{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ones_to_zeros(ET_inner_dict, ET_outer_dict):\n",
    "    \n",
    "    for k in range(0,len(ET_inner_dict)):\n",
    "        for i in range(ET_inner_dict[k].shape[0]):\n",
    "            equal_inner = np.array_equal(ET_inner_dict[k][i],np.ones_like(ET_inner_dict[k][i]))\n",
    "            equal_outer = np.array_equal(ET_outer_dict[k][i],np.ones_like(ET_outer_dict[k][i]))\n",
    "            if equal_inner or equal_outer:\n",
    "                ET_inner_dict[k][i]=np.zeros_like(ET_inner_dict[k][i])\n",
    "                ET_outer_dict[k][i]=np.zeros_like(ET_outer_dict[k][i])\n",
    "                \n",
    "    return ET_inner_dict, ET_outer_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def double_size(X_in):\n",
    "\n",
    "    X_out = np.zeros(\n",
    "        shape=(2*X_in.shape[0],2*X_in.shape[1],1),\n",
    "        dtype=np.float32\n",
    "    )\n",
    "    \n",
    "    for i in range(X_in.shape[0]):\n",
    "        for j in range(X_in.shape[1]):\n",
    "            \n",
    "            if X_in[i,j]!=0:\n",
    "                value = X_in[i,j]/4\n",
    "                X_out[2*i,2*j]=value\n",
    "                X_out[2*i+1,2*j]=value\n",
    "                X_out[2*i,2*j+1]=value\n",
    "                X_out[2*i+1,2*j+1]=value\n",
    "    return X_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insert(X_inner, X_outer):\n",
    "    X=X_outer\n",
    "    \n",
    "    for i in range(12,40):\n",
    "        for j in range(16,48):\n",
    "            X[i,j]=X_inner[i-12,j-16]\n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_HCAL_images(ET_inner_dict, ET_outer_dict):\n",
    "    \n",
    "    output_pics = {}\n",
    "    \n",
    "    pic = np.array(np.zeros(shape=(52,64,1), dtype=np.float32))\n",
    "    \n",
    "    for j in range(len(ET_inner_dict)):\n",
    "        \n",
    "        \n",
    "        output_pics[j]=np.array([pic for i in range(len(ET_inner_dict[j]))])    \n",
    "        \n",
    "    \n",
    "    for j in range(len(ET_inner_dict)):\n",
    "        \n",
    "        for k in range(len(ET_inner_dict[j])):\n",
    "            \n",
    "            ET_image = insert(ET_inner_dict[j][k], double_size(ET_outer_dict[j][k]))\n",
    "            ET_image = np.flip(ET_image, axis = 0)\n",
    "            \n",
    "            output_pics[j][k]= ET_image\n",
    "            \n",
    "        print('Done batch {0}'.format(j+1))\n",
    "                \n",
    "    return output_pics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ET_inner_dict={}\n",
    "ET_outer_dict={}\n",
    "\n",
    "i=0\n",
    "while os.path.exists('/disk/lhcb_data/davide/HCAL_project_full_event/npy/B2Dmunu_full/ET_inner/batch_'+str(i)+'.npy'):\n",
    "#while i < 2:\n",
    "    ET_inner_dict[i]=np.load('/disk/lhcb_data/davide/HCAL_project_full_event/npy/B2Dmunu_full/ET_inner/batch_'+str(i)+'.npy')\n",
    "    ET_outer_dict[i]=np.load('/disk/lhcb_data/davide/HCAL_project_full_event/npy/B2Dmunu_full/ET_outer/batch_'+str(i)+'.npy')\n",
    "\n",
    "    i+=1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_inner_ET=np.mean(ET_inner_dict[0], axis=0, keepdims=True).reshape(32,32)\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(mean_inner_ET[2:mean_inner_ET.shape[0]-2,0:mean_inner_ET.shape[0]])\n",
    "plt.subplot(1,2,2)\n",
    "mean_outer_ET=np.mean(ET_outer_dict[0], axis=0, keepdims=True).reshape(32,32)\n",
    "plt.imshow(mean_outer_ET[3:mean_outer_ET.shape[0]-3,0:mean_outer_ET.shape[0]])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(14,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ET_inner_dict, ET_outer_dict = ones_to_zeros(ET_inner_dict, ET_outer_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cutting out useless pixels\n",
    "for i in range(len(ET_inner_dict)):\n",
    "    ET_inner_dict[i]=ET_inner_dict[i][:,2:ET_inner_dict[i].shape[1]-2,:]\n",
    "    ET_outer_dict[i]=ET_outer_dict[i][:,3:ET_outer_dict[i].shape[1]-3,:]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((20000, 28, 32), (20000, 26, 32))\n"
     ]
    }
   ],
   "source": [
    "#Check cut dict dimensions\n",
    "print(ET_inner_dict[0].shape, ET_outer_dict[0].shape)\n",
    "#is\n",
    "#(?, 28, 32), (?,26, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def delete_undetected_events(X_inner, X_outer):\n",
    "\n",
    "    pos_rejected={}\n",
    "    \n",
    "    for i in range(len(X_inner)):\n",
    "        pos_rejected[i]=[]\n",
    "        \n",
    "        for j in range(X_inner[i].shape[0]):\n",
    "            \n",
    "            equal_inner = np.array_equal(X_inner[i][j],np.zeros_like(X_inner[i][j]))\n",
    "            equal_outer = np.array_equal(X_outer[i][j],np.zeros_like(X_outer[i][j]))\n",
    "            \n",
    "                \n",
    "    X_inner_filtered={}\n",
    "    X_outer_filtered={}\n",
    "    for i in range(len(X_inner)):\n",
    "    \n",
    "        X_inner_filtered[i]=np.delete(X_inner[i],pos_rejected[i],axis=0)\n",
    "        X_outer_filtered[i]=np.delete(X_outer[i],pos_rejected[i],axis=0)\n",
    "        \n",
    "    return X_inner_filtered, X_outer_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ET_inner_filtered, ET_outer_filtered = delete_undetected_events(ET_inner_dict,ET_outer_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f55a0625810>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(ET_inner_filtered[0][2].reshape(28,32),cmap='gray')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(ET_outer_filtered[0][2].reshape(26,32),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f55a0522d90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "i=11\n",
    "plt.imshow(np.flip(insert(ET_inner_filtered[0][i],double_size(ET_outer_filtered[0][i])).reshape(52,64),axis=0))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done batch 1\n",
      "Done batch 2\n",
      "Done batch 3\n",
      "Done batch 4\n",
      "Done batch 5\n",
      "Done batch 6\n",
      "Done batch 7\n",
      "Done batch 8\n",
      "Done batch 9\n",
      "Done batch 10\n",
      "Done batch 11\n",
      "Done batch 12\n"
     ]
    }
   ],
   "source": [
    "output_pics = generate_HCAL_images(ET_inner_filtered,ET_outer_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 52, 64, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_pics[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(output_pics)):\n",
    "    with open('/disk/lhcb_data/davide/HCAL_project_full_event/reco/sample'+str(i)+'.pickle', 'wb') as handle:\n",
    "        pickle.dump(output_pics[i], handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([output_pics[0][i].sum() for i in range(len(output_pics[0]))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(a/1000,range=(0.5,120),bins=70);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}