Gaussian2Dup.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",
    "import uproot\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",
      "tf.config.list_physical_devices('GPU')\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([[0.000e+00, 0.000e+00, 2.000e+00, 4.000e+00, 3.000e+00, 6.000e+00,\n",
       "         4.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
       "        [0.000e+00, 1.000e+00, 1.600e+01, 5.400e+01, 1.160e+02, 1.030e+02,\n",
       "         5.200e+01, 1.500e+01, 2.000e+00, 0.000e+00],\n",
       "        [2.000e+00, 2.300e+01, 1.440e+02, 5.320e+02, 9.550e+02, 9.130e+02,\n",
       "         4.580e+02, 1.130e+02, 1.400e+01, 1.000e+00],\n",
       "        [9.000e+00, 9.900e+01, 6.370e+02, 2.208e+03, 4.201e+03, 4.099e+03,\n",
       "         2.090e+03, 5.830e+02, 8.200e+01, 6.000e+00],\n",
       "        [1.100e+01, 1.850e+02, 1.431e+03, 4.961e+03, 8.984e+03, 8.777e+03,\n",
       "         4.526e+03, 1.251e+03, 1.600e+02, 2.100e+01],\n",
       "        [2.200e+01, 2.110e+02, 1.391e+03, 5.202e+03, 9.665e+03, 9.233e+03,\n",
       "         4.694e+03, 1.271e+03, 1.650e+02, 1.100e+01],\n",
       "        [9.000e+00, 1.230e+02, 7.200e+02, 2.567e+03, 4.639e+03, 4.514e+03,\n",
       "         2.503e+03, 6.430e+02, 8.700e+01, 7.000e+00],\n",
       "        [2.000e+00, 2.600e+01, 1.910e+02, 6.640e+02, 1.177e+03, 1.167e+03,\n",
       "         6.010e+02, 1.450e+02, 2.200e+01, 3.000e+00],\n",
       "        [1.000e+00, 5.000e+00, 2.100e+01, 9.000e+01, 1.220e+02, 1.190e+02,\n",
       "         6.900e+01, 1.700e+01, 3.000e+00, 1.000e+00],\n",
       "        [0.000e+00, 0.000e+00, 2.000e+00, 5.000e+00, 5.000e+00, 6.000e+00,\n",
       "         4.000e+00, 1.000e+00, 0.000e+00, 0.000e+00]]),\n",
       " array([-2.45482005, -1.75187568, -1.04893132, -0.34598696,  0.3569574 ,\n",
       "         1.05990177,  1.76284613,  2.46579049,  3.16873485,  3.87167922,\n",
       "         4.57462358]),\n",
       " array([-0.23863004,  0.17325008,  0.58513019,  0.99701031,  1.40889042,\n",
       "         1.82077054,  2.23265065,  2.64453077,  3.05641088,  3.468291  ,\n",
       "         3.88017111]),\n",
       " <matplotlib.collections.QuadMesh at 0x21f050737d0>)"
      ]
     },
     "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": 9,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def synthroot(filename, data, data2):\n",
    "    with uproot.recreate(filename) as file:\n",
    "        file[\"testree\"] = {\"theta0\": data, \"theta_unknown_S\": data2}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
      "['theta0', 'theta_unknown_S']\n",
      "Shape theta0_G: (100000, 2)\n",
      "Shape 0_S: (100000, 2)\n",
      "Shape theta0: (100000, 2, 2)\n",
      "Shape unknown s: (100000, 2)\n",
      "Shape theta0 Tree: (100000, 2, 2)\n",
      "Shape unknown s Tree: (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",
    "theta0t = np.stack((theta0_G, theta0_S), axis=1)  # axis=1 to maintain stack property\n",
    "\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_St = np.random.normal(0, 0.5, (N,2)) + theta_unknown_G\n",
    "\n",
    "# Save data to a fake ROOT file\n",
    "synthroot(\"synth_data.root\", theta0t, theta_unknown_St)\n",
    "\n",
    "events = uproot.open(\"synth_data.root:testree\")\n",
    "print(events.keys())\n",
    "\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",
    "\n",
    "theta0 = events[\"theta0\"].array(library=\"np\")\n",
    "theta_unknown_S = events[\"theta_unknown_S\"].array(library=\"np\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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, 1.000e+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, 1.000e+00, 1.000e+00, 4.000e+00,\n",
       "        4.000e+00, 9.000e+00, 6.000e+00, 1.500e+01, 2.600e+01, 4.300e+01,\n",
       "        6.300e+01, 7.800e+01, 1.320e+02, 1.470e+02, 2.420e+02, 2.740e+02,\n",
       "        3.630e+02, 5.290e+02, 6.050e+02, 7.760e+02, 9.350e+02, 1.215e+03,\n",
       "        1.528e+03, 1.763e+03, 2.079e+03, 2.426e+03, 2.864e+03, 3.085e+03,\n",
       "        3.458e+03, 3.815e+03, 4.034e+03, 4.398e+03, 4.613e+03, 4.759e+03,\n",
       "        4.799e+03, 4.833e+03, 4.751e+03, 4.548e+03, 4.328e+03, 4.209e+03,\n",
       "        3.967e+03, 3.604e+03, 3.320e+03, 2.982e+03, 2.592e+03, 2.192e+03,\n",
       "        1.881e+03, 1.633e+03, 1.271e+03, 1.105e+03, 8.810e+02, 7.250e+02,\n",
       "        5.260e+02, 4.190e+02, 3.040e+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": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21f096f22d0>"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21f0a02b950>"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "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": 26,
   "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": 27,
   "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": 28,
   "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": 29,
   "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": 30,
   "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 5ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/step\n",
      "10/10 [==============================] - 0s 5ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/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 3ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 4ms/step\n",
      "10/10 [==============================] - 0s 3ms/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": 31,
   "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": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21f816a2d90>"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21f0ab145d0>"
      ]
     },
     "execution_count": 34,
     "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,
   "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
}