GaussianEx_min2Denh.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",
    "\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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/device:CPU:0']\n"
     ]
    }
   ],
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([[0.0000e+00, 0.0000e+00, 1.0000e+00, 6.0000e+00, 1.5000e+01,\n",
       "         1.2000e+01, 1.1000e+01, 2.0000e+00, 0.0000e+00, 0.0000e+00],\n",
       "        [0.0000e+00, 4.0000e+00, 1.6000e+01, 9.3000e+01, 2.3800e+02,\n",
       "         2.1500e+02, 1.1800e+02, 2.6000e+01, 4.0000e+00, 0.0000e+00],\n",
       "        [2.0000e+00, 1.9000e+01, 1.4800e+02, 6.7900e+02, 1.5380e+03,\n",
       "         1.8190e+03, 9.1100e+02, 2.3700e+02, 3.1000e+01, 0.0000e+00],\n",
       "        [4.0000e+00, 4.9000e+01, 5.5000e+02, 2.6220e+03, 5.6380e+03,\n",
       "         6.0990e+03, 3.1890e+03, 8.1900e+02, 1.0000e+02, 7.0000e+00],\n",
       "        [3.0000e+00, 8.8000e+01, 9.7400e+02, 4.4670e+03, 1.0001e+04,\n",
       "         1.0870e+04, 5.4850e+03, 1.3420e+03, 1.5700e+02, 9.0000e+00],\n",
       "        [3.0000e+00, 7.5000e+01, 7.4700e+02, 3.6670e+03, 8.4390e+03,\n",
       "         8.9560e+03, 4.6130e+03, 1.1220e+03, 1.1700e+02, 3.0000e+00],\n",
       "        [2.0000e+00, 3.4000e+01, 3.1500e+02, 1.5660e+03, 3.3450e+03,\n",
       "         3.6110e+03, 1.8900e+03, 4.4100e+02, 4.8000e+01, 4.0000e+00],\n",
       "        [1.0000e+00, 2.0000e+00, 5.9000e+01, 3.0800e+02, 6.2900e+02,\n",
       "         7.3100e+02, 3.5700e+02, 7.9000e+01, 9.0000e+00, 1.0000e+00],\n",
       "        [0.0000e+00, 1.0000e+00, 5.0000e+00, 2.7000e+01, 5.7000e+01,\n",
       "         6.4000e+01, 3.2000e+01, 1.2000e+01, 1.0000e+00, 0.0000e+00],\n",
       "        [0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 4.0000e+00,\n",
       "         1.0000e+00, 3.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00]]),\n",
       " array([-2.25424075, -1.54846137, -0.842682  , -0.13690262,  0.56887675,\n",
       "         1.27465613,  1.9804355 ,  2.68621488,  3.39199425,  4.09777363,\n",
       "         4.803553  ]),\n",
       " array([-0.46269321, -0.01927457,  0.42414406,  0.8675627 ,  1.31098134,\n",
       "         1.75439998,  2.19781861,  2.64123725,  3.08465589,  3.52807453,\n",
       "         3.97149317]),\n",
       " <matplotlib.collections.QuadMesh at 0x25edc5aef50>)"
      ]
     },
     "execution_count": 5,
     "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": [
    "print(stuff.shape)\n",
    "plt.hist2d( stuff[:,0], stuff[:,1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\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": 7,
   "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",
    "\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",
    "\n",
    "theta0_S = np.random.normal(0, 0.5, (N, 2)) + theta0_G  # Detector smearing for synthetic sample but different\n",
    "\n",
    "theta0 = np.stack((theta0_G, theta0_S), axis=1)  # axis=1 to maintain stack property\n",
    "\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",
    "\n",
    "theta_unknown_S = np.random.normal(0, 0.5, (N,2)) + theta_unknown_G\n",
    "\n",
    "print(\"Shape theta0_G:\", theta0_G.shape)\n",
    "print(\"Shape 0_S:\", theta0_S.shape)\n",
    "print(\"Shape theta0:\", theta0.shape)\n",
    "print(\"Shape unknown s:\", theta_unknown_S.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 2.000e+00, 2.000e+00, 1.000e+00, 3.000e+00,\n",
       "        1.000e+00, 6.000e+00, 1.000e+01, 2.000e+01, 2.700e+01, 3.600e+01,\n",
       "        5.600e+01, 6.700e+01, 1.140e+02, 1.460e+02, 2.010e+02, 2.630e+02,\n",
       "        3.790e+02, 4.770e+02, 6.240e+02, 7.830e+02, 9.910e+02, 1.257e+03,\n",
       "        1.486e+03, 1.719e+03, 2.106e+03, 2.375e+03, 2.769e+03, 3.174e+03,\n",
       "        3.438e+03, 3.711e+03, 4.107e+03, 4.416e+03, 4.681e+03, 4.720e+03,\n",
       "        4.848e+03, 4.877e+03, 4.983e+03, 4.624e+03, 4.432e+03, 4.211e+03,\n",
       "        3.994e+03, 3.567e+03, 3.277e+03, 2.901e+03, 2.608e+03, 2.175e+03,\n",
       "        1.916e+03, 1.578e+03, 1.268e+03, 1.056e+03, 8.290e+02, 6.830e+02,\n",
       "        4.790e+02, 3.730e+02, 2.870e+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": 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[:,0], bins=np.linspace(-3,3, 100) )\n",
    "plt.hist( theta0_G[:,1], bins=np.linspace(-3,3, 100) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25edf315110>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25edf326d90>"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
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      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "myweights = of.omnifold(theta0,theta_unknown_S,20,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape theta0_G: (100000, 2)\n",
      "Shape weights: (20, 2, 100000)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape theta0_G:\", theta0_G.shape)\n",
    "print(\"Shape weights:\", myweights.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25e89283050>"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25e82589dd0>"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "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": 22,
   "metadata": {},
   "outputs": [],
   "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": "3.11.0"
  },
  "notify_time": "0",
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
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   "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
  }
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 "nbformat": 4,
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}