDA6XHqKiowIcffoi6ujo4ODhg2rRpovfdHDt2LB4/fswGPvh248YNeHp6cnYw4+7du+zfq6urlXo9oGkahw8fFqUiQGOa9iNszMfHp90ECZOTk3Ho0CEAwOzZs0VeTVNyuZzNQOIqk1Amk2ldSeS0tDRUVlay2UnajHncC9mPMDIyElFRUdDX18eGDRsED4JcuXKlyb8lEgkOHjzISWayuoQIEnJFyFKjgHA9CQHtCBLK5XK2fQXpR0gQBEEQBEHoAhIkbEN+fj5effVVDBgwACkpKVizZg3+85//sIFCmqabfDA+dOgQLl++jL///huXL18WPNNOCExfQqE/YAL89iMUKpNEWePGjYOJiQmvcwwfPhyJiYm4fPkywsLCMHjwYF7nA8D26MzIyIBUKlUpwKBNKIpCr1692E0ATUuNtsbS0hLDhw9HcHAwMjIycO/ePdy/fx+JiYl4//338cMPP/DSO4mmafz+++8AgP79+3OSzVJdXY0dO3awZbl0haOjo1rZX7GxsVp3+EBdf//993P/z9raGpMmTcLkyZPb7LUnlUrx6aefIjMzExYWFpgzZ45oGR+NmZiYYPz48di/f79gcx45cgTLly+HqampRuMoFArcuXOH/beyQcLz589rRTlFLoOE3t7eOHnyJGfjtcTW1rbJdZ9CoeB8U/qHH34ATdMIDAzkpGcjlxqXKOQqSPj06VNBggiqevDgAQYOHCj2MlpF07TgQcLq6mqsW7cOAPD++++jd+/egszb2Lffftvk8RccHAw/Pz/B18FQKBTIy8sDwF+QkMu+pEIfqOhoQcK4uDiUl5ejc+fOgh+oJQiCIAiCIAh1kCBhGzIzM+Ho6IgPPvgAMpkMc+fObRIofPbkbEpKChISEnD16tV22SMH+CdIKPQHTJqmcf/+fQDcBwlpmtaqUqM2NjaCbEzZ2dlh4MCBiI6OxqeffoodO3bwUsa1MRsbGxgYGEAqlSIzM5Oz8pVi6N27tyBBQgZFUXB1dYWrqysCAwOxefNmJCUl4cMPP8R3333HeVD52rVriI+Ph56eHkaMGKHxeJWVlfj7779RUFDAweqENXPmTJXLuGVnZ+PIkSP8LEgE/fr1Y+8DmqaRkpKCkpISbN++HTt27MDw4cMxdepU+Pr6PvfeSNM0fvzxR9y9excGBgaYO3cuu6GnDby9veHl5SVY4KyyshLHjx/HrFmzNMrAefz4cZNyocr0LI2JiWFft8Skr6+Pbt26cTaeqakpevTogUePHnE2ZmMDBgxAYGDgc8Hwuro6fPvtt5zNk52djW3btgEA5syZw9m4XGEybvX19Tkrbalt/QgZiYmJWh8kLC4uRlVVFQwMDAQL1m3duhWFhYXw8PDAxx9/LMicz1q+fDmvVRRUVVJSwgbCuMz4A7jPJCwtLRW8zLQQQULm8aANfa6ZUqNTpkzhpKS2rpNIJAgJCWH/ThDPIo8RgiAIghAfuWptQ69evfDhhx+yvQV37NiBefPmYc2aNfj3v//NZl4xGVH//ve/8eqrr3J62lPbNM4kVCgUgl3I5efno7CwEBRFsX3tuJKamqoVHyoZY8aMEayvzNixY5GZmYnc3Fx8//33bB8kvkgkEjg4OCAjIwOpqak6HSR0cnJCr169QFEUmyEplK5du2LhwoXYsmUL4uPj8cEHH+CLL77g7LUnKysL69evBwAEBgZyshm3d+9enQsQ+vn5ISQkROWfn6ZpHDhwgKdViWPChAlNepDJ5XIkJyfj1q1bSE9PR0REBCIiIhAcHIxPP/20yWvY0aNHcfr0aVAUhVmzZmldjzUAGD9+vKDZdUlJScjIyGB7GqsjNja2yb8bZ3k1Jzc3FydOnFB7Pi45OTlxvnnat29fzoOEQUFBGDx4sGBB7fXr10MqlcLHxwd9+/YVZE5VMEFCrrIIAe3rR8jQhZKjTJlWLy8vQTKzExMTcfDgQQDAr7/+ynvFC13BVEewsLDg/PfAZChyFSQUoxKMkJmEFRUVqK2t5fQ1ShU0TePatWsASKlRhomJCS5fviz2MggtRh4jBEEQBCE+ckynDZ06dWIzAqVSKQYNGoRdu3YhNjYW33//PW7dugWFQoFvv/0Wu3btAsBtORhtxGTx1dbWoq6uTrB5mfIxlpaWnH8A16bAhYuLC3r16iXYfPr6+pgxYwYoisKtW7fw8OFD3udkgrx8ZXwIhaIozJ49Gy+99JLg/XiAhtKtCxYsgIGBAeLi4rBy5Uo8ffpU43Hv3r2LJUuWIDc3F5aWlggKCtJ4TJqmdepkqI+PD1auXInJkyerFSCVyWQoKSnhYWXaQ09PD97e3li0aBFef/11+Pv7QyKRIDIyEuvWrQNN0wAastuYzKgxY8age/fuYi67Rebm5oJv6NXW1mr0/c8GL9oKZN2+fVuj+biUlpaGiooKTsfk6r3T3NwcY8eOxQcffIDRo0cLmvUaHR0NAAgLCxNsTlUwgV2ZTMbZmMXFxZyNxTVt753LlOEPCAjgfa6ioiJ8+umnUCgUeOmllzB+/Hje59QVzO+B60OMANjrS4VCwcl4VVVVnIyjCiGCyUZGRuzhpLKyMt7na0lJSQny8/NBURRGjRol2joIgiAIgiAIQhW6s2MrkNTUVPz111/46aefcObMGQANG6EKhQIGBgZQKBQYOHAgdu/ezQYKp0+fjm+++YbNNmzvGn/QEzJIWF9fDwC89LBjxtYGoaGhggecOnfuzAbDhejLxQSak5OTeZ+rvXNycsLSpUthaWmJp0+fYsmSJRr11zx27Bjeffdd1NXVoVu3bliyZAknp7EpisK8efMwdOhQjcfii5WVFUJDQ/HWW2/hxRdfROfOndUey8DAQGf7barDzs4OkyZNwowZMwA0ZA4yryVHjhxBaWkprK2tBel7qol+/fph4sSJgsxlZWWlcSZ1WFgY5s6dCw8PD/j6+rZailsmk7Eb2dri7NmznI5naGiIyZMnazRGt27d8PrrryMwMLBJ5qxQmCwfPoINXGDKrdbW1nJy7UTTtKDXkqrS5v655eXlSE1NBQCMHj2a17mYnrKFhYXw9vbGxo0beZ1P1zAtC/g45Ofk5ASAu2C6GAe2uCwt3RKKotjPqEKXU22Mye719PTUuO8wQRAEQRAEQQiFlBtt5P79+wgJCcGAAQPw4MEDWFlZwc7ODsePH4eZmRmbCaNQKBAQEIDt27dj6NChsLKywvXr19GnTx+xfwRBSCQSGBoaor6+XpQgIR+9HbQlSOju7g43NzdR5g4MDER8fDwuXLiAV1999bm+S1xiNrKTkpJ4m6Mj6dq1K5YuXYo9e/YgKysL7777Lt577z2VTvnL5XKsX78ehw8fBtBQtm/y5MmcBroMDQ0xZswY+Pj44Pjx4xpvvhoaGsLOzg5du3aFnZ0d+3eKolBUVITCwkIUFhayfy8qKoJcLn9unH79+qF///5wc3PjNEBvZmaG0tJSzsbTBV5eXhg7dizOnj2L3377DRYWFtizZw8AICQkRCeySQMCAmBoaMg+F/gwdOhQBAYGavz8oigKPXr0UKonamJiosaZi1y7f/8+/Pz84O7uztmYAwYMwJ07d5CVlaXy93bv3h0zZ84UpGxjc2iaZoOEXbt2FWUNbTEzM4OJiQlqampQXl6ucR/j+vp6NutYG2lzkDAuLg5Aw3sY3yXP161bh4SEBJiZmeHIkSNa1VNWGzAZwHz0hWQCbFwFCYU+iKivry9YiXETExNUVlaKGiRMT08H0NDrmGhQVVXFfr5NS0uDmZmZuAsitA55jBAEQRCE+EiQ8P+rrq7G66+/jpdeegkbNmxAaWkpbt68yfYdvHDhAuzs7CCXy6Gnp4fa2lrs2rULFhYWuHr1aof7IGBsbIz6+npBg2t8BgmF6JWhjLCwMFHKVgINpSvd3NyQlpaGQ4cO4bXXXuNtLhcXFwAkk5BLnTp1wsKFC3H06FEkJCRgzZo1bGZhc4EZqVSKtLQ0PHr0CI8fP0ZsbCzbF2rkyJEYNmwYb49FBwcHLF26FFFRUS1mEhkaGsLQ0BBGRkbs3zt16tQkIGhpadniGh0dHZ/LxFEoFCgrK2ODh8bGxvDy8uKtb42pqWmHCxICwJAhQ1BSUoLo6Gi2x2nnzp21sr9aS/r16wcDAwPs27eP03GDg4MxZMgQUbILtKnUaGPh4eF4/fXXOevDS1EUJk6ciD///FPp77GwsECfPn0watQowfoBN6eoqAg1NTUAwOtBHU1QFIWuXbsiPT2dkyAh8/Nqq+zsbNA0Ldq1WUtommaDhGPHjuV1ruPHj+P48eOgKAr79+9X6lBCRyKTyRATEwPgn77tXGIyCbnqnS70YZ1u3boJNifz3sp1KWtVMJmEHW1voC1M2xCCaAl5jBAEQRCEuEiQ8P+rq6tDeXk5xowZA6ChDNi4cePg6emJ2bNnIywsDHfv3oWenh5omkZubi5OnTqFM2fOdMgPASYmJigvLyeZhBwaPHiw6JkDQ4YMQVpaGg4ePIj58+fztpHNZBKmpaWhsrLyub5ahHoMDAzw4osvwsbGBpGRkdi1axeysrLw1ltv4enTp3j06BEbFExLS3uup5S+vj6mT5/OyybXsyQSCQIDA9G/f3+UlZU1CQbq6+vzsiErkUhgbW0Na2trQTY5O+opWIqiEBYWhrKyMrbH6fDhw3Uii7AxLy8vzJs3Dzt37tRoHBMTEwwZMgSDBg3iLSDdlvz8fK3trVZYWIibN29y0vuU4eDggICAgBYDo507d4aLiwtcXV3h6uoKKysrzubWBJNFaGNjI1o2ozJsbW3ZIKGmtC279VnV1dUoLy/Xun7jOTk5KCgogEQiQUhICG/zFBcXY926dQCAr7/+mvQhbEZiYiKqq6thamraaslndTUOEnIRsBYjSCgUbSo32lEqDBEEQRAEQRDtAwkS/n8WFhZQKBS4dOkSpk2bBuCfMl5btmzBzJkzsXLlSvz666+gKApubm6IjY3tsL0GmA9hJJOQG6ampggNDRV1DQDQs2dPdO7cGUVFRTh16hRefPFFXuaxtraGvb09cnNzERkZSTadOERRFEaOHInOnTvj2LFjiIiIQERERLO3NTY2hoODA+zt7WFvbw93d3fBS4iZmJg06XPannTUICHQsAn54osv4uDBg9DT02N7nuqa7t27Y9GiRdi6davK32tmZobAwEAMHDhQ9IAPk+Wirc6fP4++ffvCwsKCszFHjhyJR48eoaysDHZ2dmxA0MXFRWsPpmh7qVEGk+XYEYKEQEM2obYFCZkswuHDh/P6eD527BikUikGDx6MDz74gLd5dFnjfoR8BOCYqgh1dXWorq7W+NpC6CAhE+QUAnMQh5QbJQiCIAiCIAjVkCAhGkr26OnpYebMmThz5gzCw8MxYcIE9ut9+/bFnDlzcPHiRVRVVbEfzjpqgBD4J0hIMgm5MWrUKNEyTBqjKApDhgzByZMncfDgQUydOpWX0msURcHf3x8nT57EuXPnSJCQB76+vrCyssKePXtQW1sLc3NzNiDI/Le1cp2E5jryewTQUDJ2zpw5Yi9DY66urvj3v/+NnJwcVFdXo6qqqsl/G/+9pqYGFhYWGDp0KPz8/Djt6akuqVSK2NhYsZfRprNnz2LGjBmcjWdiYoIVK1ZALpdrxfurMpggoVD9u9TFBDHLyso0HksXgoQ5OTmCZNgrSy6XIz4+HgC/pUbr6+tx7NgxAMC//vUvcr3QAj77EQKAkZER7OzskJeXh+LiYhIkbIXY5UZLSkpQVlYGiqJ4ezwQBEEQBEEQBB9IkBD/NHCfP38+Tp8+jfXr1zfJ7KIoCt7e3tixYwcnJzjbAzGChMxc7S2T0NHREQMGDBBt/mf5+vri4sWLyMnJwdWrV3krY8UECc+fP8/L+MQ/wY3a2toOH7ASA3mvaD9MTU3h6enZ5u0UCgUoitKqzfT79+8L+l6troSEBPj5+cHDw4OzMQ0MDLQiUKsskkmonXJycsReQhMpKSmorq6GtbU1Bg4cyNs8Fy9eRElJCZydnTF9+nTe5tF1TCYhn0Ghbt26IS8vD0VFRRqXNBXy/cnCwkLQChVilxtlsgjd3d3JdTdBEARBEAShU3SrQRCPaJqGh4cH/vzzT2RkZGDNmjVsebG6ujrcunULjo6O7bYsnqpIJiF3xo8fr1UbygYGBggICAAA7N+/n7d5/Pz8AADx8fHIzc3lbZ6OTiKRkI0KkZD7veORSCRa9XoOaH+p0cbCw8Mhl8vFXoZomA1mEiTULtnZ2aBpWuxlsJhSoyNHjuTlmhho+Fx04MABAMCKFSt4m0fXVVdXs78PPoOEjfsSakrITEIhswgB8YOEqampAEipUYIgCIIgCEL3dLggoUwmey5rjDn5r1Ao0LdvX+zduxfGxsb4+uuv4eTkhHHjxmHz5s345ZdftLaPjdDaW09CsYKE/fv3F/wDtDIGDRoEiUSChIQEpKSk8DKHpaUlevToAQC4dOkSL3MQhJhIJiEhtsLCQmRlZYm9DKUVFRVh165dovaTEpMulhvVNHimC0HC6upqToIzXKitrcWDBw8A8Ftq9P79+0hJSYGRkRGWLVvG2zy67saNG1AoFLCxsWGD53xgPisUFxdrPJaQQcJu3boJNhcgfpCQOejRp08fUebXVhKJBAEBAQgICBC83C2hG8hjhCAIgiDE16GOhSYmJuLzzz9HdnY2unfvjrFjx2LOnDmQSCSQy+XQ09ODQqGAt7c3/vzzT6SlpeHUqVNwcnJCcHAwunfvLvaPoDXECBIyvfEUCgXnY5uZmaGgoIDzcdvCZOxpG6lUym788XmhzvxOO3LmCMGt8vJy0DQNS0tLsZeCqqoqsZdAdHC6+Nr65MkT/N///R8mTpwIf39/rcvM5BOTVd+5c2eRV9I6R0dHGBgYoK6uDunp6XBzc1N7LAsLC+4WxqOzZ89izpw5oj8enzx5AoVCAWdnZ/agFR+YQOSkSZNgY2PD2zy6TCqV4l//+hcAYPDgwbw+Nuzt7QFwE/wyNDTUeAxlCR0krKysBCDeIS0mk5AECZsyMTFhe3cSRHPIY4QgCIIgxNdhjuk8fPgQQ4cOhaGhIcaMGYMnT57g+++/x+LFiwE0BCvq6+vZgEiXLl0QEBCA//73v1i8eDEJED7DyMgIgLBBQqZ0Hx8lTjXZ4NKEtm7gXr16FTRNIyAgAO7u7rzMUVNTg0ePHgEAhg0bxsscRMdSWFiIdevW4eeff8bly5dFf349ffpU1PkJomvXrrCyshJ7GWo5efIktm3bhsLCQrGXIhjmZ9X235mhoSHGjRsHALhz545GY+nKZvqjR4+QmJgo9jLY6ya+g1JMH8aePXvyNoeuomka6enp+PDDDxEXFwcLCwvesy2ZQC0TBNOEkAE0BwcHweYC/rnu8vLyEnReoOFxwQQJ+/btK/j8BEEQBEEQBKGJDhEkpGkaf//9N8aMGYPt27fjk08+walTp7BkyRLExMTgpZdeAvDPycotW7YgMzNTzCVrPWNjYwB4rnQrn5gPtXwECT08PDgfUxli9kJsSVlZGe7duwcAWLBgAW/zJCYmQi6Xw8nJCa6urrzNQ3QMcrkchw4dgkwmA03TiIiIwF9//YW8vDzR1qRLZR7FoFAoUFlZiby8PDx58gTZ2dm8ZIp3ZBRF8doni2/p6elYv349IiMjRQ/68626uhrV1dUAoBWZ0G2ZMGECgIb38pqaGrXHMTIywoABA7haFq9Onz4tanlUmqbx+PFjAA1BQj4xQUKxro+1RWlpKa5evYoNGzZg+fLlGDZsGKytreHm5oa1a9cCAFauXAlra2te19E4SKhpiV+h+iXb2toKmrUI/HPdJUZPwMLCQlRUVEAikYgSpCQIgiAIgiAITXSIcqMURSErK4st4wQ0fEB65ZVXYGxsjPXr1+PDDz/EN998g+vXr+N///sfLl68iK1bt7LlEImmxMgkZIKEfGzQODo6wtDQUPCgnTYGCa9duwaFQoH+/fvzehI2Pj4eABAcHCx6+S5C9126dAk5OTkwMjLC66+/jt9//x25ubn4888/ERISgmHDhgna40IqlTZ5z+mo0tPTkZaWhqqqquf+NBdYMDIygqurK9zc3ODu7g47Ozvy+qAhb29v3Lx5U+xlaOTixYtISEjAlClT4OjoKPZyeMFkERoYGAi2ia+J3r17w8PDA0+ePEF8fDwGDRqk9lj+/v64e/cuh6vjR2VlJc6fP49JkyaJMn9eXh4qKythbGyMfv368TpXdnY2gI4XJJTL5fjtt99w6tQpxMfHt3hoVE9PDy4uLhg5ciRGjx7N+7qYIKRMJkN9fT37OUwdQmUSCp1FWF1dzfZsFCNIx2QR9urVS6PfT3tUXV3NBm4TExN14j2OEBZ5jBAEQRCE+Np9kJCmaVAUBT8/PyQnJyMpKYk9VW9iYoKZM2fi4cOHuHTpEkpKSjB06FD85z//wejRo0mAsBXMh5/2kkmop6cHNzc3PHz4kPOxW6NtQcKKigrExMQA4DeLEADi4uIANAQJCUITqampuHbtGgDg448/RnBwMIYPH461a9fi+vXruHTpEpKTkzF16lTY2toKsiZmg7Ujq6qqwt9//91qdiBFUbC0tISlpSWKiopQWVmJhw8fsq/FJiYmcHNzY4OGXbp0IUFDFTk5OcHc3BwVFRViL0UjeXl52LhxI4YOHYrQ0FAYGBiIvSROMX2RLS0tdeIxTlEUxo8fj/Xr1+Pu3bsaBQkdHR1hb2+vEwcrYmJi0K9fP7i4uAg+N1Nq1M/Pj9cMLYVCwf4uOlKQMD09HfPnz0dkZGST/9+1a1e4u7vD09MT7u7u8PDwgLOzs6CvQcbGxjA1NUV1dTUqKys1CkLp6enB2NiY96xYoQ90MKVGXVxc0KlTJ0HnBhr6hQKk1GhzmBK9zN8J4lnkMUIQBEEQ4mv3QUJmo2XChAn44osvsGbNGvz8888wNzcHAFhYWODtt9/GDz/8gIsXL+LFF1/kva9Ee8CUGxUjk5CPICEAuLu7d/ggIZNF2LdvX/Tv35+3eeRyOR48eACABAm1QVlZGTIzM2FgYIDu3bvr1AGJ6upqHD58GAAwceJE9vFkY2ODr776CufOncPatWuRnZ2N33//HSNHjkRgYCDvWYWkH2HDSWCFQgF7e3uMGTMGVlZWsLKygrW1Nftfc3Nz9vEml8vx+PFj3Lt3D3fv3kVcXBxqamrw4MED9vXCzMwMPj4+GD16NPT12/0lDCeYkqPR0dFiL4UT169fx4MHDzBlyhTR+gnzgckk1IVSo4wxY8bgt99+Q25uLnJyctTOHKIoCv7+/jh58iTHK+TH8ePH8dprrwn+GsSUGtUkIKuMoqIiSKVS6Ovrw8nJide5tMXu3buxbNkyVFVVwdTUFC+//DL69OkDDw8PUQJOzbGxsWGDhJ07d9ZoLFNT03YbJBSj1ChAgoQEQRAEQRCEbuswO2yenp7Yt28fxo8fD1NTU3z22Wfo0qULgIZehAMGDND4A1dHImYmIV8fasU4La1NQcLKykrcvn0bADB//nxeMxny8/NRW1sLIyMj0T7Md1Q0TaO4uBjp6elIT09HRkYGSktL2a+bmZlhwIAB8PPz473HjqZomsaJEydQUVEBZ2dnrFixosnXKYrC2LFjMWDAAKxduxZRUVE4f/48kpKS8MILL7DvAXwg/QiBhIQEAMC0adMwa9asNm+vp6eHXr16oVevXnjppZcgk8mQnJyMu3fv4t69e4iPj0dVVRWioqKQnZ2Nl156SbCyabrOy8ur3QQJAaCkpATbtm3D5MmT4efnJ/ZyONE4k1BXWFpaIiQkBJcuXcKdO3cwceJEtcfq27evzgQJCwsLce3aNYSEhAg2Z0lJCVv6ku9+hEwmvKura7s/jFFWVoYVK1Zg586dAIA+ffrgo48+0sqyxjY2Nnj69CkqKys1HsvMzIwtzckXOzs7Xsd/FnPdJVY/QKbcKAkSEgRBEARBELqofX/ye8aIESOwf/9+zJw5E9nZ2Zg5cyb69euH7du34+nTp/D09BR7iTpDjExC5iRvXV0dW0aWS7a2tjAzM0NVVRWn47ZGm4KE169fh1wuh5eXFwICAnidi9mAcnd3F7RPXEekUCiQn5/PBgTT09NbfYxXVVXh6tWruHr1Kjw9PeHn54devXppZXbh3bt38eDBA0gkEqxevRomJibN3s7W1hbffPMNTp06hZ9++glPnz7F77//jrCwMN4e6x09k7CiooItGxQaGqrWGPr6+ujTpw/69OmDl19+GfX19YiKisKXX36JzMxM/PXXX5g7d65gJWR1maurK1uqrj05fvw4Kisr20VvWyaT0MrKStyFqGjChAm4dOkS4uPjERYWpvZ7hZGREfz8/HDnzh2OV8iPy5cvw9fXV7DfV0REBGiaRkBAAOzt7XmdKycnB0D7LzV69epVvPzyy0hPT4dEIsGCBQvw8ssva+X1DvBPX0KugoR88vT05LUk7rMUCgUbJBTj8KFcLmeveUiQkCAIgiAIgtBFHW53fvLkybh+/TqKi4vxwQcfYMqUKThy5AhOnToFZ2dnsZenM8TIJGzcwJqP4BpFUYJviGhLkLCqqorNMlmwYAHvm63tZQNKoVCgrKxMa36PjclkMty6dQs///wz/vjjD5w+fRqJiYkqBcFTUlKwf/9+/N///R8uXLiAkpISHlesmqKiIpw6dQoAsHTpUvTs2bPV21MUhQkTJmD79u0ICAiAXC7HyZMn2ewdLpWXl+t8/zdNJSYmAmjIyujatSsnYxoaGiI4OBgbN26Eo6MjSktLsXHjRrbEF9EyiUSCXr16ib0MXly6dAmnTp1qtfelLtDFTEKgoT9ep06dUFdXh/z8fI3G4vuAEtcuXrwoyDz5+fmIjY0F0PB+xzfmIJeuX6O1RCqV4r///S9CQkKQnp4OBwcH/PLLL1i4cKHWBgiBhkxCQDeChL179+Z1/Gfl5eWhrq4OJiYmcHd3F3RuoOFgmFQqhZmZWbsqg00QBEEQBEF0HB0qk5Dh5+eHY8eOobi4GJWVlbC3t+e17Fx7JEYmoZGREfT09CCXy9lSlVxzd3dHfHw85+O2pHGZRzFFRUVBJpOhZ8+evJexAnRrA4qmaVRXV6OwsBBFRUUoKipCcXExCgsLUVJSArlcDkNDQ7zxxhtas7krlUqxceNGzgJgjbMLZ8+erRXBhhMnTkAmk2HAgAF46aWXlP4+Ozs7rFmzBh9//DFu3LiB6OhoTJgwgdO1MWU2O7L79+8DaMjg55qrqyvWr1+P//73v7h//z527NiBV155pcP0zlKXu7s77t69K/YyeMEccuH6uSwkJpOQ6ZmtK5gAdExMDLKystTuSwgADg4OcHR0ZK8RtF18fDwGDx6Mbt268TpPZGQkAGD48OGCvP+2l4NcLVmyZAm2b98OABg3bhzefPNNnShdzWWQsPHBSz4IHSRksvj69esnSqA3JSUFAODj40MqpBAEQRAEQRA6qUMGCQHAwsICFhYWYi9DZ9XU1ACAoL1KKIqCnZ0dsrOzUVhYyEtARsjSOACQnJyMmzdvYsiQIYLO25hcLmfLe82dO1eQkm1M3462sr/EQtM0IiMjkZycjKKiItTV1bV6+/r6ety7d0/Q/kStuXv3Li8ZckBDtos2BAlpmgbQ8Fqu6oYMRVHo27cvbty4wXn5xejoaJw9e5bTMXVNaWkpW25V3VKjbbGyssLatWvx6aef4ubNm7h16xYJErYiNzcXhw4dEnsZvIqOjoavry/vARu+MI/fmJgYLF68WOTVqIYJXrT1XqmM/v3760yQEADOnj2LRYsW8d7HGRAuCM4cANSFwJk6ysvLAQC+vr744IMPRF6N8pjqLVwEofg+jCB0oI7pr9i9e3dB52Uwn6MGDRokyvzajqIotgysrpcGJ/hBHiMEQRAEIT5y1I1QC7NhwfTHEApzMpXpO8G13NxcXsZtzZkzZ9isGzEkJyejqqoKNjY2CAoKEmTOx48fAwAGDBggyHyqOnfuHC5duoTs7Gx209Pe3h4BAQGYNm0aVq1ahTVr1mD37t34z3/+A6Ahc4oJXIlJLpfj+vXrvI2vLf3fxo0bB4qiEBERgWvXrqn8/cypcy5/nmvXriE8PJyz8XQVk0k5YMAAdO7cmbd5DA0NsWDBAgAN5U25CFC0R5mZmfjjjz/EXoYgwsPDteJ1WB1Lly6Fvr4+EhIS2HK9uoIpY81UmdCEth4eaklGRgYePHjA6xxMKV0+Kmg0h8kyY4Jp7c3nn38OiqIQGxsraPUQTTEZnlz0weS78gVzGFAozGsQc2BBSAqFgr3unjx5suDz6wJTU1MkJCQgISGB9yxWQjeRxwhBEARBiI8ECQm1iBUk9PLyAsBfkJD5AC60gwcPCv6BmsGcfh0/frwgmaGlpaUoLCwERVHw9fXlfT5V3bx5Ezdu3AAAvPrqq9i8eTNOnz6N3bt34/vvv8eqVaswbdo0DBw4EPb29ggODoZEIkFhYaHG/Zi4cP/+fZSVlfE2vrYECR0cHBAYGAgA+Omnn1TqtQj8s4HFVb+8y5cv4/z585yMpev4LDX6rN69e8PFxQVyuVznAitCePz4MTZv3iz2MgSTnZ2tsyVVHRwcMG/ePADAgQMHRF6Napjyh1wECS0tLeHo6KjxOEI6f/48ZDIZb+MzQUKhsrM6deoEoP0GCX19fdnejuvXr9eZfqZ5eXkAuPnsxXeQkDkMKBTmGpCLAKqqkpOTUVJSAnNzc62pKEIQBEEQBEEQqiJBQkItXH5QVUXjTEKuswVomhYtSAgAf//9t+CZjOXl5WwfjYkTJwoy56NHjwA0lATStt5L9+/fx5kzZwAAy5Ytw5w5c+Du7t7q6f1OnTqxwSqxe9HRNI2rV6/yNr6enp4oGzAtCQ0NhbW1NQoLC/HXX38p9T1yuRxnzpzhNEiYnZ2NiIgIjcdpDwoLC5GbmwuKojB8+HDe56MoCmPHjgUAxMXF8T6fLklISMDOnTvFXobgLly4wJZE1zXvvPMOACAiIoK9ztIFXAYJAaBPnz6cjCOUkpISti8mH4QOEjJZHHweOBLbl19+CVNTUyQnJ+vMAR9dyiRMSUkRNKubCRIK/bkUAJtFGBYWJnjbCoIgCIIgCILgCgkSEmphMqaEDhj06NEDFEWhqqqK882LiooKzvuTqWr37t1sLxghMEGtfv36wcHBQZA5tbXUaGpqKg4fPgwAmDZtGubMmaP0944cORJAw/0pZqm75ORkFBYW8jZ+ly5dOOmFwxUDAwNMmjQJAHDkyJFWg7Q0TSMiIgJLlizBt99+C6lUCkdHR042lC5duqTxGO0F8zsYNGgQ75uQjNGjRwMA0tLSUFpaKsic2i4mJkbnstG4Ul1drbPPSV9fX4wcORIKhYJ9P9IFzAY9V+UwdS1ICDT0JuTrGlLoICHTi7C9ZhICgJ2dHT755BMAwMaNG7X+YEF9fT17fcfFZy9jY2Ney9eWl5ejoqKCt/GfJWYmIRMknDJliuBz64rq6mr06dMHffr0Ef2zNqGdyGOEIAiCIMSnPbu9hE5hTrgL3fvByMiIbUrPdclRMbMIGeXl5WypSyEIWRaQwWQSalOQMDc3F7t374ZCoUBISAhWrFihUtP0wMBA6Onpobi4WLTHEd9ZhID2lBptzMPDgy1b+8MPP0AqlTb5Ok3TuHXrFl5//XV89tlnSE9Ph6GhIUaNGoWFCxdqHPTMzMwUvKyWtqJpmn1NYQLnQrCzs2NfT0g2YUNvzBMnTmg8jpeXl2A90LgWHR0tSo9hLjDZhCdOnND6wAWD60xCS0tLODs7czKWkPjKKBcrSNieMwkB4K233oK7uzsKCwuxZ88esZfTqoKCAtA0DX19ffb3oym+D/IUFBTwOj5DoVCwr5VCZxKWl5fjyZMnABraNhDNo2kaiYmJSExM1Nm+wQS/yGOEIAiCIMTHfwMyot1RKBTsBz8xTmx6eXnh0aNHyMrK4vS0uTYECYGG3mYBAQGcbQK0pLi4GNnZ2aAoStAeGkxApX///oLN2ZrS0lLs3LkTUqkU/fr1w0cffaTyRpyJiQmGDRuGiIgIJCQkiNJPKS0tjbdenYwuXbrwOr66xo4di0ePHiEtLQ27d+/GggULADQEjDZt2sQGjvT19TF06FAEBgZytpl9+fJlTsbhm5eXF4KCgpo8tuvr67FlyxbO5sjLy0NhYSEkEgmCgoI4G1cZY8eOxd27dxEXF4fg4GCVgvwtkclkePr0KVJTU5Geng4DAwMMGDAAvXr1EmyzXhU0TePChQu4du2aRuNYWVlh9uzZsLOzg1wuR3p6OpKSkpCUlCRoZoimTp06hUWLFnHyWBDShAkT0LNnTzx8+BCnTp3C9OnTxV5SqxQKBXvqn6vXVaAhmzAzM5Oz8YRw69YtDBw4kPP3SpJJyA9jY2N8//33mDFjBvbs2YMJEybAzs5O7GU1q3GpUa5e0ywtLXntpZ2fnw9PT0/exmcwrz8SiUTwNgbp6ekAAFdXV3Tu3FnQuQmCIAiCIAiCSyRISKisuLiY3bAQK0h47NgxzgMi2pR1cOXKFd5PpDJlAf39/QU7eVtTU4OnT58C0I5MwurqauzYsQOVlZVwd3fH119/rXY/kZEjR7JBwtGjRwu+Mc13FiGgnZmEQEP/pLCwMBw6dAjbtm2Di4sLTp06hVu3bgFo2DgaNGgQhg0bxmnwPT09nT1Brq2srKwwfvx49OzZ87mv1dXVcToXk0UYFBTE+yGHZw0fPhxr165FUVERsrKy4OTkpPIYCoUCubm5SE1NZQODMpmsyW0eP36MTp06wc/PD/7+/rCwsODqR9CIQqHAyZMncefOHY3GcXd3x4wZM9ieZHp6evDw8ICHhwfGjx+PnJwcNmAoVJaIujIyMhAfH49+/fqJvRSVSCQSvP3223jjjTdw8OBBvPDCC1oZlGZUVFSwp/65DhKePn2as/GEcu7cOZXKlSuD9CTkz/Tp0zF8+HBcuXIFGzduxOrVq8VeUrOYICGX1+vtJZOQKTVqaWkp+GtlWloagIbPpgRBEARBEAShy0iQkFAZU2rUwsIC+vrCP4SYD2I5OTlQKBSc9UjTlkxCoOE0+uDBg3kt5ypGqdGUlBTQNA0HBwfRT2tLpVLs3r0bRUVFsLW1xXfffYdOnTqpPd7gwYNhYmKCsrIyPH36VNBSadnZ2YIEq7Q1SAgAPj4+iIuLw+PHj/H5558DACiKgp+fH4YPH855MIemaa3vexYUFISQkBAYGBjwPhdN0+zBAyFLjTJMTU0xYsQInDt3DrGxsUoFCaurq1FaWspmC6alpaG2trbJbaytrTFgwAD4+fkhLy8PJ06cQElJCa5cuYLIyEj06tULAwcOhLu7u2gZa6WlpTh9+jSSk5M1GmfIkCEYM2ZMi++pFEXB0dERjo6OGDlyJIqKirBp0yatLol57tw59OrVS+fKpi5YsAAff/wxsrOzcePGDQwbNkzsJbWICQSYmZlxek3YqVMnuLu7IzU1lbMxhfDw4UM8efIEHh4enI0pdJCQuRZq75mEQMPr2v/93/8hICAAFy5cwPTp0+Ht7S32sp7DHGTk8nBmewsSinFwNSMjAwC08jFDEARBEARBEKogQUJCZUxpGjE+jAGAs7MzzMzMUFVVhfz8fNjb22s8ZlVVldZthly5cgVTp07lZeyioiLk5+dDIpEgODiYlzmaw5QaFTuLsKCgAMeOHcPTp09haGiI7777TuMAmJGREYKCgnD+/Hk8ePBA0CDh9evXBZlHiGCTuiiKwsSJE7Fhwwa2dGxISAhvgfb09HS2zJS2cXFxwcSJE9G1a1fB5szMzERpaSn09fUxZMgQweZtbOzYsTh37hzu37+PcePGtRiwePr0KS5evNhs8MHMzAy+vr4YMGAA/P394ebm1iT4N3/+fERGRuLo0aOIi4tjs+r8/PwwefJk3n625tA0jZiYGJw8eVLjscLCwjB48GClb8+UmNTmACHQ0Cvv8uXLGDdunNhLUYmZmRlee+01fPvtt9i/f79WBwmZqg58BBz69Omjc0FCADh//jxeffVVzsYTK5OwqKgIcrlcqzNZueDn54dFixZhy5Yt+Oabb7B69Wr06tVL7GU10bjcKFeECBJyeZizJWIGCUkmIUEQBEEQBNFekCAhoTJmw0bIDejGJBIJbG1tUVVVxfah0BTzIU+bJCcn8/bhmtn0pmma3XwSApOF2qNHD8HmBIATJ06wm1wymQwPHjyAQqGAvr4+vv/+e7i7u3MyD3P6/tkShXyqrq5mM7j4tmnTJsyePRvdunUTZD5VWVlZYeXKlVAoFLxvFsXExPA6vrqmTJmC/v37C57VxgSqR48ezWnJQVUMGDAAdnZ2yMvLQ1xcHPz8/Jp8vaCgABcvXkRSUhL7/2xsbODm5sZmC7bVb9DAwAAjR47EyJEjkZqairfffhvl5eXPZSDyTSaTITw8HHfv3uVkvNOnT6OsrAxDhgxpMeu2vLwcjx8/RkpKCp48eSL4z6yumzdvory8HCNHjtSpnlErVqzAmjVrEBcXh4yMDLi4uIi9pGYdO3YMAHh5X9DV4FROTg6Kioo4ebzRNA25XA4AglXvsLe3R6dOnVBeXo5NmzZxGvDUVl9//TWOHj2Kp0+f4o033kBYWBgCAwPRv39/japMcIGmacTHxwMAp1U4+D74VVdXh7S0NE6zapvDfE6SSqW8ztMc5jNxnz59BJ+bIAiCIAiCILhEgoSEypgNViEzpZ7FbJhwtYHEZLhpk9raWuTk5PCy8WZjYwN7e3vk5uYiIiICU6ZM4XyO5jDZml26dBFkPgZTWrWxIUOG4K233uIkE5XB9APjKuioDGbjSAiVlZX466+/MGHCBHh5eYm+cdYcIXrE1dTUNPuYEtuAAQNEydLNzc1lS11y3YtLFXp6epg+fTp+++033Lx5EwMGDABFUSgrK8Ply5cRGxsLmqZBURTGjRuHhQsXavT8T0pKQnl5OfT09DBq1CgOf5LWlZeXY9++fZz35b1x4wZu3LiB/v37IygoCFZWVsjIyMDjx4/x+PFjre9B2JrExEQkJibC398fISEhMDc3F3tJbXJycsL48eNx8uRJnDp1Cq+99prYS3pObGws7ty5A4lEgqFDh3I+vqYldMX08OFDBAYGajyOXC7npedja4yNjbFo0SL8+uuv+PjjjzFr1izRqocIxcHBAUlJSXjrrbewe/duhIeHIzw8HBKJBD179oSfnx/8/PzQt29ftXtXqys1NRWFhYXQ09ODq6srZ+Ny3Ze4ObGxsbwHCZnHptBtI0pLS1FUVASKotC3b19B59Y1FEWxj12xSrMT2o08RgiCIAhCfCRISKiMCRKKeaqdCRJykWVH07RWBgkB4MmTJ7xlbfXt2xe5ubk4f/68YEHCiooKABA8m2PRokVNNnU8PDwwaNAgTj+EFBQUsL1J3NzcOBu3LbGxsYLNxWA2z7p27QoPDw94eHjA1dVV8I0zsWhjgBCAaGU+IyMjATT0NxU722nChAnYuHEjCgoKEB8fj5ycHNy6dYvNmA4ODsYrr7yi8XO0uLgYP/30E4CGn5vP/rGNZWRkYP/+/aisrORtjnv37uHevXu8jS+mmJgYxMTEYNiwYQgKChIt61VZS5YswcmTJ3HmzBksWbJElD7Qrdm6dSuAhgMKXAeRpFJpk6xfXcNVkLBxdpSQj9cXXngBx48fR3p6Or744gv8+OOPgs0tFltbW+zatQtLly7FwYMHcf78eTx8+JAtK71r1y4YGBjAx8cHfn5+8Pf3R8+ePXnPeI2KigLQcACNy9cAIYKEcXFxmDhxIq/Xh9bW1gAayuPW1dUJ1oM2JSUFAODp6amVh+a0iampqVZW7SG0B3mMEARBEIT4tGu3gdAJzMluMTMJmXKOXHwwz8vL43XDVROpqam89Qz08fHBuXPnEB8fj9zcXE4z6lpSVlYGAIJtqDNmzJgBMzMzXudgsggdHR1hYmLC61yM/Px8wU9OPzt/fn4+bt68CaDh4IC7uzs8PDzQrVs3nS0V1xZtDKB4enqKUgK6oKAAiYmJAICXX35Z8Pmf1alTJ0ydOhUHDhzA4cOH2f/fv39/LFu2DN7e3pzM88svv6C+vh4ODg6cBALawmX/QQK4evUqYmJiEBwcjIEDByq18U7TNGpqap7bWOdzo33SpEno2rUr8vPzERUVhaCgIN7mUtXdu3dx79493nobMxvwuiotLQ01NTUaXw8wQUI9PT1Bg8T6+vp444038P777+OXX37Ba6+9pnV9+vjClJQGGnrYXrhwgf2TnZ2Nu3fv4u7du9i0aRM8PT3xxx9/8Hq9c+vWLQBA9+7dOR1XiCAhADx48AC+vr68jW9iYgJDQ0PU19cjNzeX02zL1jCvUf369RNkPoIgCIIgCILgEwkSEiqpr69nPxS1l0xCbc0iBBqChFKplJe+IRYWFnBzc0NaWhouXLiAefPmcT7Hs5hyo7rUF0pZYpQa1bZgVUZGBjIyMhAREQFDQ0P06tULI0aMYE95twd5eXnIzs4WexnPESJQ1Rwmi3DYsGG8lxRT1vTp03Hw4EHQNI0ePXpg2bJlCAgI4Cxz+OrVq4iIiABFUZgyZQovfWMb47r/INGgpqYGZ8+eRVRUFEJDQ+Ht7Y2KigqUlZU1+VNeXs7+Xch+s0BDz7D58+dj7dq1CA8P15ogIU3TbBahv78/LC0tOZ9Dl0uNMh4/fqxxGULmMSdG1uugQYMQGBiIGzdu4O2330Z4eHiHKwPn5OSEhQsXYuHChaBpGsnJyWzA8MSJE0hJSUFqairnATxGVVUVWzGC637eQgUJY2NjeQ0SUhQFa2tr5OXlIScnR7Ag4ZMnTwCA15+NIAiCIAiCIIRCgoSESlJSUiCXy2FiYiJ4X7nGmE2T9h4kBIDMzEzeNt/79u2LtLQ0nD9/HnPnzuV986e9BglpmhY8SKhQKBAXFyfIXOqor69HfHw84uPjERwcjGHDhrWLcqTaFpgFAHNzc1ECdMXFxWzpVW3IImQ4ODjg559/RlVVFQYNGsRpEK+yspItMxoUFMR7BjZf/QeJf5SVleHo0aM4evSo2Etp1pIlS7B27VrcvHkTVVVVvGfFK+POnTuIi4uDRCLBsGHDOB9foVC0iyDhw4cPNQ4SMpmEQpVQfNby5csRHR2N06dP44MPPsB3330nyjq0AUVR6N27N3r37o0VK1Zg/PjxOH36NGJjY3kLEt69exc0TcPGxobzKhxCBQlTU1NRVlbGy2ECRuMgoVBIJqHyampqMHz4cADAlStXBKu4QugO8hghCIIgCPHxe/ydaHcalxoV8zQxV+VG6+rqkJ6ezsWSeMOcVOWDt7c3JBIJ0tLSeA+W0jTdboOEmZmZKCwshEQiESzDNiUlBVVVVYLMpanIyEj8+uuviI2NBU3TYi9HbXK5XCsDsx4eHqK8HkdGRoKmaQwePFjrytD17dsXQ4YM4TzL7/fff0dRURE6d+6MkJAQTsd+Vl1dHTZv3kwChB2cl5cX3NzcoFAotKJHH03T2LJlCwAgICAAFhYWnM+RmZmJmpoazscV2qNHj9jKF+pigoRi9c90dnbGu+++CwBYs2ZNh+hNqCymzG58fDxvczD9CPkIQtbX13M+Zkv4vnZieqIKFSSUyWTs50eSSdg2hUKB27dv4/bt22yPaIJojDxGCIIgCEJ8JEhIqITZoBKz1CjAXbnR1NRULpbDKz6DhMbGxujduzcA4MyZM7zNAwC1tbVscLe9BQmZLEIXFxdeSsM2hyk/pSsqKipw5MgRbNq0CU+fPhV7OWp5+PAhqqurxV7Gc8TIIiwtLWUfg/Pnzxd8fjHcu3eP7Qk4efJk3vuDRUREsH1ciY6NKSeckJAg8kqA6OhoJCQkQE9Pj5csQgBaEQzlQl1dHTIyMjQagwkSipmJHxYWhldffRUA8O6772Lnzp2irUWbMFkvcXFxvByAommat36EgHCZhAB4PyTGlLUXKkiYmZkJqVQKc3NzuLm5CTInQRAEQbRFKpXi22+/RZ8+fWBiYgJbW1tMnz5do7YVqowplUpx9uxZrFq1CgEBAejSpQuMjY3RvXt3rFixQusTNAiioyNBQkIlzMaNs7OzqOvgKkj46NEjLpbDq5ycHF5P1Pfv3x8AcP78eXYzig/MZreRkRFMTU15m0cMQpcajYmJ0YrNYnVkZWVh06ZNOHLkCCoqKsRejkq0mENujAABAABJREFUsdQoINzj7v79+4iLi0NcXBzOnDkDmqbh5+eHPn36CDK/mOrq6vDDDz8AaOjBxnfPo4KCAty4cYPXOQjdMWTIEABAYmKiqOto3Itw4MCBMDc352WO9hIkBBoOl2hC7ExCxuzZszFjxgwAwMKFC3H69GlR16MNBg4cCCMjI5SUlPBy+CktLQ35+fmQSCS8BKKEDBIWFRXx2s9Z6ExCpvpKv379OlyfToIgCEI7SaVShIWF4cMPP0RhYSEmT56M3r174/DhwxgyZAjOnTvH+5gREREYN24c1q1bh8LCQgQHB2P8+PGoqanBhg0b0K9fP/IZlyC0GOlJSKiE2aASO5OQq3KjfGbpcSk1NRXe3t68jO3p6YlOnTqhrKwM165dQ2hoKC/zMKVGbWxsBP9AvXnz5iYZfv3792c3XTVVXV3NnqLiM6Orvr4eCQkJuHfvnsaZCdogNjYWsbGxePfdd9GpUyexl9OmqqoqjTd7+WBra8vLRn1zTpw48dz/6whZhHK5HOvXr0dWVhbMzc0xZswY3ue8dOkS73MQuoPJJExMTARN06JtSt+5cwcPHjyAnp4egoKCeJkjNTUVpaWlvIwthkePHmHcuHFqf782ZBICDf34li9fjtLSUpw/fx7Tpk1DXFwcevToIeq6xGRkZIRBgwYhMjIScXFxnB+gjI6OBtBwEIiPKhVCBgmBhozLbt268TJ240xCIV4jmc+PpNQoQRAEoS3WrFmDixcvYuDAgTh//jzbEmD37t2YO3cu5s2bhydPnqi096LqmBKJBC+99BLeffddDBw4kB2ntrYWy5cvx9atWzFnzhw8evRIsApcBEEoj2QSEkpLT0/H7du3AYAtUSkWIyMjANA4803IfhyayMzM5G1siUQCPz8/AMDGjRt5u0+YQEZBQYHgvfQOHTqEvXv3sn8+/PBDREREcDL2+vXrUVlZCSsrKzg6OnIyJoOmaaSlpeHo0aP45ptvcOzYsXYRIGxMF0r+AkBlZaXYS2gWXxtuzRkwYAACAgLYP4sWLWrXG2Q0TSMyMhKLFy/G8ePHAQCTJk1i33/4VFJSwvschO7o2rUrgIYP2GJiMth9fHx4OdxRW1uLY8eOcT6umDSteMG83mhD4FQikeA///kPfH19UVtbi08++UTsJYnO398fAD/X6UwfeD6yCBUKBYqKijgftzXFxcW8jW1tbQ2JRILq6mpeMxYZzDWhvb0973MRBEEQHZObm5vS1wAymQz/93//BwDYsGFDk57hc+bMwYQJE1BQUIDNmzcrPb86Y44cORJ79uxpEiAEGipi/Pbbb7C0tER6ejquX7+u9DoIghAOCRISStu6dStomsaAAQNE/1DElJXRdOOeGUfbZWVl8Tr+0KFD0alTJ2RnZ+PAgQO8zGFvbw87OzvIZDJcu3aNlzla8uabb+K9997De++9h8mTJwMAvvvuO40Dbo8ePUJ4eDgAYOrUqRpvBjJKS0sRERGBdevWYdu2bVpb5pILuhIMESIwpA4hX8O+/PJLfP/99+yfhQsXttsyW/Hx8XjzzTfxySefIDMzE6amppg8eTJ69uwpyPxCZYcSuiEvLw9Aw0a4mM85ppQfX32FT58+3e76cPbt21ej77ezswPQEIQSOvOrOQYGBli5ciUAYM+ePe36+kQZTPZgQUEB52OnpaUB+OeQAJcKCwsFPyjJZ0sDfX19ODk5ARCmZzfTk5jPn4kgCIIglHXt2jUUFRXB3d0dAQEBz339pZdeAgAcPXpUtDGNjY3Zz9JCHOghCEJ1JEhIKEWhUGDLli0AgAkTJoi8mn/KymiakaYrQcKcnBy2DyMfjIyM2BJ6W7Zs4WWzg6Iotv+h0KX0vvrqKzawcejQIYSGhqKmpgaffPIJqqur1R53+/btABqyKjTpUVZXV4fHjx/jwoUL2Lx5M37++WdcvnxZZwJomtCVn1HsUm8tsbS0FHsJWuns2bPYsWMH7t27p9JGaFpaGj7++GOsWrUKCQkJ0NfXR3BwMFatWsVmXAuBBAmJxnJzcwE0lOsWE/OBnrkG41JCQoIgm/tC0zTbulOnTjA1NWUrC2iD7t27Y+TIkQCAjz/+WOTViIsJTBUWFnI6rkwmYw+yMYFiLvHRQ7EtfAfUmFYYJEionbp06YIuXbqIvQxCi+niY+THH38ERVHsnz179oi9JJ109OhRzJgxA66urjA2NkbXrl0RGBiINWvW8HZ4jIs5KyoqcPDgQaxcuRJDhw6Fra0tDAwMYGFhgd69e2PBggU4ffo0aJrm5WdojHnvYyocPIv5HKvKeyTXY8rlcvZaVuykE4Igmkd6EhJKuXDhAtLT02FmZobg4GCxl9PhgoQymQz5+flwcHDgbY6+ffsiOjoaT58+xZ9//snLxk///v1x5swZUftt6evrY8+ePfDz80N6ejrWrFmDTz/9VOXsjNTUVERGRgKAys+J6upqZGRkID09Henp6Wx2RkekDSXUlEEyCXXHxYsX8c0337D/lkgk6Nu3L3x9feHr6wtvb28YGxs3+Z6CggJs2bKF/SBHURT8/PwQEhIiSsCOBAmJxhpnEoqJea/ieh3l5eW8VTEQk6enZ5PSTOqgKAp2dnZITU3F48eP0atXL45Wp5nFixfj8uXLCA8Px9WrVzFs2DCxlyQKJkjI9eG6zMxMyGQyGBoaavwYag7fFUqaw3dAzc3NDVevXkVcXByv8wD/BAl1pW2F2MzMzHg5gEq0H7r6GHm2dOOmTZswe/ZskVajeyoqKjB37tzn+t4XFBSgoKAAN2/exLp167B3714MHTpUq+b88ccf8fHHHzfbCqCiogLJyclITk7G9u3bERwcjB07drCHWfiQnp4O4J/rkmcx/7+oqAiVlZVKtQ3geszt27ejoKAAXbt25ez3SRAEt0iQkFDKpk2bAACjR4/Wis1yZoNK03KjYm+4qSIrK4vXICFFURg/fjw2btyI8+fPY8qUKRqXyXrWgAEDAADR0dGoqKgQbSPczs4OBw4cwPDhwxEREYH9+/dj1qxZKo2xY8cOAICXl5dSpaAyMjIQFxeHjIwMnfwQxBddySTU09ODnp4erxm96iCZhE1lZmayAcJu3bqhrKwMlZWViI2NZU85SiQSeHt7w9fXF/369cO9e/ewb98+9nfr5eWFkSNHinqamQQJicaYIKGYmYT19fXsexeX1040TatU+kiXcNWzlQkSpqSkcDIeF5ycnDBhwgScOHECH374Ia5cudJuy0+3pnEmoUKh4KzsPNOvuWvXrrzcr2IECWUyGa/jOzk5gaIo5ObmIi8vj5cMTIaBgQEAkklIEB3ZzZs32V7NjAsXLiAtLY2XXrLtjUwmw4svvohz584BaLjWWbZsGby9vVFcXIzdu3fj2rVrePr0KSZOnIirV6+iT58+WjPnw4cP2QChk5MTRo0ahYCAANja2qKmpgZRUVHYsWMHKisrERkZidDQUNy8eZOXEuJAQ2ASaAi4N6dxAK+iokKpICGXY6alpeHdd98FAPzvf//Tij1lgiCeR4KERJuKi4tx6NAhANpRahToeJmEQMMH+uZqgXPJ0dERfn5+uHPnDtatW4fffvsNenp6nI1vZ2cHR0dHZGdnIzIyUtTHU2BgIH766SesXLkSf/zxB3r27MmWQ21LZmYmmw2pTBZheno6tm7dqsFq26/y8nLI5XJOH2d8MTQ0RE1NjdjLaIKPDANdVVdXh88++wwymQyurq5YsGABKIpCcXEx0tLSkJ6ejrS0NFRUVOD+/fu4f/8+du7cyX6/i4sLRo8ezfaYEhP5vRKNMeVGxTzYxKzB0NAQJiYmnI0bFRWFJ0+ecDaetjAyMkLv3r05GYspyaRt99OCBQtw5swZXL16FadPn8b48ePFXpLgHBwcQFEUpFIpysrKOHuO8tmPsL6+nj14ICS+A2pGRkZwcHBAdnY24uLi2DYKfGCuWUkmIUF0XMwhegBYvnw5fvvtN9A0jS1btuDzzz8XcWW6YePGjWywztvbGxcvXmxyuGPFihV47733sHbtWpSWluK1117D1atXtWZOiqIwduxYvPfeexg1atRzh4QWLVqEDz74AOPGjUNycjJSU1PxwQcfPJd9CgDffvstkpKSnvv/TCnzRYsWPfe1YcOGYenSpey/mZKmXB4s4mrMsrIyvPDCCyguLsasWbOwZMkSLpZHEAQPSE9Cok07d+6EVCqFp6cnevToIfZyAHAXJNS1TEIhjBw5EgYGBnj06BHCw8M5H1+svoTNeeONN/Dyyy9DoVDgiy++UDrDb+fOnaBpGj179mwzu7O6uhoHDx7kYrntEk3TvPUa4Jq2nXgzNzfXieCqUNatW4cnT57A2NgYL774IiQSCSiKQufOneHv74/p06fjnXfewZtvvokpU6bA19cX1tbWcHR0xOzZs7Fo0SKtCBACJJOQaEobyo02LjXK1QZEfn4+zpw5w8lY2qZPnz5stpGmmA2slJQUQfraKMvW1hbTpk0DAHz00UdQKBQir0h4BgYGbBCXyyoRjTMJucb0FhWaEFl3TH9wvvsSMs9tEiRUTk1NDUJDQ9me8ATxLF17jFRVVWHv3r0AGvr0/vDDD+wBvy1btnTI90NVyOVyfPHFF+y/t2/f3mz293fffcfuHV27dk2ja0au5/z6669x5swZjBkzpsUqAq6uruzjBAD27t2L6urq5253+vRpbNu27bk/VVVVqKqqavZrzwYvmc+OLVVaa7xvquznTC7GrKmpwZQpUxAXF4eRI0fi77//VmpugiDEQYKERKtommZPSU2YMEFrSgkxGYCalhvVpVJ9BQUFqKur430eMzMzjBo1CgCwfv16tswAV7QpSEhRFP744w/069cPJSUl+Pzzz9vcxMjOzsbZs2cBAMOHD2/1tjRN4/Dhw5zfh+2NrpQcNTQ0FHsJTejS6xffzp49i5MnTwIAZs6c2eIHFYqiYGNjgwEDBmDq1KlYtWoVli1bhl69emnN+xtAgoREU0wWn5jlRpnAAleBSplMxlapaI+UrUygjC5dukAikaCyshL5+fmcjcuFuXPnwtTUFPfu3WuXfSWVwUdfQj6DhE+fPuV8TGUIGSTkuy8h05OQlBtVjkKhQEREBCIiIkjwhGiWrj1G9u3bx36+nz9/PkxNTTFz5kwADRWHmGw1onkRERHstW1ISAj8/PyavZ2enh5WrVrF/nv37t1aM6ey1+S+vr5sZYnq6mo8fvz4udtcvnwZNE0/98fV1RWurq7Nfu3ZKlXM+19L7/HM/7exsVGq1CgXY9bX1+PFF1/ElStXEBgYiKNHj2rdoWuCIJoiQUKiVZcuXUJsbCwMDAwwevRosZfDYt6UNc0k1NfX16nNWKFO/zL11Ovq6rBlyxZOx2b6Et69exelpaWcjq0OU1NTHDx4EGZmZkhISMBvv/3W6u137doFmqbh6emJbt26tXrb69evN3shSDSlK0FCbbuoJUHCBmlpafj+++8BNHzo8/DwEHlFmjM1NSVZogRL2zIJuXDx4kVRSh4KoXPnzmzgiAv6+vpsj1Rt6ksINLwPMT2d//vf//Led04bMRnoXAVwa2tr2eohfAQJxehHCDQEAfje/HdxcQHQsElfXFzM2zxMkJBkEhJEx8QcoqcoCvPnzwcALFy48LmvE807ffo0+/e22s80/vqpU6d0ak5G4/1GvjJlmcNpd+7cafbrzP9XpV+2JmPK5XLMmzcPp06dQv/+/REeHq50cJIgCPGQICHRIpqm2ZT8iRMnalWPJGaTqqKiQuMPnLpUcpQ5Wcw3PT09hIWFAQCOHDnC6aZUly5d4OzsDIVCgStXrnA2ria6d+/OnhI7fPgwbt261eztCgoK2AvFtrIInz59ivPnz3O70HZKV4KEJJNQu9A0jYiICLz//vuQyWTw8PBo83mpC+RyOeLj47Uqs5EQFxNMEzOTkMsgYV5eHm7cuKHxONrK19eX8+cvU9IyKiqK03G5MHPmTFhaWuLhw4fYs2eP2MsRHBMQZnoHaSo9PR1Aw2ERMzMzTsZk0DQtWpAQ4D/zzsTEhC0fx2c2IQkSEkTHlZycjGvXrgFo6Avn7u7O/p05qHj06FHO3hPao/j4ePbvAwcObPW2dnZ2TQ7jqJu1L8acAFBXV4eHDx+y/2ay87gWFBQEGxsbPHnyBDExMc99fd++fQCAF154gfcxaZrGkiVLcODAAXh5eeHs2bNsJTiCILSbvtgLILTXuXPnEBERAQMDA8yePVvs5TTh4OAAIyMj1NXVIT4+XqUTMc+ysbFBRkYGh6vjT2RkJPr06dNs/XSueXh4wMvLCw8ePMBPP/2En3/+ucV666rq168fMjMzce3aNUyZMoWTMTU1btw4dOnSBYWFhaitrW32NidPnoRCoYCzszN7Wrk5tbW1pA+hCjQtGywUNzc3rcriaCuTtT1LSkrChg0b2A98VlZWmD59OmevUWKoq6tDTEwMoqKiUF5eLvZyCC3CbERz1eNOHUxZLRMTE43H4rIsozbio393z549ERcXh2PHjsHGxgYLFizQmoMEpqamGDVqFA4dOtRkE66jYA4QcZVFyWep0aKiIlFL4MtkMt6rMri5uSEvLw/R0dEIDQ3lZQ4mG6K9v5YRBPG8xlmCjbMHKYrCggUL8Nlnn6G+vh47duzA22+/LcIKW3b16lXOgpd+fn6t7oe0Jjk5mf07E2Rtjbu7OzIzM9nvtbW11Yk5gYZypWVlZQAa7jPm0BfX9PX18fbbb+OTTz7BihUrcO7cOTaDce/evThx4gS6dOmCV1555bnvXbBgAW7duoWVK1di5cqVGo/5zjvvYNu2bfDw8MD58+fVvu8IghAeCRISzVIoFPjwww8BNJwMESIopQpDQ0MsXLgQf/75JyIiIuDj46N2aTa+3qj5cujQISxbtow9xcqncePG4dGjR7h//z7OnDmD8ePHczKuj48PTp48ievXr3MyHhcOHTqEwsJC2NjYIDAw8Lmvy+VytudZa6fPaJrGiRMntKKUqq7QtGywUIKCglBeXo7o6GixlwJLS0u2v0FHUlBQgI0bN7K9PvT09BAUFISgoCCty/RUVnl5OaKiorTq9ZDQLiYmJqitrRU1a4WmaQDgJBAvZrBTCHxU3ujTpw8KCgoQERGBrVu3oqSkBG+++abWlCVmMt6qq6tFXonwmGoIXLUvePLkCQB+goRiH3QSohxtjx49EBUVhaioKNA0zUsw3cHBAUBDuXOCIDoOmUyGv//+G0DDtRnTh5CxYMECfP7556BpGps2bdK6IOHq1asRERHByVhbtmzBokWL1PrexvskTDn11nTu3LnZ79X2OQsKCvCf//yH/ffq1avVGkdZ77//Pi5evIjLly+je/fuCAkJQW5uLiIjI2FgYIDt27c3e62SkZGB5OTkZgPIqo559OhR/PzzzwAasiY/+uijZtc6depUTJ06lZsfnCAIzpAgIdGsAwcO4M6dOzA1NcW8efPEXk6zpk6div3796OkpAT37t2Dv7+/WuPoWpAwPz8fFy5cwLhx43ify9LSEiNGjMC5c+fw888/Y+jQoZyUOPT29gYA3L59G5WVlVpRn3z9+vUAgMmTJze7gXnjxg0UFhbC1NQUXl5eLY5z9+5dJCQk8LbO9khXNhUpisL48eNhaGjIlpkRy+DBg3U6a05VNE3j+PHjWLduHbvJ6Ovri5EjR2pVKWxVMCUXY2NjxV4KoeVMTExQUlKCuro60dbABAm52HDX1YC+MiQSCSfZls0JDQ2FqakpTp06haNHj6KsrAwffvihVtyfxsbGAHTn/ZxLTO87roKETCYhHwc0O0KQ0NXVFQYGBigqKkJKSgq6d+/O+RzMZ8fs7GzU1dVpXc9qgiD4cfz4cbYE/AsvvPDcZxB3d3cEBwfjypUruH//Pm7duoVBgwaJsVSt1riKEHP90JrG11XqZsMLPWd9fT1efPFFNuN86tSpmDZtmsrjqMLQ0BBnzpzBDz/8gB07duDYsWPo1KkTXnjhBXzyySfw8/PjfczGbWQuXbrU4rhubm4kSEgQWogECYnnSKVS9pTLrFmztLZ+tImJCebOnYv169fjypUrGDBggFqb5tqWJamMmzdvwtfXV5AA5+DBgxEbG4v8/Hxs3LgR7733nsZjOjs7w8nJCU+fPsWWLVvw5ptvcrBS9cXGxuLq1avQ09PDpEmTmr3NiRMnADQ0cG4pi7OgoADHjx/nbZ3tla5kEgING+SjR4+GkZERLl68KNo61LnI11X5+fn4/vvvcfv2bQANrx9hYWFwdHQUeWXqoWkaR48eJcFBQmmmpqYA0GIpbCFo2v+5sfacSWhmZsZrGdBBgwbB1NQUhw8fxuXLl1FRUYEvvviCfYyIhQmSdMQgIdeZhHyVG5XJZKJnvsnlct7n0NfXh4eHB5KTk3Hz5k1egoRWVlYwNjZGbW0tMjIyeCkx3N6I/RpFaD9deIw0LjW6YMGCZm+zcOFCXLlyBQCwefNmrQoSXr58WewldAgKhQKvvPIKIiMjAQCenp7YvHmzyuOo855taGiIjz76qMUMvua09bhQZcxFixapnWFKEIT4Ok4aAqG0LVu24NGjR7CysnquhIK2mTJlCiwtLVFeXq72B19jY2OtDYS25uzZs+zJfj7p6elh4sSJABp68qWnp2s8JkVRmDFjBgDgp59+EmTToDVMFmFwcHCz5Sdyc3MRFRUFAC1mrEqlUhw4cIC/RbZj1dXVgjyWuRQcHIywsDBR5g4MDOwQp9Zpmsbp06fx8ssv4/bt29DT08O4ceOwePFinQ0QAg2ZHCRASKiCOc0sZrlRBskkbJ0QlRF8fHwwd+5c6OvrIyYmBu+++67oJc5JJiE3QcKysjIUFRUBAOc9fJ4+fQqpVMrpmKoSIpMQABsYvHnzJi/jUxTFHjIVO/CqC8zMzFBVVYWqqiq2NDFBNKYLj5Hs7GycPn0aQEM28dixY5u93cyZM9mA5+7duzvk+2JbGl8rKXMArqamhv27uu+1Qs1J0zRef/117Ny5EwDg4uKC8+fPw9raWoXVEgRBiIMECYkmqqur8dlnnwEAXn75Za0/0WVoaIjg4GAA0KjEo66VHAUaTho/fvxYkLlcXFzY/me7du3iZMxx48bBwsICT548wZEjRzgZUx0lJSVsb4GWSh4wWYQeHh6wsbFp9jZnz55Ffn4+L2ts72QymVZsfqtq8ODBmDJliijztnfFxcVYvXo1vvvuO0ilUjg5OeH111/HkCFDeM3SEQLpPUioirkWE7PcKJNJSIKErROqfLqnpycWLVoEIyMjJCUlYdWqVcjNzRVk7uYwv9OOuBnKZSYhk0VoZWXF+WEgoT4ztEaoICGT2ZeYmIiysjJe5mD6EjK/M4Ig2retW7eyB5vnzZvXYk9gc3NztqxkeXk5OUTcjMYH9Jvrg/cs5vDMs9+rbXPSNI033ngDGzduBAA4OTnh4sWLcHNzU2WpBEEQoiHlRokm1q1bh5ycHNjZ2WHy5MliL0cpoaGhOHHiBB48eICJEyeqVXLU3t4eSUlJPKyOX+fOnYOnp6cgvcmCg4ORlJSEc+fOYeHChRpn8hgbG+OFF17A9u3b8cMPP+DFF1/kaKWq2bp1K+rq6uDu7o5+/fo993WZTIbw8HAALWcRPnjwgC2FSKinoqJCJ7PjBgwYAAMDAxw8eFCQ+Xx8fDjpC6rNLl68iDVr1qCurg4SiQQjRozA0KFD20UPxpycHLKhSKiMySQUs9wolz0J23O5USF7LHfr1g1Lly7Fjh07kJmZiTfffBNr1qyBu7u7YGtgkExCboOEXJcaBYAnT55wPqaqhKocYmlpia5duyI/Px+3b9/GqFGjOJ+DOWBKMgkJov2jabpJuciWSo0yFi5cyGaSbdq0qc3bC+Xq1atKBciU4efnBxcXF7W+t1evXuz7XWpqaptBtMafnXr16qWVc9I0jRUrVuD3338H0HCNdunSJXh6eqq1XoIgCDHo/o4bwZmSkhJ89dVXAIDFixfrzEnv/v37w8rKCjU1NWpvvupiX0KgoQfe3bt3BZnL0dERnp6eoGkae/bs4WTMqVOnwsDAADdv3hQlu0ahUGDDhg3sWprb/Lx27RpKSkrQqVOnZi8QS0tLsW/fPt7X2t6p24RcG/j4+GDJkiVwdXXlfa7AwEDe5xBLWVkZPv/8c3z55Zeoq6uDvb09XnvtNQwbNqxdBAgB4MaNG2IvgdBB2lBulMsgoa5cX6pDyCAhAHTp0gWvvPIKbG1tUVhYiNdff12jyhrq6qg9CaVSKdtXWZuDhFVVVcjJyeF0THUIlUkI/JNNyLQL4BrJJFRebW0tJk6ciIkTJ4p62IXQXtr+GImIiEBKSgoAwNfXt9mDxY2NGjUKTk5OAIArV67g0aNHvK9RGatXr8a0adM4+XPx4kW119G3b1/279HR0a3eNi8vD5mZmQAa3hvVLcXN55xMgPC3334D0LBvdunSJV564rZFKpXi22+/RZ8+fWBiYgJbW1tMnz5doz1DVcaUSqU4e/YsVq1ahYCAAHTp0gXGxsbo3r07VqxYwUnrIoIg+NM+dt0ITqxZswaVlZVwd3fH6NGjxV6O0vT09DB8+HAA6pcc1cVyo4xLly4JVoKMKe168uRJFBQUaDyejY0NxowZAwBYu3atxuOp6uzZs3j8+DHMzMzYdTzr2LFjABoyxp4tK0LTNA4fPsz7OjsCXQ4SAg3lRBYuXIiXX36Zt355bm5uOt2LrzXXrl3D4sWLcfnyZVAUhZCQECxdupSXbAqxlJWVIT4+XuxlEDqIKTcq5sYZl+VG9fXbbyEToYOEAGBhYYHFixfDyckJ9fX1eOutt3gLjLSko2YSMqVGKYripI8Wk+3H9XufNmQRAuIFCfnIYCQ9CZUnl8sRHh6O8PBw0fvQE9pJ2x8jmzZtYv+uTFagRCLByy+/zP67cRYiACQlJYGiqGaznGfNmgWKopoEtRjz588HRVE4e/asKsvXOmFhYezfT5061eptmYpOADBhwgStm/PZAKGDgwMuXbrEvgcJSSqVIiwsDB9++CEKCwsxefJk9O7dG4cPH8aQIUNw7tw53seMiIjAuHHjsG7dOhQWFiI4OBjjx49HTU0NNmzYgH79+pFDswShxUiQkADQ8CH3//7v/wAAS5cubbHGurYKDQ0F0HDBxWxkqcLS0pLd4NA1VVVVuHXrliBzubq6wsXFBQqFgrPg2MyZMwEAhw8fRkZGBidjKuvPP/8E0HDRyGRqNJaTk4M7d+4AaCip8ayUlBTB19xelZeXi70EjVEUBU9PTyxduhSzZs1S+6RjS9prFuHp06exevVqlJSUwNbWFkuXLkVoaKjOvQ+1hZQkJtTFvD+J2ZOQySTkAkVR7bbkqBhBQqDhMTJ//nx0794dcrkcH330EWclxZTBZBIyWXUdBVNq1MzMTOP3LJqm2YATCRJqzsnJCUZGRigvL0dycjLn45NMQoLoGMrKyti2Enp6epg3b55S37dw4UL279u2bWsS/LS2tgbw/CHZ1NRUHDp0CMA/h1AYeXl52LdvH3x8fDB27FjVfxAAly9fBk3TnPxZtGiRWmsAgJCQEPaQ/uXLl9n9lmfJ5XL88ssv7L9nz56tdXOuXLmSDRDa29vj0qVL6Nmzp9rr1MSaNWtw8eJFDBw4EI8ePcK+ffsQGRmJXbt2ob6+HvPmzUNlZSWvY0okErz00ku4desW0tLScPjwYRw+fBgpKSlYtGgRysvLMWfOHEilUq5/fIIgOECChAQAICMjA3V1dbC0tNTJjWhvb28AQE1NjVpvOBRFwcLCgutlCYaLrD5l+fj4AABbgkFTbm5ucHZ2Bk3TbBkPoTDzDRo0qNmvM6WZunTp0mzDaiE34Nq79nShSFEUvLy88Prrr2Pq1KmcjCmRSEQ5kSgEZvPOwcEBr776arvNlmQ2kwlCVUxATRtO13ORSQig3R0CYIhZStXQ0BCzZ89Gp06doFAoBD3ExGSHChkE0gZM4J6LnsoKhYLT0qWNaUu1Bi4PG7RFT0+PDbbycb3OfG5sD4fcCIJo2a5du1BTUwMAGDdunNJtanr37s3uMeTk5DTJTmspSPjTTz9BoVBg0KBBzwUJf//9d9TX1+Odd95R+2fRFnp6evjkk0/Yfy9YsAD5+fnP3e6DDz7AvXv3AABBQUEYN25ci2OGhoaCoihQFIWtW7cKMuebb77Jtq5hAoTq9kzUlEwmY5M+NmzY0GRvc86cOZgwYQIKCgqey2rlesyRI0diz549GDhwYJOxjI2N8dtvv8HS0hLp6emitBoiCKJt7bfeD6EWfX19zjaAhKSLa9ZVfPQGE7vfWFublWKvj9BNEokEvr6+OHr0qMYbY8yHnvasR48e7boMIUGoqz0+99vjz6QN9PT0YGpqqvJJcU2R3yehjcjjkiAITalaarSxhQsXshWfNm3ahMmTJwNoONRjamraJEhYWlqKzZs3Y9KkSXB1dcWtW7dQV1cHIyMjSKVS/PHHH+jatavSmYzabtmyZTh8+DDOnTuHhIQE+Pr6YtmyZfD29kZxcTF2796Nq1evAmio+vXHH39o1ZyrV6/Gr7/+CqDhveatt95CUlISkpKSWl2Dn58fXFxcNP5ZnnXt2jUUFRXB3d0dAQEBz339pZdeQnh4OI4ePYpVq1aJMqaxsTF69uyJ6OhoZGdnK7UGgiCERXbjCIIgCIIgCIIgCIIgCIIgAMTGxiImJgZAQ9DohRdeUOn7Z8+ejXfeeQf19fU4efIk8vLy2ExEa2vrJkHC33//HZWVlXjvvffYnoOlpaWws7PD/v37kZOTg88//5yTzHVtoK+vj4MHD2Lu3Lk4ceIEcnNz8eWXXz53OycnJ+zduxd9+vTRqjmZYCLQkCX/4YcfKrWGLVu2aFSqtSWxsbEAAH9//2a/zrTOYW4nxphyuZwtq86UfiUIQruQ9BiCIAiCIAiCIAiCIAiCIAg0zSKcOXMmjI2NVfp+GxsbNntQJpNh27Zt7Nesra3ZrP/6+nqsW7cOAQEBGD58OCwtLQH805fwl19+gbGxMZYvX67Rz6NtzM3Ncfz4cRw5cgTTp0+Hs7MzjIyM0KVLFwwePBjfffcd7t+/j6FDh+r0nEJIT08H0BDgbA7z/4uKipSuNsH1mNu3b0dBQQG6du2qc/cvQXQUJJNQSzGl6YTqc8C8qDfuh6FL6uvr2b8z/UFUpVAouFqO4ORyudo/t6qY3nEymYyzxwpz31dVVSn1mGdu01YJx7aeR0x/p9ra2mZ/ltraWnZ9zd2/Ha33Dp9kMplgj2GhcdGDh6Zpzu8fZjxln0fV1dWczs9o/JrSXh8DgHb0kyP4o+n7UWuYa5z6+nrRrtGY9+n6+npOnqdC9iYTklQqFf11jPldtXRtwwemX5NCoVDrMc7VdZ3QuPz81Pg9oq6ujtO+ndryGUfo5wefzwXmmoimaa15PKr6PFL2c5emGt/35eXl5HqIeI42PUaYtdA0jV9++QW//PKLRuMdOHCg2f9vbW3Nvnfs378f2dnZWLt2LQA0CRJGR0cjKioKy5Ytg62trUZr0VYvvPCCylmaz7p8+bKgc6o6H9+YrFQzM7Nmv96pU6cmt238byHGTEtLw7vvvgsA+N///tduMmIJor2h6Pb6KV3HPX36FM7OzmIvgyC0WmZmZosnmwDyPCIIZZDnEUFojjyPCEJz5HlEEJojzyOC0FxbzyNNvfDCCzh27BhycnIwduxYlJWVISUlBfr6+ti7dy9mz56NkydPYteuXdi1axcSEhLg5eXF23oI8SxYsIDtXamsadOm4ZtvvmH/vWzZMvz1119YvXp1syVUZTIZDAwMAADZ2dlwcHBocw6uxiwrK8Pw4cMRFxeHWbNmYe/evUr9jARBCI9kEmopR0dHZGZmwtzcXOmm6+Xl5XB2dkZmZiYsLCx4XqFuI/eVarTt/qJpGhUVFXB0dGz1duo8j7SNtt332obcPy1r675p788j8thoitwfz+PiPmkPzyPy2OAeuU9Vw9fziPwemkful+bp+v3SHt6PtI2uPyaE1F7uK2WfR5qytrYGABw8eBDx8fFYu3Yt9PUbtmeZTMKkpCTs378fYWFhJEDYjmVkZCA5OVml78nJyWnyb3NzcwBosexn42xd5rZt4WLMmpoaTJkyBXFxcRg5ciT+/vtvpeYmCEIcJEiopSQSidonlywsLHT6wkxI5L5SjTbdX8zFc2s0eR5pG22677URuX9a1tp90xGeR+Sx0RS5P56n6X3SXp5H5LHBPXKfKo/P5xH5PTSP3C/N0+X7pb28H2kbXX5MCK093FfKPI80xQQJv/nmG1hYWGDp0qXPzf/jjz+ivr4e//rXv3hfDyEeLkqXurq6AmjIFG8O8/9tbGyUKjXKxZj19fV48cUXceXKFQQGBuLo0aOkzChBaDmJ2AsgCIIgCIIgCIIgCIIgCIJo75ggYVZWFpYtW9YksMoECbOystCvXz+MHj1alDUSuqN///4AgDt37jT7deb/+/r6CjKmXC7HvHnzcOrUKfTv3x/h4eFKBycJghAPCRISBEEQBEEQBEEQBEEQBEHwjAkS6uvr46233mrytcaZjO+8846g6yJ0U1BQEGxsbPDkyRPExMQ89/V9+/YBaOiFyfeYNE1jyZIlOHDgALy8vHD27FlYWVmp8NMQBCEWEiRsR4yMjPDpp5+SFG4lkPtKNeT+Eg+571tH7p+WdfT7pqP//M8i98fzyH3SgNwP3CP3qXYgv4fmkfuleeR+IZ5FHhPKI/eVat58803QNA2pVApnZ+cmX+vWrRtomgZN01i0aJE4CyR0ir6+Pt5++20AwIoVK1BRUcF+be/evThx4gS6dOmCV1555bnvXbBgAXr37o1ff/2VkzHfeecdbNu2DR4eHjh//jxsbW05+ikJguAbRdM0LfYiCIIgCIIgCIIgCIIgCIIgCIJQXn19PcaNG4fLly+ja9euCAkJQW5uLiIjI2FgYIBjx44hLCzsue8LDQ1FREQEPv30U3z22WcajXn06FFMnToVADBixAi4uLg0u9apU6eytyMIQnvoi70AgiAIgiAIgiAIgiAIgiAIgiBUY2hoiDNnzuCHH37Ajh07cOzYMXTq1AkvvPACPvnkE/j5+fE+ZklJCfv3S5cutTium5sbCRIShBYimYQEQRAEQRAEQRAEQRAEQRAEQRAE0cGQTEItpVAokJ2dDXNzc1AUJfZyCEKr0DSNiooKODo6QiJpubUqeR4RRMvI84ggNEeeRwShOfI8IgjNkecRQWiOPI8IQnPKPo8IgtAuJEiopbKzs59rYEwQRFOZmZlwcnJq8evkeUQQbSPPI4LQHHkeEYTmyPOIIDRHnkcEoTnyPCIIzbX1PCIIQruQIKGWMjc3B9DwomphYSHaOvr374/U1FSsWbMG3t7eAIBdu3Zh165dWLJkCX788Uf2ttOmTcPFixfx7rvvYsSIEZytYfny5cjMzMScOXPg6urK2bh8uHv3Ls6cOaPW93p4eGDWrFkcr6h1JSUl+OOPP2BiYoL9+/drPN7vv/+OEydO4N///jdWr17NwQqbV15eDmdnZ/Z50hJ1n0fvv/8+fv/9d8yaNQsLFizQaK2amjx5MmiaRlJSEhwcHFT+fnd3dxQXF2PDhg3PNY7evHkzDh06hIEDB2LUqFHPfe+JEydw//59tdfeXtnb22PRokUtfn3t2rWQSqX4888/4ejoqPY8hYWFWLRoESiKwvvvv6/S9546dQqxsbFK3VbZ59G+fftgamra6m0//PBDxMfHY8uWLZg+fbpyi21B586dIZPJsHXrVnTp0qXJ15R9X9i1axcyMjI0WkdbXnrpJbi7uyt129OnT+PevXuYO3cu5s6dq9T3zJ8/HyUlJVi8eDHs7OzUWuO2bduQk5Oj1vc2p1u3bpg/fz5n4zHOnTuHmJgYzl57V65cibS0NKV+R/v27cOTJ0/Unouv96PGJk6ciKtXr+L9999HcHCwWmNoiovHoy7Ly8vDli1bAAD/+te/YGhoKMo6rly5guvXr2PSpEl4/fXXBZ373r17WL16Nby8vHDz5k1OxuT7uk4sVVVV7HXAgQMHYGxsrPGYp0+fxq+//oru3btjxowZGo/Xkj/++KNJb6HWvPLKK+jatStva2kL1+8dqnr77bfx+PFj7N+/H2PHjhV8foaqz6N33nkHRkZGLd6Oi/u1traWfZy29Jr55MkT7Nu3T63xhTJq1CgMHDjwuf9fWFiIv/76CxYWFti1a1eTr3399de4ceMGxo4dq1YvMG1148YNREREsP9u6XOkJo4cOYKkpCS89tprmDx5Mqdjt6W6uhqzZs3S6vcjuVyO69evAwCGDh0KPT09QecntJe2PDaUfT8iCEK7kCChlmJKFlhYWIj6IZhJDTcxMYGZmRkAsBf3hoaGTdamr9/wcDIyMmJvy+UaDA0NW/0gow0MDAzU/l6JRCL4z8f8LimK4uR3xvz8RkZGgjxu2yrtoe7ziPk9GBgYcPpYVgdFUaBpGubm5mrdp809hxnM70tfX7/Zxx75wNG8tp6rzOPO1NRUo8dPdXU1O56qrw2q/O6UfR4p8/Mw85qammr8GtDavMq+LwhR3kSV9ybm/jE0NFT6scHFeyDX9wNf71fMdQRXr72q3Hea3kd8vR81xtd1liqYn0MXrsn40HiD28jISLQgIdfPFVUwgS49PT3Or/WEeB4JqfF7sampKUxMTDQek3ne6enp8focVKV8ntivB8z9LNZ1O5fXPlxQ9nlkZGTU6u9NnWuWlsZg5mvuNVOs11FVtPRZqbXP0o1fp9vT+yXzczX+N9c/HxePPU1p+/vRxIkTBZ+T0A3a9NggpXgJQreQ4sAEQRAEQRAEQRAEQRAEQRAEQRAE0cGQTEJCKampqUhPT8f9+/dx+/btZm9D07TAq9I+lZWVan8vuf+0R21trdhLeE5UVBSmTZum9O3r6upw5MgRFBYWtngbmUzGxdKIFkRERGDmzJlqZxjX1NRwvCLhaPp6RtN0q2OQ10txVVVVQSqVapQ935r4+Hjk5ubC3t5e7TGSkpJUKh8ql8vVnkto2vD414Y1aIqmacjlcshkMshkMsjlctTW1qKysrLJn6qqKvbvypZfFIqYj1uFQiHa3AT/VHmO5+bmdsjyw89qD6+LjdXX12s8hlQq5WAlukmXriu01eXLl+Hq6go/Pz+SkdQI09YCAF599VXerscJ3UMeGwRBaIIECYlWBQQEICUlpUnvQaChLNez/XD42ixgyhrV1dXxMj5X7t+/j8uXL6v9/WL8fEwpD64+wDElV5gyibooPT0d27ZtA9DQJ1JsXl5eSEhIwPTp0/Hmm2/iu+++a7VcVVJSEjZu3Ii///6bDRBaWFg81yumuroaFy5cAADY2to2O1Zb/ec6qrZK6jg7OyMlJQUbN25EeHg4li1bhuHDh6v04VYmk+GHH34AALX6Goq1Wcc8ZkpLSzUaJyoqCjKZDMbGxrC0tHzu60yAu62yqvb29khLS9NoLW1R5b5mAgzW1tZKf0+nTp1QVFSEqqoqldfGsLOzQ1ZWltrf/6zi4mLs2LEDc+bM4aTHFsPJyQlRUVGIi4vDvHnzMGPGDMybN0+lMk6JiYn4+++/ERUVBaChDFZbv6O8vDykpqZqtHYhMM8vMQ+yODo6ori4GCkpKWr1yRVadXU1Tp06hadPn7IBQea/ml63PltyTUjMa8idO3dA07Sgm6fM8yk5ORlFRUXo3LmzYHPrmsbXC/X19ZyUG2VeB/g8SCSVSlUKiB85cgQ5OTkYPXq0KM+LoqIiAICNjY3gcwNgr1OePn0qyvx8yMjIYHtb9+jRQ+1x9uzZAwCwsrJqcbNalWsisbT0fseUKn/24GVBQQHbs/XZvtq6zsnJCRKJBAqFAhKJ5Lme91xwdnZGQkIC4uPj8d5778HZ2RmTJ09GWFgY6XGGhveTlStXAgAWLVpEAkEEizw2CILQBCk3SrTq119/hZeXF8zMzDBixAh89NFHOH78OPLy8jBnzpwmt2VOy3Hdx4zZfKioqOB0XC4lJCTg4MGDGo1RVlbG0WqUx2xeSKVSTk6LMhu5zId1XUPTNFasWIHa2lr069cPoaGhYi8JP/zwA6ZOnQoAWLduHfz8/HDnzp0mt6mpqcH27dsRHBwMLy8v/PjjjygsLESXLl0wf/58/PXXX88F/Pbu3YvS0lLY2NigX79+zc6tTnCqI7Cysmr163PmzMHEiRNhZmaGrKwsfPbZZ1i5ciXi4+Nb/B65XI68vDzcu3cPp0+fxldffYX4+HgYGBiwv39V+Pv7ixIoZOZMT0/XaJydO3cCAIKDg5vtVcNsjrbVxyY0NJTXDexXXnlF6V4pNE2zgTpvb2+l53BycgKg2evqqFGj0KlTJ7W/vzkZGRnYsmULp+/NPj4+WLZsGdzc3KBQKLBv3z7MnDkTe/bsafM9KiEhAf/5z3+wYsUKREVFgaIo+Pr6Yvny5a3+jhQKBU6fPs3Zz8An5rCHpkF4TUyYMAEAEBMTo/VZM/n5+fjrr79w//59lJaWoqKiAjU1Naivr9c4QOjl5SVIz9OWeHt7w8DAAE+fPsXdu3cFndvJyQkeHh6Qy+U4fPiwoHPrGn19ffbauLy8nJMxmQxrPl8H1Al2RUVFYfPmzYJ/BlAoFMjMzASAFq9n+da9e3cAEPy5yJeqqiocOHAANE1j1KhRzx0MVlZ6ejobJBw3blyLhxlsbGw0CkTybfLkyXB1dW32a8x1aE1NTZP3xO3bt0OhUMDV1bXF79VVbm5ueO+997Bq1Sq8//776N27N+dzDB48GK+//joCAgKgr6+PzMxMbNiwAdOmTcP//vc/XL16VSsr/xAEQRCELiOZhESrunTpgoSEBNA03eZmCF9BQub0HVcfrrmWmJiIAwcOaDxORUUF5HI55/dfaxpvsNfU1GjcOJ7ZCCkuLtZoHLHs378fJ0+ehIGBAf71r3+JugHIMDY2xltvvYXAwECsWbMGSUlJGDRoEL744gtMmDABmzZtwo4dO9jNIolEgiFDhmDSpEkYNGhQs4+n4uJi7Nq1C0BD8KClx5wuZImIoa0Tz3p6eggICEDfvn1x48YNXLt2DYmJiVi1ahWCgoIQEhKC/Px85OTkIDc3Fzk5OcjPz2+2/Ov06dPVCnLp6elhypQp2Lhxo8rfqwkmSKhJ9p5MJsPevXsBNDw+m6NskNDIyAgzZszAH3/8ofZ6WvLqq6+q9BwpLCxEXV0djI2N4e7urvT3OTs7s9+vLlNTU0yePBm7d+9We4zm5OfnY8OGDejTpw/69u0LFxcXjTOaHB0dsWDBAjx+/Bjnz59Hfn4+/vjjDxw+fBiLFy/GmDFjmrxmxcfH4++//2ZLoTPBweDg4DazSpggB9/ZplxhgoRilrwcMWIE1q9fj9LSUqSkpLCb49omKSmJfR3hg6enJ29jK8PQ0BC+vr64ffs2jh8/Dj8/P0HnHzFiBJ48eYK9e/di6dKlgs6ta2xsbFBeXs7ZgQomSMjn5wZ1D/rk5OTg119/xfTp09G3b1+OV9W8vLw81NfXw8zMTKX3Vi4xrwf37t0TZX4uKRQKHD58GBUVFXBxccG7776r1vs6TdP4+eefoVAo0LNnT/Tq1avV2w8ZMgSPHj1Sd9m8GDp0KIKDg1utmMBchyoUCtTX18PIyAhZWVk4ceIEAGDkyJHtskymiYkJJ5nRrbGzs8PEiRMxevRoxMfH4/bt28jLy8O5c+dw7tw56OvrIzAwEMHBwQgMDOT8MBxBEARBdDQkSEi0iaIopS5ulS3/piomSKiNmYQPHjzA/v37ORuvvLxc0JIrEokEBgYGkEqlqKqqarasnyqY8h+6mElYUlKCVatWAQDmzZundac+Bw0ahE2bNuHHH3/ElStX8PHHH+Pjjz9mv25vb48JEyYgLCysxfKhjG3btkEmk6Fbt27w8vJq8XY2NjYwMjLS+lK/QlP2OWpkZITQ0FD4+/sjIiICd+7cwbVr13Dt2rVmby+RSGBpaQlra2tYWVkptanSGkdHRwQGBuLGjRtqj6EqLjIJz58/j4KCAlhZWcHf3/+5rysUCvYxqczBBua5ER4ervaaGuvUqRMWLFjQ5vPsWUxmRq9evVR6n2QyCTU9fNGzZ0+MGDECly5d0micZ9XW1iImJgYxMTGwtLSEj48P+vbtq1EmK0VR6NGjBzw9PREXF4dLly4hPz8f3333Hfbv349XX30VJiYm2LZtG5tZrUpwEGgIEB44cABJSUlqr1NozH0qZpDQ2NgYY8eOxeHDhxETE6N1QUKaphEZGcn54/xZ2lCO3N/fH7dv38aVK1dQUlIi6PXjiBEjsGnTJly8eBH5+fnPlTQn/mFjY4O0tDTODjtaW1vD0NAQ9fX1vH1u0LQawKFDh2Bubg43NzduFtQKZq0+Pj6CHrRsjMmCi4uLE/zAJ9ciIyORkpICPT09fPbZZ2oHgi5evIi7d+9CT08PYWFhbe4luLu7w87ODnl5eWrNxyUfHx+MGjWqzcohAJqU8qupqYGRkRG2bt0KmqbRvXt3XkpxdjRGRkYICAiAv78/nj59ioSEBCQlJaGsrAyRkZGIjIyERCKBv78/goODERQUJFrpYYIgCILQZSRISHCGySTkOvtKW4OEycnJ2LdvH6djCh0kBBouvKVSKSd9BJkgoy4GCd9//33k5eXBxcXluVK62sLS0hKfffYZzp07h19++QV1dXUICgrCxIkT4e/vr9RzLzMzE8ePHwcAjBkzptUP7RRFwcHBQWeybISizKZBY+bm5pg0aRIGDx6MyMhIlJWVwcrKClZWVmxA0NraGubm5py/foaGhuLBgweClSfkIkjIZLmGhoY229uorq6OLemkbJ+FgIAApKWlITExUe11AQ3PwQULFqi1+cAECVUpNQqA3WDSJJOQMXz4cJiZmbEn3LlWVlbGBsK7du2Kvn37wsfHR+XnDEMikaB///7o06cPbt26hYiICDx58gQffPABexuKotC/f38EBwcr/f4pk8mwf/9+PHz4UK11iUUbMgmBhtJrhw8fRnJyMioqKrSmP1B9fT2OHj2q8fO8LTY2NlrRQ8ve3h7dunVDVlYWTp8+Lei1S7du3dCzZ088fPgQBw8exPLlywWbW9cw7xdcfY6hKAr29vbIyMhAaWkp549FmUzGSW+9w4cPY/ny5Zz2rW1ORkYGAAiWudicbt26wdjYGDU1NUhOTlb5fV5bpKam4vLlywCA9957T+3MzFu3buGXX34B0HDdocxjlKIoDBkyBEePHlVrTi64uLhg7Nix6Natm9Lf0/jAbW1tLZ48eYLz588DaMgi7OhkMhlqa2thZmamcUYlRVFwdnaGs7Mzxo0bh9zcXDx48ABJSUkoKChAdHQ0oqOj8eOPP6Jv376YMGFCq2VuCYIgCIJoigQJCc7wlUmojT0JMzIy2B4LXBKjL2HjXgqa0tWehFeuXGHLMr777rsal13lE0VRGDt2LIYOHQqaplXenP3rr79A0zR69uypVLako6MjCRI+Q90NOVtbW0yfPp3j1bTO0NAQkydPxvbt2wWZjwkSZmdno76+XuXnUnV1NVu+efTo0c3epvFrlbJBQoqiMHnyZGRnZ6sdMO3SpQtefvlltTOu1Q0SMuVGy8rKIJVKNW5A7+/vj06dOvHyHtZYfn4+Lly4gAsXLsDFxQWBgYFq960xMDBAUFAQ/Pz8EBkZiaioKABgg4OqBCGlUin27duHx48fq7UWMTHPLzF7EgINGR/9+vVDXFwc7ty5g5CQEFHXAzTcJ3v37kVubi7vc4ldarQxf39/trTdSy+9JGiZ9BEjRuDhw4fYt28fCRK2ggkSctk2oXGQkGvZ2dnNlj9XVXl5OcLDw3m97qFpmj2UJFY/QqAhUOTp6YmEhATcu3dPJ4OEFRUVOHjwIAAgLCwMYWFhKo9RX1+Pv/76i62y4+DggKFDhyr9/T4+Pjh//jyqqqpUnlsTNjY2GDNmDHr16qVWQIkJEtbU1GDz5s0AGq71OnLbhtraWty4cQNXrlwB0PAc6dSpE8zNzWFubt7k7xYWFjA3N4e1tbVK1/UODg5wcHDAyJEjUVhYiKSkJDx48ADZ2dmIj49HfHw87ty5g3fffRdGRkZ8/rgEQRAE0S6QICHBGb4yCZmSbtoSJCwpKcGWLVt4GVuMICFz0czFB7LGPQkVCoVW9PRrS11dHV599VUAwKRJk0TdZFCFOn0XEhMTceXKFVAU1WKvt2c5OjqqPE97ZmBgAFNTU7GXoRIPDw9MnToVWVlZTf6/XC5nSzVypXEZtKdPn6pcku/48eOoqamBg4NDi5tstbW1ABp+F6ps5hgbG2PGjBn466+/VFqTubk5goOD4efnp/YhmLq6OuTn5wNQPUhoaWkJc3NzVFRUoLi4WKMynoxevXrhlVdeYTez+JaRkYGMjAyV+zg+y8TEhD0kAaj+OiiVSrFnzx48efJE7TWISVsyCYGG90smSBgcHCzq+316ejq2bt0q2HzaFCTs06cPzpw5g+zsbNy5cwcBAQGCzR0aGoo//vgDERERyMnJ6dAb4q3hOpMQ+KcvIR9BQk1LjTYWHx+PHj168JblV1RUhOrqahgYGGhUnp0LPXr0QEJCAu7evYu5c+fyPh9N02xVBYZCoVBpjE2bNrGv3VVVVaiqqoK7uzveeustldeTkZGBr776iu0rOHDgQIwZM6bZihAt0dfXx8CBA9lsRnV5e3tDJpOhrq4O9fX1z/1h7jcTExO2LYAmh5wNDQ1RXV3NthWgKAojRozQ6GfQVbW1tbh58yYiIiKa/H+FQoHy8vI2D0sMGjQIgwYNUrkfe5cuXTBs2DAMGzYMZWVluHfvHiIiInDu3DlkZGTgyy+/VLlNAEEQBEF0NCRISHCG756ENTU1kMlkKn3Y4FptbS12797N2/hiZhJyVW5UX18fMpkMaWlpWtGzpy3ffPMNkpOTYWNjg9dee03s5fCGpmn88ccfAABfX1+l+weRTb+mrK2tdbJsja+vL3x9fZv8v7q6Os6DhBRFwc7ODpmZmUhPT1f5NWDnzp0AgFGjRrV4PzOZhOpk/Hbr1g1jx47F2bNn27ytqakpgoOD4e/vr3H2HhOgtbe3V7lUKUVRcHJywoMHD1BUVMRJkBBoyFB84403sHPnTsHee86ePYsFCxZo/BxS55BEfX09du/erdOZ0czvvqysTPS+VyEhIfj1119RXl6Ox48fo2fPnqKs4/bt2zh58qSgcwrRZ01ZhoaG6NevH6Kjo3H8+HFBg4T29vbw8vLCgwcPcODAAbz55puCza1L+MokBLQ/SAg09Cd0dnZWu+x0a5hSo15eXqJXAWH6s969e5f3uaqqqhAaGorbt29rNE5BQUGTfxsYGOCzzz5TqUQsTdMIDw/HTz/9BJlMBiMjI0ybNk3toG1AQIDaQcKgoCAMHTq01cN8NE2zAUQTExNO3keZx96vv/4KoOGam9m/UBcTyNSVzxx1dXW4efOmxgHeW7du4datW/D09MSgQYPQo0cPle8DS0tLhISEwMXFBXv37kVycjJee+01fP7556KWJeaSkZER2zqAZEkSjZHHBkEQmiBBQoIzfGUSdurUic1MqaioEK0PjEKhwIEDB577QMUlMTMJuSg3amBggJ49eyIxMRHXrl3T+iDhgwcP8PXXXwMA3nzzTbU2nnXFzZs3ERcXBz09PZVOt1pbW8PY2JjN3urotKEPlbZrHCRURVFREcLDwwGg1UxX5rGo7obgkCFDkJaW1mo/ulGjRmHQoEGcbTqqW2qU4ezsjAcPHnDSl7AxW1tbvPLKK9i5cyeb6cgn5n4XOuOjrq4Ou3btYjeUdRWz6ahQKFBRUcHLpruyDA0NERYWhn379uH27duiBAnPnDmDmzdvCjqnq6ur1m26+Pv7Izo6GpGRkSguLlarZ6q6RowYgQcPHmDv3r0kSNgCPjIJ+So9LJfLeXmdPHz4MBYuXMj5Z0Rt6EfIYIKE9+7dA03TvAZ3Pv74Y40DhADwxRdfNAkI9uzZU6WS6hUVFVi7di2bNebu7o5p06Zp1KfWzMwMfn5+Kh1iGzx4MIYNG6bU5ziKomBgYKDx4a/GGl8rSiQSjUpw0zSNuLg4XLp0CRRFYdy4cWqXahdCXV0doqKicOnSJU7HTUlJQUpKCqytrTFw4EAMGDBA5f6m7u7ueO2117B3717k5eXh7bffxjvvvINJkyZxulYx6OvrY+LEiWIvg9BC5LFBEIQmtL8WIKEzmCAh1yfbKYpiN8a4PIWrqitXriAlJYXXOXJycp4rHcM35oMNV/0ffHx8AAAXLlzgZDy+FBcX4+WXX4ZMJkNgYKBW9FTiS0pKCn755RcADQESpiysMiiKYk+sExB1U15XMJuXqp6m37x5M+RyObp3795qpk5lZSUA9YOEFEXhhRdeaPZ5EBISgvfffx/Dhg3jLECoUCjY/ndeXl5qjcH0JeSj36uFhQUWL16sVI9SLpw+fVrQAzGFhYXYunWrzgcIgYYP/kzAQVtKjgLAo0ePBL8+y8jIEDxACEArDz/Z2dnByckJNE3jzJkzgs4dGhoKALh27Zog/SB1kS5lEmZnZ0MqlXI6JtDwfL116xYv4wLi9iNkuLu7Q09PD0VFRezBID4kJCSw1/Rffvkljhw5wv7ZtWuXSmP5+flh4MCB7B9lA4QKhQJnzpzBK6+8goiICFAUhdGjR2P+/PkaBQgZQ4YMUep2/v7+ePvttxEWFibqQc/G14v+/v5qf1aoqqrC1q1bceTIEZSVlbG9dq9du8bRSrlVVlaG9evXcx4gbKykpARnz57Fd999h7i4OJW/39raGq+88gq8vb2hUCiwdu1a/P7774LvtxAEQRCELiBBQoITNE2zmwNcfDh4Vl1dHQDuA5DKUigUnJzYbEtVVRUyMzN5n6cxrk/1Mr2itm/fLkjJHXVkZ2cjJCQEd+7cgYWFBd566y2dKeeiivLycuzevRuvvvoqcnNzYWlpiWHDhqk0RlVVlU6X5+OaNvWi0lZ+fn4AgHXr1uH48eNt3p6maVy5cgUfffQRAGDy5Mmt3p4pFapsydzmmJqaYsaMGey/g4KC8O9//xuhoaEqn1Ruy9mzZ5GZmQmJRKL0xtez3N3dAYC3TXhjY2PMmzdPkPfY0tJSbN68mfOsyGdJpVLcvHkT69evbzfBi4cPH6K4uBgSiUSlbA++ODs7s6+JOTk5gs4dExMj6HwM5kCctmEyTfg+zPYsU1NT9jqSj+BSe8C0SVC1X1xrmM9azOcjrvARyGNER0dzujGvUCjYwxLacG1maGjIHpLi81AKRVHsZ5bbt2/jwoUL7J9n+8DxISEhAW+88Qa+/fZbFBYWwsbGBkuXLkVQUBBnn6VsbW3Rv3//Fr/er18/vPnmm5g0aZJWvBcyh0cMDQ0xfPhwtcYoKCjAX3/91exj59me4tpAoVDg4MGDnGZIt+Xw4cM4evQo6uvrVfo+Q0NDzJgxg62ks3fvXqxfv16nA4VSqRRbt27F1q1byXsv0QR5bBAEoQlSbpTgxPXr11FRUQFjY2M4OjpyOnZFRQWbPSFWw+nU1FTOMu3aEh8fDxcXF0HmAv7Z1OEqa8bX1xehoaG4fPkyXnvtNdy4cUPUvklAw4d1ZkOloKAAM2fORFpaGjp37ozvv/+esx5f2kAulyMmJganTp3ClStX2E2pHj164IUXXlA5ACLEhoOuGD16NHr06CH2MrTeiBEjcO/ePRw/fhyzZs3CsWPHYGhoiKysLGRnZyM7O7vJ37Ozs9kSoiEhIa0GCTMyMnDx4kUA/xxIUJezszM++ugjUBTFW6/bGzduICoqCgCwevVqODk5qTUOk4GYn5+Puro6Xsod5uXlCRYAKS8vx/r167F06VJ069aN07HLyspw69Yt3Llzp92VSd68eTMAYNCgQYKWlGyNo6MjUlJSeOmN1pL6+nq1Mgq4kJSUpFLJbqEw1zhCZ5jGxsZCoVDA09OTzXgmmmI+P5iYmHA2Jh/VW0pKSnD//n3OxntWcXExMjMzOfuM07hNgjYEioCGzPzs7GwUFxfzNoe3tzd+/PFHvP322zh69Chv8zQnIiICn3/+OWiahoGBAUJCQjB48GBerqGmTJmCvn37Iioqqkl5+ODgYIwcOZLz+TQRFBQEHx8f6Ovrw8zMTOXvT0lJwY4dO1r8OtcHerlw+fJlwQ82Aw3lfJ8+fYqXXnpJpb6PFEVh+PDhMDMzw4kTJ3Dw4EFIpVK89dZbWnn/tqW+vh6LFy8GAMycOZPT8rmEbiOPDYIgNEGChAQn/vzzTwANZYe4bhzP9LWysLAQrQ+MkJtRCQkJCAsLEyywxgQJubxvV65ciejoaERHR+P333/HihUrOBtbHc31KnF0dMQPP/wABwcHEVbEvaysLJw+fRpnzpxp0jfTzs4OgwYNwoABA1Q+4VtUVITo6Giul6qTgoODERQUJPYyRBUXF9ckyOzh4dHsxhxFUVi1ahWysrJw584djB07Vqnx/fz88P7777f6OGU2UXr16sVJGVw+PzglJiayWY+vv/66RoEFGxsb2NnZIS8vD9nZ2WxmIZda69HIl7/++gvz58/XuIQjTdPIyMhAVFQUHjx4wNHqtItMJsO2bdsAABMmTBB5Nf/gq+xhaxITEwWb61n5+fmC9/1TBlNqj8/gRHOYvmGjR48WdF5dwgQJucxSZ4KEXFbBEKKk4b179zgLElZXVwNoCJDzddBHVUwZcz5Kgze2atUqGBkZ4fLly03+v1QqxaFDh3iZ8+LFi/jqq69A0zS8vLwwYcIEXkt8UhQFDw8PeHh4oLi4GDExMTA2Nla5IgqDpmnk5eUhPT0dGRkZyMjIgKWlJebNm8dJAF/dQHV0dDTbj7sl2hbEKikpQWRkpGjzFxYWYv369Zg1a5bKZfz9/f0hkUhw7NgxHDt2DHK5HP/617+07j4WWkZGhlIVPrp06SLoYXaCIAhCWNpxRU3oNKZePtB2mTh1MEFCsbIIhT6xXlNTg8ePH6NXr16CzCeTyQBwGyTs3LkzlixZgl9++QXvv/8+pk2bxnmGqSoMDQ2bbKR4e3tj9erVWrfJp6qamhpERETg1KlTTR6jxsbG6NevH/r3769REJTJ2OILUx5K6PJoqvLz89PKzBGhMeVAGQYGBtiwYQO6d+/+3G319fXx2Wef4T//+Q/S0tJga2uLzp07o0uXLux/G/+9c+fObR4wuXXrFs6dOwcAapdzEkpmZiYOHjwIAJg6dSpmzZql8Zi9e/dGXl4esrKyeAkSPnr0iPMxlbF9+3bMnDkT3t7eKn+vTCbD/fv3ERUV1W5KirYkPDwcubm5sLb+f+ydd1gUZ9fG79kFRFB67yCICIqgggVUUKzYG6ImajRGTfIaU96YxJQ3XRNNM83Yjb333hvSEQSkg/Te6+58f/DNBCJly8zsgPO7Li4Vd5/nYZn2nPuc++hj+PDhql4ODSUSctlnMjo6mrO52iIhIUHpSmamoYL1XFcSUravY8eO5XTergQbIiHlEsFUYLuqqooTC9/IyEhMnDiRkYRS6nPlU69orkRCgiDw2muv4bXXXmv1/YqKClZEwkuXLuHbb78FSZJwd3fHtGnTFD72mpqaUFtbi5qaGvpPIyOjDi3kDQwMEBAQINc8EokEubm5yMjIQEZGBrKysp5zF6iqqsKBAwewePFizqttqL6Oslj88q0lxoMHD1S9BADA4cOHMXLkSPj7+8t1PHp4eEAkEuHUqVM4d+4cUlJSWl1HqNjIi0JmZiZcXFzoxIuO0NLSQnx8vCAUCggICHRTBJFQQGn+/vtv1NfXw97eXu5sLlmgREJ5LCWYJDExkfM5Hz9+zJlIyEYlIdBsE3P58mUkJCTgrbfeooVkVXD8+HGF7F+4pqGhAY8fP0Z+fj7q6+vpr4aGBtTV1bX6e11dHSIjI1ttZBwdHTFo0CA4OzsrnVX97NkzVqs1vL29MX78eNy+fZvXIqGdnR0mT57MyAadJEmkpaWhvr4effr0Ybzqmm0MDQ3pTXhtbS2qqqqwfv16bN26tc3gTu/evfHbb78xMnd2djY2bNgAoFm0VWXSQWcUFxfj77//hlQqxYgRI/D6668zcvz0798ft27dYqU3TWVlpUpFtiNHjiAwMBCDBw/u9LVUH6ro6GiEh4fLFNToDvz1118AgPHjx/OmagYAnYjCVSVhWVmZyvvk8lEkpJ5xysvL0dTUxMkxUlJSgvT0dBAEISTSdACblYRMiYQPHz5kZBxZiI+Ph7u7u9LjUNd+vliNAtyJhFxy7tw5fPfddwCan78CAwM7faaRSCQIDQ1FdnZ2KzGwtra23X5yb731Fv35KUNkZCQeP36MrKys58Senj17wtXVFe7u7rCyssLXX3+NrKwsHD9+HHPnzuWsmqy+vh5Hjx5FcnKyTK/nU5VbTU0Nr1xm7t27h5ycHMyZMwdaWloyv8/d3R0ikQgnTpxAQkICiyvkP0VFRaipqcG+ffs6jOXFx8dj0aJFKCoqEkRCAQEBgW4Kf6IMAl0SkiRpq9EpU6awkumm6krCx48fcz5nXFwcpk6dyom9KlsioVgsxrp16/Daa6/h8OHDWLp0KSZOnMjoHKqGJEls2rQJWVlZ6N+/P9zc3ODm5gZ9fX2Zx8jOzsajR4/w6NEjREVFyd1Dy8DAAIMGDYK7uzsjm2ug+eeiKrbYYNq0afDw8ACguvNaFvT19TF37lxGrH8LCgpw4cKFVsHtAQMGwM3NDX369FF5305ZWLFiBX2dqKurw44dO1BYWIj169fjxx9/ZM12qra2Fh9//DEaGhpgZWWFSZMmsTIPE1RXV9OJM/369cNHH33E2O+2X79+AMCKSKiqKsKWnD17FjU1NfDx8QFBEGhoaEBRURH9VVxcTP/JVe9EvpCbm4tz584B4JfVKMC93aiqehG2JCsrC1VVVaxa7cmLlpYWCIIASZIoKyvjJLGOshodNGiQyhL5ugJ8Fwnr6uo4sRqliIqKYkQkfJErCbni9OnT2LJlCwBg6NChmDRpUqd7/ezsbJw+fRoFBQVyzXX37l2l72/37t3D1atX6X/r6Ohg4MCBGDBgANzd3eHo6NjqmUxfXx/r1q1DQkICLly4wFhSYEeUlZXhwIEDcn0+fKokDAsLU/USniMtLQ1//vkn5s2bJ1cS4YABA2Bqavrcc3VjYyMuXLjA9DJ5j4uLCzw9PVW9DAEWEOxkBQQEZEUQCQWUIjQ0FDExMdDQ0JDbBkRWVCkSVldXqyx4mpCQwMgmujPYsBulcHJywuzZs3HkyBGsXr0asbGxcmX58Z2YmBh6ExEbG4vDhw8DACwtLeHm5kaLQNbW1q0qsKKiomhhMCcnp9WYvXr1grm5OdTV1aGurg41NTWoqam1+jf1p6GhIaytrRnfPD59+hSZmZmMjkmxdOnSVg+fHdkLqZoFCxYofbzW1dXh5s2bCAkJee7/Hj9+jMePH6Nnz560yGxra8urYEB7aGpqIjg4GNu3b0dqaio+/fRTfPPNN4xXr1BCfGpqKrS1tTFv3jzOq6iqq6tx7949FBQUQCqVQiqVQiKRtPknVeVrbm6Or776ipE+NxR9+/YFQRCorKxERUUFY0kBAD9EQqDZ4jghIQFVVVWoqKhQ9XJ4w+7duyGVSuHq6sq7zbupqSkA0BUibFZIkySpcqtRiqdPn/IqmEYQBLS1tVFVVYWSkhJORULBarRjKDGLyfsBkyIh11VB6enpKCsrU1rc43MlIde9Qdng2LFj+OWXXwA0u39MmDChw+fThoYG3LhxQ+Gq1NDQUIwcOVLh32dkZCQtEM6fPx8TJkyAra1th+eIu7s7Pv74Y3z66acICwtD7969WbWzr6mpwY4dO1BZWSnX+5g4zylLcGXOl6amJpnsUVVBeXk5tm3b1ioRVRZMTEye24vW19e/kCKhQPdEsJMVEBCQB0EkFFCKbdu2AQBGjx7NaMCSoqamBvn5+QBUYzcaFxfH+ZwUMTExnIiEVCUhW4G9pUuX4tatW0hLS8MXX3yBr776ipV5VAG1gbC3t4eBgQEyMzNRWFiI7OxsZGdn49KlSwCagwaurq6or69HVFQU3UsGaN742djYwNHREY6OjjAxMVGpSCSVSltl4TKFqakpgoKCngsKGRgYQCQStfpM+EBwcLBSiQkkSSIqKgpXr17t9KG8trYW4eHhCA8Pp4+VAQMGwMzMjNeCoZ6eHoKDg7Fjxw6Eh4fj+++/x3vvvcfomg8fPowbN26AIAjMmzcPvXv3ZmzszmhsbMSDBw9w9+5d+jopC5qamvjmm2/kqiiWddw+ffogOTkZz549U6iHX1s0NTUhNTWVkbGY4N+JEy86UqkU27dvB8C/KkKgOaDQs2dP2oKYzV6/z549403wPSEhgVciIdCcZFRVVcVJX0KSJGmRcNy4cazP15VhsyehsvdbiUTSZhIT20RFRWHMmDFKjSFUErLHoUOH8PvvvwMARowYgXHjxnV4rKWkpODs2bNKV5TfvXsXU6ZMkft9CQkJOHPmDAAgKCgIK1eulPm9o0ePxhtvvIGff/4ZN27cQO/eveUSmeTh4sWLcguEgPIiYUNDA3777Tc0NTXh5ZdfhrW1tULjxMTE0OcdXzl9+jSePXuGSZMm8cqaXaB7w+dKPcFOVkBAQB6EO6eAwlRWVmLfvn0AoNADvSxQ1Uza2toqqUBThdUoRWpqKid2VlQlIZPBi5b07NkTb7zxBjZs2ICNGzdi4cKFcHV1ZWUuLqmursa1a9cAAH5+fvSGq7a2Fs+ePUNmZiaysrKQnZ2NioqKVk3e9fT0aFHQzs6OE1tZWYmMjJTpIVceXFxcMGPGjDaFaLFYDENDQxQWFjI6pzKMHz8eTk5OCr8/OzsbFy5cUMgWkjpWHjx4AB0dHZibm8PMzAzm5uYwNzdnJRlDGczNzTFv3jwcOHAAFy9ehJmZGV5++WVGxg4PD8cff/wBAJg4cSJnGxapVIro6GjcuHGDDuY4OTlh+vTp6NGjB13dKxaL6b9T/xaLxbCxsWG0YqQlLi4uSE5ORnZ2NmMiYWZmZrs9ggRUz99//43k5GRoa2vztu+bgYEBsrOzWRcJ+VJFCDRX37JdOSkv1PMiF0JqTk4O8vPzoaamBh8fH9bn68rw2W40KytLJUH/6OhojB49WimRk0rA4pNISCUydWWRcN++fXRiiq+vL/z8/Nr9PdXU1ODy5cuMXZvDwsLg4+MjV7Vbeno6jhw5ApIkMXnyZLz66qtyzztr1iwUFRXhwIEDOHPmDHr16qXUPqAtEhMTFY4rKJsMkJ6ejvr6egDNzxSvvPKK3ImQJEm22svymYiICOTn52PevHm82zcxRY8ePWgHIz7FEV5E+Fap196xIdjJCggIyIIgEgoozOHDh1FXVwdra2sMHDiQlTkoq1FVVBFWVFTg2bNnnM/bkri4OHh7e7M6B9uVhADg4+ODkSNH4t69e1i+fDlu3brFq8CaIpw4cQJNTU0wNDSElZUV/f2ePXvCycmJ3lxKJBLk5eUhNDQUTU1NGDFiBMzNzXlZISaRSHDz5k1Gx/Ty8sLEiRM7/HmNjY15IxJ6eHhg2LBhCr8/JCQEFy9eZGQtFRUVqKioQGJiIv09Hx8f3lm7OTk5YfLkyTh37hx27doFNzc3DB48WKkxa2pq8OGHH4IkSQwaNAhDhw5laLUdI5FIcODAAaSkpABoroBdvnw5/P39GbF7UhYXFxecOXOG0b6EfLEaFWibzz77DEBzdTNb4rOyUCKhIhUSsiKRSFTq7tAWmZmZcHR0VPUyaLgUCSMjIwEAI0eOhLa2NuvzdWX4LBJS9zquKSsrQ1ZWllKBUkEkZJ6LFy/SAuGYMWMwevTodl+bk5ODAwcOoKqqitE1PHjwQOYe9qWlpdi/fz+kUil8fHywbt06hfdXy5cvR1FREa5cuYLDhw/jzTffZMy9oqmpCWfPnlX4/cqc55WVlbh//z49Tn19Pfbt24eFCxfK1fIhLS2N8SRSNsnOzsb27dvxxhtvdMuKQjU1NcydO1fVyxAA/yr1hGNDQEBAGbrfHVOAMygxwd/fnzXBo+VmSyqVchqk5UNAmOmNV1v06tULZWVlSE1Nhbm5OWvzvPnmm4iKisLDhw+xbt06us9FV+TChQv0JnrYsGEdHv9isRi1tbV0lm1iYiI8PDzg5eWlEvG7I/Ly8hg/5h49eoSqqioMHDgQjo6OEIvFz72GC2s0WbCzs8OUKVMUvp5FREQwJhC2R3p6OqvjK4qdnR0IggBJknTwUhmioqJQX18PXV1dTJ48mTNR/cqVK0hJSYGamhpeeeUVzJo1i1cJDVRCTmZmJmpraxkRjShLbwH+QZIkbb86cuRIFa+mfagAHJu20aWlpairq2NtfEVQV1dX9RJUBpVg1l2rNJiEeu6hnDuYgLrPKntv5GKf0R5Mnc8kSTIyDhOEh4cDgMJ2jqrm+PHjAJrvNx0JhElJSdi/fz8ra5C1HzFJkrhw4QIaGxvh5uaGDRs2tLnHkBWRSIQ333wTV65cQVNTE6MOCxkZGUqda3Z2dnK/h2p7QH1GIpEIX331FX799VdkZmbir7/+woIFC2Bvby/TeLJUSfGNiooKXiblvgjw2X6TLYRKPQEBge6AIBIKKAzV6L6jjBllGTp0KPT09FBWVoakpCQ4OzuzNte/6dWrFzw9PemeK6qAC/sIJycnhIaG4sGDB6wGIU1MTPDhhx/igw8+wNatWzFkyBAsWbKEtfnY4ubNm9i0aROAZoGws4qpmpoanDp1CkCz6F1ZWYnQ0FCEhoaiT58+8PLygpOTEy82MVlZWayM++TJEzx58gRaWloYMGAA3N3d6X57RUVFyM3NZWVeeRg0aBCmTJmicIAhNjaW7ofCJnwKhlGQJImzZ8+CJEl4e3szUvUXExMDAOjTpw9ngfjo6Gi6N9Mnn3zCSws9S0tL2NvbIy0tDU+fPmWkb21XrXh4ESAIAk5OToiJiUFOTg5sbW1VvaQ2qa2tBcDuMwtfehFSODg48C6wRQWh2bR8paACy6q05e8qGBoaAgCjlbaUIK9sQqMyooqyKJsoR1Ww8iXRrKmpiX7eX716tYpXIz/p6elISkoCQRAYMWJEu6+LiIhg9XlX1uq9hIQEJCUlQSQS4b333mMkoevJkycAAF1dXUavo8o4NkyfPl3u+EdZWRnOnDlD95t2dnbGe++9BwcHB/Tr1w8bNmzA48ePsW/fPkybNk2mZ0kzMzOF1q8qjI2NERQUpNJrHJs0NTXhxIkTAICZM2fyqlqSb/abLxr/PjYEBAQE5IE/dxOBLkVLCzw2hTt1dXUEBATgyJEjiIqK4lQkBJqbmatSJGSrT2BLnJ2daZGQ7WrN4cOHY8mSJdi1axdeffVVuLm5YciQIazNxzQPHz7E559/DpIk4eHhgfHjx3co7pEkifPnz6OqqgrW1tb4888/ERcXh+PHj+P+/ftISUlBSkoK9PX14eXlhUGDBnHyO28PtkRCipqaGoSEhCAkJATGxsZwd3dHWVkZq3PKwqRJkzB06FCFhdqCggIcO3aM4VW1DZuVOooSHR2NjIwMiMVirF27lhHBmxIJuRJFcnNz6aDX4sWLFRYIy8rKQBCEXP105MXHxwdpaWlISEhQWiRsbGyUOWtfQDX07dsXMTExKrc/7wgqEMRm1S1fhAAA6NevH2bPns2L5J6WUCKhvr4+63M5ODgAaBYWysvLWb3mdXUokZDJay2VMKTsMaiqALpYLFbaJpRvIuGtW7dQVFQEMzMzzJs3T9XLkZurV68CaE4e1dLSavM1eXl5rCfEyVKd3NDQQDt3LFy4sFXl5vXr1yGRSDBu3Di5zw8qAdrBwYHR67uitr7BwcFy9UYkSRKPHj3C1atX0dTUBLFYjBUrVmDOnDn0ua6rq4vvvvsOX3/9NW7evImTJ0+irKwMo0aN6vBnNjAwgLq6Ol1FzmdcXFzoPuLdlfr6evo6U1VVxSuRkG/2my8a/z42BAQEBOSBP3cTgS4FJZyZmpqy3gti0qRJOHLkCBITE1FVVUX3XOECHR0dDBs2DA8fPuRszpZwIRjZ2dlBQ0MDJSUlePr0Kfr168fqfIsXL8bTp09x//59zJo1Cw8fPoSFhQWrczJBVFQUPvroI0ilUri5uSEwMLDTDWRsbCzi4uJAEAQ++OADaGpqYvDgwRg8eDBycnJw6tQpnDt3DqWlpbh06RKuXbuGQYMGYfjw4ZxUArSEJEnWRcKWFBYW0gEJVfLyyy8rZONDIZVK6cxxLuBbJWFNTQ0dqHnllVcYyTSura1FQkICAHCyaayursahQ4cgkUjg7e2tcIVzeXk5FixYgMbGRqxZswYzZsxgRUTw9fXF3r17kZSUhMbGRqUqLflWnSXwPFRyFJfXZ3mhRMIXoZLQw8MDgYGBvLCk/zdU7zsuREIdHR26n3BsbCyv7XBVDfU8x6RIyJTdqKpEQkNDQ6XPIUok5EOyGQAcPXoUQHMVYVcRJ1566SX691BeXg7gH1vzf0PZe7KNLJWEt27dQkVFBczNzbFw4UL6+xEREfj8888BAKmpqXj11VflOkcou9g+ffrIuer2KSsrU6iX34IFC+QSCIuKinD69Gn6WWHgwIF49913YWVl9dxrNTQ0sGHDBpiZmeHgwYO4efMmysrKEBgY2O41QSQSwczMjNfPIgDg5+cHX19f3iXxvIgI9psCAgICXQ/+7XAFugRhYWEA2K0ipLC3t4eLiwtIkqT7unGJKi3nuNhkisViODo6AgDd2JxNRCIR1q9fDxsbG2RlZWHy5Mm8r2RJSEjAe++9B4lEgr59+2LGjBmdBjcqKiroJvUvv/zyc+KrhYUFVq1ahSNHjuCtt96Cra0tmpqaEBYWht9//53zz6S8vJxRKyy+oqOjA09PT8ybNw/r169XSiAEms8ZqmcYF/BNJLxy5Qrq6+vh4ODAWJP0J0+egCRJ6Orqsp6EIpVKcfToUZSXl8PS0hIfffSRwoHLI0eOoK6uDhKJBD/99BM++OADViocHB0dYWZmBolEguTkZKXG4ovwItA+ffv2BQBeVxJSdqPdvZJw5MiRmDp1Ki8FQpIkObUbBf6pJqQqvwXahqokpEQYJujqlYRM9OSmkkb5cG148uQJEhISoK6ujpUrV6p6OTJTUlKCoqIiFBUVobGxEb1796bvOf8mLi4OmZmZrK+ps0rCgoICPHjwAEBzv3tqr9zY2IiffvqJft3Bgwfxxx9/yPzcXFRUhLS0NACQuU+fLChiNRoUFNTu7+HfSCQS3LlzB7/99huysrKgpqaGtWvXYsuWLW0KhBQikQgrV67EW2+9BYIgEBUVhf3793fYK5TvlqNBQUGdVkQKCAgICAgItI9QSSigEFyKhAAwefJkxMfHIzIyEiNGjOD04U9bWxujRo3C7du3OZuTgivryb59++LJkyd48OABli1bxvp8vXr1wjfffIM1a9YgOjoas2fPxrlz51gNMCpKWloa1q5di8bGRtjZ2bWybGkPkiRx6tQpNDQ0oF+/fq2yXP9Nz549MW3aNEydOhWRkZHYunUrUlNTcenSJcZEF1nge2aoMtjb28PR0RGOjo4wNjZm7PpRWFiIa9euMTKWrPDJbjQ9PR1RUVEAgHXr1jFmdUMlg3BhNXrlyhWkp6dDTU0Nn3/+ucKV6uXl5Th06BAAwN3dHY8fP8bDhw/xyiuv4P3334eXlxdjayYIAj4+Pjh69CgSEhKU6gss9CPkP3yvJCRJ8oXoSRgQENBhny5VU1tbSwfD2U6uoOjTpw9CQkIEkbAT+NyTUFUWdUyIhFQloaqvDcA/VYSLFi2CiYmJilcjO0uXLm2199LT02vTnaChoQGXL1/mZE0diYQkSeLcuXMgSRK+vr4YNmwY/X9Hjx5FRkYGjI2NsW7dOqxfv55+Llu5cmWnz/5UFaGFhUW7dquKIK/V6Pz582WOr1RWVuLgwYN0sqKXlxfWrVsHU1NTmeebNm0aTExMsGHDBqSmpmLnzp0IDg5u00La3Nxc5nG5xNDQEEFBQYxcVwS6NpmZmTJV7hoZGQkWpwICAgJtIIiEAgrBtUjo5+eHH3/8EcXFxXj27Fmr3gNcMHz48G4tEjo5OYEgCCQnJ6OgoICTDa65uTm+/vprrF27FlevXsWKFSuwa9cuXmX/ZWdn45133kF9fT0sLS0RFBQkk7Xfo0ePkJqaCrFYjPXr18sUhCEIAp6enli/fj1effVVPHnyBCkpKYxa3nQEF9nBXKGrq0uLgvb29qwErrm2GaVQtJIwMzMTubm5rb7X1NSk8DqamproStlp06bB1dVV4bH+DRVwZnvzdv/+fdpK+qOPPlIqc/zo0aNoamqCmZkZpk+fjuHDh+PYsWMoLCzEf//7X8yaNQsrV65kLBFi1KhROHr0KJ4+fQqJRKJwNQgfgqsCHUPZjRUVFaG+vp53NnZ1dXW0YMFWoo9UKlVptdD06dMxaNAglc0vC1QVoY6OjlIWxPIgVBLKBht2o9Q59yJXEvLFbrSgoAC3bt0CAPznP/9R6VrkxdTUVKZ7yt27dzlzG+nIbjQ6OhqZmZlQU1PDmjVr6O8XFBRgz549AIBNmzbh5Zdfho6ODtasWSOzUEjFNqjrGhM0NTUhNTVV5tfPmzdP5rYfRUVF2LdvH8rLy6GhoYG3334bAQEBCl0Thg0bhq1bt2L9+vUoKCjA3r17sXr16ueSEPhYSejk5IRZs2ZxFjNpi6ioqE6TDAVRin0yMzPh4uJCW+B3hJaWFuLj44XfiYCAgMC/EERCAbkpLS2ls+JktcJQFm1tbYwdOxaXLl1CREQE5yKhpqYmxo0bx3kPNa6CgVpaWrCyskJWVhYePHiA6dOnczKvs7MzPv30U3zwwQfYs2cPbGxs6F4SqqawsBBvv/02SkpKYGJigoULF8r0+ygqKsKVK1cAAGvWrJH74dPR0RGzZs3CsWPHcP78eaxatYqTTG8+W9l1hrGxMaytrWFrawsbGxvo6uqyLjY/fPgQ2dnZrM7RFoqIhIWFhdi1a5fSVqWxsbF08Dk9PR3FxcUwMDDAihUrlBq3JQ0NDYiPjwfAbiVheHg4fZ4uX74co0ePVnisiooKOghF2RyZmppixYoVuHr1Kh49eoTjx48jMjISGzZsYMTGqn///tDX10dpaSnS09MVTibgm0jYr18/uh+lQDP6+vpQV1dHY2MjysvLeVelQolTIpGINXGqoqJCZVXUQUFBnCXEKQP1e+CiHyEFFUx//PgxpFIpL21Y+QBVSSiIhP/ApN1oWVkZSJJUWZLhyZMnIZVK4efnB3d3d5WsgU1KS0tx584dTubS1NRs9z5SW1tLP7e98sorrarlfv31V9TV1WHkyJFYvHgxgObekABooZAkSbz22mttHidSqZSVfoSZmZlobGyU6bVz586V2RkiMzMT+/fvR319PaysrPDNN9/A0tJSmaWib9++2Lp1K15++WUUFxejtLSUvnZRmJiYQCQS8cbVxNfXF35+fipPMJZlDyGIUuxTVFSEmpoa7Nu3r8NzKT4+HosWLUJRUZHw+xAQEBD4F4JIKCA3VKadpaWlTM3FmWLy5Mm4dOkSYmNjMXHiRM6z6b28vPDw4UM6EMMFXGbF9e3bF1lZWbh//z5nIiEAeHt7Y926dfjuu+/wxRdfwMrKSuX9POrr6/Huu+8iPz8fBgYGWLx4MXr27Nnp+yQSCU6cOAGJRIIhQ4Yo/DkuXboUN2/eRHFxMe7fv49Ro0YpNI6sJCcnIy8vj9U5mMTKygo2NjawsbGBtbU1o7ZAstBSCOYaeTfmJEniwoULIEkSdnZ2rQSqpqYmuQI/VOVgS9asWaOwRWdbJCYmoqGhAVpaWs8FJ5ji8ePH9M8SHBzcoR2wLBw9ehSNjY0wNTVtlQGurq6OSZMmwdHREadOnUJaWhqWL1+ONWvWYObMmUoFNcRiMUaOHImzZ88iPj5e4aAWn+xGhw0bhvHjxyM0NBQXLlxQ9XJ4A0EQMDQ0RF5eHi9FQkr46NmzJ2uBOlWJ2UuWLOHE9pgJqqurAXArElpbW0NNTQ2VlZXIyMhgtI9Xd0IQCZ+HyUrCxsZGVFVVcbonpWhoaMC5c+cAAGvXruV8fi7gymYU6Nhq9Nq1a6ipqYGtrS3mzJlDfz80NBS3bt2CSCTCr7/+2ipZYfXq1SAIAqtXr8bhw4dBkiRWrVr13HmTmpqK0tJSqKurd9jHT15k7Rs9e/Zs9O/fX6bXxsfH4+jRo5BKpXBxccFXX33FmMW0mZkZbG1tkZSUhMLCwueew8ViMUxMTHixZ5w7d67Mnxnb/Pnnnxg8eHC7/8+GKNXSgj4qKqrdOMWLWMHo4uICT09PVS9DQEBAoEsiiIQCcsO11SjFgAEDYG1tjaysLMTHx3Nu/aSuro5Ro0bh/PnznMxHEARnllFA8+/z2rVrCAsLQ21trUyiGFNMmTIFhYWF2L17N1avXo0RI0ZgwIABnM3/b44fP46MjAz06tULL730kswiSEhICHJycqChoYH33ntP4ax6bW1trFq1Cl988QVu374NDw8P1oIfNTU1+Pvvvzt9nY6ODiwsLNCzZ09ERkayspb20NTUhKurK1xdXWFlZcXpedEWbYllXCFvNWBaWhrS0tIgFovx1VdfteonUl1dLZdI6OHh0Sqg6OLiAj8/P7nW0xmxsbEAms+B+vp6xhMloqKicPr0aQDNFoLLly9XaryamhocPHgQQHMmcVsBWycnJ7z22ms4ffo0kpKS8PPPP6O4uFjpCkxfX1+cPXsWiYmJmDJlitzB4oaGBk6TXjpi3LhxdL/hoUOHIj4+Hunp6apeFm+gREJVWm62B2VBx+YzA9c/d69evbBw4UJeWqu1hyoqCdXU1GBra4uUlBRER0cLImE7UIH26upqNDU1MeIO0ZV7Eurq6jLyHKempoYePXqgvr4epaWlKhEJ7927h4qKCtjY2GDKlCmcz882mZmZnFb3tycSlpaW0pV+b731Vqvjdtu2bQCAN954AwMHDnzuvatWrQLQLBgeOXIElpaWzyVxhoaGAgDs7OwYPSdksRqdNWsW3NzcZBovPj4ehw8fBgCMGDECGzZsYPw52c7OjhYJ27I+NTc3V7lIGBgYyBuBEGiOoXApSmVmZraaz8fHp93XChWMLx4aGhrYuXMn/XcBAQEBeRBEQgG5iYqKAvBPnxyuoIKHWVlZMjUkZgNPT0/cu3cP5eXlrM9lamrKqX2GkZERtLS0UFNTg+zsbDg6OnI2NwC8/PLLCAkJQUJCAqKiolQqElJCxciRI9ts3N4e0dHRAJqrq4yNjZVag7+/P44ePYqEhAQkJCRg6NChSo3XHiRJQltbm65CAP4RBM3Nzek/qYxtoDkoRW3W2aRPnz7w8PCAs7OzSgJZbVFYWIiMjAyVzd9RlnVb1NbWAgD9e1SGzz//vNVxwAZUUL6wsBBbt27FpEmT4OLiovS1kCRJXL16Fffv3wcATJgwAW+++abS4z5+/BiNjY3Q09PrsI9Mr169sGDBApw+fRpRUVGMWNV6eHigZ8+eqKqqQm5uLiwsLOR6v7q6Om1ZqkqmTZsGDw8P+t8EQWDq1Kn4+eefVbgqfjFw4EDExcXh9u3b8PLyUvVyWlFQUAAAjFYU/xsurc0MDAywcOFCuo9cV4G6hzNVUSIrTk5OSElJQUREBGbMmMHp3F2Fls+RNTU1ct/H24KpSkJV9PFismWEjo4OCgsLkZeXp5IgONWLMDg4WGVVmWzCta1ke5aZiYmJAJqfe/5t6Upd+3x9fdsdd8GCBfjoo49QUlLynP2nVCqlk/+YbqNibGyM/Pz8dv9/5syZMu93Kysr6V7okydPxrp161g55qh7eUNDQ5v/b2Njw3myaEsIguB9j2C2KSoqQm1trWCrKdAm6urqWLJkiaqXISAg0EXhR9RVoEsRExMDgFnPflmhHlzr6+s5nxtottmgqjfYZuTIkazP0RKCIKCrq4uamhoUFBRwLhISBEFbyHJtJftvKBFanoz84uJiFBQUgCAIRqqrCILAqFGjkJCQgKdPn7ImEmpra+Ott95CaWkpSJJE7969Ow0aBQQEICEhoZWwyDSmpqZYuHChyvtM/BtKCFYV8lZw9+nTBwRBICsrC5mZmbzfJPr5+UFfXx+bN29GVlYWjhw5gr59+2Ly5MlyCfYtaWhowPHjx+kg0+LFi7FkyRJG+mdR90M7O7tOj1WCINDU1AQAjFxf1dXVMXjwYNy9exdJSUlyi4QEQWDBggX49ddflV6LIjg7OyMgIKBNW1kDAwNYWFggJydHBSvjH6tWrcKBAwdw9epVvPrqq4yIDExB9bNlU1TjqkJo8ODBCAgIUPkziCLU1NQA4F4k7NevHy5evIhHjx5xOm9XQl1dHWpqamhqamJs/8JUJSHXxwsARp9njYyMUFhYiMzMTM4TKOrq6hASEgIArewvuxO2trYwMjLiJDlXR0cHI0aMaPP/0tLSALR97Pj4+ODw4cM4cuQIZs+e3eb7169fj5KSEtjY2GDatGmt/u/hw4fIycmBpqZmm5WIyjBr1iz4+fkhLy8Pubm5yMvLQ15eHnR1dTFlyhSZk/dIksTp06dRX18PJycnrF27ljVRmkoubO8+6OLiQouVqoAkSRQXF/POel0VCLaaAgICAgJMI3SYF5CL2tpaPH36FIBqREKqikVVIiEAuLu7sx6gMzQ0VImNBhWE7yjrkU2o7E5VB+iozbA8v2fKjmfw4MGMBTSHDx8OoNmupr2MTiYQi8UwMjKCsbGxTFnlPXr0wLhx41hbDwCMGjWKdwIhSZJ4/PixStcgr0ioqakJBwcHAM22WF2BQYMG4a+//sLixYshEonw9OlT/PLLLwgJCZE7q728vBw7duxAYmIiRCIRPvzwQyxbtowRgRAAfTzIWhlBWTQxla1OBUVl7Xvzb4yNjfHqq68yshZZMTExweLFixEUFNRh30mmM/q7Mj4+PnB3d0d9fT1nlueyQlXFsikSsv3MpaOjg0WLFiEwMFDlzx+KQgV2VSESAsCjR4/ktsN+kaD2L9TvSVkkEgmAricSmpqaMlpJSN1DMjMzGRtTVsLCwlBXVwdbW9suG6j/8ccfsWnTJvrr0KFDrc5jgiDg7e3NyVomTJjQpjWeRCKh7cfb6vtGJWaeOnWqzeTF+/fv4/fffwcArFu37rk5jh49CqDZLYhpaz6CIGBgYID+/ftj7NixWLhwId5++20sX75cLnePsLAwJCcnQywW44MPPmC17QJ1jWrvs+jRowdcXV1Zm18WVBWn4Bt37tzBuXPn6AREAQEAaGpqwrlz54RjQ0BAQCEEkVBALp48eQKpVAo9PT1O+55Q8EEkVFNT69D7nQl8fX0ZC2LLAyUSUvZhXEOJhKr0T5dIJLT9njxiHyUSMnls2NrawsLCAlKpVKa+Flzi7u4OKysrVsY2Njbu0D5FVaSnp6OiokJl8xsbG3coqrQHFcQ9e/Ystm7dSn9RfVz4iIaGBpYtW4bt27fDzc0NTU1NuHjxIrZv3073Sqmuru5QNMzOzsZff/2F/Px8aGpq4qeffmJU3G5oaEBcXByA5nNVltdTCQhMVWpTwbvs7GyFg8/m5uZYtmwZI+vpjMmTJ2PlypW0cN0RXPc95jMEQeCNN94A0BwIpQQCPkBVEipybZIVNisJBw0ahFWrVqkk8Y1JqOC4ohXXiuLg4AB1dXWUlpYiJSWF07m7EkzvX5gSCbW0tDg9Zry8vBhNADMyMgIAZGVlMTamrNy+fRtAc7UY35LaZKW2thY1NTX0V0JCApKSklq9ZuDAgazb0g4aNKjd5/7s7Gw0NDRAR0enzWcnZ2dnWFhYoK6u7jmnn8bGRqxcuRIAMGnSpOesSlNTUxEZGQmCIHhn5U1RVFSES5cuAWh2FbCzs2N1PqoqvaOEGaYrLuVFEAmbWbt2LQIDA1UaFxPgH/X19QgMDBSODQEBAYUQ7EYF5IKyVnNwcFDJhkhLSwuAakVCoLknQkhICIqLixkfW19fX2X9+PgiEqoyk7+kpAQkSYIgCJn7r1VUVNCBUiZFQoIgMGzYMBw/fhxPnz7tsOcZ1xAEgUmTJrEiNPn6+vIy4EJd/1SFor9/Z2dnnD9/Hjk5OXTGdFfBzs4OP/74I86ePYtffvkFOTk52L9/P/3/BEFAS0sL2tra0NbWpv+upqaGkJAQSCQSODg44Msvv6T7HTJFQkICpFIptLW1ZaqiooIahoaGjFVdmZiYwN7eHmlpaUhJSYGbm5tC41hbW+Oll17Cnj17GFkXhbm5Oezt7WFvbw9bW1u5st9NTU2ho6OjUmGeTwQHB+O9995DXl4eHjx4wHqykiyQJMlJJaG2tjYIgmC0Uq1Xr16YOnVqt6lYpQK7XIuE6urqcHR0RHx8PB49esS5VX1Xgdq/1NXVMTIeUz0JgebrPxe91jU1NRnf31AiIdeVhI2NjXSP4/YsLrsCW7duRc+ePQEAJ06cwJkzZxASEtLquqihoQFPT0/652Wa4cOHIyAgoN1jmUqS9PDwaFMUJwgCY8aMwf79+3Ho0CHMnz+f/r/vv/8esbGx0NXVpcXClhw7dgxAs20j19dOWZBIJDhx4gQkEgkGDx6MmTNnsj4ndS/pKGG3T58+0NLSol/LNYJI+OKRmZnZqe1xfHw8R6sREBAQ6L4IIqGAXLQUCVUBHyoJgeZqwldeeQUnT56k7VeZwsfHRyVVhIBgNwr8YzXau3dvmYMvVK8zV1dXxqspRowYQYuElHjJFywsLODh4cFoA3tDQ0OlbGwqKipQU1MDY2NjRvt1NDY2IioqirHxFEFRkbB3796YP3/+c5n2TU1NdD8dPiMSiTBt2jSMGDECf/31F+Lj41FWVoaKigqQJInq6up2+2MOHz4cH330ER2gZRLqfmhjYyPTeZmbmwuAuSpCCi8vL6SlpSE5OVlhkRAA7O3tsWDBAhw4cEDhMYyMjGBnZwcHBwfY2dnRwUdFIAgCffv2RVhYmMJjdCd69uyJ5cuXY+PGjTh+/DgvRMKysjL63GNTJBSJROjVqxcqKysZGW/gwIGYOHGiUscn31CVSAg035sokTA4OJjz+bsCfLUbBQArKyvExsYqPU5neHp6Mm6TSD1zl5SUoKqqiu5dzzaRkZGorq6GmZkZ3RqgK2Jra0sfm8HBwTh79ixSU1NRUFDQqufb0KFDWREJx40bh5EjR3b4GqofYVtWoxT+/v7Yv38/zp49i4qKCujo6CA1NRWffPIJAGD16tXPXRvLyspw8eJFAMCwYcOU+TFY486dO8jJyYGGhgb++9//chIf6MxuFGhuU+Hq6orQ0FDW19MWlHW/wItBZmYmXFxcZBKltbS06OQRAQEBAQH5EURCAbmIjo4GoJp+hAB/KgmB5oBdUFAQHjx4gCtXrjAyprm5+XNWKFwiVBK2FgllhcpcYyNoO3DgQGhpaaG6uho5OTmwtLRkfA5lGDt2LKMioaJWu01NTbh16xbu3bsHkiShpqYGc3NzWFpawtLSElZWVtDV1VVYZKXsZFWFjo6OXP1L/o2zs/Nz9o319fVdQiSkMDIywvvvv0//u6mpCeXl5SgtLaX/LCsro/90cHDAjBkzGBWLW0L1I7SxsZHp9ZRIyHTlkre3Nw4dOoSkpCSlEwn69u2LOXPmdFhxqqamhp49e6Jnz57Q1NSEgYEB7OzsYG9vz3jvOEEkbM3q1avx3XffITIyEmlpabC3t1fpeqgKel1dXaipsbul0NHRUVok1NbWxpQpU3hpZ60MUqmUDuyqQiR0cXHBiRMn8OjRI87n7irw1W4UkL2nrrIMHTqU8TE1NTXRq1cvVFVVISsri7Nzm7IanTlzpsoSO5nGzMwMPj4+uHPnDkJCQjB16lT6//T09ODi4sJopc706dMxaNCgDl9TX19PJ7gNGTKk3dc5ODjAxsYGmZmZOHXqFBYtWoTVq1ejoaEBnp6eCAgIeO49Z8+ehVQqhYWFBWvtE5Th2bNn9HH23//+F8bGxpzMK4vdKNDcdkJVImFVVRUKCws5+0wEWtPZdYDpir6ioiLU1NRg3759nV7jjYyMZN6XCQgICAg8jyASCsgMSZJCJeG/IAgCI0aMgLW1NY4ePaqwLZq2tjZGjx4NT09P1gLaskAFl4qLi9HU1MR60O/f8KEnobwiYW1tLdLT0wE0C1xMo66ujqFDh+LWrVt4+vQp70RCbW1tTJw4kc7GVQZFrXbT09Nx5swZlJSUAGgW8Gtra5GVldWqek5bW7uVaGhtbS1zVjuVIKEqnJ2deVVFygfU1NRgaGjIai+09pBIJHL1IwT+yXxmupLQzc0NGhoaqKmpQUlJidKfB1URnZubS4uBLUVBpitBOsLe3h7q6ur0veFFx9bWFtOnT8eJEydw8uRJvPXWWypdDxdWoxTK9iV0cXFBYGAgK1XFqqZldZqqKgmB5uqqxsZGTq8RXQU+242ampqyfp11dnaGnp4eK2MbGRlxKhJKJBLcvXsXQHM/wu7ErFmzcOfOHURHR2PcuHGtqq29vb0ZC/4HBQXJ1Hc4IyMDJEnCwsKiw0Q5giDg5+eH3bt34+DBg1BXV8elS5egrq6Ot95667nzpLq6GidOnADQ/HPx6fm6vr4ekZGRdNKjv78//P39OZtfFrtRoNlNxtDQkJXWK7IQGRmJ8ePHq2TuFxUjIyNoaWlh0aJFnb6WjYo+FxcXeHp6MjqmgICAgEBrBJFQQGZyc3NRXFwMkUjEetPs9uBTJWFLrK2tsXLlSpw8efK5hu+dMWbMGAwfPlylwhiFtrY2xGIxJBIJioqKGO/h1RlsVRKWlZWhoaGB/reurm67mb/yioSUDaiDgwNrAt6wYcNokdDPz4+VOZRh6NChiIiIULoCVd4qwtraWly5coWuZDQ0NMR//vMfjBw5Es+ePUNCQgLi4+MRHx+Pp0+forq6Gk+fPqUtgvX19bFixYpOLecqKyuRkpKi+A/GAN2t8qWrk5qaiurqamhoaMDU1LTT10skEvr8cHJyYnQt6urqcHJyQlxcHLKzsxkRTc3MzDi//reFmpoa+vTpo/JKXj7xxhtv4MSJE7h8+TJWrFjBmb1eW1CVhHwXCadMmYLBgwfzKhDMJFRQt3fv3ipJNLO0tKSruWJjY+Hh4cH5GvgOleTIlEjIZCWhWCyGpaUlnfDGBl5eXqyNbWhoiPT0dM76Ej5+/Bjl5eUwMDDA6NGjOZmTK9zd3dGnTx+kpKQgPDy8lUOKjY0NzMzMlLZ6XLp0qcyVPlQ/QlmEAUokPH/+PO3ws3jx4jarBH/55ReUlJRAT09PqRYHTFJaWoqQkBBERETQ+2ELCwusXbuW03VQSSed7cUJgsDAgQNx48YNLpb1HNHR0Rg7dqxKk6tfNGxsbBAfH99pb0BAqOgTEBAQ6KoIIqGAzFDWalZWVioTtKjsZIlEopJKt47Q0tLCggULcPz4cZl6e3h5eWHUqFF04IAPEASB3r17o6ysDAUFBZwGiRsbG2nxl2mRsK2Mt2vXrrUZXKEyImW1zaMEp856aigDZbGTl5eHyspKpSsqmEYkEmHSpEnYvXu3wmPo6upi4MCBMr++sbERf/31F109OG3atFYBcxsbG9jY2NBZpg0NDUhKSqJFw7CwMJSWluLWrVuYOHFih3NRFWOqRNgE8wuqqt7a2lqmIG1hYSGkUikIgkBpaalS1rFtQYmEOTk5cp1HXYG+ffsKImELxowZAzc3N8TGxuL06dMq7QFHiYRcVPMqct8zMjLCnDlzZBLyuzKUSMhWpVZniEQiODs7Izw8HKGhoYJI2AZsVRIyZXVpbW3NmkhoZGTEqjUyVa2SkZHB2hwtoaoIp0+f3u2qZgmCwOzZs7Fx40aEhoZi5MiRdHIFQRDw9vbGqVOnFBq7d+/eWLhwoVzXY+qYlOX4sbW1hYODA1JTU9HY2AhbW1vMnz+/1Wuqqqqwc+dO2v2ETUt6WcnJycG9e/cQHx8PkiQBNO9hZs+ejYCAAM5650okEly8eFEuV58BAwaoTCSsqanB3bt3MWrUqC6TACRLJS7fxTVqf90d6A6/j8zMzOdE25buElFRUawmAAkICHQ/+KOwCPAe6mGxpqZG6b5HirJnzx4AzRVAqn6ob0lJSQliYmLw+PFjWrRoD3V1dbz22mucZN7LS3l5OcrLywE0f8Zcsm/fPjQ2NsLExAQWFhaczt0SeS2cKEGTEguZhiRJ7Nq1C0DzscOn474ldnZ2GDBgAJ1MIC/Dhw+X62d79uwZSkpKoKOjg88//7xTYURDQwOurq50xvCZM2ewefNmmbIhqXNClezcuRMuLi7w9/cXGrLzAKoPi6xV9dra2tDQ0EBDQwPWrFmDCRMmYMWKFYyIKykpKbh16xYA5S0Z+Ygq7BP5DEEQeOedd7BkyRLs2bMHo0ePVpkNdVlZGQBujjtZrtX/ZsqUKd1eIAT+EZ5UVVUqlUrpnqvd8RrEBFQ/TaaS4ChhmCmRis2ExYEDB7K6Z6Sufw8fPkRtbS3rogr1TEjZ7HY3xo4di19++QUVFRXIzc1ttSdzc3NDSEiI3NWEBgYGWLx4sdyJDDo6OsjPz8evv/4KExOTTnu/L1y4EF988QXU1NTw9ttv07ELqVSKixcv4q+//kJpaSkAYPTo0TLbxTMNSZJISUnBvXv3WgXwhw4dijlz5mDIkCGc9bqUSqW4desWdu7cSbdoMDU1haamZqfv1dfXh6WlJW09zjU3b95EaWkpAgMDeZU4/m/ktemMj4+XSZj673//C2tra164UXUl2Pp9cE1mZiZcXFzo54G2oK6ZbNi/CggIdE/4ezcV4B3Dhg2Duro6ioqK8OzZM84a3VOEh4fT2YuBgYEqzxqrq6tDbGwsYmJiWvU964zGxkbapoZvhIWFgSRJeHh4cPr7TU5Oxt9//w2g2QKG6Yfdffv2tepD1JHdKFVB2DILqyN8fHwQFRWFkJAQREZGMp5Bf+LECZw7dw4AMHfuXF73U5oyZQpycnIU6k/h5uYm1+upChZPT0+FKqeoqlVZgkl8EWapKsjBgwdj9OjRQjBWRdTW1tIiYd++fWV6T+/evfH666/j2rVriI6OxqVLl3Djxg189tlnGDZsmMJrCQ0NxYcffkgnWLBp6aYqqICewD+89NJL2Lt3L65du4bvvvsO33//PWcBxZZQgQmmq///TVNTExITE+V+37lz5/Dqq692u2qff0Pdz1T1fBATE4OcnBxoaWlh2rRpKlkD36FsE5mqIqeEKqbEvaqqKkbGaYu27B6ZHt/AwAAlJSW4c+cO633KqOQeZW03+YqGhgYGDx6MO3fuICkpqZVIqKamhuXLlyM/Px/Z2dn0V0dJHL1791ZIIASAOXPm4OjRo0hKSsKGDRvg6+uLpUuXws7Ors04gL+/P9zd3aGhoUE/I8fHx+Onn36iHQkMDQ0xadIk9OnTR+71MEF8fDxu3bqF/Px8AM2JPwEBAZg/fz4cHBw4WwdJkggNDcVff/1Ft0rR0tKCj48Phg4dKnOcxcHBQWUiIdBsO1pUVIT58+fzdl8kq01nfHw8Fi1ahKKiIplEqXnz5gk9+hSArd8H1xQVFaGmpgb79u3rtC0J3ysiBQQE+IMgEgrITM+ePTFy5EjcvHkTkZGRnIpI1dXV2LRpE4Bm60UuH6LboqysDD/++KPC7w8NDWXVekcRmpqaEBERAQCYOXMmp/Nu3LgREokEs2bNwpw5cxifQ09PT+ZAClW10lFWVksMDAwwZMgQhIaG4o8//sCvv/7KWLA2LCwMv/zyCwBg3LhxjPcyY5oePXpg3rx5+O233zp9rZqaGszNzWFpaQkXFxe5A12USKhoLxEqICZLcJsvIiFFeHg4wsPDMWrUKIwYMYL1AL1AayIiIiCVSqGnpwdjY2OZ39e7d2/MmDEDQ4YMwfnz55Gbm4v169dj8eLFePnll+U+zs6dO4fvv/8eJEnCzs4O8+bN65ZiiCASPg9BENi2bRv69++PqKgonD17ViXiDFciYWpqqkK9qIuKinD9+nVMmDCBhVXxB1WLhBcuXADQXMXDJwt9vkCSJNLS0gCAMacMSiRk6nfOpkjItjsI1Rvt5s2buHTpEusiIZXkSVXPdke8vb1x584dJCcnP9d3USwWw8LCAhYWFhg6dCiA5sTZnJycVsIhdUwtWrRIYStkDQ0NBAUF4fz583j8+DHu3LmDO3fuQF9fH+7u7vDw8ICHhwesrKxoUYsScUtLS7Ft2zb6+qSmpgY/Pz94e3ur5LmeJElcvXoV9+/fp9czY8YMlVhix8bGYtu2bbR1vrq6OkaMGIHhw4fLfT+3t7fHnTt32FimzGRnZ+PPP/9EUFCQypwVOqM72XR2B7rT78PFxUUQiwUEBBhDEAkF5GLs2LG4efMmIiIiOA1I/f7778jPz4eenh4CAgI4m7ct6uvrceDAAaXGiI+PR0VFhcx977ggNjYWNTU1MDU1xYgRIzib99ChQ0hKSkLv3r2xdetWlVeIUiKhrJWEQLNlTWRkJBITE3Hz5k34+/srvY6srCx8+OGHIEkS7u7unP5OlMHExASzZ8/GsWPHWn3f2NgYlpaW9JeJiYnCm3SSJGmRsH///gqNQQUvZLHT4ZtISHH79m2EhYXBy8sLdnZ2sLCw6JYiEd94+PAhgOY+gIpcr6ysrLBs2TJcunQJYWFh2Lt3L+Lj4/HRRx/JZK1JkiR27NiBffv2AWi2cps6dSqvrZaUgbK0FGiNvb09Nm7ciDfffBO///47vLy8OO0jDPwjErJtdSVL35j2ePjwIZydnWW2Bu6KNDQ0AJCtMp5pqqurabvjZcuWcT5/VyA/Px+1tbUQiUQwMTFhZMyuIhIaGRlxksjk7u5O708LCwvlSuCRF0ok7K6VhABoV4Jnz56hpqam0+NMU1MTDg4OdBIvSZKorKyEurq60tclkUiEKVOmwNPTE9evX0dGRgZKS0tx8+ZN3Lx5E0DzcTZo0CB4eHjA3d0dDx48wJ9//kn313N3d8fYsWNVVmnW0NCA48eP0xXx8+fPR3BwMOdxgJSUFGzfvh0PHjwA0PzZenl5wdfXV+FribW1NdTU1NDU1MTkUuWmqqoKf/31F2bMmAF3d3eVroUrwsLCUFFRAV9fX97uVQW4RyKR0MK9cGwICAjIS/eMKAmwhr+/PzZs2ICoqChIpVJO7K1CQ0Nx9uxZAM1N4lXpuy6VSnHs2DEUFBQoPVZ4eDj8/PwYWJXykCSJR48eAQCmTZvG2cNERkYGdu/eDQDYunUr5wHOtqA2bLJWEgLNdk8+Pj64efMmtm/fDl9fX6XEmqqqKnz44YdoaGiAlZUVL+x15YGyDi0rK6OzjWUR42SltLQUNTU1UFdXh6Ojo0JjUP2BupLdaFvU1NTQQRKgOWPfysoK1tbWsLa2Fvq5MQxJkrRIKKvVaFuoqalhypQpsLKywpkzZxAWFoaVK1fis88+g7Ozc7vva2howMaNG3Ht2jUAwKhRozBmzJgudX2QF6GSsH3WrFmDQ4cO4d69e/j++++xceNGTo8FKpmGTRFAIpHQNnGKcurUKbz22mvdtupalZWEN27cQH19PVxcXODt7c35/F0BymrU2NiYsUSeiooKAPwXCbmq6tHT04O1tTWysrJw69YtVlxJKKhKte5cSWhsbAwHBwekpqYiJSUFAwYMkOv9BEEwKoARBAELCwssWrQIEokE2dnZSEtLQ3p6OjIzM1FUVISrV6/i6tWrrd5nbm6OSZMmcd4ipSUVFRU4cOAA8vLyIBKJsH79eowbN46TuUmSRFZWFiIiIhAaGkpXMRIEgUGDBmH06NFK7xPU1NRgY2NDX+dUzcmTJ5Gfn49x48apxIadS1auXAmg+fotVPELUNTV1dExRuHYEBAQkBdBJBSQi6FDh0JbWxvl5eVIS0tj3c+/qqqKthmlqmVUyZUrV2jffmWJiIjAqFGjeCFAPHv2DLm5uXS2JhdIJBJs2rQJjY2NmDx5skzNo7lAXrtRiuHDhyMsLAw5OTk4ffo0Zs+erdD8EokE//vf/5CVlQUdHR3Mnz+/S1YIydtjUB6oHqB9+/ZVOGmgO1QStkVOTg5ycnJo0V9HRwfW1tZ0z56WUBnWArKTnJyMoqIiqKurM3I/cnd3h5mZGQ4fPoz8/HysXr0aa9eubTMxoKKiAhs2bEBMTAwIgsDUqVMZ74HKRwSRsH1EIhF27NiBAQMGICwsDBcvXsSkSZM4mVsikaCurg4Au5WEaWlp9DyKUlZWhsuXL2Pq1KkMrYpfUJWEqhAJKSu/ZcuWdetkBWVguh8h0HUqCdm2Gm2Jq6srsrKycPPmTU5Ewu5cSQg0W46mpqYiOTlZbpGQTcRiMW0VOHr0aDQ2NuLZs2e0aPjs2TNoaGggICAAHh4eKhWKsrOzcfDgQVRVVUFTUxPfffedwm0SZKWwsBARERH017/7rrm6usLPz48+jpnA3t6eNyIhADx48ACFhYWYPXs2o0mqAgICAgIC3Z2uF3kWUCnq6uoYNWoULly4gMjISNZFwq1bt6KwsBAGBgYYO3Ysq3N1Rnh4OF1BwgRVVVWIj49nVUyRldDQUADA+PHjOas8OnHiBOLi4qClpYXff/+dN8ElRUVCDQ0NjB49GufOncOePXswYcIE9OrVS64xqqqq8PvvvyM0NBRisRhBQUFyj/EioKzVKNB9RcJ/U1FRgbi4OMTFxal6Kd0CyqLJwcGBMfHe1NQUK1aswMmTJ5GYmIjNmzfjyZMnWLt2LV35lJOTg/fffx9ZWVlQV1dHUFCQynvzckFdXZ1c1s8vIn379sUXX3yB9957D1u3bsXQoUNhZGTE+rwtfy9sVug9efKEkXEiIiLQv39/1p9bVQFVSci13Wh6ejqePHkCkUjEm0QvPsK0SFhXV0cL50yIhFKpFNXV1UqP0xZcioT9+/fHxYsXERcXh4KCAsasXf8NlXBVXl6O2tpaldj8coGXlxcOHDiA5ORkkCTJm33av1FXV4e9vT3s7e0BNCfAicVilVeRPXnyBMePH4dEIoG9vT2++uorRhxzJBIJGhoaWn0lJycjIiICkZGRdCIlhUgkgo2NDezt7dGvXz9WzgsHBwfa4YIvJCcn448//kBAQABcXFx4e/wKCAgICAjwCUEkFJAbf39/WiRkM1PzwYMHuHjxIgDV24ympaXRlqdMEhoaqnKRsKqqihYQZs6cycmc5eXl2L59OwBgy5YtKrWB+TctexLKuyn29PTEw4cPUVxcjIMHD2L58uUyvS83NxfHjx/HyZMn6Z4OM2fOZDTrvDtBiYTKZOO+KCKhALNQIqEyVqNtoampifnz5+Pu3bu4ceMGLl68iOTkZHz22WcoLy/HunXrUFdXBx0dHSxcuJC14CffEKoIZeOtt97CkSNHEBoaii1btuCLL75gPSBGJdKIRCLWrpFSqVRpq9GWnDlzBqtWrep2tqNUJSHXllLUM3pgYCAv7OL5SlpaGgDmBDPKalQkEjGyN6Ked9mAy+Oid+/esLW1RUZGBm7evIl58+axMo+2tjY0NDTQ0NCA3Nzcbpuw4+bmBm1tbVRXVyMnJ4cz61hlUXVvbpIkcefOHdy4cQNAc0Xmhg0b5Lo+kySJH374AZGRkWhoaEB9fT0tCHbW+48gCJibm8Pe3h4ODg6wtrZm/TMxMzODpqam0lX/TFNWVoYjR47A2toa48ePh5WVlaqXJCAgU59tIyMj2NjYcLAaxaBsnjuCLYcCAQEBdhFEQgG58ff3BwBER0dDIpGwEhxqaGjA5s2bATTbOKryJllcXIw9e/awMnZmZiby8/NhamrKyviyEBYWBqlUCldXV8YD3+1x+vRp1NXVwcPDAytWrOBkTlmhemiQJIm6ujq5MoRFIhHGjRuHQ4cO4eDBg5g+fTqMjY3bfX1cXByOHDmC27dv0wEaY2NjjB49mnU7mq5KQ0MD8vPzAQiVhFxz6NChVgFJa2tr+Pj4vDCfT0lJCS1YODk5MT4+QRDw9fWFpaUljhw5guTkZCxduhQSiQQSiQRmZmYIDg5G7969GZ+brwgioWyoqalhx44d8PDwwP3793HixAnMmjWL1TkpkVBDQ4M1QTI9PZ3RStLy8nJcuXIFgYGBjI3JB1RRSdjU1ITLly8DaLYaFWgfqpKQKcGspdUoE+ceW4E8c3Nzzu3y+/fvj4yMDNy4cYM1kZAgCBgaGiI3Nxd5eXndViRUU1PD4MGDcfv2bSQlJXUZkVCVNDU14cyZM4iJiQEAzJ49G6tWrZL7OTklJQWnT5/u9HUikQhqamrQ1dWFnZ0dHBwcYGdnx7nFpkgkgr29vUzihyrIysrC9u3b4ebmhrFjx0JPT0/VSxJ4ATEyMoKWlpZMzgtaWlqIj4/npVCYmZkJFxcXuV23BAQEugaCSCggN+7u7tDX10dpaSkSExOVCtS3x4MHD1BUVITevXvTjXdVgVQqxaFDh1id49GjRyrrkyOVShEeHg4ArAcUKerr63Hy5EkAwNtvv807+w91dXVoaWmhpqYG1dXVcgfdnJ2dYW1tjaysLLz77ruYNWsWxo0bR1tC1dTU4Pr16zhz5gyePn1Kv8/BwQHDhw9Hnz59ePeZ8ImcnByQJAkTE5MOBdjOEERC+dm7d+9z3xszZgw++eQTFayGeyhbZnNzc1aFOgcHB6xatQqHDx9GdnY2gGZRcs6cOSqtqFcFubm5ql5Cl8HNzQ3fffcd1q5di99++w3Dhg1j1eqPCg6wWZUXHR3N+Jjh4eGwtLTsVv08VdGT8Pbt2ygtLYWJiQkmT57M2bxdEUokZOp8pERCpipHu0M/QgrKcjQhIQF5eXmsVTIaGBggNze3296jcnJyEBISQj+DpKamYsyYMapdVBfg5MmTiIuLA0EQWLt2LaZNm6bQOLdv3wbQ/Dzo7+8PdXV1qKmpPfelakvVlvBZJKSIjY1FbGwsFi9e3G3FfQH+YmNjg/j4+E4r8OLj47Fo0SIUFRXxUiQsKipCTU0N9u3bBxcXl3ZfFx4ejldffZXDlQkICDCBIBIKyI1YLIa/vz+OHTuG0NBQVkRCqhm8nZ2dSm1D4uLiUFhYyOocERERGDlyJN3jgkvy8vJQVVUFbW1t+Pr6cjLnqVOnUFJSAmtra8ydO5eTOeXF3t4ecXFxSEpKkru3E0EQmDhxInbu3ImMjAxs2bIFP//8MyZOnAig2Z6LsooRiUQYOHAghg0bptJq0q5EY2MjACjVq7GkpIQOcMsyjjJiZHeHCk6/CJSUlAAAJ1afOjo6WLp0Ke7evQuCIODj48NoMIgkSTQ2NqK2tha1tbWoqamh/07928zMDO7u7ozNKS+pqam4e/euyubviowePRpAc88ituwDKSorKwHIlmihCImJiXQ1BtOcPn0axcXFGDt2bJdPyiFJkn5O5cqGuKysDL/88gsA4LXXXlO5vR+fIUmSdj8wNDRkZEzqXsSUKMzWOUA5c3BJr169YGpqiry8PKSmprIqEgL/7Fe7KlevXm2V6JGSkoKQkJDn+toxdex2dwoKCgA0J8FOmTJFoTEkEgkuXboEABg0aFCXqeB0dnbG+fPnVb0Mmdi7dy/eeOMNlcReZKEzsZXvYqxA+9jY2PBS+FMEFxcXeHp6tvv/gt2ogEDXRBAJBRRi0qRJOHbsGEJCQvDyyy+zNo8qM+RIksS9e/c4mevmzZucVfK1hOqT4u7uzkmQp6amBvv37wcAfPLJJ7ytihk/fjzi4uIQHh6OwYMHy71OCwsLvPXWW4iOjkZ4eDiKi4tb9bQ0NDSEp6cn3N3dWe0hRJIkbt68iezsbGhqaqJHjx7o0aMH/fd//2loaMjb3wkF1TOS2ogrQkhICIDmijBZqmBMTU3h6OiI5ORkhefsjvTv3x/vvvuuqpfBGdS50Vk/GKYQi8W06MMEjY2NOHPmDNLS0lBbWwuJRNLpe3r06IF+/foxtgZZKS4ubrNyVaBjNmzYAADw8/NjPbBYVlYGgJ0+eJWVlTh48CDj47bk3r17KCkpwcyZM7u0yFVcXIy6ujpoaGigT58+rM9HkiS2bNmC0tJSuLq6Yv369azP2ZWpqalhJLmpJcXFxQDAWEU7W9Z7stxj2EBfXx95eXmsCnj6+voAQAvAXZUffvihze8TBAEbGxs4OjrCycnphemDrCxU0owy5/qjR49QUFCAnj17dlilwzd0dHQwdepUnDlzRtVLkYkDBw5g+fLlvOpRLI8dZc+ePbF27Vro6+t36WcYgP+iKN/X92/U1dWxceNG+u8CAgIC8iCIhAIKQVVFJSQkoLy8nA7cdydSUlI42/w9fvwYI0aMYC3jtT3S09MBgDPbraNHj6K8vBxOTk6sisvK4u/vj19++QXFxcXYsWMHFixYIPcxrqWlheHDh2PYsGHIyMigbdPc3d1ha2vLSfVCVlYWbVkjCzo6Onj99dd5/UBJBbOqqqpQXV2tUID64cOHAOTrKzdq1KgXXiQ8depUq89bJBJ1+SoceaACCVTAtyvR1NSEQ4cOISUlRa73HTp0CG+//TZjwW1ZqKurw4EDBzibr7vw+++/4+zZsxCJRFiyZAnr8zFteUhBkiRtSc428fHxKC8vR1BQUJft9UlV/Dg7O3Ny77527Rpu374NsViMPXv2cN77qqtB9bHV0dFhrPKPqiRk6rqsq6sLgiAYrz5WlUhIPa+zuYejREJlEtb4gIODQ6uE3N69e8PR0REODg5d7tyWSqXIz89HRkYGampqoKGhgR49ekBDQ6Pdv2tqajL6HEu1qFCmgobqRTho0CDOe3oqi4eHB1JTUxEXF6fqpXRKUVERjh07hqCgIN7YtspqRwk0C4pdvSJN3h598ro7KQvf19ceGhoaL1QSr4CAALN0rScPAd5gaWkJd3d3REdHIzQ0FOPGjVP1khjn/v37nM53/fp1BAcHczafRCJBRkYGgOaNCNtUVFTg8OHDAID//e9/vN749OrVCz/88AM++ugj5Ofn4/fff0dwcDCsra3lHosgCNjZ2cHOzo75hXbCgwcPAADe3t4YOnQoqqur6S9KZKO+8vLyUFFRgcTERLi5uXG+VlnR0NBAz549UVtbi4KCAtjb28v1/sbGRoSFhQEA+vbtK/P7rK2t4eDgQPcWehERi8UvdH9GSiTkqpKQKaRSKY4dOya3QEhx6tQpBAcHcyIIS6VSHD16lK6UEZCNO3fu4PXXXwcALFu2TKF7lbyUlpYCYL4PXn5+PqfX2ZycHPz1118IDg7ukrbfz549AwBWrP//TWFhIX788UcAwMcff9yhzZRAM48fPwYARvtNU9dHpkRCsViM3r17o6KigpHxKFQlElLJZEIlYefMmzePV5VU8iCVSpGbm4v09HRkZGQgMzMT9fX1co1hbGyM+fPnM2anSgmrlB23vOTn59NuJ4MHD2ZkTVxCEAQCAwORnZ1Nuw3wmaSkJFy/fp1XcazuZEfZGXwXRfm+PgEBAQE24G+UXoD3TJo0CdHR0QgJCeHVwxUT5OTk0FacXJGUlISMjAzY2tpyMl9OTg4aGxuho6PDSfPuQ4cOobq6GgMHDsS8efNYn09Z+vfvj99++w0ffvghUlJSsGvXLkyfPh0DBw5U9dJkoqSkhM5gX7lyZadi2o4dO7B3715ER0fzWiQEmrPEa2trkZeXJ7dI+PjxY9TU1EBbWxsWFhZyvXf06NEvtEjIFg0NDYiIiIBIJIKHhwdvK1kpu9GuVElIkiROnTpFXwsUITk5GY8ePYK3tzeDK3ueoqIi3L17V2Ex80UlMzMT06ZNg0QigZ+fH2fJRlQAkGmRMDY2ltHxZKGiooJOBpKnwpwPUCKhq6srq/OQJInvvvsOVVVVGDJkiGAzKiOUSCjvs0pHMG03CjQLa91FJOSykrCri4RdiaamplaiYFZWltJ9sQsLC/HLL78wllyjrEh47tw5kCQJe3v7LtsHUlNTE3PmzMFff/2l6qXIxL179+iWGxRd4TlfIpEgIiICAODp6dllkzj5LoryfX1t0V2ODQEBAdUgiIQCCjNp0iR88803CA0NhVQqZdSqgWnLG3nhqhfhv7l27RqWLl3KScUGJYIOGjSIdZuNkpISHDt2DADwxRdf8MbWozNMTU3x888/48svv8S9e/dw4sQJFBYWwt/fn/c2i1QmqpeXl0zBqfHjx2Pv3r1ISUlBZWUlr63XdHV1kZeXp1BwhrIadXR0lPt3aGNjAzs7O9qmV0BxpFIpYmJicPXqVdy+fZsOqOjq6sLf3x8TJkxA3759eXWedTW7UZIkce7cOcTExCg91sWLF2Fvb89oXyKSJJGfn4/4+HjEx8ejsLCQsbFfFGpqajBjxgyUlZXByckJ7733HmfnDBt2oyRJqkQkpNi/fz/GjRsHb29vXrsdUNTV1dF2h2yLhGfOnMGjR4+grq6OPXv28DaZg29QIiGTyXhM240CzSJhZmYmY+MBqq8kFETCrkV9fT3Ky8tRVlbW6k/q78pYeHbGjh07MG/ePKV7AFJ2o4qIhE1NTTh//jyArllF2BJLS0sEBATgypUrql6KTFy/fl3VS5Cburo6eHl5AWi2t2WjP7SAalG0F6JwbAgICCgD/3fAArxl+PDh0NXVRXl5ORITE7tUc+2OKC4uxpMnT1Qyd1ZWFoqLiznxNKeEDi6sRvft24f6+np4e3sjMDCQ9fmYpGfPnvjf//6H7du3Y//+/bh79y6Kioowc+ZMurKIb9TV1dEZZHPnzpXpPVZWVnB1dUVcXBzdI5OvUFniivSCUaQfYUtGjx4tiIQKQpIkkpOTce3aNVy7dq2VfUuvXr1AkiTKy8tx4sQJnDhxAra2thg/fjwCAgJgbGyswpU305XsRkmSxJUrVxAeHs7YmMePH8fy5cuVEk9IkkROTg6ePHmChIQEOtgtID8kSWLZsmWIjIyEnp4ePv/8c057SFGVhEwGH549e0aLj6ri6tWrCAkJwfDhwzF48GDe3ucBIDs7GwBgZmYGAwMDVuf57bffAADffvttt3ne5wIqSYNJkZCtSkKmUbVIWFZWhtraWlq4YRLqfBNEQsWpq6vDkydPEBsbi7y8PNTW1qp0PYcPH8bEiROVck1QRiR88OABiouLoa2tjX79+im8Br4wfPhwpKWlvfD93PmAomKTgGroqr0QBQQEugeCSCigMOrq6ggICMDRo0cREhLSbYIGVB83VfH06VPWb/ZNTU3IysoC0NxknE3y8vJw9uxZAMBXX33Fq8ogWRGJRFixYgVsbW3x7bffIiEhATt37kRQUBAtWPGJ8PBwNDU1wd7eXq5s1AkTJiAuLg7R0dEYPnw4b39XilpJZWdnIysrCwRBoE+fPgrNbWdnB1tbW7qfp0DnZGdn4/r167h27Vqrz61Hjx7o378/BgwYQNssp6SkICYmBvHx8cjIyMC2bduwbds2DB48GOPHj4evry8rAT9Z6EqVhLdv32b8Xpafn4/r169j/Pjxcr83JyeH/r0ybWn3ovLtt9/i0KFDEIvF+Oyzzzjvp8eG3agqqwhbUllZicuXL+Pu3bvw9vaGl5cXpwKsrHDRj5AkSWzcuBF1dXUYPXo0/vOf/7A2V3ejsLAQ+fn5dG9qJqipqaHFFKYrCZlGVSKhpqYmevTogfr6ehQUFLDSxoGqJKT6enfVSo3Dhw+3sqLT1dWFq6srbG1tWdkDNDU1ITk5mX4e4BsXL15EWVkZxo8fr9DPr4zd6JkzZwA0J+92B3tAgiAwY8YM7Ny5U+k+0yKRCEZGRjA1NYWJiQlSUlKEhE0ZEMSmronQC1FAQECVCCKhgFJMmjSJFgmXLFmi6uUoTW1tLaOVF4qQlJTEehXXs2fP0NTUBH19fdZ7IO7ZsweNjY3w9/eHv78/q3Oxzfjx42FhYYF3330XeXl52LZtG4KCgmBlZaXqpdFIJBI8evQIQHMVoTybXD8/P/zwww8oKChAXl4ezM3N2VqmUihqJUVVEdra2ioV8B01ahT27t2r8PtfFCorK/Hll1/S1rdA80a/X79+cHNzg5OT03NVaU5OTnByckJdXR3i4+MRHR2NjIwMhIeHIzw8HJs2bcLUqVOxcuVKWrTjiq5SSfjgwQPcvHmTtbEdHR1lroqprKzE1atXGbE8FfiHc+fO4YMPPgAAvPnmmyrplct0JaFUKlWZi0N71NTU4MaNG7hx4wZ8fX0xbNgwxnswKgMX/Qjj4+MRExODHj16YOfOnV3GLp4PUFajFhYWjCW35ObmAmiuWGLyHtidREKg+efJz89HXl4eK/sc6vOvr69Hfn4+J73d2aCtPtthYWHo3bs33Nzc4ObmBnNzc6UEQ5IkkZmZiZiYGDx58gR1dXXKLJl1Hj58iIqKCsycOVNu5wRFKwlzcnIQGhoKoOtbjbZEW1sbq1atQnFxMSoqKlBZWUn/2fLv1dXV9Hu0tLRgZmYGExMTmJmZwdTUFEZGRq1+FyNHjkRaWhquX79OV9QLPI8gNnVdVNkLUag8FRB4sRFEQoYhSZK31TdsMHHiRABAYmIiysvLGa+q4nqDGRcXx+l8bZGeno66ujpWs9ap7DsPDw9Wj9f8/HxcunQJAPDll1+yNg+XuLm5YefOnfjwww+RmpqKnTt34pVXXoGFhYWqlwagWWSuqKiAvr4+xo4dK9d7e/XqhdGjR+PGjRuIiYnhrUioaCUhtQFX1GqUwt7eHtbW1nQ1rkDbfPTRR62s1gYMGIB+/frJdG3T1NSEh4cHPDw8UFZWhpiYGERHR6OkpAQnTpxAWloavv/+e04D1lSAgs+VhE+fPsXly5dZnePUqVP4z3/+0+lnX11djc2bN7O6lheR/Px8zJ8/HyRJYtq0aZg2bRrna2hoaKCrmZgSzTIzM1ntOaUsd+7cwZ07d7By5UqYmZmpejmQSqWciIRUT6l58+bJ1N9Y4B+oylgmBSRKJKQq2ZiCDZFQlQk1urq6yM/PZ80OlCAI6Ovr0/2xu6pI2B6VlZV48OABHjx4gAEDBmDmzJly7xebmpoQExOD+/fvK11JxjVPnjxBeXk5Zs+eLde5Rj3fZmZmYteuXXRyaUdkZWVhy5YtAIA+ffowfm6rGrFYDBMTkw57WkskElRWVkJNTQ3a2tqdHmsEQcDBwQH29vZ4+vQprl+/rlALihcBVYpNAl0LofJUQEAAEERCRqisrER9fT20tLR4lWHMBQYGBhCJRJBKpYwGTi0tLQE0Zziy1U+iLRSxB2GDzMxM9O3bl7XxqYw9tivgMjIyIJVK4erqimHDhrE6F5eYmZnh559/xieffIKwsDBcuHABr7zyiqqXBeAfAcPIyEihXkpOTk64ceNGq6xOvkFVrshrW0iJGsoGrgiCwNixY7Fr1y6lxunuUNnijo6OWLhwocLj6OnpYdSoUfD19UVKSgoOHTqEqKgonD17llOB5Nq1awAAQ0NDzuaUF6palk34LJK+COzZswfV1dVwdHTE66+/rpI1iMViaGtro7q6GsXFxYw8S3QVG9oTJ05gxYoVSvXmZILk5GTU1dVBR0dHYftsWaAqRtm2pu+OUHsX6jNkgoaGBgDNLR+YhBqXSZheozzU1NQAAKuOA9TP193viY8fP4aFhYXM+ziSJJGYmIhLly4xeuxzTXZ2Nn766SfMmTNH5kQMExMT9OzZE7W1tdi9ezd2796NgQMHYuLEiRg9enSrOFF5eTn27NmDEydO0EnmPj4+cq+zrq4OtbW10NPT67KJ6mKxWKFEBYIg4OzsjL59+yIuLg43btwQel0LCCiIUHkqICAACCKh0jx+/BhLly5FfX09ysvLMX/+fAQHB8PDw0OuqsL6+nrU19fT/+4qwZL4+HhIpVLo6OgwGjgdMWIEXalz7tw5zJ49m5MHX76IvOnp6ayKhJRYwnalJrVxZrJvSkdweR5paWnh/fffx5w5c/Ds2TNUVVVx9nN2BJXNnJSUhOLiYrnPS2pz1bt3b8bXxhRUYKahoQESiUTm3h2+vr548OAB4uLiMGrUKKXWYGtri2XLluHIkSO8SS5gCqbOoyVLluCDDz5AWloampqalA6qEwQBR0dHjBs3DhcvXsQvv/wCHx8fGBgYKDWuLJSXl+PAgQMAmo8jPlJaWoq0tDTW5/Hy8pKpglNbWxt+fn64ceMG62viI2zcj0iSpJMTZsyYobIgvFgsxtChQ3Hz5k0kJiYyIhJ2lR5MBQUFuHXrltyV+kwTEREBoLmXMJuCJdXrkqpa5Jquuj8CQPdvjYuLY+wZkRqj5WfCBHl5eYyOBwA6OjqMjykLjY2NtAUhm1bMbPSGZAtlz6NLly7B2tqaTuJtj5KSEly4cAHJyckKrZOPHD16FElJSRg7dmyneyNNTU289dZbSEhIQFRUFFJTUxETE4OYmBhs3rwZ/v7+GD9+PNLS0rBt2zZanO/bty8CAgLkqswhSRKhoaG4cuUKmpqaoKGhAVNTU5iZmdE2nSYmJioV67mCIAi4ubmhf//+iI+Px927d1m5pgkIdHeEylMBAQFBJFSCjIwM+Pv7IygoCDNmzMC9e/dw48YNXL16FZs3b4afn5/MQuHXX3+Nzz77jINVMwvVb8Pe3p5REU8sFmP9+vVYs2YN4uLi4OzsjAEDBjA2fntwVbHYGZmZmayOz7VIyFXvMK7PI0NDQ5iamiI/Px+lpaW8CBRoa2vD0tIS2dnZCAkJweTJk+V6P2UJxIefpT1aVkhSVdyy4OPjg++++w4FBQUoLCyEsbGxUuuwtrbGypUrcfz48Tb7unRVmDqPhg0bBk1NTdTV1aG8vJyxRJKhQ4ciJiYGOTk52Lp1KzZs2MDIuB1x8OBBNDU1wczMDP369WN9PkWIioriZJ6hQ4fK/NqRI0ciLi7uhbSBYuN+FBYWhidPnqBHjx4YPXo0o2PLy4gRI3Dz5k08ffqUEcGsq4iEAHD37l3069ev04A5W1RWVuLp06cAgClTprA6F3WfZPu5tD266v4IaE4mcnFxQXx8PMLCwjBmzBilx6ScFJju68ZGQJ3pFhSy8uzZM5AkCSMjI1atgbuSSMjEeXT06FGsXLmyTcv4xsZG2pK5OxIdHY3o6Gj4+vpi5MiRHe5r1dXVMWDAAAwYMADl5eW0XX5xcTEuX77cyhLexMQEEyZMkNuutqysDKdPn6YTw0QiERoaGpCVldWqFQJBEPR5QImGenp60NPT65bioUgkgqurK/r374+MjAwUFhaisbERTU1NaGxsbPX3lt+TSqWtxmlp581X1NXV8cknn9B/FxCgEI4NAQEBZRBEQiW4c+cOnJ2dsWXLFqipqWHs2LEYM2YMfv31V6xcuRI7duyQ2TZi/fr1WLduHf3viooKWFtbs7V0xqBEQjZ6Mbi4uODll1/Grl27cObMGdjY2LC+4eRLJWF2djYaGhoUsouUBSoYx3a/EK5FwvbOo3v37rXa1FI95ZjAwsIC+fn5KCkpUXrMyspKnDx5EgRBYO7cuQp/bo6OjkqLhHyuJGxZNVFXVyfzedu7d28MHToUISEhePLkCSNBdm1tbSxcuBCXLl3Co0ePlB6PDzB1PyIIAqampsjIyGBUJBSJRAgMDMS2bdtw/fp1TJw4US7hSh6ys7Oxc+dO2mrUz8+Pl3ZOUqmUE5Fw8ODBdJBaFsRiMaZOnYrt27ezuCp+wsZzHVVF6OPjo/LAtLe3NwiCQEFBAcrKypTuadaVREKguTfnqlWrVHI9iIqKAkmScHNzg62tLatzUZWEqhIJu+r+iGLSpEmIj49HSEgIoyKhUEnYPhkZGQCaqwjZOj9JkuxSIiET5xElTM2dO5f+XClr0YsXL6K8vJzRNfORO3fuIDw8HKNGjcKQIUM6vW/p6urC19cXPj4+yM7ORlRUFGJjY6GhoYHRo0fDw8NDrt7aJEkiKioKFy5cQGNjI8RiMdasWYOpU6fi2bNnSE5ORnJyMlJSUpCUlITy8nIUFhaisLCQjtlQ9OrVC/r6+tDX14eenl6rP3V0dHj5rCsrBEHAzs4OdnZ2Cr2/vr4e33zzDbOLYhgNDQ18+umnql6GAA8Rjg0BAQFlEERCJaivr0dCQgIKCgroptSjRo1Cjx498N133+GLL77An3/+KVPJdo8ePTgTUpikZSUhGyxatAghISGIj4/HyZMn8dJLL7H60MqXSkKguZE5W31muKokpGxUuDq22zuPvv7661b/VlNTw+7duzttJi8LlpaWiIyMVLoHQklJCfbu3Uv37zhx4gTmz5+v0PHu5OSEW7duISwsTG6bx65gN0oQBNTV1dHY2Ch3Nv2YMWMQEhKCuLg4xipxRCIRJk6cCIlEgvDwcEbGVCVM3o8okZDpvjTm5ubw8vJCSEgIfvjhB+zYsYPR60xhYSH27t2Ls2fPgiRJAICnpyecnJwYm4NJUlNTObHhU6S3rJWVFaysrHiflc00TD/X1dfX05a3EydOZGxcRdHR0cGAAQMQExODxMREeHt7KzWeqnv8yUthYSHy8/NZrVRqC5IkERkZCYD9KkKgudIFUJ1I2FX3RxSTJ0/G5s2b8ejRI7naULQHJUjV1dUxMh7QfEx1J5GQOlbZtBptWX3UFURCps6j+Ph4hIaGwsvLq1tai8pCTU0NLl68iJCQEPj7+8PV1bXT85AgCPpZaMqUKQqdt5WVlThz5gySkpIAAK6urnj//fdpu29KFBs3bhyA5vO6uLiYFg6Tk5Px7Nkz5ObmoqamBlVVVaiqqmpVeUiho6ODkSNHwtPTs8vdmwUEBAQEBAQUR7jrKwC1KevTpw8MDQ1x7949zJ49mxZevL29sXDhQrz99ttITU3t1r7ObFYSAs2Z5R988AGWLl2K9PR0PHz4EMOHD2dlLoA/lYRAcyZsVxcJua4kbA8rKyv6Zy4rK0NFRQV++OEHfPvtt0oHWCi7sdLSUoXHyM3NxZ9//tnqe4mJibh27Rq92ZMHCwsLaGtro7q6Go8fP4aHh4fM76WaVfM96KGoSOjj44NNmzahsLAQBQUFdABUWQiCwJQpU9DY2IiYmBhGxuwOUFUobGSY+/n54cmTJ8jJycG+ffvwyiuvKD0m1Xvw6NGj9PXR0dER/v7+MDc3V3p8tuCiitDZ2Vmufjkt6du37wsnEjLNmTNnUFpaCmNjY7mu6WwyYsQIxMTE4OnTp0qLhF2tkhBo7h/NtUiYnp6O0tJSqKurc2I5S13D8/LyUFNTw6vn5K4A5ShTUlKC8vJypStuqUpCkiTR2NjIiONIeXk54/algGrsRiUSCX2v4aIfIQC5quu7AxcuXEBRURFCQ0NVvRSVUlpaimPHjuHBgwcYN26czAnT8u47SZJEbGwszp49i4aGBohEIqxYsQJz587t8L5JWY0aGRm1SvAiSRIVFRXIy8tDTk7Oc3/m5+ejoqICFy5cwJ07dzBy5EgMHjxYsC3kGVKpFPHx8QCa3bfkqUgV6N4Ix4aAgIAyCFcMOaD8yynGjBmDgQMH4q233nouQDdjxgxoamri3LlzHK+SO4qLi5GTkwOAvUpCoFngefPNNwEAV69eZbW3EZ+CH2xmbb9oIuGiRYuwdOlSLF26FC+99BJEIhFCQ0NpC0FloERCRSsJU1NTnxMIKe7du6dQ8J8gCDg6OgIAQkJCZH5fTU0NHSjicyUh8E9fQnkDW7169aI3y3FxcYyuiSAITJ8+Hf3792d03K4MJcKyIRL26NEDkyZNAgDs378f6enpCo9VU1OD3bt3Y+7cuTh06BAkEglsbGywZMkSLFy4kNcCYX19PePHcluMGDFC4fc6OzszuJIXE8pqdPz48bwR1KhjIj09XWn7Q778TPJA9YPikoiICADNNpZcuF/o6OjQVtHdoVKea3r06EEnVyjrOEGNR0E9YytLbm4uI+O0RCQSqUQ8y83NRWNjI3R0dFi14qVEQk1NzS557VKWF10gbElOTg727NmDv//+G/n5+YyOXV1djSNHjuD48eNoaGhA37598ddffyEoKEjh444gCOjq6sLZ2Rl+fn5YsGAB3n77bWzatAl///03zp8/j7Vr18LExARVVVW4dOkSfvzxR9y/f592CBJQPbW1tXBzc4Obm1urpAUBAeHYEBAQUAZBJJSRhIQErF69GmPGjMG7776LBw8eAACOHDmCPn36YO7cubh37x69YWtqaoKFhcULUUVobm7OurgWGBiI4cOHQyqV4vjx46z10tPQ0OCNrcazZ89Y+zlfNJGwJYaGhnT2/datW5W26KMsSxUJ/sTFxWHv3r0dvubUqVNtWsF0BmWL+PDhQ5nfQ1URamhosNYPkymojFZFHn6pvkBxcXG0lSRTiEQizJo1i7e2lMqwceNGfP755/TXqVOnOv382KwkBIB+/fqhb9++kEql2LJlC23/JSsNDQ04cuQIgoODsWvXLjQ2NsLMzAzBwcFYsmQJ6/2+mIDqv8QmlpaWSvUBMzY2VrqC5kUmNzcXFy5cAABMmDBBxav5B2tra1hbW0MqlSptOdcVA+0ZGRlyX3OUoaamBk+ePAHAjdUo0BxQphJfqL2PgHxQSSZUz2dlEIlE9DM1UwF7NqxGdXV1VVK9QN0PBwwYwOr8XakfoQA3JCcn4/fff8eVK1cYEfCfPn2KX3/9FfHx8SAIAkuXLsXWrVtZTcwGmveA06dPx759+/D222/DzMwM1dXVuHLlCn788UfcvXuX8Z6oslBXV4fY2FicOHECZ86cYVyQFRAQEBAQEBDsRmXi8ePH8PPzw7Rp0zBkyBBcunQJIpEI3t7eEIlEOHfuHKZPn465c+ciODgYTk5OiI+PR1hYGLZu3arq5bMG2/0IW0IQBN555x0EBwcjPz8fN27cQEBAACvz9OrVi/H+WYogkUiQnZ3NSpCaCsaxJUJS8FEkBICRI0ciNjYWhYWF+OOPP/Duu+8qPBYlEtbW1qK2tlbmzP5Hjx7RQd/O2LFjB/7zn//IFWTv06cPCIJARkYG8vLyZLJE6wr9CCkoEVORjeqIESNAEASKi4tRVlYGfX19RtcmFosxb9487N+/XyWVJmxx+/btVv++fv06KisrsWjRonbfQx13bF1TCYLApEmTkJqaipiYGFy8eBGTJ0+W6b15eXl49913aWsyQ0ND+Pn5oX///qz2vmWalJQU1uegzhlFIQgCffv2xaNHjxhc1YvD33//DalUCldXV6XEWjYYPnw4srKy8PTpU7i6uio8TlcUCevr65Gbm0s7CrBNTEwMpFIpnJyc0LdvX07mBJp7X925c0cQCRXEzMwMjx8/ZqSSEGiuXquvr2eskpAtkVAVcNGPEPjHxUIQCQX+zf379/HkyRMEBgYq3DakoqIChw4dglQqhb29PdavX99u8qFUKkVlZSWqqqpQXV1Nf1E9B1t+TywWw8DAoNWXvr4+DA0Nn0sOVVdXR2BgICZOnIgrV65g3759yMnJwbVr13D//n0MGzYMXl5e0NTUVOhnlIXKykokJiYiISEBaWlprZJyIiIi4OTkhJEjR8LGxqZLPbcLCAgICAjwFUEk7ISMjAxMnz4dr776Kr766isAwLfffovHjx+jtrYWPXr0QK9evXDt2jV88MEHiIyMxNmzZ2FjY4MbN25wuonnGrb7Ef4bAwMDfPTRR9iwYQPu378PBwcHVnr28UUkBJqPPzZEQiq7lm2RkMpy5lvwTywWIzAwEDt37sT58+cREBCAQYMGKTRWz549YWBggJKSEpSWlsokEkZERMgsEFKcOnUKL7/8ssyv19TUhI2NDTIyMrBjxw7MnTsXJiYm0NHRaXcjlZqaCqBriIRUJWF1dbXc79XW1oaFhQWys7Px7NkzxkVCAFBTU0NQUBD27dunUCUoH/nmm2/oYEBaWhp+/PFHbN++HdbW1u32xqIqCSsqKtDU1MRKpbaenh78/Pxw5coV/PTTT/D19e30GH706BE2bdqEoqIi9O7dG2PGjMGgQYO6XN8GkiTp85Yt9PT00K9fP6XHEURCxdm/fz8AflURUowYMQKHDx9GUlISpFKpwucQ354TZCU1NZUzkTA6OhoAd1WEFFQlIeWYIvSmkg+qkpBJkbC8vJz3lYRcQ5IkZyIhVUlIEARIkhQECoFWlJWVYd++fQgICFDIqj0qKgpSqRQuLi744Ycf2nV3aWpqwurVq5GUlKTUejU1NfHjjz+2GbdSU1PDpEmTMH78eFy9ehX79u3Ds2fPcOPGDURGRuL1119n/P5dXl6OGzdu0Pc8Cmtra4wcORK5ubm4desWkpKSkJSUBGdnZ0yfPp0TC24BAQEBAYHujCASdoBUKsXdu3cRGBiItWvX0t/Pzc1FQkICBg8ejEGDBsHHxwevv/46vvrqK9TW1qKhoQHq6uq86m/HBlSwj+p9xgU+Pj6YMmUKzp07hyNHjmDlypWMB/j5YjcKAFVVVayMq6OjAwBK9fCSBarK7tSpU9i8eTOvLCxtbGwwePBghIeHY+PGjdi+fbtCm4uqqipaVJalYrKurg5Xr16Vex7KClQeBgwYgIyMDFy5cgVXrlwB0LwRNDY2hqmpKUxMTOgvgiDoymc7Ozu55+IaY2NjpKen49atW3JXFd+6dQvZ2dkA2M0C19DQoG0su4MtzqpVq+hrB9CcbLBlyxZs3rwZrq6udN+llhgZGcHExAQFBQV49OiRUn3tOsLb2xtRUVEoLCzE8ePH2xXUKysr8euvv+LixYv0+hYvXtzq5+pKZGdnK3RtkIdevXoxIp7a2dlBXV2dseqXFwnqM1NFj6/OcHNzg7q6Ompra1FeXq7wMxmfnr3kITo6Gj4+PpyIBFT1Etf3aGdnZ+jr69PuC6+//jqn83d12LA1Z4r6+nqlbffbQhV7YKlUSp8jbAv31tbWUFdXR1paGrZs2YJ169axOp9A1+TKlSu0Lbc8UNbEvr6+He6dw8PDaYFQS0sLenp60NXVfe6L+n5jYyPy8vJafeXm5qKurg6nT5/GO++80+5cYrEYEyZMwLhx47Bz5078/fffqKyslOvn6oz6+nrcvXsXDx48oFuiuLi4wMfHBz4+Pq3a+GRnZ+PQoUM4d+4cEhMT8eeff2LOnDmcJe0ICAgICAh0R7pWyjzHiEQijB49Gq+++ipMTEwAAB9//DH+/PNPLFq0CGvXroWBgQG2bdtG9/3q2bMndHV1u71AWFFRQVcSurm5cTr3m2++iX79+qG+vh4HDx5kvIk229V1fICqTkxPT2css7ktAgICYGBggGfPnnXae08VBAQEQFdXF7m5ufj9998VGiMkJARSqRRGRkYwNDTs9PV3795VqI+eLGP/G09PT4wZMwYWFhZ0cLmurg5ZWVkICwvD+fPnsWvXLmzcuBHffvstpFIpnJ2d4evrK/dcXOPl5QWgubKBsouUhfv37+Ozzz4DAAwaNIj1YKumpiZmz57N6hyq4ptvvoGnpycqKiro4+ffiEQiWrC7ffs2HcBjGrFYjFGjRgForrpqq8L0/v37WLp0KS0Qent7Y8WKFV1WIASaA0Rs8+zZM0aq68VicZdIQOAjlIXu/fv3VbyS5xGLxbCysgKgWDILRVcVCYuLiznpCwo0J8cA7Cd4/RsNDQ0sWbIEAPDpp5/yxm2jq5CTkwMAbSbSKAKVNMBE9Q5byYiqqAxuKZ6y3SvU2NgYa9asAQC89957vLw2C/CDY8eOyf3sS8U2OosnUUmnr7/+Oqqrq5GdnY0nT57gwYMHuHjxIg4dOoQ///wTGzduxIcffohPP/0Uv//+O06ePImHDx8iPT0dly5dAtCcQClLTEUsFtPtL+zs7Bg712NiYvDzzz/j7t27kEgkGDhwIH777Tf8+uuvCA4ObiUQAs2JAOvWrcNvv/0GCwsLlJWVYceOHXj06BHjiRkCAgICAgIvCoJI2AlWVla0zQ7QHGA/fPgw1q5di9deew2rVq1Ceno6bW/yovDw4UNIpVKYm5srJF4og4aGBv73v/9BX18fBQUFOHXqFKMPgy+CSKilpUX3CouKimJtHg0NDcyfPx8A8PXXX/Pus+3RowemT58OADh9+rRCAfc7d+4AgEx2fOXl5bh3757ccwCKiYQEQWD06NFYsWIF3nnnHXz44Yd444038NJLL2H69OkYM2YMPD090adPHxgbG6Nfv36YPXt2l7BcNDIyom1xjhw5ItN7QkNDsWHDBpAkCTc3N0ydOpWT6g9jY2OMGzeO9Xm4RkNDA/v27YOGhgbCwsJw8uTJNl83YcIE2Nraor6+XuHjXxb69+8PIyMjNDQ04MSJE/T3y8vL8eWXX+LDDz9EcXExDA0NsXTpUkycOJFX1c3yUldXx+r1uyVxcXGMjMNFD+PuyLRp0wA0J6Xw7T4KgA7eKSMSdmULSy7EekB1IiHQbHFqa2uL4uJifP3115zP35WhREKm9ktMioSKWLbLgipEwpbPc2yLhEDzddnPzw8SiQRz587tVj2oBZijvLwcZ86ckStWQZ3jHfX7q62txd27dwEACxcuVHh9vr6+sLS0RFVVlcyW8BEREQCYe6bLy8vDiRMnUF1dDSsrK3z++ef44YcfZNpb9+3bF3/88Qd8fX0hlUpx4cIFHDt2TKGe9QICAgICAi86/I8Ec0xaWhr++usv/PDDD3RmlUgkooMyGzduRGBgIL350NfXR//+/WnB5UWBypjkuoqQwtjYGJ999hlEIhGePHlCPyQzAR8DcGxAVXRQD/psMXXqVOjo6CAlJQWHDh1idS5FsLe3x9ChQwEAmzZtkjlg0tjYiP3799MioYuLS6fvuX79usLrZCK4pKamBgMDA9jb22PQoEEYPXo0pk6dikWLFmH16tWYP39+lwrUDh8+HABw7tw5lJeXd/jaqKgorF+/nu7xMWPGDE7F0OHDh9PVNt0JFxcXfP/99wCAP/74o82KGrFYjOXLlwMAHjx4wLg9EYVIJKKrYI8cOYKamhrcvn0bS5cuxdWrV0EQBIYPH46VK1c+l5HcFYmJieFsLqZEQq56GHc3hg0bBiMjI1RVVdEuDnyCCZGwq1YSAkBsbCxrYktLKFcTVYgRYrEYK1euBABs2bJFJUJlV0WoJOQOSiikrArZnuudd96Bra0tcnJy4Ofnx1lVsUDX4smTJ3Ltt6mKvo5Ewnv37qGurg59+vSBt7e3wmsTi8UICgoCAJnaYTQ1NdEJakw901Hz+vr6YseOHXJbePfq1QufffYZVq9eDYIgEBcXh23btnWLVg/toa6ujnfeeQfvvPNOl9q7C7CPcGwICAgogyAStiA2NhZDhgzBwYMHsWnTJqxbtw7+/v6orq6GmpoaSJKks8Co4PJvv/2GmpqaNhs9d2eoahBViYRAc781qlfkjRs3GAs8vygiIZX9FxkZyeo8PXv2xNy5cwEAX375JSfZvfIybtw46OvrIz8/H7/99lunr4+MjMTy5cuxbds22qLT3Ny8w/fk5uYqFdTnumK3K2Brawtzc3NIJBKcPn263dc9fvwY7777LiQSCZycnDB79mzOA1gikYiuWu1urFmzBhMmTEBDQwO+/PLLNnvOjRw5Eq6urpBIJLh58yZra3Fzc4OBgQEqKiqwatUqfPLJJygtLYWxsTGWLVuG8ePHd4sNE0mSnFUvAc3XL6pHjjKYmJjwsq8e3xGLxQgMDATAT8tRSiRU5hghCKJLC4VcVPW2FAlVYac2bNgweHp6orGxEevXr+d8/q5ISUkJncTEdCUhE+dLd6okBP7Zn3O119DS0sL3338Pa2trZGRkwM/PD1lZWZzMLdC1OHv2LAoKCmR6LXWO9+zZs93XUMJacHCw0q4owcHBAJoT+Tq7JsTHx6O2traVK5EypKamIiUlBSKRCKtWrVL4GZ0gCMydOxc///wzjI2NUVxcjO3bt3eaRNpV0dDQwKZNm7Bp06Yu7YoiwDzCsSEgIKAMgkj4/9TU1OC1117D/PnzcfXqVcTFxeH7779HYWEhvL29kZ+fD4Ig6IewxMRE/Pe//8Wvv/6KPXv2vFCVhE1NTXQPRldXV5WuZerUqXB1dQVJkoxVVbwoIqGtrS0IgkBOTg7y8vJYnWvGjBnQ1tZGfHx8KxtAvqChoUELOOfOnWvXbqW4uBhffPEF1q1bh8zMTGhra2PGjBmYP39+pxs0WbIzO0IQCZ+HqgwDgBMnTrTZSyMhIQFvv/02mpqa4ODggHnz5qkseGVkZISxY8eqZG42IQgCO3bsgI6ODpKSkrB79+42X0NVoURGRipVcdQRLasJMzMzQRAEfHx88Oqrr3arSs5nz57JHGxiCiaqCQmC4G014YQJEzBx4kSMGDECbm5usLa2ho6ODieWxLJA3aPu3bvHu347lEhYWFio1DhdWSQMDw9n/fdiZGQEgiBQUVGB0tJSVudqC4IgsGrVKhAEgYMHDyIkJITzNXQ1bty4AaD5HGEiQYIkScFutAOo6zWXCYmGhob4/vvvYWFhgbS0NPj5+SE7O5uz+QW6DseOHWszke7fdFZJWFZWhtDQUADKWY1SeHh4wNnZGQ0NDbRDTntQCWr29vZKPx+RJIkrV64AAGbOnNlpwq0suLq6Ytu2bXB2dkZjYyPOnTvHu2cmAQEBAQEBviKIhP9PfX09KioqEBAQAADQ09PDhAkTcPz4cfTo0QMTJkygX/v06VNs3LgRt27dws2bN+Hu7q6qZauE2NhYVFVVQVtbm7asVCWTJk0C0JzFzcRDIJ9EQjYfanv06AFLS0sA7FcT9urVC7NmzQIAfPHFF7x8WLe1taXtWjZt2tTKgkkikeDYsWNYsGABrl27BgAYMmQI1qxZA3d39043SWVlZUhNTVV4bQRBQF9fX+H3d2f69+8PHR0dlJaWPifEJicn4z//+Q8aGxtha2uLoKAglQehhw8fTleDdCcsLCywfft2AMCBAwfatEQcMGAARowYAZIklbLe7YyBAwfCyckJNjY2WL58OcaOHavy3/u/CQkJoQM8isBlFSFFd7YcnTx5MoYNGwZvb28EBARg9uzZWLZsGd566y189NFHeOutt7Bs2TLMnj0bU6dObfU1ceJETtYYEBAAdXV15Obm8s7q0draGkBzwl1NTY3C43TlKt/S0lKl7vOyoK6uTj8LqKr/maOjI70nWrduHS+f5/gEFQAfPHgwI+O13KMIdqPPQ1UScmE32hJjY2Ns2bIF5ubmSElJgZ+fH3JzczldgwD/KSgowOXLlzt9XWci4c2bNyGVSjF48GA4OzsrvS6CIGixkdrntgf1/MnEs1xsbCzy8vKgrq6OxYsXy/Se7OzsTkV4XV1dvP/++xCJREhKSkJsbKzSa+UbUqkU6enpSE9P56VLk4DqEI4NAQEBZeBUJFS2koZNdHR0IJVK6YxPoPmBycnJCTt37kRtbS1ef/11AM0Nkt955x2cOnXqhRMIgX+sRl1cXFS2CWzJmDFjIBaLUVRUxEjmJp9EQrbhynIUAGbPng1NTU1ERUXh/PnzrM+nCGPHjoWBgQGKiorwyy+/AGjuI/Haa6/hl19+QWNjIywsLLBixQpMmTKlQxuYligbzNPT0+PFucZHxGIxLe4eOXKEDlimpaXh9ddfR0NDA6ytrREcHMyLALRYLMbUqVNVvQxWmDNnDl566SVIpVJ8/fXXbYoFVG/C+Ph4PHv2jJV1iEQiBAcHY+nSpbCwsGBlDmXIzMzExYsXcf78eZSVlcn9/traWkRHRzO/sE4oKChgpHqRuu/wBW9vb7ovbVuIRCLo6OjA2toabm5u8PT0bPU1aNAgTtapra2N8ePHA+Cf5WjPnj3p5AdlLEf5JubLCxfivSr7ElIsW7YMmpqauH//Po4fP66ydXQFKJFwyJAhjIzXsgpJqCR8HlVUElKYmJhg8+bNMDU1RVJSEvz9/bt1TzQBxQgLC0N8fHyHr6HO8/ZEQiqmxkQVIcWCBQsAABERESgpKWnzNdXV1XTCmLIiYVNTE50w+NJLL0FXV7fD11dXV+Pnn3/GSy+9hCVLluDEiRMdJqnY2dlhyZIlAJpdgrjoG8wltbW1sLe3h729PWpra1W9HAEeIRwbAgICysCpSDh+/HgMGjQIu3fvlslqgStIkoRYLMbcuXMRFhb2nIAxYMAALFiwADExMXTGpYuLC0xNTVWxXJXDh36ELdHW1oa/vz8A5XvCkCT5QoqEERERrGeD6+rq0nZpn3/+OS+zz9XV1TFjxgwAwKVLl7B+/XqsWbMGycnJ0NDQwJQpU/DKK6/ILTwoW/UhWI12jKenJ9TV1ZGeno7Q0FBkZWXh7bffRn19PSwsLBAcHMwrT34rKyt4eXmpehms8NNPP8HW1ha5ubm00N4Se3t7uvLq2rVrvLwOsM3t27fpv6ekpMj9flUIhBRMVBPq6ury5prm7OxMC29dgWnTpgHgn0gI/GM5qoyVMB8SOZQhPj6esf7Y7cEHkdDY2Bjz5s0DAPz3v/9t0+pboLnXVmpqKsRiMWPJBPX19fTfmThfuptIqKpKQgozMzNs3rwZJiYmSEhIgL+/P+fW4AL85/Dhw6irq2vz/0iSpK+pbSWj5ubmIi4uDiKRCEFBQYytydHREV5eXs8lzbckJiYGJEnCwMAAenp6Ss0XFhaGsrIyGBoaYvbs2e2+jiRJ3LhxAy+//DKOHz8OqVSKpqYm/PTTT/jyyy87FEGCgoLg4OCA+vp6XLx4Uan1CggICAgIvAhwKhIuXLgQCQkJWLZsGWxsbPDll18qlXHMFFTW4eLFi0GSJLZu3YqbN2+2+v/+/fsjJydHyMbAP8EpvoiEAGjro9jYWKU2hqraVKoKKysriEQiFBcXc2JfNm/ePGhoaCAkJAS3bt1ifT5FsLa2pvvcUb03Bw0ahDfffBNDhgyhAxDyIIiE7KKpqUlbef3vf//DK6+8gtLSUpiammLRokXtZuKqEn9/f+jo6Kh6GYyjq6uLvXv3giAIXLhwoU1b0SVLlkAkEiE9PV0hkawrk5+f3+pnljfQT5IkIiIimF6WzMTExDByn+SD5aiZmRlmzZql0DVdVQQGBgJoFqP48PzcEspyVBmRsKtXEgLsOzNQImFSUhKr83RGUFAQDAwMkJKSgq1bt6p0LXyFqiLs378/tLS0GBmTEgnV1NQY6Zfa3exGqc9Elfs5CwsLbN68GUZGRnjy5AkmT578QiWgCshGe/cKiURCJ9C1tX+hYlQ+Pj6M9PCrqqrClStX8PHHH9P375bJbBRNTU04e/YsAOUdIUiSpHsfLlmypENnnr179+J///sfiouL4eTkhMuXL2PLli0Qi8W4du0aPvjgg3bfq66ujvfeew8EQSA2NpZ3Vu0CAgICAgJ8g9PIyN69e5GZmYmPP/4YIpEIGzZsgI2NDVatWoXExEQul/IcJEnCwcEBf/75JzIzM7Fx40bs2rULQPOG7NGjR7CwsJDZXrC7UlNTg4yMDACAk5OTilfzDx4eHlBXV0d9fb1SWdx8CxYyEQDoCHV1dfr3eObMGVbnAgADAwOMHTsWAHDixAnW51MUf39/ODg4wMrKCkuXLsX06dOhra2t8Hg9evRQaj19+/ZV6v0vAsOHD4e2tjaqq6vR2NgIMzMzLF68mLfX7B49etCVN1xiYGBAf7HV59LX1xfvv/8+AGDjxo3PBbNNTU3prOEbN268UNWEYrG41X1G3uOzoKAAhYWFTC9LZsrKytoMHsmLqpOMevfujQULFvCqwlgWLCwsMHz4cNb7eioCJQooY/PXq1cvppajMqKjo1m9pllbW4MgCCQlJXVqWccmPXv2xLJlywAAn3zySbv2dC8yp0+fBgBGnQOoZFWmrl1siYSqgkq+UrUYYGlpic2bN6N3794IDw/Hzp07VboeAf7Rnt28SCSiq4TbSgai9qP379/HgwcP5J63pKQEp0+fxjvvvANvb2/o6upi/Pjx+Pzzz+m+upaWlq3eU11djfXr1+P+/fsgCAIDBw6Ue96WSKVSOuHBxcWlw9dSn8HIkSMRExODgIAArF27Fj/88AMAdFqpa2NjQycgvWjJ4AICAgICAvLCuSJiYmKCTz75BBkZGdi3bx8GDBiAP/74A66urpg6dWq79gZM0dTU9JzVqVQqBUEQkEqlGDBgAA4dOgRNTU18+eWXsLKywoQJE7Bjxw789NNP3SKAoQzUpktbWxu9e/dW7WJaIBKJaC/7tvpgyTMOn6qOuBAtqeDFmTNnOAkWDBs2DECznSdfUVNTw+LFi/HKK68wIuRQP7MizJ07lxdVN3xHR0cHa9euxZIlS7Bs2TKsWLFCKWGXbZqamvD06VNO5/Tw8MAbb7xBf61cuZK1uT7//HNMnDgR9fX1+Pjjj5/Lol+wYAHU1NSQk5Oj8ooYLjEyMsKCBQsgFouhoaEBX19fud4fGxvL0spk5/bt28jNzVVqDBsbG0yaNImhFcnPggULumwl7+LFiwEAly9fVvFKWpOTkwOgORFBUagqua5MSUkJa/1WgeZ7HRWg3bdvH2vzyMLEiRPh4OCAyspK/O9//1PpWvhGRUUFXUno4+PD2LjUczoTe5X6+nrW2n+oqnKuZRsFVWNtbY2XXnoJALBhwwbWrYi7I2pqarCyssLQoUPh7Oys6uUwSnvVxSKRiK4QbCuJPjAwEL6+vmhqasKsWbOQnZ3d7hyNjY2IjIzEX3/9hddeew0DBw6EoaEhpk+fju+//x6PHj2CVCqFqakpAgIC8Pbbb2P37t1499136TGKiorwn//8B2FhYVBTU0NQUJDSe2OxWEzvbUNCQjp87ahRowA0Oyi0jIskJCQA6Lzf64ULF9DY2AhDQ0NhPy0gICAgINAJKiubUlNTQ3BwMB4+fIhHjx4hKCgIV65cwbhx4+Dh4YG9e/cyPueTJ0+wcOFC+Pv7Y+nSpThw4ACA5ocxiUQCkUgEqVSK/v37488//8SBAwewcuVKvPzyy3j06BFj/SS6MpRIaGpqynqVm7xQAT9lLWH5JCxwYddjb28PY2NjNDU1PdePkw08PT0hEomQmJio0p46XDJw4ECFepi+8sor6N+/Pwsr6p6oqanB1tYW1tbWvKsK/jepqamc93FS1h5IHsRiMQ4cOAAjIyPk5eUhJiam1f/r6+vT1YQ3b958oaoJHR0dsXbtWrz++utyCVUkSeLJkycsrkx2Tp48qXRGtpeXF6PBc1kJCgpixKJLVcyfPx/q6upITk7mlV0vJRIqU6HcXXp9s903lDpv7t+/r9JjQCwWY9WqVQCAX3755YVK+OgMKjBtbW0NW1tbxsalREJlHSpajsUGqqrYoUSA8PBwXjxXTJ8+HZaWlsjPz8fGjRtVvRxe01IQnD59Ol577TWsX78er7zyCiZPnqzSxCI26MhJgnpGaSuZUCQSYf369bC3t0deXh5mzpyJuro6NDY2Ijo6Gtu3b8fq1avh5eWF3r17w9PTEytWrMAff/yBx48fA2hO1JoyZQo++OADHDhwAAcPHsQHH3yAwMBA2NjY0DGetLQ0rFmzBikpKdDU1MTSpUsZc7ehxumsx/KgQYOgr6+PkpISOvECAN1jcOjQoe2+VyKR4NixYwAAb29v3sWuBAQEBAQE+IbKoqiNjY0oLCzE06dPIZFIsGjRInz99dewsLBAdHQ0li5dyuh8T58+xYgRI6ChoYGAgACkpqZi06ZN9DxisRgNDQ10YNnIyAhDhgzBhg0bsHTpUjg6OjK6nq4KJRLyMcBGBVuVqSQE2s/sUwVciIQEQcDb2xsAcPz4cdY39r169YKrqysAflcTMomamhpWrlyJt99+GytXrkRwcDCmTZsGPz8/DB06FC4uLrCysoKenh7EYjEsLCzwxhtvwMrKStVLF2AJVYg9XIqEAKCnp4epU6cCAO7du/fc/wcFBUFNTQ25ubmcV1Wqml69esldjZ+Xl8cbS7+CggJGbEf9/f05TcAaP358l69GMDAwwLRp0wDwp5qQJEm6uvRFryQEgLi4OFYrqYyMjOjnqL///pu1eWRhyJAh8Pb2hkQiwX//+1+VroVPUJb6Pj4+jAammawkZLOyTVWVhDY2NhCJRMjPz6cTF1SJuro6Xn31VQDApk2bWK0y7qpYW1tj+fLlrQTBQYMGwdTUtFXCn66urtI2l3yiI5HQwsICQNuVhNR7v/jiC+jo6CA0NBT9+vVD7969MWjQICxfvhy//fYbQkNDUV9fD21tbXh4eCAoKAiffvopjh8/jt27d+Odd95BQEAAzMzM2pwjIiICK1euREFBAQwNDfHqq6/S62ICqt1JXFwcysvL232dWCyGn58fANAJ/lSSlFgshqenZ7vvvX//PnJyctCzZ0+4u7sztnYBAQEBAYHuihqXk7m7u6O0tBSlpaXtCjlU1h+T2X8kSWLPnj0ICAigKxTfeecd7Ny5E3/88Qfmz5+PQ4cO0f0ddu7ciXHjxsHa2pqxNXQXqMqv9h4oVQlTlYR8Egm5qoYaOHAgrl27hvz8fNy7d4+29mALLy8vPH78GJcuXcJrr73G6lx8gSAI9OrVC7169erw/CFJUsh07OZIJBLO+/CamJioxC57+vTp2LlzJ+7du4fXX3+91bGtp6eHefPmYf/+/bh58yb69u0rHPsdEBcXp+oltOL27dvo16+fUklDBEEgMDAQ1dXVrFcheXp6KmX9zCdeeuklHDt2DFevXsWrr77KSUJRRxQXF6O+vh4EQdDW74pgaGhIu3p0Zerq6pCYmEgLeWzg6+uLuLg43LhxA0uWLFFJj1uKlStXIjQ0FCdOnMCtW7cwevRola2FD9TX19POHExXS1dXVwNgRiTsjpWEGhoasLKyQmZmJiIiIp7rraYKfH19MXDgQMTExODDDz/E7t27Vb0k3jBlyhQMHjxY5me/ESNGPOdM0VWRRSRMTk5GVVVVm8/vFhYW+Pjjj/Hee+8hIyMDQLMbUt++fekvZ2dnWFhYyP1sffnyZXz77beQSqWwsbFBUFAQ4z3edXV1YWZmhry8PDx8+BATJkxo97Vjx47F8ePHcezYMfz55590krGbm1uHcZujR48CAAYPHtxuH9eamhqUlpa2+h7XTi+KoKamhtWrV9N/FxCgEI4NAQEBZeD0qvH48WMQBAEvLy8MHToUurq6HX4xBUEQyM7ORl5eHv09LS0tLFu2DJqamti6dSvWr1+Pr7/+Gvfv38dXX32F69evY9euXSoPvPANqpKQjyIhEz0JAX6JhFwdf+rq6hg8eDDu3r2LY8eOsS4SDh06FNu3b8fVq1dRXV3NK4tXVSOIJOxSW1uLtLQ0pKWlQUtLC8OGDWN849sZaWlpqKur43ROJqoIv/jii1YWZ15eXnSlYHsEBASgR48eyM/PR0pKynNV+fPmzcPhw4eRl5eHhIQEuLi4KL1OppFKpbhy5QqkUin8/f0ZsXmTFz5Zjbbk5MmTSotUYrEYc+bMwZ49ezrsraMMDg4OmDx5cre5vk6aNAlGRkYoKipCWFgY7QagKqgqQl1dXaWPBWNjY+Tn5zO1NJURExPDqkhoamoKZ2dnJCYmYv/+/Xj//fdZm6sz7O3tERgYiNOnT2PdunUICQl5oQNT169fR2VlJYyMjNCvXz9Gx+4qdqOqqiQEmo/HzMxMhIeHd/qMwgUEQWDVqlVYtWoV9u7di48//hh9+vRR9bJUSr9+/TBp0iS5ewObmprC0dERycnJLK2MOzraexgaGtL3+D/++ANvv/12m68bPHgwfvzxRxQUFMDZ2Rnm5uZKJRjn5ubi1KlTOHToEADA1dUVM2bMYO167uTkhLy8PDx48KBDkdDFxQXm5ubIzc3FmTNnaJHQy8ur3fckJiYiJiYGIpGo3ddVVVXh+++/V+6HUBE9evTA1q1bVb0MAR4iHBsCAgLKwOkObs6cOThx4gRCQ0NhamqKd955h/V+NFRVjqenJxITE5GQkEBv2Hr27Im5c+fi6dOnuHHjBkpLSzFixAi89957GDdunCAQtkFXqCRUViTkk2DFZV+1oUOH4t69e4iJicHTp08Z6znQFk5OTrCwsEBOTg5+++03vPPOO6zNJcAs+fn5IEmSl9eAtmhqakJWVhZSU1ORmpr6nP3U7du3MWnSJAwePJiza35XtRrdtGnTc99bt24dNm7c2O5np6WlhQkTJuD06dO4d+/ecyKhrq4ugoKCsG/fPrqakE/3XpIkcebMGURFRQFovgfOmzcPRkZGnK4jNzf3uUxnPlBQUIBbt27B399fqXE0NDQQHByMXbt2obCwkKHVNWNqaoq5c+fy6rhSFnV1dQQHB+Onn37C5cuXVS4SUuKuMv0IKUxMTLqFSPj06VPWk6B8fX2RmJiIy5cv4+WXX1ZpK4AlS5bg2rVriIiIwDfffIOPPvpIZWtRNZTV6IgRIxh/jmeykpBNu1FVVRICzUkht27dQmRkJKRSKS96VPfr1w9Dhw5FaGgofv311y4rTChLr169MHnyZKUSwkaMGNEtRMKOkpIpl4Vdu3bh7NmzGDt2bLvW7G5ubkqtIy8vDzdv3sTNmzdbuZyMGDEC48aNaze5qqKiArm5uaivr+/0y8LCAlOmTHluLGdnZ9y5cwehoaFobGyEurp6m3MRBAE/Pz/s378fu3fvpu3uOxIJjxw5AqD582nP3v/ChQvtfzACAgICAgIvIJw+NR8+fBhJSUlYs2YNrl+/jtGjR2PYsGE4cuQIa9ZC1MPI5MmTkZSUhI0bN7baFOno6GDt2rUIDQ3F9evXAQArVqzgvHdTV6Er9CTsTnajXAY1dXR06Kx3qsk3W4hEIixatAgAsHHjRjroIcBvSktL8eeff+KPP/7A7du3GbWFZgqSJJGfn48HDx7g77//xsaNG7Fnzx7cvXu33f40Fy5cwO+//85J0EEikSAhIYH1ef6NnZ2d0mMEBgZi5syZmDlzJsaNGwcA2Lx5M+bMmdPhOTx9+nQAbfclBIC5c+dCQ0ODsT53THLjxg1aIASAwsJCbN26lfPfId+sRlty584d3Lp1S+nrgZaWFlasWMFo8pi/vz9WrFjBSECdb7z00ksAgLt377JaESQL1LWVKZGwu/D48WNWx7e0tESfPn1AkiQOHjzI6lydoa+vjzfffBMA8Mknn+DRo0cqXY+qkEgkOHXqFIBmEZdpmOxJ2F0rCS0tLaGhoYGKigqkpqaqbB3/ZubMmQCAHTt2vJD7Hk9PT6xZs0Zpxwg7OztGe+Opis5cTGxtbTF48GAAwPfff8+oBWZeXh4OHTqEVatWYcGCBfjjjz+QmJgIgiBgb2+POXPmICAgoF2BMDExEVu2bMHBgwdx4sQJnD9/HteuXcPdu3cRGhqKmJgYJCYmIj09Hbm5uQgPD2/TUt7CwuL/2DvvsKiu9fuvM0PvHUSa0hQVBVERsYu9l6iJvUVNbjQm3vTcJPd70zTxJsYYNZbEXrCisYsNFSuooKD0DtL7MHN+f/A754IBnHLKDOzP8/DE4MzeGzxzyl7vWi9MTU1RWVn5yhjZoUOHAqh/ZquoqIC1tTU6duzY5GszMzPZfb3mYuafPn2qlQkdykLTNPLz85Gfn6+Vz+ME8SDHBoFA0ATBS+s8PDzw008/IT09Hf/5z3+QkZGBGTNmwNPTEz/99BNvDyyenp44cOAA9uzZg48++ggFBQXs3xkYGCAgIAC2tra8zN1aKCsrw4sXLwDUV+ZrG61RJBS6+pW5kT5//jwKCwt5nSssLAzt2rVDfn4+fvvtN17nInDD5cuX2YKOS5cuITw8HDKZTORV1d8Mp6en4/Tp0/jvf/+L3377DWfPnsWzZ8+UXl9BQQF2796NPXv2NLo+cE1qaqrG5yhVcXFx4SSabOnSpXjnnXfwzjvv4JNPPsFnn30GfX19HD16FIMGDWoU6d2QsWPHgqIoJCYmNukQsrCwwD//+U8A9YJTWlqaxmvlgtu3b+Pq1atN/t3+/ftx4cIFQXqnaWvUaEMiIyNx5MgRjTeG9fX1MXToUCxdulSjXlK+vr5YsWIF+vfv36ochA0JDAyEn58famtrcfnyZVHXwsSNciESauP9pbrExMTwPgcjREVERHDuwlWVsLAwDB48GAqFArNmzRJdvBaDGzduIC8vD2ZmZs06fzSB9CR8NVKpFO7u7gCAu3fviraOl+nTpw+cnZ1RXFyMXbt2ib0cwbC1tcW8efMwbtw4To5biqIQEhLCwcrEQyqVKnVfPmzYMJiZmSEjIwN//vmnRnPm5eXhwIEDWL58OWbOnInffvsNT548YYXBMWPG4L333sOcOXNajMq+c+eOWkUpTRWTURQFb29vAPXnzpbo2LFjoyL+Xr16NbtPsnXrVtA0DS8vryYL2xv2jdVVKisr4eDgAAcHB41TtAitC3JsEAgETRAtf8PS0hIffvghkpOTsXXrVpibm+Pdd9+Fq6srPvjgA1760gwePBgHDx7E77//jiVLlmDv3r14/Pgx1qxZg4yMjDbfH+BVME2xzczMmmygLTZc9STUprhRoTc327dvDxcXFygUCt5vnvX09Bq5CbVBbCI0T0lJCVvl+dprr4GiKDx+/JhtCi8WZWVl2LZtG7Zt24Zbt26htLRUo/ESExOxYcMGpKenc7TCxsTHx/Mybkvw5YwfMmQIfvjhB1hYWODOnTsYOHBgkxuDDg4O7IZOVFRUk2MNHToUYWFhoGkaR44cEUR8a4mMjIxXngOvXbuG48eP876W7OxsFBcX8z6Ppjx8+BCHDx/mZCxHR0csWLAAI0eOVOl9VlZWmDFjBmbMmAErKytO1qKtUBSFuXPnAhA/Mos4CZsmJycHeXl5vM7h7u4ONzc3KBQK3lMgXgVFUXj33Xdhb2+PxMREfPPNN6KuRwwYF2Hfvn156ePFZU9CPuNGxb6nZ+577t27J+o6GiKRSDBx4kQA9SJGW2DAgAFYunQpK9pyRefOnTm53oiFubm5Uj2SjYyMMHr0aADAnj172F58qpCWloYvv/wS06dPx8aNG9nnEA8PD4wePRqrVq3CnDlzEBQU9Mo9kMePH+PkyZMqrwGov09g2tY0hGlv8iqREECjaPtevXo1+Zr4+HhcunQJwP/chy9z/fp1jZ8XCQQCgUBojYge0q+vr4/p06fj/Pnz+OWXX2BmZoa1a9fyJtiNGzcOUVFRKCwsxIcffojx48fj6NGj+Ouvv+Dq6srLnK0FRhisqqrSyqoUZnNaU/ddc3n4YiDGQzZTxS9EBfjw4cNhbm6OvLy8V8aMEMSlqqoKNE3D1NQUS5cuZWOThHbFNSQrKwtbtmxBRkYG52PfuXOH8zEBoLq6mpdxW8LNzY23sbt164Zly5YBqHdjNnf+ZTY5WnLWrFixAkZGRiguLmZd62KhbBRYTEwM73Fm2hSX9iri4+NRUlLCyVgSiQR9+vTBypUrleqRGxoaiuXLl8PX15eT+XWBWbNmQU9PD48fPxbVbcoIYVwIsxYWFujduzccHBzQrl07uLi4wN3dHR07doSXlxd8fX3h5+eHrl27onv37ujSpQs6dOgAR0dHmJmZaUX/sYYIcS5j+jJpg2vK3NycFUKaipdr7TDna00jFZuDKYjkIu1DGZFCXcROBGD6Zmtbf1PmfMCFyKvtjBs3DoMHD+ZFLJdIJOjXrx/n4wqFKi0AOnfujG7duoGmaXz77bf45ptvlHr2ysvLw5o1azBv3jxERkYCqC8qGTVqFN577z3MnTsXvXr1UqnwW9NCRyZ1oCHM7yIrK+uVP9eIESNgamoKKyurJvsR1tXVsf0+/f392fPAy+hC4R2BQCAQCGLA/V1bC4SFhaGsrKzRV3l5eSPHABNDwKc4EhgYiOPHj6OwsBDl5eVwcnKCnZ0db/O1Fjw8PNCxY0ckJSXhwYMHWhf1wcQsNdecWln4fGhWFTHEWCYykIn/4BM9PT34+vrizp07uH37Ntt7QWzkcjnu3r2LiooKeHl5wcXFRauOCzGws7ODRCJBRUUF0tPT2V4PLTWN5xO+XYxPnz6FXC7n3M0rhruJ7/g+Jupw/vz5zX5OmOtFS731TE1N0bFjR8TFxSE3Nxf29vbcL1ZJVCka+uuvv7B06VLenN9ib7aqyqNHjzjdvLO0tMTMmTORnp6O69ev4+nTp43+PjAwEAMGDGA3z9sSzs7OmDVrFnbs2IH9+/fjyy+/FHwNdXV1rGDBxb8BRVEYNWqU2u+naRoxMTGso0tsuOwj1RxMNG9ycjJqa2thYGDA+5wtUVRUBACcu4d0gZqaGgDcxIE2Rc+ePXH+/Hk8f/68katGHSwsLHhzuubn56OqquqVfdf4ghHhtKmoNS8vj3Xbf/zxxyKvhn8uXrwIHx8f3tKHAgICcPPmTV5bBPCBra0tRowYodJ7Jk6cCDs7O0RGRuLs2bOIj4/Hv/71ryaL6ktKSrB7926Eh4eze2w+Pj4YMmSIxs8DTYl8qtCUe9nY2BjGxsaoqqpCVlZWi0YBe3t77N+/H3K5nG0z05CDBw/i+fPnMDQ0xPDhw5sdR5ddqAQCgUAg8ImgIuGFCxfYP5uYmMDa2hqurq6wtrZu8otPLCwsmry5ILRMWFgYNm3ahDt37midSMg8JGgqEmoTQje2l8vlrEiojHODCzp16sSKhEuXLhVkzpbIz8/H4cOH2d/DlStXYGpqCh8fH/j6+qJjx45a5TYVCj09PTg4OCAnJwe//PILiouLYWlpCT8/P0HXQdM0Ll++zHsPrpqaGiQnJ8PLy4vTcYUWCY2MjHiNh87NzUV0dDQAYMmSJc2+jukdUlBQgLy8vGYjBT09PREXF4ecnBx07dqVlzUrg4mJCezt7ZXq8VVQUIBbt27xck1UKBQ6JxI+fPiQlwp/V1dXzJgxA/n5+bh9+zYoikJwcHCb3+x5//33sWPHDly9ehUZGRlwcXERdP709HQoFAoYGBhoRVw7RVHo0aMH3N3dceTIEd6io5VFCJHQ0tKS3WRNTk4W3U3L3D+p4pZpLTBpAXzdJzIRe1lZWaiurtZIjOT7Obi0tFQ0kZARysVMu3iZHTt2QCaTYeDAgSpHaesiFRUVOH78OF5//XVexpdIJBg+fDj27NnDy/h8MWPGDJU/txKJBAMGDIC7uzvCw8ORnp6OJUuWYMWKFRg3bhwoikJlZSUOHjyI3bt3s8X27u7uGDp0KCdpWdXV1Ro7mJvb17CxsUFmZuYrRUKg+bYwmZmZbIzvyJEjW7wfaev3jQQCgUAgNIegImF8fDwrALbFTfbWACMSakOk0cswES6tSSQUugI2Pz8fcrkcJiYmbGU63zCbWYzQIBY0TePWrVs4d+4cFAoFDA0NERwcjDt37qCiogL379/H/fv3IZVK2cgzHx8frdgUFQonJyfk5OTg9u3bAIDg4GBBo91kMhmOHTvWohuNS+Lj43VeJHRwcODVBXvy5EkoFAoMHjy4xcICU1NTBAQE4O7du3j06FGzDghmc0AbIsLc3NyUEgkB4Ny5c+jWrRvn15+8vDzWmaIr5ObmtigEa4q9vT0bX0sAunTpgrFjxyIiIgIHDhzAqlWrBJ2f6THE97lGVaytrTFv3jxcv36ddb6LgRAiIUVRaNeuHZKSkvD06VPRRULm/N0WnYSMSMiXm9PW1hYODg7Iy8tDXl6eRnHifLuvS0pKeE8yaI6GIiFN06Kfm168eMH2k/vmm29EX49QJCYmory8nLdiNS8vLzblSBeYPn26RulV7u7uWLp0KY4ePYrExESsW7cO9+7dg5+fH/bs2cPGvbdr1w5DhgyBp6cnZ8caU/yhCc31QbW2tkZmZiYyMzPVGpemafz444+Qy+Xo0KEDunfv3uLrbWxs1JqHQCAQCITWjqCNO3x9feHg4EAEQh1myJAhkEgkSEtLU3rzVCgYJ2FrcogKLRIyMSLe3t6CiT+dOnUCAMTFxQnunGQoKSnBzp07cebMGSgUCvTu3Ru7d+/GF198gSNHjmDt2rWYNGkSHB0dIZfL8fTpUxw/fhxr167Ftm3bWuyz1ppo164d+2cjIyMEBgYKNndpaSm2b98umEAI1EeONozD5gIxREK+kMvl+OuvvwAAb7755itfr0zkqLaJhKpw7tw5zteQmprK+ZhC8PDhQ7GX0Kb45z//CQA4ffo0J73KVOH58+cA+I81VgeJRIL+/fuL2v9LKJGfuT4nJCQIMl9LECchfyIhAHTs2BGA5tdJIZyEYsH8/uVyuSj93V/m4sWLUCgU6Nu3L/r27Sv2cgSFz76sFEW1GCupTQwYMIB95tUEExMTzJw5E2FhYaAoCpcvX8bGjRtRUlICW1tbTJ06FYsXL4aXlxenYrSmUaMAUF5e3uT3GdEuKytLrXHPnDmDe/fuQSqVYuzYsa/8uYmTkEAgEAiEphFUJGyK/Px8rF27VuxlEJTE2toaQUFBAIA7d+6IvJrGtMa4UaFFQubmXKioUaC+152dnR0UCgXu3bsn2LxAfeXhw4cPsWHDBiQnJ0MqlWLlypX49ttvYWtrC6A+Mqpnz5545513sHfvXmzZsgXz5s1jezamp6fj6NGjuHnzpqBrF4OGImFQUJBgfY+qqqqwbds2Th5QVYHpv8glQvdN41MkvHHjBgoKCmBvb49Jkya98vWMSBgXF9fsa5jNz7KyMtF7Canqgnn48CHnop6uRY0yPHr0iO0xTeCf0NBQBAcHQyaT4ciRI4LOzTgJtVEkZDAxMRFtbiGchEB9f0pAfJGwoqKCdYu0RSchIwrzWRBLRMJX0/D+VBsiR8+fPw8AeOONN0ReifDw3TPQ0dERAQEBvM6hKT4+Phg0aBBn41EUhZCQECxYsAA2NjawtLTEuHHjsHz5cnTp0oUXpyqfIiEj2qnjJCwqKsJ///tfAMCgQYOUcgmamZnpvGlBT08Pc+fOxdy5c6GnJ2g4HEHLIccGgUDQBFFEQpqmcfLkSUyePBkuLi744IMPxFgGQU2Yij0iEvKP0M465gFASJEQ+J+bUMjI0aqqKoSHh+Pw4cOQyWTo1KkTtm/fjgkTJjT7cEVRFLy8vDB37lxs3rwZ+/fvx2uvvQagvorxwYMHgq1fDBwdHaGvrw89PT307t1bkDmZ6wUToSM08fHxnI6np6fHa4/Al+FTJDxx4gQAYMGCBUoJxkz1fGJiIuu2eBkTExN2s5uLaCNNsLS0VFnUPXXqFGfuU13sR8hQXFyMjIwMsZfRZqAoinUTHj16tNnPFx8wMW98nms0RcxYcKFEQqaIJzk5WbA5m4IRrqytrVvV/biyCOEk5Mpxz3fRkpgioUQiYTdHxRYJ09LSkJCQAIlEwj4ztCU0cRJWVFQgLi4Op06dwqZNm3Do0KEmC5Cai7DXBmxtbTFp0iRehDsXFxe8/fbbWLFiBQIDA3lNAeJCJKypqWnS2auJk3DDhg2oqamBo6Oj0i5diqJ03k1oaGiIHTt2YMeOHaKmJRC0D3JsEAgETRBUJHz+/Dk++eQTuLq6Yvz48Th27JhWRIAQVCMsLAwAcO/ePc6j+NSlqqqKFdRaU9xoVVWVYL9jhULBbjgILRIy/XOYXnd88+zZM2zcuBGPHz8GRVGYN28efvnlF5Ubuzs4OGDp0qWYNm0aAOD48eN48uQJH0vWCgwMDLB48WIsWbJEsM2/R48eCRox+jJPnjzh3BElZOSovb09L+NmZ2ezn9fFixcr9R43Nzc4Ozuzkb3NoU2Ro6o6YfLy8jgrdrhx40azVde6QGxsrNhLaFNMmDABHTt2RHl5OSIjIwWZs7y8nP2carOTsC2IhFZWVjAyMkJdXR3r7hQD5nhoi1GjgPAioSb3J63ZSQg07ksoJoyLcOTIkbzdk2kzqoiE5eXlePz4MU6ePIlff/0Va9euxcGDB3H79m3k5OTg8ePHSElJ+dv7zMzMMHjwYA5XzR3Tp0+HkZERb+NTFMV7j8va2lrOHKFN3dcyIiHzb6wst27dwoULF0BRFMaPHw+pVKr0e3VdJCQQCAQCgQ94Fwmrq6uxa9cuDB48GD4+Pvj222+RnZ0NmqZB0zQMDAwwZcoUvpdB4JDg4GCYmpqiuLhYaxqFMw8g+vr6gkUgCoVQD7f5+fmoq6uDiYkJXFxcBJmTgXES8iUSHjp0CPv27cO+ffuwc+dO7N69G2VlZXB1dcWvv/6KuXPnqvRg0RCKorBs2TKMHDkSNE3j4MGDom7Q8Y29vb1gmxylpaU4fPiwIHM1R0lJCecxp0KJhGZmZpzG7BUXF6OoqAhFRUU4fPgwaJpGWFgYu2H5Kph4JKDlyFFtEglV7UsI1LuKNRW2s7Oz2Y1FXSUuLg5yuVzsZbQZJBIJFi1aBACIiIgQZE7mWmdpacnrJqimtIW4UYqiWBd2YmKiIHM2BeMAb4tRo8D/4kb5fBZxcXGBvr4+ZDIZioqK1B5HX18fxsbGHK6sMUQkrE/DuHDhAgBg1qxZoq1DTF4lLqWkpCAiIgIbNmzADz/8gEOHDuHOnTvIz88HAHTo0AETJ05kE0yaS23p27ev1hUKv/baa61CGObyfrwpkdDMzIwtUH733XeVis2uqqrCunXrAAB9+vRhr3/KousiIU3TqKioQEVFBYn3JzSCHBsEAkETeBMJ79y5g+XLl6Ndu3aYO3curly5wgqDNE2jd+/e2LBhA7Kzs3HgwAG+lkHgAQMDAzZX//Tp0+Iu5v/DPIBYWFjwXk0nNEJFjjJCiJeXF69xJU3BOAmTkpJQWFjI+fjPnj3D06dP8fTpU1bYnjRpEjZv3sxJE3mKovD++++jf//+UCgU2LNnD9uTh6A+2nJ+acn1pg5C9SXkOv5v1qxZmDx5MiZPnoxDhw4BAIYOHarSGL169QKgOyKhuhvdhw4davFnfBWnTp1S+73aQmVlJSIjI7UmcaAtMG/ePEgkEjx+/Fit3j6q8vz5cwDaHTUKtA0nIfC/yFGx+hLSNI1Hjx4BIE5CPvtdSaVS9veraSw3n/cjpaWlom5QMiKhmP2No6OjkZWVBSMjI4wfP160dYhJUVFRs+fBmpoa7Ny5E3fv3mWf5T09PTF58mR8+eWXOHr0KLZt24YVK1Zg3rx5AIDHjx83mUSlr6+vVbGj/fv3R+fOncVeBidwWSzZ3PPxlClT4ObmBplMhrfffht79+5tMTr9jz/+QG5uLiwtLdVykSrTu1CbqayshJmZGczMzETv4U7QLsixQSAQNIHTTqaFhYXYtWsXtm7dyj4kNnw4cHFxwaxZszB37lxWFCDoJnPmzMHJkycRHh4OKysr0asjmRtOLipihe4D+CpKSkoE2YDLy8sDAKVdQVzSsKkyHxvKb7/9dqNMdk9PT84f3KRSKT799FMsW7YMSUlJeET6380AAQAASURBVPbsGQICAjido60hxCa3MnC92SdUIYOTkxPvc3zyyScwNDTEihUrXvlz1dXVYd++fQDqe7Q0B+NO0gZnkq2tLSwsLNRyRMTFxcHPz0+teVuLI/7atWtISUnBpEmTdGpDhqZplJSUoLCwEC9evMCLFy/YPzNOoYav1RZyc3OhUChAUVSj6ypfMOdoOzs73ufSBDGLdoRMZmCcFGLEntM0jU2bNuHixYsA/te/vK1hZWWFsrIyFBUV8fpv7+rqisTERBQXF2s0Dp9OQplMhvz8fFGKCGQyGZsyI9a1JzMzE19//TUAYOHChaIWK4gJTdO4c+cOmyTREKlUCn19ffa6amNjg/HjxyMsLOxvxybjyJPL5c0+K/r7+yM9PR0xMTGoq6vTeO0ODg5wcXGBsbExysvLUVZWxn61JGD5+flpbfypOnDpxm3u3t7AwACvv/46du/ejfT0dGzevBnh4eGYM2cORo8e3eieJj09nTUZjBkzRq17ZqFTkwgEAoFA0AU42UE4d+4ctm7dimPHjrGVYsymiYmJCSZNmoS5c+di6NChrc7l1VZ57bXXkJSUhI8++ghbt24FIG6MCnPzykVzXj6cbJrw4sULeHt7CzIPIE5EFCMKtGvXjpfNxpEjRwrycG5gYIDg4GAkJSUhLS2NiIQa8vJmvFhw/e8oVASXOlGZLbFr1y42tq+qqgq//fYbrl69infffReRkZHYtm1bi5txP/30E+7fvw9zc3PMmTOnydfI5XKcPHkSANCjRw9O168OFEWha9euiIqKUvm9mlRev/7664iJicHVq1c13gQWm4yMDKxfvx7jx4/X2nNicnIynj17xgqBRUVFnGwwCs0XX3wBABg8eLAgPQIZF5O2x3alpaWJMm/37t0Fdba0b98eQH16Qk1NDSf3xMpA0zR+/fVX1mH+yy+/YNSoUYLMrW10794d6enpePbsGbp168bbPEyhjaZ9a/lODklJSRFFJHz+/DnkcjmcnJzQoUMHweevqKjAJ598gtLSUvTu3Rtr1qwRfA3axPXr1xEUFPQ3MUdPTw9Lly7FjRs3cP/+fRQWFmLdunXYsmULxowZg4kTJ7IFb0yfYycnp2bPbRRFYezYsRg1ahRyc3ORkZGB9PR0ZGRkKHUvZWlpCU9PT3h5eaFDhw4tFqvJZDKUlJSguLiYjeM3MjKCj48PHBwcWtWel7otOZrC1dW12b8zNDTEvHnzEBsbi8jISLx48QLr1q3D/v37MW/ePAwZMgRSqRS//fYbaJqGt7e32nsk7dq1g7u7O1JTU9X9UQgEAoFAaHWoLRKmpaVh+/bt2L59O9LT0wH8TxikKAoDBgzA3LlzMW3aNJiZmXGzWoJW8eGHH4KmaXz88cfYunUrJBIJXn/9dVHWwoiEXDh+VGmwLgRCrYeJeWnp5p0vmMiy7t27Cz4313Tt2hUA2PMiQT1omhY0pq05goKCOBeYS0pKOB2vObj+LFtZWbG/C2trazYKauPGjTh27BgCAgKwf/9+BAcH/+29ycnJ+PTTTwEAS5cubVZMjI6ORm5uLoyNjdV24XGNv7+/WiJhYWGh2hv1UqkUgYGBCAgIQG1tLaqqqlBdXY2qqqpm/5ycnCxq76VXcfz4cZiamrJ9Z7SF4uJi/Pnnn2IvQ2NOnz6NY8eOQSKRYO7cuYLMyQjhQvVZVYeSkhKN+rapS58+fTBixAhBN4otLCxgZmaG8vJyJCYmsvcjfELTNNavX48jR44AADZu3IilS5fyPq+20r17d0RERLD3tXzBFNRp6pIVQiRkeskJCRMTHxoaKrhYI5fL8e9//xupqalo3749jh49yqtjUxeorKzE7du30a9fv7/9nZWVFUaNGoUhQ4bg/v37iI6ORlFREfbv348DBw4gNDQUkydPRkxMDADlilmlUimcnZ3h7OzMHn/l5eWsYJiRkYGsrCxQFIUOHTrA09MTnp6esLGxUfp40dfXh52dndY76bmAq/NEhw4dXrlXI5FI0KNHD3Tt2hX37t3DlStXkJWVha+//hp79+7FwIEDERUVBYqiNHas9+vXj4iEBAKBQCA0QGWR8MCBA9i6dSsuXLjAioLMfzt27Ig5c+Zgzpw5bbYXRVvjo48+gkKhwKeffootW7ZAJpNh1qxZnFacKQMT+cFFRJu2OQmFWE9dXR27ica1+0gZmM0Uf39/wefmGmZT7sWLF6ioqGiz8UKaUldXpxUxfn379uV8TCFEQnt7e9b1xxcURWHSpEno0qULvvzyS6SlpSE0NBTffPMN3nvvPXZTgaZpLF26FNXV1ejRo0eLDpOIiAgA9RutfPZ0UgVHR0f4+/uzVeyqkJOTo5E7m6IoGBoaKiU0yuVyPHv2DLGxsRr1Q+STvXv34t1334WFhYXYS2G5ceOG2EvQmLKyMrz55psAgIkTJwp2HWechNosEvIt1jTFoEGDMGDAAMHFCYqi0L59ezx9+hTx8fG8i4QKhQI///wzjh07BoqisHnzZixatIjXObUdxgHP93HHFNrogpOQpmlBPwsKhYIVCZsSpfhmy5YtuHXrFgwNDXHs2DG2V2hbJyoqCr169Wr2Wd3Q0BDBwcHo06cPEhMTcevWLSQlJeHq1au4evUqewype09lZmaGzp07s+0mmMhSvj8DrQGu9nVUcfXq6emhd+/e6NGjB27duoVr164hOTmZTR/q3bu3xgKtl5cXevTogYyMjEbfVygUWrcfRCAQCASCEKgsEs6YMQMURbGbtxYWFpg2bRrmzp2L0NBQzhdI0H4++eQT0DSNzz77DDt27MCVK1ewfPly9OzZU7A1MO6J1igSCuEkLCoqAk3TMDExabFXGF8kJSUB0A6RsKamBs+fP8ezZ89gb2+P4OBglTY3zM3N0aFDByQnJyMtLa3VNK0XGm2IGu3SpQvnvWwUCoUgcaNCiv0+Pj7YvHkzfvjhB1y6dAn//Oc/cfnyZezYsQN2dnbYvXs3zp49C319fbz33nvNfp7y8vJYwUbI64cyTJgwATRN4+HDhyq9Lzs7W7AIZ6lUCl9fX/j6+qK6uhqPHz9GbGysaFGLzREeHo65c+dqxcZcZWUloqOjxV6GxnzyySdIS0uDk5OTYCJNeXk528NZm0VC5v5CKEaNGiWKc4qBEQn57kuoUCjw3//+FydOnABFUdi6dSvmz5/P65y6AJOIkZSUBLlczlvRJHOvru1OwqqqKuTm5grSI5khPT0dVVVVsLCw4DXytSlOnz6N/fv3AwD+/PNPrbuXERPmevuq/SKKouDj4wMfHx/k5+fj1q1bePDgAeRyOSiK4uz+VhvuQXQFrs5jnp6eKr/HwMAA/fv3R1BQEKKionDz5k0YGhpi4MCBGq+HoihMmDDhb9+vqanBt99+q/H4BAKBQCDoGmrHjVIUhWXLlmHt2rUt5rUT2gaffvopHB0d8f777yMpKQnvv/8+QkJCsGzZMkEaQ3MVNyqTyTR+4OaakpISyGQyXl01DaNGha58p2laNJGwqqoKz549Q0JCAhITE5GQkIDU1FS2uhQAAgMD8e6776p0HHfr1o2IhBqiDSJhSEgI52NWVFQ0Or74QmhHsKmpKT777DP06NEDv/zyC06ePIkePXpgw4YNePvttwEAc+bMafFzdPLkSdA0DQ8PD62Lb5JIJJg4cSIkEgkbeaUMmvQl1AQjIyP07NkTPXv2RFFREWJjYxEbG6sVRTBpaWm4dOkShg4dKvZSWoVAGBUVhV9++QUA8N577wkWa8cc26amplrj+n0ZmqZZ14EQTJo0SfRiJ6YvIZ8ioUKhwA8//IBTp06Boijs2LGj2T6zbQ1PT0+YmpqioqICGRkZvBWJ6EpPQqA+blxIkZA59oODgwVNtnn48CF+/PFHAMDnn3+O1157TbC5dQXGTahsDLu9vT3Gjh2LoUOH4uHDh7CwsCAJLSLAxefIyMhIo/OAsbExhg4div79+4OmacF67hIIBAKB0JZQWyQE6vtOnD9/HrNnz8asWbMEq5YnaCeLFy/GlClT8OWXX+KXX35BVFQUoqOjMWXKFMyaNYvX3pRcxY0K1StMVQoLC+Ho6Mjb+IxIKEbUaG5uLioqKqCvr49OnTrxMscff/zRaBMzNzcXiYmJSE9PbzLS0sHBAf7+/rhy5Qru3buHBQsWYM6cOZg+fbpSm6Fdu3bF8ePHSV9CDRBbJOzQoQOcnZ05H1eoc4wYn2WKojB+/Hj4+fnhq6++Qnp6OiZOnAig/vc5Y8aMZt8rl8tx6tQpANrnImSQSCSYMGECJBIJ7t+/r9R7xBIJG2JtbY2BAwdiwIABSEpKwq5du8ReEq5duwYPDw+1qsq5ora2VudFwurqaixcuBA0TWPkyJEICgoSbG5diBrNyclBZWWlIHPNnDlTK/ptMtetrKwslJSUwNLSktPx5XI51q5di9OnT0MikeDPP//EG2+8wekcuoxEIoG/vz9u3LiBZ8+e8S4S1tTUaFRIKIRImJKSwkt0e1PQNM2KhEImHOXk5OBf//oXZDIZpk6din/961+Cza1LVFVVITo6Gv3791fpfcbGxqI6tNs6XIiEHTp04OR8w0VqVGtAKpVi6tSp7J8JBAZybBAIBE1Q+Uq9du1a+Pn5gaZp0DSNxMREfP755/D09MSgQYOwY8cOjasaCbqLjY0NfvrpJzx69AijRo1CXV0d9u/fj9mzZ+PEiROQy+W8zMtV3Kg2i4R8wkSaurq68jpPUzB9W/z8/HhzIxw8eBB79uxhvy5cuIC0tDTQNI327dtj3Lhx+OKLL3D8+HFkZGQgJycH586dQ1xcHMLCwiCTybB161YsWbJEqbhBJt4oKysLMpmMl5+ptVNbWyvq/Hy4CAFhzjHm5uacbwyrgpeXFzZt2oSwsDAA9eLh+++/Dz295uuSbty4gYKCApiYmGi1+5aiKIwbN05pITM/P19rzgEURcHT0xPvvvsu6zYSk127donq3L9//z5776Cr/Oc//8GTJ09gbW2NZcuWCTo3I4Brs0goVD/CefPmaYVACNS7NZiY7GfPnnE6tlwux3fffccKhLt37yYCYRMwkaN8Hn8mJiZsko8m51EhRMKXEzr4JC8vD8XFxZBKpYIVTVRVVeGTTz5BUVERAgICsGPHDhJl2QIXL14UvRCQoBpcCA0dO3bkYCUEBiMjIxw8eBAHDx4kqW6ERpBjg0AgaILKd7CrVq3Cw4cPcePGDSxcuBBmZmagaRoKhQJXr17FwoUL4eTkhDfeeANnzpxp0qVDaP107twZp06dwqlTp9CpUycUFxfjxx9/xJIlS5R2YKhCaxcJ+e5LKKaTUIio0WXLlmHlypXs1//93//hr7/+Qk5ODjIyMnD8+HH861//wrhx49C+fXs2ctXT0xNnzpzBrl27YG9vj5SUFLzzzjv48ccfWyyGcHR0hJ2dHWiaRmZmJm8/V2tGzA0ER0dH3hxOQpxj3NzcBI8NfhljY2N89NFH+Pbbb9niopY4ceIEACAgIEDrqx4pisKYMWPQq1cvpV6fm5vL84pUw8LCAvPmzUOPHj3EXgoOHz4s2OZxQxQKBdv/UleJjY3FN998AwBYsWIFLCwsBJ1fF5yEQvQjXLJkidYlqbRr1w4AkJCQwNmYcrkc33zzDc6dOweJRIJ9+/a16A5vywghElIUxYrB2i4S1tTUCOaqZ1yEffr0ESR6maZpfPvtt0hKSoKjoyOOHTtG4jCV4NatW2IvgaACRCQkEAgEAqFtoPaTQZ8+fbBlyxZkZ2dj69at6NevH+surKysxL59+zB69Gi4uLjggw8+wKNHj7hcN0FHGDVqFGJjY/Hzzz/D3NwcSUlJWLVqFfbt28fpPFzFjRYVFXGxHM5pzSIhs4nCbKrwwbfffot169axX5988glGjhypVIQrRVF44403EB8fjwULFgCoFzTefPPNZh1CFEWxbsK0tDTufpA2hJiO9JCQEN5ENiEEI3t7e97nUAaKotCnTx8EBga2+Lpnz56xsY+veq22QFEURo0ahT59+rzytYyYok3o6elh/PjxGDNmjKjrSElJwcWLFwWdk6ZpXLlyRWuLgpShpKQEs2fPhlwuR//+/TFw4EDB18Ccy7RVJCwpKeG1H6GlpSXefvttVpDTJpi+T4mJiZyNuW3bNly4cAFSqRQHDx7EtGnTOBu7tcEUYHD5+28KpnevJvdLQhUUpaSkCDIPI4zzlQbxMk+fPsWVK1cglUpx9OhRURJZdJFLly6R5CkdQtNigq5du8La2pqj1RAIBAKBQOALjcsHTUxMMH/+fFy9ehVPnjzB+++/D0dHR1YwzMnJwdq1a9G9e3cEBgbip59+Qn5+PhdrJ+gI+vr6+Mc//oHk5GQsXLgQALBjxw5O4wSZTVhNqjfr6uoQExPD1ZI4he8HKcbx21IcIB8oFArExsYCgKC9lNTB1tYWW7duxZ9//gmgPkawJac004Ozrq5OkPW1JiorK3H58mVR5pZKpejSpQsvY+fn57PHO59ERkbyujnONTdv3gQA+Pj4sM4IXYCiKIwYMeKVjkIxo19bgqIoBAUFYe7cuaKu4/r167hz544gc8nlcpw4cUK08wsXVFRUYMyYMYiNjYWVlRVWrFghyjqYe3mhHYzKUFVVhd27d/M2fqdOnfDmm2+yfeG0Dcb1waVLlxHVZ86cicmTJ3M2bmvE398fEokERUVFvD7zMs8Gmty7C1UcyXfbBKD+WSYvLw8ABHPKR0VFAQCmTJmC4OBgQeZsLYiVJEBQHXNzc7Xf6+fnh0mTJomecNIUcrkc8fHxiIqKavSlC07XiooKUBQFiqJQUVEh9nIIWgQ5NggEgiZwmjHi4+OD77//HhkZGTh8+DDGjBkDiUTCCoYxMTFYtWoV2wPs4MGDovedIgiHra0ttmzZAgsLC9TU1CAjI4OTcRuOpYwzrDkePHggan+kluC7bxKzycc8XAtFUlISioqKYGpqir59+wo6t7owgnSPHj1adK4yMaO6JHooQ0xMDCIjI5GQkMCLeE3TNCIiIkT7LFpaWvIWd3n+/Hlexm2KP//8U5CoPS54/PgxAKBDhw4ir0R1KIrC0KFDm40169SpE7y8vARelWq4u7uLHvF68uRJTqMRm4IRjviIPBeK6upqTJw4EdevX4eZmRnWrFkjmlDFJBxosnnIB0wvbD7FmTFjxggSZaguzM/u4eHB2ZgDBgwAAJw9e5YUP70CExMTNkI/Li6Olzlqa2vZYiB13axyuZyzZ7FXIcRGZWVlJerq6kBRlGCJCvfu3QMAjBgxQpD5WhPJycm4evWq2MvgBJlMhhMnTmDfvn24desWCgoKWlXLHUdHR+jr66v13hEjRmhdj87CwkKcO3cOP/74Iw4cOIBz5841+rp06ZLYSyQQCAQCQRR4sQ1JpVJMnDgREydORHZ2NrZv347t27ezsYJ1dXVsvzpLS0u89tpr+O233/hYCkHLoCgKnTt3xq1bt5CWlsZJPn1qaiqA+h5Y6joJ5XI5rl27pvFa+IKJU+ULCwsLFBQUCO7yZdwjgwYN0jgqVigiIiIA4JWiZmsUCePi4nD06NFG37OwsICzszOcnZ3Rvn17tGvXTqPN0wcPHiA+Pl7DlaoPX66vlJQU3kWQl9m5cydmz56t1X1AFAoFu4mqqzFdhoaG6NevX5Mi8NixY7WyerohFEXBwsJC9LjtvXv3YtGiRWjfvj3nYxcWFmLPnj28R3fziUwmw2uvvYbz58/DyMgI3333nWgCtFwuR3FxMQDtEglpmsaxY8fY+0I+CAgIYJMCtBXmXo7LXomBgYGwsLBAXl4eLl++jKFDh3I2dmskODiYvZ/hIw44OTkZNE3DxMRE7c9gTk5Os7H5XCOESFhaWgoAsLa2FuSZory8nL1fHTZsGO/ztUYiIyPh7u7OaUGD0FRUVGDv3r3sc9/Tp08B1D8fdezYkf3S5V6VEokEzs7OKl9be/TooTVpA3K5HE+ePMG9e/d0poiSQCAQCASh4b2sp127dvj444+RmJiIS5cu4Y033oCxsTHrLiwuLsaWLVv4XgZBi+jUqRMA7nq1MTd6jo6Oam/GxsbGanV/otbqJLx79y4AYPjw4YLOqy5FRUWsmNxSrFBtbS37u2wtImFJSQkOHjz4t++XlpbiyZMnuHjxInbu3Invv/8e69evR3h4OO7fvw+5XK70HIWFhTh+/DiXy1YZPkRCmqZx9uxZzsdVhp07d7IFOtpIRkYGSktLoaenx/bR0kV69er1tw2g6dOn68ymkLZs4vz++++cx9KlpaVh/fr1Oi0QyuVyzJ49GydOnICBgQG++eYb+Pn5ibae4uJiKBQKUBQFExMT0dbxMufPn+e9B7q2RwrSNM2Lk1BPTw/9+/cHAM77irdGmH61fDkJmaIjJycntZ99hOyZLYRIyDzHOTg48D4XUF/UplAo4OPjI0pP99ZCeHi4zkbiFRYWYtu2bcjMzIShoSFmz56NwMBASCQSlJaW4sGDBzh8+DDWrl2LTZs24dy5c3j+/Llg4jyXuLi4qPyefv368bAS1SgqKsL58+exbt06HDp0iAiEBAKBQCC0gKANyAYOHIiBAwdiw4YN2L17N7Zt28aKBIS2AyMSpqenczJeSkoKAKgdLaNQKLQ+7qS6uho0TfPmSGGEESFFwpqaGrYHZFhYmGDzasKZM2egUCjg4eHRYrxTTk4OaJqGgYGBzogELaFQKHD48GGlX19YWIjCwkI8evQIkZGRCA0NRUBAQIt9c+RyuUpz8AUfYsmjR4+QnZ3N+bjKsmvXLsyaNQuenp6iraE5mKhRZ2dn0SMvNcHAwAChoaE4c+YMgPrqaeZapwtokxts27ZtCAwMREBAAKytrdUep7a2FjExMTh16hSHqxOempoaLFmyBPv374eenh6+/PJLwfptNQcjuJqammpNjFh0dDTbH4wvPD09BRMg1KW8vBw1NTWgKIpzV+7gwYNx8uRJHD58GL/++qva8XNtAUZMTkhIQF1dHec9v589ewYAGhXXtFaRUKioUWYPg7gINaO8vBy7d+9GUFAQvLy8tKZo6VVkZGRg165dqKmpgZOTE7777jtWLK6ursbDhw9x9+5d3LlzB8+fP0dOTg5ycnIQFRUFiUQCV1dXuLu7o0OHDnBxceH8HME1qqZ9dO7cGXZ2djyt5tU8f/4cN27c0OpCSQKBQCAQtA1R7kYsLCywbNkyLFu2DDExMdi2bZsYyyCIhK+vLwDuHk6Znhzqbtw8evRI9Ji1V6FQKCCTyXiLz2FEQiHjRmNjYyGTyeDi4qIzm+lM1OirnAQNo0a1PWpQGa5cuaL257W0tBSnTp3C1atXERISgp49eza5sXjlyhX29yYmXDsJ6+rqcOHCBU7HVIddu3ZhzJgxjWLytKGSmREJdTVqtCFBQUF48OABaJrWuf5E2rQpV1FRgatXr+Lq1avw8PBAQEAAOnfurJQgUVNTg4SEBMTFxeHJkycCrJZfkpKS8Nprr+Hu3buQSCT49NNPtcLJxoiE2hK7GR8fj7/++ov3eXShdzJzH+fi4sL5PWOPHj1gbW2NwsJCnD9/HqNGjeJ0/NaEj48PLC0tUVJSgqSkJPj4+HA6fmJiIgD1+xHSNC2oSFhbWwuZTMarsMzEjQol5DMioa4UOmoz2dnZOHHiBID6ZCAvLy94e3vD1dVVawpRGvLkyRMcOnQIcrkcPj4++OabbxolxxgZGaFXr17o1asXgPriybt377JfBQUFSE1NRWpqKq5cucKKhh4eHvDw8NBK0VBVJ6GYLsLbt2/rfIEYgUAgEAhiIPrdR/fu3fHTTz+JvQyCgDR0EnLhjmNiI9R5KNQFFyFDdXU1byKhGHGjDR+udUFIk8vl7CZkW+pHmJqaisuXL2s8TllZGc6cOYNr164hJCQEQUFB7PGclpaGK1euaDwHF3AtEt66dUtrooxPnjwp9hL+BhMN2BpEQj09PSxdupRX1zdfaJNI2JCUlBSkpKTg1KlT6NatGwICAtCuXbtGv9/q6mo8ffoUcXFxgvf95JPw8HDMnTsXFRUVsLCwwEcffaQVAiEANhJWGxyo6enpOHDgAO/z2Nvba3V/VwY++hEySKVSDBw4EEePHsX+/fuJSNgCEokEffr0wdmzZxEXF8epSCiXy1l3jLoi4YsXL1BZWcnZmpShoqICVlZWvI0vpEiYl5eH9PR0SCQSDBo0iPf52hK5ubnIzc3F9evXYWhoCE9PT3h7e8PLy0srClOio6Nx+vRp0DSNPn364F//+tcre7Hb2NggLCwMYWFhoGkaGRkZePDgAR48eICYmBi8ePGCFQ0vX74MiUQCNzc3tl9j+/btRXdum5qawtraWqnC6g4dOvDSX1oZrl+/3mSPcAKBQCAQCK9GdJGQ0Pbw9PSEVCpFVVUVCgoKNIqFKSsrQ0FBAQD1Hgrj4+PZ92s7VVVVvG3kiuEkvH37NgDd6Ud48+ZNFBYWwtzcHF26dGnxtYxIqElUnjZQVVXFeQRoRUUFzp07h2vXrqFv377o3r07jhw5wukcmsClSFhZWUkeVFugrKwMqampANTrdaKtiCEQFhcXg6IotY9fbRB7WqKmpgZ37tzBnTt34OjoiICAABgYGCAuLo6N3Wst1NTUYPXq1Vi/fj0AoEuXLvj888+1KuZSW5yEL168ECyNpG/fvjoh/jP3cXz1SBs8eDCOHj2KI0eO4LfffoORkREv87QGgoODcfbsWcTHx2PixImcjZuWloba2loYGBioXYwmpIuQgW+RUMiehEyhY+/evXn9mdo6NTU1iIuLY3t7Ojk5oUOHDujQoQPc3d15K55tCpqmceHCBVy/fh0AMGbMGLz77rsqR+VTFAVXV1e4urpi3LhxfxMNHzx4gMLCQrZIihENXVxcWNGQD6e4Mri6uiolEoaGhgqwmsbQNI3IyEitKTrlG6lUitGjR7N/JhAYyLFBIBA0gYiEBMExMDCAp6cnEhISkJaWppFIyLgILS0tVd6ooGlap24kq6ureRubER8rKipQUVHBex+9yspK9t9u6NChvM7FFYwLq1evXq+84WoNTkKapnH8+HG2MptrqqqqcPHiRVy8eJGX8dWFS5FQl84vynD8+PFGmxJdu3Zl46PVgdn0sbGxaRW9O8UiNzcXW7ZsAU3TmD9/vlqCq7Y6CZsiNzcXp0+fFnsZvNAwXhQAZsyYgYULF2pd7Jg2iISJiYlsBDjfmJqaolu3boLMpSlM4ZuHhwcv43ft2hV2dnYoKCjAmTNnMGHCBF7maQ306dMHQH1BIpc07EeornAtlkjIJ0KKhHfu3AFA+hEKDdPX78aNGwDqRStGNOQzorOurg7Hjh1j0y8WLlyIN954g5PCkaZEw/T0dNZlGBsbi4KCAqSlpSEtLQ1Xr15le84yoqGbm5sgoqGLiwtiY2NbfI2zszM6dOjA+1oaQtM0zp49i5s3bwo6r5gYGRlpZUIMQXzIsUEgEDRBu3YdCG0GX19fJCQkID09HT179lR7HEZocnR0VPm9CQkJgsZrakpVVRVvYxsYGMDIyAjV1dXIy8vj/eY+NzcXAGBlZaWRSCwUcrmcdbsp05OoNYiET548aRU9vVRBT08Pcrmck0ifxMRE3Lp1i4NVaQ+bN29u9P8URWHDhg3o3LmzWuMxIqE2OaR0kRMnTkAulwMADhw4gJUrV6rcw0eXRMLWSl5eHnr06IGysjKtixd9GTHjRmmaxrlz59hNYiHo1q2b1gm1TUHTNK9xowDYeMVDhw5h//79RCRsAUYkTE9PR1lZGWefFyZW2cnJSe0xGBe/kPApEioUCpSVlQEA788VNE3j/v37AEg/QrFJT09Heno6rly5An19fXh6esLHxwe+vr4wMTHhbJ7jx4/j0aNHoCgKH3zwAa99pymKgpubG9zc3DB8+HC8ePECsbGxuHjxIitOM+7DjIwMXL9+HcbGxnjnnXd4d3YrU4TWr18/wV33N2/ebFMCIYFAIBAIfKH9T7yEVgdN00hMTASgeRU685CrzgMhUw2oK/DttLG1tUVmZiaSkpJ4FwlramoAaH/EHcOWLVvw5MkTmJqasps+LcEIuq/qUaHNMJvAbYm6ujrs2LEDs2bNUvvcRNM0bt68ibNnz3K8OvHp3Lkz66J9/vw5qqqqEBERobZIaGdnB6BekH706BG6du3K2VrbEg0FQYlEApqmVR7DzMwMxsbGvBajEFqmtLSU3eT+9ddfRevnowpCbwTSNI3Tp08jOjpa0Hlv3ryJDh06cNpXjg+ys7NRWVkJPT093kRCAKxIePLkSTb2kvB3bG1tYW9vj/z8fOTn53N2z8s4E9XtR1hYWIji4mJO1qIKhoaGvI7PXPv47t2Wk5ODoqIi6OvrK/VMQBAGmUzWqMBxwoQJ6N69u8bXqYqKCnbP4Ntvv0Xv3r01Go+maZSWluLFixcoLCxk/8t8Nfx/vt23quLo6MgWFTeFvb09OnXqJPCqgNraWsHnJBAIBAKhNUJEQoLgxMTE4MmTJzAwMFDKldUSjGPL1tZWpffV1dWxlbi6gImJCe8bhu3bt0dmZiaePHnCewQo8wCvCzf1eXl5WL16NYD6eBllNnkYxwHj7tFF2mqPldzcXGzbtg2zZs1S2Qkql8tx8uRJtsK8tTFhwgR2ky8lJQV//PEHLl++jBUrVqi1STx27Fg8e/YMx48fx5EjR2BkZAQvLy+ul80rdXV1yM3NRVZWFrKyspCdnY2KigrQNP23L4VC0ej/HRwcMGrUKI17h02aNImNG3399dfV6j8hkUjg6+uLBw8eaLQWgvp4enrC1tYWL168QElJiVaLhMz1oby8XLA5aZrGqVOnWCeF0Ozduxfjxo1DYGCgKPMrA+PO7tevH6+CTOfOnWFtbY2ioiJERkbqTG9pMbC0tER+fj5nm/21tbXs84urq6taYzCFmkLDVwQuUH8NMzQ0RE1NDUpLS3m9h2V+f127duVd+CSoz7Fjx5CQkICxY8dq5CqMi4sDTdPw8fHRWCAsKyvDP//5T5WSWvT09GBubg5zc3OYmZk1+i/zZ2tra97FcaD+c+bj49Ns5OiYMWNUTrLggj59+iA6OhqVlZWCzy0WFRUVbBJLXl4eadtAYCHHBoFA0AQiEhIEZ+/evQCA4OBgjS9aGRkZAFSPdUxOTtYJgYrBx8eH95tuZkOS694pTaFLIuHq1atRXl4Ob29vjB8/Xqn3MCKhQqHgc2m8ostRqZpSVFTECoXKxnlVVlbiwIEDokR4iYG7uzssLCxQWlqKW7duoX///iqPQVEU3nnnHZSVleHSpUvYv38/li5dqnLRh1AwgmB2djYrCObk5Kg9Xk5ODrZv347AwEAMGzZMbeextbU13n33XVAUpVEkYufOnYlIKCIURSEkJAQnTpzA48eP4efnJ/aSmsXa2hoA/z3GGGiaRkREBO7duyfIfM1x4sQJlJaWYuDAgYK7KF8FTdOsSDho0CBe55JIJAgJCcHJkydx7NgxIhK2QMOe31zw8OFDyGQymJmZqX2fxvQ0FBIHBwdO4x+bwsTEBDU1NWxvQr5gfn8BAQG8zkPQnPj4eKSlpWH8+PFqO8GZ8+rgwYM1WotcLse///1vViA0MTGBmZnZ374Y4Y/5MjQ01KrrTadOnZoUCXv06MGrg70ljIyMMGjQIJw6dUqU+cWiLYmiBNUgxwaBQFAXIhISeKW8vBxPnz5l4z/i4+Nx7tw5AMCQIUM0Gru2tpbdoFV1U1nXeq0JEd3BiISJiYmoq6vjtf+OroiEly9fxp9//gmKorBy5UqlHTqtwUnIbAK3VSoqKrBp0ybMmzev0UOvQqFAUVERGx/GfBUUFKCurk7EFQsLRVHo2rUroqKicP78ebVEQgCQSqX46KOP2J4r4eHhWLhwoVpuOL6ora3F0aNH8fTpU16E/3v37uHp06cYMWIEunbtqtZmEBcV5B07doSBgYHWn5dbM/369WNFwmnTpom9nGYRUiRUKBQ4ceKE1gjYly9fRllZmWiOiebIzc1FUVERpFKpIBGI/fr1w8mTJ3H8+HH88ssvWrWJrU1YWloC4G7Djul17OXlpdbvXCaTiSIS8ukiZDA2NkZRURFKS0t5nYdxEmqzq5jwPyoqKrB3714EBgZixIgRKiVf1NTUICUlBYDmxRfbtm3D7du3IZVKsWjRIo16iopJc4kfw4YNE3gljenZsydu377N9uUlEAgEAoGgOkQkJHBGfHw8IiMjER8fz4qC6enpTb7WxsYGwcHBGs2XlZUFADAwMFDJkahQKHROJOzYsSPvc9jY2LB9BpKTk+Ht7c3bXIyIps2b0TKZDMuXLwdQH4uoiquD2bDXZZHQyMioxb4TbYUdO3YgJCQEZWVlbVIMbIlu3bohKioK165dQ3l5udp9HPX19fHpp59i9uzZyM7OxsWLFxEWFsbxatVDJpNh3759SE5O5nWeiooKHD58GDExMRg9erQoTl49PT14e3vj8ePHgs9NqCckJARAfc9kmqa1VnhhREK+40YVCgWOHz+OmJgYXudRlXv37qGoqAjDhg2Ds7Oz2MsBAPZzGxISIkg/5MDAQBgZGSEjIwP37t1Dz549eZ9TF+HaScj041Q3mlustAMhRELGqci3SEichLrJvXv3kJycjEmTJikd1fvixQsA9dc8TUS9S5cuYc+ePQDqo/t1VSAE6u/ZfX198fTpU/Z748aNEz3SUCKRYPjw4di9e7eo6yAQCAQCQZfRnhJYgs4il8vxn//8B127dsXy5cuxfv16nDt3jhUIra2t4e/vj7Fjx2L58uX47rvvsH37do37ODBRo7a2tiptpKWnp+uUBb9Tp06C9BmgKIrd7OI7cpT5eWQymdZGcv7444+Ii4uDpaUlFi1apNJ7W4OTEGjbkaMNiYqKwsOHD5GTk0MEwgY4OjrC3t4eCoUCV69e1Wgse3t7fPrppwDqf9/Pnz/nYokaUVdXhwMHDvAuEDbk+fPnWL9+Pa5evSrK+aNz586Cz0n4H0FBQZBKpXjx4gVyc3PFXk6zCOEkVCgUOHr0qNYJhAzJycnYsmULdu3ahZSUFNA0LdpaGkaNDhw4UJA5DQ0N0atXLwD1vb8ITcOlSJibm4vU1FRQFKV28aAYLkJAOCchwK9IWFhYiIKCAlAUBX9/f97mIfAD007g4sWLSt1jMSKhuv0/gfr7uq+//hoA0LdvX3Tr1k3tsbSFhglHrq6uWiOYe3l56VxvcwKBQCAQtAniJOQYba785oOMjAzMmjULly9fBlCfR+/r6ws3Nze4ubnB1dWVjdrhmszMTACqCxlC9NzjEl9fX8Hmat++PZKSkvDkyROl+++pQ0PRs7a2FkZGRrzNpQp1dXU4ceIEfv75Z0RGRgIAli5dym7yKEtrEQmtra1Zxy6B8DJM5OilS5dw/vx5jBo1SqPxQkNDMX78eBw/fhxHjhzBsmXLRKtMlsvlCA8PF21D9eLFi3j48CHGjh0LNzc3weYlmyviYmxsjJ49eyI6OhqPHj3SWrcB3yKhQqHA4cOHdcLV+vz5czx//hyurq4IDQ2Ft7e34M8BeXl5KCwshEQiQd++fQWbt1+/frh69SqOHTuGr776SrB5dQku40YZF6GLi4vablExrmlOTk6CuFuFEAmZ35+Pj4/a6QkE8bl69SqePXuGSZMmwd7evtnXFRQUAFBfJCwtLcVnn32Guro6dOzYUfRITq5ouDcxduxYrdr7CgsLE+3enUAgEAgEXYc4CTng/v37mDlzJgCofZNUU1OD0tLSRl/azpEjR+Dn54fLly/D2NgYH374IX788UcsXboUo0ePRteuXXkTCIHGTkJloWla56JG1W20rg6Mk5Dv35GRkRH7WSkrK+NsXHU/Ry9evMB3330HT09PTJ48GZGRkZBIJBg3bhyGDx+u8jpai0hoZWUl9hIIIqDK54ipiL537x5bca0Jy5cvh4eHByoqKnDs2DFR3DmMg0nsa0V+fj62b9+Ov/76S7Dfg6GhoaDXnNaMutcjJnJUmwUypjirpqaGc3c1TdM4evSoVv/8TZGeno69e/di06ZNePTokaApCYyLsG/fvmzkohAEBwdDIpEgNjaWN8e1Lj4fNYQpMuMimvf27dsAAE9PT7XeX1RUxMl1WlWEcBEC/4sbLSkp4W0Oph+htjinlEXXP0d8kJ2djV9//RU1NTXNvkYTJ6FcLse///1vZGdnw8rKClOmTNGqPraaYGxsjMWLF2Px4sVwcHAQezmNcHBwQFBQkNjLIBAIBAJBJ2kddyoiEhMTg9DQULRv377R91Xd0Pvmm29gaWnJfmkSa8E3lZWVWLp0KSZPnoyysjL4+vpiy5YtGDFihKCVZIxIqIqTMCcnh9eHR65xc3MTdMOHOY6Tk5N5jWSVSqUwNzcHAE4bjKv6OXrw4AEWLlwIZ2dnfPjhh0hLS4OlpSVef/117N27F6tWrVLrgY5xSoodpVpQUIBz587h1q1bar2fxI22TVT5HFlbW8PFxQVAvftNUwwNDfHZZ59BIpEgMTFR7WNXXWiaxokTJ/Do0SNB522J6OhonD9/XrD5SOQoN6h7X9evXz8A0Kpj8GVMTU3Z6xzXfQlzc3Px8OFDTscUktzcXISHh2PDhg24f/++IMVCQkeNMlhaWrKFIsePH+dlDl16PmoKrpyEMpkMd+/eBaC+45sRuIRGKJFQCCehroqEuv454pOWEoY0EQm3bNmCO3fuQE9PDzNmzBD0eV4InJ2dtaYn78sMGjRI7CXwjkQiwcCBAzFw4MBWIz4TuIEcGwQCQRPIWUMDYmJi0K9fPyxfvhxr165t9HeqimUfffQRSkpK2C+mn5+2UVtbi8GDB2PTpk2gKAozZ87E+vXr/yaSCgETN6qKkzAhIYGv5fCCkFGjAGBubs5WPfP9u2I2TrgUCVX5HP33v/9FQEAAtm3bhtraWnh7e+ODDz7AgQMHNK6MZJyE6enpovUpiouLw4YNGxAVFYXTp0+zkTmqwKcTmNAyZmZmGDlypChzq3o9YjaJL1y4wMn8HTt2xNtvvw0AOHfunKCRt7du3cKDBw8Em09ZoqKiBHNWCX3dERtzc3OEhISgR48enI6r7n0d4yRMSkpCcXExp2viCoqiWKc515Gj2iyOqkJhYSGOHz+Obdu28XofkJeXh4KCAsGjRhkYUTsiIoKX8XXl+ag5uOpJGBcXh8rKSpiYmKi9MS9Wr193d3dB5hHCScj8DnVNJNT1zxGfxMbGNvl9mqZZkZAphlOWU6dOYf/+/QCAiRMnwtHRUbNFEgDUtwXZs2cP9uzZ02KKgampqaBR/WJgbGyMyMhIREZGChLnTNAdyLFBIBA0gYiEapKZmYlBgwZh7NixWLNmDWQyGT788ENMnToVffv2xebNm1WK3jE0NISFhUWjL21kzZo1iI6OhoWFBdasWYMlS5Y06i8nJDKZDABUml+staqLGJWeTA8kvqKjGOzs7AAAqampnI2pyufo0qVLAOof9NevX49NmzZh5MiRMDAw0HgdTMzJ7du3sXPnTlFifV7elFQnEo5xJxCEp3///ujTpw8mTpwo+NyqXo/8/PwAAE+fPkVhYSEna5g4cSL69esHhUKBAwcO8Nb77GXS0tIEmUcdhHJVGhsbi3LcCY2/vz9mz56NlStXIiwsDBMmTMCqVasQGhrKyfjq3tc5OzsjMDAQCoUCmzZt4mQtfMD042opqk0d+EwxEIOsrCxeRcKYmBgA9dGfYvRIY45rro8DBl15PmoOJpFB0/vAvLw8AICjo6PaqS1VVVUarUEdHB0dBes7zhS28VlYVFRUBEA44ZMrdP1zxCfNXXMoimKfCVUpaD137hzWrFkDoL7XdpcuXTRfJAEAcObMGSQmJiIxMRHnzp1r8bWdOnUSaFUEAoFAILQeiEioJklJSejevTtyc3Nx584djB8/Hjdv3oSDgwPc3d3x008/4dtvv0V2drbYS+WMhIQEfPnllwCAt99+Gz179hR1PczDoCqbx1w7BfjG0NBQ8DkZ8Y7vKlNGABXL3ck8FI4aNQpdu3blNCp3ypQpeP/996Gnp4fk5GT8+uuvgvc369KlC+bPnw8PDw906dJF5SrWgoIC3Lt3j6fVEV4F8/no3r07ZsyYIfJqWsbMzIwtLuDqmKEoCh9++CFcXFxQUlKC8PBwQeJ7tbmPaHp6umD9pPz9/XVuE1RZBg4ciH/+85+YNGkSOnbs2CiKx9zcHEOHDhUlHaEh69evBwCcPn2aFYG0DaboiuvPTGtzsFMUxVvck1wuZ4+PUaNG8TLHq2DubXr16iXK/NoOc23UtICGKYxkkirUwdTUVKM1qAPz8wsBkwBSUFDAmwubuQ+RSqW8jE8QnpYEU29vbwDAjRs3lBrr0qVL+OabbwDUF4wOGTJE8wXqMJWVlThy5AhOnjyp8VhxcXG4c+cO+//R0dF4+vRps6/v3bs32rVrp/G8BAKBQCC0JYhIqCb9+/fH559/zsZU0TSN8PBw/Prrr9i3bx/eeustnDhxQrT+D1yjUCiwZMkSyGQy9OrVC8OGDRN7SbC2tgagmkhoYmKC3r1787UkzuHC1aYqTHwr3yIhE90ilkjIHDd8VDhTFIUxY8Zg69at8Pb2Rk1NDfbv34+IiAi1HH3q4ubmhrlz52Lq1Kkqi6BcRUcSVEcikTSKu/X19cWCBQtEXNGr6dixIwA0eoDXFDMzM3z11Ves2C7EManNIiEAwaJQKYpqdT1dBg8ejA8++ACDBg16ZfyOuj2/uCIkJARLliwBAKxbt44VCLQJRiTk+prW2hwufIoJz549Q0VFBaytrREcHMzbPC3B9PPSpXtrIWFEMsaBpi7M50yT40mMnmhCxiwaGhqyzk2+olUZVzDp89R6MDc3b/bvmPj1U6dO4Z133sGHH36IL774At999x3Wr1+P33//HXv27MGRI0ewZ88e/Pvf/wZN0wgICMDo0aM5LUDVNbKysrB582bExsbizp07qK6uVnusoqIiHD16FAAwY8YMTJs2DQBw5MiRZl3aUqm0VadiVFRUwN7eHvb29oKlrRB0A3JsEAgETSB3uGrAPKgNGTIE//jHP7Bs2TJ8+umnsLW1ZSsMly9fDoVCgYsXL4q5VM7Ytm0bLl++DCMjI6xatUorbnqZanNVo6nE6NmiLmKIhEI5CcUWCZnjhs8YJDc3N2zYsIF1gt29exfbt2/ntV8KF6SnpwvufCT8D0dHx79tBLq6uuKtt95i+4BpG56engDqj3Euo/U6dOiATz75BIAwffm0XSSMiYkRxFEJAB4eHujQoYMgc/EF4wz88MMPMWDAAKXP92KLhADwzTffwN7eHqmpqWxvI22COAmVg0+RkCkaCAsL08hhpi61tbWsGNOnTx/B59cFmIKfiooK1NbWqj0OUyigayKhkE5C4H+iJF8iIXO+I07C1kNLhSmenp4wNDREVVUVHj58iFu3buHy5cs4ffo0Dh8+jN27d2PLli34+eefsWXLFtA0DX9/f4wdO1Yr9krEgKZp3L17F1u2bGn0vKuuSCiXyxEeHg6ZTAY/Pz8sXLgQixYtYotwDx8+3Ox9sYODA4YOHarWvLpAQUEBCgoKxF4GQQshxwaBQFAX4Z8odZTs7GxkZWWhZ8+e0NPTg1wuh1QqRVhYGDw8PNhYLolEAoVCgby8PLi7u6N79+4ir1xzsrOzsXLlSgDA/PnzBX/gaw51nIQAYGVlhW7duuHhw4d8LItTxBQJc3NzUV1dzZuIxsQpJiYmQqFQCF6Vy6eTsCH6+vp48803ERgYiM8++wxZWVnYuHEjpk+frpUb8DRNv7LPA4FfmjvH2tnZYeHChdi9ezdycnIEXlXLuLm5QSqVoqCgAKmpqfDw8OBs7EGDBuHp06fYt28fjh49Cnt7+0ZOSy7RdpGwrKwMSUlJgolYgwYN4r0/LR+4urqiT58+6NSpk1qbuc7OzjA2NhalhxeDjY0NfvzxR8yePRs7d+7E4MGDRY9BbQgjShGRsGX4EhPKy8vZqDWxokafPXuGuro62Nvbt9p4Yk1p6FqWyWRq39cTJ6Hy88XHx+PZs2e8jM+IEcRJ2HpoSSTU19fHW2+9hdzcXNTW1rJfNTU1jf6f+Z6LiwsGDBjQ7PFRUVGBgwcPIisrC6ampjAxMYGpqWmzf7awsICpqanOCI4ymQynTp1qMvVCXZHw4sWLyMzMhIGBAT777DP23uPzzz/H/PnzkZqaiqtXr2LgwIFNvj8kJARPnz5FRkaGWvMTCAQCgdCWICKhEjx58gQhISEIDg7GF198gd69e0MqlbJCIZNXzyCRSLBx40aUlpaK3rdPE4qKinD16lWsW7cOFRUV8PHxwZQpU8ReFgvjqFHHRh8SEqL1IqFEIhGlMtzExITdHM3IyOBtM9rJyQlSqRSVlZXIyspinYVCIYSTsCG9evXCjh078PnnnyMxMRE7d+7EsGHD0LdvX616+Hv69CnvLlJCyzg7Ozf7d2ZmZpg3bx4OHDiApKQkAVfVMnp6enB3d0dSUhLu3LnDqUgIAIsWLUJiYiLu3r2LPXv2YPr06bz0GtF2kRCodw8JJRK6ubnB09OTN1cG13Tv3h29e/du8TOkDBKJBJ6ennj06BFHK1OPN954A9u3b8fFixfx888/47vvvhN1PQ1hxA6uPzMkblQ5YmNjQdM0/Pz8OD/fKguTONC7d2+tuo/RJhjHLaBZNC8XTkKhexKam5sLLkwyRVZ8x40SJ2Hr4VXXHHNz8xYjSZWlsrISf/75J/Ly8gAAxcXFSvXONDAwgI2NDWxtbWFjY9PozyYmJlpz7i0qKsKBAweaLWJURyRMTExEVFQUAOCTTz5pVETp4uKC1atX45tvvsHly5fRoUMHuLm5/W0MiUSCCRMmYMOGDSrPTyAQCARCW4OUwb2CvLw8LFmyBAEBAXj+/Dm+//57REdHA6h/QHg5Vu3w4cN455138NNPP2Hv3r1N3qxoK0VFRTh+/DhWrVqFwMBA2NraYsKECYiMjIREIsHq1au16qFI3bhRoP4hUhvixFpCDBchgxB9CfX09NiNXDEiR4VyEjbEyckJ69evx4gRI1jH3qFDhzSKoOIShUJBehFqAa8SvwwNDfH666/D399foBUpBxM5ymVfQgapVIrPPvsMzs7OKCkpwe+//47o6GhOo00B3RAJHz9+LKjDTdt7E5qZmWHQoEF47733MHHiRI0FQgbmeBYTiqKwceNG6OnpITo6WusKAwDuexLq6+uzfcVaA3zcN9M0zTo1xHIRAv8TCUnUaPNIJBLWVaTJ9UUXnYRiJM8wc6ampnJ+b03TNOsk1KbnYYJmcCEAvoqqqirs3LmTFQhVoba2Fjk5OXj8+DGuXr2KY8eOYdu2bVi7di2+++47bN68GRcuXBD1/jUxMRE///xziyknqoqEZWVlCA8PBwBMnDgRAwYM+Ntrhg8fjrCwMNA0jfDw8Gbvje3s7DB8+HCV5icQCAQCoS1CRMJXkJ6eDmdnZ/zwww/YvXs3YmNjGwmFL1dvPX/+HI8fP8a1a9fQo0cPEVasPGVlZU2KguvWrcP9+/dB0zRcXV0xbtw4/PTTT1onqqkbN8oQEhLC5XI4x9DQULS520JfQqGdhAyGhob44IMPsHLlSkgkEsTFxeH333/HixcvBF1HU9y/f5/k12sBykRpSqVSTJw4EUOGDBFgRcrRsWNHAPV98/gQvi0tLbFx40aEhIRAoVDgr7/+wqFDh9SOMGoKXRAJAQjqcHNxcflbYoI24OjoiEmTJmHlypUYOHAgzMzMOB1fW+55fHx8MH78eADAmTNnRF7N/+CrJyEgfEQhn/AhJmRmZiI/Px9SqRSDBw/mfHxliY+PB1DvJCQ0DxeCui46CcX4HFtYWMDIyAhyuRxpaWmcjt2wKInEjbYe+Hav19TU8NYmoKamBtnZ2bh27Rp27doleEQ6TdO4fPky9uzZ88rXqnKvrlAocPjwYdTU1MDLywvLli1r9rUrV65E+/btUVpaihMnTjRbPNinTx+dKt4nEAgEAkEMyB3uK/D19cVHH32EHj16ICgoCLt27UJMTAy+//573Lp1i30d8/C2evVqHD58GF27dhVryUpx9+5duLu7NysKfvbZZzh06BD+/PNPrFq1Sit/Hk2chADg4eHBmeOAD1q7kxAQTySUyWTsZ1ZokRCoLy6YMGECfvrpJ9ja2iI/Px+bNm1iq/LFoLa2FpGRkaLNT6jH0dGxUTxZS1AUhf79+2P58uVsj08xcXR0hKmpKaqrqxEXF8fLHBYWFvi///s/LF++nBXZN23ahKysLE7G1xWRsKl+L3yibW7CHj16YNGiRfD39+fN0WFmZqY1PZjnzp0LADh37pzWHKN8ioR89RwVAz6Oz/v37wMAhg4dKrjww1BaWsr2eOrVq5coa9AVmM+KJiIhcRIqB0VRrDjJdV9CxkUIECdha8HZ2ZnX58Da2lrs3r0bmZmZvM3BkJKSgq1bt6KwsJD3uYB6d+TevXuVfnZURSS8cuUKUlJSoKenh88//7zFPRETExN89tlnkEgkiI+Px927d5t8HRM7SiAQCAQCoXmISPgKzMzMWIFMJpOhd+/e2LNnD2JiYrBmzRpER0dDoVDg22+/ZauoGPFKWykpKcHAgQNRVFQEBwcHjB07Fp9++ikOHjzIioJDhgxhhSJthXESlpeXqxU5R1EU+vXrx/WyOENMkVBoJ+HTp095nWf+/PmYMWMGZsyYgenTp6N///7s34np2OzatSs2b94Mf39/yGQy7N+/H7GxsaKs5d69eygvLxdlbsL/UGdTzd7eHvPnz8fo0aN5WJHyUBTFugnv3bvH6zzTpk3DL7/8AicnJxQXF2Pr1q2cVGlriwDzKrKysgR1Hzs7O6NTp06CzdcSo0aNwvjx4wXp2astbsJRo0bB3t4eRUVFbJKF2HAhfDRHa3IScu04qq2tZXtqixk1yty3eXt7t6p4WD7gQlAnTkLl4asvYUORUFv6wBE0o1u3bryOf+LECUH7vL948QLr16/nNGGjKWiaxp9//onExESl36PsmvLz83HlyhUAwPvvv69UEaSvry+WLFkCADh9+nSzz7M2NjaiXje5RCKRICgoCEFBQcTZTGgEOTYIBIIm8L/DomMkJyfjwoULKC8vR+fOnTFixAhIpVIoFAro6+tDoVCgV69e2Lt3L2bOnIk1a9ZAJpPh7NmzuH37ttjLV4qEhARUVFTA1NQU27ZtE60KWVMcHR0hkUhQU1ODgoIC2NvbqzxGp06dYG9vj/z8fB5WqLsYGxsDUN+lqSzMgzxTjc4Xhw8fbvL7/v7+Sru2+MLGxgY//PAD1q9fj+PHj+PkyZPw9PQU/HMp9u+BUI+6wg9FUejVqxd8fX1x6tQp3oX35uDTXfQynTt3xpYtW/Dxxx/j4cOHePTokcbOhXbt2qG0tJSjFfKL0E6G4OBgUd3OQL2jzsPDQ9Q1iIG+vj5mz56NH3/8EREREejbt6/YS2KPv4Yb51yhDc5ornhVj1lVuXv3LmQyGVxdXUXtS1tWVgYAWp3IoS0wnxFNhCWmcJARC9VBT08PVlZWKC4uVnsMZTE0NBRNPGaeYbiOXtTT04OZmRnKy8uRkJBAYnZbAXwfo5p8XjWhpqaGV4ckRVEqF2opW5Tb8Dz56NEjDB8+/JXnTrlczsZfv+q1reX+wtjYWGf2HgnCQo4NAoGgCUQkbMCjR48wcOBABAQEID4+HlZWVnB0dMSJEydgamoKmqYhkUigUCgQFBSEnTt3IiQkBFZWVoiKikKXLl3E/hGUgolxqq2tFTx6hkuMjIwQFBSE6OhoJCQkqCUSSiQSTJw4EVu2bOFhhZrBR08vZWE2+PkWjpiHs9zcXF7nWbJkSSNnpqWlJXx8fODi4qIV1cB6enp45513EB8fj8TERJw+fRpTpkwRdA2BgYEoLCxEVFSUoPMSGpORkYH8/Hy1zmdAfRznjBkz8OLFC8THxyMuLg7Z2dkcr7J5GMG/c+fOgsxnZmaGMWPG4OHDh0hNTdV4vDFjxiAzM1PrXbXz5s2DlZWVoHOKLQYsWbKEc8HlVXAdV6cJixcvxo8//oibN29qdI7gCj6vnUyKh5C9N/li6NChnI0ll8tx8+ZNAMD06dNFrRBnhBh1+4K3JRgHjSbJFZr2YWfo1KkTewzxiZubm2jHJ1PgyHWyj0QiQffu3XH9+nVcunSJiIStgPz8fPj4+PA2fv/+/QUv2jMwMOC9zyJQn9KTl5eHtLQ0pKenIzU1lS0eaQpl7yHt7OwwefJkhIeHIyIiAqampnjzzTebveegaRrr1q3D5cuXIZFIMHPmzBZ7VGtLGgOBQCAQCNoIEQn/P5WVlVi6dCmmT5+OX3/9FcXFxbh58yZWr16NPn364MKFC3B0dIRcLodUKkV1dTX27NkDCwsLXLt2DX5+fmL/CErDbCzJZDJUVFS0eCOl7QQHByM6OhqJiYlqR4c6Oztj0KBBWtePraamRrS5hRIJmU3u/Px89rPFB+PHj9d6x6xUKsV7772HZcuW4dGjR/D29hbUJUBRFIYNGwYARCgUmfv372P48OEajWFra4vQ0FCEhoaiqKgIT548QUlJSaPX1NXVNdu7Qx1qamqQl5cHAIJeE7t37w4AyMzMRG1trUZRzebm5pg2bRq2b9/O1fI4Z8GCBaJUQuvr68PBwYH9NxaSRYsWCS4QlpWVcRJhyxWdOnXCwIEDcfnyZZw8eRLz5s0TdT2MAKBO3LsyBAcH67xIOH36dE7vPR4+fIjS0lLY2toiLCyMs3HVgYiEyqFQKNiiP02uTVyJhH5+foKIhGI6vhkHIR9CSY8ePViR8IMPPuB8/NaEt7c32rVrB5qmoVAomvyiaRpyuRxyuRw1NTWora1t9FVTU8NLpDVDQUEBb2MDQPv27dG5c2fW5SYEDg4OghTASiQSODk5wcnJCb179wZN0ygpKUFaWhorHDa8X1TlHq5r166ora3FiRMnsH//fpiZmWHWrFlNvnbz5s04efIkKIrC1KlT2bYHTVFeXi54T28CgUAgEHQJIhL+f2pqalBaWso+dFtZWWHEiBHw9PTEjBkzMHLkSNy/fx9SqRQ0TSMnJwd//fUXzpw5o1MCIVDf4JmJSykuLm5RJCwsLMSOHTtgaGiIpUuXal2j9r59++Lnn39GWloaqqqq2E0LVQkNDUVCQgKysrI4XqH6iOkkZB7I+O75xIiECoUChYWFojsjxMbX1xdz5szBH3/8gRMnTsDZ2ZntDykEjFBIURSuX78u2LyExsTExGDo0KGcnW+tra2bjCesqanhVCTMzMwEUB8jLGTMmJOTE9q1a4fs7GykpaVp3EfOzc0No0aNwl9//cXRCrlj8eLFojr62rVrJ7hIuGDBArRv317QOQHu+1lxwZtvvsmKhLNnzxb1nozZhORLJGzfvj1cXV0F7efEJf7+/pz28aRpmi3gmTJliqh9q4H/iYTa7roWm4ax/Zr8mzH3y5qKhC4uLjA3N2/R8cMF7u7uvI7fEnyKhAEBAQCAa9euQSaTkaj+l+jUqRM6d+4MHx8fzuIuFQoFZDIZampqkJ6ejqdPnyIhIYGTYloh7mcGDx4sqEgo1rM0RVGwsrKClZUVW+RaVVWFzMxMWFpaqnz+CwwMRE1NDc6ePYutW7fC1NQUkyZNavSavXv3Yt++fQCAcePGvTLF5M6dOyqtQZuprKxk9yDj4uJ0Oh2MwC3k2CAQCJpAOpn+fywsLKBQKHDp0iX2exRFwdvbG9u3b0dlZSXefvtt9vseHh6IiYlBnz59xFqyRjDN5IuKipr8e4VCgYiICMydOxcnTpzAoUOHsGHDBiGXqBROTk5wd3cHTdNITk5WexypVPq3G0+xqa2t5W3z7VUwTkK+N6H09PTYh3i+I0d1hdmzZyMgIAB1dXU4ePCg4P0sKIrC0KFD1XbmEjSnsrISCQkJYi9DZZioUTEKZxg3YUpKCifj9erVS9R+X02xdOlS0SM/hXbzzZs3T7T+MdoUNcowefJk2NnZoaCgQBA3UEvw7SQE6t2Euoi5uTlGjhzJ6ZgpKSnIz8+Hvr4+xo0bx+nY6sAIAMRJ2DLMddHY2Fgj0YQpvNH0901RFO9x4AYGBoJfKxrCCLN8iIQdOnSAhYUFKioqSM8n1P9bd+3aFdOmTcNHH32E6dOnw9/fn9N+eBKJBIaGhrCwsECXLl0wefJkrF69GrNmzUJQUBDMzc3VHjs/P5/3Z217e3v06NGD1zkawrSV0QaMjY3h5eWltnDZt29fDBgwAADw888/48yZM+zfRUREYPPmzQCAsLAwVsBvjrq6ulYlEtI0jdTUVKSmpoq2X0TQTsixQSAQNIGIhKg/kUqlUkybNg137tzBqVOnGv19t27dMHPmTMTGxjZ6ONPlqgzmBrIpkTA1NRXvvvsufvjhB5SXl7MPk0eOHMHhw4cFXacy9OrVC4DmG3p2dnacb+pogkKhYMU6oWHm5dtJCPwvQomIhPVIpVJ8+umnsLa2Rl5enihuJiIUis+9e/fEXoLKME5CofoRNoQRCbnoSwjUfwbGjh0LJycnTsbTBHNzcyxfvpwt7hETIUXK2bNni+ZGUSgUWukkNDQ0ZGNGT5w4Iepa+HYSAvWuFK77ignB+PHj1U62aA7mmjBy5EitaBNAnITKwRQwtmvXTqMIwIZxo5p+5vgu5BGzHyHAr5OQ6UsIQOvaVAiJpaUlZsyYgdWrV2PKlCnw8/MT1N0slUrh6emJMWPG4N1338WiRYsQGhqqshglk8l4jxwFgEGDBvE+B4M2iYRcMGjQILYo/7vvvsPVq1dx6dIl/PDDDwDq06BCQkJeOc6jR49IUQuBQCAQCK+AiIT430bH7NmzQdM0NmzY0OjGn6Io+Pn5ISsrq1FsjC7D3EAWFxez36utrcWOHTuwePFixMbGwsjICP/973/x8OFDfPfddwCADRs2iF69/jJM4/hnz55p/ODcu3dvrXKPiNWXkIkbFSJGh4iEf8fGxgaffPIJgPr+dLGxsYKvgQiF4vLs2TOUlpaKvQyloWlaVCchU6XN9CXkAn19fbz22mucjKUOxsbGGDx4MJYvX641UcxCiaZvvPFGi31l+CYzMxPV1dWizd8SS5YsAQBER0eL2jNRCCehRCLRucSOnj17ahx5/DKVlZWIi4sDAIwdO5bTsdWFcQrV1tYKnnigSzAioabnTkYsp2la42dRV1dXXoVmMfsRAvyKhMD/Ikcbpg+1NXr06AFfX19OikmrqqpQXl6u9rWEoii0b98eQ4cOxfLly7F8+XJ07dpV6fenpaWpNa8qWFpaCnYta20iIUVRGDFiBHr06AGapvHFF1/g//7v/wDUX2+HDBnyyjFomsatW7f4XiqBQCAQCDoPEQn/PzRNo2PHjti8eTPS0tLw/fffY8eOHQDqhZro6Gg4OztzXhksFi/HjcbExGDx4sX4448/IJPJMGbMGDx9+hQrVqyAVCrF6tWrsWDBAigUCnz11VdaVWHv7+8PqVSKsrIy5OfnazQWRVGYOHEiJk6cyM3iNESsvoSMk1BIkVDoPlfaTs+ePTF37lwA9Y4RISpdX0ZXhEILCwt4enoiODiY7SvbGnjw4IHYS1CaoqIiVFZWQl9fn/MNcmVwcnKCk5MTaJrmtIeZtbU13njjDc7GUwYTExMMHToUK1aswIABAziN7dIUfX193jegQkNDRTmGGqKNUaMM3t7eGDJkCGia/lvyhZAI4SQE8MoIMW3CysqKl2tQTEwMFAoFvL294ePjw/n46tDwvETcGc3DRGBrGr/ZMJ5f09+3RCLhtF/my4jZj1ChULAFHnyJhExR0vXr10Ur5hQbTYrB6urqkJSUhHPnzmHTpk34/vvv8cMPP2DNmjX4448/8Ndff+HevXvIyMhQ6znY3t4eU6ZMwZtvvqnU+VIIkRAA+vfvr9Lr1dlzMjY2hqmpqcrv03YoisK4cePg5+cHhUIBhUKBLl26YPTo0Uo5tFNTU0UtqiIQCAQCQVfgP0tQy6irqwNN043ED4VCAYlEAoVCgW7dumH//v349NNP8Z///AeffvopvLy8EBsbi4sXL2pFxA8XMJt86enpWLt2LU6ePAmgXjxcv349pk6d2uimi6IobNy4EcnJybh06RI+/vhjbNy4ke2RISaGhobo2bMnoqOj8ezZM403MCmKQvfu3eHm5oYjR45wuuGsKm1JJCROwr8ze/ZsxMbG4v79+zh48CAWLVokyL9JQxihEKjfEBETS0tLODg4wM7ODg4ODrC3t4ednR0MDQ0bvc7Lywu7d+/WKSdeU9y7dw9BQUE6EW3NuAh9fHwEjZxqSPfu3ZGTk4OUlBR4enpyNq6Xlxfmzp2LhIQEFBUVobCwEIWFhazjmitMTU0REhKCoKAg0X6HytCuXTveijpMTEwQGhrKy9iqoM0iIVDfn/LixYs4deoU5syZI0g0+MsI4SQE6sWo3r17Izo6mtd5uGDChAl/ux5pCk3TbNSotrgIgfr7Q6lUCrlcjoqKClhZWYm9JK2EKychUH+/XFpayoko6+fnx0t/Ln19fVH7ETIuQoA/kdDDwwPW1tYoKipCdHS0yuKPrmNnZ6dSugFN08jNzUVSUhKSkpKQkpLyt3YaFEWhqqoKKSkpf+stbW1tDUdHRzg4OKBDhw5KO1WdnJwwc+ZMpKen48KFC83G0QslEpqammLAgAG4cuVKk39vZWUFNzc3uLm5wd3dHba2tsjPz8fly5dZJ/mrcHBw0CjWWJuRSCSYPHkyuxc3fPhwpWONiYuQQCAQCATlaFMiYVxcHL788ktkZWXBy8sLw4cPx8yZMyGRSCCXyyGVSqFQKODn54fNmzcjJSUFf/31F1xcXNC/f3/RK9u5hBHSLly4wH5vyZIl+Pbbb1nR5mUMDAwQHh6Ovn374unTp/jqq6/w3//+V4jlvpJevXohOjoaz58/VyqXXhmsra0xb948XLt2TbRImbYUN5qdnc37XLoG059w0aJFyMvLQ2RkpChOOTGFQldXV3Tq1Am+vr6wtbVV6j0ODg5YuHAhdu/erdMO1ZKSEmzZsgWDBw9Gt27dtPrBn9kIFaMfIUP37t1x5swZzvoSNsTDw6PRxhRN0ygvL0dhYSErHDL/LS4uVikOzszMDP369UPPnj0FLwJQh3bt2iEmJoaXsQcOHMi5yKIqVVVVyMrKEnUNr2LChAlwcHBAXl4eoqOjObvvUQXmfKRQKHifq0+fPlovEgYHB/MSs5iRkYGCggLo6emx12FtgKIoGBkZoaKigvQlbAGuRcLU1FROft/u7u4wMTHhvI2Gm5sbpFIpp2OqAvPzmJqa8rYOppg0MjISly5danMioZ+fn9L3ozRN4+DBg4iPj2/0fVtbW/Ts2RNBQUHo2bMnTE1NkZKSguTkZCQlJSE5ORnPnz9HUVER+/XkyRNcuXIFYWFhKl3zXF1dMXfuXCQlJeHixYt/u74XFxcjLS0Nbm5uSo+pLiEhIYiJiUFJSQkcHBxYQdDNza1JUdvBwQHTpk1Dbm4uIiMj8eTJkxbH15Zoer6QSqUYNWqUSu9hjh0CgUAgEAivps2IhAkJCQgJCcG4ceMQFhaGCxcuYM2aNTh79iy2b98OqVSK2tpatnrfzs4OdnZ2CAoKEnnl/DB06FDo6emhrq4OnTt3xqZNm5R6yLG2tkZERAQ6d+6MmJgYJCcno0OHDgKsuGWYG3uuI48kEgkGDBgAT09P7N+/H2VlZZyO/yoKCgoEeWh5GabCUwh3AtN36syZM6irqxPFEaHNWFpawtbWFkVFRbw7NlqCEQrt7OyQnp4OhUIBuVzOxr409eeysrJGfU+VxcfHB76+vvDx8VHbvW1hYYH58+dj9+7drMtNFykuLsaRI0dw/fp1DBkyBD4+PlonFiYlJbHRqGJG0zL/zkKITBRFwdzcHObm5k1Gq9XW1qK0tBQlJSUoLi5GSUlJo6/S0lKYm5sjJCQEgYGBOnXe4ytKzsHBAT179uRlbFVQtjJdTAwMDBAQEIAzZ86wsfFCw7g9zM3NeZ/LxsYGZmZmWitGGRgYKNUXSR0YkSk0NFSrYuSqq6t57/3WGmBSMrjYvGf6EnIh7EkkEri7u/9NvNEUsZMPmHYYfPdFDAgIQGRkJC5evIjPP/+c17m0DVWcounp6YiPjwdFUejduzcrCnp4ePztXtbX1xe+vr6NvldcXMw6EDds2AAAePHihcprpigKnp6e6NixI86cOfM3Z9n27dsxa9YsTlMomsLQ0BBvvfUW5HK5SlHyjo6OmD59OrKyshAZGYnExMQmX+ft7c3VUlsNjBO/tUFRFBv7q23PhQRxIccGgUDQBN3ZldIAmqbx559/IiwsDDt37gQAvP/++9i+fTs2bdqE6dOnY//+/axAuH37dgwbNgyurq5iLptX/Pz88PjxY5SVlSEgIEClTTEvLy+MGjUKJ06cwIULF7Bo0SIeV6ocMpkMAH/Ot/bt22P48OEIDw/nZfzmyMzMRGBgoKBzAv9zBgjhaunduzesrKyQk5OD06dPa1WcljZw+vRpPHv2DPr6+qL3BqQoCj169GD7sShDXV0dKioq2K/KyspGf66srISBgQGcnZ3h4uICJycnzo47IyMjjBkzBps2beJkPDHJy8vDvn374OLigqFDh/K+AaYsVVVVOHr0KABg3LhxKh0bXFJdXY2IiAgA0AqhycDAgC02agqFQgGKonTy4Y2J/eLSpWtlZYU33niDU+cHTdMoLCyEsbGxShvXzCbezp07tTqymBHF+e4R2RRyuRz3798H8L9CHz6pqanRWoEQqBcx+bpfyszMBAB06dKFl/HVJTk5GQqFAg4ODpy45ForhYWFALgR0xmRsGGkpiao0/PsVTA/r1g8fPgQAHh33TLPZlFRUaisrBRdHBWSl6NCW4IR40aNGoXVq1erPJeVlRUCAwMRGBiI27dvIzo6WqPzjUKhQEJCQpN/t2vXLkybNk2jfovKoK+vr/b1wtnZGa+//jqysrJw/fr1RjGk/fr105qetdpCw3uV1oaJiQkeP34s9jIIWgg5NggEgia0CZGQoihkZmY2alhsYmKCBQsWwMjICBs2bMBHH32Eb775BlFRUfj6669x8eJF7NixQ9TIFL7R5EbyjTfeYEXChQsXir7RyfTu49ON0aVLF0RFRQkai8lsDgmNkE5CPT09DBs2DIcOHcK2bduISNiA8vJy/P777wCAIUOGaJWLQFn09PRgaWnJbm4JjZOTE/z9/REbGyvK/FyTkZGBP/74A56enhgyZAicnZ1FWwtN0zhx4gTKysrg6uqKZcuWibaWCxcuoLS0FFZWVjqxSaILbrXmYKLWzp07x8l4ZmZmmD17NidupLq6OqSmpiIhIQEJCQmsk3n06NEICgpS+l7Fzs4OCxYswK5du1BQUKDxuviA6Zfs6Ogo+NwJCQkoLy+HoaGhIOcgbf03YGgupl9TaJpmxWC+N65Vhenb2aNHD9GfAbSV6upq1vXHxfmNGYOriFA+hC2xnM1A/XkiKysLFEVh8ODBvM7Vvn17tljm2rVrGD58OK/zaRNMYe6rKCkpYZ2qU6ZM0WhOmqZZcU+Tnpd3795t8Rg9ePAgxo4dqxXFZi3h7OyMadOm4cWLF7h16xaMjIx4P+Z1kYSEBM5TpggEAoFAaM3o7i6VkjDxfIGBgZDL5Y0yyY2NjTFt2jSEhYXh0qVLKCoqQkhICP75z3/iq6++atUCoaaMGzcOxsbGyMnJ0YpKFaZ3H5+iVsO+bEKRm5vLCqBCIqRICIDtL3D8+HHk5+cLMqcusGvXLhQXF8PW1ha9evUSezk6S2t8cH7+/Dm2bNmCAwcOiLaB/uDBAzZG6pNPPuHFlaAMNE2zLu/evXvrtACnK/j7+3M21qxZs2BjY6P2+ysqKvDgwQMcOHAAa9aswa5duxAdHd0o6vjUqVM4dOgQqqurlR7X0tIS8+fPR/v27dVeG1+UlpayLkcxehDdvXsXANChQwdBPm/qxMsJiZWVFS/jMv1NJRKJ1vVFZ0TCgIAAkVeivTBihEQi4USQY0RCbXYSVlZWitZPnSkGCw4O5u0zyUBRFOsmPH/+PK9zaRtM3/pXcfv2bdA0jYCAAI0d59nZ2SguLgZFUWoXxtTU1ODy5cuvfF1ERASuXbum1hxCY2tri9GjR2PIkCGkWKMJmHsVAoFAIBAIytHqd9KYG6bRo0cjMTER33//faO+chYWFli5ciVu376NixcvAgAWL16sFX32tBkTExNMnToVQL2DQ2yEcBIC9bFaQh8bLzdYFwJGJBQibhSo/736+PhALpdj9+7dgszJBXK5HPn5+Uo/MKtCeno6Dh48CAAYMWIEKVrQACsrKwQHB4u9DF6Ij4/Hhg0bBO+7WFhYiFOnTgEAFi1a9Lc+MkJy//59JCcnQ09Pj2xYC4SZmRknvW8WLlyo1oZfUVERrl27hm3btmHt2rU4duwY4uPjWyyqiYuLw+bNm1W6ppqYmGDOnDm89ylSFcZFaG5uLoo4z2y8CRE1CrRdJyGTJuHj48O2RNAWmN5vYkVM6wKMSGhmZsaJmK4LTkJAnMhRmqbZqNFhw4YJMicjEmrDc7CQKPPMI5fL2V5wkydP1nhOJjLSxcVF7WdTJhpWGS5cuIBz586J2gueoBlFRUXsdao1UllZiS5duqBLly6cXRMIrQNybBAIBE1oE3GjAODp6YkDBw5g1KhRMDExwRdffMH2CjIwMEBAQABsbW1FXqVu8frrr2Pnzp24dOkS3nrrLcFcZ00hlEjIuAmZCEghyMzMFLz/mNBOQgAYOXIkEhISsH37dqxYsUIrKiIrKyuRm5uL3Nxc5OXlsf9l/pyfnw+FQgFjY2Ns2LCBUwF548aNUCgU8Pb2Jo3oOaB///64efOm2Mvgja1bt2Lp0qW8Rg/+9ttv7OeyqqoKdXV18Pf3x/Tp03mbUxkYF2GPHj1gZGQk6lraEt27d0diYqLa7589ezZcXFxUfl9sbCyOHDmi1pxFRUXYsmULRowYgT59+ih1nTEwMMDMmTNx5MgRrUhOAIC0tDQA4rgIq6qqWMeOUCKhtjsJ+RIJmeKPzp078zK+usjlciISKgGX/QgB3ehJCNSfZzWJhFSHxMREFBcXw9TUFCEhIYLMyYiE9+/fR2FhoUaOeF1CGZEwLy8PVVVVMDU1Rd++fTWekxEJ1X3OKisrw5UrV1R6T1RUFKqqqjB27FiSUKGDMCJ1a4WmabYnJRGzCQ0hxwaBQNCENiMSAvWRcwcPHsS0adOQlZWFadOmwd/fHzt37kRGRobWVYprO8OGDWP7Mdy9exd9+vQRbS1CiYRAfR8KPz+/Rs3C+USMvoQKhQKAsCLh0KFDsXHjRsTGxuL+/fvswzcXnDhxgq3Cp2kaMpkMlZWVKC8vR2Vl5d/+XFFRgYqKCqUjk6qqqrB8+XJs2rQJbm5uGq83OjoaN27cAEVROtXnhKZplJSUICsrC1lZWVAoFLCzs2O/+KpaVwYTExMMGTKEdYy3Rnbt2oX58+fztlHVML4RAAwNDfHxxx+L6nLNyspCVFQUAIh6DWqL+Pr6wtLSEiUlJSq/d/r06SoLTAqFAufPn8eNGzdUnu9lzpw5g9TUVIwfP16pjXKpVIrJkyfD2dn5b65duVzO9koSCsZJ6ODgIOi8APDw4UMoFApYWloKtine1p2E2iYSZmVlobq6GsbGxqSIqQW4FgmJk7B5bt26BQAYM2aMYMVCtra2cHd3R2pqKi5duqRx3z1dQZmehA3PXZreI9I0jQcPHgCA2kWzkZGRar3v/v37qKmpwaRJk0QthiaoRl1dHXvMEAgEAoFAUJ42d7czbtw4REVFYdWqVfjwww+hp6cHfX19/PXXX3B1dRV7eTqFnp4epk+fjvXr1+P8+fNtRiQEgCFDhggmEgodIwiI4yS0sLBAaGgoLl26hO3bt3MqEm7atEnt9xoZGcHS0rLZL6lUip07dyI3NxerVq3Cf//7X7XcMQx1dXX49ddfAdSLHozjWRspKytjBUHmq6XNK2Nj40aiYUPxUKFQtPgll8thbm7ObpKpQ3BwMG7fvt0ocro1UV5ejp07d2L+/Pka/Z6a4/vvv28kqDg7O4teOc84yjw9PbX6s9Ia0dPTw/Lly5GamorU1FSkpaWx4lVLTJw4EZ06dVJprqqqKoSHh3MaHfXkyRNkZ2dj6tSpSp2zJRJJkw6VmpoafPvtt5ytSxnEFAnv3LkDoN5FKITjX6FQaL2TkI/+Z3K5HNnZ2QC0TyRk+hH6+/uTKPQWYMQyrq7HXDsJ+RIJmZhVocjLy0NSUhIoisKkSZMEnTswMBCpqam4cOFCmxEJlXESMiKhqtf6pkhPT8eLFy8gkUjU2qspKCjQyFUWFxcHmUyGmTNnakXKDeHV3LlzB+Xl5WIvg0AgEAgEnaPNiYRA/Q398ePHUVhYiPLycjg5OZHNRTV5/fXXsX79ely7dg1VVVWi9MYBwDq+hBK1bG1tERwcLEh8YVlZGUpLS3nZ9G8OoXsSMowcORKXLl3Cnj17sHbtWhgaGnIybqdOnRptZEmlUhgaGsLIyAiGhobNfpmYmCi1hjlz5uCPP/5AXl4eVq1ahZ9++kntqKVjx44hNTUVJiYmGDhwoFpj8IVCocD9+/eRmJiIrKysJsU2iqLg5eUFX19f6OvrIz09Henp6cjNzUVVVRX7/+ri4OAAT09PeHl5wc3NTaXPvL6+PgYNGoQTJ06oPb+2U1xcjF27dmHevHmcbwD6+fnB1NSU0zE1obKyEseOHQNAXIRiYWBg0CgSWSaTITMzs5Fo2NB1MHLkSHTv3l2lOfLz87Fv3z5e3CklJSXYunUrwsLC0LdvX53ZABRTJBS6H2FJSQl7T6KNMMVCXJObmwu5XA4LCwu0b9+e8/E1gREJSdRoyzBiGddOwtraWtTV1Wn8zMNn3KiQMC7C0NBQODk5CTp3YGAgjhw5gnPnzgk6r5goIxIyvX+5EAkZR5iq9/wMXPSMTExMRFpaGtzd3TUei8AvtbW1uHr1qtjLIBAIBAJBJ2mTIiFQ/6AlpOjSWunTpw86duyIpKQk3LhxA0OGDBFlHUKLhAAwaNAgxMXFobS0lPe5MjMzBT1emQdAoaNVevbsCXt7e+Tn5yMiIoKzqtyJEydyJjg2hYmJCebMmYMdO3YgPz8fq1atwsaNG1V2F1RUVGDz5s0A6uORta2/2pEjR/Do0aNG3/Pw8ICvry/75eXlxUa7NqSqqgqZmZlIS0tr9JWens46gYH6Y04qlbJfzP9LJBLk5+ezPSFv3LgBKysrlfuh9ujRAzdu3ND6+DpNyM/Px65duzBr1ixRI175Zu/evZDJZLC1tYWXl5fYyyGgXoj38PBgI8EUCgWys7ORnp4Oc3NzdOnSRaXxysvLWWc1n5w7dw5VVVUYOnQo73NxQWpqKgDhRcLi4mIkJSUBUL83lKpou4uQ76jRTp06aZ14TURC5eA6btTU1BQSiQQKhQJVVVUaj8vXPaaQcaNVVVWIiYkBAEydOlWweRl69OgBfX19PHv2DKdPn8bIkSMFX4PQNLxnbwqZTIb8/HwA0PjerKqqCmfPngWgXtRobm4unjx5otEaGB48eEBEQh3g7t27nEUyEwgEAoHQ1mizIiGBGyiKwrhx4/DTTz8hPj5eNJEwPj4eAH+bNU1haGiI7t27C1KtxlW0kDJUV1cjOTkZAASP4JVKpejTpw8iIiIQExOjU9E9pqammDNnDrZv346cnBx89dVXWLNmjUoOA6ZfIqD5gzXXVFVV4fHjxwCAWbNmoVevXvD29la6Et3Y2BheXl5/+7kUCgVbES+RSFoco7S0FGfPnsWGDRsA1BcHqNqQWyKRIDg4GBERESq9T9fIzs7GH3/8gdmzZ8PMzEzs5XDO3r17sWvXLgD17gFt20Qn1CORSNC+fXu1nVDV1dUcr6h5KioqBJtLEyorK9m+jEIJdQyMe9zAwEAwV7HQiQaqwpUA9DLMRjwfUaaaUFtbi4cPHwIgDu5XwdyfcHV9kkgkkEqlbAw7F+Op21e2JUpKSlBRUSHIOSIpKQlyuRxubm7o1q0b7/O9jJmZGUaNGoXjx48jPDy8TYiECQkJkMlkzZ6b9fT0YGhoiOrqapw8eRILFixQa57CwkJ89NFHSEhIgEQiUbnICOCufycAzj8nBH7Q9nsGAoFAIBC0mZZ3ZAkEJfDz8wMAjSIENaGgoICtEmQiz4RCqA3MppxZfHHv3j3U1dWhQ4cOojxwM84IpopelzA3N8eMGTOgp6eH+/fvY8uWLSq938zMjBXRxOhF2RJJSUmgaRru7u5YuHAh/P39OYmqkkgkMDAweKVACNQ/7DM96MzNzbFgwQK1HgbVjYLVNfLy8rBjxw5B3M5Csn///kaOW+Jmab3Y2NgIdv0T43qnDmfOnEFNTQ2cnJzg6ekp6NwN4w6FigBt37694KkGqsBXSgHjAte2jemHDx+iqqoK7dq1E/Xca2lpCYlEwn516tQJv//++ytdTkLCCLxc9caSy+VsIRlXG+F8RdkK9UzI9Knt3bu3aMVCffv2BQBcvHhRlPmFprKyko0AbQqKojBmzBgAwM6dO7F69WqV+wknJydj+fLlSEhIgKGhIebNm6dWaxguj28iPukGPXv2RFBQkNjL4B2KouDu7g53d3dSKEloBDk2CASCJhCRkKAxTL+BtLQ0UeaPiooCUP8gwFdFd3MI5TwQ6sFEoVDg9u3bAIApU6aIcmPBPATqokgI1IuckyZNAlAvZqi6acBsVIv1eWoO5gG/V69eosyfnp6OFStWICsrC1ZWVpg/f77avWTt7e05Xp328uLFC2zfvh3FxcViL0Vj6urqsGvXLvz2228A6iOfBwwYIPKqCHwikUgEEfUtLS3VijITA6ZQQgwHbUNXslBFUnp6elod8caXSMi4sLTt3M30fxs5cqTom080TbNfT58+xeLFi+Hp6YmffvpJK+LmLC0tAXD3rMC0VgC4ey5wdnbmZJyXEeIelqZpNv5YrHtTAPD394dUKkVSUhJSUlJEW4eQREVFtdibsEuXLhgwYAAkEgnu3LmDRYsW4bvvvmNjSFvi7t27ePPNN5GbmwsbGxssWbJE7WQbAwMDuLi4qPXel+GqWOXhw4e4fPkyUlNTtbrfrq5CURRGjx6NkJAQsZfCKyYmJkhJSUFKSkqrbi1BUB1ybBAIBE0gIiFBYzp37gwAyMnJafQAKxSMSOjr6yv43EJtQgglEj59+hTFxcWwsLDAsGHDBJnzZXRdJATq3bX9+vUDAHzzzTcqVdB27doVgHjO3KZouBEjRnVmdHQ0Fi9ejLy8PNjZ2WH+/PkaRQszfdPaCsXFxdi+fTsyMjJQVFTU6EsXqKurw6lTpzBnzhxs3boVADBw4EAMHDhQ5JURhEAIkdDf3190wUMZZDIZTpw4AQDo37+/4PNLpVJWvBIyBr1jx46CzaUqfDsJtVUkHD16tKjr2LlzJ8LDwxEeHo4DBw5g+fLlsLOzQ0ZGBlauXAl3d3d8/fXXojoxuXYSNhTmuRIsdNlJ+OLFC5SUlEAikcDf35/3+ZrDxMSELZi9dOmSaOsQkuLiYly4cKHZv6coCoMHD8Zbb73FxoSePn0aM2fOxLZt25p9fv7rr7+wevVqyGQyuLm5YeHChbCxsdForVwVmXDxmSsoKMDhw4cRGRmJHTt24LvvvsOuXbtw/fp1ZGdnq9xCgdA0FEUhLCwM7733Hvs8TiAQCAQC4dVob34PQWews7ODjY0NCgsLkZ6eLmgvtfLyctb5JoZI2NqchMzmz9ixY3nb+HoVrUEkBIAhQ4YgOzsbSUlJ+Pzzz/Hbb78p5XRlRMLc3FzU1NSI9u/QkIYbMd27dxds3rq6Omzbtg179+4FUC8WvPHGG5z0ufH29m4zFd9AfT9HRmBTl8ePHzeKmHVxceG1D2xdXR3Onj2LXbt2ITs7G0C9u2bQoEFtIkqIUA9fTpeGCHle04TLly+juLgYVlZWavVn4gILCwtUVFQIKhIK3XtRFYyMjHgZVxudhFlZWUhLS4NEIkFYWJioa7G2tm50LzBt2jRMmDABZ86cwb59+5CVlYVPPvkEX3/9NVasWIGVK1cKniLAtZOQEQn19fU5K2rg6/yakZHB9pvmC6YAr0ePHrx9DpUlMDAQjx8/xsWLFzF//nxR1yIUN2/ehLe3d4tFHDY2Npg6dSqCg4Nx9uxZpKenY+fOnYiIiMC8efMwZswYSKVS0DSN7du3Y+fOnQDqU1XGjx/PyfHj7u6O69evazwOF2u5c+cOAMDR0RG1tbUoKirC8+fP2WPZ2NgYHTp0YL9sbGx0ooBJWzEzM8OwYcMQGhqK27dv49atWzrTf5pAIBAIBDEgTkKCxlAUJVrkaGRkJBQKBezs7ESJEGxNImFOTg5SU1NBURQmTpzI+3zNwYiERUVFgm5Cco1EIsGUKVNgZWWFrKws/Oc//1EqVsbe3h7t2rUDTdNa05dQjI2Y3NxcrFy5khUIg4KCsGDBAk4EQkD4/qWtgQ8++ADvvPMO+zVt2jQcOnQICoWC03nq6upw8uRJzJkzB2vWrEF2djZMTU0xfPhwrFixggiEbQy+RUIXFxfY2tryOgdXHD58GADQr18/SKVSUdbAFLsIeX12cnLipAcuH/DtJKypqdGaeyGmkKx///6sAKZNGBgYYNy4cfjzzz/x8ccfw93dHRUVFfj666/h6uqKlStXClqAxpeTkMtnAgMDA95coYWFhbyMyyBmwsXLBAQEAKjvS9iW3GDHjh1TKnraxcUF8+fPx2uvvQYbGxsUFRVh3bp1WLBgAa5evYr//Oc/rEDYv39/TJo0iTOB2c3NjZNxNP3c1dbWsr0cV61ahfDwcGzduhVvvfUW+vbtC2NjY1RVVSEuLg4nT57EL7/8gh07dnB+j90WMTIyQv/+/bFixQqMHj2aPTfrMlVVVejVqxd69eqlNfcIBO2AHBsEAkETiEhI4AQmclRokfD06dMA6sULoSvtFAqFYHGjBgYGvM/BbP4MGjRI1J5tZmZm7KabrrsJTUxMMH36dEilUty6dQubN29WSihk3ITa0peQEQmF2oi5du0a5syZg8ePH0NfXx/Tpk3DmDFjOK1It7OzaxUPiUJibW0NGxsb2NjYwMLCAnK5HBs2bMCqVatYp58mMOLg7NmzsXbt2r+Jg3379hXMVU3QHmxsbHh1VOuKi1ChUODo0aMA6vsRioWFhQUAYUVCiqK0NnKUr2PTwMCAFYLFjMxsSHR0NABg1KhRIq+kZaRSKcLCwrBt2zb8+9//hq+vL2pqavDTTz+hc+fOePTokSDr4MtJyPUzQVBQEAIDAzkdE6iPVuQLuVyO5ORkAOL2I2To0qUL9PX1kZWVhYSEBLGXIxilpaU4deqUUq+lKAqdO3fG8uXLMXLkSBgbGyMtLQ2ff/45Lly4AIqiMH78eAwZMoTTZ3pDQ0NOio00fQZ59OgRampq4OzsjKCgIPa6NnXqVHz99dc4fvw41q9fj/nz56N79+6gKAppaWmsGN6aUSgUSEpKQnx8PMrKynibR19fH7169cLbb7+NSZMm6XSPeoVCgTt37uDOnTtESCY0ghwbBAJBE0jcKIETxHASZmRk4PHjx6AoSpReFEJukPG9MV5RUYHY2FgAwOTJk3md61VQFAU7OztkZmYiKytL0PhaPnBycsL48eNx5MgRHDhwADdv3sTChQvRv3//Zh+Cu3XrhnPnzmmFSFhXV8fGcvK9EVNbW4vNmzcjPDwcQL2DaOrUqbxEWlIUBW9vbzaumPBq3nzzTXZDnKZp3L17F2fOnEFMTAzmzJmDd955B2PHjlV5c6e2thbnzp3Drl27kJOTA6A+aq9fv34ICgoiwmAbh6IoODs7sxvCXMMUZWg70dHRyM7OhomJCS8b+soihpMQqI8cffz4saBzKgNf7nqKomBqaorS0lIUFxfDycmJl3mUpaamBvfv3wcgfj9CZZFIJAgNDUW/fv1w9+5d/Pbbb3j+/DlGjRqFGzduwMXFhdf5mUKkiooKKBQKSCSa1eYyfd+5viZSFIXRo0cjPz+f016CL1684Gysl0lPT4dMJoOVlZVWFBAYGBiga9euuH//Pi5cuCBKCwyxePjwIXx8fJS+lkqlUvTp0wfdu3fHtWvXcOPGDUilUsyYMYO3f0s3NzdkZWVpNIamIiETNTp27NgmzwV6enro2rUrunbtijlz5uDnn3/GkSNH8ODBA51/Fm6O8vJy3L9/H3fv3m1UDGNlZQU3Nze4urrC1dUV9vb2Gp8/GyKVSuHv749u3brh+fPnfztX1dXV4fz585zNRyAQCITWhUwmww8//ICdO3ciKSkJZmZm6N+/Pz777DM2XUIVNm/ejAsXLiA2Nha5ubkoKyuDjY0NevXqhaVLl2Ls2LF/m//SpUuIiIhAVFQUUlJSUF5eDhcXF4wYMQL//Oc/1e7JTERCAicwTsLU1FTB5jxz5gwAwNPTU6leb1wjZKY935vkd+/ehUKhgK+vr2h9jhrCiIS67iRk8Pf3h0wmw4ULF5CWloZ//etf6NSpE5YsWdLkRYR50M7IyIBcLhctVg7430aMtbU1rxsxmZmZ+Oqrr9gK7ODgYAwbNozXn52IhOpDURSCgoLg6emJY8eOITU1FT/++COuXr2K1atXK1WdW1VVhRMnTuDgwYOs44CIg4Sm4Esk7NKli+i9rJTlyJEjAIA+ffoIki7QHGKJhNogBDQFny7XhiKh2MTExKCmpgYuLi46I6wzMNerH3/8Ef/4xz+QlpaGUaNG4erVq7wmCjBOQpqmUVVVpXFcOh9xowxSqRSvvfYatmzZgtLSUk7G5FMkZBIuevbsyal4oAmBgYG4f/8+Ll68iOXLl4u9HEEJDw+Hm5sb6zRXBiMjIwwbNgwhISGgKIrXSGkPDw/cvHlTozE0+dxlZmYiOzsbEolEaSf26NGjceTIEcTHx6OyspKNoNZ1aJpGSkoK7ty5g/j4eDae19zcHPb29khKSkJxcTGKi4vZAmZDQ0O4uLiwoqGrqysn50GKouDl5fU3EbampoaIhAQCgUBoEplMhpEjR+LixYtwcHDAuHHjkJ2djSNHjuDkyZOIiIhQuXf7jz/+iOfPn6Nbt27o168fTExMkJSUhJMnT+LkyZN47733sHbtWvb1ly9fxogRIwDU917u378/gPqi4l9//RW7du3C6dOn0bdvX5V/PiISEjiBcRKmp6fz3qgeqL/BPHfuHID6qFEx4OohWhn43CxnIgkAYMqUKVrRIJ3pS9haREKgfiOja9euiIqKQlRUFJ48eYJVq1Zhzpw5mD9/fqPXuru7w9zcHGVlZcjJyUH79u1FWjXYjXlfX1/ON2LkcjlOnjyJy5cv48GDB1AoFDA0NMSkSZMEqcL28PDgfY7WjrW1NebOnYtbt27h/PnzuH37NmbPno39+/e32LOqpqYGixcvbvQZd3Nzw6xZs4g4SPgb7dq142VcXYkaBep7PwHiRo0C/4sbFbJQCqg/11hZWWmFYNYQPkVCZlM4Ly+PtzmUhSmoGTVqlFbcJ6qDhYUFvvvuO7z99tt49OgRpk6dirNnz/ImMhkZGcHQ0BA1NTUoLy/XapEQqI/7nzFjBjZv3szJePn5+ZyM0xTa1I+QgSn6u3TpEmia1tnPibpERETg9ddfV/l9QohfXPQl1GRvg3nOHjp0qNKFCV5eXvD29kZiYiLi4uK06ljXhGPHjiEmJob9/y5dumD8+PEYOHAgDA0NUV5ejri4ODx+/BiPHj1CXFwcqqur8fz5c7Y4wNLSEgsWLFBJlCYQCAQCgQu+//57XLx4Eb169cL58+fZa9HevXvx+uuv44033mDdhcqyfft2dOvW7W/vuXbtGkaOHIkffvgB06ZNQ58+fQDUp6VMnz4d7733XqO0t+rqaixbtgw7duzAzJkzkZiYqPJzg3aU3hF0Hg8PD9jb20Mmk+Hu3bu8z1dVVYXc3FwAEC2CQ4ifk4HPTbHCwkKUlZXB0NAQgwYN4m0eVbC1tQUATvqcaROGhoYYPHgwVqxYwZ7Md+7ciWfPnjV6HU3TbIa82BXSzIXq5s2bOHjwIGfjPnz4EEuWLMG6detw7949KBQKuLm5YdmyZYLFNOnr65MHTA6gKArBwcFsdbRMJnvle/T09P52bKelpfEWKUnQbfgoGNHX14enpyfn4/JBTU0Nnj59CkC8wigG5p4rPj5eqc86l6gbm8IntbW1vI3NxGHu2bNHcOfmyzAPmIWFhaKuQ1OcnJzw7bffwsjICBcuXMAPP/zA+3wAN0IvI8zzKUw7Ojqy9+CakpWVhbq6Ok7GaghN06wAqQ3pJwzM7628vFzklYhDYmIip3G1XGJsbKzxfoG6kc+1tbVsH9SX48JeBfMMpkw/e12BOReGhITg999/xy+//ILhw4ez5zUzMzP07t0b8+fPxw8//ICIiAhs3rwZ77zzDoYMGQITExOUlJRg//79vJxfCAQCgdC28PDwUNo8UFdXh3Xr1gEAfv3110Z7iTNnzmTj+7dt26bSGvr27dukqBgaGorp06cDAC5cuMB+f8iQIdi3b9/f2kEZGRlh48aNsLS0RGpqKqKiolRaB0BEQgJHML0EALAOPz5hcuv19PREid2Ki4vDkydPBJuPzxhXpkeDl5eX1jh4mB502lA9zwdmZmYYPXo0unTpApqm8eOPPzZ6AExISEBFRQWMjIzg6Ogo4krr+xD269cPAFjruiYUFRXhu+++wzvvvIOkpCQYGhoiLCwMb731FubNm9ei+4xr6urqBHMEOzs7Izg4GK+99homTZqErl276kzMoTIoFArcunULADBt2rRX/jtKpVJs374df/zxB/79739j4MCBAOojFdvqBpsukp2dzXsxR1VVFW7cuMH5uA4ODqIXYSgL8zvW19fnpUerKoSGhsLZ2RmVlZW4d++eoHM7ODgIOp8yFBUV8TZ2SEgILC0tkZubix07dvA2jzIMHToUQL0LhM8YSSHw8vLC22+/DQD4+OOP8eDBA97mYoqeuOgxXVZWBgC8xjI+evSI039fPgo8Kisr2QIFse+RG8LEpjs7O7c5FyHDlStXxF5Cs0ycOFGt66epqSmGDx+udpFKfHw86urq4OzsjG7duin9vtLSUvbc5OPjo9bc2giTFuTn56dUoZZUKoW3tzcmTZqEzz77DFu2bIGhoSGysrIQERHBxpUSCATdIDIyEhRFgaIofPHFF2Ivh0BQievXr+PFixfo0KFDkw5/RtBjEoC4gNmjV7ZI0MjIiL1vUKcfs27sjhB0gtmzZwOot8RWVlbyOhcjEpqYmAj+IFZcXMypo0oZuNhcaA5m81GbHkCYhzjGLdpaGTFiBPT19REfH4+TJ0+y32c2Xj08PETfxKYoCkOHDmVdplu3bsW6devw/PlzlR7M5HI5jh07hhkzZuD06dMA6vu3vPPOOwgJCYGdnZ0on2U+0NPTY7PBZ82ahQ8//BCLFy/GiBEj0LlzZ/j7+2PKlClYvXo15s+fj379+mnVRpc6PHjwAPn5+TAwMMAbb7yh1HukUinc3NwQGhqKjz/+GB07dkR1dTWOHj1KHvq1HJqmERUVhc2bN2Pz5s28Ouv56huqjYJTc2RkZAAA7O3tRd98lkql7ANQVFSUoA4Hbfw349NZZ2BggDFjxgAADh48yPbsFQNPT094e3ujrq4Oe/fuFW0dXDF69Gj069cPdXV1eP3113lzajZsx6ApjEjIV4GRXC5HZGQkp2Py8fzCPAPa2tqK2p/1ZRiRkHEAt0WePXumta0iTE1Nlb4/BeoF/hkzZuDdd99Vq6cPAxOtOXz4cJWu37du3QJN03BwcBC0OCgpKQmPHz/m7drO9CxXtwDa2dkZ//nPf0BRFGJiYtgCxbaInZ0dK7oSCA3R9WNDLpfj0aNH2LFjB/7xj3+gb9++7L4vRVGYN2+e2EvkFSF//rlz57Ljci3cpqSk4LPPPkNoaCjs7Oygr68PMzMzdOzYEZMnT8auXbtUTqU5duwYpk6dCnd3dxgZGcHBwQF9+/bF999/z94f8glzTe/Zs2eTfx8YGNjodZry4MED7N+/H1KpFCNHjlTqPXK5HCkpKQDUS0EgPQkJnBEUFAQfHx8kJCTg6tWrbCNNPmA294Vu4i2XyxEeHi7onAARCVsr5ubmGDZsGP766y/88ssvCA0NhY2NDSsSdujQQeQV1kNRFAYOHAg9PT2cP38ex48fx/Hjx+Hk5IR+/fohNDQU3bp1g1QqbfL9T58+xbp169i4PCcnJ4wZM0b0jRSuN3f9/f0RFBSEdu3aKdW7RCKRwM3NDW5ubhg2bBhKSkqQmJiIxMREJCUl6UyMTm1tLS5dugQAWLx4MczNzVUew8DAAJ9++ikWL16M58+f49atWwgODuZ6qQQOkMvlOHXqVCMXWUREBCoqKtC/f39ORSyZTMbbBpA2Ck7NwYiE2vLAP3LkSPzxxx8oLCzEw4cPBYtA1cZ/Mz6dhADg7e2Nrl274tGjR1i7di02btzY7LWWb0aOHInExERs376ddeLpKhRFYfXq1YiPj0d8fDw++OAD/Pzzz5zPo0tOwgcPHnB+PPPx/MI8A2pbcRUTgSpmH3Ft4MqVK5g5c6bYy2gSW1tbzJs3r1lntq2tLQICAtC9e3eVegk1R0lJCRujr+q+yPXr1wFAsBYMQL3jYOfOnQDqn1F79+6NoKAgTgsTNBUJgfrN2eXLl2PDhg04e/Ys/h975x0XxfH//9cdR2/SBUFpKiAKKGLBil1UbMHeWz4xxqgxxlhiixqjxhijsfeGFTuiIigI0pXei/Te293t7w9+t18QUOAqMM/HYx8cd3szc7s7uzPzehdtbW0YGxsLqoltAkVFRaHmfSW0XdrDteHs7Iy7d++KuxliQ1S//9mzZ7h06ZJQyj58+DB+/fVXVFVV1XufzWYjMTERiYmJuHfvHvbs2YPbt2/D0tLyi+WVlJRg7ty5ePToUb33c3JykJOTA19fX/zzzz+4efMmBg8eLPDfw4P37GpqHZP3fl5eHkpLS1s8ljh//jw8PT1RXV2N5ORkvHv3DtLS0jhx4kSzQ+xfvnwZOTk50NbWbtWxIJ6EBIHBYDAwf/58AMCLFy+EWhfPSkBRUVGo9XyOh4cHvVgnSgoKCujFAUFCURQyMzMB1C5ESQodRSQEQItKNTU1OH78OKqrq2nLE0kRCXnY29tj1qxZ6NGjB6SkpJCZmYk7d+5g3bp1mD59Ovbu3QtPT0/aIr+kpAR//fUXvv32W0RHR0NaWhoTJkzAihUrxC4QAoJd3O3WrRucnJxgYGDQLIGwMVRVVWFra4s5c+Zg5cqVAmubsPHx8UFpaSn09PTg5OTU6nKMjIywZs0aALVhq3n3JoLkUFFRgatXrzYaZtLDwwPPnj0TqBdoSEiI0CITSNoC85eo60koCcjIyGDmzJkAaqNH8HLoChtlZWWJC9MsbJEQqF1clpGRQWxsrFgM1XiMGjUKLBYLQUFB+PDhg9jaIShUVVWxadMmAMA///xDRzkQJLwFfkF6EgpDJGSz2UIJFZmamirw+4OkioQ8T8KOLhLGxMRIdE75bt26Yfr06fXes7a2xtKlS7F69WrY29sLRCAEQN8nraysWmTNX1NTg/fv3wMQnUhIURSePn1K/19SUoKXL1/ijz/+gJubm8A8NHjGTikpKXzdG2bMmIFx48aBoii4uLiI5FlMIBBEw+eezOrq6hK1VilsRPH7i4uL6fUmQa+pHzt2DBs2bKAFQnt7e+zfvx9Xr17FiRMn8P3339NG5dHR0Rg5cuQX133YbDZmzJhBC4Q6OjrYunUrrl27hmPHjtGpkT59+gRHR0eEh4cL9PfUhTcWb+qY1R0/tGb93tvbGxcvXsT169fh4+MDeXl5HD9+HMuWLWvW95OSkrBhwwYAwN69e1uVx5x4EhIEyrx587B9+3YEBQUhNzdXaFbvdcONioq4uDjaqk8cpKSkNNt6oLkUFRWhqqqKDo8oKfBEwpycHHA4HLFZzYsCJpOJSZMm4cyZM3j58iU6d+4MDocDJSUlifEaqYuZmRnMzMxQXV2N+Ph4REdHIyYmBsXFxXB3d4e7uzuYTCb69++P6OhoejGnT58+GDNmjMAm3oJAUJ6EysrKmDlzpkBDw2ppaWHp0qUtTnosakpKSuj74vLly/nOazplyhS8f/8ePj4+uHPnDlauXCkxuVIFRUFBAYKDg2nrss83ZWVlifzNeXl5uH79+hfzVb1//x7l5eWYOnUq3/dtLpfbqmTbzUUSvdKaQtJEQqC2r164cAF5eXmIioqChYWF0OtkMBjQ0dERap7mliLMcKM8lJSUMH78eDx48ACnT5/GyJEjxXItqKqqYtCgQXjz5g0uXryIQ4cOibwNgsbOzg7Tpk3DvXv3sGTJEnz48EGgx5YXbjQ9PR1sNrvVRkQA6BzKwhAJAwIChJKjuaqqCtnZ2a0Kd9QUvDmgIMsUBCTc6P/h6emJ2bNni7sZTdK7d2/IycmhoqICZmZmQglbS1EUbfTZUi/C8PBwVFRUQFFREXp6egJvW2N8+PChSUNoX19f+Pr6onfv3hg8eDBffU9dXR1MJhOVlZV83RsYDAbWr1+P5ORkREVF4caNG1i2bJlEhSAmEAitw87ODubm5ujXrx/69esHIyMjXLhwAUuWLBF300SCKH7/xo0bkZqaCn19fTg7O+Pw4cMCKbeiogK//vor/f+pU6ewYsWKBvtt374dDg4OCAsLQ25uLg4cONBkG06fPg13d3cAtflsX716Vc9QbPXq1fjpp59w6NAhFBYWYtWqVXj79m2Dcvbv34+oqKgG7/PGb42FcR0yZAiWL19O/88zhhZW+o8zZ87gzJkzKCsrQ2xsLP755x8sX74c9+7dw+3bt79oLFtUVAQnJyfk5+fD2dm52cLi5xCRkCBQjI2NYW9vD29vb7x69QrOzs5CqYc3QRRWyJ3PKSkpwdWrV0VSV1MkJycLXCTkLfbq6enxtXAhaDp16gSgdpE4Pz9fohZGhYGenh769++P9+/f09eZkZFRow+fiooKxMbGIi4uDoqKihgzZoxY8hbKyMjA3Nwc5ubm4HK5SE1NRVRUFKKjo1FQUECHCNTS0sLEiRNhaGgo8jZ+DUFZnTo7OwtF/DQwMMDs2bNx48YNgZctKF6/fg02mw1zc3M6byU/MBgM/Pzzz1i2bBlyc3Ph5uaGSZMm8d9QMUNRFOLj4+Hv79+svGIyMjK0YGhoaIhhw4aJNT9pUlISLl682Kx9w8LCUFFRAWdnZ74Wa8LDw4WWN1RRUVHkkQj4gZfjSZIMRxQVFTFr1ixcvnwZb9++hbm5uUjyJWppaUmUSFhYWCgSYyZra2sEBwcjNTUVp0+frjcBFyUTJkzAmzdvcOXKFezfv18iDRpayqpVqxAUFITk5GSsWLEC9+7dE9i1zDNG4HK5qKioaFU4bh6lpaUABD/3qa6ubnQxRVAkJycLRSSUNE9CEm70/4iOjkZmZqbECbl1EbZXSlpaGvLy8sBisTB8+PAWfTcgIABAbS5YUTxXq6qqmhUF6uPHj/j48SOMjY0xePBgGBsbt7h9TCYTmpqayM7ORlJSEl/XiIyMDHbt2oVvv/0W2dnZuH//Pr755hux524WBRUVFZgwYQIA4OnTpyJbEyNIPu3h2hDXGFdSEPbv9/DwwOnTpwEAJ06coJ85gsDb25v2oLO1tW1UIARq53P79u3D5MmTAaDJaBYcDge7du2i/798+XKj478//vgDL1++REhICLy9veHm5tbAQOfZs2fw9PRssu1NrXXUFQl543jemPxzysrKGuzbGhQVFWFtbY2zZ8+CyWTizJkzOHToELZs2dLo/hUVFZgyZQo+fPgABwcHvsLIknCjBIHDCznKU/uFgSjDjXK5XNy7d0/o9XwNYeT14FlNGBgYCLxsfmCxWFBRUQHQMUKOAsDIkSPrCU11Q43m5+fj3bt3uHjxIv7880/cu3cPHz9+hK+vL+7fvy/Q8H6tgclkolu3bhg3bhzWrFmD//3vfxg9ejQmT56MVatWSaRACAjGA2TixIlCtRrv2bMnpkyZIrTy+SE7OxvBwcEAgP/9738Cm5Srqqpi8+bNAIDAwEBERkYKpFxxUFFRgXfv3uHYsWO4evVqswRCoHbRNj8/H8nJyfD09MStW7fElqMyODi42QIhj/j4eFy6dKnVoUIpihKq576kLS5/DUn0JASA6dOnQ0pKChkZGUhISBBJnZJ27iiKElgYti/BYDDohPXu7u6IiIgQep2N0b9/f6ipqSE7O7teaLq2jKysLLZu3QoWiwVXV1ecPXtWYGVLS0vTQmplZSVfZfE8/QQdctfPz6/eooagEfT8hYQbbRsII3xtWyIkJARA7fyupZGPeAu2osqz5+Xl1eSCZ2MkJCTgypUruHDhQoN8U82BN5YRxL1BS0sLO3fuBJPJRGRkZIe57rhcLjw9PeHp6SmykO+EtoG4rw03NzdMmzYN06ZNAwD06tUL06dPx/Pnz0XeFkJDysvLsXz5clAUhVmzZgncGDs7O5t+3aNHjy/uW/fzpp5Bnp6edCjS4cOHo2/fvo3uJyUlhR9++IH+//r16w32ef36NSiKarB169YN3bp1a/Szz3MY86LvNeV5z3tfXV1dYE4ECxYsAAC4uro2+nl1dTVmzJgBLy8vDBo0CK6urq0KM8qDiIQEgfPNN9+AxWIhLi6OTtYtaHgTRFGEG3379q3QfkdLyMrK4nuB4XN4noSSJhICHSsvIVC76MNbAARqF5ZevHiB48eP459//sHz58+RlJQEiqJgZGSEKVOmgMFg4OPHj3j8+LHYhUIeDAYD2trasLe3R9++fSU2VCyXy+XbS8nKygq2traCadAXsLGxgYODg9DraSkvXrwARVEYOnQoevfuLdCy+/XrR4eqcnV1FUoYNGGSmZmJBw8e4MCBA3j+/DnfgnRUVBSuXr3aqsWY1kJRFF68eIEHDx606vtpaWk4f/58qwSU+Ph4od7721KoUUByRcJOnTrReUiF6YlUF0k8d6LKhaSnpwdra2sAtTn0xLH4w2KxMGbMGABoMHFuy5iamtJhedasWYPY2FiBlc2bq/A7hheGJyGHwxFqWGegVggQ5BhVEkVCiqKISPgZkZGR9RYLOxJsNpvOidTSUKNFRUWIjo4GIBqRMDc3t9X3gJSUFNy+fbvFzyJeVISkpKRW1fs5lpaWWL9+PYDaRWDe8SMQCKKDy+VixYoVGD9+PO7fv0/PfT99+oR79+5h3LhxWLt2rZhbSdi8eTMSEhKgpqaGv//+W+Dl1x2bfW0sXfdzS0vLRvepmy984sSJXyyv7ufCMmTkzcOCgoIa/Zz3vpWVlcDq5M3/eREr6sLhcDBv3jw8ffoU1tbWePLkCd/iJBEJCQJHQ0ODtkhwc3MTSh2iykkYHR0NDw8PodbRElJTUwVaHhEJ+ScjIwORkZGtSkz7ORYWFhg1ahQA4M6dO/D29kZOTg4YDAZsbGywevVqXL16FefOncO6deuwdetWALXeVk09qAiNU1JS0iApdEvo3LkzHB0dW+w9x2azW1XvkCFDYGdn1+LvCYtPnz4hNjYWDAajyTAS/LJ06VL06NEDVVVVuHbt2hcTWksSnp6eOHnyJO1lKSiSkpJw69YtgZbZFGw2G7dv3+bbmy83NxcnT57Ey5cvkZKSgtTU1HpbSkpKvY3nOdlaYbK5SKLQ1BQcDgcZGRkAJCvcKI9Zs2aBwWAgKSmJbqcwkcRz19ikTViMGjUKLBYLUVFRYhuf8gyaHjx40K5EAGdnZ9jY2KCysrJeaCF+EYRIyOFwaG8/QYqEwjBA/JzS0lKBCemVlZW0sYwkhbIsKipCTU0NAIgsh1xb4M2bN+JugliIiYlBZWUltLS06AXF5sLzQNTW1uYrVFlzefnyJV/fj4uLw5UrV1o0RucteAYEBAgsWoijoyOmTp0KoHYOLYh5OYFAaD7r1q3DmTNnANR6dY0dOxZAbY61jRs3Ql1dHUePHsU///wjzmZ2aHx8fHDs2DEAwKFDh4RibGVvb0/f4/39/Zs0KMzLy6NDZzKZTNrQ43M+fvxIv+7fv/8X69bR0aHXtbOzs4UyP7O3t4e6ujoSEhIQGBjY4HMXFxcAoI1oBQFvvmdqalrvfYqisGzZMty+fRvm5uZ4/vw5nbaLHyQnCRmhXbF06VLcv38fDx48wJw5c6CqqirQ8nmhe3gTMmEQHh6O27dvC6381tDa0G1NwctDKOhyBYGGhgYAID09XcwtaZqysjKcOnWK/l9VVRX6+vowMDCAvr4+Onfu3CJPOgaDgSFDhiAqKgppaWlQUFDA+vXrMWDAgEYtQhwcHBAeHo67d+8iPT0d/fr1E8jv6gjIyclBWVm51ZNIZ2fnFudiKigowIULFyAlJYXvvvuuRXlAeaHmQkNDRepN1hQ8z74ePXoIzchAWloaW7duxYoVK5CVlYVTp05hyJAhGDZsmETlUK1LRkYGXr9+LbTy4+PjUVVVxVcIia9BURRcXFwE5klTUVGBt2/fiszTrDm0NU8PJpMJDofDl2GDsNDW1saQIUPw5s0bxMXFQVdXV6j1ycnJQU1NTWTee83Bz88Ptra2IrkvKSkpYejQofDw8MCNGzfg4OAg8vxLRkZGMDc3R2RkJP777z9s375dpPULCyaTiY0bN2L+/Pnw8vKCv7//VxckmgNvHCiJIeFEdV/OycmBuro63+XwjERVVFQkKs8S736koaHBVy7e9kZYWBgcHR0FHiJX0uF5yA0bNqzFEVV4HsO8tBvCRhAhPxMTE3Hy5ElYWVlh5MiRX133MTIygpycHHJycvDdd9/B0dERK1as4Hu9aPXq1QgPD0dsbCxCQ0MxZMgQvsojEAjNw9vbmxb/FBQUaC+u58+f45tvvoGKigrWrVsHBwcH3L17V5xNbZK3b9/SEQH4pW/fvujatatAyhIUlZWVWLp0KbhcLkaNGoUlS5YIpR45OTmcPHkSs2bNQk1NDZYsWYJz585h8uTJ0NfXR0lJCcLDw3HhwgUUFxdDSUkJZ86cweDBgxstr65neN10TE1hZGREO9ZER0cLPAoPi8XCjz/+iO3bt2P16tVwd3enDXpu3ryJR48eQVNTE0uXLm3w3YULF+L9+/f4/vvv8f3339PvBwQEIDw8HLNnz26wxvPo0SNaTP3cgHHdunW4ePEijI2N8eLFC4H9VslcZSO0eSZNmgRra2uEhITAxcVF4J4menp6CA4OFtoi0YcPHyQiD+HnCHriqa+vj5iYGHz8+BEzZ84UaNn8wrvJCdp7UpDUtWwBahcvioqK6BAzLBYLenp6tHBoaGjYrInypEmTcPr0aZSXl6OysvKLLuO8RUlhigbtEVlZWaxevRpBQUF49+5di8TCVatW0Z6uzaW8vBxXr16lxbWEhISvxmn/nKKiIokQCIH/8/QVtheJgYEBrl69ir///htv3rzBmzdvEBMTg4ULF4ok3HRLoCgKjx49Eno9GRkZQs3zmZWVJdBQe5KGqqqqxIXt/BJSUlIwMjJCTEwM0tPTJcp7hoeVlRXevHkjlNzJjWFoaChRImFhYSECAgIwcOBAkdTXv39/eHl5IS4uDqGhoS32VBEEM2fOxO7du/Hvv//i559/bjcigK6uLkaNGgV3d3ds2bJFIDl0eAaN/IjIUlJSkJOToz3pBPH8S01NFVnOX0H1V55IKGkexbwQqG3p2SIqPnz4IFGRMEQBL0R4r169WvxdXsQAUXjCVVVVCdRQODQ0FKGhoRg8eDCGDh3a5HNBQUEBq1evxosXLxAaGorHjx/D3d0dP/zwAxwdHVtdP4vFgpOTEw4ePIjQ0FDY29uL3IiGQOiIHDp0iA4rvm/fPgwbNqxBflBdXV3cvHkTffv2lUijx61bt8LT01MgZZ0/fx6LFy8WSFmCYvv27YiOjoa8vDxOnjwp1LqmTZuGV69eYfXq1fjw4QO9hlMXaWlpbNmyBatWrfqiwXnd9EDNiajDczT5/LuCZNOmTXj16hVev34NU1NTDB8+HJmZmXjz5g2kpaVx+fLlRiMBpKSkIDo6uoEY/enTJyxevBhr166Fra0tdHR0UFhYiOjoaMTHxwMANmzYgFmzZtHfcXV1pcPFduvWDb/++mujbZ06dSrtZd9cSLhRglBgMBjYsWMHAODu3bsC76C8UC7CWCQKCgqSSIEQ4G+BoTF4Fi5hYWESZ+HMWwCQZJEwNDT0i5+z2WykpKTAx8cHN2/exB9//NEsUaVz5850DrojR44gLS2tyX154af4XaCrrKwUeWiW8vJy+Pv7IywsTKT18pCVlcWgQYOwdu1aODk5NWvgsXz58hYv0tfU1OD69et0eF8AiIiIaHF7Jakv8ETCgoICVFRUCLUuDQ0N7Nq1Czt37oSamhqysrJw5coVoYdIaylJSUki8XwWdkjH9p7LpUePHm1u0cjExAQAvvgsECd9+vQBUHuPEsVYQpgieWtxc3MT2T1JXl6eFgZFFYL4c4YNGwZtbW1kZ2fj2rVrYmmDsFiyZAlYLBbc3d0FEtJVECIhACgqKgLgP7chUGvU4u7uznc5zUVQ80CeoRURCdsO/v7+EpM3XRTU1NTQoTctLCxa/H1RioTCMrbx8fHBH3/8gXfv3oHNZje6j5KSEqZOnYolS5agc+fOqK6uxsGDB3H69Gm+rpcRI0ZASkoKubm5Eh2NiEBoL1RVVeHx48cAaj2gV65c2eS+ffr0ocOQEkRHQEAADh8+DADYuXMnPa8UJvb29jh69ChsbW0b/bympgb//vsvDh8+/MW1JJ53PdC89c66USaE9RyVkZGBm5sbfv/9d2hoaODBgweIiIiAk5MTfH196bQMzaV///7Yvn07bGxsEB0djTt37tDzj/nz5+PNmzc4ePBgve/UfX57eHjg4sWLjW68EOYtgYiEBKExZcoU9O3bF5WVlbh586ZAy+aFCuMlxBUUFRUVePjwoUDLFCQtDW/4Nbp06QJpaWnk5+fj1atXAi2bXyTdkzArK6tVOdIuXbpUTyxqikGDBsHQ0BBsNht79+5t0uKK9+Dkx5OwoKAAf/zxBw4fPoy///4b9+/fR2BgIHJzcwU+sedyuYiJiYGLiwv+/PNPPHnyhH4QimsRQUpKCtbW1vjuu+8we/bsJq2Zli5d2uIwhVwuF3fu3MGnT58gIyODDRs2AACioqKanDg3hai8dJqDnJwcPVATVa7AYcOG4ciRI5CTk0NGRgauX78u1JDTLSUgIEAk9RCRkD969uwp7ia0GF4OAkld8DI2NoaioiKqqqpEkke4OeFmxIEo82/xvBZ9fHzEIh6zWCxMnz4dAPDXX3+1KxFAV1eXzq2+efNmvn+boERCXlQJQUQUiIqKEun4WlAiIc+TUBh5dPiBiIRNk5ubi+TkZHE3Q2RkZGSAoihoaGi0SszmiYTl5eUtnie0FGF75D9//hz//vsvwsPDm7yPdu3aFStWrMDIkSMBANeuXcORI0dabXCkqKiIESNGAECrFkfbEgoKChIXVYUgGYjy2ggNDUV1dTWAWmHoa0LOqFGjRNGsFvP69WtQFCWQTZK8CKurq7FkyRJwOBzY2Ng0mftPkOTl5WH06NEYMWIE4uPjcfjwYcTHx6O6uhqFhYV4+fIlJk6ciMLCQhw5cgQjRoxo1vqosEhKSqLDhDcXGRkZ/Prrr4iIiEBlZSVyc3Nx//599O3bt8nv8K4xnjMVjy5dumDnzp3w8PBAamoqKisrUV5ejri4OFy+fLnR0NmLFy9u1rX4eV3NgYiEBKHBYDCwc+dOAMD9+/cFOhDleRIKWiSUpEX4xhC0SMhisTB06FAAtUmFeQ94SUDSPQm/5kXYFGVlZbh06dJX+wOTycTUqVMhLS2NiIgIXL16tcnygNZ7ElZXV+PGjRv0/4WFhQgNDcWjR4/w77//4uDBg7h58ybevXuHtLS0Vk/a8vLy8OLFC/z111+4fv16gxBXXl5eePHihVgXGhkMBnr27ImlS5diyZIl9cKBLlq0qMW59yiKwpMnTxAdHQ0mk4mDBw9i4sSJ0NTURFVVFR0+oLlIWl/geRMKQrjgDWS+RteuXXH06FFIS0sjJSUFLi4uEhGypLS0tFXeoa1BmCJhcXGx0EVIcSIjI4Nu3bqJuxkthmfxKakioZSUFB1WTRTjKGVl5XrhZCQFHx8fWsQQNpqamujevTsA4M6dOyKp83N4ucbCwsLw4sULsbRBWCxYsABycnLw8/PDgwcP+CqLJxK2NDfZ5wjKk5DD4Yj8fAnak1DSxDhev5e0dkkKojKikgR4oUYtLCxaFbVARUWFnu8L25tQWKHYPq/j9u3bcHFxqecRUhcmk4lhw4bRoUYfPHiAffv2tVokHTduHADJjJQkKBQVFVFWVoaysjL62UAgAKK/NurOTXjj0i/BM3wkiIY9e/YgLCwMUlJSOHPmDN9j0a9RXl6OIUOG4NWrV1BTU4Ofnx/WrVsHY2NjSEtLQ1VVFQ4ODnj8+DFWr14NAHj//j3WrFnTaHl10y41Z/xb1yuxsZCfhK9DREKCUHF0dET//v0F7k3IEwnLy8sFmqOrpRYEokbQIiFQa42uoqKCrKwssS00NQZPJMzKyuL7HAcHByMwMJDe+LW653K5DfIRtoTi4mJcunSJXuxoClVVVdqa/cKFC43mjuGJhK3xJKQoCq6url8MgVpeXo6oqCg8f/4cZ86cwf79+3H27FncunULbm5u8PHxQVhYGFJSUlBYWFhPsKmqqkJQUBDOnTuHY8eOwdvbu8kJIlC7wPrs2TOJ8Ejo2rUr5syZgzVr1uDHH39sVXi7t2/fIjAwEADw22+/oXfv3vREGGhZyFFReei0BJ5IyI+oVFBQgMuXL8PZ2Rnz5s3Dmzdvvnr+u3fvjkOHDkFKSgpxcXG4e/eu2BcBgoODRVZXbm6u0Aw62rsXoYmJicDDdosCSRcJAaB3794ARGdsJYkhRwEIJDxlc+F5Ez58+PCLz1ZhoaSkhIkTJwIAHcaovaCurk57Sm7dupUvYxRJEwmDgoIEbmT5NQoKCgQytiOehG2T8PBwkac0EBc8kdDc3LxV32cwGPR1JOxjJsrcvlFRUTh06BA+fvzY5L3A1tYW06dPB4PBwIsXL/Dbb7+1arzbt29fSEtLo7KyUmSGOwRCR6Xu+LM53otE1BYdoaGh2L9/PwBg/fr1X/RyExTHjx9HVFQUAGDjxo1fFI4PHDiATp06AQBu3rzZ6JoS73MADXL5NUZdj8S63yU0n7a3UkJoU/ByEzo6OuL+/ftwdnaGuro63+UqKiqiU6dOKCwsRH5+PnR1dQXQWkh8OBRhiITS0tJwcHDA/fv3cf78eUycOBGqqqoCr6elqKqqQkZGBtXV1UhPT+crvJibm1uD9+zt7eHg4AAms+W2EgkJCXwvyBUWFuLSpUtYvHhxPQuZz+nduzdiY2MRFhaGvXv34tSpU/VibfMSzrfGk9Db27vF3k81NTX0BLgpFBUVoays3KowlO/fvwebzcakSZMkIm9Ya+9XISEhdAjfH374gRYGgdp8GXfv3kV0dDTYbHazRIuvHXNxwI8nYWxsLO7cuYNXr17VCxm6fft22NraYs2aNXTO1Mbo3bs39u7di82bNyMiIgKysrKYPHmyWK4ZLpeLoKAgkdaZmZn5xePTWmJiYgRepiRR1zu4LVFXJKQoSiLujZ/Dy0uYnJwskjYaGhrSRhiSRGhoKAYOHNji3LWtwcjIiM4L+OjRI8yePVvodX7OjBkzcO/ePTx79gwRERGtysElqcyePRsPHjxAWFgYrl+/jvnz57e4DIqiaIFRUDkJ+TGcq6qqwuvXr/lqR2uorq5GRUUF3+HPeAv+kpqTsDm5rTsqQUFBGD58uLibIXTqehK2Fk1NTaSnp3/VmJRfRCkS8rh79y4iIyPh6OjYqFjQu3dvyMrKwsXFBT4+Pti0aRN+//33Ft07pKSkoKuri5SUFBQUFNDzFQKBIHjqrmPx1qW+BM/AXdJ4+/Zts0So5tC3b1+hzNNbyoULF1BTUwMmkwlpaWns2bOn0f28vLzqvebt17NnT3zzzTctqrNu6q6v5Z9UUFDA4MGD8eTJE3C5XAQEBGDy5Mn19unZsycSExMBAImJiV81EuXty/suoeUQT0KC0JkwYQIGDBiAqqqqemEN+YXnTSioAW5lZaXEh1kThkgI1C7ude7cGTU1Nbh48aJQ6mgpdS0p+Q2zOHDgQAwZMgRDhgxBv379ANQKZBcvXmyVlWZrQ41+Tl5eHi5duvTFARWDwcDEiROhoqKCT58+4cSJE/U+b60nYWxsLF6+fNnyRjeDsrIyvvLUBQUFwdXVVezeYa0lLi6ODk82Z84cTJs2rd7nvXr1anHIUUkLNQq03JOQw+Hg9evX+OGHH7By5Uq4ubmhpqYGdnZ2uHz5MrZu3QppaWkEBARg2bJl+O+//77YN+zs7LB9+3YwGAwEBwfj+fPnYvFCjY+PF0nIproIw6OsqqoKcXFxAi9XkmhOGBxJxMjICAwGA+Xl5SK/1pqLmZkZpKWlUVZWJhIvJUn1JAQAd3d3kdTDYDBob8J79+6JJfSynp4enSvjyJEjIq9fmCgrK9PC6/bt21vl0VLXCEZQOQn58ST09vZu1iKeMOD33sXlcmnRRNJEQhJu9OsEBga22XF9cykuLkZJSQkYDAZfRkm8cNrtyZOwLpGRkTh48CDCw8Mb/bxHjx6YP38+pKWlERISgvXr17fYI1BYqWkkhcrKSjg6OsLR0ZFv73JC+0LU10aXLl3o182ZR0rqXHPr1q2YNm2aQDaeobi44a2LcLlc7N27F9u2bWt0qxsFxcPDg36/qXRHX6LuGoWKispX96/r7deYEwYvUg0A+Pv7f7GsrKwses1MW1tbqGOympoa7N+/H7169YK8vDy0tLQwffr0VkeXOnXqFGbNmgVzc3Ooq6tDWloaOjo6mDRpEh49etSsMiiKgoODAxgMBhgMBu3R2VKISEgQOnVzEz548EBggzXeA0lQ5UniIvznCEskZDAYtKXH/fv3JSY3o6DyEm7duhW7d+/G7t27cfDgQfz22290TrPjx4+3KDdcZWUlwsLC+GpPXXJycnD58uV68bM/R15eHk5OTgBqrXPevXtHf8Z7mLZEJMzLy8O1a9da2WLREBoairt370pEvrmWkJ6ejps3b4KiKIwZMwbLly9vsA+TyaStqZvrySmJ96fmioRFRUW4du0a5syZg507d+Ljx4+QkpLC3Llz4evrCz8/P8yfPx+7d+9GREQEJk2aBDabjZs3b2LhwoVfzFU5fPhwbNy4EQDg6+tbzxJOVIjDm4kfEb4pWpojs62hr6/fZkPcyMnJQV9fH4DkhhyVkZGBmZkZANGEHFVSUhJYFAlBk5CQILL+1Lt3bygqKiI7Oxuenp4iqfNzeFbGFy5cQE5OjljaICymT58ONTU1JCYm4syZMy3+fl1PIH7H8Px6EpaUlODNmzd8tYEf+BUkysrKwOVywWQyJS4nKQk3+nVKSkrafbQCnhehqalpvagvLYXnkSpMT0KKosRudHT79m3cunWrUc8iQ0NDLF68GLKysoiOjsbatWtb9HzhjQ/EJYQKGw6HgydPnuDJkydtbq5MEC6ivjb69OlDr0O9ffv2q8KksIzUCZJBXWGwOfPBupH8GhvbjR8/nn799OnTL5b15MkT+jUvHYIwqKmpwfjx47F582bk5uZi8uTJMDMzw7179zBw4MBWGYsePnwYd+/ehby8POzt7TF9+nR07doVjx8/xuTJk/HTTz99tYxTp07Bw8OD72g+RCQkiISxY8di0KBBqKqqwu3btwVSpqAtxCQ91CjAvxXylzAyMkKPHj1AUZTEeBMKypPwc0aMGIFz587BxMQElZWVuHLlSrPraK1FxpfIzMyEq6vrF/cxNjamPQb+/PNP1NTUgMPh0AOxloQbvXfvXusbK0LCw8MlxhKrOXA4HNy4cQNsNhv9+vXDxo0bmwxnO2LECAC119PXBvAURUl8uNGmrMPT0tIwd+5cnD59Gjk5OdDS0sK2bduQkpKCq1evYsCAAfX2NzU1xcOHD/Ho0SOYmJggLy8Pv//+O44fP95kOyZMmEAnvn79+rVIF6FKS0vFksdPGEJRe1+8a6uhRnmQvIQNkeRzKqpnF4vFgq2tLYBaIy9xYGlpCTMzM9TU1LRKSJNk5OXlsWDBAgDAnj17Wrzg5uPjAwAwMDBoVVj4uvDrSfj27Vu+6ucXfgWJut56/OZ3FDTEk7B5SGKIaEHCbz5CHjyRUJiehCUlJRIhLkVERODgwYONemrr6elh2bJlUFZWRnJyMn755ZdmRwwRdMQpAoHQOLKysrQgU1xc/MVxYFhYGJ4/fy6qprWI169fg6IogWyLFy8W988BUBvhoznt/e233+jv/Pbbb/T7rZlX1PX8+5pTQlxcHPz8/ADUGtHzIr7VZfjw4XQKh9evXzeZ4oXD4eDo0aP0/8JMwXDgwAG8evUK/fv3R2xsLFxcXPDmzRtcu3YN1dXVmDdvXotTU50/fx4FBQUICgrCw4cPcfPmTfj7++PNmzdQVFTEoUOH6GPVGJ8+fcLPP/+M8ePH8x3qloiEBJHAYDDoSbagFo94efP4yc1RF35zzIkCYU+KeTdmSRFMeQsiwpgk6evr499//6XzKCUlJTXre8KabDRHBBo1ahSYTCYKCgpQUFAANptNf9YSAVmSF5k/RxI96JqipKQEJSUlYLFY2Llz5xe9Bni5Sqqrq7+64FdTU9OqMGfChmclXVNTU+9arEtiYiLKy8uhqamJCxcuICUlBbt27aIn703h6OiIsLAwfP/99wC+7nE5c+ZMODg4ABBt/kZxWWELI1ycsHPfiBtJ9TprLrxFQ0keqxgYGAAQXRtFkfevtYjSo46XcyMtLU1kddaFwWDA3t4eQG0o8/YGb/ErIyOjxSHveF7fgshNIyMjAwCtXtgXt5cnv2HPeOMgSfQIl+S2SRLtXbDhecTxO97gXUd1wxULGkGtnwiKpsagWlpaWLRoEYBaL/0vRd6pC2+diITiJBCEz08//UR7L/3yyy+NGiVlZWVh1qxZEmGcQGg9I0aMoMNZXrhwocHnc+fOpV+fP38eZ8+ebbScrKwsODs702tIjo6OjXoSSklJYfv27fT/CxcuRHZ2doP9fvnlF4SEhAAA7O3tMW7cuJb8rGbDZrPx119/AQCOHz9ez3Nyzpw5mDhxInJycnDu3LkWlTto0KB6+T15DBkyBLNmzQLwZS/cVatWgcPhNEhN1RqE55ZEIHyGML3gCIJB0ixz+XWV/hqysrIwMDDAhw8fhFqPoGCxWEI/JgT+YbFYX10oasrDsC3Rkmuxe/fu9CS/ucjJyWHMmDE4duxYs/bnLQgQCO0Rcu9vCDkmtUjC80TSxo+CRBDzF3KtEggdB377O7lf1Kc143tyDAkE0TF48GCsWbMGR48eRVlZGUaMGIHRo0cDqA0rHB0djTNnziA/Px/Tp0/H3bt3v1heYmJiA3Gp7npdcHAwtm7dWu/zvn37Yvr06QL6ReKlLf/+sWPHYubMmbh9+zYoisLy5ctx+fJlODk5QV9fHxUVFQgICMDly5dpY2s1NTUcOnSoyTJXrFiBe/fuwd3dHeHh4bCyssKKFStgYWGB/Px8XL9+nRamVVVVcfLkSaH9Pm9vb+Tl5cHIyIiO5FKXWbNm4cmTJ3B1dcUPP/wgkDp5jgdNpZe6fPkynjx5gr/++guGhoZ810dUGwKBQCAQCAQCgUAgEAgEAoFAIBAIzeavv/5CWVkZzp49Cw6HAzc3NwDAsmXL6H3Wrl2LqVOnflUkTE5Oxu+//97k5x8+fGhg5L9o0SKJEMkEQVv//VeuXIGqqiotdHp6ejaZN7179+64ceMGunfv3mR5LBYLd+7cwdy5c/Ho0SNkZmZi9+7dDfbT19fHzZs30atXL8H8kEYIDQ0FgEZDowK1Ym3d/fglJCQEN2/ehJSUVL38jDyysrLw448/wtbWFmvWrBFIneI3PSUQCAQCgUAgEAgEAoFAIBAIBAKB0GZgMpk4c+YMnj59iilTpkBNTQ0A0KVLF0ybNg3Pnj3DkSNHxNtIgkiQlZXFmTNnEBwcjLVr18LW1hbq6upgsVhQUFCAoaEhpk+fjkuXLuHjx4+0sPYllJWV8fDhQ9y/fx/Tp0+HgYEBZGVloampiQEDBuCPP/5AWFgYBg8eLNTfxkvLpa+v3+jnvPfz8vJalX7j/PnzWLx4MebOnQt7e3v07dsX5eXlOHHiRKPi5+rVq+lcoIKK6kI8CSUUXlLm9pQjiBdDns1m0zH7+YEXS5/D4Qgkrn5biI8t7PwBvJwaXC5XIOeIX3i5GKqqqur1Bd7rryUv533+pfxdvDrYbHazjm9Tudf4haKoZtVf9zfVDYFVVVXV7GTuzd1PEuByuRKXN6MpeO2kKKpF/aeqquqL4cyEnY+wuf3o8/NQ9/+ysrJGc6fwcoGw2exWPc94fZfD4Xz1mLa0LwsCceWKbO79oiVwuVyBlidp1NTUCOW6qNvvvwS/47q6z0NJeD43Bu9YiOq+Lcx8TfwijD7aFLz7UEufPcJoQ3V1dauucUGO6wRN3flBcXFxi8KPCnLuwzvGrZ33iPsez++zWdLmKHXhXZelpaVinbu3tB+JmrY0pm8NvHtFdXU1X9eooNc4GkPScp1XV1c3+Vvrzr3Ly8ubdf3yMx5p6biurKxM5P2+7vVVXFzcJtaxCKJB3NfG4MGDMXjwYHh7e2PixIkIDw+vFzJ4xIgRX+1bzdmnPSPq379jxw7s2LGjWfu+fv262eVaW1sLXBh2cnKCk5OTQMtsCSUlJQCazkFdN69gSUlJo3kGv4S3tzcuXrxI/6+goICjR49iyZIlDfa9e/cu7ty5g19++QVWVlYtqudLMKiO3PskmE+fPsHAwEDczSAQJJrU1NQmrTgA0o8IhOZA+hGBwD+kHxEI/EP6EYHAP6QfEQj8Q/oRgcA/X+tHBIKoWLhwId6/f9+i70ybNg379u2j/1+xYgXOnDmDrVu3NhrylM1m0zkE09PToaur26q2lpWVITY2Fv/88w/OnTsHR0dH3L59G3JycgCA/Px8WFhYQElJCR8/foS8vDz9XUNDQyQnJyMyMhJmZmYtrpt4Ekooenp6SE1NhbKycrMTLxcXF8PAwACpqalQUVERcgvbNuRYtQxJO14URaGkpAR6enpf3K81/UjSkLRjL2mQ49M0Xzs27b0fkWujPuR4NEQQx6Q99CNybQgeckxbhrD6ETkPjUOOS+O09ePSHp5HkkZbvyZESXs5VqQftZz2cu75hRyH/6O5/YhAEBUpKSmIjo5u0XcyMjLq/a+srAwATYYSrevJy9u3NSgqKsLa2hpnz56lQ/keOnQIW7ZsAQCsW7cOWVlZuHbtWj2BUBAQkVBCYTKZrba4UFFR6fAPpeZCjlXLkKTjVTdsQVPw048kDUk69pIIOT5N86Vj0xH6Ebk26kOOR0P4PSbtpR+Ra0PwkGPafITZj8h5aBxyXBqnLR+X9vI8kjTa8jUhatrDsSL9qHW0h3MvCMhxqKU5/YhAEBUtCZXaFN26dQNQ60neGLz31dXVWxxqtCkWLFiAM2fOwNXVlRYJXV1dIScnh127dmHXrl319s/MzARQ6zmpoKCA77//HjNnzmx2fUQkJBAIBAKBQCAQCAQCgUAgEAgEAoFAIBDqYG1tDQAICgpq9HPe+4LMEailpQUAyMnJqfd+ZWUlPD09m/yev78/AGDq1Kktqo/ZsuYRCAQCgUAgEAgEAoFAIBAIBAKBQCAQCO0be3t7qKurIyEhAYGBgQ0+d3FxAQA4OTkJrE4PDw8AgKmpKf1eYWEhKIpqdON5O0ZGRoKiKPz4448tqo+IhO0IWVlZ/Pbbb5CVlRV3UyQecqxaBjle4oMc+y9Djk/TdPRj09F//+eQ49EQckxqIcdB8JBjKhmQ89A45Lg0DjkuhM8h10TzIceq40LOfS3kOBAI7RsWi0WLbqtXr0ZJSQn92c2bN/Ho0SNoampi6dKlDb67cOFCmJmZ4dixY/XeDwgIwMWLF1FVVdXgO48ePaJDjC5fvlyAv6RpGBRFUSKpiUAgEAgEAoFAIBAIBAKBQCAQCAQCgUBoI1RXV2PcuHF4/fo1tLW1MXz4cGRmZuLNmzeQlpbGgwcPMH78+AbfGzFiBDw9PfHbb79hx44d9Pv379/HtGnToKqqCltbW+jo6KCwsBDR0dGIj48HAGzYsAEHDx5sVvsMDQ2RnJyMyMhImJmZtfj3EU9CAoFAIBAIBAKBQCAQCAQCgUAgEAgEAuEzZGRk4Obmht9//x0aGhp48OABIiIi4OTkBF9f30YFwi/Rv39/bN++HTY2NoiOjsadO3foEKPz58/Hmzdvmi0QCgLiSUggEAgEAoFAIBAIBAKBQCAQCAQCgUAgdDBY4m4AoXG4XC7S09OhrKwMBoMh7uYQCBIFRVEoKSmBnp4emMymHaJJPyIQmob0IwKBf0g/IhD4h/QjAoF/SD8iEPiH9CMCgX9IPyIQ+Ke5/UiQEJFQQklPT4eBgYG4m0EgSDSpqanQ19dv8nPSjwiEr0P6EYHAP6QfEQj8Q/oRgcA/pB8RCPxD+hGBwD+kHxEI/PO1fiRIiEgooSgrKwOovRhUVFTE3Brh4OHhgalTp8LQ0BDHjh0Td3M6JJMnTwZFUYiOjkbnzp3F3ZxmU1xcDAMDA7qfNEVH6EeEjktZWRn09PQAALdv34acnFyLvl9eXg5nZ2fSjzoIMTEx6N+/P5SVlXH9+vWv7l9QUIAFCxaAwWCgsLBQ+A1so5DnUePcuXMHS5cuRZ8+fbB3715xN6dDEx8fj7Vr10JXVxdRUVHibk6jtOd+NH36dLx8+RLr16+Hg4ODuJvTptm9ezf8/Pxw5MgRLFmyRNzNkTjacz8idFw0NTVRU1ODCxcuQFNTU+j1kfkRgfB1duzYgb/++gtOTk5YsWJFg88ltR9xOBz4+PgAAAYPHgwpKSmh10kQPu31vDZ3XCdIiEgoofBcrVVUVNrtoENRUREAwGQy6dcE0cJgMEBRFJSVldvkdfa1kAQdoR8ROi51Bz8KCgqQl5dvVTmkH3UMlJSUANSez+Y8c6uqquj9yXn/OqQf1UdBQQFA7X2KjPHEC+/ZwGQyJf7aa4/9iMWqnW7LysqSvsAnvGMpLy/fZs6/OGiP/YjQceFdrwoKCiK9h5J+RCA0jaysLABAWlr6i/1SEvuRo6OjSOohiJb2fF5FGYpXNEFNCQQCgUAgEAgEAoFAIBAIBAKBQCAQCASCxEA8CQlig8PhiLsJhP/Pjh078L///Q9WVlYkYXAH5cmTJwgLC4OGhkaDTV1dHdLS0uJuIuEz3NzcxN0EQhuCy+W2+ntHjhzB8uXLaW9EAuFzqqur8eHDBwQEBMDf3x+enp7ibhLhMyiKEncTOjTk+AuOW7duoV+/fujXr5+4m0IgtCtiYmLg4+MDDodTb+NyuQ3e69SpE8zMzGBubo7OnTsLbA0hLy8Pfn5+8PX1ha+vL6qrqwVSLkHw5Ofno6amBjo6OuJuCkGEVFRU4MOHD+JuRquoqanBqVOnAAArV64ka1ztBHJeBQcRCQli48GDBwCALl26iLklHRdLS0t8+PABp06dwqlTp9CtWzc4OTnByckJQ4cOJTfXDsLTp0+/6p6voqJCi4aGhoYYOHAgBg0ahL59+7Y4Fx6BP1JSUvDDDz/A1dUVAGBsbEzOAeGr+Pn5AQC0tbWbtb+qqir09fXx6dMnrFu3Dtu2bcOaNWuwZs0a6OrqCrOpBDFTXl6O58+f4+HDh0hOTgaHwwGbzaa3uv9zOBzU1NQgLS2t0YU8a2tr0f8AQj00NDQAAOnp6cjKyiKLeSKGl0MrJydHzC1p+1haWsLb2xsvXryAra0thg4dinXr1mHKlCntJv8MgSAu3r59i5EjR4LNZrf4u6qqqjAzM6NFQ95fY2NjsFgsUBSFkpISFBUVoaioCIWFhQ3+RkZGwtfXF3FxcQ3KNzAwgJqamiB+JkEAREdH48CBA7h06RLYbDb69etHryH17t2bGJ23Y0JCQjBv3jxEREQAAMzMzMTcopZRXV2N77//HgCwePFist7ZTiDnVXAQkZAgFgoKCnDu3DkAwNSpU8XbmA7MoUOH4OnpCQ8PDwQEBCA5ORlHjx7F0aNHoaamBkdHRzg5OWH8+PHEg6SdkpycjNmzZwMAevXqBSUlJRQXF9NbaWkpKIqi/09MTERAQABu374NoDYOfd++fTFo0CAMGjQIAwcOhIGBgURPDiiKQmZmJlRVVem8WW2BmpoaHD16FFu3bkVlZSWkpKTg7OyMBQsWSPTxJkgGvD47dOjQZu0vJSWFM2fO4Pnz57h16xZSU1Oxb98+HDx4EAsWLMBPP/0Ec3PzRr9LURTy8/Px6dMnpKWl4dOnT1BQUICdnR26d+/eJq9XiqJQVFSEioqKeu+XlJSIqUWCJTs7Gw8fPoSrqyvc3NxaZbmvoqKCnj171tu0tLSE0FpCS1BXV0f37t0RGxuLZ8+eYdGiReJuUofC2toaV69eRWxsrLib0uaZPXs2bGxscPv2bXh4eODNmzd48+YNjIyM8MMPP2Dp0qUkNxiB0ApSUlIwefJksNlsGBsbQ0dHB0wms9FNSkoKDAYDRUVFSE5ORkZGBoqKiuDn50cbpPHg5SsrLi5uUUQLAwMDmJubw8LCAhYWFjA2NiaGABJAYGAg9u3bh7t379bzjg8MDERgYCC2b98OQ0NDWjAcMmQIWaxvJ3A4HBw6dAhbtmwBm82Guro6fv75ZwwYMEDcTSMQCAKEiIQEsXD27FlUVlbC2NgYNjY24m5Oh4XFYmHUqFEYNWoUKisrERAQAG9vb7x79w4FBQW4cuUKrly5AmlpaYwZMwZLly7FjBkzxN1sgoCoqqrCzJkzUVxcDDMzMxw+fBgyMjL19uFwOCgtLUVRURFtAZqUlITw8HBERESgsLCQnhQeOXIEAKCnp4dBgwZhyJAhcHBwgKWlJZhM0abAraqqQlJSEhISEhAfH4/4+Hj6dUJCAioqKiAvL4+ff/4Z69evl/hFJV9fX6xatYoO7WFpaYn169fDyMhIzC0jtAUKCwvx/PlzAMCIESOa/T1ZWVlMnjwZjo6O8PHxwY0bNxAeHo5z587h3LlzmDRpEkaNGoWMjAx8+vSJFgXT0tJQWVnZaJnq6uqws7PDwIEDMXDgQNjZ2YnVOpyiKISHhyM6Oho5OTlNbrm5uaipqRFbO4VBdHQ0XF1d4erqinfv3tVb8OncuTPs7e3Rs2dPSElJfXFjMplQU1ODrq5umxSAOwIDBw5EbGwsHj9+TERCEcOb5zTmHUNoOT179sSWLVuwcuVK3L9/H48ePUJiYiLWrVuHnTt34uLFi5gyZYq4m0kgtBnKysrg5OSEwsJCmJqa4ujRo5CXl2/296urq5GWlobk5GSkpKQgOTkZqampSElJQVVVFQoLC+l9paSkoKSkBCUlJSgqKtb7q62tDQsLC5iZmUn8vKwjQVEUXr9+jX379sHd3Z1+f/DgwZgzZw709PTw7t07eHt7IzAwEElJSfj777/x999/Q01NDRMnToSTkxMmTJhAjM7bKMnJyVi0aBGdSmDIkCH46aefoKqqKuaWEQgEQcOgSIIEiaS4uBiqqqooKipqd4MkNpsNExMTpKSkYOPGjZg4caK4m0T4DA6Hg/DwcLx9+xbe3t5IT0+nP1uzZg0OHz4MFkt8NgbN7R/tuR8Jgu+++w4nTpyAsrIyTp06hc6dO7fo+xRFISMjgxYMIyIiEBcX18BSVEtLCyNHjoSDgwMcHBxgamoq8IXk1NRUPHnyBE+ePEFISAhSU1Obnf9HU1MTv/76K/73v/9JXNjOgoICbN68GadOnQJFUVBRUcHKlSsxYcIEvoXXsrIyTJo0ifSjDsClS5ewaNEiGBoa4vz583yVFRYWhps3b8Lb2/urfUxNTQ2amprQ1NREcXExYmNjG/VQ69GjBwYOHIgBAwZg1KhR6NmzJ19t/BrFxcV48eIFnj59iqdPnyItLa3Z3/2831EURXsZtoV+RFEUjh49ihMnTiA6OrreZz169IC9vT3s7e1hbGxMBL92REREBFavXg0VFRXk5uZKnGV/ex7X5efn0yFfHz58SBZJBUxlZSXt8f7p0ycAwLJly/DXX39BWVlZzK0TLe25HxGEA0VRmDVrFm7duoVOnTrhv//+E1hIai6Xi+zsbFRVVdFioKysrMSPLcQ1P+JyuYiPj0dISAi9ffjwATo6OtiyZQumTp0q0mPH5XLx4MED7Nu3D+/fvwdQOwYeNWoU5syZ06ihakVFBQIDA2mj86KiIvozFRUVHDt2DPPnz5f4a4BQC0VRuHbtGlatWoWysjLIy8vj+++/x4QJE756DiV1naGsrIweh5WWlkJRUVHodRKET3s9r+IYrxGRUEJpz4P327dv45tvvoGqqipcXFwaeC4RJAuKopCUlIRnz57BxcUFADBmzBjcvHlTbN4fZBLMP1evXsX8+fMBAPv27cPAgQMFUm5lZSWio6MRERGB0NBQhIaGNvAoMjAwoAXDUaNGtSovKYfDgZ+fHx49eoTHjx83mjxbTk4Oenp69Karqws9PT106dIF2tra8PHxwdmzZ5Gamkq367fffsOiRYvEKoIDtf3u6tWr2LBhA7KzswEA48ePx6pVq9CpUyeB1CGpg/e2CEVRKCgoQHJyMnR1dVssuAubKVOm4OHDh1i0aBEWL14skDJTU1Nx//595OXlQUtLC5qamtDS0qJfa2hoNHi+19TUICEhAREREYiMjERkZCS9qFuXkSNHYvXq1XBychJIX6QoCh8/fqRFwbdv34LD4dCfy8rKwtTUFJ06dUKnTp2gqqra4LWamhpUVVUb/Ka21o+OHz+O1atXA6iNJmBtbY3BgwfD3t6+2fkqCW0PLpeLGTNmoLCwEB4eHi3yKBYF7X1cZ2hoiOTkZPz1118kT6eQqK6uxrlz5+Di4gKKomBkZITLly/D3t5e3E0TGe29HxEEz549e7Bt2zawWCwcPnwYvXv3FneTxI4oxnVVVVX48OFDA0GwtLS0ye/Y2tri999/x5gxY4QqstXU1OD69evYv38/IiMjAQAyMjKYOHEinJ2dm52XnMPhICIiAt7e3vDy8kJGRgYAYOzYsfjvv/9INBwJhLfuFxISguDgYLx9+xYeHh4AatPSbN68udnrNpI6P2qvYlJHp72eVyISEmja6+CdoigMHToU3t7eWLBgAZYuXSruJhFagJeXF/bt24fKykr06NEDDx8+RI8ePUTeDjIJ5o/w8HDY2tqisrJS6P2QzWYjMjISwcHBCAoKQkRERIOQfbq6ujAwMECXLl2gr68PfX39Bq/l5OSQn58PNzc3PH78GM+ePUNeXh5dBpPJhLm5OQYOHAgrKyt06dIFampqX51EcTgcPHv2DBcvXkROTg6A2lBWe/bswYwZM0Rm6UhRFBISEujQrV5eXggJCQEAdOvWDT/++KPAFxYldfAuiVAUhby8PCQnJyMpKanexnuPl5tOWloamzZtwq+//tqicE3Cori4GJqamqipqcG5c+ckblJeVFSEqKgoREREIDw8HMHBwbQ3cpcuXbBq1SqsWLGixcJrWloavL298fz5czx79qyBt6CBgQHs7OxgZ2cHKysryMrKtqr9bakfeXl5wcHBARwOB/PmzcPs2bOJV1MHYt++fXj+/Dl++ukn/Pnnn+JuTj3a+7hu2rRpuH//PlavXo2ZM2eKuzntmpCQEOzfvx9ZWVlgMpnYtGkTduzY0SGMUtt7PyIIlnv37mH69OkAgJ9++gmOjo5ibpFkIMxxHUVRcHFxwY8//ojMzMwGn8vIyMDY2BgmJiYwNTWFsbExAgICcOvWLdrodtiwYfj9998xZMiQ1v3AJqiqqsKFCxewf/9+JCUlAQAUFRUxdepUTJ8+Herq6q0um81mw8XFBRcuXEBNTQ3k5OSwd+9e/PDDDyTXpJioqalBVFQUgoODERwcTIvVdcMDA7VrLIsWLcK8efNadK4kdX7UXsWkjk57Pa9EJCTQtNfBO8+LkMVi4fr169DU1BR3kwgtJC4uDlu2bEF2djaUlJRw7949jB49WqRtIJPg1lNSUoL+/fsjOjoa/fr1wx9//CHSwXllZSXCwsJo0TAmJqZZiezV1dVRWFhYb18lJSU6t5mdnR1fcfGrq6tx//59XL16FcXFxQCAfv36Ye/evUKx2CwoKMD79+9pUfD9+/fIzc2tt4+MjAwWLlwIZ2dnoYSGk9TBuyTB4XBw/fp17Nq1C7GxsV/dX1lZmRYLjYyMcOzYMbGH1OZ5DXft2hUXLlyQ+BA/WVlZePjwIR4/fkxPVKWkpDBz5kysXr0aQ4YMafAbuFxuvRDZ3t7e9AIHD1lZWdjY2MDOzg4DBgyAnp6eQNrbVvpRamoq+vTpg8LCQjg4OGDr1q0Sfy0QBMurV6+we/duWFhYIDw8XNzNqUd7H9ft2rULv/32G8aMGYNff/1V3M2hSU9PR1BQEBgMBvr16ydxXvCtpaysDP/88w/c3NwAANbW1rhy5Qp69eol5pYJl/bejwiC4+PHj7Czs0NlZSWmTZuGH374QdxNkhiENa5LTk7Gd999hydPngCoDb/Zo0cPWhA0NTWFgYFBo/PygoICXLt2Da6urrSx7YQJE7Bnzx707du3lb+0lvLycpw+fRp//vknbVCnpqaGb775BpMnTxaoMVlqaioOHTqE0NBQAED//v1x5swZ9OnTR2B1EBpSUFBAR3gKDQ3Fhw8fEBYWhqqqqgb7slgsGBkZ0dekra0tunbt2uI6JXV+1F7FpI5Oez2vRCQk0LTHwXt+fj4sLCyQlZVFvAjbOPn5+di2bRsiIiLAZDJx9OhROnyZKCCT4NZBURRmz54NFxcXaGpq4vTp0wILXdlaSktLkZaWhpycHOTk5CA3N7fe35ycnHoDWCMjIwwcOBADBw5Er169BC5wlpWVwcXFBbdu3UJFRQUAwN7eHitXrsSMGTNaPeDgcrnw8vLC9evX8fr1a8TExDTYR1paGiYmJjA3N4e5uTn69u1L5zESBpI6eJcEKIrC/fv3sW3btnqL6RoaGujcuTN0dHTq/eW9lpGRgZeXF44dO0aLvtOmTcPff/8NAwMDsfwWngdLW3vuVldXw9PTE66urvXOQZ8+ffDdd9+hR48e8PHxwdu3bxvkPQFqLV9NTEzQp08fDBgwAFZWVkLxJGkL/aiiogJDhw5FYGAgTE1N8c8//0hc/lVJhMPhICMjA6mpqfW2T58+gcPhQEtLC9ra2tDW1q73WltbG5qamhJnHV9aWgonJydwuVwkJibC0NBQ3E2iae/juocPH2LKlCkwMjLCuXPnxNaO0tJSBAUFITAwEAEBAfVyjgNA165dYWdnh/79+/PlYS0peHp64vDhwyguLoa0tDQOHDiAH374ge+czpJKe+9HBMGQm5uL/v37IykpCX379sWBAwck7nklTgQ9rmOz2Th69Ci2bNmCyspKSEtLY968eZgzZ06Lx6U5OTm4dOkSnjx5QhvOfvPNN9i1axfMzMxaVFZJSQmOHz+OQ4cO0dF0NDU1MXv2bDg6OgptnMjlcvH06VOcOHECZWVlkJKSwqZNm7Bt27Z2MTatqqrCkydPkJiYCAaD8dWtU6dOMDExgbGxMTQ0NPgy4ONwOIiLi2sgCPJSq3yOoqIiLVJ3794dpqam6Natm0CMkyV1fsRms2kDonHjxok9xQxBMLTX80pEQgJNexy8L1u2DOfOnUPXrl1x+vTpDhH2pT1TXV2NgwcPwt3dHQDwv//9D3///bdQPJ4+h0yCW8fRo0exdu1aSElJ4ciRI7C0tBR3k74KRVEoLS1FTk4OlJSURJYzq7CwEFevXq1nsamsrIw5c+Zg2bJl6N+//1cH8RRFITg4GNeuXcONGzcahDvU09OjBUFzc3OYmpqK9L4oKYN3iqJQXV0NLpcLFosFFoslNg8niqLg7u6OrVu3wt/fH0Ctx+rs2bMxbdo0KCgoNKuc8vJyXLx4Ebdv3waXy4WcnBx27dqFH3/8UST3SB4lJSXQ0NBATU0Nzpw5AxMTE5HVLUhiY2Ph6uqKFy9eNGr1CtTmIO3VqxcsLS1haWkJCwuLZp8vfpCUftQUFEVh4cKFuHLlClRVVfHff/+1G28hQcLlcuHr60svpqSmpiI9Pb1e7sqWwGQyoa6ujt69e2PlypUSc8zXrl2LDx8+4NixYyI17voa7X1c9+nTJxgYGIDJZOLJkyciE994Id8DAgIQGBiIiIgI1J36MxgM2oAlNTW13mdSUlLo27cvHZbZwMCgTXof5+Xl4c8//4Sfnx8AwMHBARcuXBCb4Y4wae/9iMA/NTU1GDt2LF6/fg09PT0cP36cr0gs7RFBjuuCgoKwYsUKBAUFAag1dNuwYUOrPLPqkpaWhvPnz+PVq1egKApMJhNjxoyBiYkJDAwM0LVrV3Tt2hUGBgbQ09OrN/coKCjA0aNHcejQITr6SefOnTF37lyMGzdOZHPRvLw8HD16FF5eXgCAHj164NSpUxg+fLhI6hc04eHhOHv2LC5fvtwgOlBzUVFRgYmJSaObjIwM0tPTkZGRUe9v3ddZWVlNRmjS1dWlQ9nyNl1dXaEZzUj6/IhAaAsQkZBA095ulj4+PnTi+KNHj5Kk2O0EiqJw48YNnD59GhRFYezYsbh3757QF2bFPQmuqKjAu3fv8Pr1a3h6eqKoqKieB4G2tjZ0dHQavCfO/GTPnz/HxIkTweFwSE6cFpCbm4tnz57h6dOn9SzuHRwccPLkSZiamjb6vZiYGKxatQqvX7+m31NUVMTw4cMxZMgQWFhYiH1SLszBO5fLhZ+fH1xdXZGQkIDy8nKUlZWhvLy8wevy8vIGC/FSUlKQlpYGi8WCtLR0vde6uroYNGgQBg0ahMGDBzc7gfrXSEtLw6pVq/D48WMAtaLTzJkzMWvWrFaH2klISMBff/2FsLAwALUhbO/duyeSxUl/f3/s2bMHDx48gL6+Pi5dutQmF3jrUlJSgmfPnuHx48eoqKiAhYUFevfuDUtLS5iYmIjFEl7SJ8FXrlzBggULwGQycfDgQdjY2Iis7rZAZmYm3N3d8fz5c3z69KnB5ywWCxoaGtDU1IS6ujo0NTWhoaEBKSkpFBcXo6ioCEVFRSguLqb/Ly4urrdIw2KxsGzZMsycOVPslq3Xr1/HqVOnMGDAAHh5eUmMwZ64x3XChqIo6OjoICcnBydOnGixx0druH37Nq5cudLAy1pTU5NeKOzWrRstWFZWViIhIQFxcXGIj4+nQ6/zUFNTQ8+ePdG9e3d609HRaRPPFYqi8PDhQ5w4cQKVlZVQUVFBQEAAunfvLu6mCZT23o8I/LNp0yYcOHAACgoKOHbsmMTlqZYEBDGu43K52LdvH7Zv3w4ulwslJSV8++23mDBhgkBFmYSEBJw7dw7e3t5N7sNgMKCjowM9PT2oq6vDx8cH5eXlAGrzc8+bNw+jRo0S2/jkzZs3+Pvvv5GXlwcAmDJlChYvXoyJEydKrDc7RVFISEiAl5cXvLy88ObNG8THx9Ofa2pqok+fPmAwGLTxDUVR9AaAHicWFRUhPT291cLi58jJycHIyKhebksjIyOR5yCX9PkRgdAWICIhgaa93SwXLFiAK1euYPz48di0aZO4m0MQMD4+Pti9ezcqKysxdOhQPHr0SKjXragnwZWVlfDz84OHhwc8PDzw7t072rusJRgaGmLatGmYOXMmBg4cKLJwRy9evICjoyOqq6tJLqpWwuVyERoaiidPnsDT0xM1NTWQkZHB7t27sX79enpiVVNTg0OHDmH79u2oqamBtLQ0Bg8ejNGjR8POzk5iFmQBwQ/eq6ur4eHhgXv37sHV1RWZmZnCaHYDunbtisGDB9OioZWVVYu89SiKwsWLF/H999+jrKwM0tLScHJywty5c6GmpsZ3+7hcLtzc3PDff/+huLgYampqePToEQYPHsx32Y3V9ejRIxw6dIi2zAWADRs2YNKkSQKvjyD5k+DZs2fj5s2bmDt3LlasWCGyeiWZ8vJyeHl5wc3NDSEhIfT70tLS6N27N3R0dGhhUEVFpcXPS54HfF5eHl6/fo3k5GQAgLGxMdatWydWL/7MzEwsXrwYVVVVmD17Nq5cuSIRYeY6grgxYsQIeHp6YsuWLULP5f3x40c6x5i8vDy9WGhsbNwsAyWKopCTk4P4+HjExcUhKSmpUe8EFRUVOkwZb9PX15fYcJ6fPn3Czp07ERcXB3Nzc/j6+ra56+hLdIR+RGg98fHxMDMzA5vNxq5duzB06FBxN0ki4Xdcl5+fj4ULF9JGhyNGjMCaNWugrq4utDbHxcUhIiIC2dnZ9JaTk4Ps7Gyw2ewG+xsbG2P+/PkYNmyYRIwBSktLcfLkSTx69Ih+T01NDbNmzcKCBQswaNAgsa5d1M19zhMGPw/XLSUlhUGDBmHixImws7Nr8XGtrKxEZmYm7R1Yd8vIyACXy4W6ujo0NDSa3DQ1NaGqqioR51RS50c1NTW4evUqAGDevHkijfBDEB7t9bwSkZBA054G78XFxdDW1kZVVRX+/fdfWFhYiLtJIoPL5aKqqgpcLpf2hmmv4kxYWBh++eUXlJWVYcCAAXj9+rXQ4sq3dBJ8+vTpRr0beeeCFxO+7nsAEB0dDQ8PD3h7e6O6urredzU1NWFtbQ1ra2toaWmhsLAQhYWFyM/Pp18XFBSgoKAAhYWFDURFPT09TJ8+HTNmzMDQoUOFNph79eoVJk6ciKqqKgwePBg7duxoNw9NcZGeno7Dhw8jMDAQAGBjY4MzZ86AoigsX76cXnS2tbXFhg0bJCbM3OcIYvBeUlKCp0+f4v79+3j8+HE9zwNFRUUMHDgQFhYWkJeXh6ysLOTk5CArK9vgfzk5OTCZTHA4HLDZ7Hp/eRubzQabzcanT58QERGB8PBwxMfHN1i4lJeXR//+/WFubg4lJSUoKio2+ZfFYmH37t148uQJAMDMzAybNm0SSq6uzMxMbNmyBQkJCZCWlsbhw4exYsUKgVjJlpeX49KlS/jrr7/ofJdSUlJwcHCAs7Nzkx6vBP6R1EkwDzMzM0RHR+PAgQPo37+/yOqVNDgcDoKDg/H8+XN4eHjUWzQzMjJCnz59YGFhIXBDDoqiEBISAjc3NzpU7qRJk7BixQqxje/fv3+PX3/9FRwOB99++y2OHz8u9rFpRxA3Fi1ahEuXLmH58uWYN2+eUOvasGEDgoKCYG5ujpkzZ/It2rHZbGRkZNBbZmYmsrOzGxUO5eXlYWFhgaFDh2LIkCFCza3cGvLy8vDtt98iNzcXU6ZMwb179yRW1GwpHaEfEVrPrFmz4OLigv79++PAgQPibo7E0tJx3d27d+mc9SUlJfjpp5+QlJQEGRkZrF27FhMnThRV0xvA5XJRWFiIvLw85ObmIjc3F9ra2ujfv79E3veSkpLw7NkzvHz5sp5nnYmJCebPn4/58+eLZE5TWVmJgIAAvH37Fm/fvoW3tzcKCwvr7cNisWBmZoY+ffrAysoKvXr1oq8DQcOLuCMJ4l9zkdT5UVlZGe1VWVpaKrRzRhAt7fW8EpGQQNOeBu9nz57F8uXL0bVrV1y4cEHsCxGthaIoFBQUICUlBSkpKUhNTUV+fj4qKytRUVGBysrKeq8rKioazZ3EEwt5m4yMDP1aXV0dpqam9NalSxeJHMA1RUxMDDZu3Iji4mKsXLkSJ0+eFEo9LZ0ECwJ1dXVaFLSxsUGXLl2afS3zvApCQkLg5eVVL8wHAGhra2Pq1KmYMWMGRo4cybeIR1EUvLy8cPDgQdoib8CAAdi1a5dEebK1ZSiKgpubG44fP46SkhK6n3K5XKioqOC7777D2LFjJfp+19LB++bNm2nhn8vlIiAgAM+fP68ngKurq8Pe3h5DhgyBjY2N0AXpiooKREZGIiIiAmFhYYiIiKDza7QEaWlpLFmyBM7OzkKdgFVUVGD//v20l5+Ojg6+/fZbfPvtt60Skz98+IAzZ87gypUrKCgoAFCbQ3Hy5MmYNm0atLS0BNp+QkMkdRLMa5uysjIoisKdO3eEasUuqWRmZuLRo0dwc3Ort+CkoaEBKysr9OnTRyShn8vLy+Hu7k4bkXTq1AnfffcdRo8eLZbnxKtXr7Bnzx5QFIVVq1bh+PHjYh1vdgRxY/v27di9ezemTJmCdevWCa2ekJAQrFu3DkwmE2vWrEGnTp2EUg+bzUZ2djYyMzNp8TArK6uB14qlpSWGDRuGoUOHSozRVGRkJNauXYuamhps374dO3fuFHeTBEJH6EdtHd7Sm6jv+35+fhg4cCAYDAZOnToldKGFzWajoKAAeXl5UFFRgZ6enlDrEyQtHdc1hp6eHnbs2NHuQhqLCg6Hg5CQEDx//hxeXl6orKykPxs4cCAWLFiAMWPGoFOnTlBRUWm1wWVNTQ1KSkpQUlKCsLAwWhT08/NrYODNy33eu3dvWFlZwczMTGgG8e0BSZ0ftVcxqT2QkpLSrLC7mpqaDfK6ttfzSkRCAk17GrwPHToUb9++xcqVKzFnzhxxN+ersNlspKen00IgTxRMSUlBaWmpSNsiJycHExMTdO/enRYOjYyMJFroCQgIwM8//wyKonD58mXMnz9f4HW0dBJsbW3dYOG/bnz4xt6jKAoaGhq0MGhgYCCwCV11dTUCAwPh5eUFb2/veqKGmpoaJk+ejMmTJ2Ps2LEt6v9sNhu3b9/GwYMHaS83BoOBUaNGYePGjRJ93bRV8vPzcezYMXh4eACozVX4/fffCyRMpbARxCQYALp06YIhQ4Zg6NChMDc3F+tCM5fLxadPnxAWFoacnJx6RhtN/e3WrRvWrFkjFO/Bptp49+5d3Lx5kx4Is1gszJkzBz/88ANsbW2/+P2SkhLcuHEDZ86cwfv37+n3O3fujJkzZ2LixIlizX/a0ZDUSTAA+Pr6YtCgQdDQ0MDt27dFUqckwOFw4OfnhwcPHsDPz49+X05ODpaWlrCysmqRoY8gSU5OxqNHj+i+b2Njg3Xr1okkT+nnPHv2DAcOHABFUViyZAlOnz4tNiv1jiBu8IwmBwwYgP379wulDoqi8OOPP+LDhw+wtbWFo6OjUOppCi6Xi5ycHMTFxSEyMhJpaWn1Pu/RoweGDRuGYcOGieWar8uzZ8/wxx9/AADu3LmD6dOni7U9gqAj9CNJpaSkBElJScjKyvrilp2dDUVFRfTt2xe2trawtbVFv379YGxsLLRnUnp6OqZOnQp/f3+MGzcOv/zyC99lZmdnIzIyEnl5ecjLy0N+fn6910VFRfXm2EZGRhg6dCiGDh0KExOTdmVEaWRkVG/u06NHD3z33XcizwHXXqmoqMDbt2/h7u6OwMDARj3YZWRkoKKi0ugGgM4bXVJSUu9vXfHxc9TU1NC7d296MzU1bVOefOJGUudH7VVMauukpKTA3Ny8niNFUygoKCAyMrKeUNhezysRCQk07WXwHhcXh+7du4PJZOLGjRsS49VQUVHRINZ3Wloa0tPTkZWV1ejgg4eamho0NTWhoaEBFRUVyMjI0BvPM/Dz/xkMRoMQerzQeXVfFxUV0Za4jVnjArWiT58+fbBw4UL07dtXmIep1Vy4cAEXL16EnJwcAgIC0KtXL4GW39JJ8KNHjyT2QcFms2kPw7dv39LeQECtcDB8+HBMmjQJkyZNatLqs7i4GGfPnsWRI0eQkpICoHawPH78eMycOVPsCzEdAZ53iLW1tVjb0RJaOnifMGFCPc9ALS0t2Nvbw9DQUKIn+5IKm82Gl5cX7t69i/DwcPr9wYMHY+3atZg2bRp9vCmKgq+vL86cOYNr167Rk1oWiwV7e3tMnDgR/fr1I5NXMSCpk2AAOHHiBL777juhihKSRF5eHp48eYJHjx4hOzubft/Y2Bh9+/ZFz5496fyx4oTD4cDHxweenp7gcDhgMplYsmQJ5s6dK3IjixcvXmDfvn3gcrmYO3cuLl68KJZj1BHEjRcvXmDMmDHo1q0bLly4IJQ6goKCsGHDBjCZTKxdu1bsx6ioqAhRUVGIjIykc3PyMDQ0xKRJkzB9+nSxjSH+/fdf3L59G3JycvD39xdrvlBB0BH6kSTAZrMRFhYGPz8/vH//Hn5+foiIiAA/y2pqamro168f+vXrR4uH3bp146tvVFRU4NChQ/j9999RWVkJOTk5XLx4Edra2q0uEwA8PT2xe/duOgRiUzCZTCgqKqK0tLTesdHT06MFQ3EbFzZGS8d1krzO0N7Iy8vDy5cv8fLlS6SmpqKiokIg5crIyEBHRweWlpbo06cPevfuDT09PTK/5QNJnR+1VzGprRMUFIR+/frhypUrMDc3b3K/yMhIzJ8/H4GBgfXWwtvreRXHeE38M2VCu+bixYsAanNziVMgrKysxLVr1xAcHIy0tLR6QkxjSEtLQ1NTkxYD675u7eJJS7/H5XKRl5dHh/HJyspCRkYGKioqEBoaig0bNsDa2hpLlixBnz59WtUmYbFgwQKEhYUhMDAQM2fOhL+/P7GmawIWi0VPBteuXYuPHz/i3bt3ePfuHVJTU+mB8Lp169CzZ084Ojpi0qRJGDJkCDIzM3H06FGcOHECZWVlAGpDmE2dOhVOTk5CCzFFaEhbEgdby+rVq9vNgEsSYLFYcHBwgIODA6KionD37l14eHjAx8cHPj4+0NfXx3fffQd5eXmcOXOmnpDYtWtXODo6YsyYMW3Ca5UgHnjGCyYmJuJtiBChKApBQUF4+PAhvLy86IVIeXl5WFtbo1+/fhKXE01KSgpDhw6FpaUlnjx5gri4OJw9exYpKSnYuHGjSPMGjx49GtLS0ti9ezeuXbuG6upqXLt2jeQuFgI8i+esrCxQFCXwxUeKonD+/HkAQL9+/SRC/FFVVcWAAQMwYMAAlJaWIjo6GpGRkUhISEBSUhKOHTuGqKgosUW7+Pbbb5GQkICgoCA4OTnB39+/Q4ZlJjQNRVFITU2Fn58fLQoGBgY26u2goqICNTU1qKurQ01NrcHGe7+4uBjR0dGIiYlBTEwM4uPjUVBQgBcvXuDFixd0ed26dcOyZcuwdOlSdOnSpUVtdnFxwc8//0wbj1pYWOCHH37gSyCkKAo3b96k04loaWlBU1MTSkpKUFZWbvBXQUEBDAYDFRUViImJQWRkJOLi4pCeno6bN2/i5s2b0NDQgL29PYYNGwYrKyuJMOQhSC4aGhpwdnaGs7MzgFqjq4qKCpSXl6OsrKzRv0Ct55GCggIUFRUhLy8PRUVF+j0FBQVy3REIEoK5ubnEOsJ0FIgnoYTSHiz8uFwuDA0NkZqaim3btsHBwUEs7YiMjMS+ffuQmppa7315efl6A/a6f5WUlCTScoiiKBQWFsLX1xcBAQG0x6OtrS2WLFkCCwsLMbfw/ygoKMDKlSuRm5uLOXPm4OrVqwI7pu3Jk/BLfPr0Cb6+vnj37h1CQ0PrWWyqqKigrKyMfq9r16745ptvMGbMmFbH5Sd0LIilrOSRl5eHBw8e4OHDhw2MWWRlZTFixAg4OjrC0tJSIp9RHRFJtZQFanPRvn//Htu3b8fIkSNFUqcoqKysRFRUFD5+/Ah3d/d64zsDAwPY2trCwsKiTSz6UBSF4OBgPHr0CBRFwdbWFjt37oSCgoJI2+Ht7Y2dO3eipqYGU6ZMgYuLi0jHEh3BA6qyspIOBe3q6irw9vv7++Pnn38Gk8nEjz/+CGVlZYGWL0gqKioQEhICd3d3UBSFPn36YPfu3WI5p0VFRfjf//6HjIwMjB49Gk+fPm0T947G6Aj9SFCkpqYiNjYWubm5yM3NRV5eHv368/95hph1UVRURM+ePWFubk5vrRWYa2pqkJiYiJiYGERHRyM6OhqJiYl0RCEmk4mJEydi5cqVmDBhwhevT39/f/z444/w8fEBUJv3fuXKlXBwcOBr3Mhms/H333/Tue779++P8ePHt9gLsLq6GnFxcYiKikJMTAyqqqroz9TV1fHNN99g8uTJYp1vkPkRgcA/kjo/qutxFhkZ2azwlo3lwCMIFp4n4ecegs3dj3gSCg4iEkoo7WHw/vLlS4wePRpKSkq4c+eOyC1Ea2pqcPnyZVy5cgUURUFZWRkjR46Ejo4O1NTU2nzOpqKiIrx58wZBQUG05fyAAQOwZMkS9OzZU8ytq+Xjx4/48ccfweVycfz4cfzvf/8TSLkdRSSsS2lpKQIDA/Hu3Tv4+vqiqKgIQK0Hm7OzMwYMGCBx4VoIkg2ZBEsu1dXV8PDwoIWDsWPHwsHBgXhkCwGeJ1pMTAxteczbysvLUVpaivLycvr152HAec9fSZsEs9lsKCsro7KyEpcuXWrTYadzcnIQHh6OsLAwhIeHIzo6ul7oMhaLBWtra9ja2kJHR0eMLW09sbGxcHFxAZvNRo8ePbBv3z6RezS9f/8e27ZtQ3V1NSZMmIA7d+6IbKzcUcSNzp07IysrC6dOnUL37t0FVi5FUVi9ejUiIyMxYMAAjB8/XmBlC5P4+HjcvHkTNTU1MDAwwP79+6GnpyeWdnz//feorKzE+vXrcejQIZG3QRB0lH7UGqqrq+Ht7Y2nT5/iyZMn9aIzfA0pKSkYGxvXEwQNDAyEOu+qqqqCl5cXHj16hA8fPtDv6+npYdmyZVi2bBm6detGv5+WloZff/0Vly5dAlCbg3fOnDlwdnaGnJwcX20pLS3Fzp07ERAQAAAYP348BgwYwFeZQO04JSkpCZGRkYiKiqIX62VkZODs7IwZM2YILSoORVHw9/fH1atXGxiS84yyyfyIQGg9bUEklJeXb1bI2sZy4BEECxEJG4eEGyW0K3j5NhwcHEQuECYmJmLfvn2IjY0FAFhaWmLixIltXhisi6qqKh120tPTE6GhoXQoFHt7eyxevLjJHHaionfv3li5ciX+++8/rF27Fv3794etra1Y29RWUVJSwvDhwzF8+HBwOBzExsZCTk4OhoaG4m4agUAQMDIyMhg3bhzGjRsn7qa0a9LS0vDPP//Az89P3E0ROLGxsXQOInEsuvNDSkoKAgMDaWEwKyurwT7KysowMDCAsbExLC0t27wHfffu3bF48WJcuXIFMTExWLNmDQ4cONCiEHP8Ymdnh71792LLli14+vQpJk+eDFdX13Yz0ZYEunbtSucdF6RI6Ofnh8jISEhJSWHIkCECK1fYmJiYYNmyZbh27RpSU1OxbNkyHDp0SOSRUUxMTPDLL79gx44dOHz4MKytrbFgwQKRtoEgeNLT02lR0N3dHSUlJfRnTCYTBgYGUFVVpTcVFZVGX2tqaor8GSMrK4sxY8ZgzJgxSElJwePHj+Hm5ob09HTs3r0be/bswdixY7FixQpERERg7969dL7qsWPHYvny5QJJ9ZKZmYnNmzcjKSkJLBYLM2fObJYxMpvNxqdPn5CUlAQZGRlYWVk1eJawWCyYmprC1NQUEydOxMePH+Ht7Y3c3FxcuXIF169fx5QpU+Ds7IzOnTvz/Vt4BAcH49y5cwgLCxNYmQQCoW0gKysLFxcXJCQk4Jdffml2Drzc3FwiEkowvPPKe01oPUQkJAiF0tJS3Lp1CwBEbs2alJSEFStWgMPhQFZWFpMnT0avXr1E2gZR0qlTJzg5OWHo0KHw9PSkB9je3t4YNWoUfv75Z7Hk+eDh7OxMt8nZ2RkRERF8WzR2dKSkpGBmZibuZhAIBEKbpKqqCteuXcOVK1fA5XLBZDJhbm4OBQUFyMnJQVZWFrKysvVe8/6XkpJqUNaxY8fE9EuaJjAwEEDt4vfnbZY0KIpCUlISvLy84OnpicTExHqfMxgM6OjowMDAgN5UVVXbXcjdLl26YPny5bhy5QrS09OxfPly/Pfff/W8RYRNv3798Mcff2Dz5s14+fIlJk+ejCdPnpBxm4Do1q0b/P39kZmZKdByeTng7ezs2pzHuY6ODpYvX47r168jIyMDP/zwA44ePSpyoXD48OFYsGABLl++jGXLlsHe3h7GxsYibQOBf7y8vHD37l28fPmygQjUqVMn2NnZYcCAAbC1tW0zXpRdu3bF//73Pyxbtgxv377F48ePERQUBDc3N7i5udH79erVC6tXr/7igndLKC0txZo1a5CbmwslJSXMmTOnWUZHKSkpcHFxqRem1d3dHUuWLGlykV1KSgrW1tawsrJCVFQU3r59i/T0dNy7dw+urq44cuQIevfuzfdvunr1Ks6cOUPX2b9/f1hZWdXzCq2ursbZs2f5rotAIEgeLBYL33zzDYKCgvDLL790qBx4KSkpyM3N/ep+bTG8Ku+8EviHiIQEofDmzRtUVVWhc+fOIhczXr9+DQ6HAz09PcyePVuic3IIEnV1dUybNo32LAwPD8fLly9RVlaGXbt2QVpaWiztYjAY+OWXX7BkyRIkJibiv//+w48//iiWthBER0VFBSiKEnleJQKBQPgSfn5+OHr0KNLT0wHUimgTJkyAhoZGq8oT17P1a5w/fx4AYGVlJeaWNA5FUYiPj6eFwZSUFPozJpMJQ0NDGBgYoGvXrujSpUuHsQrV0NDAsmXLcPXqVWRmZuKnn37C0aNHoaurK7I2WFlZ4eDBg9i4cSM8PDwwb948uLi4SLzY3BbghYoXpPFefHw8oqKiwGQyYW9vL7ByRYmysjIWL14MFxcXxMfHY/fu3Th9+rTIBc/FixcjNDQUHz58wM2bN7F582aR1k9oPaGhodi0aVM90YzBYMDMzAwDBgzAgAED0KNHjzadmkFGRgYODg5wcHBAWloanjx5gufPn0NOTg5LlizByJEjBWo84+rqitzcXKipqWHRokVQVVX96nfCw8Nx+/btRj+7e/cuvv322y8anTAYDJibm8PMzAyJiYl48uQJ8vLyEBkZKRCRMDo6GgDQo0cPTJo0qdF1orp5EgkEAqE9kJKSAnNz82blYCThVTs2RCQkCAVPT08AtfnSRG3p/fHjRwCAjY1NhxEI66KlpYWZM2eib9++uHbtGnx9fbFr1y789ttvX0x0LkyUlJSwePFiHDx4EHv37sWyZcs65LnpCPCsR58+fQoAGDp0KBwdHdGvXz+RTMwrKiqQmZmJ0tLSeltZWVmD/83NzbFo0SKy8EkgdACys7Nx7NgxvHnzBgCgoqKC8ePHw8zMrN15pH38+BGvXr0Ck8nElClTxN0cGoqiEBMTQwuDaWlp9GdMJhOmpqawsLBAjx492lV4+JaipKSEBQsW4MKFC8jJycGGDRvw999/CyR0XHOxsLDAnj17sGnTJty9exdr1qzB8ePHRVZ/e4TL5cLf3x8ABObpAwDPnz8HULvo3ZZDw8rIyGDmzJk4deoUMjMz8eeff2LHjh0ivT8zmUyMGjUKHz58wMOHD4lI2AZISUnBtm3bcPnyZVAUBRaLhdGjR2PgwIGwtrZulrDVFunSpQtWrFiBFStWCKX8qqoq3LlzBwAwYsSIrx5HiqLg4+ODFy9eNLlPUVERnj17hqlTp361fgaDAWNjY3Tt2hV5eXkCE+5490gDAwOyFkEgdEDYbDbu3buHhISEFn0vMjLyq/tIsgdebm4uysvL2214Vd55BYBp06aJbd27PUCOHEEo8ERCUVuwczgcREREAKgd/HVkjI2NMWfOHFy7dg1v377Fnj17sG3bNrEJIuPGjcONGzfw6dMn/P3339i6datY2kEQPBRFISQkBC4uLvD19a33maenJzw9PaGjo4MJEyZg/Pjx0NHREXgbEhMTcffuXTx79gxsNrtZ3wkICEB2djZ+/vnnNm1ZTCC0BSiKEosYx2azcfv2bZw9exZsNhsMBgODBg3C8OHDxRqKW5j8/fffAGqNNIRxv20NJSUl2LRpU71JNpPJRI8ePWBubo6ePXt2GG/B5qCgoIAFCxbg/PnzyMjIwMaNG/H333+LdMHbxsYGW7Zswc6dO3HixAkMGDAAixYtEln97Y24uDgUFhZCRkYGRkZGAimTw+HQi/KS6jXcEuTk5DBjxgycPXsWXl5eePjwocgNHQYNGoS//voLvr6+yM7Ohra2tkjrJzSPgoIC7Nu3D0eOHEFNTQ0AwMHBAcuWLWtzeXglkadPn6KgoACqqqpfTdvC5XLx9OlTBAQEfLXc0NBQdO/evdmpYHjRGgQlEvIi3BBvQQKhY1JVVQVnZ+dm76+pqQkFBQXMnz//q/u2BQ+89hpete55LS0tJSIhH5AjRxA4ZWVltKWstbW1SOuOi4tDZWUlZGVlyaQOtWHUZs+ejRs3bsDT0xMsFgubN28Wi1DIYrGwZMkS7N69G/v378d3330HdXV1kbeDIDjYbDZev34NFxcXxMbG0u+bmZlh0KBBkJGRQXBwMD58+ICsrCxcuHABFy5cgJ2dHSZOnIjBgwfzFaqPw+HA29sb9+7dQ0hICP2+vLw85OXlIScnR2+8fGK81zU1NXj16hXc3NygoKCANWvWtDtvIgJB3NTU1ODp06e4ceMGZGVlsWvXLpEa8Hz8+BGHDx9GUlISgNq8Po6Oju16fJCTk4NLly4BAGbMmCHm1tRSXV2NrVu3IjIyElJSUujZsyfMzc3RvXt3Igx+AWVlZSxcuBDnzp1DcnIyNm7ciMOHD4s0BOPw4cOxZMkSnDt3Dt9++y3s7OwE6gXXkXj//j0AoHv37gJbvAgMDER+fj7k5eXRvXt3gZQpbrp06YLRo0fD3d0df//9N3r16gUTExOR1a+lpYUePXogJiYGjx8/xpIlS0RWd3uguroaCQkJiI6OhpSUFKysrKCvry+wMXZlZSX+/fdf7Ny5EyUlJQBqDRpWrVqFnj17CqSOjg6Hw8HNmzcBAIMHD/7iukF1dTVu375dbx74NW7fvg0DA4Nm5YTk3Surq6ubXf6X4HkSEpGQQCA0h65duyIyMvKrufzaqgcegfA5RCQkCBwfHx9wOBzo6Oigc+fOIq2bF2q0a9euZMH//9O9e3d88803cHFxwcuXL8FiscTmOTVixAhcvXoVCQkJOHDgAPbv3y/yNhD4p7S0FI8fP8adO3eQk5MDoDb5u42NDQYOHFgvt9eECRMwevRoREVFISgoCElJSXj//j3ev3+PTp06Ydy4cRg6dCj09fWhoqLSrH5bVFSEx48fw9XVFdnZ2QD+L++InZ0dunXr1qxyVFRUcO/ePdy7dw+KiopYtmxZK48IgUCoS01NDZ49e4arV68iKyuLfn/FihX4448/ROLx8vr1a+zatYvOjTpmzBhYWVm1+7HBqVOnUFNTg549e8LS0lLczQGHw8Hvv/+ODx8+QFpaGsuWLZMY78a2QKdOnbBw4UKcPXsWsbGx2Lx5Mw4cOCDScKxz585FaGgoAgMD4ezsjPfv33focLCthScSClJk5eVfs7S0bFeh0wcNGoSkpCTExsZi165d+O+//0R6zQ0ePBgxMTF48OABEQkbgaIoZGdnIzo6usGWkJAADodTb391dXVYWVnB2tqa/mtubt4ib34ul4tr165h69atSE5OBgAYGRlh1apVsLOza/fPdlHi4eGBzMxMKCgowMbGpsn9SkpKcP36dWRkZLS4DldXV8yfP/+r540nEgrak1BQoiOB0JHJzc1FaGgoQkNDkZ+fX++z5kZ2agt07dqVCH+EDgMRCQkChxdqtE+fPiKvOywsDAAJNfo5ZmZmmDlzJm7fvg03NzewWCysX79e5EIhk8nEsmXLsGXLFhw5cgRr166Frq6uSNtAaD0UReHSpUu4evUqHdpHUVERdnZ2sLW1pSdenyMtLY3evXujd+/eyM/PR3BwMEJCQlBYWIibN2/S1qpKSkrQ09ODnp4eunTpUu+vhoYG4uPjcffuXTx//hxcLhdA7WSvb9++sLW1bXEYtj59+qCqqgpPnjzBlStXoKioiNmzZ/NxhAgEyaOiogIRERH4+PEjwsPDoaioCCcnJ6HkDGaz2XBzc8Ply5dpcVBJSQmDBg1CREQE0tLSsH79evz8888YN26cQOuui7u7O/bt2weKotCrVy84Ojp2CFGjuroa//77L4BaL0JxL5pSFIV///0XXl5eYDKZmDNnDhEIW4GmpiYWLVqEc+fOISwsDFu2bMG+fftE5oUpJSWFX3/9FcuXL0dYWBh+/PFHnDx5UiR1tyd4IqGZmZlAyisrKxNbegdhw2Aw4OTkhJMnTyIlJQVHjx7Fpk2bRFb/4MGDceHCBTx79gwlJSUkdxlqvdTv3buHW7duwd/fH0VFRU3uKycnBwMDA3A4HCQlJSE/Px8eHh7w8PCg95GWloaFhQWsra2hra2N8vJylJWVNbkVFxejsLAQQO09cenSpRg7dmy7EsclAYqicP36dQDAgAEDmoz4kp2djWvXrn3xOvgSCQkJ8PPzw8CBA7+4H69+QYl6JNwogdB68vLyEBISgpCQEISGhiI1NVXcTZI42nruQh7t5XcQWg4RCQkCR1wTVoqi6nkSEupjYWGB6dOn4+7du3j8+DFYLBbWrl0r8kXEQYMGwcLCAhEREdi7dy/++ecfkdZPaD0uLi64cOECgNpwTIMGDULv3r1bFDZLXV0do0aNwsiRIxEbG4uQkBCkpaWhpKQEpaWliImJQUxMTIPvSUtL08IkAOjq6sLOzg6WlpZ8he3q378/qqqq8PLlS5w8eRIKCgoiz39DIAiSvLw8hIWF4ePHj/j48SNiY2NBUVS9fTw9PWkv8xEjRvAV9hf4P3HwypUryMzMBFBrQDBkyBD069cP0tLS6N+/P+7fv4+IiAjs378faWlpWLJkicCfQU+ePMGff/4JoDbk+eTJkztMztHbt28jIyMD6urqGDFihLibgxs3btRLIi+oPGwdkc6dO2PhwoW4ePEigoODsW3bNuzZs0dkeTXV1dWxZcsWbNy4EadOncLIkSOJUU0LqK6uRnBwMADBiYSenp7gcDjQ1NRslznYFBUVMX36dFy6dAnPnj2DjY0Nxo4dK5K6TU1NYWBggNTUVFy4cAFr1qwRSb2SRm5uLu7duwcXFxe8evWKNtADaoVcHR0ddO3aFQYGBvU2TU1N+tleXV2NpKQkxMfHIy4uDnFxcYiPj0dZWRntgdJcFBUVMXfuXMyYMaNNhaqmKAqpqan48OEDPn78CCaTCRsbG/Tt2xeampribl49/Pz8kJCQABaLhf79+ze6T2VlJU6cOMF3XW5ubjA2Nv5iCHgSbpQgSfDugR1hXsHhcJCdnY3IyEhaGGxMFNTV1UW3bt0a3MtqamroaAcdgfaSu7C9/A5C6yEiIUGgVFRUwM/PD4Do8xFmZGQgPz8fTCYTXbp0EWndbQVLS0twOBzcv38frq6uYLFYWL16tUiFQgaDgeXLl2P9+vU4fvw4xowZQ0SZNsD79+9pz4ExY8Zg0KBBfF03TCYTPXv2pPOH1NTUoKCgAPn5+cjPz6dfFxQUoLCwEDU1NWAwGOjVqxfs7OwEmt9kyJAhqKyshLe3N/766y8oKChg9OjRAimbQBA2HA4HXl5e8PPzw8ePH5Gent5gn06dOqFr167Q19dHdnY2goODERsbi7179+LUqVOYPn06Jk2a1GJvCTabjefPn+PKlSt0uClFRUXY29vD1ta2nvgoLS2NmTNn4uXLl/D29sbly5eRlpaGTZs2CUzoePDgAf766y8AgK2tLSZOnCh2bzpRQVEUjhw5AgCYOnUq38Ivvzx//hynTp0CAIwbN04iQp+2dfT19TF//nxcuXIF/v7+2LlzJ3bs2CGyc92vXz/MmzcPV65cwbJly2BrawtTU1OR1N3WCQ0NRXV1NVRUVAQm6D1//hxAbVSE9nqfMzQ0xLBhw+Dp6Yk///wTZmZmIlmMYjAYmDZtGo4ePYp//vkHq1ev7hCLwkCtoRFPGHz58mU9YbBHjx4YMWIE+vfvDwMDg2YJdTIyMujRowd69OhBv0dRFLKysuoJhnJycg3yiX++de7cuU1EBeByuUhISMCHDx/oraCgoN4+z549AwB069YN/fr1Q79+/WBlZUWLWOKAoihcu3YNQK0RZVPHmucVLQju3r2L5cuXN2nwKaxwo0QkFC4cDgdxcXG0IQAv5PyQIUMwfPhwWFtbS7wXMJfLxadPnxAdHY2YmBhER0cjNjYWNTU10NTUhKamJrS1taGtrU2/1tLSgra2NtTU1ATyzKiurkZkZCRCQ0ORk5MDNpvd5FZTUwMOhwMZGRm6fRoaGg1e171vczgcZGVl4dOnT0hLS0N6ejr9OiMjo9GQoTxR0NDQEN26dYOcnFyjba+qqupQImF7yV3YXn4HofUQkZAgUHx9fekHp6itWnlehHp6enx5FrV3rKyswOVy8eDBA9y5cwcsFgurVq0S6QKDjY0Nxo8fj2fPnmHmzJlwd3fH8OHDhVbf+fPn6y2i6enpYdSoUU2GxyTUJzU1FVu3bgVFUbCxseFbIGwMaWlpeqD9ORwOB0VFRZCTkxPaORs1ahSqqqoQEBCAvXv3Ql5eHvb29kKpqzVwOBwUFhbSImpeXh79WkdHB46OjlBSUhJ3M9slJSUlOH78OD59+gQjIyMYGRnBxMQExsbGYj3mHA4HL168wNWrVxtYdvKs+3mbiopKvc9HjhyJwMBAvH//Hrm5uTh16hTOnTuHyZMnY8aMGU0a2pSVlSEqKgqRkZGIiIhAeHg4iouLAdSKg4MHD0b//v2bFC0YDAZGjx4NdXV1PHr0CK9evUJ2djZ2796NTp068XU87ty5g2PHjgGoDZE1bty4Zt2nKIpCbGws8vPzoaysDGVlZaioqEBZWVniFzDq8vTpU/j7+0NaWhqTJ08Wa1v8/f3pnMODBg36ajgxQvPp1q0b5s6di6tXr8LHxwd79uzB9u3bRXatLl68GKGhofj48SNmzZoFHx+fNuXRIy7q5iMUxPgpMzOT9sASR3oHUTJs2DCkpKQgMTERO3bswPHjx5tclBQk48ePp3OBPn36FI6OjkKvU9Ts37+fPpZcLhe+vr5wd3evJwx2794dI0aMwPDhwwVmhMtgMNC5c2d07twZQ4YMEUiZ4oCiKJSVlSE3Nxd5eXmIi4ujRcHS0tJ6+zKZTBgYGKBr167gcrlITExEeno6kpOTkZycjLt374LBYMA92+JdAAEAAElEQVTCwgJ9+/ZFv379YGFhIVKDn6dPn9Kejk09t6uqquqFjeWXrKwseHh4YMyYMY1+Luhwo8STUDjwREGex9nHjx9RVlbWYL+HDx/i4cOH6NSpE4YMGYIRI0ZIhGDI5XKRnp5O51aNiYlBbGwsysvLG90/KysLWVlZCA8Pb/RzKSkp6Orq0vNGY2NjGBkZoUuXLl/8rdXV1YiKiqKPY3h4uMDzZyorK0NTUxM1NTXIyMhokEP289+hpaUFQ0NDGBoaomvXrm3CUENctJfche3ldxBaB1FSCALFy8sLgHisWkmo0eZjY2MDDoeDx48f4+bNm9DR0cG0adNE2oaffvoJxcXF8PHxgaOjI96/fw8LCwuh1HXnzp0G7/3777/YtWsX7OzshFJne6GwsBBbtmxBTU0NDAwM4OjoKPK+LSUlBXV1daHWwWAwMHHiRFRXV+PDhw/Yvn07/vnnH6Fdk01RVVWF4OBg+Pn5ITk5GQUFBSgoKEBxcXGDkJF1OXv2LL755hvMnDkTampqImxx+yY/Px/r169HcnIygP/Le8tDX18fZmZmMDc3h5mZGUxNTYUe/o+iKPj6+uLEiRO0OCgvLw8bGxsYGRlBX1//qwuoCgoKGDp0KAYNGoSwsDD4+voiKysL9+7dw/379/H9999j+vTp4HK5iImJgZ+fH/z9/REREdHgOlRQUKA9B5v72/v27YtOnTrhxo0bCAsLw7fffosNGzY0GdrqS3C5XFy8eBGXLl0CANjb22PUqFHNuk9VVlbiwYMHjeZdYLFYMDMzg6WlJUxNTcW+gPE1tm3bBgCYPn0634IrP+Tk5ODXX38FRVGwtLRscuGP0HqMjIwwe/ZsXL9+HV5eXvjvv/+wevVqkdQtJSWFbdu2Yfny5QgKCsLvv/+OXbt2iaTutgwvyoqgQo2+ePECQO210NJ8zG0NJpOJ6dOn47///kNiYiJOnTqFH374Qej1ysvLY+LEibh16xaOHDnSLkXCffv2Nfq+qakphg8fjhEjRkBfX1/ErZI8qqurkZaWhtTU1AZbSUlJo9+RlpamF1q7deuGLl26NDBirqioQGJiIhISEpCYmIj8/HyEh4cjPDwcly9fhoqKCiZPngwnJydoaWkJ9TcmJCTQkRhGjhzZwLiMR0vCwzYXHx8f9OnTp9GcxTxvLEF7ElZWVgqkvI4Gh8NBTk4OMjMz6S0qKgofP35sIKjJysqia9eutMdZZWUlwsPDERUVhcLCQjx69AiPHj2Curo6JkyYAEdHR+jq6orst1AUhZiYGLx48QKvX79u1HOKxWKhc+fO0NPTg56eHnR1dSEnJ4fi4mIUFRWhuLi43lZUVITS0lJwOBx8+vQJnz59wps3b+jytLW1sWLFikbnKe/evcOBAwfo3Ks8FBUVYWhoCC0tLUhJSUFKSgpMJpN+Xfd/JpOJmpoalJSUoLi4GKWlpfTrkpISsNlslJSU1LtvsVgsqKmpQV1dvd6moaEBZWXldudFLyMjg/PnzyMpKQk7d+4Ud3MIAoJ3XnmvCa2HiIQEgcKzlBVHWClero9u3bqJvO62iK2tLaqqqvDixQtcuHABDg4OIl1okJKSwvbt2/Hzzz/jw4cPmDp1Kt6/fy+Uxc3+/fvTEzMul4u4uDjk5eVh06ZNWL58OebOndtuQzXxQ2lpKTZu3IjU1FSoqKjA2dlZ4hfK+YHBYMDJyQmVlZWIiYnBrl27cPr06RaHYGwpWVlZ8PX1ha+vL/z9/Zu06GMwGFBUVISSkhK9KSgoIDY2Fjk5Obh27Rpu3ryJyZMnY9asWejcubNQ2y1quFwudu3ahcrKSmzZskXo56WoqAgbNmxAcnIyVFRUMHz4cBQUFNDWo8XFxfQEkLdgy2QyYW1tjRkzZmDgwIECn1glJyfj33//hb+/P4DaxY7BgwfD1ta2Vd48LBYL1tbWsLKyQmJiInx8fBAfH49//vkH79+/R1RUFIqKiup9p1OnTtDX10eXLl2gr68PXV3dVt0XjI2NsWLFCly/fh1ZWVn4+eefMXbsWHz33XfNfhZVVFTgjz/+oHMhDxs2DCNGjGjW/TwjIwO3bt1qEAKMB5vNRlhYGMLCwiAnJwcLCwv07t0bXbt2rVf+l8R7UREXF4egoCBISUlhzpw5Ym3L69evwWaz0blzZzg5OZFnq5AwNTXFjBkzcOvWLdy9exezZ8+GhoaGSOrW0tLCd999h/379+Pu3btEJPwKHA6HDrnVu3dvgZQZFBQEACI3ZBIXSkpKmDZtGq5cuYL79+/DyclJJPO96dOn486dO3jx4gVCQ0NhZWUl9DpFyfjx4+t5quno6GDYsGEwMDAQY6tEA8+LprCwkN6Kiorov7zXhYWFyMvL++KzXk5ODkpKStDQ0KBFQV1d3a+OAeXl5WFhYUH348LCQlo0TEhIQHFxMa5evYpr167BwcEBM2fOFJihQV2ys7OxadMmsNlsGBoaYvDgwU3uWzc/vCBpaqzA89QSlBdrTk4OABBvqC9QXl6OxMREfPr0iRYCs7KykJGRgezs7HqexnWRlZVFt27d6FCUnTt3btAHTExM4OjoiKSkJFowzM/Px9WrV3H16lX0798f06ZNw8CBA4U2fkxOTsbLly/x6tUrpKWl0e/zBEFdXV1aENTS0mq0H6uoqDRpQMHlclFSUoK8vDxkZWUhOzu73vb777/D1dUV33//PXr27Ak2m40zZ87g5s2bAGrndjzPPUNDw3o5XvmBoihUVlbSIiGTyaSFwI40VpeWlsbixYsRFBRERMJ2BO+8EviHiIQChqKoDnWT/RyeUNe9e3eR1suLoc1kMmFoaCjSutsygwYNomOc//vvv/j1119FWr+srCx27tyJb7/9FrGxsZg7dy4ePnwocCFq1KhR9RbQ2Ww2nj59iqCgIJw5cwaxsbHYtGkTmTDUoaKiAps2bUJcXBzk5OSwcOHCDhHOkslkYtq0aTh58iSysrLw559/YufOnQK9r3M4HISHh9PCYGJiYr3PVVRU0L17dxgYGNBioKKiIhQUFBqdqIwePRrR0dF4+/Yt0tLS6Jyjo0ePxpw5c2BkZCSwtouT6OhoWgxav349/vzzT6F5TJWUlOCnn35CUlISlJWVsWjRogberOXl5UhPT0daWhq9lZeXIygoCEFBQTAwMICzszPGjh3Lt0VbcXExLly4gPv374OiKDoU1LBhwwQS6o/BYNChcF68eAEfHx/a80VWVhbGxsYwMTGBqampQI1JtLS08O233+Lly5d4//49nj9/jufPn0NfX58O6WpiYgITExPo6OjU64c5OTnYsmULYmNjwWQyMWnSJNjY2Hy1ToqiEBgYiMePHze7nZWVlfR5lUR4v6VPnz5i9yp6+/YtgNq81CT0u3CxsLCAgYEBUlNTcefOHaxcuVJkdQ8YMABA7QJufn6+0L392zJ+fn7Izs6GoqKiQEKDcjgc2vu5I0VPMTExQc+ePREdHY1Tp07h999/F3qdnTt3xvDhw+Hh4YE///wTV65cEXqdouT7778Xaw48cREeHo4dO3Z8NedSXXh5vjQ0NOjcXhoaGlBXVxeY10KnTp1gY2MDGxsbcLlcREdHw9fXFykpKXj58iVevnyJXr16YebMmRg6dKhA5sulpaXYtGkTcnNzoampCWdn5y+Km2ZmZrRhnKCYOHFio+kmMjIyEB0dDQaDgdmzZwukLp7oSDxka58l6enpSEhIQHx8PO3R2lhu87owmUx06tQJnTp1gqqqKh2OUkdHp1nGkVJSUvTY3tHREdHR0QgMDERCQgL8/f3h7+8PGxsb/O9//xPYmmJWVhZevXqFV69eIS4url5bzMzM0Lt3b5iYmAhk3MpkMqGqqgpVVVUYGxvT79fU1ODdu3d48+YNHUVlwoQJSElJoa9LOzs7jBkzRijjZwaDAXl5ecjLyzfa3wgEAgEgIqFAqKqqApvNhqKiYqsXkquqquqFUeDl+GlLZGZmIiMjAwwGAyYmJiKtOyAgAABgYGBA3ItbAJPJxJQpU3Du3Dm4u7tj5MiRGDRokEjb0KlTJ+zevRtr1qzB06dPsX379lZP/Jvbj1gsFiZPngw9PT08efIEnp6eSElJwe7du5ttqVhWVoZXr17Bzc0NFRUV6NWrFywtLWFpaQldXd02bSxQXV2NLVu2ICIiAjIyMli8eLHIPBQkATk5OcycORPnzp3DmzdvcOnSJYwePRqdO3du1YS8qKgIMTExdNLzkJCQemE+GAwG9PX10aNHD3Tv3h3a2totun4YDAbMzMzQs2dPJCUl4e3bt0hISIC7uzvc3d1hb2+PuXPnNtvjQFKfRzzxAaj1nPrxxx9x6NAhgV+bZWVl+Pnnn+sJ5I0tgCsoKMDU1BSmpqYAasWngoICBAYGwt/fH6mpqTh06BDOnTuHqVOnwsnJqcUCDofDwYMHD3Dy5En6nPTs2RNjx44VyqI8L2egqqoqSktLYWJiAn19faF6EMvIyGDChAmwtLTE48ePkZWVRXto8kRhoDbcDm9RQU9PD9evX0d+fj7k5OQwZ86cZi2WV1VV4dGjRw3CxgoDUfYjnkgo7tx/BQUF+PDhA4Da65QgfOzt7XHjxg3cvn0bc+fOFZkxT6dOnWiB0sfHB5MmTRJKPZL6PGoJrq6uAGqFVUHkF0tISEBlZSVkZWWFHoJQ0hg9ejRiYmLg4+ODkJAQWFtbC73OWbNmwcPDA9evX8fevXvbpDDbHvqRoHj69CkOHjwILpcLaWlpKCsrQ0FBgTbG4228/xUVFaGiosLXOk9rYDKZMDc3h7m5OdLT0+Hn54ewsDA6HKm2tjamTZsGR0fHVkfWqK6uxrZt25CUlAQlJSXMmzfvqwazGhoa6Ny5MzIzM1tV5+cMGDCgyVDzvDGgg4ODwPodT4xpjbdsW+9HFEXBw8MDAQEBSEhIQFJSUpNhXJWUlKClpUWLgXU3JSUlgUVKkZKSor1p8/Pz4e/vj/fv3yM4OBgrV67E+PHjsWzZMmhqara47JKSEnh4eODFixd0WiKgdq7TvXt3WFpaomfPniJbO5SWlsawYcNgbW1Nt+np06f0Z1OnTu0w0QEaIyQk5KtjWE1NTb7vBWw2G25ubvXEYkLbh3deAWDcuHHEUJUPyJHjk8jISPzyyy9IT0+HlJQUNm/ejAkTJrT4YbNv37427+7M8yI0MDAQuUcWTyQUtTjZHtDX18fAgQPx7t07HD58GOfPnxe5x1j37t3x008/4ffff8fevXthbW2Nb775psXltLQf9evXD9ra2nBxcUFiYiKWLl2K3bt3N5mnkKIoRERE4PHjx3B3dwebzaY/S0hIwMOHDwEA6urqtGBoaWmJ7t27t5kHFZvNxo4dOxAcHAwWi4WFCxc2miOivdOlSxeMGTMGbm5uuHDhAi5cuAAZGRno6+vToVR4IYX09fXpe35hYSEtCPK2rKysBuXLy8vD1NQU3bt3h4mJCZ0jgx8YDAadID09PR1v375FZGQkvL294e3tDW1t7XpiT1PhYiT1ecQTCYcPH46goCAkJydj7dq1OHz4sMAsIisqKvDLL78gKioKsrKyWLx4cbMnpgwGA+rq6hgzZgyGDRuGoKAg+Pn5oaCgAOfPn8elS5cwadIkfPPNNw2MEXgCY3JyMpKSkpCSkoLk5GQkJibSuSm0tbUxbty4elapwoDBYIglV6uBgQG+/fZblJWV0aGNeFtOTg7Kysrw4cMHWoQCao/JnDlzmuVRmpWVhVu3biEvL0+Iv+L/EFU/Ki0txevXrwGIXyT08fEBAOjq6oo1L2JHokePHtDS0kJOTg4ePnwo0nCzvXv3RmpqKt6+fSs0kVBSn0fNhaIo3L9/H0CtoCsI6nrCtGWDtNagqamJfv36ISAgACdOnMCJEyeEni+pZ8+esLGxQXBwMI4cOYLDhw8LtT5hIOp+VF1djaSkJMjLy0tM6FIOh4MTJ07QeerNzMwwbdq0NmFcrKenh2nTpmH06NEICAhAQEAAsrOzcfLkSZw9exZOTk5YsGBBiwzRuFwu/vjjD4SEhIDFYmHevHnNfm5bWFgIRCTs3r07xo4d2+hnPC9CAFi4cCHfdQG110BUVBSA1nkStuXnUUpKCg4fPtwgpySLxYK2tjZ0dHTovzo6OgKZl7YUdXV1jBs3DgMGDMDLly8RFhaGZ8+e4cWLF1iwYAGcnZ2/mnOdw+Hg/fv3cHNzw5s3b+rNdQ0NDWFpaQlzc3Ox/D4eKioqmD59OmxtbfHy5UvaaF9NTU1sbZIEhg8f/tV9FBQUEBkZyZdQWFVVJbQxK0F81D2vpaWlbWbtVRIhR44PIiIiMHz4cMycOROTJ0/G/fv3sW7dOtjY2LT4xrV582asX7+e/r+4uFhiBtXNhReGS9ShRtlsNi1QEpGwdYwcORLR0dHIzc3Ff//9h59++knkbRg9ejRiY2Ph4uKChQsXomfPni0Oy9SafmRgYICVK1fCxcUFnz59ajRPYVFREdzd3fH48WMkJSXR39XU1ISNjQ06depEJ7DPyMhAfn4+vLy84OXlBaA2XJ+ZmRkcHBwwefJkiV3U4XA4+P333/Hu3TtISUlh3rx5AssB0RYZMGAAqqqqEBkZidzcXFRXV9N5QurCZDKhq6sLNpvdqCAI1E58dHV1oaurCwMDA+jr6wt1YUtPTw/Ozs7Izc2Ft7c3QkNDkZ2d3azvSuLzKCUlBSkpKXSYTSsrK1y6dAlpaWlYu3YtDh06BD09Pb7qqKqqwpYtWxAWFgZpaWksWrSo1QK5rKwsBg0aBDs7O0RERODdu3fIyMiAq6srXF1dMXToUPTq1YsWA5OTk1FaWtpkWaNGjUK/fv3aXfL4xqjrMciDw+EgNzeXFg+zs7Ohrq7eIJR0UwQHB+PBgwfCbHYDRNWPXrx4gZqaGujp6Ym9n/KEfGHkTBI2FEUhLi4OTCazTY0lGQwGBg8eDFdXV9y5cwczZswQ2aJ379698eTJk3pe3oJGEp9HLeHOnTuIiYmBjIwMHaKVX8QRLq+qqgrJycmgKAqGhoYCCXPdWkaMGIHQ0FDExMTg1atXGD16tNDrnD17NoKDg/Hff/9h+/btbc4IQpj9qLS0FHFxcfQWGxuLxMREOoefoaEhRowYgZEjR4rNC7O4uBi7du1CYGAggNrF6OHDh0vsfKwplJWVMXLkSAwdOhQfP36Er68vsrOzcefOHTx8+BDLli3DtGnTmuWxfPr0abx69YoO5dmSPOa9evXCq1ev+Pkp0NHRwYwZM5oc1/Lm0KNGjRLYdRMfH8+XF3ZbfB5VV1fj+vXruHTpErhcLlgsFuzs7NClSxfo6Ojg/7F33mFRnen7v2foTXrvVelNEHtX7L2bxGjMJpvEbMpmd/PNpmySTVZTTUxij5VEY0lsICpiwYIC0qX33vsAM+f3B79zAgrKzJw2cD7XxSUC874vzMw57/vcz3M/xsbGvDtbGBkZYdmyZQgNDcXFixdRUlKC/fv348yZM9i8eTNmzJjx2Jrz8vIQFRWFS5cuoa6ujvq6paUl/P394e3tjREjRrD9qzwRBwcHPP/881wvgzfs2rULwcHBA34/IyMD69evR01NjUpW9AsIqAqCSKggNTU1+Otf/4o1a9Zg+/btAIAXXngBHh4e2Lt3r9xZRlpaWpweuOiAq36EGRkZaG1thY6OjlwbXIE/0dDQwMKFC/Hzzz/j3LlzmDp16hNv0kzx4osvIjc3F/fv38fixYtx7949uSz1FH0fkT3HevcpzMrKwrx58xAVFYWrV69SmWhqamrw8fFBUFAQ7O3tqQMmaQ/R1dWFsrIyFBUVoaSkBMXFxWhvb8eDBw/w4MED1NXV8bKprkwmwxdffIGrV69CLBZj9erVw76/p0gkogIJMpkMDQ0NqKmpQXV1NWpqaqjPJRJJn8bnJiYmVMNz8uNpmY9MYWZmhkWLFmH69OlURRpJZ2cnDh069Nhj+Hg/IoPQLi4u0NbWhra2NjZs2ICDBw+ioqKCEgoVPTSQlku9K2itra2VXreamhp8fX3h4+ODwsJCxMXFITs7G9evX8f169cf+3ljY2OYm5vDzMwM5ubmMDc3h4WFBS0WdaqMmpoald0sD52dnTh//vxjmdNswNb7iLQaHTNmDKcBz7a2Nty9exeAaomEBEEgIyMDsbGxVCKFl5cX5syZozJ9eH19fRETE4Pa2lpER0dj3rx5rM0LAHfv3kVHRwcj9zk+3o8GS1tbG9566y0APSITXb3flLHLGywEQaCyshI5OTnIzc3tkyAHAI6OjlQyB9sW+3p6epg4cSKuXLmCPXv2YNKkSYwL4yEhIXBxcUFeXh5++ukn/POf/2R0Prqh630klUqRkpKC1NRUZGdnIycnZ8D+ZTo6OpBIJCgoKKAcOVxcXDBlyhRMmTKFNXGloKAA//d//4eysjKoq6tjyZIlKm/pp66ujsDAQAQEBFDtBSorK/Hjjz/ijz/+wEsvvYTx48cP+L48deoUfvnlFwDAwoUL5U6MISu+SGs3edHT08OaNWsGfE2Wl5dTFX/PPPOMQnP0h7JV2Kp2P0pOTsaXX36JoqIiAICbmxvmzZunMkkO9vb22LhxI9LS0nDp0iXU1NTgs88+w8mTJ/HKK6/AwcEBly5dQlRUFLKzs6nH6erqwtfXFwEBAUJsUIUYOXIkgoKCuF6GgMCwRxAJFSQnJwdaWlp47rnnAPQEojQ1NRESEoL29naOV8cNXFUSklajLi4uvMuEUiUcHR0REhKC+Ph4fPHFF9i3bx/rtrFqamr497//jZdffhn5+flYvXo1zp8/z0q5+KN9CntXAgI99mmBgYHw9fV9YiBMQ0ODsqMEegItNTU1SElJwfXr13HgwAFoa2vT1oBdWdrb2yk7DzKjdNmyZVSfNYEexGIxTExMYGJiAg8PD+rrBEGgpaUFNTU1EIlEsLKy4kwQfBL6+vqPBbwH6kPBR0iRsHefM0NDQ2zYsAGHDh1CdXU1/vKXv+CHH36As7OzXGN3dXXho48+Qnx8PNTV1bF+/XraKzREIhGcnJzg5OSE6upq3L17F21tbZQYaGZmBlNT02EvBtJJXV0dfv3110FX0KoiBEHg/PnzALi3Gr1z5w5kMhlMTExUok8aaR9+7dq1x14j6enpSE9Px5IlS+Dr68v7ahM1NTWEhYXh4sWL+PXXXxEeHs5oH1ESGxsbGBsbo76+HvHx8Zg4cSLjc6oS//vf/1BUVARLS0vabGBLS0up/u90Oz20t7cjNzcXubm5yMnJGbC6HQBVAX/lyhXo6upSgqGrqysr4npYWBji4+NRWVmJkydPMr6nFolEWLlyJT7//HN8++23eOONN1RKLFAGmUyGlJQUxMTE4Nq1a6ivr3/sZwwNDWFlZQUrKytYW1vDysoKI0aMgEQiQWZmJtLT05GTk0O5cezbtw+urq5UhSFTriVxcXH48MMP0dXVBUNDQ7kr5viOSCSCq6srnJ2dkZSUhCtXrqC0tBT//ve/ERAQgFdeeeWx89z169epBPepU6cq3NczLCwMvr6+uH79Ou7cuSPXY9esWfNEa1Ty/D1t2jTqPE0HbCRY8IHm5mbs3LmTSiLT09NDeHg4vL29eb+feRSRSAQfHx+MGjUKt2/fxrVr1/Dw4UNs2bIFIpGIqlgWi8UYOXIk/P394ebmxsoeSKAHstdlcXFxn68P1NZEQECA3wgioYKEhYVhzZo1VLUVeSOysLBAa2trn5+VSCRD/iBRUlKC/Px8AGBdXOgtEqoinZ2dqKqqQnV1NYyMjOQOcNPJ9OnTkZWVhYqKCuzevRtbtmxhfQ2Ghob4+OOP8eqrryI6Ohrvvvsutm7dytr8ZJ/C48ePo6OjA/7+/ggKClK4qkgkEsHc3BzTpk2DhoYGrly5gp07d0JbWxuLFy+md/GDoKurC+np6UhMTERiYiJSU1P7bOIWLVqk8hm2bCISiWBgYAADAwOulzJkqa6uRkZGBoDHK5TIKuBDhw6hsrISL7/8MrZv395HyH0SpMVuXFwc1NTUsHbtWloDEv1hbm7OWqXPcCU3NxeHDx/mehmMk5SUhLKyMmhraysc6KOL3lajfA5CyWQyShysrq5+4s+eOnUKaWlpmDdvHu9sqh4lKCgIV69eRXFxMW7evIlJkyYxPqdIJIKfnx9iY2Nx48YNQSTsRX5+Pj777DMAwMsvv0xb8tClS5cA9LRXoGvM5ORkxMfHo6SkRKHHt7W1ISUlBSkpKQAAKysrBAUFISQkhJb19YeGhgamTZuG33//Hfv27cOcOXPk6semCNOmTcOePXtQUVGBw4cPY9OmTYzOxyXkdTImJgaxsbF9evnq6OhQFaSkMDhQfy/y3hQQEID29nZKMCTF6NzcXOzduxfOzs4IDg5GYGAg/P39la66JQgCR44cwd69ewH0JMKuWLGCtmpeviEWixEUFARvb2/cuHEDcXFxSEpKwubNmzFnzhxs2rQJpqamSE1NpdyugoKClL5mk+LT2LFjce3aNSph/En015e7NxUVFVQVIV29CEnS09MBsGvVzCYEQeDKlSvYsWMHJeYHBQVhxowZrCd+P4pUKoVYLFZ4f6iuro4JEyYgICAAV69eRUJCAgiCgLW1NQICAuDj48Npn8HhTElJSb/uOFxSVFSEmpqafr83XAt6BAQGiyASKoBMJoNYLKYsA2UyGSUSSiSSPkGHb7/9FgDw2muvDekqt6NHjwIA/Pz8WA2kNDY2UsFjvveQkclkqKuro/opkf8+mpHp5+eHuXPnciIsa2lpYcGCBTh8+DBOnTqFKVOmyN0XkA5cXV3xzjvv4D//+Q+2bdsGU1PTPuJAW1sbo/Pb29vj9ddfBwBaM9EmTpyIzs5O3LhxA99++y20tbURHh5O2/j9IZVKkZ2djcTERCQkJCAlJeWxCrIRI0bAxcUFPj4+vH8fCQw/4uLiAPQc6vurUNDT08Nzzz2HI0eOoLS0FK+++iq++eYbSuwmCAJNTU2oqalBbW0t6urqUFNTg7q6OmRnZyM1NZWy2OUySWOo0tXVhZiYGEilUkyYMIFRQZ0gCNy6dQvR0dGMzcEnyCzxoKAg1vrQ9UdXVxdVScBXq1Ey6B0bGztg4KA/srKykJWVhQULFiAwMJC3AqiWlhbGjBmD69evIyIiAhMnTmRlrT4+PpRIKPAnb731Frq6uhAYGEibYEsQBHVtI61elaW8vBynTp2iZSySiooKnD9/Hmpqaoxah/n5+eH27duorKzEoUOH8OqrrzI2F9AjTK5YsQI//vgjvvjiCzz//PND6mxPEAQyMzMpYbB3hTXZW93b2xsuLi4KnY10dHQQGBiIwMBAtLW1UYJhXl4e8vPzkZ+fj99++w0ikQienp4ICgqihC957m8dHR3YunUrYmJiAACjR49mrbr6achkMuTn56O9vR2mpqYwNTWl9d7du4f15cuXkZqaigsXLiA6OhorV67EiRMnIJVK4e7ujnnz5tF2jzA0NMSCBQswfvx4XL16lUoYeJRp06Y9NRE1NjaW+lk6k/Zqa2tRXl4OYGiKhOXl5fj6668RHx8PoKfdxIIFC1jr3SaVStHY2IiGhoZ+P5qbm6Gjo0MlkCia1KGvr4/58+dj0qRJ6OrqgqmpKc2/iYC8yFtJzDRFRUXw9PQcVLxQR0cHZmZmLKxKQEB1EERCBXj0QCAWiynhUFdXlwqAvf/++/jkk0+QnJw8pA4R/UH62rPRPL43cXFxIAgClpaWjGeQKoNUKsWuXbsGZXuWnJyMqqoqbN68mZPXjaurKwICApCUlIRt27Zh165dnGSfTZ06FTk5OTh69CgnvT+YOkxOmzYNnZ2duHv3LrZu3QpTU1NGsq1zcnIQFRWFy5cvPyZE6+npwdnZGU5OTnB2doaxsTFvA59DDZlMRjWOFxgct27dAvBk8UFHRwfPPPMMjh49iqKiImzZsgXTp09HS0sL0tPTH+vH2BuRSIQVK1YIFrsM0NLSgiNHjqCiogJAT++yZ599lhExtqurC2fOnBkwODUUuXDhAgDurUYTExPR2toKfX19Xgbfuru7ceTIkcf6qsnDmTNnUFFRgblz59K3MJoZM2YMbt68iczMTKSmptImJD0Jco6bN2+CIAhhL4GeqtpTp05BLBbjtddeo+1vkpWVhdLSUqirq9MmxpP3VyY4c+YM7OzsYGFhwcj4YrEYs2bNwqFDh3Dq1CmsWLFC7r618jJv3jwcPHgQmZmZiIqKwpw5cxidj2lWrlxJvT47OzvR1dVFfU9TUxMjR46Et7c3XF1d5dq3tra24saNG1BTU8OUKVMee6yuri4lAra1tVEiYX5+Purq6ijL58OHD0NDQwOrV6/G8uXLqURkgiDQ3NzcJ+mW/Lhx4wY6OzshEokwd+5cjB49moa/FD0cOXIEeXl5fb7GxJ7IyMgIy5YtQ2hoKC5evIiSkhIqodvGxgbLly9nJMZgYmKCpUuXUmIhWREIAP7+/pgwYcITH19bW8tIL0IAiIyMBNDj+jXUHL5iYmLw3//+F93d3RCLxZg0aRLGjx/P2lmztbUVP/7442Nuao/S3t6Omzdv4ubNm/Dw8EBwcDDc3NwUei3y3d1huNDa2krZ+PKFmpoatLW14fDhw/D09Hzs++3t7dS1KCEhgTUh/VHIQpcnYWZmxtn6BIYvQpSSJkg/bA0NDZiamuLzzz/Htm3bEB8fDx8fH45XxzwPHz4EANabzZKZy/3dAPhEdXW1XH2RKioqUF1dzfhhdyBmz56N3NxclJSU4LvvvsM777zDyTo2btwIAHjw4EGfr0ul0j4HD1VCJBIhPDwcnZ2dSEpKwv/+9z/s27ePls1uXV0dLl++jKioKOTm5lJf19LSogRBZ2dnmJubC4E8luju7kZpaSnVv6e4uBhdXV1QV1eHtrY2dHR0+vzb+3MLCws4OTkN+SSTpyGVSgHgqYddLS0trFu3Dr/88gvy8/Nx8eLFPt8nk3j09fX7/Ovo6MjZtXYoU1tbi8OHDz8m0B48eBDPPfccnJycaJursbERv/76K5UlPhzo7u7G/fv3AbC/93qU1NRUAD1JRny7txAEgbNnzyolEJLEx8dj9OjRjIkeyqKnpwcvLy+kpqbiwYMHrIiEpL1Xb3FhuENW+02dOpXW4D95fbO2tqat+qisrIyWcQYiOjoa69atY2x8FxcX2Nvbo7i4GLdu3WLcxl9PTw/jxo1DdHQ0kpKSVF4kfLTSQl1dnRIG3dzc5O6TLJVKER8fj6ioKOprBQUFWLVq1YAuArq6uvD29oa3tzeAnvt57+rClpYWHDp0CBEREfD390dNTQ0qKyvR0dEx4Do0NDSwdu1aWvcZdNDU1PTY15js22Vvb4+NGzciNTUVMTEx0NDQwJo1axh3HrC0tMSqVatQWlqKGzduQFNTE/Pnz3/q/oB8Tg0MDGgLjEulUuzcuRPHjx8HQF8VNh+QSqXYu3cvIiIiAPTY6i5YsEDh6jqCIFBaWoq0tDRkZmZCT08Pa9eufaqNZ0VFxVMFwkchXRoMDQ0RFBSEwMBAoXWHCsLnGAVZkf4oXV1d+P777wFw40RnZmYGXV1drF+//qk/q6uri4yMDF4LhYMRO9kYR1NTk3peuXTXGQoIIuFTePjwIWpqajB+/Pgn/hxZdSSVSvHll19CV1cX169fp3oWDmWkUil1yGDT67+9vR23b98GwF97KxJFDuHFxcWcBa61tbWxZMkSHDx4EBcuXEBISAimTp3K+jrU1NSwefPmx77e2tqK+fPnD3qc+vr6PlmDurq6nGYRktmtxcXFqK2txTfffIP3339fobE6Oztx69YtREVF4fbt230aeI8aNQr+/v5wdXXlhc3OcKCzsxPFxcWUKFhUVNTvz3V3d6OlpQUtLS1PHG/EiBFUPxdjY2Mmlsx7AgMDce/ePeTn52PMmDFP/FlNTU2sXbsWly9fRnNzM2xtbWFvbw8rKyuhepNFysrKsHv37gG/f+DAAdqEwqKiIuzfv1/pcVSNhw8fQiKRQEdHR+GeuXSRlZUFoKdCgW/cvXv3sUQjZbhy5QpWr15N23h0Y21tjdTUVOTk5LAyX2FhIQD+96JkE/L1RvfZpPf+ji6am5tpG6s/cnJykJOTw2ilvoeHB4qLixEfH89Kr2/SmqyyspLxuZjmxRdf7BNMMzAwUDi4lpubi8jIyMfsnEtLS7F7926sWrXqib3oSAwNDSlbUoIgkJGRgWvXrqGyspJKjCHR1dWFoaHhYx+2tra8dBgKDAzsY4e+aNEixoPUIpEIvr6+8PX1Zb3a29bWFqtWrRr0z5N79ebmZhQVFSm9R2xvb8cnn3xCtS2YOnXqU2N6qkJTUxM++eQTyl503LhxmD59utz3B4IgUF5ejrS0NKSlpaGxsZH6XkNDA44cOYJnn332iXETZZKEGhsbERMTg5iYGIwaNQrBwcGsJZy1t7dTrShqa2thbGzMa1t5PqKjo4O//OUv2LlzJ9dLGTQaGhp45ZVXOJvfwcEBGRkZT219kJGRgfXr16OmpoaXIqE8Yudg0dXVVdj+levndSghRMyeQFJSEsaPH4/PP/98wA0FaTNKoqurCz09Pdy9e5f31W100TvIzWbD4Pj4eMhkMhgbG/M2q5tEkeqGkpISTi1SnJ2dMXHiRFy/fh2fffYZPD09YWVlxdl6lOHRjYtIJIK9vT3c3Nzg6uoKa2tr1jeEGhoaWLJkCfbu3YuYmBiMHz8e06dPH/Tjs7KycP78eVy5cqVPkMfW1hb+/v7w8fHhvEn5cKGxsREJCQnIzc1FaWkprWM3NTXh2rVruHbtGpycnBAYGAhPT0+5s7tVmeDgYOzevRv5+fmQSqVPFbzV1dUxe/ZsllYn8Ci5ubk4fPjwU3/uwIED2LBhg1I9ZxISEnDmzBmFH6/KJCcnA+ippOEyk5cgCN6KhIWFhZTFGF08fPgQxcXFsLe3p3VcuiCTy3q7CTAJmQjD92Q9NklKSgIA2oUxUiSkC4lEgs7OTlrH7I+LFy8yep1ydXXF5cuXkZiYiO7ubsYTgsiELXkcYviKiYmJ0kmTdXV1uHjxIuUq1B/Nzc3Ys2cPlixZIleve5FIBC8vL3h6eiI3NxfNzc2UEDhixAiV2wv3FglXr16NkSNHsjo/38UPNTU12NnZoaCgAMnJyUqJhNXV1Xj33XeRk5MDsViMpUuXUtWqqk5+fj7ee+89lJWVQU1NDYsXL5bLuYwgCFRUVCAtLQ3p6emPtSXpTVlZGX799VesXbt2wGsrXU4CmZmZyMzMhL6+PpycnGhpiyKTyVBfX4/a2lrU1NRQoiBpSfkozc3NmDx5srK/yrDCysoK77333mNJRxKJBD/99BNHq+I3Dg4OvBT+5GGwYqc8CPaq/EAQCQfgwYMHGD9+PF5++WW89tpr/f5Mb4GwsLAQjo6O+PDDD7Fx48Zh9eImrTPU1NRY3ayTVqMjR47k/aZXEZGwuLiYgZXIx+TJk5Gfn4+SkhJ88skn+Pbbb1WyIk1LS6vPa6SjowNFRUUoKirClStXoKurC1dXV+pDX1+flXXZ2tpi0qRJiI2NxdatW+Hn5wdzc/MBf14qleLWrVs4fvw4FSAGejJ//fz84O/v/8THK0N3dzfS09ORkJAALS0tLF68eFiLkDKZDDk5Obh//z4VIGeagoICFBQU4Pz58/Dx8UFgYCBsbGx4f/1TFjc3N4wYMQJNTU0oKyvjbXBeAEhJScHJkycH/fM///wznn/+ebn3TFKpFFFRUVQG9XCErFbiwiqnN8XFxaivr4dYLOaVbW9TUxNlL0Y3ly5dwoYNG3h57SWTuUpKStDW1sZ48h4pEg6XxMin0dDQQFVXMvXepOt1x3QVIUl1dTUSEhIYS3y0srKCrq4u2trakJaWBn9/f0bmITEyMgIwNCoJlaGzsxPXr1+nzuOD4dSpU6ioqMCMGTPkEo1FItGQ6Buto6OD119/HQRBPNUdpKCgAGKxeFjFlICewHNBQQFSUlKwcOFChca4du0avvnmG9TX10NbWxvr1q1jtF/ypUuX+ojtdnZ28Pb2ZmSPEBsbi08++QTd3d0wMjLCqlWrBp3E3dnZiTt37iApKQl1dXWDnjM/Px8nT54csJcl3XbjLS0tSE1NpazsR4wY0Uc0JK/Bj66BFAGrq6tRW1uL6upq1NXVUW0rBsPVq1ehrq4+ZCpOlYFMAhSLxXB3d3/iz6qpqT32vEgkEgZXpzhSqRTXr18HAEycOFElY5t8gU9ip/C80ocgEvZDdnY2xowZg7fffhuffPIJurq6cPbsWZSWlsLc3BxTp06FhYUFdZP88ssvERkZiY8//hhhYWG8eaOwBXnI1NPTYy1g0t3drTJWozKZTKGDZF1dHVpbW1m1cH0UNTU1LF26FD/++CPS0tJw8OBBPP/885ytR1FOnDjR5+9YVlaG+Ph4xMfHIyEhAW1tbUhJSUFKSgqAnoCDq6srvLy8GK+MmDhxIrKzs1FWVoatW7di69atj72P2tvbERkZiRMnTlCVaiKRCN7e3ggICICzszNjGdoNDQ24d+8eEhMT+2Tc7d+/H+vXr2etcXhraysIgmBNwB2I5uZmJCYmIiEhoY8lC5tIJBLcv38f9+/fh4WFBXx8fODs7AwbGxte9wZQFDU1NQQGBiI2Nha5ubmCSMhTbt269VgfyMGwf//+QQuFEokE6enpuHfvHuO9tPgOX0TCW7duAehxH+BLVUd3dzeOHz8ud4+cwVJUVIScnJynBk24QE9Pj0qqyM7OZlwwIQUxQSTsgUzgsrCwoL2/Et2VhGyJhAAQExMDHx8faGtr0z62SCSCq6srUlJScO/ePcZf8yYmJgCGr0hIEARSUlJw6dIlhV5Dt27dQnV1NZYtW8bI64Hv9CdwPMr9+/dx9uxZAD2WsFxbirMJuRckz+TyUF9fj2+//RaxsbEAeq7Dq1evfqIgK5VK+62Akodvvvnmsa95eXlhzZo1GDduHC1nM6lUip9//ply6nB2dsby5csHlQgkk8nw4MEDXLly5altLgYiIyMDZ8+exYIFCx6LUzDdk7ipqQnJycnU/dXIyAhOTk7Q1tamhMFH+58rw6VLl6ChoYHQ0FDaxlQ1WlpacObMGSoRmnS84mNynLx0dHRQbZRaWlo4jbUK0IfwvNKHIBI+Qnd3N77//nvo6+sjICAAQI9ffFlZGVpbW1FYWIjw8HC8+eabmDJlCoCestjOzk5GM5T4DLmxYrOqKDk5Gc3NzdDV1eV9wLi6uhrd3d0KPbakpIR1K5JHMTY2xoIFC3Dy5EkcOnQIU6ZMgbOzM6drUhYbGxssWrQIixYtQldXF9LS0ijRMDs7GxUVFaioqMDNmzcxatQoTJ8+XWF/7KehpqaGJUuW4KeffsK9e/dw+vRpLFmyBABQU1ODU6dO4cyZM9T7TFtbG8HBwQgNDWVMoCMIAnl5eYiPjx/QPqi6uhp79+7F+vXrGateJKmsrMTevXshlUoRGhqKSZMmsXq9IQgC+fn5uH//PtLT01mbdzBUVVXhypUrAHr68Tk6OsLR0RHOzs6wsrIaMqJhcHAwYmNjkZeXR917BfgBQRC4dOkS1fNFEfbv34+NGzf2ez+XSqXIzc1FcnIy0tLSlFkqLUycOLFPwKmrqwsXLlxgfR297Ua5hBQJ+SSYRUZGoqSkhNE5Ll++DDc3N14GTGxsbNDU1ISHDx8yKpgQBCFUEj4Ck+I9KRKqWiUhALS1teHGjRuYMWMGI+O7uLhQIuGmTZsYmYNkKNmNykNNTQ0yMjKQlpamtECak5ODPXv2YPXq1Yydr1SVlJQUSiAEgHPnzmHTpk28vNcwgb29PUQiESorK1FZWTkohwKCIHD58mV88cUXkEgkEIlEmDBhAiZNmvRE++HCwkL8/PPPSq+5t52yTCZDQUEB0tPT8e9//xtOTk5Ys2YNpk2bprAVcktLCz755BPcuXMHABAWFoaZM2cO6oyXm5uL6OhoWpIaEhMToaOjg5kzZ/b5OtMi4aM0NDRQtt5MceHCBairqyMoKIjRefhIWloazpw5Q72XCILAzZs3UV9fj8WLF/MmIVBAQIAZBJHwEdTV1fHqq6+io6MDX375Jd588034+fnh2LFjcHV1RUZGBlavXo3vv/+eClQ+99xzWLJkCWsVNXyDPGSy2Y+QtDbx8PDgfRBcEatRkuLiYs5FQgDw9fVFeno6MjMz8cMPP/Rb7aaqaGhoICAgAAEBAdi8eTPq6upw7949xMXF4dq1a8jMzMTDhw8RGBiIKVOm0J4ZDvQkGsycORORkZHYsWMHzMzMcP36dVy+fBkymQxAT1AiLCwMAQEB0NTUpH0NQE/FYlJSEu7duzcoG5Kmpib88MMP2LRpE2NJEs3NzTh69Ch1ALl9+zYSEhIwdepUhISEMGolQBAE7t27h9u3b8tly8IVnZ2dyM7ORnZ2NoAem11HR0fKouVRMVfR5AUuIG3KSkpKIJFIlO6hMxyRSCQoKiqCVCqFhYUFjIyMlL5/SqVS/PHHH33sjxVl3759lFBIEATKy8vx4MEDpKam9ts3hAumTJnyWK8SiUTCukhYU1NDVVJyKRI2NzdTz72Hhwdn6+hNQkIC7t+/z/g8lZWVKCgo4GXSlLW1NbV3YRKyp4+amtqQsAKkAzZEQrpgUyQEgJs3byI4OPipNouKQP69MzMzUVtbC1NTU9rnICErwWpqaljpgcgVBEGgsrKSOv9VV1fTOn5tbS127NiBtWvX8irJhEsyMzMfs2wvLS1FYmLisBErNDU1YW1tjbKyMiQnJz8mSD1KdXU1vvnmGypRzdLSEosWLXpq9eW9e/dw7tw5Wta8cuXKPueSlpYW3L59G3fv3kVBQQE+++wz7Nu3D6tWrcKcOXMGXUErlUqRlZWF//73vygpKYGamhoWLlw4qL6eVVVViI6ORk5OjsK/V3/ExcVBV1e3jx0n2yIhW5w5cwbq6upy9VFVZdra2nD+/HkqIdPNzQ3/+te/kJubi88//xzp6eloamrC6tWrhSotAYEhzNDc1SqAVCqlgs3u7u5455138NFHH8HMzAxfffUVtXn18fHBt99+i5kzZyIlJQXe3t4Qi8XDViAE2BcJyWwWgP9Wo4DyIiFfmDlzJrKysijRZOzYsVwviRFMTEwwa9YszJo1C/n5+dizZw/i4uKQkJCABw8eYOzYsRg/fjztFjmhoaHIyspCXl4e3n//ferrDg4OGDt2LKOCeENDA27cuKFwYHXv3r2MHPIlEgmOHj2KpqYmODg4YOPGjThw4ADy8/MRFRWFu3fvYsaMGfD09KRdtG5tbcWpU6eQm5tL67hsIpFIkJWVxVrPRCaxtraGjY0NysrKUFBQwIvkCb7T3d2NkpIS5OfnIz8//7H7ibq6OszNzWFhYdHnw8DAoM/7iSAItLe3o7GxEY2NjWhqaqI+r6qqojVwuG/fPkyYMAGZmZm0NkKng8DAQEyaNInrZQD4U4iwsbFhNUHrUeLj40EQBMzNzRkJ/MtLaWkpzpw5w9p88fHxvBQJbW1tAYBxkZCsInR1dWUseUnVYMMGWBUrCUkuX76M5cuX0z6ugYEB7OzsUFJSgpiYGEbmIDE0NIRYLIZMJkNtbS2verEqC0EQKCkpQUZGBjIyMmi18BuIo0ePYvLkyRg3btywvo7k5OTg119/7fd7Z86cwahRozi937OJg4MDysrKkJKSMqBISBAELly4gG+++QZdXV0Qi8WYPHkyxo8f/8QEUqlUivPnzyMhIYGp5UNfXx8zZszAhAkTEB8fjzt37qCyshLbt2/HwYMHsWzZMixevJhqodHS0oKioiIUFRWhuLiY+rysrIxK6DQ0NMSqVaueKn62tLTg6tWrjCZLXbp0CTo6OpRw3dnZydhcXHPq1Cmoq6vDy8uL66UwysOHD3H27Fm0tLRAJBLhmWeewfr166GhoQEXFxeYm5vjH//4B0pKSqi4j1AFLsB3ioqKBhVPMDMzG3Yt456EIBICyMrKwpkzZ7B27Vrqxuvq6opPPvkEGRkZcHJyAvBnBmdHRwc8PDxgaWnJ+yo2NmDbbjQ7OxtVVVVQV1fn3GZrMCgjEpaVlfURsLnExMQEYWFhiIuLw48//oiQkBBWsmcJgkBHR0efrz36f6ZwdnbGp59+ipSUFOzcuRNpaWm4ceMG4uPjMXnyZFr/BiKRCIsWLcIPP/yAzs5OeHt7IywsjAr2MUFDQwOuX79Oy0Hp6NGjWLx4MW3WZjKZDCdOnEBFRQW0tbXx+eefw9raGhMmTEBkZCT27duHuro6HD9+HA4ODpg1axZtf6u8vDycOnVK4b4NAswQHByMsrIy5OXlCSJhP8hkMlRUVFCiYGFh4ROrRbu7u1FeXv7YPUpLSwsWFhbQ0NCgxEA2q05JpwA+4ebmhnnz5vGmgp4v/QjJ3tB8qAJpbW3FsWPHWJ0zIyMDTU1NvEsUJM8ypaWlaG5uZsQBARD6ET5Kd3c3UlNTAahGJSEXe5y0tDSMGTOGkVYRPj4+KCkpYUyIJFFTU4OhoSHq6+sHbYfIV3777TfqjEkQBMrKyjh5XcTGxiI+Ph5jxoxBaGjosOtVWFhYiCNHjjzxZy5fvowFCxawtKKn097eDplMxkhFkYODA27fvj1gX8KKigp8+eWXuHfvHoA/24hYWFg8cdyWlhYcO3aMtSRsbW1tTJw4EWFhYUhKSkJcXBwaGhqwd+9eHDx4EJ6eniguLkZ9ff2AY2hoaMDV1RXz589/4t+6q6sLt2/fplpQMM2ZM2ego6MDT0/PIVtJSHL8+HGsWbOGN44ZdNLR0YGoqCjKvtXR0RH/+te/HjtnBwQEYPfu3fjnP/+J8vJy7N69G2vWrKHi5AICbJORkdHn/+3t7dTnSUlJaGlpwdKlSwflRqSrq4uMjAxBKPz/DHuRMCcnB2PHjkV9fT1qa2vx5ptvUlkRDg4OlC868Gfm5rVr12BnZyfYnf1/2K4kJKsI3d3dee2JLZVKkZiYSFmCKUJ3dzcqKioYFYrkYeLEiUhKSkJxcTHOnz+PhQsXMjqfTCbDli1bOO9D5evri++++w43b97Enj17UFhYiIsXL+L27duYMGECAgMDaRELR4wYgddff52xQ1dvSktLsWfPHlrHPH36NAwMDGgR72/evIns7Gyoqanhyy+/pIKeampqmDdvHqZNm4aIiAgcPXoURUVF2LNnD1asWKF0pl98fDzOnz+v9PoF6Cc4OBhnzpxBXl4e10vhHc3NzThy5AgtPUckEgmvqti5xsrKCsuXL+dFsg4JGTjjUiSUyWRUfxyuAycEQeD06dNoampife7bt29j1qxZrM/7JHR1dWFsbIz6+npkZWUhODiYkXkEkbAvOTk56OjogLa2NmxsbGgfn+6ehFy8X4CeKpTnn3+e9nG9vb0RFRWFzMxMVFRUwMrKivY5SIyMjCiRUJWh24pQGdra2hATE4OYmBi8+OKLT62YGiq0trYOqi9eQkICwsLCGO8DPxi6u7uxdetWAD32ns7OznBycoKjoyMtAi8ZqC0oKMDKlSthYWEBS0tLWFpaoqurC6dPn0Z3dzfU1NQwbdo0hIWFPTVxPzs7G3/88QcnIriGhgZCQkIQFBREJRxXV1f3seo3MDCAmZkZTE1NYWZmRn2MGDHiqdf8kpISnDhxgpXK394cO3YMb7zxhkq1r1CUiIgIvPbaazAxMeF6KbRBEAT27NmD2tpaAMCqVauwcePGASu6HRwcsGPHDrz33ntIT0/HoUOHMG3aNIwdO1YonBFgDTMzM+jq6mL9+vUD/syECRMA9JyHIiMjn3jfzMjIwPr161FTUyOIhP+fYS0Stra24rPPPsPChQsxevRovPbaa+ju7sY777zTb/l0amoqfvnlF/z000+4ceMGDA0NOVg1/yCtEtiyrSH7bfE1c0UmkyE5ORmxsbG0bNbq6+t5IxJqa2sjNDQUV69eRUpKCuMiYUNDA+cCIQnZBH3s2LGIjIzEzz//jJqaGpw/fx53797Fpk2baDkYsVWRW1VVxci4hYWFtIiEvQ9EZF/G3kilUkilUqXn6U1iYqIgEPKY4OBgiMVi1NTUoKqq6qkZw0Od+vp65OXlUR9sVVgPJzQ1NbF27VreJoX1d21ki4qKCjQ1NUFNTY2RqiB5yMnJ4SzYfevWLXh6enL+N3gUfX191NfXMxYQ7S0Sh4aGMjKHqkFeg7W1tRlJKiCfSzquRwRBMLYPfBpFRUVob2+nfb+rr68PbW1ttLe3MyoEdHV1UeIgk70PhzO7du3CK6+8Mizs7OQRWOrr63khEvZOJKusrERlZSXlLGBjY0OJhg4ODgpZyOrp6cHDwwNZWVmorq5GdXX1Y/EAe3t7LFq06KnvQYlEgqioKCQmJsq9DrpRU1ODn58ffH19kZeXh9bWVkoYVOS6LpPJcPPmTdaqB/ujrq6O10n7dDLUKiYlEgklEH711VcIDAx86mOMjY3x1Vdf4bPPPkNsbCwuXbqEzMxMLF68WLgfcshgbDUfrbxTVRwcHJCRkfHY79ve3k6Jgzdu3ICOjo5gI6ogw1okFIvFCA4OhqmpKVatWgVzc3OsXr0aACihkAxUFxQU4O9//zuysrIQGxsLX19fLpfOK8jKnYKCAlbmIyvz+HYjIggCaWlpuHr1KnXDpYPBlEizCXk4KS0tZXwu8u9oaWnZpzdcU1MTI1nag4GsZJs5cybOnTuHQ4cOoaamBmfOnMHy5ct5Y0f3NJiq/KUraD1u3DgUFxcjKysLb7/9Nvbs2QM7Ozt0d3fjzJkzOHDgABobGwH0WGPMmjVLqddESkoK/vjjD1rWLsAM+vr6GD9+PK5fv46kpCTeVe8wTUdHB/Lz85Gbm4u8vLwn2hMJ0IOrqytjVo3KEBAQAIDbKhCyH52pqSmnGcQymQzR0dGczQ/09Kz5y1/+wisxmQxmMXWvT09PR2VlJQwMDDBnzhxG5lA1yD1IQ0MDuru7abfkJ/u/0mFvW11dDYlEovQ4ilJUVES7bXhbWxtlN8VkcmVycjLa2tpgaWlJXYsF6OfgwYPYuHEjjIyMuF4KoxgaGsLc3HxQ/Z35YsOan58/4PfKyspQVlZGOT/Z29sjJCRE7tjZ6tWr0draioaGBsr6nvywtLTEpEmTnpqMkZ+fj99//506LzIF2RORRF1d/YnxAJFIpLQTRHNzM06dOvXE54INzM3NeSFcM42pqemQS07t/f4ZNWrUoB+npaWFDz74gOoJWlJSgh9//BEBAQF9BGO+VphqaGhQldBDQeAuKiqCp6fnoG01h0LyjYODw2PiX2dnJ/W8hoSEDOsex8oyrEVCHR0dPPfcc5St38qVK0EQBNasWQOCIPDPf/4TpqamkEql0NPTw48//gixWCyo0Y9A2gzV19ejsbGR0QpLmUxGiYR8KfcnCAIPHz7E1atXGbGd4ZtISP7d2RAJ6+rqAPQEXnrbb9JdQaYImpqaWLJkCUaNGoVXXnkF6enpSEhIYMzWi26Yqlik67kRi8VYtmwZfv75Z5SXl+Mf//gHnnvuORw6dAglJSUAeuwGZsyYAQ8PD6XE2YyMDJw8eZKWdQswS3h4OK5fv47k5GRMnz6dVxaQTFBRUYGMjAzk5eVRr3sB9uBDr73+CAoKAvCnswIXkCIh14fNpKSkQQVXmaS+vh6RkZFYtGgRp+voTWdnJwDmgspk5cKSJUt4E7jmGjMzM6irq6O7uxt1dXW0BxTJyj86REKuLaWZEAnJM4O5uTmjrhxkxdS8efMEizUGaW5uxqFDh/D8889TrkVDFQ8Pj0Hdx9hym3ka8iSGFxcXo7i4GFZWVnKJSSKRCPr6+tDX14ednZ1c65PJZLh48SJV7c40X375ZZ//6+npwd/fHwEBAYwIaFlZWYiIiKB9XHnR1dWFnp7esBAJvb29VSYRfLD0vn/JG78RiUSYO3cugoODsW3bNty/fx/379+ne4mMoKmpib///e9cL4M2ampq0NbWhsOHDz/V/n8oV9YNteeVS4a1SAiAEh6kUinEYjFWrVoFgiCwdu1aiEQi/O1vf8MXX3yB/Px8RERECAfhftDX14ejoyMKCwtRWFgIPz8/xuaqqamhsrW4tnslCAK5ubmIiYlRqu/g0+CrSNjU1ITm5mZGqyzISkIm+4ooi6enJ1588UXs3LkTFy5cgL29vUpkmvFdJAT+tPrbu3cvysrK8NlnnwHouW5PmTIFQUFBSgdosrOzcezYMTqWK8ACY8aMoXptZWdny5X5qEp0dHTg8uXLuHfvHtdLGda4ublxvYR+8ff3B9BTDcR0ctZA8EEk7OzsRExMDGfz9yYpKQkeHh686c9HioRM3OulUimuXr0KAJQDi0BPwM3a2hrFxcWoqalhTCSk4/3OtUjIxPzkmUFeQUFeeouEAsxSV1eHQ4cOYcOGDbwRyJjAw8ODqrx7EsrEoUhrNmXv2Z2dnQq9f+/cuYP58+crNfdg6O7uxsmTJzm11mttbUVcXBzi4uJgZ2eHgIAA+Pj4KO020N3djUuXLrEmfj4N8h6nCnEPZfHx8eF6CbSjjEhIYmlpiW3btiEmJqaP6xfQU2F7/PhxpdYoMHg8PT2pJFIBAWUY9iIhiZqaGgiCgEwmw+rVqyESifDMM8/gjz/+QG5uLu7evSsIhE/Ay8sLhYWFKCgoYFQkJKvXjIyMOM3ebGlpwalTp5CXl8f4XHwTCTU1NaGvr4+WlhaUlZXRngncGzIrmO/N61euXImEhATEx8fjt99+w+bNm3lvX8CUBRndVZ76+vpYt24d9uzZg+7ubowbNw7jx4+nxdYtPz8fR48epWGVAmyhpqaGWbNm4ddff0VSUtKQEwkJgkB6ejoiIyMZ7akk8HSsra15aTUK9FQSubm5IScnB9nZ2Rg9ejTra+CDSHjr1i1evU+OHTuGN998kxevGyZFwgcPHqC+vh4mJiaYMWMG7eOrMra2tiguLqa17QAJnXajfBAJu7q6aN0rsyESFhcXo6SkBOrq6pg5cyZj8wj8SVVVFY4cOYJnn312yNqH2dnZQUdHh7LLHQhFr+ddXV3Ys2cPZDIZ3njjDaXuC+S9X17u37+P6dOnMyr2dnZ24tdff2UlPjNYSkpKUFJSgrNnz1LVhY6OjnJXpdXU1ODEiROoqKhgaKXyQ1YQ6urqDur1q6pYWloOyWpJkUgEkUgEgiCUsgYViUSYNm0apk2b1ufrra2tvBQJpVIpEhISAPQ4swx1V6LhgvC80ofgkdGL3hfKVatWYeLEiaiurkZCQsKgGrkOZ7y9vQEw35eQD1ajZWVl2L17N2sbUL6JhAB7lqOkSMjnSkKgJxPrn//8J4yNjVFdXY2oqCiul/RUVKGSkMTMzAx/+9vf8Pbbb2PatGm0CIRFRUU4ePAgDasbPvz000/Yvn079XHx4kVO1hEeHg6gx26ntbWVkzUwQUNDAyIiIvDbb7/xSvgYrvC1ipCE3JdyZTlaWFgIgDuRUCKRUNVsfOL3338HQRCcroEgCEZFQtJqdPny5bxPiGIbsi8hWbVDF1KplBpT2UrC1tZWan/NJXS7sLAhEpJVhFOmTOFFMsBwobS0FL/88gtve1wpi1gsfuqeQ11dXeE+p/X19ZBIJOjq6kJ6erpCY5Ao0wMvMTFRqbmfRHt7Ow4dOsQrgfBRHjx4gAMHDuC7777DtWvXUFpaipqaGjQ1NaGjo6PfMzRBEEhMTMSOHTt4JRACf1YQikSiISmikZBxzqEIWXTBh1Y+bNHR0YHQ0FCEhoaio6OD6+UI0ITwvNKHUEn4CCKRCFKpFH//+98RExODpKQkuRstD0e8vLwA/Bk0YgpSlDI2NmZ0noFITk7GqVOnWJ2TryJhUVERayIh3ysJgZ6/ybvvvou///3vuH//PlxcXKj3BR8Ri8XQ0tKCRCKhdVyZTEbreCR0VnI3NTVh//79tI03XDh79uxjXzMyMkJoaCir63BycsKoUaOQmZmJ5ORkjB07ltX56UYmk+H27duIjo7meikCvfDw8OB6CU8kMDAQx48f50QkbGxsRFNTEwDA1NSU9fkBICUlhZN5n0Zubi7i4+NZvy72pncgnW6RsKurC9evXwcgWI32B1MiYV1dHWQyGcRicZ8e3YrAl/62hYWFcHR0pG08NkVCwWqUffLz83H8+HGsWLFCYbGMz3h4eDzxvqbMtbx3UkBKSgqCg4MVHksZkTA+Ph5hYWG0u0E1Nzfj8OHDlCUz36mvr0dMTEy/dulqamrQ0NCApqYmNDU1IRKJOO+7PBC9bUbNzc0VrjLlO0PRapRELBZDKpUOK5FQQEDgyQiVhAPg7e2NhIQERq0zhxJDvZJQJpMhKiqKdYEQAC8rZci/P9OBBlWpJCQZPXo01q5dCwA4ffo0GhoauF3QU2DCcpTvm0yCIPoVuwSeztixYzFx4kRMnDgR7u7uAIBt27ZxUvVGVhMmJSVxXrWjDKWlpdi1a5cgEPIMXV1dKtjPV8i+Ezk5OazPTQaCDA0NObN/Iy1l+MiFCxc4DeqRVYQAaKm87839+/fR1NQEKysrTJo0idaxhwK2trYAQLvdKBn8NjAwUDrAzrXVKAmd6yAIgjozMCUStra2Ijk5GQBY6a0m8DhZWVk4evQo7QmOfMDV1fWJ36dLJCwsLERjY6NC47S3t6O8vFzhdTQ0NCArK0vhx/dHfX099u/frzIC4dOQSqXo6OhAU1MTampqeCsQAuhTPThUKwltbW05K05gA3I/MVSrtAUEBORHEAn7QU1NDRs3bkRAQADXS1EZyL5QdXV1jPqRc1FJ2N7ejiNHjlDZo2zT1tbGuyA4W3ajZJBFFSoJSZ5//nl4eXmhq6sLJ06c4LVoxoQNGZ9/X6CnGpgrez5VZ/LkyVTPgRUrVsDExAQ1NTXYsWMH62uZNm0axGIxqqqqlApYcIVEIsGFCxewZ88eVFZWcr0cgUdwc3PjtO/xYCDtRktKSljvA8O11Wh5eTnv3/cnT57k7H5IioTa2tq0v45Jq9GVK1cK/T76gUwuYEokHAr9CEmKi4tpc59obW1FV1cXRCIRY2eGe/fuobu7Gx4eHry3ox7K5Ofn4+DBg7x02lEGHR2dJ1bWKuOo8qi9cGpqqkLj0OEYdefOHaXHIKmqqsK+fftQX19P25gCg0NfX79PHGGoioRD2WoUALWP43v8RkBAgD34HQHhEHmbCQ93DAwMqGxlRbPTBgPblYRSqRT79+/n1N9eJpPxLmOSFGnp7ifSm95ZwapSSQj09Ix47733oKGhgZKSEloPQ3TDhEjI50y0rq4unD59mutlDAk0NDSwaNEiAEBkZCSSkpJYnd/AwABTpkwBANbnVhaCILBnzx7cvXuX66UIDICTkxPXS3gqFhYWsLGxAUEQrFcTkpWEXImEDx484GReeaioqMCtW7c4mZupfoTt7e24ceMGAMFqdCBIkZDu6g9yPGX7EXZ3dzO6d5cHiURC29+pubkZQM/5hKk+meR+Xqgi5J6ysjLs3r2b9qo0rnmSzbky13NSRCPbYCjal5AOkbCgoIAWO2aJRIIff/xR6OHNEZqamn3iU0PRAhgY+iIhmUjW1dXF8UoEBAT4giASCtCCSCSi+tIwKRKSTUjptk8aiPT0dF7YPJC9f/gCeUhgsqKTbOINgPe2b49ibW2NDRs2AGC+2lIZnJ2daR+Tz88VGUQSoAcHBweqZy8X1n8hISEA6K/YYAMhY5PfxMXFqUTTc9LakO1rGxngU1awUJTedpp8hqtqR3IfTvce7Y8//kB7ezvc3NwQFhZG69hDBQMDAwCgPbmP7AOmbJJkYWEhr5K56KpGJUWQ3j2y6IZssTBmzBjG5hAYPA0NDYiIiEBERMRjlXKqysiRIwf8njKvbTL5nWxhomiFOV1CEN+dGgSeTl1dHXbu3Ini4mJkZmZi//79XC+Jdtzc3Gip3ucz5D5RFZLvBAQE2EG4QwvQBplRzqRISGbRsZHtQhAEZxajj8Knw49UKqX+LkuXLmVsHjLT2dbWlpGKN6YhAzl8DmaOHz8eq1atom0DvHDhQqq6S2B4QIrCdGQXy4u+vj4A+oOxTCMSiRi9dgooT01NDU6cOEGbFR5TkPcXtjO4SVGSq3szUz3H6IYrMYYUcZ9kXScvHR0d+PXXXwEA7777ruC4MgBkAgjdQfCHDx8CUD4RixyHD5iamlIJpspCioRMJqqR1z261ixAD1lZWfjuu+8QExOj8tUwpqamVPLPowQHBys8LmkFSe7VyZ7G8uLn56fwGkhmz55NiyOUlpYWZs+erfQ4AopTX1+Pffv2UXuDocacOXO4XgLj+Pj4AAAuXrzI8UoEBAT4wtCsCxfgBHIDyqRIqKWlRfWdYJqSkhLeWPLwyWs/LS0NTU1NMDY2xsyZMxmbh8zAd3FxYWwOJiGDp3wXMEaNGgUXFxfcvHkTqampCgnSLi4uWLhwIWdVJQLc8WjggU309PQA8P891h92dnYIDw9HZGQk10sRGICcnBxcvHgR4eHhXC9lQMi9EFP2egNB2nsp0yNJGezt7TmZV164EglJBwwHBwfaxjx79izq6+vh5OSE9evX0zbuUIMUCens19je3o6CggIAyolgBEHwSiT09PSkTWwmz0kDCSx0QF73mHRREVCca9eu4cGDB5g9ezZGjRqlsokMfn5+j7nQeHp6KnXG6t0vTkdHhxIGFBnHzs6OqqqVl1GjRtFaiRsaGooHDx6goqKCtjEFBABgxowZrLU34hIfHx9ERkYiOzsbBQUFKtFuISMjQ6nva2ho4IMPPqA+5zvK/r7DBVV7XvmMIBIK0AYblYRkQIqN6iy+VBEC/KkkJAiC6rGzdOlSaGpqMjYXKdC6uroyNgeT6OrqAlANAUNTUxNTp07F1KlTUVtbi+zsbOTk5CA3N/epj507dy5Gjx6tsodxAeUgAw8lJSXo6upidVNGvsf4XK37JEJDQ1FUVKRwbxgB5rlz5w4sLCwUzrpnGq4qCclgOVeVhGZmZtDW1ua9JSzXlYR0iYSdnZ1UpcA///lP4fD9BJioJMzJyQFBEDAwMKDsTBWhoqKCV+0LPD09aRuLzUpCQSTkL42NjTh27BhGjhyJZcuWqeS1ysfHBxcuXOjztdDQUKXG7G1VGhQUpNTfJSAgQCGR0MjICAsXLqT1vCgWi7FgwQLs3r2btjEFBKysrDB27Fiul8EKurq68PDwwMOHDxEdHY3NmzdzvaQBMTMzg66u7qAS1XR1dQfsm66pqYkPP/yQ5tXRD12/73BBVZ5XVUAQCQVogwwWNzQ0MDYHKRIyXUnY0NDAq8Atk39TecjPz0dFRQXU1dWxYMECRuciRUI6KgkPHDjQ50Dk4+PD+OZPVQUM0v4pLCwMnZ2dyM/PR1ZWFnJycvoElxwcHLBo0aJhkWUnMDAGBgbQ1NREZ2cnSktLWc1AVCUhvj9EIhEWLFiAiooK3iSCCDzOmTNnYGJiwsvsWnIvxJXdKFeVhCKRCPb29sjOzuZk/sHChUhIEATtdqMXLlxATU0N7OzsqH7LAv3DhEg4FK1GjYyMYG1tTdt45D2UKZGws7OT2msIIiH/efjwIY4dO4ZVq1axfn9UFjJon5WVBQCwtLRU+lpubm4ONTU1yGQyjB49WqmxfHx8cPbsWbkft3z5ckYSi2xsbBAaGoq7d+/SPrbA8GTBggXDqm+mr68vHj58iMuXL2PTpk28/d0dHByQkZFB7XGfhJmZGa1uGlww3H5fAf6gWrsmAV7Dht0oublkWniJj49ndHx54UsAOS4uDkDP5olpa0k6KwmPHz/+2NdefPFFrFmzRumxB0LVBQygJyNn5MiRGDlyJAiCQFVVFXJzc6Gvrw8fHx/ebiIF2EMkEsHc3BylpaUoLCxkVUjpbTdKEIRKVrNqa2tjxYoV2LlzJ9dL4Q0uLi7Iy8vjehl9OHDgAF577TXeJUVwUUlIEATnPQmBHstevouEpGDEJu3t7VSFJR29G2UyGSIiIgAA77zzDrS0tJQecyjDpEiorKhGig58gE47SKlUSp09mRIJyeppkUhEWx9vAWbJycnBiRMnsHz5clrtf9nAz8+Per+GhoYq/V7R0tLCM888A5FIBCMjI6XH8vf3x4MHD576s9bW1nBycoKPjw+jVb7Tpk1DRkYGtTcREFCUsWPHMvpa5SMjR46ElpYWKisrkZycjICAAK6XNCAODg5Ki2EymYyy6PT09OR1PIuO33e4oErPK98RREIB2mDDbpQMTjBZSdjZ2UmJYXyhoaEBMpmM04tdZWUlcnNzIRKJsGLFCsbno7OSMDg4mAqitrS0IC0tDbt27QIAxoRCVa0kHAiRSARLS0tYWlpyvRQBnkGKhAUFBZg8eTJr8/YWKLq6uhi1P2YSKysrLFiwAGfOnOF6KZwzd+5chISEoLGxEffu3UNCQgLa2tq4XhYAICIiAps2beKseq4/uKgkbG9vh0wmA8BdJSGgGn0JuagkJG0XzczMaBH0SkpKUFlZCR0dHbzwwgtKjzfU4WslYWNjI9Xrmw/QaTXa2NgIgiCgqanJWCIHKT4YGRkJgScVIjMzE6dPn8aSJUtU6nkbOXIk9Vr29fWlZUy6KsuBHsvR/kRCGxsbODo6wsnJCQ4ODqztEbS0tDBnzhwcO3aMlfkEhibW1taYMmUK18tgHXV1dXh5eSExMREXL17ktUhIB+3t7VRf1paWFirpWEC1EZ5X+hBEQgHaYKOSkA270aSkJMbGVhSZTIampials/+UgexFOHnyZFotgvqjs7OTKq2no5Jw5syZfYJl5ubmuHr1KqNCISkSSqVSSKVSlctiFRAYLGSCSGFhIavzamtrQywWQyaTQSKRqKxICACBgYEoKioaVGb2UGXatGkICQkBABgaGmL69OmYPHkyUlNTcffuXc4D3DU1NThx4gTWrFnDm2AjF5WEZLBcLBZz2u/J1taWs7kHCxciIWlPT1dCDylQBQYGclo5qiqQIiFde77W1lYUFxcDUE4k5FMVob6+Pq0iP+m2Ym1tzdi1WehHqLqkpqZCQ0MDCxYsUBnHCXV1dbz88svU53zD0dERfn5+qK2thYODA+uiYH+MGjWqj02rgMBgGDFiBEaNGgUvLy/Y29vzZn/PNn5+fkhMTMS1a9fw+uuvC64RAgLDGP7tOgRUFjJQzEZPQqaqswiC4K2nfV1dHWciYUNDA5KTkwEAq1atYny+iooKEAQBfX19RprwktVOpFBIEATWrl1L6xykSAj02CH2/r+AwFCCTBBhWyQUiUTQ1dVFS0sLJBIJDAwMWJ2fTkQiEebOnYuysjJUV1fL/XhLS0tUVlYysDJ2GDt2LCZMmPDY19XV1REQEAB/f3+Ulpbi7t27SElJ4WCFPeTk5CAiIqLPvZgLS0kSMmGKTbGut9UolwFXTU1NBAQE8DKxi4RLkZCuZC4y4KpsH6vhAt2VhKSlrqGhoVJZ0XzqR0in1SjwZ/Usk4kDgkio2iQmJkJDQwPh4eEqJRTyFZFIhCVLlnC9jD6IRCLMmTNHEAkFnoqRkRE8PT3h5eUFW1tblbkmMImjoyMMDQ3R2NiIuLg4TJ06leslCQgIcAR/dx8CKgcbIiGZxcxUJWFTUxNqa2sZGVtZyEMwF9y6dQsEQSAoKAijRo1ifL7eVqNMbdx6C4W7d++GtbU17RsidXV1dHd3o7OzUxAJBYYspEhYXFzMetVsb5FQ1dHU1MSKFSvwww8/yPW4UaNGYcmSJairq1PJ3oYBAQGYOXPmE6/1IpEIdnZ2sLOzw6xZs3Dv3j3ExsayuMo/ycnJ4WTeRyEIgkqYYvM9R/bm4oPt6pgxYwSR8BGYEgmDg4NpGW+oQ7dISIfVqEQiQW5uLi3roQM6rUaBP89HTPaRIq97TPdjF2COu3fvwtzcXEh4GMIYGRlh5syZiI6O5nopAjzD1NSUEgatrKwEYfARRCIRfH19cePGDZw5cwZTpkwR/kYCAsMUQSQUoA2y0ozJLEvyIMhUCXxHRwcj49IBF8EmEjKTefny5azMRwb8R4wYweg8kydPRmtrK+Lj43H9+nVaRcJTp06hu7sbmpqagic2T+DSGm8oY2RkBA0NDXR1daG0tJTVBtuGhoaoqqpCa2sra3Myibm5OWbPno2oqKhBP8bU1BQaGhrQ19fHiBEj0NTUxOAK6cXU1FRuCzB9fX1MmTIFISEhiI2NRXx8PIMr5C9FRUWUKM9m4JoUJrm+nnZ2dlI26HyFqf5oT4IUp9rb22kZjxS9BKvRwUH+3emyvy4pKQHwZzKOIuTl5dGyFjrQ09ODk5MTrWOSZzcmzwympqYAeirSOjo6eJEkISA/Q6VPvMDAhIWF4eHDhygqKuJ6KQIMo6enBz09PWhraz/xw9TUFObm5oLo9RQCAwMRFxeHxMREXLhwAXPnzuV6SQICrJGRkfHUnzEzM2M1zsUVgkgoQBtks+hJkyYxNgeZCWtlZcXI+Hz2IefqQNrS0kKJs3Q1T38a5O/KRuB/1KhRiI+Pp9WKqaqqiqromTVrFufBVIEehGpOZhCJRLCwsEBpaSlyc3NZ3TyZm5sjOzub0V64bOPi4iLXz9+8eRMSiQQVFRUqJRACPUKTovddPT09zJkzB6WlpVT1+XCCFEddXFxUuh+nvBAEgYyMDERFRfH+9d6fhS7TkBWEZHKXstjY2CAtLY03FbR8h7wX0ZUcVlVVBUC5CjY+2e/5+voydtZiMgDs7+8PCwsLVFVV4ffff2el9YIA/QQEBHC9BAGGEYvFeO6555Ceno7o6Gje7xMEFGPixImYOnWqIPzRiImJCaZNm4ZLly7h66+/RmBgIG2uFAICfMXMzAy6urpYv379U39WV1cXGRkZQ14o5K8iIqBSlJeX48KFCwDAmId1c3Mz1XPJ0tKSkTn4vNHgqoFwcXExgJ5ApL6+PitzsikSkpufsrIyqueIsnz33Xfo7u6Gvb09goKCaBlTQHnU1NSEagiGsLCwAADk5+dzMu9QCgKYmZnJLfrcu3ePqjhRJZTt5ycSiTB+/HiaVqNakP2T6bbu4zPV1dU4fPgwjh8/zvv3vIWFBSfPTW+RUCaTKT2enZ0dNZ7A0+GbSEgQBK8EXlUVadTU1DB79mwAwL59+zhejYAihISECMmCwwSxWAwfHx+88sorGDt2LNfLEaCZSZMmCQIhQ4wdOxYODg7o7u7G559/zmnfdQEBNnBwcEBGRgbu37//xI/Dhw+jra0NNTU1XC+ZcYRKQgFa2Lp1K7q6uuDt7U27jQwJWUVoaGjIWFWdUEn4OKRdh4+PD2tzsikS6ujowNjYGPX19cjKylK6786NGzdw48YNiEQizJ8/X9jA8gx9fX3abNgE/oRM3GC779FQFAnFYjFsbGxQUFDA9VIYh47D56hRo2BiYoK6ujoaVqQ6kCIhG32Ce0Pe0wiCYG1OiUSC2NhY3tuL9mby5Mmc3P/Nzc2hqamJtrY25Ofnw9XVVanxbG1tAQgi4WAh70V0JdVVV1cDUFwkrKiooPrpcY21tTVjSZ5sEB4ejkOHDiE6OhrFxcWwt7fnekkCchAWFsb1EgRYRlNTE7NmzYK/vz/Onj2rksl0An2ZNGmS0C+PQcRiMRYvXowffvgBycnJ+O2334Zc5byGhgbefvtt6nOBoYEyz6uDg8OQrw6UB/4qIgIqQ3l5OX744QcAwHPPPcfYTZtpq1FAqCTsD7KSkE2RkKz2YqvPmI2NDQAobTna1taG7du3AwDGjRtHCRgC/EHoD8kMXFUSkn2ahpLdKPBnYH6oQ4dIKBaLh101oVQqxf379wGwLxKyCUEQSE5Oxvfff69SAiFXVYRAz/uBPOgmJSUpPR5ZScinajQ+Q96L6KhYamlpofbBivbb45O4q6pVhCQ2NjYICAgAQRA4cOAA18sRkANPT09OesQK8ANLS0ts3LgR8+fP53opAkowefJkoYKQBYyNjTFnzhwAwK5du1g/2zONpqYmtm3bhm3btg2rdg1DHeF5pQ9BJBRQmq1bt6KzsxPe3t4YPXo0Y/OQIiGTWahCJWFfurq6qF5PbPUjBFRXJNy/fz+qq6thZGSEyZMn07E0AZphyzJ3uEFel8vLy9HW1sbavEOxkhD4MzA/1KHLxsbPz29YvbczMzPR0tICbW1t1jMf2QrOVFZW4ueff8apU6d4Uwk1WCZNmsRpEMvR0REAPSIhmbBQUVFBWV8KDAyddqNkFaGOjo7CAQ8+iYRsJhsyBRk43b9/Py12vgLsIFhOCohEIgQHB+Ptt9+Gv78/18vhJebm5ggJCcGKFSvg7OzM9XL6MHnyZEyZMoXrZQwbAgMD4eHhAZlMhk8//RQdHR1cL0lAQIAlBLtRAaWoqKhgpYoQYEck5HNmEheVhKWlpSAIAmZmZqxaBJGCaFdXF7q6uhi3AqBDJMzKysKJEycAAPPmzRPsC3iKUEnIDLq6utDX10dLSwvy8/Ph7e3Nyry9RUKCIHh9DZcHoZJQPtTV1TF27FhER0fTMh7fIa1GR44cCTU1NY5XQz9XrlzB9evXuV6GQpibm8PLy4vTNZC2/w8ePIBMJlMqAU5fXx9ubm7IycnBxYsXsX79eppWOTShUyQkRVlFqwhbW1t5Y6/n5eU1JPrBTZo0Cd9++y3y8vJw7do1IWitAtjZ2QnWsIOkvr4e165dQ1tbG5ydneHm5gZTU9Mhs7cGeq7NixcvRkBAAM6dOzcs+ksNhKmpKZycnODs7AxHR8c+yXZ1dXW8qSBTRYGwsrISjY2NsLW1VWo/0NbWhoKCAhQUFKCjowPa2trUh46OTp9/yc81NTWVfs+KRCIsWLAAO3bsQG5uLrZt24b33ntvSFwLZDIZ1U7JwcGB10UiAoNHeF7pQxAJBZSCrSpCqVRKbVSYtBvl88WEC5GQtBr19vZmdVPQu2qytbUVRkZGjM5nbW0NoGdD19DQIPd8UqkUX331FQiCgLe3N9zc3BhYpQAdCCIhc1haWqKlpQV5eXmsiYRmZmYQiUSQSqVobW0dMtVkBgYGGDFixJCrkHwUukRCAAgODh52IuFQtBotLCxUWYEQ4K4XYW+sra2hqamJ5uZm5OXlKb0nCQ0NRU5ODi5cuCCIhE+BFAnpuBdVVlYCULwfIds9gp/EUKnc0dbWxrRp03D27Fns379f5QLXwxGhivDpSCQS3LhxAzdu3KC+lpWVhaioKBgaGsLFxQVubm5wdnam3H5UHScnJ7z00ku4d+/eY1XyUqkUDx484GhlzDNu3DiEhYXBwMBgwJ/hi7A+ZcoUlXJnkkqliImJwc2bN6mvmZqawt7enkpYMDc3H3Cf2NHRgcLCQuTn56OgoIDaB8iDkZERwsLCEBgYqJTtor6+PlatWoWDBw/iypUrcHNzw5o1axQejy+0t7dTlbItLS1CbGiIIDyv9CGIhAIKU1FRgR07dgBgvoqwuLgYXV1d0NTUhLGxMWPzcB3YGQgNDQ1OqgW46EcI/Pn7SqVStLS0MC4SamlpwdTUFLW1tcjKykJoaKhcj//999/x8OFDaGhoYPbs2QytUoAOhoqIxEcsLCyQm5vLauapuro6TE1NUVNTg6ampiH1/Nra2goioRxoaWlh4sSJKi0wDZb4+HgA3IiETO+Trl27xuj4TMKHKkIAUFNTg4ODA3JycpCUlESLSHj06FFERUVBKpUOyepVumDCblTV+xGS1ahDhTlz5uDs2bP49ddf8d133yn8/Agwj7Gx8ZBMpqELgiDw4MEDXL58eUBb78bGRiQmJiIxMRFAT2Wmq6srXF1dYWtry8sE67a2NhQWFkJXVxcODg4D7lvU1NQwZsyYx74ukUiGrEg4atQozJgx46l7OdJliUtUTSBsbGzEiRMnqPgZSW1tLWpraykLeG1tbUowtLe3p4ohCgoKqDY/ytDQ0IDIyEjExsYiNDQUoaGhClfyOzk5Yc6cOTh//jx27doFJycnIfFCQGCII4iEAgrz5ZdfslJFCAAZGRkAeoLQTAao+LjRBbjpR0gQBLXJYbMfIYm2tjZaW1tZ7UtYW1uLgwcPQlNTE/7+/o+91lpaWlBRUYGKigqUl5dT/966dQsAMGvWrCdm5Qlwj5BVxBykJTGbgUmpVEpdH5uamnhxqKULS0tL6t43VJFKpbTaxI4ZM2bIi4QdHR1UoIHL4CdBELSPWVpairy8PNrHtbW1hVQqRUVFBe1j94brXoS9cXR0RE5ODh48eIDly5crNZa3tzf09PRQW1uL+Ph4hIWF0bTKoQeZ2EGHtaYylYQymQw5OTlKr4EO/Pz8WDlfMXFN6g9PT084OjqisLAQx48fx6ZNm1iZV0B+wsLCeHu25wORkZGUM8FgKSkpQUlJCWJjY6GjowM3Nzd4eHjA1dWVF1WGWVlZ+OWXX6jrgaenJxYvXqxURdXT2LRpEzU+QRCoq6tDUVERiouLUVpayti88mJqaopFixYNap+ioaEBKysrxvdNAzF16lRMmjSJk7nlpaGhAXfv3qXiQU+jo6MDOTk5jN+j29vbERsbi9jYWEydOhVjx45VqB1OSEgIKisrcf/+fXzwwQc4ffr0kLAPFxAQ6B9BJBRQiNbWVvz4448AgPXr1zMaFGlvb8fPP/8MAHB1dWVsHgCsCVLywoXwVFNTg46ODmhpaTH+d+8PExMTtLa2ori4GCNHjmR8Pk9PT6SkpCAtLQ1vvPEG3N3d4e/vj8rKSkoYbG5uHvDxbm5uCA4OZnydAsrBpF3xcMfBwQEAkJqairKyMsYFO6lUiv/+978oKSmBSCSi+hMOBZqbm3Hv3j2ul8EKTU1NCtvpPYqmpia0tLQgkUhoGY+PZGVlQSqVQl9fn9VewSRkIKy7u5v2se/fv0/7mEBPNrpEIsFvv/3GyPgkfKgiJDExMQHQ02NKWdTV1TFmzBhcuXIFERERgkj4BMhqdrKiUBna29sBKJYo2NTUhI6ODqXXQAdM29aRlXxsiaIikQgTJ05EYWEh7ty5I4iEPGao2NwyhbJuDu3t7UhJSUFKSgqAnqojd3d3eHh4cNbL8OHDhyAIAiNGjEBzczMyMjJQW1uLVatWUfdFujE3N+/TFsbCwgKGhobQ1tZGd3e3QpaRTGBhYTHo+0l2djZnAuGUKVN4LxCSyfS3b99WiYTOmJgYJCYmYubMmfD09JT7vTlnzhykpKSgs7MTlZWVlK2jgIDA0EMQCQUU4siRI2htbYWdnZ3c1ozysnfvXlRVVcHIyAjjxo1jdC6+Nq/mQtgoKSkBAIwcORLq6uxfKpycnFBcXIz09HTMmDGD8fk8PT3x6quv4vbt20hMTER2dna/FVG6urowMjKCsbExjIyMYGRkBCsrK9ja2vKmgkBgYEaMGIGQkBDKrk+APoyNjeHq6orc3Fz88ccfeOmllxibSyqV4tNPP0VMTAxEIhFWrlzJ2OGfbaRSKY4fPz6g9dNQo6KigjaR8OHDhwoJhDY2NtiwYQMKCgrw8OFDPHz4kLd//8zMTAA9lWJc3HPIamy6BYjOzk7Kzoxuzp07R6u17UB0d3czWq0gD6SIS1c/65kzZ+LKlSs4evQovvjiC4Wy0YcDAQEBSEtLQ25urtKWXGQljCLvczrEYbpg+r3n5uaG2NhY3Lt3D93d3aycWRwdHQH8eT0W4B96enq0Xf+GKrNmzUJlZSV15leWgoICFBQUIDo6GiYmJpRgaG1tDZFI1OcDwID/VwYylvPaa6/B2toa77//PqqqqrBz506sXLmSkcTnY8eO9alYraio4OUeMiMjA0VFRVRS56PU1dUhMzMTmZmZj1lmsoGvry/Gjx/PSQLcYJFKpUhLS8Pt27dRXl7O9XLkoqGhAcePH4eTkxPCw8Pl+juLxWJ0dXUB4KZ4QUBAgD14JRJ+9dVX+P777yESiXjVbF2gLwRB4PvvvwcALFq0iFEbj4yMDJw4cQIAMH/+fMaDL3wVCa2trVmfk9wcent7sz430HMAv379OtLT05UeKzk5uU9Ay8rKql/h1dTUFPPmzcPUqVORmJiI5uZmSgwk/+VLAFBAcSZNmiSIhAwRGhqK3NxcnD9/Hhs2bGDEKrm7uxuffPIJYmNjIRaLsXLlSlaqjdkiKiqKk8M5V1RUVND2/JE2nPIyf/58aGhowN3dHe7u7pg3bx7KysoowbCqqoqW9dEBmbE8UJCHachKKYlEQqtVLB33+oFoaGhgbOzedHZ28maPQAozdK0nJCQExsbGqKmpQWRkJBYsWEDLuEONgIAAHDlyhHOrTz6JhExUHffG1tYWurq6aGtrQ0pKCgIDAxmdD/jz+vvw4UPG5xJQDGNjY66XwHs0NTWxdu1a7N+/n+qBShd1dXW4c+cO7ty5M+jH2NnZ4fnnn1cqtkTGchwcHODh4YGffvoJ77//PjIzM3HkyBHMnDkTYWFhtCZZMWGTzhQXL17Epk2bIBKJQBAEKioqKGGQq71ucHAwxo0bx+tkT4IgkJqaikuXLql8v/iCggL89NNPGD9+/KAT8Ts7O6nEJUEkFBAY2vBKJKyvr0dBQYFQjcNzrl+/jpSUFGhrayM8PJyxebq7u/Hll18C6OlnwYblZW1tLeNzKAIXIiGZVciVfRaZpUtH4PD8+fN9/i8Wi/HKK68MuBnV1dXF+PHjlZ5XgJ/o6+vD1taWV30ihgpubm4wMjJCQ0MDLl++jHnz5tE6fnd3Nz7++GNcu3YNYrEYq1atgoeHB61zcElycvKwE7DpsjNqampSKMFszJgxj91jRSIRbG1tYWtri2nTpqG+vh4PHz5EVlbWYzaCBEGwGpAnK1e4EgnJSkKpVIru7m7aKsqYqiJkk87OTq6XQEEKM3SJhGpqapg+fTp+++03HDx4UBAJB4AUqOgQCZXpsVdXV6f0/HTBtEgoEong7u6OBw8e4Pbt26yIhKSFalVVFerq6ngd3B6u8FUkzMrKQlVVFfT19akPAwMD6OjocNI/UUdHB+vXr8e+fftosUlWhpKSEjx8+BCenp4KPb69vR1tbW0A/nyPmpub49tvv8XXX3+NyMhIXLx4ERUVFVRy2HCjtLQUsbGx6OjoQGZmJqfP+bhx4xAWFsZ70amkpARRUVG0VdzyhZs3b8LV1XVQ1qGk/bmGhoZQoS0gMMThlUgooBqQVYQzZ86kMsqZ4Pjx48jNzYWWlhZmzZrF2Dy94WslIdu9tjo6OqiMQq4qCZ2cnAAAaWlpSlcrBAcHU/ZDxcXFKCsrQ2RkJNauXUvHUgVUEHt7e0EkZACxWIyQkBBER0fj9OnTmDt3Lq2VRvv378e9e/eGpEBYUVGBU6dOcb0M1qFLJHzw4IHcjzEwMMDUqVOf+nPGxsYICwvrtxebRCLB559/LvfcisK1SEgGMmUyGTo6OmgJstXW1qKoqIiG1XHLUBYJAWD27Nn47bff8Pvvv6O+vp63QXguIXuglZaWoq2tDbq6ukqPqcg9lK3q2cHAtEgIoI9I+PLLLzM+n46ODszNzVFdXY2HDx8qbS0rQD9GRkZcL+ExcnJyEBER0e/3RCIR9PT0+oiHbm5urJzDR4wYgWeeeYaK8XBJfHy8wiIhGcexsLCAjo4O9XVNTU288847cHNzw44dO5CcnIzq6mqsWrWKNrt7VSI2NpazuXV0dDBmzBiEhob2eY74SFNTEy5fvozk5GSul8IYV65cwcaNG5+6zyBbDPBd0B0M6urq+Otf/0p9LjA0EJ5X+hD+egJyUVpaStl/Ll68mLF5cnJysHfvXgBAeHg4lbnOJARB8FIkNDc3Zz3TjRRPbGxsOAsC2dvbQyQSoa6uDtXV1UoJpR999BH1GioqKsLzzz+P7OxsZGVlDSmRQWDwODg44Pbt21wvY0gSGBiIK1euICcnB6mpqfD19VV4rO7ubsTGxuLEiROUzaJYLMbq1avh7u5O15I5p729HceOHeN6GZzQ0NCAjo4OpaxpCYJQSCQMDw9XqYxYmUxG2dtxJRKKxWLo6uqipaUFEomEloDBUKgiBKBQP0ymYEIkdHV1hYuLC/Ly8nDs2DH85S9/oW3soYKZmRns7OxQUlKC3Nxcpe5/ysCnSkKyjxGTuLq6QiQSoaioCAUFBVSiIZM4ODgIIiGP4VsSQ3NzM44cOTLg9wmCQEtLS59edklJSfDw8GAlDmBqaorNmzdj9+7djM/1JPLz81FdXQ1zc3O5H9vbavRRRCIRli1bBmdnZ7z77rsoLy/HTz/9hDVr1nC2nxpOGBoaIiwsDEFBQbyxZR+Irq4u3Lx5k1MxlS1KSkqQnZ391HgYWUk4FERCLS0t7Nixg+tlCNCM8LzSB/ueBgIqzc6dOyGTyeDv7w8XFxdG5mhpacGHH34IqVQKd3d3KiuXaVpbW6ksGT7BZT9CrqxGgZ4LPfm709mryMHBAatWrQIAREZGspLhLNA/XV1duHDhAk6dOoWsrCyqhxIbkDY0AvSjo6MDPz8/AMDp06cVGqOhoQGHDh3CmjVr8MknnyAjIwNisRj+/v548cUXh5RASBAETp48yaseUmxTWVmp1ONLSkrktgt3d3dXOFudK4qKitDe3g4NDQ1O9gYkpIsEHXsmmUymkMDLR4Z6JaFIJKKcPQ4ePEjbuEONgIAAAMpbjipjN8qn+wkb+2xtbW1qX3D58mXG5wP+3EeS1d0C/IJPIqFMJqOSrOWltbWV5tUMjI2NDV555RXORTNFbfdJkfBJZ7ygoCDs378fLi4u6OjowIEDBxAZGYmYmBjq49q1awrNL/A4dnZ2WLFiBbZs2YKwsDBeC4QEQSA5ORnff//9sBAISWJiYp663xhKIqGAgMCTUbqSkE6hiE/WKAKPI5FIsHPnTgDAkiVLGJmDIAhs27YNpaWlMDQ0xOLFi1nrUcnHKkIAsLKyYn1OrvsRkjg5OaGsrAzp6emYMmUKbeM+88wziI6ORk1NDW7evInJkyfTNrbA4GhpacEvv/xCVa0mJydDV1cX3t7e8PX1hZ2dHaPvfX19fZiYmPAq234oERISgsTERMTExOCvf/0rTE1NB/W4nJwcnDhxAhcvXoRMJgPQ81yNHj0awcHBjFpcc8XVq1dp6V+lylRUVFB9aBUhKSlJ7sfMmTNH5Xpgk8FoW1tbqKmpcbYOOkXCnJycPpUTqsxQFwkBYPr06di1axfi4uKQk5MDNzc3WscfCgQEBODs2bOcXdfb29t5lfTIVjKer68vsrKycPny5UHZpykLKaQIIiE/4ZNIGBsbi8LCQoUe297ezqp1qpmZGTZs2ID79+/j3LlzrM3bm/j4eEyfPl1upwcyWexpiaDW1tb4/vvvsXXrVly9ehV37txReK0C/ePl5YWwsDCVScotLS3FhQsXhmUrkoqKCmRkZDwx7jeU7EZ7u8eZmZmp3FlQoH+E55U+lBYJCwoKIBKJlO4ZJsB/Tpw4gaqqKpiZmWH8+PGMzXHt2jWIxWKsWLGCll4eg4WvIiHb1QIEQfBGJHR0dERcXBzS0tJoHVdHRwcvv/wyPv74Y1y/fh3+/v6MH8DIDC3hOtlTNRQREfFYs/S2tjbEx8cjPj4exsbG8PHxgZ+fH8zMzBhZh4ODgyASMoS1tTXs7e1RXFyMkydPYvHixZBIJOjo6Hjs346ODrS1teHGjRt9KopsbGwwZswYeHt7cyqIMElWVpaQsQzl+hJ2dXXJfY+YNm0arwKIg4XrfoQkpH03HULEULEaBYa+3SjQc/AODg5GfHw8Dh06hI8++ojW8YcCdFcSyrtv5FMVIcCeSOjh4QF1dXWUl5cjPT2d8V5u5HWYtIAW4A/W1tYYMWIE18sAAOTl5Sm1z2tra6NxNYNDJBJh9OjR8PDwwPnz5zl5jScnJyMkJESuxzzJbvRRdHR08P7772PMmDHIysrq872uri6cPXtWrrkFehgzZgzGjBmjUnvs+/fvD/vnOyYmBqNGjYJY3L/RIFlJOBSSddva2qg2Ri0tLay0tRJgHuF5pQ9aexIqY4siwH9+/PFHAMDChQsZaQZaVFSEH374AQAwe/Zs2Nra0j7Hk+CrSMh2JWFNTQ0kEgm0tLTg6urK6tyPQm7y6bQbJZk6dSrOnDmDpKQkXLx4EStXrqR9DhKCILB37160t7dj3rx5jFn18h2pVIq7d+/i4sWLT/3Z+vp6XL9+HdevX8fUqVMxadIk2tdjb2+vUAWSwOAIDQ1FcXExjh49iqNHjw7qMSKRCF5eXhgzZgzj1aRc09nZiYiICK6XwQuUsRt9+PChXOKMWCxWqmqRS0iRkOv101VJ2N7ePqSqcPhUSUj2gWPC2mvWrFmUSPjhhx8O6eu0IpAiYX5+Pjo7O5V+DuQ9X/NNJGSjJyHQ81r38vJCcnIyoqKiGBcJyQqZnJwcdHV1sd4/fjhibW0NIyMj6OjoUB+6urqPfa6np/fU6xJBEJDJZIwmoUmlUpw6dUqpMbgQCUlGjBiBVatWISMjA+fPn2fV+jQpKUkukVAqlVKJn4aGhoN6jEgkQnh4OMLDw/t8vbW1ddiLRvJgYGBA9RtUpr84F9y5cweRkZFcL4NzampqkJ6eDh8fn36/L9iNCggMH5RWekxMTFBfXw9/f3+Few+RfPnll/j++++VXZIAQ5DBdKasGRMSEkAQBBwcHOTOHKMDPtpdWVpasr7Zys7OBgD4+PgwIgbLQ25uLgAM2qpQHkQiEbZs2YJNmzYhIyMDxcXFjFlitLW1UfYVhw4dQmBgIGbNmqVyG2llKCgowPnz51FdXS33Y2NiYuDl5UV7ReFwFWvZwtPTE1ZWVqioqIBIJIKmpiY0NDQG/CArVPiS/c00gsX6nygTRJf3miKTybB//37Y2toiJCQE3t7enN/rBkteXh6AnipbLqGrMp7NgCMb8Cl40ttyh24mTJgALS0t5OfnIykpCYGBgbTPoco4OzvD2toa5eXluHPnDiZOnKjQOGSgu6mpSa7HyWvRxzQDVSYwQWBgIJKTk3Hu3DmsWrWK0YRTc3Nz6OnpobW1Fenp6fD392dsLoGeBOIxY8bQkpRQX1+PiIgIVFdXY8SIETAyMoKxsTH1L/m5gYGB0vMp+3gTExOlHq8sZPKes7MzoqOjWav+l3dfJhKJYGxsjLq6OnzwwQf48ssvOWnZMtzw8fHB/PnzeXffGQyNjY2CQNiL4uLiAUVCMgmGy6QFAQEBdlA6KhIcHIzo6GhkZ2fD3t5eqYPAYLN+BNinpaWFEtHMzc0ZmSM/Px9AT2YmF1nJfOrfQRIcHMz6nGRWP1OWsoNFIpEgKioKALBp0yZG5nB2dsbcuXNx7tw5XLx4kbEeJo++thITE5GTk4P58+fDw8OD9vn4RFNTE6Kjo5GamqrUOJGRkVi3bh2tz4+RkRFGjhwpWEUxhJqaGl588UXGM7VVFTYDp3xnoEPpYFD03l1aWorS0lJcvHgRwcHBcHFxgVQqRVdXV78f3d3d6OrqonplkkilUoXXLi9FRUUAehKIuIS0itbR0VFqHFUMKj0JvtzPu7u7qcQkPz8/2sfX1tZGSEgIbty4gdOnTwsi4SOIxWKsX78e27Ztw8WLFxUWCd3c3BAVFSW3HTPXlcaPwmZCnJOTE1xdXZGbm4u9e/fi/fffZ2wukUgEd3d3JCUl4f79+4JIyAAODg7w9fWFl5cXbS1I6urqcODAAUp8b2pqQlNTE3V/7Y2amhqMjIzg7OyMmTNnyp3QpKamhhUrVmDfvn1yr1NXVxeTJ09m3VlpIHR0dLBw4UJMnjwZaWlpSE1NRXl5OWPzyZuULhaL8cwzz+DgwYMoKyvD3/72N3z11VecJ1UNVYKDgzF69GiVFmLJOJNAD4+2gekNmXCmaG9VAQEB1YE2kbC9vf2JJcoCqg15QNXW1lY6KDQQZIY8V8EvPvWSIWEiuPMkWlpaUFxcDKAnU5xLrl69iubmZjg5OWHWrFmMzbNhwwZERUWhpKQEmZmZ8PT0pH0O0qKhN83NzYiIiICfnx9mz57Nav9NNpBKpbhz5w6io6NpGS83NxdZWVkYOXIkLeORhIWFCSIhg4hEIkEgHADh7/InytjCKZvg09bWRlkb8xmCIKggJtlzgSuam5sBCCJhb/z8/HhjN1haWgqZTAYTExPGAqTjx4/HjRs38Pvvvwt9Cfvh2WefxbZt23D79m00NjYqlAjr5uYGQP6erRoaGnB3d6ecQbiGbdeMmTNnIjc3FzExMVixYgUj+3oSDw8PSiTcuHEjY/MMJ8zNzeHr6wtfX1/a+8U/KhA+DalUitraWtTW1qKoqAirV6+Wu9eavb095s+fP2j7SjMzM4wdOxZ+fn68dDkwNDTEuHHjMG7cOFRWViIhIQHJycm0Jlt7eHgo5PZiZGSEDRs24MCBA6isrMSrr76K119/HZMmTRJssWnAwsICo0ePhp+fn8rv33JycpCRkcH1MnjFk6zKySKRoqIiEAQhvJ8EBIYwSu88Ro8eTX1+//59QSQcopAHVKYsLwiCoCoJuQp+8a2SMCgoiPUNGCmWjBo1irGK0cFy5swZAMCLL77IaDDdzMwMa9aswaFDh3Dp0iV4eHjQPt+TXlvJycnIzc3F3Llz4eXlReu8XJGfn48LFy4oZC36JC5evAgPDw9aN6aOjo6wtLRUqieagIAiCJWEPYwaNUopsYlv926mqK2tpRJOuL4/kwFWZZNb+CKq0YGvry/XS6AgM719fX0ZC+SMHTsWYrEYDx48QH5+PpydnRmZR1Xx8fFBUFAQEhIScPnyZSxdulTuMUiRsLGxEW1tbXK939zc3IatSGhpaYmAgAAkJSXhp59+wjfffMPY+4CsHr5//z4j4w+EVCrFRx99hKtXr/b5end3N6vroAt9fX34+vrCz88PlpaWjDxftbW1OHDgAJXkIi9VVVXYvn071q9fD1dXV7keGxwcjPLy8ie+TlxcXBAWFgY3NzeVCcBbWlpizpw5mDFjBjIyMpCYmIiCggKlx505c6bCjx0xYgQ2bNiAw4cPo6qqCh9++CHGjRuHv/3tb5zvnVQVX19fjB49mjO3L7rp7u7G+fPnuV4G72hoaBhQADQxMYFIJEJrayvq6uoYaQUkICDAD5SOUJF2iARBsL5BFmAPpkXCqqoqtLa2QiwWM9I/ZTDwLdDYW4BnC1Ik5NpqNCcnB2lpaVBTU2MlM5fMDK2rq2PkOtZfJWFvWltbcfz4cRw/flylezQ1NzfjxIkTOHjwIO0CIdCTAfwkKwxFEIlEGDNmDK1j8hVra2vY2NhQH6psETMUECoJe1C2Yp5v926mIKsITUxMlOrhqCwEQVAiobKVhCKRSOWz0QFAT0+PVz1uydcKk24UhoaGlDD6xx9/MDaPKvPcc88BUNzSTF9fn6oElbea0N3dXaE5mYCL/ttTp06FmpoakpOTERcXx9g8pEj44MEDuXtHKsO7776Ljz/+mKqCJz9u3brF2hroYubMmXj99dcxa9YsWFlZ8VIg7M3hw4cRFxdH9eYdLHPmzIGDg8NjX/f398dLL72EZ555Bu7u7iopwmhoaMDPzw/PPfccXn31VYwfPx56enoKjRUaGqp0LMjAwACbN2/GpEmTIBaLERcXh3Xr1uHUqVOPWcYLPI6uri7c3NwwY8YMvP3221i6dCkcHBxU8rXZHzdv3nxi1dxwpbOzc8CYlbq6OlVFLViOCggMbZSuJHR0dMT27dshlUrh5OSk1Fgff/wxPv74Y2WXJMAA5OGUqawRsorQ1NSUs8Apn+xGbW1tYW1tzeqcEokEubm5ALi3GiWrCJctW8aK/ayuri6ee+45fPPNN4iNjYW/vz+tgcuniYQk6enpSE9Px/Lly5Wy32MbqVSKu3fv4uLFi4zPVVdXR7v9kK+vLy5dujQkm3Hb2NggICAAPj4+jwX0JRIJPv/8c45WJiCIhD3BY2WD2cNFJCStwLm2Gm1tbaWCbHTYz2tpafFq/6UI3t7evKkMlslk1GuFacv68ePH48GDBzh9+jRef/11RudSRdasWYM333wTWVlZCldburm5oaysDBUVFXIJ0cbGxvDw8EBWVpbcc9INF4kAI0aMwNixY3Hjxg3s2rULYWFhjNxzbW1tYW9vj+LiYnzxxRf4z3/+Q/scj3LgwAFs3boVAPDCCy/Azs6O+l5HR4fK7OuCg4MxdepUhcWkwVJTU4MDBw6gpaWFtjGjo6NRXl6OhQsXDroinuxPuH//frS1tWH06NEIDQ2FgYEBbeviA6amppgxYwamTp2KrKwsJCUloba29rEezwMhby/CgVBXV8fUqVPh7e2NM2fOoKSkBNu3b8fly5fx1ltvCdXv/x9NTU0qedTW1hY2NjYwNDQcMoLgo9TV1T1WgS3wJ/X19QO6FpiZmaGurg5FRUUICgpieWX0oa6uTiVx8dHSWUAxhOeVPmj567366qt0DCPQD8eOHetzofb29uakMTrZmJqpSkJSJOSqH6FUKkVXVxcnc/cHWaHLJjk5OZDJZLC3t4ejoyPr85O0tbVRfexeeukl1uadN28eTpw4geLiYty8eRPTpk2jbWx5g9i//fYbNDQ0qAxlPlNQUIDz588zUjnYH7W1tbRXbKirq2P06NG4du0areNyhYGBAfz8/ODv7y9Y6/AYvogKXOLl5aX0Rn6wSRiqDlkdxtU+iYSsxFBXV6fFLnQoVBLyyWq0srISnZ2d0NPTYzwIOn78ePzwww+4du0aamtrBfupRzA3N8f8+fPx+++/4+LFi/jLX/4i9xju7u64du2a3JWEADB79mxeiIRcVBICPa/PhIQEFBUV4dy5c1i4cCHtc4jFYmzatAkffvghtm3bhpdffpnRJM+4uDhs3rwZAPDMM89g3bp1fb6vCm4kLi4umD17NisJL0wIhCSpqamorq7GqlWrBt2nUF9fH6+88gqAob8HVFNTg6enZ789QQmCQHd3dx/hsLOzE0ZGRkrbmD+KhYUFnn/+edy7dw/R0dFIS0vDCy+8gPXr12PdunWcOjNwha+vL1xdXWFrawtTU9MhKwg+CkEQiIyM5HoZvKahoQG2trb9fs/MzAxZWVnUeURV0dLSws8//8z1MgRoRnhe6WNo706GAJs3b8a6deuoj4CAAGzcuJG1gDwJ03ajeXl5ALjLkOdTFruWlhYnvT0zMzMB9ByqudwsXr58Ge3t7fDw8MCUKVNYm1ddXZ06eMfFxdFqG6RIEDsiIgK1tbW0rYFumpubcfLkSRw4cIDV6xFTfxMu7H3pxsfHB+vWrcPf/vY3zJgxQxAIeY5QSUhPpdNwqSQkD+VcVxLSZTVKouoiobGx8YABFS4gXyc+Pj6MX2NsbGzg4uICmUyGc+fOMTqXqvLss88CAC5duqSQzR3Zl5BM1pQHExMT2qpylIErkVBbWxuTJk0CAPz888+MuUVMmjQJXl5e6OjowEcffcTIHECPxdv8+fPR1dWFiRMnYsOGDYzNxQSmpqZYs2YN1q9fz8p9rLq6Gjt27GBEICSprKzE9u3bqTjGYBCLxUNeIHwaIpEIGhoa0NXVhaGhIczMzGBjY0O7QEgiFosRGhqKV199FR4eHpDJZDh48CA2b96MlJQURubkK/Pnz8fSpUvh7+8PMzOzYSMQAj1tdfjSq5evNDQ0DPg90gZYsBsVEBjaCHWYPCcgIIA65Hd1dSE5ORn79+/Hb7/9hi+++AIvvPACKxvNoS4S8inI6O/vT0t2vjxIpVJq08Sl1ShBEJTV6EsvvcT6xnXChAnw8fFBamoqrl69SlvWsaKvr19++QUvvPACr4KobFqL9kddXR0j45LVd8nJyYyMzySzZ89GQEAAZ4E4AcUY7kEiIyOjfvvzyAPfXACYhG8iIV3BPD7d3xTB19eXV0G23iIhG4wfPx55eXn4/fffKUFM4E/mzZsHsViMmpoaNDQ0yH2OIu2YSbs+ec8H48ePR3JyMqf9l7jcm4wePRp3795FXV0djh07xoiwJhKJ8NJLL2HLli3YvXs33njjDYwcOZLWOVpaWrBw4ULU19fDzc0N//rXv1RqDxEeHo7Ro0ezlhxVXV2NH374gZW5AODQoUOYNWsWwsLCeHU/EOiLoaEhVq9ejfT0dFy4cAFFRUXYsmULAgIC+lQUSqVSucZ96aWX+uxlyL1pZ2cnOjs7B/y8ra0NjY2NaGpqQmNjo9zzysuzzz47bG1WOzs7ceHCBa6XwXuetFcgk49VvZKQIAgqaUhXV1e4Zg8RhOeVPgSRkOd88sknfbz609LS8PXXXyM3Nxd/+ctfkJCQgJ9++onxdZSVlQFgRiSUSqVU/xSuKl/4JBJyUdFUUFAAiUQCExOTfm1B2OLMmTPIzs6GhoYG5SnNJuRB/9VXX0VSUhICAgKUDmIDir++ampqEBkZiUWLFim9Bro4ffo0UlNTOZufKZEQAMaMGaNSIqGVlRWWLVtGZfYJqBaqFOBjAjrEFT7du5mGL3ajQiVhX/hkNSqVSqkMb6b7EZKMHz8ehw4dwvnz59HR0SEkqzyClpYWLC0tUV5ejurqarnPUaampjA2NkZ9fT3Ky8vl3pNqaGggPDwcERERcj2OTrh8TaipqWH69Ok4fvw4jhw5gqVLl2LEiBG0z+Pr64tx48YhLi4O7733Ho4fP07r+O+88w6Sk5NhbGyMTz/9lLbrL9PY2Nhg/fr1rK63q6uLVYGQ5OLFiyAIAuPGjWN9boHBIxKJ4O3tDRcXF0RHRyMxMRFJSUlKjWlkZKT0XoYMcDc2NvYRDhsaGpCRkaHU2GQV73C2BL9z5w6tLlFDlcbGxgG/R8YbampqUFBQACcnJ5ZWRS9tbW3Q19cH0JOAw3RfXAF2EJ5X+lBKJGSyOff777/P2NgC8lFfX08FzukQTB5FLBbD0NAQNTU1yM/PZ6xa8UkYGRmxPmd/mJmZcSKUkpsmd3d3TgLXHR0d+Pbbbymf+L/+9a+cvA6Anr6fM2bMwKVLl3D48GGsWLGCyuRWFAsLC8rOVV4yMjKwYMECXggKMpkM6enpnK6ByRu+jY0NXF1dkZuby9gcdOHr64uFCxcKjZlVHCcnJxQUFHC9DE4IDAxUeown2eIMNcjXCdeVhJWVlQB6qq/pYLC9nPiIs7Mzr5I0bt++jdbWVhgbG2PUqFGszOnh4QFDQ0M0NjYiJSUFISEhrMyrStjZ2aG8vBw1NTUKVZgFBgbiypUruH//vkLnMA8PD3h6eiodaFYEXV1dzvcpnp6eMDExQV1dHdLT0xEWFsbIPC+88ALi4uJw8uRJFBUV0XpmJggCQI/oqUpW5V5eXiojaNJBZ2cn10sQGCQ6OjpYuHAhRo8ejaqqqj7f6+rqwvnz51ldj0gkgp6eHvT09GBjY9Pne+3t7bh//z7i4+PlFrpcXV2xbNmyYfU+7A+hqmhwPCmJRltbGw4ODigqKsLLL7+MH374YdhWpgoIDGWU2rV/+OGHjF1wBZGwh3//+9/UYaCzs5MS6wwMDPDll19i06ZNjK/hwoULkMlkcHJyemzTQgcikQirVq3Cjh07cP369T4Wq2yhq6uLOXPmcG5DwLbNKB8oKSnBBx98gLy8PIjFYnzyySf4xz/+wema3nrrLVRXV+PBgweIiIjAjBkzMHbsWIWvd1OmTEFLSwsSEhLkfqxEIkF5eTkveh6VlZUp1FOHTpgOQC5ZsgR79+7l1JprMPj6+nIeeFN1CILArVu30NjYCDc3Nzg5ObF+DX7mmWeQm5uLxMRExgK4pqamsLOzg52dHezt7SGRSJCdnY3s7GxK8GGbTZs20SIOsd2fmSva29sp23dra2tO10JWqtEljrm7u+PmzZu0jMUmpqamWL58OdfLoGhubsbVq1cBAC+++GIf6zQmEYlE8PDwQHx8PBISEgSRsB/Is1NNTY1Cj1+5ciWuXLmClJQUTJs2DYaGhnKPsWjRItTW1j4WDGcaOzs7VufrD5FIBBsbG9TV1SE3N5cxkdDZ2RlBQUGUy89///tf2sbeunUrrl27hvT0dHzwwQf46quvWHuPqxoaGhoICAhQujpMXvT09Bh7bQkwh42NzWPxLYlEwrpI+CR0dHQwYcIEjB8/HnV1daisrKQ+Kioq+lR/aWlpwdnZGc7OznBxcYGpqakgkAEICwtDamoqZ+ceVeFpbYdWrVqFQ4cOoaKiAi+//DK+//57qneygIDA0EDpCCOZ2fYkRCLRE3/u0e8LN7I/SUxMfOxrzz//PP73v/+xVnH2+++/AwCj9hkLFizAL7/8gtraWiQlJSE4OJixuQYiJCQEmZmZyM/PZ31uktbWVs7m5oJr167hf//7H9ra2mBhYYGIiAhMmzaN62VBW1sb27Ztw7fffotz584hOjoalZWVWLBggULCjEgkQnh4OIqKihQKEhUUFPBCJMzJyeF0flNTU8btcPX09LB27Vrs2LGD0XmUhStr5qFEWloaoqOjAQB3796FmpoaXFxc4O7uTlXHMI1YLIa7uzvc3d3R0tKC5ORkJCQkoLa2VqHxNDQ0YGtrSwmCdnZ2/faOc3BwwPTp09HU1EQJhnl5eYz399PX18f69etps8wcLiIhaTWqq6tLWwWfsmuhSyS0t7eHjo4O2tvbaRmPDWxsbLB27Vra+jLSQXR0NLq7u+Hl5YVZs2axOre7uzslEgo8Drl/U/S6PnLkSAQGBiIxMRG3bt1CeHi43GNoaWlh7dq12L17N6tnDT6IhECPTXNqairjThGLFy9GQkIC9uzZgw8++IA2O2UDAwP8/vvvCAwMRFpaGrZv34633nqL9zGTwcSJmCAkJIR1kXDatGmC3bIAo4hEIpiamsLU1BReXl7U1zs6OlBZWQl1dXVYW1vzwn2Ib6irq2Pp0qX48ccfuV4KbxkzZsxTEzh1dXXx7LPP4vDhwygrK8Mrr7yC7du3096HV0BAgDuUEgljYmKe+P3vvvsOJ0+ehFgsxqxZszB9+nS4ublBT08Pra2tyMnJweXLl3Hx4kXIZDIsXboUr776qjJL4gUEQdC2ad+1a1efIIS3tzcCAgJoGXswNDc3U9V148ePZ2weLS0trFmzBt9//z1n1YQikQiLFi3CN998w+q8vWltbaX19TNYyPnYOswRBIE9e/bg6NGjAHqyln799VdGKlUVRUNDA2+99RZcXV3x3XffITk5GbW1tVi1apVCQVoNDQ0sW7YMO3fulPux+fn5jL7/BgvXNpxLly5l5bpgZmaGZ599FgcPHmR8LkXQ0NBgRcAayrS2tuLs2bMAenp3kf2iSMHs/PnzsLCwoARDOzs7xg/d+vr6GDduHMaOHYvi4mIkJCTgwYMH1Pd1dHQoKyI9PT3o6ur2+b+xsTEsLS3lWueIESMQHByM4OBgdHd3o7CwkPob0N3/08TEBOvWraPVSlrRyhxVg0xesrKy4jQoTBAE7T2kSaFcVfrBurq6YuXKlbyq4iksLERKSgoAYMuWLawHCElLdkEk7B9yb6tMUsOaNWuQmJiI+/fvY9KkSQoJ1IaGhlizZg327Nmj8DrkhS8ioZWVFQDm97Hjxo2DhYUFqqqqcPz4caxfv562sd3c3HD8+HHMnTsX586dg7u7O696lvcHVyIhWR1WVlbG2nx0WKgLCCiCtrY2HB0duV4G77GwsMCsWbNw8eJFrpfCSyZNmjSon9PR0cEzzzyDI0eOoKSkBK+99hq++eabPsK1gICA6qKUSDh58uQBv/e3v/0Np06dgqenJ3755Rf4+vr2+3NvvvkmUlNTsWrVKpw8eRIODg748ssvlVkW65SVlSE5ORkEQcDf3x82NjZyCz0SiQQSiYT6P+k3vmrVKkYarA+Wb775Bs3NzbC1tWW8v8n8+fMRERGB2tpaJCYmYvTo0YzO1x+GhoZ47rnncODAAdbnBgCpVAqJRDLkMxHPnz9PCYR///vf8emnn9Ji8zfQ+0hRRCIRlixZAkdHR7z77rsoLS3Frl27sHr1aoUq+6ysrBTanBYVFUEqlXLah6StrQ0lJSWczT99+nRWRWRnZ2csXLgQf/zxB2tzDhZzc3NGhQI630ek0MRVj9GBuHDhAiQSCVxcXPDFF19AXV0deXl5uH37Nm7duoX09HRUVVWhqqoKN2/ehLa2NoKDgzF9+nTGRRqRSAQHBwc4ODhg7ty5kEgk0NXVZfz9r66uDldXV7i6umL27NmoqalBZmYmMjMzFQ60GRsbw8PDAyNHjoSDgwPtvwOfKwnpfB+R/QjJQDdX1NbWorW1FSKRiNZegh4eHiohEvr5+WHhwoW86gkmk8moZL758+dzks1NioTJycno6uqi1baZ7n0dFyhbSQgAo0ePhru7O7KzsxEfH//EM/jT1rJixQocP35c4bXIA1+S/8jq9eLiYkgkEtoq/B5FTU0NCxYswN69e7Fjxw5aRUIACA8Px2effYZ//vOf+O6772BmZtan9+FAFdlcvY+4EgmBnmpC0g2JacLDw3lf1SmgPEPhfjTcCQsLQ1ZW1rDtBz8Q06ZNkyv5SFtbG+vXr8fRo0dRVFSE119/Hf/4xz/6nA06OjqYWKqAgADDMNLQKDo6Gtu3b4epqSmuXLnyVFspHx8fXLlyBd7e3vjmm28wZ84czJgxg4ml0U5KSgqWLVsGDQ0NtLS0wMvLCwcOHICFhYVc43z22Wf46KOPGFqlYtTU1ODzzz8HAGzcuJHxzGTSCue7776jqgm56Lnl5OSE1atX45dffmF9bqDngMeVSMjGYS43Nxfbt28HAOqgSxdMvY+CgoKwd+9evPfeeygoKMC+ffuwcOFC+Pv7yz1WWFgYcnNz5cpm7urqQllZGezt7eWejy7y8vI4m9vR0ZFRu+OBCAwMRGdnJyIjI1mf+0kwbTVK1/uotLQU+/btA0EQWLRokULvFybIyMhAWloaRCIR3nnnHSqgTQpk69atQ2NjI+7evYvbt28jPj4ezc3NuHnzJkxNTVnNFtfU1OSkYkkkEsHc3Bzm5uaYOHEimpqakJmZiYcPHz71WmBnZ4eRI0di5MiRMDMzYyxw1tXVxeveoXTej/giEpJWo8bGxrTuz1xdXWkbiynGjRuHGTNm8C4QfO/ePVRWVkJTUxMvvPACJ2uwsbGhXGLS09Npvdbz8XwkL8r2JAR6rsmrV6/Gxx9/jLt372LcuHEKi7FeXl6YPn06Ll++rPB6BoOlpSVjYpy86OvrQ1dXF21tbcjPz2c08XXu3Lk4cOAAbt++jYSEBAQFBdE6/jvvvIPExET8+uuveO+99wb1GK7eR1yKhN7e3oiKimI8UO3n58fp+YwrCIKARCJBW1sb2tra0NraSn3+6Nf09fXh4+ODkSNHst77m06Gwv1ouCMSibB48WJOncP4hoGBgUL9VLW0tLBu3TpERESgoKAAn376KQOrExAQYBtGFJiffvoJIpEImzZtGnTfGUtLS2zatAnbtm3Dzp07VUIkzM/Px8yZM7Fp0yZs2bIFV69exUcffYTa2lpKJJTJZIMS1/71r3/hzTffpP7f1NTE+Ybz888/R1tbG9zc3DBlyhRW5iSrCWtqapCUlMRJNSHQ0/9j0aJFrGUg9qatrY3WDP3BwJbdaHt7Oz766CN0dnZizpw5eOedd2gdn8n3ka2tLXbs2IFPP/0UcXFxOH36NKqqqjB9+nS5BHTS1varr76Sa/78/HxOrwlcWo0uWbKEs/4KY8aMgZWVFX777Te0tLRwsoZHoasX2EDQ8T5qbm7Gr7/+CplMBgA4ffo0mpqaMGHCBE6D7O3t7Th37hyAHvu2gapuDA0NMXPmTMycORNSqRSHDx/Gzz//jAsXLsDV1ZXTCn8uGDFiBEJDQxEaGor29nZkZ2cjMzMTGRkZAIBRo0bBw8MD7u7u0NfXZ2VNfLcapfN+1NtulEvo7kdIoq2tDWdnZ057Qj+JWbNmYezYsVwv4zFaW1spoefll1/mzIZaJBLB3d0dSUlJSEhIoFUk5OP5SF7ISkJlr1mTJ0/Gnj17UF5ejsTERISGhio81vjx46k+8EzBh17aJCKRCFZWVsjLy0Nubi6jIqGJiQkmT56My5cvY8eOHdi7dy+t44tEIuzduxcSieSx1i8EQfRb3cTV+4hLkVBDQwMBAQG4ffs2o/OoQsyKDgiCQGVlJQoLC6mPtra2QT/+4cOH0NLSgqenJ/z9/eHo6Mi7pJunMdD7aOvWrX1+Fw0NDRgYGFAf+vr6ff4lP1dlwVSVMTQ0xJIlS3Dq1Cmul8ILpkyZovBrUVNTE2vXrkVkZORjjlMymYyXZzU1NTUsX76c+lxgaCA8r/TBiEh47949AJC7dx6ZnX/37l26l8QIf/zxB0JDQ6msiVWrVmHXrl1ISEhAWloaAgIC4ObmNiihUEtLizfZlgBQUlJCVXtt2rSJtQA9eaPZvn07p9WEQM/rt729nXXfcnk23KoEQRD4+uuvUVxcDFtbWxw8eJD21xXT7yNdXV18/PHH+Pnnn3Ho0CHExcWhqqoKy5Ytk6v608DAAGvWrEFERMSgH1NQUDBor3i6IQiCM5Fw+fLltAY+pVIpSkpKkJ+fD0NDw0FVhTk6OuLFF1/Eb7/9RgXJuYTpSkJl30fd3d04duwYmpub4ejoiJCQEPz222+4cuUKmpqaMGfOHM5E36ioKLS2tsLR0RHPPffcoB6jpqaG9evX486dO8jIyMDZs2exZs0alQtu0IWOjg78/Pzg5+cHqVQKgJvNOJ+tRgF670dkJaG1tTUt4ykKUyIh0GM5ykeRcOnSpQO2TOCaK1euoLOzE25ubliwYAGna+ktEj7//PO0jcu385EikJWEzc3NSlldqqmpYcWKFdi+fTtu3bqF0aNHK3wvFYlEmD9/PhoaGhizXeObmGthYUGJhEyzePFiXL58GYcPH8bWrVthampK6/h6enr9Bribmpr63TNz9T7iUiQEemx6mRQJp0+frlCfelVAJpOhsrISBQUFKCwsRFFRUb92ttra2jA0NISRkRGMjIwwYsQI6nNDQ0OMGDECWVlZiI6ORmVlJZKSkpCUlARDQ0P4+vrCz8+P8XMNXQz0PiITIkmkUik6Ojqeuk81MTGBr68vfH19ab9GCDwZX19fZGVlIS0tjeulcIq5ubncMftH0dDQ6HcPKpFIKFc6PqGtrc2a5boAewjPK30wor5UVVUBQB/P7sFA/jz5eL7T3NyM5ORkVFVVwcLCAp999hliY2PR1NSE7u5upKenIy4uDsHBwVwvVW7+85//oKurC76+vhgzZgyrc8+bNw9Hjx5FTU0NEhMTERISwur8vRk7diza2tpw48YN1uYcqJ8Ek7BRSXj+/HlER0dDLBbjl19+YbwaiinEYjE2btwIZ2dnfPrpp8jJycGePXuwbt06uSpAPTw8MHr0aCqp4mkUFxeju7ubE9G8qqoKzc3NrM8bEBAAb29vpcYgCAI1NTXIzc1FXl4eCgsL0dnZSX1fLBYPqurBwMAAzz77LC5dusR4VvLT4PNhmiAInD9/HiUlJdDU1MQnn3wCOzs7WFlZ4fvvv8e9e/fQ0tKCpUuXsp5Bm5WVhQcPHgDoseuSx8ZTTU0N77zzDjZt2oTs7Gw8ePBA6UPVUIDLTD2+i4R0wje7UaZEwqioKNrHVYb169fz1gq1rKwMCQkJAIAtW7ZwnjVL9iVMTEzkdB18xMjICDo6Omhvb0dNTY1SFXZz5szBgQMH0NDQgIyMDKX2SGpqali5ciV+/vlnRs7ednZ2tI+pDOT1kw2R0NvbG25ubsjJycH+/fvx9ttvMz4nH+FaJDQ1NYWLiwsjLROMjY0VsujjK52dnSgrK0NpaSmKiopQWFj4WCxPW1sbPj4+CAgIgL+/P9zc3AaVJDtx4kQ8//zzSE5ORnR0NGJjY9HY2IgbN27gxo0bsLa2ppLP5OmLxhf2799PrZsgCLS1taGurg41NTXUv7W1tX0+Ojs7UVdXh9jYWMTGxsLOzg6+vr7w9vaGnp4ex7/R0EckEmHevHkoKiriJMbBF2bMmMFZ4q6AgAA/YSTabGxsjMrKSly9enXQmfoAKMsMIyMjJpZFO6NHj0ZkZCTCwsIwbtw4HD16FH/88QemTJkCiUSCl19+GW+99RbOnTunUjf7wsJCyhpl8+bNrFdLPFpN6O/vz0lPJpJp06ahra2NCsYwDZeVhI9mwtFFeXk5VZn66aefYsKECYzMwyZTp06FnZ0d3nvvPVRVVWH37t3YsmWLXBWFs2bNQmFh4aCC3d3d3SgrK4ODg4Myy1YILvoRGhsbIzw8XOlxoqKicOfOnT5fMzQ0hJ2dHdLS0nDmzBm4u7sP6lCqpqaG2bNnw87ODr/99pvSa1MENTU13t4jCYJATEwMEhMTIRKJ8PHHH1NBwmXLlsHMzAz/+c9/kJmZib1792LBggWs2ZGR4iXQUxnk5eUl9xhOTk7YtGkTdu/ejQsXLsDLy4vTe9NwZ7iIhM3NzVQAfyiLhCYmJjAzM+ONNdHmzZupCjA+QgqqM2fO5EWlo4eHB4AekVAqlXIuWvIJkUgEW1tb5OTkKC0SamtrY9GiRTh48CDu3LmjdCKVjo4OXnjhBVy5coXWBCgTExPeVcaQLVDY2NOSfa+++OIL/PDDD3j99deHpbUg1yIh0BOvYeI5nz17NmduR4rQ0dGB0tJSNDU1obm5GU1NTX0++ktS1tXVha+vL/z9/eHv7w8PDw+Ff2exWIyAgAAEBARgy5YtiIuLQ3R0NG7fvo3y8nKUl5fjypUrWLp0KaN2wExgbm7+WKzP2dl5wJ8nbYHv3r2LS5cuIT4+HiUlJSgpKUFUVBTCw8M5TZIfLujo6GDRokU4fPgw10thHA0NDWhqalL/ampqwtHRkUrwEhAQECBhZGcTFhaG06dP48iRI3jhhRcwfvz4pz7m5s2bOHLkCEQikcpkZYWHh0NdXR35+fnIy8vDSy+9hPnz5wPoaZDu5uaGiooK6OjocLxS+YiOjoZMJoO3tzdngYd58+bh2LFjqKiowKVLlzB37lxO1gH8mWlUX1/PihUWF5WEpFVKcXExCIKgXRg+duwYOjs7MXnyZNr7EHKJu7s7duzYgS1btqC8vBzp6ekICgoa9OM1NDSwZMkS7Nq1a1A/z1avr0fhIslhxowZtFgjkX0ESYvX0aNHw9XVFQRBYNmyZWhsbER9fb1cmave3t6wsLDAwYMHWe9TaGZmxsuMv+7ubvzxxx9ISUkBALz00kuP9UuaPHkyvv76a7zzzjuorKzEnj17MHr0aEyfPl0ucV0RCIKgAht3795FTU2NQkKHm5sbgJ6ECj4Ev4YrMpmMF9a/bEBWZpmbm3N2DyDp7u4GAMYCow4ODpyLhHp6eti4cSNMTEw4XceTKC8vR1FREcRiMV588UWulwOgp2pMR0cHbW1tSEtLg5+fH9dL4hWkQERaNCvD7NmzcfDgQZSWlg6qpcVg1jZ79mx4enri0KFD1PtcGQICAnhnyU26SLAl7EyfPh179uxBfn4+9uzZg5dffpmVeflEV1cX10vAyJEjYWpqitraWtrGdHNzoxIjVIHGxkb89NNP6OjoeOLPmZmZwdPTEz4+PlSlIBMJH1paWpg6dSqmTp2KhoYGXLlyBefOnUNeXh5+/fVXTJgwAVOnTuXleYcORCJRn97ndXV1uHLlCi5evIjs7GycP38eVVVVCA8PFxJuGMbV1RUBAQGM9udlm3Xr1sHExIQSBTU0NIbse0kRWltbqfNUS0uLShXzCAyM8LzSByNXi7/85S8Aeg5C4eHh2LFjRx97t950dnbihx9+wJw5c6iDkypsosmKqxkzZmDz5s3Q0dF5LGDV3NwMKysruW1XuYbMJB2MBR9TaGpqUtYs8fHxjPXLGCxisXhQvcvo4GkbeCaws7ODWCxGTU0NSktLaR27qakJkZGRAIB///vfQ26TYmZmhnnz5gEAUlNT5X68tbX1E7MNScaNG8dZ0NLPz4/16k95rFufxLhx4wD0XI/DwsLg7u4OsVgMNTU16r2miLWNubm5XIIwXZCZ8Hyira0NBw8eREpKCkQiEd5++22sXLmy35/18/NDREQEZs6cCaCnh/GOHTuQmprKqOgmFouxfv16GBoaoqSkBG+88YZClWhkH9Hg4GCV75OlylRUVHCSUMMF9+/fB9AT6OQaUlhnypqJD72dwsLCeC0QAqCcLSZPnswb63Y1NTWqQjsuLo7j1fCPuro6AMCIESOUHsvKygoaGhqQyWRobGxUejwSBwcH2pwK+JhEQyZ6smUVrq2tjWeffRYA8OGHHw5LSzsuzrSPIhaLB9yTKsrcuXN5J4IPhEwmw8mTJ9HR0QFjY2OMHj0ac+bMwbPPPou33noLn3/+Ofbu3Yvff/8dx48fx3/+8x+sXLkSI0eOZEWgMjIywtKlS7Fz504sW7YMAHDjxg0cOXKEU3clNjExMcHy5cuxc+dObN68GUDP+ejIkSPDZq/LJarYGqo/9PX18fLLL8PNzQ0mJibQ19eHlpbWkIu9CQgIMAsjV4zZs2dj06ZNlCf3li1bYGlpifnz5+ONN97A//3f/+GNN97AggULYGlpiddee42qxti0aRNmzZrFxLJo5dGLrYODA0pLS/HNN98gMjIS//jHP3DkyBF88MEHKldJeOvWLQBQyI6NToKDg6kmuL///vuAQjNbkBUkTMPFgUpDQwP29vYA6O8n88cff6CjowP+/v6YNm0arWPzBfL3KigoUKiy7GmWIhYWFpg6dapCa6OLadOmPVYZxiR0Zf/Y2NhQ1zLSRhno6YFLJnAoeo3mIpGCb/0Ia2pqsGfPHhQXF0NDQwPbtm2jRPOBMDY2xrvvvosvv/wS9vb2aGlpwYkTJxAREcHoYdjIyAgbNmyghMI333xTLqEwPT0dSUlJEIvFGDt2LGPrFHg6OTk5XC+BNci+tXyoWmBaJOS6UhLosabjM52dnVRv1adda9mGtL4URMK+EARBq0goFospy1I6q6OAHjt2OqCjYpJuyIRTtpI+AWDBggWws7NDVVUVvvjiC9bm5Qt8ETgsLCxoEwqnT59OWyIjG1y/fh1FRUVQV1fHd999h23btuGdd97B888/j/nz52PMmDFwcXGh5dqkDOrq6nj11Vfx3nvvQV1dHXl5edi5cyftyct8RiQSYe3atfjkk08ot7Ldu3cz0jN2IAiCgEQiQWNjIy8qgdnA1tZW5fvMGxsbY+PGjbCwsKB13MrKSqSmpiInJwelpaWoq6tDW1sbYy2KBAQEuIcxv41du3ZBV1cX33//PQiCQGNjIy5cuIALFy70+Tky01AkEuG1117D119/zdSSFKKurg5SqfSpgdkNGzbg+vXr+PHHHyGRSGBjY4OYmBile0WwTUNDA9LT0wFwLxICPVWpd+/eRWVlJS5fvow5c+ZwthYdHR04OzszbjnKVdalk5MTCgsLkZSURImzytLZ2YlTp04BAN566y2VybqUF2tra3h6eiIjIwNpaWkYM2aMXI8fOXIkRowYgaampn6/v2TJEs77XohEIoSHh0NTUxM3btxgfD46LQKmTp2KjIwM3Lx5E+np6fDy8qL+1mKxWOGKMBMTE9jb26O4uJi2tT4Nujf/ylBQUICIiAh0dnbCysoKn332GZycnAb9+KCgIOzZswcRERE4ePAgsrOzsXfvXqxZs4axXkakUHjgwAFKKPzqq68GJb4eOXIEQE81JF2BVAHF4KJPKlfwqZKQfF8O1UrCMWPGMG59rCxpaWno6uqCjY0Nq2LHYBBEwv5pbW2lgq10vcbt7OxQUFCA2tpaWpMY6Vof30TCrq4uyvGHzfeNuro6XnjhBXz44YfYunUrXnrpJVhbW7M2P9fwRSQEAE9PT0yaNAnXrl1TeAwLCwuVShIrKipCbGwsAOCdd95hrQ+4MkyfPh0uLi54//33UVJSgn379mHu3LkICgoasnGERxk/fjx27tyJ//u///t/7J11XFRp+/8/M3R3NzZIiIBiIIjdXWuvrm7vs+Xu82zHs/Go3w27u7A7MBADEBTpku5uGGDm/P7gd86KgsJwauC8X695qcjc9wUzc85935/r+lwoKCjA1q1b4eDgAFdXVwwYMEDufWtZWRlSU1NRVlYGiUSChoaGlx4SiYQ6n9XV1cVbb73V7W37RCIRpk+fjokTJyI9PR0pKSlITU2ltVKfSUxMTLBkyRLa19DV1dXYtm1bu/+vpqYGdXV1aGhoQF1d/aW+u3xbBwgICHQMxk6dRSIR/vzzT8ybNw8bNmzA5cuX26wEU1NTw6RJk/Dpp5/ybtGVlpaGsWPH4o033sDatWthaWn50vc83wti9+7dSEhIgKqqKgwMDHhvV9QW4eHhAFqqb/iQJaelpYVPP/0Un332GcLDw+Hk5AQ7OzvO4unbt2+3FgmDg4MRFRVFW1/CW7duoaysDJaWlpg/fz4NUfKX0aNHIyEhAbGxsZ0WCcViMQYPHozbt2+/9H/+/v4wNzenK8wuIRKJEBAQgEGDBuHmzZtUQgHdqKur02pxY2xsDDc3N0RFRWHXrl3YuHEjtfDX0NDo0nvdzc2NVZGQS7vR0NBQSqyur6/Hw4cPIZPJ4OTkhJ9++kmue4aqqiqWLVuG4cOH46uvvkJhYSF27NiBefPmoVevXnT/CABahMJly5ZRQuGSJUtga2sLW1tb2NnZUX+3tramNjzp6enUwXdH+iwLMANBEHj48CEyMzO5DoUVqqqqkJSUBECoJGQDReiJTlqNTp48mXcWUk5OThCJRHj27BkKCwt5aY/NBWQVoYqKCm0iNHnYT45NF921kjA7OxsymQzGxsawtrZmdW5fX184OTkhPj4e3333HbZv387q/FzCJ5EQAPz8/FBQUIDk5GS5nj9lyhSF6RHX0NCA06dPgyAIqvedouDg4ICtW7fit99+w71793Dx4kXk5ORg0qRJLwkR3RVHR0ds3boVv/76K8LCwpCeno709HRcvHgR/fv3h4uLy2t7RkqlUmRmZiIlJQUpKSmdrjyvqqrC5cuXMWfOnB4h0KqqqqJfv37o168fCIJAcXExJRhy3fqoPaysrLBo0SK5Wqe8jrCwsFf+P+nKpChiqoCAQMdgvDRl+PDhGD58OGWPk5eXh5qaGmhra8PKygqurq5QVVVlOgy5uH79OnUzVldXx8qVK6nDeoIgIJPJqBtzeno6HBwcMGDAAC5D7jJkP0I+/Ryenp6YPHkyLl26hHPnzuHtt9/mbIHYr18/XLt2jdE5uBIJyb6EZWVlyM7Ohq2tbZfGIwgCgYGBAIAPPviAt59zuvD398eWLVuQk5OD8vLyTgsmHh4eL4mEVlZWrPcC7AiGhoaYO3cusrOzcf36deTk5NA6PhMZi6NGjUJ0dDSePHlCVeYA8vUjfB5nZ2dcvHixq+F1CFVVVU7tgO7cufPS1/z8/PDFF190uT9f7969sWXLFnzzzTeIi4vD4cOHMX78eHh7ezOyMSWFwsOHD6OkpITaQD8Paelma2tLbawHDBjAmx5gPY3GxkacP38ecXFxXIfCGqT9t5mZGW29wrpCd+5JOHDgQF78jl9FUVERcnJyqMp+vqGtrQ17e3ukp6fj4cOHmDFjBtch8QJSyNPR0aHtfkYKXXSLhHStMfgmEj5vNcr2YbdIJMLatWvxwQcfYNeuXVi1atVr2wx0F/jQk/B5RCIRZs6ciV27dnVaMBk8eDDVmoPvEASBixcvorKyEpaWlvjwww+5DqnTaGtr44cffsCxY8ewc+dOREVFoaCgAPPmzeNFIjsb6Ovr49dff0V+fj5u3ryJGzduICsrC3FxcYiLi4OGhgacnZ3h6uoKa2triEQiVFVVITU1FSkpKUhLS2tVpCESieDu7o7+/ftDR0cHWlpa0NbWbvUgv5aZmYm1a9ciPj4ecXFxGDhwIIe/CfYRiUQwNTWFqakphg8fDolEgrS0NKSkpCAjIwPl5eVchwh7e3ssWLCgy3vwtpBIJLh//z7t4woICPAf1vzrVFVVFW5BPGzYMCxduhR9+vTB5s2bIZPJ8N5770FfXx8ikYgSCDds2ICrV6/i119/VfjGt2Q/Qr7ZpK5duxbh4eEoLi7GzZs3OTscMTAwgKmpKaPe8FxlXSorK8PW1hYZGRl48uRJl0XCiIgIpKWlQUNDA2vWrKEpSv5iZGQEd3d3PHnyBLGxsRg5cmSnnq+trY2BAwciNjaW+tqMGTN4VynwPDY2Nli5ciUSEhIQFBRE24KZCZFQX18fnp6eCA8Px65duzB37lwAXRcJ1dXV4ezszIpwYWpqymkm55gxY1rZ3vbv3x9Tp06l7T1qaGiIjRs3YuPGjbh27RquXr2K4uJiTJw4kZHMbX19fbz99tsoLy9HcXExSkpKqEdxcTEaGxuRnZ3dqlKUj6J9T6C0tBTHjx/vVA/J7gCf+hEC/4iE7VljdxUuLa345mbSFmSCy4gRI3jrVuLs7ExVXgsiYQvPi4R0QYqEdPck7K4iIekCw5VFr4uLCwICAnDz5k28+eabiIiI6PbJk0DLnpYudxq6UFdXx4IFC7B58+YOP0dXVxcBAQEMRkUvUVFRiIuLg0gkwldffaWwdpEikQgLFy5E3759KevNbdu2Yc6cOejTpw/X4bGGhYUFFi9ejDfeeAPJyckICgrCzZs3UV5ejoiICERERMDAwACqqqooLCxs9VxDQ0MMGTIEQ4YMgaenZ4ffC3379sWyZcuwb98+XLhwAXZ2dpxbwnOJmpoaBgwYQBVSVFdXIzMzE5mZmcjKymK1byTQUrgwZ84cxtrRkK4VAgICPQ9um1zxHIIg8ODBA+zduxdSqRQ7duyAjo4OgoODMWDAAPz8888AWg5NGhsbFd5WRyaTUWXlfOhH+Dza2tr49NNPsW7dOoSFhcHJyanLIpa89OvXj9GFAJdZl/b29sjIyEBUVBSmT5/epbFOnDgBAHjrrbd4n51PFwEBAXKLhADg5eVFiYQTJkxQiIolkUgEJycn9O3bFxEREbRU2jK1mR05ciQeP36MxMREqj+uhoZGl8d1dXVlTSTkko8++ojxgwZVVVWsW7cODg4O2LZtGyIjI1FaWoq5c+cyYqUiFothZGT0Ug9EgiBQXV3dSjg0MTFp03ZcgFmSkpJw7NgxrsPgBFIU4otIyHRPQiUlJWhqaqKuro6R8dvDzs6O95/tpqYmREdHA2ixvOMrZHW90JfwH0iRkE4nAFIkrKiogFQqpS2RpjvajUokEuTm5gLgTiQEgPfeew+PHj1CTEwMfv/9d3z11VecxcIWUqkUTU1NvBNEjY2NsW7dOhQWFkIkEkEsFr/yTx0dHYWxuSwpKcHly5cBAKtXr+aVO5S8DB48GAcOHMC3336LxMREHD16FJMnT1b45PzOIhKJKDvMtWvX4vHjx7hx4wZu377dKlHXyckJQ4YMwdChQ9G7d2+5kznfeOMN3L9/HykpKbh48SIWLFjAK8GfS3R0dDBw4ECqwrKurg7Z2dmUaEjec5jA1dUV06ZNY8z6WCqVUu5y3RElJSVMmjSJ+rtA90B4XelDEAlfgZubGxwcHJCZmYlvvvkGGhoa+M9//gNlZWWsXbuW+r5ly5Zh5syZnNrA0UFycjLKy8uhqqrKWC+oruDt7Y0JEybg6tWrOHfuHNauXcvJgr1fv34ICQlhbPympiZaN/ydwd7eHgDw9OnTLmV+pqWlISIiAmKxWCEtTuTF19cXGzduRFFREYqKijot6tjY2GDYsGGQyWTw9vZmKEpmUFZWxtChQ+Hm5oaQkBCqKlkemBCDgJZkAx8fH4SEhFAVOnTM1bt3b+jp6THuyc+1SMgWIpEI8+fPh62tLb799ltkZGRg165dWLhwIUxMTFiLQVdXF7q6unB0dGRlToHWEASBO3fu4O7du1yHAqAli9jW1hZGRkat7o3Nzc149OgRI3PyTSQkP391dXVobm5mJINZW1ubdZFw2LBhrM4nD/Hx8WhoaICZmRmvD0bJA7OIiAhIJBJGbLAUDSYqCY2MjKCuro6GhgaUl5fTllTWHSsJs7KyQBAELCwsOO3xra+vj/fffx8///wzfvjhB8yaNYt3SblMUFdXxzuREGipKLSzs+M6DFppbm7GqVOn0NzcDA8PD8yfP5/rkGjD1NQUf/75J/744w9cuXIFFy9eRE1NDXx9fXukcKWkpAQvLy94eXnhX//6F8LDw9Hc3IzBgwfTlpytrKyML7/8EqtWrUJycjKioqI4TbTgM5qampSAC7S0SCgsLERBQQEKCwupR1NTU5fm8fLywsSJExl9z8fGxjLmGMIH1NXVcenSJa7DEKAZ4XWlD0Ek/P+0J8o0Njbi7t27sLe3R1JSEpSUlKChoYGnT59i4MCBVOaxoguEwD/Nafv168dY6XpXeffddxEREYGSkhLcv38ffn5+rMdgaWkJbW1t1NTUMDZHbW0tJ+8pKysrKCsro7y8HBkZGXBwcJBrnAsXLgAAZs+eLfcYioiOjg6GDh2KBw8eIC4urtOijkgkUqjG8m2hoaGBcePGYciQIXjy5AmioqI6LZ7V1dVBJpMxYrU6bNgwhIWFUT0a6KgkFIvF8PPzw7lz57o81qtoamrinW0Tk/j4+GD79u34z3/+g/z8fOzcuRMffPABtLW1uQ5NgAWCgoI4rUZSU1ODnZ0d7OzsYG9vD3Nz8zavSRKJhBGRsLKyEsnJyQBAHTpwja6uLlRUVNDU1ITS0lJGHDR0dHRYtW0yMDBQCNuyp0+fAgAcHR15nSFrZWUFfX19VFRU4MmTJxg6dCjXIXEOEyKhSCSClZUVnj17hrKyMtpEQjU1NaiqqrbqYyUPzc3NtMRDB8/3I+SagIAABAUFISwsDJ999lmPONCqq6vrMY4yXPPw4UMUFBRATU0NX375Ja9bVsiDqqoqPvvsMxgbG+PgwYO4c+cOtLS04OnpKdd4DQ0NOH78ODIzM1t9nSAIOsJlDQ0NDYwaNYqRsR0cHLBixQrs3r0bV65cgbOzMy9Ef5lMhtraWtTX11OPhoaGVv8mH5aWlvDz82N17aSqqgobG5tWfUwJgkB5eflL4qFEIoGysjJUVFSgrKzc7t9tbGxY6atLngkLCAj0TPipBLFMcnIyLly4gEWLFsHCwgJAy2GsiooKhgwZArFYjA8++ABXrlxBVFQUjh07hu+++w5KSkr48MMPeb1Z7wykMNjVjSGTaGtrY+nSpdi4cSOysrI4iYG0HWFSJIyMjIS/vz9j47cH2ZcwLS0NkZGRcgt85KakoqKCxugUgz59+uDBgweora3lOhRO0dPTg5+fH0aNGoX09HRcu3atwwe/CQkJ2L9/P2bNmkWb9RWJuro6/P39KVvU5xfvXcHV1RXh4eHIz8+nZby2uHnzJnJycjB58uQe0xfCwcEBW7ZswezZs9HU1IT6+npBJOwhsC2Gd1QUZAuyitDMzIz266C8iEQiuLm5ISIiAufOncPKlStpTypj+9pWXl6OlJQU3lRrtoe6ujqAlkPg3NxcWFlZcRxR24hEImhra6OiooKzHtt8gxSpXrS17gpSqZTq0UpntWZ+fj4t+0CZTEZDNPRA2iPzoWpMJBJh3LhxCAsLY3S9yCf4mnjcnZHJZJBIJFyHwQgikQgrV65EfX09Tp48iczMTLlEQqlUisDAQOr63BX+/vvvVu5WZmZm8PX15aw1Dp3U19dT1pNisZgTAVUmk6GkpAR5eXnUo6CgoMMV62lpaSgtLcXs2bM5PbcViUQwNDSEoaEhr22A2e6vKCAgwC96/KotNTUVPj4+KC8vR2lpKT7++GMYGxtTN/p+/fph6dKlMDc3x/nz5+Hg4IAvv/wSSkpKmDp1arcRCAFQolRycjKqqqp4Wx1JHuozbe3XHgRBoKSkhNE57t69C29vb04ajTs6OlJ2oXPmzJFrjDlz5uDcuXO4ceMG7t69C19fX5qj5C/kYQR5oKcIEASB9PR0SCQS2Nvb01JdRyISieDo6NjpA6OsrCz88ccfmD9/Pvr3709bPAAwZMgQ6tCCroNhsViM+fPnY9euXYwmECQlJSEpKQnTpk2Du7t7j6gqrKmpgUwmg7KyMq2HrAL8JiAgAFZWVrhx40arXitdRVlZGcbGxjAxMWn1MDAw4FXWfXh4OADw7iDh008/xbJly5Cfn4+rV6/S3h/PwMCA1vE6wtGjR/HWW29RiYJ8ZMaMGaipqUF2dja+//57bNq0iRfZ/C8ik8lQWFgIAD3KSeJVJCYmAqBXpIqJiUFVVRU0NDRoS3YC0C37EJGH2ny5vsfHxwNQDJtjOhDWbewxbNgwpKSkIDs7G9999x02bdrUbS2fSTcveRISCILA5cuXkZaWRkssZOLp8+zevRsODg4YNWoU/Pz8eJGk0FkaGhrw5ZdfIi4uDqqqqli2bBnj7yeZTIbS0lJKDMzPz0dBQUGXbToTEhJw8uRJzJkzp1ud39INQRC8sgtngtraWsrtq6ioiJPzVgH6EV5X+ujRImFtbS1++eUXTJs2DZ6ennj//ffR3NyMzz//nLJtGT58OP7zn/9g9uzZcHd3pyzwPv/8c46jpx9LS0sMGDAACQkJiIqK4q2wQ/bEqaqq4sR6r7KysssLlY4QEhKCCRMmMD7Pi/Tq1QtBQUF4+vQpGhsb5TqEsrCwwOTJk3H+/Hl89dVXCA4O7hFiBvCPSEin0MYUzc3NiI6OxsOHD1sJ31ZWVnB0dESvXr1gbW3d5cV0cXGx3ML68ePH4enpiXHjxtHWg1QkEsltTfMq9PT0sGDBAuzatYv2sV/k/PnziI2NxdSpU7u9jVNqaiqAln4kfDnkU1SqqqqQm5tLWfI0NDS0+yArgtp76OjoQEtLi7HewCKRCAMGDECfPn0QHh6OGzdudHoMExMTWFhYtBID9fX1FeJ9xFeR0MzMDD/88APWrVuHyMhI2NjYwM3NjbbxubqeHTlyBKtWreJN1eaLqKqqYs6cOdiyZQtSUlKwfft2vP/++1yH9RKlpaVoamqCkpISrK2tuQ6HcwiCQEJCAgB6RcL79+8DaEl0out6VlVVhejoaFrG4pNdHxkLX/YhMTExAIARI0ZwHAnzaGtrCwfyLKKkpETdJ1JTU/H333/j008/5TosRiDfV/KIhKGhoXj8+DFtsYwYMYJKPiUIArm5uXj27BnS09ORnp6Offv2wd7ennLYsbe3p2Xe+vr6Vv3utLW14enpSUuiv0QiwVdffYWnT59CRUUFS5cuZTyRqr6+Hvv376cSjegmMTERgYGBmDt3rnBdagc+WYUzCdu9zwXYQXhd6aFHi4RisRiDBw+GkZER5s+fDxMTEyxYsAAAKKGwb9+++PLLL6GpqQmAPxsMphgzZgwSEhIQGRnJe5GwubkZ9fX11GvDFqS9D9OEhYXBx8eH9QMrMzMzaGlpoba2FvHx8XB3d5drnMWLF+PKlSsICQlBUFCQwvfa6yiKIBLW19cjIiICYWFhbdqi5ubmIjc3FyEhIVBRUYGDgwMcHR3h6OgIY2PjTl8HySx6eYmIiEBWVhbmzJlDff75ipWVFebOnYvAwEDG50pLS8Off/6JMWPGYNCgQaxfC9kiJSUFAGBubs5xJIpNVlYW9u7d26nndKRxvZmZGSZMmEDboceLKCsrY9iwYXBzc0NwcHC7/f+0tbVhZWUFKysrWFtbw9LSkvaMZ5lM9pIVH1OWXqRISHclNR14e3tj2bJl2L9/Py5cuAALC4tO9+BtDy4qCYGWiuUjR45g5cqVvK280NXVxaxZs3D06FGcPn0agwYN4p3QUFBQAACwtbUVbAYBbNiwAUVFRZSdPx0QBIF79+4BoPf6QGdvVT6KhHxIDqmvr6cSn/j22WWCnmKNzyd0dXUxb948HDx4EJcuXcLAgQM5SToGWlrYHDlyBMnJyZBKpa999OvXD+vWreuQgCOvSJiUlITr16/L9fO0x4gRI15aN9TX1yMpKQnx8fF49uwZMjIysG/fPuzbtw92dnbw8/ODk5NTm3vqF79WXV2NoqIiSgwk/06eObz4XBcXFwwdOhQ+Pj6ws7Pr8L69vr4eCQkJiI2Nxb1795CSkgJlZWUsWbKEcYtzmUyGkydPMiYQkiQlJQlC4StgoxBCQECA3/To3ZuGhgaWLVtGlaLOmzcPBEFg4cKFIAgCn3/+OUxMTKCuro709HQ4ODj0CJHw77//xpMnT7gOpV1UVVVhYGCA8vJyVFZWdluREACCg4Mxbdo01uYD/rGHjImJQUREhNwioYmJCaZPn46TJ0/iq6++wpgxY7r95wfgt0hYUVGB0NDQTjWkbmpqQnJyMpKTkwG0bD6dnJwwatSoDluqdlUkBFpsA7Zs2YL3338fhoaGXR6PSZycnBAQEICbN2+yMl9QUBCCgoLg7OyMQYMGwdHRsVt91sgDNUEklJ/8/PxOC4QdpbCwEPv374eLiwvGjh3L2KGglpYWJk2aBC8vLwQHB6OmpgaWlpawtraGlZUVdHV1GX3fNzQ0YNeuXSgtLWVsDhIyUUMsFqNPnz6MzycPS5YsQVxcHCIiInDixAmsXr2aFnGNK5EQaLnPBAYGYsGCBbwVuPr27QsfHx88fPgQP/74Iw4cOAAzMzOuw6Ig+6wxlTSgSBw6dAifffYZAGD16tW0rQufPXuGgoICKCkpoVevXrSM2dTURPVBpQM+iYR8Ij4+HjKZDHZ2dj2i0pavrUu6O46OjvDz88OdO3ewfv169OnTh7ZrRUfJzs7GDz/8QK3hO0JWVhasra2xdOnS136vPCJhfn4+jh071uHv7woaGhpwd3eHu7v7S4JhZmYm9u/fT8s86urq0NPTg56eHioqKlBUVITo6GhER0djx44dsLCwoARDNze3Vg5RpaWliI2NRUxMDGJjY5GcnNzq2q2srIzFixfTamndHjdu3KDN/vV1JCUl4cSJE5g7dy5v13pc0VMqCQUEBNqnx18VSYFQKpVSPaUIgsCiRYsgEonw0UcfYf369cjMzMTBgwe7baUGyahRoyAWi5GdnY2ioiLaMsPpxtTUFOXl5aiqqmK9hwzT/Qif58mTJxg+fDjr/RyeFwlXrVol9zgLFy7ExYsXER4ejkuXLtHeu4iP8LEnYX5+Ph48eIDY2Nguj1VVVYXQ0FDExsZi0qRJr7XCq6ysRF5eXpfnJQkMDMTKlSsZsziki+HDh6O0tBRRUVGszRkXF4e4uDjo6elRG9PuYEVKHjDwuV8YnykpKcGOHTsYnycmJgYxMTEYP348vL29GavaMDExkbtfble4evUqKwIh8E9FD909YulESUkJ//nPf/DWW2+huLgYFy5coOV10dLSgqamJmeWMc+ePcPx48cxf/583h4eBQQEICsrC7m5ufjxxx/xxx9/8CZWUiTs6f0Ir1+/juXLlwMA5s6di3nz5tE2Nmk12qdPH9rWQk+fPkV9fT0tYwH8Egn5VElIrsN7QhUhIFQScomvry9ycnKQmpqKb7/9Ftu2bYO2tjYrcwcFBeG3335Dc3Mz1NTU4OfnB3V1dYjF4jYfSkpKKCgowPXr17Fv3z54enrCycnplXN0ViSsqqrC0aNHu/yzycPzgmFDQwOSkpKQkJCAysrKVt/X3nVTVVUV+vr6lBj4/OPF5KyKigqkpKQgOTkZaWlpyM/Px5kzZ3DmzBmoq6vDy8sL6urqiIuLa3N/rqurC1tbW9jY2KBv376s7COfPn3Kej/c5ORkqqKQL+snPiBUEgoICAhXxP+PkpISCIKATCbDggULIBKJsGTJEpw/fx7Pnj3Do0ePur1ACLT01PL29qa82rmyp3gdpqamSEpKemlxxQZsVhICwJ07dzB79mxW5ySzDcnfsbyWp4aGhpg5cyaOHj2Kr7/+GpMmTeLFJp1J+FRJWFZWhitXrnQqi7Oj1NTU4MSJE+jfvz8mTpzYbrYwHVWEz1NQUICrV69i6tSptI5LNyKRCFOmTEF5eTkyMzNZnbuyshLBwcEIDg6Go6Mj3N3dMWDAAIXcBJWVlaGsrAwikYhX1TKKQmVlJQ4ePMjqnNeuXcOTJ08wefJk2uz1uCYhIQFPnz5lbT6+9iN8EX19fXz77bd4//33ERcXh3HjxnW5ckQkEsHGxgZJSUk0Rdl5UlNTcezYMcyfP5+XCSlKSkqYPXs2tm7diri4OOzduxerV6/mOiwA/9iN9mSRMDIyEjNmzIBUKsXo0aOxdu1aWscnrUb79etHy3gEQdB+QMtHkZAPDgs9qR8hIFQScolIJMLMmTOxfft25Obm4vfff8f333/PuOPC33//jcuXLwNo6cM6a9asDr0PHBwckJ+fj5iYGPz888/YuXPnK8/eyD2NVCp97diNjY04duxYm/acbKOurg43Nzdaezk/j76+Pry8vODl5YXGxkakp6dTjkA1NTUICQlp9f1mZmaUKGhra8t6m5vc3FycPXuW1TlJkpOTceLECcybN08h98hMIFQSCggICFfD5yAXTQRBYP78+dixYweioqLw+PFjuLi4cBwdewQEBCA0NBSRkZG8FQnJvmRsi4QEQbBaSQi0ZJ0OHz6cVas9HR0dmJiYoLi4GI8fP4a/v7/cY82fPx/nzp1DVFQUTp8+zUkFCFsQBMELkbC5uRkPHjzA7du3GZ8rMTERiYmJmDhxIry8vF7afNItEgLA48ePYWtry9gGiy6UlJQwb9487N69G2VlZZzEkJaWhrS0NKirq8PW1rbV69ORjTXXkAK3kZERLw/r+UxNTQ0OHDjQob6CdFNUVIS9e/fCzc0NY8eOpVwbFBEyIYJN+NyP8EWcnZ3Rp08fJCcnIzMzk5b1srW1NaciIdBSUXj06FEsXLiQl9ceAwMDzJgxA4GBgThy5Ajc3Nzg7e3NdVg9XiR89uwZxo4di/r6enh4eOCLL76gNTkuIyMDqampEIlE6Nu3Ly1jpqSk0F4lzUeRkOskRalUiri4OAAtbhM9AUEk5BZNTU3MmzcPe/bsQUhICI4fP44FCxYwMld6ejq+//57KjHS19eXcqjqKJMmTUJWVhby8vLw999/Y926de1+b0crCQmCwJkzZ6gqdyaQyWSt4hCJRLxISlBVVUW/fv3Qr18/EASBgoICpKSkQCqVwsbGBtbW1pw6H9XU1OD48eOczQ+03P/47h7BJkIloYCAQPcu6ZEDkUgEmUyGjz/+GLdv38bt27d7lEAItPQlBFoO4vm0yXse0gaV7cPPmpoaNDQ0sDonAFbEnhchqwm72qNET08Pc+fOBQB88803CiFMyEtDQwOVgcWVSJiZmYnt27ez/p65cuUK9uzZg6KiIuprdXV1yMjIYGS+s2fPtpqLr2hqamLRokVch4GGhgYkJycjKSmJejBRYUo3KSkpAIR+hJ2lvr4ehw4d4kycJnn69CnWr1+PR48edapvDF8gCALnz59ndU6ZTEbZjfK9kpDE1dUVQEs/ITpgo/9NR0hPT8eRI0fQ2NjIdSht4uTkBE9PTwDAt99+y5od7qvoyXajRUVFGD9+PMrLy9G7d2/88MMPtAvMFy9eBNBSRUiXww0TNm982j/ypZIwNTUVDQ0N0NPTg7OzM6exsIUgEnKPlZUVxo8fDwDYvn07vv32W1r3TwRB4NKlS1i9ejUyMzOhra2NpUuXwt/fv9PCvLq6OmbOnAmRSISrV6++ci/bUZEwKCiIkYTV5/n999/x448/Uo9NmzbhyZMnvFr3ikQiWFhYwNfXF/7+/ujduzenAqFUKsWJEyd4Ud2ZmpqK48ePM1pFRyaS5+TkIDY2Fvfv38fly5dx7NgxnDlzBjExMbRafstLT6gkFIvFGDVqVKeTGAT4jfC60oeQLtEOzs7OePz4MXXw0ZPw8fGBhoYGysrKEB8fz8uNDFlJyLZIWFhYyOp8JMnJycjNzYWVlRVrc/bq1QuhoaGIiIjo8lhz5szB6dOnkZCQgMDAQMayGLmGPJAXi8WcVB7Ex8cjMDCQ9XlJcnJysHXrVqxZswbm5uZITk5mdL5jx45h0aJFMDY2ZnSermJkZIRly5bR1qS+pyCVSqnrjyASdo5z585xdr9qi8uXLyMsLAzOzs5wdHSEtbU1dcDDV8rKynD79m1KqGaLpKQkVFVVQV1dHfb29qzOLS+urq44efIkbSKhpaUlLePQQUZGBg4ePIh58+bxsr/W+PHjkZ2djcLCQmzcuBE///wzZ7E0NjZSlvyK8t6lk+XLl+PZs2ewsLDAb7/9RnsFdWNjI65fvw4AGDx4MC1jVldXIz09nZaxnodPh+N8EQmjo6MBtFQR9pQDLL66CNTV1eHBgwdobGzEwIEDYWNjw/n7g0m8vLxQU1ODe/fu4e7du7h37x6GDBmCESNGYNiwYXL3naurq8PGjRtx8+ZNAC1nBzNnzuzS625nZ4cRI0YgJCQEv/zyC2xtbanE5echX69XXWtSU1Px4MEDuWORl7KyMpw/fx7379/HuHHj0KdPn279/pKH27dvIzs7m+swKFJTU3Hp0iVMnz6dkfEPHTqEtLS0dv8/OjoaYrEYM2fOxMCBAxmJoSP0hEpCDQ0N3Llzh+swBGiGrdc1ISHhtd9jbGys0C1XBJGwDZSUlLBy5coeezNXU1PDzJkzceTIEfz666/Yvn077/oxkjajqqqqrM5LWoBxQUpKCqsiIZnJX1hYiOrq6i4djmlra2PGjBk4ePAgDh8+3G1FQrIhuqWlJSfXj4qKCtbnbIv4+HiYm5tTtmNMUV5ejs2bN2P+/Pm8t+Wzt7fH6NGjcevWLa5DUQhkMhk2btyIqKgoiMVi9OnTh+uQFAq2+2B2hNLSUty9exd3796FiooK7Ozs4ODgAEdHR5iZmfFmzVVZWYm7d+/i8ePHnMxPbnD69+/PeyGVxMDAAABoq7gj3x98eR/n5ORg586dmDdvHqytrbkOpxXKysqYNWsWtm7diocPH6K8vJx6Pdjm+vXrkMlksLGx6ZGJHWFhYQCAL7/8EoaGhrSPHxcXh+rqamhpacHR0ZGWMZmy4CsrKwNBELy4rpPOHlxX1t+9exdAS1uPnkJ8fDzl/sMHyOSzq1evUl979OgRDAwMqD5x8gpmfEYkEmH06NFwdnbGpUuXkJ2djYcPH+Lhw4cQiURwcXHByJEjMWLEiHav3QRBoKGhATU1NaiurkZhYSE2b96M3Nxcavzhw4fT8pkfNWoUMjIykJ2djXfffRebN29+SSgkP0+vutbm5OR0OZauUFpaiqNHj6JXr14YN24crz4LXEIQBKt9vjsKU1WNNTU1lEBoZmYGU1NT6k9TU1MUFhbi/v37yMrKwunTpyEWi+Hk5MRILK+DibWLgEB3wNjYGJqamli8ePFrv1dTUxMJCQkKKxQKImE78GFTwyV//fUX7t69i5ycHGzYsAFfffUVr34nZDYmm5ZUOTk5rFcUPA/b2VZqamrQ0dFBdXU1srOzu7xY8ff3x8GDB3Ht2jVUVVV1OwuapKQkXLp0CQAwduxYTmLgS+Y+aWNTXl7OynzHjx+Hr68v/Pz8eHWdepGeaL8mDwRBYPPmzbh8+TJEIhFmz54tbKw7AXmQw2eampqQmppKWd5qamrC3t4ejo6O6N+/PyfVB7W1tQgJCaEO+rmCzMj38PDgNI7OQPZqprPSzsPDgzciIdByeLR7925MmzYNgwYN4jqcVpiamsLCwgL5+fl48OABJk+ezHoMBEFQvTv/9a9/9ZhKqbZgan1LVtb36tWLtt8vWflJN7W1tSgtLeWF0wO5fmCiYrKj1NXVITY2FkBLr/aeQnBwMHr16sULC+m0tDRcvXq1zfd8eXk57ty5gzt37sDOzg5ubm5wcnKCmpoaB5Eyh5mZGVasWIHi4mIkJCQgMTERBQUFiI6ORnR0NDZv3ow+ffrA3t4e1dXVqK6uRk1NDaqqqlBTU9NmlZGuri7mzJlD62uspKSERYsW4eDBg8jLy3tJKCwsLKSqqn18fNodhy8tTp49e4atW7fC09MT/v7+vEu+Z5v8/HzU1NRwHcZLMHWgn5eXB6ClSnbfvn1tfs+qVavw22+/4fr16zh58iTmz5+Pfv36MRLPqzAyMoKLiwtiYmJYn1tAgM/Y2toiISGB2vO2R0JCAhYvXoySkhJBJBToXhgZGeH48eMYOXIkbt26hUGDBmHKlClchwWg5SCC3Gix+cHjuiw9JycHMpmM1YMXY2NjVFdXIysrq8siob29PWxsbJCdnY1Lly5h4cKFNEXJPTKZDH/99RcAwMXFhbMbgrm5OdTU1CCRSDiZn4RtkRBoySgtKCjAzJkzOe2x8CrMzMy4DkEh2LNnD06fPg0AmD59OmfZlIoK159/eairq0N8fDzi4+Nx8eJFfPbZZ4wfotTW1qKkpATFxcUoKCjocv9dOpDJZFQPHkUSCclDVzrFEScnJ1y9epUXPVqe5/z58ygoKMC4ceN4VenZv39/5OfnIyQkhBORMCUlBdnZ2VBXV8eqVatYn58PMJ2kRIqEdFURAsyJhECLKMcnkfBVVm9MQ/6e9fT0WHWF4QN79uzB6tWrObORLi8vx40bNzpkEQa0ODFkZmbi/PnzcHV1hZubG+zt7btN4oNIJKIqmEaNGoWKigokJiYiMTERWVlZSElJeWVStEgkgoaGBjQ0NGBtbY3x48dT1bp0oq6ujiVLlrQpFB4/fhwEQcDBweGVnye+9VeLiIhAREQExo8fDy8vL16tIdgkKSmJ6xDaxM7OjpFxc3NzAeCVrkdisRiff/45mpubcevWLZw4cQILFy5E7969GYnpVfj5+XVrkbC2tpZKrM/IyOCtLbZA52DjdbW1tVVY4a8zCCKhQLsMGzYMv/76Kz7//HP8/fffcHJyonVjKi/5+fkoKSmBWCxmbaOVnZ2NZ8+esTJXezQ2NqKwsBAWFhaszWlkZIT09HRaqhhFIhF8fX1x+PBhnDp1qluJhDdu3EB8fDyUlZU5qyIEWhaY9vb2nC++y8vLIZFIWBUJgZbenbt27cL8+fOpvqV8QkVFBerq6ryv8uKSI0eO4NChQwCASZMmwc3NjeOIFA++iSryEBISgvHjx3d5HIIgUFNTg+LiYupBCoN1dXU0REovUVFRKCsrg6amJu8tlJ+HiUpCZWVluLm5ITQ0lLYx6SI8PBxFRUVYsGABbypNBgwYgNu3b+PRo0eora1l/dDj4cOHAICJEyfysncjm5A98OiksrKS6vOsSCKhl5cXY+N3FFIkzM7ORlNTEyc9w8lrZE8TCEl27txJ9Stni8bGRty7dw8hISFyj0FW2FlbW2P58uXdUtTR19fH0KFDMXToUNTW1iIlJQW1tbXQ0NCAuro6JQiS/1ZVVWXNtaUtofDnn3/G+fPnAQAjRox45fP5Ukn4IteuXUNERARGjhwJOzs76Onp8doJh27IexnfYOr6TFYSvq4yUElJCV9++SWampoQEhKCY8eOYdGiRayfvxoaGsLT05NKTOqOvK4aTEAxEV5XeugeKVECjPHJJ59g0qRJaGxsxA8//MCLw0fSatTS0pK1jR7XVYQkWVlZrM5nZGQEgD6r05EjRwIALl26xMsDWnmora3F9u3bAbT0UOD6cIwvlpaZmZmcZHCWlpZiy5YtuHbtGi/fY9ra2lyHwFvOnDmDnTt3AgDGjBnDi8NFRaQ7iNChoaFd7rGalpaGLVu2YOPGjTh48CCuXr2KyMhIZGZm8vLaAABBQUEAAHd3d4U6jGSikhAABg8eTOt4dJKRkYEDBw7w5r1kYmICIyMjyGQyTixzSTGXiypGvkAe8jIhEpKVzqamprStMwmCYFQkzMjIYOR30Vl0dXWhpqYGqVTKWY8y8vfMt56mbLJ9+3ZG328kpOPQ5s2buyQQPk9OTg6ioqJoGYtuSkpKkJGRQUtPYC0tLbi7u2P48OHw8PCAk5MTHBwcYG5uDj09PaipqbEuZpFCoZWVFSQSCT799FNIpVJYWFi8ds/LV5EQaNmvnj17Fn/++Sc2bNiAY8eOISQkBOnp6Zw4gkgkEhQWFiIpKQlhYWG4du0a7t+/j9raWlrnqaysREFBAa1j0oGdnR2UlemvnyEIghIJO5L8p6ysjK+//hrDhg2DVCrF0aNHObHe9/X1ZX1OAQEBfiBUEgq8ErFYjH379sHNzQ2ZmZnYtGkTPvvsM05jYttqNDMzk1OLmufJzs7GkCFDWJuPtAmiS5zs27cvzM3NUVBQgGvXrmHmzJm0jMslBw8eRHl5OQwNDTF06FCuw+FNX0K2Be0XCQ0NRWhoKPz9/TF06FCoqqpyGg+Jtra2kOX0AgRB4NKlS5Rlr6+vL4YPH85xVIpLdxAJgZbknBkzZnT6edXV1bh+/Tq1VlAkyH6EfOt59zrIaxrdIqGxsTHs7Ox41ZvwefLy8rBv3z4sWbKE8wQhoOUA6v79+wgJCcHo0aNZm7esrAyJiYkAWirAeypMHp6TIiGdFQVVVVVt9hiji/r6ehQUFLDqgNIWpL1idnY20tLSOEmm6+mVhCQHDhzA8uXLqSRUupHJZDh27Ngr7TLl5d69e7xL4CksLMS2bduof1tYWMDGxga2trawsbFhrD8q26irq2Px4sU4dOgQZd04YsSI115z+SwSPk9tbS2SkpJaOQGZmJjA2toaVlZWMDU1haamJrS0tOQSahsbG1FTU4Pa2lqqx2RFRUWrR3t7h6CgILi7u2PIkCG0VALztYqQKavRyspK1NXVQSwWUz01X4eKigq+/fZbfP311wgPD8ehQ4ewdOlSVnu76ujoYPjw4bh//z5rcwoICPADQSQUeC0mJiY4fPgwAgICcPnyZXh4eCAgIICzeMhKQrZEQr5UEQL0VfR1FHITl5eXB6lU2uWNkUgkwsiRIxEYGIhTp04pvEhYVFSEwMBAAMCECRMYyUDrLORGguvqhvz8fE7nJ7l9+zbCw8MxatQoeHh4cL6558NBMp9IT0/Hli1bKEuToUOHws/Pj9ugFJzuIhI+ffoUPj4+He7lKZPJ8OjRI1y9epXhyJhBIpFQVQ98rqBrC6ZEQqDld8FXkRBoqRDau3cvli5dCn19fU5jGTBgAO7fv4+wsDA0NjaylhxDVi56enpyLgh1RwiCoO6RHT1k7Ahk/2gmSU9P58V7ghQJ09PTOZmfrKDr6SJhTU0N9u/fjxUrVsDAwID28aOiohgRCAGgoqICT58+5U2/4MbGRpw8ebLV1/Lz85Gfn4/w8HAALT0wbWxsqIeZmZnC9lYkhcIzZ85ASUmpQ1VZfOtJ2BlIi/wnT560+rpYLKYEw+f/1NTUBEEQlBBI/llTU9PlZJCoqChERUXB3t4eQ4YMQd++feV+H3HdEqU9mO5H2Lt3706tyVRVVfHDDz/g3//+Nx4/foyDBw9i+fLlrPZ2FURCAYGeiWKuEgRYx9/fH19//TUAYOPGjdQNj20qKioooYwNkTA9PR0ZGRmMz9NRqqqqUFlZydp8enp6UFJSQlNTE23WEKR9wYULFzix06CTwMBAyGQy2Nvbo0+fPlyHA6BFiOWD5WhpaSnXIVDU1tbi8uXL2Lx5M2JjYzm1vxKaY7dQWVmJP/74A2+++SYiIiIgFovh6+uLcePG9ai+HEzAB1twuiAr615Hbm4udu7cqbACIdDS062+vh6Ghoa8qQjvCARBMNKTkGTAgAHQ0NCgfVw6KS8vx549ezivEre0tISuri7q6+upyjM2IPsR9mSrUYA5u9Hs7GwUFRVBLBbTepDJhvUjX/ZQZF9CrkRCoZLwH6qrq7F///4uW4q/iEQiwa1bt2gd80VCQkJ4U5129erV195zKisrERsbiytXrmDHjh3YsGEDLl++jJycHNb2QmTVGh2oq6tj4cKFmDdvXodEKr68VnQik8lQU1ODwsJCpKenIzY2FuHh4bhz5w6Cg4MRERGBxMREZGdno7y8nNZq8YyMDBw/fhx///03Hj582OmkRIlEgmfPntEWD50wZQXdGavRF1FTU8NPP/0EV1dXNDU14cCBA7Tbv74KDQ0NTJkyhbX5BAQE+IEgEgp0mK+//hojR45EXV0dfvjhB0YtatojJiYGQEt1I9OHRgRB8KqKkIRNG0exWEx7X0InJycYGxujqqqqwwfAfKSqqgpnz54FAN5ZI/LhgLmmpobrEF6ivLwcp06dwo4dO/Ds2TNOxMKeXknY1NSEwMBAzJ07F+fOnQNBEBgwYADeffdd+Pv7CwIhDXSXSkIASElJeeUhc319PS5evIhdu3bxssdJZ3jealSRPge5ubloamqCkpISI9c3ZWVluLu70z4u3VRXV2Pz5s2cVtGLRCL069cPAGjrxfU6GhsbqSo34TCpBbrXFuTv187OjtZe7GyIhJmZmbw4qOdaJBR6EramsrISBw4cQFVVFW1j3rt3j/ED9IqKCsrRiEtiY2NfqjDrCHV1dXj06BF2796NTZs2ITg4GGVlZQxE2HIdDA8Px//93//hzz//xN69e/H48WNWE4T5cO3pjlRUVOD69ev47bffKNG5I3sPPguETDkvkCIhuTbrLBoaGvjll1/g6OgIiUTCejKkh4eHcN8SEOhhCCKhQIdRVlbGkSNHoKuri+TkZOzbt4/1GEiRkI0qwpiYGM77qrUF25ajZF9CuuYVi8UYMWIEAFAimyJy4sQJNDc3w8zMjFb7JzrgQyUhnzdmBQUFOHToEP7++29cvHgRCQkJrAkr2trarMzDRwiCwBdffIEtW7agqakJZmZmWLp0KebNmwdDQ0Ouw+s2dCeREACOHTuGo0ePIiQkBJmZmVSC0rNnz7B582ZWK6aYJDg4GIDi9SN89OgRgJYKGabsnPli79YRduzYwdiha0cYMGAAAOD+/fusJMLExMSgvr4e5ubmCvU6MQFTlYRM9CMEwErla2NjI2fuM89DioT5+fmcVNsLlYQvU15ejn379tFie5uZmYl79+7RENXruXfvHqeOJA0NDTh16lSXxykrK8OdO3fw999/48qVK5DJZDRE1wJBEDh//jyuXLlCfS0rKwsXLlzAr7/+irt377KSbK7IdqOKAik6//bbb1i/fj327duHCxcu4OHDh0hJSUF5eTlkMhlkMhkeP37MdbhtwtS5IkEQlEiorq4u9ziampr4/PPPAbQkCLB5FigSibqdS4RYLIanpyc8PT0V1n5Z4GWE15U+uG+gJaBQWFtbY9++fZg1axYCAwMxdepUWpoYdxTSx5zpjJaYmBicOXOG0Tnkha0eMy/OR2fmH9l4mU+WlJ0hJiYGR44cAdBin8q3qg9DQ0MYGRlx+vtVVlbm/easvLwckZGR1AGclZUVevXqBUdHR1hbWzNy4M333wmTZGRk4PHjxxCLxZg8eTLc3d2FRRwDdLeMT4lEguTkZCQnJ1NfMzMzQ2FhIYdR0Q/ZnykmJkZhNuRFRUXYtm0bAPmslDqKsbExzM3NFaZa9MyZM1ixYgUn1zcy4aK2thYymYzxPrxkVc3YsWN79PVcJpNRlnp024qTCYt0X9urq6tpHa894uPjWesj3x6amppQUlKCVCpFVVUVqxbGUqmUem+wuWdWBMrLy7F161bMnDkTLi4ucu2nEhMTcfz4cQaia5uysjJIpVLO+tDTKeaRhIeHo6SkBHPmzKHls3Hr1i1ERUW1+/+3b99GREQERo8eDVdXV8buHZaWlpxVD/dEamtrUVtb+1IfaSUlJWhpadFaOUwnZHIV3YhEIhgaGqKgoAA//fQTMjMzsXTp0k6vy2QyGVUoQY7LJubm5vDy8qKSAhUdDQ2NbvOzCPyD8LrSR8/dzQnIzYwZMxAQEICmpibs3LmTtXllMhnVjJzJpr3R0dE4ffo0Y+N3FVdXV1bna2xsBEDvoQeZUauIh9k1NTX4+eefQRAEXF1d4eTkxHVILyESieDt7c36vFpaWvDw8MCiRYt4Z8HaEXJzc3H37l3s27cPv//+O44ePYqwsDBa+6YoqjBOB2Tz8169esHDw6NHHygzSd++fRVGZJKX7iYQAsBXX30FALh+/TpvLZmeRyaT4bfffkNjYyOsrKwwZMgQRuczMzNjdHw6ycnJoa53bEPer0xNTRkXCIEWAQgAfHx8GJ+LzxQWFkIikUAsFsPExIS2cQmCoCqt9PT0aBsX+Gd9zzRxcXGMCBudhVxzsB1LdXU1VXlG53ujO3HmzBns3bu30xUyERERrAqEJFy0XCHR1NRkxMEmLS0Nu3bt6nKFcURERIeqOqurq3Hu3Dns3LkTaWlpXZqzPQICAjBr1iyhHzzHkMkZfGTEiBGMnkctX74crq6uIAgCBw4cwIcffthhW/qKigocPXoUS5YswebNmwEAo0eP5uT8rKev8QQEehLCCZ1ApxGJRFi/fj1EIhFu3bqFhIQEVubNy8tDfX09lJWVKQtMusnIyOBtBSHQIo6SljlsQVrX0bnAVtTeHARBYOPGjSgsLISBgQEmTZrEdUjtwlYPJwMDA/j4+GDFihX4+OOPMXXqVPTp04d3FqydpbGxEcnJybh69Sr+/PNPBAUF0XKgxqUNHdeQPbKYrDgSaMHT0xPz58/nOgyBTuDl5YV58+aBIAhWE7Dk5fTp03j8+DGUlZUxc+ZMxkV/RTtcv3XrFif9CSsrKwGwU7Ekk8moPcDQoUMZn4/PkL1TTUxMaK0wqqqqotYedPf8ZEskrKmpeWVvWbYgqy/YFgnLy8sBtFT5clV9pghkZ2djz549OHHixGsT6giCQHBwMC5dusRSdK1h67PTHgMHDmRk3LKyMmzevJlKyu4sycnJnX5NCgoKcPDgQRw+fJj2PqkikQguLi549913MXjwYFrHFlB8bG1t4e/vz+gcampqmDlzJmbNmgUVFRXExcVh2bJluHHjRpvfTxAEnj59ih9//BGzZ8/Gjh07kJeXBzU1NYwaNYpq2cM2BgYGwv5dQKCHIKxUBeTC3d0dS5cuxf79+7Ft2zb88ccfjJe+k1ZjZmZmjBxGVVVVYf/+/bSPSydsCT/PQ9qMCiIhcO3aNdy+fRsikQizZs2Cmpoa1yG1i6qqKnx8fPDw4UPaxzYxMYGzszP69+8PU1PTNj/7lpaWUFFR4TTblk7u37+P6OhojBs3Ds7OznJf73pqJWFRURF1De/bty/H0byaxsZGFBcXo7CwEIWFhSgqKkJxcTH09fUxadIkRivZ6aR///5YsWIF9u7dy3UoAh3kp59+wqlTpxAWFoaoqChO7vkdIT09HVu3bgUAjB8/HkZGRozPyXaCFB2cOXMGq1evhoqKCmtzkpWEbFReZmdno7a2FhoaGnBxcWF8Pj5D2qvR/Xsn18taWlq0CkxSqZTV3tExMTG091TsLFyJhM9X9wq8noSEBCQkJMDLywujRo16af8pk8lw+fJlTvsRc723GTBgAM6dO8fY+EeOHIGTkxPGjh0LfX39Dj0nNzcXR48elXvO1NRUpKamYt68ebTbP2poaGDKlClwc3PDxYsXaemDKaD4zJ49mzVXGxcXF9jY2OD06dPIzs7Gf//7X4SFheGjjz6CtrY2qqurcf36dVy4cKGVXauVlRUGDx6MgQMHsrqWbIshQ4YgMTGR0xjooK6ujnICi4+Ph6amJscRCdCB8LrShyASCsjNTz/9hKNHjyI6Ohr3799nPLMlNTUVADPZ0c3NzThx4gTt49INU5mDr0KoJGwhOzsbGzZsAAD4+/srROze3t60i4RTpkyBh4fHa0UyJSUl2NraKoRtXkeprq7GqVOnEBERgYkTJ3bqMLCwsBBRUVFdtvFRVEjrIVtbW2hra3MczT+QvTNIMbCwsJDK+m/re3fu3IlRo0Zh5MiRrFj5dRVbW1u88847OHToEG+tfgT+oU+fPlizZg22bNmC7du3Y8uWLbzredvU1IT//ve/kMlk6N27N2vZ+Yp4wF5cXIybN29iwoQJrM3JZu8z0mrUy8urx1dIkZVyTImEurq6tI7LdiVUQkICJk+ezOn7hCu7UUEklI9Hjx7h0aNH8Pf3h4+PD1RUVNDc3IzTp0+z5mLUHlxXEqqpqWHAgAGM/h7i4+MRHx+PkSNHYsSIEVBVVW33e8vKyrBr1y5a5j1x4gQmTpzISNsMGxsbvPXWWwgNDUVQUBDt4wsoDgsXLqT9vvo69PX1sXz5coSEhCA4OBg3b95EXFwcXF1dcfPmTSpxR1lZGa6urvD09ISFhQWrMb4KOzu7btETniAISoglrcAFFB/hdaWPnr2jE+gS1tbW+Oyzz/Dzzz9jx44dGDp0KKObP9L6gomb5ZUrV5Cbm0v7uHTi7OxMSzPxzkJWEtJ1sE8QBOsi4bZt21plX1lZWWH8+PEd/pmamprw008/obm5Gfb29grTb09fXx+LFy/GzZs3u2x75ubmhrFjx3ZKLLa3t+9WIiFJZmYmtm3bhiFDhsDPzw/q6uptfl9tbS1iYmLw9OlTFBQUsBwlvyBFwn79+nEcyT+kp6fjwIEDnX5ecHAwUlJSMGPGDIWwQDQxMcGbb76Jw4cPC9nTCsA333yDPXv2IDExEcHBwfDz8+M6pFbs27cPqampUFNTw7Rp01gTMXV1daGqqsr54WxnCQsLw9ChQztcjdFVSLtRNioJSZGwp1uNAv9UEtItzpLXbEUXCSUSCVJSUmivEOoMXFcSKsJ6gY/cvn0bERER8PX1RWxsbKsqG67gupIQaKlMYkMsDQkJwZMnTzBmzBi4urq+dM+vra3F4cOHaZ3zypUrqKysxJgxY2hfYygpKWH48OFwdnbGlStXKJcTgZ6Dj48PZ642YrEYo0aNgqOjI06fPo2CggLqjMDU1BSenp5wdXXlpVuVSCTC0KFDGa1iFhDoLnTk/mxsbAxbW1sWoukcgkgo0CXWrVuHnTt3Ijs7GxcuXMDMmTMZmYcgCMZEwsePH+Px48e0jskEXNmOkSIhXSXbVVVV1OaKLdu+ixcvvvS1bdu2YfLkyZg1a9ZrL867d+9GcnIy5SvPljUFHfTq1QuOjo7IyMjAgwcPqIrcjmJiYoLJkyfDzs6u03M7ODh0+jmKRFhYGGJjYzFmzBi4ublBJBJBKpUiOTkZT58+RVJSEtch8oKqqipERUUB4E8/wri4OJw8eVLu5+fl5WHLli0YP348hgwZwrtqrxfR1dXFihUrcPLkyW4p3HcnzMzMsG7dOnz//ffYtWsXRowYwZsqrZiYGMpObPr06bT3SHsVIpEIJiYmvE/oaovIyEgEBASwMhebdqOCSPgPQiXh64mNjeVUJOSqkpB0JxAqCeWnurqas/6DbcGHZJU+ffpATU2N2qczSU1NDc6ePYtHjx5h/PjxsLGxAdAilh47doyRfusPHjxAVVUVpk+fzsgaSF9fHwsWLEB6evpLlVHNzc24desW7XMKcI+VlRVr67FXYWNjg7Vr1+LWrVtobGyEh4cHrK2teb+fHDhwIG7cuIG6ujquQxEQ4CXGxsbQ1NTE4sWLX/u9mpqaSEhI4J1QyI9TBwGFRUdHB99//z3efvtt7N+/H2PHjmXESq64uBiVlZUQiUS0brJyc3Nx4cIF2sZjCl1dXU56eUilUkrQo8tulDzwMDU1ZS1LysfHh9pgEASB5ORkFBUV4dy5czh37hyGDBmC2bNnw9PT86XFWUREBI4fPw4AmDFjBuvWFHQgEong4OAABwcHFBYW4uHDh3j69OlrnxcQEAAfHx+5bRUtLCwUsvKjM9TW1uLcuXOIjIyEhYUFYmNjUV9fz3VYvOLhw4cgCAKmpqYwNDTkOhyEh4fjypUrtIx17do1JCUlYfr06axVCsmLuro63njjDcTGxuL69euoqanhOiSBdvjkk0+wdetW5Obm4uLFi5gxYwbXIaGurg6//PILCIKAm5sbJ4f9pqamCikSPnnyBH5+foxbFBMEQVUSMm03WldXRwljQ4YMYXQuRYD8XQiVhO0THx8PiUTCWYUEV5WE5GdSEAm7D3yoJFRWVsaAAQOoJDw2yM3NxZ49e+Di4oLRo0fj6tWryMnJYWy+2NhYVFdXY8GCBe26tnQFkUgER0fHl85YJBKJIBJ2U+bMmcObdhFqamqYOHEi12F0CmVlZXh6euLu3btchyIgwEtsbW2RkJDw2hZDCQkJWLx4MUpKSgSRUKD7sWrVKvz5559ITEzE0aNHsXr1atrnIKsITU1Nacsmq62tVYg+hADg6urKSfXa89mJdIuEbPb0GzVqVKtDidGjRyMjIwOhoaFITk5GWFgYwsLCYGdnh1mzZmHs2LHQ0NBARUUFfvnlFwDA4MGDeVMF1RXMzMwwY8YM+Pv7IywsrM2ehf369cOECRO6LHqIxWLY2dlRn9/uTE5ODqMbZUXm/v37ALivIiQIArdv30ZISAit42ZkZODPP//EtGnT4O7uzussUJFIBBcXF/Tt2xd37txBaGgo1yEJtIGOjg6++eYbvPfeezhw4ADGjx/Pid3482zevBn5+fnQ09Njtcfe8yiqXV9tbS2SkpLg5OTE+DxSqRRisZjx31VSUhJkMhlsbW1Zc4XgK8/3QaFbJCQPGegWCdmoPmqLhIQEzpxRuBIJyUpCRb1+CbwMX5IfBw4cyKpISBITE4OYmBhW5srMzMSePXvwxhtvQE9Pj5U5Bbon8+fP531CpyLg5eUliIQCAq/A1taWd8JfZ1AczzwB3qKsrIzff/8dAHDy5ElGqhNIi0Q6N9+XL19GVVUVbeMxCddWo+rq6rSJs1yIhC9CVtYtXLgQ77//Pry9vaGsrIzMzEz83//9H5YuXYr8/Hz83//9H8rKymBiYoLx48dzFi8T6OnpYdy4cVi3bh3GjBkDHR0dynplwYIFtC2i7e3taRlHQDFpbGzEgwcPAHAvEoaFhdEuED7P+fPncefOHcbGpxM1NTWMHz8ea9euVehFbHdm9erV6NWrF8rLy+XqnUknERERuHz5MgBg5syZjGT0dwRFrsRhw9aeFCOMjY0Zt6gVrEb/obi4GPX19bS7nZBjA92jkhBoqQziCjLZUiqVsjqvUEnY/eBKZH8RBwcHXjh0ME1xcTH27t3L+mdXoPvg7e3N+T60u6CtrQ1XV1euwxAQEGAIoZJQgBamTJkCc3NzFBQUIDc3F/369aN1/OrqagCg1cpUUap+LCwsYGRkxMncpEc/nT1WGhoaANB/4CEvhoaGmDhxIvz9/REVFYXQ0FCUlJTg448/RkFBAUQiEWbNmgUVFRWuQ2UEdXV1DB8+HMOGDQMA2qughKzPnk1xcTGkUilUVFQYt797HWz04gsPD8fIkSN500PudZiZmWH58uWIjo7GtWvXBKtcHqGqqor169dj5syZOHbsGFxdXeHj48NJLOfOnQMAeHp6ytWfli64voZ0hYKCAsbnIBPqevfuzfhcpaWlrM3Fd563BKV7rUiuyQiCYGRctsnKyoJMJmPdHYUgCOr+RleP9Y5CJqT2BDGnp5CQkMALm2WxWIwpU6ZwnkjEBpWVlbRfBwX4i7u7O1RUVKCiogJlZWXq789/jSAINDQ0oKGhARKJpM2/NzQ0wNzcHGPHjuX6R+pWeHt7Izo6musw5EIkElHOHnx2/xHoHMLrSh+KcYolwHtEIhEsLS1RUFDASPNqVVVVAPRmfypKNhqXlVikfRIT2UJ8u3irq6tj6NChcHJywo4dO6gDPTc3N4U+mOwoTL0eQt+zng15OKapqcn5Z762tpbxORoaGpCUlARnZ2fG56ILkUhEXee2bdvGdTgCzzFjxgy8++672Lx5M3799VcEBgZS6yG2qKiooCyDvby8WJ37RbS0tGBsbPzaPg98pK6ujlFxhCAIqkrL39+fkTmeh6xE46qqlE+QlWJ02fI/j4WFBbKzs1FeXg4HBwfaxuXKvripqQmlpaWsW2+SB8dAy++UTci1h5A0133IzMxEfn4+6++ltnBwcMDbb7+Nc+fOIS8vj+twGMPZ2VlhEvAEuoanpycmT57MdRgCr8DS0hJmZmZUQYEioampibi4OK7DEKAZ4XWlD8FuVIA2yIUyEyIhmZnb3NxM25h0jsUkXGbtZ2VlAQBcXFxoH5uv2YC6urqYN28exGIxlJSU4Ofnx3VICg0bwowAfyGrwLnupwaw9158+vQpK/PQjZmZGVauXMl1GAIvsHHjRpibm6OqqoqTzc/NmzdBEAQsLCx4YZfH5ZqoKxAEgbq6OsbGz8vLQ1lZGZSUlDB8+HDG5iFpamoCgFb9nnsqTIqEZJIaaSVLF1zek3Nzc1mfk/z9GRkZsf6eFUTC7klYWBjXIVCYmprizTff7NbVUgMHDuQ6BAGWEJKP+I9IJMKgQYO4DkNAQIABBJFQgDZIkZC0IKITMnOeTmFPUSoJuToQk0gkyM/PB9BSTdeTsLW1xVtvvYW33npL2NR3EUEkbI2+vj5GjhzJdRisQVYSci0SEgTB2nsxJSWFsp9TNGxsbLB48WKuwxB4DlVVVYwZMwYAEBkZyfr8165dA8Bdb+QXUeQ+t0xW1pNVhKNGjWLleiuIhP9AioR0tkQgsbS0BNBS0UsnXN6Tuah2IkVCtiu/pFIplRwg7Ce6F0+fPuWVW4pYLMawYcPw3nvvKWwyTXuoqakJ1tY9CEEkVAyYKCIQEBDgHkEkFKANMttVsBulD3Nzc84WSjk5OSAIAubm5oxUD/C1kpDEzMyMF1UTio4gErZm4cKFGD16NJYsWcJ1KKzAl0pCiUTC6jX/6tWrvL/GtUevXr0EoZBncCUSPnv2DCkpKRCLxbzJ4lfkw0+mDpRlMhlVZTp69GhG5ngRQST8B0WsJOTyEJZMQGQTrkTC5/v8CiJh9+PRo0dch/ASRkZGWLZsWbeya+zfv79gNdqDEERCxUBTU1Oh2muQ1NXVwdnZGc7Ozow6fAiwi/C60odwtxWgDUWyG5XJZApxgGtra8vZ3GQ/QrqzhLjuSybALoJI+A8LFiyghGdHR0f861//wsmTJ5Gdnc1xZMxBVhJyveFj+32Ynp6O+Ph4hdw8AS1C4Zo1a3D48GFeZcr3VAICAgAAycnJqK6uho6ODivzklWE/fr1g6amJitzvg4dHR0YGhoystZkGqY+S5mZmdT7wtvbm5E5XoQUCbm+tvMBJkVCpioJlZWVoaKiQr2ObFJQUACpVAolJSXW5iRFQvL3yRbk2kNVVVUQ1LshERERGDlyJO8ELJFIBE9PT/Tp0wcXL15Eamoq1yF1CabW0gRB4P79+y/twxQhkbwtVq5ciYiICERHR3MdSpcQrpWKw6BBgxSuDxxBEIiPj6f+LtA9EF5X+hAqCQVog41KQrpEQkXpR8ilrRbZj7CnWY0K0IsgErYwevRo9OvXr9XXdHV1sWzZMgwZMoSjqJiHFAm5Fhi4eB9ev34djY2NrM9LF+bm5njzzTdhbGzMdSg9Hmtra/Tr1w8ymQxRUVGszNnY2IigoCAA/FsHKGo1IVlZTTek1aivry+VVMc05LVNOMxjkOQcGwAAlZNJREFUViQkEzBra2tpv59wJfA2NzejuLiY1TlJkZXcq7KF0I+we1NXV4eYmBiuw2gXPT09LFq0CDNmzOA6FLnR0NCAo6Mj7eMSBIHr16/j5s2bSE5ObvV49uwZ7fMxzciRI2FjY4OZM2di5cqVrFdN04mQfKQ4ODo6Cvc3AYFuBr/SngQUGkXqSagoGWJcVRI2NzcjJycHAODq6srIHEKGR89AqEICBg4ciBEjRrT5f0pKSpgwYQJsbW0RGBjIcmTMwxe7US5EwqqqKty7d481+z8m0NfXx8qVK3H8+HGqulyAG8aMGYOkpCRERkay0tf01KlTKC8vh66uLu96Adnb2+PJkydch9FpmLgfSqVSKnOWzWuNYDf6D0yKhNra2tDR0UF1dTUqKipotcHX0NBgTLh+Hbm5uawKdlxXEgqHqN2X0NBQuLu789YpRyQSwc3NDU5OTigvL0dDQ0O7D4lEgrq6OmRkZHAdNsWAAQMYqTq+f/8+QkNDAQDLli1rdW2VSCT466+/aJ+TKbS1tVvtM21sbLBq1SpERUXh5s2bCme9J4iEioNIJIK7uzuCg4O5DkVAQIAmBJFQgDaeryQkCILWxTLdPQkVQSQ0MTFh5MChI+Tl5UEqlcLAwADW1ta0js3XTZQA/TQ2NnJiZcUnLCwsMG3atNe+752cnPDee+/hxIkTKCoqYik65iErCXuiSAgAISEhcHd3h6GhISfz04GGhgYWL16Ms2fPKpylTHdizJgx2Lx5Mx4/fsz4XJWVldi7dy+AFuGJTVvAjqColYRMXIdSU1PR0NAAIyMjVis+BZHwH8j7nLa2NiPjm5ubo7q6GuXl5bSLhFyRl5eHwYMHszKXTCajKgnZrq4hEwMEkbD7UlRUhPT0dEaq3ehERUWlw9cPiUSCuLg4PH36lHIW4gomrEafPHmCmzdvAgDefvttzJs3r9X/19bWKpRIGBAQQJ2VkYjFYnh4eMDJyQnBwcGUIKoICCKhYjFo0CBBJBQQ6EYIIqEAbZAiYWNjI2pra2ndLJP2SXQJDopgN8rlIRhZMeLq6sqYqCeTyRgZV4A/9HSrUW1tbSxYsKDD9m9GRkZYtWoVLl26hKdPnzIcHTuQVQpcZ7Fy+V48d+4cFi1apNCH6crKypg9ezZ0dXXx8OFDrsPpkfj5+UEsFiM7OxtFRUW0igUvEhgYiKamJpibmzPmJtAV9PT0oK+vT3ufNqZhopKQtBr19/dnVcwlrS/5JiBzAZOVhEBL9VtKSgrt7Ry4FAnz8/NZm6uiogIEQUBFRQVGRkaszQv8s/bQ1dVldV4BdomMjOS9SNgZ1NTU4OHhAQ8PD5SVlSE6OhpPnz5l/Z6rpaVFe+uVoqIiXLhwAUBLr/gXBUJFw9LS8pUJQurq6hg/fjw8PDxw7do1hbBSFURCxUJPTw8LFy6kXMhImpubhT2jgIACIvQkFKANDQ0NmJiYAGhpSk8n5GFYSUkJLVWAXFXodQa2LXGeh+wV0rdvX9rHJt8jimgVJtA5mLAeViTmz5/f6YMhFRUVTJ8+HePGjWMoKnYhD02CgoJw584dzmyG9fX1OZkXaOnvun//foUXzUUiEcaNG4cJEyZwHUqPRF9fn8qoZ/qQp7CwEEBLhTNfq/9tbGy4DqHT0C0U1NfXU1ajY8aMoXXs10G6TJw4cYLVefkI0/e1Pn36AABSUlJoHZfL6jY2kzVJIb1fv34Qi9k9+iATItnqFSrADYqQfCwvhoaG8PPzwwcffIDly5fD3d2dtbm9vb0Z/czW1tYqfPuTUaNGdWidZmJigjfeeAODBg1iISr5MTMz69A5XWpqKhISEoSkc57Qt29fjB49utVj1KhRXIclICAgB4JIKEArpKiUnZ1N67gODg7Q1dVFU1MT8vLyujyeiooKPDw8aIiMOQwMDDibm8yKZqJfyODBg6GkpISkpCTaDzwE+AXdyQKKxJw5c+S26hWJRPDx8cHUqVNpjop9PvroI8yePRsAEBwcjMDAQKoChU1cXFyoBAUuyM/Px549e6i+SIrMkCFDMHfuXK7D6JH069cPAF7K1qUbMjGLq35lHYHNfmZ04ePjQ+t4sbGxkMlkcHR0ZCSp61UsWLAAAHDgwAGkp6ezOjffIMVfpirmyV6T6enptH4muRTa2aqsk8lkiIyMBABO1lTKyi2mTRKJhPW5BdiDFPK7MyKRCHZ2dpg+fTq+/PJLTJo0ifY5NDQ04OzsjKlTp+Kjjz6Cr68v7XOYmppi1qxZEIlEuHDhAjZv3qzQQmFnnKdEIhECAgIYjKZr9O7dG0uXLn2t6PngwQMcPnwYJ06cwLZt2xAXF6fQr6EAu5DXMjs7O94mQgp0HuF1pQ/BblSAVvr06YP79+/TfoAlFovh5uaGkJAQZGRk0LKx9fT0ZKW3j7xwKRKSdiJMHMJpa2vDzc0Njx8/xoULF/Dxxx/TPocAP+ipImFAQAAtPTQ8PDygrq6OwMBAGqLqOnv27GmVDd+3b1/4+/u/ciGmpKSE9957D46OjtiwYQMSEhJQWlqKBQsWsHqNE4vFGDt2LI4cOcLanC9SVlaG3bt3Y/HixQopcDyPk5MTli1bhv3793MdSo+CFIKYFgnNzMwA/NNrjY+w3Vusq/Tq1Yv2mElHhokTJ7K+IXZycoKnpyciIiLw66+/Yvv27azOHxQURFUbEASBxsZG1NTUoLa2FjU1NS/9nfy3s7Mzvv76a1qr6HR0dAAwJxJaWFjA2dkZcXFxiI2NpU1strW1pWUceWBLJExKSkJVVRX09PTg5+fHypzPQ/YJE0TC7g2ZwNNTUFVVhZeXF5ycnHDjxg1aWiSMHj0aPj4+lLDOJAMHDkRzczPOnTuHU6dOIT8/H8bGxtT/09XehmnMzc073cpAS0sLo0aN4l0PuVGjRsHX1/eVlaMEQeDOnTu4e/cu9bXi4mKcPHkSpqam8PPzQ//+/QWBQOCVaGpqIiMjg+swBGhGeF3pQxAJBWiFqUpCoKUpLikSjhw5ssvjWVhYwMrKCrm5uTRERy9isZg6dGAbqVRKHQwydZDt4+ODx48f4+LFi4JI2I3piSLhoEGDMHz4cNrGc3JywhtvvIHDhw93eazhw4e32nw3Nzfj/v37HX7+6dOnX/paSEgIPvvsM2hqar7yuZMmTYKtrS2++eYbFBUVYfv27Zg/fz4cHBw6/gN0kd69e8PR0RFpaWmszfkitbW12L59O5YuXcrqz84E9vb2+Pzzz1FYWIiGhgY0NDSgvr6+1Z/ko6qqiqpQF5AfslqBaZGQrLrl82umaEL7iBEjaB2voKAA+fn5VAIEFyxduhQRERHYs2cP/vOf/7AqOpEV6p3l6tWrOH78OHbt2oXx48fTEgvTIiHQknwUFxeHmJgY2kRCHR0d6OnpcfI5Z0skjIiIANCyBiEFOzYhE6sEkbD7Ym1tTfueXSqV4t69e5BIJHBzc6MSd/iGlpYWZsyYATc3N1y8eFGuvqnu7u4YPXo06+ce7u7uaG5uxqVLl/DgwQNW56YLeZPmfXx8EBERwZs2CIsWLXptNS5BELh+/TpCQ0Pb/P+ioiKcOHEC5ubm8PPzQ9++fbuNWEgQBMrKyqCurq4QLZMEBAQUG0EkFKAVJrPcSQ/8rKwsNDc305Jp5unpyUuRUF9fn/W+GSSkQKiqqspYpc+wYcOwefNmBAcHIzo6Gq6urozMI8AdjY2NPa4noYmJCSZPnkz7pqR3795YsWIF9u7dK9fzBw4ciAkTJry0sZBIJJ0SCb28vKjrbmNjIx4/fow7d+4gLS0N33//Pezt7V8bx/bt2/HVV18hOTkZBw8exIQJE+Dl5cXKRo7sqbdt2zbG53odBw4cwNy5c+Hk5MR1KF1CQ0Pjta870HLgdf36dYSHhzMfVDeG7UpCPouEGhoacHV1RXR0NNehvBYrK6tOWYJ1BNIJw9fXl7Peci4uLhg0aBCePHmC3377DZs3b2ZtbkdHx1brZGVlZWhoaEBDQwPq6urU35//mpKSEk6cOIGcnBxMmDABq1atwoYNG7osWDFtNwoA/v7++Pvvv5Gfn4+SkpJWVS9dwcbGptuKhCUlJVRS0LRp0xifry0EkbD7079/f1rHq6iowMmTJ6nziYcPH8LS0hKDBg3CwIEDoa6uTut8dODg4IC3334bISEhraq8XoWNjQ0mTJgAS0tLhqNrH09PTxgaGr6U3N7c3Ix79+5xFFXHkTcxR01NDb6+vrhy5QrNEXUOc3NzzJs377XnTTKZDBcvXqTcE15FQUEBjh07BktLS/j5+aF3794KJxZKpVIUFBQgKyuLetTV1UFNTQ3vv/++IBQKCAgwiiASCtDK85WEBEHQelO2t7eHgYEBysvLkZubS8uBi7OzM65du4aGhgYaIqQPPliNmpmZMbaosrS0xMiRIxESEoLly5cjLCyslY2hgOJTVFTEdQis4+3tDSUlJUbGtrW1xZo1azpl6aarq4vJkyfT1qsqICCgla2Nm5sbAgMDkZWVhdWrV+PLL7+keie1h4mJCf766y/873//w82bN3HlyhUUFhZi0qRJjP3unsfMzIw61OaawMBAvP/++zA0NOQ6FMZRUlLCxIkTYWZmhgsXLnAdjsJCZloXFRVBIpF02maqo5A9Cevr69HY2MhJBU5HCAgIUAiRcMSIEbSup5qbmxETEwOgxWqUS5YuXYonT55g586d+Pe//w0rKytW5v3rr7/kOigbN24cdu3ahVOnTmHXrl24du0adu/e3aVqTDYqCfX19eHt7Y2wsDDExMTA39+flnFtbGwQGxtLy1idgQ2R8NGjRwBaEhO5qjwWRMLuD50iYUpKCk6dOgWJREJZej58+BB5eXnIy8vD1atX4ezsjEGDBvGu75KysjL8/f0xcOBAXLp0CZmZmW1+n56eHsaMGQNnZ2dexO/o6AhHR8dWX5NIJAohEnal/c7gwYMRGhrKWa90d3d3TJo06bXnP1KpFGfPnu30fSovLw9HjhyBtbU1xo4dy6m99uuQSCTIycmhBMGcnBw0Nze3+X23b9/GlClTOIiy+1BfX0/1O7179y40NDQ4jkiADoTXlT64KVUS6Lb07t0bAFBTU0N7LxuRSAQ3NzcAQHp6Oi1jqqioUBWKfEJfX5+zuZnsR/g8H330EXR1dfHkyRP88ssvjM4lwD490WqU6YpYc3NzvPvuux06XPP29sY777xDm0DYFjY2NlizZg0cHBzQ3NyMH3/8EX/99ddre3moqanhP//5D9566y0ALRUxBw4cQE1NDWOxPg9dh6t0cOvWLa5DYBUPDw+sXLkS2traXIeikBgbG1PrAyZdELS1tSkBhs99CXV1dV+bmMA19vb2tPerSkxMRENDA0xNTTF48GBax+4s7u7ucHV1RVNTE37//XdOY+kI6urqeO+99/DHH3/A0tIS2dnZGDduHNasWSP3e52NSkIAGDNmDAAgJiYGBEHQMiYdPd7lgWmRsLGxkUoGmj59OqNzvQqhJ2H3xsTEBEZGRl0eRyaT4datWzhy5AgkEgn69++P/fv346effsKpU6fw9ttvw87ODlKpFNHR0di/fz82bdqEkJAQVFdX0/CT0IeJiQmWLVvWZvWuv78/3n33XQwcOJAXAqEio6ur2yUXASUlJc7WT1OmTMG0adNeKxA2NzcjMDCwS4ksOTk52Lt3Ly2fE4lEgsePHyM0NBTZ2dltCnkdobGxEampqbhx4wZ27NiB3377DYcOHcLdu3eRkZGB5uZm6OrqYtiwYVizZg02bdqE9evXA2jZMxcWFnb5Z+nJyGQyREREICIiAjKZjOtwBGhCeF3pQ6gkFKAVDQ0N2NraIisrC9nZ2bRbIA0aNAh37tyhtSmpp6dnu/7mXMGHSkKmRUJDQ0N88MEH+Omnn/DDDz9g+vTplAgsoPjk5+dzHQKreHt7s1JtY2xsjJUrV+LgwYNt2rmamppi6tSpsLa2ZjwWoKUfyeLFi3H79m3cu3cPZ86cQVJSEr777juqr1lbiEQiLFy4EPb29vj222+RlZWFDRs2QFtbG3p6eq0e+vr61N/V1dW7fLCgo6MDPz8/3Llzp0vj0EFcXByGDh3K2uvFB2xsbLB69WocP34ceXl5XIejUIhEIvTt2xfh4eHIycl5KfudTkxNTZGeno7KykrarA2ZgOxxTK5d+ISVlRUWLFhA+2EoKX5MmDCBlQrs17F06VJ8+umn2LZtG7788kuF6Bfp5uaGXbt2YefOnThz5gx27NiBq1evYvfu3ZQY11HYqCQE/ukrTDqq0HHfMDMzg5qaGusiFtMiYUxMDJqammBlZQVPT09G53oVQiVh94aOBJCamhqcOnWKOtuYMWMG3n77bWpPoa+vj3nz5mHu3LlISEjA5cuXce3aNZSVleHWrVu4ffs2nJycMH78eNb7+rWHSCTCoEGD0LdvX9y5cwcEQcDX15e1XqQ9AToq45ydnfHgwQPW9ux6enqYO3duhxwHGhsbcezYMdoKAyIiIuROEi0oKMCjR48QHR3dShgUi8WwtLSEtbU1rK2tYWNj0+Z7vLm5Gbm5uUhLS0NGRgZycnJeEjEsLCwwcOBAuLi4wMXFBba2ti+1HvL19cXdu3dx/fp1LF68WBDaBQQEGEEQCQVop2/fvlSp/MCBA2kde9CgQQBa7Eybmpposag0MjKCo6Mj1beCD3ApEpK9Sdhokj569GgEBwdTtqPh4eE92na0pKQEycnJUFJSgouLCzQ1NbkOSW56Wpabl5cXa3Pp6elhxYoVOHToUKuKTX9/fwwfPpz1Q2OxWIyAgABYW1vj9OnTiI+Px5IlS/Dzzz+/tsrFx8cHu3btwnfffYf09HTU1NSgpqam3SopVVVV2NraYvz48V0SLoYNG4bIyEheZGAHBQVh2bJlPWqzp6urixUrVuDixYt4+vQp1+FAQ0MD9fX1XIfRIfr06UOJhEzyvEjIZ5SVlTFu3DicOHGC61BaYWZmhjfeeIN2S9iKigpqvTphwgRax5YXDw8PODs7Iy4uDj/99BM2bdrEdUgdQkNDAx988AF8fX3x+++/IysrC2PHjsXatWvx+++/d/jAnS2RUENDA35+fggKCkJMTAwtIqFYLEavXr0QHx9PQ4QdQ1VVldG+agRBUFaj06ZN46zHOyCIhN2drlqNZmZm4uTJk6ipqYGysvIrbftFIhGcnJzg5OSEd999F3fu3MHly5cRGxuLuLg4JCcnY+LEiXB3d+fNelJLSwuTJ0/mOoxuCR1V4CKRCGPGjMHBgwdpiKh9TExM4OPjAxcXF6q3/atoaGjAkSNHXuoV2RUiIiIwcuTIDs0PAE1NTYiLi0NERESrPamdnR2srKwQHx+PiooK5OTktFqP6+rqwsbGBtbW1mhubkZ6ejqysrJeqjok2194eHjA3d39lYm1JGvWrMG9e/eQlpaGlJQURt2CBAQEei6CSChAO3379kVQUBCtN3YSa2trGBsbo6SkBDk5OXBwcKBlXE9PT0Ek/P+wVUkItCxOP/roIzx9+hRRUVHYtGkT/vWvfzE+Lx958OABbty4Qf07KCgIa9as4XUFR3sQBNGjREIHBwfWXyctLS0sW7YM586dg0wmw9ixYzl/r/Tr1w9r167FiRMnUFBQgE8//RSbN2+Gk5PTK59na2uL3bt3o7KyEoWFhSgqKkJhYSH1IP9dUVFBWbSkp6fj7bffltvmSUVFBaNHj8a5c+fkej6dZGZmIi0tDb169eI6FFZRVlbG9OnTYW5ujmvXrnESg7u7Ozw9PWFgYID//e9/nMTQWZ7v/cwkZKIQV/1qOkP//v1hbW3NuHDaUYyNjbFkyRJG+mGQorqHhwcsLCxoH18eRCIRli5dinXr1lHX/HfeeYfrsDqMu7s7du/ejR07duDs2bPYtm0bQkNDO9y7lqwcqK2tZTJMAC19OIOCghAXF4cJEybQIgb07t2bVZGQ6WqivLw8FBYWQklJiXMhXRAJuy86OjqwtLSU+/kZGRk4cOAACIKAvb09vv/++w5Xh2loaGDixImYOHEiUlNTsWHDBiQmJuL8+fPIy8sThLkeAF1W0Y6OjujVqxeePXtGy3jP06tXL/j4+MDR0bFT96pTp07Rvsatq6tDbGxsh9oMSaVSbN26lVr/isVijBo1CtOmTYObmxtEIhEIgkBeXh7i4+MRFxeHuLg4PHv2DFVVVdS/n8fAwACDBg2ihEELC4tO378tLS0xd+5cHD9+HMHBwV0SCZ89e4a6ujr07duXsf7mAgICiokgEgrQDlk+z0QWm0gkwoABAxASEoKioiLaRMJ+/fpBXV0dDQ0NtIzXVbiyC3le3GHL/s7Q0BALFizAjh078ODBgx4rEr5oO0EQBG8yQeXBwMAAxcXFXIfBCiNHjuRkXjU1NfTv3x8EQdDSE4UODAwM8Oabb+LAgQPIzs7G06dPXysSAi3Xdn19fejr67dr3ySRSHDx4kVs2rQJUqm0y37zbm5uSEtLQ0xMTJfGoQO6+kspGiKRCEOHDoWuri4CAwNZmVNPTw/e3t5wd3dvVa09ffp0XojGr4Ps59jY2MjoPKRonZKSgoCAAEbn6irx8fG8EQgNDAywZMkSqqcj3ZSVlQEApxaKbeHl5YWAgADcvHkT33//Pd58802FOnjS0NDAhx9+CH19fezbtw8lJSUdfi4pFBQXF6O5ubnDlQry4OnpCWVlZdTW1qKqqoqWtg5s9yU0NDRkdHyy+rl///6c2xuSFSimpqacxiFAP/Ic8j/PgwcPQBAEhg0bhq+++krupJLevXtj06ZNCAwMxPbt2xEREUHZFQp0T4yNjWl1fAoICKBVJHR3d4ePj4/c1z2m9kQpKSkdEgkJgmgVw6pVq7Bw4cJW3yMSiWBlZQUrKyuMHTsWjY2NuHr1Kv7v//6vzTHJdhoGBgbQ09OT+9pBnn12ZQ8QFRXVar/j7OwMFxcX9O7dmxcW9gICAtzCnf+GQLclISEBQEs5PhOQmdN0ZreLxWLaBEc6YCL7vCOUl5ejoaEBKioqjPY6ehGyarEnVZ+9yPDhw7F06VI4ODigV69eWLt2LW+En84iEomwcuVK2u2G+Yaenh5WrlzJybVDKpXi/PnzOHv2LM6dO4ejR48ybnXWUZSVlalqaDo3G3l5eTh27BiAlh6QHbFmeRUikQizZs3C7Nmz6QhPbkxNTXtcFeGLsHVIraOjg1WrVmHYsGEv2Tm7ubl16PCAa8i1D9OH335+fgBa7stsVEjJy5MnT3Dy5EmuwwDQ8j5+8803GX1tSMsqJu0a5UEkEuGLL76AsbExioqKWBP96YQgCKpH+Zo1azr8PDs7O+jo6KC5uZnxCl9lZWXaq3zp7h//Oph2KiF7ufGheo+s/PX19eU4EgG66YqQUVFRgZSUFADAO++80+V9v5KSEhYsWIBJkyYBAC5fvtzlRDoBfmJkZIQlS5bQaqNsYWEBFxcXWsaaNm0apk+f3qXEiJkzZzKSTJKZmdmhz62ysjLefPNN6ixsx44d+OWXX9psS1BcXIzdu3dj/vz5lEAoFovh5uYGX19fWFtbQyQSITs7G2fOnMFXX32FqVOn4v3338f+/fsRFxcHqVTa4Z/h6tWrACD365WYmPhSQmRcXByOHTuG9evX48KFCx3+PQkICHRPhEpCAdohRUKmMthIkZC0xaQLe3t7KnYuUVVV5SyLJy8vD0BLBQGbvQFJQaEni4QikQgODg60Ck5SqZTqu2Zvbw9bW1vWXld1dXXMmjULvXr1UojqnM7i5OSEqVOncnJQ29DQgBMnTrRq5p6SkoLt27djzpw5rFcFtAW5uaCrGvbBgwf49ttv0dzcDCMjI1ormwYOHAhzc3OcOHGCk+rXkSNHKnTVMB2w9TmaP38+VYn3IiKRCJMnT0ZxcXG7PTH5AFlJxrTjgK6uLiwsLJCfn4+SkhLGKuO6QlhYGHVgwjUDBw7E9OnTGa0iA1r65ADgZZWesrIypk2bhj179mDTpk1YvHgx1yF1ivj4eCQmJkJFRaVTIqFIJIKLiwsePHiAtLQ0xhOHLCwskJubi/Lyctjb23d5PBUVFVb7sjJtk0uKhHxInIqOjgYgiITdka4coj9+/BhAi220lZUVXSFh9erVCAoKQmFhISIiIuDt7U3b2ALcY2pqiiVLlrS7ju0K/v7+XXZW8ff3x6BBg7oci5aWFpYsWYI9e/bQ2ju+trYWJSUlHUoy1dbWxhtvvIF79+7hzp07uH79OhITE/HNN9/A0dER0dHROHPmDO7evUtdC3R0dODp6YnBgwdTa2Z/f380NDQgPT0daWlpSEtLQ1lZGWJjYxEbG4t9+/bByMgIc+bMwdSpU1+51s7NzUVUVBSAlsTGzpKeno7jx4+3+/8NDQ14/PgxHj9+DF1dXQwcOBCurq4wNTXtdvtUrlu0CDCD8LrSgyASCtBKWVkZioqKADAvEtLdJ4cvlYRcVREC/4iEbDdCJrPFCgoKWJ23O1NUVIQzZ85Qv9N79+5BLBbDzs4Ojo6OcHR0hLm5Oa2ZiC8iEong7u4OGxsbnDp1Cvn5+YzNxSZTpkyBh4cHJwvmiooKHD58uE0rtKqqKuzZswdjxozBsGHDOF3Q0yUSEgSBo0ePYufOnQBakjnmzp1LHQLShbGxMVatWoWLFy+yaj9qaGjYITvW7o6KigqUlJQ6lU3bWaZPn/7awzhlZWXMmzcPO3bs4G31HCkSsmGjZ2trS4mETLlDyEtxcTFvBMJRo0Zh1KhRrFxzyUpCuq+BdDFlyhQcPHgQYWFhePToEby8vLgOqcOcOnUKALB48eJOV6q7urpSIiHT9rykvSmd+yBdXd1uJxKy9fO0R2NjI9XrcdSoUZzGIkA/8lbqSaVSqt/p1KlT6QwJ+vr6eOedd/DHH38gKCgIzs7OvEzwEeg8FhYWWLx48UsuGHRhYGCAIUOGICwsTK7nDx48mNb2G/r6+li8eDG2bt1K25hASzVhR+/vYrEYvr6+sLOzw6lTp5CVlYU1a9bA1ta2VaKunZ0dvL290b9//zbPVdTV1TFgwAAMGDAAQMu9mxQM09LSUFpaiu3bt2PPnj2YN28eZs2a1WYlJbnm7dWrV6cdAHJzc3HgwIEOf39VVRUePHiABw8ewNHREYsXL+42QqGWllaPaYnTkxBeV/oQ7EYFaIWsxDM1NWVM7Hq+kpDOUnhjY2NGMrM6C5ciISni9O/fn9V5yUrCqqoq3vSFVFRkMhkePHiA7du3o6CgAGpqaggICICxsTFkMhnS09Nx8+ZN7Ny5E+vXr0dgYCAiIiJQVlbGmLWEkZERVq5cCR8fH0bGZwtTU1O88847GDx4MCcL5dzcXPz555+v7ZUUFBTEuf0o+V7qiggtkUjw008/UQKhp6cnoxtkVVVVzJw5k7JrYoMRI0YwKtQrEkxWEw4ZMqTDVqK6urqYO3cuY7F0FVIYYGO9QlYld6Y/G1uEhIRwHQKAFlssPz8/1u4JpEjIx0pCoGU9R1rVbtq0idtgOkFxcTGCg4MBAB9++GGnn+/q6goAtPZ1ag8mHFXY6t2nqanJ+FzkZ4NrkTAhIQFNTU0wMzNjPflSgHnk3TMlJyejpqYGBgYGGD58OM1RtSRq9OnTB01NTQgKCqJ9fAH26d27N5YuXcrY/odE3opnLy8vTJo0ifZ1kKmpKVauXEnrmBkZGZ1+jp2dHdasWYPevXtDKpUiPT0dSkpK8PDwwNq1a7F8+XI4OTl1eD9nYGCAwYMHY+7cufj0008xbdo0GBkZoampCYcPH8a8efPwxx9/UMn7QEtyASkSdrZas7i4GLt27erUc54nLS2Ndgc3AQEB/iJUEtKMTCbr0Qd+iYmJAJirIgT+6WXR2NiIuro62jLkSLtHNqtI2oKrPjMEQVCLkX79+rE6t7a2NlRUVNDU1ITCwkLeVSwoCuXl5Th79iyysrIAAEOHDsWnn34KIyMjEASB7OxsREZGIjIyElFRUaitrUV8fDyV6WxpaYlZs2Yx0gtRWVkZ48aNg6OjI86ePcvbKp328PT0xLhx41i14X2ehIQEnDhxosPfz7X9aFcrCYuLi/H1118jKSkJIpEIEydOZKUiRSQSwcvLC5aWlggMDERlZSVjc1lYWFAHywItCTJMXBcMDAwwbty4Tj3Hzs4OkyZNwuXLl2mPp6uwWUlI3ov5JhKWlJRwvlYDgOXLl7O+XuF7JSHQIpzeuHEDR48exf/+978u9SZii7Nnz0Imk2HUqFFy2XiR/YGery5gCiYcVdgSCS0sLBgX1MnPRkNDA6RSKWctHJ63Gu0uFRgC/yBvJWFkZCQAYMKECYzsKZSUlPDhhx/ivffeQ1RUFDw8PHjRhkCg4+jr61OuP/b29qxVg2pqamL06NG4devWa79XT08Pnp6eGDRoEKPx2djYYNGiRThy5Agt45H99jp7TdbS0sKiRYvw5MkTNDU1wdXVlZbEfiUlJQwaNAju7u5ITEzE/fv3kZubi3PnzuH8+fPw9/fHwoULUVZWhpKSEmhoaHTqnK6iogIHDx7scpxFRUVUUr+AgED3RhAJaSA1NRWhoaFYvHgxxGKxXEKhRCJp1WC9qqqK7jBZgel+hEDL5s/Y2BglJSWoqKigdWHCB5GQq0rC0tJSNDY2Qk1NjfVDL5FIBAMDAxQVFXVJJOwun6POQhAEnjx5gitXrqC5uRnKysr46KOPWmX1iUQi2NrawtbWFjNnzoRUKkViYiIlGsbExCAvLw/bt2/HrFmzGKsm7d27N9auXYtz584hNTWVkTnoxMTEBGPHjkWfPn04mZ8gCDx8+BA3btzo9HPltR+l43PUlUrChIQEfP311ygtLYWamhoWLFhAS9+lzmBlZYW33noLZ86cYeR9OnbsWHh7e3N2eMlHmEqQGThwoFzvQ09PT+Tl5VH9PzoLU/cjUhhguich8M9ajm8iIZdVhLq6uvDy8oKHhwfjWf1tweeehCQDBgxA//79kZiYiF27duHf//633GOxsa6TSCS4ePEiAPmqCIF/RMKioiJUV1cz+vlkym6UDchETyZ5XkBvaGjgzG7x6dOnAPhhNcrm/sjU1BRubm5wcnJCUFAQ4uLiGJuLS+SpJKytraWqjSdPnkx3SBTOzs6YMGECrl69isuXL2P16tUKlcROEAQkEglqamrafBgZGWHo0KGsJ24y9TnS0NCAo6MjHBwc4OjoyKkYM3ToUDx69KjdXoC9e/eGl5cXevfuzdp7qk+fPpg1axZOnz7d5bFqampQVlYmV0K0SCSCh4dHl2Nob2xy7ZSZmYl79+7h2bNnuHXrFm7dukXdo11cXDrc+7qmpgYHDx6kpa9jYWEh60UETFFfX4+JEycCAK5cucKpi5sAfQivK30IImEXqaysxLBhw6ChoYHS0lJ8+OGHcgmFv/zyC77//nsGI2UHUiRkWmSysLBASUkJysvLaW34zYe+hFxd0Mgqwt69e3NycE2KhF3pS9hdPkedobq6GhcuXEBKSgqAFsurdevWUYdI7aGkpARnZ2c4Oztj6dKlKCkpwQ8//ICYmBgcP34cw4YNQ0BAACMbAG1tbSxatAihoaG4fv067ePTgY6ODvz8/ODu7s7Zxlomk+HKlSuIiIjo0jhBQUHIzMzE5MmTWx2ePb/RfR46PkfyVhLeuHEDv/76K2QyGUxNTbFgwQLONsuamppYtGgRQkJCcPv2bVrG9PLygp+fHyfiAt9hSiR0dnaW63kikQiTJ09GcXExcnNzO/18pu5HZCUhmyJhRUUFmpqaOKukfp6ysjKqQodN7O3t4e3tjX79+nF62KoIlYRASzXhL7/8gq1bt+Lzzz/v8KHWi7CxrgsKCkJVVRXs7e0xbdo0ucbQ09ODnZ0dMjMzkZaWJlc1YkchKwlra2vR2NhIy3uBzUpCplFWVoZIJAJBEKivr+dEJGxubqbEMXkt/OiE6c+RlpYWXFxc4Obm1koInjVrFjQ1NfHo0SPG5uYKeSoJyUpjR0dHWs8v2uKtt97CrVu3UFBQgMjISF73hyUIAnfu3EFaWholBJL3uvaIiorC9OnTGU1Mf5H2Pkeqqqqt9jtisRgqKirtPpSVlaGiogI9PT04OjrC3NycN9XGKioq8PPzw4ULF6ivaWhowN3dHZ6enm32ymMDFxcX1NXV0dKLOiMjgxHXJDoQiUSwt7eHvb098vPz8eDBA8TFxVGCdEetRhsaGnDo0CFqz9BVioqKaBmHD8hkMspeXt6KcAH+Ibyu9CGIhF2kqakJ2tra8PDwoBrek0JhZyxOvvzyS3z88cfUv6uqqhTSGoKNSkKgZZMZExNDaxYt0GLvYGlp2coDnG24Fgm5yhIihYDCwkK5x+gun6OOEhcXhwsXLkAikUAsFuOtt97CnDlz5BJ5jY2NsXHjRuzYsQOBgYF48OABcnNzMXv2bEYOo0UiEXx8fNC3b188ffoUsbGxtH+e5WX06NGMZKg2Njbi5s2bKCoqgqamJrS0tF76k/y7SCSitYotJSUFf/zxR4e+l47PUWcrCaVSKXbt2oVjx44BaLkOzZw5k/NqGZFIBF9fX/Tq1QtPnjxBYmKiXJaYffr0wdixY2FiYsJAlP/0PpK3ao4PMHHvMzY27pLVobKyMubNm4cdO3Z0+nVn4n4kk8mo6yQbh/p6enrQ1dVFVVUVSktLWakCeh1sVxF6eHjA29sbZmZmrM7bHooiEvr5+WHr1q3IycnBuXPnMHv2bLnGYXpdRxAETp48CQB47733upQk5+rqyopIqK2tTX0uy8vLaXlv6unp0RDZ62FDJBSJRFBVVYVEIuGsL2FycjIaGhpgaGgod6IKnTD1OXJ2doabmxt69erV5tpDLBZj4sSJ0NTUpA7vugvyVBKmpaUBAAYPHkx3OC9hYGCAtWvX4q+//kJQUBCcnJw4q6p9HRkZGbh79+5LX9fS0oKhoWGrh7a2Ni5duoSSkhLs3bsX3t7eCAgIYOWe2N7n6OOPP+Z8v0In7u7uePbsGWpqajBo0CA4OzvzIklsyJAhqKura/O90hkyMzNZ+Qx2FQsLC8yePRv+/v6IiIiAtrZ2h9bhjY2NOHLkSJfO1F6kO4mEAgICr0YQCbuIsbExhg8fjvHjx+P+/fs4dOgQVFVV8fbbbyM+Pp6yn3kdampqCr+4qK+vp5oBs1FJCNBrtUPi5eWFc+fO0T5uR+GqJyEpEjJlM/k6yMy0rixousPnqKNERETg0qVLAFoEiC+//LLLlbDKysp455134OzsjJ9//hmZmZnYsWMHli5dypi4YWRkhNGjR8Pf3x+5ubmIjY1FXFwcampqGJnvVQwZMgS+vr6MVXkdPXpUrobpbEPH56gzlYTZ2dn48ccfqWrYESNGYPTo0bzJqgVa7EetrKwwadIkZGZmIj4+HgkJCa8VjkxNTTFu3Dj06tWLkbhKS0tx69Ytqq9obGwsFi5cyKvfXUeRSqW0j+ns7Nzl34Wuri7mzp2Lffv2dep5TNyP8vPzqc+WtrY2rWO3BWlTHRsbi+LiYs5FwuLiYrntXzuDnp4eZSnKN7saRbAbBVpETF9fX5w/fx47duyQWyRkcl1XUlKCAwcOICMjA+rq6njzzTflHqu5uZkSSdjqS0inSMhG0oGamhprzgCkSMhV/2vSatTX15cXiTt0fo709fUxfPhwDBw4sEN7VpFIBD8/P2hpafGyz6+8SCSSTvc2I/cAbAkU06ZNw+XLl5Gamorg4GBMmjSJlXk7y8OHDwG0JGnOmjULhoaGMDAwaPf9NWfOHGzZsgVXrlxBeHg4kpOTMWPGDMbPn3rKOYNYLMbcuXNZn7e+vp4SAEeOHNnmftzPzw+1tbVUb095yMzMlPu5XGBoaNjh3upSqRSBgYHIzs6mNYbi4mKqpY2AgED3RviUdwGyUpAs4/7xxx/xzTff4MiRI9i0aROampoQExMDFRUVXmwQmObJkycgCAIGBgbQ19dndC5y0cjEoSLX9gOlpaWczNvQ0ACgRfjmAvI9w7feR3yFtIrU0tLCn3/+SetB5qhRo+Do6IhvvvkGGRkZ2LNnD1asWNGlipzXIRKJYG1tDWtra4wbNw7p6ek4fvw4dSDKJC4uLvD392f08Kq6ulohBEK6ILOV8/PzX/l9t27dwi+//EJtPKZNm9bh5BouEIvFcHBwgIODAyZOnNiuYKilpQV/f38MGjSIkft/VVUV7ty5g6ioqFbZ7CkpKcjJyVG4Cuo7d+5QQied0JX0Ymdnx7nLAEEQ+OCDDwAAvXr1Yq2SzMzMDLGxsbT0NOkKzc3NlGMHUxgbG2PMmDHo06cPL9ft1dXVkEgkEIlEnFl+dZRLly5RiUx8i7W8vBxHjhzB+fPn0djYCKClQkTevUtycjKWLFmC8PBwAIC1tTVdobYLKeqRa/euwsbBt5qaGmsJLEZGRqiurkZ0dDQnyY9kf/uRI0eyPjdT6OjowNfXF4MGDZKr4tbLywu5ubmUgKrolJSUICoqqsP2f8A/CXRsHbQrKSlh6dKl+Oabb3gtjJDnOQ4ODh2qvNXW1sbnn38Of39/bNiwAYWFhdi3bx+GDh2KgIAAQchQMAiCQEJCAq5cuUIlCT958gRjx459aR8lEokwadIkiMViuW2M2bLXZpvm5mZaXYhepKKigrNzQgEBAfYQ7qBdgNzoTJw4ETExMVi8eDF++eUXeHt7Izc3F2+99Ra16epsj0JFJDQ0FADg5OTE+CaQXEAwUXWXlZVF+5idITk5udOZiXRAbvjYEGXagrS0pMs7vbvj6emJhw8fora2FleuXMGsWbNoHd/GxgZ//PEHPvvsM6SkpFBCIRuWa2KxGJmZmYy/F/v27Qt/f39WqmNycnIYn4NPWFlZ4cmTJ5QF9Ys0NjZi8+bNOH/+PIAWEWbWrFkKtXF7UTDMyclBSkoK1NXV4enpyciha11dHUJCQhAeHk757Q8dOhRvvvkmzp49i0uXLuHhw4cKIxKSfWi6ah3UFioqKrQlNpSWlnIqEALAgQMHcPr0aSgpKeHzzz9nbV7yM8mVbR/JzZs3abVOaouSkhLo6urydr1OHvL27t2bt7ZxMpkMu3btwtGjRwEAixYtwu7duzmOqoXKykocP34cZ86cocS1ESNG4Mcff4Sfn1+nxyMIAlu2bMEnn3wCiUQCLS0tfPjhhxgzZgzNkbcPXXsFNvq3VFdXs7YfHjBgADIyMhAcHIx58+YxPt/zEARB9SMcPnw4q3MzBV02/HRXt3DN+fPnYW9v3+EkQxsbG1RUVCAmJoa1akLSZaasrIyT84WO4OLigrS0NFy5cgWLFi3q8DXCy8sLu3btoqoKQ0NDkZqaipkzZ8LS0pLhqAXooKqqCleuXEFiYiKAlpZFysrKSEtLw8WLF/H48WNMmjSpVQ9PsViMSZMmYcSIEYiJiUFMTEyH14cODg6M3xOampqQmpqKpqYmWFhYwMjIiPH7Xm1tLY4fP87oNfZVIuGdO3deSkQQ+sIJCCgmgkjYBciLvY6ODh48eAAA+OSTT1BRUYHx48cjIiICv/32G9atW8fbAwc6eV4kZJruLBLW1dUhNzeXlUzk5yGz7sisarYRRMLOoaamBn9/f1y8eBE7duzAuHHjaLef09PTw4YNG/DZZ58hKSkJe/fuxbJlyxjvKZOamspo36n+/fvD19eXld44JD1NJCSvX4mJiS/1583JycF3332HZ8+eAWjJtPfz81Po+6RYLIatrS1j/XglEgkePnyIBw8eUOK5q6srVq1aRVVezp49G5cuXUJiYiLKy8tZs3WTF4IgcOvWLdy7d4+R8S0tLWl7T0VHR9Myjrykp6fjnXfeAQCsWLECffv2ZW1uPoiEz549o9aYTHP69Gm88847vDxIJQ9g+Fpt3dDQgF9++YUS/b/55ht89913nP8uGxsbcebMGRw8eJCq+Pb29saPP/6IsWPHyhVfTU0N5s+fT9knenh4YN26dYw6LjyPPP3Q2ByvvTlqa2sZ6XP9IgMGDMCVK1cQHx+PwsJCVnuKZmdno6qqCurq6p2qMuMjgwYNwujRo2nZX5SUlHTLPd6ZM2ewfPnyDq03bG1tERMTw+qawtzcHGKxGM3NzaiurmY1GU8ikeDSpUsQi8WYNm1au78jJycnXL58GXl5eQgPD8fQoUM7PAdZVThy5EisX78eJSUl2LVrF0aOHAlfX98u9ZntKDt27Gj1s2lra8PR0RGOjo6wsLBQuP2NTCbDhQsXIJVKMWnSJEbO2wiCQGRkJK5fv46mpiaIRCIsXrwYixcvhpKSEs6ePYvt27cjLy8Pu3btwuDBgzF69OhWFqS6uroYPnw4hg8fjsLCQkowrKqqanNOd3d3TJkyhZH3RHNzM1JTUxEXF4fY2NhW/6esrAxzc3OYm5vDwsICFhYWMDU1pS2OoqIiHD16FBUVFbSM1x7tjV9RUdHtes4KCPRkBJGQBoYMGYJTp05h4cKFCA4Oxv3796GpqYnPPvsMN2/exOrVq3lntUMXjY2NiI6OxqNHj3D79m0Aii0SEgTBuUgItFQT9jSRkNy0MNFnsrsyaNAghIWFobi4GIcPH8aaNWton0NHRwfr16/HZ599hsTEROzbtw/Lli1jLEOzsrIShw8fZmRsZ2dnjBw5ktXDIpLc3FzW5+QSExMTqKiooK6uDllZWVQm8/P2ourq6pgzZw5j/fq6A83NzXj06BHu3buHuro6AC09SFetWgUvL69Wh9sODg7w8vLCo0ePEBYWhgkTJnAV9mshCAJBQUFUghUTPJ913BUIguDUHq2pqQlLly5FXV0dXFxcsGDBAlbn51okrK2txdmzZ1mbr6SkBKWlpby0VCLXp66urhxH8jIymQzr1q1DdHQ0VFRUsGfPHixevJjTmAiCwN27d7Fjxw6qEtjd3R0//vgjJk+eLLd4WV5ejkmTJiE0NBSqqqpYs2YNZsyYoXAHwc/DVsZ/VVUVKyKhjo4O7OzskJmZyXo1IWk16u3tzZotNN04ODhg3LhxtDptJCcn0zYWn8jOzsb9+/c7ZC1LJpLFx8ez1t+LFCjy8vJQVlbGmkjY2NiIo0ePUsktZmZm8PHxafN7VVVVKYeckydPdkokJPHx8cGePXvwxx9/UA4VoaGhUFNTg7KyMlRUVKCsrNzm30UiUav7QWfb2bwofpeUlCAjIwO3bt2ChoYGHBwcKNGQ7wl8QMs1jOz/XFRUhMWLF9OaiFxSUoILFy5Qa5oBAwbg008/haOjI/U9s2fPhp+fH7Zv344bN24gMjISsbGxGDduHAYNGvTS/dvMzAxmZmYICAhAZmYmoqOjER8fT7VnGT16NEaMGEFr0pJUKkVaWhri4uKQmJhIzfUizc3NyMnJaZUwLBaLYWZmBnNzczg6OsLJyUmuNURycjLl3MA07YmEZPWinZ0dli1bRn1dIpHgt99+YyO0TtNWv0sBxUd4XelBEAlpwN7eHlFRUWhqasLly5fRu3dvAMCvv/4KZWXlbiMQymQyJCUlITw8HI8ePcKjR48QFRXVSlRSV1dHv379GI+FKZGwoqKCtv4eXSE5ORmjR49mdU6uRUKhkrDziMVijB07FkeOHMGJEycwffp0RqwztbW18b///Q9ffPEF4uLisH//frzzzjvQ09OjdR6pVMpIzykXFxeMHDkSJiYmtI/dEaRSaY8TCcViMaysrJCRkYH4+HhYWVm9ZC86e/ZsVg4LFZX6+nrs2bOH6tNqY2ODlStXwtfXt92N5Jw5c/Do0SNERkbCz8+PkezfrkIQBK5fv854ZRhdImFmZiYqKytpGUsePvnkE9y7dw+ampr48ssvWcmKfx7yM8qFSEgQBM6fP0+t+dgiPz+fdyKhRCJBUVERAH5WEkZGRiI6OhoaGhq4du0a573YkpKSsGXLFqpix9LSEv/973+xZMmSLol5RUVFGDduHJ4+fQodHR389ttvGDBgAF1hdxpFshsFwGpvUycnJ05EQrKKRFGsRp9/D5mYmCAgIAB9+vShvQK4u4qEQEsCXO/evV/rUGJiYgJ1dXU0NDTg2bNnrJyZAC3uHqRIaG9vz/h8TU1NOHbsWCv7wevXr8PJyandvaO3tzdCQ0MRGRmJZ8+eyZVAqKenh2+//RYjR47E77//DolEwsq5xq+//kqttwmCQEZGBiIjI/HkyRPU1tYiPj6e6rttaGhICYaOjo6s9IPtDM3Nzbhz5w7178LCQuzduxdLliyRu28viVQqxf379xEcHAyZTAZlZWWsXbsWM2bMaHNta2RkhH//+9+YPHky/vzzT6Snp+PChQuUBWlbCcsikQj29vawt7fHpEmTkJqaCnV1ddre9zKZDBkZGYiLi0NCQoLca2OZTIb8/Hzk5+fjyZMnCA4OxqhRo+Ds7Nyhay9BEAgNDcX169flml8eXicSenp6wt/fn/p6bW0tL0VCLS0tylVCoPugqK9re615SNjeAwOCSPhaysrKIJVK2z1clslkUFdXx9WrV6GsrExZQBEEoTA9gV4FQRDYvn07jh8/jsjIyDY3d7q6uujXrx/69++P4cOHQ0NDg/G4mBIJi4uLaR1PXgoLC1FZWUm7CPMqBJFQMenduzccHByQnp6OXbt24auvvmJkHm1tbfz+++/417/+heTkZJw5cwZLly6lNXP+5s2btHrpu7u7Y8SIETAyMpLr+U1NTYiOjoaSkhLc3NzkPjApKipCc3OzXM9VZEiR8O7duzh79izVSL072IsyDUEQOHPmDEpKSmBoaIiVK1diwoQJrxWIvLy8qOqJJ0+etJu1zRUEQeDq1asIDw9nfC66qvG5rCLcu3cv/v77bwDAl19+yapFMgm5DuFCJIyIiODkYDk/P593QlxpaSkAwMDAgJfJhxcuXAAArFq1ilOBsLi4GLt27aIOztTU1LBu3Tp8/vnnXe7jmJ2djTFjxiA5ORkGBgZYv359q8oHRYYNu1EA7drAMQFXlqNkP8Jhw4axMl9X+OKLL1gRKerr61/qV9XdOH36NN56661X9m0UiUSwtrZGamoq4uPjWRMJyaQp8j7CJM3NzThx4gTS09Nf+r+rV69i/vz5bT5PX18fTk5OiIuLw8mTJ7Fu3Tq5Yxg9ejSGDRuGoqIiSCQS6tHY2IjGxkbq7+SfL1YONjY24uDBgx2eb+DAga3uL+7u7pgxYwakUikSExMRERGBiIgIxMXFoaysDGVlZYiIiICGhgaWL1/Omk11R4iMjHxJDCorK6PcAeSNNS8vD+fOnaOSnYYMGYKPPvqoQ8nNbm5u2LFjB86cOYOdO3ciNzcXO3fuhKenJ0aPHt3u2aOysjL69+8vV7wvQvaavXnzJiO2niUlJTh16hRCQkLg5+eH/v37t3vuIJVKcfHiRaraky1eJxIOHDiQxWgEBBQbY2NjaGpqcu660haCSPgK0tLSMHbsWLzxxhtYu3btK+31XrTY5Lr/Bl1s3LgRn376KfVvdXV19O3blxIF+/fvDwsLC9Z/3u4uEgItGZdeXl6szUeKhGS/K7Yh7U/43Fidj4hEIowbNw7bt2/HzZs3MWfOHNoWxC+iqamJb775BsuXL0dmZmaH7XU6QlJSEh4+fEjLWDo6OlixYoXcli5NTU2IjIzE/fv3qWtNVFQUpk2bJtfhbE/rR0hCijSkIKSmpoY5c+ZQ1fYC7XP37l2kpKRASUkJv/32W4d/ZyKRCHPnzsX69esRFhaGIUOGyCXGymQy1NbWQlNTk7bKNYIgcPnyZURERNAy3qvQ0dGhxVKrqamJ9U04SWhoKGUhvXz5cowYMYKTOMgEHtLuli2Ki4upfm9sk5+fz8m8r4JcnzLV97QrlJaW4v79+wDAiO15R6ivr8exY8dw/PhxyvJryZIl+O9//0tLwkBKSgrGjBmDrKwsmJmZYf369ay3BXgeukU9Nu1G2UJHRwe2trbIyspirZqwoqKCOjBVBJGQLVJSUrgOgXFKSkoQFBSEiRMnvvL7SJEwLi4OM2fOZCU2UiRkuqWHVCrFyZMnqaTAF0lMTERSUlK74ujQoUMRFxeH69evd7ldj7q6utz3y9ra2k6JhO2hpKQEZ2dnODs7Y9myZaitrUVUVBS1xywqKsKePXuwYsUKTlphvIhEIqF6Cr9IdXU1tm7dijfffLPT977m5mYcOHAAEokEampq+PTTTxEQENCpsx5lZWXMnTsX/v7+2LZtG27evImIiAjExMRgzJgx8PDwYCz5NDc3F9euXaM1kbk9ioqKcOLECVhYWMDf3x+9e/du9Xuqq6vDiRMnOEm6aEsklEgkKCwsBCCIhAICncHW1hYJCQmUY1R7REZG4q233mIpqhYEkfAVXL9+Henp6bh48SLU1dWxcuVKKtuFIAjIZDLq8CwrK4uXG/eucOrUKUogXLhwIcaMGQM7OzvWra7agjy4p7tqkU8iYUpKCqsiIfm6cl1J2NzcjJqaGsGGsBOYm5vDzc0NT58+xdatW/HHH38wJrJaWVnhk08+wW+//Ybbt2/D0dGxy7Z+FRUVOHbsGE0RAr169ZJLIGxLHDQ1NUVZWRkyMzOxdetWjB079qVecK+jp4uEQMtCaPbs2az1QlFkUlNTKaufTz/9tNOi6pgxY7Br1y5UVFQgISEBzs7OnXp+eXk5/vrrL+rfampq0NLSgqamZpt/amhoQF1dnXpoaGi81GeHIAhcunQJkZGRnYpFXuiyGk1MTKRlnM6Sl5eHWbNmoampCSNHjsSSJUs4iQPgpidhc3MzI9bTHSU/P593yUrkJtLOzo7jSF7mypUrkMlkGD58eKevN/Jy5MgRqt9bU1MTrl27Rv2ORowYgY0bN9K2ho6JicHYsWNRWFgIGxsbrF+/njeVH4Ld6KtxdnZmVSQkrUYHDBjAy4pfrujOVqPPEx4ejsGDB7/y+kCujV9nMUYn5JxMVhLKZDKcOnUKSUlJr/y+K1euwMHBoc1+ndbW1rC2tkZOTg7OnTuHFStWMBUuJ2hpaWH48OEYPnw4li9fjs8++wzJycnYu3cvli9fzkjLkM7w8OHD1yaE7d69G4sXL+6UHWxJSQkkEgm0tbVx6NChLjllGRsb46uvvsKUKVPw119/IT09HZcuXcLjx48xceJEWp3cqqqqcPPmTcq2nE3y8/Nx5MgRWFtbw9/fHw4ODigpKcHRo0cZF/vbo7a2Fk1NTa2qpXNyckAQBMzNzXln098eDQ0NmD17NoCW824+tuYQ6DyK+Lra2tq+VkMS7EZ5xrBhw7B06VL06dMHmzdvhkwmw3vvvQd9fX2IRCJKVNmwYQOuXr2KX3/9FYMHD+Y4anoIDQ3FokWLAAAzZszA6tWreXVY0hMqCVNSUtDY2Mha03uuKwnV1NSgoqKCpqYmlJeX0yoS/vrrr63ev6amppg4cSIvD9sqKytBEESnff9Hjx6N2NhYREdH4/79+4xWnYwfPx7h4eG4ffs2Tp06hTVr1shtV9Tc3IzAwEBa4+ts34G2xEEzMzO88cYbmDBhAkpKSvD7778jKioKV65cQUJCAqZPn97h16i7iITBwcGtxB9VVVXo6upSVVu6urqtNg7a2tqYMmUKGhsb5a5o62lUVFTg5MmTAICpU6diwoQJnR5DTU0N06ZNw4EDBxAaGtqpQ/va2locOnSo1ddIm6bOWEErKSm1Eg7J3htsQZdIyIXVaENDA2bNmoX8/HzY29vjiy++4PSzQ4qETU1NaG5ufkkAZoKbN29SmclcIJFIUFFRIXc1OhM835uUT8hkMly8eBEAWM10PXLkyEtfc3BwwO+//47Zs2fTtmcJDw/HmDFjUF1djV69euH333/nhfhDdyUhW3ajbIuEz1uOFhUVMS7uklajitKPkA2kUmm7lWXdkbt372LOnDnt/j+5PsnLy0N5eTkr9xlyzrKyMshkMtrXFDKZDGfOnOmQ8FlZWYk7d+5g3Lhxbf7/0KFDcfLkSZw7dw6LFi3iXc8+utDV1cX69evx+eefIzExEfv27cOyZcs4sZUHWtb/wcHBHfreQ4cOYc6cOR3eX5Dnaw4ODrS10nF3d8fOnTtx9uxZbN++Hfn5+dizZw/c3NwwZswYaGtryz12U1MTHjx40Ko3I1fk5OTg4MGDsLGxYaWS8XVUVFS0asNFxsRWghgdSKVSyqnkRathAcVFeF3pQzixewUEQeDBgwf497//jTVr1mDHjh3Yv38/Zs2ahf/85z/U9xkbG6OxsZEXNgF0IJFIMGnSJDQ2NsLHxwfvvfcerwTCpqYmykqITpGQIAheiYQA2vTzZwpS9CZ/t2wjEomow0gmMh0JgqAehYWF2LdvH9VEnC9UVlbijz/+wJ9//tnpDFNdXV3K2mj37t1MhEchEonw8ccfw8zMDOXl5V1aRD948AB5eXn0BYfOVVrIZDJs374d165dQ01NDczMzPDJJ5/g4MGDmDp1KlRUVGBhYYENGzbggw8+gLKyMjIyMrBly5YO9SSoq6vrNn02Hz58iJCQEOpx8+ZNnDlzBgcOHMCmTZvw+++/v7SBGTx4MHx8fASBsIOcP38eEokE/fv3x3vvvSf3ONOnT4dYLEZOTk6nPl9nz56l5f0qlUpRW1uL0tJS5Obmsm7fSIcNYE1NDZ49e0ZDNJ3jxx9/RFhYGHR0dPDTTz9BU1OT9RieR0tLi/r8slFNWF5ejtDQUMbneR1035e6Cl8rCZ88eYLCwkIYGBhg7ty5rM375ptv4u2336Yef//9N+Lj/197dx4fw/3/Afy1m0gkJEGirrhTlcStiKvuokXrqqLU0YNqHHW07qOU0kNLW0X5UvfZFqUUddURJYc44owjrrgSSSTZ3ffvj/x2ZOVEsrPZeT0fjz6azMzGe987n9mZz3s+nzmJLl265Ng1y927d9GsWTPExsbCz88P3377rU0UCHODPU43CjyechRAhtPo5SQWCdO6fPmyateWajA/dy4j+fPnVzrZrXUdWqJECXh4eMBgMCijXXPSgQMHnurvHjx4MMPRUL6+vvDw8MCDBw+we/funArRJrm5uWHWrFnw9fVFYmIilixZolpbedpHfqxbtw7Xrl3L1rbm/rWnvYk3Kw4ODujcuTNWr16tTPMbEhKCOXPmPPMNKSKCBQsW2ESBMDVbKBACaaccNd8IzalGiewHRxJmonr16ihfvjwiIyMxYcIEuLi4YOzYsXB0dMSAAQOU7d5991107NjRbqZRu3LlCu7duwcnJyeMHz/eJqYXTS31SLecvKPdaDSqNoouI9a8mDZ3AlqroyA95rsFHz16ZJV/79q1a2meJ6qm1CeAV69eha+v71O9/uWXX8a+fftw6dKlHI4srYIFC6J3796YNWsWbty48cx/J6eLB46Ojk81CjMhIUEpSg8fPhytW7e2GA1nptfr0bFjR9StWxeBgYG4d+8eoqOjs/y3rP0cr9zUrl07i9zExcXh9u3biI6Oxo0bN5CcnIyrV6/a3EiXvMTcHoYOHfpco8iLFCmCqlWrIiQkBHfu3Mn0mcqpZfeC39Y97Ujs9Fi7M9vM/MzGvn375tiIyOeh1+vh5OSER48ewWAw5Pq/Zys3Vdjasdt8XmRLoxuBx8XURo0a5fgjADLzzTff5Pp1V1hYGOLj41GkSBF89dVXVn1/9kqNRxp4e3vj8uXLVhmdbC58PM1UfPbO2qNHbcHdu3czvaGgcOHCuH37drZuNswJDg4OaNSoEbZs2ZLl84+exbPMmBIdHZ3u95ler0elSpUQFBRkN+ekmSlYsCBmzZqFt99+Gw8fPnyqc/ac9Cz7xdWrV7N1nmq+wSy3zl8KFy6MUaNGoX379vj0008RGxuLu3fvPtOsVElJSTY3aMCWPFnENs/ApMY+S0S5g0XC/2c0GtMthiUlJWHv3r0oV64czpw5AwcHB7i4uCAkJARVqlRRDoj2UiBMLV++fLwg1hBbGi1Kz8baBf28MNf302jXrl2W25QqVQqenp6qPQ9ATQMGDECBAgXSXTdt2jT8/fffVo7IfuXEd6+WR2/aw/dZRm2NyBbZ2g2FOalgwYK8HsrD1Pg+sIfvIMo9auwfeWnaTq21H/PzvdV49pQ15fbn6uvri8KFC2vyxgS1aa3NEtkz7fYgpRIREYHZs2dbjGgxjygzP0dp8ODB2Lp1K4KDgzF48GBMmjQJq1at4ny3RERERERERERERERElOdofiThuXPnUL9+fdy7dw937tzBJ598Ai8vL2VKtZdeegm9e/dG8eLF8ccff6B8+fIYPXo0HBwc0L59e7u+c5aIiIiIiIiIiIiIiIjsk6aLhHFxcZg+fTo6dOiAl19+GYGBgTAYDBg1ahS8vLwApDx4fOzYsejcuTNq1KgBk8kEvV6PUaNG5WpsIgJAnWfimIfoiwji4uKs/u9nxTyvOZAyL3ZOjea0xrN2nlZycrLVHmBtfv/Jycmqfe7m5yHGxcVluu+b15nbSUayWm8wGFR7QHh6Uj8T81liS729NT5D879nMpmeOY85PRpbRJ4qlmfJmXk/zU77VOP5O08ru+0os2d0mY8fttam8hpzrhMSEp67DZvb1tN8j2S1L+QViYmJz70fPm3bzW47yuq8ztyWEhMTbeYczBx7TuQ1K7ZyzLTm+Vd25OSxISeZc5ScnPxc1yxPe15njesjc55NJpNN5Rx4tuN7ZqzV7p72HC0nWPP6JrvXMbnladuRNT6L1Nc2WpFVuzS3X2t+z5s/h9w4T3+Wa7nMcmT+e0lJSaoce83XOzlxfZRd5mNHUlKSKucez/IZZndfsubn+bx5tKXzPlv0ZLs15/vRo0dpPtunbUfW+t5MHWdMTAxnBrQT9vq5mt+XNftodGIvPULPICEhAYsXL4anpye6deuGNWvW4O2338aIESMsCoXx8fFwdXUFkPLhWGPO5atXr6J06dK5/u8Q5WVXrlyBt7d3huvZjoiyxnZE9PzYjoieH9sR0fNjOyJ6fmxHRM+P7Yjo+WXVjnKSpouEQEpltkCBAsrvq1evRvfu3TF8+HCMGjUKRYsWhclkQmRkJMqXL2+1uEwmE6KiouDm5pbtomRMTAxKly6NK1euwN3dPZcjzNuYq6dja/kSEcTGxqJkyZLQ6zN+tOqztCNbY2u5tzXMT8ayyo29tyPuG5aYj7RyIif20I64b+Q85vTp5FY74ueQPuYlfXk9L/bwfWRr8vo+YU32kiu2o6dnL5/982IeHrN2O2LuM8f8ZM5W85PddpSTND3dKAClQGg0GqHX69GtWzeICHr06AGdToehQ4fiq6++QmRkJH799VdlRGFu0+v1z1wpdnd3t6kd25YxV0/HlvLl4eGR5TbP045sjS3l3hYxPxnLLDdaaEfcNywxH2k9b07spR1x38h5zGn25WY74ueQPuYlfXk5L/byfWRr8vI+YW32kCu2o2djD599TmAeUqjRjpj7zDE/mbPF/GSnHeUkzRcJzRwcHCAiMJlMePvtt6HT6dCrVy/88ccfOH/+PIKCgqxWICQiIiIiIiIiIiIiIiLKTdYZr5hH6HQ66HQ6iAi6deuGxo0b4/bt2zh27Bhq1KihdnhEREREREREREREREREOYIjCZ+g0+lgNBoxcuRI7N69G8HBwahataraYWWLs7MzJk6cCGdnZ7VDsXnM1dNhvtTD3GeO+cmY1nOj9ff/JOYjLeYkBfOQ85hT28DPIX3MS/qYF3oS94nsY660i599CuZBPcx95pifzDE/j+lERNQOwtYYjUb873//Q+3atTmCkIiIiIiIiIiIiIiIiOwOi4QZEBHodDq1wyAiIiIiIiIiIiIiIiLKcXwmYQZYICQiIiIiIiIiIiIiIiJ7xSIhERERERERERERERERkcawSEhERERERERERERERESkMSwSEhEREREREREREREREWkMi4REREREREREROkwmUxqh0BERERElGtYJMzjTCYTL1qINExE1A7B5iQkJCA5OVntMGwevzuIKCMmkwlGo9FiGb9vyJ5pff9mm09fWFgYjh8/Dr2e3SaUObaX7GOutImf+2PMhfWwzyN7uE9mTEu54dluHnby5En06dMHrVq1wgcffIBVq1apHVKepqWGT3lXVFQUtm3bhq1btyIqKgo6nY77birh4eEYNGgQjhw5AoPBoHY4NufcuXNYtmwZAECv12vupJlthVK7e/cubt++rXYYNufUqVP46KOP0Lp1a4wfPx6///47APD75jmcP38eX3zxBXr37o2lS5fi2rVraodE/+/evXsAUvZvrWKbT19oaCiqV6+OTZs2qR0K2ajjx4+je/fuALR9DMmO2NhYREdHIz4+nrnSELaRx9gGrEfrfR7ZwX7FzCUmJiIuLg6Ato5dLBLmUadPn0ajRo3g5OSE119/HRcvXsS4ceMQGBiodmg278yZM/jkk0/w9ttvY8aMGTh27BgAXghn5NatW7h//77aYRBS7mZu2rQphg8fjgEDBqB///64deuWpr60MnPixAk0atQILi4uKFeuHBwdHZV1bNvAgwcP0KBBA4wdOxbfffcdAPs9aeZxPq2LFy/i22+/xfDhw7F69Wq1w1HdhQsXUKdOHcyZMwdRUVFqh2MzTp8+jQYNGuDhw4coX748Dhw4gGHDhmH8+PEAtN2GntWJEyfQuHFj/Pvvv7h+/ToGDRqEuXPnAuDdzWoLCQnBq6++iuDgYLVDUQ3bfPqCg4MREBCAkSNHYsKECWqHQzYoJCQEjRo1QqlSpSyWa7G9ZCUsLAzNmjVDs2bNULlyZYwcORLHjx8HwHzZM7aRx9gGrEdLfR7Piv2KmTt16hTeeustNG3aFAEBAfj999+RlJSkdljWIZTnPHr0SHr27CmDBw9WliUkJEj16tVFp9NJjx49VIzOtoWHh4uHh4e0a9dO3nnnHSlevLg0btxYvv76a2Ubk8mkYoS25eTJk+Lk5CRdunSRBw8eqB2Opl24cEGKFSsmY8aMkRs3bsiqVavE19dXTp48qWxjNBpVjFBdDx48kKZNm8rQoUOVZefPn5ezZ89KdHS0iGg7PyIit2/flvLly0vnzp2lcePGMnv2bGWdwWBQMbKcxeN8WqGhoeLt7S0tW7aUBg0aiF6vl5kzZ6odlqp++ukn0el0UrNmTZk2bZpcv35dWWcymTS3j4ikvO9hw4bJW2+9pSy7cuWKfPvtt+Li4iIjR45UMbq86fLly+Lr6yuffvqpsmzVqlXi6uoqZ8+eVTEyCg4OFicnJ4vPxtzutdL+2ebTd/bsWdHr9TJt2jQREUlKSpKVK1fKxIkTZenSpRIUFKRyhKS24OBgKVCggIwYMULtUGzepUuXxMvLSz7++GP5+++/ZfLkydK0aVOpUaOG7Nq1S0S0c8zVEraRx9gGrEsrfR7Piv2KmQsPDxcvLy8ZMGCALFiwQF5//XUpX768REZGqh2aVbBImEe1aNFCJk2aJCIpBUIRkVGjRkmnTp2kVq1aMmvWLDXDs0lJSUnSu3dv6d+/v7IsMjJSBgwYILVq1ZKpU6cqy/klLXLjxg1p2LChtGjRQry8vKRr164sFKpo9uzZ0r59e4tlzZs3l2XLlsnatWuVzkatfqHfv39f6tWrJydOnJDExETp2LGj1KhRQ7y9vaV69epy+PBhEdFufszeeecd+fXXX2XAgAHy8ssvy48//igiKUUke8DjfFqXLl0SHx8fGTVqlHJh9Msvv0jx4sU1XaQICQmRd999V6ZOnSolS5aUzz//XO7du6d2WKpr3769dOrUyWJZTEyM/PDDD1KkSBH55ptvVIos7zEajTJ//nzp3LmzXL16VUwmkxiNRrl37574+vrKoUOH1A5Rs8LCwsTFxUXGjx+vLLt7966cO3dOxajUwTZvyWAwyJw5c0Sn08nixYtFJOW6u3r16uLn5yfe3t7SoEEDWbZsmbqBkmquXr0qhQoVkm7duolIyrnnp59+Kp07d5aAgAD5+eef5cKFCypHaTt+/fVXadiwoSQnJyvL9uzZI926dZMXX3xR9u3bp2J0lBvYRiyxDVifvfd5PA/2K2bs9u3b0qRJEwkMDLRY/uKLL8qECRNUisq6ON1oHiMiiI+PR1JSEs6fPw+DwYD8+fPj2rVrWL16Ndq1awc/Pz/8+eefaodqc/Lly4fr168rw/lFBGXKlMGECRPwyiuvYPPmzVi+fDkAbc05nJHjx4+jXLlymD59OrZs2YKdO3fivffeQ0xMjNqhaVJsbCxCQ0Nx69YtAMD06dOxZ88ezJ49G9OmTYO/vz/+++8/6PXaO6yLCK5du4bz58/DxcUFI0aMQHx8PObMmYPvv/8e/v7+aNasGU6cOKHJ/ACA0WgEkPIMNgD4/PPPUadOHaxYsQL+/v7o3LkzEhMT8/w0HDzOWzKZTFi1ahV8fHwwZswYODg4AADq1q2LfPnyKfuFFokI/v33X4wZMwYffvgh5s+fjyVLlqBTp04YO3as2uFZnbnNvPLKK7hx4wYiIiKUdW5ubnjrrbfQv39//Pbbb7hx44ZaYeYper0e5cuXR40aNVCqVCnodDro9Xq4ubkhKSkJV69eVTtETYqOjkaHDh3g7++PKVOmAAD69euHVq1aISAgAE2bNsW///5r9881ZptPn4ODAzp16oTp06djyJAhKFasGDw8PLBu3TqEh4djy5YtKFOmDBYtWoSbN2+qHS6p4MKFC6hevTpu3ryJo0ePokOHDjh06BBeeOEFlC1bFt999x1mzJiB69evqx2qTUhMTMTp06eVa1gg5bgzbNgwVK9eHVOnTsXly5dVjJByGtuIJbYB69FKn8fzYL9ixs6dOwdnZ2e8++67AKBMMVqnTh0kJCSoGZrVaO9Tz+N0Oh1cXV0xffp0LF++HC1atEDv3r3x0ksvoVWrVujbty8+/fRTBAUF4cyZM5zf+v8ZjUYkJyfD29sb9+7dw6NHjwCkdKCWKFECw4YNQ+HChbFmzRqVI7UdtWvXxnvvvYc6deqgbt26FoXCBw8eKNtxH7OOl19+Gd7e3ggICMA777yDsWPH4rfffsPu3bvx999/44033sDw4cOVh+tqiU6ng5+fH+rUqYPp06fj9OnT+PTTT9GoUSN07NgRX3/9NZo1a4Yff/wRBoNBk/usuSDWtm1bhIWFwcvLC9OnT8etW7cQGRmJdu3awdnZOU/P18/jfFp6vR7169dHjRo14OHhoSz38/ODo6OjZi7O01O9enWUL18ekZGRmDBhAgIDAzF27Fjs2rULTZo0UTs8qzMfI6pXr46oqCgsX75cucAGAC8vL7zxxhs4dOgQLl68qFaYeU7Lli0xZswYAJbnS/nz57e4+N64caPybBrKXUWKFEGzZs2U66l69erh+vXreP/997F06VIkJiaiV69eOHXqFAD7Pc9lm89YyZIl0bt3b4waNQq1atXChAkT4OPjAwCoVq0a+vXrh927d7NTV6MaN26MCRMmwM3NDQ0aNICIYP369fjxxx+xatUqDBo0CJs2bcLZs2fVDlVV5mNnxYoV4enpiQMHDlhcY9SrVw89e/bE2bNnceHCBbXCpFzANpKCbcD6tNDn8bzYr5ixgIAAdO/eHbVr1wYA5QbrF154IU0+EhMTrR6fNbBImEc1bNgQhw4dQpkyZeDs7IyZM2diwYIFAFLu3CldujSKFy+uiZESmTHfSeLg4IB8+fLh3XffxR9//IH58+crd3SbTCaUKVMGkydPxqZNmxAcHKxu0CpKPaqkaNGiaNq0KYCUTvaAgAD8+eef2LlzJ95//33ExMQgOTkZ8+bNw44dO1SKWDvatGmDSZMmYfTo0ShdujQGDBiAdu3aoWDBgvD09ISPjw9MJhNcXFzUDlU1jRs3xj///IODBw+iSJEiAFL23eLFi6No0aK4efMmHB0dNXlcNHdGu7m54d9//wUADB8+HPfv30fr1q1x9OhRfPnllxbb5hU8zqeV+lj+yiuvYPr06QAeX6zqdDrodDokJycr2+3cuRO3b9+2bqBWktGIyaSkJOzduxcAcObMGTg4OMDFxQUhISGIioqyZoiquHjxIhYuXIjZs2fjr7/+AgC0atUKQ4YMwbRp0zBv3jyLPLz44ovw9fVVK9w8IXVOt23bBiDlmGowGKDT6WAymaDT6eDm5gZ3d3cAwOjRo/Huu++icOHCaoauCSaTCXq9HvPnz4e/vz9mz56NYsWKYcmSJfjwww/Rtm1bHDx4EM7Ozvjqq68A2Neoc7b59KXXbkuUKIE+ffpgypQp8PPzAwClQ9HNzQ2+vr7w9PRULWZSh3mEcfPmzREYGIiBAwdi3Lhx8PT0VPaPjz76CCaTCbt27VIzVNUkJydbjMRu2rQpqlWrhmHDhqU5/37zzTeRP39+bNmyxcpRUm5hG2EbUJM993nkFPYrps98fOrTp4/yu7lImJiYaDES+LvvvsO8efPsstDsqHYA9Ozq1KmDpUuXprl43bdvH4oVK2ZXF7XPIiIiAps2bUKPHj1QokQJAECTJk3w5ZdfYtiwYXB1dcV7772nfDkULFgQfn5+cHV1VTNs1aSXLzNzjurVq4etW7eibdu2eP/991GgQAEsW7ZMuduacoe5U6tly5YAgClTpiAsLMxim9jYWBQvXhyJiYma+0IXEeh0OowePRoXLlzAL7/8gs8//xzz589XioWurq4oWLAgDAYDHBwcNHt8rFevHtavX4/u3btjz549OHDgAFxdXTFy5EjlBgBzzvICHufTSi8n5jai0+lgMBiQmJgIvV6vFCnGjBmDGTNm2OX0h+nlIzk5Gfny5UO9evWg1+sxePBgbN26FcHBwVi1ahUmTZoEBwcHDBkyRLk4sDcnTpxAkyZNULNmTZw6dQqFChWCl5cXtm7diqFDh8JgMGDKlCmIjIxEhw4dUKVKFcydOxfR0dEoV66c2uHbpPRyOnPmTGzatAkFChQA8LhgGBcXh6SkJEyePBnff/899uzZw7zmori4OIgIHB0dkT9/fjg4OGDOnDkoWbIkqlatihdeeAFASuemo6MjqlWrZnfT67PNpy+9vMyYMQNbtmxBqVKlULx4ceV7wHwu8dtvv8Hd3R2FChVSMXKyluvXryMqKgq1a9eGo6MjjEYjHBwc0KpVK5QrVw5ly5YFAOWmtFu3bqFs2bKoXr26ypFb3+nTp/H111/j1KlTCAgIQOfOnVG/fn2sXbsWTZo0QdeuXbF06VJl2nuDwYCSJUuiTJkyaodOz4Ft5DG2Adtgb30eOYX9ihl7smhsPl7p9Xq4urrCzc0NADBhwgRMnToVoaGh9llotuYDECl3hYaGykcffSTu7u4SHBysdjiqOnv2rBQpUkR0Op2MHj1abt++rayLi4uTyZMni06nk7Fjx8rRo0fl9u3b8tlnn0mFChXkxo0bKkaujszylZ79+/eLTqeTIkWKyH///WelKMls8eLFUqNGDfn2229l69atMmrUKClcuLCcOHFC7dBUYzAYlJ8HDhwoFStWlDp16sjEiROld+/e4uHhIeHh4SpGaBsSEhKkdOnSUrx4cTl27Jiy/PLlyxIVFaViZE+Px/m0snMsNxqNkpCQIBUrVpSjR4/KlClTpECBAnLkyBEVIs5dWeVj0aJFotPppESJEhIUFKQs//LLLyUiIsLa4VpNXFycNGzYUAYOHCgiIvfu3ZOtW7dKlSpVpHLlykr7WLRokbRs2VJcXV3Fz89PypYta3HcoMcyy6m/v7+SU6PRKMnJyRIQECC+vr6SP39+OXr0qJqh272wsDAJCAiQGjVqSKlSpeSLL75Qzl1NJpMkJCRYbG8ymaRz584ybtw45fe8jm0+fZnlxc/PT8mLeR84deqUjBgxQjw8PCQkJES1uMl6Tp06JYULF5a2bdvK4cOHleWprzueNGHCBKlcubJERkZaI0SbERoaKp6entK3b18ZMmSIVKlSRUaOHClGo1FERGJjY6V58+ZSokQJGT58uMybN0+GDBkihQoVkjNnzqgcPT0rtpHH2AZshz31eeQm9itmznwc++yzz2TEiBEyffp0u792Y5HQTjx69Eg2bNggb7/9tuYvWh4+fCj9+vWTPn36yNy5c0Wn08nIkSPl1q1byjZGo1GWLl0qxYsXl5IlS0rlypWlVKlSdn0hnJGM8pVRoTAxMVEGDBggbm5uLLqoqF+/flKpUiUpW7as1K9fXxM3BphMpkwvOFKvW7lypfTt21caN24sPXr0kNDQUGuEqKo7d+5YHOeeZL5ACQ8Pt7gQyYsdoDzOp/W0x/KaNWtKnTp1xMnJyaJAZi+yk48zZ87IuHHj5Pjx4yLyuI3Yu7t370rVqlVlw4YNyjKTySQRERFSq1YtqVKlirL85s2bEhYWJseOHbPb4npOyCqn1atXV5Y/ePBAqlatKoUKFdLEd5OaLl68KJ6enjJo0CDZuHGjjBs3TqpXry6tW7eWHTt2pNneYDDI2LFjpUSJEnL27FkVIs4dbPPpyyovNWrUUJafOnVKunfvLjVq1FC+M8i+3bx5Uxo3bizNmzeXSpUqSefOnS2KIE+eP69fv14CAwPFw8NDc/vIpUuXpHz58jJ69Ghl2YwZM6Rnz57y8OFDSU5OVpaPHj1a2rRpIy+99JK0atVKc7myJ2wjj7ENWI+W+jyeVVY5Sk2L/YqnT5+W/fv3Z3v7kSNHik6nkwIFCthlv0lqLBLakUePHsnDhw/VDkN18fHx8sMPP8iqVatERGT16tXpdiCLpHQe7NmzR7Zt2yZXr15VI1zVZZav9DqXjxw5Iv7+/nY56kRNFy5ckG+++UY++eQT5bN40pMd2CdPnpRz587JnTt3rBGiqs6cOSNDhgyR119/XSZPnizR0dFptkmvgz8pKSnTwqK9OH/+vFSoUEHGjx8v165dS3cbeyqA8DifVnaP5QaDQe7cuSMeHh7i4OBgt0WK7OYjLi5O+VkrF48Gg0H8/f0lMDAwzbqQkBCpVKmSDBgwQIXI8q7s5HTQoEHKsrVr1/JGKyv46aefpFGjRhbLNm/eLO3bt5dXXnlF9u3bpyzfunWrdOjQIc1d5/aAbT59T9tuQ0JCOAJBQ44ePSrdunWT48ePS1BQkLz44otpiiCpzZw5U5o3by5hYWFWjlRdRqNRli1bJoGBgXLz5k1l+ZAhQ6R27dry0ksvSbdu3WTOnDnKuvj4eLl//77FORjlPWwjKdgGrEdrfR7P4llypKV+xePHj4urq6t8//33GW7zZH4mTpwoBQsWlJMnT+Z2eKpjkZDs0pPF0lWrVolOp5MRI0YonYPJycl2N8XBs8osX+ZijNFolMuXL4tIyp23lHNCQ0PF29tbWrZsKQ0aNBC9Xi8zZ87McPsLFy5YMTr1hYaGygsvvCBdunSRDz/8UJycnGTSpEkW26T+Ir906ZK1Q1TdTz/9JDqdTmrWrCnTpk2T69evK+ueHIFpL8c9HufTys6xPDk5WaKjo2Xbtm12P5VIZvkwF5ONRqOmjqnmQuikSZOkfv36smXLljTrJ06cKI0bN+aNZ9n0NDmNiYlRI0TN+vHHH6VcuXIWnXYiIjt27JA2bdpIr1695ObNm2I0GuXkyZMybNgwu+sAYJtP39PkJTY2Vo0QSWWxsbEWIyoOHz4sPj4+0rlzZzl06JCyPCkpSfn5/v37Vo3RVly5csWi8DN+/HhxcXGRb7/9Vn766ScZOHCgVKtWTQ4ePKhilJTT2EYeYxuwDi32eTytp8mRlq6BRUSCg4PF1dVVhg8fnuE2GfUramV/YpGQ7JrBYFAuAleuXKmMIrh27ZoMGzZMOnXqJA8fPtTM6IGsZJWvN998U+Lj41WO0r5cunRJfHx8ZNSoUcoX9i+//CLFixdPd6qrr776Slq2bGnX82CnduHCBSlXrpzF1B2TJk2Sjz76yOKCw0xr+TELCQmRd999V6ZOnSolS5aUzz//XO7du5dmO3vMD4/zaWWVk44dO2rqztXs7CNayodIyl2mAQEB8tprr8nu3bst1q1evVoqVqyY7WlqKAVzanv++OMPKVq0qOzcuVNELKclX758uRQqVCjbz1DK67h/po95ocyYjwnma44jR44oRZDDhw+L0WiUKVOmyPLly9UM0yak7lgdOXKkbNq0Sfk9NDRU3N3dZfXq1WqERrmIbeQxtoHcp+U+j+xijtIXEREhzs7OMnbsWBFJOWZt2LBB5syZI6tWrUpzQ6E5P1or7DuCyI45ODhARGAymfD2229Dp9OhV69e+OOPP3D+/HkEBQWhQIECaodpM7LK15EjR+Di4qJ2mHbDZDJh1apV8PHxwZgxY+Dg4AAAqFu3LvLlywej0ZjmNV5eXkhKSkKxYsWsHa7VGY1GrF+/Hm3btsVnn32mLL969SrCw8PRsGFD1K5dG6+99hrat28PQFv5SU1E8O+//2Lx4sUwGo2YP38+3NzcsGfPHvj6+mLatGkA7DM/PM6nlVlOzp07h6NHj8LV1VXtMK0mO/uIlvIhIqhQoQLmz5+PHj16YObMmbh06RL69OmDxMREHDlyBCVLluT3/VNgTm1T+/bt0aRJE/Tp0wf79u1D2bJlYTAY4OjoiB49emDq1KnYvHkz6tatCwDKeZi94f6ZPuaFUrt48SJ27tyJhw8fwtfXF61bt4aDgwNMJhPy5csHk8mEOnXqYOXKlejevTtmzZqF5ORkbN++HUFBQWqHb1Xp5Uqv1yvH15kzZwJIudbV6/UoXLgw/Pz8ULx4cZUjp+fBNvIY24A6tNznkV3MUVoGgwFz585FwYIFUaNGDQDAG2+8gaioKMTFxSEyMhJt2rTBJ598gqZNmwJ4nB9vb2/1AleDOrVJIusymUzKKILmzZtLkSJF7PY5TDmB+bKePXv2yGeffWaxzGg0Svny5dPc0Wz24MEDK0RmG65cuWJx987nn38uDg4OMnbsWPn++++lTp060qJFC4tnxGgpP6m9+uqrcvHiRRFJeeZDgQIFxMPDQ/766y+L7ew1PzxupcWcWNJaPpKTk9OMuDbf5Wz+f3h4uHTs2FF8fHykVKlS0qRJEylcuLAcP37c2uHmCcxp3mH+PG7duiWNGjWSChUqWEwl+ujRI2nYsKH8/PPPaoWY47h/po95oayEhYVJkSJFpEWLFlKyZEnx8/OTZs2aKVPwms8dzPvLwYMHRafTaXIfyU6unpy9Y8yYMVKtWjWLae8ob2EbeYxtQF1a7/PIDuYorYiICPnggw8kICBASpcuLa+//rqcOXNGDAaDhIWFib+/v3Tu3NniNVrKjxmLhKQZBoNBhg0bJjqdTkJCQtQOx+YxX7kno+mszCeTJpNJKlSoINu3b1fW/f3335o5qcwoP9HR0TJ06FDZunWrsuzkyZOi0+ksltm7jPLTtGlTWbJkiYiI9O/fX9zd3aV48eIyc+bMDB9abW943EqLObGklXyEh4fLW2+9JY0aNZI+ffrIihUrlHXmY4i5I+f27dsSFBQkU6ZMkUWLFqU71TUxp3lBRtNKX7hwQVq0aCFeXl4yffp0WbRokYwYMUIKFy4sERERVo4yd3D/TB/zQlmJi4uThg0bysCBA0VE5N69e7J161apUqWK+Pv7y40bN0Tk8f6SkJAggYGB4uHhIeHh4arFrYbs5srs9OnTMmrUKClUqJDFs+sob2EbeYxtwHrY55E15ihzT+bn3Llz0qtXL2nXrl2a8/+///5bdDqdhIaGWkwdrDV6tUcyElmTv78/jh07hmrVqqkdSp7AfOW8iIgIzJ49G9evX1eWiQgAQKfTwWAwID4+Hnq9Hu7u7gCAMWPGoFWrVjCZTKrEbE3p5cfM09MT06ZNQ5s2bZSpA00mE2rVqoVSpUqpEK31pZef5ORkAEC9evWg1+sxePBgbN26FcHBwRg8eDAmTZqEVatWpTt9rT3icSst5sSSvecjIiICDRo0gJOTE1q1aoULFy5g1qxZ6Nu3L4CUKRWTkpKg16dcBnh5eeHll1/G+PHj0bdvX/j4+KgZvk1iTvMGnU6X7vLy5cvj77//Rt++ffHHH39gxowZCAoKwq5du/Diiy9aOcqcx/0zfcwLZUdiYiJiYmLQqlUrAEChQoXQunVrbNiwAc7OzmjTpg2Ax1OX37hxA1u3bsVff/0FPz8/NUO3uqxy1bp1a2XbiIgIzJw5E3v27ME///yD6tWrqxU2PSe2kcfYBqyDfR5ZY44yl15+KlasiKlTp+Ljjz9GuXLlAKT0xYoIHj16hEqVKqFYsWLKeaEmqVmhJLK2jO4wpvQxXznr7NmzUqRIEdHpdDJ69Gi5fft2mm2MRqMkJCRIxYoV5ejRozJlyhQpUKCAHDlyRIWIrSuz/KQeZZna2LFjpV69enLr1i2rxqqGrPafRYsWiU6nkxIlSkhQUJCy/Msvv7SbkRLZweNWWsyJJXvOh8lkkrFjx0qXLl2UZXFxcTJ37lypWrWqvPXWWxbbL1q0SC5fvmztMPMU5tR2RUREyKhRo2Tw4MEyffp0iY2NTXO+8OTdwNHR0fLgwQOJiYmxery5gftn+pgXyi6DwSD+/v4SGBiYZl1ISIhUqlRJBg0aZLE8Li7OWuHZlKfN1cmTJ9OMrKK8h23kMbaB3Mc+j6wxR5nLKj/p9QWMGjVKWrRoIffv37dWmDaJRUIiIit4+PCh9OvXT/r06SNz584VnU4nI0eOTLdQKCJSs2ZNqVOnjjg5OVl8sdurp81PeHi4jBs3Ttzd3e16ykCz7OTnzJkzMm7cOOW5D1qeJoFIy/r06SONGjWyWBYfHy8LFy6UmjVrKs/BPXDggPj4+Mg777yT4XQ1lII5tT3h4eHi7u4ur732mrz++uvi5eUlderUkQ0bNkhiYqKIWH4P2vPNRNw/08e8UFbMHYWTJk2S+vXry5YtW9KsnzhxojRu3Fh53phWPU2uYmNj1QiRcgHbyGNsA7mPfR5ZY44yl538pC4ShoWFydixY8Xd3V1CQ0PVCNmmOKo9kpGISAv0ej1q164NT09PdOvWDUWLFsXbb78NABg1ahS8vLwAAEajEQ8ePMCFCxfw8OFDHD9+HFWrVlUzdKvIbn4A4PLlyxg3bhxOnz6NvXv32u2UgallJz+VKlXC6NGj4erqCiDjadeIyD6JCHQ6HWrVqoUzZ87g9OnTqFy5MgDAxcUFXbt2RUREBHbv3o179+6hQYMGGDVqFFq2bAkHBweVo7dNzKltSkpKwrhx4/DWW29hwYIFAICYmBh07NgR06ZNQ3x8PLp16wZHx5RL3eHDh+Py5cv46quvULZsWTVDz1HcP9PHvFB2mc+Ve/XqhW3btuGHH36Aq6srmjZtqqz38/PDsmXLEB8fjwIFCqgYrbqeJlcJCQkoWLCgitFSTmEbeYxtIPexzyNrzFHmspMfcz4uXbqEkSNHIiIiAnv27NFEv2uW1K1REhFpx5N3161atUp0Op2MGDFCoqOjRUQkOTlZoqOjZdu2bXLixAk1wlRNdvJjMBjk5s2bcuXKFbly5YoaYaoms/yYR0gYjUa5cOGCGuERkY04d+6ceHl5Sd++fdNMqRgVFSV6vV7WrVunUnR5E3Nqe1q0aCFjxowREZGkpCQRSRkl1q5dO6lZs6bFNO3r16+XIkWKyLVr11SJNbdx/0wf80LZYR5REBoaKlWqVJG2bdvK4sWLRUTk0aNHMnz4cI4M+n/MlTbxc3+Much97PPIGnOUuez2K966dUsuXrwokZGRaoRpk1gkJCKyMoPBoJxgrly5UhkCf+3aNRk2bJh07NjRbufxz46s8vPmm29KQkKCylGqJ6v8dOrUSdP7DxGJ7Nq1S5ydnWXQoEEW06tER0dL7dq1Zffu3eoFl0cxp7bDaDRK8+bNpXPnzsoy8xSjiYmJUqVKFenQoYPFa+y9w477Z/qYFzJLTk5WbigwM0/BZv5/eHi4dOzYUXx8fKRUqVLSpEkTKVy4sDJlm1YwV9rEz/0x5kJ97PPIGnOUOfYrPj2diIjaoxmJiLRGUm7SgF6vx+rVq9GrVy9UqFAB586dw9GjR1GjRg21Q1RVRvk5f/48jhw5gpo1a6odoqoyy09QUJDm9x8iAjZt2oSuXbvitddeQ9euXVGtWjX8+uuv+N///oegoCCULl1a7RDzHOZUffL/U0n+888/eP311zF16lQMGzYMAJCQkAAXFxfs3bsXXbp0wa5du+Dn5we9Xq+8zp5x/0wf80InT57E5MmTERUVBR8fH7z66qvo3r07gJRHPTg4OMBkMkGv1yM6OhqXLl3C1q1b4e3tjcaNG8PHx0fld2A9zJU28XN/jLmwHezzyBpzlDn2Kz4dFgmJiFRiPvzqdDq0aNECwcHB+OeffzgX9v9jfjLH/BBRVo4dO4ZPPvkEFy9ehKOjI/Lly4eVK1fygug5MKe2ITY2Fl9++SWWL1+O4cOH4+OPP1bW7d+/H3379sXff/9tV88gzA7un+ljXrQrIiICdevWRfv27fHiiy9i586diI2NRfXq1bF48WIAKc85dXJyUjlS9TFX2sTP/THmwvawzyNrzFHmmJ/sY5GQiEhFRqMRI0eOxOzZsxEcHIxq1aqpHZJNYX4yx/wQUVZiYmJw9+5dPHz4EMWLF4eXl5faIeV5zKm6DAYDHB0dce7cOcybNw/Lly9H//79MWLECBgMBnz//fdYt24d9uzZg6JFi6odrtVx/0wf86I9IoLx48fjzJkzWLt2LQAgPj4eixcvxs8//wxfX1+sXr1a2X7x4sVo2bKlJkeXMlfaxM/9MebCdrHPI2vMUeaYn+zRqx0AEZHW+fv749ixY/yiygDzkznmh4gy4+7ujnLlyqFKlSrsFM8hzKl6jEYjHB0dcenSJfz7778YOHAgxo0bh7lz56JKlSp45ZVXsHDhQixbtkyTBUKA+2dGmBft0el0uHbtGm7cuKEsc3V1Rb9+/TBkyBCcPXsWo0ePBgD8+++/+OKLLzBmzBgYjUa1QlYNc6VN/NwfYy5sG/s8ssYcZY75yZqj2gEQEWmZg4MD+vXrZ/fPyXlWzE/mmB8iItIK8wjCS5cuoVKlSujevTt69+6NQYMGoVOnTtizZw/c3d1RpUoVlClTRu1wiUhF5ueQ1qpVC2fOnMHp06dRuXJlAICLiwu6du2KiIgI7N69G/fu3UODBg0watQotGzZEg4ODipHb13MlTbxc3+MubBt7PPIGnOUOeYnezjdKBEREREREdms1AXCWrVqoWPHjpg3bx7y5csHk8kEvZ4T5BBRWufPn0dAQADat2+P7777Dm5ubsq669evw9vbG2vWrEHnzp1VjNI2MFfaxM/9MeaCiLSMIwmJiIiIiIjIJj1ZIOzQoQN+/vlnODqmXMqyQEhEGalYsSLWrFmDtm3bwtXVFZMmTVKmm3VyckLNmjXh6empcpS2gbnSJn7ujzEXRKRlLBISERERERGRzUn9DEJzgXDhwoVKgZCIKCvNmjXD2rVr0bVrV0RFRaFr166oVq0afv31V1y9ehUVK1ZUO0SbwVxpEz/3x5gLItIqTjdKRERERERENikyMhJ+fn7o1q0bFixYwOf/ENEzOXbsGD755BNcvHgRjo6OyJcvH1auXImaNWuqHZrNYa60iZ/7Y8wFEWkNi4RERERERERkc4xGIwIDA5GUlIR58+ZxBCERPZeYmBjcvXsXDx8+RPHixZWpBCkt5kqb+Lk/xlwQkZawSEhEREREREQ26f79+3B3d+ezB4mIiIiIiHIBi4REREREREREREREREREGsPbMYmIiIiIiIiIiIiIiIg0hkVCIiIiIiIiIiIiIiIiIo1hkZCIiIiIiIiIiIiIiIhIY1gkJCIiIiIiIiIiIiIiItIYFgmJiIiIiIiIiIiIiIiINIZFQiIiIiIiIiIiIiIiIiKNYZGQiIiIiIiIiIiIiIiISGNYJCQiIiIiIiIiIiIiIiLSGBYJiYiIiIiIiIiIiIiIiDSGRUIiIiIiIiIiIiIiIiIijWGRkIiIiIiIiIiIiIiIiEhjWCQkIiIiIiIiIiIiIiIi0hgWCYmIiIiIiIiIiIiIiIg0hkVCIiIiIiIiIiIiIiIiO/bPP/9Ap9NBp9Nh0qRJaodDNoJFQiIiIiIiIiICAHzzzTfQ6XRwcnJC2bJl8dFHH+HOnTtqh0VERBpi/i4y/7dq1Sq1QyLSjEuXLmH9+vUYPXo0Xn31VXh6eiptsVy5cmqHR7mARUIiIiIiG3D//n1Mnz4dAQEBKFKkCAoWLIiqVati2rRpiI2NBQC8++67ysn55cuXVY6YiIjs0bFjxwAAycnJuHz5Mn766Se0b99e5aiI8r779+9Dr9dbFD6y+s/T01PtsIlUsWjRIovff/nlF5UiIdKWOXPmoHz58ujSpQtmzJiBHTt24O7du2qHRbmMRUIiIiIilW3cuBEVK1bEmDFjcPjwYdy7dw9xcXE4ceIExo0bhwYNGuD69es4ffo0AMDT0xNlypRROWoiIrJHH374IdavX49PPvlEWXbw4EGEhISoGBVR3nfgwAGIyFO9platWrkUDZHtOnToEMLDwy2W7dy5E5cuXVInICINMRqNFr+7uLigWrVqKkVD1sIiIWkaR20QPR+2IaLnt3z5cnTp0kW5O69t27aYM2cOVq9ejZkzZ8LHxwcnTpzAoEGDcObMGQBAzZo11QyZiIjsWOPGjdGpUyd8/fXX6Nq1q7I8KChIxaiI8r7KlStj48aN6f43Y8YMeHh4WGyv1+vx+uuvqxQtkXpSjxocOHAgAEBEsHjxYrVCItKMcuXKYdCgQVi0aBFCQkIQExOD33//Xe2wKJfp5GlvYyKyExs3bsR7772X4ZDpKlWqYPv27XjzzTdx5MgReHp6Ijo62spREtkutiGi5xcaGoqAgAAkJCTAy8sL69atQ5MmTSy2efjwIRo0aICwsDBl2ahRo/Dll19aO1wiItKYxYsXo1+/fgCAjz/+GHPmzFE5IiL7Eh8fj/Hjx2POnDlITk4GADg6OqJHjx4YPXo0KleurHKERNYVFxeHEiVKIDY2Fj4+PggJCUGJEiUQExOD0qVL49KlS9DrOeaF6Fn9888/aNasGQBg4sSJmDRpUpavuXTpEsqXLw8AKFu2LEf12iEeVUmTOGqD6PmwDRE9PxFBr169kJCQAL1en26BEAAKFiyIL774wmIZp54iyr7+/fsrI9qPHDmS7jb//fcfvL29odPp4O7ujk2bNlk5SiLblHp6qdDQUBUjIbI/hw4dQo0aNfDNN98oBcIOHTrg5MmTWLJkCQuEpElr1qxRZiXq1asXXF1dlVHtV65cwY4dO9QMj8jm/fXXX+jYsSNKlCiB/Pnzo0yZMujUqRO2b9+udmhkwxzVDoDI2kJDQ/H+++/DZDJlOGpj4MCBaNCgATZu3KgsY4csUQq2IaKcsWbNGqXDNTAwMN0Codkrr7xi8TvbE1H2HT9+HADg4OCAqlWrplm/Zs0a9OnTBwkJCahQoQL++OMP+Pv7WztMIpvk7+8PBwcHGI1GixHtRPR85s6di2HDhsFgMAAAXnzxRcyfPx9NmzZVNzAilZmnGtXpdOjVqxeAlEeYmJf/8ssvaN26tWrxEdkqk8mEDz/8EAsXLrRYfuXKFVy5cgUbN27E4MGD0bFjR5UiJFvGkYSkKRy1QfR82IaIcs4333wDAHBycsKoUaMy3dbd3R1ubm7Kzz4+PrkeH5E9SE5ORnh4OICUZ0G5uLgo60QEEyZMQLdu3ZCQkIBmzZohKCiIBUKiVM6cOQOdTgcAuHfvHq5cuaJyRER5m4jg448/RmBgIAwGA3Q6HYYMGYKQkBAWCEnzzpw5gwMHDgAAGjVqpExv2KhRI1SoUAEA8Pvvv/MxJkTpGDZsmFIgdHBwQK9evbBo0SIsX74cI0eORJEiRfD9999z6nhKF4uEpCkctUH0fNiGiHLGjRs3EBQUBABo3rw5SpYsmeVr4uPjAQA1atRQOmyJKHPh4eFISkoCYDntdVxcHDp37ozPP/8cAPDRRx9h+/btKFKkiCpxEtkig8GAfv36KSOdACAkJETFiIjyvkGDBuGHH34AABQoUABr167F7NmzLW5iIdIq82hBIGX0oJlOp0Pv3r0BAElJSVi2bJnVYyOyZQcOHFCKf66urti1axeWLl2Kvn37okePHpg5cyZOnDiBypUrY8OGDSpHS7aIRULSFI7aIHo+bENEOWPnzp0QEQApRcKsREVFwWg0AmDBnehpmKcaBR63ncjISGVK7Hz58mHevHn44Ycf4OjIJzEQpTZz5kwcO3bMYhmfS0j07GbMmIGffvoJAODp6Yl9+/ahc+fOKkdFZBsMBgOWLl0KAHBxcVGeQ2jWu3dv5UbJ1MVEIgK+/vprpX9h+vTpaW7aB4ASJUpg9erVcHBwsHZ4lAewSEiawVEbRM+HbYgo50RGRio/lytXLsvtDx8+rPycejQUEWUudYGjZs2a2L9/P+rUqYPQ0FB4eXlhx44d+PDDD1WMkMg2nTp1ClOmTAGQcnOYGYuERM9mz549GDduHADAw8MD27dv5zkdUSqbNm3CzZs3AQBvvPEG3N3dLdaXL18ejRs3BgCcOHECR44csXqMRLYoMTERW7ZsAZByg/4HH3yQ4bbVqlXDq6++aq3QKA9hkZA0g6M2iJ4P2xBRzrl165by85MXwOlZuXKl8nNW7SkmJgbDhg1DmTJl4OTkBFdXV9SvXx+7du169oCJ8qjUIwmPHj2KFi1a4Pbt2wBSptDObNpsIq0ymUzo168fEhMTodPpsGDBAmUdpxslenrJyckYOHCgcm20ePFiXh8RPSH16EDz1KJPSj0F6aJFi3I9JqK8ICQkRHm8QsOGDZE/f/5Mt2/RooU1wqI8hkVC0gyO2iB6PmxDRDkn9bSGV69ezXTbkydP4rfffgOQMvWOr69vhtv+999/qFy5Mr777juUKVMGXbp0QdmyZXHo0CG0adNGGQ1MpAUiYlHQGDlypHIBDbBziSgj3377LQ4dOgQAGDx4MHr37g0PDw8AwNmzZ/Ho0aNMX5+cnIwZM2bA398fLi4uKFq0KDp16mRRtCfSkkWLFuHUqVMAgP79+6Njx44qR0RkW6KiorBt2zYAQPHixTMc6dS1a1e4uroCSLmJ0jxzEZGWRUVFKT+/+OKLWW7PRwFRelgkJM3IrVEbmzdvRocOHVCiRAnky5cPOp0uzX9OTk5ITk5+vjdApLLcaENnz56Fi4sLihcvjpiYmAz/ltFoRIECBaDT6XDt2rWniJrINpUvX175ee/evRlul5CQgH79+infIdWqVcvwGQLnz59H8+bNkZycjH379mH//v1YsWIFwsPD0b17dyQnJ2PatGk5+0aIbNjZs2fx8OFD5Xe9Xo/PPvsMzs7OAIAVK1bgxIkTaoVHZJPOnTuH8ePHA0jpRPriiy8AANWrVweQck4WHh6e4euTk5PRpk0bjB49GtHR0Wjfvj0qV66MjRs3IiAgADt27Mj9N0FkY+bMmQMAcHZ2xqRJk9QNhsgG/e9//1NG2vbs2TPD6x03NzelyB4TE4N169ZZLUYiW5X6esdcRM9MgQIFcjMcyqNYJCTNyI1RG4GBgWjfvj3+/PNPVKpUCV27dkWVKlXSbOfn54d8+fI9e/BENiA32tCAAQPw6NEjfPnll5kWHh0cHJTOqf379z9l5ES2J/UUH8uXL0/3mRrR0dF48803LUblZnbTysCBAxETE4MFCxagYcOGynK9Xo/Ro0cDYPshbUk9aqlw4cL4888/MX36dOU5HSaTSXk+FBGljL7t378/EhISoNPpsGjRIqWzyXweBmQ+5ejMmTOxa9cu1KlTB2fPnsWaNWuwb98+rFixAklJSejZs6dFZxaRvTt58qRSWO/YsSO8vb1VjojItoiIxewOGU01apZ6ytHUU5QSaVXBggWVn7MzujYuLi43w6E8ikVC0oycHrUxZ84czJ07F97e3vjvv/+wZ88erFixAqGhofjwww8BAK+88gqSk5Nx7NixHH43RNaX021o8+bN2LVrF3x9fdGrV68s//0KFSoAgDJVD1FeVrlyZbRv3x5AyqiMZs2aYfDgwVixYgWWLFmCoUOHonLlyti+fTvq1q2rvK527drp/r2QkBDs2LEDjRo1wptvvplmvbn9smOWtCR1kfCXX35B69atAQBjxoyBi4sLAOD3339XplUk0roff/xROccbPHgwGjdurKyrUaOG8nNoaGi6rzcYDPj222+Vv5X6BrDu3bvjtddew+3btznVL2nKzp07lZ/btWunYiREtmnPnj04f/48gJQbUqpVq5bp9i1atFCK7Xv37sXZs2ct1p8+fRo6nS7d56699dZb0Ol0qFq1app1vXr1gk6nw/bt25/1rRCpolSpUsrP586dy3L77GxD2sMiIWlGTo7aePDgAcaNGwe9Xo/ly5db3Fmr0+nw8ccfAwAOHjwIEYFez6ZGeV9Oj3wyT3s4fPjwbLURT09PAMD169efKm4iW7VgwQJUrlwZQModf3PmzEHPnj3Rp08ffPfdd4iPj8eMGTPQtWtX5TVNmjRJ92/9/vvvAIDOnTunu/7evXsAUp7xQaQVqW/SSl1sL168OAYNGqT8PmbMGKvGRWSLIiMj8dlnnwGwnGbULDtFwgMHDuDOnTsoX748Xn755TTru3XrBuDxdxaRFqS+wTF1vwERpUg9GjCrUYRAyiwp77zzjvL7kzeeFC5cGAAQGxtrsfzixYvYsGEDgMfXRmY3b97EmjVrUKVKlQyfh0hkq6pVq6Y8TmH//v1ZPjs69c0rRGasXJBm5OSojZUrVyImJgYdOnTAK6+8kma9eWrF5ORkjtogu5GTbSgsLAyHDh2Ci4uL0mFkFhISgn/++QciYrHcZDIp/zaRPShWrBgOHz6McePGoXLlynB2dkahQoVQo0YNjBkzBqdOncKnn36Kbdu2AQDKli2b4UPGzSf6jRo1Snf9vn37AGQ8EpHIHgUHBwMAihYtanGHLQB8+umnytQ8u3fv5nPSSPPef/99PHz4EHq93mKaUTN/f39l6vmMioTmaUgz+q4x3ziW2XSlRPYm9WMaihQpomIkRLbnwYMHWL9+PYCUR4z07NkzW69LPeXokiVLLPoIMioSzp49GyaTCXXr1k1TJJw3bx6SkpIwbNiwZ3ofRGpydnbGa6+9BiDlWZ0LFy7McNsTJ05wtCyli0VC0pScGrXx119/AQA6deqU7r/z4MEDAEC+fPkyfc4aUV6TU21o69atAICGDRtazJ8OAB06dFCmAUktKioKAFCiRIkcfU9EanJ3d8fnn3+OU6dO4dGjR7h37x6OHz+OadOmoWzZsggLC8OuXbsAAH379k33b5hMJmVaRfOI2yeZL747dOiQC++CyPZcvXoVt2/fBmA5AsrMy8sLQ4YMUX4fO3astUIjsjkLFy5UCuWBgYEW04yaOTs7K+eAd+7cwbVr19JsExkZCQAZPnPNvPzOnTu8kZI0I/Vz3W/cuKFiJES2Z8WKFUhISAAAtG7dGsWKFcvW6ypXrqzcmHz9+nX8+eefyjonJye4urpaFAnv37+PRYsWoV27dqhbty7i4+ORmJgIIOXm/p9//hkvvPBCtouURLZmxIgRSh/aZ599hv3796fZ5ubNm+jWrRtvvKd0OWa9CZH9MI/amDVrFtatW4eLFy/CxcUF5cqVw2uvvYYPPvgAZcuWRcuWLQFkPGrDfPdrQEBAuv+OearFWrVqpfssNqK8KqfakLmg8eSd5pcvX8bly5fRsGHDNK8xjwjhSCjSiri4OPTt2xciAjc3N+V5t0+KiIhQLoIjIyMtnh8KpEz/9ttvv6FkyZJ46623cj1uIluQ+nmENWvWTHeb4cOHY+7cuXjw4AGCgoKwYcOGDG8AI7JX165dw4gRIwCkP81oajVq1MCJEycApIwmfHKErvm7qECBAum+PvWNYbGxsWluFCOyR6VLl1Z+Xrt2bYaPYiDSoqedajS1d999V3kEyi+//KLMegSkjCZMXSScN28eHj58iBEjRiijqO7fv49ixYph7dq1uH79OiZPnqxM2UiU1zRo0ACBgYH4/vvvERcXh6ZNm6Jnz55o1qwZnJ2dERwcjIULF+Lu3bvo1KmTMvVuRsaNG2fxu3kwDJDSdp5cX7hwYQwfPjzn3hBZnxCRhdDQUNHpdAJAJk2alO42BQoUEABy48aNdNf3799fAMi0adNyM1Qim5SdNtSgQQMBIHPnzrVYPnfuXAEgHTt2tFgeEREhACRfvnxy9+7dXIudyFqOHTsmsbGxGa4/e/asBAQECAABIN99912G2y5btkzZrk2bNpKUlKSsCw0NlTJlyohOp5MNGzbk6HsgsmWTJ09W2sXKlSsz3G7KlCnKdr6+vmIwGKwYJZH62rVrJwBEr9fL3r17M9121qxZSnuZPn16mvXvvfeeAJBx48al+/rk5GTl9VFRUTkSP5Gt2759u7LfOzk5yerVq9UOicgmBAcHK23Dw8NDEhISnur1d+7cEScnJwEgjo6OFv1zVapUEb1eLyIiiYmJUrJkSXn55ZdFRGTmzJkCQE6dOiUiIvXq1ZP8+fPLrVu3cuidEanDaDQq/dEZ/TdkyBDZvXu38vvEiRPT/VuZ/Y30/itbtqxV3yvlPE43SpRKdkdtuLm5AQDOnTuXZt1///2HJUuWwM3NDe+9916uxktka7LbhpKTkwE8fs6g2YIFCwAABoPBYvn8+fMBAO3bt1eeMUCUl02cOBHe3t7o2bMnZs+ejdWrV2P16tX49ttv8eabb8LX1xeHDh0CkPKcqMGDB2f4t44ePQoA+Oijj7B9+3ZUrlwZPXr0QPPmzVGzZk1cvXoV3333HTp27GiV90ZkC1KPJExvulGzoUOHKtP0njp1Cr/++mtuh0ZkM5YtW4bNmzcDAAYPHpzuNKOppW5L6T2X0HyNlNFUonFxcWm2JbJ3rVq1Qtu2bQEASUlJ6NatGwICAjB58mT8+uuv2LRpE0JDQ9M8j53I3qUeRdi1a1fkz5//qV5fpEgRZfSgwWDAkiVLlHWFCxeGyWRCXFwcVqxYgaioKGWUk4eHBwDg3r17CAoKwuHDh9GrVy8ULVr0ed8Skar0ej0WLlyIrVu3okOHDnjhhRfg5OQEb29vdOzYEdu2bcPs2bPVDpNslcpFSiKryclRG2+//bYAkNatW0tiYqKy/MSJE1K+fHkBIPPnz8/R+InUlpNtqEuXLgJA3njjDWXZ7NmzxcnJSdq2bSslSpRQ2taBAwfE2dlZHB0dJTg4OMfeD5GaSpYsmeXdeC4uLvLNN9+IyWTK9G81btxYAEhYWJj8/vvvUrt2bXFxcZHChQtLhw4d5ODBg1Z6V0RElFfcvHlTPD09BYD4+PhIXFxclq+5ffu28h3l7++fZv3s2bMFgHTp0iXd1584cUIASJEiRZ47fqK8JCYmRt58881Mz/tq1qwply9fVjtUIrvQoUMHASDXr1+XqlWrSpkyZSQ5OVlERFatWiUAZMuWLdKzZ0/R6XRy8uRJlSMmIlIXn0lImjFx4kTs3bsXr7/+OurUqYMSJUoAAKKiorBnzx5s2bJFGb2U1aiNiRMn4s8//8Rff/2Fl156CQEBAbh16xb27t0LEcHMmTPx/vvvW+V9EVlLTrahXr16Yd26dfj999/RpEkT5M+fHzt27MCECRPg4eGBrVu3IiAgAN7e3vjrr7+QlJSEuXPnonr16lZ5r0S5benSpdi0aRP27duHW7du4c6dOxAReHp6okqVKmjZsiX69u2rjHDKiMlkQnBwMFxcXODr64sqVaqgQ4cOVnoXRESUV73wwguIjo5+qtd4eXllOtrJPNLw2LFj6a43L+f5HGmNm5sbNm7ciMOHD2PZsmU4dOgQIiMj8eDBAyQlJQFIGQHfr18/7NixQ+VoifI+8+xD69evR1hYGL7++ms4OqZ0gZtHEp4+fRpr165FmzZt4Ovrq1qsRES2QCeZneUT2ZFSpUohKioq021cXFwwbdo0DB06FDqdLtNtT548iQkTJmDPnj148OABihUrhmbNmmHYsGGoWbNmToZOZBNyug3NmzcPX3/9Na5cuYIyZcpg0KBBGDx4MBISEjBw4EBs3rwZSUlJePnll/HZZ5+hdevWOfl2iOzC6dOn4evri/r16+Pff/9VOxwiItIwg8GAYsWK4e7duzh69Chq165tsb59+/bYvHkzZs+ejSFDhqgUJZFtOXnyJJo2bYrbt28DSJmW19XVVeWoiPK2YcOGYfbs2ShVqhRiY2Nx5coVuLu7AwAOHjyIBg0aoFSpUrh27Rp27NiBli1bqhwxEZG6OJKQNCOnRm2Y+fn5Yd26dbkcNZHtyOk2NGDAAAwYMCDNcldXV4vnCRBRxv777z8ASNMRS0REZG2Ojo4YOnQoJkyYgEGDBmHHjh3KswdXr16NzZs3w8vLC/369VM5UiLb4efnh+bNm2P16tXQ6/VwcHBQOySiPM88kvDatWsYPny4UiAEHo8kvHbtGqpVq8YCIRERWCQkDWnRogVatGihdhhEeRbbEJHtMRcJa9WqpXIkREREwKeffopdu3bhn3/+gY+PD5o0aYIbN25g3759yJcvH3799VelcEhEQFJSEkJCQgAALVu2hLOzs8oREeV95iKho6NjmpHr5iIhkDLikIiION0oEREREREREeWQpKQkfPXVV1i2bBkuXLiAggULolGjRpgwYQJvaiH6f/Hx8fjjjz/www8/YP/+/dDr9di2bRtatWqldmhERESkMSwSEhERERERERERWcmOHTvw6quvKr/PmjULI0aMUDEiIiIi0ipON0pERERERERERGQlYWFheOGFF1C/fn0MGTIEzZo1UzskIiIi0iiOJCQiIiIiIiIiIiIiIiLSGL3aARARERERERERERERERGRdbFISERERERERERERERERKQxLBISERERERERERERERERaQyLhEREREREREREREREREQawyIhERERERERERERERERkcawSEhERERERERERERERESkMSwSEhEREREREREREREREWkMi4REREREREREREREREREGsMiIREREREREREREREREZHGsEhIREREREREREREREREpDH/B4R8CR0xp0eHAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1810x1810 with 64 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import corner\n",
+    "\n",
+    "figure = corner.corner(samples,labels=[ r\"$g_{\\sigma}$\", r\"$g_{\\omega}$\",r\"$g_{\\rho}$\",r\"$\\kappa$\", r\"$\\lambda_0$\",r\"$\\zeta$\",r\"$\\Lambda_w$\",\"d1\",\"d2\"],\n",
+    "                    smooth=0.9,\n",
+    "                    label_kwargs=dict(fontsize=22),\n",
+    "                    title_kwargs=dict(fontsize=22),\n",
+    "                    quantiles=[0.16, 0.84],\n",
+    "                    levels=(1 - np.exp(-0.5), 1 - np.exp(-2), 1 - np.exp(-9 / 2.)),\n",
+    "                    plot_density=False,\n",
+    "                    plot_datapoints=False,\n",
+    "                    fill_contours=True,\n",
+    "                    show_titles=True,\n",
+    "                    max_n_ticks=3,\n",
+    "                    title_fmt= '.2f')\n",
+    "                       #,range=[(10,80),(0.56,0.64),(0.10, 0.2),(150,600),(40,80),(50,75)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4481115b400f107b26d360e6549f546bb0e8bc1af70e4e66085bfa77a017a39"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/test_TOVsolver.ipynb.txt b/_sources/test_TOVsolver.ipynb.txt
new file mode 100644
index 0000000..3c55fef
--- /dev/null
+++ b/_sources/test_TOVsolver.ipynb.txt
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TOVsolver module guide\n",
+    "\n",
+    "TOV solver part we have multiple function, we can generate a Mass radius function from 'Test_EOS.csv' file here easily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mass Radius file will be generated and stored as  2-d array. The first column is Radius, second one is mass\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import TOVsolver.main as main\n",
+    "import TOVsolver.EoS_import as EoS_import\n",
+    "from matplotlib import pyplot\n",
+    "import matplotlib.pyplot as plt\n",
+    "import TOVsolver.constant\n",
+    "from itertools import repeat\n",
+    "import numpy\n",
+    "# Main has two functions\n",
+    "#   Output.MRT() returns the mass, radius, tidal\n",
+    "#   OutputC_s() returns the speed of sound\n",
+    "# Using the main functions with either arrays of density and pressure or a .csv with density and pressure columns\n",
+    "#   Function argument is either (filename = ) OR (density = , pressure = )\n",
+    "#   Example: main.OutputMR(filename = 'file.csv') OR main.OutputMR(density = array1, pressure = array2)\n",
+    "MR = main.OutputMR(\"Test_EOS.csv\").T\n",
+    "# Plot the Mass-Radius curve\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MR[0], MR[1],lw=2)\n",
+    "ax.set_ylabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.set_xlim(8., 20.)\n",
+    "ax.set_ylim(0,3)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can generate the tidal property from it also,like tidal deformability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mass Radius and tidal will be generated as the 3-d array. The first column is Radius, second one is mass,last is tidal\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# try solve tidal property\n",
+    "MRT = main.OutputMRT(\"Test_EOS.csv\").T\n",
+    "# Plot the Radius and Tidal curve\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "        \n",
+    "ax.plot(MRT[0], MRT[2],lw=2)\n",
+    "ax.set_ylabel(r'$\\Lambda$', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.set_xlim(8., 20.)\n",
+    "ax.set_ylim(0,1000)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot out Tidal with mass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Mass and Tidal curve\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MRT[1], MRT[2],lw=2)\n",
+    "ax.set_ylabel(r'$\\Lambda$', fontsize=16)\n",
+    "ax.set_xlabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlim(0., 3.)\n",
+    "ax.set_ylim(0,1000)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Can generate the speed of sound from the given equation of state. This do not need to solve TOV equation, the speed of sound is defined by the derivative of P(rho) curve, this quantity is very of interest, since whether there is a upper limit of this speed of sound (except for the casaulity limit c_s < 1 ) is a very interesting and hot topic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from TOVsolver.constant import c,G \n",
+    "# here we plotting out speed of sound with Test_EOS, the density is in g/cm3\n",
+    "C_s,rho = main.OutputC_s(\"Test_EOS.csv\")\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(rho, C_s,lw=2)\n",
+    "ax.set_xlabel(r'$\\epsilon$', fontsize=16)\n",
+    "ax.set_ylabel(r'C_s', fontsize=16)\n",
+    "plt.xscale(\"log\")\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This package can easily integrated into a Baysian inference flow, to do bayesian inference, Here, we generated several (50) EoSs from RMF model, and try to use a loop to compute out all of their MRT property. That could be a in-between step of doing bayesian inference of neutron star EoS. Remember these EoS could also be polytrope, or anything that generate from your own EoS computation code. Next step for us will be integrate our EoS computation into this package and also the Bayesian analysis."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "e4481115b400f107b26d360e6549f546bb0e8bc1af70e4e66085bfa77a017a39"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000..8141580
--- /dev/null
+++ b/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/_static/basic.css b/_static/basic.css
new file mode 100644
index 0000000..7ebbd6d
--- /dev/null
+++ b/_static/basic.css
@@ -0,0 +1,914 @@
+/*
+ * Sphinx stylesheet -- basic theme.
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin-top: 10px;
+}
+
+ul.search li {
+    padding: 5px 0;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css
new file mode 100644
index 0000000..88ba55b
--- /dev/null
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\ No newline at end of file
diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 0000000..0398ebb
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,149 @@
+/*
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 0000000..72513f8
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '1.9.1',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/_static/file.png b/_static/file.png
new file mode 100644
index 0000000..a858a41
Binary files /dev/null and b/_static/file.png differ
diff --git a/_static/fonts/Lato/lato-bold.eot b/_static/fonts/Lato/lato-bold.eot
new file mode 100644
index 0000000..3361183
Binary files /dev/null and b/_static/fonts/Lato/lato-bold.eot differ
diff --git a/_static/fonts/Lato/lato-bold.ttf b/_static/fonts/Lato/lato-bold.ttf
new file mode 100644
index 0000000..29f691d
Binary files /dev/null and b/_static/fonts/Lato/lato-bold.ttf differ
diff --git a/_static/fonts/Lato/lato-bold.woff b/_static/fonts/Lato/lato-bold.woff
new file mode 100644
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+++ b/_static/js/badge_only.js
@@ -0,0 +1 @@
+!function(e){var t={};function r(n){if(t[n])return t[n].exports;var o=t[n]={i:n,l:!1,exports:{}};return e[n].call(o.exports,o,o.exports,r),o.l=!0,o.exports}r.m=e,r.c=t,r.d=function(e,t,n){r.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},r.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return r.d(t,"a",t),t},r.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},r.p="",r(r.s=4)}({4:function(e,t,r){}});
\ No newline at end of file
diff --git a/_static/js/theme.js b/_static/js/theme.js
new file mode 100644
index 0000000..1fddb6e
--- /dev/null
+++ b/_static/js/theme.js
@@ -0,0 +1 @@
+!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return n[i].call(o.exports,o,o.exports,t),o.l=!0,o.exports}t.m=n,t.c=e,t.d=function(n,e,i){t.o(n,e)||Object.defineProperty(n,e,{enumerable:!0,get:i})},t.r=function(n){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(n,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(n,"__esModule",{value:!0})},t.t=function(n,e){if(1&e&&(n=t(n)),8&e)return n;if(4&e&&"object"==typeof n&&n&&n.__esModule)return n;var i=Object.create(null);if(t.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:n}),2&e&&"string"!=typeof n)for(var o in n)t.d(i,o,function(e){return n[e]}.bind(null,o));return i},t.n=function(n){var e=n&&n.__esModule?function(){return n.default}:function(){return n};return t.d(e,"a",e),e},t.o=function(n,e){return Object.prototype.hasOwnProperty.call(n,e)},t.p="",t(t.s=0)}([function(n,e,t){t(1),n.exports=t(3)},function(n,e,t){(function(){var e="undefined"!=typeof window?window.jQuery:t(2);n.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(n){var t=this;void 0===n&&(n=!0),t.isRunning||(t.isRunning=!0,e((function(e){t.init(e),t.reset(),t.win.on("hashchange",t.reset),n&&t.win.on("scroll",(function(){t.linkScroll||t.winScroll||(t.winScroll=!0,requestAnimationFrame((function(){t.onScroll()})))})),t.win.on("resize",(function(){t.winResize||(t.winResize=!0,requestAnimationFrame((function(){t.onResize()})))})),t.onResize()})))},enableSticky:function(){this.enable(!0)},init:function(n){n(document);var e=this;this.navBar=n("div.wy-side-scroll:first"),this.win=n(window),n(document).on("click","[data-toggle='wy-nav-top']",(function(){n("[data-toggle='wy-nav-shift']").toggleClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift")})).on("click",".wy-menu-vertical .current ul li a",(function(){var t=n(this);n("[data-toggle='wy-nav-shift']").removeClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift"),e.toggleCurrent(t),e.hashChange()})).on("click","[data-toggle='rst-current-version']",(function(){n("[data-toggle='rst-versions']").toggleClass("shift-up")})),n("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),n("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),n("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n('<button class="toctree-expand" title="Open/close menu"></button>'),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}if(t.length>0){$(".wy-menu-vertical .current").removeClass("current").attr("aria-expanded","false"),t.addClass("current").attr("aria-expanded","true"),t.closest("li.toctree-l1").parent().addClass("current").attr("aria-expanded","true");for(let n=1;n<=10;n++)t.closest("li.toctree-l"+n).addClass("current").attr("aria-expanded","true");t[0].scrollIntoView()}}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return n=i+o,r}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()}).call(window)},function(n,e){n.exports=jQuery},function(n,e,t){}]);
\ No newline at end of file
diff --git a/_static/js/versions.js b/_static/js/versions.js
new file mode 100644
index 0000000..818bc99
--- /dev/null
+++ b/_static/js/versions.js
@@ -0,0 +1,224 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = "True";
+const themeLanguageSelector = "True";
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${config.projects.translations
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 0000000..c7fe6c6
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,192 @@
+/*
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
index 0000000..d96755f
Binary files /dev/null and b/_static/minus.png differ
diff --git a/_static/nbsphinx-broken-thumbnail.svg b/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 0000000..4919ca8
--- /dev/null
+++ b/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
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new file mode 100644
index 0000000..a3fb27c
--- /dev/null
+++ b/_static/nbsphinx-code-cells.css
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+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
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+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
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+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
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+}
+
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+}
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+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
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+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
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+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
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+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
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new file mode 100644
index 0000000..365c27a
--- /dev/null
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+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
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+    max-width: 100%;
+    max-height: 100%;
+}
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+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
diff --git a/_static/nbsphinx-no-thumbnail.svg b/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 0000000..9dca758
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diff --git a/_static/pygments.css b/_static/pygments.css
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+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
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+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
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\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 0000000..2c774d1
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,632 @@
+/*
+ * Sphinx JavaScript utilities for the full-text search.
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename, kind] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+// Global search result kind enum, used by themes to style search results.
+class SearchResultKind {
+    static get index() { return  "index"; }
+    static get object() { return "object"; }
+    static get text() { return "text"; }
+    static get title() { return "title"; }
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename, kind] = item;
+
+  let listItem = document.createElement("li");
+  // Add a class representing the item's type:
+  // can be used by a theme's CSS selector for styling
+  // See SearchResultKind for the class names.
+  listItem.classList.add(`kind-${kind}`);
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = Documentation.ngettext(
+      "Search finished, found one page matching the search query.",
+      "Search finished, found ${resultCount} pages matching the search query.",
+      resultCount,
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.setAttribute("role", "list");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+            SearchResultKind.title,
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+            SearchResultKind.index,
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+        SearchResultKind.object,
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
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index 0000000..8a96c69
--- /dev/null
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diff --git a/cluster.html b/cluster.html
new file mode 100644
index 0000000..cc08409
--- /dev/null
+++ b/cluster.html
@@ -0,0 +1,222 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
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+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation Guide &mdash; CompactObject 1.9.1 documentation</title>
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+    <script src="_static/js/theme.js"></script>
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+<li class="toctree-l2"><a class="reference internal" href="#computation-time-scaling">Computation Time Scaling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#parallelization">Parallelization</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
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+            <a href="_sources/cluster.rst.txt" rel="nofollow"> View page source</a>
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+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-guide">
+<h1>Computation Guide<a class="headerlink" href="#computation-guide" title="Link to this heading"></a></h1>
+<section id="computation-time-scaling">
+<h2>Computation Time Scaling<a class="headerlink" href="#computation-time-scaling" title="Link to this heading"></a></h2>
+<p>Fully converged Bayesian inference requires numerous iterations to explore the entire prior parameter space. This process demands substantial computation time, which can be very expensive depending on the Equation of State (EOS) you are using.</p>
+<blockquote>
+<div><p><strong>1. Using Integrated EOS Models</strong></p>
+</div></blockquote>
+<p>If you are utilizing the EOS models integrated into this package, here is an overview of the computational resources required:</p>
+<ol class="loweralpha simple">
+<li><p><strong>Relativistic Mean Field (RMF) Model</strong></p>
+<ul class="simple">
+<li><p><strong>Optimization:</strong> Please use the Numba-accelerated version of EOS computation.</p></li>
+<li><p><strong>Resources:</strong> With one NICER observation constraint and utilizing <strong>256 CPU cores</strong>, the computation can be completed within approximately <strong>1 hour</strong>.</p></li>
+<li><p><strong>Scalability:</strong> The computation time will increase as you include more observations.</p></li>
+</ul>
+</li>
+<li><p><strong>Speed of Sound Model and Polytropic Model</strong></p>
+<ul class="simple">
+<li><p><strong>Nature:</strong> These are meta-models with explicit mathematical formulas, allowing for analytical computations.</p></li>
+<li><p><strong>Resources:</strong> Using <strong>24 CPU cores</strong>, the inference can be completed within <strong>6 hours</strong>.</p></li>
+<li><p><strong>Scalability:</strong> The computation time scales with the number of observations included.</p></li>
+</ul>
+</li>
+<li><p><strong>Strangeon Model and MIT Bag Model</strong></p>
+<ul class="simple">
+<li><p><strong>Nature:</strong> Although these are physics-based models, the computations are relatively straightforward and fast.</p></li>
+<li><p><strong>Resources:</strong> On a laptop with <strong>24 cores</strong>, the computation can be completed within <strong>5-6 hours</strong>.</p></li>
+<li><p><strong>Scalability:</strong> The computation time increases with the addition of more observations.</p></li>
+</ul>
+</li>
+</ol>
+<p><strong>2. Using Custom EOS Models</strong></p>
+<p>If you are defining your own EOS and wish to perform inference using this package:</p>
+<ul class="simple">
+<li><p><strong>Performance Requirement:</strong> Ensure that the computation time for your EOS is <strong>less than 1 second</strong> per evaluation. Otherwise, the overall computation will become prohibitively expensive due to the large number of samples required for Bayesian inference.</p></li>
+</ul>
+</section>
+<section id="parallelization">
+<h2>Parallelization<a class="headerlink" href="#parallelization" title="Link to this heading"></a></h2>
+<p>Given the computational intensity of Bayesian inference, parallelization strategies are essential to optimize performance. Below are the steps to parallelize computations using <strong>UltraNest</strong> without modifying the package’s code.</p>
+<p><strong>Requirements</strong></p>
+<ul class="simple">
+<li><p><strong>OpenMPI:</strong> Ensure that OpenMPI is installed and available on your system.</p></li>
+<li><p><strong>Python Packages:</strong> Install <cite>h5py</cite> and <cite>mpi4py</cite>.</p></li>
+</ul>
+<p><strong>Steps to Parallelize</strong></p>
+<ol class="arabic">
+<li><p><strong>Set the Number of Threads</strong></p>
+<p>Control the number of threads per process by setting the <cite>OMP_NUM_THREADS</cite> environment variable. For example, to set it to 1:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">OMP_NUM_THREADS</span><span class="o">=</span><span class="m">1</span>
+</pre></div>
+</div>
+</li>
+<li><p><strong>Run the Inference Script with MPI</strong></p>
+<p>Use <cite>mpiexec</cite> to execute your inference script across multiple cores. Replace <cite>inference.py</cite> with the name of your inference script.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpiexec<span class="w"> </span>-np<span class="w"> </span><span class="m">4</span><span class="w"> </span>python<span class="w"> </span>inference.py
+</pre></div>
+</div>
+<ul class="simple">
+<li><p><strong>Explanation:</strong> This command runs the inference using <strong>4 CPU cores</strong>. Adjust the <cite>-np</cite> value according to the number of available cores you wish to utilize.</p></li>
+</ul>
+</li>
+</ol>
+<p><strong>Recommendations</strong></p>
+<ul class="simple">
+<li><p><strong>Virtual Environment:</strong> Regardless of where you are running the code, it is highly recommended to use a virtual environment to manage your installations. This practice helps maintain dependencies and avoids conflicts with other projects.</p></li>
+</ul>
+<p><strong>Additional Resources</strong></p>
+<p>For more detailed information on performance and parallelization strategies with <strong>UltraNest</strong>, refer to the <a class="reference external" href="https://johannesbuchner.github.io/UltraNest/performance.html">UltraNest Performance Page</a>.</p>
+</section>
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diff --git a/crust_EOS.html b/crust_EOS.html
new file mode 100644
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Crust Equation of state interpolation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#EOSgenerators.crust_EOS.PolyInterpolate"><code class="docutils literal notranslate"><span class="pre">PolyInterpolate()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
+        </div>
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+            <a href="_sources/crust_EOS.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="crust-equation-of-state-interpolation">
+<span id="crust-eos-conect"></span><h1>Crust Equation of state interpolation<a class="headerlink" href="#crust-equation-of-state-interpolation" title="Link to this heading"></a></h1>
+<p>Function to connect the crust EoS given by user with the core part by function calls</p>
+<dl class="py function" id="module-EOSgenerators.crust_EOS">
+<dt class="sig sig-object py" id="EOSgenerators.crust_EOS.PolyInterpolate">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.crust_EOS.</span></span><span class="sig-name descname"><span class="pre">PolyInterpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps_crust</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure_crust</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/crust_EOS.html#PolyInterpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.crust_EOS.PolyInterpolate" title="Link to this definition"></a></dt>
+<dd><p>Polytrope connecting crust part of equation of state to core part</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eps_crust</strong> (<em>array</em>) – the energy density of crust EoS in MeV/fm3, times a G/c**2 factor</p></li>
+<li><p><strong>pres_crust</strong> (<em>array</em>) – the pressure from crust EoS model in MeV/fm3, times a G/c**4 factor</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>EOS ingredient, combined crust and inter-crust part  energy density in
+MeV/fm3, times a G/c**2 factor
+pres_combine (float): EOS ingredient, combined crust and inter-crust part pressure in MeV/fm3,
+times a G/c**2 factor</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>eps_combine (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">RMF EOS solver (numba speed up version)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#EOSgenerators.fastRMF_EoS.Energy_density_Pressure"><code class="docutils literal notranslate"><span class="pre">Energy_density_Pressure()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#EOSgenerators.fastRMF_EoS.compute_EOS"><code class="docutils literal notranslate"><span class="pre">compute_EOS()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#EOSgenerators.fastRMF_EoS.initial_values"><code class="docutils literal notranslate"><span class="pre">initial_values()</span></code></a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+            <a href="_sources/fastRMF_EoS.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="rmf-eos-solver-numba-speed-up-version">
+<span id="fast-rmf-eos"></span><h1>RMF EOS solver (numba speed up version)<a class="headerlink" href="#rmf-eos-solver-numba-speed-up-version" title="Link to this heading"></a></h1>
+<p>Functions to compute RMF Equation of state from given parameters. (numba speed up)</p>
+<dl class="py function" id="module-EOSgenerators.fastRMF_EoS">
+<dt class="sig sig-object py" id="EOSgenerators.fastRMF_EoS.Energy_density_Pressure">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.fastRMF_EoS.</span></span><span class="sig-name descname"><span class="pre">Energy_density_Pressure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/fastRMF_EoS.html#Energy_density_Pressure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.fastRMF_EoS.Energy_density_Pressure" title="Link to this definition"></a></dt>
+<dd><p>Generate pressure and energy density two EOS ingredient from given RMF term and given parameters,
+(fast version)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array</em>) – An array that consists of the initial values of sigma, omega, rho, and chemical</p></li>
+<li><p><strong>function.</strong> (<em>potential obtained from the initial_values</em>)</p></li>
+<li><p><strong>rho</strong> (<em>float</em>) – The central density from which the computation of the equation of state begins.</p></li>
+<li><p><strong>theta</strong> (<em>array</em>) – An array representing the parameters used to determine a RMF model in the</p></li>
+<li><p><strong>case</strong> (<em>Lagrangian. In this</em>)</p></li>
+<li><p><strong>parameters.</strong> (<em>the RMF model is defined by 10</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>EOS ingredient, energy density in g/cm3
+pressure (float): EOS ingredient, pressure in dyn/cm3</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>energy_density (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="EOSgenerators.fastRMF_EoS.compute_EOS">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.fastRMF_EoS.</span></span><span class="sig-name descname"><span class="pre">compute_EOS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps_crust</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pres_crust</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/fastRMF_EoS.html#compute_EOS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.fastRMF_EoS.compute_EOS" title="Link to this definition"></a></dt>
+<dd><p>Generate core part equation of state, main function, from RMF model (fast version)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eps_crust</strong> (<em>array</em>) – the energy density of crust EoS in MeV/fm3, times a G/c**2 factor</p></li>
+<li><p><strong>pres_crust</strong> (<em>array</em>) – the pressure from crust EoS model in MeV/fm3, times a G/c**4 factor</p></li>
+<li><p><strong>theta</strong> (<em>array</em>) – An array representing the parameters used to determine a RMF model in the</p></li>
+<li><p><strong>case</strong> (<em>Lagrangian. In this</em>)</p></li>
+<li><p><strong>parameters.</strong> (<em>the RMF model is defined by 10</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>EOS ingredient, energy density in g/cm3
+pressure (float): EOS ingredient, pressure in dyn/cm3</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>energy_density (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="EOSgenerators.fastRMF_EoS.initial_values">
+<span class="sig-prename descclassname"><span class="pre">EOSgenerators.fastRMF_EoS.</span></span><span class="sig-name descname"><span class="pre">initial_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rho</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EOSgenerators/fastRMF_EoS.html#initial_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#EOSgenerators.fastRMF_EoS.initial_values" title="Link to this definition"></a></dt>
+<dd><p>Outputs the the sigma, omega, rho term and chemical potential of electron and neutron at
+given initial density (fast version)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rho</strong> (<em>float</em>) – given nuclear density</p></li>
+<li><p><strong>theta</strong> (<em>array</em>) – paramters of determine a RMF model in lagrangian, here we have 10 parameters.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>sigma term in lagrangian
+omega (float): omega term in lagrangian
+rho_03 (float): rho term in lagrangian
+mu_n (float): chemical potential of neutron matter
+mu_e (float): chemical potential of electron portion</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>sigma (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="publications.html">Publications</a></li>
+<li class="toctree-l1"><a class="reference internal" href="cluster.html">Computation Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#C"><strong>C</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#J"><strong>J</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ 
+</div>
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    CompactObject
+
+      <ul>
+        <li><a href="index.html#module-CompactObject">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.compute_EOS">compute_EOS() (in module EOSgenerators.fastRMF_EoS)</a>
+
+      <ul>
+        <li><a href="RMF_EOS.html#EOSgenerators.RMF_EOS.compute_EOS">(in module EOSgenerators.RMF_EOS)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.Energy_density_Pressure">Energy_density_Pressure() (in module EOSgenerators.fastRMF_EoS)</a>
+
+      <ul>
+        <li><a href="RMF_EOS.html#EOSgenerators.RMF_EOS.Energy_density_Pressure">(in module EOSgenerators.RMF_EOS)</a>
+</li>
+      </ul></li>
+      <li><a href="EoS_import.html#TOVsolver.EoS_import.EOS_check">EOS_check() (in module TOVsolver.EoS_import)</a>
+</li>
+      <li><a href="EoS_import.html#TOVsolver.EoS_import.EOS_import">EOS_import() (in module TOVsolver.EoS_import)</a>
+</li>
+      <li>
+    EOSgenerators.crust_EOS
+
+      <ul>
+        <li><a href="crust_EOS.html#module-EOSgenerators.crust_EOS">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    EOSgenerators.fastRMF_EoS
+
+      <ul>
+        <li><a href="fastRMF_EoS.html#module-EOSgenerators.fastRMF_EoS">module</a>
+</li>
+      </ul></li>
+      <li>
+    EOSgenerators.RMF_EOS
+
+      <ul>
+        <li><a href="RMF_EOS.html#module-EOSgenerators.RMF_EOS">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="EoS_import.html#TOVsolver.EoS_import.file_read">file_read() (in module TOVsolver.EoS_import)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="prior.html#InferenceWorkflow.prior.flat_prior">flat_prior() (in module InferenceWorkflow.prior)</a>
+</li>
+      <li><a href="RMF_EOS.html#EOSgenerators.RMF_EOS.functie">functie() (in module EOSgenerators.RMF_EOS)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    InferenceWorkflow.BayesianSampler
+
+      <ul>
+        <li><a href="BayesianSampler.html#module-InferenceWorkflow.BayesianSampler">module</a>
+</li>
+      </ul></li>
+      <li>
+    InferenceWorkflow.Likelihood
+
+      <ul>
+        <li><a href="Likelihood.html#module-InferenceWorkflow.Likelihood">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    InferenceWorkflow.prior
+
+      <ul>
+        <li><a href="prior.html#module-InferenceWorkflow.prior">module</a>
+</li>
+      </ul></li>
+      <li><a href="fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.initial_values">initial_values() (in module EOSgenerators.fastRMF_EoS)</a>
+
+      <ul>
+        <li><a href="RMF_EOS.html#EOSgenerators.RMF_EOS.initial_values">(in module EOSgenerators.RMF_EOS)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="J">J</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="Likelihood.html#InferenceWorkflow.Likelihood.Jliklihood">Jliklihood() (in module InferenceWorkflow.Likelihood)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="Likelihood.html#InferenceWorkflow.Likelihood.Kliklihood">Kliklihood() (in module InferenceWorkflow.Likelihood)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="Likelihood.html#InferenceWorkflow.Likelihood.Lliklihood">Lliklihood() (in module InferenceWorkflow.Likelihood)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="TOV_solver.html#TOVsolver.solver_code.m1_from_mc_m2">m1_from_mc_m2() (in module TOVsolver.solver_code)</a>
+</li>
+      <li><a href="Likelihood.html#InferenceWorkflow.Likelihood.Masslikihood_Gaussian">Masslikihood_Gaussian() (in module InferenceWorkflow.Likelihood)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="index.html#module-CompactObject">CompactObject</a>
+</li>
+        <li><a href="crust_EOS.html#module-EOSgenerators.crust_EOS">EOSgenerators.crust_EOS</a>
+</li>
+        <li><a href="fastRMF_EoS.html#module-EOSgenerators.fastRMF_EoS">EOSgenerators.fastRMF_EoS</a>
+</li>
+        <li><a href="RMF_EOS.html#module-EOSgenerators.RMF_EOS">EOSgenerators.RMF_EOS</a>
+</li>
+        <li><a href="BayesianSampler.html#module-InferenceWorkflow.BayesianSampler">InferenceWorkflow.BayesianSampler</a>
+</li>
+        <li><a href="Likelihood.html#module-InferenceWorkflow.Likelihood">InferenceWorkflow.Likelihood</a>
+</li>
+        <li><a href="prior.html#module-InferenceWorkflow.prior">InferenceWorkflow.prior</a>
+</li>
+        <li><a href="EoS_import.html#module-TOVsolver.EoS_import">TOVsolver.EoS_import</a>
+</li>
+        <li><a href="main.html#module-TOVsolver.main">TOVsolver.main</a>
+</li>
+        <li><a href="TOV_solver.html#module-TOVsolver.solver_code">TOVsolver.solver_code</a>
+</li>
+        <li><a href="speed_of_sound.html#module-TOVsolver.speed_of_sound">TOVsolver.speed_of_sound</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="Likelihood.html#InferenceWorkflow.Likelihood.MRlikihood_Gaussian">MRlikihood_Gaussian() (in module InferenceWorkflow.Likelihood)</a>
+</li>
+      <li><a href="Likelihood.html#InferenceWorkflow.Likelihood.MRlikihood_kernel">MRlikihood_kernel() (in module InferenceWorkflow.Likelihood)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="prior.html#InferenceWorkflow.prior.normal_Prior">normal_Prior() (in module InferenceWorkflow.prior)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="main.html#TOVsolver.main.OutputC_s">OutputC_s() (in module TOVsolver.main)</a>
+</li>
+      <li><a href="main.html#TOVsolver.main.OutputMR">OutputMR() (in module TOVsolver.main)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="main.html#TOVsolver.main.OutputMRpoint">OutputMRpoint() (in module TOVsolver.main)</a>
+</li>
+      <li><a href="main.html#TOVsolver.main.OutputMRT">OutputMRT() (in module TOVsolver.main)</a>
+</li>
+      <li><a href="main.html#TOVsolver.main.OutputMRTpoint">OutputMRTpoint() (in module TOVsolver.main)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="crust_EOS.html#EOSgenerators.crust_EOS.PolyInterpolate">PolyInterpolate() (in module EOSgenerators.crust_EOS)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="TOV_solver.html#TOVsolver.solver_code.solveTOV">solveTOV() (in module TOVsolver.solver_code)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="TOV_solver.html#TOVsolver.solver_code.solveTOV_tidal">solveTOV_tidal() (in module TOVsolver.solver_code)</a>
+</li>
+      <li><a href="speed_of_sound.html#TOVsolver.speed_of_sound.speed_of_sound_calc">speed_of_sound_calc() (in module TOVsolver.speed_of_sound)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="TOV_solver.html#TOVsolver.solver_code.tidal_deformability">tidal_deformability() (in module TOVsolver.solver_code)</a>
+</li>
+      <li><a href="Likelihood.html#InferenceWorkflow.Likelihood.TidalLikihood_kernel">TidalLikihood_kernel() (in module InferenceWorkflow.Likelihood)</a>
+</li>
+      <li><a href="TOV_solver.html#TOVsolver.solver_code.TOV_def">TOV_def() (in module TOVsolver.solver_code)</a>
+</li>
+      <li>
+    TOVsolver.EoS_import
+
+      <ul>
+        <li><a href="EoS_import.html#module-TOVsolver.EoS_import">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    TOVsolver.main
+
+      <ul>
+        <li><a href="main.html#module-TOVsolver.main">module</a>
+</li>
+      </ul></li>
+      <li>
+    TOVsolver.solver_code
+
+      <ul>
+        <li><a href="TOV_solver.html#module-TOVsolver.solver_code">module</a>
+</li>
+      </ul></li>
+      <li>
+    TOVsolver.speed_of_sound
+
+      <ul>
+        <li><a href="speed_of_sound.html#module-TOVsolver.speed_of_sound">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="BayesianSampler.html#InferenceWorkflow.BayesianSampler.UltranestSampler">UltranestSampler() (in module InferenceWorkflow.BayesianSampler)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="BayesianSampler.html#InferenceWorkflow.BayesianSampler.UltranestSamplerResume">UltranestSamplerResume() (in module InferenceWorkflow.BayesianSampler)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
+           </div>
+          </div>
+          <footer>
+
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+
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+    <p>&#169; Copyright 2023, Chun Huang, Nicole Obsborn, Nathan Whitsett.</p>
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+  <section id="compactobject-package-tutorials">
+<span id="readme"></span><span id="module-CompactObject"></span><h1>CompactObject Package Tutorials<a class="headerlink" href="#compactobject-package-tutorials" title="Link to this heading"></a></h1>
+<p><strong>An Open-Source Package for Neutron Stars</strong>
+<strong>Comprehensive Workflow for Bayesian Inference Constraining</strong>
+<strong>Neutron Star EOS Package</strong></p>
+<p><strong>CompactObject</strong> is an open-source package designed to perform Bayesian inference
+on neutron star equation of state (EOS) constraints. It offers a comprehensive workflow
+that integrates astrophysical observations and nuclear measurements to explore the interior
+composition of neutron stars. The package is built to be user-friendly, easily extendable,
+and thoroughly documented, making it an essential tool for researchers in nuclear astrophysics.
+The package integrates three independent modules:</p>
+<ol class="arabic simple">
+<li><p><strong>Tolman–Oppenheimer–Volkoff (TOV) Equation Solver</strong>
+A user-friendly solver for determining neutron star structure based on a given EOS.</p></li>
+<li><p><strong>Neutron Star EOS Generator</strong>
+Generates EOS across the full density range. Currently, it includes the relativistic mean
+field theory EOS solver and will support polytropes and additional EOS models in the future.</p></li>
+<li><p><strong>Bayesian Inference Workflow</strong>
+A complete package for constraining the EOS of neutron stars. This includes defining the
+likelihood from observations (X-ray timing, gravitational waves, radio timing) and nuclear
+experiments (nuclear quantities, pQCD, chiral EFT), as well as from simulated astrophysical
+observations. It also involves defining priors for the parameters and running nested sampling
+of the posterior space.</p></li>
+</ol>
+<p>These three components are independent, allowing users to integrate them into their workflows
+seamlessly. The package’s functionality extends beyond inference studies, and we welcome
+contributions of new features.</p>
+<p>For installation instructions, please refer to the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/installation.html">Installation Page</a>.</p>
+<p><strong>Publications</strong></p>
+<p>Papers generated using this package include:</p>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id1" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., Raaijmakers, G., Watts, A. L., Tolos, L., &amp; Providência, C.</strong> (2024).
+<em>Constraining fundamental nuclear physics parameters using neutron star mass-radius measurements I: Nucleonic models</em>.
+<em>Monthly Notices of the Royal Astronomical Society</em>, 529.
+<a class="reference external" href="https://academic.oup.com/mnras/article/529/4/4650/7634362">DOI:10.1093/mnras/stae844</a></p>
+</aside>
+<aside class="footnote brackets" id="id2" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., Tolos, L., Providência, C., &amp; Watts, A.</strong> (2024).
+<em>Constraining a relativistic mean field model using neutron star mass-radius measurements II: Hyperonic models</em>.
+<em>arXiv preprint arXiv:2410.14572</em>.
+<a class="reference external" href="https://arxiv.org/abs/2410.14572">https://arxiv.org/abs/2410.14572</a></p>
+</aside>
+<aside class="footnote brackets" id="id3" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., &amp; Zheng, X.-P.</strong> (2024).
+<em>Bayesian Insights into post-Glitch Dynamics: Model comparison and parameter constraint from decades-long observation data of the Crab pulsar</em>.
+<em>arXiv preprint arXiv:2409.18432</em>.
+<a class="reference external" href="https://arxiv.org/abs/2409.18432">https://arxiv.org/abs/2409.18432</a></p>
+</aside>
+</aside>
+<p>If you use our software, please consider citing us with the following standard citation:</p>
+<p>The inference conducted here relies on the framework in the <span class="math notranslate nohighlight">\(\textit{CompactObject}\)</span> package <a class="reference internal" href="publications.html#compactobject" id="id4"><span>[CompactObject]</span></a>.
+This is an open-source, full-scope package designed to implement Bayesian constraints on the neutron star EOS. Other work based on this package is ….</p>
+<section id="concept">
+<h2>Concept<a class="headerlink" href="#concept" title="Link to this heading"></a></h2>
+<p>Bayesian inference studies of the neutron star equation of state have become a trending
+field, particularly due to significant advancements such as the Neutron Star
+Interior Composition Explorer (NICER) measuring neutron star masses and radii through X-ray timing, and the detection of neutron star merger events through gravitational wave observations by the LIGO detector.</p>
+<p>Below is the overall pipeline of this field:</p>
+<img alt="Workflow Diagram" src="_images/workflow.png" />
+<p>As depicted in this diagram, fundamental physics provides the EOS for neutron stars. By inputting the EOS into the Tolman–Oppenheimer–Volkoff (TOV) equation, we can obtain parameters related to the neutron star structure, such as mass, radius, and tidal properties. Instruments like NICER and LIGO measure these properties, providing data for Bayesian inference. This process constrains the EOS by determining the region in the Mass-Radius space where neutron stars can exist, ultimately offering insights into the fundamental composition of their interiors.</p>
+</section>
+<section id="equation-of-state">
+<h2>Equation of State<a class="headerlink" href="#equation-of-state" title="Link to this heading"></a></h2>
+<p>The neutron star equation of state (EOS) plays a crucial role in determining the composition of a neutron star. It is closely connected to the microphysical properties of neutron stars. Here, we introduce an EOS derived from the Relativistic Mean Field (RMF) theory. The Lagrangian of this model is represented as follows:</p>
+<div class="math notranslate nohighlight">
+\[\mathcal{L} = \sum_N \mathcal{L}_N + \mathcal{L}_{\mathcal{M}} + \sum_l \mathcal{L}_l\]</div>
+<p>Where:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\mathcal{L}_N\)</span> is the nucleonic Lagrangian</p></li>
+<li><p><span class="math notranslate nohighlight">\(\mathcal{L}_{\mathcal{M}}\)</span> is the meson part of the Lagrangian</p></li>
+<li><p><span class="math notranslate nohighlight">\(\mathcal{L}_l\)</span> is the lepton Lagrangian</p></li>
+</ul>
+<p>Details of the Lagrangian are illustrated below:</p>
+<img alt="Lagrangian Diagram" src="_images/lagrangian.png" />
+<p>In the equation:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\Psi_{N}\)</span> and <span class="math notranslate nohighlight">\(\psi_{l}\)</span> are the nucleon and lepton spinors</p></li>
+<li><p><span class="math notranslate nohighlight">\(\bar{I}_{N}\)</span> is the nucleon isospin operator</p></li>
+<li><p><span class="math notranslate nohighlight">\(g\)</span> denotes the strong interaction coupling of a meson to a nucleon</p></li>
+<li><p><span class="math notranslate nohighlight">\(m\)</span> represents the masses of the nucleons, mesons, and leptons</p></li>
+<li><p>The parameters <span class="math notranslate nohighlight">\(\kappa\)</span>, <span class="math notranslate nohighlight">\(\lambda_0\)</span>, <span class="math notranslate nohighlight">\(\zeta\)</span>, and <span class="math notranslate nohighlight">\(\Lambda_{\omega}\)</span> are coupling constants to be determined by the inference method</p></li>
+</ul>
+<p>These free parameters represent the degrees of freedom in the RMF model and can be determined through nuclear experiments. Additionally, astrophysical observations can help constrain these parameters. The complete list of parameters includes:</p>
+<img alt="Free Parameters Diagram" src="_images/free_para.png" />
+<p>These parameters should be inputted to generate the EOS using our EOS generators module. Different EOS parameters will affect mass-radius relations, as demonstrated here. For more details on the physics, refer to <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/1996cost.book.....G/abstract">Glendenning (1996)</a>.</p>
+</section>
+<section id="tolmanoppenheimervolkoff-tov-equation">
+<h2>Tolman–Oppenheimer–Volkoff (TOV) Equation<a class="headerlink" href="#tolmanoppenheimervolkoff-tov-equation" title="Link to this heading"></a></h2>
+<p>The TOV equation is a general relativity equation that constrains the structure of a spherically symmetrical body under gravity. The original equation is:</p>
+<div class="math notranslate nohighlight">
+\[\frac{d P}{d r} = -\frac{G m}{r^2} \rho \left(1 + \frac{P}{\rho c^2}\right) \left(1 + \frac{4 \pi r^3 P}{m c^2}\right) \left(1 - \frac{2 G m}{r c^2}\right)^{-1}\]</div>
+<p>To solve this equation, the essential ingredient is the EOS. The basic strategy is as follows: at a given central density, input it into the neutron star EOS to obtain the pressure. Then, integrate the density from the center to the boundary, repeating this process across the entire possible density range.</p>
+<p>In our code, we provide two different functions:</p>
+<ol class="arabic simple">
+<li><p><strong>Default Solver:</strong>
+Solves the TOV equation within a predefined density range (log(14.3, 15.6) on a scale of 10).</p></li>
+<li><p><strong>Point-by-Point Solver:</strong>
+Allows solving the equation for any chosen central density range.</p></li>
+</ol>
+<p>For more information on the TOV equation, visit the <a class="reference external" href="https://en.wikipedia.org/wiki/Tolman–Oppenheimer–Volkoff_equation">Wikipedia page</a>.</p>
+</section>
+<section id="bayesian-inference">
+<h2>Bayesian Inference<a class="headerlink" href="#bayesian-inference" title="Link to this heading"></a></h2>
+<p>Using Bayesian inference tools to explore constraints on the neutron star EOS is increasingly common. The foundational equation is Bayes’ Theorem:</p>
+<div class="math notranslate nohighlight">
+\[P(A \mid B) = \frac{P(B \mid A) P(A)}{P(B)}\]</div>
+<p>Where:</p>
+<ul class="simple">
+<li><p><strong>Posterior Probability</strong> (<span class="math notranslate nohighlight">\(P(A \mid B)\)</span>): The probability of hypothesis A after observing B.</p></li>
+<li><p><strong>Prior Probability</strong> (<span class="math notranslate nohighlight">\(P(A)\)</span>): The initial probability of hypothesis A before observing B.</p></li>
+<li><p><strong>Likelihood</strong> (<span class="math notranslate nohighlight">\(P(B \mid A)\)</span>): The probability of observing B given that hypothesis A is true.</p></li>
+<li><p><strong>Evidence</strong> (<span class="math notranslate nohighlight">\(P(B)\)</span>): The probability of observing B under all possible hypotheses.</p></li>
+</ul>
+<p>In this context:</p>
+<ul class="simple">
+<li><p><strong>Posterior</strong> is the updated probability after considering new observations or experiments.</p></li>
+<li><p><strong>Prior</strong> is the initial belief before new data is considered.</p></li>
+<li><p><strong>Likelihood</strong> is the influence of new data on the belief.</p></li>
+</ul>
+<p>The likelihood in our studies primarily comes from four sources:</p>
+<ol class="arabic simple">
+<li><p><strong>Mass-Radius Measurements from X-ray Timing</strong> (e.g., NICER)</p></li>
+<li><p><strong>Tidal Measurements from Gravitational Wave Detection</strong></p></li>
+<li><p><strong>Mass Measurements from Radio Timing</strong></p></li>
+<li><p><strong>Nuclear Physics Constraints from Experiments</strong></p></li>
+</ol>
+<p>NICER’s mass-radius measurements are a significant achievement in neutron star physics, comparable to gravitational wave detections. For the connection between our EOS and nuclear quantities, refer to <a class="reference external" href="https://journals.aps.org/prc/abstract/10.1103/PhysRevC.90.044305">Chen &amp; Piekarewicz (2014a)</a>.</p>
+<p>The nuclear physics quantities we consider are:</p>
+<ul class="simple">
+<li><p><strong>K</strong>: Compressibility of nuclear matter</p></li>
+<li><p><strong>J</strong>: Symmetry energy at saturation density</p></li>
+<li><p><strong>L</strong>: Slope of symmetry energy at saturation density</p></li>
+</ul>
+<p>There are more on constraining the EOS by Pure neutron matter (PNM):</p>
+<ul class="simple">
+<li><p><strong>chiral EFT constraint</strong></p></li>
+<li><p><strong>pQCD constraint</strong></p></li>
+</ul>
+<p>These can be computed from posterior samples (we will add the nuclear properties computation code in the near future). They also serve as independent constraints on the neutron star EOS.</p>
+<p>When performing real astrophysical sampling, it is crucial to sample the neutron star central density corresponding to each measurement used. For instance, if investigating the constraining effect of two mass-radius measurements on the EOS, you need to define two additional free parameters—the central densities for each measurement. Otherwise, the inference would not represent a full-scope EOS analysis. Therefore, our likelihood functions include a parameter <span class="math notranslate nohighlight">\(d_1\)</span>, representing the density parameter of each observation.</p>
+</section>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading"></a></h2>
+<p><strong>CompactObject</strong> provides a comprehensive suite of tools for Bayesian inference on neutron star EOS constraints. By integrating astrophysical observations with nuclear experiments, it facilitates a deeper understanding of neutron star interiors.</p>
+<p>For more information, tutorials, and support, please refer to our documentation or contact the development team.</p>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#using-anaconda-recommended">Using Anaconda (Recommended)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#optional-using-python-virtual-environment">Optional: Using Python Virtual Environment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#summary">Summary</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="publications.html">Publications</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="publications.html#citing-compactobject">Citing CompactObject</a></li>
+<li class="toctree-l2"><a class="reference internal" href="publications.html#acknowledgements">Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="publications.html#contact">Contact</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="cluster.html">Computation Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="cluster.html#computation-time-scaling">Computation Time Scaling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="cluster.html#parallelization">Parallelization</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="starthere.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="starthere.html#depends-on-your-scientific-goal">Depends on Your Scientific Goal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="starthere.html#acknowledgements">Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="starthere.html#contact">Contact</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="Contributor.html#introduction">Introduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Contributor.html#contributing-an-equation-of-state-eos">Contributing an Equation of State (EOS)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Contributor.html#contributing-inference-components">Contributing Inference Components</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Contributor.html#guidelines-for-key-functions-and-permissions">Guidelines for Key Functions and Permissions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Contributor.html#acknowledgements">Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Contributor.html#contact">Contact</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="test_EOSgenerators.html#RMF-EOS">RMF EOS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_EOSgenerators.html#Example-of-RMF">Example of RMF</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_EOSgenerators.html#Illustrating-the-power-of-Numba-accelerated-EoS-Generation">Illustrating the power of Numba accelerated EoS Generation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_EOSgenerators.html#Polytrope-EOS">Polytrope EOS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_EOSgenerators.html#Strangeon-matter-EOS">Strangeon matter EOS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_EOSgenerators.html#Speed-of-sound-EOS">Speed of sound EOS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_EOSgenerators.html#MIT-bag-model-EOS">MIT bag model EOS</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="test_Inference.html#Set-up-prior">Set up prior</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Inference.html#Set-up-likelihood">Set up likelihood</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Inference.html#Set-up-sampler">Set up sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Inference.html#Posterior-plot">Posterior plot</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html#Set-up-prior">Set up prior</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html#Set-up-likehood">Set up likehood</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html#Set-up-sampler">Set up sampler</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html#(a)-Import-packages">(a) Import packages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html#(b)-Set-up-priors">(b) Set up priors</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html#(c)-Set-up-likehood">(c) Set up likehood</a></li>
+<li class="toctree-l2"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html#(d)-Set-up-sampler">(d) Set up sampler</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="crust_EOS.html#EOSgenerators.crust_EOS.PolyInterpolate"><code class="docutils literal notranslate"><span class="pre">PolyInterpolate()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.Energy_density_Pressure"><code class="docutils literal notranslate"><span class="pre">Energy_density_Pressure()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.compute_EOS"><code class="docutils literal notranslate"><span class="pre">compute_EOS()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="fastRMF_EoS.html#EOSgenerators.fastRMF_EoS.initial_values"><code class="docutils literal notranslate"><span class="pre">initial_values()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="EoS_import.html#TOVsolver.EoS_import.EOS_check"><code class="docutils literal notranslate"><span class="pre">EOS_check()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="EoS_import.html#TOVsolver.EoS_import.EOS_import"><code class="docutils literal notranslate"><span class="pre">EOS_import()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="EoS_import.html#TOVsolver.EoS_import.file_read"><code class="docutils literal notranslate"><span class="pre">file_read()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="main.html#TOVsolver.main.OutputC_s"><code class="docutils literal notranslate"><span class="pre">OutputC_s()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="main.html#TOVsolver.main.OutputMR"><code class="docutils literal notranslate"><span class="pre">OutputMR()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="main.html#TOVsolver.main.OutputMRT"><code class="docutils literal notranslate"><span class="pre">OutputMRT()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="main.html#TOVsolver.main.OutputMRTpoint"><code class="docutils literal notranslate"><span class="pre">OutputMRTpoint()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="main.html#TOVsolver.main.OutputMRpoint"><code class="docutils literal notranslate"><span class="pre">OutputMRpoint()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="TOV_solver.html#TOVsolver.solver_code.TOV_def"><code class="docutils literal notranslate"><span class="pre">TOV_def()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="TOV_solver.html#TOVsolver.solver_code.m1_from_mc_m2"><code class="docutils literal notranslate"><span class="pre">m1_from_mc_m2()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="TOV_solver.html#TOVsolver.solver_code.solveTOV"><code class="docutils literal notranslate"><span class="pre">solveTOV()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="TOV_solver.html#TOVsolver.solver_code.solveTOV_tidal"><code class="docutils literal notranslate"><span class="pre">solveTOV_tidal()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="TOV_solver.html#TOVsolver.solver_code.tidal_deformability"><code class="docutils literal notranslate"><span class="pre">tidal_deformability()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="speed_of_sound.html#TOVsolver.speed_of_sound.speed_of_sound_calc"><code class="docutils literal notranslate"><span class="pre">speed_of_sound_calc()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="BayesianSampler.html#InferenceWorkflow.BayesianSampler.UltranestSampler"><code class="docutils literal notranslate"><span class="pre">UltranestSampler()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="BayesianSampler.html#InferenceWorkflow.BayesianSampler.UltranestSamplerResume"><code class="docutils literal notranslate"><span class="pre">UltranestSamplerResume()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="prior.html#InferenceWorkflow.prior.flat_prior"><code class="docutils literal notranslate"><span class="pre">flat_prior()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="prior.html#InferenceWorkflow.prior.normal_Prior"><code class="docutils literal notranslate"><span class="pre">normal_Prior()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="Likelihood.html#InferenceWorkflow.Likelihood.Jliklihood"><code class="docutils literal notranslate"><span class="pre">Jliklihood()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="Likelihood.html#InferenceWorkflow.Likelihood.Kliklihood"><code class="docutils literal notranslate"><span class="pre">Kliklihood()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="Likelihood.html#InferenceWorkflow.Likelihood.Lliklihood"><code class="docutils literal notranslate"><span class="pre">Lliklihood()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="Likelihood.html#InferenceWorkflow.Likelihood.MRlikihood_Gaussian"><code class="docutils literal notranslate"><span class="pre">MRlikihood_Gaussian()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="Likelihood.html#InferenceWorkflow.Likelihood.MRlikihood_kernel"><code class="docutils literal notranslate"><span class="pre">MRlikihood_kernel()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="Likelihood.html#InferenceWorkflow.Likelihood.Masslikihood_Gaussian"><code class="docutils literal notranslate"><span class="pre">Masslikihood_Gaussian()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="Likelihood.html#InferenceWorkflow.Likelihood.TidalLikihood_kernel"><code class="docutils literal notranslate"><span class="pre">TidalLikihood_kernel()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+</div>
+</section>
+<section id="indices-and-tables">
+<h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading"></a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
+<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
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+              <p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#using-anaconda-recommended">Using Anaconda (Recommended)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#optional-using-python-virtual-environment">Optional: Using Python Virtual Environment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#summary">Summary</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="publications.html">Publications</a></li>
+<li class="toctree-l1"><a class="reference internal" href="cluster.html">Computation Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+             
+  <section id="installation">
+<h1>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h1>
+<p>CompactObject is an open-source tool designed for comprehensive neutron star equation inference. It is built to be easy to install and use. Follow the step-by-step installation guide below to get started.</p>
+<section id="using-anaconda-recommended">
+<h2>Using Anaconda (Recommended)<a class="headerlink" href="#using-anaconda-recommended" title="Link to this heading"></a></h2>
+<ol class="arabic">
+<li><p><strong>Create a Virtual Environment</strong></p>
+<p>We recommend creating a virtual environment for CompactObject using Anaconda:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>CompactObject
+</pre></div>
+</div>
+<p>When prompted to proceed, type <cite>y</cite> and press Enter:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Proceed ([y]/n)?
+</pre></div>
+</div>
+</li>
+<li><p><strong>Activate the Environment</strong></p>
+<p>Activate your newly created environment with the following command:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>activate<span class="w"> </span>CompactObject
+</pre></div>
+</div>
+<p><strong>Note:</strong> Once you create this environment, you don’t need to create it again. Simply activate it whenever you want to use CompactObject.</p>
+</li>
+<li><p><strong>Install CompactObject</strong></p>
+<p>Use <cite>pip</cite> to install the CompactObject package:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>CompactObject-TOV
+</pre></div>
+</div>
+<p>To upgrade to the latest version, run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>CompactObject-TOV<span class="w"> </span>--upgrade
+</pre></div>
+</div>
+</li>
+<li><p><strong>You’re Ready to Use CompactObject!</strong></p>
+<p>You are now ready to use CompactObject. Whenever you want to use this package, remember to activate the environment first:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>activate<span class="w"> </span>CompactObject
+</pre></div>
+</div>
+<p>Our package automatically installs all necessary dependencies for you. The dependencies include:</p>
+<ul class="simple">
+<li><p><cite>corner</cite></p></li>
+<li><p><cite>csv</cite></p></li>
+<li><p><cite>itertools</cite></p></li>
+<li><p><cite>math</cite></p></li>
+<li><p><cite>matplotlib</cite></p></li>
+<li><p><cite>numba</cite></p></li>
+<li><p><cite>numbaminpack</cite></p></li>
+<li><p><cite>numpy</cite></p></li>
+<li><p><cite>os</cite></p></li>
+<li><p><cite>pandas</cite></p></li>
+<li><p><cite>scipy</cite></p></li>
+<li><p><cite>sys</cite></p></li>
+<li><p><cite>ultranest</cite></p></li>
+</ul>
+</li>
+</ol>
+<p>Some of the dependencies like numbaminpack may hard to install if don’t have a fortran complier, please
+refer to this page <cite>NumbaMinpack documentation &lt;https://pypi.org/project/NumbaMinpack/&gt;</cite>, and you can skip
+this NumbaMinpack if you are not using “fastRMF_EoS” and “pQCD”</p>
+</section>
+<section id="optional-using-python-virtual-environment">
+<h2>Optional: Using Python Virtual Environment<a class="headerlink" href="#optional-using-python-virtual-environment" title="Link to this heading"></a></h2>
+<p>If you are not using Anaconda, you can create a virtual environment using Python’s <cite>venv</cite> module:</p>
+<ol class="arabic">
+<li><p><strong>Create a Virtual Environment</strong></p>
+<p>Run the following command to create a virtual environment named <cite>CompactObject</cite>:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-m<span class="w"> </span>venv<span class="w"> </span>CompactObject
+</pre></div>
+</div>
+<p><em>You can specify a different path by replacing `CompactObject` with your desired directory name.</em></p>
+</li>
+<li><p><strong>Activate the Environment</strong></p>
+<p>Activate the virtual environment with:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">source</span><span class="w"> </span>CompactObject/bin/activate
+</pre></div>
+</div>
+</li>
+<li><p><strong>Install CompactObject</strong></p>
+<p>Once the environment is activated, install CompactObject using <cite>pip</cite>:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>CompactObject-TOV
+</pre></div>
+</div>
+<p>To upgrade to the latest version, run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>CompactObject-TOV<span class="w"> </span>--upgrade
+</pre></div>
+</div>
+</li>
+<li><p><strong>Using the Package</strong></p>
+<p>You are now ready to use CompactObject. Each time you want to use the package, ensure you activate the environment:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">source</span><span class="w"> </span>CompactObject/bin/activate
+</pre></div>
+</div>
+</li>
+</ol>
+</section>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading"></a></h2>
+<ul class="simple">
+<li><p><strong>Using Anaconda:</strong>
+1. Create and activate the <cite>CompactObject</cite> environment.
+2. Install CompactObject with <cite>pip</cite>.
+3. Activate the environment whenever you use the package.</p></li>
+<li><p><strong>Using Python Virtual Environment:</strong>
+1. Create and activate the <cite>CompactObject</cite> virtual environment.
+2. Install CompactObject with <cite>pip</cite>.
+3. Activate the environment whenever you use the package.</p></li>
+</ul>
+<p>If you encounter any issues or have questions, feel free to reach out for support. Happy computing!</p>
+</section>
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+
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+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
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+             
+  <section id="compactobject-package-tutorials">
+<span id="readme"></span><h1>CompactObject Package Tutorials<a class="headerlink" href="#compactobject-package-tutorials" title="Link to this heading"></a></h1>
+<p><strong>An Open-Source Package for Neutron Stars</strong>
+<strong>Comprehensive Workflow for Bayesian Inference Constraining</strong>
+<strong>Neutron Star EOS Package</strong></p>
+<p><strong>CompactObject</strong> is an open-source package designed to perform Bayesian inference
+on neutron star equation of state (EOS) constraints. It offers a comprehensive workflow
+that integrates astrophysical observations and nuclear measurements to explore the interior
+composition of neutron stars. The package is built to be user-friendly, easily extendable,
+and thoroughly documented, making it an essential tool for researchers in nuclear astrophysics.
+The package integrates three independent modules:</p>
+<ol class="arabic simple">
+<li><p><strong>Tolman–Oppenheimer–Volkoff (TOV) Equation Solver</strong>
+A user-friendly solver for determining neutron star structure based on a given EOS.</p></li>
+<li><p><strong>Neutron Star EOS Generator</strong>
+Generates EOS across the full density range. Currently, it includes the relativistic mean
+field theory EOS solver and will support polytropes and additional EOS models in the future.</p></li>
+<li><p><strong>Bayesian Inference Workflow</strong>
+A complete package for constraining the EOS of neutron stars. This includes defining the
+likelihood from observations (X-ray timing, gravitational waves, radio timing) and nuclear
+experiments (nuclear quantities, pQCD, chiral EFT), as well as from simulated astrophysical
+observations. It also involves defining priors for the parameters and running nested sampling
+of the posterior space.</p></li>
+</ol>
+<p>These three components are independent, allowing users to integrate them into their workflows
+seamlessly. The package’s functionality extends beyond inference studies, and we welcome
+contributions of new features.</p>
+<p>For installation instructions, please refer to the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/installation.html">Installation Page</a>.</p>
+<p><strong>Publications</strong></p>
+<p>Papers generated using this package include:</p>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id1" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., Raaijmakers, G., Watts, A. L., Tolos, L., &amp; Providência, C.</strong> (2024).
+<em>Constraining fundamental nuclear physics parameters using neutron star mass-radius measurements I: Nucleonic models</em>.
+<em>Monthly Notices of the Royal Astronomical Society</em>, 529.
+<a class="reference external" href="https://academic.oup.com/mnras/article/529/4/4650/7634362">DOI:10.1093/mnras/stae844</a></p>
+</aside>
+<aside class="footnote brackets" id="id2" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., Tolos, L., Providência, C., &amp; Watts, A.</strong> (2024).
+<em>Constraining a relativistic mean field model using neutron star mass-radius measurements II: Hyperonic models</em>.
+<em>arXiv preprint arXiv:2410.14572</em>.
+<a class="reference external" href="https://arxiv.org/abs/2410.14572">https://arxiv.org/abs/2410.14572</a></p>
+</aside>
+<aside class="footnote brackets" id="id3" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., &amp; Zheng, X.-P.</strong> (2024).
+<em>Bayesian Insights into post-Glitch Dynamics: Model comparison and parameter constraint from decades-long observation data of the Crab pulsar</em>.
+<em>arXiv preprint arXiv:2409.18432</em>.
+<a class="reference external" href="https://arxiv.org/abs/2409.18432">https://arxiv.org/abs/2409.18432</a></p>
+</aside>
+</aside>
+<p>If you use our software, please consider citing us with the following standard citation:</p>
+<p>The inference conducted here relies on the framework in the <span class="math notranslate nohighlight">\(\textit{CompactObject}\)</span> package <a class="reference internal" href="publications.html#compactobject" id="id4"><span>[CompactObject]</span></a>.
+This is an open-source, full-scope package designed to implement Bayesian constraints on the neutron star EOS. Other work based on this package is ….</p>
+<section id="concept">
+<h2>Concept<a class="headerlink" href="#concept" title="Link to this heading"></a></h2>
+<p>Bayesian inference studies of the neutron star equation of state have become a trending
+field, particularly due to significant advancements such as the Neutron Star
+Interior Composition Explorer (NICER) measuring neutron star masses and radii through X-ray timing, and the detection of neutron star merger events through gravitational wave observations by the LIGO detector.</p>
+<p>Below is the overall pipeline of this field:</p>
+<img alt="Workflow Diagram" src="_images/workflow.png" />
+<p>As depicted in this diagram, fundamental physics provides the EOS for neutron stars. By inputting the EOS into the Tolman–Oppenheimer–Volkoff (TOV) equation, we can obtain parameters related to the neutron star structure, such as mass, radius, and tidal properties. Instruments like NICER and LIGO measure these properties, providing data for Bayesian inference. This process constrains the EOS by determining the region in the Mass-Radius space where neutron stars can exist, ultimately offering insights into the fundamental composition of their interiors.</p>
+</section>
+<section id="equation-of-state">
+<h2>Equation of State<a class="headerlink" href="#equation-of-state" title="Link to this heading"></a></h2>
+<p>The neutron star equation of state (EOS) plays a crucial role in determining the composition of a neutron star. It is closely connected to the microphysical properties of neutron stars. Here, we introduce an EOS derived from the Relativistic Mean Field (RMF) theory. The Lagrangian of this model is represented as follows:</p>
+<div class="math notranslate nohighlight">
+\[\mathcal{L} = \sum_N \mathcal{L}_N + \mathcal{L}_{\mathcal{M}} + \sum_l \mathcal{L}_l\]</div>
+<p>Where:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\mathcal{L}_N\)</span> is the nucleonic Lagrangian</p></li>
+<li><p><span class="math notranslate nohighlight">\(\mathcal{L}_{\mathcal{M}}\)</span> is the meson part of the Lagrangian</p></li>
+<li><p><span class="math notranslate nohighlight">\(\mathcal{L}_l\)</span> is the lepton Lagrangian</p></li>
+</ul>
+<p>Details of the Lagrangian are illustrated below:</p>
+<img alt="Lagrangian Diagram" src="_images/lagrangian.png" />
+<p>In the equation:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\Psi_{N}\)</span> and <span class="math notranslate nohighlight">\(\psi_{l}\)</span> are the nucleon and lepton spinors</p></li>
+<li><p><span class="math notranslate nohighlight">\(\bar{I}_{N}\)</span> is the nucleon isospin operator</p></li>
+<li><p><span class="math notranslate nohighlight">\(g\)</span> denotes the strong interaction coupling of a meson to a nucleon</p></li>
+<li><p><span class="math notranslate nohighlight">\(m\)</span> represents the masses of the nucleons, mesons, and leptons</p></li>
+<li><p>The parameters <span class="math notranslate nohighlight">\(\kappa\)</span>, <span class="math notranslate nohighlight">\(\lambda_0\)</span>, <span class="math notranslate nohighlight">\(\zeta\)</span>, and <span class="math notranslate nohighlight">\(\Lambda_{\omega}\)</span> are coupling constants to be determined by the inference method</p></li>
+</ul>
+<p>These free parameters represent the degrees of freedom in the RMF model and can be determined through nuclear experiments. Additionally, astrophysical observations can help constrain these parameters. The complete list of parameters includes:</p>
+<img alt="Free Parameters Diagram" src="_images/free_para.png" />
+<p>These parameters should be inputted to generate the EOS using our EOS generators module. Different EOS parameters will affect mass-radius relations, as demonstrated here. For more details on the physics, refer to <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/1996cost.book.....G/abstract">Glendenning (1996)</a>.</p>
+</section>
+<section id="tolmanoppenheimervolkoff-tov-equation">
+<h2>Tolman–Oppenheimer–Volkoff (TOV) Equation<a class="headerlink" href="#tolmanoppenheimervolkoff-tov-equation" title="Link to this heading"></a></h2>
+<p>The TOV equation is a general relativity equation that constrains the structure of a spherically symmetrical body under gravity. The original equation is:</p>
+<div class="math notranslate nohighlight">
+\[\frac{d P}{d r} = -\frac{G m}{r^2} \rho \left(1 + \frac{P}{\rho c^2}\right) \left(1 + \frac{4 \pi r^3 P}{m c^2}\right) \left(1 - \frac{2 G m}{r c^2}\right)^{-1}\]</div>
+<p>To solve this equation, the essential ingredient is the EOS. The basic strategy is as follows: at a given central density, input it into the neutron star EOS to obtain the pressure. Then, integrate the density from the center to the boundary, repeating this process across the entire possible density range.</p>
+<p>In our code, we provide two different functions:</p>
+<ol class="arabic simple">
+<li><p><strong>Default Solver:</strong>
+Solves the TOV equation within a predefined density range (log(14.3, 15.6) on a scale of 10).</p></li>
+<li><p><strong>Point-by-Point Solver:</strong>
+Allows solving the equation for any chosen central density range.</p></li>
+</ol>
+<p>For more information on the TOV equation, visit the <a class="reference external" href="https://en.wikipedia.org/wiki/Tolman–Oppenheimer–Volkoff_equation">Wikipedia page</a>.</p>
+</section>
+<section id="bayesian-inference">
+<h2>Bayesian Inference<a class="headerlink" href="#bayesian-inference" title="Link to this heading"></a></h2>
+<p>Using Bayesian inference tools to explore constraints on the neutron star EOS is increasingly common. The foundational equation is Bayes’ Theorem:</p>
+<div class="math notranslate nohighlight">
+\[P(A \mid B) = \frac{P(B \mid A) P(A)}{P(B)}\]</div>
+<p>Where:</p>
+<ul class="simple">
+<li><p><strong>Posterior Probability</strong> (<span class="math notranslate nohighlight">\(P(A \mid B)\)</span>): The probability of hypothesis A after observing B.</p></li>
+<li><p><strong>Prior Probability</strong> (<span class="math notranslate nohighlight">\(P(A)\)</span>): The initial probability of hypothesis A before observing B.</p></li>
+<li><p><strong>Likelihood</strong> (<span class="math notranslate nohighlight">\(P(B \mid A)\)</span>): The probability of observing B given that hypothesis A is true.</p></li>
+<li><p><strong>Evidence</strong> (<span class="math notranslate nohighlight">\(P(B)\)</span>): The probability of observing B under all possible hypotheses.</p></li>
+</ul>
+<p>In this context:</p>
+<ul class="simple">
+<li><p><strong>Posterior</strong> is the updated probability after considering new observations or experiments.</p></li>
+<li><p><strong>Prior</strong> is the initial belief before new data is considered.</p></li>
+<li><p><strong>Likelihood</strong> is the influence of new data on the belief.</p></li>
+</ul>
+<p>The likelihood in our studies primarily comes from four sources:</p>
+<ol class="arabic simple">
+<li><p><strong>Mass-Radius Measurements from X-ray Timing</strong> (e.g., NICER)</p></li>
+<li><p><strong>Tidal Measurements from Gravitational Wave Detection</strong></p></li>
+<li><p><strong>Mass Measurements from Radio Timing</strong></p></li>
+<li><p><strong>Nuclear Physics Constraints from Experiments</strong></p></li>
+</ol>
+<p>NICER’s mass-radius measurements are a significant achievement in neutron star physics, comparable to gravitational wave detections. For the connection between our EOS and nuclear quantities, refer to <a class="reference external" href="https://journals.aps.org/prc/abstract/10.1103/PhysRevC.90.044305">Chen &amp; Piekarewicz (2014a)</a>.</p>
+<p>The nuclear physics quantities we consider are:</p>
+<ul class="simple">
+<li><p><strong>K</strong>: Compressibility of nuclear matter</p></li>
+<li><p><strong>J</strong>: Symmetry energy at saturation density</p></li>
+<li><p><strong>L</strong>: Slope of symmetry energy at saturation density</p></li>
+</ul>
+<p>There are more on constraining the EOS by Pure neutron matter (PNM):</p>
+<ul class="simple">
+<li><p><strong>chiral EFT constraint</strong></p></li>
+<li><p><strong>pQCD constraint</strong></p></li>
+</ul>
+<p>These can be computed from posterior samples (we will add the nuclear properties computation code in the near future). They also serve as independent constraints on the neutron star EOS.</p>
+<p>When performing real astrophysical sampling, it is crucial to sample the neutron star central density corresponding to each measurement used. For instance, if investigating the constraining effect of two mass-radius measurements on the EOS, you need to define two additional free parameters—the central densities for each measurement. Otherwise, the inference would not represent a full-scope EOS analysis. Therefore, our likelihood functions include a parameter <span class="math notranslate nohighlight">\(d_1\)</span>, representing the density parameter of each observation.</p>
+</section>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading"></a></h2>
+<p><strong>CompactObject</strong> provides a comprehensive suite of tools for Bayesian inference on neutron star EOS constraints. By integrating astrophysical observations with nuclear experiments, it facilitates a deeper understanding of neutron star interiors.</p>
+<p>For more information, tutorials, and support, please refer to our documentation or contact the development team.</p>
+</section>
+</section>
+
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+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Main functions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.main.OutputC_s"><code class="docutils literal notranslate"><span class="pre">OutputC_s()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.main.OutputMR"><code class="docutils literal notranslate"><span class="pre">OutputMR()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.main.OutputMRT"><code class="docutils literal notranslate"><span class="pre">OutputMRT()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.main.OutputMRTpoint"><code class="docutils literal notranslate"><span class="pre">OutputMRTpoint()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.main.OutputMRpoint"><code class="docutils literal notranslate"><span class="pre">OutputMRpoint()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+             
+  <section id="main-functions">
+<span id="main"></span><h1>Main functions<a class="headerlink" href="#main-functions" title="Link to this heading"></a></h1>
+<p>Controls most functionality of package by calling the following functions.
+* :ref:’EoS_importing’
+* :ref: ‘TOV_solving’
+* :ref: ‘__s’</p>
+<dl class="py function" id="module-TOVsolver.main">
+<dt class="sig sig-object py" id="TOVsolver.main.OutputC_s">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.main.</span></span><span class="sig-name descname"><span class="pre">OutputC_s</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/main.html#OutputC_s"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.main.OutputC_s" title="Link to this definition"></a></dt>
+<dd><dl class="simple">
+<dt>Calls function to open csv (if needed) and check equation of state validity.</dt><dd><p>Then calls function to calculate speed of sound.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file_name</strong> (<em>string</em><em>, </em><em>optional</em>) – string. CSV file to be opened.</p></li>
+<li><p><strong>density</strong> (<em>array</em><em>, </em><em>optional</em>) – numpy 1Darray. Passed into a check function and returned if valid.</p></li>
+<li><p><strong>pressure</strong> (<em>array</em><em>, </em><em>optional</em>) – numpy 1Darray. Passed into a check function and returned if valid.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy 1D array. List of speeds of sound.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>C_s (array)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.main.OutputMR">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.main.</span></span><span class="sig-name descname"><span class="pre">OutputMR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/main.html#OutputMR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.main.OutputMR" title="Link to this definition"></a></dt>
+<dd><p>Outputs the mass, radius, and tidal deformability
+:param file_name: string. CSV file to be opened.
+:type file_name: string, optional
+:param density: numpy 1Darray. Passed into a check function and returned if valid.
+:type density: array, optional
+:param pressure: numpy 1Darray. Passed into a check function and returned if valid.
+:type pressure: array, optional</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple with mass, radius.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>MR (tuple)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.main.OutputMRT">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.main.</span></span><span class="sig-name descname"><span class="pre">OutputMRT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/main.html#OutputMRT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.main.OutputMRT" title="Link to this definition"></a></dt>
+<dd><p>Outputs the mass, radius, and tidal deformability
+:param file_name: string. CSV file to be opened.
+:type file_name: string, optional
+:param density: numpy 1Darray. Passed into a check function and returned if valid.
+:type density: array, optional
+:param pressure: numpy 1Darray. Passed into a check function and returned if valid.
+:type pressure: array, optional</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple with mass, radius, and tidal deformability.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>MRT (tuple)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.main.OutputMRTpoint">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.main.</span></span><span class="sig-name descname"><span class="pre">OutputMRTpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">central_density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">energy_density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/main.html#OutputMRTpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.main.OutputMRTpoint" title="Link to this definition"></a></dt>
+<dd><p>Outputs the mass, radius, and tidal deformability (single point)
+:param central_density: central density that we want to compute
+:type central_density: float
+:param density: numpy 1Darray. Density of EoS
+:type density: array, optional
+:param pressure: numpy 1Darray. pressure of EoS
+:type pressure: array, optional</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple with mass, radius and tidal.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>MRT (tuple)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="TOVsolver.main.OutputMRpoint">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.main.</span></span><span class="sig-name descname"><span class="pre">OutputMRpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">central_density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">energy_density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/main.html#OutputMRpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.main.OutputMRpoint" title="Link to this definition"></a></dt>
+<dd><p>Outputs the mass, radius, and tidal deformability (single point)
+:param central_density: central density that we want to compute
+:type central_density: float
+:param density: numpy 1Darray. Density of EoS
+:type density: array, optional
+:param pressure: numpy 1Darray. pressure of EoS
+:type pressure: array, optional</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple with mass, radius.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>MR (tuple)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
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+<li class="toctree-l1"><a class="reference internal" href="publications.html">Publications</a></li>
+<li class="toctree-l1"><a class="reference internal" href="cluster.html">Computation Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Prior Definition Module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.prior.flat_prior"><code class="docutils literal notranslate"><span class="pre">flat_prior()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#InferenceWorkflow.prior.normal_Prior"><code class="docutils literal notranslate"><span class="pre">normal_Prior()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+  <section id="prior-definition-module">
+<span id="prior-definition"></span><h1>Prior Definition Module<a class="headerlink" href="#prior-definition-module" title="Link to this heading"></a></h1>
+<p>Functions to define prior distribution of parameters</p>
+<dl class="py function" id="module-InferenceWorkflow.prior">
+<dt class="sig sig-object py" id="InferenceWorkflow.prior.flat_prior">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.prior.</span></span><span class="sig-name descname"><span class="pre">flat_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">up</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/prior.html#flat_prior"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.prior.flat_prior" title="Link to this definition"></a></dt>
+<dd><p>Generate a flat prior distribution for a given parameter,</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>low</strong> (<em>float</em>) – lower bound of this flat distribution.</p></li>
+<li><p><strong>up</strong> (<em>float</em>) – upper bound of this flat distribution.</p></li>
+<li><p><strong>random</strong> (<em>float</em>) – random number generated to do inference, this is follow the</p></li>
+<li><p><strong>UltraNest</strong> (<em>definition</em><em> of </em><em>baysian workflow of</em>)</p></li>
+<li><p><strong>cube</strong><strong>[</strong><strong>i</strong><strong>]</strong> (<em>here default to be</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>ppf of this distribution function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ppf (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="InferenceWorkflow.prior.normal_Prior">
+<span class="sig-prename descclassname"><span class="pre">InferenceWorkflow.prior.</span></span><span class="sig-name descname"><span class="pre">normal_Prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">width</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/InferenceWorkflow/prior.html#normal_Prior"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#InferenceWorkflow.prior.normal_Prior" title="Link to this definition"></a></dt>
+<dd><p>Generate a normal prior distribution for a given parameter,</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>center</strong> (<em>float</em>) – center value of this gaussian distribution.</p></li>
+<li><p><strong>width</strong> (<em>float</em>) – width of this gaussian distribution, this is the 1-sigma width.</p></li>
+<li><p><strong>random</strong> (<em>float</em>) – random number generated to do inference, this is follow the</p></li>
+<li><p><strong>UltraNest</strong> (<em>definition</em><em> of </em><em>baysian workflow of</em>)</p></li>
+<li><p><strong>cube</strong><strong>[</strong><strong>i</strong><strong>]</strong> (<em>here default to be</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>ppf of this distribution function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ppf (float)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="BayesianSampler.html" class="btn btn-neutral float-left" title="Bayesian Inference smapler setup module" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Publications</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#citing-compactobject">Citing CompactObject</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#acknowledgements">Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#contact">Contact</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="cluster.html">Computation Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="publications">
+<h1>Publications<a class="headerlink" href="#publications" title="Link to this heading"></a></h1>
+<p><strong>CompactObject</strong> has been implemented in several studies and ongoing research projects. If your work is based on or has contributed to our package and you would like to be mentioned on this page, please send us an email at <a class="reference external" href="mailto:chun&#46;h&#37;&#52;&#48;wustl&#46;edu">chun<span>&#46;</span>h<span>&#64;</span>wustl<span>&#46;</span>edu</a>. This allows us to promote your work within our documentation, thereby extending the impact of the <strong>CompactObject</strong> package. Please ensure you specifically cite us if you are using this package, even if you utilize only one functionality, so that we can acknowledge your contribution. We greatly appreciate feedback and contributions to enhance our functionalities.</p>
+<p>The initial papers based on or contributing to this package are listed below:</p>
+<p><strong>1. Definition of RMF Models</strong></p>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id1" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., Raaijmakers, G., Watts, A. L., Tolos, L., &amp; Providência, C.</strong> (2024).
+<em>Constraining fundamental nuclear physics parameters using neutron star mass-radius measurements I: Nucleonic models</em>.
+<em>Monthly Notices of the Royal Astronomical Society</em>, 529.
+<a class="reference external" href="https://academic.oup.com/mnras/article/529/4/4650/7634362">DOI:10.1093/mnras/stae844</a></p>
+</aside>
+<aside class="footnote brackets" id="id2" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., Tolos, L., Providência, C., &amp; Watts, A.</strong> (2024).
+<em>Constraining a relativistic mean field model using neutron star mass-radius measurements II: Hyperonic models</em>.
+<em>arXiv preprint arXiv:2410.14572</em>.
+<a class="reference external" href="https://arxiv.org/abs/2410.14572">https://arxiv.org/abs/2410.14572</a></p>
+</aside>
+</aside>
+<p><strong>2. Application of Likelihood Functions and analysis glitch data</strong></p>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id3" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
+<p><strong>Huang, C., &amp; Zheng, X.-P.</strong> (2024).
+<em>Bayesian Insights into post-Glitch Dynamics: Model comparison and parameter constraint from decades-long observation data of the Crab pulsar</em>.
+<em>arXiv preprint arXiv:2409.18432</em>.
+<a class="reference external" href="https://arxiv.org/abs/2409.18432">https://arxiv.org/abs/2409.18432</a></p>
+</aside>
+</aside>
+<p><strong>3. Strangeon Star EOS Inference</strong></p>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id4" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></span>
+<p><strong>Yuan, W.-L., Huang, C., Zhang, C., Zhou, E.-P., &amp; Xu, R.-X.</strong> (2024).
+<em>Bayesian inference of strangeon matter equation of state using the new NICER mass-radius measurements J0437-4751 and GW190814 data</em>.
+<em>In preparation</em>.</p>
+</aside>
+</aside>
+<p><strong>4. pQCD Computation Implementation</strong></p>
+<span class="target" id="pqcd-publications"></span><aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></span>
+<p><strong>Gorda, T., Komoltsev, O., &amp; Kurkela, A.</strong>
+<em>Ab-initio QCD Calculations Impact the Inference of the Neutron-star-matter Equation of State</em>.
+e-Print: [2204.11877 [nucl-th]](<a class="reference external" href="https://arxiv.org/abs/2204.11877">https://arxiv.org/abs/2204.11877</a>)
+DOI: [10.3847/1538-4357/acce3a](<a class="reference external" href="https://doi.org/10.3847/1538-4357/acce3a">https://doi.org/10.3847/1538-4357/acce3a</a>)
+Published in: <em>Astrophysical Journal</em>, 950 (2023) 2, 107</p>
+</aside>
+</aside>
+<p><strong>5. Chiral Effective Field Theory Constraint Implementation</strong></p>
+<span class="target" id="chiral-eft-publications"></span><aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id6" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></span>
+<p><strong>Marquez, K.D., Malik, T., Pais, H., Menezes, D.P., &amp; Providência, C.</strong>
+<em>Nambu–Jona-Lasinio Description of Hadronic Matter from a Bayesian Approach</em>.
+e-Print: [2407.18452 [nucl-th]](<a class="reference external" href="https://arxiv.org/abs/2407.18452">https://arxiv.org/abs/2407.18452</a>)
+DOI: [10.1103/PhysRevD.110.063040](<a class="reference external" href="https://doi.org/10.1103/PhysRevD.110.063040">https://doi.org/10.1103/PhysRevD.110.063040</a>)
+Published in: <em>Physical Review D</em>, 110 (2024) 6, 063040</p>
+</aside>
+</aside>
+<p><strong>6. Density-dependent RMF Implementation</strong></p>
+<span class="target" id="density-function-publications"></span><aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id7" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>7<span class="fn-bracket">]</span></span>
+<p><strong>Malik, T., Ferreira, M., Agrawal, B.K., &amp; Providência, C.</strong> (2022).
+<em>Relativistic Description of Dense Matter Equation of State and Compatibility with Neutron Star Observables: A Bayesian Approach</em>.
+<em>Astrophysical Journal</em>, 930(1), 17.
+<a class="reference external" href="https://doi.org/10.3847/1538-4357/ac5d3c">https://doi.org/10.3847/1538-4357/ac5d3c</a></p>
+</aside>
+<aside class="footnote brackets" id="id8" role="doc-footnote">
+<span class="label"><span class="fn-bracket">[</span>8<span class="fn-bracket">]</span></span>
+<p><strong>Providência, C., Malik, T., Bastos Albino, M., &amp; Ferreira, M.</strong>
+<em>Neutron Star Equation of State: Identifying Hadronic Matter Characteristics</em>.
+e-Print: [2307.05086 [nucl-th]](<a class="reference external" href="https://arxiv.org/abs/2307.05086">https://arxiv.org/abs/2307.05086</a>)
+DOI: [10.1201/9781003306580-5](<a class="reference external" href="https://doi.org/10.1201/9781003306580-5">https://doi.org/10.1201/9781003306580-5</a>)</p>
+</aside>
+</aside>
+<p>If your work relates to pQCD computations, chiral effective field theory constraints, or density-function implementations within the <strong>CompactObject</strong> framework, please ensure to update the corresponding sections with your publications.</p>
+<section id="citing-compactobject">
+<h2>Citing CompactObject<a class="headerlink" href="#citing-compactobject" title="Link to this heading"></a></h2>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>If you use our software, please consider citing us with the following standard citation:
+
+The inference conducted here relies on the framework in the :math:`\textit{CompactObject}` package [CompactObject]_.
+This is an open-source, full-scope package designed to implement Bayesian constraints on the neutron star EOS. Other work based on this package is ....
+</pre></div>
+</div>
+<div role="list" class="citation-list">
+<div class="citation" id="compactobject" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>CompactObject<span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://chunhuangphy.github.io/CompactObject/">https://chunhuangphy.github.io/CompactObject/</a></p>
+</div>
+</div>
+</section>
+<section id="acknowledgements">
+<h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Link to this heading"></a></h2>
+<p>We welcome feedback and contributions to expand the functionalities of <strong>CompactObject</strong>. Your support helps enhance the tool for the entire research community.</p>
+</section>
+<section id="contact">
+<h2>Contact<a class="headerlink" href="#contact" title="Link to this heading"></a></h2>
+<p>For inquiries, contributions, or to be featured in our publications list, please contact us at <a class="reference external" href="mailto:chun&#46;h&#37;&#52;&#48;wustl&#46;edu">chun<span>&#46;</span>h<span>&#64;</span>wustl<span>&#46;</span>edu</a>.</p>
+</section>
+</section>
+
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+        <a href="installation.html" class="btn btn-neutral float-left" title="Installation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+
+   <h1>Python Module Index</h1>
+
+   <div class="modindex-jumpbox">
+   <a href="#cap-c"><strong>c</strong></a> | 
+   <a href="#cap-e"><strong>e</strong></a> | 
+   <a href="#cap-i"><strong>i</strong></a> | 
+   <a href="#cap-t"><strong>t</strong></a>
+   </div>
+
+   <table class="indextable modindextable">
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-c"><td></td><td>
+       <strong>c</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="index.html#module-CompactObject"><code class="xref">CompactObject</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-e"><td></td><td>
+       <strong>e</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-1" style="display: none" alt="-" /></td>
+       <td>
+       <code class="xref">EOSgenerators</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="crust_EOS.html#module-EOSgenerators.crust_EOS"><code class="xref">EOSgenerators.crust_EOS</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="fastRMF_EoS.html#module-EOSgenerators.fastRMF_EoS"><code class="xref">EOSgenerators.fastRMF_EoS</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="RMF_EOS.html#module-EOSgenerators.RMF_EOS"><code class="xref">EOSgenerators.RMF_EOS</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-i"><td></td><td>
+       <strong>i</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-2" style="display: none" alt="-" /></td>
+       <td>
+       <code class="xref">InferenceWorkflow</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="BayesianSampler.html#module-InferenceWorkflow.BayesianSampler"><code class="xref">InferenceWorkflow.BayesianSampler</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="Likelihood.html#module-InferenceWorkflow.Likelihood"><code class="xref">InferenceWorkflow.Likelihood</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="prior.html#module-InferenceWorkflow.prior"><code class="xref">InferenceWorkflow.prior</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-t"><td></td><td>
+       <strong>t</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-3" style="display: none" alt="-" /></td>
+       <td>
+       <code class="xref">TOVsolver</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="EoS_import.html#module-TOVsolver.EoS_import"><code class="xref">TOVsolver.EoS_import</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="main.html#module-TOVsolver.main"><code class="xref">TOVsolver.main</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="TOV_solver.html#module-TOVsolver.solver_code"><code class="xref">TOVsolver.solver_code</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="speed_of_sound.html#module-TOVsolver.speed_of_sound"><code class="xref">TOVsolver.speed_of_sound</code></a></td><td>
+       <em></em></td></tr>
+   </table>
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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diff --git a/searchindex.js b/searchindex.js
new file mode 100644
index 0000000..47b3fad
--- /dev/null
+++ b/searchindex.js
@@ -0,0 +1 @@
+Search.setIndex({"alltitles": {"(a) Comparing the time it takes to generate the EoS": [[23, "(a)-Comparing-the-time-it-takes-to-generate-the-EoS"]], "(a) Connecting outer crust with polytropic part": [[23, "(a)-Connecting-outer-crust-with-polytropic-part"]], "(a) Defining the EOS parameters": [[23, "(a)-Defining-the-EOS-parameters"], [23, "id2"]], "(a) Defining the parameter": [[23, "(a)-Defining-the-parameter"]], "(a) Import packages": [[21, "(a)-Import-packages"]], "(a) Load crust EOS and prepare interface EOS": [[23, "(a)-Load-crust-EOS-and-prepare-interface-EOS"]], "(b) Computing the mass-radius relation": [[23, "(b)-Computing-the-mass-radius-relation"]], "(b) Defining the paramters": [[23, "(b)-Defining-the-paramters"]], "(b) Set up priors": [[21, "(b)-Set-up-priors"]], "(b) Solve TOV with this EOS": [[23, "(b)-Solve-TOV-with-this-EOS"], [23, "id1"], [23, "id3"]], "(c) Connecting Core and crust EOS": [[23, "(c)-Connecting-Core-and-crust-EOS"]], "(c) Set up likehood": [[21, 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+<li class="toctree-l2"><a class="reference internal" href="#TOVsolver.speed_of_sound.speed_of_sound_calc"><code class="docutils literal notranslate"><span class="pre">speed_of_sound_calc()</span></code></a></li>
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+  <section id="calculate-speed-of-sound">
+<span id="c-s"></span><h1>Calculate Speed of Sound<a class="headerlink" href="#calculate-speed-of-sound" title="Link to this heading"></a></h1>
+<p>Calculates and returns speed of sound from an equaiton of state inputed by the user.</p>
+<dl class="py function" id="module-TOVsolver.speed_of_sound">
+<dt class="sig sig-object py" id="TOVsolver.speed_of_sound.speed_of_sound_calc">
+<span class="sig-prename descclassname"><span class="pre">TOVsolver.speed_of_sound.</span></span><span class="sig-name descname"><span class="pre">speed_of_sound_calc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">density</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pressure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TOVsolver/speed_of_sound.html#speed_of_sound_calc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TOVsolver.speed_of_sound.speed_of_sound_calc" title="Link to this definition"></a></dt>
+<dd><p>Function that calculates the speed of sound by taking the gradient of the euqation of state.
+:param density: numpy 1Darray.
+:type density: array
+:param pressure: numpy 1Darray.
+:type pressure: array</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy 1Darray.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>speed_of_sound (array)</p>
+</dd>
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+<li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#depends-on-your-scientific-goal">Depends on Your Scientific Goal</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#acknowledgements">Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#contact">Contact</a></li>
+</ul>
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+             
+  <section id="start-here">
+<h1>Start Here!<a class="headerlink" href="#start-here" title="Link to this heading"></a></h1>
+<p>Welcome to <strong>CompactObject</strong>! This guide will help you get started after successfully installing the package and activating your virtual environment. Follow the steps below to achieve your scientific goals using the functionalities of the <strong>CompactObject</strong> package.</p>
+<section id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"></a></h2>
+<ul class="simple">
+<li><p><strong>Package Installation:</strong> Ensure you have installed <strong>CompactObject</strong> successfully.</p></li>
+<li><p><strong>Virtual Environment:</strong> Activate your virtual environment where <strong>CompactObject</strong> is installed.</p></li>
+</ul>
+</section>
+<section id="depends-on-your-scientific-goal">
+<h2>Depends on Your Scientific Goal<a class="headerlink" href="#depends-on-your-scientific-goal" title="Link to this heading"></a></h2>
+<blockquote>
+<div><ol class="arabic simple">
+<li><p><strong>Understanding Basic Concepts</strong></p></li>
+</ol>
+</div></blockquote>
+<p>If you are new to <strong>CompactObject</strong> and wish to familiarize yourself with its basic concepts and introduction:</p>
+<ul class="simple">
+<li><p><strong>Index Page:</strong> Explore the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/index.html">Index Page</a> for an overview of the package.</p></li>
+<li><p><strong>README:</strong> Review the <a class="reference external" href="https://github.com/ChunHuangPhy/CompactObject/blob/main/README.md">README</a> on GitHub to understand the package’s purpose and features.</p></li>
+</ul>
+<blockquote>
+<div><ol class="arabic simple" start="2">
+<li><p><strong>Generating Equations of State (EOS)</strong></p></li>
+</ol>
+</div></blockquote>
+<p>To use <strong>CompactObject</strong> for generating equations of state:</p>
+<ul class="simple">
+<li><p><strong>EOS Generators Tutorial:</strong> Start with the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/test_EOSgenerators.html">EOS Generators Tutorial</a> which showcases all currently integrated EOS computations in the package. The supported EOS models include:
+- <strong>Relativistic Mean Field (RMF) Model</strong>
+- <strong>Strangeon Star Model</strong>
+- <strong>Quark Star Model:</strong> MIT Bag Model
+- <strong>Polytropic Model</strong>
+- <strong>Speed of Sound Model</strong>
+- <strong>Density-Dependent EOS Model</strong>
+- <em>…and more</em></p></li>
+</ul>
+<blockquote>
+<div><ol class="arabic simple" start="3">
+<li><p><strong>Using the Tolman–Oppenheimer–Volkoff (TOV) Solver</strong></p></li>
+</ol>
+</div></blockquote>
+<p>To utilize the TOV solver integrated within <strong>CompactObject</strong>:</p>
+<ul class="simple">
+<li><p><strong>TOV Solver Tutorial:</strong> Refer to the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/test_TOVsolver.html">TOV Solver Tutorial</a>. This tutorial demonstrates how to perform TOV computations using table-based EOS.</p></li>
+<li><p><strong>Inputting an Array:</strong> If you prefer to input an array instead of a table, refer back to the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/test_EOSgenerators.html">EOS Generators Tutorial</a>. The quantities currently available are:
+- <strong>Mass</strong>
+- <strong>Radius</strong>
+- <strong>Tidal Deformability</strong></p></li>
+</ul>
+<blockquote>
+<div><ol class="arabic simple" start="4">
+<li><p><strong>Performing Bayesian Inference on the EOS</strong></p></li>
+</ol>
+</div></blockquote>
+<p><strong>CompactObject</strong> offers robust tools for Bayesian inference related to neutron star EOS. There are two primary scenarios:</p>
+<blockquote>
+<div><ol class="loweralpha simple">
+<li><p><strong>Using Integrated EOS for Inference</strong></p></li>
+</ol>
+</div></blockquote>
+<p>If you wish to perform Bayesian inference using the EOS models already integrated into <strong>CompactObject</strong>, follow these examples:</p>
+<ol class="arabic simple">
+<li><p><strong>RMF EOS Inference Pipeline:</strong> Explore the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/test_Inference.html">RMF EOS Inference Pipeline</a>.</p></li>
+<li><p><strong>Strangeon EOS Inference Pipeline:</strong> Check out the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS Inference Pipeline</a>.</p></li>
+<li><p><em>…and more examples</em></p></li>
+</ol>
+<blockquote>
+<div><ol class="loweralpha simple" start="2">
+<li><p><strong>Using Your Own EOS for Inference</strong></p></li>
+</ol>
+</div></blockquote>
+<p>If you have developed your own EOS and wish to perform Bayesian inference:</p>
+<ol class="arabic simple">
+<li><p><strong>Contributor Instructions:</strong> Follow the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/Contributor.html">Instructions for Contributors</a> to define your EOS without conflicts and understand the necessary tests to ensure computational accuracy.</p></li>
+<li><p><strong>Constructing a New Pipeline:</strong> Utilize the existing inference pipelines, such as the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/test_Inference.html">RMF EOS Inference Pipeline</a> and <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS Inference Pipeline</a>, as reference points to build your own inference workflow based on <strong>CompactObject</strong>.</p></li>
+<li><p><strong>Performance Considerations:</strong> Ensure that the computation time for a single EOS is within the sub-second range to handle millions of samples required for Bayesian inference efficiently.</p></li>
+</ol>
+<p><strong>Contribution Invitation:</strong>
+Consider contributing your EOS to our community if you are using our package! This will boost the influence of your work and make you a collaborator on this project. We can also promote your results on our page. Please check the <a class="reference external" href="https://chunhuangphy.github.io/CompactObject/Contributor.html">Instructions for Contributors</a> if you wish to contribute.</p>
+<blockquote>
+<div><ol class="arabic simple" start="5">
+<li><p><strong>Performing Bayesian Inference in Other Fields</strong></p></li>
+</ol>
+</div></blockquote>
+<p>If you aim to use the inference modules of <strong>CompactObject</strong> for Bayesian inference outside of EOS studies, such as in high-energy physics or other astrophysical fields:</p>
+<ul class="simple">
+<li><p><strong>Example Notebook:</strong> Refer to the <a class="reference external" href="https://github.com/ChunHuangPhy/CompactObject/blob/main/Test_Case/Sample_glitchBayesian.ipynb">Sample Glitch Bayesian Inference Notebook</a> for an example of how to apply <strong>CompactObject</strong> to different inference scenarios.</p></li>
+</ul>
+<blockquote>
+<div><ol class="arabic simple" start="6">
+<li><p><strong>Advanced Usage: Modifying Solvers or APIs</strong></p></li>
+</ol>
+</div></blockquote>
+<p>If the existing tutorials do not cover your specific needs, such as:</p>
+<ul class="simple">
+<li><p><strong>Modifying the TOV Solver:</strong> For example, changing to a double-fluid TOV solver.</p></li>
+<li><p><strong>Investigating Additional EOS-Related Quantities:</strong> Beyond pressure and density used in TOV solving.</p></li>
+</ul>
+<p>You can:</p>
+<ul class="simple">
+<li><p><strong>API Documentation:</strong> Consult the detailed API documentation in this documentation to identify and utilize the functions you need.</p></li>
+<li><p><strong>GitHub Repository:</strong> Visit our <a class="reference external" href="https://github.com/ChunHuangPhy/CompactObject/tree/main">GitHub Page</a> to explore the source code and understand the internal workings of <strong>CompactObject</strong>.</p></li>
+</ul>
+<p><strong>Note:</strong>
+The key functions, including the <strong>TOVsolver module</strong> code, form the foundation for all inference and computations. Modifying them may require substantial code restructuring. Therefore, if you clone the repository locally and need to make changes to the <strong>TOVsolver</strong>, proceed with caution to avoid extensive code rebuilding. Only original contributors have permission to modify the <strong>TOVsolver</strong> module code, and such commits must undergo detailed reviews by the original members of the project.</p>
+</section>
+<section id="acknowledgements">
+<h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Link to this heading"></a></h2>
+<p>We welcome feedback and contributions to expand the functionalities of <strong>CompactObject</strong>. Your support helps enhance the tool for the entire research community.</p>
+</section>
+<section id="contact">
+<h2>Contact<a class="headerlink" href="#contact" title="Link to this heading"></a></h2>
+<p>For inquiries, contributions, or to be featured in our publications list, please contact us at <a class="reference external" href="mailto:chun&#46;h&#37;&#52;&#48;wustl&#46;edu">chun<span>&#46;</span>h<span>&#64;</span>wustl<span>&#46;</span>edu</a>.</p>
+</section>
+</section>
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+<li class="toctree-l1"><a class="reference internal" href="publications.html">Publications</a></li>
+<li class="toctree-l1"><a class="reference internal" href="cluster.html">Computation Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="starthere.html">Start Here!</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Contributor.html">Call for Contributions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">MIT bag EOS Inference pipline</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#(a)-Import-packages">(a) Import packages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#(b)-Set-up-priors">(b) Set up priors</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#(c)-Set-up-likehood">(c) Set up likehood</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#(d)-Set-up-sampler">(d) Set up sampler</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+             
+  <section id="MIT-bag-EOS-Inference-pipline">
+<h1>MIT bag EOS Inference pipline<a class="headerlink" href="#MIT-bag-EOS-Inference-pipline" title="Link to this heading"></a></h1>
+<p>The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state <span class="math notranslate nohighlight">\(p=\frac{\epsilon}{3}-\frac{4B}{3}\)</span>. There is only one parameter, the “bag constant” <span class="math notranslate nohighlight">\(B\)</span>. This represents the vacuum energy density, which creates a “bag” in which quarks are confined. See <a class="reference external" href="https://doi.org/10.1103/PhysRevD.9.3471">Chodos et al. (1974)</a>.</p>
+<p>In this notebook, we use our Bayesian inference code to constrain the value of <span class="math notranslate nohighlight">\(B\)</span> using observations.</p>
+<section id="(a)-Import-packages">
+<h2>(a) Import packages<a class="headerlink" href="#(a)-Import-packages" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">InferenceWorkflow.BayesianSampler</span> <span class="k">as</span> <span class="nn">sampler</span>
+<span class="kn">import</span> <span class="nn">InferenceWorkflow.Likelihood</span> <span class="k">as</span> <span class="nn">likelihood</span>
+<span class="kn">import</span> <span class="nn">InferenceWorkflow.prior</span> <span class="k">as</span> <span class="nn">prior</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.MITbag_EOS</span> <span class="k">as</span> <span class="nn">MITbag</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.unit</span> <span class="kn">import</span> <span class="n">MeV</span><span class="p">,</span> <span class="n">fm</span><span class="p">,</span> <span class="n">g_cm_3</span><span class="p">,</span> <span class="n">dyn_cm_2</span><span class="p">,</span> <span class="n">km</span><span class="p">,</span> <span class="n">Msun</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="(b)-Set-up-priors">
+<h2>(b) Set up priors<a class="headerlink" href="#(b)-Set-up-priors" title="Link to this heading"></a></h2>
+<p>Next, we need to set up the priors. We first use a parameters array to specify the variable name. This process should be consistent with what you need to call them.</p>
+<p>Define a prior transform function to define prior. Cube is a set of random numbers from 0 to 1. This prior setting is the standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.</p>
+<p>We provided two options call from prior:”normal_Prior” and “flat_prior”.</p>
+<p>We note that since we are doing Equation of state inference from mass-radius data of neutron star measurement. The central density of the star should be also sampled. Otherwise, this will be a partially-defined prior that did not span all of the parameter space, and proved to be different from full-scope inference.</p>
+<p>This request will randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of the compact star — different equations of state will predict different upper bounds, so here we need to use the prior-setting EoS parameters computing the mass-radius curve for this equation of state, then find out the last stable point of this equation of state (first mass points that give the direvative to be
+negative). We can find that index with the len() function, then reset this max_d to be upper limit of this density range.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span><span class="s1">&#39;d1&#39;</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">prior_transform</span><span class="p">(</span><span class="n">cube</span><span class="p">):</span>
+    <span class="n">params</span> <span class="o">=</span> <span class="n">cube</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">flat_prior</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="n">B</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="n">epsilon</span><span class="p">,</span><span class="n">p</span> <span class="o">=</span> <span class="n">MITbag</span><span class="o">.</span><span class="n">MITbag_compute_EOS</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
+
+    <span class="n">RFSU2R</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">MFSU2R</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">14.3</span><span class="p">,</span> <span class="mf">15.6</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">epsilon</span><span class="p">[:],</span> <span class="n">epsilon</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">p</span><span class="p">[:],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
+        <span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">,</span><span class="n">epsilon</span><span class="p">,</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">MR</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">)</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1">#this line for showing how to add one more observation</span>
+    <span class="k">else</span><span class="p">:</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">])):</span>
+            <span class="n">RFSU2R</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+            <span class="n">MFSU2R</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">20</span> <span class="ow">and</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">break</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MFSU2R</span><span class="p">)</span><span class="o">==</span><span class="kc">False</span><span class="p">:</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1">#params[3] = 0</span>
+        <span class="c1">#this line for showing how to add one more observation</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">max_index</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">MFSU2R</span><span class="p">)</span>
+        <span class="n">max_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">max_index</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">14.3</span> <span class="o">+</span> <span class="p">(</span><span class="n">max_d</span> <span class="o">-</span> <span class="mf">14.3</span><span class="p">)</span> <span class="o">*</span> <span class="n">cube</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="c1">#params[3] = 14.3 + (max_d - 14.3) * cube[3]</span>
+        <span class="c1">#this line for showing how to add one more observation</span>
+    <span class="k">return</span> <span class="n">params</span>
+</pre></div>
+</div>
+</div>
+<p>In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.</p>
+<p>Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement. For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. “d1” for the centre density of this measurement, in the meantime add “params[2]” to this code.</p>
+<p>If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. “d1” and “d2” for the centre density of these two measurements, in the meantime add “params[3]” to the above code.</p>
+</section>
+<section id="(c)-Set-up-likehood">
+<h2>(c) Set up likehood<a class="headerlink" href="#(c)-Set-up-likehood" title="Link to this heading"></a></h2>
+<p>We need to set up a likelihood, Using standard definition way of UltraNest, that is below.</p>
+<p>Here the likelihood is generated from a simulated mass radius measurement, which is 𝑀=1.4𝑀⊙ and 𝑅=13 km, With a 5% Mass radius measurement uncertainty,</p>
+<p>so here</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>likelihood.MRlikihood_Gaussian
+</pre></div>
+</div>
+<p>function will be use for our likelihood, please check <a class="reference external" href="https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py">likelihood.MRlikihood_Gaussian</a> to see the original code, and more choice of likelihood. eg:</p>
+<p>1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into</p>
+<p>likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)</p>
+<p>set the KDE kernel as a input for this function</p>
+<p>2.If we gain measurement from radio-timing, say only measure the neutron star mass, then</p>
+<p>likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)</p>
+<p>Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.</p>
+<p>3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:</p>
+<p>likelihood.Kliklihood(theta,K_low,K_up) likelihood.Jliklihood(theta,K_low,K_up) likelihood.Lliklihood(theta,K_low,K_up)</p>
+<p>We are defaulting a hard-cut flat constrain, so if you don’t like this default hard cut, also could define the likelihood by youself with similiar style.</p>
+<p>4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:</p>
+<p>likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)</p>
+<p>Where x is sampled distribution from real measurements, the standard is</p>
+<p>kernel, chrip = x,</p>
+<p>where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">stats</span>
+
+
+<span class="k">def</span> <span class="nf">likelihood_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
+    <span class="c1"># This is a demonstration code for only introduce one constraint from one mass-radius observation.</span>
+    <span class="c1"># Could be very easy to implement more constraint from nuclear quantity, since that do not need to</span>
+    <span class="c1"># sample more central density of real neutron star. If user want to expand to two mass radius measurement</span>
+    <span class="c1"># the code could be:</span>
+
+    <span class="n">B</span><span class="p">,</span> <span class="n">d1</span> <span class="o">=</span> <span class="n">theta</span>
+
+    <span class="c1">####################################################################################################################</span>
+    <span class="c1">############ This is the block to compute out all the EoS you need based on your parameters#########################</span>
+
+    <span class="n">epsilon</span><span class="p">,</span><span class="n">p</span> <span class="o">=</span> <span class="n">MITbag</span><span class="o">.</span><span class="n">MITbag_compute_EOS</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
+
+    <span class="c1">####################################################################################################################</span>
+
+    <span class="n">probMRgaussian</span> <span class="o">=</span> <span class="n">likelihood</span><span class="o">.</span><span class="n">MRlikihood_Gaussian</span><span class="p">(</span><span class="n">epsilon</span><span class="p">,</span><span class="n">p</span><span class="p">,(</span><span class="mf">1.4</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mf">0.07</span><span class="p">,</span><span class="mf">0.65</span><span class="p">),</span><span class="n">d1</span><span class="p">)</span>
+
+    <span class="n">prob</span> <span class="o">=</span> <span class="n">probMRgaussian</span>
+
+    <span class="k">return</span> <span class="n">prob</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="(d)-Set-up-sampler">
+<h2>(d) Set up sampler<a class="headerlink" href="#(d)-Set-up-sampler" title="Link to this heading"></a></h2>
+<p>Here next, we define sampler, there is two different sampler we provided for.</p>
+<p>Considering where you need resume file:</p>
+<p>sampler.UltranestSampler and sampler.UltranestSamplerResume</p>
+<p>Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).</p>
+<p>live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.</p>
+<p>max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">step</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span>
+<span class="n">live_point</span> <span class="o">=</span> <span class="mi">400</span>
+
+<span class="n">max_calls</span> <span class="o">=</span> <span class="mi">60000</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">UltranestSampler</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span><span class="n">likelihood_transform</span><span class="p">,</span><span class="n">prior_transform</span><span class="p">,</span><span class="n">step</span><span class="p">,</span><span class="n">live_point</span><span class="p">,</span><span class="n">max_calls</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="test_Bayesian_inference_Strangeon_EOS.html" class="btn btn-neutral float-left" title="Strangeon EOS inference pipeline" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/test_Bayesian_inference_MITbag_EOS.ipynb b/test_Bayesian_inference_MITbag_EOS.ipynb
new file mode 100644
index 0000000..c733e26
--- /dev/null
+++ b/test_Bayesian_inference_MITbag_EOS.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "edb2cf2a",
+   "metadata": {},
+   "source": [
+    "## MIT bag EOS Inference pipline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc633d3",
+   "metadata": {},
+   "source": [
+    "The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state $p=\\frac{\\epsilon}{3}-\\frac{4B}{3}$. There is only one parameter, the \"bag constant\" $B$. This represents the vacuum energy density, which creates a \"bag\" in which quarks are confined. See [Chodos et al. (1974)](https://doi.org/10.1103/PhysRevD.9.3471).\n",
+    "\n",
+    "In this notebook, we use our Bayesian inference code to constrain the value of $B$ using observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acdcb64a",
+   "metadata": {},
+   "source": [
+    "### (a) Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6f20856",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import EOSgenerators.MITbag_EOS as MITbag\n",
+    "from TOVsolver.unit import MeV, fm, g_cm_3, dyn_cm_2, km, Msun\n",
+    "import TOVsolver.main as main"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edfa0f72",
+   "metadata": {},
+   "source": [
+    "### (b) Set up priors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38136542",
+   "metadata": {},
+   "source": [
+    "Next, we need to set up the priors. We first use a parameters array to specify the variable name. This process should be consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube is a set of random numbers from 0 to 1. This prior setting is the standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.\n",
+    "\n",
+    "We provided two options call from prior:\"normal_Prior\" and \"flat_prior\".\n",
+    "\n",
+    "We note that since we are doing Equation of state inference from mass-radius data of neutron star measurement. The central density of the star should be also sampled. Otherwise, this will be a partially-defined prior that did not span all of the parameter space, and proved to be different from full-scope inference.\n",
+    "\n",
+    "This request will randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of the compact star --- different equations of state will predict different upper bounds, so here we need to use the prior-setting EoS parameters computing the mass-radius curve for this equation of state, then find out the last stable point of this equation of state (first mass points that give the direvative to be negative). We can find that index with the len() function, then reset this max_d to be upper limit of this density range.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b87dd3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = ['B','d1']\n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = prior.flat_prior(20,100,cube[0])\n",
+    "\n",
+    "    B = params[0]\n",
+    "    \n",
+    "    epsilon,p = MITbag.MITbag_compute_EOS(B)\n",
+    "\n",
+    "    RFSU2R = [] \n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50) \n",
+    "    if all(x<y for x,y in zip(epsilon[:], epsilon[1:])) and all(x<y for x, y in zip(p[:], p[1:])):\n",
+    "        MR = main.OutputMR(\"\",epsilon,p).T  \n",
+    "        \n",
+    "    else:\n",
+    "        MR = []\n",
+    "    if len(MR) == False: \n",
+    "        params[1] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "   \n",
+    "        for i in range(len(MR[1])):\n",
+    "            RFSU2R.append(MR[1][i])\n",
+    "            MFSU2R.append(MR[0][i])   \n",
+    "            if i > 20 and MR[0][i] - MR[0][i-1]< 0:    \n",
+    "                break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[1] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[1] = 14.3 + (max_d - 14.3) * cube[1]\n",
+    "        #params[3] = 14.3 + (max_d - 14.3) * cube[3]\n",
+    "        #this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8e4822",
+   "metadata": {},
+   "source": [
+    "In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.\n",
+    "\n",
+    "Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement.\n",
+    "For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. \"d1\" for the centre density of this measurement, in the meantime add \"params[2]\" to this code.\n",
+    "\n",
+    "If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. \"d1\" and \"d2\" for the centre density of these two measurements, in the meantime add \"params[3]\" to the above code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ab6d43",
+   "metadata": {},
+   "source": [
+    "### (c) Set up likehood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfd12ff",
+   "metadata": {},
+   "source": [
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is  𝑀=1.4𝑀⊙\n",
+    "  and  𝑅=13\n",
+    "  km, With a 5% Mass radius measurement uncertainty, \n",
+    "  \n",
+    " so here\n",
+    " \n",
+    "      likelihood.MRlikihood_Gaussian\n",
+    "      \n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood. eg:\n",
+    "\n",
+    "1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into\n",
+    "\n",
+    "likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2.If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.\n",
+    "\n",
+    "3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Where x is sampled distribution from real measurements, the standard is\n",
+    "\n",
+    "kernel, chrip = x,\n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d3a8359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "      \n",
+    "    B, d1 = theta\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "\n",
+    "    epsilon,p = MITbag.MITbag_compute_EOS(B)\n",
+    "\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(epsilon,p,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3802c4",
+   "metadata": {},
+   "source": [
+    "### (d) Set up sampler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76bce7fb",
+   "metadata": {},
+   "source": [
+    "Here next, we define sampler, there is two different sampler we provided for. \n",
+    "\n",
+    "Considering where you need resume file:\n",
+    "\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f05849a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 400\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test_Bayesian_inference_Strangeon_EOS.html b/test_Bayesian_inference_Strangeon_EOS.html
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="test_EOSgenerators.html">Equation of State Generators illustration notebook</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Strangeon EOS inference pipeline</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#Set-up-prior">Set up prior</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#Set-up-likehood">Set up likehood</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#Set-up-sampler">Set up sampler</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
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+</ul>
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+             
+  <section id="Strangeon-EOS-inference-pipeline">
+<h1>Strangeon EOS inference pipeline<a class="headerlink" href="#Strangeon-EOS-inference-pipeline" title="Link to this heading"></a></h1>
+<p>This is an example notebook about how to use our tools to analysis a observation constraint on strangeon star equation of state.</p>
+<p>Here in this notebook, we are using a strangeon matter EOS.</p>
+<p>The following is the package that we need.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">InferenceWorkflow.BayesianSampler</span> <span class="k">as</span> <span class="nn">sampler</span>
+<span class="kn">import</span> <span class="nn">InferenceWorkflow.Likelihood</span> <span class="k">as</span> <span class="nn">likelihood</span>
+<span class="kn">import</span> <span class="nn">InferenceWorkflow.prior</span> <span class="k">as</span> <span class="nn">prior</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.Strangeon_EOS</span> <span class="k">as</span> <span class="nn">Strangeon</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.constant</span> <span class="kn">import</span> <span class="n">oneoverfm_MeV</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span><span class="n">G</span><span class="p">,</span><span class="n">c</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/Users/a9/CompactOject/InferenceWorkflow/Likelihood.py:314: SyntaxWarning: invalid escape sequence &#39;\(&#39;
+  &#34;&#34;&#34;
+</pre></div></div>
+</div>
+<p>The following is the strangeon matter EOS function.</p>
+<p>We note that because the parameter Nq is an integer in the strangeon matter EOS.</p>
+<p>In the following, we use a possible value of this parameter, Nq=18, as an example to carry out the Bayesian analysis.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Nq</span><span class="o">=</span><span class="mi">18</span>
+<br/></pre></div>
+</div>
+</div>
+<section id="Set-up-prior">
+<h2>Set up prior<a class="headerlink" href="#Set-up-prior" title="Link to this heading"></a></h2>
+<p>Next step, we need to set up the prior, first use parameters array to specify the variable name, this process should consistent with what you need to call them.</p>
+<p>Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.</p>
+<p>We provided two options call from prior:”normal_Prior” and “flat_prior”.</p>
+<p>Then the Parameters prior should all set.</p>
+<p>We note that since we are doing Equation of state Inference from mass-radius data of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.</p>
+<p>This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of compact star — different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by</p>
+<p>strangeon.compute_EOS Compute out EOS, put into</p>
+<p>main.OutputMR</p>
+<p>find out Mass Radius of this equation of state, then find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)</p>
+<p>and find out that index by len() function, then reset this max_d to be upper limit of this density range.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span> <span class="s1">&#39;epsilon&#39;</span><span class="p">,</span> <span class="s1">&#39;ns&#39;</span><span class="p">,</span> <span class="s1">&#39;d1&#39;</span><span class="p">]</span>
+<span class="c1"># for two or more MR measurements, define</span>
+<span class="c1"># parameters = [&#39;epsilon&#39;, &#39;ns&#39;, &#39;d1&#39;, &#39;d2&#39;]</span>
+
+<span class="k">def</span> <span class="nf">prior_transform</span><span class="p">(</span><span class="n">cube</span><span class="p">):</span>
+    <span class="n">params</span> <span class="o">=</span> <span class="n">cube</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">flat_prior</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">170</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>   <span class="c1">#epsilon=10-170MeV</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">flat_prior</span><span class="p">(</span><span class="mf">0.17</span><span class="p">,</span><span class="mf">0.36</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="c1">#ns=0.17-0.36fm^-3</span>
+
+    <span class="n">epsilon</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">ns</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">theta1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">Nq</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">,</span> <span class="n">ns</span><span class="p">])</span>
+    <span class="n">n_min</span> <span class="o">=</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">theta1</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="n">theta1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">n_max</span> <span class="o">=</span> <span class="mf">0.16</span> <span class="o">*</span> <span class="mi">8</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">/</span> <span class="n">theta1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">n_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">n_min</span><span class="p">,</span> <span class="n">n_max</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span>
+    <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span> <span class="o">=</span> <span class="n">Strangeon</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">n_values</span><span class="p">,</span> <span class="n">theta1</span><span class="p">)</span>
+
+
+
+    <span class="n">eps_total</span><span class="o">=</span><span class="n">energy_density</span>
+
+    <span class="n">pres_total</span><span class="o">=</span><span class="n">pressure</span>
+
+    <span class="n">RFSU2R</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">MFSU2R</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">14.3</span><span class="p">,</span> <span class="mf">15.6</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eps_total</span><span class="p">[:],</span> <span class="n">eps_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pres_total</span><span class="p">[:],</span> <span class="n">pres_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
+        <span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">energy_density</span><span class="p">,</span><span class="n">pressure</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">MR</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">)</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1">#params[3] = 0</span>
+        <span class="c1">#this line for showing how to add one more observation</span>
+    <span class="k">else</span><span class="p">:</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+            <span class="n">RFSU2R</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+            <span class="n">MFSU2R</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">20</span> <span class="ow">and</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">break</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MFSU2R</span><span class="p">)</span><span class="o">==</span><span class="kc">False</span><span class="p">:</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1">#params[3] = 0</span>
+        <span class="c1">#this line for showing how to add one more observation</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">max_index</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">MFSU2R</span><span class="p">)</span>
+        <span class="n">max_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">max_index</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mf">14.3</span> <span class="o">+</span> <span class="p">(</span><span class="n">max_d</span> <span class="o">-</span> <span class="mf">14.3</span><span class="p">)</span> <span class="o">*</span> <span class="n">cube</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+        <span class="c1">#params[3] = 14.3 + (max_d - 14.3) * cube[3]</span>
+        <span class="c1">#this line for showing how to add one more observation</span>
+    <span class="k">return</span> <span class="n">params</span>
+</pre></div>
+</div>
+</div>
+<p>In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.</p>
+<p>Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement. For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. “d1” for the centre density of this measurement, in the meantime add “params[2]” to this code.</p>
+<p>If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. “d1” and “d2” for the centre density of these two measurements, in the meantime add “params[3]” to the above code.</p>
+</section>
+<section id="Set-up-likehood">
+<h2>Set up likehood<a class="headerlink" href="#Set-up-likehood" title="Link to this heading"></a></h2>
+<p>We need to set up a likelihood, Using standard definition way of UltraNest, that is below.</p>
+<p>Here the likelihood is generated from a simulated mass radius measurement, which is 𝑀=1.4𝑀⊙ and 𝑅=13 km, With a 5% Mass radius measurement uncertainty,</p>
+<p>so here</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>likelihood.MRlikihood_Gaussian
+</pre></div>
+</div>
+<p>function will be use for our likelihood, please check <a class="reference external" href="https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py">likelihood.MRlikihood_Gaussian</a> to see the original code, and more choice of likelihood. eg:</p>
+<p>1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into</p>
+<p>likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)</p>
+<p>set the KDE kernel as a input for this function</p>
+<p>2.If we gain measurement from radio-timing, say only measure the neutron star mass, then</p>
+<p>likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)</p>
+<p>Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.</p>
+<p>3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:</p>
+<p>likelihood.Kliklihood(theta,K_low,K_up) likelihood.Jliklihood(theta,K_low,K_up) likelihood.Lliklihood(theta,K_low,K_up)</p>
+<p>We are defaulting a hard-cut flat constrain, so if you don’t like this default hard cut, also could define the likelihood by youself with similiar style.</p>
+<p>4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:</p>
+<p>likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)</p>
+<p>Where x is sampled distribution from real measurements, the standard is</p>
+<p>kernel, chrip = x,</p>
+<p>where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">stats</span>
+
+
+<span class="k">def</span> <span class="nf">likelihood_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
+    <span class="c1"># This is a demonstration code for only introduce one constraint from one mass-radius observation.</span>
+    <span class="c1"># Could be very easy to implement more constraint from nuclear quantity, since that do not need to</span>
+    <span class="c1"># sample more central density of real neutron star. If user want to expand to two mass radius measurement</span>
+    <span class="c1"># the code could be:</span>
+
+    <span class="n">epsilon</span><span class="p">,</span> <span class="n">ns</span><span class="p">,</span> <span class="n">d1</span> <span class="o">=</span> <span class="n">theta</span>
+    <span class="c1"># comment this line if you need two measuremnts.</span>
+    <span class="c1">#epsilon, ns, d1, d2 = theta</span>
+
+    <span class="c1">####################################################################################################################</span>
+    <span class="c1">############ This is the block to compute out all the EoS you need based on your parameters#########################</span>
+
+
+    <span class="n">theta1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">Nq</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">,</span> <span class="n">ns</span><span class="p">])</span>
+    <span class="n">n_min</span> <span class="o">=</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">theta1</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="n">theta1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">n_max</span> <span class="o">=</span> <span class="mf">0.16</span> <span class="o">*</span> <span class="mi">8</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">/</span> <span class="n">theta1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">n_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">n_min</span><span class="p">,</span> <span class="n">n_max</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span>
+    <span class="n">energy_density</span><span class="p">,</span> <span class="n">pressure</span> <span class="o">=</span> <span class="n">Strangeon</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">n_values</span><span class="p">,</span> <span class="n">theta1</span><span class="p">)</span>
+
+    <span class="c1">####################################################################################################################</span>
+
+    <span class="n">probMRgaussian</span> <span class="o">=</span> <span class="n">likelihood</span><span class="o">.</span><span class="n">MRlikihood_Gaussian</span><span class="p">(</span><span class="n">energy_density</span><span class="p">,</span><span class="n">pressure</span><span class="p">,(</span><span class="mf">1.4</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mf">0.07</span><span class="p">,</span><span class="mf">0.65</span><span class="p">),</span><span class="n">d1</span><span class="p">)</span>
+
+    <span class="n">prob</span> <span class="o">=</span> <span class="n">probMRgaussian</span>
+
+    <span class="k">return</span> <span class="n">prob</span>
+</pre></div>
+</div>
+</div>
+<p>In the following, we will show how to modify the likehood_transform function when considering more observations.</p>
+<p>def likelihood_transform(theta):</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Nq, epsilon, ns, d1 = theta
+theta = np.array([epsilon, ns])
+n_min = 3 * theta[1] / Nq      #n_min=3*ns/Nq
+n_max = 0.16 * 8 * 3 / Nq      #n_max=0.16*8*3/Nq
+n_values = np.linspace(n_min, n_max, 100)  # 100 points between n_min and n_max
+energy_density_total, pressure_total = Strangeon_compute_EOS(n_values, theta)
+
+#1. This line is to compute MR likelihood from a Simulated MR measurement:
+
+probMRgaussian = MRlikihood_Gaussian(energy_density_total,pressure_total,(1.4,13,0.07,0.65),d1)
+
+#probMRgaussian = likelihood.MRlikihood_Gaussian(energy_density_total,pressure_total,(2.08,13,0.08,0.65),d2)
+
+#2. This is  a block that constrain from given real MR measurement, say J0030:
+
+#J0030 = numpy.loadtxt(&#39;data/PST_equal_sampled_MR.txt&#39;, delimiter=&#39; &#39;)
+#J30R_list, J30M_list = zip(*J0030)
+#J30R_list = numpy.array(J30R_list).T
+#J30M_list = numpy.array(J30M_list).T
+#Rmin = J30R_list.min()
+#Rmax = J30R_list.max()
+#Mmin = J30M_list.min()
+#Mmax = J30M_list.max()
+#X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]
+#positions = numpy.vstack([X3.ravel(), Y3.ravel()])
+#values = numpy.vstack([J30R_list, J30M_list])
+#kernel3 = stats.gaussian_kde(values)
+#probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)
+
+#3. This is to compute the constraint from experiment of nuclearmatter
+# 250&lt;K&lt;400, 25&lt;J&lt;38, 30&lt;L&lt;86:
+# hint: since this K,J,L sampling don&#39;t need to sample central density so this
+# theta should be redifined.
+#probK = likelihood.Kliklihood(theta,250,400)
+#probJ = likelihood.Jliklihood(theta,250,400)
+#probL = likelihood.Lliklihood(theta,250,400)
+
+#4. This is block to cosntrain from a GW event, say GW170817, here the file of this
+# event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:
+#GW170817 = np.load(&#39;GW170817_McQL1L2weights.npy&#39;)
+#chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])
+#kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])
+#probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)
+
+
+prob = probMRgaussian
+
+#prob =  probGW#+ probMRJ0030 + probK + probJ + probL + probGW
+
+return prob
+</pre></div>
+</div>
+</section>
+<section id="Set-up-sampler">
+<h2>Set up sampler<a class="headerlink" href="#Set-up-sampler" title="Link to this heading"></a></h2>
+<p>Here next, we define sampler, there is two different sampler we provided for.</p>
+<p>Considering where you need resume file:</p>
+<p>sampler.UltranestSampler and sampler.UltranestSamplerResume</p>
+<p>Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).</p>
+<p>live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.</p>
+<p>max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">step</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span>
+<span class="n">live_point</span> <span class="o">=</span> <span class="mi">400</span>
+
+<span class="n">max_calls</span> <span class="o">=</span> <span class="mi">60000</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">UltranestSampler</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span><span class="n">likelihood_transform</span><span class="p">,</span><span class="n">prior_transform</span><span class="p">,</span><span class="n">step</span><span class="p">,</span><span class="n">live_point</span><span class="p">,</span><span class="n">max_calls</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="test_Inference.html" class="btn btn-neutral float-left" title="RMF EOS inference pipeline" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+    <p>&#169; Copyright 2023, Chun Huang, Nicole Obsborn, Nathan Whitsett.</p>
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diff --git a/test_Bayesian_inference_Strangeon_EOS.ipynb b/test_Bayesian_inference_Strangeon_EOS.ipynb
new file mode 100644
index 0000000..3d57d0d
--- /dev/null
+++ b/test_Bayesian_inference_Strangeon_EOS.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "edb2cf2a",
+   "metadata": {},
+   "source": [
+    "## Strangeon EOS inference pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc633d3",
+   "metadata": {},
+   "source": [
+    "This is an example notebook about how to use our tools to analysis a observation constraint on strangeon star equation of state.\n",
+    "\n",
+    "Here in this notebook, we are using a strangeon matter EOS.\n",
+    "\n",
+    "The following is the package that we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f6f20856",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/a9/CompactOject/InferenceWorkflow/Likelihood.py:314: SyntaxWarning: invalid escape sequence '\\('\n",
+      "  \"\"\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "from TOVsolver.constant import oneoverfm_MeV, m_rho, m_w,G,c\n",
+    "import TOVsolver.main as main"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b494a39",
+   "metadata": {},
+   "source": [
+    "The following is the strangeon matter EOS function.\n",
+    "\n",
+    "We note that because the parameter Nq is an integer in the strangeon matter EOS.\n",
+    "\n",
+    "In the following, we use a possible value of this parameter, Nq=18, as an example to carry out the Bayesian analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c9d879d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nq=18\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edfa0f72",
+   "metadata": {},
+   "source": [
+    "### Set up prior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38136542",
+   "metadata": {},
+   "source": [
+    "Next step, we need to set up the prior, first use parameters array to specify the variable name, this process should consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling.\n",
+    "\n",
+    " We provided two options call from prior:\"normal_Prior\" and \"flat_prior\".\n",
+    " \n",
+    "Then the Parameters prior should all set.\n",
+    "\n",
+    "We note that since we are doing Equation of state Inference from mass-radius data of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.\n",
+    "\n",
+    "This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of compact star --- different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by\n",
+    "\n",
+    "strangeon.compute_EOS\n",
+    "Compute out EOS, put into\n",
+    "\n",
+    "main.OutputMR\n",
+    "\n",
+    "find out Mass Radius of this equation of state, then find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)\n",
+    "\n",
+    "and find out that index by len() function, then reset this max_d to be upper limit of this density range.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b87dd3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = [ 'epsilon', 'ns', 'd1']\n",
+    "# for two or more MR measurements, define \n",
+    "# parameters = ['epsilon', 'ns', 'd1', 'd2'] \n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = prior.flat_prior(10, 170,cube[0])   #epsilon=10-170MeV\n",
+    "    params[1] = prior.flat_prior(0.17,0.36,cube[1]) #ns=0.17-0.36fm^-3    \n",
+    "    \n",
+    "    epsilon = params[0]\n",
+    "    ns = params[1]\n",
+    "\n",
+    "    theta1 = np.array([Nq, epsilon, ns])\n",
+    "    n_min = 3 * theta1[2] / theta1[0]  \n",
+    "    n_max = 0.16 * 8 * 3 / theta1[0]   \n",
+    "    n_values = np.linspace(n_min, n_max, 2000) \n",
+    "    energy_density, pressure = Strangeon.compute_EOS(n_values, theta1)\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "    eps_total=energy_density\n",
+    "    \n",
+    "    pres_total=pressure\n",
+    "    \n",
+    "    RFSU2R = [] \n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50) \n",
+    "    if all(x<y for x,y in zip(eps_total[:], eps_total[1:])) and all(x<y for x, y in zip(pres_total[:], pres_total[1:])):\n",
+    "        MR = main.OutputMR('',energy_density,pressure).T  \n",
+    "        \n",
+    "    else:\n",
+    "        MR = []\n",
+    "    if len(MR) == False: \n",
+    "        params[2] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "   \n",
+    "        for i in range(len(MR[0])):\n",
+    "            RFSU2R.append(MR[1][i])\n",
+    "            MFSU2R.append(MR[0][i])   \n",
+    "            if i > 20 and MR[0][i] - MR[0][i-1]< 0:    \n",
+    "                break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[2] = 0\n",
+    "        #params[3] = 0\n",
+    "        #this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[2] = 14.3 + (max_d - 14.3) * cube[2]\n",
+    "        #params[3] = 14.3 + (max_d - 14.3) * cube[3]\n",
+    "        #this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8e4822",
+   "metadata": {},
+   "source": [
+    "In the upper part, we define a flat (uniform) prior for the parameters in the strangeon matter equation of state, due to the lack of constraints from terrestrial experiments.\n",
+    "\n",
+    "Note that the above code is an example of Bayesian analysis for a given mass and radius observation measurement.\n",
+    "For example, if you use the NICER data for the measurements of J0030, then you should define another parameter, except the strangeon EOS parameters, e.g. \"d1\" for the centre density of this measurement, in the meantime add \"params[2]\" to this code.\n",
+    "\n",
+    "If you further consider the adjoint analysis with J0030+J0740, then you should define the other two parameters, e.g. \"d1\" and \"d2\" for the centre density of these two measurements, in the meantime add \"params[3]\" to the above code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ab6d43",
+   "metadata": {},
+   "source": [
+    "### Set up likehood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfd12ff",
+   "metadata": {},
+   "source": [
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is  𝑀=1.4𝑀⊙\n",
+    "  and  𝑅=13\n",
+    "  km, With a 5% Mass radius measurement uncertainty, \n",
+    "  \n",
+    " so here\n",
+    " \n",
+    "      likelihood.MRlikihood_Gaussian\n",
+    "      \n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/CompactOject/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood. eg:\n",
+    "\n",
+    "1.If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into\n",
+    "\n",
+    "likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2.If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement.\n",
+    "\n",
+    "3.If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4.If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "\n",
+    "Where x is sampled distribution from real measurements, the standard is\n",
+    "\n",
+    "kernel, chrip = x,\n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d3a8359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "      \n",
+    "    epsilon, ns, d1 = theta \n",
+    "    # comment this line if you need two measuremnts.\n",
+    "    #epsilon, ns, d1, d2 = theta\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "\n",
+    "\n",
+    "    theta1 = np.array([Nq, epsilon, ns])\n",
+    "    n_min = 3 * theta1[2] / theta1[0]  \n",
+    "    n_max = 0.16 * 8 * 3 / theta1[0]   \n",
+    "    n_values = np.linspace(n_min, n_max, 2000) \n",
+    "    energy_density, pressure = Strangeon.compute_EOS(n_values, theta1)\n",
+    "\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(energy_density,pressure,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96d26ce7",
+   "metadata": {},
+   "source": [
+    "In the following, we will show how to modify the likehood_transform function when considering more observations.\n",
+    "\n",
+    "def likelihood_transform(theta):\n",
+    "\n",
+    "    Nq, epsilon, ns, d1 = theta \n",
+    "    theta = np.array([epsilon, ns]) \n",
+    "    n_min = 3 * theta[1] / Nq      #n_min=3*ns/Nq\n",
+    "    n_max = 0.16 * 8 * 3 / Nq      #n_max=0.16*8*3/Nq\n",
+    "    n_values = np.linspace(n_min, n_max, 100)  # 100 points between n_min and n_max\n",
+    "    energy_density_total, pressure_total = Strangeon_compute_EOS(n_values, theta)\n",
+    "    \n",
+    "    #1. This line is to compute MR likelihood from a Simulated MR measurement:\n",
+    "    \n",
+    "    probMRgaussian = MRlikihood_Gaussian(energy_density_total,pressure_total,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    #probMRgaussian = likelihood.MRlikihood_Gaussian(energy_density_total,pressure_total,(2.08,13,0.08,0.65),d2)\n",
+    "    \n",
+    "    #2. This is  a block that constrain from given real MR measurement, say J0030:\n",
+    "    \n",
+    "    #J0030 = numpy.loadtxt('data/PST_equal_sampled_MR.txt', delimiter=' ')\n",
+    "    #J30R_list, J30M_list = zip(*J0030)\n",
+    "    #J30R_list = numpy.array(J30R_list).T    \n",
+    "    #J30M_list = numpy.array(J30M_list).T\n",
+    "    #Rmin = J30R_list.min()\n",
+    "    #Rmax = J30R_list.max()\n",
+    "    #Mmin = J30M_list.min()\n",
+    "    #Mmax = J30M_list.max()\n",
+    "    #X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]\n",
+    "    #positions = numpy.vstack([X3.ravel(), Y3.ravel()])\n",
+    "    #values = numpy.vstack([J30R_list, J30M_list])\n",
+    "    #kernel3 = stats.gaussian_kde(values)\n",
+    "    #probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)\n",
+    "    \n",
+    "    #3. This is to compute the constraint from experiment of nuclearmatter\n",
+    "    # 250<K<400, 25<J<38, 30<L<86:\n",
+    "    # hint: since this K,J,L sampling don't need to sample central density so this \n",
+    "    # theta should be redifined.\n",
+    "    #probK = likelihood.Kliklihood(theta,250,400)\n",
+    "    #probJ = likelihood.Jliklihood(theta,250,400)\n",
+    "    #probL = likelihood.Lliklihood(theta,250,400)\n",
+    "    \n",
+    "    #4. This is block to cosntrain from a GW event, say GW170817, here the file of this\n",
+    "    # event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:\n",
+    "    #GW170817 = np.load('GW170817_McQL1L2weights.npy')\n",
+    "    #chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])\n",
+    "    #kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])\n",
+    "    #probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)\n",
+    "    \n",
+    "    \n",
+    "    prob = probMRgaussian\n",
+    "    \n",
+    "    #prob =  probGW#+ probMRJ0030 + probK + probJ + probL + probGW\n",
+    "    \n",
+    "    return prob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3802c4",
+   "metadata": {},
+   "source": [
+    "### Set up sampler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76bce7fb",
+   "metadata": {},
+   "source": [
+    "Here next, we define sampler, there is two different sampler we provided for. \n",
+    "\n",
+    "Considering where you need resume file:\n",
+    "\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. Some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f05849a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 400\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "240dec67",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test_EOSgenerators.html b/test_EOSgenerators.html
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+++ b/test_EOSgenerators.html
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+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
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+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Equation of State Generators illustration notebook &mdash; CompactObject 1.9.1 documentation</title>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Equation of State Generators illustration notebook</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#RMF-EOS">RMF EOS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#(a)-Load-crust-EOS-and-prepare-interface-EOS">(a) Load crust EOS and prepare interface EOS</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#Example-of-RMF">Example of RMF</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#(b)-Defining-the-paramters">(b) Defining the paramters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#(c)-Connecting-Core-and-crust-EOS">(c) Connecting Core and crust EOS</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#Illustrating-the-power-of-Numba-accelerated-EoS-Generation">Illustrating the power of Numba accelerated EoS Generation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#(a)-Comparing-the-time-it-takes-to-generate-the-EoS">(a) Comparing the time it takes to generate the EoS</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#Polytrope-EOS">Polytrope EOS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#(a)-Connecting-outer-crust-with-polytropic-part">(a) Connecting outer crust with polytropic part</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#(b)-Solve-TOV-with-this-EOS">(b) Solve TOV with this EOS</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#Strangeon-matter-EOS">Strangeon matter EOS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#(a)-Defining-the-EOS-parameters">(a) Defining the EOS parameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id1">(b) Solve TOV with this EOS</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#Speed-of-sound-EOS">Speed of sound EOS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id2">(a) Defining the EOS parameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id3">(b) Solve TOV with this EOS</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#MIT-bag-model-EOS">MIT bag model EOS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#(a)-Defining-the-parameter">(a) Defining the parameter</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#(b)-Computing-the-mass-radius-relation">(b) Computing the mass-radius relation</a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="test_TOVsolver.html">TOVsolver module guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Inference.html">RMF EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
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+             
+  <section id="Equation-of-State-Generators-illustration-notebook">
+<h1>Equation of State Generators illustration notebook<a class="headerlink" href="#Equation-of-State-Generators-illustration-notebook" title="Link to this heading"></a></h1>
+<section id="RMF-EOS">
+<h2>RMF EOS<a class="headerlink" href="#RMF-EOS" title="Link to this heading"></a></h2>
+<p>First import all of the package that will be used here.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">EOSgenerators.crust_EOS</span> <span class="k">as</span> <span class="nn">crust</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.RMF_EOS</span> <span class="k">as</span> <span class="nn">RMF</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.Polytrope_EOS</span> <span class="k">as</span> <span class="nn">Polytrope</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.Strangeon_EOS</span> <span class="k">as</span> <span class="nn">Strangeon</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.unit</span> <span class="kn">import</span> <span class="n">g_cm_3</span><span class="p">,</span> <span class="n">dyn_cm_2</span><span class="p">,</span> <span class="n">km</span><span class="p">,</span> <span class="n">Msun</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.maxium_central_density</span> <span class="kn">import</span> <span class="n">maxium_central_density</span>
+</pre></div>
+</div>
+</div>
+<section id="(a)-Load-crust-EOS-and-prepare-interface-EOS">
+<h3>(a) Load crust EOS and prepare interface EOS<a class="headerlink" href="#(a)-Load-crust-EOS-and-prepare-interface-EOS" title="Link to this heading"></a></h3>
+<p>Define constants, and load the crust EOS into this notebook, since when we solve the equation of state to TOV equation, we need full scope of the equation of state. Here, crust eos file is ‘Tolos_crust_out.txt’, while we need to times g_cm_3 for energy density and dyn_cm_2 for pressure, that will be easier for treatment, no need worry about G and c.</p>
+<p>Then just connect the crust part with the interface part(the part between core equation of state and crust) by a PolyInterpolate function</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Tolos_crust_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s2">&quot;Test_Case/Tolos_crust_out.txt&quot;</span><span class="p">)</span>
+<span class="n">eps_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">g_cm_3</span>
+<span class="n">pres_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">dyn_cm_2</span>
+<span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span> <span class="o">=</span> <span class="n">crust</span><span class="o">.</span><span class="n">PolyInterpolate</span><span class="p">(</span><span class="n">eps_crust_T_out</span><span class="p">,</span> <span class="n">pres_crust_T_out</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+<section id="Example-of-RMF">
+<h2>Example of RMF<a class="headerlink" href="#Example-of-RMF" title="Link to this heading"></a></h2>
+<section id="(b)-Defining-the-paramters">
+<h3>(b) Defining the paramters<a class="headerlink" href="#(b)-Defining-the-paramters" title="Link to this heading"></a></h3>
+<p>Defining the free prameters that we need to compute the equation of state, you could check our intro part of documentation or just check the original paper of us about details. <a class="reference external" href="https://arxiv.org/abs/2303.17518">Huang et al,2023</a></p>
+<p>After defined the equation of state parameters, we could call the ‘compute_EOS’ function from RMF, to compute full core equation of state.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m_sig</span> <span class="o">=</span> <span class="mf">495.0</span> <span class="o">/</span> <span class="mf">197.33</span>
+<span class="n">m_w</span> <span class="o">=</span> <span class="mf">3.96544</span>
+<span class="n">m_rho</span> <span class="o">=</span> <span class="mf">3.86662</span>
+
+<span class="n">g_sigma</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">107.5751</span><span class="p">)</span>
+<span class="n">g_omega</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">182.3949</span><span class="p">)</span>
+<span class="n">g_rho</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">206.4260</span><span class="p">)</span>
+
+<span class="n">kappa</span> <span class="o">=</span> <span class="mf">3.09114168</span> <span class="o">/</span> <span class="mf">197.33</span>
+<span class="n">lambda_0</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.00168015405</span>
+<span class="n">zeta</span> <span class="o">=</span> <span class="mf">0.024</span>
+<span class="n">Lambda_w</span> <span class="o">=</span> <span class="mf">0.045</span>
+<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+    <span class="p">[</span><span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">eps</span><span class="p">,</span> <span class="n">pres</span> <span class="o">=</span> <span class="n">RMF</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="(c)-Connecting-Core-and-crust-EOS">
+<h3>(c) Connecting Core and crust EOS<a class="headerlink" href="#(c)-Connecting-Core-and-crust-EOS" title="Link to this heading"></a></h3>
+<p>These two line can easily connect the Equation of state we computed for core part by setting the parameters and the crust equation of state, to a full equation of state that prepared for next step output</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">eps_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">*</span><span class="n">eps_com</span><span class="p">,</span> <span class="o">*</span><span class="n">eps</span><span class="p">])</span>
+<span class="n">pres_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">*</span><span class="n">pres_com</span><span class="p">,</span> <span class="o">*</span><span class="n">pres</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<p>Here below is one of the possiblity, use our defined function, ‘OutputMR’ to compute out what the mass radius curve corresponding to this equation of state.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">eps_total</span><span class="p">,</span> <span class="n">pres_total</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">km</span><span class="p">,</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">Msun</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;M [$M_{\odot}$]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;R [km]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_11_0.png" src="_images/test_EOSgenerators_11_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Illustrating-the-power-of-Numba-accelerated-EoS-Generation">
+<h2>Illustrating the power of Numba accelerated EoS Generation<a class="headerlink" href="#Illustrating-the-power-of-Numba-accelerated-EoS-Generation" title="Link to this heading"></a></h2>
+<p>We shall now run the same code but with the Numba accelerated version, comparing the times for EoS Generation.</p>
+<p>Keep in mind all previous definitions are still valid, all we need to change is a single line of code.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">EOSgenerators.fastRMF_EoS</span> <span class="k">as</span> <span class="nn">FastRMF</span>
+<span class="kn">import</span> <span class="nn">time</span>
+
+<span class="n">Tolos_crust_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s2">&quot;Tolos_crust_out.txt&quot;</span><span class="p">)</span>
+<span class="n">eps_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">g_cm_3</span>
+<span class="n">pres_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">dyn_cm_2</span>
+<span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span> <span class="o">=</span> <span class="n">crust</span><span class="o">.</span><span class="n">PolyInterpolate</span><span class="p">(</span><span class="n">eps_crust_T_out</span><span class="p">,</span> <span class="n">pres_crust_T_out</span><span class="p">)</span>
+
+<span class="n">m_sig</span> <span class="o">=</span> <span class="mf">495.0</span> <span class="o">/</span> <span class="mf">197.33</span>
+<span class="n">m_w</span> <span class="o">=</span> <span class="mf">3.96544</span>
+<span class="n">m_rho</span> <span class="o">=</span> <span class="mf">3.86662</span>
+
+<span class="n">g_sigma</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">107.5751</span><span class="p">)</span>
+<span class="n">g_omega</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">182.3949</span><span class="p">)</span>
+<span class="n">g_rho</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">206.4260</span><span class="p">)</span>
+
+<span class="n">kappa</span> <span class="o">=</span> <span class="mf">3.09114168</span> <span class="o">/</span> <span class="mf">197.33</span>
+<span class="n">lambda_0</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.00168015405</span>
+<span class="n">zeta</span> <span class="o">=</span> <span class="mf">0.024</span>
+<span class="n">Lambda_w</span> <span class="o">=</span> <span class="mf">0.045</span>
+<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+    <span class="p">[</span><span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span><span class="p">]</span>
+<span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<section id="(a)-Comparing-the-time-it-takes-to-generate-the-EoS">
+<h3>(a) Comparing the time it takes to generate the EoS<a class="headerlink" href="#(a)-Comparing-the-time-it-takes-to-generate-the-EoS" title="Link to this heading"></a></h3>
+<p>The reason why we test the time it takes to generate the EoS here is due to the fact that Numba is compiling the code, and as such the first run will take considerably longer to complete, but all other runs will be greatly faster.</p>
+<p>Warning: Do not rerun this cell, as the compilation time will be gone.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">### Normal RMF ####</span>
+
+<span class="n">first_run_time_normal</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="n">eps_fast</span><span class="p">,</span> <span class="n">pres_fast</span> <span class="o">=</span> <span class="n">RMF</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+<span class="n">first_run_time_normal</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">first_run_time_normal</span>
+
+<span class="n">time_for_20_runs_normal</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">):</span>
+    <span class="n">eps</span><span class="p">,</span> <span class="n">pres</span> <span class="o">=</span> <span class="n">RMF</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+<span class="n">time_for_20_runs_normal</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">time_for_20_runs_normal</span>
+
+
+<span class="c1">##### Fast RMF #####</span>
+
+<span class="n">first_run_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="n">eps_fast</span><span class="p">,</span> <span class="n">pres_fast</span> <span class="o">=</span> <span class="n">FastRMF</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+<span class="n">first_run_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">first_run_time</span>
+
+<span class="n">time_for_20_runs</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">):</span>
+    <span class="n">eps_fast</span><span class="p">,</span> <span class="n">pres_fast</span> <span class="o">=</span> <span class="n">FastRMF</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+<span class="n">time_for_20_runs</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">time_for_20_runs</span>
+
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;--------------------------------------------------------------------------------------&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">84</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">32</span> <span class="o">+</span> <span class="s2">&quot;Time Comparion Table&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">32</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">84</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for first run  with RMF-Normal  = </span><span class="si">{</span><span class="n">first_run_time_normal</span><span class="o">*</span><span class="mi">1000</span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">19</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for first run  with RMF-Fast    = </span><span class="si">{</span><span class="n">first_run_time</span><span class="o">*</span><span class="mi">1000</span><span class="w">       </span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">19</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">84</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for   20  runs with RMF-Normal  = </span><span class="si">{</span><span class="n">time_for_20_runs_normal</span><span class="o">*</span><span class="mi">1000</span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">19</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for   20  runs with RMF-Fast    = </span><span class="si">{</span><span class="n">time_for_20_runs</span><span class="o">*</span><span class="mi">1000</span><span class="w">       </span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">19</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">84</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for  all  runs with RMF-Normal  = </span><span class="si">{</span><span class="p">(</span><span class="n">first_run_time_normal</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">time_for_20_runs_normal</span><span class="p">)</span><span class="o">*</span><span class="mi">1000</span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">19</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for  all  runs with RMF-Fast    = </span><span class="si">{</span><span class="p">(</span><span class="n">first_run_time</span><span class="w">        </span><span class="o">+</span><span class="w"> </span><span class="n">time_for_20_runs</span><span class="w">       </span><span class="p">)</span><span class="o">*</span><span class="mi">1000</span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms (Has Compilation) &quot;</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">84</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for   1   run  with RMF-Normal  = </span><span class="si">{</span><span class="n">time_for_20_runs_normal</span><span class="o">*</span><span class="mi">1000</span><span class="o">/</span><span class="mi">20</span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms  &quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">17</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Time for   1   run  with RMF-Fast    = </span><span class="si">{</span><span class="n">time_for_20_runs</span><span class="w">       </span><span class="o">*</span><span class="mi">1000</span><span class="o">/</span><span class="mi">20</span><span class="si">:</span><span class="s2">6.0f</span><span class="si">}</span><span class="s2"> ms&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">19</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">84</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">84</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;|&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">30</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;Total Speed Up = </span><span class="si">{</span><span class="n">time_for_20_runs_normal</span><span class="o">/</span><span class="n">time_for_20_runs</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> x&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span><span class="o">*</span><span class="mi">30</span> <span class="o">+</span> <span class="s2">&quot;|&quot;</span> <span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;--------------------------------------------------------------------------------------&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+--------------------------------------------------------------------------------------
+|                                                                                    |
+|                                Time Comparion Table                                |
+|                                                                                    |
+|                 Time for first run  with RMF-Normal  =    550 ms                   |
+|                 Time for first run  with RMF-Fast    =   1027 ms                   |
+|                                                                                    |
+|                 Time for   20  runs with RMF-Normal  =  10099 ms                   |
+|                 Time for   20  runs with RMF-Fast    =    287 ms                   |
+|                                                                                    |
+|                 Time for  all  runs with RMF-Normal  =  10649 ms                   |
+|                 Time for  all  runs with RMF-Fast    =   1315 ms (Has Compilation) |
+|                                                                                    |
+|                 Time for   1   run  with RMF-Normal  =    505 ms                   |
+|                 Time for   1   run  with RMF-Fast    =     14 ms                   |
+|                                                                                    |
+|                                                                                    |
+|                              Total Speed Up = 35.15 x                              |
+--------------------------------------------------------------------------------------
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">eps_total_fast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">*</span><span class="n">eps_com</span><span class="p">,</span> <span class="o">*</span><span class="n">eps_fast</span><span class="p">])</span>
+<span class="n">pres_total_fast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">*</span><span class="n">pres_com</span><span class="p">,</span> <span class="o">*</span><span class="n">pres_fast</span><span class="p">])</span>
+
+<span class="n">compare_fig1</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">eps_total_fast</span><span class="p">,</span> <span class="n">pres_total_fast</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Fast Version&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">eps_total</span><span class="p">,</span> <span class="n">pres_total</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Normal Version&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;P $[fm^{-4}]$&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$\epsilon$ $[fm^{-4}]$&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">compare_fig1</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_16_0.png" src="_images/test_EOSgenerators_16_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MR_fast</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">eps_total_fast</span><span class="p">,</span> <span class="n">pres_total_fast</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+<span class="n">compare_fig2</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR_fast</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">km</span><span class="p">,</span> <span class="n">MR_fast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">Msun</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Fast Version&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">km</span><span class="p">,</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">Msun</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Normal Version&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;M [$M_{\odot}$]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;R [km]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">compare_fig2</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_17_0.png" src="_images/test_EOSgenerators_17_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Polytrope-EOS">
+<h2>Polytrope EOS<a class="headerlink" href="#Polytrope-EOS" title="Link to this heading"></a></h2>
+<p>First import all the package that will be used.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">ode</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.Polytrope_EOS</span> <span class="k">as</span> <span class="nn">Polytrope</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.Strangeon_EOS</span> <span class="k">as</span> <span class="nn">Strangeon</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.crust_EOS</span> <span class="k">as</span> <span class="nn">crust</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.unit</span> <span class="kn">import</span>  <span class="n">km</span><span class="p">,</span> <span class="n">Msun</span><span class="p">,</span> <span class="n">MeV</span><span class="p">,</span><span class="n">fm</span><span class="p">,</span><span class="n">g_cm_3</span><span class="p">,</span><span class="n">dyn_cm_2</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span><span class="n">c</span>
+</pre></div>
+</div>
+</div>
+<p>The polytropic equation of state (Polytrope) is the most commonly used EOS in astrophysics, its advantage is assumed to be capable to simulate all EOS shape with given parameters. Here, we provide one of the computation for polytrope.</p>
+<section id="(a)-Connecting-outer-crust-with-polytropic-part">
+<h3>(a) Connecting outer crust with polytropic part<a class="headerlink" href="#(a)-Connecting-outer-crust-with-polytropic-part" title="Link to this heading"></a></h3>
+<p>For polytrope, we first connecting it with crust as we did in RMF</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Tolos_crust_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s2">&quot;Test_Case/Tolos_crust_out.txt&quot;</span><span class="p">)</span>
+<span class="n">eps_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">g_cm_3</span>
+<span class="n">pres_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">dyn_cm_2</span>
+</pre></div>
+</div>
+</div>
+<p>We define a set of parameters, first three in theta are the parameters control the slope of each polytrope, and the last two are transition point from one segment to another. Details see the documentation about polytrope</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">gammas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.03</span><span class="p">,</span> <span class="mf">2.62</span><span class="p">,</span> <span class="mf">1.38</span><span class="p">])</span>
+<span class="n">rho_ts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">8.087e13</span><span class="p">,</span> <span class="mf">1.8078e15</span><span class="p">])</span> <span class="o">*</span> <span class="n">g_cm_3</span>
+<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gammas</span><span class="p">,</span> <span class="n">rho_ts</span><span class="p">)</span>
+
+<span class="n">eps_set</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">11.7</span><span class="p">,</span> <span class="mf">15.6</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">g_cm_3</span>
+<span class="n">pres_out</span> <span class="o">=</span> <span class="n">Polytrope</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_set</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">eps_crust_T_out</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">pres_crust_T_out</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="n">eps_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">eps_crust_T_out</span><span class="p">,</span> <span class="n">eps_set</span><span class="p">))</span>
+<span class="n">pres_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">pres_crust_T_out</span><span class="p">,</span> <span class="n">pres_out</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="(b)-Solve-TOV-with-this-EOS">
+<h3>(b) Solve TOV with this EOS<a class="headerlink" href="#(b)-Solve-TOV-with-this-EOS" title="Link to this heading"></a></h3>
+<p>Here below we use the strangeon matter EOS to compute the mass radius curve.</p>
+<p>The following code calculates the mass and radius of the strange stars for the given EOS of the polytrope using our main.OutputMR</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">eps_total</span><span class="p">,</span> <span class="n">pres_total</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">km</span> <span class="p">,</span> <span class="n">MR</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">Msun</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="c1"># ax.scatter(R /km, M / Msun, c=&#39;r&#39;)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;M [$M_{\odot}$]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;R [km]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_28_0.png" src="_images/test_EOSgenerators_28_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Strangeon-matter-EOS">
+<h2>Strangeon matter EOS<a class="headerlink" href="#Strangeon-matter-EOS" title="Link to this heading"></a></h2>
+<p>First import all the package that will be used.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">ode</span>
+<span class="kn">from</span> <span class="nn">EOSgenerators</span> <span class="kn">import</span> <span class="n">Strangeon_EOS</span> <span class="k">as</span> <span class="n">Strangeon</span>
+
+<span class="kn">from</span> <span class="nn">TOVsolver.unit</span> <span class="kn">import</span>  <span class="n">km</span><span class="p">,</span> <span class="n">Msun</span><span class="p">,</span> <span class="n">MeV</span><span class="p">,</span><span class="n">fm</span>
+</pre></div>
+</div>
+</div>
+<p>The strangeon matter EOS describes the strongly interacting matter in the solid state, and the compact star can be a self-bound quark star composed of strangeon matter. Therefore, in the following we present the bare quark star EOS without a crust.</p>
+<p>Note that the strangeon matter parameters we input are in the natural unit system. The quentity returned by the Strangeon_compute_EOS function is in the Geometric Unit System. Thus, here, the parameter epsilon and ns are in the units of <span class="math notranslate nohighlight">\(MeV/fm^{-3}\)</span>. So follow the unite conversion rule, if we are using this unit, we just time this unit to do compute</p>
+<section id="(a)-Defining-the-EOS-parameters">
+<h3>(a) Defining the EOS parameters<a class="headerlink" href="#(a)-Defining-the-EOS-parameters" title="Link to this heading"></a></h3>
+<p>Define the strangeon matter EOS that we will use to calculate the energy densities and pressures of the system. The following is an example of how to define the theta and Nq. For example, theta=[50, 0.24] means the example values for epsilon and ns: epsilon in units of MeV, ns in units of <span class="math notranslate nohighlight">\(fm^-3\)</span>. Nq is an integer, e.g. Nq=9, 12, 15, 18, 21, 24, 27</p>
+<p>For the definition of parameter n, the minimum value is 3<em>ns/Nq, the maximum value is about 0.16</em>8*3/Nq. Then we can generate,for example, 1000 points for the input of the srangeon matter EOS. Special note: If you are using this EOS, need a very fine grid for the EOS. From our test, 1000 points for this EOS is the minimum requirement</p>
+<p>Once we define the EOS parameters already, we can use the Strangeon_compute_EOS function to calcute the energy_density and pressure.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Nq</span><span class="o">=</span><span class="mi">18</span>
+<span class="n">epsilon</span><span class="o">=</span><span class="mi">50</span>
+<span class="n">ns</span><span class="o">=</span><span class="mf">0.24</span>
+<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">Nq</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">,</span> <span class="n">ns</span><span class="p">])</span>
+<span class="n">n_min</span> <span class="o">=</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">n_max</span> <span class="o">=</span> <span class="mf">0.16</span> <span class="o">*</span> <span class="mi">8</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">/</span> <span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">n_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">n_min</span><span class="p">,</span> <span class="n">n_max</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span>
+
+<span class="n">energy_densities</span><span class="p">,</span> <span class="n">pressures</span> <span class="o">=</span> <span class="n">Strangeon</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">n_values</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n values:&quot;</span><span class="p">,</span> <span class="n">n_values</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Energy densities:&quot;</span><span class="p">,</span> <span class="n">energy_densities</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Pressures:&quot;</span><span class="p">,</span> <span class="n">pressures</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+n values: [0.04       0.04008671 0.04017342 ... 0.21315991 0.21324662 0.21333333]
+Energy densities: [  204.61935484   205.0631334    205.50734538 ... 46616.1606005
+ 46711.98281964 46807.96367981]
+Pressures: [0.00000000e+00 1.98864796e-01 4.00749542e-01 ... 1.88749504e+05
+ 1.89139331e+05 1.89529799e+05]
+</pre></div></div>
+</div>
+</section>
+<section id="id1">
+<h3>(b) Solve TOV with this EOS<a class="headerlink" href="#id1" title="Link to this heading"></a></h3>
+<p>Here below we use the strangeon matter EOS to compute the mass radius curve.</p>
+<p>The following code calculates the mass and radius of the strange stars for the given EOS of the strange matter EOS. Since our energy_density and pressure are all in same unit <span class="math notranslate nohighlight">\(MeV/fm^{-3}\)</span> so all we need is to time this unit</p>
+<p>Input a given central pressure and central energy density, you will obtain the radius where the pressure is zero and the mass at that radius.</p>
+<p>Solve the TOV equations using each central pressure and energy density</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="n">MR</span><span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">energy_densities</span><span class="o">*</span> <span class="n">MeV</span><span class="o">/</span><span class="n">fm</span><span class="o">**</span><span class="mi">3</span>   <span class="p">,</span> <span class="n">pressures</span><span class="o">*</span> <span class="n">MeV</span><span class="o">/</span><span class="n">fm</span><span class="o">**</span><span class="mi">3</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">km</span> <span class="p">,</span> <span class="n">MR</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">Msun</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;M [$M_{\odot}$]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;R [km]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="c1"># ax.set_xlim(8.0, 20.0)</span>
+<span class="c1"># ax.set_ylim(0, 3)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_39_0.png" src="_images/test_EOSgenerators_39_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Speed-of-sound-EOS">
+<h2>Speed of sound EOS<a class="headerlink" href="#Speed-of-sound-EOS" title="Link to this heading"></a></h2>
+<p>First import all the package that will be used.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">EOSgenerators</span> <span class="kn">import</span> <span class="n">SpeedofSound_EOS</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.crust_EOS</span> <span class="k">as</span> <span class="nn">crust</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.unit</span> <span class="kn">import</span> <span class="n">g_cm_3</span><span class="p">,</span> <span class="n">dyn_cm_2</span><span class="p">,</span> <span class="n">km</span><span class="p">,</span> <span class="n">Msun</span>
+</pre></div>
+</div>
+</div>
+<p>The Speed of sound EOS describes the core EOS of a compact star, so it should be connected with the crust EOS to form a full EOS. See <a class="reference external" href="https://arxiv.org/abs/1812.08188">https://arxiv.org/abs/1812.08188</a> for details.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Tolos_crust_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s2">&quot;Test_Case/Tolos_crust_out.txt&quot;</span><span class="p">)</span>
+<span class="n">eps_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">g_cm_3</span>
+<span class="n">pres_crust_T_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">dyn_cm_2</span>
+<span class="n">eps_com</span><span class="p">,</span> <span class="n">pres_com</span> <span class="o">=</span> <span class="n">crust</span><span class="o">.</span><span class="n">PolyInterpolate</span><span class="p">(</span><span class="n">eps_crust_T_out</span><span class="p">,</span> <span class="n">pres_crust_T_out</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="id2">
+<h3>(a) Defining the EOS parameters<a class="headerlink" href="#id2" title="Link to this heading"></a></h3>
+<p>To construct the Speed of sound EOS, we need to specify the outer crust EOS and the interface EOS, and then connect them with the core EOS.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_last</span> <span class="o">=</span> <span class="n">eps_com</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">y_last</span> <span class="o">=</span> <span class="n">pres_com</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">dydx_last</span> <span class="o">=</span> <span class="p">(</span><span class="n">pres_com</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">pres_com</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">eps_com</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">eps_com</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
+<span class="n">CS_EOS</span> <span class="o">=</span> <span class="n">SpeedofSound_EOS</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">x_last</span><span class="p">,</span> <span class="n">y_last</span><span class="p">,</span> <span class="n">dydx_last</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>In order to generate the parameters of the Speed of sound EOS, we need a list of 5 uniform random numbers between 0 and 1.</p>
+<p>After generate the parameters, we use function <code class="docutils literal notranslate"><span class="pre">check_a</span></code> to check if the parameters are valid.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cs_a</span> <span class="o">=</span> <span class="n">CS_EOS</span><span class="o">.</span><span class="n">gen_a</span><span class="p">((</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">CS_EOS</span><span class="o">.</span><span class="n">check_a</span><span class="p">(</span><span class="n">cs_a</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+True
+</pre></div></div>
+</div>
+</section>
+<section id="id3">
+<h3>(b) Solve TOV with this EOS<a class="headerlink" href="#id3" title="Link to this heading"></a></h3>
+<p>Here below we use the speed of sound EOS to compute the mass radius curve.</p>
+<p>First we calculate the core EOS.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">core_e_min</span> <span class="o">=</span> <span class="n">x_last</span>
+<span class="n">core_e_max</span> <span class="o">=</span> <span class="mf">2e16</span> <span class="o">*</span> <span class="n">g_cm_3</span>
+<span class="n">n_core_e</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="n">core_e</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">geomspace</span><span class="p">(</span><span class="n">core_e_min</span><span class="p">,</span> <span class="n">core_e_max</span><span class="p">,</span> <span class="n">n_core_e</span><span class="p">)</span>
+<span class="n">core_p</span> <span class="o">=</span> <span class="n">CS_EOS</span><span class="o">.</span><span class="n">cal_core_p</span><span class="p">(</span><span class="n">core_e</span><span class="p">,</span> <span class="n">cs_a</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Then we concat the core EOS with the crust EOS.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">full_e</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">eps_com</span><span class="p">,</span> <span class="n">core_e</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
+<span class="n">full_p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">pres_com</span><span class="p">,</span> <span class="n">core_p</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">full_e</span> <span class="o">/</span> <span class="n">g_cm_3</span><span class="p">,</span> <span class="n">full_p</span> <span class="o">/</span> <span class="n">dyn_cm_2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$\epsilon\quad [g/cm^3]$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$P\quad [dyn/cm^2]$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_53_0.png" src="_images/test_EOSgenerators_53_0.png" />
+</div>
+</div>
+<p>Finally we compute the mass radius curve.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">full_e</span><span class="p">,</span> <span class="n">full_p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">km</span><span class="p">,</span> <span class="n">MR</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">Msun</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">17</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Radius [km]&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Mass [Msun]&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_55_0.png" src="_images/test_EOSgenerators_55_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="MIT-bag-model-EOS">
+<h2>MIT bag model EOS<a class="headerlink" href="#MIT-bag-model-EOS" title="Link to this heading"></a></h2>
+<p>The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state <span class="math notranslate nohighlight">\(p=\frac{\epsilon}{3}-\frac{4B}{3}\)</span>. There is only one parameter, the “bag constant” <span class="math notranslate nohighlight">\(B\)</span>. This represents the vacuum energy density, which creates a “bag” in which quarks are confined. See <a class="reference external" href="https://doi.org/10.1103/PhysRevD.9.3471">Chodos et al. (1974)</a>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">EOSgenerators</span> <span class="kn">import</span> <span class="n">MITbag_EOS</span>
+<span class="kn">from</span> <span class="nn">TOVsolver.unit</span> <span class="kn">import</span> <span class="n">MeV</span><span class="p">,</span> <span class="n">fm</span><span class="p">,</span> <span class="n">g_cm_3</span><span class="p">,</span> <span class="n">dyn_cm_2</span><span class="p">,</span> <span class="n">km</span><span class="p">,</span> <span class="n">Msun</span>
+</pre></div>
+</div>
+</div>
+<section id="(a)-Defining-the-parameter">
+<h3>(a) Defining the parameter<a class="headerlink" href="#(a)-Defining-the-parameter" title="Link to this heading"></a></h3>
+<p>A “typical” value of 57 <span class="math notranslate nohighlight">\(MeVfm^{-3}\)</span> for the bag constant is taken from <a class="reference external" href="http://dx.doi.org/10.1086/164679">Alcock (1986)</a>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">B</span> <span class="o">=</span> <span class="mi">57</span>
+
+<span class="n">epsilon</span><span class="p">,</span><span class="n">p</span> <span class="o">=</span> <span class="n">MITbag_EOS</span><span class="o">.</span><span class="n">MITbag_compute_EOS</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;energy density range:&#39;</span><span class="p">,</span><span class="n">epsilon</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s1">&#39;to&#39;</span><span class="p">,</span><span class="n">epsilon</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;pressure range:&#39;</span><span class="p">,</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s1">&#39;to&#39;</span><span class="p">,</span><span class="n">p</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+energy density range: 1.1554421785237932 to 2.888605446309483
+pressure range: 0.0 to 0.5777210892618965
+</pre></div></div>
+</div>
+</section>
+<section id="(b)-Computing-the-mass-radius-relation">
+<h3>(b) Computing the mass-radius relation<a class="headerlink" href="#(b)-Computing-the-mass-radius-relation" title="Link to this heading"></a></h3>
+<p>We can compute a mass-radius relation for this EOS by solving the TOV equation.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.EoS_import</span> <span class="k">as</span> <span class="nn">EoS_import</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">repeat</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">epsilon</span><span class="p">,</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">km</span><span class="p">,</span> <span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">Msun</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;B = </span><span class="si">{</span><span class="n">B</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> MeV $fm^</span><span class="si">{</span><span class="o">-</span><span class="mi">3</span><span class="si">}</span><span class="s1">$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;M [$M_{\odot}$]&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;R [km]&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_EOSgenerators_66_0.png" src="_images/test_EOSgenerators_66_0.png" />
+</div>
+</div>
+</section>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="Contributor.html" class="btn btn-neutral float-left" title="Call for Contributions" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2023, Chun Huang, Nicole Obsborn, Nathan Whitsett.</p>
+  </div>
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/test_EOSgenerators.ipynb b/test_EOSgenerators.ipynb
new file mode 100644
index 0000000..5faa8b4
--- /dev/null
+++ b/test_EOSgenerators.ipynb
@@ -0,0 +1,996 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Equation of State Generators illustration notebook\n",
+    "\n",
+    "## RMF EOS \n",
+    "\n",
+    "First import all of the package that will be used here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import EOSgenerators.crust_EOS as crust\n",
+    "import EOSgenerators.RMF_EOS as RMF\n",
+    "import EOSgenerators.Polytrope_EOS as Polytrope\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import math\n",
+    "from TOVsolver.unit import g_cm_3, dyn_cm_2, km, Msun\n",
+    "from TOVsolver.maxium_central_density import maxium_central_density"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  (a) Load crust EOS and prepare interface EOS\n",
+    "\n",
+    "Define constants, and load the crust EOS into this notebook, since when we solve the equation of state to TOV equation, we need full scope of the equation of state. Here, crust eos file is 'Tolos_crust_out.txt', while we need to times g_cm_3 for energy density and dyn_cm_2 for pressure, that will be easier for treatment, no need worry about G and c.\n",
+    "\n",
+    "Then just connect the crust part with the interface part(the part between core equation of state and crust) by a PolyInterpolate function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example of RMF"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Defining the paramters\n",
+    "\n",
+    "Defining the free prameters that we need to compute the equation of state, you could check our intro part of documentation or just check the original paper of us about details. [Huang et al,2023](https://arxiv.org/abs/2303.17518)\n",
+    "\n",
+    "After defined the equation of state parameters, we could call the 'compute_EOS' function from RMF, to compute full core equation of state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m_sig = 495.0 / 197.33\n",
+    "m_w = 3.96544\n",
+    "m_rho = 3.86662\n",
+    "\n",
+    "g_sigma = math.sqrt(107.5751)\n",
+    "g_omega = math.sqrt(182.3949)\n",
+    "g_rho = math.sqrt(206.4260)\n",
+    "\n",
+    "kappa = 3.09114168 / 197.33\n",
+    "lambda_0 = -0.00168015405\n",
+    "zeta = 0.024\n",
+    "Lambda_w = 0.045\n",
+    "theta = np.array(\n",
+    "    [m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w]\n",
+    ")\n",
+    "eps, pres = RMF.compute_EOS(eps_com, pres_com, theta)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (c) Connecting Core and crust EOS\n",
+    "\n",
+    "These two line can easily connect the Equation of state we computed for core part by setting the parameters and the crust equation of state, to a full equation of state that prepared for next step output\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eps_total = np.array([*eps_com, *eps])\n",
+    "pres_total = np.array([*pres_com, *pres])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below is one of the possiblity, use our defined function, 'OutputMR' to compute out what the mass radius curve corresponding to this equation of state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR(\"\", eps_total, pres_total).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[1] / km, MR[0] / Msun, lw=2)\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Illustrating the power of Numba accelerated EoS Generation\n",
+    "\n",
+    "We shall now run the same code but with the Numba accelerated version, comparing the times for EoS Generation.\n",
+    "\n",
+    "Keep in mind all previous definitions are still valid, all we need to change is a single line of code.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import EOSgenerators.fastRMF_EoS as FastRMF\n",
+    "import time\n",
+    "\n",
+    "Tolos_crust_out = np.loadtxt(\"Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)\n",
+    "\n",
+    "m_sig = 495.0 / 197.33\n",
+    "m_w = 3.96544\n",
+    "m_rho = 3.86662\n",
+    "\n",
+    "g_sigma = math.sqrt(107.5751)\n",
+    "g_omega = math.sqrt(182.3949)\n",
+    "g_rho = math.sqrt(206.4260)\n",
+    "\n",
+    "kappa = 3.09114168 / 197.33\n",
+    "lambda_0 = -0.00168015405\n",
+    "zeta = 0.024\n",
+    "Lambda_w = 0.045\n",
+    "theta = np.array(\n",
+    "    [m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Comparing the time it takes to generate the EoS\n",
+    "\n",
+    "The reason why we test the time it takes to generate the EoS here is due to the fact that Numba is compiling the code, and as such the first run will take considerably longer to complete, but all other runs will be greatly faster.\n",
+    "\n",
+    "Warning: Do not rerun this cell, as the compilation time will be gone.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------------\n",
+      "|                                                                                    |\n",
+      "|                                Time Comparion Table                                |\n",
+      "|                                                                                    |\n",
+      "|                 Time for first run  with RMF-Normal  =    550 ms                   |\n",
+      "|                 Time for first run  with RMF-Fast    =   1027 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                 Time for   20  runs with RMF-Normal  =  10099 ms                   |\n",
+      "|                 Time for   20  runs with RMF-Fast    =    287 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                 Time for  all  runs with RMF-Normal  =  10649 ms                   |\n",
+      "|                 Time for  all  runs with RMF-Fast    =   1315 ms (Has Compilation) |\n",
+      "|                                                                                    |\n",
+      "|                 Time for   1   run  with RMF-Normal  =    505 ms                   |\n",
+      "|                 Time for   1   run  with RMF-Fast    =     14 ms                   |\n",
+      "|                                                                                    |\n",
+      "|                                                                                    |\n",
+      "|                              Total Speed Up = 35.15 x                              |\n",
+      "--------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Normal RMF ####\n",
+    "\n",
+    "first_run_time_normal = time.time()\n",
+    "eps_fast, pres_fast = RMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "first_run_time_normal = time.time() - first_run_time_normal\n",
+    "\n",
+    "time_for_20_runs_normal = time.time()\n",
+    "for _ in range(20):\n",
+    "    eps, pres = RMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "time_for_20_runs_normal = time.time() - time_for_20_runs_normal\n",
+    "\n",
+    "\n",
+    "##### Fast RMF #####\n",
+    "\n",
+    "first_run_time = time.time()\n",
+    "eps_fast, pres_fast = FastRMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "first_run_time = time.time() - first_run_time\n",
+    "\n",
+    "time_for_20_runs = time.time()\n",
+    "for _ in range(20):\n",
+    "    eps_fast, pres_fast = FastRMF.compute_EOS(eps_com, pres_com, theta)\n",
+    "time_for_20_runs = time.time() - time_for_20_runs\n",
+    "\n",
+    "\n",
+    "print(f\"--------------------------------------------------------------------------------------\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*32 + \"Time Comparion Table\" + \" \"*32 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for first run  with RMF-Normal  = {first_run_time_normal*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for first run  with RMF-Fast    = {first_run_time*1000       :6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   20  runs with RMF-Normal  = {time_for_20_runs_normal*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   20  runs with RMF-Fast    = {time_for_20_runs*1000       :6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for  all  runs with RMF-Normal  = {(first_run_time_normal + time_for_20_runs_normal)*1000:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for  all  runs with RMF-Fast    = {(first_run_time        + time_for_20_runs       )*1000:6.0f} ms (Has Compilation) \" + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   1   run  with RMF-Normal  = {time_for_20_runs_normal*1000/20:6.0f} ms  \" + \" \"*17 + \"|\")\n",
+    "print(f\"|\" + \" \"*17 + f\"Time for   1   run  with RMF-Fast    = {time_for_20_runs       *1000/20:6.0f} ms\" + \" \"*19 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*84 + \"|\")\n",
+    "print(f\"|\" + \" \"*30 + f\"Total Speed Up = {time_for_20_runs_normal/time_for_20_runs:.2f} x\" + \" \"*30 + \"|\" )\n",
+    "print(f\"--------------------------------------------------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eps_total_fast = np.array([*eps_com, *eps_fast])\n",
+    "pres_total_fast = np.array([*pres_com, *pres_fast])\n",
+    "\n",
+    "compare_fig1, ax = plt.subplots(1, 1, figsize=(9,6))\n",
+    "\n",
+    "ax.plot(eps_total_fast, pres_total_fast, lw=2, label=\"Fast Version\")\n",
+    "ax.plot(eps_total, pres_total, lw=6, label=\"Normal Version\", alpha=0.3)\n",
+    "ax.set_ylabel(r\"P $[fm^{-4}]$\", fontsize=16)\n",
+    "ax.set_xlabel(r\"$\\epsilon$ $[fm^{-4}]$\", fontsize=16)\n",
+    "ax.set_xlim(0, 6)\n",
+    "ax.set_ylim(0, 2)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "plt.legend()\n",
+    "compare_fig1.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MR_fast = main.OutputMR(\"\", eps_total_fast, pres_total_fast).T\n",
+    "\n",
+    "compare_fig2, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "\n",
+    "ax.plot(MR_fast[1] / km, MR_fast[0] / Msun, lw=2, label=\"Fast Version\")\n",
+    "ax.plot(MR[1] / km, MR[0] / Msun, lw=6, label=\"Normal Version\", alpha=0.3)\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "plt.legend()\n",
+    "compare_fig2.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polytrope EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.integrate import ode\n",
+    "import EOSgenerators.Polytrope_EOS as Polytrope\n",
+    "import EOSgenerators.Strangeon_EOS as Strangeon\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "from TOVsolver.unit import  km, Msun, MeV,fm,g_cm_3,dyn_cm_2, G,c "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The polytropic equation of state (Polytrope) is the most commonly used EOS in astrophysics, its advantage is assumed to be capable to simulate all EOS shape with given parameters. Here, we provide one of the computation for polytrope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Connecting outer crust with polytropic part"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For polytrope, we first connecting it with crust as we did in RMF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define a set of parameters, first three in theta are the parameters control the slope of each polytrope, and the last two are transition point from one segment to another. Details see the documentation about polytrope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gammas = np.array([1.03, 2.62, 1.38])\n",
+    "rho_ts = np.array([8.087e13, 1.8078e15]) * g_cm_3\n",
+    "theta = np.append(gammas, rho_ts)\n",
+    "\n",
+    "eps_set = np.logspace(11.7, 15.6, 100, base=10) * g_cm_3\n",
+    "pres_out = Polytrope.compute_EOS(eps_set, theta, eps_crust_T_out[-1], pres_crust_T_out[-1])\n",
+    "\n",
+    "eps_total = np.hstack((eps_crust_T_out, eps_set))\n",
+    "pres_total = np.hstack((pres_crust_T_out, pres_out))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS\n",
+    "\n",
+    "Here below we use the strangeon matter EOS to compute the mass radius curve.\n",
+    "\n",
+    "The following code calculates the mass and radius of the strange stars for the given EOS of the polytrope using our main.OutputMR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR('',eps_total, pres_total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[:,1]/km , MR[:,0]/Msun, lw=2)\n",
+    "# ax.scatter(R /km, M / Msun, c='r')\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "ax.set_xlim(8.0, 20.0)\n",
+    "ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Strangeon matter EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.integrate import ode\n",
+    "from EOSgenerators import Strangeon_EOS as Strangeon\n",
+    "\n",
+    "from TOVsolver.unit import  km, Msun, MeV,fm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The strangeon matter EOS describes the strongly interacting matter in the solid state, and the compact star can be a self-bound quark star composed of strangeon matter. Therefore, in the following we present the bare quark star EOS without a crust.\n",
+    "\n",
+    "Note that the strangeon matter parameters we input are in the natural unit system. The quentity returned by the Strangeon_compute_EOS function is in the Geometric Unit System. Thus, here, the parameter epsilon and ns are in the units of $MeV/fm^{-3}$. So follow the unite conversion rule, if we are using this unit, we just time this unit to do compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the EOS parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the strangeon matter EOS that we will use to calculate the energy densities and pressures of the system.\n",
+    "The following is an example of how to define the theta and Nq.\n",
+    "For example, theta=[50, 0.24] means the example values for epsilon and ns:\n",
+    "epsilon in units of MeV, ns in units of $fm^-3$.\n",
+    "Nq is an integer, e.g. Nq=9, 12, 15, 18, 21, 24, 27\n",
+    "\n",
+    "For the definition of parameter n, the minimum value is 3*ns/Nq, the maximum value is about 0.16*8*3/Nq.\n",
+    "Then we can generate,for example, 1000 points for the input of the srangeon matter EOS. Special note: If you are using this EOS, need a very fine grid for the EOS. From our test, 1000 points for this EOS is the minimum requirement\n",
+    "\n",
+    "Once we define the EOS parameters already, we can use the Strangeon_compute_EOS function to calcute the energy_density and pressure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "n values: [0.04       0.04008671 0.04017342 ... 0.21315991 0.21324662 0.21333333]\n",
+      "Energy densities: [  204.61935484   205.0631334    205.50734538 ... 46616.1606005\n",
+      " 46711.98281964 46807.96367981]\n",
+      "Pressures: [0.00000000e+00 1.98864796e-01 4.00749542e-01 ... 1.88749504e+05\n",
+      " 1.89139331e+05 1.89529799e+05]\n"
+     ]
+    }
+   ],
+   "source": [
+    "Nq=18\n",
+    "epsilon=50\n",
+    "ns=0.24\n",
+    "theta = np.array([Nq, epsilon, ns])\n",
+    "n_min = 3 * theta[2] / theta[0]  \n",
+    "n_max = 0.16 * 8 * 3 / theta[0]   \n",
+    "n_values = np.linspace(n_min, n_max, 2000) \n",
+    "\n",
+    "energy_densities, pressures = Strangeon.compute_EOS(n_values, theta)\n",
+    "print(\"n values:\", n_values)\n",
+    "print(\"Energy densities:\", energy_densities)\n",
+    "print(\"Pressures:\", pressures) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below we use the strangeon matter EOS to compute the mass radius curve.\n",
+    "\n",
+    "The following code calculates the mass and radius of the strange stars for the given EOS of the strange matter EOS. Since our energy_density and pressure are all in same unit $MeV/fm^{-3}$ so all we need is to time this unit\n",
+    "\n",
+    "Input a given central pressure and central energy density, you will obtain the radius where the pressure is zero and the mass at that radius."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Solve the TOV equations using each central pressure and energy density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "MR= main.OutputMR('',energy_densities* MeV/fm**3   , pressures* MeV/fm**3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(9, 6))\n",
+    "ax.plot(MR[:,1]/km , MR[:,0]/Msun, lw=2)\n",
+    "\n",
+    "ax.set_ylabel(r\"M [$M_{\\odot}$]\", fontsize=16)\n",
+    "ax.set_xlabel(\"R [km]\", fontsize=16)\n",
+    "# ax.set_xlim(8.0, 20.0)\n",
+    "# ax.set_ylim(0, 3)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "ax.tick_params(top=1, right=1, which=\"both\", direction=\"in\", labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Speed of sound EOS \n",
+    "\n",
+    "First import all the package that will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import TOVsolver.main as main\n",
+    "import matplotlib.pyplot as plt\n",
+    "from EOSgenerators import SpeedofSound_EOS\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "from TOVsolver.unit import g_cm_3, dyn_cm_2, km, Msun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Speed of sound EOS describes the core EOS of a compact star, \n",
+    "so it should be connected with the crust EOS to form a full EOS.\n",
+    "See https://arxiv.org/abs/1812.08188 for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Tolos_crust_out = np.loadtxt(\"Test_Case/Tolos_crust_out.txt\")\n",
+    "eps_crust_T_out = Tolos_crust_out[:, 3] * g_cm_3\n",
+    "pres_crust_T_out = Tolos_crust_out[:, 4] * dyn_cm_2\n",
+    "eps_com, pres_com = crust.PolyInterpolate(eps_crust_T_out, pres_crust_T_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the EOS parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To construct the Speed of sound EOS, we need to specify the outer crust EOS and \n",
+    "the interface EOS, and then connect them with the core EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_last = eps_com[-1]\n",
+    "y_last = pres_com[-1]\n",
+    "dydx_last = (pres_com[-1] - pres_com[-2]) / (eps_com[-1] - eps_com[-2])\n",
+    "CS_EOS = SpeedofSound_EOS.compute_EOS(x_last, y_last, dydx_last)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to generate the parameters of the Speed of sound EOS, we need a list\n",
+    "of 5 uniform random numbers between 0 and 1.\n",
+    "\n",
+    "After generate the parameters, we use function `check_a` to check if the parameters\n",
+    "are valid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "cs_a = CS_EOS.gen_a((0.2, 0.2, 0.3, 0.4, 0.5))\n",
+    "print(CS_EOS.check_a(cs_a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Solve TOV with this EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here below we use the speed of sound EOS to compute the mass radius curve.\n",
+    "\n",
+    "First we calculate the core EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_e_min = x_last\n",
+    "core_e_max = 2e16 * g_cm_3\n",
+    "n_core_e = 1000\n",
+    "core_e = np.geomspace(core_e_min, core_e_max, n_core_e)\n",
+    "core_p = CS_EOS.cal_core_p(core_e, cs_a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we concat the core EOS with the crust EOS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "full_e = np.concatenate((eps_com, core_e[1:]))\n",
+    "full_p = np.concatenate((pres_com, core_p[1:]))\n",
+    "plt.figure(dpi=200)\n",
+    "plt.plot(full_e / g_cm_3, full_p / dyn_cm_2)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(r\"$\\epsilon\\quad [g/cm^3]$\")\n",
+    "plt.ylabel(r\"$P\\quad [dyn/cm^2]$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we compute the mass radius curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MR = main.OutputMR(\"\", full_e, full_p)\n",
+    "plt.figure(dpi=200)\n",
+    "plt.plot(MR[:, 1] / km, MR[:, 0] / Msun)\n",
+    "plt.xlim(6, 17)\n",
+    "plt.ylim(0, 2)\n",
+    "plt.xlabel(\"Radius [km]\")\n",
+    "plt.ylabel(\"Mass [Msun]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MIT bag model EOS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The MIT bag model, which has commonly been applied to strange quark stars, relates pressure to energy density with the simple equation of state $p=\\frac{\\epsilon}{3}-\\frac{4B}{3}$. There is only one parameter, the \"bag constant\" $B$. This represents the vacuum energy density, which creates a \"bag\" in which quarks are confined. See [Chodos et al. (1974)](https://doi.org/10.1103/PhysRevD.9.3471)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from EOSgenerators import MITbag_EOS\n",
+    "from TOVsolver.unit import MeV, fm, g_cm_3, dyn_cm_2, km, Msun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Defining the parameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A \"typical\" value of 57 $MeVfm^{-3}$ for the bag constant is taken from [Alcock (1986)](http://dx.doi.org/10.1086/164679)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy density range: 1.1554421785237932 to 2.888605446309483\n",
+      "pressure range: 0.0 to 0.5777210892618965\n"
+     ]
+    }
+   ],
+   "source": [
+    "B = 57\n",
+    "\n",
+    "epsilon,p = MITbag_EOS.MITbag_compute_EOS(B)\n",
+    "\n",
+    "print('energy density range:',epsilon[0],'to',epsilon[-1])\n",
+    "print('pressure range:',p[0],'to',p[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) Computing the mass-radius relation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute a mass-radius relation for this EOS by solving the TOV equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import TOVsolver.main as main\n",
+    "import TOVsolver.EoS_import as EoS_import\n",
+    "from itertools import repeat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MR = main.OutputMR('',epsilon,p).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MR[1]/km, MR[0]/Msun,lw=2,label=f'B = {B:.2f} MeV $fm^{-3}$')\n",
+    "ax.set_ylabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/test_Inference.html b/test_Inference.html
new file mode 100644
index 0000000..abd789d
--- /dev/null
+++ b/test_Inference.html
@@ -0,0 +1,473 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
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+  <title>RMF EOS inference pipeline &mdash; CompactObject 1.9.1 documentation</title>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">RMF EOS inference pipeline</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#Set-up-prior">Set up prior</a></li>
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+  <section id="RMF-EOS-inference-pipeline">
+<h1>RMF EOS inference pipeline<a class="headerlink" href="#RMF-EOS-inference-pipeline" title="Link to this heading"></a></h1>
+<p>This is an example notebook about how to use our tools to analysis a observation/nuclear constraint on neutron star equation of stat.</p>
+<p>Here in this notebook, we are using a RMF EoS model</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Package we need:</span>
+<span class="kn">import</span> <span class="nn">InferenceWorkflow.BayesianSampler</span> <span class="k">as</span> <span class="nn">sampler</span>
+<span class="kn">import</span> <span class="nn">InferenceWorkflow.Likelihood</span> <span class="k">as</span> <span class="nn">likelihood</span>
+<span class="kn">import</span> <span class="nn">InferenceWorkflow.prior</span> <span class="k">as</span> <span class="nn">prior</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">TOVsolver.constant</span> <span class="kn">import</span> <span class="n">oneoverfm_MeV</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span><span class="n">G</span><span class="p">,</span><span class="n">c</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.crust_EOS</span> <span class="k">as</span> <span class="nn">crust</span>
+<span class="kn">import</span> <span class="nn">EOSgenerators.fastRMF_EoS</span> <span class="k">as</span> <span class="nn">RMF</span>
+</pre></div>
+</div>
+</div>
+<p>We need to define the crust, this crust and interface is unversial for all the equation of state here, we just change the core part of equation of state and that is come from the RMF computation. Crust as below, “Tolos_crust_out.txt” is BPS crust model. Then as in README, since that file energy_density and presurssure all in MeV/fm3, so we need some conversion. then just call</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>crust.PolyInterpolate
+</pre></div>
+</div>
+<p>To finish the interface and make it ready to connect with core part</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Tolos_crust_out = np.loadtxt(&#39;Tolos_crust_out.txt&#39;, delimiter=&#39;  &#39;)</span>
+<span class="n">Tolos_crust_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;Tolos_crust_out.txt&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">comments</span><span class="o">=</span><span class="s1">&#39;#&#39;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<span class="n">eps_crust_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">G</span> <span class="o">/</span> <span class="n">c</span><span class="o">**</span><span class="mi">2</span>
+<span class="n">pres_crust_out</span> <span class="o">=</span> <span class="n">Tolos_crust_out</span><span class="p">[:,</span><span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">G</span> <span class="o">/</span> <span class="n">c</span><span class="o">**</span><span class="mi">4</span>
+
+<span class="n">eps_crust</span><span class="p">,</span> <span class="n">pres_crust</span> <span class="o">=</span> <span class="n">crust</span><span class="o">.</span><span class="n">PolyInterpolate</span><span class="p">(</span><span class="n">eps_crust_out</span><span class="p">,</span> <span class="n">pres_crust_out</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<section id="Set-up-prior">
+<h2>Set up prior<a class="headerlink" href="#Set-up-prior" title="Link to this heading"></a></h2>
+<p>Next step, we need to set up the prior, first use parameters array to specify the variable name, should consistent with what you need to call them.</p>
+<p>Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling. We provided</p>
+<p>“normal_Prior” and “flat_prior”</p>
+<p>two options call from prior. Here then the Parameters prior should all set</p>
+<hr class="docutils" />
+<p>However, since we are doing Equation of state Inference from mass radius of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.</p>
+<p>This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of neutron star — different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>RMF.compute_EOS
+</pre></div>
+</div>
+<p>Compute out EOS, put into</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>main.OutputMR
+</pre></div>
+</div>
+<p>find out Mass Radius of this equation of state, find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)</p>
+<p>found out that index by len() function, then reset this max_d to be upper limit of this density range.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;g_sigma&#39;</span><span class="p">,</span> <span class="s1">&#39;g_omega&#39;</span><span class="p">,</span><span class="s1">&#39;g_rho&#39;</span><span class="p">,</span> <span class="s1">&#39;kappa&#39;</span><span class="p">,</span> <span class="s1">&#39;lambda_0&#39;</span><span class="p">,</span> <span class="s1">&#39;zeta&#39;</span><span class="p">,</span> <span class="s1">&#39;Lambda_w&#39;</span><span class="p">,</span><span class="s1">&#39;d1&#39;</span><span class="p">]</span>
+<span class="c1"># for two or more MR measurements, define d2 or more depend on complexity.</span>
+<span class="c1"># parameters = [&#39;g_sigma&#39;, &#39;g_omega&#39;,&#39;g_rho&#39;, &#39;kappa&#39;, &#39;lambda_0&#39;, &#39;zeta&#39;, &#39;Lambda_w&#39;,&#39;d1&#39;, &#39;d2&#39;]</span>
+
+<span class="k">def</span> <span class="nf">prior_transform</span><span class="p">(</span><span class="n">cube</span><span class="p">):</span>
+    <span class="n">params</span> <span class="o">=</span> <span class="n">cube</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">prior</span><span class="o">.</span><span class="n">normal_Prior</span><span class="p">(</span><span class="mf">107.5</span><span class="p">,</span> <span class="mf">7.5</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">prior</span><span class="o">.</span><span class="n">flat_prior</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span><span class="mi">210</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">prior</span><span class="o">.</span><span class="n">flat_prior</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span><span class="mi">210</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">normal_Prior</span><span class="p">(</span><span class="mf">2.525</span><span class="o">/</span><span class="n">oneoverfm_MeV</span><span class="p">,</span> <span class="mf">1.525</span><span class="o">/</span><span class="n">oneoverfm_MeV</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">normal_Prior</span><span class="p">(</span><span class="mf">0.0045</span><span class="p">,</span> <span class="mf">0.0205</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">flat_prior</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.04</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
+    <span class="n">params</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">flat_prior</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.045</span><span class="p">,</span><span class="n">cube</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span>
+
+    <span class="n">g_sigma</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">g_omega</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">g_rho</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+    <span class="n">kappa</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+    <span class="n">lambda_0</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+    <span class="n">zeta</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
+    <span class="n">Lambda_w</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
+    <span class="n">m_sig</span> <span class="o">=</span> <span class="mi">495</span> <span class="o">/</span> <span class="n">oneoverfm_MeV</span>
+
+    <span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span>
+                         <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span><span class="p">])</span>
+
+    <span class="n">ep</span><span class="p">,</span> <span class="n">pr</span> <span class="o">=</span> <span class="n">RMF</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">,</span> <span class="n">pres_crust</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+
+    <span class="n">eps_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">eps_crust</span><span class="p">,</span><span class="n">ep</span><span class="p">))</span>
+
+    <span class="n">pres_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">pres_crust</span><span class="p">,</span><span class="n">pr</span><span class="p">))</span>
+
+    <span class="n">RFSU2R</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">MFSU2R</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">14.3</span><span class="p">,</span> <span class="mf">15.6</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eps_total</span><span class="p">[:],</span> <span class="n">eps_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pres_total</span><span class="p">[:],</span> <span class="n">pres_total</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
+        <span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">eps_total</span><span class="p">,</span><span class="n">pres_total</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">params</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="c1">#params[8] = 0</span>
+            <span class="c1"># this line for showing how to add one more observation</span>
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">])):</span>
+                <span class="n">RFSU2R</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+                <span class="n">MFSU2R</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">20</span> <span class="ow">and</span> <span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">break</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">MFSU2R</span><span class="p">)</span><span class="o">==</span><span class="kc">False</span><span class="p">:</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1"># params[8] = 0</span>
+        <span class="c1"># this line for showing how to add one more observation</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">max_index</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">MFSU2R</span><span class="p">)</span>
+        <span class="n">max_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">max_index</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">params</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="o">=</span> <span class="mf">14.3</span> <span class="o">+</span> <span class="p">(</span><span class="n">max_d</span> <span class="o">-</span> <span class="mf">14.3</span><span class="p">)</span> <span class="o">*</span> <span class="n">cube</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span>
+        <span class="c1"># params[8] = 14.3 + (max_d - 14.3) * cube[8]</span>
+        <span class="c1"># this line for showing how to add one more observation</span>
+    <span class="k">return</span> <span class="n">params</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Set-up-likelihood">
+<h2>Set up likelihood<a class="headerlink" href="#Set-up-likelihood" title="Link to this heading"></a></h2>
+<p>We need to set up a likelihood, Using standard definition way of UltraNest, that is below.</p>
+<p>Here the likelihood is generated from a simulated mass radius measurement, which is <span class="math notranslate nohighlight">\(M = 1.4 M_{\odot}\)</span> and <span class="math notranslate nohighlight">\(R = 13\)</span> km, With a 5% Mass radius measurement uncertainty, so here</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>likelihood.MRlikihood_Gaussian
+</pre></div>
+</div>
+<p>function will be use for our likelihood, please check <a class="reference external" href="https://github.com/ChunHuangPhy/EoS_inference/blob/main/InferenceWorkflow/Likelihood.py">likelihood.MRlikihood_Gaussian</a> to see the original code, and more choice of likelihood. eg: 1. If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>likelihood.MRlikihood_kernel<span class="o">(</span>eps_total,pres_total,x,d1<span class="o">)</span>
+</pre></div>
+</div>
+<p>set the KDE kernel as a input for this function</p>
+<ol class="arabic" start="2">
+<li><p>If we gain measurement from radio-timing, say only measure the neutron star mass, then</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>likelihood.Masslikihood_Gaussian<span class="o">(</span>eps_total,pres_total,x,d1<span class="o">)</span>
+</pre></div>
+</div>
+<p>Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement</p>
+</li>
+<li><p>If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>likelihood.Kliklihood<span class="o">(</span>theta,K_low,K_up<span class="o">)</span>
+likelihood.Jliklihood<span class="o">(</span>theta,K_low,K_up<span class="o">)</span>
+likelihood.Lliklihood<span class="o">(</span>theta,K_low,K_up<span class="o">)</span>
+</pre></div>
+</div>
+<p>We are defaulting a hard-cut flat constrain, so if you don’t like this default hard cut, also could define the likelihood by youself with similiar style.</p>
+</li>
+<li><p>If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>likelihood.TidalLikihood_kernel<span class="o">(</span>eps_total,pres_total,x,d1<span class="o">)</span>
+</pre></div>
+</div>
+<p>Where x is sampled distribution from real measurements, the standard is</p>
+</li>
+</ol>
+<p>kernel, chrip = x,</p>
+<p>where the kernel is a whole set sampling from GW event, that is [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling.</p>
+<ol class="arabic" start="5">
+<li><p>To calculate the log-likelihood for the pure neutron matter (PNM) equation of state (EoS) using chiral effective field theory (chiEFT) constraints, the function</p>
+<p><code class="docutils literal notranslate"><span class="pre">likelihood.chiEFT_PNM(EoS_PNM,</span> <span class="pre">type=&quot;Gaussian&quot;,</span> <span class="pre">contraint_quantity=&quot;e&quot;,</span> <span class="pre">enlargement=0)</span></code></p>
+</li>
+</ol>
+<p>EoS_PNM is a 3D array of number density (rho), energy density (e) and pressure (p) with unit fm^-3, MeV.fm^-3 and MeV.fm^-3.</p>
+<p>compares PNM EoS data with chiEFT-derived constraints for either energy per neutron ( E/N ) or pressure ( p ), depending on the specified <code class="docutils literal notranslate"><span class="pre">contraint_quantity</span></code> parameter (“e” for energy or “p” for pressure).</p>
+<p>The likelihood model can be either “Gaussian” or “Super Gaussian” with an optional <code class="docutils literal notranslate"><span class="pre">enlargement</span></code> factor for the Super Gaussian to flatten its peak. e.g., 0.05 for 5% enlargement.</p>
+<p>The function calculates the sum of log-likelihoods over densities of 0.08, 0.12, and 0.16 fm(^-3), utilizing energy constraints from Huth et al. (Nature, 2022) and pressure constraints from Hebeler et al. (ApJ, 2013). This enables a statistical comparison of the PNM EoS data against chiEFT-derived reference values.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">stats</span>
+<span class="k">def</span> <span class="nf">likelihood_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
+    <span class="c1"># This is a demonstration code for only introduce one constraint from one mass-radius observation.</span>
+    <span class="c1"># Could be very easy to implement more constraint from nuclear quantity, since that do not need to</span>
+    <span class="c1"># sample more central density of real neutron star. If user want to expand to two mass radius measurement</span>
+    <span class="c1"># the code could be:</span>
+
+    <span class="c1"># g_sigma, g_omega,g_rho, kappa, lambda_0, zeta, Lambda_w, d1, d2 = theta</span>
+    <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span><span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span><span class="p">,</span> <span class="n">d1</span> <span class="o">=</span> <span class="n">theta</span> <span class="c1"># comment this line if you need two measuremnts.</span>
+
+    <span class="c1">####################################################################################################################</span>
+    <span class="c1">############ This is the block to compute out all the EoS you need based on your parameters#########################</span>
+    <span class="n">m_sig</span> <span class="o">=</span> <span class="mi">495</span> <span class="o">/</span> <span class="n">oneoverfm_MeV</span>
+    <span class="n">m_w</span> <span class="o">=</span> <span class="mf">3.96544</span>
+    <span class="n">m_rho</span> <span class="o">=</span> <span class="mf">3.86662</span>
+    <span class="n">theta1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">m_sig</span><span class="p">,</span> <span class="n">m_w</span><span class="p">,</span> <span class="n">m_rho</span><span class="p">,</span> <span class="n">g_sigma</span><span class="p">,</span> <span class="n">g_omega</span><span class="p">,</span> <span class="n">g_rho</span><span class="p">,</span> <span class="n">kappa</span><span class="p">,</span> <span class="n">lambda_0</span><span class="p">,</span> <span class="n">zeta</span><span class="p">,</span> <span class="n">Lambda_w</span><span class="p">])</span>
+    <span class="n">ep</span><span class="p">,</span> <span class="n">pr</span> <span class="o">=</span> <span class="n">RMF</span><span class="o">.</span><span class="n">compute_EOS</span><span class="p">(</span><span class="n">eps_crust</span><span class="p">,</span> <span class="n">pres_crust</span><span class="p">,</span> <span class="n">theta1</span><span class="p">)</span>
+
+    <span class="n">eps_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">eps_crust</span><span class="p">,</span><span class="n">ep</span><span class="p">))</span>
+    <span class="n">pres_total</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">pres_crust</span><span class="p">,</span><span class="n">pr</span><span class="p">))</span>
+    <span class="c1">####################################################################################################################</span>
+
+    <span class="c1"># probMRgaussian1 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(1.4,13,0.07,0.65),d1)</span>
+    <span class="c1"># probMRgaussian2 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(2.0,12,0.1,0.6),d2)</span>
+    <span class="c1"># probMR = probMRgaussian1 + probMRgaussian2</span>
+
+    <span class="c1"># Same could be extended to more distributions. Notice the prior definition should be change accordingly</span>
+    <span class="c1"># by define more density parameters like here d2.</span>
+
+    <span class="c1">#1. This line is to compute MR likelihood from a Simulated MR measurement:</span>
+
+
+    <span class="n">probMRgaussian</span> <span class="o">=</span> <span class="n">likelihood</span><span class="o">.</span><span class="n">MRlikihood_Gaussian</span><span class="p">(</span><span class="n">eps_total</span><span class="p">,</span><span class="n">pres_total</span><span class="p">,(</span><span class="mf">1.4</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mf">0.07</span><span class="p">,</span><span class="mf">0.65</span><span class="p">),</span><span class="n">d1</span><span class="p">)</span>
+
+    <span class="c1">#2. This is  a block that constrain from given real MR measurement, say J0030:</span>
+    <span class="c1">#J0030 = numpy.loadtxt(&#39;data/PST_equal_sampled_MR.txt&#39;, delimiter=&#39; &#39;)</span>
+    <span class="c1">#J30R_list, J30M_list = zip(*J0030)</span>
+    <span class="c1">#J30R_list = numpy.array(J30R_list).T</span>
+    <span class="c1">#J30M_list = numpy.array(J30M_list).T</span>
+    <span class="c1">#Rmin = J30R_list.min()</span>
+    <span class="c1">#Rmax = J30R_list.max()</span>
+    <span class="c1">#Mmin = J30M_list.min()</span>
+    <span class="c1">#Mmax = J30M_list.max()</span>
+    <span class="c1">#X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]</span>
+    <span class="c1">#positions = numpy.vstack([X3.ravel(), Y3.ravel()])</span>
+    <span class="c1">#values = numpy.vstack([J30R_list, J30M_list])</span>
+    <span class="c1">#kernel3 = stats.gaussian_kde(values)</span>
+    <span class="c1">#probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)</span>
+
+    <span class="c1">#3. This is to compute the constraint from experiment of nuclearmatter</span>
+    <span class="c1"># 250&lt;K&lt;400, 25&lt;J&lt;38, 30&lt;L&lt;86:</span>
+    <span class="c1"># hint: since this K,J,L sampling don&#39;t need to sample central density so this</span>
+    <span class="c1"># theta should be redifined.</span>
+    <span class="c1">#probK = likelihood.Kliklihood(theta,250,400)</span>
+    <span class="c1">#probJ = likelihood.Jliklihood(theta,250,400)</span>
+    <span class="c1">#probL = likelihood.Lliklihood(theta,250,400)</span>
+
+    <span class="c1">#4. This is block to cosntrain from a GW event, say GW170817, here the file of this</span>
+    <span class="c1"># event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:</span>
+    <span class="c1">#GW170817 = np.load(&#39;GW170817_McQL1L2weights.npy&#39;)</span>
+    <span class="c1">#chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])</span>
+    <span class="c1">#kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])</span>
+    <span class="c1">#probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)</span>
+
+
+    <span class="c1">#5. This block constraints pure neutron matter EOS</span>
+    <span class="c1">## For pure neutron matter alpha = 0</span>
+    <span class="c1">#theta1    = np.append(theta, 0)</span>
+    <span class="c1">#rho, e, p = RMF.get_eos_alpha(theta1)</span>
+    <span class="c1">#EoS_PNM   = np.array([rho, e*oneoverfm_MeV, p*oneoverfm_MeV]) # energy density and pressure are converted from fm^-4 to MeV fm^-3</span>
+
+    <span class="c1">#probPNM = likelihood.chiEFT_PNM(EoS_PNM, type=&quot;Super Gaussian&quot;, contraint_quantity=&quot;e&quot;, enlargement=0.1) # 10% enlargement</span>
+
+    <span class="n">prob</span> <span class="o">=</span>  <span class="n">probMRgaussian</span> <span class="c1">#+ probMRJ0030 + probK + probJ + probL + probGW + probPNM</span>
+    <span class="k">return</span> <span class="n">prob</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Set-up-sampler">
+<h2>Set up sampler<a class="headerlink" href="#Set-up-sampler" title="Link to this heading"></a></h2>
+<p>Here next, we define sampler, there is two different sampler we provided for. Considering where you need resume file:</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>sampler.UltranestSampler<span class="w">   </span>and<span class="w">  </span>sampler.UltranestSamplerResume
+</pre></div>
+</div>
+<p>Here since it is our first run, so we only use first one. some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).</p>
+<p>live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.</p>
+<p>max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">step</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span>
+<span class="n">live_point</span> <span class="o">=</span> <span class="mi">2000</span>
+
+<span class="n">max_calls</span> <span class="o">=</span> <span class="mi">60000</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">UltranestSampler</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span><span class="n">likelihood_transform</span><span class="p">,</span><span class="n">prior_transform</span><span class="p">,</span><span class="n">step</span><span class="p">,</span><span class="n">live_point</span><span class="p">,</span><span class="n">max_calls</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+</section>
+<section id="Posterior-plot">
+<h2>Posterior plot<a class="headerlink" href="#Posterior-plot" title="Link to this heading"></a></h2>
+<p>Since we only run our code at my labtob so here is the result corner plot(still have a lot flucatuation)</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">corner</span>
+
+<span class="n">figure</span> <span class="o">=</span> <span class="n">corner</span><span class="o">.</span><span class="n">corner</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span> <span class="sa">r</span><span class="s2">&quot;$g_{\sigma}$&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$g_{\omega}$&quot;</span><span class="p">,</span><span class="sa">r</span><span class="s2">&quot;$g_{\rho}$&quot;</span><span class="p">,</span><span class="sa">r</span><span class="s2">&quot;$\kappa$&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$\lambda_0$&quot;</span><span class="p">,</span><span class="sa">r</span><span class="s2">&quot;$\zeta$&quot;</span><span class="p">,</span><span class="sa">r</span><span class="s2">&quot;$\Lambda_w$&quot;</span><span class="p">,</span><span class="s2">&quot;d1&quot;</span><span class="p">,</span><span class="s2">&quot;d2&quot;</span><span class="p">],</span>
+                    <span class="n">smooth</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span>
+                    <span class="n">label_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">22</span><span class="p">),</span>
+                    <span class="n">title_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">22</span><span class="p">),</span>
+                    <span class="n">quantiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.16</span><span class="p">,</span> <span class="mf">0.84</span><span class="p">],</span>
+                    <span class="n">levels</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">),</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">9</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">)),</span>
+                    <span class="n">plot_density</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">plot_datapoints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                    <span class="n">fill_contours</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                    <span class="n">show_titles</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                    <span class="n">max_n_ticks</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+                    <span class="n">title_fmt</span><span class="o">=</span> <span class="s1">&#39;.2f&#39;</span><span class="p">)</span>
+                       <span class="c1">#,range=[(10,80),(0.56,0.64),(0.10, 0.2),(150,600),(40,80),(50,75)])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_Inference_11_0.png" src="_images/test_Inference_11_0.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
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+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+
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+    <p>&#169; Copyright 2023, Chun Huang, Nicole Obsborn, Nathan Whitsett.</p>
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diff --git a/test_Inference.ipynb b/test_Inference.ipynb
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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RMF EOS inference pipeline\n",
+    "\n",
+    "This is an example notebook about how to use our tools to analysis a observation/nuclear constraint on neutron star equation of stat. \n",
+    "\n",
+    "Here in this notebook, we are using a RMF EoS model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Package we need:\n",
+    "import InferenceWorkflow.BayesianSampler as sampler\n",
+    "import InferenceWorkflow.Likelihood as likelihood\n",
+    "import InferenceWorkflow.prior as prior\n",
+    "import math\n",
+    "import numpy as np\n",
+    "\n",
+    "from TOVsolver.constant import oneoverfm_MeV, m_rho, m_w,G,c\n",
+    "import TOVsolver.main as main\n",
+    "import EOSgenerators.crust_EOS as crust\n",
+    "import EOSgenerators.fastRMF_EoS as RMF"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to define the crust, this crust and interface is unversial for all the equation of state here, we just change the core part of equation of state and that is come from the RMF computation. Crust as below, \"Tolos_crust_out.txt\" is BPS crust model. Then as in README, since that file energy_density and presurssure all in MeV/fm3, so we need some conversion. then just call \n",
+    "\n",
+    "   ```sh\n",
+    "   crust.PolyInterpolate\n",
+    "   ```\n",
+    "\n",
+    "To finish the interface and make it ready to connect with core part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Tolos_crust_out = np.loadtxt('Tolos_crust_out.txt', delimiter='  ')\n",
+    "Tolos_crust_out = np.loadtxt('Tolos_crust_out.txt', delimiter=None, comments='#', usecols=(0, 1, 2, 3, 4))\n",
+    "eps_crust_out = Tolos_crust_out[:,3] * G / c**2\n",
+    "pres_crust_out = Tolos_crust_out[:,4] * G / c**4\n",
+    "\n",
+    "eps_crust, pres_crust = crust.PolyInterpolate(eps_crust_out, pres_crust_out)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up prior\n",
+    "\n",
+    "Next step, we need to set up the prior, first use parameters array to specify the variable name, should consistent with what you need to call them.\n",
+    "\n",
+    "Define a prior transform function to define prior. Cube are set of random number from 0 to 1. This prior setting is standard set-up of UltraNest package, since we are using UltraNest to do nest-sampling. We provided \n",
+    "\n",
+    "\"normal_Prior\" and \"flat_prior\"\n",
+    "\n",
+    "two options call from prior. Here then the Parameters prior should all set\n",
+    "\n",
+    "------------------\n",
+    "\n",
+    "However, since we are doing Equation of state Inference from mass radius of neutron star measurement. The center density of the star should be also sampled. Otherwise will be a partially-defined prior, did not span all parameters space, and proved to be different with full-scope inference.\n",
+    "\n",
+    "This request as randomly generate a density from a EoS range, however, this process is not that trivial, since we need to determine the upper limit of the central density of neutron star ---  different equation of state will predict different upper bound, so here we need to use the prior-setting EoS parameters computing the EOS by\n",
+    "\n",
+    "```sh\n",
+    "RMF.compute_EOS\n",
+    "```\n",
+    "\n",
+    "Compute out EOS, put into\n",
+    "\n",
+    "```sh\n",
+    "main.OutputMR\n",
+    "```\n",
+    "\n",
+    "find out Mass Radius of this equation of state, find out the last stable point of this equation of state.(first mass points that give the direvative to be negative)\n",
+    "\n",
+    "found out that index by len() function, then reset this max_d to be upper limit of this density range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = ['g_sigma', 'g_omega','g_rho', 'kappa', 'lambda_0', 'zeta', 'Lambda_w','d1']\n",
+    "# for two or more MR measurements, define d2 or more depend on complexity.\n",
+    "# parameters = ['g_sigma', 'g_omega','g_rho', 'kappa', 'lambda_0', 'zeta', 'Lambda_w','d1', 'd2']\n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = math.sqrt(prior.normal_Prior(107.5, 7.5,cube[0]))\n",
+    "    params[2] = math.sqrt(prior.flat_prior(75,210,cube[2]))\n",
+    "    params[1] = math.sqrt(prior.flat_prior(150,210,cube[1]))\n",
+    "    params[3] = prior.normal_Prior(2.525/oneoverfm_MeV, 1.525/oneoverfm_MeV,cube[3])\n",
+    "    params[4] = prior.normal_Prior(0.0045, 0.0205,cube[4])\n",
+    "    params[5] = prior.flat_prior(0,0.04,cube[5])\n",
+    "    params[6] = prior.flat_prior(0,0.045,cube[6])\n",
+    "    \n",
+    "    g_sigma = params[0]\n",
+    "    g_omega = params[1]\n",
+    "    g_rho = params[2]\n",
+    "\n",
+    "    kappa = params[3]\n",
+    "    lambda_0 = params[4]\n",
+    "    zeta = params[5]\n",
+    "    Lambda_w = params[6]\n",
+    "    m_sig = 495 / oneoverfm_MeV\n",
+    "\n",
+    "    theta = np.array([m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa,\n",
+    "                         lambda_0, zeta, Lambda_w])\n",
+    "    \n",
+    "    ep, pr = RMF.compute_EOS(eps_crust, pres_crust, theta)\n",
+    "\n",
+    "    eps_total = np.hstack((eps_crust,ep))\n",
+    "    \n",
+    "    pres_total = np.hstack((pres_crust,pr))\n",
+    "    \n",
+    "    RFSU2R = []\n",
+    "    MFSU2R = []\n",
+    "    density = np.logspace(14.3, 15.6, 50)\n",
+    "    if all(x<y for x,y in zip(eps_total[:], eps_total[1:])) and all(x<y for x, y in zip(pres_total[:], pres_total[1:])):\n",
+    "        MR = main.OutputMR('',eps_total,pres_total).T\n",
+    "        if len(MR[1]) == False: \n",
+    "            params[7] = 0\n",
+    "            #params[8] = 0\n",
+    "            # this line for showing how to add one more observation\n",
+    "        else:\n",
+    "   \n",
+    "            for i in range(len(MR[1])):\n",
+    "                RFSU2R.append(MR[0][i])\n",
+    "                MFSU2R.append(MR[1][i])   \n",
+    "                if i > 20 and MR[1][i] - MR[1][i-1]< 0:\n",
+    "                    break\n",
+    "    if len(MFSU2R)==False:\n",
+    "        params[7] = 0\n",
+    "        # params[8] = 0\n",
+    "        # this line for showing how to add one more observation\n",
+    "    else:\n",
+    "        max_index = len(MFSU2R)\n",
+    "        max_d = np.log10(density[max_index-1])\n",
+    "        params[7] = 14.3 + (max_d - 14.3) * cube[7]\n",
+    "        # params[8] = 14.3 + (max_d - 14.3) * cube[8]\n",
+    "        # this line for showing how to add one more observation\n",
+    "    return params"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up likelihood\n",
+    "\n",
+    "We need to set up a likelihood, Using standard definition way of UltraNest, that is below.\n",
+    "\n",
+    "Here the likelihood is generated from a simulated mass radius measurement, which is $M = 1.4 M_{\\odot}$ and $R = 13$ km, With a 5% Mass radius measurement uncertainty, so here \n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.MRlikihood_Gaussian\n",
+    "   ```\n",
+    "function will be use for our likelihood, please check [likelihood.MRlikihood_Gaussian](https://github.com/ChunHuangPhy/EoS_inference/blob/main/InferenceWorkflow/Likelihood.py) to see the original code, and more choice of likelihood.\n",
+    "eg:\n",
+    "1. If we have some real mass-radius measurements, say PSR J0030 or PSR J0740, come from NICER, a KDE kernel could be trained to feed into \n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.MRlikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "set the KDE kernel as a input for this function\n",
+    "\n",
+    "2. If we gain measurement from radio-timing, say only measure the neutron star mass, then\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.Masslikihood_Gaussian(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "Which will give the likelihood from single mass measurement, x is the parameters of that measurement, you should specify where this measurement mass is located and what is the sigma width of this mass measurement\n",
+    "\n",
+    "3. If we have nuclear measurements, and want to constrain this RMF model by nuclear properties like K(The Incompressibility of nuclear matter),J ( the symmetry energy at saturation density) and L( the slope of symmetry energy at saturation density). You can choose:\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.Kliklihood(theta,K_low,K_up)\n",
+    "   likelihood.Jliklihood(theta,K_low,K_up)\n",
+    "   likelihood.Lliklihood(theta,K_low,K_up)\n",
+    "   ```\n",
+    "We are defaulting a hard-cut flat constrain, so if you don't like this default hard cut, also could define the likelihood by youself with similiar style.\n",
+    "\n",
+    "4. If we have a Tidal measurements from Gravitational wave detector, we can use it to do constraint:\n",
+    "\n",
+    "   ```sh\n",
+    "   likelihood.TidalLikihood_kernel(eps_total,pres_total,x,d1)\n",
+    "   ```\n",
+    "Where x is sampled distribution from real measurements, the standard is \n",
+    "\n",
+    "kernel, chrip = x, \n",
+    "\n",
+    "where the kernel is a whole set sampling from GW event, that is  [chrip mass, M2/M1, tidal of M1, tidal of M2] four quantities. Chrip is the single smapling that comes only the chrip mass sampling.\n",
+    "\n",
+    "\n",
+    "\n",
+    "5. To calculate the log-likelihood for the pure neutron matter (PNM) equation of state (EoS) using chiral effective field theory (chiEFT) constraints, the function\n",
+    "   \n",
+    "     `likelihood.chiEFT_PNM(EoS_PNM, type=\"Gaussian\", contraint_quantity=\"e\", enlargement=0)`\n",
+    "\n",
+    "EoS_PNM is a 3D array of number density (rho), energy density (e) and pressure (p) with unit fm^-3, MeV.fm^-3 and MeV.fm^-3.  \n",
+    "\n",
+    "compares PNM EoS data with chiEFT-derived constraints for either energy per neutron \\( E/N \\) or pressure \\( p \\),\n",
+    "depending on the specified `contraint_quantity` parameter (\"e\" for energy or \"p\" for pressure). \n",
+    "\n",
+    "The likelihood model can be either \"Gaussian\" or \"Super Gaussian\" with an optional `enlargement` factor for the Super Gaussian to flatten its peak. e.g., 0.05 for 5% enlargement. \n",
+    "\n",
+    "The function calculates the sum of log-likelihoods over densities of 0.08, 0.12, and 0.16 fm\\(^-3\\), utilizing energy constraints from Huth et al. (Nature, 2022) and pressure constraints from Hebeler et al. (ApJ, 2013). This enables a statistical comparison of the PNM EoS data against chiEFT-derived reference values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "def likelihood_transform(theta):\n",
+    "    # This is a demonstration code for only introduce one constraint from one mass-radius observation.\n",
+    "    # Could be very easy to implement more constraint from nuclear quantity, since that do not need to\n",
+    "    # sample more central density of real neutron star. If user want to expand to two mass radius measurement \n",
+    "    # the code could be:\n",
+    "    \n",
+    "    # g_sigma, g_omega,g_rho, kappa, lambda_0, zeta, Lambda_w, d1, d2 = theta\n",
+    "    g_sigma, g_omega,g_rho, kappa, lambda_0, zeta, Lambda_w, d1 = theta # comment this line if you need two measuremnts.\n",
+    "    \n",
+    "    ####################################################################################################################\n",
+    "    ############ This is the block to compute out all the EoS you need based on your parameters#########################\n",
+    "    m_sig = 495 / oneoverfm_MeV\n",
+    "    m_w = 3.96544\n",
+    "    m_rho = 3.86662\n",
+    "    theta1 = np.array([m_sig, m_w, m_rho, g_sigma, g_omega, g_rho, kappa, lambda_0, zeta, Lambda_w])\n",
+    "    ep, pr = RMF.compute_EOS(eps_crust, pres_crust, theta1)\n",
+    "\n",
+    "    eps_total = np.hstack((eps_crust,ep))\n",
+    "    pres_total = np.hstack((pres_crust,pr))\n",
+    "    ####################################################################################################################\n",
+    "    \n",
+    "    # probMRgaussian1 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(1.4,13,0.07,0.65),d1)\n",
+    "    # probMRgaussian2 = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(2.0,12,0.1,0.6),d2)\n",
+    "    # probMR = probMRgaussian1 + probMRgaussian2\n",
+    "    \n",
+    "    # Same could be extended to more distributions. Notice the prior definition should be change accordingly\n",
+    "    # by define more density parameters like here d2.\n",
+    "    \n",
+    "    #1. This line is to compute MR likelihood from a Simulated MR measurement:\n",
+    "    \n",
+    "    \n",
+    "    probMRgaussian = likelihood.MRlikihood_Gaussian(eps_total,pres_total,(1.4,13,0.07,0.65),d1)\n",
+    "    \n",
+    "    #2. This is  a block that constrain from given real MR measurement, say J0030:\n",
+    "    #J0030 = numpy.loadtxt('data/PST_equal_sampled_MR.txt', delimiter=' ')\n",
+    "    #J30R_list, J30M_list = zip(*J0030)\n",
+    "    #J30R_list = numpy.array(J30R_list).T    \n",
+    "    #J30M_list = numpy.array(J30M_list).T\n",
+    "    #Rmin = J30R_list.min()\n",
+    "    #Rmax = J30R_list.max()\n",
+    "    #Mmin = J30M_list.min()\n",
+    "    #Mmax = J30M_list.max()\n",
+    "    #X3, Y3 = numpy.mgrid[Rmin:Rmax:500j, Mmin:Mmax:100j]\n",
+    "    #positions = numpy.vstack([X3.ravel(), Y3.ravel()])\n",
+    "    #values = numpy.vstack([J30R_list, J30M_list])\n",
+    "    #kernel3 = stats.gaussian_kde(values)\n",
+    "    #probMRJ0030 = likelihood.MRlikelihhood_kernel(eps_total,pres_total,kernel3,d1)\n",
+    "    \n",
+    "    #3. This is to compute the constraint from experiment of nuclearmatter\n",
+    "    # 250<K<400, 25<J<38, 30<L<86:\n",
+    "    # hint: since this K,J,L sampling don't need to sample central density so this \n",
+    "    # theta should be redifined.\n",
+    "    #probK = likelihood.Kliklihood(theta,250,400)\n",
+    "    #probJ = likelihood.Jliklihood(theta,250,400)\n",
+    "    #probL = likelihood.Lliklihood(theta,250,400)\n",
+    "    \n",
+    "    #4. This is block to cosntrain from a GW event, say GW170817, here the file of this\n",
+    "    # event is origanized by [chrip mass, M2/M1, tidal of M1, tidal of M2, sampling weight]:\n",
+    "    #GW170817 = np.load('GW170817_McQL1L2weights.npy')\n",
+    "    #chrip170817 = stats.gaussian_kde(GW170817[:,0],weights = GW170817[:,4])\n",
+    "    #kernelGW = stats.gaussian_kde(GW170817.T[0:4],weights = GW170817[:,4])\n",
+    "    #probGW = likelihood.TidalLikihood_kernel(eps_total,pres_total,(kernelGW,chrip170817),d1)\n",
+    "\n",
+    "\n",
+    "    #5. This block constraints pure neutron matter EOS\n",
+    "    ## For pure neutron matter alpha = 0\n",
+    "    #theta1    = np.append(theta, 0)\n",
+    "    #rho, e, p = RMF.get_eos_alpha(theta1)\n",
+    "    #EoS_PNM   = np.array([rho, e*oneoverfm_MeV, p*oneoverfm_MeV]) # energy density and pressure are converted from fm^-4 to MeV fm^-3\n",
+    " \n",
+    "    #probPNM = likelihood.chiEFT_PNM(EoS_PNM, type=\"Super Gaussian\", contraint_quantity=\"e\", enlargement=0.1) # 10% enlargement \n",
+    "\n",
+    "    prob =  probMRgaussian #+ probMRJ0030 + probK + probJ + probL + probGW + probPNM \n",
+    "    return prob"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up sampler\n",
+    "\n",
+    "Here next, we define sampler, there is two different sampler we provided for. Considering where you need resume file:\n",
+    "\n",
+    "```sh\n",
+    "sampler.UltranestSampler   and  sampler.UltranestSamplerResume\n",
+    "```\n",
+    "\n",
+    "Here since it is our first run, so we only use first one. some of the sampler parameters is requested, first is step number, our choice for UltraNest sampler is slicesampler, which could easily be sliced up your total computation load, and parallelize, speed up sampling. So step as suggested by documentation of UltraNest, we use 2*len(parameters).\n",
+    "\n",
+    "live_point we set 2000, it will influence the sampling precision, We suggest for 7 dimension space, maybe 5000 is a better choice, however, since my computer only have limited resources, we set 2000.\n",
+    "\n",
+    "max_calls set 10000, it is how many iteration after it will stop, we suggest to set this number significantly higher, otherwise maybe will broken before the inference converging to a definite value. That result will be un-phyiscal\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step = 2 * len(parameters)\n",
+    "live_point = 2000\n",
+    "\n",
+    "max_calls = 60000\n",
+    "samples = sampler.UltranestSampler(parameters,likelihood_transform,prior_transform,step,live_point,max_calls)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Posterior plot\n",
+    "Since we only run our code at my labtob so here is the result corner plot(still have a lot flucatuation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1810x1810 with 64 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import corner\n",
+    "\n",
+    "figure = corner.corner(samples,labels=[ r\"$g_{\\sigma}$\", r\"$g_{\\omega}$\",r\"$g_{\\rho}$\",r\"$\\kappa$\", r\"$\\lambda_0$\",r\"$\\zeta$\",r\"$\\Lambda_w$\",\"d1\",\"d2\"],\n",
+    "                    smooth=0.9,\n",
+    "                    label_kwargs=dict(fontsize=22),\n",
+    "                    title_kwargs=dict(fontsize=22),\n",
+    "                    quantiles=[0.16, 0.84],\n",
+    "                    levels=(1 - np.exp(-0.5), 1 - np.exp(-2), 1 - np.exp(-9 / 2.)),\n",
+    "                    plot_density=False,\n",
+    "                    plot_datapoints=False,\n",
+    "                    fill_contours=True,\n",
+    "                    show_titles=True,\n",
+    "                    max_n_ticks=3,\n",
+    "                    title_fmt= '.2f')\n",
+    "                       #,range=[(10,80),(0.56,0.64),(0.10, 0.2),(150,600),(40,80),(50,75)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4481115b400f107b26d360e6549f546bb0e8bc1af70e4e66085bfa77a017a39"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/test_TOVsolver.html b/test_TOVsolver.html
new file mode 100644
index 0000000..8d648b7
--- /dev/null
+++ b/test_TOVsolver.html
@@ -0,0 +1,329 @@
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+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_Strangeon_EOS.html">Strangeon EOS inference pipeline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="test_Bayesian_inference_MITbag_EOS.html">MIT bag EOS Inference pipline</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UnitConventionForDeveloper.html">Unit Convention for Developer</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">EOS generators module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="crust_EOS.html">Crust Equation of state interpolation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="EOS.html">EOS solver</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fastRMF_EoS.html">RMF EOS solver (numba speed up version)</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">TOV solver module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="EoS_import.html">Import Equation of State</a></li>
+<li class="toctree-l1"><a class="reference internal" href="main.html">Main functions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TOV_solver.html">Solve TOV Equation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="speed_of_sound.html">Calculate Speed of Sound</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Bayesian Inference module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="BayesianSampler.html">Bayesian Inference smapler setup module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior Definition Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Likelihood.html">Likelihood function definition module</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Postprocessing module</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Cornerplot.html">Postprocessing module: Cornerplot</a></li>
+</ul>
+
+        </div>
+      </div>
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+             
+  <section id="TOVsolver-module-guide">
+<h1>TOVsolver module guide<a class="headerlink" href="#TOVsolver-module-guide" title="Link to this heading"></a></h1>
+<p>TOV solver part we have multiple function, we can generate a Mass radius function from ‘Test_EOS.csv’ file here easily</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">TOVsolver.main</span> <span class="k">as</span> <span class="nn">main</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.EoS_import</span> <span class="k">as</span> <span class="nn">EoS_import</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">TOVsolver.constant</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">repeat</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="c1"># Main has two functions</span>
+<span class="c1">#   Output.MRT() returns the mass, radius, tidal</span>
+<span class="c1">#   OutputC_s() returns the speed of sound</span>
+<span class="c1"># Using the main functions with either arrays of density and pressure or a .csv with density and pressure columns</span>
+<span class="c1">#   Function argument is either (filename = ) OR (density = , pressure = )</span>
+<span class="c1">#   Example: main.OutputMR(filename = &#39;file.csv&#39;) OR main.OutputMR(density = array1, pressure = array2)</span>
+<span class="n">MR</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMR</span><span class="p">(</span><span class="s2">&quot;Test_EOS.csv&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+<span class="c1"># Plot the Mass-Radius curve</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MR</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">MR</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;M [$M_{\odot}$]&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;R [km]&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Mass Radius file will be generated and stored as  2-d array. The first column is Radius, second one is mass
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_TOVsolver_1_2.png" src="_images/test_TOVsolver_1_2.png" />
+</div>
+</div>
+<p>We can generate the tidal property from it also,like tidal deformability</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># try solve tidal property</span>
+<span class="n">MRT</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputMRT</span><span class="p">(</span><span class="s2">&quot;Test_EOS.csv&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+<span class="c1"># Plot the Radius and Tidal curve</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MRT</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">MRT</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\Lambda$&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;R [km]&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">8.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Mass Radius and tidal will be generated as the 3-d array. The first column is Radius, second one is mass,last is tidal
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_TOVsolver_3_2.png" src="_images/test_TOVsolver_3_2.png" />
+</div>
+</div>
+<p>Plot out Tidal with mass</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot the Mass and Tidal curve</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">MRT</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">MRT</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\Lambda$&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;M [$M_{\odot}$]&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_TOVsolver_5_1.png" src="_images/test_TOVsolver_5_1.png" />
+</div>
+</div>
+<p>Can generate the speed of sound from the given equation of state. This do not need to solve TOV equation, the speed of sound is defined by the derivative of P(rho) curve, this quantity is very of interest, since whether there is a upper limit of this speed of sound (except for the casaulity limit c_s &lt; 1 ) is a very interesting and hot topic</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="kn">from</span> <span class="nn">TOVsolver.constant</span> <span class="kn">import</span> <span class="n">c</span><span class="p">,</span><span class="n">G</span>
+<span class="c1"># here we plotting out speed of sound with Test_EOS, the density is in g/cm3</span>
+<span class="n">C_s</span><span class="p">,</span><span class="n">rho</span> <span class="o">=</span> <span class="n">main</span><span class="o">.</span><span class="n">OutputC_s</span><span class="p">(</span><span class="s2">&quot;Test_EOS.csv&quot;</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rho</span><span class="p">,</span> <span class="n">C_s</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\epsilon$&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;C_s&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/test_TOVsolver_7_1.png" src="_images/test_TOVsolver_7_1.png" />
+</div>
+</div>
+<p>This package can easily integrated into a Baysian inference flow, to do bayesian inference, Here, we generated several (50) EoSs from RMF model, and try to use a loop to compute out all of their MRT property. That could be a in-between step of doing bayesian inference of neutron star EoS. Remember these EoS could also be polytrope, or anything that generate from your own EoS computation code. Next step for us will be integrate our EoS computation into this package and also the Bayesian analysis.</p>
+</section>
+
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="test_EOSgenerators.html" class="btn btn-neutral float-left" title="Equation of State Generators illustration notebook" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2023, Chun Huang, Nicole Obsborn, Nathan Whitsett.</p>
+  </div>
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diff --git a/test_TOVsolver.ipynb b/test_TOVsolver.ipynb
new file mode 100644
index 0000000..6a479e3
--- /dev/null
+++ b/test_TOVsolver.ipynb
@@ -0,0 +1,260 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TOVsolver module guide\n",
+    "\n",
+    "TOV solver part we have multiple function, we can generate a Mass radius function from 'Test_EOS.csv' file here easily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mass Radius file will be generated and stored as  2-d array. The first column is Radius, second one is mass\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import TOVsolver.main as main\n",
+    "import TOVsolver.EoS_import as EoS_import\n",
+    "from matplotlib import pyplot\n",
+    "import matplotlib.pyplot as plt\n",
+    "import TOVsolver.constant\n",
+    "from itertools import repeat\n",
+    "import numpy\n",
+    "# Main has two functions\n",
+    "#   Output.MRT() returns the mass, radius, tidal\n",
+    "#   OutputC_s() returns the speed of sound\n",
+    "# Using the main functions with either arrays of density and pressure or a .csv with density and pressure columns\n",
+    "#   Function argument is either (filename = ) OR (density = , pressure = )\n",
+    "#   Example: main.OutputMR(filename = 'file.csv') OR main.OutputMR(density = array1, pressure = array2)\n",
+    "MR = main.OutputMR(\"Test_EOS.csv\").T\n",
+    "# Plot the Mass-Radius curve\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MR[0], MR[1],lw=2)\n",
+    "ax.set_ylabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.set_xlim(8., 20.)\n",
+    "ax.set_ylim(0,3)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can generate the tidal property from it also,like tidal deformability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mass Radius and tidal will be generated as the 3-d array. The first column is Radius, second one is mass,last is tidal\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# try solve tidal property\n",
+    "MRT = main.OutputMRT(\"Test_EOS.csv\").T\n",
+    "# Plot the Radius and Tidal curve\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "        \n",
+    "ax.plot(MRT[0], MRT[2],lw=2)\n",
+    "ax.set_ylabel(r'$\\Lambda$', fontsize=16)\n",
+    "ax.set_xlabel('R [km]', fontsize=16)\n",
+    "ax.set_xlim(8., 20.)\n",
+    "ax.set_ylim(0,1000)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot out Tidal with mass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Mass and Tidal curve\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(MRT[1], MRT[2],lw=2)\n",
+    "ax.set_ylabel(r'$\\Lambda$', fontsize=16)\n",
+    "ax.set_xlabel(r'M [$M_{\\odot}$]', fontsize=16)\n",
+    "ax.set_xlim(0., 3.)\n",
+    "ax.set_ylim(0,1000)\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Can generate the speed of sound from the given equation of state. This do not need to solve TOV equation, the speed of sound is defined by the derivative of P(rho) curve, this quantity is very of interest, since whether there is a upper limit of this speed of sound (except for the casaulity limit c_s < 1 ) is a very interesting and hot topic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from TOVsolver.constant import c,G \n",
+    "# here we plotting out speed of sound with Test_EOS, the density is in g/cm3\n",
+    "C_s,rho = main.OutputC_s(\"Test_EOS.csv\")\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, figsize=(9,6))\n",
+    "ax.plot(rho, C_s,lw=2)\n",
+    "ax.set_xlabel(r'$\\epsilon$', fontsize=16)\n",
+    "ax.set_ylabel(r'C_s', fontsize=16)\n",
+    "plt.xscale(\"log\")\n",
+    "ax.legend()\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "ax.tick_params(top=1,right=1, which='both', direction='in', labelsize=14)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This package can easily integrated into a Baysian inference flow, to do bayesian inference, Here, we generated several (50) EoSs from RMF model, and try to use a loop to compute out all of their MRT property. That could be a in-between step of doing bayesian inference of neutron star EoS. Remember these EoS could also be polytrope, or anything that generate from your own EoS computation code. Next step for us will be integrate our EoS computation into this package and also the Bayesian analysis."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4481115b400f107b26d360e6549f546bb0e8bc1af70e4e66085bfa77a017a39"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}