QISUnfoldingPaperPlots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Qiskit includes\n",
    "from qiskit import QuantumRegister, ClassicalRegister\n",
    "from qiskit import QuantumCircuit, execute\n",
    "from qiskit import Aer\n",
    "from qiskit.providers.aer import noise\n",
    "from qiskit.providers.aer.noise.errors.readout_error import *\n",
    "from qiskit.circuit import Instruction\n",
    "from qiskit.ignis.mitigation.measurement import (complete_meas_cal,\n",
    "                                                 CompleteMeasFitter, \n",
    "                                                 MeasurementFilter)\n",
    "#Other python tools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from scipy.optimize import minimize\n",
    "import seaborn as sns\n",
    "import matplotlib.lines as mlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(3)\n",
    "\n",
    "N_data = 10000\n",
    "sigma = 3\n",
    "true_data = np.clip(np.random.randn(N_data)*sigma,-10.,10.)\n",
    "true_data_int = [round(x) for x in true_data]\n",
    "randnoise = np.random.uniform(0.,1.,N_data)\n",
    "reco_data_int = []\n",
    "for i in range(len(true_data_int)):\n",
    "    reco_data_int+=[true_data_int[i]]\n",
    "    if (true_data_int[i]==-10 and randnoise[i]<0.25):\n",
    "        reco_data_int[i]+=1\n",
    "    elif (true_data_int[i]==10 and randnoise[i]<0.25):\n",
    "        reco_data_int[i]-=1\n",
    "    elif (abs(true_data_int[i]) < 10 and randnoise[i]<0.25):\n",
    "        reco_data_int[i]-=1\n",
    "    elif (abs(true_data_int[i]) < 10 and randnoise[i]<0.5):\n",
    "        reco_data_int[i]+=1 \n",
    "        pass\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Counts')"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m,b,_ = plt.hist(reco_data_int,bins=np.linspace(-10.5,10.5,22),label='m')\n",
    "t,b,_ = plt.hist(true_data_int,bins=np.linspace(-10.5,10.5,22),facecolor=\"none\",edgecolor='black',label=\"t\")\n",
    "plt.legend(frameon=False)\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's assume we know R exactly.\n",
    "R = []\n",
    "for i in range(len(m)):\n",
    "    R += [[]]\n",
    "    for j in range(len(t)):\n",
    "        if (i==0):\n",
    "            if (j==0):\n",
    "                R[i] += [0.75]\n",
    "            elif (j==1):\n",
    "                R[i] += [0.25]\n",
    "            else:\n",
    "                R[i] += [0.]\n",
    "        elif (i==len(t)-1):\n",
    "            if (j==len(t)-1):\n",
    "                R[i] += [0.75]\n",
    "            elif (j==len(t)-2):\n",
    "                R[i] += [0.25]\n",
    "            else:\n",
    "                R[i] += [0.]\n",
    "        else:\n",
    "            if (abs(i-j)==0):\n",
    "                R[i] += [0.5]\n",
    "            elif (abs(i-j)==1):\n",
    "                R[i] += [0.25]\n",
    "            else:\n",
    "                R[i] += [0.] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfolding Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MatrixInversion(ymes,Rin):\n",
    "    #Rin is a matrix where the first coordinate is the measured value and the second coordinate is the true value.\n",
    "    return np.ravel(np.matmul(np.linalg.inv(np.matrix(Rin)),ymes))\n",
    "\n",
    "def fun(x,ymes,Rin):\n",
    "    mat_dot_x = np.ravel(np.matmul(Rin,x))\n",
    "    return sum((ymes - mat_dot_x)**2)\n",
    "\n",
    "def Ignis(ymes,Rin):\n",
    "    #Rin is a matrix where the first coordinate is the measured value and the second coordinate is the true value.\n",
    "    #This is copied from https://github.com/Qiskit/qiskit-ignis/blob/master/qiskit/ignis/mitigation/measurement/filters.py\n",
    "    x0 = np.random.rand(len(ymes))\n",
    "    x0 = x0 / sum(x0)\n",
    "    nshots = sum(ymes)\n",
    "    cons = ({'type': 'eq', 'fun': lambda x: nshots - sum(x)})\n",
    "    bnds = tuple((0, nshots) for x in x0)\n",
    "    res = minimize(fun, x0, method='SLSQP',constraints=cons, bounds=bnds, tol=1e-6, args=(ymes,Rin))\n",
    "    return res.x\n",
    "\n",
    "def IBU(ymes,t0,Rin,n):\n",
    "    #This is the iterative Bayesian unfolding method.\n",
    "    #Rin is a matrix where the first coordinate is the measured value and the second coordinate is the true value.\n",
    "    #n is the number of iterations.\n",
    "    \n",
    "    tn = t0\n",
    "    for q in range(n):\n",
    "        out = []\n",
    "        for j in range(len(t0)):\n",
    "            mynum = 0.\n",
    "            for i in range(len(ymes)):\n",
    "                myden = 0.\n",
    "                for k in range(len(t0)):\n",
    "                    myden+=Rin[i][k]*tn[k]\n",
    "                    pass\n",
    "                mynum+=Rin[i][j]*tn[j]*ymes[i]/myden\n",
    "                pass\n",
    "            out+=[mynum]\n",
    "        tn = out\n",
    "        pass\n",
    "    '''\n",
    "    tn = t0\n",
    "    for i in range(n):\n",
    "        Rjitni = [np.array(Rin[:][i])*tn[i] for i in range(len(tn))]\n",
    "        Pm_given_t = Rjitni / np.matmul(Rin,tn)\n",
    "        tn = np.dot(Pm_given_t,ymes)\n",
    "        pass\n",
    "    '''\n",
    "    return tn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "that_matrix = MatrixInversion(m,R)\n",
    "that_ignis = Ignis(m,R)\n",
    "that_IBU = IBU(m,np.ones(len(m)),R,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def myre(x):\n",
    "    return np.concatenate([[x[0]],x])\n",
    "\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(2, 1, width_ratios=[1], height_ratios=[3.5, 1])\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax1.set_xticklabels( () )\n",
    "\n",
    "mybincenters = np.linspace(-10.5,10.5,22)\n",
    "plt.errorbar(0.5*(b[1:]+b[:-1]),m,yerr=np.sqrt(m),label='m',marker='o',linestyle='none',color='black')\n",
    "t,b,_ = plt.hist(true_data_int,bins=np.linspace(-10.5,10.5,22),facecolor=\"none\",edgecolor='black',label=\"t\")\n",
    "plt.step(mybincenters,myre(that_matrix),color='blue',linestyle=':',label=r\"$\\hat{t}_{matrix}$\")\n",
    "plt.step(mybincenters,myre(that_ignis),color='red',linestyle='--',label=r\"$\\hat{t}_{ignis}$\")\n",
    "plt.step(mybincenters,myre(that_IBU),color='green',linestyle='-.',label=r\"$\\hat{t}_{IBU}$\")\n",
    "plt.legend(frameon=False)\n",
    "plt.ylabel('Counts')\n",
    "\n",
    "ax2 = plt.subplot(gs[1])\n",
    "ratio_ignis = np.zeros(len(m))\n",
    "ratio_matrix = np.zeros(len(m))\n",
    "ratio_IBU = np.zeros(len(m))\n",
    "for i in range(len(m)):\n",
    "    ratio_matrix[i]=1.\n",
    "    ratio_ignis[i]=1.\n",
    "    if (t[i] > 0):\n",
    "        ratio_matrix[i] = that_matrix[i]/t[i]\n",
    "        ratio_ignis[i] = that_ignis[i]/t[i]\n",
    "        ratio_IBU[i] = that_IBU[i]/t[i]\n",
    "        pass\n",
    "    pass\n",
    "sns.set_style(\"white\")\n",
    "plt.ylim([0.,2.])\n",
    "plt.step(mybincenters,myre(ratio_ignis),color='red',linestyle='--')\n",
    "plt.step(mybincenters,myre(ratio_matrix),color='blue',linestyle=\":\")\n",
    "plt.step(mybincenters,myre(ratio_IBU),color='green',linestyle=\"-.\")\n",
    "plt.ylabel('Extracted / Truth')\n",
    "plt.xlabel('Value')\n",
    "xx2 = [1.,1.]\n",
    "plt.plot([mybincenters[0],mybincenters[len(mybincenters)-1]],xx2,color='black',linestyle=':')\n",
    "plt.savefig(\"plots/fig1.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the response matrices from the Tokyo IBM Q machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mybin(input,q,r=1):\n",
    "  if (r==0):\n",
    "    return bin(input).split('b')[1].zfill(q) \n",
    "  else:\n",
    "    return bin(input).split('b')[1].zfill(q)[::-1]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12f850d68>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nqbits = 5\n",
    "bin_convert = {}\n",
    "for i in range(2**nqbits):\n",
    "  bin_convert[bin(i).split('b')[1].zfill(nqbits)]=i\n",
    "  pass\n",
    "\n",
    "measured = np.zeros(2**nqbits)\n",
    "truth = np.zeros(2**nqbits)\n",
    "response = np.zeros((2**nqbits,2**nqbits))\n",
    "\n",
    "for i in range(2**nqbits):\n",
    "  myfile = open(\"tokyodata/output_gLR0_Tokyo_Unfold.dat\")\n",
    "  for line in myfile:\n",
    "    if \"Unfold_1\" in line:\n",
    "      myline = line.split(\"}\")[i].split(\",\")\n",
    "      for j in myline:\n",
    "        if \":\" not in j:\n",
    "          continue\n",
    "        outbin = 0\n",
    "        counts = 0\n",
    "        if \"{\" in j:\n",
    "          outbin = int(bin_convert[j.split(\"{\")[len(j.split(\"{\"))-1].split(\":\")[0].split(\"'\")[1]])\n",
    "          counts = int(j.split(\"{\")[len(j.split(\"{\"))-1].split(\":\")[1])\n",
    "        else:\n",
    "          outbin = int(bin_convert[j.split(\":\")[0].split(\"'\")[1]])\n",
    "          counts = int(j.split(\":\")[1])\n",
    "          pass\n",
    "        truth[i] += counts\n",
    "        measured[outbin] += counts\n",
    "        response[outbin,i] += counts\n",
    "        pass\n",
    "      pass\n",
    "    pass\n",
    "  pass\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "plt.bar(range(2**nqbits),truth,alpha=0.3,color='b',label='Truth')\n",
    "plt.bar(range(2**nqbits),measured,alpha=0.3,color='r',label='Measured')\n",
    "xvals = []\n",
    "xlabs = []\n",
    "for i in range(2**nqbits):\n",
    "  xvals+=[i+0.5]\n",
    "  xlabs+=[r'$|'+mybin(i,nqbits)+r'\\rangle$']\n",
    "  pass\n",
    "plt.xticks(xvals,xlabs,rotation='vertical',size=10)\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.legend(frameon=False,loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_normalized = np.zeros((2**nqbits,2**nqbits))\n",
    "response_normalized_reversed = np.zeros((2**nqbits,2**nqbits))\n",
    "response_labels = np.zeros((2**nqbits,2**nqbits))\n",
    "for i in range(2**nqbits):\n",
    "  mysum = 0.\n",
    "  for j in range(2**nqbits):\n",
    "    mysum+=response[j,i]\n",
    "    pass\n",
    "  for j in range(2**nqbits):\n",
    "    response_labels[j][i] = '0'\n",
    "    if (mysum > 0):\n",
    "      response_normalized[j,i] = 100.*response[j,i]/mysum\n",
    "      response_normalized_reversed[i,j] = response_normalized[j,i]\n",
    "      response_labels[j][i] = '%1.0f' % (response_normalized[j,i])\n",
    "      pass\n",
    "    pass\n",
    "  pass\n",
    "\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "sns.set(font_scale=1.5)\n",
    "ax = sns.heatmap(response_normalized,annot=response_labels,annot_kws={\"size\": 8},cmap=\"Blues\")\n",
    "ax.figure.axes[-1].yaxis.label.set_size(18)\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Pr(Measured | True) [%]', labelpad=30)\n",
    "plt.xticks(xvals,xlabs,rotation='vertical',fontsize=10)\n",
    "plt.yticks(xvals,xlabs,rotation='horizontal',fontsize=10)\n",
    "plt.xlabel('True',fontsize=18)\n",
    "plt.ylabel('Measured',fontsize=18)\n",
    "plt.subplots_adjust(bottom=0.18,left=0.18)\n",
    "plt.savefig(\"plots/fig4.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fun0(x,Rin):\n",
    "    myR = []\n",
    "    for i in range(2**nqbits):\n",
    "        myR += [[]]\n",
    "        for j in range(2**nqbits):\n",
    "            meas = mybin(i,nqbits)\n",
    "            true = mybin(j,nqbits)\n",
    "            myp = 1.\n",
    "            for k in range(len(meas)):\n",
    "                if (meas[k]=='0' and true[k]=='1'):\n",
    "                    myp *= x[0]\n",
    "                elif (meas[k]=='1' and true[k]=='0'):\n",
    "                    myp *= x[1]\n",
    "                elif (meas[k]=='0' and true[k]=='0'):\n",
    "                    myp *= (1.-x[0])\n",
    "                else:\n",
    "                    myp *= (1.-x[1])\n",
    "                    pass\n",
    "                pass\n",
    "            myR[i]+=[myp]\n",
    "            pass\n",
    "        pass\n",
    "    return np.sum((Rin/100. - myR)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fun1(x,Rin):\n",
    "    x = np.reshape(x,[nqbits,2])\n",
    "    myR = []\n",
    "    for i in range(2**nqbits):\n",
    "        myR += [[]]\n",
    "        for j in range(2**nqbits):\n",
    "            meas = mybin(i,nqbits)\n",
    "            true = mybin(j,nqbits)\n",
    "            myp = 1.\n",
    "            for k in range(len(meas)):\n",
    "                if (meas[k]=='0' and true[k]=='1'):\n",
    "                    myp *= x[k][0]\n",
    "                elif (meas[k]=='1' and true[k]=='0'):\n",
    "                    myp *= x[k][1]\n",
    "                elif (meas[k]=='0' and true[k]=='0'):\n",
    "                    myp *= (1.-x[k][0])\n",
    "                else:\n",
    "                    myp *= (1.-x[k][1])\n",
    "                    pass\n",
    "                pass\n",
    "            myR[i]+=[myp]\n",
    "            pass\n",
    "        pass\n",
    "    return np.sum((Rin/100. - myR)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = [0.5,0.5]\n",
    "bnds = tuple((0, 1.) for x in x0)\n",
    "res = minimize(fun0, x0, method='SLSQP',bounds=bnds, tol=1e-6, args=(response_normalized_reversed))\n",
    "universal = res.x\n",
    "\n",
    "x0 = []\n",
    "for i in range(nqbits):\n",
    "    x0+=[[0.5,0.5]]\n",
    "    pass\n",
    "x0 = np.array(x0)\n",
    "x0 = np.reshape(x0,[2*len(x0),1])\n",
    "bnds = tuple((0, 1.) for x in x0)\n",
    "res = minimize(fun1, x0, method='SLSQP',bounds=bnds, tol=1e-6, args=(response_normalized_reversed))\n",
    "perqubit = res.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can also check from the matrix itself what the transition probabilities look like when all the other qubits are fixed.\n",
    "\n",
    "one_to_zero_x = []\n",
    "one_to_zero_y = []\n",
    "zero_to_one_x = []\n",
    "zero_to_one_y = []\n",
    "for k in range(nqbits):\n",
    "    mydict = {}\n",
    "    for i in range(2**nqbits):\n",
    "        for j in range(2**nqbits):\n",
    "            meas = mybin(i,nqbits)\n",
    "            true = mybin(j,nqbits)\n",
    "            issame = True\n",
    "            mylab = true[:k]+true[k+1:]\n",
    "            if mylab not in mydict:\n",
    "                mydict[mylab]={}\n",
    "                pass\n",
    "            if (true[:k]+true[k+1:] == meas[:k]+meas[k+1:]):\n",
    "                if (meas[k]=='0' and true[k]=='0'):\n",
    "                    mydict[mylab][0,0] = response_normalized[i,j]\n",
    "                elif (meas[k]=='0' and true[k]=='1'):\n",
    "                    mydict[mylab][1,0] = response_normalized[i,j]\n",
    "                elif (meas[k]=='1' and true[k]=='0'):\n",
    "                    mydict[mylab][0,1] = response_normalized[i,j]\n",
    "                else:\n",
    "                    mydict[mylab][1,1] = response_normalized[i,j]\n",
    "                pass\n",
    "            pass\n",
    "        pass\n",
    "    for val in mydict:\n",
    "        one_to_zero_x+=[k]\n",
    "        one_to_zero_y+=[mydict[val][1,0]/(mydict[val][1,0]+mydict[val][1,1])]\n",
    "        zero_to_one_x+=[k]\n",
    "        zero_to_one_y+=[mydict[val][0,1]/(mydict[val][0,0]+mydict[val][0,1])]\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(5,5))\n",
    "plt.plot(range(nqbits),[perqubit[2*i] for i in range(nqbits)],marker='^',linestyle='none',color='blue',label=r'Per qubit $p_{0\\rightarrow 1}$',markersize=12)\n",
    "plt.plot(range(nqbits),[perqubit[2*i+1] for i in range(nqbits)],marker='v',linestyle='none',color='red',label=r'Per qubit $p_{1\\rightarrow 0}$',markersize=12)\n",
    "plt.plot(range(nqbits),np.ones(nqbits)*universal[0],color='blue',linestyle=\":\",label=r'Global $p_{0\\rightarrow 1}$')\n",
    "plt.plot(zero_to_one_x,zero_to_one_y,linestyle='none',color='blue',marker='^', mfc='none',alpha=0.2,label=r'Single qubit $0\\rightarrow 1$')\n",
    "plt.plot(one_to_zero_x,one_to_zero_y,linestyle='none',color='red',marker='v', mfc='none',alpha=0.2,label=r'Single qubit $1\\rightarrow 0$')\n",
    "plt.plot(range(nqbits),np.ones(nqbits)*universal[1],color='red',linestyle=\":\",label=r'Global $p_{1\\rightarrow 0}$')\n",
    "plt.xlabel('Qubit Label')\n",
    "plt.ylabel('Readout Error')\n",
    "plt.legend(frameon=False,loc='upper center',fontsize=11,ncol=2)\n",
    "plt.ylim([0,0.35])\n",
    "plt.text(0., 0.355, \"IBMQ Tokyo, 5 Qubits\", fontsize=18)\n",
    "plt.savefig(\"plots/fig5.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative spread in filled markers for 0 -> 1: 0.29849781269309067\n",
      "Relative spread in filled markers for 1 -> 0: 0.3989394525749185\n"
     ]
    }
   ],
   "source": [
    "print(\"Relative spread in filled markers for 0 -> 1:\",np.std([perqubit[2*i] for i in range(nqbits)])/np.mean([perqubit[2*i] for i in range(nqbits)]))\n",
    "print(\"Relative spread in filled markers for 1 -> 0:\",np.std([perqubit[2*i+1] for i in range(nqbits)])/np.mean([perqubit[2*i+1] for i in range(nqbits)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's show the pathological example for a QIS simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SHO_groundstate(x):\n",
    "    return np.exp(-x**2/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here is how you could setup an entangled Gaussian.  This is not needed for this example (more in the next cells), but it is useful and thus shown here.\n",
    "nq = 5\n",
    "\n",
    "qr = QuantumRegister(nq, 'qr')\n",
    "cr = ClassicalRegister(nq, 'cr')\n",
    "circ = QuantumCircuit(qr, cr)\n",
    "\n",
    "qrs = []\n",
    "for i in range(nq):\n",
    "    qrs+=[qr[i]]\n",
    "    pass\n",
    "\n",
    "desired_vector = []\n",
    "for i in range(2**nq):\n",
    "    desired_vector += [SHO_groundstate(2.*(-2**(nq-1)+i+0.5)/2**(nq-1))]\n",
    "    pass\n",
    "desired_vector /= np.sqrt(np.sum([x**2 for x in desired_vector]))\n",
    "\n",
    "circ.initialize(desired_vector, qrs)\n",
    "circ.measure(qr, cr)\n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "result = execute(circ, simulator,shots=10000).result()\n",
    "mycounts = result.get_counts(circ)\n",
    "           \n",
    "xvals = []\n",
    "yvals = []\n",
    "for j in range(2**nq):\n",
    "    xvals+=[j]\n",
    "    yvals+=[mycounts[mybin(j,nq)[::-1]]]\n",
    "    \n",
    "sns.set_style(\"white\")\n",
    "plt.errorbar(xvals,yvals,yerr=np.sqrt(yvals),linestyle='none',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "nqubits = 4\n",
    "ntest = 1000000\n",
    "ntrain = 1000000\n",
    "\n",
    "#This sets up some useful arrays and functions for later.\n",
    "mymapping = {}\n",
    "mymapping_inverse = {}\n",
    "for i in range(2**nqubits):\n",
    "    mymapping[i] = bin(i).split('b')[1]\n",
    "    mymapping_inverse[bin(i).split('b')[1]] = i\n",
    "    pass\n",
    "\n",
    "nqubitarray = []\n",
    "for i in range(nqubits):\n",
    "    nqubitarray+=[i]\n",
    "    pass\n",
    "\n",
    "def mybin(input,q,r=1):\n",
    "    if (r==0):\n",
    "        return bin(input).split('b')[1].zfill(q) \n",
    "    else:\n",
    "        return bin(input).split('b')[1].zfill(q)[::-1]  \n",
    "\n",
    "xvals = []\n",
    "xlabs = []\n",
    "xlabs0 = []\n",
    "for i in range(2**nqubits):\n",
    "    xvals+=[i]\n",
    "    xlabs+=[r'$|'+mybin(i,nqubits)+r'\\rangle$']\n",
    "    xlabs0+['']\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's make the noise model\n",
    "\n",
    "epsilon = 0.25\n",
    "\n",
    "mynoise = []\n",
    "first_row = [1.-epsilon,epsilon]\n",
    "last_row = []\n",
    "for i in range(2,2**nqubits):\n",
    "    first_row+=[0.]\n",
    "    last_row+=[0.]\n",
    "    pass\n",
    "last_row += [epsilon,1.-epsilon]\n",
    "mynoise+=[first_row]\n",
    "for i in range(1,2**nqubits-1):\n",
    "    middle_rows = []\n",
    "    for j in range(2**nqubits):\n",
    "        if (j==i-1 or j==i+1):\n",
    "            middle_rows+=[epsilon]\n",
    "        elif (j==i):\n",
    "            middle_rows+=[1.-2*epsilon]\n",
    "        else:\n",
    "            middle_rows+=[0.]\n",
    "            pass\n",
    "        pass\n",
    "    mynoise+=[middle_rows]\n",
    "    pass\n",
    "mynoise+=[last_row]\n",
    "c = ReadoutError(mynoise)\n",
    "noise_model = noise.NoiseModel()\n",
    "noise_model.add_readout_error(c,nqubitarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is a special measure function that allows us to simultaneously measure multiple qubits at the same time.\n",
    "measure2 = Instruction(\"measure\", nqubits, nqubits, [])\n",
    "\n",
    "#Test distribution is a Gaussian, the ground state of a SHO.  Note that we could prepare\n",
    "#the initial state to be in a superposition of qubits so that every measurement follows\n",
    "#a Gaussian.  However, in that case, we don't know the true distribution in the finite\n",
    "#statistics limit.  For studying readout errors in isolation in simulation, we don't need\n",
    "#the qubits to be entangled.\n",
    "\n",
    "mymean = 1. #1 means the Gaussian is centered.\n",
    "mystd = 3.\n",
    "myvals = mystd*np.random.randn(ntest)+2**(nqubits-1)*mymean\n",
    "#myvals = np.random.uniform(0,2**(nqubits),ntest)\n",
    "\n",
    "for i in range(len(myvals)):\n",
    "    myvals[i] = int(myvals[i])\n",
    "    if (myvals[i] < 0):\n",
    "        myvals[i]=0\n",
    "        pass\n",
    "    if (myvals[i] > 2**(nqubits)-1):\n",
    "        myvals[i] = 2**(nqubits)-1\n",
    "        pass\n",
    "scult_vals = {}\n",
    "for i in range(2**nqubits):\n",
    "    scult_vals[i] = len([x for x in myvals if x==i])\n",
    "    pass\n",
    "\n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "many_counts = []\n",
    "many_counts_sculpt = []\n",
    "many_counts_sculpt_corrected = []\n",
    "\n",
    "#First, let's compute what qiskit-ignis would do\n",
    "qr_calib = QuantumRegister(nqubits)\n",
    "meas_cals, state_labels = complete_meas_cal(qubit_list=nqubitarray, qr=qr_calib)\n",
    "\n",
    "#Calibration circuit - ignis's built in one doesn't work for multi-qubit readout errors.\n",
    "meas_cals_mine = []\n",
    "for i in range(2**nqubits):\n",
    "    binnumber_initial = mybin(i,nqubits)\n",
    "    qr = QuantumRegister(nqubits, 'qr')\n",
    "    cr = ClassicalRegister(nqubits, 'cr')\n",
    "    circ = QuantumCircuit(qr, cr)\n",
    "    for j in range(nqubits):\n",
    "        if (binnumber_initial[j]=='1'):\n",
    "            circ.x(qr[j])\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "    circ.name='cal_'+binnumber_initial[::-1] #Their convention is backwards of mine.\n",
    "    circ.name=circ.name\n",
    "    circ.append(measure2, nqubitarray, nqubitarray)\n",
    "    meas_cals_mine+=[circ]\n",
    "    pass\n",
    "\n",
    "#These are our custom calibration circuits.\n",
    "job = execute(meas_cals_mine, backend=simulator, shots=int(ntrain/(2**nqubits)), noise_model=noise_model)\n",
    "cal_results = job.result()\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels)\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "#Let's first quickly process these\n",
    "many_counts = []\n",
    "for i in range(2**nqubits):\n",
    "    binnumber_initial = mybin(i,nqubits)\n",
    "    counts = cal_results.get_counts(meas_cals_mine[i])\n",
    "    many_counts+=[counts]\n",
    "    pass\n",
    "\n",
    "#Let's make our own matrix based on cal_results.\n",
    "measured_cal = np.zeros(2**nqubits)\n",
    "truth_cal = np.zeros(2**nqubits)\n",
    "response_cal = np.zeros((2**nqubits,2**nqubits))\n",
    "for j in range(len(many_counts)):\n",
    "    counts = many_counts[j]\n",
    "    for i in range(2**nqubits):\n",
    "        binnumber_initial = mybin(i,nqubits,0)\n",
    "        if (binnumber_initial in counts):\n",
    "            measured_cal[i]+=counts[binnumber_initial]\n",
    "            truth_cal[j]+=counts[binnumber_initial]\n",
    "            response_cal[i,j]+=counts[binnumber_initial]\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "response_normalized_cal = np.zeros((2**nqubits,2**nqubits))\n",
    "for i in range(len(truth_cal)):\n",
    "    for j in range(len(truth_cal)):\n",
    "        response_normalized_cal[i,j]=response_cal[i,j]/truth_cal[j]\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "#Okay, now for the \"test\" aka the measurement.\n",
    "for i in range(2**nqubits):\n",
    "    binnumber_initial = mybin(i,nqubits)\n",
    "    \n",
    "    #Calibration circuit\n",
    "    qr = QuantumRegister(nqubits, 'qr')\n",
    "    cr = ClassicalRegister(nqubits, 'cr')\n",
    "    circ = QuantumCircuit(qr, cr)\n",
    "    for j in range(nqubits):\n",
    "        if (binnumber_initial[j]=='1'):\n",
    "            circ.x(qr[j])\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "    circ.append(measure2, nqubitarray, nqubitarray)\n",
    "    counts_sculpt = {}\n",
    "\n",
    "    if (scult_vals[i] > 0):\n",
    "        result2 = execute(circ, simulator,noise_model=noise_model,shots=scult_vals[i]).result()\n",
    "        counts_sculpt = result2.get_counts(circ)\n",
    "        pass\n",
    "    many_counts_sculpt+=[counts_sculpt]\n",
    "    pass\n",
    "\n",
    "measured_test = np.zeros(2**nqubits)\n",
    "truth_test = np.zeros(2**nqubits)\n",
    "response_test = np.zeros((2**nqubits,2**nqubits))\n",
    "for j in range(len(many_counts_sculpt)):\n",
    "    counts = many_counts_sculpt[j]\n",
    "    for i in range(2**nqubits):\n",
    "        binnumber_initial = mybin(i,nqubits,0)\n",
    "        if (binnumber_initial in counts):\n",
    "            measured_test[i]+=counts[binnumber_initial]\n",
    "            truth_test[j]+=counts[binnumber_initial]\n",
    "            response_test[j,i]+=counts[binnumber_initial]\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "many_counts = {}\n",
    "for i in range(2**nqubits):\n",
    "    binnumber_final = mybin(i,nqubits,0)\n",
    "    many_counts[binnumber_final] = 0\n",
    "    for j in many_counts_sculpt:\n",
    "        if binnumber_final in j:\n",
    "            many_counts[binnumber_final]+=j[binnumber_final]\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "ignis_counts = meas_filter.apply(many_counts)\n",
    "\n",
    "measured_test_ignis = []\n",
    "for i in range(2**nqubits):\n",
    "    binnumber_initial = mybin(i,nqubits)\n",
    "    measured_test_ignis+=[ignis_counts[binnumber_initial[::-1]]]\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "sns.set(font_scale=1.5)\n",
    "ax = sns.heatmap(response_normalized_cal,annot_kws={\"size\": 8},cmap=\"Blues\")\n",
    "ax.figure.axes[-1].yaxis.label.set_size(18)\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Pr(Measured | True) [%]', labelpad=30)\n",
    "plt.xticks(xvals,xlabs,rotation='vertical',fontsize=10)\n",
    "plt.yticks(xvals,xlabs,rotation='horizontal',fontsize=10)\n",
    "plt.xlabel('True',fontsize=18)\n",
    "plt.ylabel('Measured',fontsize=18)\n",
    "plt.subplots_adjust(bottom=0.18,left=0.18)\n",
    "plt.savefig(\"plots/fig4.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, for the unfolding.\n",
    "unfolded_Matrix = MatrixInversion(measured_test,response_normalized_cal)\n",
    "unfolded_IBU = IBU(measured_test,truth_cal,response_normalized_cal,10)\n",
    "\n",
    "bincenters = np.array(xvals)+0.5\n",
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(2, 1, width_ratios=[1], height_ratios=[3.5, 1])\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax1.set_xticklabels( () )\n",
    "\n",
    "def myre(x):\n",
    "    return np.concatenate([[x[0]],x])\n",
    "\n",
    "#mybincenters = np.linspace(-0.5,31.5,33)\n",
    "mybincenters = np.linspace(-0.5,15.5,17)\n",
    "plt.step(mybincenters,myre(truth_test),color='black',label=r\"t\")\n",
    "plt.errorbar(0.5*(mybincenters[1:]+mybincenters[:-1]),measured_test,yerr=np.sqrt(measured_test),label='m',marker='o',linestyle='none',color='black')\n",
    "plt.step(mybincenters,myre(unfolded_Matrix),color='blue',linestyle=':',label=r\"$\\hat{t}_{matrix}$\",linewidth=3.0)\n",
    "plt.step(mybincenters,myre(measured_test_ignis),color='red',linestyle='--',label=r\"$\\hat{t}_{ignis}$\")\n",
    "plt.step(mybincenters,myre(unfolded_IBU),color='green',linestyle='-.',label=r\"$\\hat{t}_{IBU}$\")\n",
    "\n",
    "_=plt.xticks(xvals,xlabs0,rotation='vertical',fontsize=10)\n",
    "plt.ylabel('Counts')\n",
    "plt.ylim([0,2.*max(truth_test)])\n",
    "plt.legend(loc='upper left',fontsize=12,frameon=False)\n",
    "_=plt.text(10., max(truth_test)*1.8, \"Qiskit Simulator\", fontsize=18)\n",
    "\n",
    "ax2 = plt.subplot(gs[1])\n",
    "ratio_ignis2 = np.zeros(2**nqubits)\n",
    "ratio_ignis = np.zeros(2**nqubits)\n",
    "ratio_matrix = np.zeros(2**nqubits)\n",
    "ratio_unfold = np.zeros(2**nqubits)\n",
    "for i in range(len(ratio_unfold)):\n",
    "    ratio_unfold[i]=1.\n",
    "    ratio_matrix[i]=1.\n",
    "    ratio_ignis[i]=1.\n",
    "    ratio_ignis2[i]=1.\n",
    "    if (truth_test[i] > 0):\n",
    "        ratio_unfold[i] = unfolded_IBU[i]/truth_test[i]\n",
    "        ratio_matrix[i] = unfolded_Matrix[i]/truth_test[i]\n",
    "        ratio_ignis[i] = measured_test_ignis[i]/truth_test[i]\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "plt.ylim([0.8,1.2])\n",
    "plt.step(bincenters,ratio_ignis,color='red',linestyle=\"--\")\n",
    "plt.step(bincenters,ratio_matrix,color='blue',linestyle=\":\",linewidth=3.0)\n",
    "plt.step(bincenters,ratio_unfold,color='black')\n",
    "plt.ylabel('Extracted / Truth')\n",
    "xx2 = [1.,1.]\n",
    "plt.plot([xvals[0],xvals[len(xvals)-1]],xx2,color='black',linestyle=':')\n",
    "_=plt.xticks(xvals,xlabs,rotation='vertical',fontsize=12)\n",
    "plt.savefig(\"plots/fig4.pdf\",bbox_inches='tight')\n",
    "\n",
    "#Save this random number !!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use the realistic response matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "nqubits_r = 5\n",
    "ntest_r = 1000000\n",
    "ntrain_r = 1000000\n",
    "\n",
    "#This sets up some useful arrays and functions for later.\n",
    "mymapping_r = {}\n",
    "mymapping_inverse_r = {}\n",
    "for i in range(2**nqubits_r):\n",
    "    mymapping_r[i] = bin(i).split('b')[1]\n",
    "    mymapping_inverse_r[bin(i).split('b')[1]] = i\n",
    "    pass\n",
    "\n",
    "nqubitarray_r = []\n",
    "for i in range(nqubits_r):\n",
    "    nqubitarray_r+=[i]\n",
    "    pass \n",
    "\n",
    "xvals_r = []\n",
    "xlabs_r = []\n",
    "xlabs0_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    xvals_r+=[i]\n",
    "    xlabs_r+=[r'$|'+mybin(i,nqubits_r)+r'\\rangle$']\n",
    "    xlabs0_r+['']\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can directly use the Tokyo response matrix.\n",
    "mynoise_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    hold = []\n",
    "    for j in range(2**nqubits_r):\n",
    "        #if (i==j):\n",
    "        #    hold+=[1.]\n",
    "        #else:\n",
    "        #    hold+=[0.]\n",
    "        hold+=[response_normalized[j][i]/100.]\n",
    "        pass\n",
    "    mynoise_r+=[hold]\n",
    "    pass\n",
    "c_r = ReadoutError(mynoise_r)\n",
    "noise_model_r = noise.NoiseModel()\n",
    "noise_model_r.add_readout_error(c_r,nqubitarray_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is a special measure function that allows us to simultaneously measure multiple qubits at the same time.\n",
    "measure2_r = Instruction(\"measure\", nqubits_r, nqubits_r, [])\n",
    "\n",
    "#Test distribution is a Gaussian, the ground state of a SHO.  Note that we could prepare\n",
    "#the initial state to be in a superposition of qubits so that every measurement follows\n",
    "#a Gaussian.  However, in that case, we don't know the true distribution in the finite\n",
    "#statistics limit.  For studying readout errors in isolation in simulation, we don't need\n",
    "#the qubits to be entangled.\n",
    "\n",
    "mymean_r = 1. #1 means the Gaussian is centered.\n",
    "mystd_r = 3.5\n",
    "myvals_r = mystd_r*np.random.randn(ntest_r)+2**(nqubits_r-1)*mymean_r\n",
    "#myvals = np.random.uniform(0,2**(nqubits),ntest)\n",
    "\n",
    "for i in range(len(myvals_r)):\n",
    "    myvals_r[i] = int(myvals_r[i])\n",
    "    if (myvals_r[i] < 0):\n",
    "        myvals_r[i]=0\n",
    "        pass\n",
    "    if (myvals_r[i] > 2**(nqubits_r)-1):\n",
    "        myvals_r[i] = 2**(nqubits_r)-1\n",
    "        pass\n",
    "scult_vals_r = {}\n",
    "for i in range(2**nqubits_r):\n",
    "    scult_vals_r[i] = len([x for x in myvals_r if x==i])\n",
    "    pass\n",
    "\n",
    "simulator_r = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "many_counts_r = []\n",
    "many_counts_sculpt_r = []\n",
    "many_counts_sculpt_corrected_r = []\n",
    "\n",
    "#First, let's compute what qiskit-ignis would do\n",
    "qr_calib_r = QuantumRegister(nqubits_r)\n",
    "meas_cals_r, state_labels_r = complete_meas_cal(qubit_list=nqubitarray_r, qr=qr_calib_r)\n",
    "\n",
    "#Calibration circuit - ignis's built in one doesn't work for multi-qubit readout errors.\n",
    "meas_cals_mine_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial = mybin(i,nqubits_r)\n",
    "    qr_r = QuantumRegister(nqubits_r, 'qr')\n",
    "    cr_r = ClassicalRegister(nqubits_r, 'cr')\n",
    "    circ_r = QuantumCircuit(qr_r, cr_r)\n",
    "    for j in range(nqubits_r):\n",
    "        if (binnumber_initial[j]=='1'):\n",
    "            circ_r.x(qr_r[j])\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "    circ_r.name='cal_'+binnumber_initial[::-1] #Their convention is backwards of mine.\n",
    "    circ_r.name=circ_r.name\n",
    "    circ_r.append(measure2_r, nqubitarray_r, nqubitarray_r)\n",
    "    meas_cals_mine_r+=[circ_r]\n",
    "    pass\n",
    "\n",
    "#These are our custom calibration circuits.\n",
    "job_r = execute(meas_cals_mine_r, backend=simulator_r, shots=int(ntrain_r/(2**nqubits_r)), noise_model=noise_model_r)\n",
    "cal_results_r = job_r.result()\n",
    "meas_fitter_r = CompleteMeasFitter(cal_results_r, state_labels_r)\n",
    "meas_filter_r = meas_fitter_r.filter\n",
    "\n",
    "#Let's first quickly process these\n",
    "many_counts_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial = mybin(i,nqubits_r)\n",
    "    counts_r = cal_results_r.get_counts(meas_cals_mine_r[i])\n",
    "    many_counts_r+=[counts_r]\n",
    "    pass\n",
    "\n",
    "#Let's make our own matrix based on cal_results.\n",
    "measured_cal_r = np.zeros(2**nqubits_r)\n",
    "truth_cal_r = np.zeros(2**nqubits_r)\n",
    "response_cal_r = np.zeros((2**nqubits_r,2**nqubits_r))\n",
    "for j in range(len(many_counts_r)):\n",
    "    counts = many_counts_r[j]\n",
    "    for i in range(2**nqubits_r):\n",
    "        binnumber_initial = mybin(i,nqubits_r,0)\n",
    "        if (binnumber_initial in counts):\n",
    "            measured_cal_r[i]+=counts[binnumber_initial]\n",
    "            truth_cal_r[j]+=counts[binnumber_initial]\n",
    "            response_cal_r[i,j]+=counts[binnumber_initial]\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "response_normalized_cal_r = np.zeros((2**nqubits_r,2**nqubits_r))\n",
    "for i in range(len(truth_cal_r)):\n",
    "    for j in range(len(truth_cal_r)):\n",
    "        response_normalized_cal_r[i,j]=response_cal_r[i,j]/truth_cal_r[j]\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "#Okay, now for the \"test\" aka the measurement.\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial = mybin(i,nqubits_r)\n",
    "    \n",
    "    #Calibration circuit\n",
    "    qr_r = QuantumRegister(nqubits_r, 'qr')\n",
    "    cr_r = ClassicalRegister(nqubits_r, 'cr')\n",
    "    circ_r = QuantumCircuit(qr_r, cr_r)\n",
    "    for j in range(nqubits_r):\n",
    "        if (binnumber_initial[j]=='1'):\n",
    "            circ_r.x(qr_r[j])\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "    circ_r.append(measure2_r, nqubitarray_r, nqubitarray_r)\n",
    "    counts_sculpt_r = {}\n",
    "\n",
    "    if (scult_vals_r[i] > 0):\n",
    "        result2_r = execute(circ_r, simulator_r,noise_model=noise_model_r,shots=scult_vals_r[i]).result()\n",
    "        counts_sculpt_r = result2_r.get_counts(circ_r)\n",
    "        pass\n",
    "    many_counts_sculpt_r+=[counts_sculpt_r]\n",
    "    pass\n",
    "\n",
    "measured_test_r = np.zeros(2**nqubits_r)\n",
    "truth_test_r = np.zeros(2**nqubits_r)\n",
    "response_test_r = np.zeros((2**nqubits_r,2**nqubits_r))\n",
    "for j in range(len(many_counts_sculpt_r)):\n",
    "    counts = many_counts_sculpt_r[j]\n",
    "    for i in range(2**nqubits_r):\n",
    "        binnumber_initial = mybin(i,nqubits_r,0)\n",
    "        if (binnumber_initial in counts):\n",
    "            measured_test_r[i]+=counts[binnumber_initial]\n",
    "            truth_test_r[j]+=counts[binnumber_initial]\n",
    "            response_test_r[j,i]+=counts[binnumber_initial]\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "many_counts_r = {}\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_final_r = mybin(i,nqubits_r,0)\n",
    "    many_counts_r[binnumber_final_r] = 0\n",
    "    for j in many_counts_sculpt_r:\n",
    "        if binnumber_final_r in j:\n",
    "            many_counts_r[binnumber_final_r]+=j[binnumber_final_r]\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "ignis_counts_r = meas_filter_r.apply(many_counts_r)\n",
    "\n",
    "measured_test_ignis_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial_r = mybin(i,nqubits_r)\n",
    "    if (binnumber_initial_r[::-1] in ignis_counts_r):\n",
    "        measured_test_ignis_r+=[ignis_counts_r[binnumber_initial_r[::-1]]]\n",
    "    else:\n",
    "        measured_test_ignis_r+=[0.]\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fxMbGIiIiwug5Q3UZOg7UpZ6vWrWq9d88EREpBhsm1kkRjR3AcCK5oWRzY+eYek5ERET1FNPYsSSmnhMREcCeHWtl0Tk7TD0nIiIiuVm0Z0eODfyIiIhMxo4dq8RhLCIiors4jGWdFLH0nIiIiEguikk9N5RgztRzIiJqLdxnxzoppmfHULo5U8+JiIjoXihmzo6hdHNrSD0XIZJkLldCukhK8aVNUZLLAoDXi9KX+F9JF6tbqraWkN7DXXqS+aXS25LLerq1N+VxrI5IkvltjfSE9PYOTEgHxJLMdSJ/4WSaScxeGOukmJ4dc2HqORERmUwl04ssyuoaO6Zi6jkREZF1Uswwlrkw9ZyIiEzFYSzrZHU9O0w9JyIiooYU09iJi4tDZGQkioqK4Ovri8zMTKPHDZ1rmKAeEhKC4OBg9OvXz+Dxevn5+WzsEBHd57j03DopZhjLUII5U8+JiKi1sGFinRTTs2NJTD0nIiKyXhbt2WHqORERKQl7dqwTU8+JiIjIqilmzg4REZHFsWPHKrGxQ0REdBeHsayTYlLPRZPKjZ0TTUNn6jkREZH1UsRqLFOSyo2dE0lDB5h6TkREdbjPjnVSxDCWKUnlhq7p27evUBr6vaSe39HqJJWzt5WnTSmSZC4SJiySUiz6zkSSzB+I2iy57IWN0yWXrRRIrnbr4CC5bMnv0hPHvQQSx0WSoEWSzJWQ6C7iZlWN5LLOjtJ/tIl8viJJ5pXVlk9IF/m66XTSPweRnxEibJTwjUZWSRE9O6YklbeUYi71HvWYek5ERCqVPC+yLEX07CgBU8+JiIhDTtZJET07piSVt5RiLvUe9Zh6TkREZJ0U0dgxJam8pRRzqfeox9RzIiLiMJZ1UsQwVsNEcq1Wi8mTJ+sTyQ0dN3ZNXFwcDh06hLKyMvj6+iI2NhYREREGywN1qeerVq1q/TdPREREslJEYwcQTyo3dk40DZ2p50REBHDOjrVSTGPHkph6TkREAIecrJVF5+ww9ZyIiIjkxtRzIiKiu+TaMJEsi8NYREREZBEzZsy45zpUKhXS09ONlmFjh4iI6C7O2Wldhw4dgqurK9q3lx5109Dt27dRXl7eYjmLNnaio6OxcuVKeHl5ISEhAQUFBXB3d0dWVpa+zL59+7B8+XLodDpERERg9uzZLZ4TrUutVmPRokVYt25dK71zIiJSIq7Gan2LFi3C008/bdK1n332GRYuXNhiOUVsKgiIJ5Uz9ZyIiKhtGzJkCNzc3Ey+3t3dHYMHD26xnGKGsUSTypWQei41zbxGKz1N2M5Wnn9ViPxjRSlp2L9ujpZcttfLn0guW7JhquSy5bfuSC7b3V16N+zFUnkS0rUCydW2AhMxlfA9IZJkfqtaekJ6h3byJKQ7tZMnIV2kXpGvm8jEXLm+z5SAHTuta/v27fd0/dixYzF27NgWyymmZ6c5TD0nIiK6f/3nP//BP/7xD8yePRuLFy/GwYMHTapH0Y2d1sTUcyIiUqlUsrxIXEZGBl566SX8+uuv6NixI86cOYMXXngBGRkZwnUpZhirOUw9JyKi1sSGiXKkp6dj9erVmDBhgv7YqlWrkJKSgmnTpgnVpeieHaaeExERWa+YmBj897//bfZcTU1Nk8anSqWCVit9jls9xfTsiCaVM/WciIjMjR07rcvb2xt/+ctfMGzYMMybNw9Dhw7Vn5s2bRpef/11fPLJJ3Bzc8OlS5dw7NgxzJ8/X/g+qtpakfn65tVwnx1LUqvVePXVV4XDQKskLvhQwmosEUpYeQOIrfhQwmqszh3sJZflaix5KWE1lo3AB6GE1VgiXzclrMYSWJwnZOj/zZOl3u//L0cODCktLUVKSgo+/vhjDB8+HK+99pp+OfnXX3+NL774Ar/99hu6du2Kp556Cv7+/sL3UEzPjiUx9ZyIiADO2bEENzc3LFy4EH/5y1+QkpKC6OhojBgxAvPmzYOfnx/8/Pzu+R5MPSciIrpLpZLnRS3r2rUr/v73v2PPnj3o0aMHpk+fjldeeQU//fTTPddt0cZOeHg4GztERET3MZ1Oh++//x5ffPEFvvvuO3To0AGLFy/Gnj174OHhgcjISPz1r3/FqVOnTL4Hh7GIiIju4jBW6/rll18wZ84cnD9/Xn/MxcUFS5cuRVBQEJYsWYLZs2dj/fr1mDJlin7R0cMPPyx0H4v27ERHR+tjHfbt24egoCCMHz8eKSkp+jIJCQnw8fFBaGhok+tFrzFUXq1WY86cOeZ+e0RERGTE3//+d7i7uyM3Nxc//vgj9u/fj2effRYLFixARUUFAMDT0xNvv/02cnJy0KlTJ0RERAjfRxH77IiGeppyDYNAiYioJUqZs5OVlYWJEydi8ODBCA4Oxq5du4yW1+l0+Ne//oXAwEAMHjwYTz/9NLKzsxuVqampwXvvvQc/Pz8MGTIE06ZNw/Hjx8UfzoxOnz6NkJAQ9O7dG/b29ujatSumT5+OqqoqXLhwoVHZ7t27Y+XKlcjJyRG+jyKGsURDPU25Ro4gUKlElpPf0eokl5UaRCpK9C+mXMtbRcqKLCcf+H+yWy5017EVE1ouZAKR5eSXyqQvU+/WWXq9IkS+FnItzxYhspxcrmXqIkSWk9/WSF+m3t5Ber0iRJaTK2GZugglDGPl5OQgPj4eMTExGDt2LPbu3YsFCxbA0dGx0W7CDa1YsQLbt29HXFwcHn74YWRnZ+P111+Hs7OzfjXT8uXL8emnnyI+Ph7dunVDWloaZs6cic8++0z/u7G1DRkyBKmpqXBwcICnpydu3LiBrVu3okuXLo32wmvIlGdVRGOnuZDOllqbote0VD4wMBCrV6+WpbFjzSy3SxMRkXVau3YtgoODkZCQAAB44oknoFarkZSU1Gxjp7i4GNu2bcPSpUv1Qzw+Pj44f/489u/fDz8/P1y8eBHbt2/H4sWL8fzzzwOoSwwPCgpCamoqlixZ0npvsIHVq1fjnXfewdKlS3Hnzh2oVCoMHToU69evh6Ojo9nuo4jGjhIwCJSIiCzdsVNSUoLi4mLExcU1Oh4UFITc3FyUlJQ06dnYu3cvHB0d8eyzzzY6vnXrVv3/Hzx4EFqtFkFBQfpjDg4O8Pf3R0FBgfnfiERdu3bFmjVroNPpUFpaChcXF9jbS9+gVSpFzNkRDfU05RoGgRIRkdKdO3cOANC7d+9Gx3v27AkAzf6jvLCwEL1798a3336LZ555Bn/+85/x1FNPNZrbcu7cObi4uMDNza1JvZcvX0ZVVZW534ok7777Lk6fPg0bGxt06dJFuKFz+vRpvPvuuy2WU0RjRzTU05RrGARKREQtUalUsrwqKipw8eLFJq/6FUf1bty4AQBwdnZudLxDhw4AgJs3bzZ55tLSUvz6669YtGgRoqKikJqaikGDBmH+/Pk4ePCg/ro/1tmw3lu3bt37h2eClJQU/WIhU/z888/YsGFDi+UUMYxlSqgng0CJiMjc5BrGSk9PR3JycpPjc+fORWxsrP7PLcVV2tg07aO4c+cOSktLsX79ejz55JMAgFGjRuHcuXNITk7GqFGjWqzXkhOzt2/fjgMHDph0bXMLmJqjiMYOAIP5F8a6p0SvMVRerVZDo9Gga9euAk9MREQkTUxMDMLCwpoc/2OKQMeOHQE07Wmp79GpP99Qhw4dYGtrizFjxuiP2djYYPTo0dixYweAup6i5npv6uttrtentRw+fBiHDx82+XopDTXFNHYsiUGgREQEyNfD0alTJ0nxSPVzdYqLizFgwAD98fo9Z/44lweom3ej0+lQU1MDBwcH/fH61U1A3SKc8vJyqNVquLi4NKrXy8ur0XWt6fTp061yHwaBEhERKUTPnj3h5eWFL774otHxPXv2oFevXujWrVuTa5544gnU1tYiNzdXf6ympgb79+/H8OHDAQCjR48GAOzevVtfRqPRoKCgQH/Omlm0Z4d72hARkZJYeuk5AMyZMwcJCQlwcXGBv78/8vLykJubi7Vr1wKom5BcXFyMvn37wtnZGT4+PvDz88OyZctQWVmJXr16ISMjA5cuXcKaNWsA1EUuhIWF6cv07NkTaWlpqKiowKxZsyz5dlsFh7GIiIjuUsIOyuHh4dBoNNi4cSMyMzPRvXt3JCYmIiQkBABQUFCAhIQEbN68GSNHjgQAvP/++0hKSkJKSgrUajX+/Oc/Y+PGjfD29tbXu3TpUnTq1AkpKSmorKzEoEGDkJaWpl/Wbs1UtS1N0SaDqqTvMi+ZEuIiRIh+9yghdkCuuAhHe3m25pcrLkKun+lKiIsQoYS4CBFKiIsQIVdchKNMX4ox/2+/LPUe+D9PyFIvSaOY1HNDSeVMPSciotailCBQMi/Ldw3cZSjdnKnnRERE95/a2lqUlpZCrVbfc12W75O9y1C6uTWknosQGZqq0UrvHhZJXhchZ0K6yBCHyNDJicQQyWUHJ3zRcqG7fkoMllxWhKer9KGpX8ulb/n+UGfzhew1JPJ1E/l+kOtfx20tIV1kaKqyWvqQl0jyugiRoSmdwJAXINfPNHbDWEpNTQ12796Nr7/+GkeOHMGVK1f0myHa2trioYcego+PD3x9feHn5ycULaGYxo4opp4TEZG5sbHT+qqrq7Fx40ZkZGTg+vXr8PDwQP/+/eHv7w9nZ2fodDqUl5fjypUr2L17Nz755BN07doV0dHRmD59uj7ywpg229gxN6aeExERta78/HwsW7YMdnZ2iI6OxoQJE9CjRw+j1/z888/Izs7Gxx9/jIyMDLz55pst5mm22cYOU8+JiMjc2LHTulatWoXXX38dwcHBknvV+vfvj/79++Nvf/sbPv/8cyQmJrbY2FHMBGVRTD0nIiJq27KzsxESEmLS8KFKpcKkSZMa7RxtiGIaO3FxcYiMjERRURF8fX2RmZlp9HjD1POQkBAEBwc3Sj3/4zXGygN1XWls7BAR3d9UKpUsL2pecynuctShmGEsQ0nlTD0nIiK6f5SXl2P9+vXIz8/HlStXsH79erRr1w6bN2/G3/72N/Tq1Uu4TsX07FgSU8+JiAjgpoKWdv36dUyePBlbt26Fi4sLNBoNAODGjRv48ssvMXXqVPzyyy/C9TL1nIiI6C4OY1nWu+++C7VajV27dmH9+vX6fXb8/PywY8cO2NjYICkpSbheizZ2wsPD2dghIiIiAHUhp1FRUejbt2+TRuLAgQMxffp0/Pe//xWuVzFzdoiIiCyNnTCWdevWrUYbAP+Rq6srbty4IVwv5+wQERGRIvTp0wffffedwfN79+5F7969hetVTOq5oURyQ8cB8aR0pp4TEZExNiqVLC+SJjo6Grm5uVi7di2Ki4sBABqNBqdPn0ZcXBwOHjyIyMhI4XoV0bNjKJHcWFI5IJaUztRzIiJqCVdjWVZ4eDjmzp2LDRs26Bs1r7zyCsLCwpCTk4OoqCiTGjuKmLNjKJF8xIgRRpPKRZLS20rquQiRJHOtQJqwSEqxKJG/9CIJyDYizyxQVCTJ3H/115LL7vnbE5LLVt/RSS77oIs8SeZyEfl+uKOV/jnY28rz7zi5kszlIpJkflsjPSFdJHldJNle6O8xWa25c+di0qRJ+PLLL1FSUgKtVgsvLy88+eSTjTYDFqGIv7mGEslFk81NuUc9pp4TERGXiStD9+7d8eKLL5qtPkU0dpSAqedERESWlZyc3GIZlUolPM9WEY0dQ4nkosnmptyjHlPPiYiII2mWZayxo1KpYG9vDzs7u7bZ2GmYSO7h4YHs7GysWbMGvXv3bva4Oe9Rj6nnRETEYSzLysvLa3JMp9Ph+vXryMnJQX5+PrZu3SpcryIaOw0TybVaLSZPnqyfhGToOFCXbn7o0CGUlZXB19cXsbGxiIiIMHjcWF35+flYtWpVq793IiIiquPp6dns8e7du+PRRx9FeXk5VqxYgQ8++ECoXkU0dgDDieSGjgPiSelMPSciImPYsaNsI0eOxDvvvCN8nSL22bE0pp4TEREp3w8//AA7O/F+Gov27DD1nIiIlEQlshEXmZ2hCcp37tzBqVOnsH//fjz77LPC9Vq0scM9bYiISEm4GsuyjK3GsrOzw/jx47Fw4ULhehUzZ4eIiIjub82txgIAW1tbdO7cGcOKd9UAACAASURBVI6Opu0Sz8YOERHRXVx6blmLFy/G008/jbCwMLPWq5jUc9GkcmPXMPWciIio7Tl8+DCqq6vNXm+LPTumbLSnUqmwd+9eoWvCw8MRFRWFBQsW6I/VJ5WnpaXBw8MDzz33HAICAvThnc1dY0pdDVPPXV1dhd8vERFZB3bsWNYjjzyCI0eOmJRsbkyLjZ1u3bo1OXbixAncvn0bAwYMwJ/+9CfodDqUlJTg5MmTcHNzw9ixY4UfxJSk8vs99VyESJK5UhLSRRKQlfDMX8X5Si4bvO5byWV3x46RXLbi9h3JZTu1t5dcVglEksyVkJDe1ogkmVfdkZ6Q7mgvvV4lsGFrx6KCgoKQlJSEp59+Go8++ijc3d1hY9P476gs2Vhbtmxp9OcdO3bgxIkT2Lx5M0aMGNHo3LFjxzB79mx4e3sLPYQhTD0nIiK6f6xcuRIAcObMGZw5c6bZMq0SBJqSkoIZM2Y0aegAwLBhwxATE4O0tDTMmDFDtGqLYuo5ERGxY8eyDK3GulfCjZ3r16/Dzc3N4HknJyeo1ep7eqh6TD0nIiKyXrt27cJjjz2m//1rKBvrXgkPVg8YMAA7duxAZWVlk3OlpaXYtm0bhgwZYpaHa5hUrtFokJ2djYCAAFnqYuo5ERGpVCpZXtS8hIQEHDt2TPb7CPfszJs3D7Nnz0ZoaChCQ0PRvXt3VFVV4cKFC/j8889x584dJCUlCT+IKUnlTD0nIiJzYrukddXWSl9cci+EGzujR49GSkoKVq9e3WivGpVKhcceewwLFy7EoEGDhB9ENKnc2DVMPSciIqJ6Ju2gPHr0aOzcuROlpaW4dOkSVCoVPD092+weNUw9JyIigEvPLaG8vByXL18Wuqa5bXGMMTkuoqamBhcuXMCvv/6KESNGwN7eHmq1Gi4uLpLrYOo5ERHR/W3FihVYsWKF5PIqlQonT54UuodJjZ3c3FwsX74cv//+OwBg48aN0Gg0eO211zB37lzMmjVLUj3c04aIiJSE/Tqtb/jw4foNf+Ui3Nj55ptv8Prrr+PRRx/FrFmz8M477wAAvLy80L9/f6xZswZdu3bFpEmTzP6wREREcuLKqdY3depUPP3007LeQ3jp+bp16+Dt7Y3Nmzc3atD06dMHGRkZGDZsGNLT0836kERERESmEm7snDp1ChMnTmySVQEAdnZ2CA0NlbwTccPUc0OJ5IYSzI2dY+o5ERGZwkYlz4ssS7ixY29vj5qaGoPny8vLYW8vFjBYn0iempqK7OxsZGVl4ezZswDq5vWkpqY2e52hc80dN3aPhqnnRERE1Dq6desGJycn2e8jPGdnxIgR2LFjB6Kiopqcu3btGjIyMjB8+HChOo0lkhtKNgeYei4XuRLSReuWq16dwDOLJK+LzGwUSTKfmnZEctmt0Y9KfwgrJpJkXqOV/v1gZ8t/ogNiSeZtLSGdc3Za11dffdUq9xHu2Zk/fz6uX7+OZ555Bv/85z+hUqmQl5eHlStXIjQ0FDdv3sS8efOE6mwukfzq1auij3ZP9wgMDMTevXvNek8iImpbVCp5XmRZwo2dvn37Ytu2bXjggQewZcsW1NbWYuvWrUhPT0ePHj2wadMmDBw4UI5nlRVTz4mIiKyT8DDWmTNn0L9/f2zZsgXl5eUoLi6GTqeDp6enyXEL5kw3N/UeTD0nIiIOY1kn4Z6dmTNnYs2aNQCAzp07Y/DgwRg6dOg95UqZM93c1Hsw9ZyIiMg6CTd2Kisrzd4DYmdnp08kDwkJQXBwsD6RPC4uDpGRkSgqKoKvry8yMzP11xk619xxY/cA6lLP2dghIrq/cem5dRIexoqJiUFaWhoGDRqERx55xGwPYiiR3FCCubFzTD0nIiJTcBirdYkGgBrSUjCocGPnxIkTuHbtGqZMmQJHR0d07ty5yQaDKpWqTa1sYuo5ERFR6wsICLinBmZtbS1UKhVOnTpltJxwY6e6uhre3t4mP1hDTD0nIiIlYb9O69q8eXOr3Ee4sbNlyxaz3Zwb+BEREd2/RowY0Sr3EW7sEBERWSsbztmxSsKNHakrlvLy8losEx0djZUrV8LLywsJCQkoKCiAu7s7srKy9GUMHTd2ztDxffv2Yfny5dDpdIiIiMDs2bMB1E1QXrRoEdatWyfpvRERkXViW8c6CS8979atW5PXgw8+CDs7O1y6dAn29vbw9/cXfhCRUE9TrmEQKBER0f3JrHN2Tpw4gVmzZpk0BicS6mnKNQwClYdosKdIcKhcoaEi4Z5yhYbWCuSnfhQjPVg3ast/JZfNEKjXmomEezI0VJxIuGf1HZ30eu2E/60uCZeeWyezfrd4e3sjKipKkcNBDAIlIiK6P5m9adylSxecP3/e3NXKjkGgRESklNTzrKwsTJw4EYMHD0ZwcDB27dol+dpff/0Vw4cPxz//+c9Gx48cOYIBAwY0eb388sviD9jGmHU11vXr1/HRRx+1uJOhJTAIlIiIWqKE1Vg5OTmIj49HTEwMxo4di71792LBggVwdHTEhAkTjF5bW1uLRYsW4ebNm03OFRYWwsnJCWlpaY2OW3K/O1NimkzZuNhsq7E0Gg1KS0uh1Wrx1ltviVYru4ZBoB4eHsjOztYHmgIMAiUiImVYu3YtgoODkZCQAAB44oknoFarkZSU1GJjJyMjA+fOnWv23OnTp9GvXz8MHTrU7M9squY6R06cOIHbt29jwIAB+NOf/gSdToeSkhKcPHkSbm5uGDt2rPB9hBs7hnptbG1tMXLkSISGhpq0GisuLg6HDh1CWVkZfH19ERsbi4iICIPHTbmmPghUq9Vi8uTJTYJAV61aJfzcRERkPSzdsVNSUoLi4mLExcU1Oh4UFITc3FyUlJToF9o0d+3q1auRlJSEl156qcn5U6dOYeDAgbI8t6n+uOhpx44dOHHiBDZv3txksdOxY8cwe/Zsk1IcLLqDckOioZ6mXMMgUCIisoSKigpUVFQ0Od6pU6dGw0j1vTK9e/duVK5nz54AgKKiomYbOzqdDgsXLkRwcDB8fX2bnNdqtThz5gxcXV0RFhaGM2fOoEuXLpgxYwZeeOEFxaxCS0lJwYwZM5pd1T1s2DB9GPmMGTOE6jXLnJ07d+7gwIEDsLW1hY+PD+zs2tbGzAwCJSIiQL6l5+np6UhOTm5yfO7cuYiNjdX/+caNGwAAZ2fnRuU6dOgAAM3Oxamv/+LFi1i/fn2z58+fP4+qqioUFRUhLi4Orq6uyMvLw6pVq3Dz5k3MmzfPpPdlbtevX4ebm5vB805OTlCr1cL1CrdKNBoNli1bhosXL2Ljxo3QaDSYOnUqTp8+DQDo06cP0tPT4e7u3mJdDAIlIiIlkWf3HiAmJgZhYWFNjv/xd2BtC5tw2dg0fcJffvkF7733Ht5//3107Nix2es8PDywYcMGDBw4UD+K4ePjg6qqKmzYsAEvvvhikwaWJQwYMAA7duxAREQEnJycGp0rLS3Ftm3bMGTIEOF6hRs7ycnJ+OSTTzB58mQAwK5du3Dq1CnMmDEDAwcOxDvvvIOkpCQsXbq0xbq4gR8REd0P/jhcZUh9Y+XWrVuNjtf36PyxMaPVapGQkIAJEyZgzJgxqKmp0Z/T6XSoqamBnZ0dnJ2dmx3e8vf3R2ZmJoqKivDII48Ivy9zmzdvHmbPno3Q0FCEhoaie/fuqKqqwoULF/D555/jzp07SEpKEq5XuLGTm5uL5557DsuWLQMA7N69Gx07dsQbb7wBOzs7lJSUIDMzU/hBiIiILM3Sc1fq5+oUFxdjwIAB+uMXLlxodL7er7/+ih9++AE//PBDk714PvjgA3zwwQcoLCxEYWEhjh49ioiICNjb2+vLVFVVAQBcXV1leT+iRo8ejZSUFKxevRopKSn64yqVCo899hgWLlyIQYMGCdcr3Ni5cuWKftna7du3cfjwYfj7++vn6Tz00EPNTsIiIiIi43r27AkvLy988cUXGD9+vP74nj170KtXryYroh944AHs2LGjST3PPfccnn/+ef0ozIULF7BkyRJ4eHg02mYlJycHXl5e8PT0lOkdiRs9ejR27tyJ0tJSXLp0CSqVCp6envfUIBMenuzSpQt+++03AMD+/fuh0WgaLTUvLCzEAw88IKmu6OhofYbVvn37EBQUhPHjxzdqzRk6bso1CQkJ8PHxQWhoaKN61Go15syZI+0DICIiq2WjkuclYs6cOcjKysLSpUuxb98+vPXWW8jNzcVrr70GoG7uyvfff4+bN2/CwcEBjzzySJMXUNcQqv9/f39/DBo0CIsXL0ZmZib279+PN954A1999RUWLlxo8R6tP6qpqcGFCxdQUlKCBx98EPb29iZNTK4n3NgZOXIk0tPTkZaWhlWrVqF9+/YYN24cKioqkJaWhk8++QRPPvmkUJ2GEsmNJZWbco2hlHSmnhMREaCMxk54eDiWLFmCb775BnPmzMHhw4eRmJiIkJAQAEBBQQGmTp2Kn376SXKdDg4OSE1Nxbhx45CcnIxXX30VZ8+eRXJycqMeJCXIzc2Fv78/pk2bhtdffx1nzpzB0aNH4efn1+zvcCmEh7EWLVqEq1evIjExEU5OTli2bBk6deqEo0ePIjExEY8//jjmzp0rVKehRPIRI0YYTCo35RpjCeqmpJ7f0UpL6LW3ld6mFEnDVlhDXBKRJHO5EsdFyFWvyNdO5F9cIknmrk8slFz24t5lkstWVmsll3Xv6CC57M2qmpYL3dWpvX3Lhe7SCfylE0kyL/69UnJZL7f2ksuK/IwQ+ft247b0z9fZUZ7tRdrZy7UWqu2JjIxEZGRks+fCw8Nb/F1VWFjY5Jibm5ukxUOW9M033+D111/Ho48+ilmzZuGdd94BAHh5eaF///5Ys2YNunbtikmTJgnVK/yd1alTJ6SlpeHbb7/FwYMHMXHiRADAwIEDsX37dmzZskV4ObmhRHJjSeWmXGMMU8+JiEilUsnyImnWrVsHb29vbN68uVGDpk+fPsjIyMCwYcOQnp4uXK/JzWg3Nzc4OPzvv8qcnJz0a99LS0tNrdZimHpORERkWadOncLEiROb3U/Izs4OoaGhJv2uNqkv8qOPPsL+/ftRWVkJne5/h3K0Wi1u3bqFs2fP4sSJE5LrM5RIbiyp3JRrjGHqORERyTRyTRLZ29s32ivoj8rLyxstnZdKuGdnw4YN+olTP//8Mw4fPoySkhL8+OOPOHr0KM6fP4/o6GihOhsmkms0GmRnZyMgIMDgcVOvMYap50REpFLJ8yJpRowYgR07dqC6urrJuWvXriEjIwPDh0ufm1hPuGdn586dGDhwILZs2YKysjKMHz8emzdvRrdu3bB9+3a8/fbbwls529nZGUwkN3TclGuMJagz9ZyIiMiy5s+fj8jISDzzzDPw9fWFSqVCXl4eCgoK8Omnn0Kj0ZiU4yXc2Ll06RLi4uLg7OwMZ2dnuLi44MiRIwgLC8O0adNw9OhRpKenY8KECUL1GkokN3TclGsMpaEz9ZyIiADAht0wFtW3b19s27YNy5Ytw5YtWwAAW7duBQB4e3vjH//4BwYOHChcr3Bjx87OTp++CtTt9thwidvIkSOxdu1a4QexJKaeExERWd6ZM2fQv39/bNmyBeXl5SguLoZOp4Onp+c9dUgIz9np06cPjh07pv9z7969G01Gru8lkYKp50REpCQ2Mr1ImpkzZ2LNmjUAgM6dO2Pw4MEYOnToPY+8CH8NwsPDsXPnTsTHx6OyshIBAQE4cuQIkpOTkZOTg/T0dDz88MOS62Jjh4iIlIITlC2rsrJSlpXRwsNYzz//PK5cuYJt27bBzs4OTz31FPz9/ZGcnAwAcHZ2Rnx8vNkflIiIiKxbTEwM0tLSMGjQIH2ulzmYtM/O/PnzERsbq086X79+PY4cOYLy8nIMGzYM7u7uZntAIiKi1sIJypZ14sQJXLt2DVOmTIGjoyM6d+7cZINBlUolnHhg8lCinZ0drl27hh9++AE3btzA4MGDERAQINTQaZh6biiRnKnnRERE94fq6mp4e3vjscceg7e3N7y8vNCtW7dGr4ceeki4XpN6do4ePYrly5fj1KlTAICNGzdCq9Vi0aJFWLhwoT6ZVUR4eDiioqKwYMEC/bH6BPO0tDR4eHjgueeeQ0BAAPr27WvwXO/evQ1e09w9gMap566urqZ8JEREZAXYsWNZ9cvNzU24sXP8+HG88MILeOihhxATE6MP5HJxcYGdnR3i4+PRoUMHg3vjGNJcIrmhZHOlpJ5LTTO/UyMtHR0A7O04b7+eSOK4EhLS25rrBSsll+0aIr1s2e5F0p+houkuqQafoVM7yWVFErw7tpf+Y1AkcbyHu5PksiIJ6SL1agX+Xoh8DiIJ9CIJ6SIJ9IA8f4/548E6CTd2kpKS4OXlhZ07d6KyshKbNm0CUBff8Pnnn+P555/Hhx9+KNzYaU5zCebHjx83es7YNcYEBgZi9erVQo0dIiIiMh+psU15eXlC9Qo3do4dO4ZXX30Vjo6OuH37dqNzzs7OmDJlCt5//33Rai2OqedERMQJypbVrVu3Jsd0Oh1+++03XLhwAb169cKYMWOE6zVpzo6Dg4PBc9XV1Y2S0O8FU8+JiIjuH8bm7Jw4cQKzZs3CiBEjhOsVniAyZMgQZGVlNXuusrISmZmZZlsbz9RzIiJqTdxUULm8vb0RFRWFdevWCV8r3LMzb948REdHIyoqCoGBgVCpVDh+/DjOnDmDLVu24PLly1iyZInwgxhKJGfqOREREQFAly5dcP78eeHrhBs7w4YNw4cffoi33noLiYmJAKAP/uzatSveffddjBo1SvhBDCWSM/WciIhaC1djKdf169fx0UcfNTuvpyUmzdkZM2YMvvzyS5w8ebJRIqm3t7d+V+W2hKnnREQEACqZlrSTNIamk2g0GpSWlkKr1eKtt94SrrfFlklCQoLkyrZv3w6gbivnFStWtFieqedERERUz1Cvja2tLUaOHInQ0FD4+/sL19tiY+fTTz+F6u7sqlqJGz5JbexwTxsiIlISDmNZlsV2UB4wYAAKCwvh5uaGwMBAjB8/Hj4+PrC3t5flgYiIiIjq3blzBwcOHICtrS18fHxMmi7T4hWfffYZLl68iL179+LLL7/EK6+8AicnJ/j7+2P8+PHw8/ODo6OjSW+AiIhISdizY1kajQbLli3DxYsXsXHjRmg0GkydOhWnT58GAPTp0wfp6elCoeOAxH12vLy8MHPmTGzbtg3ffPMN3njjDVRUVCA+Ph6jRo3Cq6++ik8//RRqtVro5g1Tz0WTyo1dw9RzIiIyhUqlkuVF0iQnJ+OTTz7RJ5vv2rULp06dQnR0NFasWIHr168jKSlJuF7hTQXd3NwwZcoUpKSk4D//+Q+WL1+Odu3aYdmyZRgzZgxmzpyJjIwMoTrrE8xTU1ORnZ2NrKwsnD17FkDdvJ7U1FTJ15hSV8PUcyIiIrKM3NxcPPfcc1i+fDkAYPfu3ejYsSPeeOMNhIWFYfr06cjPzxeu957WiTs7O2PixImYOHEizpw5g8TERHzzzTf47rvvMG3aNMn1GEs3N5RUroTUc6lEksxrtNJTf+1s+a+FekIJ6QLJykrIyREJghZ5XJHuepEkc/fINMllL2+Jkf4QAkQSvOVKSBchkmR+uaxKctkHO0tPihchkmR+q1r659uhneW3LuEwlmVduXIFQ4cOBQDcvn0bhw8fhr+/v36ezkMPPYSKigrheu/pO+v777/HV199hby8PJw7dw42NjZ4/PHHMW7cOKF6TEkqZ+o5ERGRdenSpQt+++03AMD+/fuh0WgaLTUvLCzEAw88IFyvUGNHo9Hg22+/RV5eHvLz8/H777+jXbt2GDNmDGbNmoUnn3wSnTt3Fn4IJWDqORERKaAz9742cuRIpKeno127dti2bRvat2+PcePGoaKiAv/+97/xySefIDIyUrjeFhs7ZWVlKCgoQF5eHg4cOIDbt2/D1dUV/v7+GDduHMaMGXPPq7FMSSpn6jkREZmbEoau72eLFi3C1atXkZiYCCcnJyxbtgydOnXC0aNHkZiYiMcffxxz584VrrfFxs6YMWNQW1sLLy8vTJ06FePGjcPw4cPNOru8YVK5h4cHsrOzsWbNGpOu6d27t3BdAFPPiYiILK1Tp05IS0tDaWkpnJ2d4eDgAAAYOHAgtm/fjiFDhphUb4uNHZ1OBwAoKSlBeno60tPTW6xUpVLh5MmT0h/CSIK5oaRypp4TEZG5cYKyMri5uTX6s5OTk76hU1pa2uR8S1ps7ISFhQlVaCrRpHJj1zD1nIiIqG366KOPsH//flRWVuo7XIC6LWdu3bqFs2fP4sSJE0J1ttjYWblypfiTtjFMPSciIoATlC1tw4YNWLNmDRwcHODs7IyysjI8+OCDKC8vx+3bt+Ho6Ijo6GjheoU3FTQnpp4TEZGS2EAly4uk2blzJwYOHIhvv/0W27dvR21tLTZv3owjR47gzTffRHV1tUnzdiza2AkPD2djh4iIiADUrYyeNGkSnJ2d0b17d7i4uODIkSOwtbXFtGnTEBISImnu8B9ZtLFDRESkJCqVPC+Sxs7ODh06dND/uWfPnigsLNT/eeTIkTh//rxwvWzsEBERkSL06dMHx44d0/+5d+/ejSYj1y8oEmXRxk7D1HNDieRMPSciotZio5LnRdKEh4dj586diI+PR2VlJQICAnDkyBEkJycjJycH6enpePjhh4XrVUzPjqFEcqaeExFRa7FRqWR5kTTPP/88Xn75ZRQUFMDOzg5PPfUU/P39kZycjLi4OFRVVSE+Pl64XstHzN5lKJHcGlLPRYgkmTMh3TQiP3iUkJAu189JkaR4rU7653AtY6bksg/N2CK93q0zJJcV0dYS0ru5So/nEUlIF6lXhEiSuUhCuqOdYn59kZnNnz8fsbGx+qTz9evX48iRIygvL8ewYcPg7u4uXKdienZENZdufvXqVYPHWxIYGIi9e/fK8qxERNQ2cIKyMtjZ2eHatWv44YcfcOPGDQwePBgBAQEmNXSANtzYMTemnhMREVne0aNHER4eDj8/P0RGRuLEiRM4dOgQ/P39kZOTY1Kdbbaxw9RzIiIyN87Zsazjx4/jhRdewK1btxATE6M/7uLiAjs7O8THx+Prr78WrrfNNnYapp5rNBpkZ2cjICDA4PGWMPWciIg4jGVZSUlJ8PLywmeffYbZs2ej9u68yUceeQSff/45+vTpgw8//FC4XsU0duLi4hAZGYmioiL4+voiMzPT6PGGqechISEIDg5Gv379DB43VhdQl3rOxg4REZHlHDt2DOHh4XB0dITqD61EZ2dnTJkyBWfOnBGuVzHT2Q0lkjP1nIiIWotiegDuYw4ODgbPVVdXN0pCl4pfVzD1nIiISAmGDBmCrKysZs9VVlYiMzMTjzzyiHC9Fu3ZYeo5EREpyR+HTqh1zZs3D9HR0YiKikJgYCBUKhWOHz+OM2fOYMuWLbh8+TKWLFkiXK+qtlZg1zRqpEr6/ley4KaC8lPCpoJKILKpoAglbCooQgmbCopQwqaCIkQ2FXTvIM/nu/lIiSz1znisuyz1WqMDBw7grbfearIJcNeuXfGPf/wDQUFBwnVa/m8jERER0V1jxozBl19+iZMnT6K4uBg6nQ6enp7w9vbW76osio0dIiKiu6y5h7YtUalUGDRoEAYNGmSW+iza2ImOjsbKlSvh5eWFffv2Yfny5dDpdIiIiMDs2bMBwOBxoC7FvKCgAO7u7o0mNBm6xlB5tVqNRYsWYd26da30zomIiCghIUH4GpVKhRUrVghdo4ienfqk8rS0NHh4eOC5555DQEAAevfu3ezxvn37AqhLMY+KisKCBQtarKtv377Nlgcap567urq26nsnIiLlYL9O6/r000/1k8KlTiFus40dUxLMgeYT0Q3V1VZSz0UwIV1+QgnpApN4RRLH5SIy6dhWpucVmXTcJ/ZTyWV/evcZyWVva7SSy3Z2Mrz/hxKJTDq+WHpblnohMLddJCFdLhzFal0DBgxAYWEh3NzcEBgYiPHjx8PHxwf29vZmvY8i9tkxZ4I5U8+JiIjahs8++wx79+7F7Nmzce7cObzyyivw8fFBfHw8du/ejaoq6SsKjbF8M1ohmHpORETcZ6f1eXl5YebMmZg5cyZKS0uxd+9e7N27F/Hx8bC1tcXo0aMxfvx4BAQEwMXFxaR7KKJnx5wJ5kw9JyKiti4rKwsTJ07E4MGDERwcjF27dhktf+3aNcTHx8PHxwePPvooXn31VVy4cKFRmZqaGrz33nvw8/PDkCFDMG3aNBw/flzOtyHMzc0NU6ZMQUpKCv7zn/9g+fLlaNeuHZYtW4YxY8Zg5syZyMjIEK5XEY0dcyaYM/WciIhMZSPTS0ROTg7i4+MxduxYrFu3DiNGjMCCBQvwxRdfNFu+uroas2bNwo8//og333wTa9aswbVr1xAVFYWKigp9ueXLl2PTpk146aWXsHbtWtja2mLmzJkoKZFnI8V75ezsjIkTJ2Lt2rX4+OOPMWrUKBw8eBBvv/22cF2KGMZqmFSu1WoxefJkfVK5oeNAXYr5oUOHUFZWBl9fX8TGxiIiIsLgNYbKA3Wp56tWrWr9N09ERIqhhGGstWvXIjg4WL8s+4knnoBarUZSUhImTJjQpHx+fj4KCwvx73//G97e3gCAfv36ITAwELt370ZERAQuXryI7du3Y/HixXj++ecBAGPHjkVQUBBSU1NNimCQ2/fff4+vvvoKeXl5OHfuHGxsbPD4449j3LhxwnUporEDiCeYA4ZTzJl6TkREbVFJSQmKi4sRFxfX6HhQUBByc3NRUlKiX21cb+zYsfjoo4/0DR0A+tVMGo0GAHDw4EFotdpGUQsODg7w9/dHQUGBTO9GjEajwbfffou8vDzk5+fj999/R7t27TBmiVPqGwAAH81JREFUzBjMmjULTz75JDp37mxS3Ypp7FgSU8+JiAiw/D47586dAwD07t270fGePXsCAIqKipo0dpydnfHoo48CAO7cuYNffvkFiYmJcHV1xfjx4/X1uri4wM3NrUm9ly9fRlVVFRwdWz8fraysDAUFBcjLy8OBAwdw+/ZtuLq6wt/fH+PGjcOYMWPM8lxMPSciIpJZRUVFo/kz9Tp16tTo9+CNGzcA1DVgGurQoQMA4ObNm0bvExsbi/z8fNjY2GD58uV44IEH9Nf9sc6G9d66dcsijZ0xY8agtrYWXl5emDp1KsaNG4fhw4ebfTjRoo2dtrSBHxERWT+55uykp6cjOTm5yfG5c+ciNjZW/+eWdhG2sTE+3fmll15CTEwMPv/8c/2cn/Dw8BbrtdRcJZ1OB6Bu+C49PR3p6ektXqNSqXDy5Emh+3AYi4iI6C65lijHxMQgLCysyfE/jm507NgRQF1PS0P1PTr15w0ZPnw4AMDHxweXLl3Chx9+iPDwcDg7Ozeps2G9zfX6tIbmPhM5KCYIVDTUE2AQKBERtQ1/HK4ypH6uTnFxMQYMGKA/Xr9nzh/n8gDAyZMnUVRUhIkTJzY6PmjQIPz4448A6jbOLS8vh1qtbrQx34ULF+Dl5QUHB8tEoaxcubJV7qOIfXaAum621NTURsfqQz1TU1ORnZ2NrKwsnD171uRrmisPNA4CJSKi+5dKpZLlJVXPnj3h5eXVZE+dPXv2oFevXujWrVuTaw4ePIjXX38dxcXF+mNarRYHDx5E//79AQCjR48GAOzevVtfRqPRoKCgQH/OEi5dunTPdUjZJ0gxw1iioZ6mXGNtQaAiRMM9GRwqTiTcUwmhoSLhnkoIDf056VnJZb0X5Egue+r/TWy50F1ltzSSy7p2aFuhoV5u7SWXvSQQGuopUK/I95nl103JZ86cOUhISICLiwv8/f2Rl5eH3NxcrF27FgBQWlqK4uJi9O3bF87OzggPD8eWLVvw17/+FbGxsXB0dMS2bdvw888/Y+PGjQAAT09PhIWFYdmyZaisrETPnj2RlpaGiooKzJo1y2LvdfLkyQgNDcVLL70kKe2goZKSEqSkpGDPnj347rvvjJZVTM9OcxgEahls6BDR/Uol00tEeHg4lixZgm+++QZz5szB4cOHkZiYiJCQEABAQUEBpk6dip9++gkA0LlzZ2zduhX9+/fH0qVL8dprr6Gqqgrp6ekYOXKkvt6lS5ciMjISKSkpmD9/PrRaLdLS0vTL2i3hs88+w7Vr1xAYGIiZM2di27ZtBntqamtrUVhYiG3btmH69Ol46qmn8Pvvv+Ozzz5r8T6K6dmxNAaBEhGRAjZQBgBERkYiMjKy2XPh4eFNRiE8PT31PT+GODg4YNGiRVi0aJHZnvNeeXh44P3338fhw4exadMmrFixAsuWLUP79u3h6ekJZ2dn1NbWoqysDNeuXUNVVRVUKhUCAgKwbds2/f5CLVF0Y4dBoERERNbv8ccfx+OPP44rV65g3759+O9//4uSkhKUl5fDxsYGDz30EIYPH45Ro0Zh7NixTTZHbImiGzsNQz09PDyQnZ2NNWvWmP0agEGgREQE2FjxXKC2wNHREVOmTMGUKVPMWq9i5uzExcUhMjISRUVF8PX1RWZmZqOA0JCQEAQHBzcJAhW5prny9fLz89nYISIisqBnn31Wlm1gFNOzIxrqaco1DAIlIiJjlDJn535VVlYmy+9ixfTsWBKDQImICABUMv1H0oSGhiIzMxO//fabWetlECgREREpgo2NDc6ePQs/Pz/06NED7u7uTfLAVCqVpAythhgESkREdBeHsSzrwIEDcHV1BQBUV1fj8uXLZqlXMXN2iIiI6P721VdfyVIvGztERER3cem5Zdy5cwdnz55FTU0N+vbti/btpceMSGHRCcrR0dH6rKp9+/YhKCgI48ePR0pKir5MQkICfHx8EBoa2uR6Q+dE61Kr1ZgzZ4453xoREbVBKpU8LzJs06ZNGD16NMLDwzFlyhSMGjUKiYmJqKmpMds9FLEay5SkckPnmHpORETUNuzatQvvvPMOOnXqhOnTpyM6Ohq9evXCpk2bsGrVKrPdRxHDWKYmlTP1XB5MSJcfE9LF6xV5ZyJJ5qOW5Uku+/WCJwWewnqJJJlfUVdJLuvRydGUxzEr9sK0royMDAwdOhTp6elo164dgLrAz/nz52P79u2Ij4+Hg4PDPd9HET07piaVm7Mupp6bRqShQ0RE1NAvv/yCp59+Wt/QAeqWls+cORMajQbnzp0zy30U0bOjBEw9JyIibgDYum7fvo2OHTs2Oe7l5YXa2lpUVFSY5T6KaOyYmlRuzrqYek5ERDKNBJMBOp0OqmbGDm1tbQHUzcM1B0UMYzVMKtdoNMjOzkZAQECr1sXUcyIiIuukiJ6dhknlWq0WkydPbpRUfujQIZSVlcHX1xexsbGIiIgwes6UuvLz880685uIiNoeDmO1vvLy8iY7JavVagBAaWlps7sod+vWTegeqtraWovNMI2OjsbKlSstPnykVqvx6quvCoeBVplvC4A2S3SCMldjiVPCaiwRcq3GkutzkGs1Vjt7RXScW5xcq7Ha25vyNC376vTvstQb8LC7LPW2dQ8//HCzw1hA3aqs5s6pVCqcPHlS6D6K6NmxNKaeExERwKXnrS0sLKxV7sPUcyIiors4jNW6Vq5c2Sr3Yeo5ERERWTUOYxEREd2lgGlvJAPOoCMiIiKrppjUc0OJ5Ew9JyKi1qKS6T+yLMX07BhKJGfqORERtRaVSp4XWZZi5uwYSiRn6rmyMSFdfkIJ6QLbZtnI9BNYaO8ckeeVqd5vF0nfrT3g3X2SyxbE+0kuKxeRXdTk+oX8oIv0vXOuqqsll+3p3q7lQkR3KaZnx1yYek5ERKZSyfQiy7K6xo6pmHpORERknRQzjGUuTD0nIiJTyTW8S5ZldT07TD0nIiKihhTT2ImLi0NkZCSKiorg6+uLzMxMo8cNnWuYoB4SEoLg4OBGqeeG6srPz2djh4joPsc5O9ZJMcNY7777rtBxY+f8/Pzg59d0JYSh8mq1GhqNBl27dpXwpEREZLXYMrFKiunZsSSmnhMREVkvpp4TERHdxd2OrRNTz4mIiMiqKWbODhERkaVx5bl1YmOHiIjoLrZ1rJNiUs8NJZUbOm7snGiCOlPPiYiIrJciVmMZSio3lmBuSro5U8+JiMgobrRjlRQxjGUoqXzEiBEGE8xNSTdn6rnliSSZa2p0kss62Fm+3a6EhGmRre7vaKV/vva28ny+Is9bo5X+AYt8n4n8IhJJMn8wZqvkskUbnpdctlKjlVzWrYOD5LLnr1dKLtujS3vJZUX+Xni4MMmc5GH53xAwnFRuLMHc1HRzQ5h6TkREKpn+I8tSRM+OEjD1nIiIuBrLOimiZ8dQUrmxBHNT080NYeo5ERGRdVJEY8dQUrmxBHNT080NYeo5ERFxfrJ1UsQwVsOkcq1Wi8mTJ+uTyg0dN3ZNXFwcDh06hLKyMvj6+iI2NhYREREGjwN1qeerVq2yzAdAREREslFEYwcwnFRu6Lixc6IJ6kw9JyIiAOyGsVKKaexYElPPiYgIYBCotbLonB2mnhMREZHcmHpORER0F5eeWydFrMYiIiIikgvn7BAREd3Fjh3rpJjUc0OJ5Ew9JyKiVsONdqySYoaxmkskZ+o5ERER3SvFDGM1l0huLNmcqefWTyTJ/I5AQrq9TAnpIhMblZCQLpJkroSEdJEkcyU87+W06ZLL9pm7U3LZon9Olly2/NYdyWV7dXWSXFYkIV2kXq1O4C+GTN0lXHpunRTTs9Mcpp4TERHRvVJMz46lMfWciIi49Nw6Kbqxw9RzIiJqTWzrWCdFD2Mx9ZyIiIjulWIaO3FxcYiMjERRURF8fX2RmZnZKNk8JCQEwcHBzaae//Fcc3UZOw7UpZ6zsUNEdJ/j0nOrpJhhLEOJ5Ew9JyKi+01WVhb+9a9/oaSkBJ6ennj55Zfx7LPPSro2MTERp06dwqZNmxodP3LkCKZPb7pK0N/fHx9++KE5HluxFNPYsSSmnhMREaCMpec5OTmIj49HTEwMxo4di71792LBggVwdHTEhAkTjF67ZcsWbNy4ET4+Pk3OFRYWwsnJCWlpaY2O3w+B3BZt7DD1nIiIlEQJq7HWrl2L4OBgJCQkAACeeOIJqNVqJCUlGWzsXL16FatWrUJOTg46duzYbJnTp0+jX79+GDp0qGzPrlT/f3t3HxtV9edx/NMiiFIoFLVIawsIi0RAWAMoECwtUkVUqCgVRNgNuEFQHkIiEIjhwVUaTIlUSEB5sCYGkcgvUkBMgRBRo7AYohBiLHRKGsE40tLyq5WW/cN2wtDOcO90pnPm8H6ZRrnzndMzd8b2cM499xPVa3ZycnIY7AAA0KCsrEwej0djx471O56dna2SkhKVlZU1+7z8/HydOnVKW7duVb9+/ZqtOX36tPr27Rv2PscCYy5QBgAg2qJ9fXJJSYkkqWfPnn7H09PTJSng/eBmzpypoqIiPfLII80+XldXp19++UW//fabJk6cqP79+ysjI0NbtmzRNTe3dI9RXLMDAECEVVZWqrKyssnxTp06+a1wXL58WZKUkJDgV9ehQwdJUlVVVbPt9+7dO+j3P3funGpqanT27FktXLhQXbp0UXFxsfLy8lRVVaXXX3/d1euJNcaknrtNMA/lOaSeAwCCitDUzvbt25WVldXka/v27X7f/mazLPHxof3aTk5O1ubNm/XJJ59o3LhxevTRR7Vs2TJNmjRJmzdvDjiIsoURy1ihJJiTeg4ACLe4CP0zffp0FRcXN/maPn263/dvvLi4urra73jjYCTQxcc3k5CQoFGjRjW5xUpGRoZqa2utj0syYhkrlARzUs9xPTdJ5rUuEtLdJK+7YXNC+tU65x12k2TuRqz1102Seb9FRY5rf3z7yVC6c1Nuksw9fzhPSE9NuiOU7sSEG5erAmm8Vsfj8fhdTFxaWur3uFtnzpzR8ePH9fzzz6tt27a+4zU1NZKkLl26hNRurDBiZieUBHNSzwEA4RYXF5kvp9LT05Wamqr9+/f7HT9w4IB69Oih7t27h/S6SktLtWLFCh05csTv+N69e5WamqqUlJSQ2o0VRszsmIDUcwCACebMmaMlS5YoMTFRGRkZKi4u1r59+5Sfny9J8nq98ng86t27d5MLmQPJyMjQgw8+qOXLl8vr9apbt2764osvdPDgQa1fv15xJtxgKIKMGOyEkmBO6jkAINxM+JWfk5Oj2tpabdmyRTt37tR9992nNWvWaNy4cZKkw4cPa8mSJfroo480bNgwR222a9dOH3zwgdatW6eCggJ5vV716dNHBQUFGjNmTCRfjhGMGOxcn2CenJysoqIivfvuu2F/TjCkngMAjBjtSMrNzVVubm6zj+Xk5AS9vrSwsLDZ40lJSVq5cmVY+hdrjLhmJ5QEc1LPAQCAE0bM7EjuE8xDeQ6p5wCAYEwIAkX4GTGzE22kngMAYC9SzwEAaGD5pqRbVlQHO9zADwBgEsY6dmIZCwAAWM2YIFC34Z0SQaAAgDCLUBAoosuYmR234Z0EgQIAACeM2XruNryTIFCEyk24598uQkPdhJG6EWuhoW7CMk0I4TShv/Uu3rif88Y5rh207EvHtSf/9wnHtW6kdY1MaOh/JDtv1w22ntvJmJkdtwgCBQCEW7SDQBEZMTvYCTeCQAEAsJMxy1huEQQKAAg3JmHsFLMzO9cHgdbW1qqoqEiZmZkht0cQKAAAdjJmsOM2vJMgUABA2LH13ErGLGO5De+UCAIFAIQXu7HsZMzMTjQRBAoAgL0IAgUAoAHbxO1EECgAALCaMdfsAAAQbUzs2InBDgAADVjGspMxqeeBEswDHZfcp5uTeg4AwK3HiN1YgRLMgyWbS+7TzUk9BwAEx412bGTEMlagBPOhQ4cGTDaX3Celk3oOt9wkmcdaQroJ3CSDX/mrznHtnbe3cVzrJineTX/r6p033CbexRvnotRNkvkTBd84rv3X/zziuLbWxf8X9yVFJskcMGJmJ1CCebiTzYMh9RwAQOq5nYyY2TEBqecAAMYldjJiZidQgnm4k82DIfUcAAA7GTHYCZRgHu5k82BIPQcAsIxlJyMGO4ESzIMlm0vu081JPQcA4NZjzDU7gRLMAx2X3Kebk3oOAAiG1HM7GTPYiSZSzwEAkrhC2VJRXcYi9RwAAEQaqecAADRgYsdORlygDAAAEClcswMAQAO2idvJmNTzQInkpJ4DAFpLXIT+QXQZs4zVXCI5qecAAKCljFnGai6RPFAaOqnnMFGsJaTHGjdJ5v+udZ6Qfkc75+3Wu4hId5NkHrGEdBf2vvqo49rcbccc137630Mc117+91XHtXe0jdCvLyZhrGT0T1FSzwEAQEsZM7MTbaSeAwCY2LGT0TM7pJ4DAICWMnqwQ+o5AKA1kXpuJ2MGO80lkpN6DgBoTWw9t5Mx1+wESiQn9RwAALSEMYOdaCL1HAAgseRkK1LPAQCA1Ug9BwAAVmMZCwCABixj2cmY3VgAAACRYEzqeaB080BJ5cEeI/UcABAKtp7byYiZnWDp5oGSyoM9Ruo5AABoZMQ1O8HSzYMllZN6jljlKiG9zkVCehsj/v4SdW6SzCOVkO6GmyTzehcJ6fFuEtJdlLpJMn+p8P8c1259cZDzTkQI1+zYyYifjK2Zbh4IqecAgLgIfSG6jBjsmIDUcwAA7GTEMlZrppsHQuo5AIBpGDsZMbPTmunmgZB6DgCAnYwY7ARLNw+WVE7qOQAgnNh6bicjlrGkwOnmgZLKgz1G6jkAIBTsxrKTETM70UbqOQAA9iL1HACABqZsPd+zZ4+eeuopDRw4UE8++aR2794dtL66ulorVqzQiBEjNHjwYM2aNUvnzp3zq7l69arWrVunxx57TA899JCmTJmikydPhtC72BPVwU5OTg6DHQAArrN3714tWrRII0eO1Pvvv6+hQ4fqjTfe0P79+wM+Z8GCBdq/f78WLVqkNWvW6MKFC3r55Zd1+fJlX81bb72lbdu2adasWcrPz1ebNm00Y8YMlZWVtcbLiqq4a9euOb8dJ/zUXI12D3Ar4A7KkWXCHZTdiNQdlOtd/CqId3FhS6TuoNyxfWQ+61f+jsyvxDvbOj9njz/+uPr376/8/Hzfsfnz5+vMmTPat29fk/pjx45p6tSp2rx5s0aNGiVJ8nq9ysrK0uzZs/XKK6/o/PnzGjt2rJYvX64XX3xRklRbW6vs7GyNGjVKK1asaOErNBs/GQEAaBDt3VhlZWXyeDwaO3as3/Hs7GyVlJQ0Owtz9OhRdejQQSNGjPAdS0pK0pAhQ3TkyBFJ0nfffae6ujplZ2f7atq1a6eMjAxfjc2MST13m1QeynNIPQcAmKykpESS1LNnT7/j6enpktTsnf5LSkqUnp6uNm38Zx7T0tJ89SUlJUpMTFRSUlKTdsvLy1VTUxO212AiY2Z23CaVh/IcUs8BAMHExUXmq7KyUufPn2/yVVlZ6ff9G6+xSUhI8DveoUMHSVJVVVWTPldVVTWpb3xOY32wGumfC5xtZsx9dkJJKo926nl7Y84ebNbeRUI63Gt/W/Svw3EnUjeCiUy7n/3Xf0ak3UiJ1M/1zdu3q6CgoMnxuXPn6rXXXvP9+WaX0cbHN/15EOw5jfU3azfO8hsM8eu6QVZWltauXetqsAMAgBPTp0/XxIkTmxy/cUdyx44dJTWdaWmcoWl8/HoJCQnN/kW+urraN5uTkJDQ7OxNY7vNzfrYhMFOA1LPAQCR0qlTJ0e3Wmm8Vsfj8ahv376+46WlpX6P3/icb7/9VteuXfOboSktLfXV9+rVS5cuXVJFRYUSExP9alJTU9WuXbvQXliMYH68AannAIBoS09PV2pqapN76hw4cEA9evRQ9+7dmzxn5MiRqqys1DfffOM75vV6dezYMQ0fPlySfP/+8ssvfTW1tbU6fPiw7zGbMbPTgNRzAIAJ5syZoyVLligxMVEZGRkqLi7Wvn37fPfd8Xq98ng86t27txISEjRkyBANHTpUCxcu1KJFi9S5c2etX79eHTt29N1TJyUlRRMnTtTq1at15coVpaena+vWraqsrNTMmTOj+XJbhTEzO6EklZN6DgCwTU5OjlasWKGvv/5ac+bM0Q8//KA1a9Zo3LhxkqTDhw9r8uTJ+vnnn33PKSgoUGZmpvLy8rR48WJ169ZN27Zt81uyWrlypXJzc7Vp0yYtWLBAdXV12rp1q29bu82iegfladOm6e2334768lFFRYVeffVVwkABALCQMTM70UTqOQAA9iL1HAAAWI0gUAAAYDWWsQAAgNUY7AAAAKtxnx2HKioqdOjQIX311Vc6d+6cUlNTlZWVpaysLHXt2pVaaqmlltoI1QItxTU7DsydO1cVFRXKyMhQZmamevbsqfLychUXF+vQoUP6+++/VVhYSC211FJLbZhrgbC4hpuqqKhw/Di11FJLLbXhqwXCgZkdAABgNa7ZcejXX39VcXGxLl68KEm65557lJWVpfvvv59aao2vDWTXrl167rnnqKU2JmsBp9iN5cCmTZu0cOFCSdKAAQM0YMAASf9kbW3atIlaao2uDWb9+vXUUhuztYBTLGM5kJ2drT179qht27Z+x2trazV+/HgdOHCAWmqNrX366acVyNmzZ/XTTz9RS62xtUA4sIzlQFxcnC5evKiUlBS/47///rvi4uKopdbo2j/++EMffvhhk2iWa9euKTc3l1pqja4FwoHBjgNLly7VjBkzlJ6ernvvvVeSVF5eLo/Ho+XLl1Mbwdply5ZR28LajIwMVVdXq1+/frrRsGHDqKXW6FogHFjGcqi+vl4nT57UhQsXJEnJyckaMGCA2rRpQy21xtcCwK2MmR2Hzp49q++//95v50vHjh2b3flCrfva+Ph4DRo0qMnx5nZmUOu+1oQdYTbXBmLCjiWbawGn2I3lgAk7amyuDcaEHR+xXmvCe2xzbTAmfh5sqgWcYhnLARN21Nhca8KOD5trTXiPba414T22uRYIB5axHDBhR43NtSbs+LC51oT32OZaE95jm2uBcGCw44DpO5ZifXeTCTs+bK41/TPJ55faYLVAOLCM5ZAJO2psrkVkmfAe21wLwGzM7Dhkwo4lm3c3mbCjxubdNyZ8Jvn8mlMbiKmfX6Cl2I3lgAk7PmzeHWLCOeP8mlPr5rWZUGvCObP5/ALhwDKWAybs+LB5d4gJ54zza06tCeeM82tOLRAOLGM5YMKOD5t3h5hwzji/5tSacM44v+bUAuHAYMcB03eo3Fhrwg4Km3cLmXDOOL/m1Jpwzmw+v0A4sIzlkAk7PmzeHWLCOeP8mlMba0w4ZzafX6ClGOwAAACrsYzlUKxtAaW/9Jf+0l9b+gu0FFvPHYi1LaD0l/7SX/prS3+BcGAZy4FY2wJKf+kv/aW/tvQXCAeWsRyItS2g9Jf+0l/6a0t/gXBgsONApEIMTd8KS3/pL/2lv9HuLxAOLGM5FGtbQOkv/aW/9NeW/gItxcyOQ5EKMYzUTgf6S3/pL/21pb9AS7Eby4FI7UigXdqlXdql3eDtAuHAMpYDkdqRQLu0S7u0S7vB2wXCgWUsByK1I4F2aZd2aZd2g7cLhAODHQciFbJHu7RLu7RLu8HbBcKBZSyHIrUjgXZpl3Zpl3aByGKw40B5ebmjuk6dOqmyspJaaqmlltow1SYkJDiqBYJhsOPAtGnTbloTFxennJwc7dq1S3FxcQp2Wq+vpV3apV3apd3AtRMmTLhpu8DNMNgBAABW4z47AADAagx2AACA1RjsAAAAqzHYAQAAVuOmgkCMWbx4sT7//POb1k2cOFHvvPNOK/QIAMzGbiwgxpw4cUIej8f35+PHj2vHjh2aPHmyHn74Yd/xtLQ0DR48OBpdBACjMLMDxJjBgwf7DWLq6uq0Y8cODRo0SM8++2wUewYAZuKaHQAAYDVmdgCLZWZmavjw4aqvr9eePXvUuXNn7d69W5MmTVJKSooKCwub1N94/MSJE3rvvff0448/SvpnZmn+/PkaOHBgq74WAAgVMzuA5YqKinTmzBktXbpUL7zwgpKSkhw/9+jRo5o2bZouX76sefPmafbs2SovL9fUqVN17NixCPYaAMKHmR3AcjU1NdqwYYOSk5NdPa++vl5vvvmmBgwYoI8//tiXXP3SSy9pwoQJWr16tXbv3h2JLgNAWDGzA1guLS3N9UBHkk6dOqWysjKNGTNGFRUV8nq98nq9qqmp0ejRo3X69GlduHAhAj0GgPBiZgewXNeuXUN6XuP29ry8POXl5TVbU15eHtJACgBaE4MdwHKNy09O1NXV+f67vr5ekjRv3jwNGjSo2fpevXq1rHMA0AoY7AC3oPj4eNXW1vodu3r1qv7880+lpaVJklJSUiRJd955p4YPH+5Xe/LkSVVUVKh9+/at02EAaAGu2QFuQXfddZfOnj2rmpoa37GDBw/qr7/+8v25f//+uvvuu1VYWKjq6mrf8aqqKs2fP19LlixxNWsEANHCzA5wCxo/frxWrVqlmTNn6plnnlFpaak+/fRT32yOJLVt21bLli3TggULlJOTo0mTJun222/Xzp07VV5errVr1+q22/gRAsB8/KQCbkFTpkzRpUuX9Nlnn2nVqlV64IEHVFBQoC1btujKlSu+uieeeEKJiYnauHGjNmzYoPj4ePXp00cbN27U6NGjo/gKAMA5gkABAIDVuGYHAABYjcEOAACwGoMdAABgNQY7AADAagx2AACA1RjsAAAAqzHYAQAAVmOwAwAArMZgBwAAWI3BDgAAsNr/A54Mwks1GkALAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "sns.set(font_scale=1.5)\n",
    "ax = sns.heatmap(response_normalized_cal_r,annot_kws={\"size\": 8},cmap=\"Blues\")\n",
    "ax.figure.axes[-1].yaxis.label.set_size(18)\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Pr(Measured | True) [%]', labelpad=30)\n",
    "plt.xticks(xvals_r,xlabs_r,rotation='vertical',fontsize=10)\n",
    "plt.yticks(xvals_r,xlabs_r,rotation='horizontal',fontsize=10)\n",
    "plt.xlabel('True',fontsize=18)\n",
    "plt.ylabel('Measured',fontsize=18)\n",
    "plt.subplots_adjust(bottom=0.18,left=0.18)\n",
    "plt.savefig(\"plots/fig4_r.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, for the unfolding.\n",
    "unfolded_Matrix_r = MatrixInversion(measured_test_r,response_normalized_cal_r)\n",
    "unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,1000)\n",
    "\n",
    "bincenters_r = np.array(xvals_r)+0.5\n",
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(2, 1, width_ratios=[1], height_ratios=[3.5, 1])\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax1.set_xticklabels( () )\n",
    "\n",
    "mybincenters = np.linspace(-0.5,31.5,33)\n",
    "#mybincenters = np.linspace(-0.5,15.5,17)\n",
    "plt.step(mybincenters,myre(truth_test_r),color='black',label=r\"t\")\n",
    "plt.errorbar(0.5*(mybincenters[1:]+mybincenters[:-1]),measured_test_r,yerr=np.sqrt(measured_test_r),label='m',marker='o',linestyle='none',color='black')\n",
    "plt.step(mybincenters,myre(unfolded_Matrix_r),color='blue',linestyle=':',label=r\"$\\hat{t}_{matrix}$\",linewidth=3.0)\n",
    "plt.step(mybincenters,myre(measured_test_ignis_r),color='red',linestyle='--',label=r\"$\\hat{t}_{ignis}$\")\n",
    "plt.step(mybincenters,myre(unfolded_IBU_r),color='green',linestyle='-.',label=r\"$\\hat{t}_{IBU}$\")\n",
    "\n",
    "_=plt.xticks(xvals_r,xlabs0_r,rotation='vertical',fontsize=10)\n",
    "plt.ylabel('Counts')\n",
    "plt.ylim([0,2.*max(truth_test_r)])\n",
    "plt.legend(loc='upper left',fontsize=12,frameon=False)\n",
    "_=plt.text(20., max(truth_test_r)*1.8, \"Qiskit Simulator\", fontsize=18)\n",
    "_=plt.text(20., max(truth_test_r)*1.7, \"IBMQ Tokyo Readout Errors\", fontsize=12)\n",
    "\n",
    "ax2 = plt.subplot(gs[1])\n",
    "ratio_ignis2_r = np.zeros(2**nqubits_r)\n",
    "ratio_ignis_r = np.zeros(2**nqubits_r)\n",
    "ratio_matrix_r = np.zeros(2**nqubits_r)\n",
    "ratio_unfold_r = np.zeros(2**nqubits_r)\n",
    "for i in range(len(ratio_unfold_r)):\n",
    "    ratio_unfold_r[i]=1.\n",
    "    ratio_matrix_r[i]=1.\n",
    "    ratio_ignis_r[i]=1.\n",
    "    ratio_ignis2_r[i]=1.\n",
    "    if (truth_test_r[i] > 0):\n",
    "        ratio_unfold_r[i] = unfolded_IBU_r[i]/truth_test_r[i]\n",
    "        ratio_matrix_r[i] = unfolded_Matrix_r[i]/truth_test_r[i]\n",
    "        ratio_ignis_r[i] = measured_test_ignis_r[i]/truth_test_r[i]\n",
    "        pass\n",
    "    pass\n",
    "\n",
    "plt.ylim([0.8,1.2])\n",
    "plt.step(mybincenters,myre(ratio_ignis_r),color='red',linestyle=\"--\")\n",
    "plt.step(mybincenters,myre(ratio_matrix_r),color='blue',linestyle=\":\",linewidth=3.0)\n",
    "plt.step(mybincenters,myre(ratio_unfold_r),color='green')\n",
    "plt.ylabel('Extracted / Truth')\n",
    "xx2 = [1.,1.]\n",
    "plt.plot([xvals_r[0],xvals_r[len(xvals_r)-1]],xx2,color='black',linestyle=':')\n",
    "_=plt.xticks(xvals_r,xlabs_r,rotation='vertical',fontsize=12)\n",
    "plt.savefig(\"plots/fig4_rr.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now compute the pulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "on strap 0\n",
      "on strap 10\n",
      "on strap 20\n",
      "on strap 30\n",
      "on strap 40\n",
      "on strap 50\n",
      "on strap 60\n",
      "on strap 70\n",
      "on strap 80\n",
      "on strap 90\n",
      "on strap 100\n",
      "on strap 110\n",
      "on strap 120\n",
      "on strap 130\n",
      "on strap 140\n",
      "on strap 150\n",
      "on strap 160\n",
      "on strap 170\n",
      "on strap 180\n",
      "on strap 190\n",
      "on strap 200\n",
      "on strap 210\n",
      "on strap 220\n",
      "on strap 230\n",
      "on strap 240\n",
      "on strap 250\n",
      "on strap 260\n",
      "on strap 270\n",
      "on strap 280\n",
      "on strap 290\n",
      "on strap 300\n",
      "on strap 310\n",
      "on strap 320\n",
      "on strap 330\n",
      "on strap 340\n",
      "on strap 350\n",
      "on strap 360\n",
      "on strap 370\n",
      "on strap 380\n",
      "on strap 390\n",
      "on strap 400\n",
      "on strap 410\n",
      "on strap 420\n",
      "on strap 430\n",
      "on strap 440\n",
      "on strap 450\n",
      "on strap 460\n",
      "on strap 470\n",
      "on strap 480\n",
      "on strap 490\n",
      "on strap 500\n",
      "on strap 510\n",
      "on strap 520\n",
      "on strap 530\n",
      "on strap 540\n",
      "on strap 550\n",
      "on strap 560\n",
      "on strap 570\n",
      "on strap 580\n",
      "on strap 590\n",
      "on strap 600\n",
      "on strap 610\n",
      "on strap 620\n",
      "on strap 630\n",
      "on strap 640\n",
      "on strap 650\n",
      "on strap 660\n",
      "on strap 670\n",
      "on strap 680\n",
      "on strap 690\n",
      "on strap 700\n",
      "on strap 710\n",
      "on strap 720\n",
      "on strap 730\n",
      "on strap 740\n",
      "on strap 750\n",
      "on strap 760\n",
      "on strap 770\n",
      "on strap 780\n",
      "on strap 790\n",
      "on strap 800\n",
      "on strap 810\n",
      "on strap 820\n",
      "on strap 830\n",
      "on strap 840\n",
      "on strap 850\n",
      "on strap 860\n",
      "on strap 870\n",
      "on strap 880\n",
      "on strap 890\n",
      "on strap 900\n",
      "on strap 910\n",
      "on strap 920\n",
      "on strap 930\n",
      "on strap 940\n",
      "on strap 950\n",
      "on strap 960\n",
      "on strap 970\n",
      "on strap 980\n",
      "on strap 990\n"
     ]
    }
   ],
   "source": [
    "pulls_unfold_r = []\n",
    "pulls_matrix_r = []\n",
    "pulls_ignis_r = []\n",
    "for strap in range(1000):\n",
    "    if (strap%10==0):\n",
    "        print(\"on strap\",strap)\n",
    "        pass\n",
    "    myvals_r = mystd_r*np.random.randn(int(ntest_r/100))+2**(nqubits_r-1)*mymean_r\n",
    "\n",
    "    for i in range(len(myvals_r)):\n",
    "        myvals_r[i] = int(myvals_r[i])\n",
    "        if (myvals_r[i] < 0):\n",
    "            myvals_r[i]=0\n",
    "            pass\n",
    "        if (myvals_r[i] > 2**(nqubits_r)-1):\n",
    "            myvals_r[i] = 2**(nqubits_r)-1\n",
    "            pass\n",
    "    scult_vals_r = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        scult_vals_r[i] = len([x for x in myvals_r if x==i])\n",
    "        pass\n",
    "\n",
    "    many_counts_r = []\n",
    "    many_counts_sculpt_r = []\n",
    "    many_counts_sculpt_corrected_r = []\n",
    "\n",
    "    #Okay, now for the \"test\" aka the measurement.\n",
    "    for i in range(2**nqubits_r):\n",
    "        binnumber_initial = mybin(i,nqubits_r)\n",
    "\n",
    "        #Calibration circuit\n",
    "        qr_r = QuantumRegister(nqubits_r, 'qr')\n",
    "        cr_r = ClassicalRegister(nqubits_r, 'cr')\n",
    "        circ_r = QuantumCircuit(qr_r, cr_r)\n",
    "        for j in range(nqubits_r):\n",
    "            if (binnumber_initial[j]=='1'):\n",
    "                circ_r.x(qr_r[j])\n",
    "                pass\n",
    "            pass\n",
    "        pass\n",
    "        circ_r.append(measure2_r, nqubitarray_r, nqubitarray_r)\n",
    "        counts_sculpt_r = {}\n",
    "\n",
    "        if (scult_vals_r[i] > 0):\n",
    "            result2_r = execute(circ_r, simulator_r,noise_model=noise_model_r,shots=scult_vals_r[i]).result()\n",
    "            counts_sculpt_r = result2_r.get_counts(circ_r)\n",
    "            pass\n",
    "        many_counts_sculpt_r+=[counts_sculpt_r]\n",
    "        pass\n",
    "\n",
    "    measured_test_r = np.zeros(2**nqubits_r)\n",
    "    truth_test_r = np.zeros(2**nqubits_r)\n",
    "    response_test_r = np.zeros((2**nqubits_r,2**nqubits_r))\n",
    "    for j in range(len(many_counts_sculpt_r)):\n",
    "        counts = many_counts_sculpt_r[j]\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            if (binnumber_initial in counts):\n",
    "                measured_test_r[i]+=counts[binnumber_initial]\n",
    "                truth_test_r[j]+=counts[binnumber_initial]\n",
    "                response_test_r[j,i]+=counts[binnumber_initial]\n",
    "                pass\n",
    "            pass\n",
    "        pass\n",
    "\n",
    "    many_counts_r = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        binnumber_final_r = mybin(i,nqubits_r,0)\n",
    "        many_counts_r[binnumber_final_r] = 0\n",
    "        for j in many_counts_sculpt_r:\n",
    "            if binnumber_final_r in j:\n",
    "                many_counts_r[binnumber_final_r]+=j[binnumber_final_r]\n",
    "                pass\n",
    "            pass\n",
    "        pass\n",
    "\n",
    "    ignis_counts_r = meas_filter_r.apply(many_counts_r)\n",
    "\n",
    "    measured_test_ignis_r = []\n",
    "    for i in range(2**nqubits_r):\n",
    "        binnumber_initial_r = mybin(i,nqubits_r)\n",
    "        if (binnumber_initial_r[::-1] in ignis_counts_r):\n",
    "            measured_test_ignis_r+=[ignis_counts_r[binnumber_initial_r[::-1]]]\n",
    "        else:\n",
    "            measured_test_ignis_r+=[0.]\n",
    "        pass\n",
    "\n",
    "    unfolded_Matrix_r = MatrixInversion(measured_test_r,response_normalized_cal_r)\n",
    "    unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100)\n",
    "    for i in range(len(ratio_unfold_r)):\n",
    "        pulls_unfold_r += [unfolded_IBU_r[i]-truth_test_r[i]]\n",
    "        pulls_matrix_r += [unfolded_Matrix_r[i]-truth_test_r[i]]\n",
    "        pulls_ignis_r += [measured_test_ignis_r[i]-truth_test_r[i]]\n",
    "        pass\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qiskit-terra': '0.9.0',\n",
       " 'qiskit': None,\n",
       " 'qiskit-ignis': '0.1.1',\n",
       " 'qiskit-aer': '0.2.3',\n",
       " 'qiskit-ibmq-provider': None,\n",
       " 'qiskit-aqua': None}"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import qiskit\n",
    "qiskit.__qiskit_version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(5, 5))\n",
    "n,b,_=plt.hist(pulls_unfold_r,bins=np.linspace(-60,60,50),alpha=0.5,label=r\"$\\hat{t}_{IBU}, \\sigma=$\"+'%s' % float('%2.3g' % np.std(pulls_unfold_r)))\n",
    "plt.hist(pulls_matrix_r,bins=b,alpha=0.5,label=r\"$\\hat{t}_{matrix}, \\sigma=$\"+'%s' % float('%2.3g' % np.std(pulls_matrix_r)))\n",
    "plt.hist(pulls_ignis_r,bins=b,alpha=0.5,label=r\"$\\hat{t}_{ignis}, \\sigma=$\"+'%s' % float('%2.3g' % np.std(pulls_ignis_r)))\n",
    "plt.xlabel(\"Predicted - True\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.legend(frameon=False,loc='upper left',fontsize=12)\n",
    "plt.savefig(\"plots/fig_pull.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "QiskitDevenv",
   "language": "python",
   "name": "qiskitdevenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}