initial_prof.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial T S Profiles\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'/Volumes/Extreme SSD/MITgcm outputs/qf700_strat/state_001.nc'\n",
<<<<<<< HEAD
    "state1 = xr.open_dataset('/work/oceans/wbao/MITgcm_results/iceplume/4_Sz_maxhs/state_001.nc')\n",
    "state2 = xr.open_dataset('/work/oceans/wbao/MITgcm_results/iceplume/4_Sz_maxhs/state_002.nc')\n",
    "state3 = xr.open_dataset('/work/oceans/wbao/MITgcm_results/iceplume/4_Sz_maxhs/state_003.nc')\n",
    "state4 = xr.open_dataset('/work/oceans/wbao/MITgcm_results/iceplume/4_Sz_maxhs/state_004.nc')\n",
=======
    "state1 = xr.open_dataset('/Users/weiyangbao/Documents/Model_outputs/4_Sz_maxhs/state_001.nc')\n",
    "state2 = xr.open_dataset('/Users/weiyangbao/Documents/Model_outputs/4_Sz_maxhs/state_002.nc')\n",
    "state3 = xr.open_dataset('/Users/weiyangbao/Documents/Model_outputs/4_Sz_maxhs/state_003.nc')\n",
    "state4 = xr.open_dataset('/Users/weiyangbao/Documents/Model_outputs/4_Sz_maxhs/state_004.nc')\n",
>>>>>>> 8c6f201a3f7b8accc4c7542c8ae898ea859e991d
    "\n",
    "\n",
    "s_ini1 = state1.S.data[0,:,:,:]\n",
    "s_ini2 = state2.S.data[0,:,:,:]\n",
    "s_ini3 = state3.S.data[0,:,:,:]\n",
    "s_ini4 = state4.S.data[0,:,:,:]\n",
    "\n",
    "pt_ini1 = state1.Temp.data[0,:,:,:]\n",
    "pt_ini2 = state2.Temp.data[0,:,:,:]\n",
    "pt_ini3 = state3.Temp.data[0,:,:,:]\n",
    "pt_ini4 = state4.Temp.data[0,:,:,:]\n",
    "\n",
    "s_end1 = state1.S.data[-1,:,:,:]\n",
    "s_end2 = state2.S.data[-1,:,:,:]\n",
    "s_end3 = state3.S.data[-1,:,:,:]\n",
    "s_end4 = state4.S.data[-1,:,:,:]\n",
    "\n",
    "pt_end1 = state1.Temp.data[-1,:,:,:]\n",
    "pt_end2 = state2.Temp.data[-1,:,:,:]\n",
    "pt_end3 = state3.Temp.data[-1,:,:,:]\n",
    "pt_end4 = state4.Temp.data[-1,:,:,:]\n",
    "\n",
    "depth = state1.Z.data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
<<<<<<< HEAD
   "outputs": [],
=======
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1.,   -3.,   -5.,   -7.,   -9.,  -11.,  -13.,  -15.,  -17.,\n",
       "        -19.,  -21.,  -23.,  -25.,  -27.,  -29.,  -31.,  -33.,  -35.,\n",
       "        -37.,  -39.,  -42.,  -46.,  -50.,  -54.,  -58.,  -62.,  -66.,\n",
       "        -70.,  -74.,  -78.,  -82.,  -86.,  -90.,  -94.,  -98., -102.,\n",
       "       -106., -110., -114., -118., -122., -126., -130., -134., -138.,\n",
       "       -142., -146., -150., -154., -158., -163., -169., -175., -181.,\n",
       "       -187., -193., -199., -205., -211., -217., -223., -229., -235.,\n",
       "       -241., -247., -253., -259., -265., -271., -277., -283., -289.,\n",
       "       -295., -301., -307., -313., -319., -325., -331., -337., -343.,\n",
       "       -349., -355., -361., -367., -373., -379., -385., -391., -397.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
>>>>>>> 8c6f201a3f7b8accc4c7542c8ae898ea859e991d
   "source": [
    "depth"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "metadata": {},
   "outputs": [],
=======
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IwP799+fWA29lSM4QR9vluNoKmHMEbF0Be58NpxeAQ0ONpdU+fnbnfGr9QT645giG9Gz/zeSJRHs2XYC4XLizs0kZPIjU0aNJGT4cd4+eiMeDqavDv3UrtatWUbtiBb6NGwlUVcWlx6MSV9+MvjxwxAPce9i99EjtweJNiznj1TN48tsnCQQDbZ+gq0rNgrOfAG8GfPUsLPmPY03JTfdywl7WEOd/l3S/3o0GmwQnLhfurCxSBg20As+IEXh62oHH58O/bRt14YGnslIDTxdQUFBAQUFBu54jIpww4gRePvVlThhxAtX+au5ZfA8Xv3Uxq0pXxailSaDv7nDy/db3b14LG5Y51pSzxls9zf8uKSIY7F7/TzXYJBERwZ2ZiXfgQO599lkunnEra8vLEa+3IfCsXk3t8uX4NmzQwJPEpk2bxrRp0zr03B5pPbj3sHt54IgH6JPeh2VblnHWK2fxyNeP4A/6o9zSJLHP2ZD/SwjUwXMXQXWJI804cGQvBuWls76kmk9WbXOkDU7RYJOk/vCHP/DHP13F9L//HdeIEUy5806O/OUvOey889iwcSP+7dsbB54KDTzdzRFDj+ClU1/i1F1OpS5Yx9+X/J0L37yQlSUrnW6aM46/GwaMhR1r4JVLwYH/Dy6XcGb+YACeK1wX9+s7SYNNEqqqquKGG25g8eLFbN26lcLCQnJ79uSTxYv57PPPGX7ABDy9eyPeFIzfbwWeNVbgqduwgUBFhQaebiInJYfbD76dmUfPpF9GP77a+hVnvXoWD331UPfr5XjTrHtuUnPgu1fhs1mONCMUbN76ZhNlNT5H2uAEDTZJaObMmeTn59OvXz9EhPz8fLKzs5k8eTJvvvkmrox0vP37k7rbrqSOGmUFnhQr8AS2b6duzRrt8XQzhww6hBdPeZEzdj0DX9DH/Uvv54I3Luh+vZyeI+GUf1nf/+9mWL8k7k0Y0jODCSN7UuML8vqXG+N+fadosElCK1eu5PDDD6empoYTTzyRQCDA3XffzaxZs7j33ntZsmQJ06dP56qrrsLvdluBZ9edA094j0fneLq+7JRsZhw0g9lHz6Z/Zn++3vY1Z796Ng9/9XD36uXseQr8bBoEffDcxY7M35yZbyUKPN+NstL0PpsEvM+mLQsXLmT69On06dOH//u//+Oiiy5i3bp1+Hw+brvtNt555x3uvvtuXn/9dbKzszniiCMaPd8Yg6mpIVBaSqCsDFNXV79PPB7cObm4cnNwZWTw/fffJ+z70JVFUkGgM8rryrmv8L765Qv26b0PdxxyByNyR8TkegnHXwsPHwsbl8EeJ1vp0XG8/6ay1s/+d86nqi7Awj9NZHjv5KvgrffZdAMTJ07k/fff5/nnnyc1NZW5c+fy0UcfsWjRIo4//vhGxzZ306eI4EpP37nHUz/HY2e1rVhBoLSU6i++0B5PFxPq5cw6ehb9Mvrx5dYvOevVs3ji2ye6R/UBTyqc9Z+G+ZvFD8X18pmpHo6377l5YWn36N1osOmCzjnnHGbMmMGCBQs48MADWz22UeDZbVdSR47E06t3fTp1sLKSNedMZuVRR1P8l79Q/c03GnjiwBgTl/f54EEH88IpL/DzUT+nNlDLvYvv5ZK3L2FdeTfIlOo5Ek7+h/X92zfCpq/ievkzx1mJAs8vXd8t7rnRYbQIh9GGX/d6XNqz5u6T4nKdthhjCFZX892XX5J6zbWNlr9OGTaM7BNPIPfEE0nddVcHW6mi6d2f3uXWT25lW8020j3pXL3/1Zy565ldv5L0K5fD0seg164w7T1Iic+QVjBoOOSeBWworWHu1AlMGNkrLteNFh1GU1EhIrgzMnDn5rLLwncZ9uQT9Dj3XNy9elG3di3bZs5i1ck/Z9XJJ7N15kzq1qxxusmqk44YegQvnvIixw0/jmp/Nbd9chu/e+d3bK7a3PaTk9nxd0OfPWDbD1aV6DhxuYRT97OqP7+4dH3crusU7dkkYYJAR//S7Mi/ddP3wfj9VC1aRNmbb1L2v3kES0vr96WNGUPOSSeRc+IJePtrmfvOyM/PB2DJkvin5gK8ufpN7vj0DsrqyshNzeXmCTdz3PDjHGlLXBR/axXs9NfAGQ/D3mfG5bI/bi7n6L+9T1aqh8Kbjk6qdW50PZt2SsZgE+7222/nm2++4c4772TUqFFRP39r74Opq6Pi448pe+MNKua/Q7CqytohQkZ+PjknnUj2ccfh6dkz6u3q6mKdjRaJzVWbmf7xdD5a/xFgrQp6wwE3kJOS41ibYmrxw/D6lVbSwG8+hB7D4nLZ0Do3/zp3PybtMzAu14wGDTbtlOzBpqysjG+//Za//e1vFBQUcOqppwLWvTh/+tOfuOKKKzp1/kjfh2BNDRXvvU/Z669TsXBhQzq1203mQQeRO+kkso46GndW8qV4OiERgk3o+s+teI6/Fv6Van81/TL6cechd3LAgAMcbVdMGAPPnA/fvwZDDoCL3wB37FfMfeTD1dz22rcctXtfHr54/5hfL1o02LRTMgebqqoqrrnmGkaPHs2LL77IggULAPjkk0/4z3/+w8yZM3G7O9ct78j7EKiooOKddyh9/XUqP/4E/NYNg5KaStaRR5A7aRKZhx6KKyWlU23ryhIl2ISsKV3DDR/ewFdbrYyti/a8iMvHXU6Ku4v9G1Zth5kHQflGmHg9TLwu5pfcUl7LhLveQYBFNx5Nz8zkeE81QaAbaVq2BmDRokU89NBDPPjgg50ONB3lzsoi95RTGFpQwK4fvE//GTPIGD8eU1tL+ZtvUfT7S/nhkEPZePPNVH76GSbYDe7rSHLDc4fz+AmP87t9f4db3Dz27WP84vVf8MOOH5xuWnRl9ITTZgMC790DP30W80v2yU7lkF164w8aXv+q65av0WCTxJqWrVmyZAkzZ85k1qxZeDyx7/5HwtOjBz0mn8OwJ59glwXv0PfqP5G6xx4Ey8ooee6//HTxxfx4xJEU3/sXar77LmH+klc787g8/Hbsb3n8hMcZmj2UFTtWMPm1yTz13VNd699t5OFw0GVggvDCFKgpi/klT9nXmqt5+fOum5Wmw2hJPIzWtGzN8OHDGTlyJF6vl7Fjx3L//fd3+hqxeh9qf/yR0tdeo+y11/EVNdxBnbLLKHInnUzOpEmkDB4U9esmi0QbRmuqylfFvYvvrS93c/Cgg7nj4Dvond7b4ZZFib8OHjoKNn0J+54Pp/47pperrPWTf8c8anxBPrz2CAb3SPwlo3XOpp2SOdjEQ6zfB2MM1Z8vo+y1Vyl7400CJSX1+9Lz88k9+WRyTjged25uzNqQiKZOnQrQ7tU6423+2vnM+GQGpbWl9Ezrye0H385hgw9zulnRsfl7KDjcSoc++wnY8+cxvdxlT3/Oq19s4JrjR/O7ibvE9FrRoMGmnTTYtC6e74Px+aj46CPKXnmV8gULMDU1AIjXS9bEieT8/GSyDj9cEwsSTHFlMTd+dCOfbbTmN87f43z+mP/HrpE88FkBvHk1pPeE330K2f1idql53xYz5fFC9hiQw5tXHBqz60SLBpt20mDTOqfeh0BFJeXz51H2yitUfvJp/aqKrtxcck44ntyfn0L6fvt2/VIqSSJogvzn6//wr8//hd/4Gd1jNPcefi8jc0c63bTOCQbhydNh1buw63Fw7jMxqw5d6w+w/x3zKavxM//Kw9ilb3ZMrhMtmo2mugR3ViZ5p57K0EceYZeF79L36qtJHT2aYGkpJXOfYe2557Ly+OPZ8uCD1BV1vaq5S5Yscax6QEe4xMWv9v4Vj5/wOEOyh7B8x3ImvzaZl358KWHnnSLicsEp/4a0XPjhbVj6eMwulepx11eCfuWLrpeVpj0b7dm0KtHeh5rlyyl9+RXKXn0V/5Yt9dszxo8n99RTyD7uONzZif0XYSQSPUGgNZW+Su749A5eW/UaYFUeuHnCzWR6k/iG3i+fgxd+DSnZ8LuPIW9oTC7z/ootXPjIIkb2yeSdKw9P6J67DqO1kwab1iXq+2ACASo/+ZTSl1+mfN68hvmd1FSyjz6a3NNOI/PACYhD9xp1VjIHm5CXf3yZOz+7k2p/NUOzh/LXw//KHr0S77MUEWPg2Qvhu1dgxGFwwctWryfK/IEgP/vzO2yvrOONyw9lz4GJWxpIh9FUtyBuN1mHHMygv9zLrh9+wIA77yTjZz/D1NZS9vrrrPv1r/nxyKPY/Le/U7tqtdPN7ZZO2eUUnpn0DKN7jOan8p84743zeOb7Z5IzgIrApL9DRm9Y/T4UPhyTy3jcrvqhtDe62A2eGmxU0nNnZZF3xukMe/wxRs2fT+/LL8M7ZAj+4mK2FRSw6sQTWXPOZHbMfYZAebnTze1WRuSO4KmTnuLs3c7GF/Rxx2d38Kf3/kRFXYXTTWu/zN4w6W/W9/Nuge2x+SNm0t4DAHj9q43JGZhboMNoOozWqmR9H4wxVC9ZQslLL1H+5lsE7X9jSU0l+9hjyTv9NDIOOACJwVBINHSFYbSm3lr9FjM+mUGlr5Kh2UP528S/MbrnaKeb1X7P/RK+eQGGHwoXvRr17DR/IMiEu95ha0Udr19+CGMGJuY9ZjqMprj99tuZPHkyK1eudLopjhERMsaPZ+Add7DrB+8z8N57yJgwwRpme/VVfvrlJaw8+hi2/PNf1BV13RIhieT4EcfvNKz24g8vOt2s9jvxr9Zw2poPYMmjUT+9x+3i2DHWUNpbX2+K+vmdoj2bLtizCV924KmnnuKSSy5h5cqVBAIBXnrpJQYMGBDxuZL5fWhOXVERpS++RMmLL+DfYI+Ji5B54ATyzjyTrKOPToibRrtizyakxl/DXYvu4oUfXgDg1F1O5cYDbiTNk+Zwy9rh6xfgv7+0stN+/ynkDo7q6T/8YSvnP/wZI/tksuCqiVE9d7Roz6abq6qq4oYbbmDx4sVs3bqVxYsXk5OTw8cff8ynn37arkDTFaUMHkyfyy5ll/nzGfrIw+RMmoR4vVR+/Anrr7yKHw89jE1//jM1y1c42s7CwkIKCwsdbUOspHnSuPWgW7nj4DtIc6fx0o8vceGbF7KufJ3TTYvcmNNg90lQVw6vX1V/03G0TBjZk7wML6u2VPJDcdeYZ9Rg08U0XXYgPz+f7OxsJk+ezBtvvOF08xKGuFxkHnQQg/76F3b94H363XwTqXvsQaC0lB2PP8HqU05h9dnnsOPZZwlU7NzzjbX8/Pz6paG7qlN2OYUnT3ySIdlD+G77d5zz2jm8X/S+082KjIg1nJaaAyvesuZwosjjdnHMHlZpnK4ylKbBJgnV1tbyq1/9imHDhpGdnc1+++3Hm2++Cey87EAgEODuu+9m1qxZ3HvvvYB1d/r06dO58sorqQutqNmNuXNz6XneeYx88QWGP/9f8n4xGVd2NjVffsmm6bfw42GHsfHm6VR/9XWXHNZy0uieo5k7aS4Th0ykvK6cS9+5lFlfzCJokmCNo5wBcMxt1vdvXmstvBZFoRTot77RYKMc4vf7GTJkCO+99x6lpaXcfvvtnH322axZs4azzz6biy++mJdffplLL72USy65hIMPPphjjz2W66+/HoC5c+dyyy23MHHiRD766COHX01iSR8zhgG33MKu77/HgLvvIj0/n2BVFSXPPceas85i9RlnsOPpp2OeQj116tT6ys9dXU5KDvcfcT+X7XcZAP9e9m+uePcKyuuSYPho3EUw9CCo3ALzb4nqqQ/epTeZKW6+2VBG0Y6qqJ7bCZog0EUSBPbZZx9uueUWzjjjjDaPvfrqq7n77rt5/fXXycnJYeLEiS0em2zvQyzUrlxJybPPUfryy/VLIEh6OrmTTiLvnMmk7zUm6tfsygkCrfmg6AOu/eBayuvKGZ4znAeOfIARuSOcblbrtiyHWYdAoA5++SYMOyhqp/79U0t5/auNTJ+0J5cckljvgyYIdEPFxcWsWLGCMWMi+6V3zjnnMGPGDBYsWMCBBx4Y49Ylv9RRo+h3/XXs8t5CBv71r1algupqSp77L2vOPJPVZ57FjueeI1iV/H99Ou3QwYfyzEnPsGuPXVlTtoZzXz838edx+oyGQ660vn/tSmvhtSg5dow1bzPv2+KondMp2rNJ8p6Nz+fjhBNOYNSoUcyePTvq50+W9yHealetouSZZyl56SWCpaUAuLKyyP35z+lx3rmkjhrVqfN3155NSJWvips+uol5a+chCFeMu4JL9rokcQtT+mpg5oGwfRUcdQscemVUTlta5WPcHfMAWHLT0eRlOJ+WH6KFONsp4mAzI0538c4ojfjQYDDIueeeS1lZGS+//DJerzfqzdFg07pgTQ3lb7/NjrnPUP355/XbMw44gB7nnkv2UUciHk+7z9vdgw1Ya+QUfFnAv5dZSzL/fNTPueXAWxJ3UbYf37HWvvFmwO8XQd6QqJz23Dmf8vHKbfzjnH05db/EWSpdh9G6CWMMv/rVryguLub555+vDzQi0uGHaj9XWhq5p5zC8Kf/jxEvvUje2Wcj6elUffYZ66+4gh+POpotDz7YaDkEFRmXuPjN2N/wj4n/IN2TzisrX+HX//s126q3Od205u1yFOx5Cviq4H83Ru20R9kp0O98vzlq53SC9mySdBjtN7/5DcuWLWP+/PlkZWU1e8ztt9/ON998w5133smoDg7rJPr7kIgCZWWUvvQSO/7vaerWrLE2er3kHHMMPc47l/Rx49oM7tqzaey7bd9x2YLLKK4qZmDmQP551D/ZrcduTjdrZ6VF8K/9rYBzwUsw6ohOn3LN1kom/nUh2Wkelt58DF53YvQRtGfTDaxdu5bZs2ezbNky+vfvT1ZWFllZWTz11FONjrviiiv4wx/+wPXXX09JSQkTJ05k4sSJDBkyhPvvv9+h1nd97pwcel54ISPfeJ2hjzxM1tFHQSBA2RtvsPa881l9xhmUvPgSwVbucRo3bhzjxo2LY6sT2x699uDpk55mn977sKFyAxe8cQEfrv/Q6WbtLHcwHHa19f0bV0PA1+lTDu+dyag+mZTX+Fm8Jrr38sST9myStGfTlqqqKq655hpGjx7Niy++yIIFCwD45JNP+M9//sPMmTNxR7CwWLK/D4nCt2EDO555lpLnniOw3fqF4e7dmx6TJ9Nj8jl4evd2uIXJocZfw/SPpvPmmjdxi5ubJ9zMGbu1ne4fV/46eHACbF8Jx98NE37b6VPe+fq3zPlgNdMOG8n1JybG/0ft2Shg57I1AIsWLeKhhx7iwQcfjCjQqOjxDhxI3z/+gV3eXcCAO+8kdfRoAlu3svVf/+LHI45kw/U3UPPdd043M+GledK4+7C7mbL3FAImwIxPZvDA0gcSa7jRkwLH3Wl9v/CuqFQWOGJ0X+t0y5N37k+DTRfVtGzNkiVLmDlzJrNmzcLTgewoFR2u1FTyzjidES+9yNBHHyXryCMxfj+lL77I6tNOZ+2FF1H+zjuYQMDppiYsl7i4fNzlTD9wOm5xM+erOdzw4Q34ojBkFTW7HQ8jj4CaUnj3z50+3fjhPclMcbO8uJwNJdVRaGD8abDpopqWrZk0aRIrVqzgmGOO4YorrnC6ed2eiJA54QCGPPhvRr39Fj0uvABXRgZVixZR9PtL+d+uu3Fhz54EKpJwRcs4OWu3s/jnkf8k3ZPOa6te4zfzf0NZXZnTzbKIwHF/BnFB4SOwuXO91hSPi4N3sYZak7V3o3M2XXTOJlr0fYifQHk5pS+8wPYnnsRXVARYN4r2+MVkelxwAd6+fR1uYWL6dtu3/P6d37O1eiu75O1CwTEF9Mno43SzLK9fBYsfgl2OhvOf79Sp/u+zn7jhxa84ds9+FFw4PkoN7Dids1EqSbmzs+l50UWMevstLl1fxOKqKoIVFWyb8xArjzqajTffTO2q2Kx7n8z27LUnT534FCNyR/BjyY9c8OYFrCtLkLVxJt5gLUPw43xY9V6nTnXYblbP5pOV2/AHkqAqdhNJE2xEZKGI1IhIhf1Y3mT/USLyvYhUici7IjLMqbYq1RnidrOgooKL1v3E8Gfmkn3MMRi/n5Ln/suqk06i6LLLqP7iC6ebmVAGZg3kseMfY69ee7G+Yj0XvnUhy7cvb/uJsZbZCw62h63n3wLBjgeJwT0yGNE7k/JaP18URV5pJFEkTbCxXWqMybIfo0MbRaQ38AJwM9ATKASecaiNSkVN+tixDP7nA4x843XyzjoL8XgonzefNedMZu35F1Dx3nuJlYnloB5pPXjouIc4oP8BbK3eyi/f/iXLNi9zullW6nNWf9jwOXz7UqdOdeiuVu/mwx+2RqFh8ZVswaYlpwPfGGOeM8bUADOAsSKyu7PNUio6UkeMYMDttzHqnfn0mjIFV3Y2VYWFrJv2G1b//BRKX34Z4/c73UzHZXoz+ffR/+aooUdRXlfOlP9Ncf7mz5RMmHid9f07t3XqRs9D7CSBD35IviSBZAs2d4nIVhH5SEQmhm0fA9SPKxhjKoGV9nalugxv3770vepKdnl3AX2vvhpP377U/vADG669jpUnnEjJf/+L8SVQCrADUt2p/PXwv3LaLqdRE6jhsgWXseCnBc42ar8LoNeusGM1LH28w6c5cFQv3C7h83UlVNQm1x8XyRRsrgVGAoOAAuBVEQkV/MoCmg5ilgLZzZ1IRKaKSKGIFPr1r0GVgGbPnt3qkhHurCx6/eoSRs2fx4A77yRl+HB869ax8aabWXn8Cex49llMN17y2+PycOtBt3LBnhfgD/q5auFVvLP2Heca5PbAkTdZ339wH/hrO3Sa7DQv+wzOJRA0SVe6JiGCjT35b1p4fAhgjPnMGFNujKk1xjwGfAScaJ+iAshpctocoNl1ZY0xBcaY8caY8XqDo0pEkS4L7UpJIe+M0xn5+msM/MtfSBk5Et/69Wyafgs/Hn88O+bObbUGW1cmIlw9/mp+OeaX+I2fP733J+atnedcg/b4OfTbC8rWd6p3M2FkLwA+XZWg1a9bkBDBxhgz0RgjLTwOaelpQKh07jfA2NAOEckERtnbleryxO0m9+RJjHz1FQbe91dSdhmFf8NGNs24lZXHHsf2p54iWNuxv6aTmYjwx/w/8qu9foXf+Ln6vat5a81bzjTG5YLDr7W+/+A+a8G1DjgwFGxWarCJOhHJE5HjRCRNRDwich5wGPC2fciLwF4icoaIpAHTgS+NMd871WalOqOgoICCgoJ2P0/cbnJPOomRr7zCoH/8ndRdd8W/aRPFt9/BymOOZfsTT3a7no6ItdJnqJ7ade9fx5ur33SmMbtPgn57Q/lGWPJoh06RP6wHHpfw1fpSymqSZ34uKYIN4AXuALYAW4HLgFONMcsBjDFbgDOAO4EdwAHAZGea6rzbb7+dyZMns3LlSqebojpo2rRpTJs2rcPPF5eLnOOPZ8TLLzHogftJ3X13/Js3U3znnaw8/nhKXnypW9VfExEu2+8yfjP2NwRMgOs/uJ53fnJgDsflashM+/DvHZq7yUz1MHZIHkEDhUk0b5MUwcYYs8UYs78xJtsYk2eMmWCMmdfkmPnGmN2NMen2sNwah5rruPB1bHw+HxdccAEHHXQQBxxwABs3bnS6eSqOxOUi59hjGfHC8wz+1z+tns6GjWy8/npWn3oq5QsWdJv7dESE3+/7+/oeztXvXc2nGz+Nf0N2Pwn6joGKTfDF3A6d4mcjegJQuGZHNFsWU0kRbFTkqqqquOGGG1i8eDFbt25l8eLF5OTk8PHHH/Ppp58yYMAAp5uoHCAuF9lHH82Il15k4D134x04kNoffqTod79n7bnnUVVY6HQT4+ay/S7jF7v/Al/Qx+ULLueLLXGuxiACh/zR+v6j+yHY/h7m/sN7ABpslIOarmOTn59PdnY2kydP5o033nC6ecph4naTe8opjHzrTfrdcAPunj2p/vxz1p5/AT9Nm0bN8gQo8RJjIsJ1P7uOk0eeTLW/mt/O/238S9uMOQ3yhloLrH33arufnj/U6tksKyqh1p8cw6EabJLU+eefz4ABA8jJyWG33XbjoYceAnZexyYQCHD33Xcza9Ys7r33XgCWLFnC9OnTufLKK6nrZpPFyuJKSaHnhRcw6n//o/ell+LKyKDyvfdZfepprL/6Gnzr1zvdxJhyiYvbDr6NI4ccSXldOdPmTeOnsp/i1wC3Bw663Pr+w79DO4cyczO8jO6XTZ0/yFdJUidNg02Suv7661mzZg1lZWW88sor3HTTTSxZsmSndWwuueQSDj74YI499liuv/56AObOncstt9zCxIkT+eijjxx+JcpJ7qxM+lz6e0bN+x89LrwAPB7KXn2VlSeexJYH/kmwOjkX6oqEx+Xh3sPv5YABB7CtZhu/nf9bdtTEcVhqv/Mhsw9sXAZrPmj308eHhtLWJsdQmgabJDVmzBhSU1MBa1hARFi5ciUTJ07k/fff5/nnnyc1NZW5c+fy0UcfsWjRIo4//vidzhNaMlp1b55eveh/ww2MevNNck48EVNby9YHH2TliSdR9sYbXTaJINWdygNHPMAePffgp/KfuOLdK6gNxOl+JG86jP+V9f1nLVeLaMl+Q61gs+ynkig2KnY02CSx3/3ud2RkZLD77rszYMAATjzxxLafBJxzzjnMmDGDBQsWcOCBB8a4laojjDGO/IJPGTyIQX+7j2FPPkHqnnvg37iR9VdexdoLLqDm22/j3p54yPBm8K+j/kW/jH58vvlzbv7oZoImTuvFjL8EXF5Y/gbsWNuup+47JA+AZetKot+uGNCVOiNcqXPvx/aOS3u+uuirdh0fCAT45JNPWLhwIddeey1erzeq7dGVOrsvEwhQ8vzzbPn7Pwjs2AEi5J11Fn3+cAWenj2dbl7ULd++nAvfvJAqfxVT95nKZftdFp8LvzAVvnwGDroMjr0j4qcFg4axt/2P8ho/n1x/JANy02PYyJ3pSp3djNvt5pBDDqGoqIiZM2c63RzVhYjbTY+zz2bU22/R86KLwO2m5NlnWXnc8Wx//Ikud1Po6J6juW/ifbjFTcGXBbz040vxufAB9s27Sx+Hup3/8G2JyyUNvZskGErTKpQRam+PI978fj8rV67s1BxMd+/lJpL8/HzAyhx0mjsnh37XX0fe2WdRfNfdVH74IcV//jOlr7/GgNtvJ2233ZxuYtQcMugQrv/Z9dzx2R3c9slt7JK3C3v13iu2Fx2UD4P3h6LF8PXzMO7CiJ+675A8PvhhK8uKSjhh78S+h057Nklo8+bNzJ07l4qKCgKBAG+//TZPP/00Rx55ZP1YvzGG2267jXPOOYcff/yx0faWHipxLF26lKVLlzrdjEZSR41iyJwCBv/7X3j69aPmiy9ZffoZbHnggS5Vb+2c3c/hnNHn4Av6+OPCP8YnQy3/l9bXpU+062l7DcoF4NsNZdFuUdRpsElCIsLMmTMZPHgwPXr04E9/+hP/+Mc/OOWUUxodF162pqSkhIkTJzJx4kSGDBnC/fff71DrVTITEbKPOoqRr71K3i8mg9/P1gdnsvrU06hKgF5YtFyz/zXs03sfNlVu4tr3ryXQgbv822XMqZCSDUWLYHPk9YNDwebr9aUJ/wejBpsk1KdPH9577z1KSkooKyvjq6++YsqUKY2OaVq2Ji8vj4ULF3LXXXdxwgkncOmllzrUetUVuLOzGXDLLQx76klSRoygbtUq1p53PhtvvZVARYXTzeu0FHcK9028j55pPflk4yf8e9m/Y3zBTNj7TOv7zyPv3QzMTSMvw8uOKh8bSju2ZEG8aLDpopqWrQFYtGgRDz30EA8++CBut9vhFqquICM/nxEvvUiv3/4GPB5Knp7LqpMmUfHBh043rdP6Z/bn3sPuxSUu5nw1h4XrFsb2gqG5mmX/F3E1aBFhr4ENvZtEpsGmi2patmbJkiXMnDmTWbNmoauTqmhypabS94orGPH886Ttsw/+4mLWTZlC8d33JP1czgEDDqhPgZ7+0XS2Vm+N3cUG7met5Fm9HX6cH/HTxgy0FilO9HkbDTZdVNOyNZMmTWLFihUcc8wxXHHFFU43T3VBaaN3Y/jT/0efP/4R3G62P/ooa86ZTO2qVU43rVMu2esSDhhwADtqdzDj4xmxmxsRaRhK++q/ET9tdP9sAFYUl8eiVVGjf+J2UaGyNSG6jk1yaToHlyzE7ab3tKlkTjiA9X+6mtrvvmP1GWfS74bryTvzzKQsj+QSF3ccfAenv3I67xW9xws/vMAZu50Rm4vtdQbMnwHL34TaCkjNavMpoWCzPMGDjfZslEpAHV0WOlGkjx3LiBdfIPeUn2Oqq9l083TW/+GPBEoTe16hJf0z+3PjATcCcM/ie1hXti42F8obCkMOAH81rHgroqeM6pOF2yWs2VpJjS9xb7TVYKOUigl3VhYD77mHgX+5F1dmJuVvv82qU0+j+qvEvkG6JSeOOJHjhx9Ptb+aGz+6MXb10/Zq31BamtfN8F4ZBA38uDlxMwE12CiVgJYsWZIQ1QOiIffkkxnx0oukjd0H/8aNrD3/Akpfe93pZrWbiHDThJvok96Hzzd/zgs/vBCbC405FRBY+Y41lBaB3fpZQ2kabJRS7TJ+/HjGjx/vdDOiJmXIEIY/8QR5Z52Fqa1lw5/+xOZ//AMTjFN15SjJTc3lmv2vAeAfS/8Rm+oCWX2t8jWBOlj1bkRPGdnHqoe5amvktdXiTYONUiouJCWF/rfdSr8bbwS3m22zZlN0+eUEm6m6nsiOG34cEwZMoLS2lPuXxqgSx2h77anlb0Z0+MjeViLBqi3as1FKKUSEnhecz5CC2bhycqiY/w5rzj0vqZahFhFuOOAGPC4Pz//wPF9s+SL6Fxltr0214m2IoFTOiFDPZkviBm4NNkqpuMs6+GCGPzOXlOHDqV2+nNVnnZ1Ui7ONyB3BxWMuBuDOT++MfrJAn90hbxhUbYWiwjYPH2X3bFZvrUzYGmkabJRSjkgdMYLhz8wl86CDCGzfztqLf5lUmWpT9p5Cv4x+fLf9O+atnRfdk4vAbsdZ30cwb5Ob4aVHhpdqX4At5XFa1rqdNNgopRzjzs1lyKyZZB19FMGyMn765SVUL1vmdLMikuHNYOo+UwF4cNmD0a8MPeIw6+vqDyI6fHCPDADW7aiObjuiRINNF3T77bczefJkVq5c6XRTlGqTpKQw+O9/J/u44whWVPDTr36dNMsVnLbLaQzKGsSq0lW8tSaymzAjNuxgQKxlB3xtB5AhPa1loYt2VEW3HVGiwaYLCl/HxufzccEFF3DQQQdxwAEHaNmaJFFYWEhhYdtj9V2FeL0Muu+v5Jx0EsHKSn6aMpXKRYucblabvG4v0/axlnWe+cVM/EF/9E6e0RP672WlQK9r+70YEurZbNdgo+Kg6To2ixcvJicnh48//phPP/2UAQMSe+lYZcnPz69fGrq7EI+HgffeY5W4qapi3dRpVH8Rg0yvKJs0ahJDsoewtmwtb6x+I7onHx4aSnu/9eOAwT2sns267TqMpuKg6To2+fn5ZGdnM3nyZN54I8r/EZSKMnG7GfDnP5N76qmYmhrW/f7ShE+L9rq8TNnbKpz65LdPRjcbbNhB1teixW0eOjDPCjYbSjXYqBj44YcfSEtL4/zzzwd2XscmEAhw9913M2vWLO69917AKoUyffp0rrzySuqSfL2Rrmrq1KlMnTrV6WY4QtxuBtx+GxkHTiCwdSvrfvs7AhWJe/8IwAkjTiAvNY/vtn8X3ftuBttVJDZ8Dm1UW+iXkwbA5jLNRlMx8Pvf/57999+//uem69hccsklHHzwwRx77LFcf/31AMydO5dbbrmFiRMn8tFHHznVdNWKOXPmMGfOHKeb4Rjxehn8j3+QMmIEtStWsOGqqzCBxK1onOZJ44xdrWUHnv7+6eidOLs/5AyC2jLY9mOrhw7ItYLNprLEXB5ag00Smzt3Lnl5eRx11FH120Lr2Dz//POkpqYyd+5cPvroIxYtWsTxxx+/0zmScX0R1T2E0qLdublUvPcem+2eeaI6e/TZuMTF/9b+L7oreg4aZ31d33qGXs/MFFLcLkqrfQm51IAGmyRVVlbG9OnTue+++9r93HPOOYcZM2awYMECDjzwwBi0TqnoSBk2jMH/+id4vWx/7HF2PPec001q0cCsgUwcPBF/0M9/V0S+0mabBtmJIutbz04UEfrmpAKwqTTxeje6UmeEvtt9j7hcZ4/vv4vouJtvvplf/epXDBkypN3X6GoVhVXXlrH//gyYMYONN95I8W23k7bbbqSPHet0s5p1zu7nsGDdAl784UWm7D0Ft8vd+ZMOtHs2G5a1eWi/nDSKdlRTXFbD8N6Znb92FGnPJgktW7aM+fPn88c//tHppigVF3lnnE6Pc3+B8fkouvwK/FujOEwVRRMGTGBQ1iA2VG7gk42fROekA/axvhZ/DYHW7+Ppm231bDYnYMka7dlEKNIeRzwsXLiQNWvWMHToUAAqKioIBAJ8++23fP755x0+b6IW8FMKoN9111Hz3fdUf/456/94JUMfeRjxep1uViMucXHGrmfwwOcP8PyK5zlk0CGdP2l6D+gxHHasgS3fWzd6tqA+Iy0Bg432bJLQ1KlTWblyJcuWLWPZsmX85je/4aSTTuLtt9/GGFP/uO222zjnnHP48ccfG21v6aESx7hx4xg3bpzTzUgokpLCoH/8A3ef3lQtXszmv/7V6SY169RdTsUtbhauWxi9RIEB9rDhxtbTqvuEejYJmJGmwSYJZWRk0L9///pHVlYWaWlp9OnTp9Fx4WVrSkpKmDhxIhMnTmTIkCHcf3+MFn1SUdGVloWOJm+/vgy+/37weNj+2OOUJeCNyn0y+nDY4MPwGz8v//hydE46YF/r68ZlrR6WyMNoGmy6gBkzZvDkk0822ta0bE1eXh4LFy7krrvu4oQTTuDSSy91qLVKdU7GuHH0u+46ADbedDO1q1c73KKdhe65eenHl6IzahDq2WxqfQmGvvYwWiIuM6DBpotqWrYGYNGiRTz00EM8+OCDuN1RyJJRyiE9zjuX7BOOJ1hVxfo//JFgTWINGx086GB6p/dmTdma6FQU6G8nCWz6qtVKAn2yQj2bxHo/QINNl9W0bM2SJUuYOXMms2bNwuPRvJBEJyJ6w20rRIQBt99OyrBh1C5fTvGddzrdpEY8Lg8njzwZsHo3nZbVB7IHQF0F7Gi5Jxe6z0Z7NipumpatmTRpEitWrOCYY47hiiuucLp5SnWaOyuLQff/A0lJoeS5/1Ly0ktON6mRU3c5FYC31rxFtT8KxTH77219bWUorUdGCm6XsKPKR50/yktVd5L+idtFhcrWhOg6NqorStt9d/rdfBObbp7OpltvI32ffUgdOdLpZgEwMm8k+/Tehy+3fsk7P73DpJGTOnfC/vvAD/+DTV/CmFObPcTtEnplprC5vJatFbX1laATgfZslFJJLe/MM8mZNAlTXc36q/5EMIEqmf981M8BeG3la50/Wej+muJvWj2stz1vs7UisYbSNNgopZKaiNB/xi14Bw+m9rvv2HLf35xuUr3jhh+Hx+Xhk42fsKVqS+dO1i/CYJOtwUYppWLCnZXFoPv+at9/8xgV77e9smU85KXlceigQwmaIG+ufrNzJ+s5EjzpULoOqne0eFgoIy3RkgQ02CiluoT0sWPpc/nlAGy47nr8WzrZk4iSk0dZWWmvrerkUJrLDX3tgsDF37Z4WO/sFAC2ViTOcCJosGlVdy/hEmxjZUAVO7Nnz2b27NlONyPp9Pr1r8iYMIHA9u1suPHGhPg/fNjgw8hOyea77d+xsmRl507Wb4z1tfjrFg/Rnk2SSUtLY9u2bQnxYY03Ywx1dXWsX7+ezMzEKlPeXXTnZaE7Q1wuBt5zN67cXCrf/4CSZ551ukmkulM5ZtgxAJ0fSuu7p/V1c8uFgUMJAtsqE6tno6nPLRg8eDBFRUVsSZCueLx5PB5yc3Pp3bu3001Rql28/frRf/rNbLjqTxTfcw+ZB04gZdgwR9t0/PDjeeGHF3hrzVv8ft/fd/yG3X6hYNPyMFqvLGsYbVuCJQhosGmB1+tlxIgRTjdDdVMFBQUA2rvpoNyTTqLinQWUvfEGG669jmFPPoE4WDnjZ/1/Rq+0XqwtW8u3279lTK8xHTtReM/GGGgmaPXKtHs2OmejlGrLtGnTmDZtmtPNSGr9p9+Mp29fqpctY9tDDznaFrfLzbHDjwXgrdVvdfxEmX0goxfUlkHZ+mYP6R3q2VQmVs8mYYKNiFwqIoUiUisijzaz/ygR+V5EqkTkXREZFrZPROQeEdlmP+4VLSylVLfmzstjwJ//DMCWfz9IzfIVjrbnxBEnAlb5mg7PBYu0OW/TI9MKNtsr6wgEE2fOOWGCDbABuAN4pOkOEekNvADcDPQECoFnwg6ZCpwKjAX2ASYB+mehUt1c1iEHk3fOOeDzsfGGGzD+1pdVjqV9+uxD34y+bKrcxNdbW84ma1Of3a2vW75vdrfX7SI33UvQQGm1r+PXibKECTbGmBeMMS8B25rZfTrwjTHmOWNMDTADGCsi9rvORcB9xpgiY8x64D7g4ti3WimV6Ppe/Sc8AwZQ8803bPvPfxxrh0tc9Vlp/1v7v46fqM9o62sLwQagV33vJnGG0hIm2LRhDFC/KIQxphJYaW/fab/9fYszcCIy1R6yK/Q7+JeOUir23FlZDLjtNgC2/vNf1K5a5Vhbjh1mzdvMWzuv40NpoZ7N5paDTc/MUEZa4iQJJEuwyQJKm2wrBbJb2F8KZLU0b2OMKTDGjDfGjNe1XZTq+rIOPYTc00/H1NWx8YYbMYGAI+3Yt+++9Envw/qK9Xy7reX05VbVD6MttzLSmlEfbBLoXpu4BBsRWSgipoXHhxGcogLIabItByhvYX8OUGG64x2ZSqlm9bvu2vrstB3PPNP2E2LAJS6OGnoUYPVuOiSzN6T3hLpyKNvQ7CH199p0t2BjjJlojJEWHodEcIpvsCb/ARCRTGCUvX2n/fb3rZdGVSqBGWO6ZfWKWHLn5NDv5psA2PK3v+Mr3uxIO44aZgWbd9e927ETiDTM22z7odlDQj2bku4WbCIhIh4RSQPcgFtE0kQkNMb1IrCXiJxhHzMd+NIYExq0fBy4UkQGichA4Crg0Ti/BKVUgss++miyjjySYEUFxXZadLzl98snJyWHVaWrWF3a8hLPreq9q/V1a/PBpkeGnSBQpcGmOTcB1cB1wPn29zcBGGO2AGcAdwI7gAOAyWHPnQ28CnwFfA28bm9TSql6IkL/m25EMjIof/ttyt/tYO+iE7wuL4cPPhzoRO+m927W163N3zsU6tns0J7NzowxM5oZYpsRtn++MWZ3Y0y6PSy3JmyfMcZcY4zpaT+u0fkalczy8/PJz893uhldknfgQPpcfhkAm26/nWBVVdzbcOTQIwF456d3OnaCNoJN/Y2dVXqfjVKqFUuXLmXp0qVON6PL6nn++aTuuQf+DRvZOrsg7tc/aOBBpLpT+WrLV2yt3tr+E/TaxfraxjCa9myUUspB4vEwYPp0ALY/8gh1a9bE9foZ3gx+1v9nGAwfro8kIbeJvGHg8lr10eoqd9rdI8MLwA6ds1FKKWel77uvde+Nz8emO/8c9+y/0LzN+0UdWMLa7YGedlX67TvfpJpn92xKdBhNKaWc1/fKP+LKzqbygw+oiHOywGGDDwPgo/Uf4Qt0ICiEhtK2/bjTrpw0D26XUFHrp86fGCvuarBRSnVbnt696XOZlSxQ/Oe7CNbGr5bYgKwB7NZjN6r8VSwuXtz+E/QaZX3dunOwERHy0q2htJLqxBhK02CjlOrWepz7C1J33RVfURHbH388rtcO7920W6hn08wwGkCePW+TKENpGmyUSkBTpkxhypQpTjejWxCPh77XXQvAtlmz8W9rrvB8bBw88GCgg8Gm50jra4vBxpq3SZRlBjTYKJWACgoK6peGVrGXdfDBZB5+GMHKSrb8859xu+7YvmPJ9GaysnQlGys2tu/JbQUbexgtUdKfNdgopRTQ7+qrwe2m5NnnqP2h+ftXos3r8jJhwAQAPtzQzhTo7IHgToXKzVBbvtPu3NAwmvZslFItWbJkCUuWLHG6Gd1K6i670OOcsyEYpPgvf4nbdQ8eZA2lfVjUzmDjcoWlP+9cYy0vPZT+rD0bpVQLxo8fz/jx451uRrfT+9JLcWVmUvn+B1QuWhSXa4bmbRZvWow/2M7FHHvYwWbHmp125drDaDpno5RSCcbTsyc9f3UJYC1DEI8bPQdmDWRo9lDKfeXtX1Ctx3Dra7PBxiqar8FGKaUSUK+LLsLdsyfVy5bF7UbP0LzNpxs/bd8TWws2GaGeTTt7SzGiwUYppcK4MjPp/dvfArDl73+PyxLSBww4AIDPNn7Wvie22rPRYTSllEpoeeecjXfQIGp/+JGy116L+fV+1v9nCMLnmz+n2l8d+RM12CilVPJypaTQ+9JLAdj64EyMP7ZDUXlpeezec3d8QR9fbPmiHU8can0tXQfBxjXQQsGmvEaDjVJKJazckyfhHTqUurVrKXv99Zhfb//++wNWVlrEUjIgsw8E6qCiuNGunDQr2JTpnI1SqiWFhYUUFhY63YxuTTye+rmbePRuQsGmcFM7/91DvZuSnxptzrF7NmXas1FKtUSXhU4M8ezdjOs3DkH4cuuX7Zu3aSHYpHpcpLhd1PmD1Phin+TQFg02SinVAvF46P2b3wCwdeasmGam5aTksHvP3fEH/R2btylZ02iziJBj32tTlgBJAhpslEpAU6dOZerUqU43QwG5Pz8Z76BB1K1ZQ/n8d2J6rfx+Vm/28+LPI39S7hDra2nRTruyQ/M2Nc7P22iwUSoBzZkzhzlz5jjdDIXVuwlVFdg2Z05MqwqM6zcOgKWbl0b+pNzB1tdmg43Vs0mEjDQNNkop1Ya800/H3bMnNV9/TdWn7bzLvx3267sfAF9s+SLyOmkRBJuKWu3ZKKVUwnOlpdHzwgsB2BrDdYZ6p/dmaPZQqv3VLN+xPLInhQebJr2u7NTQvTYabJRSKin0OPcXuDIzqfrkU6q//iZm1wn1biKet0nLg5QsqKuAmpJGu3QYTSmlkow7J4e8M88EYMcTj8fsOvv23ReAL7d+GdkTRCBnkPV92YZGu7Lqg432bJRSKmn0uOB8cLkofeNNfJs3x+Qa+/TZB4Avt0QYbAByBlpfmwSb7FSds1FKtWLcuHGMGzfO6WaoJlIGDyb7qCPB56Nk7tyYXGNU7igyPBmsr1jP1uqtkT2pvmezvtHmUOpzhfZslFLN0WWhE1fPiy4CYMfTcwnW1kb9/G6Xm7167wW0o3fTQs8mS7PRlFIqOaXn55O2554Eduyg7PU3YnKNdg+l5Qywvjbp2WSm6pyNUkolJRGhx3nnArDjmdgMpe3Vy+rZRLxMdLbdsynf1GhzVqob0J6NUqoFIoKION0M1YKcE0/ElZ1NzRdfUvNthAGhHcb0HgPAN9u+iaxiQXZ/62uTYJOZYvVsKjXYKKVU8nGlp5N76qkA7Hjm2aifv19GP3ql9aKsroyi8p0rA+wk2x5GK9/YaHOmZqMppVRy6zH5HADKXn2VQEVlVM8tIo16N23K7A3ihqpt4G9IWsiyg01lnQYbpZRKSqmjRpGx//4Eq6pistbNnr32BCIMNi43ZPWzvg8bSgv1bCprdT0bpZRKWnlnngFA6YsvRv3ce/TcA4Dl2yOskZbV1/pauaV+U6adIKBzNkoplcSyjzkGV2Ym1cuWUbtqVVTPvXvP3QFYvmN5ZEkCoZ5NRXH9pnSvGxGo9QfxB4JRbV97abBRSqkOcmVkkH3C8QCUvvhSVM89IHMAOSk5bK/ZzpbqLW0/IXvnYCMi9RlpVQ4vDa3BRqkENHv2bGbPnu10M1QE8k47DYDSl1+O6rLRIlLfu/l++/dtP6G+Z9O4ZltGijWUVuXwvI0GG6USkC4LnTzSx43DO3Qo/s2bqYzywmq79dgNiHDeJtOes2lyr00o2DidkabBRimlOkFEyJ10EkDUy9eEgs0PJT+0fXBWH+trVePinRn2MFp1nfZslFJNFBQUUBDDFSFVdOWcZAWb8nnzCNbVRe28u/bYFYAfdkQQbDLtYFPReH6nfhhNg41Sqqlp06Yxbdo0p5uhIpQ6ahSpu+9OsLycyg8+iNp5R+aOBGBN6Rp8gTZW28zcOfUZIL0+2OgwmlJKJb2ck04EiOoNnhneDAZnDcZv/KwpW9P6wZm9ra+VTYfRrGCjw2hKKdUF5J5oBZvydxcSrKmJ2nl36bELACtLVrZ+YHoPcHmgtrRRyZrQnI0OoymlVBfgHTSItDFjMNXVVH78cdTOGxpKW126uvUDRSC9p/V91fb6zaFhtGq9z0YppbqG7GOOBqB83vyonTMUbFaVRlChIKOX9TUsIy3dawWbGg02SinVNWQfcwwAFQsWYPzRmZAfkTsCiDDYhOZtqrbVbwoFG52zUUqpLiJ11ChSRowgUFpKVWFhVM4ZCjZrStcQCLYRMDJCw2hhwSaUjaY9G6VUU8aYyIovqoSTffRRAFS8uzA650vJpk96H+qCdWyq2tT6wfXDaA1zNqke69e89myUUqoLyTr8cAAq3n8/auccmjMUgLVla1s/ML2H9bV6R/2mNHsYrdavwQYAEblURApFpFZEHm2yb7iIGBGpCHvcHLZfROQeEdlmP+4VXcBdKeWA9H33xZWdTd3q1dStWxeVcw7LGQbAT2U/tXHxZrLR6hMEdImBkA3AHcAjrRyTZ4zJsh+3h22fCpwKjAX2ASYBevu1Slr5+fnk5+c73QzVAeLxkHnwwUD0ejdDszvfs9FsNJsx5gVjzEvAtraObcZFwH3GmCJjzHrgPuDiKDZPqbhaunQpS5cudboZqoOyDjsMiF6wqe/ZlLfVswkFm4aeTZrXnrPRYNMua0WkSET+IyK9w7aPAb4I+/kLe1uzRGSqPWRX6I9SeqJSSoVkHmL1bKoWF2KiUJhzSPYQAIrKi1o/sD7YlNRvqp+z0WG0iGwF9geGAflANvBU2P4soDTs51Igq6V5G2NMgTFmvDFmvMfjiVGTlVLdlbdvX1J2GYWpqqL66687fb5BWYMAWF+xvvUsxfQ862tNw6/DUDZat0gQEJGF9gR/c48P23q+MabCGFNojPEbY4qBS4FjRSTHPqQCyAl7Sg5QYTR3VCnlkMwDJgBQ+cknnT5XVkoWuam51AZq2Vq9teUD03KtrzUlDZu6U4KAMWaiMUZaeBzSkVPaX0M9l2+wkgNCxtrblFLKERkTDgCg6tPPonK+8N5Ni9LyrK/dtWcTCRHxiEga4AbcIpImIh573wEiMlpEXCLSC3gAWGiMCb2jjwNXisggERkIXAU86sDLUEopADL33x9EqF62jGB1dafPFwo2RRWtzNt408HlBX8N+KzK092qZxOhm4Bq4DrgfPv7m+x9I4G3gHLga6AW+EXYc2cDrwJf2ftft7cplZSmTJnClClTnG6G6gR3Xh6po0djfD6qv/qq0+cLBZtNla1UERBpGEqrLQMgxe7Z1AWcDTYJMztujJkBzGhh39PA06081wDX2A+lkp4uCd01ZIzbj9rvv6d66edk/uxnnTpX/8z+AGys2Nj6gWk5VtXnmjLI6tswjKapz0op1TWl7zcOgKrPO3/PVCjYtFkfLTXb+lprzTIkSs9Gg41SCWjJkiUsWbLE6WaoTsoYtx8A1cu+wAQ798u+vmdT2UbPJtVOzK0tByDFbQcbf9DR4q4JM4ymlGowfvx4AK38nOQ8Awfi6dcPf3ExdatWkbrLLh0+14DMAUAbczYQlv5szdl43C7cLiEQNPiDBq/bmbKR2rNRSqkYERHSx1p3ZXT25s4eqT3wuryU15VT7W8luy0ly/pq92ygoXfjc3AoTYONUkrFUNpeewFQ83Xnbv0TEfqk9wFga1UrN3am2sGmrrJ+U6g3U+fXYKOUUl1S2pg9AaiJQtmaPhlWsNlcvbnlg0I9m7qwnk0CJAlosFFKqRhKH2PVBK75/ntMJwv/9s3oC8CW6i0tHxTq2dRW1G9qGEZzbg5Qg41SSsWQOy8P7+DBmJoaaleu6tS5eqdbxe63VLUSbFLs1Oe6hmDj9TRkpDlFg41SSsVYw7xN54bSQj2bzVWtDKM107PxujXYKKWaUVhYSGFhodPNUFGStsceANQs/75T5+mV1guAbdWtrDGZkml9rWtuGM25YKP32SiVgHRJ6K4ldfRuANQuX9Gp8/RKt4NNTWvBxu7Z+KrqN4WG0TT1WSmlurC00aMBqF2+vFM36tYHm9Z6Nt4M62t46rPLSn3WBAGlVCNTp05l6tSpTjdDRYmnf39cOTkESkrwb25lcr8N9cNorfZsdh5G8ybAMJoGG6US0Jw5c5gzZ47TzVBRIiKk7WYPpa1Y3uHzhILNjpodBE0LgaM+2Ow8jKb32SilVBeXGjaU1lFet5eclBwCJkBJbUnzB9UHm4ZhtBS7goBfh9GUUqprS93dCjY10UoSaGneJjRnE54goMNoSinVPTQMo3Uy2LQ1bxM+Z2MnI3g02CilVPeQMnIkAHWrV2MCHV81s0daDwBKakqaP8DtBZcXTBACdYBmoymlVLfhzs7G068fpq4O3/r1HT5Pz7SeAGyv2d7yQU2G0kLDaH7t2Silwo0bN45x48Y53QwVZamjrN5N7Y8rO3yOvNQ8AHbU7mj5IG+69dXOSPPYCQK+oPZslFJhdFnorilllLVSZ92qjgeb0DDajppWgk1KqGdjLbKmPRullOpG6ns2naj+HBpGazXYNBlG87g09VkppbqNUJJA7coo9GxaHUZrEmzcelOnUqoZIoKION0MFWWpu9jDaCtXdrhGWo/UCIbR6udsrBs7vXpTp1JKdR/uHj1w5eQQrKwksK2V+matyE3NBaC0trTlg0LBxl8DgMdl/aoPBLVno5RSXZ6IkDJsGAB1a9d26ByhbLSS2pKWe0eeNOurnSCQCNloLa5nIyLvR3iOGmPMsVFqj1JKdWkpw4ZR89VX1K1ZQ0YH1i1K86SR5k6jJlBDtb+ajND8TLjQNrtn0zCMlpiLp+0P/KaN5wtwf/Sao5RSXVt9z2ZNx3o2ADmpOdRU1VBaW9pCsGncs3G7QuVqErBnA3xsjHmsrROIyLlRbI9SSnVpKcM7N4wG1lDa5qrNlNSWMCBrwM4HeOw5m/r7bKyeTSARb+o0xhwVyQl0CE0ppSLX2TkbCEsSqGshSSDUs7GH0dyh+2wcTBBorWejlHLI7NmznW6CipHwYGOCQcTV/jyt8CSBZnmb9GwSfBitnoiMBf4O7AtkhTYDxhiTEpumKdV96ZLQXZc7Nxd3Xp61RPTWrXj79m33OXJScgAoqy1r/gBPk9TnRB5Ga+Jp4CPgMGAP+7G7/VUppVQ7eAcPBsBXVNSh59cHm7qWgk2q9XWnYbTEDzb9genGmK+NMSvDH7FsnFLdVUFBAQUFBU43Q8WId0gng01qG8Gmfhgt+W7qfAzQrDOl4mTatGlMmzbN6WaoGEkZPASAunXrOvT8tofRWkgQSPQ5G+Bu4BMRuQEoDt9hjDky6q1SSqkurGEYrWOLqGWnZANQXlfe/AFNgk2o6rOTczaRBpv/AquBF4Hq2DVHKaW6vpTQMFpnezYtztk06dm4nZ+ziTTY7Av0MsbUxbAtSinVLXiH2MNoMU8QqLV+TICeTaRzNh8Ae8ayIUop1V14+/cHlwt/cTGmrv1/w9cnCLQ0Z9PkPpvQnI0vQWujhVsN/E9EXmTnOZvpUW+VUkp1YeL14unXD//GjfiKi0mxezqRyvJatztW+CqaP8Bt3/4YsAJZKBst2ME1dKIh0p5NBvA6kAIMCXsMjlG7lFKqS/MOsGqa+dZvaPdzQwkCFXUVzS8z0FI2WqLP2RhjfhnrhiilGnR0FUeVPLwDB1K9dCm+De0PNinuFFLdqdQGaqkJ1JAeqhgQUj9nE+rZOD9n09p6NunGmDYzzyI9Ltn4fD6KioqoqalxuikxkZaWxuDBg/F6vU43RaluyTtwIAC+je0PNmANpdUGaimvK28l2CTHfTbFQE4E51gP9IxOcxJHUVER2dnZDB8+vMutBW+MYdu2bRQVFTFixAinm6NUt1QfbDrQswFrKG1bzTYq6irom9GkvlrTbLQEqI3WWrBJE5HHIzhHl/zTuKampksGGrCWpu3VqxdbtmxxuimqBfn2Co5LlixxuCUqVrwD7TmbTgQbaCH9OTRnE2ic+pyoSwzcGeE57o5GQxJRVww0IV35tXUFS5cudboJKsY627NpNSPNHTaMZkz9Sp0JmSBgjLk1ng1RSqnuxNPf6tn4NxVjjGn3H4BZKa0EG5cLXB4I+iHgwy3OD6O1f9UepZRSnebOysSVnY2prSVQUtLu54d6NpV1lS1coKF3EypXE9Rgo9rjscceIysri6ysLNLS0nC73fU/5+XlUVtb63QTlVIR8PbvB4B/06Z2P7fVng00JAkE6up7Nsmwno1KIBdddBEVFRVUVFRwww03MGnSpPqfS0pKSE1NdbqJSqkIhIbSfBs7EGzaqiIQlpHmToD7bDTYJLlly5YxduxYp5uhlOoAb//+APg2bWz3czO9mYBVRaBZ9SVrahtu6nTwZuGIKgiISApwMVb156zwfcaYCzvbCBFJBR4Ejsa6Z+dH4AZjzJthxxwF/BsYCnwGXGyMWWvvE6ysuF/bhz8MXGu6wW3Yy5Yt4/zzz3e6GSrKpkyZ4nQTVBx46ofRits4cmf1JWsi6Nm40u1g4+BNne1ZqfMPQDmwsskjGjzAOuBwIBe4GXhWRIYDiEhv4AV7e0+gEHgm7PlTgVOBscA+wCQg6sscikiLj/AlfAsKClo9Nlx+fn6z2yNRVlbGmjVr2HfffRttv/HGGzn00EM588wzqaqq6tBrVc7SZaG7B29oGC0WPZuwYONJltpowPHACGNMSSwaYYypBGaEbXpNRFYD+cAa4HTgG2PMcwAiMgPYKiK7G2O+By4C7jPGFNn77wOmALNi0d5E8cUXX5Cdnd2oCsDXX3/NypUr+eCDD5g1axaPPPIIl156qYOtVEq1pDM9m/psNF8b2WiBuoY5mySo+vwTELdZZxHpB+wGfGNvGgN8EdpvB6eV9vad9tvfj6EFIjJVRApFpNDv90fcLmNMi4+pU6fWHzd16tRWjw23ZMmSZrdHYtmyZeyzzz6NekUffPABJ5xwAgAnnHACH374YbvPq5y3ZMkSrR7QDXj72cGmuP3BJtSzqfS3FGwalhlwifOpz60V4jwy7MfHgZdF5H52Xs9mQTQbJCJe4CngMbvXAtY8UdPaKqVAdtj+0ib7skREmpu3McYUAAUAmZmZSTuvs2zZsp2G0Hbs2MEAu3R5bm4u27dvd6BlqrPGjx8PaPXnrs5jBxvfli3tvrGzPti0dJ+Nxw42STCM9nAz2/7c5GcDjGzrIiKyEGs+pjkfGWMOsY9zAU8AdUD42E8FOxcFzcGaQ2pufw5Q0dUTBL744gt++9vfNtrWo0cPSkutuFtaWkrPnl2uRqpSXYYrKwvJyMBUVRGsqMCdnd32k2xt92wahtFcroYgFgyaRj/HS2vlaqJWDtgYM7GtY+yMsoeBfsCJxhhf2O5vsOZlQsdmAqNoGGb7Bis5YJH989iwfV1WYWHhTtsOOeQQ7rrrLi666CLefvttDj74YAdappSKhIjg7duXujVr8BcXdyzYtFhBwK6RHGhY08YfNASMwUX8g01EczYi8nIL21+IYltmAnsAJzezPs6LwF4icoaIpAHTgS/DhtkeB64UkUEiMhC4Cng0im1LGnvvvTfDhg3j0EMP5e233+aSSy5xuklKqVbUD6W1c94mvGfT/GqdjRdQczl8Y2ek2WhHtLB9YjQaISLDsFKVa4FNYeOW04wxTxljtojIGcC/gCex7rOZHHaK2VjDeV/ZPz9kb+uW7rrrLqeboJSKkKeftRaNv3hz+57n8pDmTqMmUEO1v5oMb0bjA+qH0azyVU4X42w12IjIbfa3KWHfh4wE1kajEfbNma3264wx84HdW9hngGvsh1JKJY36jLTN7c9Iy/BmUBOoocpf1UywsYfR/E3XtHEm2LQ1jDbEfrjCvh8CDMa6CfOsmLZOKaW6OE8fu2ezuX09GwgbSmvuXpv6QpzW9HdoGM2p9OdWezbGmF8CiMjHxpg58WmSUqq55A/VNXn6WsHGF+1gE3afDeD4jZ0RzdkYY+aIyK7A2cBAYAPwrDHmh1g2TqnuKrQstOr6QsHGv7n9y7RneKyhs9aDjT1n43DPJtJstHOBz7HqjlUCewNL7e1dVle+TacrvzalkklDsIntMJrTa9pEmo12B9a9L++HNojIoVg3YP5fLBrmNLfbjc/nIyUlxemmxITP58PjifSfX8VbqPyRFuPs+jx9+wDg37IFEwwirshXfml9GK1xgoDTa9pE+qqygU+abPsUyIxucxJHXl4excXFBINBp5sSdcFgkOLiYnJzc51uimrBnDlzmDNHp0m7A1dKCu68PAgECGzb1q7nth5sGioIAIRiWDCR52yAvwF/FpGbjTE1IpIO3Gpv75J69+5NUVERy5cvd7opMZGZmUnv3r2dboZSCmsoLVBSgq94M54+fSJ+XijYVPmaWUrE3VAbDcBjR5tEH0b7HdAfuEJEdgA9sO6L2Sgi9cW5jDFDo99EZ7hcLoYO7TIvRymVwDx9+1K7YgX+LZtppWD9Tlqtj+ZpnI0WKoeWkKnPYXQpSKWUipH6JIEt7ctIiyz12U4QSJLU5/di3RCllOqu6pME2pn+nO5JB9oYRrNTn10Ol6uJNPU5VUTuFJFVIlJqbztWRHQJSKWU6qSOpj93pGfjVM5TpNlofwf2As7DWsMGrBL+v23xGUqpDhs3bhzjxo1zuhkqTryxCDb1VZ+bpD4n8jAacBqwizGmUkSCAMaY9SIyKHZNU6r70iWhu5fYzNk0Xs8mKYbRsFbObBSYRKQP0L6kcKWUUjtpqI/WvsrPoUrPVf7m5mwa32fjSZKbOp8DHhOREQAiMgBrbZm5sWqYUkp1F55evUCEwLbtGL8/4udleiIpV5MYi6dFGmxuANZgLU6WB/yAVYzz1pi0SqluTkQIW0RQdXHi9eLu1QuCQfztqCIQWbkau+qz/XlyqoJARMHGGFNnjPmDMSYL6AdkG2P+aIypi23zlFKqe2hIf448SaD1CgKNezZO10aLKEFARPYEDgV6AtuBD4BvY9gupZTqVrx9+lLLd+0KNumedAShJlCDP+jH4wr7lV6fIJAE2Whi9eMfBi4CirCGzgYBA0XkCeASo7XqlVKq0zz1y0NHHmxEhExvJhW+Cqr8VeSk5ISdMJT63KRnE0jMYbSpwERggjFmmDHmQLv+2YFYPZ1pMW6fUkp1C/UZacUdzEhrOpTmblobzdmeTVvB5gLgcmPM4vCN9s9/sPcrpZTqpI6WrGkxSWCnZaGtHxN1pc49gZbqor1n71dKKdVJ3tAwWjt7NqH05wpfReMdSbbEgNsYU97cDmNMuYhEvqScUipis2fPdroJKs4a5mzaGWxSWujZhN9nY0z9fTaJuniaV0SOwFq7piPPV0p1QGhZaNV9hIKNr7id9dE8LaQ/u9wgbjABCPodryDQVrDYDDzSxn6llFKd5M7LQ1JSCJaVEayqwpWREdHzslKygGaG0cDq3fiqwF9bnyCQkMNoxpjhcWqHUipMQUEBoD2c7kRE8PTti6+oCF9xMakjRkT0vDarCPiAQF3CJwgopRwwbdo0pk3TOwu6G0//UJJA+6sINB9sGuZt3A4nCGiwUUqpBOHtGwo2myJ+TijYtDiMBuCvbejZJOh9NkoppeKkI0kCWV5rzqayrrXVOusaUp8TtIKAUkqpOPEO6A+Af9PGiJ8Tac/GlQxVn5VSSsWep78VbHwbIx9GC/Vsmg029T2bWjxuZ7PRNNgopVSC8IaCzaZ2BJtQ6nNdW3M2ybF4mlJKqRgL9Wz87Qg22SnZQAQJAqLBRinVhDEGXb2j+/H07g1eL4EdOwjW1ET0nMhTn3UYTSmlFCAuF157qYFIezfZXqtnU17XTBnLsJ5NqFyN3tSplFIKz4D2zduEF+LcqTccVvk5VIjTFwxGp6HtpMFGqQSUn59Pfn6+081QDvAOHAiAb/2GyI53eUlzpxEwAar91Y13etKsrwHnezZatVmpBLR06VKnm6AcUh9sNkQWbMBKEqiprqG8rrx+5U4gbBitRudslFJKNehIsAmlP+80bxPq2fjrHF9iQIONUkolEO/AQQD41q+P+Dmh9OdyX9NgE9azsYujabBRSimFd1AHhtFaykir79nUas9GKaVUg/phtE2bMIFARM+p79nsFGx0zkYppVQzXKmpuHv3Br8f/+bIqj+3OWcTcH7ORrPRlEpAU6ZMcboJykHeQQMJbN2Kb/16vAMGtHl8mz0bX3V9z8YXcOY+Gw02SiWg0LLQqntKGTSImi++tJIExo9v8/iclByguQSB8J6NNZClSwwopZQCwDtoMAB1EWak1QebCHo2uniaUqrekiVLWLJkidPNUA7xDmpf+nNoGK2stqzxjgTKRtNhNKUS0Hh76EQrP3dPDcEmsvTnludsQsGmBrcunqaUUipcfbApKoro+LZTn53v2WiwUUqpBFN/Y+emTRi/v83jW64gENazqb/PRqs+K6WUwrrXxtOnj3WvTXFxm8eHEgR2nrNpuKkzlI3WrRMERCRVRB4WkbUiUi4in4vICWH7h4uIEZGKsMfNYftFRO4RkW32414Rew1UpZRKQqGhtEgy0sJ7No3m+cISBEI9m4BD84CJkiDgAdYBhwM/AScCz4rI3saYNWHH5RljmutTTgVOBcYCBpgHrAJmxbDNSikVM97Bg6letiyiJIFUdyqp7lRqA7XUBGpI96RbO3TOpjFjTKUxZoYxZo0xJmiMeQ1YDUS6etRFwH3GmCJjzHrgPuDiGDVXKaVirqPpz42SBJqbs+nOw2hNiUg/YDfgmya71opIkYj8R0R6h20fA3wR9vMX9raWzj9VRApFpNAfweSbUvFWWFhIYWGh081QDqpPEuhMRlr4nI1bezaNiIgXeAp4zBjzvb15K7A/MAyrt5NtHxOSBZSG/VwKZLU0b2OMKTDGjDfGjPd4EmUkUakGuiy0inbPxtMdstFEZKE9wd/c48Ow41zAE0AdcGlouzGmwhhTaIzxG2OK7X3HikiOfUgFkBN2yRygwugdcUqpJJUy2CpZ0+4qAnVhGWmhnk2gDrtj07UrCBhjJrZ1jN0LeRjoB5xojPG1dsrQ0+yv32AlByyyfx7LzkNwSiWNqVOnAlqQszvzDBgAIta9Nj4f4vW2enyO105/Dg82IuBOhUAt3mAtAD6ds2EmsAdwsjGmOnyHiBwgIqNFxCUivYAHgIXGmNDQ2ePAlSIySEQGAlcBj8ax7UpF1Zw5c5gzZ47TzVAOcqWk4OnbF4JBfBHca9NWyRq3/fd7t56zEZFhwDRgX2BT2L0059mHjATeAsqBr4Fa4Bdhp5gNvAp8Ze9/3d6mlFJJyxsaSitqeygtJ7X1ys9eUwd08/tsjDFraRgSa27/08DTrew3wDX2QymlugTvoIFUL1kS0bxNWz0bT9AONt25Z6OUUmpn7clIa6sYp9sONn6HVurUYKOUUgkqpQPBplGCAIT1bKwEAe3ZKKWUaqQ9PZtms9GgvmfjsoONU+vZJMScjVKqsXHjxjndBJUA6otxbuhEgoDXqpPm9JyNBhulEpAuCa0AvP37g8uFf1Mxpq4OSUlp8diWl4a2ezaBGsDq2RhjiHdhfB1GU0qpBCUpKXj69YvoXptQsKnwVTTeYc/ZSKCuvhinE50bDTZKKZXA6gtytjFvk+1tyEZrvKZNwzIDTq7WqcFGqQQkInEf5lCJqT4jrY3qz163l3RPOgEToNofVoSluWKcDpSs0WCjlFIJzDvIqiIQ0Yqd3laKcYavaePAOJoGG6WUSmCdvrEzbGloJ1fr1GCjlFIJrD7YRFAfrdVlBvw1uF3Wr3yds1FKKdVIQzHOtlfsDN1r0yj9OdSz8emcjVJKqRZ4+/cDtxv/li0E6+paPbbZ9Of6BdQastF0GE0ppVQj4vHg7dcPjMG/cWOrxzafIBA2Z+N2LthoBQGlEtDs2bock2rgHTQI34YN1BUVkTJsWIvHNZ8gkBjZaBpslEpAoWWhlQJ73mbx4jYz0nJSmqmPptloSimlItGQ/ryh1eNaTX32VWs2mlKqsYKCAgoKCpxuhkoQ3girCCTyfTY6jKZUApo2bRqgw2nKkjK4fcGm+ftsqrWCgFJKqZbV92w2tD6M1vqcTZ3eZ6OUUqplnr59weOx7rWprW3xuOZ7Ng2FOPU+G6WUUi0Sj8daSI3WkwSa79k0LDHg5H02GmyUUioJ1JetaSX9OSslC7AqCASNnXHWqGej2WhKKaVaEckiah6XhwxPBkETpMpXZW8M69noMJpSSqnWNNxr087052bmbDQbTSkFgDGm8dK+qttLiWAYDZpJEtCejVJKqUiFejZ1baxrE0oSaAg2oZ5Nw302voDO2SillGpGh++1cXsBgaAfr1hBJuhAr1mDjVIJKD8/n/z8fKeboRKIp08f8HoJbN1KsKamxeN2mrMRqe/dpEoA0Js6lVK2pUuXsnTpUqeboRKIuN14BwwAWp+3aW2ZgTR8gM7ZKKWUakV9jbT2BhtvOgBpLmulT58GG6WUUi1pSH9uO9g0V4wzFSvYBDRBQCmlVEsiCTatFeNMtYfR9D4bpZRSLYok/bm1nk2aBhullFJtac8wWoWvomGj3bNJEecSBHTxNKUS0JQpU5xugkpA7ZqzqW1mzsbYczYabJRSgC4JrZrl6dMH8XoJbN9OsKoKV0bGTse0NmfjNTqMppRSqg3icuEdaFd/bqGSQGv32aSEstF0iQGlFMCSJUtYsmSJ081QCaghSaD56s9Z3pbXtPHawcaJno0OoymVgMaPHw+glZ/VTtpaRM3tcpPlzaLCV0GFr8IaVgv1bOxhtICWq1FKKdWa+iSBSNKfaxtXfvaGEgS0EKdSSqnWdCj9uUmw0UKcSimlWtWh+mj2MFp9sNFsNKWUUq3p0L02oZ5NsBbQbDSllFJtcPfujaSmEigpIVBR2ewxO6/WGerZ6H02SimlIiAiDffatNC72enGTrtn43GwgoAGG6USUGFhIYWFhU43QyWottKf6+dsfI3nbNz2MJreZ6OUAtAloVWrvINa79nsnCBg92yCofVstGejlFKqDQ332jRfRWDnBAGrZ+Op79logoBSCpg6dSpTp051uhkqQaWEhtE2tG/Oxq1zNkqpcHPmzGHOnDlON0MlqPr6aG0MozXNRnMH9D4bRORJEdkoImUiskJEft1k/1Ei8r2IVInIuyIyLGyfiMg9IrLNftwrIhL/V6GUUrHXVsma+p6Nr0nPpv4+m24cbIC7gOHGmBzg58AdIpIPICK9gReAm4GeQCHwTNhzpwKnAmOBfYBJwLS4tVwppeLI3bMnkp5OsKyMQFnZTvtbqiDgcjAbLWGCjTHmG2NMbehH+zHK/vl04BtjzHPGmBpgBjBWRHa3918E3GeMKTLGrAfuAy6OW+OVUiqORKQhI62ZdW1aykarH0YLdPMEARF5UESqgO+BjcAb9q4xwBeh44wxlcBKe/tO++3vx9ACEZkqIoUiUuj3+6P4CpRSKj5aK1uT6c1EECp9lfiD/vpg4wroMBoAxpjfAdnAoVjDZqGeThZQ2uTwUvvY5vaXAlktzdsYYwqMMeONMeM9Hr3VSCmVfFJaSX92iYusFHsRtbqKsGBTYz2nq95nIyILRcS08Pgw/FhjTMAY8yEwGPitvbkCyGly2hygvIX9OUCF0ZWnVJIaN24c48aNc7oZKoF5B1npzy1lpDVKErCDjdg3dQYd+NUYlz/rjTETO/A0Dw1zNt9gzcsAICKZ9r5vwvaPBRbZP48N26dU0tEloVVbGobRdp6zgSbpz1k9AHD5u3mCgIj0FZHJIpIlIm4ROQ74BbDAPuRFYC8ROUNE0oDpwJfGmO/t/Y8DV4rIIBEZCFwFPBrnl6GUUnHT1lIDjW7sDPVsHJyzSZQJC4M1ZDYLKwCuBf5gjHkZwBizRUTOAP4FPAl8BkwOe/5sYCTwlf3zQ/Y2pZTqkrxtLKLWKCPN7QFxIyaAm4Aj5WoSItgYY7YAh7dxzHxg9xb2GeAa+6FU0gvltui0o2qJOy8PycggWF5OoLQUd25uo/3Npj/7KknFp4U4lVJKRUZEGjLSmundtHRjZyp1BBz4I0aDjVJKJan6GmnNpD+Hgk1prX1XiDcdwOrZdNcEAaWUUu1XnyTQTBWBnSs/Wz2bNKnruvfZKKWUir7W0p9bKsapPRullFLt0toiai3P2fh08TSllFKRay39uaVinE71bBIi9Vkp1djs2XqbmGpbeDaaMYbwcpAt9mzE50gFAQ02SiUgXRJaRcKVm4srM5NgZSWBkhI8PXrU7wvN2TSs1mn1bNKkDhOEYNDgcsVvjUkdRlNKqSQlIngHWwU5myYJtNSzyRAfEP/6aBpslEpABQUFFBQUON0MlQRaShLI8GTgFjfV/mp8QV99zybDFQo28U0S0GCjVAKaNm0a06bpyuaqbS0V5BSRJmvahO6zCQDxL8apwUYppZJY/fLQzWWkecOG0uyeTbo9jKbBRimlVMRS6udsWk5/LqsrC+vZ6JyNUkqpdmooWbNzsMlJDctIq89G056NUkqpdmooxrl+pyUpGi+gpj0bpZRSHeTOycGVk4OpriawY0ejfY3Sn5v2bOJcjFODjVJKJbmW0p9DCQKNhtGwgo1PU5+VUsYYXaVTRayljLRQzyY89TnV7tkEdRhNKaVUe7S0YmfjbLSGxdNA52yUUkq1k3eQlf5c12qwaVhiADQbTSkF5Ofnk5+f73QzVJJoaamB3NRcoHGCQIpDPRut+qxUAlq6dKnTTVBJpCFBoPmeTXjqcyp1AAQ0QUAppVR7eJusaxPSXLma+p6Npj4rpZRqD3dWFu7cXExtLYGtW+u3N9uzMaGejQYbpZRS7dRc9efGCQJWz8Zbf5+NBhullFLtVF+2JizYpHvS8bg81AZqqbWXjPbaczZ6n41SSql2a27FThFpqI9mrHVsUozdswnEN0FAs9GUSkBTpkxxugkqybS0iFp2Sjbba7ZTburoDXgdmrPRYKNUAtIloVV7tViyJpSRFrCCTCjYaAUBpZRS7dZazwagPFgLgMfUAUaz0ZRSsGTJEpYsWeJ0M1QS8Q4MLaK2ofG9NqFg46sElxcXBi8BrSCglILx48cDaOVnFTF3VibuvDwCJSX4t2zB27cv0Ez6c52PNOrwxzlBQHs2SinVRTRkpDUMpTW3WmcqPgJx/kNGg41SSnURDfM2DenPza3WmUqdztkopZTqmOZW7Gy2ZyM+rY2mlFKqY5pbaqBRz8bbsICa9myUUkp1SHMrdtYnCPgaL6Cm99kopZTqkNaKcZbXhs/Z+OK+no2mPiuVgAoLC51ugkpC3oF2FYENGzDBIOJy1c/ZhC8NnSZ1+OI8Z6PBRqkEpEtCq45wZWTg7tWLwLZt1r02/fqRkxqeIDAAsBZQC2rqs1JKqY5qOpTW/NLQOmejlAKmTp3K1KlTnW6GSkL1BTnt9OdUdyoprhTqgnXUulOsbZqNppQCmDNnDnPmzHG6GSoJpTRTRaA+I81t/cpPkzq9z0YppVTHNbdiZ/1QmssNOJONpsFGKaW6kIYqAjvXRytzWUtD65yNUkqpTvEOsofRwkrW1PdsxA42orXRlFJKdYJ3oJXe7Nu0CRMIAGFzNmINnaXhi/t9NhpslFKqC3GlpeHu0xv8fvybNwMNwaYCK8A4cZ+N3tSpVAIaN26c001QSSxl4CCqt2zFV1SEd8CAhmE0rJ6OztkopQBdFlp1TmgRtbomN3aWGTvYiGajKaWU6qSmVQTqs9GCPsBaPK3b3mcjIk+KyEYRKRORFSLy67B9w0XEiEhF2OPmsP0iIveIyDb7ca+InXahlFLdTNP05/oF1Ewo2MS/gkAizdncBfzKGFMrIrsDC0Xkc2NM+FhCnjHG38xzpwKnAmMBA8wDVgGzYtxmpWIi9LeSifMkruoami6iVj9nE6gFuvmcjTHmG2NMbehH+zEqwqdfBNxnjCkyxqwH7gMujn4rlVIq8aU0WR66fs4mUAPY5Wq685yNiDwoIlXA98BG4I0mh6wVkSIR+Y+I9A7bPgb4IuznL+xtSinV7XhC69oUF2P8/obUZ7tnk4K/e9/UaYz5HZANHAq8AIR6OluB/YFhQL59zFNhT80CSsN+LgWyWpq3EZGpIlIoIoV+f3OjckoplbxcKSl4+vaFQAB/cXHDMJq/GujCVZ9FZKE9wd/c48PwY40xAWPMh8Bg4Lf2tgpjTKExxm+MKQYuBY4VkRz7aRVATthpcoAK08KAtzGmwBgz3hgz3uNJpGkrpZSKjvqCnEXrG4bR/JUY7Gy0rhhsjDETjTHSwuOQFp7moeU5m9C7FOq5fIOVHBAy1t6mlFLdkjdsqYFUdyqp7lT8JkC1iH2fTRcMNm0Rkb4iMllEskTELSLHAb8AFtj7DxCR0SLiEpFewAPAQmNMaOjsceBKERkkIgOBq4BHHXgpSimVEOoXUWtyr025y+XIfTaJMoZksIbMZmEFwLXAH4wxL9v7RwJ/BvoCZVipzb8Ie/5s+5iv7J8fsrcplZRmz9aPr+qc5paH3lK9hXKXi77d9T4bY8wW4PBW9j8NPN3KfgNcYz+USnq6JLTqrBbTn10uhlKHrzunPiullIqO+gSBDY1v7KxwCSkSIGgvPxAvGmyUSkAFBQUUFBQ43QyVxDwDBoAI/k3FGJ+vPtiUelIBkEBdXNujwUapBDRt2jSmTZvmdDNUEnOlpODp1w+CQXzFxQ3FOD1eADxBDTZKKaWiILwgZ302mh1sXBpslFJKRUND+nNRQxUBt5UX5gnWxLUtGmyUUqqLSgm7sbMh2LgBcBvt2SillIqChpI1YT0bl/Vr3xusbfF5saDBRimluqiGGzs3hN1nY1X5cturdsaLBhullOqiwuujhRIEKuxi+F4dRlNKGWN0lU7Vad5+/cDlwl9cTJakAVAu1ufKa+ri+hnTYKOUUl2UeL14+vcDY8jYVgVAOVaZmnivaaPBRimlurCUgda8Tepmq0h+OQF7TRtfXNe00WCjVALKz88nPz/f6WaoLiA0bxPcuIl0TzoBsNe0qYtrzyYhqj4rpRpbunSp001QXUT4UgPZA7Op9ldT5nJpz0YppVT0hJesCV9mwFpALX7LDGiwUUqpLsw7uKFnk5Mavlqnn4BmoymllIqGlPBhtLAqAvGes9Fgo5RSXZinXz9wu/Fv3kyuZABQ7hZrziagwUYppVQUiMeDt39/APqVWb/yQwkCmo2mVDc3ZcoUp5uguhDv4MH41q+nV0kQxAo2udTFNRtNg41SCUiXhFbRFMpI67nDDz2tOZu+ohUElFJKRVFoEbVsu2RNmctFGnX4NPVZqe5tyZIlLFmyxOlmqC4itIhaxtYKACrsOZtgHFOfdRhNqQQ0fvx4AK38rKIiNIyWEqqPphUElFJKRVso2LiLtwF2NprO2SillIomT9++4PEg20rw+kxDz0bvs1FKKRUt4nbjHTAAgD5lztxno8FGKaW6gdBQWp8SQ4VL8FKHP6jZaEoppaIoVJBzSLkXI0LArT0bpZRSURYqyDmg3AuA3+XHF8c5G019VioBFRYWOt0E1cWEhtH6lQkAta6g3mejVHenS0KraAsFm94l1jxNrSug99kopZSKLq9dRSBvRx0AVW4I+uvidn0NNkoloKlTpzJ16lSnm6G6EE+fPuD1klHuI8W+1ybgq43b9TXYKJWA5syZw5w5c5xuhupCxOXCO9C+16bUKlmDX4ONUkqpKEsZZA2l9S0xlLsE46+J27U12CilVDcRShLoW2pVEcBXHbdra7BRSqluoj4jrdRYwUYTBJRSSkVbeM+m3OVCAjqMppRSKspCJWv6lJi4JwjoTZ1KJaBx48Y53QTVBe3Us9Fgo1T3pktCq1jw9O4NKV5yqn3U+l1IQFOflVJKRZm4XHgGDgQgtUJw6ZyNUkqpWEixy9ZklQnGVxW362qwUSoBiQgi4nQzVBcUurGzTyn4gpVxu64GG6WU6kbqV+wsNdQEtWejlFIqBlIGN2Sk1QUr4nZdDTZKKdWNhPdsarVno5RSKhbq77UpgVqjtdGUUkrFgLtXL3weIasG/DWaIKCUUioGRISKvBQAPGXdONiIyK4iUiMiTzbZfpSIfC8iVSLyrogMC9snInKPiGyzH/eK5o2qJGaMwZj4rQ+vupfqvDQAUsrid1NnIpar+TewOHyDiPQGXgB+DbwK3A48A0ywD5kKnAqMBQwwD1gFzIpLi5VSKonsesd9bC3fws9H7h23a0oi/fUkIpOB04FvgV2MMefb26cCFxtjDrJ/zgS2AvsZY74XkY+BR40xBfb+XwFTjDETmrtOuMzMTFNZGb+upFJKdQUiUmWMyYz0+IQZRhORHOA24Kpmdo8Bvgj9YIypBFba23fab38/BqWUUgkhkYbRbgceNsasa2a6JQvY0mRbKZAdtr+0yb4sERHTTNfN7ilNtX80IhK//L/o8QB+pxsRZ93tNXe31wv6mpNJensOjkuwEZGFwOEt7P4IuBQ4GtivhWMqgJwm23KA8hb25wAVzQUaAHu4raDNhicwESk0xox3uh3x1N1ec3d7vaCvuSuLS7Axxkxsbb+I/AEYDvxk92qyALeI7GmMGQd8A1wUdnwmMMrejv11LLDI/nls2D6llFIOS5Q5mwKs4LGv/ZgFvA4cZ+9/EdhLRM4QkTRgOvClMeZ7e//jwJUiMkhEBmLN+zwat9YrpZRqVULM2RhjqoD6Ij0iUgHUGGO22Pu3iMgZwL+AJ4HPgMlhp5gNjAS+sn9+yN7WlSX1MGAHdbfX3N1eL+hr7rISKvVZKaVU15Qow2hKKaW6MA02SimlYk6DTYISkT1EZIGIlIrIjyJyWivHjhSR10SkXES2isi98WxrtET6mu1aeHeIyHr72IUikvA38YrIpSJSKCK1IvJok30t1v5r5jw9ReRFEakUkbUicm7MG99B0XjNIpIqIg/br7VcRD4XkRPi8gI6IFr/zmHPabZeZLLRYJOARMQDvAy8BvTEugH1SRHZrZljU7BqwS0A+gODsZIokkp7XjNwFnAJcKh97CfAE3FqamdsAO4AHgnfGFb772as11OIVfuvJf8G6oB+wHnAzAQOttF4zR5gHda9ern2c54VkeGxaXKnRevfOWSnepFJKVRdVh+J8wD2wrpRVcK2/Q+4vZljpwIfON3mOL/ma4Fnw34eg5W96PjriPC13oFVyy/83/DjsJ8zgWpg92aem4kVaHYL2/YEcLfTrytWr7mF830JnOH064r1a8bKun0WmAE86fRr6sxDezaJqbnlEQTrF3JTE4A1IvKmPYS2UETiV8o1etrzmucCu4jIbiLixbrh961YNi7G2qr9F243IGCMWRG2LRlrAbbnNTciIv2w3odku3G7Xa+5jXqRSUeDTWL6HtgMXC0iXhE5FmsIIaOZYwdj/fXzADAQ62bYl+3htWTSnte8EfgAWI71l+FZwB/j1dAYaFrbDxrX/uvosYmsQ6/D/uPiKeAx03BTd7Jo72uurxcZ01bFiQabBGSM8WGtz3MSsAnrL5tngaJmDq8GPjTGvGmMqQP+CvQC9ohPa6Ojna/5FmB/YAiQBtwKLBCR5gJTMmir9l9Hj01k7X4dIuLCGjKsw6qnmGwifs0isi9Wvci/x75Z8aHBJkEZY740xhxujOlljDkOq0LComYO/RJrwbik147XPBZ4xhhTZIzxG2MeBXoAe8axudEUqu0HNFv7L9wKwCMiu4ZtS8ZagO15zYhVNPFhrKSIM+w/TpJNe17zRBrqRW4C/gScISJLY9/M2NBgk6BEZB8RSRORDBH5EzCA5uu9PQlMEJGjRcQN/AFrYbnv4tbYKGnHa14MnCUi/UTEJSIXAF7gxzg2t91ExGPX9nNjFZpNs7Pw2qr9V88e538BuE1EMkXkYOAUEjQbLxqv2TYTq7d+sjEmoZcEidJrbqteZPJxOkNBH80/gL8AO7C63m9irVwKMNTeNjTs2NOxftGWAQuBMU63P5avGWvo7N9YczdlwFLgeKfbH8Hrm4HVCw1/zLD3HY01b1Vt/xsOD3veDcCbYT/3BF4CKoGfgHOdfm2xfM3AMPt5NfbnIPQ4z+nXF8t/52bOmdTZaFobTSmlVMzpMJpSSqmY02CjlFIq5jTYKKWUijkNNkoppWJOg41SSqmY02CjlFIq5jTYKKWUijkNNkoppWJOg41Sqp6IGHsF0DujfN4F9mqTH0bzvCp5aLBRSU9EKsIeQRGpDvv5PKfb11EiskZEjnbg0mONMTc2acsJIjJPRJqtQiwi59pLIVeIyEZ7faVDQvuNMUcCv4lxu1UC02Cjkp4xJiv0wKoVdnLYtqecbl9z7MKMyXSNY4EzaWbtFRG5EvgH8GesqsxDgQexCoQqBWiwUd2AiAwUkedFZIuIrBaRy8P2rRGRq0XkS3v46GG7mvSbIlIuIvNFpEeT468XkW9FZIeI/Meu4Bvpta4VkS+BSrs68HUistK+1rcicpp97BNYv7RftXsL19jbjYjsEnbOR0XkjlauMbSl9rTTLGAO8H6T9zYXazXJ3xtjXjDGVBpjfMaYV40xV3fwWqoL0mCjujR7wa1XsZbjHQQcBfxBRMJLtZ8BHIO11PDJWBWnbwB6Y/0fafoL+jysUu+j7Ofc1I5r/QJrgbg8Y4wfa1ngQ4FcrEXgnhSRAcaYC2jcS7u3HS87dI2eWGXtW2tPRIwxy40xZxtjHm+y60CsKtwvtvecqnvRYKO6uv2BPsaY24wxdcaYVVh/oU8OO+afxphiY8x6rOWmPzPGfG6MqcX6Jbpfk3P+yxizzhizHbgT65d7pNd6wH5uNYAx5jljzAZjTNAY8wzwA/CzTr7mB4y1lPBeEbSns3oBW+3AqVSLYj5urJTDhgEDRaQkbJsbK6iEFId9X93Mz1lNzhm+JvxaYGA7rtVoPXkRuRC4EmtVRuxr9W72lUQudI1I2tNZ24DeIuLRgKNao8FGdXXrgNXGmF3bPDJyQ8K+HwpsaMe16heQEpFhWD2No4BPjDEBEVkGSNNjw1QBGWE/9weKWrhGLF57U59gLWp2KvDfGF5HJTkdRlNd3SKgzJ40TxcRt4jsJSL7d+KcvxeRwSLSE2tu55kOXisTKzBsARCRX2INfYUUAyObPGcZcK597uOBw1tpZyxeeyPGmFKs5Y3/LSKnirWkt9dOlW7PPJPq4jTYqC7NGBPAmvTfF1gNbAUewpqQ76j/A/4HrLIfd3TkWsaYb4H7sHoHxcDewEdhh9wF3CQiJSLyJ3vbFfY1SrASFV5qqZExeu3NXedvWEOBN2EFznXApa21TXU/uiy0Uu0gImuAXxtj5jvdllgQkRqgFivJ4OYonnceMAFYZIw5KlrnVclD52yUUvWMMWltH9Wh8x4Ti/Oq5KHDaEoppWJOh9GUUkrFnPZslFJKxZwGG6WUUjGnwUYppVTMabBRSikVcxpslFJKxZwGG6WUUjGnwUYppVTMabBRSikVc/8PDHZZarCr/wMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
>>>>>>> 8c6f201a3f7b8accc4c7542c8ae898ea859e991d
   "source": [
    "xi, yi = 10, 35\n",
    "\n",
    "fig1 = plt.figure(figsize=(6,9))\n",
    "fig1.tight_layout()  \n",
    "\n",
    "ax1 = fig1.add_subplot(111)\n",
    "ax1.set_ylim([-400, 0])\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "ax1.plot(pt_ini1[:,yi,xi], depth, 'k--', lw=2, label=r'$T_0$')\n",
    "ax2.plot(s_ini1[:,yi,xi], depth, '-', lw=2, label=r'$\\frac{\\partial s_0}{\\partial z}$')\n",
    "ax2.plot(s_ini2[:,yi,xi], depth, '-', lw=2, label=r'$2\\frac{\\partial s_0}{\\partial z}$')\n",
    "ax2.plot(s_ini3[:,yi,xi], depth, '-', lw=2, label=r'$3\\frac{\\partial s_0}{\\partial z}$')\n",
    "ax2.plot(s_ini4[:,yi,xi], depth, '-', lw=2, label=r'$4\\frac{\\partial s_0}{\\partial z}$')\n",
    "#ax2.plot(s_ini4[:,yi,xi], depth, '-', lw=2, label=r'case 4')\n",
    "ax1.legend(loc='center left', fontsize=12)\n",
    "ax2.legend(loc='upper left', fontsize=12)\n",
    "\n",
    "\n",
    "ax1.set_ylabel(r'Depth [m]', size=12)\n",
    "ax1.set_xlabel(r'Temperature [$^\\circ$C]', size=12)\n",
    "ax2.set_xlabel(r'Salinity [psu]', size=12)\n",
    "\n",
    "ax1.yaxis.set_tick_params(labelsize=12)\n",
    "ax1.xaxis.set_tick_params(labelsize=12)\n",
    "ax2.xaxis.set_tick_params(labelsize=12)\n",
    "#ax2.set_xticks(fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xj, yj = 3, 35\n",
    "\n",
    "fig2 = plt.figure(figsize=(6,10))\n",
    "fig2.tight_layout()  \n",
    "\n",
    "ax1 = fig2.add_subplot(111)\n",
    "ax1.set_ylim([-150, 0])\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "p1t = ax1.plot(pt_end1[:,yj,xj], depth, '--', lw=2)\n",
    "p1s = ax2.plot(s_end1[:,yj,xj], depth, '-', lw=2)\n",
    "\n",
    "p2t = ax1.plot(pt_end2[:,yj,xj], depth, '--', lw=2)\n",
    "p2s = ax2.plot(s_end2[:,yj,xj], depth, '-', lw=2)\n",
    "\n",
    "p3t = ax1.plot(pt_end3[:,yj,xj], depth, '--', lw=2)\n",
    "p3s = ax2.plot(s_end3[:,yj,xj], depth, '-', lw=2)\n",
    "\n",
    "p4t = ax1.plot(pt_end4[:,yj,xj], depth, '--', lw=2)\n",
    "p4s = ax2.plot(s_end4[:,yj,xj], depth, '-', lw=2)\n",
    "\n",
    "\n",
    "from matplotlib.legend_handler import HandlerBase\n",
    "\n",
    "\n",
    "class AnyObjectHandler(HandlerBase):\n",
    "    def create_artists(self, legend, orig_handle,\n",
    "                       x0, y0, width, height, fontsize, trans):\n",
    "        l1 = plt.Line2D([x0,y0+width], [0.7*height,0.7*height],\n",
    "                           linestyle=orig_handle[1],color=orig_handle[0])\n",
    "        l2 = plt.Line2D([x0,y0+width], [0.3*height,0.3*height], \n",
    "                           color=orig_handle[0])\n",
    "        return [l1, l2]\n",
    "    \n",
    "plt.legend([(\"#1f77b4\",\"--\"), (\"#ff7f0e\",\"--\"), (\"#2ca02c\",\"--\"), (\"#d62728\",\"--\")], ['case 1', 'case 2', 'case 3', 'case 4'],\n",
    "           handler_map={tuple: AnyObjectHandler()}, loc='center left', fontsize=15)\n",
    "#ax2.legend(title=r'$Q_{sg}=700\\;m^3/s$', loc='center left', fontsize=15)\n",
    "\n",
    "ax1.set_ylabel(r'Depth (m)', size=15)\n",
    "ax1.set_xlabel(r'Temperature ($^\\circ$C)', size=15)\n",
    "ax2.set_xlabel(r'Salinity (psu)', size=15)\n",
    "\n",
    "ax1.yaxis.set_tick_params(labelsize=15)\n",
    "ax1.xaxis.set_tick_params(labelsize=15)\n",
    "ax2.xaxis.set_tick_params(labelsize=15)\n",
    "#ax2.set_xticks(fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
=======
   "execution_count": 7,
>>>>>>> 8c6f201a3f7b8accc4c7542c8ae898ea859e991d
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_path = \"/Users/weiyangbao/Documents/Results_Figs/Model/\"\n",
    "if not os.path.exists(fig_path):\n",
    "    os.makedirs(fig_path)\n",
    "\n",
    "fig1.savefig(fig_path + '/initial_TS.png', dpi=200, bbox_inches='tight')\n",
    "#fig2.savefig(fig_path + '/Final_TS.png', dpi=200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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