GaussianEx_2Dup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T03:48:59.924067Z",
     "start_time": "2020-11-17T03:48:57.190005Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import uproot\n",
    "\n",
    "from keras.layers import Dense, Input\n",
    "from keras.models import Model\n",
    "\n",
    "import omnifold as of\n",
    "import os\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = np.random.normal( loc=[1.1, 1.8], scale=[0.8,0.5], size=(100000, 2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "\n",
    "def get_available_devices():\n",
    "    local_device_protos = device_lib.list_local_devices()\n",
    "    return [x.name for x in local_device_protos]\n",
    "\n",
    "print(get_available_devices())\n",
    "print(\"tf.config.list_physical_devices('GPU')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(stuff.shape)\n",
    "plt.hist2d( stuff[:,0], stuff[:,1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "print(physical_devices)\n",
    "if physical_devices:\n",
    "  tf.config.experimental.set_memory_growth(physical_devices[0], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(stuff.shape)\n",
    "plt.hist2d( stuff[:,0], stuff[:,1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "print(gpus)\n",
    "#tf.config.experimental.set_virtual_device_configuration(\n",
    "#    gpus[1],[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)]) #in MB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def synthroot(filename, data, data2):\n",
    "    with uproot.recreate(filename) as file:\n",
    "        file[\"testree\"] = {\"theta0\": data, \"theta_unknown_S\": data2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T03:49:24.308730Z",
     "start_time": "2020-11-17T03:48:59.981078Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape theta0_G: (100000, 2)\n",
      "Shape 0_S: (100000, 2)\n",
      "Shape theta0: (100000, 2, 2)\n",
      "Shape unknown s: (100000, 2)\n"
     ]
    }
   ],
   "source": [
    "N = 10**5\n",
    "# Synthetic\n",
    "theta0_G = np.column_stack((\n",
    "    np.random.normal(1.1, 0.8, N),   # 1 dee\n",
    "    np.random.normal(1.8, 0.5, N)    # 2 dee\n",
    "))\n",
    "theta0_S = np.random.normal(0, 0.5, (N, 2)) + theta0_G  # Detector smearing for synthetic sample but different\n",
    "theta0t = np.stack((theta0_G, theta0_S), axis=1)  # axis=1 to maintain stack property\n",
    "# Natural\n",
    "theta_unknown_G = np.column_stack([\n",
    "    np.random.normal(1, .5, N),   # First dimension\n",
    "    np.random.normal(1.5, 0.3, N) # Second dimension\n",
    "])\n",
    "theta_unknown_St = np.random.normal(0, 0.5, (N,2)) + theta_unknown_G\n",
    "# Save data to a fake ROOT file\n",
    "synthroot(\"synth_data.root\", theta0t, theta_unknown_St)\n",
    "events = uproot.open(\"synth_data.root:testree\")\n",
    "print(events.keys())\n",
    "print(\"Shape theta0_G:\", theta0_G.shape)\n",
    "print(\"Shape 0_S:\", theta0_S.shape)\n",
    "print(\"Shape theta0:\", theta0t.shape)\n",
    "print(\"Shape unknown s:\", theta_unknown_St.shape)\n",
    "print(\"Shape theta0 Tree:\", events[\"theta0\"].array(library=\"np\").shape)\n",
    "print(\"Shape unknown s Tree:\", events[\"theta_unknown_S\"].array(library=\"np\").shape)\n",
    "theta0 = events[\"theta0\"].array(library=\"np\")\n",
    "theta_unknown_S = events[\"theta_unknown_S\"].array(library=\"np\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00,\n",
       "        0.000e+00, 1.000e+00, 0.000e+00, 2.000e+00, 2.000e+00, 3.000e+00,\n",
       "        3.000e+00, 5.000e+00, 1.100e+01, 1.400e+01, 2.700e+01, 4.800e+01,\n",
       "        5.200e+01, 8.600e+01, 1.240e+02, 1.590e+02, 1.980e+02, 2.490e+02,\n",
       "        3.380e+02, 5.100e+02, 6.300e+02, 8.030e+02, 9.360e+02, 1.188e+03,\n",
       "        1.500e+03, 1.814e+03, 1.980e+03, 2.472e+03, 2.752e+03, 3.128e+03,\n",
       "        3.573e+03, 3.874e+03, 4.144e+03, 4.305e+03, 4.573e+03, 4.705e+03,\n",
       "        4.825e+03, 4.804e+03, 4.651e+03, 4.654e+03, 4.571e+03, 4.277e+03,\n",
       "        4.028e+03, 3.456e+03, 3.290e+03, 2.941e+03, 2.581e+03, 2.189e+03,\n",
       "        1.889e+03, 1.579e+03, 1.396e+03, 1.066e+03, 8.520e+02, 6.680e+02,\n",
       "        5.690e+02, 4.060e+02, 3.030e+02]),\n",
       " array([-3.        , -2.93939394, -2.87878788, -2.81818182, -2.75757576,\n",
       "        -2.6969697 , -2.63636364, -2.57575758, -2.51515152, -2.45454545,\n",
       "        -2.39393939, -2.33333333, -2.27272727, -2.21212121, -2.15151515,\n",
       "        -2.09090909, -2.03030303, -1.96969697, -1.90909091, -1.84848485,\n",
       "        -1.78787879, -1.72727273, -1.66666667, -1.60606061, -1.54545455,\n",
       "        -1.48484848, -1.42424242, -1.36363636, -1.3030303 , -1.24242424,\n",
       "        -1.18181818, -1.12121212, -1.06060606, -1.        , -0.93939394,\n",
       "        -0.87878788, -0.81818182, -0.75757576, -0.6969697 , -0.63636364,\n",
       "        -0.57575758, -0.51515152, -0.45454545, -0.39393939, -0.33333333,\n",
       "        -0.27272727, -0.21212121, -0.15151515, -0.09090909, -0.03030303,\n",
       "         0.03030303,  0.09090909,  0.15151515,  0.21212121,  0.27272727,\n",
       "         0.33333333,  0.39393939,  0.45454545,  0.51515152,  0.57575758,\n",
       "         0.63636364,  0.6969697 ,  0.75757576,  0.81818182,  0.87878788,\n",
       "         0.93939394,  1.        ,  1.06060606,  1.12121212,  1.18181818,\n",
       "         1.24242424,  1.3030303 ,  1.36363636,  1.42424242,  1.48484848,\n",
       "         1.54545455,  1.60606061,  1.66666667,  1.72727273,  1.78787879,\n",
       "         1.84848485,  1.90909091,  1.96969697,  2.03030303,  2.09090909,\n",
       "         2.15151515,  2.21212121,  2.27272727,  2.33333333,  2.39393939,\n",
       "         2.45454545,  2.51515152,  2.57575758,  2.63636364,  2.6969697 ,\n",
       "         2.75757576,  2.81818182,  2.87878788,  2.93939394,  3.        ]),\n",
       " <BarContainer object of 99 artists>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( theta0_G[:,0], bins=np.linspace(-3,3, 100) )\n",
    "plt.hist( theta0_G[:,1], bins=np.linspace(-3,3, 100) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x107cb1750>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,_,_=plt.hist(theta0_G[:,0],bins=np.linspace(-3,3,20),color='blue',alpha=0.5,label=\"MC, true\")\n",
    "_,_,_=plt.hist(theta0_S[:,0],bins=np.linspace(-3,3,20),histtype=\"step\",color='black',ls=':',label=\"MC, reco\")\n",
    "_,_,_=plt.hist(theta_unknown_G[:,0],bins=np.linspace(-3,3,20),color='orange',alpha=0.5,label=\"Data, true\")\n",
    "_,_,_=plt.hist(theta_unknown_S[:,0],bins=np.linspace(-3,3,20),histtype=\"step\",color='black',label=\"Data, reco\")\n",
    "plt.xlabel(\"x1\")\n",
    "plt.ylabel(\"events\")\n",
    "plt.legend(frameon=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x107c91750>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,_,_=plt.hist(theta0_G[:,1],bins=np.linspace(-3,3,20),color='blue',alpha=0.5,label=\"MC, true\")\n",
    "_,_,_=plt.hist(theta0_S[:,1],bins=np.linspace(-3,3,20),histtype=\"step\",color='black',ls=':',label=\"MC, reco\")\n",
    "_,_,_=plt.hist(theta_unknown_G[:, 1],bins=np.linspace(-3,3,20),color='orange',alpha=0.5,label=\"Data, true\")\n",
    "_,_,_=plt.hist(theta_unknown_S[:, 1],bins=np.linspace(-3,3,20),histtype=\"step\",color='black',label=\"Data, reco\")\n",
    "plt.xlabel(\"x2\")\n",
    "plt.ylabel(\"events\")\n",
    "plt.legend(frameon=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # test 4d\n",
    "# np.random.seed(42)\n",
    "# a = np.random.randint(0, 5, 100)\n",
    "# b = np.random.randint(0, 5, 100)\n",
    "# c = np.random.rand(100)\n",
    "# d = np.random.rand(100)\n",
    "\n",
    "# fig, axs = plt.subplots(5, 5, sharex='col', sharey='row', figsize=(10, 10))\n",
    "\n",
    "# for i in range(5):\n",
    "#     for j in range(5):\n",
    "#         #filter\n",
    "#         mask = (a == i) & (b == j)\n",
    "#         current_c = c[mask]\n",
    "#         current_d = d[mask]\n",
    "\n",
    "#         #scatter in sub\n",
    "#         axs[i, j].scatter(current_c, current_d, s=10)\n",
    "\n",
    "#         #labels\n",
    "#         axs[i, j].set_xlabel('c')\n",
    "#         axs[i, j].set_ylabel('d')\n",
    "\n",
    "#         #title\n",
    "#         axs[i, j].set_title(f'a={i}, b={j}')\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data, true\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.hist2d(theta_unknown_S[:, 0], theta_unknown_S[:, 1], bins=(20, 20), cmap='Oranges', alpha=0.5, label=\"Data, true\")\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"x1\")\n",
    "plt.ylabel(\"x2\")\n",
    "plt.title(\"Hist: Data, true\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Data, true\n",
    "# theshape=theta_unknown_S.shape\n",
    "# print(theshape)\n",
    "# N = theshape[0] * theshape[1]\n",
    "# plt.figure(figsize=(10, 8))\n",
    "# plt.hist(theta_unknown_S.reshape(N), cmap='Oranges', alpha=0.5, label=\"Data, true\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# _, _, _ = plt.hist2d(theta0_G[:, 0], theta0_G[:, 1], bins=(20, 20), cmap='Blues', alpha=0.5, label=\"MC, true\")\n",
    "# _, _, _ = plt.hist2d(theta_unknown_G[:, 0], theta_unknown_G[:, 1], bins=(20, 20), cmap='Oranges', alpha=0.5, label=\"Data, true\")\n",
    "# _, _, _ = plt.hist2d(theta0_G[:, 0], theta0_G[:, 1], bins=(20, 20), weights=myweights[-1, 0, :], cmap='Greys', alpha=0.5, label=\"OmniFolded\")\n",
    "# plt.xlabel(\"x1\")\n",
    "# plt.ylabel(\"x2\")\n",
    "# plt.legend(frameon=False)\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input((2,))  # 2D input\n",
    "hidden_layer_1 = Dense(50, activation='relu')(inputs)\n",
    "hidden_layer_2 = Dense(50, activation='relu')(hidden_layer_1)\n",
    "hidden_layer_3 = Dense(50, activation='relu')(hidden_layer_2)\n",
    "outputs = Dense(1, activation='sigmoid')(hidden_layer_3)\n",
    "model = Model(inputs=inputs, outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta0G (100000, 2), labels (100000,), labels unknown (100000,), theta 0(100000, 2, 2), theta unknown s(100000, 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-17 10:37:34.344066: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-11-17 10:37:34.822332: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-17 10:37:37.224661: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-11-17 10:37:37.399669: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-11-17 10:37:37.824101: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "2023-11-17 10:41:08.366743: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "/Users/dirkin/Documents/Research/lhc/alt/OmniFold/omnifold.py:81: RuntimeWarning: overflow encountered in multiply\n",
      "  weights_pull = weights_push * reweight(theta0_S,model)\n",
      "2023-11-17 10:41:08.558756: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
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     ]
    }
   ],
   "source": [
    "myweights = of.omnifold(theta0,theta_unknown_S,20,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #needless\n",
    "# plt.hist2d(theta0_G[:, 0], theta0_G[:, 1], bins=(20, 20), cmap='Blues', alpha=0.5, label=\"MC, true\")\n",
    "# plt.hist2d(theta_unknown_G[:, 0], theta_unknown_G[:, 1], bins=(20, 20), cmap='Oranges', alpha=0.5, label=\"Data, true\")\n",
    "# plt.hist2d(theta0_G[:, 0], theta0_G[:, 1], bins=(20, 20), weights=myweights[-1, 0, :], cmap='Greys', alpha=0.5, label=\"OmniFolded\")\n",
    "# plt.xlabel(\"x1\")\n",
    "# plt.ylabel(\"x2\")\n",
    "# plt.legend(frameon=False)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape theta0_G: (100000, 2)\n",
      "Shape weights: (10, 2, 100000)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape theta0_G:\", theta0_G.shape)\n",
    "print(\"Shape weights:\", myweights.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dirkin/Documents/Research/lhc/lib/python3.11/site-packages/numpy/lib/function_base.py:1448: RuntimeWarning: invalid value encountered in subtract\n",
      "  a = op(a[slice1], a[slice2])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x352778cd0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,_,_=plt.hist(theta0_G[:,0],bins=np.linspace(-3,3,80),color='blue',alpha=0.5,label=\"MC, true\")\n",
    "_,_,_=plt.hist(theta_unknown_G[:,0],bins=np.linspace(-3,3,80),color=['orange'],alpha=0.5,label=\"Data, true\")\n",
    "_,_,_=plt.hist(theta0_G[:,0],bins=np.linspace(-3,3,80),weights=myweights[-1, 0, :],color='black',histtype=\"step\",label=\"OmniFolded\")\n",
    "plt.xlabel(\"X1\")\n",
    "plt.ylabel(\"events\")\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29e78e8d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,_,_=plt.hist(theta0_G[:,1],bins=np.linspace(-3,3,100),color=['blue'],alpha=0.5,label=\"MC, true\")\n",
    "_,_,_=plt.hist(theta_unknown_G[:,1],bins=np.linspace(-3,3,100),color=['orange'],alpha=0.5,label=\"Data, true\")\n",
    "_,_,_=plt.hist(theta0_G[:,1],bins=np.linspace(-3,3,100),weights=myweights[-1, 0, :],color='black',histtype=\"step\",label=\"OmniFolded\")\n",
    "plt.xlabel(\"X2\")\n",
    "plt.ylabel(\"events\")\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x32424a7d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tqqiocLfJzs5Wt27dGjwkJdFzg2ZWxw2mfYhpftDyqmrzY1aaH9P1fWX9jRrpW5fvkwl/as+J06bHzDlaYnrMTz5v+ITFhjh58ntT40nS6W9Omx6zTbs2pscsuzLS9JgRbUPqb9RIznbmxjwn83P0qpkfnFnf8x2lC5OT33zzTT333HMXHZ+Tk6Pc3FwNGTJE4eHhysnJ0dSpUzVu3Dh34XLfffdp7ty5mjhxombMmKEDBw7ohRde0OLFixuVK8UNAACoV33Pd5Sk119/XYZhaMyYMRcdb7fb9frrr2vOnDkqKytTQkKCpk6d6jEPJyIiQuvXr1d6eroGDBigDh06aNasWY1aBi5R3AAAYEnNfZ+b+p7vKEmTJ0/2Woj0799fO3furPf79O7dWx9++GGjcvspihsAACyIZ0t5R3EDAIAFUdx4x2opAAAQUOi5AQDAipp5tZSVUNwAAGBBDEt5x7AUAAAIKPTcAABgQfTceEdxAwCABdnkY3ETwJNuGJYCAAABhZ4bAAAsiGEp7yhuAACwIpaCe8WwFAAACCj03AAAYEEMS3lHcQMAgAVR3HhHcQMAgAXZbBc2X44PVMy5AQAAAYWeG9TJMMyOZ3JASZXV5sesqDQ/5qnSCtNjHi85b3rMg9+6TI+5qeA702Pm7j5mesxTJ06ZGq+65ISp8SSpdftY02O2DW5resz2V7QxPWZCe/NjxrUzN2apKk2NV5cLPTe+DEuZmMwlhuIGAAAr8nFYiqXgAAAAFkHPDQAAFsRqKe8obgAAsCBWS3nHsBQAAAgo9NwAAGBBQUE2BQU1vfvF8OHYSx3FDQAAFsSwlHcMSwEAgIBCzw0AABbEainvKG4AALAghqW8o7gBAMCC6Lnxjjk3AAAgoNBzAwCABdFz4x3FDQAAFsScG+8YlgIAAPXaunWr7rzzTsXFxclms2n16tUe70+YMMHdm1Sz3X777R5tTp48qbFjx8rhcCgyMlITJ07U2bNnPdrs27dPt9xyi0JDQxUfH68FCxY0OleKGwAALMgm20XFRKM2Na7rprS0VH369NGSJUu8trn99tt1/Phx9/a///u/Hu+PHTtWBw8eVHZ2ttasWaOtW7dq8uTJ7vddLpeGDRumTp06KS8vTwsXLtScOXP08ssvNypXhqUAALCg5h6WGj58uIYPH15nG7vdLqfTWet7hw4d0rp16/TRRx/puuuukyT9/ve/1x133KFnn31WcXFxWrFihcrLy7Vs2TKFhISoR48eys/P16JFizyKoPrQcwMAwGXM5XJ5bGVlZU2OtXnzZkVHR6tbt2565JFH9N1337nfy8nJUWRkpLuwkaSUlBQFBQUpNzfX3WbQoEEKCQlxt0lNTVVBQYFOnTrV4DwobgAAsCCfhqR+tNIqPj5eERER7i0rK6tJ+dx+++167bXXtHHjRj3zzDPasmWLhg8frqqqKklSUVGRoqOjPY5p1aqVoqKiVFRU5G4TExPj0abmdU2bhmBYKoBUG4b5MavNjVdVbX6O5yuqTI95srTc9Jhfl3xvesz8YpfpMd/a9ZXpMf+/j782PWapq9T0mMZne8wN2PYKc+NJckQ5TI95zc87mh5zSPcOpse8LjbS9JhXRYWZGu9M60pT49XFrGGpY8eOyeH44ffKbrc3Kd7o0aPdX/fq1Uu9e/dW165dtXnzZg0dOrTpiTYBPTcAAFzGHA6Hx9bU4uanunTpog4dOujw4cOSJKfTqRMnTni0qays1MmTJ93zdJxOp4qLiz3a1Lz2NpenNpYqbupbhlabzZs3q3///rLb7br66qu1fPlyv+cJAIC/mTUs5S9ffvmlvvvuO8XGxkqSkpOTdfr0aeXl5bnbbNq0SdXV1UpKSnK32bp1qyoqKtxtsrOz1a1bN11xRcN7Qi1V3DRkGdqPHT16VCNGjNCQIUOUn5+vKVOm6KGHHtL777/v50wBAPCvmmEpX7bGOHv2rPLz85Wfny/pwmdsfn6+CgsLdfbsWU2fPl07d+7U559/ro0bN+quu+7S1VdfrdTUVEnStddeq9tvv12TJk3Srl27tH37dmVkZGj06NGKi4uTJN13330KCQnRxIkTdfDgQa1atUovvPCCpk2b1qhcLTXnpiHL0H5s6dKlSkhI0HPPPSfpwoXdtm2bFi9e7L7YAABYUXM/fmH37t0aMmSI+3VNwTF+/Hi99NJL2rdvn1599VWdPn1acXFxGjZsmObPn+8xzLVixQplZGRo6NChCgoK0qhRo/Tiiy+634+IiND69euVnp6uAQMGqEOHDpo1a1ajloFLFituGisnJ0cpKSke+1JTUzVlyhSvx5SVlXksg3O5zJ+wCQCA1QwePFhGHQtXGjIqEhUVpZUrV9bZpnfv3vrwww8bnd+PWWpYqrG8LSlzuVz6/vvaV65kZWV5LImLj49vjlQBAGgcX4ekeLbU5SMzM1MlJSXu7dixYy2dEgAAF7nUJxS3pIAelvK2pMzhcKhNmza1HmO3201bBgcAAJpfQBc3ycnJeu+99zz2ZWdnKzk5uYUyAgDAHM39bCkrsdSwVF3L0KQLQ0ppaWnu9g8//LA+++wzPf744/rkk0/03//933rjjTc0derUlkgfAADTMCzlnaWKm927d6tfv37q16+fpAvL0Pr166dZs2ZJko4fP+4udCQpISFBa9euVXZ2tvr06aPnnntOf/rTn1gGDgBAALPUsFR9y9Bqu/vw4MGDtXfvXj9mBQBA82NYyjtLFTcAAOCC5r6Jn5VYalgKAACgPvTcAABgQfTceEdxAwCABTHnxjuKGwAALIieG++YcwMAAAIKPTctpI4V7T4ENT9kRVW1qfHKKs2NJ0lnz1eaHvPk2XLTYxacPGt6zC0F35kes/CLk6bHrDb590iSjG+/ND1mq59fb2q8jj/raGo8Serfy2l6zJuvvsL0mH2iHabHvDIyzPSY7ezmfgwaZc33scqwlHcUNwAAWBDDUt4xLAUAAAIKPTcAAFiQTT4OS5mWyaWH4gYAAAsKstkU5EN148uxlzqGpQAAQECh5wYAAAtitZR3FDcAAFgQq6W8o7gBAMCCgmwXNl+OD1TMuQEAAAGFnhsAAKzI5uPQUgD33FDcAABgQUwo9o5hKQAAEFDouQEAwIJs//qfL8cHKoobAAAsiNVS3jEsBQAAAgo9NwAAWBA38fOO4gYAAAtitZR3DEsBAICAQnEDAIAFBdlsPm+NsXXrVt15552Ki4uTzWbT6tWr3e9VVFRoxowZ6tWrl9q2bau4uDilpaXp66+/9ojRuXNn93Bazfb00097tNm3b59uueUWhYaGKj4+XgsWLGj0tWFYqoEM48JmXjwTg/1LZbUfYlaZG7P0fKWp8SSptMz8mMfPnTc95uFvzY951g/X0xHRxvSYIfYQ02O2d95geszOnSJNjXfj1e1NjSdJnaPspsdMjHKYHrN9O/PzDA81/yMrzB5sarxKk+PVpbmHpUpLS9WnTx89+OCDuvvuuz3eO3funPbs2aPf/e536tOnj06dOqVHH31U//7v/67du3d7tJ03b54mTZrkfh0eHu7+2uVyadiwYUpJSdHSpUu1f/9+Pfjgg4qMjNTkyZMbnCvFDQAAFtTcE4qHDx+u4cOH1/peRESEsrOzPfb94Q9/0A033KDCwkJdddVV7v3h4eFyOp21xlmxYoXKy8u1bNkyhYSEqEePHsrPz9eiRYsaVdwwLAUAwGXM5XJ5bGVlZabELSkpkc1mU2RkpMf+p59+Wu3bt1e/fv20cOFCVVb+0AOdk5OjQYMGKSTkh97e1NRUFRQU6NSpUw3+3vTcAABgQWYNS8XHx3vsnz17tubMmdP0wJLOnz+vGTNmaMyYMXI4fhj2/M1vfqP+/fsrKipKO3bsUGZmpo4fP65FixZJkoqKipSQkOARKyYmxv3eFVdc0aDvT3EDAIAFNWVS8E+Pl6Rjx455FCB2u2/zpSoqKvTLX/5ShmHopZde8nhv2rRp7q979+6tkJAQ/epXv1JWVpbP3/fHGJYCAOAy5nA4PDZfioyawuaLL75Qdna2R9FUm6SkJFVWVurzzz+XJDmdThUXF3u0qXntbZ5ObShuAACwIJsJm5lqCptPP/1UGzZsUPv29a8WzM/PV1BQkKKjoyVJycnJ2rp1qyoqKtxtsrOz1a1btwYPSUkMSwEAYEnNvVrq7NmzOnz4sPv10aNHlZ+fr6ioKMXGxuo//uM/tGfPHq1Zs0ZVVVUqKiqSJEVFRSkkJEQ5OTnKzc3VkCFDFB4erpycHE2dOlXjxo1zFy733Xef5s6dq4kTJ2rGjBk6cOCAXnjhBS1evLhRuVLcAACAeu3evVtDhgxxv66ZPzN+/HjNmTNH7777riSpb9++Hsd98MEHGjx4sOx2u15//XXNmTNHZWVlSkhI0NSpUz3m4URERGj9+vVKT0/XgAED1KFDB82aNatRy8AlihsAACwpyHZh8+X4xhg8eHCdN6Ct7+a0/fv3186dO+v9Pr1799aHH37YuOR+guIGAAAL4qng3jGhGAAABBR6bgAAsKgA7nzxCcUNAAAWxLCUdxQ3AABYUHNPKLYS5twAAICA0uSemyNHjuj555/XoUOHJEmJiYl69NFH1bVrV9OSAwAAtWNYyrsm9dy8//77SkxM1K5du9S7d2/17t1bubm56tGjh7Kzs83OEQAA/MSl9viFS0mTem5mzpypqVOn6umnn75o/4wZM3TbbbeZkhwAAEBjNam4OXTokN54442L9j/44IN6/vnnfc3pkmQYRr13X2yMavNCuVVWmR/0+4oqU+OVV1WbGk+Svi83N0dJKqs0P2ZIK/OnuMVe0cb0mDGR5sesrDb/535NdDvTY/68Y6ip8bpGhJsaT5Ki2oaYHjMirLXpMdvag02PGeyHGbDBJg/NmB2vLkE2m4J8+H6+HHupa9Jf244dOyo/P/+i/fn5+e4newIAAP+x2XzfAlWTem4mTZqkyZMn67PPPtPAgQMlSdu3b9czzzzj8QAsAACA5tak4uZ3v/udwsPD9dxzzykzM1OSFBcXpzlz5ug3v/mNqQkCAICLsVrKuyYVNzabTVOnTtXUqVN15swZSVJ4uPljywAAoHa+Di0FcG3TtOLm6NGjqqys1DXXXONR1Hz66adq3bq1OnfubFZ+AAAAjdKkCcUTJkzQjh07Ltqfm5urCRMm+JpTnZYsWaLOnTsrNDRUSUlJ2rVrl9e2y5cvd3fb1WyhoeauhgAAoCXUrJbyZQtUTSpu9u7dq5tuuumi/TfeeGOtq6jMsmrVKk2bNk2zZ8/Wnj171KdPH6WmpurEiRNej3E4HDp+/Lh7++KLL/yWHwAAzYXVUt41qbix2WzuuTY/VlJSoqoq8+8PUmPRokWaNGmSHnjgASUmJmrp0qUKCwvTsmXL6szV6XS6t5iYGL/lBwBAc/npyERTtkDVpOJm0KBBysrK8ihkqqqqlJWVpZtvvtm05H6svLxceXl5SklJce8LCgpSSkqKcnJyvB539uxZderUSfHx8brrrrt08ODBOr9PWVmZXC6XxwYAAKyjSROKn3nmGQ0aNEjdunXTLbfcIkn68MMP5XK5tGnTJlMTrPHtt9+qqqrqop6XmJgYffLJJ7Ue061bNy1btky9e/dWSUmJnn32WQ0cOFAHDx7UlVdeWesxWVlZmjt3run5AwBgpiA1sYfiR8cHqiadW2Jiovbt26df/vKXOnHihM6cOaO0tDR98skn6tmzp9k5NllycrLS0tLUt29f3XrrrXrrrbfUsWNH/fGPf/R6TGZmpkpKStzbsWPHmjFjAAAahmEp75rUcyNduGnfU089ZWYuderQoYOCg4NVXFzssb+4uFhOp7NBMVq3bq1+/frp8OHDXtvY7XbZ7XafcgUAAC2nyb1SH374ocaNG6eBAwfqq6++kiT95S9/0bZt20xL7sdCQkI0YMAAbdy40b2vurpaGzduVHJycoNiVFVVaf/+/YqNjfVLjgAANBebTQryYQvgjpumFTd///vflZqaqjZt2mjPnj0qKyuTdGG1lD97c6ZNm6b/+Z//0auvvqpDhw7pkUceUWlpqR544AFJUlpamvtxEJI0b948rV+/Xp999pn27NmjcePG6YsvvtBDDz3ktxwBAGgOvhQ2NVugatKw1JNPPqmlS5cqLS1Nr7/+unv/TTfdpCeffNK05H7q3nvv1TfffKNZs2apqKhIffv21bp169yTjAsLCxUU9EO9durUKU2aNElFRUW64oorNGDAAO3YsUOJiYl+yxEAALSsJhU3BQUFGjRo0EX7IyIidPr0aV9zqlNGRoYyMjJqfW/z5s0erxcvXqzFixf7NR8AAFoCD870rknDUk6ns9ZJudu2bVOXLl18TgoAANSNYSnvmlTcTJo0SY8++qhyc3Nls9n09ddfa8WKFXrsscf0yCOPmJ0jAABAgzVpWGrmzJmqrq7W0KFDde7cOQ0aNEh2u13Tp09nsi4AAM3A1+dDBfCoVNOKG5vNpv/3//6fpk+frsOHD+vs2bNKTEzUH//4RyUkJKioqMjsPFuc8a/NvHhmRrugoqra9JjVhrl5Vpufos6UV5oeM6x1k28B5VV8RIjpMTu2NT/PcHuw6TE7hpl/76jYsFDTY7ZrY+71bGc3/+fT1g8xW7cy/1OudbD597/1x1OszQ4Z1IxjPb4+2Zungv9LWVmZMjMzdd111+mmm27Se++9p8TERB08eFDdunXTCy+8oKlTp/orVwAA8C9BJmyBqlHnNmvWLL300kvq3Lmzjh49qnvuuUeTJ0/W4sWL9dxzz+no0aOaMWOGv3IFAAAtZOvWrbrzzjsVFxcnm82m1atXe7xvGIZmzZql2NhYtWnTRikpKfr000892pw8eVJjx46Vw+FQZGSkJk6cqLNnz3q02bdvn2655RaFhoYqPj5eCxYsaHSujSpu3nzzTb322mv629/+pvXr16uqqkqVlZX6v//7P40ePVrBweZ3ZwMAgIvVzLnxZWuM0tJS9enTR0uWLKn1/QULFujFF1/U0qVLlZubq7Zt2yo1NVXnz593txk7dqwOHjyo7OxsrVmzRlu3btXkyZPd77tcLg0bNkydOnVSXl6eFi5cqDlz5ujll19uVK6NGrz98ssvNWDAAElSz549ZbfbNXXq1IBeKw8AwKUoSD7OuVHjjh0+fLiGDx9e63uGYej555/XE088obvuukuS9NprrykmJkarV6/W6NGjdejQIa1bt04fffSRrrvuOknS73//e91xxx169tlnFRcXpxUrVqi8vFzLli1TSEiIevToofz8fC1atMijCKr/3BqhqqpKISE/TIps1aqV2rVr15gQAADgEuJyuTy2mkcqNcbRo0dVVFSklJQU976IiAglJSUpJydHkpSTk6PIyEh3YSNJKSkpCgoKUm5urrvNoEGDPGqN1NRUFRQU6NSpUw3Op1E9N4ZhaMKECe6nZp8/f14PP/yw2rZt69HurbfeakxYAADQSGYtBY+Pj/fYP3v2bM2ZM6dRsWpWSdc8DqlGTEyM+72ioiJFR0d7vN+qVStFRUV5tElISLgoRs17V1xxRYPyaVRxM378eI/X48aNa8zhAADAJL7eZbjm2GPHjsnhcLj313RgWFmjiptXXnnFX3kAAIAW4HA4PIqbpnA6nZKk4uJixcbGuvcXFxerb9++7jYnTpzwOK6yslInT550H+90OlVcXOzRpuZ1TZuGCORl7gAABCyb7Ycb+TVlM3MtUEJCgpxOpzZu3Oje53K5lJubq+TkZElScnKyTp8+rby8PHebTZs2qbq6WklJSe42W7duVUVFhbtNdna2unXr1uAhKYniBgAAS2rupeBnz55Vfn6+8vPzJV2YRJyfn6/CwkLZbDZNmTJFTz75pN59913t379faWlpiouL08iRIyVJ1157rW6//XZNmjRJu3bt0vbt25WRkaHRo0crLi5OknTfffcpJCREEydO1MGDB7Vq1Sq98MILmjZtWqNyNf8+3gAAIODs3r1bQ4YMcb+uKTjGjx+v5cuX6/HHH1dpaakmT56s06dP6+abb9a6desUGvrDo1JWrFihjIwMDR06VEFBQRo1apRefPFF9/sRERFav3690tPTNWDAAHXo0EGzZs1q1DJwSbIZhskPDwowLpdLERER+vqb0z6PSf6Y2c9skqRzZVWmx/y+wtyYpefNz/Fr1/emxzxbYf7zqr4+c77+Ro10vtL8h3XxbCkT4/FsKVNZ4dlSLpdLMe0jVFJSYupnxk+/R0REhJ54Z49C24Y3Oc750jN68q7+fs21pdBzAwCABdn+9T9fjg9UFDcAAFiQWUvBAxETigEAQECh5wYAAAui58Y7ihsAACzIZrP59ODqQH7oNcNSAAAgoNBz00DV1Yaqq81bvl1pYqwaQX7oYww2ubL3Rzdo+7CQ+hs1kqOytekxY9uav3RZfriRg721+UvB24SYHzO0tfn/Ngs1OU97K/NzbBXsh//O/fAfpj+WbcMTw1LeUdwAAGBBZj0VPBAxLAUAAAIKPTcAAFhQzQMwfTk+UFHcAABgQcy58Y5hKQAAEFDouQEAwIp8nFAcwI+WorgBAMCKgmRTkA8Vii/HXuoobgAAsCCWgnvHnBsAABBQ6LkBAMCCWC3lHcUNAAAWxH1uvGNYCgAABBR6bgAAsCAmFHtHcQMAgAUFycdhqQBeCs6wFAAACCj03AAAYEEMS3lHcQMAgAUFybfhl0AeugnkcwMAAJchem4AALAgm80mmw9jS74ce6mjuGkhwX64NWRltWF6TLN/98Ps5v/K+eNahrQ1v1PTHz+fVsHmn7s/buzV1h5sekybH1Z6mP275I+fjz9+3w3zfzXRDGzy7cHegVvaUNwAAGBJ3KHYO+bcAACAgELPDQAAFhW4fS++obgBAMCCuM+NdwxLAQCAenXu3Nm9QuvHW3p6uiRp8ODBF7338MMPe8QoLCzUiBEjFBYWpujoaE2fPl2VlZWm50rPDQAAFtTcS8E/+ugjVVVVuV8fOHBAt912m+655x73vkmTJmnevHnu12FhYe6vq6qqNGLECDmdTu3YsUPHjx9XWlqaWrduraeeeqrJ51EbihsAACyoue9Q3LFjR4/XTz/9tLp27apbb73VvS8sLExOp7PW49evX6+PP/5YGzZsUExMjPr27av58+drxowZmjNnjkJCQhp7Cl5ZblhqyZIl6ty5s0JDQ5WUlKRdu3bV2f7NN99U9+7dFRoaql69eum9995rpkwBALj0uVwuj62srKzeY8rLy/XXv/5VDz74oEcP0IoVK9ShQwf17NlTmZmZOnfunPu9nJwc9erVSzExMe59qampcrlcOnjwoKnnZKniZtWqVZo2bZpmz56tPXv2qE+fPkpNTdWJEydqbb9jxw6NGTNGEydO1N69ezVy5EiNHDlSBw4caObMAQAwV23zXxq7SVJ8fLwiIiLcW1ZWVr3fe/Xq1Tp9+rQmTJjg3nfffffpr3/9qz744ANlZmbqL3/5i8aNG+d+v6ioyKOwkeR+XVRUZMIV+YGlhqUWLVqkSZMm6YEHHpAkLV26VGvXrtWyZcs0c+bMi9q/8MILuv322zV9+nRJ0vz585Wdna0//OEPWrp0abPmDgCAmcy6Q/GxY8fkcDjc++12e73H/vnPf9bw4cMVFxfn3jd58mT317169VJsbKyGDh2qI0eOqGvXrj5k2niW6bkpLy9XXl6eUlJS3PuCgoKUkpKinJycWo/JycnxaC9d6ALz1h4AgMuNw+Hw2Oorbr744gtt2LBBDz30UJ3tkpKSJEmHDx+WJDmdThUXF3u0qXntbZ5OU1mmuPn2229VVVVVa5eWt+4sb11gdXV/lZWVXTT+CADApcasYanGeuWVVxQdHa0RI0bU2S4/P1+SFBsbK0lKTk7W/v37PaaSZGdny+FwKDExsUm5eGOZ4qa5ZGVleYw9xsfHt3RKAABcJMiErbGqq6v1yiuvaPz48WrV6oeZLUeOHNH8+fOVl5enzz//XO+++67S0tI0aNAg9e7dW5I0bNgwJSYm6v7779f//d//6f3339cTTzyh9PT0Bg2FNYZlipsOHTooODi41i4tb91Z3rrA6ur+yszMVElJiXs7duyY78kDAGCylui52bBhgwoLC/Xggw967A8JCdGGDRs0bNgwde/eXY899phGjRqlf/zjH+42wcHBWrNmjYKDg5WcnKxx48YpLS3N4744ZrHMhOKQkBANGDBAGzdu1MiRIyVdqCA3btyojIyMWo9JTk7Wxo0bNWXKFPe+7OxsJScne/0+drvd9AoSAIBAMGzYMBmGcdH++Ph4bdmypd7jO3Xq1Cy3ZLFMcSNJ06ZN0/jx43Xdddfphhtu0PPPP6/S0lL36qm0tDT97Gc/cy9je/TRR3Xrrbfqueee04gRI/T6669r9+7devnll1vyNAAA8JlZq6UCkaWKm3vvvVfffPONZs2apaKiIvXt21fr1q1zTxouLCxUUNAPI20DBw7UypUr9cQTT+i3v/2trrnmGq1evVo9e/ZsqVMAAMAUPDjTO5tRW/8S3FwulyIiIvRl8SmP+wD4yh8Xvayy2vyYFVX1N2qEKvNTND1HSQppZf50tMpq83/qrYLN/+sU5Ie/eG3twabHtPnh353BQebG9MfPx+wcJckfnwKB/MFZF5fLpZj2ESopKTH1M+On3yMiIkIrd/x/CmsX3uQ4586e0X0Df+7XXFuKpXpuWlJQkE1BJv5RqfLDB12wH/6ahLY290PJH0Wd3Q+FSJAfptr78oA7b0KCrbEmwA+fx375kDe7XvJHAeYPl2shYnVBsinIh98xX4691FHcAABgQQxLeWeNf/YBAAA0ED03AABYkO1f//Pl+EBFcQMAgAUxLOUdw1IAACCg0HMDAIAF2XxcLcWwFAAAuKQwLOUdxQ0AABZEceMdc24AAEBAoecGAAALYim4dxQ3AABYUJDNt0eb+OMJJpcKhqUAAEBAoecGAAALYljKO4obAAAsiNVS3jEsBQAAAgo9NwAAWJBNvg0tBXDHDcUNAABWxGop7xiWAgAAAYWemxbij4lcrYLND2oY5sb0x3nbWpsfs7ra/JjBfvhnkiHD9Jj+yNMf/LHSI5AnWCLwsFrKO4obAAAsiNVS3lHcAABgQTb5Nik4gGsb5twAAIDAQs8NAAAWFCSbgnwYWwoK4L4bihsAACyIYSnvGJYCAAABhZ4bAACsiK4bryhuAACwIO5z4x3DUgAAoF5z5syRzWbz2Lp37+5+//z580pPT1f79u3Vrl07jRo1SsXFxR4xCgsLNWLECIWFhSk6OlrTp09XZWWl6bnScwMAgBX5eBO/pnTc9OjRQxs2bHC/btXqhzJi6tSpWrt2rd58801FREQoIyNDd999t7Zv3y5Jqqqq0ogRI+R0OrVjxw4dP35caWlpat26tZ566ikfTuRiFDcAAFhQS0y5adWqlZxO50X7S0pK9Oc//1krV67UL37xC0nSK6+8omuvvVY7d+7UjTfeqPXr1+vjjz/Whg0bFBMTo759+2r+/PmaMWOG5syZo5CQEB/OxhPDUgAAXMZcLpfHVlZW5rXtp59+qri4OHXp0kVjx45VYWGhJCkvL08VFRVKSUlxt+3evbuuuuoq5eTkSJJycnLUq1cvxcTEuNukpqbK5XLp4MGDpp4TxQ0AAFZkM2GTFB8fr4iICPeWlZVV67dLSkrS8uXLtW7dOr300ks6evSobrnlFp05c0ZFRUUKCQlRZGSkxzExMTEqKiqSJBUVFXkUNjXv17xnJoalAACwILNWSx07dkwOh8O9326319p++PDh7q979+6tpKQkderUSW+88YbatGnT5Dz8gZ4bAAAsqOap4L5skuRwODw2b8XNT0VGRurnP/+5Dh8+LKfTqfLycp0+fdqjTXFxsXuOjtPpvGj1VM3r2ubx+ILiBgAANNrZs2d15MgRxcbGasCAAWrdurU2btzofr+goECFhYVKTk6WJCUnJ2v//v06ceKEu012drYcDocSExNNzY1hKQAALKi5V0v953/+p+6880516tRJX3/9tWbPnq3g4GCNGTNGERERmjhxoqZNm6aoqCg5HA79+te/VnJysm688UZJ0rBhw5SYmKj7779fCxYsUFFRkZ544gmlp6c3uLeooShuAACwomaubr788kuNGTNG3333nTp27Kibb75ZO3fuVMeOHSVJixcvVlBQkEaNGqWysjKlpqbqv//7v93HBwcHa82aNXrkkUeUnJystm3bavz48Zo3b54PJ1E7m2EYhulRA4jL5VJERIS+/ua0x4QrX1X74bL74ydpdkyfbjjVjDGrq82PGRxkfqKGzP+h+yNPf/DHreP98buEy4vL5VJM+wiVlJSY+pnx0+8RERGhLfuPqV1407/H2TMu3dor3q+5thR6bhrox5OvzBDsh7+i/ihuzM7SH5W0Pz6Qgv0wG80/z3HhAx64XPFsKe8obgAAsCBf/9EdyP+QYbUUAAAIKPTcAABgQS3xbCmroLgBAMCKqG68YlgKAAAEFHpuAACwIFZLeUdxAwCABbFayjuKGwAALIgpN94x5wYAAAQUem4AALAium68orgBAMCCmFDsnWWGpU6ePKmxY8fK4XAoMjJSEydO1NmzZ+s8ZvDgwbLZbB7bww8/3EwZAwCAlmCZnpuxY8fq+PHjys7OVkVFhR544AFNnjxZK1eurPO4SZMmeTxOPSwszN+pAgDgd6yW8s4Sxc2hQ4e0bt06ffTRR7ruuuskSb///e91xx136Nlnn1VcXJzXY8PCwuR0OpsrVQAAmgVTbryzxLBUTk6OIiMj3YWNJKWkpCgoKEi5ubl1HrtixQp16NBBPXv2VGZmps6dO1dn+7KyMrlcLo8NAABYhyV6boqKihQdHe2xr1WrVoqKilJRUZHX4+677z516tRJcXFx2rdvn2bMmKGCggK99dZbXo/JysrS3LlzTcsdAAC/oOvGqxYtbmbOnKlnnnmmzjaHDh1qcvzJkye7v+7Vq5diY2M1dOhQHTlyRF27dq31mMzMTE2bNs392uVyKT4+vsk5AADgD6yW8q5Fi5vHHntMEyZMqLNNly5d5HQ6deLECY/9lZWVOnnyZKPm0yQlJUmSDh8+7LW4sdvtstvtDY4JAAAuLS1a3HTs2FEdO3ast11ycrJOnz6tvLw8DRgwQJK0adMmVVdXuwuWhsjPz5ckxcbGNjrXIJtNQSZOLTcM00K5BflhBpXZeVrl3wmBvIoAQGBgtZR3lphQfO211+r222/XpEmTtGvXLm3fvl0ZGRkaPXq0e6XUV199pe7du2vXrl2SpCNHjmj+/PnKy8vT559/rnfffVdpaWkaNGiQevfu3ZKnAwCAz2wmbIHKEhOKpQurnjIyMjR06FAFBQVp1KhRevHFF93vV1RUqKCgwL0aKiQkRBs2bNDzzz+v0tJSxcfHa9SoUXriiSda6hQAADAPE4q9skxxExUVVecN+zp37izjR2Mo8fHx2rJlS3OkBgAALiGWKW4AAMAPWC3lHcUNAABW5OOE4gCubawxoRgAAKCh6LkBAMCCmE/sHcUNAABWRHXjFcNSAAAgoNBzAwCABbFayjuKGwAALIjHL3jHsBQAAKhXVlaWrr/+eoWHhys6OlojR45UQUGBR5vBgwfLZrN5bA8//LBHm8LCQo0YMUJhYWGKjo7W9OnTVVlZaWqu9NwAAGBBzT2feMuWLUpPT9f111+vyspK/fa3v9WwYcP08ccfq23btu52kyZN0rx589yvw8LC3F9XVVVpxIgRcjqd2rFjh44fP660tDS1bt1aTz31lA9n44niBgAAK2rm6mbdunUer5cvX67o6Gjl5eVp0KBB7v1hYWFyOp21xli/fr0+/vhjbdiwQTExMerbt6/mz5+vGTNmaM6cOQoJCWn0adSGYSkAACzIZsL/fFFSUiLpwrMff2zFihXq0KGDevbsqczMTPcDrSUpJydHvXr1UkxMjHtfamqqXC6XDh486FM+P0bPDQAAlzGXy+Xx2m63y26313lMdXW1pkyZoptuukk9e/Z077/vvvvUqVMnxcXFad++fZoxY4YKCgr01ltvSZKKioo8ChtJ7tdFRUVmnI4kihsAACzJJh9XS/3r/+Pj4z32z549W3PmzKnz2PT0dB04cEDbtm3z2D958mT317169VJsbKyGDh2qI0eOqGvXrk1PtpEobgAAsCCzptwcO3ZMDofDvb++XpuMjAytWbNGW7du1ZVXXlln26SkJEnS4cOH1bVrVzmdTu3atcujTXFxsSR5nafTFMy5AQDgMuZwODw2b8WNYRjKyMjQ22+/rU2bNikhIaHe2Pn5+ZKk2NhYSVJycrL279+vEydOuNtkZ2fL4XAoMTHR95P5F3puUKdAvskTAFhZc9/ELz09XStXrtQ777yj8PBw9xyZiIgItWnTRkeOHNHKlSt1xx13qH379tq3b5+mTp2qQYMGqXfv3pKkYcOGKTExUffff78WLFigoqIiPfHEE0pPT6+3x6gx6LkBAMCSbCZsDffSSy+ppKREgwcPVmxsrHtbtWqVJCkkJEQbNmzQsGHD1L17dz322GMaNWqU/vGPf7hjBAcHa82aNQoODlZycrLGjRuntLQ0j/vimIGeGwAAUC/DMOp8Pz4+Xlu2bKk3TqdOnfTee++ZlVatKG4AALAgni3lHcUNAAAW1NyPX7AS5twAAICAQs8NAAAWxLCUdxQ3AABYkK/Ph/L12VKXMoobAACsiEk3XjHnBgAABBR6bgAAsCA6bryjuAEAwIKYUOwdw1IAACCg0HMDAIAFsVrKO4obAACsiEk3XjEsBQAAAgo9NwAAWBAdN95R3AAAYEGslvKOYSkAABBQ6LlpIYFcMQMAmoNvq6UCeWCK4gYAAAtiWMo7hqUAAEBAobgBAAABhWEpAAAsiGEp7yhuAACwIB6/4B3DUgAAIKDQcwMAgAUxLOUdxQ0AABbE4xe8Y1gKAAAEFHpuAACwIrpuvKK4AQDAglgt5R3DUgAAIKDQcwMAgAWxWso7ihsAACyIKTfeUdwAAGBFVDdeWWbOzX/9139p4MCBCgsLU2RkZIOOMQxDs2bNUmxsrNq0aaOUlBR9+umn/k0UAIAAtmTJEnXu3FmhoaFKSkrSrl27Wjqli1imuCkvL9c999yjRx55pMHHLFiwQC+++KKWLl2q3NxctW3bVqmpqTp//rwfMwUAwP9sJvyvsVatWqVp06Zp9uzZ2rNnj/r06aPU1FSdOHHCD2fYdJYpbubOnaupU6eqV69eDWpvGIaef/55PfHEE7rrrrvUu3dvvfbaa/r666+1evVq/yYLAICf1Uwo9mVrrEWLFmnSpEl64IEHlJiYqKVLlyosLEzLli0z/wR9ELBzbo4ePaqioiKlpKS490VERCgpKUk5OTkaPXp0rceVlZWprKzM/bqkpESSdMbl8m/CAADLq/msMAzD79/L5ePnUs3xP41jt9tlt9sval9eXq68vDxlZma69wUFBSklJUU5OTk+5WK2gC1uioqKJEkxMTEe+2NiYtzv1SYrK0tz5869aP/VCfHmJggACFjfffedIiIi/BI7JCRETqdT15jwudSuXTvFx3vGmT17tubMmXNR22+//VZVVVW1fq5+8sknPudiphYtbmbOnKlnnnmmzjaHDh1S9+7dmykjKTMzU9OmTXO/Pn36tDp16qTCwkK//aJagcvlUnx8vI4dOyaHw9HS6bQYrsMFXIcLuA4/4FpcUFJSoquuukpRUVF++x6hoaE6evSoysvLfY5lGIZsPxmfqq3XxmpatLh57LHHNGHChDrbdOnSpUmxnU6nJKm4uFixsbHu/cXFxerbt6/X47x1x0VERFzW/8HWcDgcXAdxHWpwHS7gOvyAa3FBUJB/p7SGhoYqNDTUr9/jpzp06KDg4GAVFxd77C8uLnZ/5l4qWrS46dixozp27OiX2AkJCXI6ndq4caO7mHG5XMrNzW3UiisAAHBhOGzAgAHauHGjRo4cKUmqrq7Wxo0blZGR0bLJ/YRlVksVFhYqPz9fhYWFqqqqUn5+vvLz83X27Fl3m+7du+vtt9+WJNlsNk2ZMkVPPvmk3n33Xe3fv19paWmKi4tz/1AAAEDDTZs2Tf/zP/+jV199VYcOHdIjjzyi0tJSPfDAAy2dmgfLTCieNWuWXn31Vffrfv36SZI++OADDR48WJJUUFDgXt0kSY8//rhKS0s1efJknT59WjfffLPWrVvXqK48u92u2bNnB8QYpC+4DhdwHS7gOlzAdfgB1+KCQL8O9957r7755hvNmjVLRUVF6tu3r9atW3fRJOOWZjOaY70aAABAM7HMsBQAAEBDUNwAAICAQnEDAAACCsUNAAAIKBQ3tfiv//ovDRw4UGFhYYqMjGzQMYZhaNasWYqNjVWbNm2UkpKiTz/91L+J+tnJkyc1duxYORwORUZGauLEiR5L72szePBg2Ww2j+3hhx9upozNsWTJEnXu3FmhoaFKSkrSrl276mz/5ptvqnv37goNDVWvXr303nvvNVOm/tWY67B8+fKLfu7NfYMxf9i6davuvPNOxcXFyWazNeihu5s3b1b//v1lt9t19dVXa/ny5X7P098aex02b9580e+DzWar89E3VpCVlaXrr79e4eHhio6O1siRI1VQUFDvcYH6N+JSRnFTi/Lyct1zzz2NutnfggUL9OKLL2rp0qXKzc1V27ZtlZqaqvPnz/sxU/8aO3asDh48qOzsbK1Zs0Zbt27V5MmT6z1u0qRJOn78uHtbsGBBM2RrjlWrVmnatGmaPXu29uzZoz59+ig1NVUnTpyotf2OHTs0ZswYTZw4UXv37tXIkSM1cuRIHThwoJkzN1djr4N04c60P/65f/HFF82YsX+UlpaqT58+WrJkSYPaHz16VCNGjNCQIUOUn5+vKVOm6KGHHtL777/v50z9q7HXoUZBQYHH70R0dLSfMmweW7ZsUXp6unbu3Kns7GxVVFRo2LBhKi0t9XpMoP6NuOQZ8OqVV14xIiIi6m1XXV1tOJ1OY+HChe59p0+fNux2u/G///u/fszQfz7++GNDkvHRRx+59/3zn/80bDab8dVXX3k97tZbbzUeffTRZsjQP2644QYjPT3d/bqqqsqIi4szsrKyam3/y1/+0hgxYoTHvqSkJONXv/qVX/P0t8Zeh4b+t2Jlkoy33367zjaPP/640aNHD4999957r5GamurHzJpXQ67DBx98YEgyTp061Sw5tZQTJ04YkowtW7Z4bROofyMudfTcmODo0aMqKipSSkqKe19ERISSkpIuucfAN1ROTo4iIyN13XXXufelpKQoKChIubm5dR67YsUKdejQQT179lRmZqbOnTvn73RNUV5erry8PI+fY1BQkFJSUrz+HHNycjzaS1Jqaqplf+5S066DJJ09e1adOnVSfHy87rrrLh08eLA50r2kBOLvgy/69u2r2NhY3Xbbbdq+fXtLp2O6mpvG1vWQTH4nWoZl7lB8KasZR67tMfBWHWMuKiq6qAu5VatWioqKqvOc7rvvPnXq1ElxcXHat2+fZsyYoYKCAr311lv+Ttln3377raqqqmr9OX7yySe1HlNUVBRQP3epadehW7duWrZsmXr37q2SkhI9++yzGjhwoA4ePKgrr7yyOdK+JHj7fXC5XPr+++/Vpk2bFsqsecXGxmrp0qW67rrrVFZWpj/96U8aPHiwcnNz1b9//5ZOzxTV1dWaMmWKbrrpJvXs2dNru0D8G2EFl01xM3PmTD3zzDN1tjl06JC6d+/eTBm1jIZeh6b68ZycXr16KTY2VkOHDtWRI0fUtWvXJsfFpS05OVnJycnu1wMHDtS1116rP/7xj5o/f34LZoaW0K1bN3Xr1s39euDAgTpy5IgWL16sv/zlLy2YmXnS09N14MABbdu2raVTQS0um+Lmscce04QJE+ps06VLlybFrnnUe3FxsWJjY937i4uL3U8kv1Q09Do4nc6LJo9WVlbq5MmTjXq0fVJSkiTp8OHDl3xx06FDBwUHB6u4uNhjf3FxsddzdjqdjWpvBU25Dj/VunVr9evXT4cPH/ZHipcsb78PDofjsum18eaGG24ImEIgIyPDvciivp7JQPwbYQWXzZybjh07qnv37nVuISEhTYqdkJAgp9OpjRs3uve5XC7l5uZ6/Gv2UtDQ65CcnKzTp08rLy/PfeymTZtUXV3tLlgaIj8/X5I8ir5LVUhIiAYMGODxc6yurtbGjRu9/hyTk5M92ktSdnb2Jfdzb4ymXIefqqqq0v79+y3xczdTIP4+mCU/P9/yvw+GYSgjI0Nvv/22Nm3apISEhHqP4XeihbT0jOZL0RdffGHs3bvXmDt3rtGuXTtj7969xt69e40zZ86423Tr1s1466233K+ffvppIzIy0njnnXeMffv2GXfddZeRkJBgfP/99y1xCqa4/fbbjX79+hm5ubnGtm3bjGuuucYYM2aM+/0vv/zS6Natm5Gbm2sYhmEcPnzYmDdvnrF7927j6NGjxjvvvGN06dLFGDRoUEudQqO9/vrrht1uN5YvX258/PHHxuTJk43IyEijqKjIMAzDuP/++42ZM2e622/fvt1o1aqV8eyzzxqHDh0yZs+ebbRu3drYv39/S52CKRp7HebOnWu8//77xpEjR4y8vDxj9OjRRmhoqHHw4MGWOgVTnDlzxv3fvyRj0aJFxt69e40vvvjCMAzDmDlzpnH//fe723/22WdGWFiYMX36dOPQoUPGkiVLjODgYGPdunUtdQqmaOx1WLx4sbF69Wrj008/Nfbv3288+uijRlBQkLFhw4aWOgVTPPLII0ZERISxefNm4/jx4+7t3Llz7jaXy9+ISx3FTS3Gjx9vSLpo++CDD9xtJBmvvPKK+3V1dbXxu9/9zoiJiTHsdrsxdOhQo6CgoPmTN9F3331njBkzxmjXrp3hcDiMBx54wKPAO3r0qMd1KSwsNAYNGmRERUUZdrvduPrqq43p06cbJSUlLXQGTfP73//euOqqq4yQkBDjhhtuMHbu3Ol+79ZbbzXGjx/v0f6NN94wfv7znxshISFGjx49jLVr1zZzxv7RmOswZcoUd9uYmBjjjjvuMPbs2dMCWZurZknzT7eacx8/frxx6623XnRM3759jZCQEKNLly4efyesqrHX4ZlnnjG6du1qhIaGGlFRUcbgwYONTZs2tUzyJqrtGvz0s+By+htxKbMZhmE0WzcRAACAn102c24AAMDlgeIGAAAEFIobAAAQUChuAABAQKG4AQAAAYXiBgAABBSKGwAAEFAobgAAQEChuAEuczabrc5tzpw5LZ0iADTKZfNUcAC1O378uPvrVatWadasWSooKHDva9eunftrwzBUVVWlVq340wHg0kXPDXCZczqd7i0iIkI2m839+pNPPlF4eLj++c9/asCAAbLb7dq2bZsmTJigkSNHesSZMmWKBg8e7H5dXV2trKwsJSQkqE2bNurTp4/+9re/Ne/JAbgs8c8vAPWaOXOmnn32WXXp0kVXXHFFg47JysrSX//6Vy1dulTXXHONtm7dqnHjxqljx4669dZb/ZwxgMsZxQ2Aes2bN0+33XZbg9uXlZXpqaee0oYNG5ScnCxJ6tKli7Zt26Y//vGPFDcA/IriBkC9rrvuuka1P3z4sM6dO3dRQVReXq5+/fqZmRoAXITiBkC92rZt6/E6KChIhmF47KuoqHB/ffbsWUnS2rVr9bOf/cyjnd1u91OWAHABxQ2ARuvYsaMOHDjgsS8/P1+tW7eWJCUmJsput6uwsJAhKADNjuIGQKP94he/0MKFC/Xaa68pOTlZf/3rX3XgwAH3kFN4eLj+8z//U1OnTlV1dbVuvvlmlZSUaPv27XI4HBo/fnwLnwGAQEZxA6DRUlNT9bvf/U6PP/64zp8/rwcffFBpaWnav3+/u838+fPVsWNHZWVl6bPPPlNkZKT69++v3/72ty2YOYDLgc346cA5AACAhXETPwAAEFAobgAAQEChuAEAAAGF4gYAAAQUihsAABBQKG4AAEBAobgBAAABheIGAAAEFIobAAAQUChuAABAQKG4AQAAAYXiBgAABJT/H/XjJCSqoK/oAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the response matrix    \n",
    "#plt.figure(1)\n",
    "#G, xbins2d, ybins2d, patches2d = plt.hist2d(theta_unknown_S[:,0],theta_unknown_G[:,0], bins=np.linspace(-1,2,20), weights=myweights[0, -1, :], cmap='Blues')\n",
    "#plt.xlabel('True')\n",
    "#plt.ylabel('Reco')\n",
    "#plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x323ec7cd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qohsaAQBwpbFYfF1U3NJnEDhBndzExsZq6dKl+vOf/6w///nPio+PV3Jysj799FOvbbKzsz1uZhQfH9+MEQMA0DAW+bhbyqcFO1c2U01LNVbPnj3Vs2dP9+uhQ4fq0KFDWrx4sf7whz9css3cuXOVmZnpfu1yuUhwAAAwkaBObi7lpptu0pYtW7y+b7Va6707IwAAV4LmXlBsJlddclNYWKjY2NiWDgMAAN8081ZwMzFVclNRUaGDBw+6X5eUlKiwsFAdOnRQly5dNHfuXH399dd66623JEnPP/+8EhISdOONN+rMmTN67bXXtGHDBn300UctdQoAACDATJXc7Nq1y+OmfHVrY8aNG6ecnBwdOXJEpaX/vMFVdXW1Zs6cqa+//loRERHq27ev1q9ff8kb+wEAYCo+TksZTEtdGZKTk2XUc+vYnJwcj9ezZ8/W7NmzAxwVAADNz9c1N/54ttSVylTJDQAAOI/kxrugvs8NAAC4+jByAwCAGbFbyiuSGwAATIhpKe+YlgIAAEGFkRsAAEyIkRvvSG4AADAhkhvvmJYCAABBhZEbAABMiJEb70hu0KzqucG0D536v8vaWv93evac//usPlfr9z5PV9f4vc+y02f83ueX3/m/z+qz/r2esfHX+rU/STrRro3f+wzp1cvvfXaOj/R7n327+f96Du1q82t/pytOaptfe6wHW8G9YloKAAAEFUZuAAAwIaalvCO5AQDAhEhuvCO5AQDAhEhuvGPNDQAACCqM3AAAYEbslvKK5AYAABNiWso7pqUAAEBQYeQGAAATYuTGO5IbAABMyCIfk5sgXnTDtBQAAAgqjNwAAGBCTEt5R3IDAIAZsRXcK6alAABAUGHkBgAAE2JayjuSGwAATIjkxjuSGwAATMhiOV98aR+sWHMDAACCCiM3aFaGjAD06X+B6LPG8H+v52r932dtAOKMaRPu9z77XtfW73126eDfOI9VnPVrf5LUqV1rv/fZzur//+d2vybC7312bu//PqMi/Hs9T570//fHm/MjN75MS/kxmCsMyQ0AAGbk47QUW8EBAABMgpEbAABMiN1S3pHcAABgQuyW8o5pKQAAcFnZ2dn68Y9/rPbt2ys6OlqjRo1ScXGxR53k5GT3iFJdmTJliked0tJSpaWlKSIiQtHR0Zo1a5bOnTvnUWfjxo0aOHCgrFarevTooZycnEbFSnIDAIAJhYRYfC6NsWnTJmVkZGj79u3Kzc3V2bNnNWLECFVWVnrUmzRpko4cOeIuCxcudL9XU1OjtLQ0VVdXa9u2bXrzzTeVk5OjrKwsd52SkhKlpaXpjjvuUGFhoaZPn65HH31U69ata3CsTEsBAGBCzT0ttXbtWo/XOTk5io6OVkFBgYYNG+Y+HhERIbvdfsk+PvroI3322Wdav369YmJi1L9/fz311FOaM2eO5s+fr7CwMC1dulQJCQlatGiRJKlXr17asmWLFi9erNTU1AbFysgNAABXMZfL5VGqqqoa1K68vFyS1KFDB4/jy5YtU8eOHdW7d2/NnTtXp06dcr+Xn5+vPn36KCYmxn0sNTVVLpdLRUVF7jopKSkefaampio/P7/B58TIDQAAJuSv3VLx8fEex+fNm6f58+fX27a2tlbTp0/XLbfcot69e7uPP/jgg+ratavi4uK0Z88ezZkzR8XFxXrvvfckSU6n0yOxkeR+7XQ6663jcrl0+vRptWnT5rLnRnIDAIAJ+Wta6vDhw7LZbO7jVqv1sm0zMjK0b98+bdmyxeP45MmT3X/v06ePYmNjNXz4cB06dEjdu3dverCNxLQUAAAmdOGupKYUSbLZbB7lcsnNtGnTtHr1av3tb39T586d6607ZMgQSdLBgwclSXa7XWVlZR516l7XrdPxVsdmszVo1EYiuQEAAA1gGIamTZumlStXasOGDUpISLhsm8LCQklSbGysJMnhcGjv3r06evSou05ubq5sNpuSkpLcdfLy8jz6yc3NlcPhaHCsJDcAAJiQv0ZuGiojI0N//OMftXz5crVv315Op1NOp1OnT5+WJB06dEhPPfWUCgoK9I9//EN//etflZ6ermHDhqlv376SpBEjRigpKUkPP/yw/v73v2vdunV64oknlJGR4R4xmjJlir744gvNnj1bBw4c0EsvvaR33nlHM2bMaHCsJDcAAJhQ3ZobX0pjvPzyyyovL1dycrJiY2PdZcWKFZKksLAwrV+/XiNGjFBiYqJmzpyp0aNH6/3333f3ERoaqtWrVys0NFQOh0MPPfSQ0tPTtWDBAnedhIQErVmzRrm5uerXr58WLVqk1157rcHbwCUWFAMAgAYwDKPe9+Pj47Vp06bL9tO1a1d98MEH9dZJTk7W7t27GxXfD5HcAABgQhb5uBVcwftwKZIbAABMiAdneseaGwAAEFQYuQEAwIT8dYfiYERyg+ZV/3q0Jqm9zCK3K6XP0EY+gbchwlv7f/C1Q7swv/cZEeb/XzWR4a393qe/hQTgwyMQ33Nr61C/9xnWKhDnHog4/Xs9rbr83X39hWkp75iWAgAAQcVUyc3mzZt17733Ki4uThaLRatWrbpsm40bN2rgwIGyWq3q0aOHcnJyAh4nAACB1tw38TMTUyU3lZWV6tevn5YsWdKg+iUlJUpLS9Mdd9yhwsJCTZ8+XY8++qjWrVsX4EgBAAis5r6Jn5mYas3NyJEjNXLkyAbXX7p0qRISErRo0SJJUq9evbRlyxYtXry4UXc6BADgSsOCYu9MNXLTWPn5+UpJSfE4lpqaqvz8fK9tqqqq5HK5PAoAADCPoE5unE6nYmJiPI7FxMTI5XK5H/R1oezsbEVGRrpLfHx8c4QKAEDj+DolFbwDN8Gd3DTF3LlzVV5e7i6HDx9u6ZAAALgIC4q9M9Wam8ay2+0qKyvzOFZWViabzaY2bdpcso3VanU/dh0AAJhPUCc3DofjoieP5ubmyuFwtFBEAAD4Bzfx885U01IVFRUqLCxUYWGhpPNbvQsLC1VaWirp/JRSenq6u/6UKVP0xRdfaPbs2Tpw4IBeeuklvfPOO5oxY0ZLhA8AgN8wLeWdqZKbXbt2acCAARowYIAkKTMzUwMGDFBWVpYk6ciRI+5ER5ISEhK0Zs0a5ebmql+/flq0aJFee+01toEDABDETDUtlZycLKOeZ/5c6u7DycnJ2r17dwCjAgCg+TEt5Z2pkhsAAHAeN/HzzlTTUgAAAJfDyA0AACbEyI13JDcAAJgQa268I7kBAMCEGLnxjjU3AAAgqDByg2YViP8phAbgPx+WADxRLjTE+20MmqpViP/jtLb2f5ztw/3fZ22t37tUqJ+vpyH/n3frUP//nzQQ/4EPRJyBEOLnk2/O82ZayjuSGwAATIhpKe/MkVoDAAA0ECM3AACYkEU+Tkv5LZIrD8kNAAAmFGKx+LRmyN/rja4kTEsBAICgwsgNAAAmxG4p70huAAAwIXZLeUdyAwCACYVYzhdf2gcr1twAAICgwsgNAABmZPFxaimIR25IbgAAMCEWFHvHtBQAAAgqjNwAAGBClv/740v7YEVyAwCACbFbyjumpQAAQFBh5AYAABPiJn7ekdwAAGBC7JbyjmkpAABwWdnZ2frxj3+s9u3bKzo6WqNGjVJxcbFHnTNnzigjI0PXXnut2rVrp9GjR6usrMyjTmlpqdLS0hQREaHo6GjNmjVL586d86izceNGDRw4UFarVT169FBOTk6jYiW5AQDAhEIsFp9LY2zatEkZGRnavn27cnNzdfbsWY0YMUKVlZXuOjNmzND777+vd999V5s2bdI333yjn/zkJ+73a2pqlJaWpurqam3btk1vvvmmcnJylJWV5a5TUlKitLQ03XHHHSosLNT06dP16KOPat26dQ2O1WIYhtGos7vKuFwuRUZGquy7ctlstpYOx/TM8tNmyP+BmuXczSIQ19Pfw/QBGfU3yVRCYz84g4XL5VLMtZEqLw/cZ0bd59K9L25U6zbtmtzP2dMVen9acpNjPXbsmKKjo7Vp0yYNGzZM5eXl6tSpk5YvX65/+7d/kyQdOHBAvXr1Un5+vm6++WZ9+OGH+pd/+Rd98803iomJkSQtXbpUc+bM0bFjxxQWFqY5c+ZozZo12rdvn/trjRkzRidOnNDatWsbFBsjNwAAmFDdgmJfii/Ky8slSR06dJAkFRQU6OzZs0pJSXHXSUxMVJcuXZSfny9Jys/PV58+fdyJjSSlpqbK5XKpqKjIXeeHfdTVqeujIVhQDADAVczlcnm8tlqtslqt9bapra3V9OnTdcstt6h3796SJKfTqbCwMEVFRXnUjYmJkdPpdNf5YWJT937de/XVcblcOn36tNq0aXPZc2LkBgAAE6rbLeVLkaT4+HhFRka6S3Z29mW/dkZGhvbt26e33347wGfZNIzcAABgQk1ZFHxhe0k6fPiwx5qby43aTJs2TatXr9bmzZvVuXNn93G73a7q6mqdOHHCY/SmrKxMdrvdXWfnzp0e/dXtpvphnQt3WJWVlclmszVo1EZi5AYAgKuazWbzKN6SG8MwNG3aNK1cuVIbNmxQQkKCx/uDBg1S69atlZeX5z5WXFys0tJSORwOSZLD4dDevXt19OhRd53c3FzZbDYlJSW56/ywj7o6dX00BCM3AACYkEW+bZ5rbNuMjAwtX75cf/nLX9S+fXv3GpnIyEi1adNGkZGRmjhxojIzM9WhQwfZbDb97Gc/k8Ph0M033yxJGjFihJKSkvTwww9r4cKFcjqdeuKJJ5SRkeFOqqZMmaIXX3xRs2fP1iOPPKINGzbonXfe0Zo1axocK8kNAAAm1NyPX3j55ZclScnJyR7H33jjDY0fP16StHjxYoWEhGj06NGqqqpSamqqXnrpJXfd0NBQrV69WlOnTpXD4VDbtm01btw4LViwwF0nISFBa9as0YwZM/TCCy+oc+fOeu2115Samtrwc+M+N/XjPjf+ZZafNu5zc+XjPjdXNu5zE/j73Ixe+rHP97n585TbAhprS2HkBgAAEwqxnC++tA9WJDcAAJgQTwX3jt1SAAAgqDByAwCASQXx4ItPSG4AADAhpqW8I7kBAMCEWFDsHWtuAABAUGnyyM2hQ4f0/PPPa//+/ZKkpKQkPfbYY+revbvfggMAAJfGtJR3TRq5WbdunZKSkrRz50717dtXffv21Y4dO3TjjTcqNzfX3zECAIALWPxQglWTRm4ef/xxzZgxQ7/+9a8vOj5nzhzdddddfgkOAACgsZqU3Ozfv1/vvPPORccfeeQRPf/8877GhCBmllFQSwD+T2OWpy8E4nsUkEclBGDFoL/jNMvPO8wpxGLx6TEXwfyIjCb9eujUqZMKCwsvOl5YWKjo6GhfYwIAAJdhsfheglWTRm4mTZqkyZMn64svvtDQoUMlSVu3btXTTz+tzMxMvwYIAADQGE1Kbn7+85+rffv2WrRokebOnStJiouL0/z58/Wf//mffg0QAABcjN1S3jUpubFYLJoxY4ZmzJihkydPSpLat2/v18AAAIB3vk4tBXFu07TkpqSkROfOndMNN9zgkdR8/vnnat26ta6//np/xQcAANAoTVpQPH78eG3btu2i4zt27ND48eN9jaleS5Ys0fXXX6/w8HANGTJEO3fu9Fo3JyfHPWxXV8LDwwMaHwAAzaFut5QvJVg1KbnZvXu3brnllouO33zzzZfcReUvK1asUGZmpubNm6dPP/1U/fr1U2pqqo4ePeq1jc1m05EjR9zlyy+/DFh8AAA0F3ZLedek5MZisbjX2vxQeXm5ampqfA7Km+eee06TJk3ShAkTlJSUpKVLlyoiIkKvv/56vbHa7XZ3iYmJCVh8AAA0lwtnJppSglWTkpthw4YpOzvbI5GpqalRdna2br31Vr8F90PV1dUqKChQSkqK+1hISIhSUlKUn5/vtV1FRYW6du2q+Ph43XfffSoqKqr361RVVcnlcnkUAABgHk1aUPz0009r2LBh6tmzp2677TZJ0scffyyXy6UNGzb4NcA63377rWpqai4aeYmJidGBAwcu2aZnz556/fXX1bdvX5WXl+vZZ5/V0KFDVVRUpM6dO1+yTXZ2tp588km/xw8AgD+FqIkjFD9oH6yadG5JSUnas2eP/v3f/11Hjx7VyZMnlZ6ergMHDqh3797+jrHJHA6H0tPT1b9/f91+++1677331KlTJ73yyite28ydO1fl5eXucvjw4WaMGACAhmFayrsmjdxI52/a96tf/cqfsdSrY8eOCg0NVVlZmcfxsrIy2e32BvXRunVrDRgwQAcPHvRax2q1ymq1+hQrAABoOU0elfr444/10EMPaejQofr6668lSX/4wx+0ZcsWvwX3Q2FhYRo0aJDy8vLcx2pra5WXlyeHw9GgPmpqarR3717FxsYGJEYAAJqLxSKF+FCCeOCmacnNn//8Z6WmpqpNmzb69NNPVVVVJen8bqlAjuZkZmbqd7/7nd58803t379fU6dOVWVlpSZMmCBJSk9Pdz8OQpIWLFigjz76SF988YU+/fRTPfTQQ/ryyy/16KOPBixGAACagy+JTV0JVk2alvrFL36hpUuXKj09XW+//bb7+C233KJf/OIXfgvuQvfff7+OHTumrKwsOZ1O9e/fX2vXrnUvMi4tLVVIyD/zte+//16TJk2S0+nUNddco0GDBmnbtm1KSkoKWIwAAKBlWQzDMBrbKCIiQp999pmuv/56tW/fXn//+9/VrVs3ffHFF0pKStKZM2cCEWuLcLlcioyMVNl35bLZbC0dDkys8f/SWkYghqoDce5miDOYh/1xaS6XSzHXRqq8PHCfGXWfSxlv75I1ol2T+6k6VaElYwYHNNaW0qRpKbvdfslFuVu2bFG3bt18DgoAANSPaSnvmpTcTJo0SY899ph27Nghi8Wib775RsuWLdPMmTM1depUf8cIAADQYE1ac/P444+rtrZWw4cP16lTpzRs2DBZrVbNmjWLxboAADQDX58PFczTpk1+ttT/+3//T8ePH9e+ffu0fft2HTt2TJGRkUpISPB3jEBQ8MdD7pqjmOXczRAnEEg8Fdy7RiU3VVVVmjt3rgYPHqxbbrlFH3zwgZKSklRUVKSePXvqhRde0IwZMwIVKwAA+D8hfijBqlHTUllZWXrllVeUkpKibdu26ac//akmTJig7du3a9GiRfrpT3+q0NDQQMUKAABwWY1Kbt5991299dZb+td//Vft27dPffv21blz5/T3v/89qJ9RAQDAlYY1N941Krn56quvNGjQIElS7969ZbVaNWPGDBIbAACaWYh8WzcTouD97G7UlFtNTY3CwsLcr1u1aqV27Zp+AyEAAAB/a9TIjWEYGj9+vPup2WfOnNGUKVPUtm1bj3rvvfee/yIEAAAXYVrKu0YlN+PGjfN4/dBDD/k1GAAA0DC+3mU4mO9Q3Kjk5o033ghUHAAAAH7RpDsUAwCAlmWxyKcFxUxLAQCAKwprbrwL5hsUAgCAqxDJDQAAJlS3oNiX0hibN2/Wvffeq7i4OFksFq1atcrj/fHjx8tisXiUu+++26PO8ePHNXbsWNlsNkVFRWnixImqqKjwqLNnzx7ddtttCg8PV3x8vBYuXNj4a9PoFgAAoMVZ/PCnMSorK9WvXz8tWbLEa527775bR44ccZf/+Z//8Xh/7NixKioqUm5urlavXq3Nmzdr8uTJ7vddLpdGjBihrl27qqCgQM8884zmz5+vV199tVGxsuYGAAATau6t4CNHjtTIkSPrrWO1WmW32y/53v79+7V27Vp98sknGjx4sCTpt7/9re655x49++yziouL07Jly1RdXa3XX39dYWFhuvHGG1VYWKjnnnvOIwm6HEZuAAC4irlcLo9SVVXV5L42btyo6Oho9ezZU1OnTtV3333nfi8/P19RUVHuxEaSUlJSFBISoh07drjrDBs2zONpCKmpqSouLtb333/f4DhIbgAAMCF/rbmJj49XZGSku2RnZzcpnrvvvltvvfWW8vLy9PTTT2vTpk0aOXKkampqJElOp1PR0dEebVq1aqUOHTrI6XS668TExHjUqXtdV6chmJYCAMCE6hbt+tJekg4fPiybzeY+XveIpcYaM2aM++99+vRR37591b17d23cuFHDhw9vcpxNwcgNAABXMZvN5lGamtxcqFu3burYsaMOHjwoSbLb7Tp69KhHnXPnzun48ePudTp2u11lZWUedepee1vLcykkNwAAmFBzbwVvrK+++krfffedYmNjJUkOh0MnTpxQQUGBu86GDRtUW1urIUOGuOts3rxZZ8+eddfJzc1Vz549dc011zT4a5PcAABgQnV3KPalNEZFRYUKCwtVWFgoSSopKVFhYaFKS0tVUVGhWbNmafv27frHP/6hvLw83XffferRo4dSU1MlSb169dLdd9+tSZMmaefOndq6daumTZumMWPGKC4uTpL04IMPKiwsTBMnTlRRUZFWrFihF154QZmZmY2KleQGAABc1q5duzRgwAANGDBAkpSZmakBAwYoKytLoaGh2rNnj/71X/9VP/rRjzRx4kQNGjRIH3/8scc017Jly5SYmKjhw4frnnvu0a233upxD5vIyEh99NFHKikp0aBBgzRz5kxlZWU1ahu4xIJiAABMKcRi8enBmY1tm5ycLMMwvL6/bt26y/bRoUMHLV++vN46ffv21ccff9yo2C5EcgMAgAk19038zIRpKQAAEFQYuQEAwIyasCj4wvbBiuQGAAATCpFFIT5kKL60vdKR3AAAYEJN2c59YftgxZobAAAQVBi5AQDAhNgt5R3JDQAAJtTc97kxE6alAABAUGHkBgAAE2JBsXckNwAAmFCIfJyWCuKt4ExLAQCAoMLIDQAAJsS0lHckNwAAmFCIfJt+Ceapm2A+NwAAcBVi5AYAABOyWCyy+DC35EvbKx3JDQAAJmSRbw/2Dt7UhuQGAABT4g7F3rHmBgAABBVGbgAAMKngHXvxDckNAAAmxH1uvGNaCgAABBVGbgAAMCG2gntHcgMAgAlxh2LvTHduS5Ys0fXXX6/w8HANGTJEO3furLf+u+++q8TERIWHh6tPnz764IMPmilSAADQEkyV3KxYsUKZmZmaN2+ePv30U/Xr10+pqak6evToJetv27ZNDzzwgCZOnKjdu3dr1KhRGjVqlPbt29fMkQMA4F9101K+lGBlquTmueee06RJkzRhwgQlJSVp6dKlioiI0Ouvv37J+i+88ILuvvtuzZo1S7169dJTTz2lgQMH6sUXX2zmyAEA8C+LH0qwMk1yU11drYKCAqWkpLiPhYSEKCUlRfn5+Zdsk5+f71FfklJTU73WBwAA5meaBcXffvutampqFBMT43E8JiZGBw4cuGQbp9N5yfpOp9Pr16mqqlJVVZX7tcvl8iFqAAACg91S3plm5Ka5ZGdnKzIy0l3i4+NbOiQAAC4S4ocSrExzbh07dlRoaKjKyso8jpeVlclut1+yjd1ub1R9SZo7d67Ky8vd5fDhw74HDwCAn7Gg2DvTJDdhYWEaNGiQ8vLy3Mdqa2uVl5cnh8NxyTYOh8OjviTl5uZ6rS9JVqtVNpvNowAAAPMwzZobScrMzNS4ceM0ePBg3XTTTXr++edVWVmpCRMmSJLS09N13XXXKTs7W5L02GOP6fbbb9eiRYuUlpamt99+W7t27dKrr77akqcBAIDPfN3xFLzjNiZLbu6//34dO3ZMWVlZcjqd6t+/v9auXeteNFxaWqqQkH8ORg0dOlTLly/XE088of/+7//WDTfcoFWrVql3794tdQoAAPgFD870zmIYhtHSQVzJXC6XIiMjVfZdOVNUAIB6uVwuxVwbqfLywH1m1H0uLd/2v4po177J/ZyqOKkHh/4ooLG2FFON3AAAgPNCZFGID5NLvrS90pHcAABgQkxLeWea3VIAAAANwcgNAAAmZPm/P760D1YkNwAAmBDTUt4xLQUAAIIKIzcAAJiQxcfdUsE8LcXIDQAAJlQ3LeVLaYzNmzfr3nvvVVxcnCwWi1atWuXxvmEYysrKUmxsrNq0aaOUlBR9/vnnHnWOHz+usWPHymazKSoqShMnTlRFRYVHnT179ui2225TeHi44uPjtXDhwkZfG5IbAABMqLmTm8rKSvXr109Lliy55PsLFy7Ub37zGy1dulQ7duxQ27ZtlZqaqjNnzrjrjB07VkVFRcrNzdXq1au1efNmTZ482f2+y+XSiBEj1LVrVxUUFOiZZ57R/PnzG/3YJKalAADAZY0cOVIjR4685HuGYej555/XE088ofvuu0+S9NZbbykmJkarVq3SmDFjtH//fq1du1affPKJBg8eLEn67W9/q3vuuUfPPvus4uLitGzZMlVXV+v1119XWFiYbrzxRhUWFuq5557zSIIuh5EbAABMyOKHP9L50ZIflqqqqkbHUlJSIqfTqZSUFPexyMhIDRkyRPn5+ZKk/Px8RUVFuRMbSUpJSVFISIh27NjhrjNs2DCFhYW566Smpqq4uFjff/99g+MhuQEAwIRCLL4XSYqPj1dkZKS7ZGdnNzoWp9MpSe4HWdeJiYlxv+d0OhUdHe3xfqtWrdShQwePOpfq44dfoyGYlgIA4Cp2+PBhjwdnWq3WFozGPxi5AQDAhPw1LWWz2TxKU5Ibu90uSSorK/M4XlZW5n7Pbrfr6NGjHu+fO3dOx48f96hzqT5++DUaguQGAAATau7dUvVJSEiQ3W5XXl6e+5jL5dKOHTvkcDgkSQ6HQydOnFBBQYG7zoYNG1RbW6shQ4a462zevFlnz55118nNzVXPnj11zTXXNDgekhsAAHBZFRUVKiwsVGFhoaTzi4gLCwtVWloqi8Wi6dOn6xe/+IX++te/au/evUpPT1dcXJxGjRolSerVq5fuvvtuTZo0STt37tTWrVs1bdo0jRkzRnFxcZKkBx98UGFhYZo4caKKioq0YsUKvfDCC8rMzGxUrKy5AQDAhCzy7S7DjW25a9cu3XHHHe7XdQnHuHHjlJOTo9mzZ6uyslKTJ0/WiRMndOutt2rt2rUKDw93t1m2bJmmTZum4cOHKyQkRKNHj9ZvfvMb9/uRkZH66KOPlJGRoUGDBqljx47Kyspq1DZwSbIYhmE08vyuKi6XS5GRkSr7rtxjwRUAABdyuVyKuTZS5eWB+8yo+1z6oKBEbds1/WtUVrh0z6CEgMbaUpiWAgAAQYVpKQAATOiHO56a2j5YkdwAAGBCvu548uduqSsNyQ0AACZkUeMXBV/YPlix5gYAAAQVRm4AADChEFkU4sPcUkgQj92Q3AAAYEJMS3nHtBQAAAgqjNwAAGBGDN14RXIDAIAJcZ8b75iWAgAAQYWRGwAAzMjHm/gF8cANyQ0AAGbEkhvvmJYCAABBhZEbAADMiKEbr0huAAAwIXZLeUdyAwCACfFUcO9YcwMAAIIKIzcAAJgQS268I7kBAMCMyG68YloKAAAEFUZuAAAwIXZLeUdyAwCACbFbyjumpQAAQFBh5AYAABNiPbF3JDcAAJgR2Y1XTEsBAICgwsgNAAAmxG4p70huAAAwIXZLeUdyAwCACbHkxjvW3AAAgKDCyA0AAGbE0I1XJDcAAJgQC4q9M8201PHjxzV27FjZbDZFRUVp4sSJqqioqLdNcnKyLBaLR5kyZUozRQwAAFqCaUZuxo4dqyNHjig3N1dnz57VhAkTNHnyZC1fvrzedpMmTdKCBQvcryMiIgIdKgAAAcduKe9Mkdzs379fa9eu1SeffKLBgwdLkn7729/qnnvu0bPPPqu4uDivbSMiImS325srVAAAmgVLbrwzxbRUfn6+oqKi3ImNJKWkpCgkJEQ7duyot+2yZcvUsWNH9e7dW3PnztWpU6fqrV9VVSWXy+VRAACAeZhi5MbpdCo6OtrjWKtWrdShQwc5nU6v7R588EF17dpVcXFx2rNnj+bMmaPi4mK99957XttkZ2frySef9FvsAAAEBEM3XrVocvP444/r6aefrrfO/v37m9z/5MmT3X/v06ePYmNjNXz4cB06dEjdu3e/ZJu5c+cqMzPT/drlcik+Pr7JMQAAEAjslvKuRZObmTNnavz48fXW6datm+x2u44ePepx/Ny5czp+/Hij1tMMGTJEknTw4EGvyY3VapXVam1wnwAA4MrSoslNp06d1KlTp8vWczgcOnHihAoKCjRo0CBJ0oYNG1RbW+tOWBqisLBQkhQbG9ukeAEAuFKwW8o7Uywo7tWrl+6++25NmjRJO3fu1NatWzVt2jSNGTPGvVPq66+/VmJionbu3ClJOnTokJ566ikVFBToH//4h/76178qPT1dw4YNU9++fVvydAAA8JnFDyVYmSK5kc7vekpMTNTw4cN1zz336NZbb9Wrr77qfv/s2bMqLi5274YKCwvT+vXrNWLECCUmJmrmzJkaPXq03n///ZY6BQAA/KeZs5v58+dfdGPcxMRE9/tnzpxRRkaGrr32WrVr106jR49WWVmZRx+lpaVKS0tTRESEoqOjNWvWLJ07d64pZ18vU+yWkqQOHTrUe8O+66+/XoZhuF/Hx8dr06ZNzREaAABXhRtvvFHr1693v27V6p9pxIwZM7RmzRq9++67ioyM1LRp0/STn/xEW7dulSTV1NQoLS1Ndrtd27Zt05EjR5Senq7WrVvrV7/6lV/jNE1yAwAA/qkldku1atXqkht5ysvL9fvf/17Lly/XnXfeKUl644031KtXL23fvl0333yzPvroI3322Wdav369YmJi1L9/fz311FOaM2eO5s+fr7CwsCafy4VMMy0FAAB+wPLPRcVNKXW5zYU3rq2qqvL6JT///HPFxcWpW7duGjt2rEpLSyVJBQUFOnv2rFJSUtx1ExMT1aVLF+Xn50s6f0PePn36KCYmxl0nNTVVLpdLRUVFfr00JDcAAFzF4uPjFRkZ6S7Z2dmXrDdkyBDl5ORo7dq1evnll1VSUqLbbrtNJ0+elNPpVFhYmKKiojzaxMTEuG+263Q6PRKbuvfr3vMnpqUAADAhf92g+PDhw7LZbO7j3u71NnLkSPff+/btqyFDhqhr165655131KZNGx8i8T9GbgAAMCM/7Zay2WwepaE3so2KitKPfvQjHTx4UHa7XdXV1Tpx4oRHnbKyMvcaHbvdftHuqbrX/n7ANckNAABotIqKCh06dEixsbEaNGiQWrdurby8PPf7xcXFKi0tlcPhkHT+hrx79+71eOJAbm6ubDabkpKS/Bob01IAAJhQc++W+q//+i/de++96tq1q7755hvNmzdPoaGheuCBBxQZGamJEycqMzNTHTp0kM1m089+9jM5HA7dfPPNkqQRI0YoKSlJDz/8sBYuXCin06knnnhCGRkZfn/sEckNAAAm1NyPX/jqq6/0wAMP6LvvvlOnTp106623avv27e7HKC1evFghISEaPXq0qqqqlJqaqpdeesndPjQ0VKtXr9bUqVPlcDjUtm1bjRs3TgsWLGj6SXhhMX545ztcxOVyKTIyUmXflXssuAIA4EIul0sx10aqvDxwnxl1n0t//6JM7ds3/WucPOlSv24xAY21pTByAwCACflrt1QwIrkBAMCMyG68IrkBAMCEWuLxC2bBVnAAABBUGLkBAMCELPJxt5TfIrnykNwAAGBCLLnxjmkpAAAQVBi5AQDAhJr7Jn5mQnIDAIApMTHlDdNSAAAgqDByAwCACTEt5R3JDQAAJsSklHdMSwEAgKDCyA0AACbEtJR3JDcAAJgQz5byjuQGAAAzYtGNV6y5AQAAQYWRGwAATIiBG+9IbgAAMCEWFHvHtBQAAAgqjNwAAGBC7JbyjuQGAAAzYtGNV0xLAQCAoMLIDQAAJsTAjXckNwAAmBC7pbxjWgoAAAQVRm4AADAl33ZLBfPEFMkNAAAmxLSUd0xLAQCAoEJyAwAAggrTUgAAmBDTUt6R3AAAYEI8fsE7pqUAAEBQYeQGAAATYlrKO5IbAABMiMcveMe0FAAACCqM3AAAYEYM3XhFcgMAgAmxW8o7pqUAAEBQYeQGAAATYreUdyQ3AACYEEtuvCO5AQDAjMhuvDLNmptf/vKXGjp0qCIiIhQVFdWgNoZhKCsrS7GxsWrTpo1SUlL0+eefBzZQAACC2JIlS3T99dcrPDxcQ4YM0c6dO1s6pIuYJrmprq7WT3/6U02dOrXBbRYuXKjf/OY3Wrp0qXbs2KG2bdsqNTVVZ86cCWCkAAAEnsUPfxprxYoVyszM1Lx58/Tpp5+qX79+Sk1N1dGjRwNwhk1nmuTmySef1IwZM9SnT58G1TcMQ88//7yeeOIJ3Xffferbt6/eeustffPNN1q1alVggwUAIMDqFhT7Uhrrueee06RJkzRhwgQlJSVp6dKlioiI0Ouvv+7/E/RB0K65KSkpkdPpVEpKivtYZGSkhgwZovz8fI0ZM+aS7aqqqlRVVeV+XV5eLkk66XIFNmAAgOnVfVYYhhHwr+Xy8XOprv2F/VitVlmt1ovqV1dXq6CgQHPnznUfCwkJUUpKivLz832Kxd+CNrlxOp2SpJiYGI/jMTEx7vcuJTs7W08++eRFx3skxPs3QABA0Pruu+8UGRkZkL7DwsJkt9t1gx8+l9q1a6f4eM9+5s2bp/nz519U99tvv1VNTc0lP1cPHDjgcyz+1KLJzeOPP66nn3663jr79+9XYmJiM0UkzZ07V5mZme7XJ06cUNeuXVVaWhqwH1QzcLlcio+P1+HDh2Wz2Vo6nBbDdTiP63Ae1+GfuBbnlZeXq0uXLurQoUPAvkZ4eLhKSkpUXV3tc1+GYchywfzUpUZtzKZFk5uZM2dq/Pjx9dbp1q1bk/q22+2SpLKyMsXGxrqPl5WVqX///l7beRuOi4yMvKr/wdax2WxcB3Ed6nAdzuM6/BPX4ryQkMAuaQ0PD1d4eHhAv8aFOnbsqNDQUJWVlXkcLysrc3/mXilaNLnp1KmTOnXqFJC+ExISZLfblZeX505mXC6XduzY0agdVwAA4Px02KBBg5SXl6dRo0ZJkmpra5WXl6dp06a1bHAXMM1uqdLSUhUWFqq0tFQ1NTUqLCxUYWGhKioq3HUSExO1cuVKSZLFYtH06dP1i1/8Qn/961+1d+9epaenKy4uzv1NAQAADZeZmanf/e53evPNN7V//35NnTpVlZWVmjBhQkuH5sE0C4qzsrL05ptvul8PGDBAkvS3v/1NycnJkqTi4mL37iZJmj17tiorKzV58mSdOHFCt956q9auXduooTyr1ap58+YFxRykL7gO53EdzuM6nMd1+CeuxXnBfh3uv/9+HTt2TFlZWXI6nerfv7/Wrl170SLjlmYxmmO/GgAAQDMxzbQUAABAQ5DcAACAoEJyAwAAggrJDQAACCokN5fwy1/+UkOHDlVERISioqIa1MYwDGVlZSk2NlZt2rRRSkqKPv/888AGGmDHjx/X2LFjZbPZFBUVpYkTJ3psvb+U5ORkWSwWjzJlypRmitg/lixZouuvv17h4eEaMmSIdu7cWW/9d999V4mJiQoPD1efPn30wQcfNFOkgdWY65CTk3PR9725bzAWCJs3b9a9996ruLg4WSyWBj10d+PGjRo4cKCsVqt69OihnJycgMcZaI29Dhs3brzo58FisdT76BszyM7O1o9//GO1b99e0dHRGjVqlIqLiy/bLlh/R1zJSG4uobq6Wj/96U8bdbO/hQsX6je/+Y2WLl2qHTt2qG3btkpNTdWZM2cCGGlgjR07VkVFRcrNzdXq1au1efNmTZ48+bLtJk2apCNHjrjLwoULmyFa/1ixYoUyMzM1b948ffrpp+rXr59SU1N19OjRS9bftm2bHnjgAU2cOFG7d+/WqFGjNGrUKO3bt6+ZI/evxl4H6fydaX/4ff/yyy+bMeLAqKysVL9+/bRkyZIG1S8pKVFaWpruuOMOFRYWavr06Xr00Ue1bt26AEcaWI29DnWKi4s9fiaio6MDFGHz2LRpkzIyMrR9+3bl5ubq7NmzGjFihCorK722CdbfEVc8A1698cYbRmRk5GXr1dbWGna73XjmmWfcx06cOGFYrVbjf/7nfwIYYeB89tlnhiTjk08+cR/78MMPDYvFYnz99dde291+++3GY4891gwRBsZNN91kZGRkuF/X1NQYcXFxRnZ29iXr//u//7uRlpbmcWzIkCHGf/zHfwQ0zkBr7HVo6L8VM5NkrFy5st46s2fPNm688UaPY/fff7+RmpoawMiaV0Ouw9/+9jdDkvH99983S0wt5ejRo4YkY9OmTV7rBOvviCsdIzd+UFJSIqfTqZSUFPexyMhIDRky5Ip7DHxD5efnKyoqSoMHD3YfS0lJUUhIiHbs2FFv22XLlqljx47q3bu35s6dq1OnTgU6XL+orq5WQUGBx/cxJCREKSkpXr+P+fn5HvUlKTU11bTfd6lp10GSKioq1LVrV8XHx+u+++5TUVFRc4R7RQnGnwdf9O/fX7Gxsbrrrru0devWlg7H7+puGlvfQzL5mWgZprlD8ZWsbh75Uo+BN+scs9PpvGgIuVWrVurQoUO95/Tggw+qa9euiouL0549ezRnzhwVFxfrvffeC3TIPvv2229VU1Nzye/jgQMHLtnG6XQG1fddatp16Nmzp15//XX17dtX5eXlevbZZzV06FAVFRWpc+fOzRH2FcHbz4PL5dLp06fVpk2bFoqsecXGxmrp0qUaPHiwqqqq9Nprryk5OVk7duzQwIEDWzo8v6itrdX06dN1yy23qHfv3l7rBePvCDO4apKbxx9/XE8//XS9dfbv36/ExMRmiqhlNPQ6NNUP1+T06dNHsbGxGj58uA4dOqTu3bs3uV9c2RwOhxwOh/v10KFD1atXL73yyit66qmnWjAytISePXuqZ8+e7tdDhw7VoUOHtHjxYv3hD39owcj8JyMjQ/v27dOWLVtaOhRcwlWT3MycOVPjx4+vt063bt2a1Hfdo97LysoUGxvrPl5WVuZ+IvmVoqHXwW63X7R49Ny5czp+/HijHm0/ZMgQSdLBgwev+OSmY8eOCg0NVVlZmcfxsrIyr+dst9sbVd8MmnIdLtS6dWsNGDBABw8eDESIVyxvPw82m+2qGbXx5qabbgqaRGDatGnuTRaXG5kMxt8RZnDVrLnp1KmTEhMT6y1hYWFN6jshIUF2u115eXnuYy6XSzt27PD43+yVoKHXweFw6MSJEyooKHC33bBhg2pra90JS0MUFhZKkkfSd6UKCwvToEGDPL6PtbW1ysvL8/p9dDgcHvUlKTc394r7vjdGU67DhWpqarR3715TfN/9KRh/HvylsLDQ9D8PhmFo2rRpWrlypTZs2KCEhITLtuFnooW09IrmK9GXX35p7N6923jyySeNdu3aGbt37zZ2795tnDx50l2nZ8+exnvvved+/etf/9qIiooy/vKXvxh79uwx7rvvPiMhIcE4ffp0S5yCX9x9993GgAEDjB07dhhbtmwxbrjhBuOBBx5wv//VV18ZPXv2NHbs2GEYhmEcPHjQWLBggbFr1y6jpKTE+Mtf/mJ069bNGDZsWEudQqO9/fbbhtVqNXJycozPPvvMmDx5shEVFWU4nU7DMAzj4YcfNh5//HF3/a1btxqtWrUynn32WWP//v3GvHnzjNatWxt79+5tqVPwi8ZehyeffNJYt26dcejQIaOgoMAYM2aMER4ebhQVFbXUKfjFyZMn3f/+JRnPPfecsXv3buPLL780DMMwHn/8cePhhx921//iiy+MiIgIY9asWcb+/fuNJUuWGKGhocbatWtb6hT8orHXYfHixcaqVauMzz//3Ni7d6/x2GOPGSEhIcb69etb6hT8YurUqUZkZKSxceNG48iRI+5y6tQpd52r5XfElY7k5hLGjRtnSLqo/O1vf3PXkWS88cYb7te1tbXGz3/+cyMmJsawWq3G8OHDjeLi4uYP3o++++4744EHHjDatWtn2Gw2Y8KECR4JXklJicd1KS0tNYYNG2Z06NDBsFqtRo8ePYxZs2YZ5eXlLXQGTfPb3/7W6NKlixEWFmbcdNNNxvbt293v3X777ca4ceM86r/zzjvGj370IyMsLMy48cYbjTVr1jRzxIHRmOswffp0d92YmBjjnnvuMT799NMWiNq/6rY0X1jqzn3cuHHG7bffflGb/v37G2FhYUa3bt08fk+YVWOvw9NPP210797dCA8PNzp06GAkJycbGzZsaJng/ehS1+DCz4Kr6XfElcxiGIbRbMNEAAAAAXbVrLkBAABXB5IbAAAQVEhuAABAUCG5AQAAQYXkBgAABBWSGwAAEFRIbgAAQFAhuQEAAEGF5Aa4ylkslnrL/PnzWzpEAGiUq+ap4AAu7ciRI+6/r1ixQllZWSouLnYfa9eunfvvhmGopqZGrVrxqwPAlYuRG+AqZ7fb3SUyMlIWi8X9+sCBA2rfvr0+/PBDDRo0SFarVVu2bNH48eM1atQoj36mT5+u5ORk9+va2lplZ2crISFBbdq0Ub9+/fSnP/2peU8OwFWJ/34BuKzHH39czz77rLp166ZrrrmmQW2ys7P1xz/+UUuXLtUNN9ygzZs366GHHlKnTp10++23BzhiAFczkhsAl7VgwQLdddddDa5fVVWlX/3qV1q/fr0cDockqVu3btqyZYteeeUVkhsAAUVyA+CyBg8e3Kj6Bw8e1KlTpy5KiKqrqzVgwAB/hgYAFyG5AXBZbdu29XgdEhIiwzA8jp09e9b994qKCknSmjVrdN1113nUs1qtAYoSAM4juQHQaJ06ddK+ffs8jhUWFqp169aSpKSkJFmtVpWWljIFBaDZkdwAaLQ777xTzzzzjN566y05HA798Y9/1L59+9xTTu3bt9d//dd/acaMGaqtrdWtt96q8vJybd26VTabTePGjWvhMwAQzEhuADRaamqqfv7zn2v27Nk6c+aMHnnkEaWnp2vv3r3uOk899ZQ6deqk7OxsffHFF4qKitLAgQP13//93y0YOYCrgcW4cOIcAADAxLiJHwAACCokNwAAIKiQ3AAAgKBCcgMAAIIKyQ0AAAgqJDcAACCokNwAAICgQnIDAACCCskNAAAIKiQ3AAAgqJDcAACAoEJyAwAAgsr/DyyaSNbIHl2ZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the response matrix    \n",
    "#plt.figure(1)\n",
    "#G, xbins2d, ybins2d, patches2d = plt.hist2d(theta_unknown_S[:,1],theta_unknown_G[:,1], bins=np.linspace(-1,2,20), weights=myweights[0, -1, :], cmap='Blues')\n",
    "#plt.xlabel('True')\n",
    "#plt.ylabel('Reco')\n",
    "#plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "notify_time": "0",
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}