notebooks/02-interaktive_visualisierung.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interaktive Visualisierung erstellen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### module importieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn import image\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from ipywidgets import interact, interact_manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dateiname des strukturellen Bildes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpr_file = '../data/sub-01_T1w_space-MNI152NLin2009cAsym_preproc.nii.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data/sub-01_T1w_space-MNI152NLin2009cAsym_preproc.nii.gz'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpr_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daten aus Bild auslesen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpr_img = image.load_img(mpr_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nibabel.nifti1.Nifti1Image at 0x7f1e5b305a58>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpr_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Voxelwerte auslesen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpr_data = mpr_img.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 229, 193)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpr_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### eine Schicht auswählen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_slice = mpr_data[100,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### in Tabelle umwandeln"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "one_slice_df = pd.DataFrame(one_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_slice_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Voxelwerte visualisieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e58a9d160>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(one_slice_df.T[::-1],square=True,cmap='Greys_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### interaktive Visualisierung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def viewer_tool(my_slice):\n",
    "    one_slice = mpr_data[my_slice,:,:]\n",
    "    one_slice_df = pd.DataFrame(one_slice)\n",
    "    sns.heatmap(one_slice_df.T[::-1],square=True,cmap='Greys_r')\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f59e528197454d11993da531e2a065dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=96, description='my_slice', max=192), Button(description='Run Interact',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact_manual( viewer_tool, my_slice=(0,192) );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### in allen drei Raumrichtungen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_viewer_tool(x,y,z):\n",
    "    \n",
    "    fig,(ax1,ax2,ax3) = plt.subplots(1,3,figsize=(16,6))\n",
    "    \n",
    "    \n",
    "    one_slice = mpr_data[x,:,:]\n",
    "    \n",
    "    slice_df = pd.DataFrame(one_slice)\n",
    "    sns.heatmap(slice_df.T[::-1],square=True,cmap='Greys_r',cbar=None,ax=ax1)\n",
    "    \n",
    "    ax1.axvline(y)\n",
    "    ax1.axhline(mpr_data.shape[2]-z)\n",
    "    \n",
    "    ax1.set_xticks([])\n",
    "    ax1.set_yticks([])\n",
    "    \n",
    "    \n",
    "    one_slice = mpr_data[:,y,:]\n",
    "    \n",
    "    slice_df = pd.DataFrame(one_slice)\n",
    "    sns.heatmap(slice_df.T[::-1],square=True,cmap='Greys_r',cbar=None,ax=ax2)\n",
    "    \n",
    "    ax2.axvline(x)\n",
    "    ax2.axhline(mpr_data.shape[2]-z)\n",
    "    \n",
    "    ax2.set_xticks([])\n",
    "    ax2.set_yticks([])\n",
    "    \n",
    "    \n",
    "    one_slice = mpr_data[:,:,z]\n",
    "    \n",
    "    slice_df = pd.DataFrame(one_slice)\n",
    "    sns.heatmap(slice_df.T[::-1],square=True,cmap='Greys_r',cbar=None,ax=ax3)\n",
    "    \n",
    "    ax3.axvline(x)\n",
    "    ax3.axhline(mpr_data.shape[1]-y)\n",
    "\n",
    "    ax3.set_xticks([])\n",
    "    ax3.set_yticks([])\n",
    "\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ad3a78bdda24f24bec9bee712e06f99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=96, description='x', max=192), IntSlider(value=114, description='y', max…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact_manual(my_viewer_tool,\n",
    "                x=(0,mpr_data.shape[0]-1),\n",
    "                y=(0,mpr_data.shape[1]-1),\n",
    "                z=(0,mpr_data.shape[2]-1));"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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