test_Chern_insulator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# API"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\n",
    "import scipy.sparse as sp\n",
    "import scipy.sparse.linalg as sla\n",
    "import scipy.linalg as la\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import pickle\n",
    "from copy import copy\n",
    "# %load_ext line_profiler"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "plt.rcParams['text.usetex']=True\n",
    "plt.rcParams['font.family']='serif'\n",
    "plt.rcParams['font.size']=9\n",
    "plt.rcParams['axes.titlesize']=plt.rcParams['font.size']\n",
    "plt.rcParams['figure.figsize']=(6.8,4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# ipcluster start --engines=MPI -n 4\n",
    "import ipyparallel as ipp\n",
    "rc = ipp.Client()\n",
    "rc[:].execute(\"from Chern_insulator import *\")\n",
    "rc[:].execute(\"import numpy as np\")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AsyncResult: execute>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# %%writefile Chern_insulator.py\n",
    "import numpy as np\n",
    "import scipy.linalg as la\n",
    "import numpy.linalg as nla\n",
    "import numpy.matlib\n",
    "\n",
    "\n",
    "class Params:\n",
    "    def __init__(self,\n",
    "            Lx=4,\n",
    "            Ly=16,\n",
    "            Delta=1,\n",
    "            t=1,\n",
    "            m=1,\n",
    "            bcx=-1,\n",
    "            bcy=1,\n",
    "            T=0,\n",
    "            dxmax=16,\n",
    "            dymax=16,\n",
    "            history=False,\n",
    "            ):\n",
    "        self.Lx = Lx\n",
    "        self.Ly = Ly\n",
    "        self.Delta = Delta\n",
    "        self.t = t\n",
    "        self.m = m\n",
    "        self.bcx = bcx\n",
    "        self.bcy = bcy\n",
    "        self.T = T\n",
    "        self.history = history\n",
    "        self.sigmax = np.array([[0, 1], [1, 0]])\n",
    "        self.sigmay = np.array([[0, -1j], [1j, 0]])\n",
    "        self.sigmaz = np.array([[1, 0], [0, -1]])\n",
    "        # check which one is faster, use sparse or dense?\n",
    "        if Lx<np.inf and Ly<np.inf:\n",
    "            hopx = np.diag(np.ones(Lx-1), -1)\n",
    "            hopx[0, -1] = bcx\n",
    "            hopy = np.diag(np.ones(Ly-1), -1)\n",
    "            hopy[0, -1] = bcy\n",
    "            hopxmat = np.kron(np.eye(Ly),hopx)\n",
    "            hopymat = np.kron(hopy,np.eye(Lx))\n",
    "            self.hopxmat=hopxmat\n",
    "            self.hopymat=hopymat\n",
    "            onsitemat = np.eye(Lx*Ly)\n",
    "            self.Hamiltonian = ((np.kron(hopxmat-hopxmat.T, self.sigmax)+np.kron(hopymat-hopymat.T, self.sigmay))* 1j*Delta-t*np.kron(hopxmat+hopxmat.T+hopymat+hopymat.T, self.sigmaz))/2+m*np.kron(onsitemat, self.sigmaz)\n",
    "        elif Lx==np.inf and Ly==np.inf:\n",
    "            self.dxmax=dxmax\n",
    "            self.dymax=dymax\n",
    "            self.dx=lambda kx: Delta*np.sin(kx)\n",
    "            self.dy=lambda ky: Delta*np.sin(ky)\n",
    "            self.dz=lambda kx,ky: m-t*np.cos(kx)-t*np.cos(ky)\n",
    "        else:\n",
    "            raise ValueError('The size of system {:d,:d} is not supported.'.format(Lx,Ly))\n",
    "            \n",
    "\n",
    "    def bandstructure(self):\n",
    "        val, vec = nla.eigh(self.Hamiltonian)\n",
    "        sortindex = np.argsort(val)\n",
    "        self.val = val[sortindex]\n",
    "        self.vec = vec[:, sortindex]\n",
    "\n",
    "    def fermi_dist_k(self,kx,ky,branch,E_F=0):\n",
    "        if self.T==0:\n",
    "            return np.heaviside(E_F-self.E_k(kx,ky,branch),0)\n",
    "        else:\n",
    "            return 1/(1+np.exp((self.E_k(kx,ky,branch)-E_F)/self.T))\n",
    "\n",
    "    def fermi_dist(self, energy, E_F):\n",
    "        if self.T == 0:\n",
    "            return np.heaviside(E_F-energy, 0)\n",
    "        else:\n",
    "            return 1/(1+np.exp((energy-E_F)/self.T))\n",
    "\n",
    "    def correlation_matrix_inf_fft(self,threshold=[1024,512]):\n",
    "        '''\n",
    "        self.dxmax/self.dymax: the maximal distance in x/y direction (in terms of unit cell) \n",
    "        Directly call fft to evaluate the integral\n",
    "        '''\n",
    "        assert self.Lx==np.inf and self.Ly==np.inf, \"Wire length should be inf\"\n",
    "        # cov_mat=[]\n",
    "        Nxmax=max(2*self.dxmax,threshold[0])\n",
    "        Nymax=max(2*self.dymax,threshold[1])\n",
    "        if self.T>0:\n",
    "            pass    #to be filled\n",
    "        else:\n",
    "            kxlist=np.arange(0,2*np.pi,2*np.pi/Nxmax)\n",
    "            kylist=np.arange(0,2*np.pi,2*np.pi/Nymax)\n",
    "            kxmap,kymap=np.meshgrid(kxlist,kylist)\n",
    "            dxmap=self.dx(kxmap)\n",
    "            dymap=self.dy(kymap)\n",
    "            dzmap=self.dz(kxmap,kymap)\n",
    "            Ekmap=np.sqrt(dxmap**2+dymap**2+dzmap**2)\n",
    "            Ekxymap=np.sqrt(dxmap**2+dymap**2)\n",
    "            costheta=dzmap/Ekmap\n",
    "            sintheta=Ekxymap/Ekmap\n",
    "            Ekxymap[0,0]=np.inf #to avoid 0/0 in cos(phi) & sin(phi); the order of this line matters\n",
    "            cosphi=dxmap/Ekxymap\n",
    "            sinphi=dymap/Ekxymap\n",
    "            fftcostheta=np.fft.ifft2(costheta)\n",
    "            constmap=np.zeros((Nymax,Nxmax))\n",
    "            constmap[0,0]=0.5\n",
    "            A_11=constmap-fftcostheta/2\n",
    "            A_22=constmap+fftcostheta/2\n",
    "            A_12=np.fft.ifft2(-(cosphi-1j*sinphi)/2*sintheta)\n",
    "            A_21=np.fft.ifft2(-(cosphi+1j*sinphi)/2*sintheta)\n",
    "            mat=np.stack([[A_11,A_12],[A_21,A_22]])\n",
    "            C_f=np.zeros((2*self.dxmax*self.dymax,2*self.dxmax*self.dymax))*1j\n",
    "            for i in range(self.dxmax*self.dymax):\n",
    "                for j in range(i):\n",
    "                    ix,iy=i%self.dxmax,i//self.dxmax\n",
    "                    jx,jy=j%self.dxmax,j//self.dxmax\n",
    "                    dx,dy=(ix-jx)%Nxmax,(iy-jy)%Nymax\n",
    "                    C_f[2*i:2*i+2,2*j:2*j+2]=mat[:,:,dy,dx]\n",
    "            C_f=C_f+C_f.T.conj()\n",
    "            for i in range(self.dxmax*self.dymax):\n",
    "                C_f[2*i:2*i+2,2*i:2*i+2]=mat[:,:,0,0]\n",
    "            self.C_f=C_f\n",
    "\n",
    "    def correlation_matrix(self, E_F=0):\n",
    "        '''\n",
    "        G_{ij}=<f_i^\\dagger f_j>\n",
    "        '''\n",
    "        if not (hasattr(self, 'val') and hasattr(self, 'vec')):\n",
    "            self.bandstructure()\n",
    "        occupancy_mat = np.matlib.repmat(self.fermi_dist(self.val, E_F), self.vec.shape[0], 1)\n",
    "        self.C_f = ((occupancy_mat*self.vec)@self.vec.T.conj())\n",
    "\n",
    "    def covariance_matrix(self, E_F=0):\n",
    "        '''\n",
    "        c.f. notes\n",
    "        Maybe differs by a minus sign\n",
    "        '''\n",
    "        if not hasattr(self, 'C_f'):\n",
    "            if self.Lx < np.inf and self.Ly<np.inf:\n",
    "                self.correlation_matrix()\n",
    "            else:\n",
    "                self.correlation_matrix_inf_fft()\n",
    "        G = self.C_f\n",
    "        Gamma_11 = 1j*(G-G.T)\n",
    "        Gamma_21 = -(np.eye(G.shape[0])-G-G.T)\n",
    "        Gamma_12 = -Gamma_21.T\n",
    "        Gamma_22 = -1j*(G.T-G)\n",
    "        Gamma = np.zeros((2*G.shape[0], 2*G.shape[0]), dtype=complex)\n",
    "        even = np.arange(2*G.shape[0])[::2]\n",
    "        odd = np.arange(2*G.shape[0])[1::2]\n",
    "        Gamma[np.ix_(even, even)] = Gamma_11\n",
    "        Gamma[np.ix_(even, odd)] = Gamma_12\n",
    "        Gamma[np.ix_(odd, even)] = Gamma_21\n",
    "        Gamma[np.ix_(odd, odd)] = Gamma_22\n",
    "        assert np.abs(np.imag(Gamma)).max() < 1e-10, \"Covariance matrix not real {:.5f}\".format(np.abs(np.imag(Gamma)).max())\n",
    "        self.C_m = np.real(Gamma-Gamma.T.conj())/2\n",
    "        self.C_m_history = [self.C_m]\n",
    "\n",
    "    def linearize_index(self, subregion, n, proj=False, k=2):\n",
    "        '''\n",
    "        If proj ==True, then the index used for projection operator will be returned\n",
    "        k, the size of measurement, is useful only when proj=True\n",
    "        '''\n",
    "        subregion_x, subregion_y = (subregion)\n",
    "        subregion_x = np.array(subregion_x)\n",
    "        subregion_y = np.array(subregion_y)\n",
    "        \n",
    "        X, Y = np.meshgrid(subregion_x, subregion_y)\n",
    "        if self.Ly<np.inf:\n",
    "            assert subregion_x.max()<self.Lx and subregion_y.max()<self.Ly, 'Range exceeds'\n",
    "            linear_index = ((X+Y*self.Lx).flatten('F'))\n",
    "        else:\n",
    "            assert subregion_x.max()<self.dxmax and subregion_y.max()<self.dymax, 'Range exceeds'\n",
    "            linear_index = ((X+Y*self.dxmax).flatten('F'))\n",
    "        if proj:\n",
    "            return sorted(np.concatenate([n*linear_index+i for i in range(0, n, k)]))\n",
    "        else:\n",
    "            return sorted(np.concatenate([n*linear_index+i for i in range(n)]))\n",
    "\n",
    "    def square_index(self, subregion):\n",
    "        subregion=np.unique(np.array(subregion)//4)\n",
    "        if self.Lx<np.inf and self.Ly<np.inf:\n",
    "            return subregion%self.Lx,subregion//self.Lx\n",
    "        else:\n",
    "            return subregion%self.dxmax,subregion//self.dxmax\n",
    "\n",
    "\n",
    "    def c_subregion_f(self, subregion, linear=True):\n",
    "        '''\n",
    "        subregion: [subregion_x, subregion_y] index of unit cell\n",
    "        '''\n",
    "        if not hasattr(self, 'C_f'):\n",
    "            self.correlation_matrix()\n",
    "        try:\n",
    "            subregion = list(subregion)\n",
    "        except:\n",
    "            raise ValueError(\"The subregion is ill-defined\"+subregion)\n",
    "\n",
    "        if len(subregion) == 2:\n",
    "            if isinstance(subregion[0], int) and isinstance(subregion[1], int):\n",
    "                linear = True\n",
    "            elif (not isinstance(subregion[0], int)) and (not isinstance(subregion[1], int)):\n",
    "                linear = False\n",
    "            else:\n",
    "                raise ValueError('Illegal subregion')\n",
    "        else:\n",
    "            linear = True\n",
    "        if linear:\n",
    "            # linearized index\n",
    "            subregion_index = subregion\n",
    "        else:\n",
    "            # 2D index\n",
    "            subregion_index = self.linearize_index(subregion, 2)\n",
    "        # This is a dumb way...\n",
    "        return self.C_f[np.ix_(subregion_index, subregion_index)]\n",
    "\n",
    "    def von_Neumann_entropy_f(self, subregion):\n",
    "        c_A = self.c_subregion_f(subregion)\n",
    "        val = nla.eigvalsh(c_A)\n",
    "        self.val_sh = val\n",
    "        val = np.sort(val)\n",
    "        return np.real(-np.sum(val*np.log(val+1e-18j))-np.sum((1-val)*np.log(1-val+1e-18j)))\n",
    "\n",
    "    def c_subregion_m(self, subregion, Gamma=None):\n",
    "        '''\n",
    "        subregion: [subregoin_x, subregion_y] index of unit cell\n",
    "        '''\n",
    "        if not hasattr(self, 'C_m'):\n",
    "            self.covariance_matrix()\n",
    "        if Gamma is None:\n",
    "            Gamma = self.C_m_history[-1]\n",
    "        try:\n",
    "            subregion = list(subregion)\n",
    "        except:\n",
    "            raise ValueError(\"The subregion is ill-defined\"+subregion)\n",
    "\n",
    "        if len(subregion) == 2:\n",
    "            if isinstance(subregion[0], int) and isinstance(subregion[1], int):\n",
    "                linear = True\n",
    "            elif (not isinstance(subregion[0], int)) and (not isinstance(subregion[1], int)):\n",
    "                linear = False\n",
    "            else:\n",
    "                raise ValueError('Illegal subregion')\n",
    "        else:\n",
    "            linear = True\n",
    "        if linear:\n",
    "            # linearized index\n",
    "            subregion_index = subregion\n",
    "        else:\n",
    "            # 2D index\n",
    "            subregion_index = self.linearize_index(subregion, 4)\n",
    "\n",
    "        return Gamma[np.ix_(subregion_index, subregion_index)]\n",
    "\n",
    "    def von_Neumann_entropy_m(self, subregion):\n",
    "        c_A = self.c_subregion_m(subregion)\n",
    "        val = nla.eigvalsh(1j*c_A)\n",
    "        self.val_sh = val\n",
    "        val = np.sort(val)\n",
    "        val = (1-val)/2+1e-18j  # \\lambda=(1-\\xi)/2\n",
    "        return np.real(-np.sum(val*np.log(val))-np.sum((1-val)*np.log(1-val)))/2\n",
    "\n",
    "    def mutual_information_f(self, subregion_A, subregion_B):\n",
    "        subregion_A = self.linearize_index(subregion_A, 2)\n",
    "        subregion_B = self.linearize_index(subregion_B, 2)\n",
    "        s_A = self.von_Neumann_entropy_f(subregion_A)\n",
    "        s_B = self.von_Neumann_entropy_f(subregion_B)\n",
    "        assert np.intersect1d(\n",
    "            subregion_A, subregion_B).size == 0, \"Subregion A and B overlap\"\n",
    "        subregion_AB = np.concatenate([subregion_A, subregion_B])\n",
    "        s_AB = self.von_Neumann_entropy_f(subregion_AB)\n",
    "        return s_A+s_B-s_AB\n",
    "\n",
    "    def mutual_information_m(self, subregion_A, subregion_B,linear=False):\n",
    "        if not linear:\n",
    "            subregion_A = self.linearize_index(subregion_A, 4)\n",
    "            subregion_B = self.linearize_index(subregion_B, 4)\n",
    "        assert np.intersect1d(\n",
    "            subregion_A, subregion_B).size == 0, \"Subregion A and B overlap\"\n",
    "        s_A = self.von_Neumann_entropy_m(subregion_A)\n",
    "        s_B = self.von_Neumann_entropy_m(subregion_B)\n",
    "        subregion_AB = np.concatenate([subregion_A, subregion_B])\n",
    "        s_AB = self.von_Neumann_entropy_m(subregion_AB)\n",
    "        return s_A+s_B-s_AB\n",
    "\n",
    "    def log_neg(self, subregion_A, subregion_B, Gamma=None,linear=False):\n",
    "        if not linear:\n",
    "            subregion_A = self.linearize_index(subregion_A, 4)\n",
    "            subregion_B = self.linearize_index(subregion_B, 4)\n",
    "        assert np.intersect1d(subregion_A, subregion_B).size == 0, \"Subregion A and B overlap\"\n",
    "        if not hasattr(self, 'C_m'):\n",
    "            self.covariance_matrix()\n",
    "\n",
    "        if Gamma is None:\n",
    "            Gamma = self.C_m_history[-1]\n",
    "        subregion_A = np.array(subregion_A)\n",
    "        subregion_B = np.array(subregion_B)\n",
    "        Gm_p = np.block([\n",
    "            [-Gamma[np.ix_(subregion_A, subregion_A)], 1j *\n",
    "             Gamma[np.ix_(subregion_A, subregion_B)]],\n",
    "            [1j*Gamma[np.ix_(subregion_B, subregion_A)],\n",
    "             Gamma[np.ix_(subregion_B, subregion_B)]]\n",
    "        ])\n",
    "        Gm_n = np.block([\n",
    "            [-Gamma[np.ix_(subregion_A, subregion_A)], -1j *\n",
    "             Gamma[np.ix_(subregion_A, subregion_B)]],\n",
    "            [-1j*Gamma[np.ix_(subregion_B, subregion_A)],\n",
    "             Gamma[np.ix_(subregion_B, subregion_B)]]\n",
    "        ])\n",
    "        idm = np.eye(Gm_p.shape[0])\n",
    "        # Gm_x=idm-(idm+1j*Gm_p)@nla.inv(idm-Gm_n@Gm_p)@(idm+1j*Gm_n)\n",
    "        Gm_x = idm-(idm+1j*Gm_p)@(la.solve((idm-Gm_n@Gm_p), (idm+1j*Gm_n)))\n",
    "        Gm_x = (Gm_x+Gm_x.T.conj())/2\n",
    "        xi = nla.eigvalsh(Gm_x)\n",
    "        subregion_AB = np.concatenate([subregion_A, subregion_B])\n",
    "        eA = np.sum(np.log(((1+xi+0j)/2)**0.5+((1-xi+0j)/2)**0.5))/2\n",
    "        chi = nla.eigvalsh(1j*Gamma[np.ix_(subregion_AB, subregion_AB)])\n",
    "        sA = np.sum(np.log(((1+chi)/2)**2+((1-chi)/2)**2))/4\n",
    "        self.eA=eA\n",
    "        self.sA=sA\n",
    "        return np.real(eA+sA)\n",
    "\n",
    "    def projection(self, s,type='onsite',ignore=False):\n",
    "        '''\n",
    "        For type:'onsite'\n",
    "            occupancy number: s= 0,1 \n",
    "            (-1)^0 even parity, (-1)^1 odd parity\n",
    "        For type:'link'\n",
    "            (o,+)|(o,-)|(e,+)|(e,-)\n",
    "        '''\n",
    "        if type=='onsite':\n",
    "            assert (s == 0 or s == 1), \"s={} is either 0 or 1\".format(s)\n",
    "            blkmat = np.array([[0, -(-1)**s, 0, 0],\n",
    "                            [(-1)**s, 0, 0, 0],\n",
    "                            [0, 0, 0, (-1)**s],\n",
    "                            [0, 0, -(-1)**s, 0]])\n",
    "            return blkmat\n",
    "        if type=='link':\n",
    "            assert (s in ['o+','o-','e+','e-']), \"s={} for {} is not defined\".format(s,type)\n",
    "            if s=='o+':\n",
    "                antidiag=[1,-1,1,-1]\n",
    "                blkmat=np.diag(antidiag)\n",
    "                blkmat=np.fliplr(blkmat)\n",
    "            if s=='o-':\n",
    "                antidiag=[-1,1,-1,1]\n",
    "                blkmat=np.diag(antidiag)\n",
    "                blkmat=np.fliplr(blkmat)\n",
    "            if not ignore:\n",
    "                if s=='e+':\n",
    "                    blkmat=np.array([[0,-1,0,0],\n",
    "                                    [1,0,0,0],\n",
    "                                    [0,0,0,-1],\n",
    "                                    [0,0,1,0]])\n",
    "                if s=='e-':\n",
    "                    blkmat=-np.array([[0,-1,0,0],\n",
    "                                    [1,0,0,0],\n",
    "                                    [0,0,0,-1],\n",
    "                                    [0,0,1,0]])\n",
    "            else:\n",
    "                if s=='e+':\n",
    "                    antidiag=[-1,-1,1,1]\n",
    "                    blkmat=np.diag(antidiag)\n",
    "                    blkmat=np.fliplr(blkmat)\n",
    "                if s=='e-':\n",
    "                    antidiag=[1,1,-1,-1]\n",
    "                    blkmat=np.diag(antidiag)\n",
    "                    blkmat=np.fliplr(blkmat)\n",
    "            proj=np.zeros((8,8))\n",
    "            proj[:4,:4]=blkmat\n",
    "            proj[4:,4:]=blkmat.T\n",
    "            return proj   \n",
    "        if type=='correlated':\n",
    "            assert (s in ['10','01']), \"s={} for {} is not defined\".format(s,type)\n",
    "            if s=='10':\n",
    "                blkmat=np.array([[0,-1,0,0],\n",
    "                                 [1,0,0,0],\n",
    "                                 [0,0,0,1],\n",
    "                                 [0,0,-1,0]])\n",
    "            if s=='01':\n",
    "                blkmat=np.array([[0,1,0,0],\n",
    "                                 [-1,0,0,0],\n",
    "                                 [0,0,0,-1],\n",
    "                                 [0,0,1,0]])\n",
    "            proj=np.zeros((8,8))\n",
    "            proj[:4,:4]=blkmat\n",
    "            proj[4:,4:]=blkmat.T\n",
    "            return proj\n",
    "\n",
    "\n",
    "        raise ValueError(\"type '{}' is not defined\".format(type))\n",
    "\n",
    "    def measure(self, s, ix,type='onsite',ignore=False):\n",
    "        if not hasattr(self, 'C_m'):\n",
    "            self.covariance_matrix()\n",
    "        if not hasattr(self, 's_history'):\n",
    "            self.s_history = []\n",
    "        if not hasattr(self, 'i_history'):\n",
    "            self.i_history = []\n",
    "\n",
    "        mat = self.C_m_history[-1].copy()\n",
    "\n",
    "        for i_ind, i in enumerate(ix):\n",
    "            mat[[i, -(len(ix)-i_ind)]] = mat[[-(len(ix)-i_ind), i]]\n",
    "            mat[:, [i, -(len(ix)-i_ind)]] = mat[:, [-(len(ix)-i_ind), i]]\n",
    "\n",
    "        self.mat = mat\n",
    "\n",
    "        Gamma_LL = mat[:-len(ix), :-len(ix)]\n",
    "        Gamma_LR = mat[:-len(ix), -len(ix):]\n",
    "        Gamma_RR = mat[-len(ix):, -len(ix):]\n",
    "\n",
    "        proj = self.projection(s,type=type,ignore=ignore)\n",
    "        Upsilon_LL = proj[:-len(ix), :-len(ix)]\n",
    "        Upsilon_RR = proj[-len(ix):, -len(ix):]\n",
    "        Upsilon_RL = proj[-len(ix):, :-len(ix)]\n",
    "        zero = np.zeros((mat.shape[0]-len(ix), len(ix)))\n",
    "        zero0 = np.zeros((len(ix), len(ix)))\n",
    "        mat1 = np.block([[Gamma_LL, zero], [zero.T, Upsilon_RR]])\n",
    "        mat2 = np.block([[Gamma_LR, zero], [zero0, Upsilon_RL]])\n",
    "        mat3 = np.block([[Gamma_RR, np.eye(len(ix))],\n",
    "                        [-np.eye(len(ix)), Upsilon_LL]])\n",
    "        self.mat2 = mat2\n",
    "        if np.count_nonzero(mat2):\n",
    "            Psi = mat1+mat2@(la.solve(mat3, mat2.T))\n",
    "            # Psi=mat1+mat2@(la.lstsq(mat3,mat2.T)[0])\n",
    "            assert np.abs(\n",
    "                np.trace(Psi)) < 1e-5, \"Not trace zero {:e}\".format(np.trace(Psi))\n",
    "        else:\n",
    "            Psi = mat1\n",
    "\n",
    "        for i_ind, i in enumerate(ix):\n",
    "            Psi[[i, -(len(ix)-i_ind)]] = Psi[[-(len(ix)-i_ind), i]]\n",
    "            Psi[:, [i, -(len(ix)-i_ind)]] = Psi[:, [-(len(ix)-i_ind), i]]\n",
    "        Psi = (Psi-Psi.T)/2   # Anti-symmetrize\n",
    "        if self.history:\n",
    "            self.C_m_history.append(Psi)\n",
    "            self.s_history.append(s)\n",
    "            self.i_history.append(ix[0])\n",
    "        else:\n",
    "            self.C_m_history = [Psi]\n",
    "            self.s_history = [s]\n",
    "            self.i_history = [ix[0]]\n",
    "\n",
    "    def measure_all_Born(self, proj_range,prob=None,linear=False,type='onsite',pool=4,ignore=False):\n",
    "        if not linear:\n",
    "            if type=='onsite':\n",
    "                proj_range = self.linearize_index(proj_range, 4, proj=True)\n",
    "            if type=='link' or type=='correlated':\n",
    "                proj_range = self.linearize_index(proj_range, 4, proj=True,k=4)\n",
    "        # self.proj_range=proj_range\n",
    "        # print(proj_range)\n",
    "        self.P_0_list = []\n",
    "        self.f_parity= []\n",
    "        if not hasattr(self, 'C_m'):\n",
    "            self.covariance_matrix()\n",
    "        if type=='onsite':\n",
    "            for index,i in enumerate(proj_range):\n",
    "                if prob is None:\n",
    "                    P_0 = (self.C_m_history[-1][i, i+1]+1)/2    # Use Born rule\n",
    "                else:\n",
    "                    if isinstance(prob,list):\n",
    "                        assert len(prob)==len(proj_range), \"len of prob {:d} not equal to len of proj_range {:d}\".format(len(prob),len(proj_range))\n",
    "                        P_0=prob[index]\n",
    "                    else:    \n",
    "                        P_0=prob\n",
    "                self.P_0_list.append(P_0)\n",
    "                if np.random.rand() < P_0:\n",
    "                    self.measure(0, [i, i+1])\n",
    "                    self.f_parity.append(0)\n",
    "                else:\n",
    "                    self.measure(1, [i, i+1])\n",
    "                    self.f_parity.append(1)\n",
    "            return self\n",
    "\n",
    "        if type=='link':\n",
    "            for i in proj_range:\n",
    "                Gamma=self.C_m_history[-1][i:i+4,i:i+4]\n",
    "                P={}\n",
    "                if prob is None:\n",
    "                    gamma1234=-Gamma[0,1]*Gamma[2,3]+Gamma[0,2]*Gamma[1,3]-Gamma[0,3]*Gamma[1,2]\n",
    "                    P['o+']=(1+Gamma[1,2]-Gamma[0,3]+gamma1234)/4\n",
    "                    P['o-']=(1-Gamma[1,2]+Gamma[0,3]+gamma1234)/4\n",
    "                    if not ignore:\n",
    "                        P['e+']=(1+Gamma[0,1]+Gamma[2,3]-gamma1234)/4\n",
    "                        P['e-']=(1-Gamma[0,1]-Gamma[2,3]-gamma1234)/4\n",
    "                    else:\n",
    "                        #ignore symmetry\n",
    "                        P['e+']=(1+Gamma[1,2]+Gamma[0,3]-gamma1234)/4\n",
    "                        P['e-']=(1-Gamma[1,2]-Gamma[0,3]-gamma1234)/4\n",
    "                else:\n",
    "                    P['o+'],P['o-'],P['e+'],P['e-']=tuple(prob)\n",
    "\n",
    "                # print((P.values()))\n",
    "                if pool==4:\n",
    "                    s=np.random.choice(['o+','o-','e+','e-'],p=[P['o+'],P['o-'],P['e+'],P['e-']])\n",
    "                elif pool==2:\n",
    "                    s=np.random.choice(['o+','o-'],p=[P['o+']/(P['o+']+P['o-']),P['o-']/(P['o+']+P['o-'])])\n",
    "                elif pool==-2:\n",
    "                    s=np.random.choice(['e+','e-'],p=[P['e+']/(P['e+']+P['e-']),P['e-']/(P['e+']+P['e-'])])\n",
    "                self.measure(s,[i,i+1,i+2,i+3],type='link',ignore=ignore)\n",
    "            return self\n",
    "        if type=='correlated':\n",
    "            for i in proj_range:\n",
    "                Gamma=self.C_m_history[-1][i:i+4,i:i+4]\n",
    "                P={}\n",
    "                if prob is None:\n",
    "                    gamma1234=-Gamma[0,1]*Gamma[2,3]+Gamma[0,2]*Gamma[1,3]-Gamma[0,3]*Gamma[1,2]\n",
    "                    P['10']=(1+Gamma[0,1]-Gamma[2,3]+gamma1234)/4\n",
    "                    P['01']=(1-Gamma[0,1]+Gamma[2,3]+gamma1234)/4\n",
    "                else:\n",
    "                    P['10'],P['01']=tuple(prob)\n",
    "                s=np.random.choice(['10','01'],p=[P['10']/(P['10']+P['01']),P['01']/(P['10']+P['01'])])\n",
    "                self.measure(s,[i,i+1,i+2,i+3],type=type)\n",
    "            return self\n",
    "\n",
    "\n",
    "\n",
    "    def fermion_number(self,proj_range,linear=False,type='C_m'):\n",
    "        if type=='C_m':\n",
    "            if not linear:\n",
    "                proj_range = self.linearize_index(proj_range, 4, proj=True,k=2)\n",
    "            return 1/2*(1-np.diagonal(self.C_m_history[-1],1)[proj_range]).sum()\n",
    "        # if type=='C_f':\n",
    "        #     if not linear:\n",
    "        #         proj_range = self.linearize_index(proj_range, 2, proj=True,k=1)\n",
    "        #     return np.trace(self.C_f[proj_range])\n",
    "\n",
    "    def snap_prob(self,proj_range,occ,linear=False):\n",
    "\n",
    "        if not linear:\n",
    "            proj_range= self.linearize_index(proj_range,4,proj=True,k=2)\n",
    "        return (1-(-1)**occ*np.diagonal(self.C_m,1)[proj_range])/2\n",
    "\n",
    "\n",
    "def cross_ratio(x,L):\n",
    "    if L<np.inf:\n",
    "        xx=lambda i,j: (np.sin(np.pi/(L)*np.abs(x[i]-x[j])))\n",
    "    else:\n",
    "        xx=lambda i,j: np.abs(x[i]-x[j])\n",
    "    eta=(xx(0,1)*xx(2,3))/(xx(0,2)*xx(1,3))\n",
    "    return eta"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Test"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "source": [
    "params=Params(m=0,Lx=24,Ly=24,bcx=0,bcy=0)\n",
    "params.covariance_matrix()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(params.val,'.')\n",
    "# ax.set_ylim([-1,1])\n",
    "# ax.set_xlim([params.val.shape[0]//2-30,params.val.shape[0]//2+30])\n",
    "ax.grid('on')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "source": [
    "params.val[params.vec.shape[0]//2-4:params.vec.shape[0]//2+4]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([-1.22407006, -1.14428229, -1.14284125, -1.05954057,  1.05954057,\n",
       "        1.14284125,  1.14428229,  1.22407006])"
      ]
     },
     "metadata": {},
     "execution_count": 224
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "source": [
    "fig,ax=plt.subplots(2,1)\n",
    "level=params.vec.shape[0]//2\n",
    "ax[0].pcolormesh(params.vec[:,level][::2].reshape((params.Lx,params.Ly)).__abs__())\n",
    "ax[1].pcolormesh(params.vec[:,level][1::2].reshape((params.Lx,params.Ly)).__abs__())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f182c7b0190>"
      ]
     },
     "metadata": {},
     "execution_count": 230
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "source": [
    "fig,ax=plt.subplots()\n",
    "level=params.vec.shape[0]//2+0\n",
    "ax.pcolormesh(params.vec[:,level][::2].reshape((params.Lx,params.Ly)).__abs__()+params.vec[:,level][1::2].reshape((params.Lx,params.Ly)).__abs__())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f182c6676d0>"
      ]
     },
     "metadata": {},
     "execution_count": 231
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "source": [
    "z1=params.c_subregion_f([np.arange(12,20),np.arange(12,20)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "source": [
    "z2=params.c_subregion_f([np.arange(6),np.arange(6)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "source": [
    "val,vec=nla.eigh(z1)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(val,'.')\n",
    "ax.set_xlim([val.shape[0]//2-10,val.shape[0]//2+10])\n",
    "ax.set_yticks([0,.5,1])\n",
    "ax.grid('on')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "source": [
    "val,vec=nla.eigh(z2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(val,'.')\n",
    "ax.set_xlim([val.shape[0]//2-10,val.shape[0]//2+10])\n",
    "ax.set_yticks([0,.5,1])\n",
    "ax.grid('on')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "\n",
    "params.fermion_number([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "16*16/4"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(params.C_m[:4,:4],5)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.measure_all_Born([params.])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_inf=Params(m=1,dxmax=32,dymax=8,Lx=np.inf,Ly=np.inf,bcx=-1,bcy=1)\n",
    "params_inf.covariance_matrix()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(np.round(params.C_m[0,:],10))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(np.round(params_inf.C_m[0,:],10))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "zz=params_inf.C_f[0,params_inf.linearize_index([np.arange(params_inf.dxmax),[0]],2)[::2]]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot((np.real(zz)),'.')\n",
    "# ax.set_yscale('log')\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "zz[0]*np.exp(-1)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(np.real(zz))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "isinstance([1,2,3],list)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Gmat_real_jl=np.loadtxt('Gmat_real.txt')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Gmat_imag_jl=np.loadtxt('Gmat_imag.txt')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.abs(Gmat_imag_jl*1j+Gmat_real_jl-params.C_m).max()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(np.real(params.C_f[:8,:8]),3)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.C_m"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(np.round(params.hopxmat,0))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(np.round(params.hopymat,0))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(np.real(params.C_f[:8,:8]),3)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(np.real(params.C_f[0,:]),3)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(np.real(params.C_m[:8,:8]),3)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.Hamiltonian"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.imag(params.Hamiltonian[:8,:8])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.trace(params_L.C_f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.fft.fftshift(np.arange(8))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(-1)%8"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "[np.arange(params_L.Lx//4),np.arange(params_L.Ly)]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "[np.arange(params_L.Lx//4)+params_L.Lx//2,np.arange(params_L.Ly)]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_L.log_neg([np.arange(params_L.Lx//4),np.arange(params_L.Ly)],[np.arange(params_L.Lx//4)+params_L.Lx//2,np.arange(params_L.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_L.eA,params_L.sA"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(params_L.C_m[4:8,0:4],10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params=Params(Lx=np.inf,Ly=np.inf,m=1,dxmax=32,dymax=8)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.covariance_matrix()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.trace(params.C_f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "[np.arange(params.dxmax//4),np.arange(params.dymax)]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "[np.arange(params.dxmax//4)+params.dxmax//2,np.arange(params.dymax)]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.log_neg([np.arange(params.dxmax//4),np.arange(params.dymax)],[np.arange(params.dxmax//4)+params.dxmax//2,np.arange(params.dymax)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.log_neg([np.arange(8),np.arange(params.dymax)],[np.arange(8)+16,np.arange(params.dymax)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "list(np.diag(np.round(params.C_m@params.C_m,10)))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(params_L.C_m@params_L.C_m,10)[:4,:4]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(params.C_m[4:8,0:4],10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.eA,params.A"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.imag(params_L.C_f).max()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params_L.C_f[156:158,76:78]),10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params_L.C_f[80:82,0:2]),10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params_L.C_f[64:66,0:2]),10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.correlation_matrix_inf_fft()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(params.C_f[64:66,0:2],10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(np.imag(params.C_f)).max()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(np.imag(params_L.C_f)).max()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.covariance_matrix()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params.C_f[80:82,:2]),10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params.C_f[156:158,76:78]),10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.where(np.imag(params_L.C_f)==np.imag(params_L.C_f).max())"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_L.C_f[0:2,32:34]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_L.C_f[28:30,60:62]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(params_L.C_f[6:8,0:2])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(params_L.C_f[32:34,0:2])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "17//8"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(params_L.C_f[34:36,0:2])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.array([[A11[1,2],A12[1,2]],[A21[1,2],A22[1,2]]])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(params_L.C_f[2:4,0:2])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.array([[A11[1,0],A12[1,0]],[A21[1,0],A22[1,0]]])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "zz[:,:,1,0]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "zz=np.stack([[A11,A12],[A21,A22]])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "A11[0,1],A12[0,1],A21[0,1],A22[0,1]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "A12[1,0]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.imag(params.C_f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(np.round(np.real(params.C_m@params.C_m),9))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params=Params(Lx=64,Ly=4,m=1.,history=True)\n",
    "proj_range_0=params.linearize_index([np.arange(params.Lx//4,3*params.Lx//8),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "proj_range_1=params.linearize_index([np.arange(3*params.Lx//8,3*params.Lx//8+1),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "proj_range_2=params.linearize_index([np.arange(3*params.Lx//8+1,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "params.measure_all_Born(proj_range_0,type='link',prob=[1,0,0,0],linear=True)\n",
    "params.measure_all_Born(proj_range_1,type='link',pool=-2,linear=True)\n",
    "params.measure_all_Born(proj_range_2,type='link',prob=[1,0,0,0],linear=True)\n",
    "# params.measure_all_Born(proj_range_0[len(proj_range_0)//2:len(proj_range_0)//2+1],type='link',prob=[0,0,1,0],linear=True)\n",
    "# params.measure_all_Born(proj_range_1,type='link',linear=True)\n",
    "(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.fermion_number()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params=Params(Lx=64,Ly=4,m=1,history=True)\n",
    "proj_range_0=params.linearize_index([np.arange(params.Lx//4)+params.Lx//4,np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "proj_range_1=params.linearize_index([np.arange(params.Lx//4)+3*params.Lx//4,np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "params.measure_all_Born(proj_range_0,type='link',linear=True,ignore=False)\n",
    "params.measure_all_Born(proj_range_1,type='link',linear=True,ignore=False)\n",
    "# params.measure_all_Born(proj_range_1,type='link',prob=[1,0,0,0],linear=True)\n",
    "(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.fermion_number([np.arange(params.Lx),np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params=Params(Lx=64,Ly=4,m=-1.2,history=True)\n",
    "proj_range=params.linearize_index([np.arange(params.Lx//4,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=2)\n",
    "prob_list=[(1-(-1)**i)/2 for i,x in enumerate(proj_range)]\n",
    "params.measure_all_Born(proj_range_0,type='onsite',prob=prob_list,linear=True)\n",
    "(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.C_m_history[-1][proj_range[0]:proj_range[0]+4,proj_range[0]:proj_range[0]+4]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.measure_all_Born(proj_range[-1:],type='link',prob=[0,1,0,0],linear=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round(params.C_m_history[-1][proj_range[-2]:proj_range[-2]+8,proj_range[-2]:proj_range[-2]+8],3)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.s_history"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "print(params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n",
    "# print(params.mutual_information_f([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "sum(params.f_parity)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params.C_m_history)[0][16:24,16:24],2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params.C_m_history)[1][16:24,16:24],2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "np.round((params.C_m_history)[2][16:24,16:24],2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.i_history"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.s_history"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.P_0_list"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.von_Neumann_entropy_f([np.arange(params.Lx//4),np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.von_Neumann_entropy_m([np.arange(params.Lx//4),np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(params.mutual_information_f([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.measure_all_Born([np.arange(params.Lx//4)+params.Lx//4,np.arange(params.Ly)],1)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.von_Neumann_entropy_f([[0,1],np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.von_Neumann_entropy_m([[0,1],np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.mutual_information_f([[0,1],np.arange(params.Ly)],[[2,3],np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.mutual_information_m([[0,1],np.arange(params.Ly)],[[2,3],np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.log_neg([[0,1],np.arange(params.Ly)],[[16,17],np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Bandstructrue"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=np.linspace(0,3)\n",
    "eigval_pbc_list=[]\n",
    "for m in m_list:\n",
    "    params=Params(m=m,bcx=1,bcy=1,Lx=16,Ly=16,Delta=1)\n",
    "    params.bandstructure()\n",
    "    eigval_pbc_list.append(params.val)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=np.linspace(0,3)\n",
    "eigval_apbc_list=[]\n",
    "for m in m_list:\n",
    "    params=Params(m=m,bcx=-1,bcy=1,Lx=16,Ly=16,Delta=1)\n",
    "    params.bandstructure()\n",
    "    eigval_apbc_list.append(params.val)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "func=lambda kx,ky,m,t,Delta:np.sqrt((t*np.sin(kx))**2+(t*np.sin(ky))**2+(m-Delta*np.cos(kx)-Delta*np.cos(ky))**2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "KX,KY=np.meshgrid(np.linspace(-np.pi,np.pi),np.linspace(-np.pi,np.pi))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "en_max=[]\n",
    "en_min=[]\n",
    "for m in m_list:\n",
    "    en=func(KX,KY,m,1,1)\n",
    "    en_max.append(en.max())\n",
    "    en_min.append(en.min())"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2,sharey=True,sharex=True,tight_layout=True)\n",
    "ax[0].plot(m_list,eigval_apbc_list,'c',lw=0.5)\n",
    "ax[1].plot(m_list,eigval_pbc_list,'c',lw=0.5)\n",
    "ax[0].set_xlabel(r'm')\n",
    "ax[0].set_ylabel(r'Energy')\n",
    "ax[1].plot(m_list,en_min,'r',lw=3)\n",
    "ax[1].plot(m_list,en_max,'r',lw=3)\n",
    "ax[0].margins(x=0)\n",
    "ax[0].set_title('x: APBC, y: PBC')\n",
    "ax[1].set_title('x,y: PBC')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/bs.pdf',bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# No measurement"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## finite"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,40))\n",
    "m_list=np.linspace(-4,4,20)\n",
    "LN_no_list=[]\n",
    "MI_no_list=[]\n",
    "Lx,Ly=64,4\n",
    "for m in m_list:\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    MI_no_list.append(params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n",
    "    LN_no_list.append(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(m_list,LN_no_list/np.log(2),'.-',label='LN')\n",
    "ax.plot(m_list,MI_no_list/np.log(2),'.-',label='MI')\n",
    "ax.legend()\n",
    "ax.set_xlabel('m')\n",
    "ax.grid('on')\n",
    "ax.set_ylabel('[log(2)]')\n",
    "ax.set_title('Finite system')\n",
    "# fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_CI_no_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Finite system')"
      ]
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Inf"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# m_list=np.linspace(1,3,20)\n",
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,20))\n",
    "LN_no_inf_list=[]\n",
    "MI_no_inf_list=[]\n",
    "for m in m_list:\n",
    "    params=Params(m=m,Lx=np.inf,Ly=np.inf,dxmax=32,dymax=8)\n",
    "    MI_no_inf_list.append(params.mutual_information_m([np.arange(params.dxmax//4),np.arange(params.dymax)],[np.arange(params.dxmax//4)+params.dxmax//2,np.arange(params.dymax)]))\n",
    "    LN_no_inf_list.append(params.log_neg([np.arange(params.dxmax//4),np.arange(params.dymax)],[np.arange(params.dxmax//4)+params.dxmax//2,np.arange(params.dymax)]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(m_list,LN_no_inf_list,'.-',label='LN')\n",
    "ax.plot(m_list,MI_no_inf_list,'.-',label='MI')\n",
    "ax.legend()\n",
    "ax.set_xlabel('m')\n",
    "ax.set_title('Infinite system')\n",
    "# fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_CI_no_inf_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Measure to 0"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "m_list=np.linspace(1,3,20)\n",
    "es=1\n",
    "LN_empty_list=np.zeros((len(m_list),es))\n",
    "MI_empty_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=16,4\n",
    "for m_i,m in enumerate(m_list):\n",
    "    # st=time.time()\n",
    "    # Serial\n",
    "    params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    for es_i in range(es):\n",
    "        params=copy(params0)\n",
    "        params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)],0)\n",
    "        MI_empty_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "        LN_empty_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    # # Parallel\n",
    "    # async_result=rc[:].map_sync((lambda p:Params(m=p[0],Lx=p[1],Ly=p[2]).measure_all_Born([np.arange(p[1]//4,p[1]//2),np.arange(p[2])]).log_neg([np.arange(p[1]//4),np.arange(p[2])],[np.arange(p[1]//4)+p[1]//2,np.arange(p[2])])),[(m,Lx,Ly) for _ in range(es)])\n",
    "    # LN_empty_list[m_i,:]=async_result \n",
    "\n",
    "    # print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "plt.plot(m_list,LN_empty_list)\n",
    "plt.plot(m_list,MI_empty_list)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd62f698460>]"
      ]
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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cPcLZmGohMsiXNqGOXU0gNiKAe0Z34p7RnThsKeKnbZks3JbJG0v28PriPcRGBHBxz2gmD2hD51ZBDq3NYcY/YzRq+/ZeuOdXaGGbw5pCCNeitIMWW0xISNApKSn239G6D4wLDx/aDiFtTj895uVldGkVyMxbEuxfQwMcyy9m0fajLNyWyap9xzlVoRnaKZxbhnQgsXuU+61ycHg9fHAR9JwE18wyuxohhJ0opdZrrWv8Q+t+F5DU0Fr6RGEpB7ILmZLQzsTCztYqyI8bB3XgxkEdyC4o4Yt1acxZm8rdszcQHezHDYPac/3AdrQKdpOZbm0GwKjHYek/oesEYxabEKJZcbP/jca4Bqdaa+lNaRbA/udvGisi0Jf7xnRmxfQxvH9LAl2jg3hl0W6GPr+E/5uzgbX7j+OokahdDX8Y2g40RqC5h82uRgjhYG44wjm38drG1BN4KOjTNsSkohrG00MxvkcU43tEcSC7kE/XHOLLlDR+2JJBt6ggbhrSgUn92rjuygaeXkbvnPeGw7f3wM3fgof7/T+PEKJm7vdfe965raU3plnoFh1MgAv9oY6NCODJy3qw9s+JvHhNH7y9FH/9dhuD/7WYp77bxp6j+WaX2DjhneDif8GB5fDbDLOrEUI4kOv8BW6o/AzoeGaVgYoKzaY0C5f1ObdVgSto4ePJlIHtuDahLZvSLHyy5hCfr0vj49WHGBwXxi1DOjK+RxTerjTJYMCtsHshLPqbMU26VXezKxJCOIAL/ZVqgBpaS+/PLiC/uNwuF3w6klKKfu1b8sqUvqz50zieuCSe9BNF3PvpBoa/sITXkndzNM9FruZXCq54E3yD4Os7obzU7IqEEA7gXoFTQ2vpyhWi+7t44FQVFuDD3aM6sfyxMfzn1gS6xwTz+uI9DHt+CdPnbeZYvgsET2AruOINyNwKy/5ldjVCCAdwr0NqNbSW3phmIcjPi7iIQJOKsh9PD3W6kdyh44V8tOogs9ccYv6WDO4d05nbh8fi5+3ErQHiL4V+N8PK16DLxdBhiNkVCSHsyM1GOOe2lt6YaqFvu1C3b5jWITyAv13ek0UPjWJo5whe+mkXia8s58etGc49pXrCc9CyA3yTBMV5ZlcjhLAj9wqcaq2lT5aWsyszj35Oev2NPXSMCOD9WxL49I5BBPp6ce+nG7hu5hq2Hc41u7Sa+QbBpJmQmw4L/2R2NUIIO3KvwKnWWnpLei4Vuu4On+5qWOcI5j8wgn9O6sXeYwVc/tZKHp+3xTnP77QfZFwUumk2bP+f2dUIIezEvQKnWmvpygkDFzSjEU5Vnh6KGwd1YOmjo7ljeCxfb0xn7MvLeXfZPorLTpld3tlGPQ4xF8D30yD/qNnVCCHswL0Cp1pr6Y2pJ+gY7k9YQPNu+xzSwpu/XNqDnx8axeC4cF5YuJPxry5n4TYnOr/j5QNXvw9lJ+G7+8BZ6hJC2Ix7BU7+mVUGtNZsTLM0y8NptYmNCGDWHxL45PYL8ff24u7ZG5j6/hp+P+Ik53ciu8H4f8DeRZDyH7OrEULYmPsETrXW0kdyi8nKL3HaBTvNNKJLJPMfGM4zV/ViV2Y+l725kie+2kJWfonZpcHAO6HTWPj5Scjea3Y1Qggbcp/AqdZaemOq0eHT1VcYsBcvTw9uHtyBZY+N4bZhscxbn86Yl5fx3vJ9lJSbeH7HwwOufBs8fYxVCE6VmVeLEMKm3CdwqrWW3pRqwdfLg/joYBOLcn4hLbz562U9+PmhkQyKDeP5BTsZ/8oKlu48Zl5Rwa3h8tfgyAZY8bJ5dQghbMp9Aqdaa+mNaRZ6twnBx8t9fkV7iosM5INbB/LxbRfi6+XBHz9ax1PfbTNvNlvPSdDnOljxEqQ7oFOsEMLu3OevcZURTml5BVsP58r5m0YY2TWSHx4Yzh3DY/l49SGufOtXdmWa1Aph4kvGaOfrJCgtNKcGIYTN1Bs4SqkkpVSiUqrGnsDWbf1r2+4wla2lA1qxMzOP0vIKmaHWSL5enjx5WQ8++uNAjheWcMVbK/lk9UHHT6H2C4Gr3oWc/fDTXxy7byGEzdUZOEqp6UCK1joZGF/D9kQgVGu9AYizT4kNlJcBgVHg6XX6gk+ZMNA0o7u1YsG0kQyOC+ev3/1O0ifrySl0cCuB2BEw9H5Y/yGs+8Cx+xZC2FR9I5yBgKXygVKqf9WN1iD6k1JqLpBc/Zuto6MUpVRKVlaWDcqtQ/4RCIoGjBlqrYJ8iQnxs+8+m4HIIF8+vHUgf72sB8t3ZXHJ6ytYtTfbsUWM+xt0nQDzH4Ht3zl230IImznfcziWqg+UUnHA48Ai4IXqL9Zaz9RaJ2itEyIjIxtdZINUaS29Kc1Cv/ahKOXeK0Q7ioeH4vbhsXx971ACfL248YO1vLBwJ2WnKhxTgKcXTP4Q2g6Er+6Agysds18hhE3VFzjrgNDKB1rr/dW2T9ZaJ2utZwJzrQFkjvwjEBxDTmEpB4+flPM3dtCrTQg/3D+c6xLa8e6yfUx+bzWHjjvoZL6PP9zwBbSMhc+mGo3bhBAupb7AmQkkWA+lLap8UimVZP1ynlJqsnV7Tg2B5BilJ6E4F4Ji2JRmXPApM9Tsw9/Hi+ev6cM7N/bnQFYBl76xkm82pjto52Fw89dGS4PZ18CJQ47ZrxDCJuoMHK21xXpYbIPWel6V52da7/drredV3+5w+Wcar21MteChoE/bENPKaQ4m9o5hwYMj6RETzENfbObBzzeSX+yAVQFC2sJNX0F5Ccy+GgodfD5JCNFo7nEdTpXW0pvSLMRHB+Pv417ds51Rm9AWzLlzEA8lduV/m49w6RsrTy8pZFetusMNXxpN2z69FkoK7L9PIUSTuUfgWFtLVwTGsCnVItOhHcjL04NpiV348q4hnKrQXPveat5eupdTFXa+Zqf9ILj2I8jYDF/eImuuCeEC3CNwrK2lD5QGk19SLudvTJDQMYwfp41gQq9oXvppFzfNWktmrp27i3a7xFhzbd9io4dOhYNmzQkhGsU9AsfaWnr9UWPdL5mhZo6QFt68ObUfL07uw+Z0CxNeX8FPv2fad6f9b4Gxf4UtX0DyU/bdlxCiSdwjcKytpTemWgj28yIuIsDsipotpRRTEtrxw/3DaduyBXd9sp4/f7OVk6Xl9tvpiEfgwiRY9aZxE0I4JfcIHGtr6Y2pJ7igXSgeHnLBp9niIgP5+p5h3DUqjs9+S+WyN1ey7bCdOosqBROeN1aY/vlJ2PyFffYjhGgS9wic/AzKAqLZfTRfDqc5ER8vD/50SXc+vX0QhSXlTHrnV2Ys30eFPSYUeHjCpBkQOxK+uxf2nLPSkhDCZK4fONbW0sd0GBVaFux0RkM7R7Bw2kjGxrfiuQU7uekDO00o8PKF6z41pk1/eQukr7f9PoQQjeb6gWNtLb2vxOjs2bdtqLn1iBq1DPDhvZsG8PzVvdmYakwoWLjNDhMK/ILhxq8gIALmXAvZe22/DyFEo7h+4Fgbr23LDyA2IoCWAT4mFyRqo5Ti+gvbM/+B4bRr6c/ds9fzxFdbbD+hICgKbv4GUPDJpNPXaQkhzOX6gWNdZWBtti/95PoblxAXGchX9wzlntGd+CIljcveWMmWdIttdxLeCW6aB0U58OlkY609IYSpXD9wrCOcnYWBcv7Ghfh4efD4hHg+vWMQJ0tPcfU7q3h32T7brlDQuh9c9wlk7YLPboAyO1+IKoSok+sHTn4GGg+yCaFvO5mh5mqGdopg4YMjuKhnFC8s3MmNs9aQkVtkux10GguT3oNDK+HrO6DilO1+thDivLh+4ORlkO8dhpeXN/ExQWZXIxoh1N+Ht2/oz4uT+7AlPZcJr/3Cgq02PO/Se7Jxnc6O7+HHR0HbeZ03IUSNXD9w8o9wVIfRp20I3p6u/+s0V5UrFMx/YAQdw/2559MNTJ+3mcISG00oGHwPDHsQUv4D395rtDcQQjiUy/+Frsg7wsHSEFmw003ERgQw756h3DemE3PXp3PpG7+wOc1imx+e+HcY8xfYPAc+vgoKj9vm5wohGsT1Ayf3CBkVobLCgBvx9vTgsYvj+ezOwZSWV3DNu6t4c/EeSsqbeP5FKRg1Ha75AA6vh1njIGu3bYoWQtTLtQOn9CRepXlk6pYyQ80NDY4LZ8G0kVzcK5p/L9rN2JeX89X69KbPZOs9GW6dD6UF8EEi7F9mk3qFEHWrN3CUUklKqUSl1ORatk+2bp9u+/LqYb0Gp9gvipiQFg7fvbC/EH9v3r6hP7NvH0RYgA+PzN3MxNd/YfGOo+imnPxvNxDuWAzBbWD2NbD+I5vVLISoWZ2BYw2RFK11MjC+hu2JQJh1+zz7lFgH6zU4wa3aO3zXwrGGd4ngu/uG8ebUfpSUn+L2/6YwZcZq1h/KafwPbdkBbvsJ4kbD99Pgp7/ItGkh7Ki+Ec5AwFL5QCnVv9r28dbnE4HE6t9sHR2lKKVSsrKymljqufKzUgGIaRdr858tnI+Hh+LyC1qz6OFRPHNVLw5kn+Sad1dzx39T2H00v3E/1C8Ypn4BF94Fq9+Cz2+EkgLbFi6EAM7/HI6l2uNQYH/lCEgpFVp1o9Z6ptY6QWudEBkZ2egia5N5+CAAnTp1tfnPFs7L29ODmwd3YMX00Tx6UVfW7j/OhNdW8NjczRyxNOKiUU8vmPgiTHwZ9vwE/5kAuem2L1yIZq6+wFmHESoAaK33V9tu6vrveUdTKdB+9Ixta2YZwiT+Pl7839guLJ8+htuGxfLdpiOMfnkZ/5y/nROFpef/Ay+8E26YCycOwvtjjZlsQgibqS9wZgIJ1kNpiyqfVEolgTGCAfpbD6kt0lpb7FVoTcpzD3PCM4IWPp6O3K1wMmEBPjx5WQ+WPDqKy/u0ZtbKA4x8aSlvL91LUel5npPpkgi3/2z01vnwUvj9W7vULERzpJo00+c8JCQk6JSUFJv9vFMVmq1PX0hQUBCdHl1is58rXN+uzHxe+mknyTuO0SrIl2mJXZiS0O78VqIoyILPb4D032DcUzD8YeM6HiFEnZRS67XWCTVtc9nrcPZlFRBBDl6hbcwuRTiZbtFBzPrDQObePYT2Yf785ZttXPTqCuZvyWj4VOrASPjD99BrMiz+hyyHI4QNuGzgbDqUQxQnZEq0qNXAjmHMvXsIs25JwNtTcd+cDVz59q98t+lww1Yt8PaDa2bB6D/JcjhC2IDLBs6eAwfwVqcIkcARdVBKkdgjigXTRvLS5D7kFpUx7fNNDHt+CS8u3En6iZP1/QAY/YQshyOEDbhs4GSkGRPmPEJam1yJcAWeHoprE9qx9JHR/Pe2C+nXviXvLd/HyBeXcsd/17F01zEq6loyp/dk4xBbSb4shyNEI3mZXUBjFJSUU3wiHbyBIAkc0XAeHopRXSMZ1TWSw5YiPlubyufr0kjesY72Yf7cOKg91ya0IyzA59xvbj8I7lwCc64zlsO55EUYeLvjfwkhXJRLjnC2pFuI4oTxIDjG3GKEy2oT2oJHL+7GqifG8ubUfsSE+PHcgp0Mfm4xD3+xifWHTpw7yaBlB7j9J4gbA/Mfhh8ehlNl5vwCQrgYlxzhbEy1EKVy0MoDFdDK7HKEi/Px8uDyC1pz+QWt2X00n0/XHOKrDYf5euNhesQEc/OQDlzZtzX+Ptb/XPxC4IYvIPnvsOoNyNoFU/4LARGm/h5CODuXHOFsTLXQxS8fFRhlLEsihI10jQri6St7sfbP4/jnpF5UaM2fvt7KoH8u5u//+529x6xrtnl4wkXPwKSZkL4OZo6BzG3mFi+Ek3O5wNFasynNQqxvHgTJ4TRhHwG+Xtw4qAMLpo3gq3uGkNgjijlrU0l8ZQXXz1zN/C0ZlJ2qgAuugz8ugIoy+OAi2P4/s0sXwmm5XOCknygiu6CEaJUDwTJhQNiXUooBHcJ49bq+rP7TWB6fEE/6iSLum7OB0S8tY9Yv+8mP6AN3LoVW8fDlzbDseaioMLt0IZyOywXORmt/+6DSLBnhCIcKD/TlntGdWP7YGGbdkkDbli14dv4Ohj63hOdWWsi4+iu4YCosew7m/kHaHAhRjcudANmUaiHUuwzP0jyZoSZM4elhXEya2COKLekW3v/lALNWHuCDlQe4os9dTB/cmei1/4T/7Ifr5xgz24QQrjjCOcHIqHLjgVyDI0zWp20ob07tx7JHR3PLkI4s3H6Uwcu682L4M5TnHEK/PwYOrjS7TCGcgksFTkn5KX4/nMegiGLjiaBocwsSwqpdmD9PXd6D1U+M4/EJ8czLjeeigr+RXuxHxX+vpHztLLNLFMJ0LhU424/kUXqqgl5B1vWvZNKAcDIh/t7cM7oTKx8fy72TL2Fa4MusKO+J14JH2DbzDnIL6lm7TQg35lKBs8k6YSDON894QiYNCCfl4+XB5AFt+eqhS1A3fskPgdfS68hcdr00jpe+WVX/oqFCuCGXmjSwMdVCTIgfQWVZ4BMIfsFmlyREnZRSjIqPhvhZpC8fRr9l04nZdAtJKY/Qqddg7hwRS5+2oWaXKYRDuNQIZ2PaCfq2C4W8IzK6ES6n7ag/4n37QloHevKt79N47vyeK976lZtmreW3AzlmlyeE3blM4GQXlJCWU0S/9qGQnylTooVrajsAz7uW4RPTg9fUv5nXfTm7MixMmbGaqTPXsHqfNHgT7qvewFFKJSmlEpVSk+t4Tf+6ttvCplQLAP3at4T8DJkSLVxXcAzc+iNcMJWEAzNY0/kTnp7Qkb1ZBUx9fw1TZqxm1d7shrfDFsJF1Bk4SqnpQIrWOhkYX8dLE4EwWxZW3ca0E3h5KHrFBBmBIyMc4cq8/eCqd+GiZ/HcPZ8/7EhiZVIn/n55Dw4dL+SGWWuZMmM1v+zJkuARbqO+Ec5AwFL5QCnVv/oLlFKJQHJN32wdHaUopVKysrKaUieX9WnNc1f3pkXZCagolxGOcH1KwdD74ca5YEnD98Nx3NrmMMsfG8M/ruxJ+okibv7gN655dxXLdh2T4BEu73zP4ViqPlBK9beOfmqktZ6ptU7QWidERkY2pr7TuscEc21CO2PCAMgIR7iPzolGJ9EWYfDxlfht+ohbhnRk2WOjefaqXhzNK+HWD9dx1TurWLLzqASPcFn1Bc46ILTygdZ6f/UXWEc4icAApVRo9e02l59h3MsIR7iTiM5w5+IqnUQfwpdT3DS4A0sfHc1zV/fmeEEJt32UwhVv/cqi7RI8wvXUFzgzgQTrobRFlU8qpZIAtNYbrCOccPuVWI2McIS7quwkOmwapPwHPrkKCrPx8fJg6oXtWfroaF68pg+5RWXc+XEKl76xkoXbMqmokOARrkE56v+SEhISdEpKStN/0JJn4Zd/w5NZ0u1TuK8tX8L/7oeAVjB1DkT3Pr2p7FQF3206wltL9nDw+Enio4OYNq4LF/eMxsNDmVi0EKCUWq+1Tqhpm8tch3NaXgZIa2nh7vpMgT/+WKWT6HenN3l7GsvmJD88ilevu4DS8gru+XQDia8u5+PVByksKTexcCFq53qBky+rDIhmos0ASFoGUT3hy1tg6b/O6iTq5enBpH5tWfTwKF6/vi9Bvl489d3vDH5uMc/+sJ3U47Jem3AurjdMyMuA8E5mVyGEYwRFw63z4YeHYPkLcPR3mDQDfANPv8TTQ3Fl3zZccUFrNqRa+GjVQT5adZAPfj1AYvco/ji0I0M6haOUHG4T5nK9wMk/Ah2Hm12FEI7j5QtXvg1RveDnvxiH2KbOgZYdz3qZUooBHVoyoENLMid2Z/aaQ8z5LZVF248SHx3ErUM7cmXfNrTw8TTn9xDNnmsdUis9CcW5MkNNND9KwZB74cZ5kJcOM8fAgRW1vjw6xI9HL+7GqifG8uI1fQB44uutDHl+MS8s3MkRS5GjKhfiNNcKHLkGRzR3ncfBnUshIBI+vgp+ex/qmGnq5+3JlIHtWDBtBJ8nDWZwbDgzlu9jxItLue/TDaw7mCPX8wiHca1DanINjhDGOcw7kuHrO+HHRyFzK0x8Gbx8av0WpRSD48IZHBdOWs5JZq85xGe/pTJ/awa92gRz69BYLr8gBl8vOdwm7EdGOEK4Ir9guH4ODH8YNvwXPr4CCo416Fvbhfnzp4ndWfPncTx7VS+Kyyp4dO5mhj2/hFd+3kVmbrGdixfNlWtd+LnyNUj+GzyRJt0+hai0dR58dx/4BsHlb0D8xPP6dq01K/dm89GvB1myywitgR3CuKR3NJf0iiE6xM8eVQs3VdeFn64VOAseh42z4c+HbVOUEO4icxt8czcc3Qp9b4QJzxlL5Zyng9mFfLvpMD9uzWD30QIAEjq05JLeMUzsHU1MSAtbVy7cjPsEzhc3w7EdcL8NlsgRwt2Ul8KKF+GXV4zrd658CzqNbfSP23ssnx+3ZvLj1gx2ZuYD0L99KBN7x3BJ7xjahEr4iHO5T+DMSgTvFvCH721TlBDuKH09fHs3ZO+GhNth/D/OulC0MfZlFbBgawbzt2ayIyMPgL7tQploPezWLszfFpULN+A+gfNKT+Oiz6tn2KYoIdxVWZGx0O3qt40LRK96FzoMscmPPpBdyI9bM/hxawa/HzHC54K2IVzSO4ZLe0v4NHfuETgVFfBspNEhMfHvNqtLCLd28Ff49h6wpMKQ+2DsX4321jZy6HghP27NZMG2DLak5wLQu00Il/SOZkLPaGIjAmRJnWbGPQKn4Bi83AUueQkGJdmuMCHcXUkBLPqr0WMnohtMeg/anNMtvsnSck4aI59tmWxOswAQFezLoNhwBsWFMSg2nE6REkDuzj0C58gmmDkKrpsN3S+3WV1CNBt7k+G7+6HgKIx4BEY+VufFok2RfuIkS3dlsXb/cdYeyCErvwSAiEBfBsWGnQ6gLq0CpYePm6krcFxnpQG56FOIpumcCPeuhoVPGLPZdi8wVp6O6mnzXbVt6c/Ngztw8+AOaK05kF3I2gM5pwNo/lbjv+eW/t5cGBt2ehTUPTpYAsiNuU7gyLI2QjRdi1DjkFr8ZfDDgzBjFIz5s9HW2sM+y9oopYiLDCQuMpCpF7ZHa01aThFrDhxn7f4c1h44zk+/HwUgpIU3AzuGMdg6AurROhhPCSC34TqBk58BysNouSuEaJrul0H7wUafncVPw64f4ar3IKKz3XetlKJ9uD/tw/2ZktAOgMOWImP0Yw2g5B1GAAX5ejGgY0t6tQ6hR+tgesQE0z7MX0ZBLqreczhKqSRgPxCqtZ5XbVsoEFd501q/WNvPafI5nIO/wuH1MOyBxv8MIcTZtIZtX8H8R6C8xJgBemESeJi7zGJmbjFrDxxnzf4c1h/KYV9WIacqjL9VAT6exMcY4VMZQt2ig/DzloVHnUGjJw0opaYDyVrrDUqpGVrru6ptT7Ju36+UWg+M01pbavpZNrkORwhhH3kZ8P0DsOdnaHshjH4cOo0z+vA4geKyU+w5WsD2jFy2H8ljR0Y+2zPyKCgpB8BDQafIQLpXCaEerYOJCPQ1ufLmpymBMxd43BooM4AZWusNdexkQLXnkoAkgPbt2w84dOhQY38HIYS9aQ2bPoWl/4K8wxDTF0Y+Ct0uNX3EU5OKCk36iaLTIbQ9wwiiw1Way7UK8qVH62C6xxi3TpEBdAwPIMDXdc4muBpbBs4LWuv9NbxuOjCvpm2VZIQjhIsoL4XNn8HKV+HEAYjsbkyj7jkJPJ3/D7XlZCnbM/JOh9D2I3nsPVZAecWZv3VRwb50DA8gNiKAjhHGfWxEAO3D/OXQXBPZ7ZCa9TWJGOd4kMARwo2cKoffv4Ff/g1ZO6BlLAx/CC6Yarfrd+ylpPwU+44VciC7kIPHC9mfZdwfzC7keGHp6dcpBa1DWliDyJ/YiEBiI/zpGB5AuzB/vD2db6TnbJoSOKHAFCAFY1LAPOvzSVrrmdaweQHIAcKqH1KrSgJHCBdVUWHMYvvlZTiyEYLbwNAHoP8t4OP666blFpVx0BpEB7KtoWS9zysuP/06Tw9Fu5Yt6BgRQNuWLWgd2oI2ocZ969AWRAX54iWB5CYrDQghzKU17FsMK/4NqasgINJYny3hdrdsiKi1Jqew1BpEJ0+H0IHsQo7kFmE5WXbW6z0URAX7nQ6g1qF+tAltQUzIma9DWni7/dI+EjhCCNs6tApWvGwEkF8IDLrbuPmHmV2ZwxSWlJORW8RhSzFHLEVkWM58fSS3iAxLMaWnKs76Hn8fzzOBFOJHVLAfkUG+tAryNe6D/YgI9MHXy3XPI0ngCCHs4/AG4xzPzh/AOwAG3gZD7oegKLMrM11FhSa7sIQMawgdthRxpEogHbEUkV1QWuP3hvp7Exnoe3YYBZ0dTpFBvk45YpLAEULY19HtsPIV4yJSD2/j/M7Q+6FlB7Mrc2plpyo4XlBKVn4Jx/KLrfclZFlvx/KLySoo4VheCSXlFed8v4+nB5FBvkQE+tAywIewAB/CA4yvwwN8aOnvQ3ig9T7AlyA/L7uv0iCBI4RwjOP74NfXYNNnUFEGbQZA/KXGtTyR3ZzmQlJXo7Umv6ScY3nWMCoo4VieEUZZeSVkF5ZyorCUHOutqOxUjT/H00PR0t+HsABvwqwBFRbgQ5i/cR8R5MtlfZq2QLIEjhDCsXLTYfPnxuy2w+uN58I6GeETfym0HWi3xUIFFJWeIudkKTkFpcZ9YQk5hWVn3Z8oLON4YQknTpZx4mQpWkNEoA8pT45v0r4lcIQQ5snLMIJn53w4sMIY+QREQtcJxqrVcaPAu4XZVTZrpyo0uUVlFBSX0z68aVPdJXCEEM6hONdoBLdzPuxZBCV54O0PnccZ4dPlomY1080duUcDNiGE6/MLgV7XGLfyUjj4ixE+u36EHd+D8oQOQ43wiZ8Ioe3NrljYkIxwhBDmq6iAjI1G+Oz80VhKByC6t3HorU0CxFwAQdEy8cDJySE1IYRrOb7vzMgndQ1g/TsV0Api+hjhU3kL7SAh5EQkcIQQrqskHzK3QcZmyNxi3B/bAdo69dcvBKKrhVB4Z5kFZxI5hyOEcF2+QdBhiHGrVFYMx7Yb4VN5++19OFVibPf2h6heZ4dQZLzLrXLtbiRwhBCux9sP2vQ3bpVOlUH27iohtMXo67PufWO7hzeEtIGQdsZkhJB2ENruzOPgNhJIdiaBI4RwD57eENXTuPW9wXiuogJy9kPmZsjcCpZUsKTBviWQn8npc0MAKAiKqRJC7c4NJ58AM34ztyGBI4RwXx4eENHZuPW65uxt5SVGK21LmhFEuWnG17lpkL4Otn8LFeVnf0+LMCN4AqPAP9x47B9mfH36Zn3coqURguI0CRwhRPPk5QthccatJhWnjFHQ6SBKPRNIBcfg2E4oyoHSgtr34RdSJZiqBZJ/mLHdJwh8A8En0Hpvfezla5/f20QSOEIIURMPT+s5nzbQfnDtrysrNoLn5HHrLafavfVWkGlMdDh5HMpONmD/3mcH0OlACjQmUlR97BMAXn7GEkH13vuCVwtj9OdgEjhCCNEU3n7g3RqCz2OV5dKTRkgV5xkjpJJ8631B3Y+LcyH3cJVt+aDPbVvQIJ4+RvB4+50Jo4BIuPWHxv28Bqg3cJRSScB+IFRrPe98twshhKjGx9+4hTTx52gNZUXGiKmsyDgvVV5kjLoac+9l30VU6wwcpdR0IFlrvUEpNQOYdz7bhRBC2JFSZ8LLBdR3EG8gYKl8oJTqfz7blVJJSqkUpVRKVlZW0yoVQgjh0s73rJHlfLZrrWdqrRO01gmRkZHnuSshhBDupL7AWQeEVj7QWu8/z+1CCCEEUH/gzAQSrIfKFlU+aZ0oUOt2IYQQojpZLVoIIYTN1LVatOOv/BFCCNEsSeAIIYRwCAkcIYQQDuGwczhKqSzgUBN/TASQbYNyHEFqtR9XqldqtQ+p1T5sUWsHrXWN18E4LHBsQSmVUtvJKGcjtdqPK9UrtdqH1Gof9q5VDqkJIYRwCAkcIYQQDuFqgTPT7ALOg9RqP65Ur9RqH1Krfdi1Vpc6hyOEEMJ1udoIRwghhIuSwHEwpVRc1Xtn4ax1VaeUClVKhdbwvEvU74qc9b111rqqk8/sGU4XOEqpRKXUC3VsT7K+ZnJdzzlCXbVaP2T9lVKTrY3qsH7oZlib1TlUfbVSQ13O+L4CicBcpdQipdR663scinnv6zn/zjW8xik+s/XV6kyf2YbUWlNdzvi+4nyf2URrrTW+R3b9vGqtne4GzK3l+elAf+vXM2p7zklqTQLirF+vx2jjEFpZq5O9r+fU5cTva1yVrxNrq9+BdZ7z71zf+2jWe9uAWp3mM9uAWp3mM9uAWp3mMwv0ByZX1lrDdrt+Xp1uhFOPmjqM1teV1BTaaD63v8pji/XLBOv/KSTV/J2mqV6Xs76v+wGUUpO11slVNpnyvtbx71zJaT6z9dXqTJ/ZBryvNdXlrO+r03xmtdYbgGTrPp+r4SV2/by6WuBUZ2ngc6axDrGvBeODaP1wJlc+5wwaWJfFgSU1xMDKL5zhfa3671wPSwOfs5v6anWmz2xttTrjZ7YBnwGn+MxaAzEZuK4BL7c08LkGcbXAqanDqNN2HVVKJQLzqjxOqunkodlqqcvZ39eqj019X6v/O1fjVJ/Zemp1qs9sXbU622e2ge9r1cemvK9KqelKqTjrexPXwPfQdu+ro48hNuAYYyLGcdD+VZ5L0meOeyZx9nHIc55zklorty3CeqzUWmt/67YkJ6r1nLqc9X21fj252mNneF9P/zs762e2AbU6zWe2ge+rU3xm66vVmT6zQFzl+wNMd/TnVS78FEII4RCudkhNCCGEi5LAEUII4RASOEIIIRxCAkcIIYRDSOAIIYRwCAkcIYQQDiGBI4QQwiH+H4ChsRBJ9j9vAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Measure to 0.5"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=np.linspace(1,3,20)\n",
    "es=50\n",
    "LN_half_list=np.zeros((len(m_list),es))\n",
    "MI_half_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=16,8\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # Serial\n",
    "    params0=Params(m=m,Lx=16,Ly=8)\n",
    "    for es_i in range(es):\n",
    "        params=copy(params0)\n",
    "        params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)],0.5)\n",
    "        # MI_half_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "        LN_half_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    #  # Parallel\n",
    "    # async_result=rc[:].map_sync((lambda p:Params(m=p[0],Lx=p[1],Ly=p[2]).measure_all_Born([np.arange(p[1]//4,p[1]//2),np.arange(p[2])],0.5).log_neg([np.arange(p[1]//4),np.arange(p[2])],[np.arange(p[1]//4)+p[1]//2,np.arange(p[2])])),[(m,Lx,Ly) for _ in range(es)])\n",
    "    # LN_half_list[m_i,:]=async_result \n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.plot(m_list,LN_half_list.mean(axis=1))\n",
    "# plt.plot(m_list,LN_empty_list)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Measure to 1"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "m_list=np.linspace(1,3,20)\n",
    "es=1\n",
    "LN_occ_list=np.zeros((len(m_list),es))\n",
    "MI_occ_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=16,8\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # Serial\n",
    "    params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    for es_i in range(es):\n",
    "        params=copy(params0)\n",
    "        params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)],1)\n",
    "        MI_occ_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "        LN_occ_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    # # Parallel\n",
    "    # async_result=rc[:].map_sync((lambda p:Params(m=p[0],Lx=p[1],Ly=p[2]).measure_all_Born([np.arange(p[1]//4,p[1]//2),np.arange(p[2])]).log_neg([np.arange(p[1]//4),np.arange(p[2])],[np.arange(p[1]//4)+p[1]//2,np.arange(p[2])])),[(m,Lx,Ly) for _ in range(es)])\n",
    "    # LN_occ_list[m_i,:]=async_result \n",
    "\n",
    "    # print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-21-450f7102a9e6>:401: LinAlgWarning: Ill-conditioned matrix (rcond=8.32667e-17): result may not be accurate.\n",
      "  Psi = mat1+mat2@(la.solve(mat3, mat2.T))\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "plt.plot(m_list,LN_occ_list)\n",
    "# plt.plot(m_list,LN_empty_list)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd62f648550>]"
      ]
     },
     "metadata": {},
     "execution_count": 24
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "source": [
    "# prob=list(np.random.permutation([1,0]*(Lx//4*Ly)))\n",
    "prob=([1,0]*(Lx//4*Ly))\n",
    "# prob=([0,1]*(Lx//4*Ly))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "source": [
    "prob_list=[prob]\n",
    "prob1=copy(prob)\n",
    "for i in range(Lx//4*Ly):\n",
    "    prob1[2*i],prob1[2*i+1]=prob1[2*i+1],prob1[2*i]\n",
    "    prob2=copy(prob1)\n",
    "    prob_list.append(prob2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "source": [
    "prob=[1,0]*(Lx//4*1)+[0,1]*(Lx//4*(Ly-1))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "source": [
    "params.square_index([1,2,3,4,5,6,7,8,9])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), array([0, 0, 0]))"
      ]
     },
     "metadata": {},
     "execution_count": 318
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "source": [
    "Lx*Ly*2//4"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "metadata": {},
     "execution_count": 450
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "source": [
    "params.fermion_number([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='C_m')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "metadata": {},
     "execution_count": 357
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "source": [
    "def run(p):\n",
    "    Lx,Ly,m,prob=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    # prob=list(np.random.permutation([1,0]*(Lx//4*Ly)))\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],prob=prob)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    fn=params.fermion_number([np.arange(Lx//4,Lx//2),np.arange(Ly)])\n",
    "    total_prob=params.snap_prob([np.arange(Lx//4,Lx//2),np.arange(Ly)],occ=np.array(prob)).prod()\n",
    "    return LN,fn,total_prob"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "source": [
    "run((Lx,Ly,1,prob_list[0]))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.6941249447928497, 32.0, 7.25798592078013e-09)"
      ]
     },
     "metadata": {},
     "execution_count": 198
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "source": [
    "run((Lx,Ly,1,prob_list[0]))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.6941249238817029, 32.0, 7.257985923839278e-09)"
      ]
     },
     "metadata": {},
     "execution_count": 196
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "source": [
    "run((Lx,Ly,1,prob))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.6957639042394876, 32.0, 9.723760770203726e-11)"
      ]
     },
     "metadata": {},
     "execution_count": 156
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "source": [
    "inputs[1][3]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0])"
      ]
     },
     "metadata": {},
     "execution_count": 87
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "import itertools"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "nchoosek=list(itertools.combinations(np.arange(Lx*Ly//4),1))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "source": [
    "nchoosek"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[()]"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "source": [
    "Lx=32\n",
    "Ly=4"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "source": [
    "prob=([1,0]*(Lx//4*Ly))\n",
    "prob1_list=[prob]\n",
    "for k in range(1,2):\n",
    "    nchoosek=list(itertools.combinations(np.arange(Lx*Ly//4),k))\n",
    "    for i1 in (nchoosek):\n",
    "        for i2 in (nchoosek):\n",
    "            prob1=copy(prob)\n",
    "            prob1=np.array(prob1)\n",
    "            prob1[2*np.array(list(i1))]=0\n",
    "            prob1[2*np.array(list(i2))+1]=1\n",
    "            prob1_list.append(list(prob1))\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "source": [
    "np.array(prob1_list[4])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0])"
      ]
     },
     "metadata": {},
     "execution_count": 116
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "source": [
    "LN_list[4]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.06532565187147998"
      ]
     },
     "metadata": {},
     "execution_count": 117
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "prob_list=[prob]\n",
    "prob1=copy(prob)\n",
    "for i in range(Lx//4*Ly):\n",
    "    prob1[2*i],prob1[2*i+1]=prob1[2*i+1],prob1[2*i]\n",
    "    prob2=copy(prob1)\n",
    "    prob_list.append(prob2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "source": [
    "# es=100\n",
    "Lx=32\n",
    "Ly=4\n",
    "inputs=[(Lx,Ly,1,prob) for prob in prob_list]\n",
    "LN_samesite_list=np.zeros(len(prob_list))\n",
    "fn_samesite_list=np.zeros(len(prob_list))\n",
    "total_prob_samesite_list=np.zeros(len(prob_list))\n",
    "\n",
    "sync=rc[:].map_sync(run,inputs)\n",
    "for es_i,result in enumerate(sync):\n",
    "    LN,fn,total_prob=result\n",
    "    LN_samesite_list[es_i]=(LN)\n",
    "    fn_samesite_list[es_i]=(fn)\n",
    "    total_prob_samesite_list[es_i]=(total_prob)\n",
    "    "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "source": [
    "total_prob_samesite_list"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([7.25798592e-09, 8.40088797e-10, 9.72376077e-11, 1.12549440e-11,\n",
       "       1.30272399e-12, 1.50786160e-13, 1.74530187e-14, 2.02013143e-15,\n",
       "       2.33823791e-16, 2.70643605e-17, 3.13261369e-18, 3.62590077e-19,\n",
       "       4.19686487e-20, 4.85773767e-21, 5.62267693e-22, 6.50806980e-23,\n",
       "       7.53288391e-24, 8.71907366e-25, 1.00920506e-25, 1.16812277e-26,\n",
       "       1.35206497e-27, 1.56497223e-28, 1.81140560e-29, 2.09664440e-30,\n",
       "       2.42679924e-31, 2.80894298e-32, 3.25126220e-33, 3.76323264e-34,\n",
       "       4.35582215e-35, 5.04172567e-36, 5.83563719e-37, 6.75456453e-38,\n",
       "       7.81819372e-39])"
      ]
     },
     "metadata": {},
     "execution_count": 743
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "source": [
    "plt.plot(LN_samesite_list/np.log(2))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f153818cfa0>]"
      ]
     },
     "metadata": {},
     "execution_count": 745
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "source": [
    "# es=100\n",
    "Lx=32\n",
    "Ly=4\n",
    "inputs=[(Lx,Ly,1,prob) for prob in prob1_list]\n",
    "LN_list=np.zeros(len(prob1_list))\n",
    "fn_list=np.zeros(len(prob1_list))\n",
    "total_prob_list=np.zeros(len(prob1_list))\n",
    "\n",
    "sync=rc[:].map_sync(run,inputs)\n",
    "for es_i,result in enumerate(sync):\n",
    "    LN,fn,total_prob=result\n",
    "    LN_list[es_i]=(LN)\n",
    "    fn_list[es_i]=(fn)\n",
    "    total_prob_list[es_i]=(total_prob)\n",
    "    "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "source": [
    "LN_list[4]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.20317456384611177"
      ]
     },
     "metadata": {},
     "execution_count": 89
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "source": [
    "kmax=2\n",
    "Lx=32\n",
    "Ly=4\n",
    "m=1\n",
    "with open('CI_kmax{:d}_Lx{:d}_Ly{:d}_m{:.1f}.pickle'.format(kmax,Lx,Ly,m),'rb') as f:\n",
    "    [LN_map,fn_map,total_prob_map]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "source": [
    "plt.pcolormesh((LN_map[1]/np.log(2)).reshape((32,32)))\n",
    "plt.colorbar()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f15385200a0>"
      ]
     },
     "metadata": {},
     "execution_count": 131
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "fn_list"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([32., 32., 32., ..., 32., 32., 32.])"
      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "source": [
    "plt.hist(LN_list/np.log(2),bins=20)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([376.,  72., 112., 112.,   0.,   0.,   0.,  40.,  96.,  88.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 129.]),\n",
       " array([0.03291518, 0.08138407, 0.12985296, 0.17832185, 0.22679075,\n",
       "        0.27525964, 0.32372853, 0.37219742, 0.42066631, 0.46913521,\n",
       "        0.5176041 , 0.56607299, 0.61454188, 0.66301077, 0.71147966,\n",
       "        0.75994856, 0.80841745, 0.85688634, 0.90535523, 0.95382412,\n",
       "        1.00229302]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "metadata": {},
     "execution_count": 106
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "source": [
    "np.sum(total_prob_list*LN_list)/np.sum(total_prob_list)/np.log(2)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.29595625387742514"
      ]
     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "source": [
    "np.sum(total_prob_list*LN_list)/np.sum(total_prob_list)/np.log(2)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.588092687258667"
      ]
     },
     "metadata": {},
     "execution_count": 93
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "source": [
    "np.sum(total_prob)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "6.508069802127115e-23"
      ]
     },
     "metadata": {},
     "execution_count": 412
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "source": [
    "params=Params(m=.1,Lx=Lx,Ly=Ly)\n",
    "params.covariance_matrix()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "source": [
    "params.snap_prob([np.arange(Lx//4,Lx//2),np.arange(Ly)],occ=np.array(prob))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798, 0.5398798,\n",
       "       0.5398798, 0.5398798, 0.5398798, 0.5398798])"
      ]
     },
     "metadata": {},
     "execution_count": 430
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "source": [
    "params.snap_prob([np.arange(Lx//4,Lx//2),np.arange(Ly)],occ=np.array([0]*(Lx//4*Ly*2)))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.25385153, 0.74614847, 0.25385153, 0.74614847, 0.25385153,\n",
       "       0.74614847, 0.25385153, 0.74614847, 0.25385153, 0.74614847,\n",
       "       0.25385153, 0.74614847, 0.25385153, 0.74614847, 0.25385153,\n",
       "       0.74614847, 0.25385153, 0.74614847, 0.25385153, 0.74614847,\n",
       "       0.25385153, 0.74614847, 0.25385153, 0.74614847, 0.25385153,\n",
       "       0.74614847, 0.25385153, 0.74614847, 0.25385153, 0.74614847,\n",
       "       0.25385153, 0.74614847, 0.25385153, 0.74614847, 0.25385153,\n",
       "       0.74614847, 0.25385153, 0.74614847, 0.25385153, 0.74614847,\n",
       "       0.25385153, 0.74614847, 0.25385153, 0.74614847, 0.25385153,\n",
       "       0.74614847, 0.25385153, 0.74614847, 0.25385153, 0.74614847,\n",
       "       0.25385153, 0.74614847, 0.25385153, 0.74614847, 0.25385153,\n",
       "       0.74614847, 0.25385153, 0.74614847, 0.25385153, 0.74614847,\n",
       "       0.25385153, 0.74614847, 0.25385153, 0.74614847])"
      ]
     },
     "metadata": {},
     "execution_count": 379
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "source": [
    "m_list=np.linspace(0,3,20)\n",
    "# m_list=-np.linspace(1,3,20)\n",
    "es=1\n",
    "LN_pn_list=np.zeros((len(m_list),es))\n",
    "MI_pn_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=32,4\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # Serial\n",
    "    params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    for es_i in range(es):\n",
    "        params=copy(params0)\n",
    "        params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)],prob=prob)\n",
    "        MI_pn_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "        LN_pn_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    # # Parallel\n",
    "    # async_result=rc[:].map_sync((lambda p:Params(m=p[0],Lx=p[1],Ly=p[2]).measure_all_Born([np.arange(p[1]//4,p[1]//2),np.arange(p[2])]).log_neg([np.arange(p[1]//4),np.arange(p[2])],[np.arange(p[1]//4)+p[1]//2,np.arange(p[2])])),[(m,Lx,Ly) for _ in range(es)])\n",
    "    # LN_pn_list[m_i,:]=async_result \n",
    "\n",
    "    # print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "source": [
    "LN_pn_list_L32=LN_pn_list"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "source": [
    "LN_pn_list_L64=LN_pn_list"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "source": [
    "# plt.plot(m_list,LN_pn_list_L32/np.log(2),'k')\n",
    "# plt.plot(m_list,LN_pn_list_L64/np.log(2),'b')\n",
    "plt.plot(m_list,LN_pn_list/np.log(2))\n",
    "plt.xlabel('$m$')\n",
    "plt.ylabel('LN [log2]')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'LN [log2]')"
      ]
     },
     "metadata": {},
     "execution_count": 464
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Satisfies Born rule with post selection of half-filling?"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "source": [
    "# plt.plot(m_list,LN_pn_list_L64/np.log(2),'r')\n",
    "plt.plot(m_list,MI_pn_list_L64/np.log(2),'r')\n",
    "# plt.plot(m_list,LN_pn_list_L128/np.log(2))\n",
    "plt.plot(m_list,MI_pn_list_L128/np.log(2),'b')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd62c496310>]"
      ]
     },
     "metadata": {},
     "execution_count": 90
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "source": [
    "m_list=np.linspace(-3,3,20)\n",
    "# m_list=-np.linspace(1,3,20)\n",
    "es=1\n",
    "LN_pn_list_L128=np.zeros((len(m_list),es))\n",
    "MI_pn_list_L128=np.zeros((len(m_list),es))\n",
    "Lx,Ly=128,4\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # Serial\n",
    "    params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    for es_i in range(es):\n",
    "        params=copy(params0)\n",
    "        params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)],prob=([0,1]*(params.Lx//4*params.Ly)))\n",
    "        MI_pn_list_L128[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "        LN_pn_list_L128[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "source": [
    "m_list=np.linspace(-3,3,20)\n",
    "# m_list=-np.linspace(1,3,20)\n",
    "es=1\n",
    "LN_pn_list_L64=np.zeros((len(m_list),es))\n",
    "MI_pn_list_L64=np.zeros((len(m_list),es))\n",
    "Lx,Ly=64,4\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # Serial\n",
    "    params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    for es_i in range(es):\n",
    "        params=copy(params0)\n",
    "        params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)],prob=([0,1]*(params.Lx//4*params.Ly)))\n",
    "        MI_pn_list_L64[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "        LN_pn_list_L64[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "params.fermion_number([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "64.0"
      ]
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Born measurement"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Correlated link"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "m=1\n",
    "Lx=32\n",
    "Ly=4\n",
    "params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=-1)\n",
    "params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='correlated')\n",
    "# ,prob=([1,0]*(Lx//4*Ly))\n",
    "params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.007711877141066914"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "np.log(2)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6931471805599453"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "source": [
    "nocc=1/2*(1-(np.diag(params.C_m_history[-1],1)[::2])).reshape((params.Ly,params.Lx,2))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "source": [
    "plt.pcolormesh(nocc[:,:,0])\n",
    "plt.colorbar()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f153bd3a4f0>"
      ]
     },
     "metadata": {},
     "execution_count": 804
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "source": [
    "plt.pcolormesh(nocc[:,:,1])\n",
    "plt.colorbar()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f153bc7d520>"
      ]
     },
     "metadata": {},
     "execution_count": 805
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "source": [
    "def run(p):\n",
    "    Lx,Ly,m=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=0)\n",
    "    # prob=list(np.random.permutation([1,0]*(Lx//4*Ly)))\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='correlated',prob=[1,0])\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    fn=params.fermion_number([np.arange(Lx//4,Lx//2),np.arange(Ly)])\n",
    "    # total_prob=params.snap_prob([np.arange(Lx//4,Lx//2),np.arange(Ly)],occ=np.array(prob)).prod()\n",
    "    return LN,fn"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "source": [
    "run((64,4,1))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.5286744127255583, 64.0)"
      ]
     },
     "metadata": {},
     "execution_count": 161
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "source": [
    "l_list=np.arange(4,Lx)\n",
    "LN_Ly_list=[]\n",
    "fn_Ly_list=[]\n",
    "for l in l_list:\n",
    "    LN,fn=run((32,l,1))\n",
    "    fn_Ly_list.append(fn)\n",
    "    LN_Ly_list.append(LN)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "source": [
    "plt.plot(LN_Ly_list/np.log(2))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f154d514fa0>]"
      ]
     },
     "metadata": {},
     "execution_count": 850
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "es=10\n",
    "Lx=32\n",
    "Ly=4\n",
    "m_list=np.linspace(1,3,11)\n",
    "LN_Born_list=np.zeros((m_list.shape[0],es))\n",
    "fn_Born_list=np.zeros((m_list.shape[0],es))\n",
    "# total_prob_Born_list=np.zeros((m_list.shape[0],es))\n",
    "for m_i,m in enumerate(m_list):\n",
    "    inputs=[(Lx,Ly,m) for _ in range(es)]\n",
    "    sync=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for es_i,result in enumerate(sync):\n",
    "        LN,fn=result\n",
    "        LN_Born_list[m_i,es_i]=(LN)\n",
    "        fn_Born_list[m_i,es_i]=(fn)\n",
    "        # total_prob_Born_list[m_i,es_i]=(total_prob)\n",
    "    \n",
    "    "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "es=100\n",
    "Lx=32\n",
    "Ly=8\n",
    "with open('/mnt/d/KITP/Rp_SPT/8/CI_Born_En{:d}_Lx{:d}_Ly{:d}_{:s}.pickle'.format(es,Lx,Ly,'correlated'),'rb') as f:\n",
    "    [m_list,MI_Born_list,LN_Born_list,fn_Born_list]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "fig,ax=plt.subplots()\n",
    "\n",
    "ax.errorbar(m_list,MI_Born_list.mean(axis=1)/np.log(2),MI_Born_list.std(axis=1)/np.log(2)/np.sqrt(es),capsize=3,fmt='.-',label='MI')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_list.mean(axis=1)/np.log(2),LN_Born_list.std(axis=1)/np.log(2)/np.sqrt(es),capsize=3,fmt='.-',label='LN')\n",
    "ax.set_xlabel(r'$m$')\n",
    "ax.set_ylabel('[log 2]')\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/CI_correlated_link_es{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 867,
   "source": [
    "plt.plot(m_list,[LN[fn==32].mean() for fn,LN in zip(fn_Born_list,LN_Born_list)])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f153a9b67f0>]"
      ]
     },
     "metadata": {},
     "execution_count": 867
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## onsite"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_list=np.zeros((len(m_list),es))\n",
    "MI_Born_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=64,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)])\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # # Serial\n",
    "    # params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    # for es_i in range(es):\n",
    "    #     params=copy(params0)\n",
    "    #     params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)])\n",
    "    #     # MI_Born_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "    #     LN_Born_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    # Parallel\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_list[m_i,index],LN_Born_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0:33.7\n",
      "1:35.7\n",
      "2:28.2\n",
      "3:28.0\n",
      "4:28.4\n",
      "5:28.1\n",
      "6:31.5\n",
      "7:33.2\n",
      "8:33.0\n",
      "9:33.4\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "es,Lx,Ly=500,16,32\n",
    "with open('/mnt/d/KITP/Rp_SPT/8/MI_LN_CI_Born_En{:d}_Lx{:d}_Ly{:d}.pickle'.format(es,Lx,Ly),'rb') as f:\n",
    "    [m_list,MI_Born_list,LN_Born_list,MI_no_list,LN_no_list]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_list.mean(axis=1)/np.log(2),LN_Born_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Onsite measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_list.mean(axis=1)/np.log(2),MI_Born_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Onsite measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'[log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "# fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_onsite_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ensemble Size=20, Lx=64, Ly=4')"
      ]
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## m=2"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,10))\n",
    "# m_list=np.linspace(-4,4,40)\n",
    "LN_Born_GS_list=[]\n",
    "MI_Born_GS_list=[]\n",
    "Lx,Ly=64,8\n",
    "\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1,history=True)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link')\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    FN=params.fermion_number([np.arange(Lx//4,Lx//2),np.arange(Ly)])\n",
    "    return MI,LN,FN"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "run((2,32,16))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## |GS>  in trivial regime |01>, m->inf"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,10))\n",
    "# m_list=np.linspace(-4,4,40)\n",
    "LN_Born_GS_list=[]\n",
    "MI_Born_GS_list=[]\n",
    "Lx,Ly=64,32\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range=params.linearize_index([np.arange(params.Lx//4,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=2)\n",
    "    prob_list=[1-(1-(-1)**i)/2 for i,x in enumerate(proj_range)]\n",
    "    params.measure_all_Born(proj_range,type='onsite',prob=prob_list,linear=True)\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    LN=(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n",
    "    return MI,LN\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_GS_list.append(results[0])\n",
    "    LN_Born_GS_list.append(results[1])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "\n",
    "ax.plot(m_list,LN_Born_GS_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_GS_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_GS_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## |GS>  in trivial regime |10>, m->-inf"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "LN_Born_GS2_list=[]\n",
    "MI_Born_GS2_list=[]\n",
    "Lx,Ly=64,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range=params.linearize_index([np.arange(params.Lx//4,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=2)\n",
    "    prob_list=[(1-(-1)**i)/2 for i,x in enumerate(proj_range)]\n",
    "    params.measure_all_Born(proj_range,type='onsite',prob=prob_list,linear=True)\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    LN=(params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)]))\n",
    "    return MI,LN\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_GS2_list.append(results[0])\n",
    "    LN_Born_GS2_list.append(results[1])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "\n",
    "ax.plot(m_list,LN_Born_GS2_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_GS2_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_GS2_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Link"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params=Params(m=1,Lx=64,Ly=8,bcx=-1,bcy=1,history=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)],type='link')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "[np.sum(np.array(params.s_history)==s) for s in ['o+','o-','e+','e-']]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])/np.log(2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=np.linspace(1,3,10)\n",
    "# m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,10))\n",
    "es=1000\n",
    "LN_Born_link_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=128,4\n",
    "outcome_Born_link_list=np.zeros((len(m_list),es,4))\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1,history=True)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link')\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    outcome=[np.sum(np.array(params.s_history)==s) for s in ['o+','o-','e+','e-']]\n",
    "    return MI,LN,outcome\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # # Serial\n",
    "    # params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    # for es_i in range(es):\n",
    "    #     params=copy(params0)\n",
    "    #     params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)])\n",
    "    #     # MI_Born_link_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "    #     LN_Born_link_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    # Parallel\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_list[m_i,index],LN_Born_link_list[m_i,index],outcome_Born_link_list[m_i,index,:]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "es,Lx,Ly=1000,64,8\n",
    "# '/mnt/d/KITP/Rp_SPT/8/'\n",
    "with open('/mnt/d/KITP/Rp_SPT/8/MI_LN_CI_Born_link_En{:d}_Lx{:d}_Ly{:d}.pickle'.format(es,Lx,Ly),'rb') as f:\n",
    "    [m_list,MI_Born_link_list,LN_Born_link_list,outcome_Born_link_list,s_history_Born_link_list]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "half_index=(outcome_Born_link_list@np.array([0,2,1,1]))==(Lx*Ly//4)\n",
    "deviatefromhalf=(outcome_Born_link_list@np.array([0,2,1,1]))/(Lx*Ly//4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_link_half_list=[LN_Born_link_list[i,j] for i,j in enumerate(half_index)]\n",
    "MI_Born_link_half_list=[MI_Born_link_list[i,j] for i,j in enumerate(half_index)]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_link_half_list_ave=[np.mean(i) for i in LN_Born_link_half_list]\n",
    "LN_Born_link_half_list_err=[np.std(i)/np.sqrt(es-1) for i in LN_Born_link_half_list]\n",
    "MI_Born_link_half_list_ave=[np.mean(i) for i in MI_Born_link_half_list]\n",
    "MI_Born_link_half_list_err=[np.std(i)/np.sqrt(es-1) for i in MI_Born_link_half_list]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "outcome_Born_link_list[-1,half_index[-1]].shape"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.plot(LN_Born_link_half_list_ave)\n",
    "plt.plot(MI_Born_link_half_list_ave)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,axs=plt.subplots(3,3,tight_layout=True,figsize=(6.8,6.8))\n",
    "# ax.errorbar(m_list,LN_Born_link_list.mean(axis=1)/np.log(2),LN_Born_link_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "# ax.errorbar(m_list,MI_Born_link_list.mean(axis=1)/np.log(2),MI_Born_link_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "# ax.legend()\n",
    "# ax.grid('on')\n",
    "# ax.set_xlabel(r'm')\n",
    "# ax.set_ylabel(r'log neg [log(2)]')\n",
    "# ax.margins(x=0)\n",
    "# ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "\n",
    "axs[0,0].hist(deviatefromhalf[0],density=False,bins=20)\n",
    "axs[0,1].hist(LN_Born_link_list[0]/np.log(2),density=False,bins=10)\n",
    "axs[0,2].scatter(deviatefromhalf[0],LN_Born_link_list[0]/np.log(2),s=1)\n",
    "axs[0,2].scatter(deviatefromhalf[0][half_index[0]],LN_Born_link_list[0][half_index[0]]/np.log(2),s=1)\n",
    "\n",
    "axs[1,0].hist(deviatefromhalf[5],density=False,bins=20)\n",
    "axs[1,1].hist(LN_Born_link_list[5]/np.log(2),density=False,bins=10)\n",
    "axs[1,2].scatter(deviatefromhalf[5],LN_Born_link_list[5]/np.log(2),s=1)\n",
    "axs[1,2].scatter(deviatefromhalf[5][half_index[5]],LN_Born_link_list[5][half_index[5]]/np.log(2),s=1)\n",
    "\n",
    "\n",
    "axs[2,0].hist(deviatefromhalf[-1],density=False,bins=20)\n",
    "axs[2,1].hist(LN_Born_link_list[-1]/np.log(2),density=False,bins=10)\n",
    "axs[2,2].scatter(deviatefromhalf[-1],LN_Born_link_list[-1]/np.log(2),s=1)\n",
    "axs[2,2].scatter(deviatefromhalf[-1][half_index[-1]],LN_Born_link_list[-1][half_index[-1]]/np.log(2),s=1)\n",
    "\n",
    "[ax.set_title('$m={:.1f},L_x={:d},L_y={:d}$'.format(m_list[5],Lx,Ly)) for ax in axs[1]]\n",
    "[ax.set_title('$m={:.1f},L_x={:d},L_y={:d}$'.format(m_list[-1],Lx,Ly)) for ax in axs[-1]]\n",
    "[ax.set_title('$m={:.1f},L_x={:d},L_y={:d}$'.format(m_list[0],Lx,Ly)) for ax in axs[0]]\n",
    "\n",
    "[ax.set_xlabel('Filling') for ax in axs[:,0]]\n",
    "[ax.set_ylabel('Counts') for ax in axs[:,0]]\n",
    "[ax.set_xlabel('LN [log 2]') for ax in axs[:,1]]\n",
    "[ax.set_ylabel('Counts') for ax in axs[:,1]]\n",
    "[ax.set_xlabel('Filling') for ax in axs[:,2]]\n",
    "[ax.set_ylabel('LN [log 2]') for ax in axs[:,2]]\n",
    "\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/Correlation_Born_link_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,axs=plt.subplots(2,1,tight_layout=True,sharex=True)\n",
    "ax=axs[0]\n",
    "ax.errorbar(m_list,LN_Born_link_list.mean(axis=1)/np.log(2),LN_Born_link_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_list.mean(axis=1)/np.log(2),MI_Born_link_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}, [Original]'.format(es,Lx,Ly))\n",
    "\n",
    "ax=axs[1]\n",
    "ax.errorbar(m_list,LN_Born_link_half_list_ave/np.log(2),LN_Born_link_half_list_err/np.log(2),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_half_list_ave/np.log(2),MI_Born_link_half_list_err/np.log(2),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}, [Post-select half-filling]'.format(es,Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/CI_half_filling_Born_link_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.hist(deviatefromhalf[0],bins=25)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.hist(LN_Born_link_list[5],density=True,bins=10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.scatter(deviatefromhalf[5],LN_Born_link_list[5])\n",
    "plt.scatter(deviatefromhalf[5][half_index[5]],LN_Born_link_list[5][half_index[5]])\n",
    "# plt.ylim([0,1])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_link_list.mean(axis=1)/np.log(2),LN_Born_link_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_list.mean(axis=1)/np.log(2),MI_Born_link_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "# fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "outcome_Born_link_list.shape"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# labels = ['o+','o-','e+','e-']\n",
    "labels=m_list\n",
    "mean={'o+':outcome_Born_link_list[:,:,0].mean(axis=1)/(Lx*Ly//4),'o-':outcome_Born_link_list[:,:,1].mean(axis=1)/(Lx*Ly//4),'e+':outcome_Born_link_list[:,:,2].mean(axis=1)/(Lx*Ly//4),'e-':outcome_Born_link_list[:,:,3].mean(axis=1)/(Lx*Ly//4)}\n",
    "std={'o+':outcome_Born_link_list[:,:,0].std(axis=1)/(Lx*Ly//4)/np.sqrt(es-1),'o-':outcome_Born_link_list[:,:,1].std(axis=1)/(Lx*Ly//4)/np.sqrt(es-1),'e+':outcome_Born_link_list[:,:,2].std(axis=1)/(Lx*Ly//4)/np.sqrt(es-1),'e-':outcome_Born_link_list[:,:,3].std(axis=1)/(Lx*Ly//4)/np.sqrt(es-1)}\n",
    "\n",
    "ax.bar(labels,mean['o+'],0.05,label='o+',yerr=std['o+'])\n",
    "ax.bar(labels,mean['o-'],0.05,bottom=mean['o+'],label='o-',yerr=std['o-'])\n",
    "ax.bar(labels,mean['e+'],0.05,bottom=mean['o-']+mean['o+'],label='e+',yerr=std['e+'])\n",
    "ax.bar(labels,mean['e-'],0.05,bottom=mean['o-']+mean['o+']+mean['e+'],label='e-',yerr=std['e-'])\n",
    "ax.legend()\n",
    "ax.set_xlabel('m')\n",
    "ax.set_ylabel('Probability')\n",
    "# fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_outcome_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Link force to o+"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "LN_Born_link_op_list=[]\n",
    "MI_Born_link_op_list=[]\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link',prob=[1,0,0,0])\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_link_op_list.append(results[0])\n",
    "    LN_Born_link_op_list.append(results[1])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.plot(m_list,LN_Born_link_op_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_link_op_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_op_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Link force to o-"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "LN_Born_link_om_list=[]\n",
    "MI_Born_link_om_list=[]\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link',prob=[0,1,0,0])\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_link_om_list.append(results[0])\n",
    "    LN_Born_link_om_list.append(results[1])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.plot(m_list,LN_Born_link_om_list/np.log(2),'.-',label='Link (o-) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_link_om_list/np.log(2),'.-',label='Link (o-) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_om_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Link force to e+"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "LN_Born_link_ep_list=[]\n",
    "MI_Born_link_ep_list=[]\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link',prob=[0,0,1,0])\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_link_ep_list.append(results[0])\n",
    "    LN_Born_link_ep_list.append(results[1])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.plot(m_list,LN_Born_link_ep_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_link_ep_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_ep_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Link, Born but with only {o+,o-}"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,10))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_link_pool2_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_pool2_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link',pool=2)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_pool2_list[m_i,index],LN_Born_link_pool2_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_link_pool2_list.mean(axis=1)/np.log(2),LN_Born_link_pool2_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_pool2_list.mean(axis=1)/np.log(2),MI_Born_link_pool2_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_pool2_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Link, Born but with only {e+,e-}"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,10))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_link_poolm2_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_poolm2_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link',pool=-2)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_poolm2_list[m_i,index],LN_Born_link_poolm2_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_link_poolm2_list.mean(axis=1)/np.log(2),LN_Born_link_poolm2_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_poolm2_list.mean(axis=1)/np.log(2),MI_Born_link_poolm2_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_poolm2_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "## see if there is a connected path ,and that fine"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## all o+ but the last is e+"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "LN_Born_link_op_ep_list=[]\n",
    "MI_Born_link_op_ep_list=[]\n",
    "Lx,Ly=64,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range_0=params.linearize_index([np.arange(params.Lx//4)+params.Lx//4,np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    params.measure_all_Born(proj_range_0[:-1],type='link',prob=[1,0,0,0],linear=True)\n",
    "    params.measure_all_Born(proj_range_0[-1:],type='link',prob=[0,0,1,0],linear=True)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "# async_result=(map(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_link_op_ep_list.append(results[0])\n",
    "    LN_Born_link_op_ep_list.append(results[1])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.plot(m_list,LN_Born_link_op_ep_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_link_op_ep_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_op_ep_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## all o+ but the middle is e+"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "LN_Born_link_op_ep_middle_list=[]\n",
    "MI_Born_link_op_ep_middle_list=[]\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range_0=params.linearize_index([np.arange(params.Lx//4)+params.Lx//4,np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    params.measure_all_Born(proj_range_0[:len(proj_range_0)//2],type='link',prob=[1,0,0,0],linear=True)\n",
    "    params.measure_all_Born(proj_range_0[len(proj_range_0)//2:len(proj_range_0)//2+1],type='link',prob=[0,0,1,0],linear=True)\n",
    "    params.measure_all_Born(proj_range_0[len(proj_range_0)//2+1:],type='link',prob=[1,0,0,0],linear=True)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "# async_result=(rc[:].map_sync(run,inputs))\n",
    "async_result=(map(run,inputs))\n",
    "\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_link_op_ep_middle_list.append(results[0])\n",
    "    LN_Born_link_op_ep_middle_list.append(results[1])\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.plot(m_list,LN_Born_link_op_ep_middle_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_link_op_ep_middle_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_op_ep_middle_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## one chain of Ly is o+, others are e+"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## one ring is {o+,o-}, in between {e+,e-}"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_link_pool22_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_pool22_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range_0=params.linearize_index([np.arange(params.Lx//4,3*params.Lx//8),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_1=params.linearize_index([np.arange(3*params.Lx//8,3*params.Lx//8+1),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_2=params.linearize_index([np.arange(3*params.Lx//8+1,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    params.measure_all_Born(proj_range_0,type='link',pool=2,linear=True)\n",
    "    params.measure_all_Born(proj_range_1,type='link',pool=-2,linear=True)\n",
    "    params.measure_all_Born(proj_range_2,type='link',pool=2,linear=True)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_pool22_list[m_i,index],LN_Born_link_pool22_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_link_pool22_list.mean(axis=1)/np.log(2),LN_Born_link_pool22_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_pool22_list.mean(axis=1)/np.log(2),MI_Born_link_pool22_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_pool22_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## one ring is {o+,o-}, in between no measurement"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_link_pool2no2_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_pool2no2_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=64,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range_0=params.linearize_index([np.arange(params.Lx//4,3*params.Lx//8),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_1=params.linearize_index([np.arange(3*params.Lx//8,3*params.Lx//8+1),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_2=params.linearize_index([np.arange(3*params.Lx//8+1,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    params.measure_all_Born(proj_range_0,type='link',pool=2,linear=True)\n",
    "    # params.measure_all_Born(proj_range_1,type='link',pool=-2,linear=True)\n",
    "    params.measure_all_Born(proj_range_2,type='link',pool=2,linear=True)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_pool2no2_list[m_i,index],LN_Born_link_pool2no2_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_link_pool2no2_list.mean(axis=1)/np.log(2),LN_Born_link_pool2no2_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_pool2no2_list.mean(axis=1)/np.log(2),MI_Born_link_pool2no2_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_pool2no2_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## one ring is o+, in between Born"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_link_op_ep_ring_middle_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_op_ep_ring_middle_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=64,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range_0=params.linearize_index([np.arange(params.Lx//4,3*params.Lx//8),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_1=params.linearize_index([np.arange(3*params.Lx//8,3*params.Lx//8+1),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_2=params.linearize_index([np.arange(3*params.Lx//8+1,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    params.measure_all_Born(proj_range_0,type='link',prob=[1,0,0,0],linear=True)\n",
    "    params.measure_all_Born(proj_range_1,type='link',pool=-2,linear=True)\n",
    "    params.measure_all_Born(proj_range_2,type='link',prob=[1,0,0,0],linear=True)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_op_ep_ring_middle_list[m_i,index],LN_Born_link_op_ep_ring_middle_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.plot(m_list,LN_Born_link_op_ep_ring_middle_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_link_op_ep_ring_middle_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_op_ep_ring_middle_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## one ring is o+, in between no measure"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "LN_Born_link_op_ep_ring_middle_no_list=[]\n",
    "MI_Born_link_op_ep_ring_middle_no_list=[]\n",
    "Lx,Ly=64,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range_0=params.linearize_index([np.arange(params.Lx//4,3*params.Lx//8),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_1=params.linearize_index([np.arange(3*params.Lx//8,3*params.Lx//8+1),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    proj_range_2=params.linearize_index([np.arange(3*params.Lx//8+1,params.Lx//2),np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    params.measure_all_Born(proj_range_0,type='link',prob=[1,0,0,0],linear=True)\n",
    "    # params.measure_all_Born(proj_range_1,type='link',prob=[0,0,1,0],linear=True)\n",
    "    params.measure_all_Born(proj_range_2,type='link',prob=[1,0,0,0],linear=True)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "    \n",
    "inputs=[(m,Lx,Ly) for m in m_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    MI_Born_link_op_ep_ring_middle_no_list.append(results[0])\n",
    "    LN_Born_link_op_ep_ring_middle_no_list.append(results[1])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.plot(m_list,LN_Born_link_op_ep_ring_middle_no_list/np.log(2),'.-',label='Link (o+) measurement (LN)')\n",
    "ax.plot(m_list,MI_Born_link_op_ep_ring_middle_no_list/np.log(2),'.-',label='Link (o+) measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, Ly={:d}'.format(Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_op_ep_ring_middle_no_Lx{:d}_Ly{:d}.pdf'.format(Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## ignore symmetry, use four bell states"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=(lambda x:(x-2)**3+2 )(np.linspace(1,3,25))\n",
    "# m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_link_nosymm_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_nosymm_list=np.zeros((len(m_list),es))\n",
    "FN_Born_link_nosymm_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=-1,bcy=1)\n",
    "    proj_range_0=params.linearize_index([np.arange(params.Lx//4)+params.Lx//4,np.arange(0,params.Ly)],4,proj=True,k=4)\n",
    "    params.measure_all_Born(proj_range_0,type='link',linear=True,ignore=True)\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    FN=params.fermion_number()\n",
    "    return MI,LN,FN\n",
    "\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_nosymm_list[m_i,index],LN_Born_link_nosymm_list[m_i,index],FN_Born_link_nosymm_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_list,'.-',label='No measurement')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_link_nosymm_list.mean(axis=1)/np.log(2),LN_Born_link_nosymm_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_nosymm_list.mean(axis=1)/np.log(2),MI_Born_link_nosymm_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "\n",
    "# ax.errorbar(m_list,FN_Born_link_nosymm_list.mean(axis=1)/np.log(2),FN_Born_link_nosymm_list.std(axis=1)/np.log(2)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (Fermion number)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, Lx={:d}, Ly={:d}'.format(es,Lx,Ly))\n",
    "\n",
    "# ax2=ax.twinx()\n",
    "# ax2.errorbar(m_list,FN_Born_link_nosymm_list.mean(axis=1),FN_Born_link_nosymm_list.std(axis=1)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (Fermion number)',color='g')\n",
    "\n",
    "# ax2.axhline(y=128*4,ls='--')\n",
    "# ax2.set_ylabel('fermion number')\n",
    "# # ax2.set_ylim([500,514])\n",
    "\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_nosymm_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Inf onsite"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_inf_list=np.zeros((len(m_list),es))\n",
    "MI_Born_inf_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=128,4\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,dxmax=Lx,dymax=Ly,Lx=np.inf,Ly=np.inf,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)])\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # # Serial\n",
    "    # params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    # for es_i in range(es):\n",
    "    #     params=copy(params0)\n",
    "    #     params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)])\n",
    "    #     # MI_Born_inf_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "    #     LN_Born_inf_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    # Parallel\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_inf_list[m_i,index],LN_Born_inf_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "# ax.plot(m_list,LN_no_inf_list,'.-',label='No measurement (LN)')\n",
    "# ax.plot(m_list,MI_no_inf_list,'.-',label='No measurement (MI)')\n",
    "\n",
    "ax.errorbar(m_list,LN_Born_inf_list.mean(axis=1)/np.log(2),LN_Born_inf_list.std(axis=1)/np.sqrt(es-1),capsize=3,fmt='.-',label='Onsite measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_inf_list.mean(axis=1)/np.log(2),MI_Born_inf_list.std(axis=1)/np.sqrt(es-1),capsize=3,fmt='.-',label='Onsite measurement (MI)')\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, dx={:d}, dy={:d}'.format(es,Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_inf_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Inf link"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=np.linspace(1,3,10)\n",
    "es=20\n",
    "LN_Born_link_inf_list=np.zeros((len(m_list),es))\n",
    "MI_Born_link_inf_list=np.zeros((len(m_list),es))\n",
    "Lx,Ly=64,8\n",
    "def run(p):\n",
    "    m,Lx,Ly=p\n",
    "    params=Params(m=m,dxmax=Lx,dymax=Ly,Lx=np.inf,Ly=np.inf,bcx=-1,bcy=1)\n",
    "    params.measure_all_Born([np.arange(Lx//4,Lx//2),np.arange(Ly)],type='link')\n",
    "    LN=params.log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    MI=params.mutual_information_m([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)])\n",
    "    return MI,LN\n",
    "\n",
    "for m_i,m in enumerate(m_list):\n",
    "    st=time.time()\n",
    "    # # Serial\n",
    "    # params0=Params(m=m,Lx=Lx,Ly=Ly)\n",
    "    # for es_i in range(es):\n",
    "    #     params=copy(params0)\n",
    "    #     params.measure_all_Born([np.arange(params.Lx//4,params.Lx//2),np.arange(params.Ly)])\n",
    "    #     # MI_Born_link_inf_list[m_i,es_i]=params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "    #     LN_Born_link_inf_list[m_i,es_i]=params.log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])\n",
    "\n",
    "    # Parallel\n",
    "    inputs=[(m,Lx,Ly) for _ in range(es)]\n",
    "    async_result=rc[:].map_sync(run,inputs)\n",
    "\n",
    "    for index,results in enumerate(async_result):\n",
    "        MI_Born_link_inf_list[m_i,index],LN_Born_link_inf_list[m_i,index]=results\n",
    "\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.errorbar(m_list,LN_Born_link_inf_list.mean(axis=1)/np.log(2),LN_Born_link_inf_list.std(axis=1)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (LN)')\n",
    "ax.errorbar(m_list,MI_Born_link_inf_list.mean(axis=1)/np.log(2),MI_Born_link_inf_list.std(axis=1)/np.sqrt(es-1),capsize=3,fmt='.-',label='Link measurement (MI)')\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "ax.set_xlabel(r'm')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Ensemble Size={:d}, dx={:d}, dy={:d}'.format(es,Lx,Ly))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/LN_CI_Born_link_inf_En{:d}_Lx{:d}_Ly{:d}.pdf'.format(es,Lx,Ly),bbox_inches='tight')\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Scaling at m=2"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## no measurement"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import matplotlib.patches as mpatches\n",
    "def place_region(x,y,fig,ax,color='ybyk'):  \n",
    "    # fig,ax=plt.subplots()\n",
    "    arc=[mpatches.Arc((x,y),50,50,-theta,0,90,transform=None,ec=ec,lw=5) for theta,ec in zip(np.arange(4)*90,color)]\n",
    "    [fig.artists.append(arc) for arc in arc]\n",
    "    ax.text(x+25,y+25,r'$A$',transform=None,ha='center',va='center')\n",
    "    ax.text(x+25,y-25,r'$A^\\prime$',transform=None,ha='center',va='center')\n",
    "    ax.text(x-25,y-25,r'$B$',transform=None,ha='center',va='center')\n",
    "    ax.text(x-25,y+25,r'$B^\\prime$',transform=None,ha='center',va='center')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "eta_no_list=[]\n",
    "MI_no_list=[]\n",
    "LN_no_list=[]\n",
    "x_list=[]\n",
    "L=32\n",
    "params=Params(m=2,Lx=L,Ly=16)\n",
    "for ii in range(500):\n",
    "    print(ii)\n",
    "    x=sorted(np.random.choice(np.arange(1,L),3,replace=False))\n",
    "    x=[0]+x\n",
    "    while x in x_list:\n",
    "        x=sorted(np.random.choice(np.arange(1,L),3,replace=False))\n",
    "        x=[0]+x\n",
    "    x_list.append(x)\n",
    "    eta=cross_ratio(x,L)\n",
    "    eta_no_list.append(eta)\n",
    "    subregionA=[np.arange(x[0],x[1]),np.arange(params.Ly)]\n",
    "    subregionB=[np.arange(x[2],x[3]),np.arange(params.Ly)]\n",
    "    MI=params.mutual_information_m(subregionA,subregionB)    \n",
    "    MI_no_list.append(MI)\n",
    "    LN=params.log_neg(subregionA,subregionB)\n",
    "    LN_no_list.append(LN)\n",
    "eta_no_list=np.array(eta_no_list)\n",
    "MI_no_list=np.array(MI_no_list)\n",
    "LN_no_list=np.array(LN_no_list)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(eta_no_list,MI_no_list,s=1,color='b',label='L={:d}, MI'.format(L))\n",
    "ax.scatter(eta_no_list,LN_no_list,s=1,color='r',label='L={:d}, LN'.format(L))\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlim([np.min(eta_no_list),1])\n",
    "# ax.set_ylim([1e-10,1])\n",
    "\n",
    "mask_MI=(MI_no_list>1e-10)&((eta_no_list)<1)\n",
    "lr_MI=scipy.stats.linregress(np.log(eta_no_list[mask_MI]),np.log(MI_no_list[mask_MI]))\n",
    "x_int=np.linspace(np.min(eta_no_list),1,10)\n",
    "y_int=np.exp(lr_MI.slope*np.log(x_int)+lr_MI.intercept)\n",
    "ax.plot(x_int,y_int,'c',label=r'MI$\\propto\\eta^{{{:.2f}}}$'.format(lr_MI.slope))\n",
    "\n",
    "mask_LN=(LN_no_list>1e-10)&((eta_no_list)<1)\n",
    "lr_LN=scipy.stats.linregress(np.log(eta_no_list[mask_LN]),np.log(LN_no_list[mask_LN]))\n",
    "x_int=np.linspace(np.min(eta_no_list),1,10)\n",
    "y_int=np.exp(lr_LN.slope*np.log(x_int)+lr_LN.intercept)\n",
    "ax.plot(x_int,y_int,label=r'LN$\\propto\\eta^{{{:.2f}}}$'.format(lr_LN.slope),color='orange')\n",
    "ax.set_xlabel(r'$\\eta$')\n",
    "ax.legend()\n",
    "\n",
    "# place_region(200,200,fig,ax,'ykyk')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/MI_LN_no.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Born measurement"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "eta_Born_onsite_Ap_list=[]\n",
    "MI_Born_onsite_Ap_list=[]\n",
    "LN_Born_onsite_Ap_list=[]\n",
    "# x_list=[]\n",
    "# subregion_list=[]\n",
    "es=50\n",
    "L=16\n",
    "params_init=Params(m=2,Lx=L,Ly=8)\n",
    "\n",
    "for _ in range(200):\n",
    "    MI_ensemble_list=[]\n",
    "    LN_ensemble_list=[]\n",
    "    x=sorted(np.random.choice(np.arange(1,L),3,replace=False))\n",
    "    x=[0]+x\n",
    "    subregionA=[np.arange(x[0],x[1]),np.arange(params_init.Ly)]\n",
    "    subregionB=[np.arange(x[2],x[3]),np.arange(params_init.Ly)]\n",
    "    subregionAp=[np.arange(x[1],x[2]),np.arange(params_init.Ly)]\n",
    "    eta=cross_ratio(x,L)\n",
    "    for _ in range(es):\n",
    "        params=copy(params_init)\n",
    "        params.measure_all_Born(subregionAp)\n",
    "        MI_ensemble_list.append(params.mutual_information_m(subregionA,subregionB))\n",
    "        LN_ensemble_list.append(params.log_neg(subregionA,subregionB))\n",
    "\n",
    "    eta_Born_onsite_Ap_list.append(eta)\n",
    "    MI_Born_onsite_Ap_list.append(MI_ensemble_list)\n",
    "    LN_Born_onsite_Ap_list.append(LN_ensemble_list)\n",
    "eta_Born_onsite_Ap_list=np.array(eta_Born_onsite_Ap_list)\n",
    "MI_Born_onsite_Ap_list=np.array(MI_Born_onsite_Ap_list)\n",
    "LN_Born_onsite_Ap_list=np.array(LN_Born_onsite_Ap_list)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "with open('/mnt/d/kitp/Rp_SPT/8/MI_LN_CI_Born_En100_pts200_Lx32_Ly16_Ap.pickle','rb') as f:\n",
    "    [eta_Born_onsite_Ap_list,MI_Born_onsite_Ap_list,LN_Born_onsite_Ap_list]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "MI_Born_onsite_Ap_list_mean=MI_Born_onsite_Ap_list.mean(axis=1)\n",
    "LN_Born_onsite_Ap_list_mean=LN_Born_onsite_Ap_list.mean(axis=1)\n",
    "fig,ax=plt.subplots()\n",
    "ax.scatter(eta_Born_onsite_Ap_list,MI_Born_onsite_Ap_list_mean,s=1,color='b',label='L={:d}, MI'.format(L))\n",
    "ax.scatter(eta_Born_onsite_Ap_list,LN_Born_onsite_Ap_list_mean,s=1,color='r',label='L={:d}, LN'.format(L))\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlim([np.min(eta_Born_onsite_Ap_list),1])\n",
    "# ax.set_ylim([1e-10,1])\n",
    "\n",
    "mask_MI=(MI_Born_onsite_Ap_list_mean>1e-10)&((eta_Born_onsite_Ap_list)<1)\n",
    "lr_MI=scipy.stats.linregress(np.log(eta_Born_onsite_Ap_list[mask_MI]),np.log(MI_Born_onsite_Ap_list_mean[mask_MI]))\n",
    "x_int=np.linspace(np.min(eta_Born_onsite_Ap_list),1,10)\n",
    "y_int=np.exp(lr_MI.slope*np.log(x_int)+lr_MI.intercept)\n",
    "ax.plot(x_int,y_int,'c',label=r'MI$\\propto\\eta^{{{:.2f}}}$'.format(lr_MI.slope))\n",
    "\n",
    "mask_LN=(LN_Born_onsite_Ap_list_mean>1e-10)&((eta_Born_onsite_Ap_list)<1)\n",
    "lr_LN=scipy.stats.linregress(np.log(eta_Born_onsite_Ap_list[mask_LN]),np.log(LN_Born_onsite_Ap_list_mean[mask_LN]))\n",
    "x_int=np.linspace(np.min(eta_Born_onsite_Ap_list),1,10)\n",
    "y_int=np.exp(lr_LN.slope*np.log(x_int)+lr_LN.intercept)\n",
    "ax.plot(x_int,y_int,label=r'LN$\\propto\\eta^{{{:.2f}}}$'.format(lr_LN.slope),color='orange')\n",
    "ax.set_xlabel(r'$\\eta$')\n",
    "ax.legend()\n",
    "\n",
    "# place_region(200,200,fig,ax,'ycyk')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/MI_LN_Born_onsite_Ap.pdf')\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "eta_Born_onsite_ApBp_list=[]\n",
    "MI_Born_onsite_ApBp_list=[]\n",
    "LN_Born_onsite_ApBp_list=[]\n",
    "# x_list=[]\n",
    "# subregion_list=[]\n",
    "es=50\n",
    "L=32\n",
    "params_init=Params(m=2,Lx=L,Ly=8)\n",
    "\n",
    "for _ in range(200):\n",
    "    MI_ensemble_list=[]\n",
    "    LN_ensemble_list=[]\n",
    "    x=sorted(np.random.choice(np.arange(1,L),3,replace=False))\n",
    "    x=[0]+x\n",
    "    subregionA=[np.arange(x[0],x[1]),np.arange(params_init.Ly)]\n",
    "    subregionB=[np.arange(x[2],x[3]),np.arange(params_init.Ly)]\n",
    "    subregionAp=[np.arange(x[1],x[2]),np.arange(params_init.Ly)]\n",
    "    subregionBp=[np.arange(x[3],L),np.arange(params_init.Ly)]\n",
    "    eta=cross_ratio(x,L)\n",
    "    for _ in range(es):\n",
    "        params=copy(params_init)\n",
    "        params.measure_all_Born(subregionAp)\n",
    "        MI_ensemble_list.append(params.mutual_information_m(subregionA,subregionB))\n",
    "        LN_ensemble_list.append(params.log_neg(subregionA,subregionB))\n",
    "\n",
    "    eta_Born_onsite_ApBp_list.append(eta)\n",
    "    MI_Born_onsite_ApBp_list.append(MI_ensemble_list)\n",
    "    LN_Born_onsite_ApBp_list.append(LN_ensemble_list)\n",
    "eta_Born_onsite_ApBp_list=np.array(eta_Born_onsite_ApBp_list)\n",
    "MI_Born_onsite_ApBp_list=np.array(MI_Born_onsite_ApBp_list)\n",
    "LN_Born_onsite_ApBp_list=np.array(LN_Born_onsite_ApBp_list)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "with open('/mnt/d/kitp/Rp_SPT/8/MI_LN_CI_Born_En100_pts200_Lx32_Ly16_ApBp.pickle','rb') as f:\n",
    "    [eta_Born_onsite_Ap_list,MI_Born_onsite_Ap_list,LN_Born_onsite_Ap_list]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "MI_Born_onsite_ApBp_list_mean=MI_Born_onsite_ApBp_list.mean(axis=1)\n",
    "LN_Born_onsite_ApBp_list_mean=LN_Born_onsite_ApBp_list.mean(axis=1)\n",
    "fig,ax=plt.subplots()\n",
    "ax.scatter(eta_Born_onsite_ApBp_list,MI_Born_onsite_ApBp_list_mean,s=1,color='b',label='L={:d}, MI'.format(L))\n",
    "ax.scatter(eta_Born_onsite_ApBp_list,LN_Born_onsite_ApBp_list_mean,s=1,color='r',label='L={:d}, LN'.format(L))\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlim([np.min(eta_Born_onsite_ApBp_list),1])\n",
    "# ax.set_ylim([1e-10,1])\n",
    "\n",
    "mask_MI=(MI_Born_onsite_ApBp_list_mean>1e-10)&((eta_Born_onsite_ApBp_list)<1)\n",
    "lr_MI=scipy.stats.linregress(np.log(eta_Born_onsite_ApBp_list[mask_MI]),np.log(MI_Born_onsite_ApBp_list_mean[mask_MI]))\n",
    "x_int=np.linspace(np.min(eta_Born_onsite_ApBp_list),1,10)\n",
    "y_int=np.exp(lr_MI.slope*np.log(x_int)+lr_MI.intercept)\n",
    "ax.plot(x_int,y_int,'c',label=r'MI$\\propto\\eta^{{{:.2f}}}$'.format(lr_MI.slope))\n",
    "\n",
    "mask_LN=(LN_Born_onsite_ApBp_list_mean>1e-10)&((eta_Born_onsite_ApBp_list)<1)\n",
    "lr_LN=scipy.stats.linregress(np.log(eta_Born_onsite_ApBp_list[mask_LN]),np.log(LN_Born_onsite_ApBp_list_mean[mask_LN]))\n",
    "x_int=np.linspace(np.min(eta_Born_onsite_ApBp_list),1,10)\n",
    "y_int=np.exp(lr_LN.slope*np.log(x_int)+lr_LN.intercept)\n",
    "ax.plot(x_int,y_int,label=r'LN$\\propto\\eta^{{{:.2f}}}$'.format(lr_LN.slope),color='orange')\n",
    "ax.set_xlabel(r'$\\eta$')\n",
    "ax.legend()\n",
    "\n",
    "# place_region(200,200,fig,ax,'ycyc')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/MI_LN_Born_onsite_ApBp.pdf')\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Check area law of EE"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "EE_list={}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "l_EE_list=np.arange(2,32)\n",
    "for Ly in (8,12,16):\n",
    "    params=Params(m=1,Lx=32,Ly=Ly)\n",
    "    EE_list[Ly]=[(params.von_Neumann_entropy_f([np.arange(l),np.arange(params.Ly)])) for l in l_EE_list]\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for Ly in (8,12,16):\n",
    "    ax.plot(l_EE_list,EE_list[Ly]/np.log(2),'.',label='Ly={:.1f}'.format(Ly))\n",
    "ax.set_xlabel(r'$L_x$')\n",
    "ax.set_ylabel('EE [log 2]')\n",
    "ax.legend()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for Ly in (8,12,16):\n",
    "    ax.plot(l_EE_list,EE_list[Ly]/np.log(2),'.',label='Ly={:.1f}'.format(Ly))\n",
    "ax.set_xlabel(r'$L_x$')\n",
    "ax.set_ylabel('EE [log 2]')\n",
    "ax.legend()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# LN scaling behavior"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_scaling_list={}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## fix dist, |A|_x, increase |A|_y"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Ay={}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Ly_list=np.arange(1,16)\n",
    "Lx=32\n",
    "LN_Ay=[Params(m=1,Lx=Lx,Ly=Ly).log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)]) for Ly in Ly_list]\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(Ly_list,LN_Ay/np.log(2),'.',label=r'$L_x={:d}$'.format(Lx),color='b')\n",
    "ax.set_xlabel(r'$L_y$')\n",
    "ax.set_ylabel(r'LN [log 2]')\n",
    "lr=scipy.stats.linregress(Ly_list,LN_Ay/np.log(2))\n",
    "xfit=Ly_list[[0,-1]]\n",
    "ax.plot(xfit,lr.slope*xfit+lr.intercept,'c',label=r'LN={:e}~$L_y$+ {:e}'.format(lr.slope,lr.intercept))\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('//mnt/d/kitp/Rp_SPT/8/LN_Ay.pdf',bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## fix dist, |A|_y, increase |A|_x"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Lx_list=np.arange(8,20)\n",
    "Ly=32\n",
    "dist=8\n",
    "LN_Ax=[]\n",
    "\n",
    "def run(p):\n",
    "    Lx,Ly,dist=p\n",
    "    return Params(m=0,Lx=2*Lx+2*dist,Ly=Ly).log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+dist+Lx,np.arange(Ly)])\n",
    "\n",
    "inputs=[(Lx,Ly,dist) for Lx in Lx_list]\n",
    "async_result=(rc[:].map_sync(run,inputs))\n",
    "\n",
    "for index,results in enumerate(async_result):\n",
    "    LN_Ax.append(results)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Lx_list=np.arange(2,20)\n",
    "Ly=32\n",
    "dist=8\n",
    "LN_Ax=[Params(m=1,Lx=2*Lx+2*dist,Ly=Ly).log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+dist+Lx,np.arange(Ly)]) for Lx in Lx_list]\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(Lx_list,LN_Ax/np.log(2),'.',label=r'$L_y={:d}$'.format(Ly),color='b')\n",
    "ax.set_xlabel(r'$A_x$')\n",
    "ax.set_ylabel(r'LN [log 2]')\n",
    "lr=scipy.stats.linregress(Lx_list,LN_Ax/np.log(2))\n",
    "xfit=Lx_list[[0,-1]]\n",
    "ax.plot(xfit,lr.slope*xfit+lr.intercept,'c',label=r'LN={:e}~$L_x$+ {:e}'.format(lr.slope,lr.intercept))\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('//mnt/d/kitp/Rp_SPT/8/LN_Ax.pdf',bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## fix |A|_x, |A|_y, increase dist "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "dist_list=np.arange(2,16)\n",
    "Lx=32\n",
    "Ly=4\n",
    "dist=8\n",
    "# LN_dist=[Params(m=1,Lx=2*Lx+2*dist,Ly=Ly).log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+dist+Lx,np.arange(Ly)]) for dist in dist_list]\n",
    "LN_dist=[]\n",
    "for dist in dist_list:\n",
    "    st=time.time()\n",
    "    LN_dist.append(Params(m=1,Lx=2*Lx+2*dist,Ly=Ly).log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+dist+Lx,np.arange(Ly)]))\n",
    "    print(\"{:d}:{:.2f}\".format(dist,time.time()-st))\n",
    "    with open('dist.pickle','wb') as f:\n",
    "        pickle.dump([dist_list,LN_dist],f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "dist_list=np.array(dist_list)\n",
    "LN_dist=np.array(LN_dist)\n",
    "fig,ax=plt.subplots(3,1,figsize=(6.8,8),tight_layout=True)\n",
    "ax[0].plot((dist_list),(LN_dist/np.log(2)),'.')\n",
    "ax[0].set_title('Linear(x)-Linear(y)')\n",
    "ax[1].plot((dist_list),(LN_dist/np.log(2)),'.')\n",
    "ax[1].set_yscale('log')\n",
    "ax[1].set_title('Linear(x)-Log(y)')\n",
    "\n",
    "ax[2].plot((dist_list),(LN_dist/np.log(2)),'.')\n",
    "ax[2].set_xscale('log')\n",
    "ax[2].set_yscale('log')\n",
    "ax[2].set_title('Log(x)-Log(y)')\n",
    "\n",
    "ax[-1].set_xlabel(r'$A^\\prime_x$')\n",
    "[ax.set_ylabel(r'LN [log 2]') for ax in ax]\n",
    "\n",
    "mask=dist_list<10\n",
    "lr=scipy.stats.linregress((dist_list[mask]),np.log(LN_dist[mask]/np.log(2)))\n",
    "xfit=dist_list[mask]\n",
    "ax[1].plot(xfit,np.exp(lr.slope*xfit+lr.intercept),'c',label=r'LN=exp({:.4f}~d+ {:.4f}) $~R^2={:.4f}$'.format(lr.slope,lr.intercept,lr.rvalue))\n",
    "ax[1].legend()\n",
    "mask=dist_list<10\n",
    "lr=scipy.stats.linregress(np.log(dist_list[mask]),np.log(LN_dist[mask]/np.log(2)))\n",
    "xfit=dist_list[mask]\n",
    "ax[2].plot(xfit,np.exp(lr.slope*np.log(xfit)+lr.intercept),'c',label=r'LN=exp({:.4f}~log(d)+ {:.4f}) $~R^2={:.4f}$'.format(lr.slope,lr.intercept,lr.rvalue))# ax[0].legend()\n",
    "ax[2].legend()\n",
    "\n",
    "fig.savefig('//mnt/d/kitp/Rp_SPT/8/LN_dist.pdf',bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.plot(xfit,np.exp(lr.slope*xfit+lr.intercept))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "lr.rvalue"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(dist_list,LN_dist,'.')\n",
    "# ax.set_xscale('log')\n",
    "ax.set_yscale('log')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Ly_list=np.arange(2,16)\n",
    "Lx_list=np.arange(2,8)\n",
    "LN_scaling_list=np.zeros((Lx_list.shape[0],Ly_list.shape[0]))\n",
    "for Ly_i,Ly in enumerate(Ly_list):\n",
    "    for Lx_i,Lx in enumerate(Lx_list):\n",
    "        params=Params(m=1,Lx=4*Lx,Ly=Ly)\n",
    "        LN_scaling_list[Lx_i,Ly_i]=params.log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+2*Lx,np.arange(Ly)])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Lx_map,Ly_map=np.meshgrid(Lx_list,Ly_list)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.plot()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.plot((Ly_map/Lx_map).flatten(),LN_scaling_list.flatten(),'.')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for Ly_i,Ly in enumerate(Ly_list):\n",
    "    ax.plot(Lx_list,LN_scaling_list[:,Ly_i],'.-',label='Ly={:d}'.format(Ly))\n",
    "ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "ax.legend()\n",
    "ax.set_xlabel('$L_x$')\n",
    "ax.set_ylabel('log neg')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for Ly_i,Ly in enumerate(Ly_list):\n",
    "    ax.plot(Lx_list,LN_scaling_list[:,Ly_i],'.-',label='Ly={:d}'.format(Ly))\n",
    "ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "ax.legend()\n",
    "ax.set_xlabel('$L_x$')\n",
    "ax.set_ylabel('log neg')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(Ly_list,LN_scaling_list[1,:])\n",
    "ax.plot(Ly_list,LN_scaling_list[2,:])\n",
    "ax.plot(Ly_list,LN_scaling_list[3,:])\n",
    "ax.plot(Ly_list,LN_scaling_list[4,:])\n",
    "ax.plot(Ly_list,LN_scaling_list[5,:])\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "l_LN_list=np.arange(2,8)\n",
    "for Ly in (8,12,16):\n",
    "    LN_scaling_list[Ly]=[]\n",
    "    for Lx in l_LN_list:\n",
    "        params=Params(m=1,Lx=4*Lx,Ly=Ly)\n",
    "        LN_scaling_list[Ly].append(params.log_neg([np.arange(Lx),np.arange(params.Ly)],[np.arange(Lx)+2*Lx,np.arange(params.Ly)]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for k,v in LN_scaling_list.items():\n",
    "    ax.plot(np.log(l_LN_list),np.log(v/np.log(2)),'.',label='Ly={}'.format(k))\n",
    "ax.legend()\n",
    "ax.set_xlabel(r'$L_x$')\n",
    "ax.set_ylabel('Log neg [log 2]')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for k,v in LN_scaling_list.items():\n",
    "    ax.plot(l_LN_list,v/np.log(2),label='Ly={}'.format(k))\n",
    "ax.legend()\n",
    "ax.set_xlabel(r'$L_x$')\n",
    "ax.set_ylabel('Log neg [log 2]')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for k,v in LN_scaling_list.items():\n",
    "    ax.plot(l_LN_list,v/np.log(2),label='Ly={}'.format(k))\n",
    "ax.legend()\n",
    "ax.set_xlabel(r'$L_x$')\n",
    "ax.set_ylabel('Log neg [log 2]')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Scaling of states to the GS"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m,Lx=1,64\n",
    "# '/mnt/d/KITP/Rp_SPT/8/'\n",
    "with open('/mnt/d/KITP/Rp_SPT/8/CI_scaling_m{:d}_Lx{:d}.pickle'.format(m,Lx),'rb') as f:\n",
    "    [Ly_list,MI_Born_scaling_list,LN_Born_scaling_list]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(Ly_list,LN_Born_scaling_list/np.log(2),'.-',label='Onsite measurement (LN)')\n",
    "ax.plot(Ly_list,MI_Born_scaling_list/np.log(2),'.-',label='Onsite measurement (MI)')\n",
    "\n",
    "ax.legend()\n",
    "ax.grid('on')\n",
    "# ax.set_yticks([0,0.5,1])\n",
    "ax.set_xlabel(r'$L_y$')\n",
    "ax.set_ylabel(r'log neg [log(2)]')\n",
    "ax.margins(x=0)\n",
    "ax.set_title('Lx={:d}, m={:d}'.format(Lx,m))\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/CI_scaling_m{:d}_Lx{:d}.pdf'.format(m,Lx),bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Infinite "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## fix dist, |A|_x, increase |A|_y"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Ay={}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Ly_list=np.arange(2,32)\n",
    "Lx=8\n",
    "LN_inf_Ay=[Params(m=1,dxmax=Lx,dymax=Ly,Lx=np.inf,Ly=np.inf).log_neg([np.arange(Lx//4),np.arange(Ly)],[np.arange(Lx//4)+Lx//2,np.arange(Ly)]) for Ly in Ly_list]\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(Ly_list,LN_inf_Ay/np.log(2),'.',label=r'$L_x={:d}$'.format(Lx),color='b')\n",
    "ax.set_xlabel(r'$L_y$')\n",
    "ax.set_ylabel(r'LN [log 2]')\n",
    "lr=scipy.stats.linregress(Ly_list,LN_inf_Ay/np.log(2))\n",
    "xfit=Ly_list[[0,-1]]\n",
    "ax.plot(xfit,lr.slope*xfit+lr.intercept,'c',label=r'LN={:.4f}~$L_y$+ {:.4f}'.format(lr.slope,lr.intercept))\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_inf_Ay.pdf',bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## fix dist, |A|_y, increase |A|_x"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Lx_list=np.arange(2,20)\n",
    "Ly=32\n",
    "dist=8\n",
    "LN_inf_Ax=[Params(m=1,dxmax=2*Lx+2*dist,dymax=Ly,Lx=np.inf,Ly=np.inf).log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+dist+Lx,np.arange(Ly)]) for Lx in Lx_list]\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.plot(Lx_list,LN_inf_Ax/np.log(2),'.',label=r'$L_y={:d}$'.format(Ly),color='b')\n",
    "ax.set_xlabel(r'$A_x$')\n",
    "ax.set_ylabel(r'LN [log 2]')\n",
    "lr=scipy.stats.linregress(Lx_list,LN_inf_Ax/np.log(2))\n",
    "xfit=Lx_list[[0,-1]]\n",
    "ax.plot(xfit,lr.slope*xfit+lr.intercept,'c',label=r'LN={:e}~$L_x$+ {:e}'.format(lr.slope,lr.intercept))\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_inf_Ax.pdf',bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## fix |A|_x, |A|_y, increase dist "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "dist_list=np.arange(2,16)\n",
    "Lx=8\n",
    "Ly=32\n",
    "dist=8\n",
    "# LN_inf_dist=[Params(m=1,Lx=2*Lx+2*dist,Ly=Ly).log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+dist+Lx,np.arange(Ly)]) for dist in dist_list]\n",
    "LN_inf_dist=[]\n",
    "for dist in dist_list:\n",
    "    st=time.time()\n",
    "    LN_inf_dist.append(Params(m=1,Lx=2*Lx+2*dist,Ly=Ly).log_neg([np.arange(Lx),np.arange(Ly)],[np.arange(Lx)+dist+Lx,np.arange(Ly)]))\n",
    "    print(\"{:d}:{:.2f}\".format(dist,time.time()-st))\n",
    "    # with open('dist.pickle','wb') as f:\n",
    "    #     pickle.dump([dist_list,LN_inf_dist],f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "dist_list=np.array(dist_list)\n",
    "LN_inf_dist=np.array(LN_inf_dist)\n",
    "fig,ax=plt.subplots(3,1,figsize=(6.8,8),tight_layout=True)\n",
    "ax[0].plot((dist_list),(LN_inf_dist/np.log(2)),'.')\n",
    "ax[0].set_title('Linear(x)-Linear(y)')\n",
    "ax[1].plot((dist_list),(LN_inf_dist/np.log(2)),'.')\n",
    "ax[1].set_yscale('log')\n",
    "ax[1].set_title('Linear(x)-Log(y)')\n",
    "\n",
    "ax[2].plot((dist_list),(LN_inf_dist/np.log(2)),'.')\n",
    "ax[2].set_xscale('log')\n",
    "ax[2].set_yscale('log')\n",
    "ax[2].set_title('Log(x)-Log(y)')\n",
    "\n",
    "ax[-1].set_xlabel(r'$A^\\prime_x$')\n",
    "[ax.set_ylabel(r'LN [log 2]') for ax in ax]\n",
    "\n",
    "mask=dist_list<10\n",
    "lr=scipy.stats.linregress((dist_list[mask]),np.log(LN_inf_dist[mask]/np.log(2)))\n",
    "xfit=dist_list[mask]\n",
    "ax[1].plot(xfit,np.exp(lr.slope*xfit+lr.intercept),'c',label=r'LN=exp({:.4f}~d+ {:.4f}) $~R^2={:.4f}$'.format(lr.slope,lr.intercept,lr.rvalue))\n",
    "ax[1].legend()\n",
    "mask=dist_list<10\n",
    "lr=scipy.stats.linregress(np.log(dist_list[mask]),np.log(LN_inf_dist[mask]/np.log(2)))\n",
    "xfit=dist_list[mask]\n",
    "ax[2].plot(xfit,np.exp(lr.slope*np.log(xfit)+lr.intercept),'c',label=r'LN=exp({:.4f}~log(d)+ {:.4f}) $~R^2={:.4f}$'.format(lr.slope,lr.intercept,lr.rvalue))# ax[0].legend()\n",
    "ax[2].legend()\n",
    "\n",
    "fig.savefig('//mnt/d/kitp/Rp_SPT/8/LN_inf_dist.pdf',bbox_inches='tight')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Measurement based on geometry\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## A inside A' inside B"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "source": [
    "Lx,Ly=16,16\n",
    "params=Params(m=1,Lx=Lx,Ly=Ly)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "source": [
    "proj_total_A=params.linearize_index([np.arange(params.Lx//8*3,params.Lx//8*5),np.arange(params.Ly//8*3,params.Ly//8*5)],4,proj=True,k=2)\n",
    "proj_total_A_Ap=params.linearize_index([np.arange(params.Lx//4,params.Lx//4*3),np.arange(params.Ly//4,params.Ly//4*3)],4,proj=True,k=2)\n",
    "proj_total_Ap=np.setdiff1d(proj_total_A_Ap,proj_total_A)\n",
    "\n",
    "sub_A=params.linearize_index([np.arange(params.Lx//8*3,params.Lx//8*5),np.arange(params.Ly//8*3,params.Ly//8*5)],4)\n",
    "sub_A_Ap=params.linearize_index([np.arange(params.Lx//4,params.Lx//4*3),np.arange(params.Ly//4,params.Ly//4*3)],4)\n",
    "sub_A_Ap_B=params.linearize_index([np.arange(params.Lx//8,params.Lx//8*7),np.arange(params.Ly//8,params.Ly//8*7)],4)\n",
    "sub_B=np.setdiff1d(sub_A_Ap_B,sub_A_Ap)\n",
    "\n",
    "# poke a hole\n",
    "sub_A0=params.linearize_index([np.arange(params.Lx//16*7,params.Lx//16*9),np.arange(params.Ly//16*7,params.Ly//16*9)],4)\n",
    "sub_A=np.setdiff1d(sub_A,sub_A0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "source": [
    "proj_total_Ap.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(96,)"
      ]
     },
     "metadata": {},
     "execution_count": 129
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "source": [
    "fig,ax=plt.subplots()\n",
    "\n",
    "ix,iy=params.square_index(sub_B)\n",
    "ax.scatter(ix,iy)\n",
    "\n",
    "ix,iy=params.square_index(sub_A)\n",
    "ax.scatter(ix,iy)\n",
    "\n",
    "ix,iy=params.square_index(proj_total_Ap)\n",
    "ax.scatter(ix,iy)\n",
    "ax.set_xlim([0,Lx-1])\n",
    "ax.set_ylim([0,Ly-1])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.0, 15.0)"
      ]
     },
     "metadata": {},
     "execution_count": 130
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "source": [
    "def run(inputs):\n",
    "    Lx,Ly,m=inputs\n",
    "    # Lx,Ly,m,bcx,bcy=inputs\n",
    "    bcx=0\n",
    "    bcy=0\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=bcx,bcy=bcy)\n",
    "    proj_total_A=params.linearize_index([np.arange(params.Lx//8*3,params.Lx//8*5),np.arange(params.Ly//8*3,params.Ly//8*5)],4,proj=True,k=4)\n",
    "    proj_total_A_Ap=params.linearize_index([np.arange(params.Lx//4,params.Lx//4*3),np.arange(params.Ly//4,params.Ly//4*3)],4,proj=True,k=4)\n",
    "    proj_total_Ap=np.setdiff1d(proj_total_A_Ap,proj_total_A)\n",
    "\n",
    "    sub_A=params.linearize_index([np.arange(params.Lx//8*3,params.Lx//8*5),np.arange(params.Ly//8*3,params.Ly//8*5)],4)\n",
    "    sub_A_Ap=params.linearize_index([np.arange(params.Lx//4,params.Lx//4*3),np.arange(params.Ly//4,params.Ly//4*3)],4)\n",
    "    sub_A_Ap_B=params.linearize_index([np.arange(params.Lx//8,params.Lx//8*7),np.arange(params.Ly//8,params.Ly//8*7)],4)\n",
    "    sub_B=np.setdiff1d(sub_A_Ap_B,sub_A_Ap)\n",
    "    #poke a hole\n",
    "    sub_A0=params.linearize_index([np.arange(params.Lx//16*7,params.Lx//16*9),np.arange(params.Ly//16*7,params.Ly//16*9)],4)\n",
    "    sub_A=np.setdiff1d(sub_A,sub_A0)\n",
    "    \n",
    "    # ,bcx=1,bcy=1\n",
    "    # params.measure_all_Born(proj_total_Ap,linear=True,type='correlated')\n",
    "    params.measure_all_Born(proj_total_Ap,linear=True,type='correlated',prob=[1,0])\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "source": [
    "run((16,16,1))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3.174396283431312"
      ]
     },
     "metadata": {},
     "execution_count": 172
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "source": [
    "pool=rc[:].map_"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "source": [
    "m_list=np.linspace(1,3,6)\n",
    "# m_list=[1,2,3]\n",
    "\n",
    "es=1\n",
    "LN_Born_list=np.zeros((len(m_list),es))\n",
    "MI_Born_list=np.zeros((len(m_list),es))\n",
    "\n",
    "LN_list=np.zeros((len(m_list),es))\n",
    "Lx=16\n",
    "Ly=16\n",
    "for m_i,m in enumerate(m_list):\n",
    "    inputs=[(Lx,Ly,m) for _ in range(es)]\n",
    "    pool=(rc[:].map_sync(run,inputs))\n",
    "    for es_i,result in enumerate(pool):\n",
    "        LN=result\n",
    "        LN_list[m_i,es_i]=LN"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "source": [
    "plt.plot(m_list,LN_list.mean(axis=1),'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1581888df0>]"
      ]
     },
     "metadata": {},
     "execution_count": 141
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "source": [
    "plt.plot(m_list,LN_list.mean(axis=1))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1581e47be0>]"
      ]
     },
     "metadata": {},
     "execution_count": 70
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "source": [
    "def run2(Lx,Ly):\n",
    "    m_list=np.linspace(1,3,6)\n",
    "\n",
    "    es=1\n",
    "    LN_Born_list=np.zeros((len(m_list),es))\n",
    "    MI_Born_list=np.zeros((len(m_list),es))\n",
    "\n",
    "    LN_list=np.zeros((len(m_list),es))\n",
    "    for m_i,m in enumerate(m_list):\n",
    "        inputs=[(Lx,Ly,m) for _ in range(es)]\n",
    "        pool=(rc[:].map_sync(run,inputs))\n",
    "        for es_i,result in enumerate(pool):\n",
    "            LN=result\n",
    "            LN_list[m_i,es_i]=LN\n",
    "    return LN_list"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "def run2_es(Lx,Ly,es):\n",
    "    m_list=np.linspace(1,3,6)\n",
    "\n",
    "    # es=1\n",
    "    LN_Born_list=np.zeros((len(m_list),es))\n",
    "    MI_Born_list=np.zeros((len(m_list),es))\n",
    "\n",
    "    LN_list=np.zeros((len(m_list),es))\n",
    "    for m_i,m in enumerate(m_list):\n",
    "        inputs=[(Lx,Ly,m) for _ in range(es)]\n",
    "        pool=(rc[:].map_sync(run,inputs))\n",
    "        for es_i,result in enumerate(pool):\n",
    "            LN=result\n",
    "            LN_list[m_i,es_i]=LN\n",
    "    return LN_list"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "source": [
    "LN_16_16_list=run2(16,16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "LN_16_16_20_list=run2_es(16,16,20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "plt.plot(m_list,LN_16_16_20_list.mean(axis=1),'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0abaac4340>]"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.plot(m_list,LN_16_16_list,'.')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f15816658e0>]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "source": [
    "LN_24_24_list=run2(24,24)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "LN_24_24_20_list=run2_es(24,24,20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "source": [
    "plt.plot(m_list,LN_24_24_list,'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f15815cae50>]"
      ]
     },
     "metadata": {},
     "execution_count": 170
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "plt.plot(m_list,LN_24_24_20_list.mean(axis=1),'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0abaa04df0>]"
      ]
     },
     "metadata": {},
     "execution_count": 14
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "source": [
    "LN_16_8_list=run2(16,8)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "source": [
    "plt.plot(m_list,LN_16_8_list,'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f15817a0c10>]"
      ]
     },
     "metadata": {},
     "execution_count": 158
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "source": [
    "LN_24_8_list=run2(24,8)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "source": [
    "plt.plot(m_list,LN_24_8_list,'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f15816fa820>]"
      ]
     },
     "metadata": {},
     "execution_count": 160
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "source": [
    "LN_32_32_list=run2(32,32)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "source": [
    "plt.plot(m_list,LN_32_32_list,'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f15816e1790>]"
      ]
     },
     "metadata": {},
     "execution_count": 162
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "##  A // B inside C"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "Lx,Ly=32,16\n",
    "params=Params(m=1,Lx=Lx,Ly=Ly)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_A=params.linearize_index([np.arange(params.Lx//8,params.Lx//8*3),np.arange(params.Ly//4,params.Ly//2)],4,proj=True,k=4)\n",
    "proj_total_B=params.linearize_index([np.arange(params.Lx//8*5,params.Lx//8*7),np.arange(params.Ly//4,params.Ly//2)],4,proj=True,k=4)\n",
    "proj_total_Ap_A_B=params.linearize_index([np.arange(0,params.Lx),np.arange(0,params.Ly//4*3)],4,proj=True,k=4)\n",
    "proj_total_Ap=np.setdiff1d(proj_total_Ap_A_B,np.hstack((proj_total_A,proj_total_B)))\n",
    "\n",
    "sub_A=params.linearize_index([np.arange(params.Lx//8,params.Lx//8*3),np.arange(params.Ly//4,params.Ly//2)],4)\n",
    "sub_B=params.linearize_index([np.arange(params.Lx//8*5,params.Lx//8*7),np.arange(params.Ly//4,params.Ly//2)],4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "\n",
    "ix,iy=params.square_index(sub_B)\n",
    "ax.scatter(ix,iy)\n",
    "\n",
    "ix,iy=params.square_index(sub_A)\n",
    "ax.scatter(ix,iy)\n",
    "\n",
    "ix,iy=params.square_index(proj_total_Ap)\n",
    "ax.scatter(ix,iy)\n",
    "ax.set_xlim([0,Lx-1])\n",
    "ax.set_ylim([0,Ly-1])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "source": [
    "def run(inputs):\n",
    "    Lx,Ly,m=inputs\n",
    "    # Lx,Ly,m,bcx,bcy=inputs\n",
    "    bcx=1\n",
    "    bcy=1\n",
    "    params=Params(m=m,Lx=Lx,Ly=Ly,bcx=bcx,bcy=bcy)\n",
    "    proj_total_A=params.linearize_index([np.arange(params.Lx//8,params.Lx//8*3),np.arange(params.Ly//4,params.Ly//2)],4,proj=True,k=4)\n",
    "    proj_total_B=params.linearize_index([np.arange(params.Lx//8*5,params.Lx//8*7),np.arange(params.Ly//4,params.Ly//2)],4,proj=True,k=4)\n",
    "    proj_total_Ap_A_B=params.linearize_index([np.arange(0,params.Lx),np.arange(0,params.Ly//4*3)],4,proj=True,k=4)\n",
    "    proj_total_Ap=np.setdiff1d(proj_total_Ap_A_B,np.hstack((proj_total_A,proj_total_B)))\n",
    "\n",
    "    sub_A=params.linearize_index([np.arange(params.Lx//8,params.Lx//8*3),np.arange(params.Ly//4,params.Ly//2)],4)\n",
    "    sub_B=params.linearize_index([np.arange(params.Lx//8*5,params.Lx//8*7),np.arange(params.Ly//4,params.Ly//2)],4)\n",
    "    \n",
    "    # ,bcx=1,bcy=1\n",
    "    # params.measure_all_Born(proj_total_Ap,linear=True,type='correlated')\n",
    "    params.measure_all_Born(proj_total_Ap,linear=True,type='correlated',prob=[1,0])\n",
    "\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "source": [
    "run((32,16,1))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.2640355317503018"
      ]
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list=np.linspace(1,3,11)\n",
    "# m_list=[1,2,3]\n",
    "\n",
    "es=10\n",
    "LN_Born_list=np.zeros((len(m_list),es))\n",
    "MI_Born_list=np.zeros((len(m_list),es))\n",
    "\n",
    "LN_list=np.zeros((len(m_list),es))\n",
    "Lx=32\n",
    "Ly=16\n",
    "for m_i,m in enumerate(m_list):\n",
    "    inputs=[(Lx,Ly,m) for _ in range(es)]\n",
    "    pool=(rc[:].map_sync(run,inputs))\n",
    "    for es_i,result in enumerate(pool):\n",
    "        LN=result\n",
    "        LN_list[m_i,es_i]=LN"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "plt.plot(m_list,LN_list.mean(axis=1),'.')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## load from pickle"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### A in A' in B"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "m_Born_g1_list={}\n",
    "MI_Born_g1_list={}\n",
    "LN_Born_g1_list={}\n",
    "m_Force_g1_list={}\n",
    "MI_Force_g1_list={}\n",
    "LN_Force_g1_list={}\n",
    "m_g1_list={}\n",
    "MI_g1_list={}\n",
    "LN_g1_list={}\n",
    "\n",
    "for Lx,Ly in zip([16,24,32],[16,24,32]):\n",
    "    with open('/mnt/d/kitp/Rp_SPT/8/CI_Born_En30_Lx{:}_Ly{:}_geo1_Born.pickle'.format(Lx,Ly),'rb') as f:\n",
    "        [m_Born_g1_list[(Lx,Ly)],MI_Born_g1_list[(Lx,Ly)],LN_Born_g1_list[(Lx,Ly)]]=pickle.load(f)\n",
    "    with open('/mnt/d/kitp/Rp_SPT/8/CI_Born_En1_Lx{:}_Ly{:}_geo1_Force.pickle'.format(Lx,Ly),'rb') as f:\n",
    "        [m_Force_g1_list[(Lx,Ly)],MI_Force_g1_list[(Lx,Ly)],LN_Force_g1_list[(Lx,Ly)]]=pickle.load(f)\n",
    "    with open('/mnt/d/kitp/Rp_SPT/8/CI_Born_En1_Lx{:}_Ly{:}_geo1_.pickle'.format(Lx,Ly),'rb') as f:\n",
    "        [m_g1_list[(Lx,Ly)],MI_g1_list[(Lx,Ly)],LN_g1_list[(Lx,Ly)]]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "source": [
    "fig,axs=plt.subplots(3,1,figsize=(6,6),sharex=True)\n",
    "ax=axs[0]\n",
    "for Lx,Ly in zip([16,24,32],[16,24,32]):\n",
    "    ax.plot(m_g1_list[(Lx,Ly)],LN_g1_list[(Lx,Ly)].mean(axis=1)/np.log(2),'.-',label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_ylabel('LN[log 2]')\n",
    "ax.set_title('No measure')\n",
    "ax=axs[1]\n",
    "for Lx,Ly in zip([16,24,32],[16,24,32]):\n",
    "    ax.plot(m_Force_g1_list[(Lx,Ly)],LN_Force_g1_list[(Lx,Ly)].mean(axis=1)/np.log(2),'.-',label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_ylabel('LN[log 2]')\n",
    "ax.set_title(r'Force GS: $|01\\rangle$')\n",
    "\n",
    "ax=axs[2]\n",
    "for Lx,Ly in zip([16,24,32],[16,24,32]):\n",
    "    ax.errorbar(m_Born_g1_list[(Lx,Ly)],LN_Born_g1_list[(Lx,Ly)].mean(axis=1)/np.log(2),LN_Born_g1_list[(Lx,Ly)].std(axis=1)/np.sqrt(20)/np.log(2),fmt='.-',capsize=3,label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_title(r'Born: $|01\\rangle$,$|10\\rangle$')\n",
    "\n",
    "ax.set_xlabel('m')\n",
    "ax.set_ylabel('LN[log 2]')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/Geo1.pdf')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "m_Force_g1_list={}\n",
    "MI_Force_g1_list={}\n",
    "LN_Force_g1_list={}\n",
    "\n",
    "for Lx,Ly in zip([16,24,32,48,64],[16,24,32,48,64]):\n",
    "    with open('/mnt/d/kitp/Rp_SPT/8/CI_Born_En1_Lx{:}_Ly{:}_geo1_Force.pickle'.format(Lx,Ly),'rb') as f:\n",
    "        [m_Force_g1_list[(Lx,Ly)],MI_Force_g1_list[(Lx,Ly)],LN_Force_g1_list[(Lx,Ly)]]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "fig,ax=plt.subplots()\n",
    "for Lx,Ly in zip([16,24,32,48,64],[16,24,32,48,64]):\n",
    "    ax.plot(m_Force_g1_list[(Lx,Ly)],LN_Force_g1_list[(Lx,Ly)].mean(axis=1)/np.log(2),'.-',label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_ylabel('LN[log 2]')\n",
    "ax.set_title(r'Force GS: $|01\\rangle$')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Force GS: $|01\\\\rangle$')"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "(ln_m1_list.flatten())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([3.25133863, 3.50846154, 3.57914904, 3.62836738, 3.67262138])"
      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "6*np.log(2)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "4.1588830833596715"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "source": [
    "fig,axs=plt.subplots(1,2)\n",
    "L_list=np.array([16,24,32,48,64])\n",
    "\n",
    "ln_m1_list=np.array([LN_Force_g1_list[(Lx,Lx)][0] for Lx in L_list])\n",
    "ln_m3_list=np.array([LN_Force_g1_list[(Lx,Lx)][-1] for Lx in L_list])\n",
    "\n",
    "ax=axs[0]\n",
    "ax.plot((L_list),ln_m1_list,'.')\n",
    "# ax.set_xscale('log')\n",
    "ax.set_title('m=1')\n",
    "ax.set_ylabel('LN [log2]')\n",
    "ax.set_xlabel('$L$')\n",
    "\n",
    "ax=axs[1]\n",
    "ax.plot(L_list,ln_m3_list,'.')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('$L$')\n",
    "ax.set_title('m=3')\n",
    "lr=scipy.stats.linregress(L_list,np.log(ln_m3_list.flatten()))\n",
    "ax.plot(L_list,np.exp(lr.slope*np.array(L_list)+lr.intercept),'r',label='LN$\\sim \\exp({:.2f}L)$'.format(lr.slope))\n",
    "ax.legend()\n",
    "fig.savefig('/mnt/d/KITP/Rp_SPT/8/Geo1_scaling.pdf')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "source": [
    "plt.plot(np.log(L_list),ln_m1_list,'.')\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4816d1e340>]"
      ]
     },
     "metadata": {},
     "execution_count": 119
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "source": [
    "lr=scipy.stats.linregress(L_list,np.log(ln_m3_list))\n"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 5 and the array at index 1 has size 1",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_7243/579536116.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinregress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mln_m3_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/scipy/stats/_stats_mstats_common.py\u001b[0m in \u001b[0;36mlinregress\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;31m#   ssxm = mean( (x-mean(x))^2 )\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;31m#   ssxym = mean( (x-mean(x)) * (y-mean(y)) )\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m     \u001b[0mssxm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mssxym\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mssym\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[0;31m# R-value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcov\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[1;32m   2413\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mrowvar\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2414\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2415\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2417\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mddof\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 5 and the array at index 1 has size 1"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "source": [
    "x=np.linspace(16,64)\n",
    "y=3*np.log(x)+1"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "source": [
    "plt.plot(np.log(x),y)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4818af8670>]"
      ]
     },
     "metadata": {},
     "execution_count": 88
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "scipy.optimize.curve_fit(lambda )"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "source": [
    "x_list=np.linspace(3.687,3.689,11)\n",
    "lr=[scipy.stats.linregress(L_list,np.log(x-ln_m1_list).flatten()) for x in x_list]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "source": [
    "rsq_list=[lr.rvalue for lr in lr]\n",
    "plt.plot(x_list,rsq_list,'.')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4818f358b0>]"
      ]
     },
     "metadata": {},
     "execution_count": 72
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 489.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "source": [
    "x_list[np.argmin(rsq_list)]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3.6877999999999997"
      ]
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "np.exp(lr.intercept)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1.1171243966861173"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### A // B in A'"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "m_Born_g2_list={}\n",
    "MI_Born_g2_list={}\n",
    "LN_Born_g2_list={}\n",
    "m_Force_g2_list={}\n",
    "MI_Force_g2_list={}\n",
    "LN_Force_g2_list={}\n",
    "m_g2_list={}\n",
    "MI_g2_list={}\n",
    "LN_g2_list={}\n",
    "\n",
    "for Lx,Ly in zip([32,32,32],[16,24,32]):\n",
    "    with open('/mnt/d/kitp/Rp_SPT/8/CI_Born_En30_Lx{:}_Ly{:}_geo2_Born.pickle'.format(Lx,Ly),'rb') as f:\n",
    "        [m_Born_g2_list[(Lx,Ly)],MI_Born_g2_list[(Lx,Ly)],LN_Born_g2_list[(Lx,Ly)]]=pickle.load(f)\n",
    "    with open('/mnt/d/kitp/Rp_SPT/8/CI_Born_En1_Lx{:}_Ly{:}_geo2_Force.pickle'.format(Lx,Ly),'rb') as f:\n",
    "        [m_Force_g2_list[(Lx,Ly)],MI_Force_g2_list[(Lx,Ly)],LN_Force_g2_list[(Lx,Ly)]]=pickle.load(f)\n",
    "    with open('/mnt/d/kitp/Rp_SPT/8/CI_Born_En1_Lx{:}_Ly{:}_geo2_.pickle'.format(Lx,Ly),'rb') as f:\n",
    "        [m_g2_list[(Lx,Ly)],MI_g2_list[(Lx,Ly)],LN_g2_list[(Lx,Ly)]]=pickle.load(f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "fig,axs=plt.subplots(3,1,figsize=(6,6),sharex=True)\n",
    "ax=axs[0]\n",
    "for Lx,Ly in zip([32,32,32],[16,24,32]):\n",
    "    ax.plot(m_g2_list[(Lx,Ly)],LN_g2_list[(Lx,Ly)].mean(axis=1)/np.log(2),'.-',label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_ylabel('LN[log 2]')\n",
    "ax.set_title('No measure')\n",
    "ax=axs[1]\n",
    "for Lx,Ly in zip([32,32,32],[16,24,32]):\n",
    "    ax.plot(m_Force_g2_list[(Lx,Ly)],LN_Force_g2_list[(Lx,Ly)].mean(axis=1)/np.log(2),'.-',label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_ylabel('LN[log 2]')\n",
    "ax.set_title(r'Force GS: $|01\\rangle$')\n",
    "\n",
    "ax=axs[2]\n",
    "for Lx,Ly in zip([32,32,32],[16,24,32]):\n",
    "    ax.errorbar(m_Born_g2_list[(Lx,Ly)],LN_Born_g2_list[(Lx,Ly)].mean(axis=1)/np.log(2),LN_Born_g2_list[(Lx,Ly)].std(axis=1)/np.sqrt(20)/np.log(2),fmt='.-',capsize=3,label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_title(r'Born: $|01\\rangle$,$|10\\rangle$')\n",
    "\n",
    "ax.set_xlabel('m')\n",
    "ax.set_ylabel('LN[log 2]')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/Geo2.pdf')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "fig,axs=plt.subplots(3,1,figsize=(6,6),sharex=True)\n",
    "ax=axs[0]\n",
    "for Lx,Ly in zip([32,32,32],[16,24,32]):\n",
    "    ax.plot(m_g2_list[(Lx,Ly)],MI_g2_list[(Lx,Ly)].mean(axis=1)/np.log(2),'.-',label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_ylabel('MI[log 2]')\n",
    "ax.set_title('No measure')\n",
    "ax=axs[1]\n",
    "for Lx,Ly in zip([32,32,32],[16,24,32]):\n",
    "    ax.plot(m_Force_g2_list[(Lx,Ly)],MI_Force_g2_list[(Lx,Ly)].mean(axis=1)/np.log(2),'.-',label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_ylabel('MI[log 2]')\n",
    "ax.set_title(r'Force GS: $|01\\rangle$')\n",
    "\n",
    "ax=axs[2]\n",
    "for Lx,Ly in zip([32,32,32],[16,24,32]):\n",
    "    ax.errorbar(m_Born_g2_list[(Lx,Ly)],MI_Born_g2_list[(Lx,Ly)].mean(axis=1)/np.log(2),MI_Born_g2_list[(Lx,Ly)].std(axis=1)/np.sqrt(20)/np.log(2),fmt='.-',capsize=3,label='Lx={:},Ly={:}'.format(Lx,Ly))\n",
    "ax.legend()\n",
    "ax.set_title(r'Born: $|01\\rangle$,$|10\\rangle$')\n",
    "\n",
    "ax.set_xlabel('m')\n",
    "ax.set_ylabel('MI[log 2]')\n",
    "# fig.savefig('/mnt/d/kitp/Rp_SPT/8/Geo2.pdf')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MI[log 2]')"
      ]
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## genus (0,0) --a"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "source": [
    "params_g00_a=Params(m=1,Lx=np.inf,Ly=np.inf,dxmax=16,dymax=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g00_a=params_g00_a.linearize_index([np.arange(params_g00_a.dxmax),np.arange(params_g00_a.dymax)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "proj_A_g00_a=params_g00_a.linearize_index([np.arange(params_g00_a.dxmax//4-ax//2,params_g00_a.dxmax//4+ax//2),np.arange(params_g00_a.dymax//2-ay//2,params_g00_a.dymax//2+ay//2)],4,proj=True)\n",
    "proj_B_g00_a=params_g00_a.linearize_index([np.arange(3*params_g00_a.dxmax//4-ax//2,3*params_g00_a.dxmax//4+ax//2),np.arange(params_g00_a.dymax//2-ay//2,params_g00_a.dymax//2+ay//2)],4,proj=True)\n",
    "proj_range_g00_a=np.setdiff1d(proj_total_g00_a,np.union1d(proj_A_g00_a,proj_B_g00_a))\n",
    "sub_A_g00_a=params_g00_a.linearize_index([np.arange(params_g00_a.dxmax//4-ax//2,params_g00_a.dxmax//4+ax//2),np.arange(params_g00_a.dymax//2-ay//2,params_g00_a.dymax//2+ay//2)],4)\n",
    "sub_B_g00_a=params_g00_a.linearize_index([np.arange(3*params_g00_a.dxmax//4-ax//2,3*params_g00_a.dxmax//4+ax//2),np.arange(params_g00_a.dymax//2-ay//2,params_g00_a.dymax//2+ay//2)],4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g00_a.square_index(proj_total_g00_a),color='k')\n",
    "ax.scatter(*params_g00_a.square_index(sub_A_g00_a),color='c')\n",
    "ax.scatter(*params_g00_a.square_index(sub_B_g00_a),color='r')\n",
    "ax.scatter(*params_g00_a.square_index(proj_range_g00_a),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g00_a=np.linspace(1,3,20)\n",
    "es_g00_a=50\n",
    "LN_Born_inf_list_g00_a=np.zeros((len(m_list_g00_a),es_g00_a))\n",
    "MI_Born_inf_list_g00_a=np.zeros((len(m_list_g00_a),es_g00_a))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=np.inf,Ly=np.inf,dxmax=16,dymax=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g00_a=[]\n",
    "for m_i,m in enumerate(m_list_g00_a):\n",
    "    inputs=[(proj_range_g00_a,sub_A_g00_a,sub_B_g00_a,m) for _ in range(es_g00_a)]\n",
    "    async_result_g00_a.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g00_a=[]\n",
    "for m_i,m in enumerate(m_list_g00_a):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g00_a.append(async_result_g00_a[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g00_a=np.array(LN_Born_inf_list_g00_a)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g00_a.square_index(proj_total_g00_a),color='k')\n",
    "ax[0].scatter(*params_g00_a.square_index(sub_A_g00_a),color='c')\n",
    "ax[0].scatter(*params_g00_a.square_index(sub_B_g00_a),color='r')\n",
    "ax[0].scatter(*params_g00_a.square_index(proj_range_g00_a),color='b')\n",
    "ax[1].errorbar(m_list_g00_a,LN_Born_inf_list_g00_a.mean(axis=1),LN_Born_inf_list_g00_a.std(axis=1)/np.sqrt(es_g00_a-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_inf_g00_a.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## geneus (0,1) --a"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g01_a=Params(m=1,Lx=np.inf,Ly=np.inf,dxmax=16,dymax=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g01_a=params_g01_a.linearize_index([np.arange(params_g01_a.dxmax),np.arange(params_g01_a.dymax)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "ax2,ay2=8,8\n",
    "proj_A_g01_a=params_g01_a.linearize_index([np.arange(params_g01_a.dxmax//4-ax//2,params_g01_a.dxmax//4+ax//2),np.arange(params_g01_a.dymax//2-ay//2,params_g01_a.dymax//2+ay//2)],4,proj=True)\n",
    "sub_A_g01_a=params_g01_a.linearize_index([np.arange(params_g01_a.dxmax//4-ax//2,params_g01_a.dxmax//4+ax//2),np.arange(params_g01_a.dymax//2-ay//2,params_g01_a.dymax//2+ay//2)],4)\n",
    "proj_B_1_g01_a=params_g01_a.linearize_index([np.arange(3*params_g01_a.dxmax//4-ax//2,3*params_g01_a.dxmax//4+ax//2),np.arange(params_g01_a.dymax//2-ay//2,params_g01_a.dymax//2+ay//2)],4,proj=True)\n",
    "proj_B_2_g01_a=params_g01_a.linearize_index([np.arange(3*params_g01_a.dxmax//4-ax2//2,3*params_g01_a.dxmax//4+ax2//2),np.arange(params_g01_a.dymax//2-ay2//2,params_g01_a.dymax//2+ay2//2)],4,proj=True)\n",
    "proj_B_g01_a=np.setdiff1d(proj_B_2_g01_a,proj_B_1_g01_a)\n",
    "proj_range_g01_a=np.setdiff1d(proj_total_g01_a,np.union1d(proj_A_g01_a,proj_B_2_g01_a))\n",
    "\n",
    "sub_B_1_g01_a=params_g01_a.linearize_index([np.arange(3*params_g01_a.dxmax//4-ax//2,3*params_g01_a.dxmax//4+ax//2),np.arange(params_g01_a.dymax//2-ay//2,params_g01_a.dymax//2+ay//2)],4)\n",
    "sub_B_2_g01_a=params_g01_a.linearize_index([np.arange(3*params_g01_a.dxmax//4-ax2//2,3*params_g01_a.dxmax//4+ax2//2),np.arange(params_g01_a.dymax//2-ay2//2,params_g01_a.dymax//2+ay2//2)],4)\n",
    "sub_B_g01_a=np.setdiff1d(sub_B_2_g01_a,sub_B_1_g01_a)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g01_a.square_index(proj_total_g01_a),color='k')\n",
    "ax.scatter(*params_g01_a.square_index(sub_A_g01_a),color='c')\n",
    "ax.scatter(*params_g01_a.square_index(sub_B_g01_a),color='r')\n",
    "ax.scatter(*params_g01_a.square_index(proj_range_g01_a),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g01_a=np.linspace(1,3,20)\n",
    "es_g01_a=50\n",
    "LN_Born_inf_list_g01_a=np.zeros((len(m_list_g01_a),es_g01_a))\n",
    "MI_Born_inf_list_g01_a=np.zeros((len(m_list_g01_a),es_g01_a))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=np.inf,Ly=np.inf,dxmax=16,dymax=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g01_a=[]\n",
    "for m_i,m in enumerate(m_list_g01_a):\n",
    "    inputs=[(proj_range_g01_a,sub_A_g01_a,sub_B_g01_a,m) for _ in range(es_g01_a)]\n",
    "    async_result_g01_a.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g01_a=[]\n",
    "for m_i,m in enumerate(m_list_g01_a):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g01_a.append(async_result_g01_a[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g01_a=np.array(LN_Born_inf_list_g01_a)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g01_a.square_index(proj_total_g01_a),color='k')\n",
    "ax[0].scatter(*params_g01_a.square_index(sub_A_g01_a),color='c')\n",
    "ax[0].scatter(*params_g01_a.square_index(sub_B_g01_a),color='r')\n",
    "ax[0].scatter(*params_g01_a.square_index(proj_range_g01_a),color='b')\n",
    "ax[1].errorbar(m_list_g01_a,LN_Born_inf_list_g01_a.mean(axis=1),LN_Born_inf_list_g01_a.std(axis=1)/np.sqrt(es_g01_a-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_inf_g01_a.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## geneus (0,1) --b"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g01_b=Params(m=1,Lx=np.inf,Ly=np.inf,dxmax=16,dymax=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g01_b=params_g01_b.linearize_index([np.arange(params_g01_b.dxmax),np.arange(params_g01_b.dymax)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "ax2,ay2=8,8\n",
    "ax3,ay3=12,12\n",
    "proj_A_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax//2,2*params_g01_b.dxmax//4+ax//2),np.arange(params_g01_b.dymax//2-ay//2,params_g01_b.dymax//2+ay//2)],4,proj=True)\n",
    "sub_A_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax//2,2*params_g01_b.dxmax//4+ax//2),np.arange(params_g01_b.dymax//2-ay//2,params_g01_b.dymax//2+ay//2)],4)\n",
    "\n",
    "proj_B_1_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax2//2,2*params_g01_b.dxmax//4+ax2//2),np.arange(params_g01_b.dymax//2-ay2//2,params_g01_b.dymax//2+ay2//2)],4,proj=True)\n",
    "proj_B_2_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax3//2,2*params_g01_b.dxmax//4+ax3//2),np.arange(params_g01_b.dymax//2-ay3//2,params_g01_b.dymax//2+ay3//2)],4,proj=True)\n",
    "proj_range_g01_b=np.setdiff1d(proj_B_1_g01_b,proj_A_g01_b)\n",
    "\n",
    "sub_B_1_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax2//2,2*params_g01_b.dxmax//4+ax2//2),np.arange(params_g01_b.dymax//2-ay2//2,params_g01_b.dymax//2+ay2//2)],4)\n",
    "sub_B_2_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax3//2,2*params_g01_b.dxmax//4+ax3//2),np.arange(params_g01_b.dymax//2-ay3//2,params_g01_b.dymax//2+ay3//2)],4)\n",
    "sub_B_g01_b=np.setdiff1d(sub_B_2_g01_b,sub_B_1_g01_b)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g01_b=params_g01_b.linearize_index([np.arange(params_g01_b.dxmax),np.arange(params_g01_b.dymax)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "ax2,ay2=8,8\n",
    "ax3,ay3=12,12\n",
    "proj_A_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax//2,2*params_g01_b.dxmax//4+ax//2),np.arange(params_g01_b.dymax//2-ay//2,params_g01_b.dymax//2+ay//2)],4,proj=True)\n",
    "sub_A_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax//2,2*params_g01_b.dxmax//4+ax//2),np.arange(params_g01_b.dymax//2-ay//2,params_g01_b.dymax//2+ay//2)],4)\n",
    "\n",
    "proj_B_1_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax//2,2*params_g01_b.dxmax//4+ax//2),np.arange(params_g01_b.dymax//2-ay//2,params_g01_b.dymax//2+ay//2)],4,proj=True)\n",
    "proj_B_2_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax2//2,2*params_g01_b.dxmax//4+ax2//2),np.arange(params_g01_b.dymax//2-ay2//2,params_g01_b.dymax//2+ay2//2)],4,proj=True)\n",
    "proj_range_g01_b=np.setdiff1d(proj_B_2_g01_b,proj_B_1_g01_b)\n",
    "\n",
    "sub_B_1_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax2//2,2*params_g01_b.dxmax//4+ax2//2),np.arange(params_g01_b.dymax//2-ay2//2,params_g01_b.dymax//2+ay2//2)],4)\n",
    "sub_B_2_g01_b=params_g01_b.linearize_index([np.arange(2*params_g01_b.dxmax//4-ax3//2,2*params_g01_b.dxmax//4+ax3//2),np.arange(params_g01_b.dymax//2-ay3//2,params_g01_b.dymax//2+ay3//2)],4)\n",
    "sub_B_g01_b=np.setdiff1d(sub_B_2_g01_b,sub_B_1_g01_b)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g01_b.square_index(proj_total_g01_b),color='k')\n",
    "ax.scatter(*params_g01_b.square_index(sub_A_g01_b),color='c')\n",
    "ax.scatter(*params_g01_b.square_index(sub_B_g01_b),color='r')\n",
    "ax.scatter(*params_g01_b.square_index(proj_range_g01_b),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g01_b=np.linspace(1,3,20)\n",
    "es_g01_b=50\n",
    "LN_Born_inf_list_g01_b=np.zeros((len(m_list_g01_b),es_g01_b))\n",
    "MI_Born_inf_list_g01_b=np.zeros((len(m_list_g01_b),es_g01_b))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=np.inf,Ly=np.inf,dxmax=16,dymax=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g01_b=[]\n",
    "for m_i,m in enumerate(m_list_g01_b):\n",
    "    inputs=[(proj_range_g01_b,sub_A_g01_b,sub_B_g01_b,m) for _ in range(es_g01_b)]\n",
    "    async_result_g01_b.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g01_b=[]\n",
    "for m_i,m in enumerate(m_list_g01_b):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g01_b.append(async_result_g01_b[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g01_b=np.array(LN_Born_inf_list_g01_b)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g01_b.square_index(proj_total_g01_b),color='k')\n",
    "ax[0].scatter(*params_g01_b.square_index(sub_A_g01_b),color='c')\n",
    "ax[0].scatter(*params_g01_b.square_index(sub_B_g01_b),color='r')\n",
    "ax[0].scatter(*params_g01_b.square_index(proj_range_g01_b),color='b')\n",
    "ax[1].errorbar(m_list_g01_b,LN_Born_inf_list_g01_b.mean(axis=1),LN_Born_inf_list_g01_b.std(axis=1)/np.sqrt(es_g01_b-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_inf_g01_b.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Finite size"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## genus (0,0) --aa"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g00_aa=Params(m=1,Lx=16,Ly=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g00_aa=params_g00_aa.linearize_index([np.arange(params_g00_aa.Lx),np.arange(params_g00_aa.Ly)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "proj_A_g00_aa=params_g00_aa.linearize_index([np.arange(params_g00_aa.Lx//4-ax//2,params_g00_aa.Lx//4+ax//2),np.arange(params_g00_aa.Ly//2-ay//2,params_g00_aa.Ly//2+ay//2)],4,proj=True)\n",
    "proj_B_g00_aa=params_g00_aa.linearize_index([np.arange(3*params_g00_aa.Lx//4-ax//2,3*params_g00_aa.Lx//4+ax//2),np.arange(params_g00_aa.Ly)],4,proj=True)\n",
    "proj_range_g00_aa=np.setdiff1d(proj_total_g00_aa,np.union1d(proj_A_g00_aa,proj_B_g00_aa))\n",
    "sub_A_g00_aa=params_g00_aa.linearize_index([np.arange(params_g00_aa.Lx//4-ax//2,params_g00_aa.Lx//4+ax//2),np.arange(params_g00_aa.Ly//2-ay//2,params_g00_aa.Ly//2+ay//2)],4)\n",
    "sub_B_g00_aa=params_g00_aa.linearize_index([np.arange(3*params_g00_aa.Lx//4-ax//2,3*params_g00_aa.Lx//4+ax//2),np.arange(params_g00_aa.Ly)],4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g00_aa.square_index(proj_total_g00_aa),color='k')\n",
    "ax.scatter(*params_g00_aa.square_index(sub_A_g00_aa),color='c')\n",
    "ax.scatter(*params_g00_aa.square_index(sub_B_g00_aa),color='r')\n",
    "ax.scatter(*params_g00_aa.square_index(proj_range_g00_aa),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g00_aa=np.linspace(1,3,20)\n",
    "es_g00_aa=50\n",
    "LN_Born_inf_list_g00_aa=np.zeros((len(m_list_g00_aa),es_g00_aa))\n",
    "MI_Born_inf_list_g00_aa=np.zeros((len(m_list_g00_aa),es_g00_aa))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=16,Ly=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g00_aa=[]\n",
    "for m_i,m in enumerate(m_list_g00_aa):\n",
    "    inputs=[(proj_range_g00_aa,sub_A_g00_aa,sub_B_g00_aa,m) for _ in range(es_g00_aa)]\n",
    "    async_result_g00_aa.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g00_aa=[]\n",
    "for m_i,m in enumerate(m_list_g00_aa):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g00_aa.append(async_result_g00_aa[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g00_aa=np.array(LN_Born_inf_list_g00_aa)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g00_aa.square_index(proj_total_g00_aa),color='k')\n",
    "ax[0].scatter(*params_g00_aa.square_index(sub_A_g00_aa),color='c')\n",
    "ax[0].scatter(*params_g00_aa.square_index(sub_B_g00_aa),color='r')\n",
    "ax[0].scatter(*params_g00_aa.square_index(proj_range_g00_aa),color='b')\n",
    "ax[1].errorbar(m_list_g00_aa,LN_Born_inf_list_g00_aa.mean(axis=1),LN_Born_inf_list_g00_aa.std(axis=1)/np.sqrt(es_g00_aa-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_g00_aa.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## genus (0,0) --ab"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g00_ab=Params(m=1,Lx=16,Ly=16,bcx=-1,bcy=1)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g00_ab=params_g00_ab.linearize_index([np.arange(params_g00_ab.Lx),np.arange(params_g00_ab.Ly)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "proj_A_g00_ab=params_g00_ab.linearize_index([np.arange(params_g00_ab.Lx//4-ax//2,params_g00_ab.Lx//4+ax//2),np.arange(params_g00_ab.Ly)],4,proj=True)\n",
    "proj_B_g00_ab=params_g00_ab.linearize_index([np.arange(3*params_g00_ab.Lx//4-ax//2,3*params_g00_ab.Lx//4+ax//2),np.arange(params_g00_ab.Ly)],4,proj=True)\n",
    "proj_range_g00_ab=np.setdiff1d(proj_total_g00_ab,np.union1d(proj_A_g00_ab,proj_B_g00_ab))\n",
    "sub_A_g00_ab=params_g00_ab.linearize_index([np.arange(params_g00_ab.Lx//4-ax//2,params_g00_ab.Lx//4+ax//2),np.arange(params_g00_ab.Ly)],4)\n",
    "sub_B_g00_ab=params_g00_ab.linearize_index([np.arange(3*params_g00_ab.Lx//4-ax//2,3*params_g00_ab.Lx//4+ax//2),np.arange(params_g00_ab.Ly)],4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g00_ab.square_index(proj_total_g00_ab),color='k')\n",
    "ax.scatter(*params_g00_ab.square_index(sub_A_g00_ab),color='c')\n",
    "ax.scatter(*params_g00_ab.square_index(sub_B_g00_ab),color='r')\n",
    "ax.scatter(*params_g00_ab.square_index(proj_range_g00_ab),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g00_ab=np.linspace(1,3,20)\n",
    "es_g00_ab=20\n",
    "LN_Born_inf_list_g00_ab=np.zeros((len(m_list_g00_ab),es_g00_ab))\n",
    "MI_Born_inf_list_g00_ab=np.zeros((len(m_list_g00_ab),es_g00_ab))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=16,Ly=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g00_ab=[]\n",
    "for m_i,m in enumerate(m_list_g00_ab):\n",
    "    inputs=[(proj_range_g00_ab,sub_A_g00_ab,sub_B_g00_ab,m) for _ in range(es_g00_ab)]\n",
    "    async_result_g00_ab.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g00_ab=[]\n",
    "for m_i,m in enumerate(m_list_g00_ab):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g00_ab.append(async_result_g00_ab[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g00_ab=np.array(LN_Born_inf_list_g00_ab)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g00_ab.square_index(proj_total_g00_ab),color='k')\n",
    "ax[0].scatter(*params_g00_ab.square_index(sub_A_g00_ab),color='c')\n",
    "ax[0].scatter(*params_g00_ab.square_index(sub_B_g00_ab),color='r')\n",
    "ax[0].scatter(*params_g00_ab.square_index(proj_range_g00_ab),color='b')\n",
    "ax[1].errorbar(m_list_g00_ab,LN_Born_inf_list_g00_ab.mean(axis=1),LN_Born_inf_list_g00_ab.std(axis=1)/np.sqrt(es_g00_ab-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "# fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_g00_ab.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## geneus (0,1) --aa"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g01_aa=Params(m=1,Lx=16,Ly=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g01_aa=params_g01_aa.linearize_index([np.arange(params_g01_aa.Lx),np.arange(params_g01_aa.Ly)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "ax2,ay2=8,8\n",
    "proj_A_g01_aa=params_g01_aa.linearize_index([np.arange(params_g01_aa.Lx//4-ax//2,params_g01_aa.Lx//4+ax//2),np.arange(params_g01_aa.Ly)],4,proj=True)\n",
    "sub_A_g01_aa=params_g01_aa.linearize_index([np.arange(params_g01_aa.Lx//4-ax//2,params_g01_aa.Lx//4+ax//2),np.arange(params_g01_aa.Ly)],4)\n",
    "proj_B_1_g01_aa=params_g01_aa.linearize_index([np.arange(3*params_g01_aa.Lx//4-ax//2,3*params_g01_aa.Lx//4+ax//2),np.arange(params_g01_aa.Ly//2-ay//2,params_g01_aa.Ly//2+ay//2)],4,proj=True)\n",
    "proj_B_2_g01_aa=params_g01_aa.linearize_index([np.arange(3*params_g01_aa.Lx//4-ax2//2,3*params_g01_aa.Lx//4+ax2//2),np.arange(params_g01_aa.Ly//2-ay2//2,params_g01_aa.Ly//2+ay2//2)],4,proj=True)\n",
    "proj_B_g01_aa=np.setdiff1d(proj_B_2_g01_aa,proj_B_1_g01_aa)\n",
    "proj_range_g01_aa=np.setdiff1d(proj_total_g01_aa,np.union1d(proj_A_g01_aa,proj_B_2_g01_aa))\n",
    "\n",
    "sub_B_1_g01_aa=params_g01_aa.linearize_index([np.arange(3*params_g01_aa.Lx//4-ax//2,3*params_g01_aa.Lx//4+ax//2),np.arange(params_g01_aa.Ly//2-ay//2,params_g01_aa.Ly//2+ay//2)],4)\n",
    "sub_B_2_g01_aa=params_g01_aa.linearize_index([np.arange(3*params_g01_aa.Lx//4-ax2//2,3*params_g01_aa.Lx//4+ax2//2),np.arange(params_g01_aa.Ly//2-ay2//2,params_g01_aa.Ly//2+ay2//2)],4)\n",
    "sub_B_g01_aa=np.setdiff1d(sub_B_2_g01_aa,sub_B_1_g01_aa)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g01_aa.square_index(proj_total_g01_aa),color='k')\n",
    "ax.scatter(*params_g01_aa.square_index(sub_A_g01_aa),color='c')\n",
    "ax.scatter(*params_g01_aa.square_index(sub_B_g01_aa),color='r')\n",
    "ax.scatter(*params_g01_aa.square_index(proj_range_g01_aa),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g01_aa=np.linspace(1,3,20)\n",
    "es_g01_aa=50\n",
    "LN_Born_inf_list_g01_aa=np.zeros((len(m_list_g01_aa),es_g01_aa))\n",
    "MI_Born_inf_list_g01_aa=np.zeros((len(m_list_g01_aa),es_g01_aa))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=16,Ly=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g01_aa=[]\n",
    "for m_i,m in enumerate(m_list_g01_aa):\n",
    "    inputs=[(proj_range_g01_aa,sub_A_g01_aa,sub_B_g01_aa,m) for _ in range(es_g01_aa)]\n",
    "    async_result_g01_aa.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g01_aa=[]\n",
    "for m_i,m in enumerate(m_list_g01_aa):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g01_aa.append(async_result_g01_aa[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g01_aa=np.array(LN_Born_inf_list_g01_aa)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g01_aa.square_index(proj_total_g01_aa),color='k')\n",
    "ax[0].scatter(*params_g01_aa.square_index(sub_A_g01_aa),color='c')\n",
    "ax[0].scatter(*params_g01_aa.square_index(sub_B_g01_aa),color='r')\n",
    "ax[0].scatter(*params_g01_aa.square_index(proj_range_g01_aa),color='b')\n",
    "ax[1].errorbar(m_list_g01_aa,LN_Born_inf_list_g01_aa.mean(axis=1),LN_Born_inf_list_g01_aa.std(axis=1)/np.sqrt(es_g01_aa-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_g01_aa.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## geneus (0,1) --ab"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g01_ab=Params(m=1,Lx=16,Ly=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g01_ab=params_g01_ab.linearize_index([np.arange(params_g01_ab.Lx),np.arange(params_g01_ab.Ly)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "ax2,ay2=8,8\n",
    "proj_A_g01_ab=params_g01_ab.linearize_index([np.arange(params_g01_ab.Lx//4-ax//2,params_g01_ab.Lx//4+ax//2),np.arange(params_g01_ab.Ly//2-ay//2,params_g01_ab.Ly//2+ay//2)],4,proj=True)\n",
    "sub_A_g01_ab=params_g01_ab.linearize_index([np.arange(params_g01_ab.Lx//4-ax//2,params_g01_ab.Lx//4+ax//2),np.arange(params_g01_ab.Ly//2-ay//2,params_g01_ab.Ly//2+ay//2)],4)\n",
    "proj_B_1_g01_ab=params_g01_ab.linearize_index([np.arange(3*params_g01_ab.Lx//4-ax//2,3*params_g01_ab.Lx//4+ax//2),np.arange(params_g01_ab.Ly//2-ay//2,params_g01_ab.Ly//2+ay//2)],4,proj=True)\n",
    "proj_B_2_g01_ab=params_g01_ab.linearize_index([np.arange(3*params_g01_ab.Lx//4-ax2//2,3*params_g01_ab.Lx//4+ax2//2),np.arange(params_g01_ab.Ly)],4,proj=True)\n",
    "proj_B_g01_ab=np.setdiff1d(proj_B_2_g01_ab,proj_B_1_g01_ab)\n",
    "proj_range_g01_ab=np.setdiff1d(proj_total_g01_ab,np.union1d(proj_A_g01_ab,proj_B_2_g01_ab))\n",
    "\n",
    "sub_B_1_g01_ab=params_g01_ab.linearize_index([np.arange(3*params_g01_ab.Lx//4-ax//2,3*params_g01_ab.Lx//4+ax//2),np.arange(params_g01_ab.Ly//2-ay//2,params_g01_ab.Ly//2+ay//2)],4)\n",
    "sub_B_2_g01_ab=params_g01_ab.linearize_index([np.arange(3*params_g01_ab.Lx//4-ax2//2,3*params_g01_ab.Lx//4+ax2//2),np.arange(params_g01_ab.Ly)],4)\n",
    "sub_B_g01_ab=np.setdiff1d(sub_B_2_g01_ab,sub_B_1_g01_ab)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g01_ab.square_index(proj_total_g01_ab),color='k')\n",
    "ax.scatter(*params_g01_ab.square_index(sub_A_g01_ab),color='c')\n",
    "ax.scatter(*params_g01_ab.square_index(sub_B_g01_ab),color='r')\n",
    "ax.scatter(*params_g01_ab.square_index(proj_range_g01_ab),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g01_ab=np.linspace(1,3,20)\n",
    "es_g01_ab=50\n",
    "LN_Born_inf_list_g01_ab=np.zeros((len(m_list_g01_ab),es_g01_ab))\n",
    "MI_Born_inf_list_g01_ab=np.zeros((len(m_list_g01_ab),es_g01_ab))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=16,Ly=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g01_ab=[]\n",
    "for m_i,m in enumerate(m_list_g01_ab):\n",
    "    inputs=[(proj_range_g01_ab,sub_A_g01_ab,sub_B_g01_ab,m) for _ in range(es_g01_ab)]\n",
    "    async_result_g01_ab.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g01_ab=[]\n",
    "for m_i,m in enumerate(m_list_g01_ab):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g01_ab.append(async_result_g01_ab[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g01_ab=np.array(LN_Born_inf_list_g01_ab)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g01_ab.square_index(proj_total_g01_ab),color='k')\n",
    "ax[0].scatter(*params_g01_ab.square_index(sub_A_g01_ab),color='c')\n",
    "ax[0].scatter(*params_g01_ab.square_index(sub_B_g01_ab),color='r')\n",
    "ax[0].scatter(*params_g01_ab.square_index(proj_range_g01_ab),color='b')\n",
    "ax[1].errorbar(m_list_g01_ab,LN_Born_inf_list_g01_ab.mean(axis=1),LN_Born_inf_list_g01_ab.std(axis=1)/np.sqrt(es_g01_ab-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_g01_ab.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## geneus (0,1) --ac"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g01_ac=Params(m=1,Lx=16,Ly=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g01_ac=params_g01_ac.linearize_index([np.arange(params_g01_ac.Lx),np.arange(params_g01_ac.Ly)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "ax2,ay2=8,8\n",
    "proj_A_g01_ac=params_g01_ac.linearize_index([np.arange(params_g01_ac.Lx//4-ax//2,params_g01_ac.Lx//4+ax//2),np.arange(params_g01_ac.Ly)],4,proj=True)\n",
    "sub_A_g01_ac=params_g01_ac.linearize_index([np.arange(params_g01_ac.Lx//4-ax//2,params_g01_ac.Lx//4+ax//2),np.arange(params_g01_ac.Ly)],4)\n",
    "proj_B_1_g01_ac=params_g01_ac.linearize_index([np.arange(3*params_g01_ac.Lx//4-ax//2,3*params_g01_ac.Lx//4+ax//2),np.arange(params_g01_ac.Ly//2-ay//2,params_g01_ac.Ly//2+ay//2)],4,proj=True)\n",
    "proj_B_2_g01_ac=params_g01_ac.linearize_index([np.arange(3*params_g01_ac.Lx//4-ax2//2,3*params_g01_ac.Lx//4+ax2//2),np.arange(params_g01_ac.Ly)],4,proj=True)\n",
    "proj_B_g01_ac=np.setdiff1d(proj_B_2_g01_ac,proj_B_1_g01_ac)\n",
    "proj_range_g01_ac=np.setdiff1d(proj_total_g01_ac,np.union1d(proj_A_g01_ac,proj_B_2_g01_ac))\n",
    "\n",
    "sub_B_1_g01_ac=params_g01_ac.linearize_index([np.arange(3*params_g01_ac.Lx//4-ax//2,3*params_g01_ac.Lx//4+ax//2),np.arange(params_g01_ac.Ly//2-ay//2,params_g01_ac.Ly//2+ay//2)],4)\n",
    "sub_B_2_g01_ac=params_g01_ac.linearize_index([np.arange(3*params_g01_ac.Lx//4-ax2//2,3*params_g01_ac.Lx//4+ax2//2),np.arange(params_g01_ac.Ly)],4)\n",
    "sub_B_g01_ac=np.setdiff1d(sub_B_2_g01_ac,sub_B_1_g01_ac)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g01_ac.square_index(proj_total_g01_ac),color='k')\n",
    "ax.scatter(*params_g01_ac.square_index(sub_A_g01_ac),color='c')\n",
    "ax.scatter(*params_g01_ac.square_index(sub_B_g01_ac),color='r')\n",
    "ax.scatter(*params_g01_ac.square_index(proj_range_g01_ac),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g01_ac=np.linspace(1,3,20)\n",
    "es_g01_ac=50\n",
    "LN_Born_inf_list_g01_ac=np.zeros((len(m_list_g01_ac),es_g01_ac))\n",
    "MI_Born_inf_list_g01_ac=np.zeros((len(m_list_g01_ac),es_g01_ac))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=16,Ly=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g01_ac=[]\n",
    "for m_i,m in enumerate(m_list_g01_ac):\n",
    "    inputs=[(proj_range_g01_ac,sub_A_g01_ac,sub_B_g01_ac,m) for _ in range(es_g01_ac)]\n",
    "    async_result_g01_ac.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g01_ac=[]\n",
    "for m_i,m in enumerate(m_list_g01_ac):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g01_ac.append(async_result_g01_ac[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g01_ac=np.array(LN_Born_inf_list_g01_ac)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g01_ac.square_index(proj_total_g01_ac),color='k')\n",
    "ax[0].scatter(*params_g01_ac.square_index(sub_A_g01_ac),color='c')\n",
    "ax[0].scatter(*params_g01_ac.square_index(sub_B_g01_ac),color='r')\n",
    "ax[0].scatter(*params_g01_ac.square_index(proj_range_g01_ac),color='b')\n",
    "ax[1].errorbar(m_list_g01_ac,LN_Born_inf_list_g01_ac.mean(axis=1),LN_Born_inf_list_g01_ac.std(axis=1)/np.sqrt(es_g01_ac-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_g01_ac.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## geneus (0,1) --ba"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params_g01_ba=Params(m=1,Lx=16,Ly=16)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "proj_total_g01_ba=params_g01_ba.linearize_index([np.arange(params_g01_ba.Lx),np.arange(params_g01_ba.Ly)],4,proj=True)\n",
    "ax,ay=4,4\n",
    "ax2,ay2=8,8\n",
    "ax3,ay3=12,12\n",
    "proj_A_g01_ba=params_g01_ba.linearize_index([np.arange(2*params_g01_ba.Lx//4-ax//2,2*params_g01_ba.Lx//4+ax//2),np.arange(params_g01_ba.Ly//2-ay//2,params_g01_ba.Ly//2+ay//2)],4,proj=True)\n",
    "sub_A_g01_ba=params_g01_ba.linearize_index([np.arange(2*params_g01_ba.Lx//4-ax//2,2*params_g01_ba.Lx//4+ax//2),np.arange(params_g01_ba.Ly//2-ay//2,params_g01_ba.Ly//2+ay//2)],4)\n",
    "\n",
    "proj_B_1_g01_ba=params_g01_ba.linearize_index([np.arange(2*params_g01_ba.Lx//4-ax2//2,2*params_g01_ba.Lx//4+ax2//2),np.arange(params_g01_ba.Ly//2-ay2//2,params_g01_ba.Ly//2+ay2//2)],4,proj=True)\n",
    "proj_B_2_g01_ba=params_g01_ba.linearize_index([np.arange(2*params_g01_ba.Lx//4-ax3//2,2*params_g01_ba.Lx//4+ax3//2),np.arange(params_g01_ba.Ly)],4,proj=True)\n",
    "proj_range_g01_ba=np.setdiff1d(proj_B_1_g01_ba,proj_A_g01_ba)\n",
    "\n",
    "sub_B_1_g01_ba=params_g01_ba.linearize_index([np.arange(2*params_g01_ba.Lx//4-ax2//2,2*params_g01_ba.Lx//4+ax2//2),np.arange(params_g01_ba.Ly//2-ay2//2,params_g01_ba.Ly//2+ay2//2)],4)\n",
    "sub_B_2_g01_ba=params_g01_ba.linearize_index([np.arange(2*params_g01_ba.Lx//4-ax3//2,2*params_g01_ba.Lx//4+ax3//2),np.arange(params_g01_ba.Ly)],4)\n",
    "sub_B_g01_ba=np.setdiff1d(sub_B_2_g01_ba,sub_B_1_g01_ba)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(*params_g01_ba.square_index(proj_total_g01_ba),color='k')\n",
    "ax.scatter(*params_g01_ba.square_index(sub_A_g01_ba),color='c')\n",
    "ax.scatter(*params_g01_ba.square_index(sub_B_g01_ba),color='r')\n",
    "ax.scatter(*params_g01_ba.square_index(proj_range_g01_ba),color='b')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "m_list_g01_ba=np.linspace(1,3,20)\n",
    "es_g01_ba=50\n",
    "LN_Born_inf_list_g01_ba=np.zeros((len(m_list_g01_ba),es_g01_ba))\n",
    "MI_Born_inf_list_g01_ba=np.zeros((len(m_list_g01_ba),es_g01_ba))\n",
    "Lx,Ly=16,16\n",
    "\n",
    "def run(inputs):\n",
    "    proj_range,sub_A,sub_B,m=inputs\n",
    "    params=Params(m=m,Lx=16,Ly=16)\n",
    "    params.measure_all_Born(proj_range,linear=True)\n",
    "    return params.log_neg(sub_A,sub_B,linear=True)\n",
    "\n",
    "async_result_g01_ba=[]\n",
    "for m_i,m in enumerate(m_list_g01_ba):\n",
    "    inputs=[(proj_range_g01_ba,sub_A_g01_ba,sub_B_g01_ba,m) for _ in range(es_g01_ba)]\n",
    "    async_result_g01_ba.append(rc[:].map_async(run,inputs))\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "LN_Born_inf_list_g01_ba=[]\n",
    "for m_i,m in enumerate(m_list_g01_ba):\n",
    "    st=time.time()\n",
    "    LN_Born_inf_list_g01_ba.append(async_result_g01_ba[m_i])\n",
    "    print('{:d}:{:.1f}'.format(m_i,time.time()-st))\n",
    "LN_Born_inf_list_g01_ba=np.array(LN_Born_inf_list_g01_ba)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "fig,ax=plt.subplots(1,2)\n",
    "ax[0].scatter(*params_g01_ba.square_index(proj_total_g01_ba),color='k')\n",
    "ax[0].scatter(*params_g01_ba.square_index(sub_A_g01_ba),color='c')\n",
    "ax[0].scatter(*params_g01_ba.square_index(sub_B_g01_ba),color='r')\n",
    "ax[0].scatter(*params_g01_ba.square_index(proj_range_g01_ba),color='b')\n",
    "ax[1].errorbar(m_list_g01_ba,LN_Born_inf_list_g01_ba.mean(axis=1),LN_Born_inf_list_g01_ba.std(axis=1)/np.sqrt(es_g01_ba-1),fmt='.-')\n",
    "ax[1].set_xlabel('m')\n",
    "ax[1].set_ylabel('LN')\n",
    "fig.savefig('/mnt/d/kitp/Rp_SPT/8/LN_geo_g01_ba.pdf')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# profiler"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "params=Params(Lx=32,Ly=16,history=False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "def test(A):\n",
    "    return A+A.T"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "z=np.random.rand(2048,2048)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f test test(z)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.measure params.measure_all_Born([np.arange(params.Lx//4)+params.Lx//4,np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.measure params.measure_all_Born([np.arange(params.Lx//4)+params.Lx//4,np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.bandstructure params.covariance_matrix()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.c_subregion_m params.c_subregion_m([np.arange(params.Lx//4),np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.von_Neumann_entropy_m Params(Lx=16,Ly=16).von_Neumann_entropy_m([np.arange(params.Lx//4),np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.von_Neumann_entropy_m Params(Lx=16,Ly=16).von_Neumann_entropy_m([np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.mutual_information_m params.mutual_information_m([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%lprun -f Params.log_neg Params(m=m,Lx=16,Ly=16).log_neg([np.arange(params.Lx//4),np.arange(params.Ly)],[np.arange(params.Lx//4)+params.Lx//2,np.arange(params.Ly)])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Check scaling behavior"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
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