MISA_Project_PreProcesing_Step(1)_Registration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Import-Libraries\" data-toc-modified-id=\"Import-Libraries-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Import Libraries</a></span></li><li><span><a href=\"#Reading-the-Training-and-validation-Data\" data-toc-modified-id=\"Reading-the-Training-and-validation-Data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Reading the Training and validation Data</a></span></li><li><span><a href=\"#Function-For-registration\" data-toc-modified-id=\"Function-For-registration-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Function For registration</a></span></li><li><span><a href=\"#Performing-Registration\" data-toc-modified-id=\"Performing-Registration-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Performing Registration</a></span></li><li><span><a href=\"#Dice-Similarity-Fuction\" data-toc-modified-id=\"Dice-Similarity-Fuction-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Dice Similarity Fuction</a></span></li><li><span><a href=\"#Cheaking-inverse-registration-will-work-or-not\" data-toc-modified-id=\"Cheaking-inverse-registration-will-work-or-not-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Cheaking inverse registration will work or not</a></span></li><li><span><a href=\"#Creating-the-excel-File-of-the-Registered-Training-and-Validation-Path.\" data-toc-modified-id=\"Creating-the-excel-File-of-the-Registered-Training-and-Validation-Path.-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Creating the excel File of the Registered Training and Validation Path.</a></span></li><li><span><a href=\"#Registartion-For-Test-Imaes\" data-toc-modified-id=\"Registartion-For-Test-Imaes-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Registartion For Test Imaes</a></span></li><li><span><a href=\"#Creating-A-Excel-File-For-Registered-Test-Data\" data-toc-modified-id=\"Creating-A-Excel-File-For-Registered-Test-Data-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Creating A Excel File For Registered Test Data</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Notebook is to 1st Preprocessing Steps for the MISA project. Registering the Training and Validation Data to\n",
    " MNI 1mm Template (MNI152_T1_1mm_Brain.nii.gz) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from numpy.linalg import inv, det, norm\n",
    "from math import sqrt, pi\n",
    "from functools import partial\n",
    "from scipy.spatial.distance import dice\n",
    "import time\n",
    "import operator\n",
    "import matplotlib.pyplot as plt\n",
    "import SimpleITK as sitk\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Reading the Training and validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "mylist = pd.read_csv(\n",
    "        \"TrainAndValidation.csv\",\n",
    "        dtype=object,\n",
    "        keep_default_na=False,\n",
    "        na_values=[]).as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1'\n",
      " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/TrainingValidationTestSets/Training_Set/IBSR_01/IBSR_01.nii'\n",
      " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/TrainingValidationTestSets/Training_Set/IBSR_01/IBSR_01_seg.nii']\n"
     ]
    }
   ],
   "source": [
    "print(mylist[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Function For registration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Helper Function for the registartion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, fixed\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# =============================================================================\n",
    "# Function Definitions\n",
    "# =============================================================================\n",
    "\n",
    "# Callback invoked by the interact IPython method for scrolling through the image stacks of\n",
    "# the two images (moving and fixed).\n",
    "def display_images(fixed_image_z, moving_image_z, fixed_npa, moving_npa):\n",
    "    # Create a figure with two subplots and the specified size.\n",
    "    plt.subplots(1,2,figsize=(10,8))\n",
    "    \n",
    "    # Draw the fixed image in the first subplot.\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.imshow(fixed_npa[fixed_image_z,:,:],cmap=plt.cm.Greys_r);\n",
    "    plt.title('fixed image')\n",
    "    plt.axis('off')\n",
    "    \n",
    "    # Draw the moving image in the second subplot.\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.imshow(moving_npa[moving_image_z,:,:],cmap=plt.cm.Greys_r);\n",
    "    plt.title('moving image')\n",
    "    plt.axis('off')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "# Callback invoked by the IPython interact method for scrolling and modifying the alpha blending\n",
    "# of an image stack of two images that occupy the same physical space. \n",
    "def display_images_with_alpha(image_z, alpha, fixed, moving):\n",
    "    img = (1.0 - alpha)*fixed[:,:,image_z] + alpha*moving[:,:,image_z] \n",
    "    plt.imshow(sitk.GetArrayViewFromImage(img),cmap=plt.cm.Greys_r);\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    \n",
    "# Callback invoked when the StartEvent happens, sets up our new data.\n",
    "def start_plot():\n",
    "    global metric_values, multires_iterations\n",
    "    \n",
    "    metric_values = []\n",
    "    multires_iterations = []\n",
    "\n",
    "# Callback invoked when the EndEvent happens, do cleanup of data and figure.\n",
    "def end_plot():\n",
    "    global metric_values, multires_iterations\n",
    "    \n",
    "    del metric_values\n",
    "    del multires_iterations\n",
    "    # Close figure, we don't want to get a duplicate of the plot latter on.\n",
    "    plt.close()\n",
    "\n",
    "# Callback invoked when the IterationEvent happens, update our data and display new figure.    \n",
    "def plot_values(registration_method):\n",
    "    global metric_values, multires_iterations\n",
    "    \n",
    "    metric_values.append(registration_method.GetMetricValue())                                       \n",
    "    # Clear the output area (wait=True, to reduce flickering), and plot current data\n",
    "    clear_output(wait=True)\n",
    "    # Plot the similarity metric values\n",
    "    plt.plot(metric_values, 'r')\n",
    "    plt.plot(multires_iterations, [metric_values[index] for index in multires_iterations], 'b*')\n",
    "    plt.xlabel('Iteration Number',fontsize=12)\n",
    "    plt.ylabel('Metric Value',fontsize=12)\n",
    "    plt.show()\n",
    "    \n",
    "# Callback invoked when the sitkMultiResolutionIterationEvent happens, update the index into the \n",
    "# metric_values list. \n",
    "def update_multires_iterations():\n",
    "    global metric_values, multires_iterations\n",
    "    multires_iterations.append(len(metric_values))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Performing Registration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Defininf The Output Folder\n",
    "Output_Registered_image_path='C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/'\n",
    "Output_Registered_label_path='C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/'\n",
    "Output_Registered_transformation_path='C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final metric value: -0.5335141688363023\n",
      "Optimizer's stopping condition, GradientDescentOptimizerv4Template: Convergence checker passed at iteration 9.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d49e3267df847f99c5aa0f920d56fb1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=91, description='image_z', max=182), FloatSlider(value=0.5, description=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1fba8c45bb544702ab8e09d4259ee492",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=91, description='image_z', max=182), FloatSlider(value=0.5, description=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fixed_image = sitk.ReadImage('MNI152_T1_1mm_Brain.nii.gz', sitk.sitkFloat32)\n",
    "for im in mylist:\n",
    "    ###Getting Suvject MRI\n",
    "    img_fn = str(im[1])\n",
    "    label_fn=str(im[2])\n",
    "    img_name = img_fn.split('/')[-1].split('.')[0]\n",
    "    label_name = label_fn.split('/')[-1].split('.')[0]\n",
    "    print(img_name)\n",
    "    print(label_name)\n",
    "    \n",
    "    ########### Output Name & Folder ##########\n",
    "    Registered_imageName=img_name+'.nii.gz'\n",
    "    Registered_labelName=label_name+'.nii.gz'\n",
    "    Transformation_imageName=img_name+'.tfm'\n",
    "    \n",
    "    # =============================================================================\n",
    "    # Loading the data\n",
    "    # =============================================================================\n",
    "    moving_image =  sitk.ReadImage(img_fn, sitk.sitkFloat32)\n",
    "    interact(display_images, fixed_image_z=(0,fixed_image.GetSize()[2]-1), moving_image_z=(0,moving_image.GetSize()[2]-1), fixed_npa = fixed(sitk.GetArrayViewFromImage(fixed_image)), moving_npa=fixed(sitk.GetArrayViewFromImage(moving_image)));\n",
    "    # =============================================================================\n",
    "    # initial Alighment \n",
    "    # =============================================================================\n",
    "    initial_transform = sitk.CenteredTransformInitializer(fixed_image, \n",
    "                                                          moving_image, \n",
    "                                                          sitk.Euler3DTransform(), \n",
    "                                                          sitk.CenteredTransformInitializerFilter.GEOMETRY)\n",
    "\n",
    "    moving_resampled = sitk.Resample(moving_image, fixed_image, initial_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n",
    "\n",
    "    interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n",
    "    # =============================================================================\n",
    "    # Registration\n",
    "    # =============================================================================\n",
    "    registration_method = sitk.ImageRegistrationMethod()\n",
    "\n",
    "    # Similarity metric settings.\n",
    "    registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)\n",
    "    registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)\n",
    "    registration_method.SetMetricSamplingPercentage(0.01)\n",
    "\n",
    "    registration_method.SetInterpolator(sitk.sitkNearestNeighbor)\n",
    "\n",
    "    # Optimizer settings.\n",
    "    registration_method.SetOptimizerAsGradientDescent(learningRate=1.0, numberOfIterations=100, convergenceMinimumValue=1e-6, convergenceWindowSize=10)\n",
    "    registration_method.SetOptimizerScalesFromPhysicalShift()\n",
    "\n",
    "    # Setup for the multi-resolution framework.            \n",
    "    registration_method.SetShrinkFactorsPerLevel(shrinkFactors = [4,2,1])\n",
    "    registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[2,1,0])\n",
    "    registration_method.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()\n",
    "\n",
    "    # Don't optimize in-place, we would possibly like to run this cell multiple times.\n",
    "    registration_method.SetInitialTransform(initial_transform, inPlace=False)\n",
    "\n",
    "    # Connect all of the observers so that we can perform plotting during registration.\n",
    "    registration_method.AddCommand(sitk.sitkStartEvent, start_plot)\n",
    "    registration_method.AddCommand(sitk.sitkEndEvent, end_plot)\n",
    "    registration_method.AddCommand(sitk.sitkMultiResolutionIterationEvent, update_multires_iterations) \n",
    "    registration_method.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration_method))\n",
    "\n",
    "    final_transform = registration_method.Execute(sitk.Cast(fixed_image, sitk.sitkFloat32), \n",
    "                                                  sitk.Cast(moving_image, sitk.sitkFloat32))\n",
    "\n",
    "    # =============================================================================\n",
    "    # post processing Analysis\n",
    "    # =============================================================================\n",
    "    print('Final metric value: {0}'.format(registration_method.GetMetricValue()))\n",
    "    print('Optimizer\\'s stopping condition, {0}'.format(registration_method.GetOptimizerStopConditionDescription()))\n",
    "    #Visualize Expected Results\n",
    "\n",
    "    moving_resampled = sitk.Resample(moving_image, fixed_image, final_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n",
    "\n",
    "    interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n",
    "\n",
    "    sitk.WriteImage(moving_resampled, os.path.join(Output_Registered_image_path, Registered_imageName))\n",
    "    sitk.WriteTransform(final_transform,os.path.join(Output_Registered_transformation_path,Transformation_imageName))\n",
    "    \n",
    "    # =============================================================================\n",
    "    # Label Registration With the Previous Transformation\n",
    "    # =============================================================================\n",
    "    ###Read The label\n",
    "    moving_label=sitk.ReadImage(label_fn, sitk.sitkFloat32)\n",
    "    ###Get the Transformation\n",
    "    transform=final_transform\n",
    "    \n",
    "    ###resampling the labels\n",
    "    moving_resampled_label = sitk.Resample(moving_label, fixed_image, transform, sitk.sitkNearestNeighbor, 0.0, moving_label.GetPixelID())\n",
    "\n",
    "    interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled_label));\n",
    "\n",
    "    sitk.WriteImage(moving_resampled_label, os.path.join(Output_Registered_label_path,Registered_labelName))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Dice Similarity Fuction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def dice_similarity(Seg_img, GT_img,state):\n",
    "    \"\"\"   \n",
    "    Inputs:\n",
    "        Seg_img (numpy.ndarray): Segmented Image.\n",
    "        GT_img (numpy.ndarray): Ground Truth Image.\n",
    "        State: \"nifti\" if the images are nifti file\n",
    "               \"arr\"   if the images are an ndarray\n",
    "    output:\n",
    "        Dice Similarity Coefficient: dice_CSF, dice_GM, dice_WM.\"\"\"\n",
    "    \n",
    "    if (state==\"nifti\"):\n",
    "       segmented_data = Seg_img.get_data().copy()\n",
    "       groundtruth_data = GT_img.get_data().copy()\n",
    "    elif (state==\"arr\"):\n",
    "       segmented_data = Seg_img.copy()\n",
    "       groundtruth_data = GT_img.copy()\n",
    "    \n",
    "    #Calculte DICE\n",
    "    def dice_coefficient(SI,GT):\n",
    "        #   2 * TP / (FN + (2 * TP) + FP)\n",
    "        intersection = np.logical_and(SI, GT)\n",
    "        return 2. * intersection.sum() / (SI.sum() + GT.sum())\n",
    "    \n",
    "    #Dice  for CSF\n",
    "    Seg_CSF = (segmented_data == 1) * 1\n",
    "    GT_CSF = (groundtruth_data == 1) * 1\n",
    "    dice_CSF = dice_coefficient(Seg_CSF, GT_CSF)\n",
    "    #Dice  for GM\n",
    "    Seg_GM = (segmented_data == 2) * 1\n",
    "    GT_GM = (groundtruth_data == 2) * 1\n",
    "    dice_GM = dice_coefficient(Seg_GM, GT_GM)\n",
    "    #Dice  for WM\n",
    "    Seg_WM = (segmented_data == 3) * 1\n",
    "    GT_WM = (groundtruth_data == 3) * 1\n",
    "    dice_WM = dice_coefficient(Seg_WM, GT_WM)\n",
    "    \n",
    "    return dice_CSF, dice_GM, dice_WM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Cheaking inverse registration will work or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IBSR_01_seg.nii.gz', 'IBSR_03_seg.nii.gz', 'IBSR_04_seg.nii.gz', 'IBSR_05_seg.nii.gz', 'IBSR_06_seg.nii.gz', 'IBSR_07_seg.nii.gz', 'IBSR_08_seg.nii.gz', 'IBSR_09_seg.nii.gz', 'IBSR_11_seg.nii.gz', 'IBSR_12_seg.nii.gz', 'IBSR_13_seg.nii.gz', 'IBSR_14_seg.nii.gz', 'IBSR_16_seg.nii.gz', 'IBSR_17_seg.nii.gz', 'IBSR_18_seg.nii.gz']\n"
     ]
    }
   ],
   "source": [
    "My_Registered_label_list=os.listdir(Output_Registered_label_path)\n",
    "print(My_Registered_label_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IBSR_01.tfm', 'IBSR_03.tfm', 'IBSR_04.tfm', 'IBSR_05.tfm', 'IBSR_06.tfm', 'IBSR_07.tfm', 'IBSR_08.tfm', 'IBSR_09.tfm', 'IBSR_11.tfm', 'IBSR_12.tfm', 'IBSR_13.tfm', 'IBSR_14.tfm', 'IBSR_16.tfm', 'IBSR_17.tfm', 'IBSR_18.tfm']\n"
     ]
    }
   ],
   "source": [
    "My_Registered_Trans_File=os.listdir(Output_Registered_transformation_path)\n",
    "print(My_Registered_Trans_File)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#############Loading Results If we Do inverse Registration############### \n",
      "data: IBSR_01\n",
      "CSF DICE = 0.9825621309899767 GM DICE = 0.9912496715782244 WM DICE = 0.9880310024988352\n",
      "############################ \n",
      "data: IBSR_03\n",
      "CSF DICE = 0.9697968972494941 GM DICE = 0.991009179006726 WM DICE = 0.9870380013130924\n",
      "############################ \n",
      "data: IBSR_04\n",
      "CSF DICE = 0.9775035832242787 GM DICE = 0.991514231804458 WM DICE = 0.987651804670913\n",
      "############################ \n",
      "data: IBSR_05\n",
      "CSF DICE = 0.9825619414371867 GM DICE = 0.9900341642668938 WM DICE = 0.9893874286823152\n",
      "############################ \n",
      "data: IBSR_06\n",
      "CSF DICE = 0.9862122788761707 GM DICE = 0.9866776751765345 WM DICE = 0.9871989311140422\n",
      "############################ \n",
      "data: IBSR_07\n",
      "CSF DICE = 0.998586537992884 GM DICE = 0.9994064054436964 WM DICE = 0.9993687508790062\n",
      "############################ \n",
      "data: IBSR_08\n",
      "CSF DICE = 0.9986510373057959 GM DICE = 0.9985595970632075 WM DICE = 0.9984952139443508\n",
      "############################ \n",
      "data: IBSR_09\n",
      "CSF DICE = 0.9968720448097766 GM DICE = 0.9984399123036154 WM DICE = 0.9983404133068171\n",
      "############################ \n",
      "data: IBSR_16\n",
      "CSF DICE = 0.9507329161241548 GM DICE = 0.9824083459155735 WM DICE = 0.9735339391230945\n",
      "############################ \n",
      "data: IBSR_18\n",
      "CSF DICE = 0.9591194295136732 GM DICE = 0.9817603190815379 WM DICE = 0.9746477068175066\n",
      "############################ \n",
      "data: IBSR_11\n",
      "CSF DICE = 0.997624703087886 GM DICE = 0.9985968288140679 WM DICE = 0.9986385574634562\n",
      "############################ \n",
      "data: IBSR_12\n",
      "CSF DICE = 0.9964391691394658 GM DICE = 0.9980941459737718 WM DICE = 0.9977821939864278\n",
      "############################ \n",
      "data: IBSR_13\n",
      "CSF DICE = 0.9747929583201339 GM DICE = 0.9918151106915374 WM DICE = 0.9866391946408345\n",
      "############################ \n",
      "data: IBSR_14\n",
      "CSF DICE = 0.978958385967445 GM DICE = 0.9904732572623514 WM DICE = 0.9878724958249686\n",
      "############################ \n",
      "data: IBSR_17\n",
      "CSF DICE = 0.9666451820818562 GM DICE = 0.9812091136119974 WM DICE = 0.9711705490642771\n",
      "############################ \n"
     ]
    }
   ],
   "source": [
    "print(\"#############Loading Results If we Do inverse Registration############### \")\n",
    "\n",
    "for im in mylist:\n",
    "    ###Getting Suvject MRI\n",
    "    img_fn = str(im[1])\n",
    "    label_fn=str(im[2])\n",
    "    img_name = img_fn.split('/')[-1].split('.')[0]\n",
    "    label_name = label_fn.split('/')[-1].split('.')[0]\n",
    "    \n",
    "    ##The Original Spaced Image\n",
    "    label_Original_fixed=sitk.ReadImage(label_fn, sitk.sitkFloat32)\n",
    "    ###REgisted MNI label\n",
    "    label_Registered_moving=sitk.ReadImage(Output_Registered_label_path+label_name+'.nii.gz', sitk.sitkFloat32)\n",
    "    #print(Output_Registered_label_path+label_name+'.nii.gz')\n",
    "    \n",
    "    ######Load the Transformation\n",
    "    initial_transform_for_InterTransformation=sitk.ReadTransform(Output_Registered_transformation_path+img_name+'.tfm')\n",
    "    inverse_Transformation=initial_transform_for_InterTransformation.GetInverse()\n",
    "    \n",
    "    Original_resampled_label = sitk.Resample(label_Registered_moving, label_Original_fixed, inverse_Transformation, sitk.sitkNearestNeighbor, 0.0, label_Registered_moving.GetPixelID())\n",
    "    \n",
    "    np_img3 = sitk.GetArrayFromImage(Original_resampled_label)\n",
    "    np_img4 = sitk.GetArrayFromImage(label_Original_fixed)\n",
    "    \n",
    "    \n",
    "    print(\"data:\",img_name)\n",
    "    dice_CSF, dice_GM, dice_WM = dice_similarity(np_img3,np_img4,\"arr\")\n",
    "    print(\"CSF DICE = {}\".format(dice_CSF), \"GM DICE = {}\".format(dice_GM), \"WM DICE = {}\".format(dice_WM))\n",
    "    print(\"############################ \")\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Creating the excel File of the Registered Training and Validation Path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_01.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_01_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_01.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_03.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_03_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_03.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_04.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_04_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_04.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_05.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_05_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_05.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_06.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_06_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_06.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_07.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_07_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_07.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_08.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_08_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_08.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_09.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_09_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_09.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_16.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_16_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_16.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_18.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_18_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_18.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_11.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_11_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_11.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_12.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_12_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_12.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_13.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_13_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_13.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_14.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_14_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_14.tfm\n",
      "###############################\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_17.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_17_seg.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_17.tfm\n",
      "###############################\n"
     ]
    }
   ],
   "source": [
    "id_list=[]\n",
    "sub_t1=[]\n",
    "label_list=[]\n",
    "tfm_list=[]\n",
    "\n",
    "\n",
    "for im in mylist:\n",
    "    ###Getting Suvject MRI\n",
    "    img_fn = str(im[1])\n",
    "    label_fn=str(im[2])\n",
    "    img_name = img_fn.split('/')[-1].split('.')[0]\n",
    "    id_list.append(img_name)\n",
    "    label_name = label_fn.split('/')[-1].split('.')[0]\n",
    "    \n",
    "    ######Getting Path\n",
    "    reg_img_path=Output_Registered_image_path+img_name+'.nii.gz'\n",
    "    sub_t1.append(reg_img_path)\n",
    "    \n",
    "    reg_label_path=Output_Registered_label_path+label_name+'.nii.gz'\n",
    "    label_list.append(reg_label_path)\n",
    "    \n",
    "    trans_tfm_file_path=Output_Registered_transformation_path+img_name+'.tfm'\n",
    "    tfm_list.append(trans_tfm_file_path)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    print(reg_img_path)\n",
    "    print(reg_label_path)\n",
    "    print(trans_tfm_file_path)\n",
    "    print(\"###############################\")\n",
    "\n",
    "Inf0_data=pd.DataFrame(list(zip(id_list, sub_t1, label_list,tfm_list)),\n",
    "columns=['id','subj_folder','subj_label','registartion_tmf'])\n",
    "Inf0_data.to_csv(\"MISAPreorocessingReg_info.csv\", encoding='utf-8', index=False)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Registartion For Test Imaes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IBSR_02.nii.gz', 'IBSR_10.nii.gz', 'IBSR_15.nii.gz']\n"
     ]
    }
   ],
   "source": [
    "Test_data_raw_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/\"\n",
    "Test_data_list=os.listdir(Test_data_raw_path)\n",
    "print(Test_data_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_02.nii.gz\n"
     ]
    }
   ],
   "source": [
    "print(Test_data_raw_path+Test_data_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final metric value: -0.3417778050822575\n",
      "Optimizer's stopping condition, GradientDescentOptimizerv4Template: Convergence checker passed at iteration 9.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0222ebe7be7747e8a8a6dfbbf52fde65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=91, description='image_z', max=182), FloatSlider(value=0.5, description=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "REegistered_Test_output_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/\"\n",
    "Registered_Testdata_tmf_file=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/\"\n",
    "\n",
    "fixed_image = sitk.ReadImage('MNI152_T1_1mm_Brain.nii.gz', sitk.sitkFloat32)\n",
    "\n",
    "for im in range(0,3):\n",
    "    \n",
    "    complete_test_img_path=Test_data_raw_path+Test_data_list[im]\n",
    "    \n",
    "    ###Getting Suvject MRI\n",
    "    img_fn = str(complete_test_img_path)\n",
    "    img_name = img_fn.split('/')[-1].split('.')[0]\n",
    "    \n",
    "    print(img_name)\n",
    "    \n",
    "    ########### Output Name & Folder ##########\n",
    "    Registered_imageName=img_name+'.nii.gz'\n",
    "    Transformation_imageName=img_name+'.tfm'\n",
    "    \n",
    "    # =============================================================================\n",
    "    # Loading the data\n",
    "    # =============================================================================\n",
    "    moving_image =  sitk.ReadImage(img_fn, sitk.sitkFloat32)\n",
    "    interact(display_images, fixed_image_z=(0,fixed_image.GetSize()[2]-1), moving_image_z=(0,moving_image.GetSize()[2]-1), fixed_npa = fixed(sitk.GetArrayViewFromImage(fixed_image)), moving_npa=fixed(sitk.GetArrayViewFromImage(moving_image)));\n",
    "    # =============================================================================\n",
    "    # initial Alighment \n",
    "    # =============================================================================\n",
    "    initial_transform = sitk.CenteredTransformInitializer(fixed_image, \n",
    "                                                          moving_image, \n",
    "                                                          sitk.Euler3DTransform(), \n",
    "                                                          sitk.CenteredTransformInitializerFilter.GEOMETRY)\n",
    "\n",
    "    moving_resampled = sitk.Resample(moving_image, fixed_image, initial_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n",
    "\n",
    "    interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n",
    "    # =============================================================================\n",
    "    # Registration\n",
    "    # =============================================================================\n",
    "    registration_method = sitk.ImageRegistrationMethod()\n",
    "\n",
    "    # Similarity metric settings.\n",
    "    registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)\n",
    "    registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)\n",
    "    registration_method.SetMetricSamplingPercentage(0.01)\n",
    "\n",
    "    registration_method.SetInterpolator(sitk.sitkNearestNeighbor)\n",
    "\n",
    "    # Optimizer settings.\n",
    "    registration_method.SetOptimizerAsGradientDescent(learningRate=1.0, numberOfIterations=100, convergenceMinimumValue=1e-6, convergenceWindowSize=10)\n",
    "    registration_method.SetOptimizerScalesFromPhysicalShift()\n",
    "\n",
    "    # Setup for the multi-resolution framework.            \n",
    "    registration_method.SetShrinkFactorsPerLevel(shrinkFactors = [4,2,1])\n",
    "    registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[2,1,0])\n",
    "    registration_method.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()\n",
    "\n",
    "    # Don't optimize in-place, we would possibly like to run this cell multiple times.\n",
    "    registration_method.SetInitialTransform(initial_transform, inPlace=False)\n",
    "\n",
    "    # Connect all of the observers so that we can perform plotting during registration.\n",
    "    registration_method.AddCommand(sitk.sitkStartEvent, start_plot)\n",
    "    registration_method.AddCommand(sitk.sitkEndEvent, end_plot)\n",
    "    registration_method.AddCommand(sitk.sitkMultiResolutionIterationEvent, update_multires_iterations) \n",
    "    registration_method.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration_method))\n",
    "\n",
    "    final_transform = registration_method.Execute(sitk.Cast(fixed_image, sitk.sitkFloat32), \n",
    "                                                  sitk.Cast(moving_image, sitk.sitkFloat32))\n",
    "\n",
    "    # =============================================================================\n",
    "    # post processing Analysis\n",
    "    # =============================================================================\n",
    "    print('Final metric value: {0}'.format(registration_method.GetMetricValue()))\n",
    "    print('Optimizer\\'s stopping condition, {0}'.format(registration_method.GetOptimizerStopConditionDescription()))\n",
    "    #Visualize Expected Results\n",
    "\n",
    "    moving_resampled = sitk.Resample(moving_image, fixed_image, final_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n",
    "\n",
    "    interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n",
    "\n",
    "    sitk.WriteImage(moving_resampled, os.path.join(REegistered_Test_output_path, Registered_imageName))\n",
    "    sitk.WriteTransform(final_transform,os.path.join(Registered_Testdata_tmf_file,Transformation_imageName))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Creating A Excel File For Registered Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IBSR_02\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_02.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_02.tfm\n",
      "###############################\n",
      "IBSR_10\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_10.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_10.tfm\n",
      "###############################\n",
      "IBSR_15\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_15.nii.gz\n",
      "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_15.tfm\n",
      "###############################\n"
     ]
    }
   ],
   "source": [
    "test_id_list=[]\n",
    "test_sub_t1=[]\n",
    "test_tfm_list=[]\n",
    "\n",
    "\n",
    "REegistered_Test_output_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/\"\n",
    "Registered_Testdata_tmf_file=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/\"\n",
    "\n",
    "fixed_image = sitk.ReadImage('MNI152_T1_1mm_Brain.nii.gz', sitk.sitkFloat32)\n",
    "\n",
    "for im in range(0,3):\n",
    "    \n",
    "    complete_test_img_path=Test_data_raw_path+Test_data_list[im]\n",
    "    \n",
    "    ###Getting Suvject MRI\n",
    "    img_fn = str(complete_test_img_path)\n",
    "    img_name = img_fn.split('/')[-1].split('.')[0]\n",
    "    print(img_name)\n",
    "    test_id_list.append(img_name)\n",
    "    \n",
    "\n",
    "    ######Getting Path\n",
    "    reg_img_path=REegistered_Test_output_path+img_name+'.nii.gz'\n",
    "    test_sub_t1.append(reg_img_path)\n",
    "    \n",
    "    \n",
    "    trans_tfm_file_path=Registered_Testdata_tmf_file+img_name+'.tfm'\n",
    "    test_tfm_list.append(trans_tfm_file_path)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    print(reg_img_path)\n",
    "    print(trans_tfm_file_path)\n",
    "    print(\"###############################\")\n",
    "\n",
    "Inf0_data=pd.DataFrame(list(zip(test_id_list, test_sub_t1, test_tfm_list)),\n",
    "columns=['id','subj_folder','registartion_tmf'])\n",
    "Inf0_data.to_csv(\"MISAPreorocessingTestReg_info.csv\", encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}