notebooks/Regression_Comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import all the necessary packages as well as the module to perform Patch2Self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from model.patch2self import patch2self\n",
    "from dipy.io.image import save_nifti, load_nifti\n",
    "from dipy.data import read_stanford_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To showcase the denoising from Patch2Self we will make use of the Stanford HARDI dataset available as a part of DIPY [https://dipy.org]. The 'get_data()' utility is the fetcher that automatically downloads the dataset and loads the Nifti file as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shrey\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).\n",
      "\n",
      "* deprecated from version: 3.0\n",
      "* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "hardi_img, gtab, labels_img = read_stanford_labels()\n",
    "data = hardi_img.get_data()\n",
    "affine = np.eye(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Patch2Self via Ordinary Least Squares Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 1, 652536)\n",
      "Patch Extraction Done...\n",
      "Training with patch-radius:  [0 0 0]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Denoising Volume  117  Complete...\n",
      " -> Trained to Denoise Volume:  118\n",
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      "Denoising Volume  159  Complete...\n",
      "Time Taken:  2001.6924583911896\n"
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "denoised_sh = patch2self(data, model='ols')\n",
    "t2 = time.time()\n",
    "print('Time Taken: ', t2-t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### Next we will visualize the output of the denoising and plot the residuals using sum of squared differences\n",
    "- #### We show the output for slice 35 and volume 127"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2160x2160 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sli = 35\n",
    "vol = 127\n",
    "orig1 = data[:, :, sli, vol]\n",
    "\n",
    "den1 = denoised_sh[:, :, sli, vol]\n",
    "rms_diff1 = np.sqrt((orig1 - den1) ** 2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(30, 30))\n",
    "ax[0].imshow(orig1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[0].set_title('Original')\n",
    "ax[0].set_axis_off()\n",
    "ax[1].imshow(den1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[1].set_title('Patch2Self')\n",
    "ax[1].set_axis_off()\n",
    "ax[2].imshow(rms_diff1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[2].set_title('Residual')\n",
    "ax[2].set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Patch2Self via Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 1, 652536)\n",
      "Patch Extraction Done...\n",
      "Training with patch-radius:  [0 0 0]\n",
      " -> Trained to Denoise Volume:  0\n",
      "Denoising Volume  0  Complete...\n",
      " -> Trained to Denoise Volume:  1\n",
      "Denoising Volume  1  Complete...\n",
      " -> Trained to Denoise Volume:  2\n",
      "Denoising Volume  2  Complete...\n",
      " -> Trained to Denoise Volume:  3\n",
      "Denoising Volume  3  Complete...\n",
      " -> Trained to Denoise Volume:  4\n",
      "Denoising Volume  4  Complete...\n",
      " -> Trained to Denoise Volume:  5\n",
      "Denoising Volume  5  Complete...\n",
      " -> Trained to Denoise Volume:  6\n",
      "Denoising Volume  6  Complete...\n",
      " -> Trained to Denoise Volume:  7\n",
      "Denoising Volume  7  Complete...\n",
      " -> Trained to Denoise Volume:  8\n",
      "Denoising Volume  8  Complete...\n",
      " -> Trained to Denoise Volume:  9\n",
      "Denoising Volume  9  Complete...\n",
      " -> Trained to Denoise Volume:  10\n",
      "Denoising Volume  10  Complete...\n",
      " -> Trained to Denoise Volume:  11\n",
      "Denoising Volume  11  Complete...\n",
      " -> Trained to Denoise Volume:  12\n",
      "Denoising Volume  12  Complete...\n",
      " -> Trained to Denoise Volume:  13\n",
      "Denoising Volume  13  Complete...\n",
      " -> Trained to Denoise Volume:  14\n",
      "Denoising Volume  14  Complete...\n",
      " -> Trained to Denoise Volume:  15\n",
      "Denoising Volume  15  Complete...\n",
      " -> Trained to Denoise Volume:  16\n",
      "Denoising Volume  16  Complete...\n",
      " -> Trained to Denoise Volume:  17\n",
      "Denoising Volume  17  Complete...\n",
      " -> Trained to Denoise Volume:  18\n",
      "Denoising Volume  18  Complete...\n",
      " -> Trained to Denoise Volume:  19\n",
      "Denoising Volume  19  Complete...\n",
      " -> Trained to Denoise Volume:  20\n",
      "Denoising Volume  20  Complete...\n",
      " -> Trained to Denoise Volume:  21\n",
      "Denoising Volume  21  Complete...\n",
      " -> Trained to Denoise Volume:  22\n",
      "Denoising Volume  22  Complete...\n",
      " -> Trained to Denoise Volume:  23\n",
      "Denoising Volume  23  Complete...\n",
      " -> Trained to Denoise Volume:  24\n",
      "Denoising Volume  24  Complete...\n",
      " -> Trained to Denoise Volume:  25\n",
      "Denoising Volume  25  Complete...\n",
      " -> Trained to Denoise Volume:  26\n",
      "Denoising Volume  26  Complete...\n",
      " -> Trained to Denoise Volume:  27\n",
      "Denoising Volume  27  Complete...\n",
      " -> Trained to Denoise Volume:  28\n",
      "Denoising Volume  28  Complete...\n",
      " -> Trained to Denoise Volume:  29\n",
      "Denoising Volume  29  Complete...\n",
      " -> Trained to Denoise Volume:  30\n",
      "Denoising Volume  30  Complete...\n",
      " -> Trained to Denoise Volume:  31\n",
      "Denoising Volume  31  Complete...\n",
      " -> Trained to Denoise Volume:  32\n",
      "Denoising Volume  32  Complete...\n",
      " -> Trained to Denoise Volume:  33\n",
      "Denoising Volume  33  Complete...\n",
      " -> Trained to Denoise Volume:  34\n",
      "Denoising Volume  34  Complete...\n",
      " -> Trained to Denoise Volume:  35\n",
      "Denoising Volume  35  Complete...\n",
      " -> Trained to Denoise Volume:  36\n",
      "Denoising Volume  36  Complete...\n",
      " -> Trained to Denoise Volume:  37\n",
      "Denoising Volume  37  Complete...\n",
      " -> Trained to Denoise Volume:  38\n",
      "Denoising Volume  38  Complete...\n",
      " -> Trained to Denoise Volume:  39\n",
      "Denoising Volume  39  Complete...\n",
      " -> Trained to Denoise Volume:  40\n",
      "Denoising Volume  40  Complete...\n",
      " -> Trained to Denoise Volume:  41\n",
      "Denoising Volume  41  Complete...\n",
      " -> Trained to Denoise Volume:  42\n",
      "Denoising Volume  42  Complete...\n",
      " -> Trained to Denoise Volume:  43\n",
      "Denoising Volume  43  Complete...\n",
      " -> Trained to Denoise Volume:  44\n",
      "Denoising Volume  44  Complete...\n",
      " -> Trained to Denoise Volume:  45\n",
      "Denoising Volume  45  Complete...\n",
      " -> Trained to Denoise Volume:  46\n",
      "Denoising Volume  46  Complete...\n",
      " -> Trained to Denoise Volume:  47\n",
      "Denoising Volume  47  Complete...\n",
      " -> Trained to Denoise Volume:  48\n",
      "Denoising Volume  48  Complete...\n",
      " -> Trained to Denoise Volume:  49\n",
      "Denoising Volume  49  Complete...\n",
      " -> Trained to Denoise Volume:  50\n",
      "Denoising Volume  50  Complete...\n",
      " -> Trained to Denoise Volume:  51\n",
      "Denoising Volume  51  Complete...\n",
      " -> Trained to Denoise Volume:  52\n",
      "Denoising Volume  52  Complete...\n",
      " -> Trained to Denoise Volume:  53\n",
      "Denoising Volume  53  Complete...\n",
      " -> Trained to Denoise Volume:  54\n",
      "Denoising Volume  54  Complete...\n",
      " -> Trained to Denoise Volume:  55\n",
      "Denoising Volume  55  Complete...\n",
      " -> Trained to Denoise Volume:  56\n",
      "Denoising Volume  56  Complete...\n",
      " -> Trained to Denoise Volume:  57\n",
      "Denoising Volume  57  Complete...\n",
      " -> Trained to Denoise Volume:  58\n",
      "Denoising Volume  58  Complete...\n",
      " -> Trained to Denoise Volume:  59\n",
      "Denoising Volume  59  Complete...\n",
      " -> Trained to Denoise Volume:  60\n",
      "Denoising Volume  60  Complete...\n",
      " -> Trained to Denoise Volume:  61\n",
      "Denoising Volume  61  Complete...\n",
      " -> Trained to Denoise Volume:  62\n",
      "Denoising Volume  62  Complete...\n",
      " -> Trained to Denoise Volume:  63\n",
      "Denoising Volume  63  Complete...\n",
      " -> Trained to Denoise Volume:  64\n",
      "Denoising Volume  64  Complete...\n",
      " -> Trained to Denoise Volume:  65\n",
      "Denoising Volume  65  Complete...\n",
      " -> Trained to Denoise Volume:  66\n",
      "Denoising Volume  66  Complete...\n",
      " -> Trained to Denoise Volume:  67\n",
      "Denoising Volume  67  Complete...\n",
      " -> Trained to Denoise Volume:  68\n",
      "Denoising Volume  68  Complete...\n",
      " -> Trained to Denoise Volume:  69\n",
      "Denoising Volume  69  Complete...\n",
      " -> Trained to Denoise Volume:  70\n",
      "Denoising Volume  70  Complete...\n",
      " -> Trained to Denoise Volume:  71\n",
      "Denoising Volume  71  Complete...\n",
      " -> Trained to Denoise Volume:  72\n",
      "Denoising Volume  72  Complete...\n",
      " -> Trained to Denoise Volume:  73\n",
      "Denoising Volume  73  Complete...\n",
      " -> Trained to Denoise Volume:  74\n",
      "Denoising Volume  74  Complete...\n",
      " -> Trained to Denoise Volume:  75\n",
      "Denoising Volume  75  Complete...\n",
      " -> Trained to Denoise Volume:  76\n",
      "Denoising Volume  76  Complete...\n",
      " -> Trained to Denoise Volume:  77\n",
      "Denoising Volume  77  Complete...\n",
      " -> Trained to Denoise Volume:  78\n",
      "Denoising Volume  78  Complete...\n",
      " -> Trained to Denoise Volume:  79\n",
      "Denoising Volume  79  Complete...\n",
      " -> Trained to Denoise Volume:  80\n",
      "Denoising Volume  80  Complete...\n",
      " -> Trained to Denoise Volume:  81\n",
      "Denoising Volume  81  Complete...\n",
      " -> Trained to Denoise Volume:  82\n",
      "Denoising Volume  82  Complete...\n",
      " -> Trained to Denoise Volume:  83\n",
      "Denoising Volume  83  Complete...\n",
      " -> Trained to Denoise Volume:  84\n",
      "Denoising Volume  84  Complete...\n",
      " -> Trained to Denoise Volume:  85\n",
      "Denoising Volume  85  Complete...\n",
      " -> Trained to Denoise Volume:  86\n",
      "Denoising Volume  86  Complete...\n",
      " -> Trained to Denoise Volume:  87\n",
      "Denoising Volume  87  Complete...\n",
      " -> Trained to Denoise Volume:  88\n",
      "Denoising Volume  88  Complete...\n",
      " -> Trained to Denoise Volume:  89\n",
      "Denoising Volume  89  Complete...\n",
      " -> Trained to Denoise Volume:  90\n",
      "Denoising Volume  90  Complete...\n",
      " -> Trained to Denoise Volume:  91\n",
      "Denoising Volume  91  Complete...\n",
      " -> Trained to Denoise Volume:  92\n",
      "Denoising Volume  92  Complete...\n",
      " -> Trained to Denoise Volume:  93\n",
      "Denoising Volume  93  Complete...\n",
      " -> Trained to Denoise Volume:  94\n",
      "Denoising Volume  94  Complete...\n",
      " -> Trained to Denoise Volume:  95\n",
      "Denoising Volume  95  Complete...\n",
      " -> Trained to Denoise Volume:  96\n",
      "Denoising Volume  96  Complete...\n",
      " -> Trained to Denoise Volume:  97\n",
      "Denoising Volume  97  Complete...\n",
      " -> Trained to Denoise Volume:  98\n",
      "Denoising Volume  98  Complete...\n",
      " -> Trained to Denoise Volume:  99\n",
      "Denoising Volume  99  Complete...\n",
      " -> Trained to Denoise Volume:  100\n",
      "Denoising Volume  100  Complete...\n",
      " -> Trained to Denoise Volume:  101\n",
      "Denoising Volume  101  Complete...\n",
      " -> Trained to Denoise Volume:  102\n",
      "Denoising Volume  102  Complete...\n",
      " -> Trained to Denoise Volume:  103\n",
      "Denoising Volume  103  Complete...\n",
      " -> Trained to Denoise Volume:  104\n",
      "Denoising Volume  104  Complete...\n",
      " -> Trained to Denoise Volume:  105\n",
      "Denoising Volume  105  Complete...\n",
      " -> Trained to Denoise Volume:  106\n",
      "Denoising Volume  106  Complete...\n",
      " -> Trained to Denoise Volume:  107\n",
      "Denoising Volume  107  Complete...\n",
      " -> Trained to Denoise Volume:  108\n",
      "Denoising Volume  108  Complete...\n",
      " -> Trained to Denoise Volume:  109\n",
      "Denoising Volume  109  Complete...\n",
      " -> Trained to Denoise Volume:  110\n",
      "Denoising Volume  110  Complete...\n",
      " -> Trained to Denoise Volume:  111\n",
      "Denoising Volume  111  Complete...\n",
      " -> Trained to Denoise Volume:  112\n",
      "Denoising Volume  112  Complete...\n",
      " -> Trained to Denoise Volume:  113\n",
      "Denoising Volume  113  Complete...\n",
      " -> Trained to Denoise Volume:  114\n",
      "Denoising Volume  114  Complete...\n",
      " -> Trained to Denoise Volume:  115\n",
      "Denoising Volume  115  Complete...\n",
      " -> Trained to Denoise Volume:  116\n",
      "Denoising Volume  116  Complete...\n",
      " -> Trained to Denoise Volume:  117\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Denoising Volume  117  Complete...\n",
      " -> Trained to Denoise Volume:  118\n",
      "Denoising Volume  118  Complete...\n",
      " -> Trained to Denoise Volume:  119\n",
      "Denoising Volume  119  Complete...\n",
      " -> Trained to Denoise Volume:  120\n",
      "Denoising Volume  120  Complete...\n",
      " -> Trained to Denoise Volume:  121\n",
      "Denoising Volume  121  Complete...\n",
      " -> Trained to Denoise Volume:  122\n",
      "Denoising Volume  122  Complete...\n",
      " -> Trained to Denoise Volume:  123\n",
      "Denoising Volume  123  Complete...\n",
      " -> Trained to Denoise Volume:  124\n",
      "Denoising Volume  124  Complete...\n",
      " -> Trained to Denoise Volume:  125\n",
      "Denoising Volume  125  Complete...\n",
      " -> Trained to Denoise Volume:  126\n",
      "Denoising Volume  126  Complete...\n",
      " -> Trained to Denoise Volume:  127\n",
      "Denoising Volume  127  Complete...\n",
      " -> Trained to Denoise Volume:  128\n",
      "Denoising Volume  128  Complete...\n",
      " -> Trained to Denoise Volume:  129\n",
      "Denoising Volume  129  Complete...\n",
      " -> Trained to Denoise Volume:  130\n",
      "Denoising Volume  130  Complete...\n",
      " -> Trained to Denoise Volume:  131\n",
      "Denoising Volume  131  Complete...\n",
      " -> Trained to Denoise Volume:  132\n",
      "Denoising Volume  132  Complete...\n",
      " -> Trained to Denoise Volume:  133\n",
      "Denoising Volume  133  Complete...\n",
      " -> Trained to Denoise Volume:  134\n",
      "Denoising Volume  134  Complete...\n",
      " -> Trained to Denoise Volume:  135\n",
      "Denoising Volume  135  Complete...\n",
      " -> Trained to Denoise Volume:  136\n",
      "Denoising Volume  136  Complete...\n",
      " -> Trained to Denoise Volume:  137\n",
      "Denoising Volume  137  Complete...\n",
      " -> Trained to Denoise Volume:  138\n",
      "Denoising Volume  138  Complete...\n",
      " -> Trained to Denoise Volume:  139\n",
      "Denoising Volume  139  Complete...\n",
      " -> Trained to Denoise Volume:  140\n",
      "Denoising Volume  140  Complete...\n",
      " -> Trained to Denoise Volume:  141\n",
      "Denoising Volume  141  Complete...\n",
      " -> Trained to Denoise Volume:  142\n",
      "Denoising Volume  142  Complete...\n",
      " -> Trained to Denoise Volume:  143\n",
      "Denoising Volume  143  Complete...\n",
      " -> Trained to Denoise Volume:  144\n",
      "Denoising Volume  144  Complete...\n",
      " -> Trained to Denoise Volume:  145\n",
      "Denoising Volume  145  Complete...\n",
      " -> Trained to Denoise Volume:  146\n",
      "Denoising Volume  146  Complete...\n",
      " -> Trained to Denoise Volume:  147\n",
      "Denoising Volume  147  Complete...\n",
      " -> Trained to Denoise Volume:  148\n",
      "Denoising Volume  148  Complete...\n",
      " -> Trained to Denoise Volume:  149\n",
      "Denoising Volume  149  Complete...\n",
      " -> Trained to Denoise Volume:  150\n",
      "Denoising Volume  150  Complete...\n",
      " -> Trained to Denoise Volume:  151\n",
      "Denoising Volume  151  Complete...\n",
      " -> Trained to Denoise Volume:  152\n",
      "Denoising Volume  152  Complete...\n",
      " -> Trained to Denoise Volume:  153\n",
      "Denoising Volume  153  Complete...\n",
      " -> Trained to Denoise Volume:  154\n",
      "Denoising Volume  154  Complete...\n",
      " -> Trained to Denoise Volume:  155\n",
      "Denoising Volume  155  Complete...\n",
      " -> Trained to Denoise Volume:  156\n",
      "Denoising Volume  156  Complete...\n",
      " -> Trained to Denoise Volume:  157\n",
      "Denoising Volume  157  Complete...\n",
      " -> Trained to Denoise Volume:  158\n",
      "Denoising Volume  158  Complete...\n",
      " -> Trained to Denoise Volume:  159\n",
      "Denoising Volume  159  Complete...\n",
      "Time Taken:  485.2488148212433\n"
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "denoised_sh_ridge = patch2self(data, model='ridge')\n",
    "t2 = time.time()\n",
    "print('Time Taken: ', t2-t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the outputs as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2160 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sli = 35\n",
    "vol = 127\n",
    "orig1 = data[:, :, sli, vol]\n",
    "\n",
    "den1 = denoised_sh_ridge[:, :, sli, vol]\n",
    "rms_diff1 = np.sqrt((orig1 - den1) ** 2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(30, 30))\n",
    "ax[0].imshow(orig1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[0].set_title('Original')\n",
    "ax[0].set_axis_off()\n",
    "ax[1].imshow(den1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[1].set_title('Patch2Self')\n",
    "ax[1].set_axis_off()\n",
    "ax[2].imshow(rms_diff1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[2].set_title('Residual')\n",
    "ax[2].set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Patch2Self via Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 1, 652536)\n",
      "Patch Extraction Done...\n",
      "Training with patch-radius:  [0 0 0]\n",
      " -> Trained to Denoise Volume:  0\n",
      "Denoising Volume  0  Complete...\n",
      " -> Trained to Denoise Volume:  1\n",
      "Denoising Volume  1  Complete...\n",
      " -> Trained to Denoise Volume:  2\n",
      "Denoising Volume  2  Complete...\n",
      " -> Trained to Denoise Volume:  3\n",
      "Denoising Volume  3  Complete...\n",
      " -> Trained to Denoise Volume:  4\n",
      "Denoising Volume  4  Complete...\n",
      " -> Trained to Denoise Volume:  5\n",
      "Denoising Volume  5  Complete...\n",
      " -> Trained to Denoise Volume:  6\n",
      "Denoising Volume  6  Complete...\n",
      " -> Trained to Denoise Volume:  7\n",
      "Denoising Volume  7  Complete...\n",
      " -> Trained to Denoise Volume:  8\n",
      "Denoising Volume  8  Complete...\n",
      " -> Trained to Denoise Volume:  9\n",
      "Denoising Volume  9  Complete...\n",
      " -> Trained to Denoise Volume:  10\n",
      "Denoising Volume  10  Complete...\n",
      " -> Trained to Denoise Volume:  11\n",
      "Denoising Volume  11  Complete...\n",
      " -> Trained to Denoise Volume:  12\n",
      "Denoising Volume  12  Complete...\n",
      " -> Trained to Denoise Volume:  13\n",
      "Denoising Volume  13  Complete...\n",
      " -> Trained to Denoise Volume:  14\n",
      "Denoising Volume  14  Complete...\n",
      " -> Trained to Denoise Volume:  15\n",
      "Denoising Volume  15  Complete...\n",
      " -> Trained to Denoise Volume:  16\n",
      "Denoising Volume  16  Complete...\n",
      " -> Trained to Denoise Volume:  17\n",
      "Denoising Volume  17  Complete...\n",
      " -> Trained to Denoise Volume:  18\n",
      "Denoising Volume  18  Complete...\n",
      " -> Trained to Denoise Volume:  19\n",
      "Denoising Volume  19  Complete...\n",
      " -> Trained to Denoise Volume:  20\n",
      "Denoising Volume  20  Complete...\n",
      " -> Trained to Denoise Volume:  21\n",
      "Denoising Volume  21  Complete...\n",
      " -> Trained to Denoise Volume:  22\n",
      "Denoising Volume  22  Complete...\n",
      " -> Trained to Denoise Volume:  23\n",
      "Denoising Volume  23  Complete...\n",
      " -> Trained to Denoise Volume:  24\n",
      "Denoising Volume  24  Complete...\n",
      " -> Trained to Denoise Volume:  25\n",
      "Denoising Volume  25  Complete...\n",
      " -> Trained to Denoise Volume:  26\n",
      "Denoising Volume  26  Complete...\n",
      " -> Trained to Denoise Volume:  27\n",
      "Denoising Volume  27  Complete...\n",
      " -> Trained to Denoise Volume:  28\n",
      "Denoising Volume  28  Complete...\n",
      " -> Trained to Denoise Volume:  29\n",
      "Denoising Volume  29  Complete...\n",
      " -> Trained to Denoise Volume:  30\n",
      "Denoising Volume  30  Complete...\n",
      " -> Trained to Denoise Volume:  31\n",
      "Denoising Volume  31  Complete...\n",
      " -> Trained to Denoise Volume:  32\n",
      "Denoising Volume  32  Complete...\n",
      " -> Trained to Denoise Volume:  33\n",
      "Denoising Volume  33  Complete...\n",
      " -> Trained to Denoise Volume:  34\n",
      "Denoising Volume  34  Complete...\n",
      " -> Trained to Denoise Volume:  35\n",
      "Denoising Volume  35  Complete...\n",
      " -> Trained to Denoise Volume:  36\n",
      "Denoising Volume  36  Complete...\n",
      " -> Trained to Denoise Volume:  37\n",
      "Denoising Volume  37  Complete...\n",
      " -> Trained to Denoise Volume:  38\n",
      "Denoising Volume  38  Complete...\n",
      " -> Trained to Denoise Volume:  39\n",
      "Denoising Volume  39  Complete...\n",
      " -> Trained to Denoise Volume:  40\n",
      "Denoising Volume  40  Complete...\n",
      " -> Trained to Denoise Volume:  41\n",
      "Denoising Volume  41  Complete...\n",
      " -> Trained to Denoise Volume:  42\n",
      "Denoising Volume  42  Complete...\n",
      " -> Trained to Denoise Volume:  43\n",
      "Denoising Volume  43  Complete...\n",
      " -> Trained to Denoise Volume:  44\n",
      "Denoising Volume  44  Complete...\n",
      " -> Trained to Denoise Volume:  45\n",
      "Denoising Volume  45  Complete...\n",
      " -> Trained to Denoise Volume:  46\n",
      "Denoising Volume  46  Complete...\n",
      " -> Trained to Denoise Volume:  47\n",
      "Denoising Volume  47  Complete...\n",
      " -> Trained to Denoise Volume:  48\n",
      "Denoising Volume  48  Complete...\n",
      " -> Trained to Denoise Volume:  49\n",
      "Denoising Volume  49  Complete...\n",
      " -> Trained to Denoise Volume:  50\n",
      "Denoising Volume  50  Complete...\n",
      " -> Trained to Denoise Volume:  51\n",
      "Denoising Volume  51  Complete...\n",
      " -> Trained to Denoise Volume:  52\n",
      "Denoising Volume  52  Complete...\n",
      " -> Trained to Denoise Volume:  53\n",
      "Denoising Volume  53  Complete...\n",
      " -> Trained to Denoise Volume:  54\n",
      "Denoising Volume  54  Complete...\n",
      " -> Trained to Denoise Volume:  55\n",
      "Denoising Volume  55  Complete...\n",
      " -> Trained to Denoise Volume:  56\n",
      "Denoising Volume  56  Complete...\n",
      " -> Trained to Denoise Volume:  57\n",
      "Denoising Volume  57  Complete...\n",
      " -> Trained to Denoise Volume:  58\n",
      "Denoising Volume  58  Complete...\n",
      " -> Trained to Denoise Volume:  59\n",
      "Denoising Volume  59  Complete...\n",
      " -> Trained to Denoise Volume:  60\n",
      "Denoising Volume  60  Complete...\n",
      " -> Trained to Denoise Volume:  61\n",
      "Denoising Volume  61  Complete...\n",
      " -> Trained to Denoise Volume:  62\n",
      "Denoising Volume  62  Complete...\n",
      " -> Trained to Denoise Volume:  63\n",
      "Denoising Volume  63  Complete...\n",
      " -> Trained to Denoise Volume:  64\n",
      "Denoising Volume  64  Complete...\n",
      " -> Trained to Denoise Volume:  65\n",
      "Denoising Volume  65  Complete...\n",
      " -> Trained to Denoise Volume:  66\n",
      "Denoising Volume  66  Complete...\n",
      " -> Trained to Denoise Volume:  67\n",
      "Denoising Volume  67  Complete...\n",
      " -> Trained to Denoise Volume:  68\n",
      "Denoising Volume  68  Complete...\n",
      " -> Trained to Denoise Volume:  69\n",
      "Denoising Volume  69  Complete...\n",
      " -> Trained to Denoise Volume:  70\n",
      "Denoising Volume  70  Complete...\n",
      " -> Trained to Denoise Volume:  71\n",
      "Denoising Volume  71  Complete...\n",
      " -> Trained to Denoise Volume:  72\n",
      "Denoising Volume  72  Complete...\n",
      " -> Trained to Denoise Volume:  73\n",
      "Denoising Volume  73  Complete...\n",
      " -> Trained to Denoise Volume:  74\n",
      "Denoising Volume  74  Complete...\n",
      " -> Trained to Denoise Volume:  75\n",
      "Denoising Volume  75  Complete...\n",
      " -> Trained to Denoise Volume:  76\n",
      "Denoising Volume  76  Complete...\n",
      " -> Trained to Denoise Volume:  77\n",
      "Denoising Volume  77  Complete...\n",
      " -> Trained to Denoise Volume:  78\n",
      "Denoising Volume  78  Complete...\n",
      " -> Trained to Denoise Volume:  79\n",
      "Denoising Volume  79  Complete...\n",
      " -> Trained to Denoise Volume:  80\n",
      "Denoising Volume  80  Complete...\n",
      " -> Trained to Denoise Volume:  81\n",
      "Denoising Volume  81  Complete...\n",
      " -> Trained to Denoise Volume:  82\n",
      "Denoising Volume  82  Complete...\n",
      " -> Trained to Denoise Volume:  83\n",
      "Denoising Volume  83  Complete...\n",
      " -> Trained to Denoise Volume:  84\n",
      "Denoising Volume  84  Complete...\n",
      " -> Trained to Denoise Volume:  85\n",
      "Denoising Volume  85  Complete...\n",
      " -> Trained to Denoise Volume:  86\n",
      "Denoising Volume  86  Complete...\n",
      " -> Trained to Denoise Volume:  87\n",
      "Denoising Volume  87  Complete...\n",
      " -> Trained to Denoise Volume:  88\n",
      "Denoising Volume  88  Complete...\n",
      " -> Trained to Denoise Volume:  89\n",
      "Denoising Volume  89  Complete...\n",
      " -> Trained to Denoise Volume:  90\n",
      "Denoising Volume  90  Complete...\n",
      " -> Trained to Denoise Volume:  91\n",
      "Denoising Volume  91  Complete...\n",
      " -> Trained to Denoise Volume:  92\n",
      "Denoising Volume  92  Complete...\n",
      " -> Trained to Denoise Volume:  93\n",
      "Denoising Volume  93  Complete...\n",
      " -> Trained to Denoise Volume:  94\n",
      "Denoising Volume  94  Complete...\n",
      " -> Trained to Denoise Volume:  95\n",
      "Denoising Volume  95  Complete...\n",
      " -> Trained to Denoise Volume:  96\n",
      "Denoising Volume  96  Complete...\n",
      " -> Trained to Denoise Volume:  97\n",
      "Denoising Volume  97  Complete...\n",
      " -> Trained to Denoise Volume:  98\n",
      "Denoising Volume  98  Complete...\n",
      " -> Trained to Denoise Volume:  99\n",
      "Denoising Volume  99  Complete...\n",
      " -> Trained to Denoise Volume:  100\n",
      "Denoising Volume  100  Complete...\n",
      " -> Trained to Denoise Volume:  101\n",
      "Denoising Volume  101  Complete...\n",
      " -> Trained to Denoise Volume:  102\n",
      "Denoising Volume  102  Complete...\n",
      " -> Trained to Denoise Volume:  103\n",
      "Denoising Volume  103  Complete...\n",
      " -> Trained to Denoise Volume:  104\n",
      "Denoising Volume  104  Complete...\n",
      " -> Trained to Denoise Volume:  105\n",
      "Denoising Volume  105  Complete...\n",
      " -> Trained to Denoise Volume:  106\n",
      "Denoising Volume  106  Complete...\n",
      " -> Trained to Denoise Volume:  107\n",
      "Denoising Volume  107  Complete...\n",
      " -> Trained to Denoise Volume:  108\n",
      "Denoising Volume  108  Complete...\n",
      " -> Trained to Denoise Volume:  109\n",
      "Denoising Volume  109  Complete...\n",
      " -> Trained to Denoise Volume:  110\n",
      "Denoising Volume  110  Complete...\n",
      " -> Trained to Denoise Volume:  111\n",
      "Denoising Volume  111  Complete...\n",
      " -> Trained to Denoise Volume:  112\n",
      "Denoising Volume  112  Complete...\n",
      " -> Trained to Denoise Volume:  113\n",
      "Denoising Volume  113  Complete...\n",
      " -> Trained to Denoise Volume:  114\n",
      "Denoising Volume  114  Complete...\n",
      " -> Trained to Denoise Volume:  115\n",
      "Denoising Volume  115  Complete...\n",
      " -> Trained to Denoise Volume:  116\n",
      "Denoising Volume  116  Complete...\n",
      " -> Trained to Denoise Volume:  117\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Denoising Volume  117  Complete...\n",
      " -> Trained to Denoise Volume:  118\n",
      "Denoising Volume  118  Complete...\n",
      " -> Trained to Denoise Volume:  119\n",
      "Denoising Volume  119  Complete...\n",
      " -> Trained to Denoise Volume:  120\n",
      "Denoising Volume  120  Complete...\n",
      " -> Trained to Denoise Volume:  121\n",
      "Denoising Volume  121  Complete...\n",
      " -> Trained to Denoise Volume:  122\n",
      "Denoising Volume  122  Complete...\n",
      " -> Trained to Denoise Volume:  123\n",
      "Denoising Volume  123  Complete...\n",
      " -> Trained to Denoise Volume:  124\n",
      "Denoising Volume  124  Complete...\n",
      " -> Trained to Denoise Volume:  125\n",
      "Denoising Volume  125  Complete...\n",
      " -> Trained to Denoise Volume:  126\n",
      "Denoising Volume  126  Complete...\n",
      " -> Trained to Denoise Volume:  127\n",
      "Denoising Volume  127  Complete...\n",
      " -> Trained to Denoise Volume:  128\n",
      "Denoising Volume  128  Complete...\n",
      " -> Trained to Denoise Volume:  129\n",
      "Denoising Volume  129  Complete...\n",
      " -> Trained to Denoise Volume:  130\n",
      "Denoising Volume  130  Complete...\n",
      " -> Trained to Denoise Volume:  131\n",
      "Denoising Volume  131  Complete...\n",
      " -> Trained to Denoise Volume:  132\n",
      "Denoising Volume  132  Complete...\n",
      " -> Trained to Denoise Volume:  133\n",
      "Denoising Volume  133  Complete...\n",
      " -> Trained to Denoise Volume:  134\n",
      "Denoising Volume  134  Complete...\n",
      " -> Trained to Denoise Volume:  135\n",
      "Denoising Volume  135  Complete...\n",
      " -> Trained to Denoise Volume:  136\n",
      "Denoising Volume  136  Complete...\n",
      " -> Trained to Denoise Volume:  137\n",
      "Denoising Volume  137  Complete...\n",
      " -> Trained to Denoise Volume:  138\n",
      "Denoising Volume  138  Complete...\n",
      " -> Trained to Denoise Volume:  139\n",
      "Denoising Volume  139  Complete...\n",
      " -> Trained to Denoise Volume:  140\n",
      "Denoising Volume  140  Complete...\n",
      " -> Trained to Denoise Volume:  141\n",
      "Denoising Volume  141  Complete...\n",
      " -> Trained to Denoise Volume:  142\n",
      "Denoising Volume  142  Complete...\n",
      " -> Trained to Denoise Volume:  143\n",
      "Denoising Volume  143  Complete...\n",
      " -> Trained to Denoise Volume:  144\n",
      "Denoising Volume  144  Complete...\n",
      " -> Trained to Denoise Volume:  145\n",
      "Denoising Volume  145  Complete...\n",
      " -> Trained to Denoise Volume:  146\n",
      "Denoising Volume  146  Complete...\n",
      " -> Trained to Denoise Volume:  147\n",
      "Denoising Volume  147  Complete...\n",
      " -> Trained to Denoise Volume:  148\n",
      "Denoising Volume  148  Complete...\n",
      " -> Trained to Denoise Volume:  149\n",
      "Denoising Volume  149  Complete...\n",
      " -> Trained to Denoise Volume:  150\n",
      "Denoising Volume  150  Complete...\n",
      " -> Trained to Denoise Volume:  151\n",
      "Denoising Volume  151  Complete...\n",
      " -> Trained to Denoise Volume:  152\n",
      "Denoising Volume  152  Complete...\n",
      " -> Trained to Denoise Volume:  153\n",
      "Denoising Volume  153  Complete...\n",
      " -> Trained to Denoise Volume:  154\n",
      "Denoising Volume  154  Complete...\n",
      " -> Trained to Denoise Volume:  155\n",
      "Denoising Volume  155  Complete...\n",
      " -> Trained to Denoise Volume:  156\n",
      "Denoising Volume  156  Complete...\n",
      " -> Trained to Denoise Volume:  157\n",
      "Denoising Volume  157  Complete...\n",
      " -> Trained to Denoise Volume:  158\n",
      "Denoising Volume  158  Complete...\n",
      " -> Trained to Denoise Volume:  159\n",
      "Denoising Volume  159  Complete...\n",
      "Time Taken:  2487.2162415981293\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "t1 = time.time()\n",
    "denoised_sh_lasso = patch2self(data, model='lasso')\n",
    "t2 = time.time()\n",
    "print('Time Taken: ', t2-t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2160 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sli = 35\n",
    "vol = 127\n",
    "orig1 = data[:, :, sli, vol]\n",
    "\n",
    "den1 = denoised_sh_lasso[:, :, sli, vol]\n",
    "rms_diff1 = np.sqrt((orig1 - den1) ** 2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(30, 30))\n",
    "ax[0].imshow(orig1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[0].set_title('Original')\n",
    "ax[0].set_axis_off()\n",
    "ax[1].imshow(den1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[1].set_title('Patch2Self')\n",
    "ax[1].set_axis_off()\n",
    "ax[2].imshow(rms_diff1.T, cmap='gray', origin='lower', interpolation='None')\n",
    "ax[2].set_title('Residual')\n",
    "ax[2].set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the denoised data from the `Regression_MLP` Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "denoised_sh_mlp, _ = load_nifti('denoised_StanfordHardi_p2s_mlp.nii.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing the self-supervised loss for each of the 160 volumes of the Stanford HARDI dataset for each of the regression models used above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "self_supervised_loss_OLS = []\n",
    "for i in range(0, data.shape[3]):\n",
    "    loss = np.mean((data[..., i] - denoised_sh[..., i])**2)\n",
    "    self_supervised_loss_OLS.append(loss.mean())\n",
    "    \n",
    "self_supervised_loss_ridge = []\n",
    "for i in range(0, data.shape[3]):\n",
    "    loss = np.mean((data[..., i] - denoised_sh_ridge[..., i])**2)\n",
    "    self_supervised_loss_ridge.append(loss.mean())\n",
    "    \n",
    "self_supervised_loss_lasso = []\n",
    "for i in range(0, data.shape[3]):\n",
    "    loss = np.mean((data[..., i] - denoised_sh_lasso[..., i])**2)\n",
    "    self_supervised_loss_lasso.append(loss.mean())\n",
    "    \n",
    "self_supervised_loss_mlp = []\n",
    "for i in range(0, data.shape[3]):\n",
    "    loss = np.mean((data[..., i] - denoised_sh_mlp[..., i])**2)\n",
    "    self_supervised_loss_mlp.append(loss.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making a dataframe of all losses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\"Volume\":np.arange(160), # 160 because the Stanford HARDI dataset has 160 volumes\n",
    "                   \"OLS\":self_supervised_loss_OLS, \n",
    "                   \"Ridge\":self_supervised_loss_ridge,\n",
    "                   \"Lasso\":self_supervised_loss_lasso,\n",
    "                   \"Multi-Layer Perceptron\":self_supervised_loss_mlp}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Line plots corresponding to each regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29e90760508>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.plot(x=\"Volume\", y=[\"OLS\", \"Ridge\", \"Lasso\", \"Multi-Layer Perceptron\"], kind=\"line\")\n",
    "plt.legend(fontsize='xx-large')"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}