{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import yaml\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline\n",
    "\n",
    "from npgamma import calc_gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('data_unequal_grid.yml', 'r') as file:\n",
    "    data = yaml.load(file)\n",
    "    \n",
    "x_reference = data['x_mephisto']\n",
    "y_reference = data['d_mephisto']\n",
    "dose_reference = data['mephisto_dose_grid']\n",
    "\n",
    "x_evaluation = data['x_monaco']\n",
    "y_evaluation = data['d_monaco']\n",
    "dose_evaluation = data['monaco_dose_grid']\n",
    "\n",
    "coords_reference = (y_reference, x_reference)\n",
    "coords_evaluation = (y_evaluation, x_evaluation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distance_threshold = 2\n",
    "distance_step_size = distance_threshold / 10\n",
    "\n",
    "dose_threshold = 0.02 * np.max(dose_evaluation)\n",
    "\n",
    "lower_dose_cutoff = np.max(dose_evaluation) * 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gamma = calc_gamma(\n",
    "    coords_reference, dose_reference,\n",
    "    coords_evaluation, dose_evaluation,\n",
    "    distance_threshold, dose_threshold,\n",
    "    lower_dose_cutoff=lower_dose_cutoff, \n",
    "    distance_step_size=distance_step_size,\n",
    "    maximum_test_distance=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HfAe4lpcvWly/oM+t/PQFj8dHHbvKdV4DHAPeuKD9lcDlQ9tfA7ZPqcZf5OVnQ7YB/96d\n15k6l12/n2PwWe0rV/tcLqhjCxe+wDn1uTlinVOdmyPWOPW5OUqdszI3u3PzCeDPL9JnYvOz14+G\n6gJfNZHkD7r9f83g6eNbGEzk/wJ+/2Jjp1jn+4BfAO5NAnC2Bl9KtRF4qGtbB3yqqr4wpRp/B/jD\nJGeB/wZ21mBmzNq5BLgN+Puq+s+h4atyLs9L8gCDu1nWJzkBvB+4dKjOqc/NEeuc6twcscapz80R\n64QZmJvAjcDbgSeTHOra3ssg9Cc+P32yWJIa55PFktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEG\ngSQ1ziCQpMb9L/d/3c0neiN+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31b68fae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_gamma = gamma[~np.isnan(gamma)]\n",
    "valid_gamma[valid_gamma > 2] = 2\n",
    "\n",
    "plt.hist(valid_gamma, 30);\n",
    "plt.xlim([0,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91008174386920981"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(valid_gamma <= 1) / len(valid_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f31e322f908>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iLwIuL3/eOpr9m5m1ipiYvVxy6qG/imLS0rkR8VpJrwPeGxF/knjqXOAWScPH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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31b69373c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dx = x_evaluation[1] - x_evaluation[0]\n",
    "x_pcolor = np.arange(x_evaluation[0]-dx/2, x_evaluation[-1] + dx, dx)\n",
    "\n",
    "dy = y_evaluation[1] - y_evaluation[0]\n",
    "y_pcolor = np.arange(y_evaluation[0]-dy/2, y_evaluation[-1] + dy, dy)\n",
    "\n",
    "cut_off_gamma = np.ma.array (gamma, mask=np.isnan(gamma))\n",
    "cmap = cm.viridis\n",
    "cmap.set_bad('white',1.)\n",
    "\n",
    "plt.pcolormesh(\n",
    "    x_pcolor, y_pcolor, cut_off_gamma, cmap=cmap, vmin=0, vmax=2)\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.colorbar()\n",
    "\n",
    "plt.xlabel('x (mm)')\n",
    "plt.ylabel('depth (mm)')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}