generate fake data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/__init__.py:1357: UserWarning:  This call to matplotlib.use() has no effect\n",
      "because the backend has already been chosen;\n",
      "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "  warnings.warn(_use_error_msg)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pylab as plt\n",
    "import nibabel as nb\n",
    "import numpy as np\n",
    "import os\n",
    "from nilearn.image import resample_img, smooth_img\n",
    "from nilearn.plotting import plot_glass_brain, plot_stat_map\n",
    "from scipy import stats\n",
    "import joblib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/data/smoothness_and_volume.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def hist_match(source, t_quantiles, t_values):\n",
    "    \"\"\"\n",
    "    Adjust the pixel values of a grayscale image such that its histogram\n",
    "    matches that of a target image\n",
    "\n",
    "    Arguments:\n",
    "    -----------\n",
    "        source: np.ndarray\n",
    "            Image to transform; the histogram is computed over the flattened\n",
    "            array\n",
    "        template: np.ndarray\n",
    "            Template image; can have different dimensions to source\n",
    "    Returns:\n",
    "    -----------\n",
    "        matched: np.ndarray\n",
    "            The transformed output image\n",
    "    \"\"\"\n",
    "\n",
    "    oldshape = source.shape\n",
    "    source = source.ravel()\n",
    "\n",
    "    # get the set of unique pixel values and their corresponding indices and\n",
    "    # counts\n",
    "    s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,\n",
    "                                            return_counts=True)\n",
    "\n",
    "    # take the cumsum of the counts and normalize by the number of pixels to\n",
    "    # get the empirical cumulative distribution functions for the source and\n",
    "    # template images (maps pixel value --> quantile)\n",
    "    s_quantiles = np.cumsum(s_counts).astype(np.float64)\n",
    "    s_quantiles /= s_quantiles[-1]\n",
    "    \n",
    "    # interpolate linearly to find the pixel values in the template image\n",
    "    # that correspond most closely to the quantiles in the source image\n",
    "    interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)\n",
    "\n",
    "    return interp_t_values[bin_idx].reshape(oldshape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_fake_map(orig_inside_mask, map_nii, row, mask_nii, t_quantiles, t_values, i, j):\n",
    "    np.random.seed(j)\n",
    "    new_data = np.random.choice(orig_inside_mask, size=map_nii.shape, replace=True)\n",
    "\n",
    "    shuffled_nii = nb.Nifti1Image(new_data, map_nii.affine, map_nii.header)\n",
    "    smoothed_nii = smooth_img(shuffled_nii, np.array([row.FWHMx_mm, row.FWHMy_mm, row.FWHMz_mm]))\n",
    "    new_data = smoothed_nii.get_data()\n",
    "    new_data[mask_nii.get_data() != 1] = np.nan\n",
    "    new_inside_mask = stats.zscore(new_data[mask_nii.get_data() == 1])\n",
    "\n",
    "    new_data[mask_nii.get_data() == 1] = new_inside_mask #hist_match(new_inside_mask, t_quantiles, t_values)\n",
    "\n",
    "    masked_nii = nb.Nifti1Image(new_data, map_nii.affine, map_nii.header)\n",
    "        \n",
    "    masked_nii.to_filename(\"/data/images/fake_maps/%04d/%04d.nii.gz\"%(i, j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0003.nii.gz\n",
      "generating fake data for map images/resampled/0004.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0005.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0056.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0057.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0058.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0106.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0109.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0110.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0111.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0112.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0113.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0115.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0116.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0117.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0118.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0119.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0120.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0121.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0122.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0123.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0124.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0125.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0126.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0127.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0130.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0131.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0132.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0135.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating fake data for map images/resampled/0136.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n",
      "/opt/conda/envs/python2/lib/python2.7/site-packages/nilearn/image/image.py:201: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if fwhm == 'fast':\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-ba0f3d259cd9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[0mjoblib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mParallel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelayed\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m     \u001b[0mParallel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msave_fake_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig_inside_mask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap_nii\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask_nii\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt_quantiles\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/opt/conda/envs/python2/lib/python2.7/site-packages/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    766\u001b[0m                 \u001b[1;31m# consumption.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 768\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    769\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    770\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/opt/conda/envs/python2/lib/python2.7/site-packages/joblib/parallel.pyc\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    717\u001b[0m                     \u001b[0mensure_ready\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m                     \u001b[0mbackend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 719\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    720\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "!mkdir -p /data/images/fake_maps\n",
    "\n",
    "for i,row in df.iterrows():\n",
    "    print \"generating fake data for map images/resampled/%04d.nii.gz\"%i\n",
    "    map_nii = nb.load(\"/data/images/resampled/%04d.nii.gz\"%i)\n",
    "    mask_nii = nb.load(\"/data/images/resampled_masks/%04d.nii.gz\"%i)\n",
    "    \n",
    "    orig_inside_mask = map_nii.get_data()[mask_nii.get_data() == 1]\n",
    "    \n",
    "    folder_name = \"%04d\"%i\n",
    "    if os.path.exists(\"/data/images/fake_maps/%04d/\"%(i)):\n",
    "        continue\n",
    "    !mkdir -p /data/images/fake_maps/{\"%04d\"%i}\n",
    "    \n",
    "    template = orig_inside_mask.ravel()\n",
    "    \n",
    "    t_values, t_counts = np.unique(template, return_counts=True)\n",
    "\n",
    "    # take the cumsum of the counts and normalize by the number of pixels to\n",
    "    # get the empirical cumulative distribution functions for the source and\n",
    "    # template images (maps pixel value --> quantile)\n",
    "    t_quantiles = np.cumsum(t_counts).astype(np.float64)\n",
    "    t_quantiles /= t_quantiles[-1]\n",
    "    \n",
    "    from joblib import Parallel, delayed\n",
    "    Parallel(n_jobs=4)(delayed(save_fake_map)(orig_inside_mask, map_nii, row, mask_nii, t_quantiles, t_values, i, j) for j in range(200))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filenames = !ls images/fake_maps/0003/*.nii.gz\n",
    "\n",
    "image_ids = []\n",
    "FWHMx_mms = []\n",
    "FWHMy_mms = []\n",
    "FWHMz_mms = []\n",
    "volume_voxs = []\n",
    "\n",
    "for fname in filenames:\n",
    "    image_id = fname.split(\"/\")[-1][:-7]\n",
    "    output = !smoothest -z {fname} -m images/resampled_masks/0003.nii.gz -V\n",
    "    FWHMx_mm, FWHMy_mm, FWHMz_mm = [float(val[len(\"FWHMx = \"):-3])  for val in output[-7].split(\", \")]\n",
    "    volume_vox = output[-2].split()[1]\n",
    "    image_ids.append(image_id)\n",
    "    FWHMx_mms.append(FWHMx_mm)\n",
    "    FWHMy_mms.append(FWHMy_mm)\n",
    "    FWHMz_mms.append(FWHMz_mm)\n",
    "    volume_voxs.append(volume_vox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bca6ed0>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9+DJaR+TYtLM0KaUswHPAZiAKPKW1bs/YfhfppQ8BeoBPaa3jecgqFpjhQIyRYJyWBjf2\nMqvZcQrGopoKrIaFzoEQO8wOIxaUbM7wHwfKtda7gWeAZ6ds/wrwT7XW7wNeApbmNqJYqC53p+eF\nXy7dOTewZg7PjMjwTJE72RT8PaQLOVrrA/DeSYdSajUwCHxeKfU6UKO1bstDTrHApFIprvYEsFkt\n745MEe+Z7Nbpkm4dkUPZFHwvMJrxeFwpNfm6OuAe4IvAQ8BDSqn7c5pQLEhD/hjByBjNPjc2q1xK\nmuq98fhS8EXuZLPSgh/IvKJmaK0nx4sNAhe11hcAlFIvkf4F8PqdGvT5ivsCneSfGbs9ids1hMvt\nePe505eHAVizrAZPxvPZ8LgdREJ2DKNsxq+dTj7andpmNm173A4q3XZ6hsI4nfabpos2iFNX56Gy\ncn7/W8pnv7hlU/D3AY8CLyildgGnMra1A26l1IqJC7n3Ac9P12B/f/Eu1uzzeST/DPn9AYKh2Lsr\nPaVSKdquD2OzWqjx2AkEo9O08B6P20EgGCUUimMYCcorsn9tNvLRbmabk/mzsbjGyflrI1y6Pszi\n2hu7vcKhGAMDAeLx+ft1JJ99c+Xij1U2n5a9QEwptY/0aJzfU0o9oZR6Sms9BvwG8B2l1AHgmtb6\nJ3NOJRa04UCMQHiMJunOuSNZBUvk2rRn+FrrFPD0lKcvZGx/HdiZ21hiIbvakz7LWiZj7++oYWL2\nzM7+ENuV2WnEQiCnV2JepVIprsjonKzYrAYNNU5GgnFCMjxT5IAUfDGvRoIT3Tl1LunOyYKM1hG5\nJN84Ma+u9KSn/ZWpFLIj/fgil6Tgi3kzebOV1bDQ5HObHacoeF12PM4yugfCJGRxczFHUvDFvBkJ\nxvGH4jT5XJTZ5KOXrcY6F2OJJP0ye6aYI/nWiXkzOTpHunNm5r1FUaRbR8yNFHwxbya7c5qlO2dG\nFtU4MQwLnf2y7KGYGyn4Yl6MhsYYle6cWbFZDRqqK9LDM6MyPFPMnnzzxLzoGEj3Py9tkO6c2ZDR\nOiIXpOCLedE5EMUwLDTXS3fObDTVpY+brIIl5kIKvsi7nqEI/vA4TXXSnTNbXlcZ7ooyugdleKaY\nPfn2ibw7fik9FbKMzpk9i8VCs8/F2HiSvuGw2XFEkZKCL/LuxKVhDAs018vcOXMx2R3W0SfdOmJ2\npOCLvOoeDNE9FKWh2oHdJguVz0VDjZMym8H1viCplHTriJmTgi/y6tD5PgCafbldmaoUWQ0LjXUu\ngpEx/OFxs+OIIiQFX+TV4fN96UJVIwU/F5ZMdIt1D+V2pS9RGqZdAEUpZQGeAzYDUeCpieUMJ7f/\nS+ApoG/iqd/SWrflIasoMt2DITr6Q6xfWimjc3Kkqc6NxQJdg1Lwxcxls6bt40C51nq3Umon8OzE\nc5O2A5/WWh/LR0BRvA7rfgA2r6wmPhY3Oc3CUG63Ul9VQe9whEB4DK/X7ESimGRz2rUHeAlAa30A\n2DFl+3bgGaXUW0qpP8hxPlHEJrtzNiyvNDvKgjI5WufM1VGTk4hik03B9wKZn6xxpVTm674D/DPg\nAWCPUuojOcwnilTvUJjrfUHWL6/BWZ7ND0mRrSUTBf/0lRGTk4hik03B9wOZd8wYWutkxuM/01oP\naa3HgR8BW3MZUBSnydE5d62pNznJwuN12fFU2NDX/cTHEmbHEUUkm1OvfcCjwAtKqV3AqckNSikv\ncFoptQaIAA8Cfzldgz5fcd9xKfmnd/ziIDarhYd2LSMxFsHtGsLlzs1IHY/bQSRkxzDK8OSozUn5\naHdqm7loe+kiF6cvj9I1EuWudYvm3F625LNf3LIp+HuBh5VS+yYeP6mUegJwaa2fV0o9A7xOegTP\nq1rrl6ZrsL8/MNu8pvP5PJJ/Gr3DYdq7Rtm4opZIKIbfHyAYipFk7iNLPG4HgWCUUCiOYSQor8jt\naJV8tJvZ5mT+uarzpL+6bxy5zjLf/NzBLJ99c+Xij9W0BV9rnQKenvL0hYzt3wK+NeckYsE4PNGd\ns2ONz+QkC1et147LYeXExQGSqRSGxWJ2JFEEZHC0yLnD5/uxGha2rpKCny8Wi4V1SysZCcbfXTpS\niOlIwRc51TcS4WpvgLXLqnFXlJkdZ0Fbv6wKgGNtAyYnEcVCCr7IqXe7c5SMzsm3tS1eymwGRy/0\nmx1FFAkp+CKnDp/vw7BY2LZaunPyrbzMyoblNXQNhOiUpQ9FFqTgi5zpH4lwpUe6c+bTjon7HI7o\nvmn2FEIKvsihw1putppvm1fWYTUsHNHSrSOmJwVf5Mxkd87WVXVmRykZToeN9ctruN4XpHdIlj4U\ndyYFX+TEwEiEy90B1iytwuO0mx2npExeID8s3TpiGlLwRU5MToW8Q7pz5t2WVeluncPnpVtH3JkU\nfJETh7WMzjGLu6KMdctquNobkG4dcUdS8MWcDY5Gae/yo1qq8Ep3jil2rkv/sjpwttfkJKKQScEX\ncyZTIZtv6yofZTaDX5ztJZVKmR1HFCgp+GLODp3vTXfnKOnOMUtFuY3NrXX0DIW51hs0O44oUFLw\nxZz0T4zOWbtUunPMtnNtAwAHzkm3jrg1KfhiTt7tzpkoNsI8m1bWUFFu48DZXpLSrSNuQQq+mJND\n59ILlcvoHPOV2axsVz6GAzEuXJP1bsXNpOCLWesdDnO1N8C6ZTUyd06BuHdDernDfae7TU4iCtG0\nK14ppSzAc8Bm0ssYPqW1br/Ffl8GBrXW/y7nKUVBOngu3Z1z91oZnVMoVi2poq7SweHz/fzaw+M4\n7NmsYipKRTZn+I8D5Vrr3cAzwLNTd1BK/RawIcfZRIE7dK4Pm1XmzikkhsXCvRsXExtLyIRq4ibZ\nFPw9wEsAWusDwI7MjUqpe4C7gC/nPJ0oWN2DITr6g2xYXovTId05hWT3ZLfOKenWETfKpuB7gdGM\nx+NKKQNAKbUI+M/A7wCyinIJOXRucnSOdOcUGl9VBWtaqjh/bYT+kYjZcUQByabg+wFP5mu01smJ\nf/5loBb4MfAHwCeVUp/JbURRiA6e78NmNdjSKt05hejejYsBePuknOWL92RzRWcf8CjwglJqF3Bq\ncoPW+kvAlwCUUr8OKK31N6Zr0OfzTLdLQSv1/Fe7/XQNhLhn42Jamqun3d9uT+J2DeFyO+b0vpM8\nbgeRkB3DKMOTozYn5aPdqW3mom2DOHV1Hiorb/3f8kP3VvC3r7ax73Q3n318IzZrbgbklfpnv9hl\nU/D3Ag8rpfZNPH5SKfUE4NJaPz+bN+3vD8zmZQXB5/OUfP6X37kMwOYVNVm15fcHCIZiJInO6X0h\nXSwDwSihUBzDSFBeMfc2M+Wj3cw2J/PPVTgUY2AgQDx++0K+a/0iXj3Swc/2X2F7Dqa9kM++uXLx\nx2ragq+1TgFPT3n6wi32+/qc04iCl0qlOHiuF7vNYPNK6c4pZPdvaeTVIx28frwzJwVfFD+58UrM\nSHuXn97hCNtW+yi3W82OI+6gyedmVXMlZy4P0ScXbwVS8MUMvXOmB4B7Job+icJ2/5YmAN441mly\nElEIpOCLrI0nkhw824vXZWfdsukv1grz7Vjjw11RxpsnuoiNJcyOI0wmBV9k7eSlQULRcXata8Bq\nyEenGJTZrNy/tZFQdJz9E7/OROmSb63I2v7T6YKxW7pzisoDW5uxGhZeOXRdVsMqcVLwRVaCkTFO\nXBqgyediSb3b7DhiBqo95dy9tp7uwTBnrgyZHUeYSKbSEzdIpVIEAv6bnt93up/xRIptrVW33H4n\ngYAf5MQyp2733+l2dq+tZv+ZXn68/zIttXf+2ns8XiwWmSllIZKCL24QCPh55cBFKpyuG55/7UR6\n5sVEYpy3Zzgp19BAL06XF6e7tO9yzKVIOMQbR4eoqqnN+jV1Xjvnr/n50S+uUem69YR3kXCIh3e2\n4vVW5iqqKCBS8MVNKpwunK73inMgHGfQP8biWid1NVUzbi8ckkW188FR4bzhv9N0NrVa+PnRTi52\nR7lvc00ek4lCJX34YlrtXemugxWNXpOTiLlo8rmo9pRzpTtAIBw3O44wgRR8cUepVIr2Lj82q4WW\nBumSKWYWi4UNK2pIAafb5eJtKZKCL+6ofyRKIDzGkno3ZTb5uBS7pYs8eJxlXOr0E46OmR1HzDP5\nBos7utiRXvtmZZNcxFsIjImz/GQqxSk5yy85UvDFbcXHE1zp8eOuKGNxrdPsOCJHVjZW4q4oo+36\nCMGInOWXEin44raudAUYT6Roba6UcdkLiGFY2NxaSzKVni5DlA4p+OK2LnSMYLFAq3TnLDjLG71U\nuuxc6hzFH5IRO6VCCr64pcHRKEP+GE0+N06H3K6x0BgWC5tX1ZFKwfGLA2bHEfNk2m+yUsoCPAds\nBqLAU1rr9oztnwD+LZAEvq21/mKesop51NYxAsDqZjm7X6iWNrip9abH5a9bFqGussLsSCLPsjnD\nfxwo11rvBp4Bnp3coJQygP8OPAjsBn5bKSW38BW5sfEk7V1+nA4bjXWu6V8gipLFYmG7qgfg8Pl+\nmUmzBGRT8PcALwForQ8AOyY3aK2TwFqtdRCom2hPOgSL3JXeMOOJFKqlCsOQi7UL2aJaJ831bvqG\nI1zvkykwFrpsCr4XGM14PD5xZg+ki75S6mPAceB1IJTThGJeJVMpLnWHMAwLq6Q7pyRsX12HxQJH\ndD+JpJzlL2TZXI3zA5n31BsTZ/bv0lrvBfYqpb4OfAb4+p0a9PmK+xb9hZz//LUBgpEEa5ZW46vJ\nzbz3kZAdwyjD43bkpD2P25HzNiflo92pbeai7Vzm9LgdbFhZx6mLA1ztj1JX56Gy8tafkYX82S8F\n2RT8fcCjwAtKqV3AqckNSikP8APgg1rrOOmz++QtW8nQ3x+YXdoC4PN5FnT+H+27CqQnSgsEozl5\nz1AojmEkKK+Ye3set4NAMJrTNjPlo93MNifz57LNXFi/tIq2a8OcuDjCxSuDtDTe/ON/oX/2C10u\n/lhl06WzF4gppfYBXwB+Tyn1hFLqKa11APgm8KZS6k3Sxf6bc04lTNEzFObs1VFqPGXUVeb2zFkU\nNnuZlW2rfSSSKb7/TofZcUSeTHuGr7VOAU9PefpCxvbngedznEuY4KcHr5ECVjfJEoalaGWTl/NX\nBzl2cZjTlwfZsDz7xVVEcZAbrwQA/lCcfad7qPXaaayTs/tSZLFY2NZahWGBb7ykicbHzY4kckwK\nvgDg50c7GBtP8v5NDRgyb07JqnKX8eDWRQyMRvnem+3Tv0AUFSn4gthYgp8f7cTlsLFzrfyML3WP\n7FhMQ42TVw93cLFzdPoXiKIhBV/w5okugpExHtjWRHmZ1ew4wmRlNoMnP7yGFPC1H59jbHzagXei\nSEjBL3Fj4wl+8ourlJdZeXjHErPjiAKxekkVD25ronswzA/fuWJ2HJEjUvBL3NsnuxkJxnlgaxMe\np93sOKKAfOL9K6nxlvPjX1yVaRcWCCn4JWw8keTHv7hKmc3gkZ0tZscRBaai3MZnHllDIpnir34k\nXTsLgRT8ErbvVDeD/hj3b2mi0iVn9+Jmm1bWsmfjYq72Bvj2y+fNjiPmSAp+iRobT/AP+65QZjP4\nkJzdizt44qFV1FdV8OJrbehrw2bHEXMgBb9E/fxoJ8OBGA9tb6baU252HFHAKsptfO6xdVgsFr76\nw7OEorLwebGSgl+CIrFxfrT/KhXlNj68a6nZcUQRWNlUya8+tJohf4y/eVnLYilFSgp+CXr54DWC\nkTE+vLMFd0WZ2XFEkfiVh1azssnLwXN9/OJMr9lxxCxIwS8xg6MRXj54Ha/LLuPuxYxYrQafe2w9\n5XYr33xF0z8SMTuSmCEp+CXmb35yjthYgo+/bwXldrmrVsxMfVUFn3p4NZFYgr/4/mnGEzJUs5hk\nswCKWCCu9Ph59dB1ltS72bNxsdlxRAFKpVIEAv5bbrPbk/j9ATa0VHCXquGQHuJbL5/lY3um/6Xo\n8XixyKR8ppOCX8RefesQSSO78fOpVIpXT6a/yKsaDF7df/KW+8VDI1hcDTnLKIpLJBzijaNDVNXc\nPIme2zVEMBQDoKnWzvkKG2+c7CM+NkZTXcUd23x4Zyter6yRbDYp+EUsZdgpc9VltW97l59+/zjL\nG700N90MCd4kAAASmElEQVT+7D4aCSEdPaXNUeHE6bp5OT2X20GS95ZUvH+bgx/vv8rhtlEW+apk\nao4iMG3BV0pZgOeAzUAUeEpr3Z6x/QngXwBjwCmt9W/nKauYpfhYgiO6D6thYc/mRpAhdSIHqj3l\n7FzXwDune3jzRDcf2tmC1ZBum0KWzUXbx4FyrfVu4Bng2ckNSikH8IfA+7XW9wFVSqlH85JUzNqJ\ni4NEYgk2rqjB65KbrETutDZXsrLRy+BolCO6z+w4YhrZFPw9wEsAWusDwI6MbTFgt9Y6NvHYBhm/\n+YTphvxRzl8dxuMsY/3yGrPjiAXo7nUNVLrtnL86wuWuW1/wFYUhm4LvBTKXvRlXShmQXuBca90P\noJT6XcCltf5Z7mOK2UgmU+w/3UMKuHttA1arjMIVuVdmM7h/SyNlVoP9Z3oYDsSmf5EwRTYXbf1A\n5hUcQ2v97uDbiT7+PwFWAR/P5k19vpsvCBWTQsnv8TiwOW+/4PhR3cegP4ZqqWbN8vdGXXjcd1ik\nPOrAcJTjutM+MxQJ2TGMsju/7wx43I6ctzkpH+1ObTMXbZv173+n5x+6u4Wf7L/CG8e7+JUPrH73\nPg+DOHV1Hiorzf/eFMp31yzZFPx9wKPAC0qpXcCpKdu/AkS01o9n+6b9/YHsExYYn89TMPkDgSi2\n5K170PyhOAfP9OCwW9ncWksgmN7P43a8+8+3bDMYxZqI3TAaY65CoTiGkaC8Yu5tTubPZZuZ8tFu\nZpvTHf/ZtJlLd2p3uuy+ynI2rqjhVPsQL+2/zAPbmrBYLIRDMQYGAsTj5v7CLKTv7mzk4o9VNgV/\nL/CwUmrfxOMnJ0bmuIAjwJPAW0qp14AU8Gda6+/POZmYtVQq3ZWTSKa4d10DDrmjVsyTzavqGBiN\n0tEf4uSlQTa3ZjdsWMyPaQu+1joFPD3l6QszaUPMrwvXR+gdjrCk3s3SBrfZcUQJMSwW7tvcyI/e\nucKJi4PUeh3UuMxOJSbJVbwFJhgZ44jup8xmsHNdg9zOLuadw27l/q1NWA0Lb53sxh+W+fMLhRT8\nBSSVSnHgbC/jiRQ71tTjdMiPL2GO2koHuzcuYmw8yb4zQ4Si42ZHEkjBX1Audo7S2R9ica2T1iav\n2XFEiVu+2MvGFTWEogm+/tN2mVmzAEjBXyAC4TiHzvVRZjPYvWGRdOWIgrBlVR2NtQ4udAT47qsX\nzY5T8qTgLwDJVIp9p3oYT6S4e209LlnFShQIi8XCXaurWFzj4NWjHbx+vNPsSCVNCv4CcO7KMH3D\nEVoa3KxolK4cUVjKbAZPfaQVd0UZ3/rpBc5eGTI7UsmSgl/khgMxjl0YwGG3smu9jMoRhanWW87v\nfHwjFgv8+d7TdA6EzI5UkqTgF7FEMsXbJ7tJplLcs2ERDruMyhGFa/WSKp78yFoisXH+99+dYDQo\nc+7MNyn4Rez0tQjDgRitzZUsqZcbrEThu2f9Ih6/bzmD/ihffPEksbGE2ZFKihT8InWxc5Sz1yO4\nK8rYscZndhwhsvbY7mXcu3ERl7sDfOUfzpBMyoI880UKfhGKxRM8/8OzpIB7Ny7CbpO5ckTxsFgs\n/PqH1rB2aTXH2gb421fbSMkqbPNCOn2L0N+9dpG+4Qhrmxw01DjNjiPEHaVSKQKBmxdG+fRDLXzx\nexF+dqQDR1mKh7YtmnHbHo9XBirMgBT8InP84gCvHeukqc7FpmWyXKEofJFwiDeODlFVU3vTtm2r\nKnntxBg//EUn3YMhli/K/gQmEg7x8M5WvN7KXMZd0KTgF5HRYIy/+tE5bFaD3/xH69Ftl8yOJERW\nHBVOnK6b53N3uuDhuyp46cA1jraN4PW4ZABCHkkffpFIplL85Y/OEYyM8cv3r5QvhVgwqtzlfGB7\nM1arhTePd9E7HDY70oIlBb9I/OxwB6cvD7FhRQ0P7Wg2O44QOeWrquD9W5pIplK8dqST4UBuV/IS\naVLwi8C13gAvvH4Rj7OM3/joOrlIJRakJp+LezcuJj6e5JVDHXJjVh5M24c/sUj5c8BmIAo8pbVu\nn7KPE/gp8Fmt9YWbWxGzFR9L8JUfnGU8keKzH1lLpctudiQh8mZFo5fxRJJfnOnlp4c6+NDOJXic\n8pnPlWzO8B8HyrXWu4FngGczNyqltgNvACtyH09897WLdA2E+MC2ZlkfVJSE1Uuq2LHGRyQ2zk8P\nXicYkRWzciWbgr8HeAlAa30A2DFlu530H4XzuY0mjrcN8NrRTpp8Ln75gZVmxxFi3qxbVsPWVXWE\nouO8cug6YVkxKyeyKfheYDTj8bhS6t3Xaa33a607AelYzqGRYIy/+nF6COZvPbYee5ncTStKy8aV\ntWxcUUMgPMYrh64TiUnRn6tsxuH7gcwBtIbWek5rlfl8N4/HLSb5zp9Ipvji904RjIzxucc3sHX9\n4lvu5/E4sDkdM27f477Da6IODEc5rjvtM0ORkB3DKLvz+86Ax+3IeZuT8tHu1DZz0bZZ//6zeb+5\nZL1vazNWq5Xjbf28criDx9+38t0Ffgzi1NV5qKzM/vtY7LVnrrIp+PuAR4EXlFK7gFNzfdP+/sBc\nmzCNz+fJe/6/f6ud4xf62bSyll3Kd9v3CwSi2JIzG77mcTsIBG//mkAwijURI0nuhsWFQnEMI0F5\nxdzbnMyfyzYz5aPdzDanO/6zaTOX7tTubLPPNevGFdWMjSc4c3mIF19r44N3LcFVUUY4FGNgIEA8\nnt1gw/n47uZTLv5YZXOk9gIxpdQ+4AvA7ymlnlBKPTVlP5n9KAdOXhrkH/Zdoa7SwecekyGYQlgs\nFratrnu3e+dluZA7a9Oe4WutU8DTU56+aeil1vrBXIUqVQMjEb76gzPYrAa//bENuByyNq0QkC76\nW1bVYRgWTlwc5OUD17hvQ43ZsYqO3HhVIMbGE/z5358mFB3nUx9czbJFsjatEJksFgubW+vYMjF6\n5/UTA3QNyDQMMyEFv0B852dtXO0JcO/GRdy36dYXaYUQsGllLXetqSc6luSLf3+BC9dHzI5UNKTg\nF4A3T3Tx+vEultS7+dQHlfTbCzGNtcuquVtVER9P8IXvHud424DZkYqCFHyTnb86zN+8rHE5bPzz\nj22gXMbbC5GVlnonn/tIKxYL/H/fO8VbJ7vMjlTwpOCbqHcozJ/vTY9y/Z2Pb6S+WlavEmIm1rZU\n8vu/upWKcitf+/F5XnzjEklZLvG2pOCbZCQY49m/O04oOs5nPqRQLdVmRxKiKK1squSZT22nvqqC\nH+2/ynN7TxONy125tyIF3wTh6BjPfvcE/SNRHtu9jPs2NZodSYii1ljn4j/8+g7WtFRx9EI//883\njzI4KnPqTyUFf55FYuP82Qsn6egPcv/WJh6/b7nZkYRYENwVZXz+n2zh/i2NXO8L8t++cZi2DhnB\nk0kK/jyKxMb5X393graOUe5eW8+nHl4tI3KEyCGb1eDTjyg++dAqAuE4/++3jvGDd66QTEq/PkjB\nnzeh6BjPfvc4FztH2bmugc89tg7DkGIvRK5ZLBYe2rGEf/PEVirddva+2c7//NtjDI5GzI5mOin4\n86B/JMJ//5sjXOryc8/6RXzu0XVYDTn0QuSTaqnmv372brat9nH+2gi/+z9f41hbv9mxTCVVJ88u\ndo7yx984TPdgmA/tbOE3Hl0rZ/ZCzBN3RRn//GMb+PQjilg8wZdePMVXf3CWQDhudjRTZDM9spiF\nVCrFTw9d54XX0+OCP/XB1Ty4rdnsWEKUHIvFwgNbm9i5sZE//eZh9p/p4VT7IP/4/pXs2bi4pE7A\npODnwZA/yjde1py8NEily85v/qP1rF0q4+yFMNPSxV7+w2e287PDHex9q52//sl5fn60g199cBVr\nSuT7KQU/hxLJJK8f6+LFNy4RjSdYu7Sa33xsHZXucrOjCSEAq2HwyN0t3LWmnhffuMT+M738yXeO\nsaalil/as5zVS6oW9Mg5Kfg5kEylOKr7+d6b7fQMhXGW2/inH17DfZsWL+gPjxDFqsbr4HOPreeh\nHUvY+1Y7p9uHOP/tYyxf7OHhu5awQ9Vjsy68S5zTFnyllAV4DtgMRIGntNbtGdsfA/4jMAZ8TWv9\nfJ6yFpxIbJx3Tvfw6pEOeobCGBYL929t4pfuXSZn9UIUgeWLvXz+V7ZwsXOUn/ziKsfbBvjKP5zl\n2xVt7FzXwM51Daxo9GIskBO3bM7wHwfKtda7lVI7gWcnnkMpZZt4vB2IAPuUUt/XWi/YsU/BcJzD\n5/s4eL6PkxcHiI8nsVkt7N6wiMd2L6OhRiZAE6LYtDZV8ruf2ETfcJjXjnWyf+JE7tUjHVS67Wxe\nWcvqJVWsaq6irtJRtL/csyn4e4CXALTWB5RSOzK2rQXatNZ+AKXU28D7gBdzHdQMsbEEvUNhOvtD\nXO72c6nLz9UeP5M37TXUOLlnfQPv39JEpctublghxJzVVzv5Jw+u4hPvX8npy0Mc1f0cvzjAmye6\nefNENwDVnnJWNVeypN5NQ7WThhon9dUVRTG1eTYF3wuMZjweV0oZWuvkLbYFgMoc5suLjr4gnQMh\n4mMJYmMJ4uNJIrFx/KE4o6E4o8E4I6EYo8Ebx+paDQtrltWwurmSzSvraGlwF+1feiHE7dmsBlta\n69jSWkcimeR6X5C266Nc6BihrWOUg+f6OHiu74bXVLntVLrK8bjK8DrteJ12KsqtlJdZsZel/7/M\nZmA1LFgsFiyW9JDRZp+LGq9jfv69stjHD3gyHk8W+8ltmYuveoCCnq0okUzyR984THw8edt97GUG\nVa5y1rRUsbjWxeJaJ8sWe2mpd9PUWEV/f2AeE99eIh4hPt43/Y4ZrAkH4eDtZxFMjkWJp0JzjXaD\naCSEYdgIh+Z+3AzihEOxnLaZKR/tZrY5mT+XbebSndqdbfZ8ZY2Ec/s5vR2rYbBskZdli7w8fNcS\nUqkU/SMRugbD9A6l/9czFGZgNEr3UIirvbevLbeyuNbJH39uV57S38iSmmaxAKXUx4FHtdafVUrt\nAv6j1vqjE9tswBlgJxAG3gEe01p35ze2EEKImcqm4E+O0tk08dSTpC/SurTWzyulPgr8Z8AC/KXW\n+i/ymFcIIcQsTVvwhRBCLAwL784CIYQQtyQFXwghSoQUfCGEKBFS8IUQokTkZfK0iSkY/ofW+gGl\n1DrgyxOb2kjPxZOcsv8R3ruB67LW+jfykSsbmdkznvsk8Dta691T9r3jPENmmEn+iW0Fc+wn8mR+\ndrYAPwQuTGz+P1rr/z9j34I+/tPln9i/kI+/D/gqUAVYgc9orS9n7FtQx38m2Sf2L+Rj/x2ggfTo\nx2XAfq31JzP2ndWxz3nBV0r9PvBpIDjx1B8Df6C13qeU+hrwGPD9jP3LAbTWD+Y6y0zdIjtKqa3A\nZ2/zktvOM2SGmeYvpGMPt8y/HfiC1vp/3eYlhX7875i/CI7/nwDf1Fq/oJS6H1gDZBbNgjn+M81e\n6Mdea/3ExPNVwM+BfznlJbM69vno0rkIfCzj8ccnir0dWMSNUzFA+i+USyn1slLqZxPhzXJDdqVU\nLfBHwL+4zf43zDME7LjNfvNlpvkL6djDzZ+d7cBHlVJvKKWeV0q5puxf0Mef6fMX+vG/F2hWSr0C\nfBJ4fcr+hXT8Z5q90I/9pP8KfElrPfWW+lkd+5wXfK31XmA843FKKdUCnAZqgRNTXhIG/lRr/Qjw\nNPAtpZQp1xYys09keB74PBAi/dNqqlvOM5TvnLczi/wFc+zh5s8OcAD4fa31+4F24L9MeUnBHv8J\n0+Uv9OO/DBjSWj8MXAf+YMpLCub4zyJ7oR97JrqlHgT++hYvmdWxn5d/Qa31Na31atJ9+VN/3l4A\nvjWxXxswCCyej1zT2Aa0Av8H+A6wVin17JR97jTPkNmyyV+ox37S32utj038815gy5TthXz8Yfr8\nhX78B4EfTPzzD0j/YslUyMd/uuyFfuwB/jHwba31re6OndWxz3vBV0p9XynVOvEwACSm7PJZ4AsT\n+zaS/pcwey4ei9b6sNZ640Qf368CZ7XWn5+y3z7gIwAT8wydmuect5Nt/kI89plezpiO+wPAkSnb\nC/X4T5ouf6Ef/7eYOL6kpz0/M2V7IR//6bIX6rHP/CX+EPCT2+w3q2M/H0sc/g/gr5VSMdI/o54C\nUEp9Hfj3wF9ObH8LSAKfLYCzhDvON5GRfS/wsFJq38SmJ/MdLEvZ5i/EY5/paeBLSqk40AP8JhTF\n8Z80Xf5CP/7/GnheKfU06e6DT0LRHP/pshfqsc/87q4m3RX4rrkee5lLRwghSoTceCWEECVCCr4Q\nQpQIKfhCCFEipOALIUSJkIIvhBAlQgq+EEKUCCn4QghRIqTgCyFEifi/VVPBxS6KPNwAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116596f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(FWHMx_mms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15.604200000000001, 16.566099999999999, 16.220300000000002)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row.FWHMx_mm, row.FWHMy_mm, row.FWHMz_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14d1c8b10>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TrIbBgzuX8PTL5zl0ppM9WxrNjiTySDpn+PsAh9Z6F/Bt4InxHUqpUuBPgHu1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\nSMEXQogiIQVfCCGKhBR8IYQoEv8/wXiyBUvTI/wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d1be650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(FWHMy_mms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b040450>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GVVkB20yIkJbvyCgK/AzlOA4PPzuxht1v3rHa5WpkuasoyeeOzSvoHxzhYHM3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\nSyjwRUSyhAJfRCRL/H9ehAwYbJrpxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x159b7ff50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(FWHMz_mms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d8aca90>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nV9p2OWqfN6US+huMMV+y1m4HPkB8CGcbsMYYUwAUAzVA3VCerKmpPYUSskNlZVDtyxJv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b8e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cdf_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110888710>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UDlkc9eL5PA6W97iYjGX5/nOHeXHfIM/uPHjW4yjnSzqT45s/38ujLx6hK+ji\nc+9fyoJ2mYzYTDzOwmUx1qLPVagmKbRx9sU7q5SynWdfFGgHgtOV0VqbSqnlwFtAN7B31pE3mGQ6\nhwl4nFJLmI3Vi1zYDNADMdzeAF7f/I/7jyYy3PeD3bymx+kMOtl6RSdv9w/IpLQm43EVvqOt+rCd\nahrMwhQu8iW2in6AMGcPsQgCExcqo7U+DqxXSn0G+BrwqQv98t7exl2+dmhkkGCgMGomkYkDEPQ5\nCPjd+ANnRtMkYi5sNueZY6e8tmpbIgZ+v8fyOAC6OzysWZyn/2Sck6NxVi5w09MTpL39wp+Paj8/\nhaGl59Y82toKi/CdGo3yd/++h1OjMVYs9PG+G9fgdNgYPp2b0/ceDHiq/L+qj89M5baLiX8+tlVb\nrrPNBSTI5s3ydhvpqj5vzaCapLAD+DDwE6XUDcC+in37gbVKqQ4gDtwM3Ffcd04ZpdSjwP+ltT4I\nRIAZx32NjESqfCv1KRItTFgbnSgkBTt5orEUec4s9haLpbHZcri9yWlfW7stWSdxpFnR7eTQKYPd\n756m29fD6GiEdPr8ld3e3mDVn59wOMSzOw+eVQMpLcI3FDL5p5/tI5rI8N6rF9Lht5FMpknO8XsP\nBjxEosmqytbXZ6awraeXquOfj23VljNzhcvSZCRV/v7GY6kZP2/15FJupqtJCo8AdymldhRff1op\n9XHAXxxp9AXgGQodxw9prQeVUueUKf79t8B3lFIpCknks7OOvMEkijMkpflo9jwuG6uXtHFwIMSp\nseTMBS6S1+fH5z/zZTJNk137B/nBC4OYpsnHblvBxmUu9h6J1fx3i/rhdhgYFGaot6IZk4LW2gQ+\nP2XzgYr9TwBPVFEGrfUrFDqhW06pfVI6mi/NhpWdHBwIoQeicz766PhgiF2H49gMg60busjlMvxm\n1wA+fxu+QPM3I7QqwzDwum0t26fQGHWhJiA1hdpoD7hZuiDAeCTDkaG5u2MfGo+z+3AcA7jj2iWs\nXtqLzx+Uhe1ahNdlJ57KtuQENkkK80SSQu1sWFV4Ittzrw/Nyc+PxNO88PopTGDL2gB93ZIIWo3X\nbcc0C6MGW40khXmSSGWx2wzkKZyXbkGHl66gk7ePhjg1WtvaQiab5zd7TpLK5NiwzMOCdmnva0Wl\nCWyt2IQkSWGexFNZvG6HzGauAcMwWL+ksL7Q4zsOlmc412KW8ytvDzEZTaOWd7C8x1WLcEUDKi1v\nH2vBzuYKKtf+AAAVS0lEQVTWW9jDAnmzMJu5p8MFtF4b5Vzo9GbxuQxefXeM7jYHXpf9kp/jfGos\nwZHBCD3tHq67bAHjo3PTPCXqX2l5+3gLPldBagrzIJkqzGaWh+vUjmEYrOnzkjfh2HAGnz94SbOc\n48ksew6GCiONrlyEzSY1ulbmK65kLM1HYk6UOpm9Lbji4lxa2u3C47KjT0xe8rOcf/7SAMl0ns1r\nu+kIyFpGrU76FMScKicFeTRjTdltBpet6CSTzdM/MDnrn/PWkTFefXeMDr+TDau6ahihaFTl5iNJ\nCmIulNolfZIUam79sg4cdoN3jk6Qz1fXX1N6hGY4HGJ4dJyHn9iPzYAt6zqk2UgAhRsOt9Pekn0K\ncpWaB2fVFKSfuaY8Ljtrl7bz7rFJToxWt1ppJBIur3P0+sFJJqJpVnYbeBytdwEQ5+fzOIjE0y03\ngU1qCvMgITWFOXXFii4MAw5cxNIXXp+fSMrOocE47QEXapk8QlOcze9xkM2Zl9xf1WgkKcyDeKow\nK1L6FOZGwOdkxcIgoViWNw9X17eQzeV56a0hDGDrxkXYpdlITFF6FGer9StIUpgHiWQWm83A5ZTT\nPVeuWteDYcDPdwyQysy8NME7xyJE4hkuX9lJb4d3HiIUjcZXXL2y1Z7AJlepeZBIZfHJbOY51eZ3\nsX5JgIlomidfOXbBYw8PRjlwMkbQ5+SqdT3zFKFoNKXm3lbrbJakMMfypkkinS0PcRNz5/LlAdr9\nTn75ynFOFx9qNNVYKMm3nzpUbjZy2OUrIKZ3pvmotZa6kG/EHEulc5im9CfMB4fdxke3LiWby/O1\nH+5lLHT2aKRkOsvXf/om0USWzWvaWdjlsyhS0QikT0HMCZmjML+uWdfF3VtXMjyZ4EsP7GBgOEre\nNHn32AR/873dnBiOsnVDD2v6JCGICysnhRZrPpIr1RyT2czz756bV5HLm/zylWP812+/ittlJ5XO\nYQC3XrWYj1y/iJffkcXuxIW5HHYcdqPlagpypZpjiaQkhflmGAb/8dbVXLa6m5f3nuLoUJh2v4v/\n+fa1rOprIxwOWR2iaBB+j1OSgqit8sQ1WQxvXhmGwe3XLmPj8g6rQxENzOtxEIqlyVW5hEozkD6F\nORaX5iMhGpa/+L1NpFrnsZySFOZYQmYzC9GwSjX8RAs9q1mSwhxLpLLYDAO3zGYWouGUk4LUFESt\nFJ7NbJfZzEI0oNJSF4l06yyKJ0lhDuVNk0QqK01HQjQon/QpiFqKJwuzmWXkkRCNSZqPRE1F4jLy\nSIhG5nHZsRmt1dEsV6s5FI7LEhdWM02TSCR81rZIJCxPwBNVMQwDn8fZUjUFuVrNoUhCagpWq3z0\nZsn46Gl8/jZ8gaCFkYlG4XU7GJ3MtMwEthmvVkopA3gA2Awkgc9qrQ9X7L8b+DKQAR7WWj94vjJK\nqauArwNZIAX8vtZ6pMbvqW6EY5IU6oHX58fnP5MA4rGohdGIRuP3OBgBIvEMnS0wQb6aPoV7ALfW\neivwReD+0g6llKP4+k7gNuBepVTvBcr8A/CHWus7gEeAv6jR+6hLpZqCzyPPUhCiUZU6m0Ox1niu\nQjVJYRvwFIDWeiewpWLf5UC/1jqstc4A24FbpylzbfH4j2mt9xX/7QDOXvC+yUTihQ+R1BSEaFyl\npDAZTVscyfyo5mrVBlQuK5lVStm01vlp9kWBdiA4ZXuuWOY0gFJqK/CHwC2XEny9i8Sz2AxwO6Wm\nMB+mdiq7XHnpVBaXrDRQpFVqCtUkhTCFi3xJKSGU9rVV7AsCExcqo5T6GIUmpQ9qrcdm+uW9vY3b\nGRiOZ/F5nLQFzzwY3kx4sHvd+AOe8rZEzIXN5iRY3Db1tVXbEjHw+z2Wx1H9ORrjNX2arq7iUseH\nxxkdOY0/0D7PcdRuWzDgqYs4ZrPtYuKfj20XU64UO0BPZ2HkUTrf2NejalWTFHYAHwZ+opS6AdhX\nsW8/sFYp1QHEgZuB+4r7zimjlPokcC9wm9Z6spoAR0Yi1RxWd/KmSSSepavNQySaLG+PRpPY8yny\nnNkWi6Wx2XK4vclpX1u7LVkncVR7juzkOfOlHh4em9P3MJfvMxgofHasjmO223p6qTr++dh2MeWC\nQeeZ761ZuAceHIk1zPXoUpJXNUnhEeAupdSO4utPK6U+DviLI42+ADwDGMBDWutBpdTUMp9SStmA\n/w84BjyilDKBF7TW/++so69jkXiGvMxmFqLhlfoEJ2PSpwCA1toEPj9l84GK/U8AT1RRBqB7FjE2\npMlICpCJa0I0OrutsMpxq/QpyDIXc2QiWkgKXqkpCNHwfG47oWga02z+UQuSFObIZFRqCkI0C4/L\nTiZnEmuB5zVLUpgj5eYjqSkI0fB87sKlcqL4vW5mkhTmiNQUhGgeHldhrpEkBTFrE5HCSIXSk5uE\nEI3L5y4lheQMRzY+SQpzZCKSwuW04XTIKRai0XndUlMQl2gymqLNJ01HQjQDrzQfiUuRyeaJJjKS\nFIRoEl7paBaXIlTsZJakIERzcNhteFz28vyjZiZJYQ5MlJOCdDIL0Sw6Ak4mwpIUxCyU1l1v80tN\nQYhm0e53EU9lSaWb+3nNkhTmQKndMSjNR0I0jQ5/oebf7E1IkhTmQGniWrskBSGaRnugsCR7s3c2\nS1KYA5PlmoL0KQjRLMo1hSafwCZJYQ5I85EQzafdLzUFMUuT0RRBnxOH3bA6FCFEjXQESjUFSQri\nIpimyUQ0RWfAbXUoQogakpqCmJVEKkc6k6cjKElBiGbi99hx2G2SFMTFGS92QnVKUhCiqRiGQWfQ\nJUlBXJzRUCEp9LR7LI5ECFFr3W0eQrE0mWzzTmCTpFBjY8Wk0C1JQYim09PuBWCsiZe7kKRQY6Wk\n0NPmtTgSIUSt9XQUbvZGJxMWRzJ3JCnU2GhYagpCNKtSs/BIqHknsElSqLGxUAKH3ShPiRdCNI9S\n85HUFETVxkJJuto82AyZuCZEs+ntKCQFqSmIqqQyOcLxjIw8EqJJtQdcOOwGYyGpKYgqjJf6E9ok\nKQjRjGyGQXebh5FJqSmIKsgcBSGaX0+Hl2giQzKdtTqUOSFJoYZkjoIQza+3vTQstTlrCzOu7ayU\nMoAHgM1AEvis1vpwxf67gS8DGeBhrfWDVZS5H3hXa/0vtXwzVjtTU5A5CkI0q55iZ/NoKMnSBQGL\no6m9amoK9wBurfVW4IvA/aUdSilH8fWdwG3AvUqp3vOVUUr1KKV+CdxdyzdRL8bC0nwkRLM7M1eh\nOTubq0kK24CnALTWO4EtFfsuB/q11mGtdQbYDtw6TZlri8cHgK8A36tJ9HVmNJTAbjPokGWzhWha\nZ+YqNGfzUTVJoQ0IVbzOKqVs59kXBdqB4JTtOaWUTWt9VGv9GtCUg/jHQkk6g25stqZ8e0IIKpa6\naOGaQpjCRb5cRmudr9jXVrEvCEzMUKYpZbJ5JqNpaToSoskFvU7cTnvTDkut5iHCO4APAz9RSt0A\n7KvYtx9Yq5TqAOLAzcB9xX3nK3NRenuDMx9UB06NRgFYsjBYjnloZJBg4OwkYSY82L1u/BXbEzEX\nNpuzfOzU11ZtS8TA7/dYHselnCO/vz7imO22YMBTF3HMZtvFxD8f2y6mXCn2EhtpenqCtLcXvtsL\nu32MTibo6QlgNNnqBdUkhUeAu5RSO4qvP62U+jjgL440+gLwDIUmoYe01oNKqXPKTPmZZrUBjoxE\nqj3UUgeOjgMQcDvOijkSPftuIhpNYs+nyHNmeyyWxmbL4fYmp31t7bZkncRx8ecoGPDURRyz3RYM\neIhEk5bHMdttPb1UHf98bLuYcsGg86zvbjyWYnQ0QjpdaFzp9Ls4PhTh6IkJAl4n9eZSbqZnTApa\naxP4/JTNByr2PwE8UUWZyv1/dXFh1r/hiUL7ojQfCdH8SmsgDU8k6jIpXAqZvFYjg6MxABb3+C2O\nRAgx10rf81PF730zkaRQI6fGCh+Ovm6fxZEIIeZaOSmMSVIQ53FqNEZ3mxuPq5puGiFEI5Oagrig\neDLLZDRNX7c0HQnRCgJeJ21+lyQFMb3BMelPEKLVLOnxMxpKNt1qqZIUauCUdDIL0XJK3/fBsbjF\nkdSWJIUaKH0opJNZiNbRrP0KkhRq4MzII6kpCNEqlkhSEOdzajRGm9/VdJNYhBDnV6opnJSkICql\n0jnGQkkWS9OREC2lWUcgSVK4REPjcUykk1mIVrS428doKEkqnbM6lJqRpHCJSncJ0p8gROtZ0lN4\nHOfgePPUFiQpXKJTMkdBiJa1uKfQbDwwLElBFB06GcIAljXhA7yFEBe2sq/wjLFDp0IzHNk4JClc\ngmwuz+FTYZb0+mXkkRAtaPnCAG6XnQMnJq0OpWYkKVyCY0MR0tk865Z1WB2KEMICdpuNtUvaGRyL\nE46lrQ6nJiQpXIIDA4W7g/VLJSkI0arWF28K+weao7YgSeES9J8otCOul5qCEC1r/dJ2AA6caI5+\nBUkKs5Q3TfoHJulp99AZdFsdjhDCIqsXt+GwG03TryBJYZZOjcaIJbNSSxCixTkddlb1tXF8OEIi\n1fjLaEtSmKX+4l2BJAUhxPplHZhmYYh6o5OkMEsHBgr/+euK7YlCiNZVujnUTdCEJElhFtKZHG8e\nGqMj4GJRlyyEJ0SrW7ukHZfDxmv7h8mbptXhXBJJCrOw+8AIiVSWm67swzAMq8MRQljM63aw5bIF\nDE8mOHC8sWsLkhRmYfveUwBs29RncSRCiHpxc/F6sP3NQYsjuTSSFC7S6Yk47x6f5LLlHSzslKYj\nIUTB+mUdLOj0slsPE0827igkSQoX6cXiXcAtmxdbHIkQop4YhsHNm/pIZ/Ps3H/a6nBmTZLCRUim\ns7z45iA+t4Nr1vdaHY4Qos5s3diHYcCvdw+QzeWtDmdWJClchB/8qp9QLM0d1y7F5bRbHY4Qos50\nBt3cvKmPk6MxHttx1OpwZsUx0wFKKQN4ANgMJIHPaq0PV+y/G/gykAEe1lo/eL4ySqk1wHeAPPCW\n1voPa/x+5sxuPcL2NwdZvjDAR25aaXU4Qog69bE71vHO0QmeePkom9Z0s3ZJY81lqqamcA/g1lpv\nBb4I3F/aoZRyFF/fCdwG3KuU6r1AmfuBL2mtbwVsSqmP1uqNzKVjQxG++9S7OB027r17Aw67VLCE\nENPzuh185kOXgwn/8tjb5Uf2Nopqrm7bgKcAtNY7gS0V+y4H+rXWYa11BtgO3DpNmWuLx1+rtd5e\n/PeTFJJJ3cpk8zzx8lH++7/uIprI8PE718ljN4UQM1LLO/notlWMhpL81Xde4/nXTzZMH8OMzUdA\nG1C5oEdWKWXTWuen2RcF2oHglO05pZQdqJzpFSkeWzfGw0kOngwxHk5xZDDMvsNjJNM5OgIuPvOh\nK9iwqsvqEIUQDeIj21axuMfPd596l399WvPTFw6xeW0PyxcE6GrzcMXKTnye+ntiYzVJIUzhIl9S\nSgilfW0V+4LAxHnK5JRS+SnH1tXUv6/+8A0Gx+Ll1z3tHm69ajEfunHlrB636XDYiYeGztqWyyZJ\nx8+uTiYTMWw2B/FYZNrXVm1LxuMkkznL45jtObKRros4ZrvNRpp4LGV5HLPdFouGq45/PrZdTDmH\nA3L5M/ewifjsmoC2XLaA1YvbePKV4+zpH+Glt4Z4qbRP9fJ//IcrZ/Vz51I1SWEH8GHgJ0qpG4B9\nFfv2A2uVUh1AHLgZuK+4b7oye5RSt2itfwt8APj1DL/b6O0NznBI7fzLl+6q6c/r7Q1y+fo1Nf2Z\n4mJtsjoA0eJ6e4OoNY0zhN0wZ1i8qWIkUenb9WkKfQT+4kijDwFfodA09JDW+pvTldFaH1BKrQO+\nBTgpJJTPaa0be/UoIYRoIjMmBSGEEK1DxlYKIYQok6QghBCiTJKCEEKIMkkKQgghyqoZkjrvlFID\nwIHiy5e11n9ZHNr6DxTWWHpWa/1XlgU4g5nWi6pXSqndnJl0eAT4GxpgrSql1PXA32mtbz/f+lpK\nqc8B91L4/Py11voJq+KtNCX2q4DHOfPZ/4bW+sf1GHtxiZtvAysBF/DXwDs0yLk/T/wnaJzzb6Mw\nklNRON//O5CiBue/7moKxS/1bq31HcU/f1nc9Q3gd7XWNwPXK6U2WxfljM67XlS9Ukq5ASrO+2do\ngLWqlFJ/RuHL4S5uOidmpdRC4D8DNwLvB/5WKWX5VNJpYr8W+GrF/8GP6zV24JPAqNb6Fgpx/RMN\ndO45O/4PUIj/Ghrn/N8NmFrrbRQWJP0banT+67GmcC2wVCn1awoT4v4UGAJcWuujxWOeprBu0l5L\nIpzZWWs/KaW2zHB8PdgM+JVSTwN24C+Ba6asVXUX8KhF8Z3PQeA/AN8rvp66vtbvULhzelFrnQXC\nSql+CnNods93sFOcEzuwXil1D4W71T8F3kN9xv4j4MfFf9uBLOd+Xur53FfGb6NwF30tcFkjnH+t\n9aNKqV8UX66gsJLEnbU4/5bWFJRSf6CU2qeUerP0NzAI/I3W+g7gb4F/p7CURriiaN2tmzTFtOtF\nWRVMleLAfVrr9wGfp3De63qtKgCt9SMULkglU2Nu49y1uEprdFlqmth3An9WvNM7TGFS6PnWF7OU\n1jqutY4ppYIULq5/SWOd+6nx/z/Aq8B/aYTzD6C1ziulvgN8Hfg+NTr/ll6otNbf1lpfqbXeVPob\n2AU8Vty/A+hj+jWW6mrdpCkutF5UvTpAIRGgte4HxoCFFfvr/ZyXTLe+VqN8fn6utX699G/gKgpf\n6LqMXSm1jMJSNd/VWv8PGuzcTxN/Q51/AK31p4D1wIOAt2LXrM9/Pd69fgX4PwGK/QYntNYRIKWU\nWlXsxH0fhWW669UO4IMA06wXVa/+APgqgFJqMYUP0jNKqVuL+z9AfZ/zkj1KqVuK/y7F/BqwTSnl\nUkq1A5cBb1kV4AU8XdHU+F4KVfy6jL3YVv008Oda6+8WN7/eKOf+PPE30vn/pFLqL4ovk0AO2DXN\n9/Wi46/HPoW/A/6tuKZSBvhUcfvnKVSRbMAzWuvXrAmvKo8AdymldhRff9rKYKr0EPCwUmo7hTu+\nT1GoLTxY7JjaD/zEuvCq9l+Ab1XGrLU2lVJfB16kUMX+ktY6bWWQ5/F54B+VUmkK/Wj3aq2jdRr7\nF4EO4MtKqf8KmMCfUIi/Ec79dPH/KfAPDXL+f0bh+/oChev4HwPvMuX7OpvzL2sfCSGEKKvH5iMh\nhBAWkaQghBCiTJKCEEKIMkkKQgghyiQpCCGEKJOkIIQQokySghBCiDJJCkIIIcr+f3fWebDE7ylO\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d98d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(im2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10df9c250>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a6sEeLeZ1P4OsolGs63QGqZUYnGjnKVtNyuE61uAgAOHc3COrSRw8EupCdCRa\nM9Oaw1IWZ0vpWO/Fchraw4PhHdc6ndmk4SNoqW8Y1OnQxjWdhncUhBSX0zpej6+TFNJVHKWlfrxJ\nqAvRcaM+TStuc7o4iWWkN0DnFk2UgtHhO5c0uh3qj66lXqSGRcSaHro1jHKi5aA0LPXHqEK6XICE\n+vEmoS5Ex9sr6WTpx/rPAND2FWvrJhNjBhn7zmGCTpCG/KPsflFoRqwqPlnqSbo/aiYxGG5nqGRj\n1lUbo9RPtLREEsq6eseVhLoQQJREnF+7SM7MMpZL9/1cWEoD++yZe4M72+lTD61H+yM0aqYjWxaD\n/lvHTjTSgP/QKKfL8CYJwaz0qx9XEupCAFfWr+PHPhPZcXy/TbvdYnYx/fEYHwvx2z5saqzvR/cL\nwKiVhvpCULp1bKKZRWn4UC1h9Kdh79+U5QKOKwl1caxtjE3/yfy7AAwZJT64XubijQqr6zbZrM/8\nWpVrs6t3zBx1goDIMNB7vO7L3XJGQI4G5bBIrNP3ziQGwy2bOaNGYyAHQO3KFZlZekxJqItjrVar\n8t0rP+DdlfcxMMjHWTKZDLVaHlAMDARkMlksy77jeY4fElmPtpW+YVCtEWOylmzqgulMRHqv2EAr\nRePCezKz9JiSUBfHXpKBetRg2BrAUOmPxMpy+mepv33vE7TGCULCfQx1gOVk4Nax8UYGpRWXcxVU\nsYiqVtF7vCaNOJgk1MWxt9hOVz4csdIZmXEM66sKxwnJZuN7rreDEAX7Fur9qoJBQjkevHUsEynO\nxiWWrAatUh6ShGh+fl/qE/tLQl0ce4t+GurDVhqSlXWLJFGUivdppQPOxt6k+xTqpkoYtWvUdB+t\nJO12CcOQofW0D/1S0U+PzUztS31if0moi2MtiANW/DUGnH6yRjo0cG01nXhUKt0/1LPtTmg+4pEv\nm53MrAMwFw7fOnYm6kNpuDqZ1hVOywiY40hCXRxrV2s3SEg4WRgHIElgfdXGcW5vMH03pxPqgb33\ny+4+yKSzBmjmo6FbxzLaYKzlcHNEoU2TcFpa6seRhLo41i5WrgBwsnACgNV1izhWDI8kD9yl7lZL\n3d6/lnrWiBg0qqzEJdrx7X9cTjQctKGoDeaIFhdJ2q2HvIo4iiTUxbGltcZbv4KtLIZzaX/6wlLa\nRz08+uD90zdCPXgEG2Q8zIS5DChmN80uHW+mOyJNjyjQmvaNG/tWn9gfEuri2JprLFAJa4xlRzCU\nQZLAYtmm2xX+AAAfsklEQVTGthNK/Q+euHO7pb4/oa41xGHEMIsA3Gz1E8UhaLC0wXgjw9RI+mtG\n+/r1falR7B8JdXFsvb98EYBxZxSA8qpJGBkMjYQP7HoByLZ8EqUeyQYZ95PEEeX1Fu1alT5doRyV\nmF+Pbs14PVnLsDiUTpbyp2/sS41i/0ioi2PrwspFFDDqpAt4zS6kLe/hkYdP2sm2fVrZDA9N/j1m\nmgaWZTLGEqBYsc/cOjfazODnLfyMQXtGRsAcNxLq4lhqhk2uV6c4XZjEMTNEkWahbJHLxhT67p1w\ndEuS4PgBbcd5dMU+xBgLGMQsGqdurbFuasUT0TBLAybh4iJJ+/5DM8XRJKEujqUPVi+R6AS3/0kA\nbs7GxLFiYix4aAPcbvsYWqct9QPAJmLMWMZXeeaDIpDeAD5XL7A8aIHWrHgfyuJex8iWd3pc11XA\nV4BngTbwnOd51zad/zzwXwMhcN7zvL+7R7UK0RNaa95ZSDfEGA5LrMYVvMtpkg8P1kE/uBXuNNIh\ngm3nYIQ6wElzkYVknMvNEYb7q4RhSHNmgfX+9Md7/lsv4Dz2OKVS/xavJI6CblrqnwMcz/M+DXwR\n+PLGCdd1s8D/Cvw1z/N+GRhwXfeze1KpED1Sqa5zYfVDHJXhm2+9wfnLa8zOaxzHZ2ltiTC8/6Qj\nrTXGyioAVcu4Y331/VRUdQq6yqxfohmnN0gzto2R6wMgastqjcdJN6H+GeAFAM/zXgM+semcD3za\n8zy/89gibc0LcWDdbMwT6oiJwhh9xX6q1QFAMTLSxLYf3AIP2m36r9wAwFivPjD8HzWlYDyeQaO4\n3rq9bEC/USQ2IK6u72N14lHrJtRLQGXT48h1XQPA8zzteV4ZwHXd/wooeJ737d6XKUTveJ1ZpOO5\nUZJEsbaaw7Y1/aWtZ1/m43RSUpw9GDdKN4zoBWwVc709QtLZPGM4dFgrWRSqLSLZs/TY6Gb2RBUo\nbnpseJ53a7pdp8/97wMfAf5GN286Olrc+qIDTOrfX7ut33v9CoYyeHx4ksvNJeLY4PQ5TTZnkWCR\ncWycrE3GsTBMEydrY2cslIJsp3UeOhks00CbYEZgWSZWYmBaBqAwbRPTNLGsdOihYRuYEUSAaZsY\nhoFpGtjW7etQGsswMTvHNl9nWQZ0hjGaloFlmERx+jzTMsiYmif71viwNsKyGuFEZpWsk8EfyGOt\nV1le9nCfenzfv/f77bDX341uQv1l4LPA867rfgo4f9f5PwBanud9rts3LZdr3Vd4wIyOFqX+fbTb\n+peaZWbrC4w7o/itmJXVAqAZHQtpVSPCICLwQ3w7JPAjDCvBb4eEQTq5x/JDYsPA1xo7TkgS0ElC\nFMVEUUIcJSRKEYcxcRwToYiimCRM0EkCpkEcxiRJQhwnhNHt62KliYyYuHNs83VRlBDF6fvEUZJe\n13mejtJrnnDKfFgb4UprjCG1hO+HOLk+oIr37o/5Kfcz+/q9329Hof5udBPqXwd+03XdlzuPf7sz\n4qUAvAX8NvBD13W/R3rr6B96nvcX2y9ZiL33TvkCACdy46yta3zfoVRq4zgGW3a+aE3GDwgy9r5O\nPHqQfstnxK6xHA5QT3L0A9lCCZijdXOKMImwjf1dr0bsvS3/D3uep4Ev3HX40nZeQ4iD4p3yBQwU\nE9lR3v8gPTY41CJtozxcJooxk4Qwc/A+8lpDGIU85iywHBa51j7BWH6OqC/9ugZX21xcvcRPjzyz\nz5WKvSaTj8SxsdZeZ6o6w2PFs5g6w40pMM2IUsnf+slAoZ3ebAxse4srH70kjlhZb+K05nF0k9l4\ngpmVgFBponyW0bWItxbe2e8yxSMgoS6OPK011WqF12beBODJ3FluzkMQQqlU77onpdjqLLl7gCYe\nbWaYFpZlcUpPo5XBvDoNWqMKRfK+5sr0eVbWl2V26REnoS6OvFqtynev/IBXFt8AYGV1lWs30gFc\n/aV6169TbKVTMPwDGuobxljA1gGzyQnq7ZhVK13Lpn+lyfMX/5JaTSYjHWUS6uJYMByL1WCNkewQ\nOaufpWWD4SHIZLqfQLTRUj/ooW6ScEJPE2NxpTVKWEr71UfXIpaTtX2uTuw1CXVxLCy0l9DAqeIk\nC0sWoDhzanuvUWr5aEhHvxxw48xiEuG1xmjmsgBMriYs+ksEsUxEOsok1MWxMN9Odwkas4e4uZB+\n7MdH/W2t31Js+Wl/+gEczng3i4hJY4F2YnMxHCHMWJxY9ImT+Na+rOJoklAXR147alP2V3B0junZ\niNV1m2zWZ6a8TBR11/1iRxHZMDrwXS+bnTbmMEi4FpzC7+8jEyYMVmPeW/1gv0sTe0hCXRx5FytX\n0Gj6zRFqlRygGBz0H7p4192KzcNxk3QzRwWcy67S1DkWculCX48tK7zKFVqRrLt3VEmoiyPv/XUP\ngH41zHI5/cj3D3Q3Nn1DqbOOut/pnz4sns4vApp3jMcAOLsCkY55r/z+/hYm9oyEujjS2lGbi+tX\nKBg5jCjP+poilwvIZB6yZd19lOqdzTEO2OqMWylZPuPmClfNCWLTZGwp/Tp+svTuPlcm9oqEujjS\n3lv+gEhHTDhjrK/agKLUxRK7m2mtKVYbwOEKdQ1EUcQ5YwqtDBZyw2SrTc4xzIerl2mGzf0uUewB\nCXVxpL25mE6NP+GMsrqSDkXsL22vP9lvthiuNWgbCl8nWz/hgIjjiOX1JklzmQG1ztXMSQCeXs4S\n65gfz7whs0uPIAl1cWTVwwYfrl7iZH4cK85TXTfpKybb7noxw4hcnNC0D95CXlsxDRPLtHjMnmc2\nOwpAdqYMwEs3X5bZpUeQhLo4krTW/Hj6dRKd8FThSeYXTEAxMrr9lnahmq7B3bDMHlf56AwbFar5\nAgmKwXKD4ewgq1GFetjY79JEj0moiyOpVqvy0uwrAFTXqszMpR/1HYV6JQ31+iFsqW9QCs7kVlh0\nhsivVDiXO4FG3xoZJI4OCXVxJFWCKmtRhdHcMHmrn9V1i0IhJpvb/mtthHrDPrwtdYBxa5W53BCm\nTji1nI63l4lIR4+EujiSzq9dBOBM8RQLZQutFUMj3S/etVnfre6Xw9tSBwgCn6aZfg+a75QZygxw\nvTZFxZd+9aNEQl0cOVpr3lx+B4XiTHGSuaU0jIeGdxbqhUqdwDQIzcP/46Ky6XDO3M1FxjMTaODt\npbu3HRaH2eH/lApxl6uVGyy2ypzIjqNih+VVk1Ixwsluf/ieGUXkGk0ah7g/fbPYUtQyeSabSwQL\noyjgLZmIdKRIqIsj54ezrwJwrnCaqZkYrRUToztbbravM+moeQD3Jd2poOSQ0RHN83XO9Z3lWuUG\na+31/S5L9IiEujhSakGdt5fOM5YdYTgzyPWpCICJsZ11vfSvVQCoH4I11LvVLqXr10xUFsk1TgDw\nyvTrMhHpiJBQF0eG1prvXf8BsY55ynmCWq3N3HxCsS/CNJrbWjt9Q/9aehOx5hyNUNcaqvn0aznb\nmueDCxkUih/OvyoTkY4ICXVxZFSq63x/7lVMDN67cIOfvN9Ca3CyFa7eXCUMt99a71+rEpvmoZxN\nej9JHFFuBFT6i0y2y4S1DBl/kFrcYLYxv9/liR6QUBdHhle5Qlv7nO6bpNg3QK1WBDTDQ+G21k7f\nYEQxxWqd2mD/odjtqFtKmSyPDGLphMn2Eu35MwC8sfzOPlcmemHL5ofrugr4CvAs0Aae8zzv2l3X\n5IFvAf+553mX9qJQIbbyo8XXAXi8eIb3gwrNZoaBwQTbToi2t9wLAKVKFQWslgqQ7KxP/iDygwC/\nM7H26eZVppY+Q/Gswzur7/OfxAEZ8/BsBCLu1U1L/XOA43nep4EvAl/efNJ13Y8D3wce7315QnTn\nRnWaa7UpRp1h+jMlqtU+AMYmdr6qYl95FQC/0SQKo57UeVD4AyU0cM5P926NlyfxY1/GrB8B3YT6\nZ4AXADzPew34xF3nM6TBf7G3pQnRvRenvg/Ak32PobWmWuvDMBKGR3Ye6oPVOgB+sdCTGg+SxDRp\n5RxK7SYjA3Vas6cAeHnu9X2uTOxWN6FeAiqbHkeu6956nud5r3qeNwscnU5HcagsNcu8W77AZH6C\nkcwQi0sQRRb9/W3MXSzXMlitExuK9hEazrhZo5DH0PC0cxMryaGrI1ytXGexsbTfpYld6OaWfhUo\nbnpseJ63q50CRkeLW190gEn9+2tz/Vpr/uzVr6HR/Nrkp0hUwutvR0DC+ESIk82RcSyixCLj2DhZ\nm4xjYZgmhm1gZ2wymdvHN67LGVBstKgXcli2iWkZgMK0TUxlYJomlmmgTTAjsCwTKzHuvM40sSwD\ny0rfy4wgAkzbxDAMTNPAtm5fh9JYhonZObb5OssywExfy7QMLMMkitPnmRv/mQZaKegsZ2CZaZ1m\nnL6WjrmjvlZ/HyyvMVGt8OzHNG9PT5IpLfNO5V1+69zf2PJ7fxgd9vq70U2ovwx8Fnjedd1PAbvu\ndCuXa7t9iX0zOlqU+vfR3fXPrszyo5nXyZs5rl2fxswUmZrO4Tg+tt3Cb+cI/IgwiAj8EN8OCfwI\nw0owEpMwCAmCEL8d3nFdplxBAXUnQxQlxFFCohRxGGMYmjiOiRQkCegkIYrie66L45gIRRTFJGGC\nThIwDeIwJkkS4jghjG5fFytNZMTEnWObr4uihChO3yeOkvS6zvN0533jOEnfL06I0UQKDH37taLk\nzvpq2SwaGJxbov3LLWamT1EJP+Bb3o/4hfzHGewfRG0a9XPUPjuHTbf/IHXT/fJ1wHdd92XgS8Dv\nuK77edd1n7vrOpmOJh65VxbfIEHzzPBPkc/nuTFjoYGB/uquRiH2z6ddELV8tjeFHkCxZVLPOQyu\nrNNYKnP61DzxyknausUfv/08c3M3ZZbpIbRlS93zPA184a7D9wxb9Dzv13tVlBDdaEdtflz+CRkj\nw2P951hprjA1Y2HbUCw2gJ0Hcv/8EpqjHeoAlUKeYsvH/2CG+hNnGMuMssoUV2qrfPeNq/wHv16i\nVOrf7zLFNsjkI3FovTL3Ou24zWOFM1iGyc15Cz9QPH4ODGPnLUwjjimWV6j0FYh3c6f1EFjvywNw\ndqVKJuNwbrKEag1A3yqL4c4WQRP7S0JdHEpBHPLi9PfJGDYnrVEajSbeNRvD0Dx+1t9VZ2D/8jpG\nnFAeLPWu4AOq4dhU81kml1ax/AClYDKXDm+8lngE4a7GRIh9IKEuDqUfzf2YalDj4wM/w7WZCq++\n3abtm/QP1JheWiaKdjYDVGvN4MwcADeLefQRv1XUDkMWHQtTa8avTgMwlhtBRVkYnuWb79zY3wLF\ntkmoi0NFa83y2jLfvP5dMkaGj2Y+gmk4zN90MIyEEydaO1rnZUPQbnNiep5EKaLVNaLgaM0kvZ/1\nwX5iBR+5PouKY5QyGFXjKCPhR4uvU6n7+12i2AYJdXGoVKtV/uTDf0U9anA2f4p3586zuOgQhorh\n4QaWtbuWdc4P6QtCWv1F7KzTo6oPttAyWSgWyLd9xi9dB2DYGMeMs6jRG/zl67Kc02EioS4OFT8K\nmApmsQ2Ln554BmXlWVkuYduakeH6rl//9Eq6A1Bj6HiN+JguFQgNgzNvvke4VoEETvmPo8yYl5d+\nSK0pN00PCwl1cah8Z+plgiTAHXwSx8xw5UYBrQ3OnIsxzd33f58tr6OB+vDg7os9ROpJzFQxT8YP\neOqlV6muruHXm1hJFjUyxf/zylv7XaLokoS6ODTW/QrfuPY9MoaNO/gkq2sJswsOmUzAxIndj9Jw\n2j5jlQb1fJbYOX7Lzy4NDRA6GSZW1ik2mmSyOT7W/xGUoXmr8QNmF8pUqxWZkHTASaiLQ+Mvrv4V\nfhzwdPGnMLH54Ss+oBgbX0P14JN8Ym4pXT+9/+ivD3I/2lCUHzuNAp65Ok0UBlSXFVZrEFVc4f/4\n//6SF1+7ItveHXAS6uLA01pzfu59Xl/4CZOFCU7nJ3n7vZCVVc3J8TZ9fe2evMf4zDwaWCsdvaV2\nu9UYHqCZyzK5uIK9uo5tZTiXOQexxWL+JzRN6Vs/6CTUxYFXqVb4F5e/BsBoWOL6jQbnL0QUCvDY\nqdXerDq0vMrIepVV26R1nLsXlGJ2uB8D+PkPb+C3WmRUhvH6sygz5s3WS8TJDraREo+MhLo48H5c\nfpNqXOdU/gQ3ruR5670+UJozjzWYXlghinY/lvzE9RkUsFg42mu9dGO11IdvGpxstDGjCL/VwtZ1\nqIwT5db42sXv7HeJ4iEk1MWBttgs88LN72Irm+yqy/zCBEppnvnpiOERG8vqwQYWWjN5dYpYKRZy\nx2Ns+sNopVgsFrC05tRsulplLpfjI6Vn0IHD2403eb98eZ+rFA8ioS4OrDiJ+Wcf/BlhElFcf4bz\n72WxrIgnnlxlcKh3XSQTs4vkGi1WSgViQ34kABZKfekeptdnodMdNTqcIbfiojX8wXv/N2vt9f0t\nUtyXfILFgZQkCV+78BfcqE6Ta5xi1jtBqZjw+OOL5HK9m7pvJAnuhcskSjF3zCYcPUxgmazkswxU\n6wys3R7tcrJfkS0/Q6TafOm1f8LK+rIMcTxgJNTFgfTGtTf5fvnHEGZYveiSz7c4eWYBRe9GX2it\n+diHVyk0WkydO0XbPpp7ke7UfGcU0LnrM7eOBe02Z3OQLJ9kLS7zf735T5mdnZFgP0Ak1MWBs+5X\n+Fc3v4FG4V95loG+kMefqJDN9jZ0Ry/f4Kn5FZqOzYzSPbnhepSsZTNU81lOzC1itdNFvaLQp9Kq\nczrzOEm9nzVjmT969wUZu36ASKiLA6Ua1PjSG/+IVtIknHaZHBpgcnKdXnZ1a62xyiv87LuXiJRi\n5vEzZHIy6uVufhiy6NiYiebk5albxy07w8REhnH/WZJ2joXcFV68/sY+Vio2k1AXB4LWmkuLl/l7\nr/w+q8EK0cJZxu0Bzj4W72qv0fvxmy0++fp5rCTBG+wjOCarMe7E2lA/kaH4yPWbmP6dXV+nT1kM\nVD6Gjmy+s/wif/LK90gS2VRjv0moi32nteYbH3yL37vwh9SSdeKFczxmlij1Vfbk/R6bXWSgHbBc\nyLIkQxgfKjZN5ob6ccKIydfeveOcUjDa55NfOg1a8Urzm/yPf/b/4t1YkDVi9pGEutg36YYXZX7/\ntT/kG4vfIYkN4mvP8pHBU7SC1T3p4840mvzM5RtEhuLK6CA9/zXgCJofKFLNZRl/7yITMwv3hPWg\noxhtP45Cszr6Kv/nt7/F3//T97h4fWGfKj7erP0uQBxfr145z7+89nViu0FSL2EvPM0zboGMA831\n3o9E0XHCkz94DTuKuTYxTGCZIDdHt9SKIj4s5fkF3+dXLk3zYyA+feaOa0ayg+S1y011FfX4BcrL\nK/yDP2/w2Ogsv/LsCT759Dg5R+LmUZDvsnhkWn7Exek1fnJpjgvNN/EHL6FsDctn6K8PM37KJ+Ps\nzWJaKtE8/do7DC0us+TYzOdze/I+R1XUV2Bh8gSj73zAL12a5qqTZ+4XfuaOa+x2hvHkNGt9i7RH\n5rGHyszMPcYff2uVP/32ZZ59YoBPPnOSjz02jJMx9+krOfq2DHXXdRXwFeBZoA0853netU3n/zrw\nPwAh8Eee5311j2oVh0wUx7x/dQFvpsqlmzVuLNagfxF78grGcB0jyFKsnWO4YNNOKihV6nkNWmuy\nC2U++eEVxqoNmvkcl4ZLKKXozUpgx0dQ6uP14RI/v1LjifMXyTaavPPRJ++4pi9T4kT+LNfW3qdm\nr2KfuoxzYppk4RxvXDnFG94qhoKTwznOjBc4O17gmcfGOTFSwJCusJ7opqX+OcDxPO/Truv+IvDl\nzjFc17U6jz8OtICXXdf9C8/zyntVsDh4/DBmrdZmdnGN5YrP0nqb2eUm04sN2omPUVzDLK3gPLsI\ndhs09CejFGolCn1ZDMukTaOnNWWCkBMzC5x8/TzjM/MALOSzLJw7SdRsItOMdqZhm7x9YoifXasz\neW2afHkF7+wEzVyBsHONUopsJY+R2JgTsGLMw6RH/uQVsrVJ4rVJ5soJN5dbvPL+MjBFNmNwZqzA\nEyeLPHmyj48+cYKMLa35negm1D8DvADged5rrut+YtO5p4HLnudVAVzX/RHwK8DXel2oeHSSRNMO\nIlp+xPJahXorotoIqTY3/dcIqTVD1hsB7aSNcloYThPltFBOE1VoYTzTJJdpQacBZmgTu5ajPx7j\n9MknWG8t96xmq+0zXF5lZGWNkcVlBtZrG29L3ckwOz7MUsYmb0pQ7FZgmlw4e5Kzs4uMVxt86sJV\n9IWrrJX6WD45RnDuNCt+gFnIM1E4w0n9OPONKdb0Iq3SNJSmKZy16Y9PEFXyRPUBWrV+Lt2scelm\nDQDLuMy5iU7ITxY5MZRlYnQIQ9bm2VI3oV4CNo8ti1zXNTzPS+5zrgYc+gU0ojih6Ueg01/fNema\nRlprtGWyst7qHLvrnObWcTQknVEC6UNNkty+LtEarTVJ57nJxvP17evuPrfxHK0hTjStVuve65Lb\nNSQ6XUMlijVxAlGS4Dg2rVZAnMQ0wjatMKIdhLSCCD+KaYcRoQ5QZoSyQjAjlBmBGaKsKH1sBaiB\nADUaoKyAnHH/sclmYqL8DCVrkJHCSQpGP/NLNzAfMh3fiCLMICTf8rGDiPzqOubyKpkwwmo1MWoN\n8gkUUGTaPh+t1clFMdnw9g3PBFi1TNYKOSrFAi3bIptzIJCbor2SGAY3z04ytbJKf7XOyUQxUK0z\nVK3DxWt8DAgNA7/vPdr5HGdMg6qlaA/nqWZ8Grk27cxVYlvBiMIZU+SNLIlRImpmCJpZrvsO16/Z\nvHgpg45tLEwGCg4DBYdCNoNjmeRsm1zGppDJYhkGlqUo9hXIWCa2ZWCZBkqlvz2sNkMqlVb6GHXr\nePr49t+Nzp8ohbHp+N1/Zm3zQP420U2oV4HN+3ttBPrGuc0doUXgUC/dFicJv/uPXmWt5u93KXsq\n89RrmANr9xw3O/9tKQEjMbGSDEZkEtYCzNiilB8kWPfJZ/owLZvaehWnL4+ONXXWadTrWJZFdW2V\neq2CYRgYlkmjXifr+3zyX38HK+5+E4YA8BVUMzYN22BOJ7T68lTaPgXHxCAhqtfxwzbt/7+9uw+R\noo7jOP6enV0tvUxD7QklQvpKIAb1hxI+/JGZJUb/9JeBF0GSEVQQWWT+0wNkSWBpas+SYZZRFj2B\nBYoWBdUl8fWfSMJIuyvcvfVmn6Y/frOnZ+7TnXezO3xfcHDD/e7ms7e//e7M7Mx3CmUKQUA+30/G\n9ykTUikUSWcynCoWKQUFBooBlUoZ3/fJZrNkfJ9UMI6B/hxlPDwvRcb3yObyZNIpShXwwpAwrHCq\nWBwyrrqekAons/14YQg+BAMB2Vw/49M+//T2Da6nTEja9ykWArK5PKViaXBcoVKhFBQIwwrZ/hxp\n36dQKA7JhwclPEpBgUoYkkmn8FM+A4UipWKJclip+Thy+QHKpRKZcRlOZrND/hfV9fT2XjAkj++l\nyAcD/DXO48SUqWT7+rgkKDIZj64goMuDC7P9dJ3MMbX6hP1e58kEvr6+i59kAkyCc90lNhd9nT0X\nC4fnUO69sul5cz6Mz/g8u3o+F09sr/vZNlPUDwDLgd0iMg/oOeNnvwKzRGQykMcdenmuwd/zpk1r\n73tAvrX+lrgjjIHb4w5gzP/cGHeABPAaXfV1xtkv1fOXunEfjE5U1e0ichvwJG4P5lVV3TKKeY0x\nxtTRsKgbY4zpHPZRsjHGJIgVdWOMSRAr6sYYkyBW1I0xJkHGvKGXiEwC3gW6cL1kVqrq8bHOMVwi\nkuJ0a4TxwHpV/TTeVK0TkdnAIWC6qp6/G3+Oomju7MBdG5EBHlbVQ/GmaqxR/6R2F7UDeQ24Cnf6\n+FOq+nGsoVokItOB74GbVPVI3HlaJSKPAitw8/5lVX291tg4ttRXAT+r6kJgF/BIDBlG4i4graoL\ncD1wZjUY33ZE5CJgA67AdJKHgK9UdTHu1NqX4o3TtMH+ScBa3EZBJ1kJ/B29ZpcBm2LO05LoTWkL\n7lqajiMii4D50fxZDMyoNz6Oot7D6atQJ8F5vD382FgKHBORvcBWoKO2WCJbccWl0yb5C8Ar0fcZ\nXBO5TjCkfxJwQ/3hbWcXrhMruJpRrDO2HW0ANgPH4g4yTEuBX0TkQ+AjYG+9waN6+EVE7gYexLUj\nqfY6vR+4WUQOA1OABaOZYSTOyl91AjilqstFZCHwBrAohngN1ch/FNipqj3RYYG2VGPudKvqDyJy\nGfA28ECMEVtRr39S21PVPAzu4b0HPB5vouaJyCrguKp+KSKPxZ1nmKYCM3FX9l+NK+yzaw0e84uP\nROR94DNV3SYic4Adqjp3TEOMgIjsBHap6p5o+U9VvTzmWE0TkSPAH7hCOQ/4Njqc0RGiOfMO7nj6\nF3HnaYaIPA8cVNXd0fJRVZ3Z4NfaiojMAD4ANqnqm3HnaZaIfIPr8QZwHaDAig77HO8Z3BvTxmj5\nR9xnA+dscxrHnY/6OL3VcoKhzcI6wX7gVmCPiMylYYui9qKq11S/F5HfgCUxxmmJiFyLOxRwp6r2\nNBrfRur1T2p7InIp8DmwRlX3xZ2nFao6uBctIvuAezupoEf24/ZKN4rIFcAEoLfW4DiK+jpgu4is\nidZ/TwwZRmIbsFlEDkbLq+MMM0LVQxud4mncGUcvRoeO/lXVO2LO1Iw9wBIRORAtd8cZZhjWApOB\nJ0RkHW7eLFPVTmtl2pE9UVT1ExFZICLf4V6v96lqzcdivV+MMSZB7OIjY4xJECvqxhiTIFbUjTEm\nQayoG2NMglhRN8aYBLGibowxCWJF3RhjEsSKujHGJMh/Drz5PHag1DoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1065d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbr_bins = 256\n",
    "imhist_orig, bins_orig = np.histogram(orig_inside_mask,nbr_bins,normed=True)\n",
    "cdf_orig = imhist_orig.cumsum() #cumulative distribution function\n",
    "cdf_orig = 255 * cdf_orig / cdf_orig[-1] #normalize\n",
    "\n",
    "imhist_new, bins_new = np.histogram(new_inside_mask,nbr_bins,normed=True)\n",
    "cdf_new = imhist_new.cumsum() #cumulative distribution function\n",
    "cdf_new = 255 * cdf_new / cdf_new[-1] #normalize\n",
    "\n",
    "im2 = np.interp(new_inside_mask, bins_orig[:-1], cdf_new)\n",
    "im3 = np.interp(im2, cdf_orig, bins_orig[:-1])\n",
    "\n",
    "sns.distplot(orig_inside_mask)\n",
    "sns.distplot(new_inside_mask)\n",
    "sns.distplot(im3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112263190>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3dQUALRbkxMAiuqNxTz592/sLBIJMlmMsD8aJ2A5vnXylU6WKPiOhLsRtCOXXaOgBLo3n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\nQvQQCXUhhOghEupCCNFDJNSFEKKH/P/hWoHSs7dUOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119209b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(orig_inside_mask)\n",
    "sns.distplot(new_inside_mask)\n",
    "sns.distplot(hist_match(new_inside_mask, orig_inside_mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!fslview /tmp/fake_data.nii.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 filo  wheel   873K Jan  4 11:21 /tmp/fake_data.nii.gz\r\n"
     ]
    }
   ],
   "source": [
    "!ls -alh /tmp/fake_data.nii.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez(\"/tmp/fake.pklz\", new_inside_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 filo  wheel   893K Jan  4 11:36 /tmp/fake.pklz.npz\r\n"
     ]
    }
   ],
   "source": [
    "!ls -alh /tmp/fake.pklz.npz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FWHMx_mm</th>\n",
       "      <th>FWHMy_mm</th>\n",
       "      <th>FWHMz_mm</th>\n",
       "      <th>volume_vox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>19.3934</td>\n",
       "      <td>20.3302</td>\n",
       "      <td>15.624</td>\n",
       "      <td>190626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FWHMx_mm  FWHMy_mm  FWHMz_mm  volume_vox\n",
       "564   19.3934   20.3302    15.624      190626"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.index == 564]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}