ReScience.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "os.getcwd()\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "from mpl_toolkits.axes_grid1 import AxesGrid\n",
    "\n",
    "def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name=\"ternary_centered\"):\n",
    "    \"\"\"\n",
    "    Function to offset the “center” of a colormap. Useful for\n",
    "    data with a negative min and positive max and you want the\n",
    "    middle of the colormap’s dynamic range to be at zero\n",
    "    Input\n",
    "    -----\n",
    "      cmap : The matplotlib colormap to be altered\n",
    "      start : Offset from lowest point in the colormap’s range.\n",
    "          Defaults to 0.0 (no lower ofset). Should be between\n",
    "          0.0 and `midpoint`.\n",
    "      midpoint : The new center of the colormap. Defaults to\n",
    "          0.5 (no shift). Should be between 0.0 and 1.0. In\n",
    "          general, this should be  1 - vmax/(vmax + abs(vmin))\n",
    "          For example if your data range from -15.0 to +5.0 and\n",
    "          you want the center of the colormap at 0.0, `midpoint`\n",
    "          should be set to  1 - 5/(5 + 15)) or 0.75\n",
    "      stop : Offset from highets point in the colormap’s range.\n",
    "          Defaults to 1.0 (no upper ofset). Should be between\n",
    "          `midpoint` and 1.0.\"\"\"\n",
    "    cdict = {\n",
    "        \"red\": [],\n",
    "        \"green\": [],\n",
    "        \"blue\": [],\n",
    "        \"alpha\": []\n",
    "    }\n",
    "    # regular index to compute the colors\n",
    "    reg_index = np.linspace(start, stop, 257)\n",
    "    # shifted index to match the data\n",
    "    shift_index = np.hstack([\n",
    "        np.linspace(start, midpoint, 128, endpoint=False),\n",
    "        np.linspace(midpoint, stop, 129, endpoint=True)\n",
    "    ])\n",
    "    for ri, si in zip(reg_index, shift_index):\n",
    "        r, g, b, a = cmap(ri)\n",
    "        cdict[\"red\"].append((si, r, r))\n",
    "        cdict[\"green\"].append((si, g, g))\n",
    "        cdict[\"blue\"].append((si, b, b))\n",
    "        cdict[\"alpha\"].append((si, a, a))\n",
    "    newcmap = colors.LinearSegmentedColormap(name, cdict)\n",
    "    return newcmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for tp in timepoint:\n",
    "    Cor = Image.open(\"../Data_ReScience/iCPu/Predicted_VS_Path_MPI{}_iCPu.png\".format(tp))\n",
    "    image.append(Cor)\n",
    "\n",
    "rnd_seed = Image.open(\"../Data_ReScience/iCPu/Random_Seed.png\")\n",
    "rnd_patho = Image.open(\"../Data_ReScience/iCPu/Random_Patho_iCPu.png\")\n",
    "rnd_adj = Image.open(\"../Data_ReScience/iCPu/Random_Adja_iCPu.png\")\n",
    "\n",
    "fig= plt.figure()\n",
    "G = gridspec.GridSpec(nrows=6, ncols=6)\n",
    "plt.tight_layout()\n",
    "V = gridspec.GridSpec(nrows=6, ncols=6)\n",
    "plt.tight_layout()\n",
    "\n",
    "axes_1 = fig.add_subplot(G[0:4, 0:2])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.annotate(\"r = 0.56\",(1475,1400), fontsize=6, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2 = fig.add_subplot(G[0:4,2:4])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"B\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.annotate(\"r = 0.69\",(1475,1400), fontsize=6, color=\"black\")\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3 = fig.add_subplot(G[0:4, 4:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"C\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.annotate(\"r = 0.65\",(1475,1400), fontsize=6, color=\"black\")\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4 = fig.add_subplot(V[3:7,0:2])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"D\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(rnd_seed)\n",
    "\n",
    "axes_5 = fig.add_subplot(V[3:7,2:4])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"E\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(rnd_patho)\n",
    "\n",
    "\n",
    "axes_6 = fig.add_subplot(V[3:7,4:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"F\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(rnd_adj)\n",
    "\n",
    "\n",
    "plt.savefig('../Data_ReScience/iCPu/Fig2.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/iCPu/Fig2.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for tp in timepoint:\n",
    "    Cor = Image.open(\"../Data_ReScience/SNCA/Predicted_VS_Path_MPI{}_iCPu_SNCA.png\".format(tp))\n",
    "    image.append(Cor)\n",
    "    \n",
    "fig = plt.figure()\n",
    "G = gridspec.GridSpec(nrows=3, ncols=6)\n",
    "\n",
    "axes_1 = fig.add_subplot(G[0:4, 0:2])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.annotate(\"r = 0.58\",(1400,1400), fontsize=6, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2 = fig.add_subplot(G[0:4, 2:4])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"B\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.annotate(\"r = 0.72\",(1400,1400), fontsize=6, color=\"black\")\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3 = fig.add_subplot(G[0:4, 4:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.annotate(\"C\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.annotate(\"r = 0.74\",(1400,1400), fontsize=6, color=\"black\")\n",
    "plt.imshow(image[2])\n",
    "\n",
    "plt.savefig('../Data_ReScience/SNCA/Fig4.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/SNCA/Fig4.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Figure 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##     a) Piriform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI1_Breg_2.1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI1_Breg_1.08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">4.</span>5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 1/3 [01:20<02:40, 80.17s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI3_Breg_2.1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI3_Breg_1.08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">4.</span>5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 2/3 [02:40<01:20, 80.09s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI6_Breg_2.1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI6_Breg_1.08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Pir_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">4.</span>5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [03:58<00:00, 79.56s/it]\n"
     ]
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "cmap= plt.get_cmap(\"Reds\")\n",
    "data_pir = pd.read_csv('../output/predicted_pathology_iPir.csv')\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "\n",
    "data_pir.head()\n",
    "\n",
    "# Matching with BrainRender\n",
    "for r in data_pir.index:\n",
    "    \n",
    "    names = reg_names[reg_names['ROInames'] == data_pir.loc[r, 'regions']]['BrainR']\n",
    "    \n",
    "    if len(names) > 1:\n",
    "         data_pir.loc[r, 'br_regions'] = list(names)[0]\n",
    "    elif len(names) == 1:    \n",
    "        data_pir.loc[r, 'br_regions'] = names.values\n",
    "        \n",
    "for r in data_pir.index:\n",
    "    \n",
    "    if data_pir.loc[r, 'regions'][0] == 'i':\n",
    "        data_pir.loc[r, 'hem'] = 'right'\n",
    "    elif data_pir.loc[r, 'regions'][0] == 'c':\n",
    "        data_pir.loc[r, 'hem'] = 'left' \n",
    "        \n",
    "# No midpoint required ==> COntinous cmap\n",
    "max_Pir= np.max(data_pir['MPI6'])\n",
    "min_Pir = np.min(data_pir['MPI1'])\n",
    "\n",
    "#Normalisation\n",
    "data_pir['MPI6'] = (data_pir['MPI6'] - min_Pir) / (max_Pir - min_Pir)\n",
    "data_pir['MPI3'] = (data_pir['MPI3'] - min_Pir) / (max_Pir - min_Pir)\n",
    "data_pir['MPI1'] = (data_pir['MPI1'] - min_Pir) / (max_Pir - min_Pir)\n",
    "data_pir = data_pir.dropna()\n",
    "\n",
    "data_pir.head()\n",
    "\n",
    "from brainrender import Scene, atlas\n",
    "from tqdm import tqdm\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "\n",
    "timepoints = [1,3,6]\n",
    "\n",
    "for t in tqdm(timepoints):\n",
    "    \n",
    "    for section in list_coor:\n",
    "        bregma = np.array([5400,  # AP    \n",
    "              0,  # DV    \n",
    "              5700,]) # ML\n",
    "\n",
    "        bregma[0] = bregma[0] + section\n",
    "\n",
    "        # Create a brainrender scene\n",
    "        scene = Scene(inset=False,screenshots_folder=\"../Screenshot_Brainrender/PIR_MPIs\")\n",
    "        frontal_camera = {\n",
    "             'pos': (-19781, 3777, -5832),\n",
    "             'viewup': (0, -1, 0),\n",
    "             'clippingRange': (24465, 26836),\n",
    "            'focalPoint': (5705, 3991, -5691),\n",
    "             'distance': 25487}\n",
    "\n",
    "        # Add brain regions\n",
    "        for r in data_pir.index:\n",
    "\n",
    "            region_to_plot = data_pir.loc[r, \"br_regions\"]\n",
    "            color = cmap(100*data_pir.loc[r, 'MPI{}'.format(t)])[:3]\n",
    "            alpha =  cmap(data_pir.loc[r, 'MPI{}'.format(t)])[:3]\n",
    "            scene.add_brain_region(region_to_plot, color=color, alpha=1, hemisphere=data_pir.loc[r, 'hem'], silhouette=True) #alpha=data_cpu.loc[r, 'MPI{}'.format(t)]\n",
    "        \n",
    "        #scene.add_brain_region(\"PIR\", color=\"blue\", alpha=1, hemisphere='right', silhouette=True)\n",
    "\n",
    "\n",
    "        plane_1 = scene.atlas.get_plane(pos=bregma+50, plane=\"frontal\", norm=(-1, 0, 0), alpha=1, color=\"white\") # Thickness: 150 um\n",
    "        plane_2 = scene.atlas.get_plane(pos=bregma-50, plane=\"frontal\", norm=(1, 0, 0), alpha=1, color=\"white\")\n",
    "        scene.slice(plane_1, close_actors=True)\n",
    "        scene.slice(plane_2, close_actors=True)\n",
    "\n",
    "\n",
    "        brainrender.settings.SHADER_STYLE = \"cartoon\" #[metallic, plastic, shiny, glossy]\n",
    "        brainrender.settings.ROOT_ALPHA = 0.17\n",
    "        brainrender.settings.LW =2.4\n",
    "        brainrender.settings.ROOT_COLOR  = [0.5019, 0.5019, 0.5019]\n",
    "        brainrender.settings.OFFSCREEN = True\n",
    "        brainrender.settings.SHOW_AXES = False\n",
    "        brainrender.settings.BACKGROUND_COLOR = \"white\"\n",
    "        scene.render(atlas_name='allen_mouse_50um', camera= frontal_camera, zoom=1.5)\n",
    "        scene.screenshot(name=\"50um_Pir_MPI{}_Breg_{}_mm\".format(t,-section/1000), scale = 3)\n",
    "        scene.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for tp in timepoint:\n",
    "    for section in list_coor:\n",
    "        Cor = Image.open(\"../Screenshot_Brainrender\\PIR_MPIs/50um_Pir_MPI{}_Breg_{}_mm.png\".format(tp,-section/1000))\n",
    "        image.append(Cor)\n",
    "        \n",
    "fig = plt.figure()\n",
    "G = gridspec.GridSpec(nrows=15, ncols=12, wspace=0.2)\n",
    "\n",
    "\n",
    "axes_1_1 = fig.add_subplot(G[0:3, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2_1 = fig.add_subplot(G[3:6, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3_1 = fig.add_subplot(G[6:9, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4_1 = fig.add_subplot(G[9:12, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[3])\n",
    "\n",
    "axes_5_1 = fig.add_subplot(G[12:15, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[4])\n",
    "\n",
    "\n",
    "###############\n",
    "\n",
    "\n",
    "axes_1_3 = fig.add_subplot(G[0:3, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.text(3000,-100,\"MPI 3\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[5])\n",
    "\n",
    "axes_2_3 = fig.add_subplot(G[3:6, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[6])\n",
    "\n",
    "axes_3_3 = fig.add_subplot(G[6:9, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[7])\n",
    "\n",
    "axes_4_3 = fig.add_subplot(G[9:12, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[8])\n",
    "\n",
    "axes_5_3 = fig.add_subplot(G[12:15, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[9])\n",
    "\n",
    "\n",
    "###############\n",
    "\n",
    "axes_1_6 = fig.add_subplot(G[0:3, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.text(3000,-100,\"MPI 6\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[10])\n",
    "\n",
    "axes_2_6 = fig.add_subplot(G[3:6, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[11])\n",
    "\n",
    "axes_3_6 = fig.add_subplot(G[6:9, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[12])\n",
    "\n",
    "axes_4_6 = fig.add_subplot(G[9:12, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[13])\n",
    "\n",
    "axes_5_6 = fig.add_subplot(G[12:15, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[14])\n",
    "\n",
    "plt.savefig('../Data_ReScience/Pir SN/Pir.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/Pir SN/Pir.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) SN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_1.36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_2.82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_4.5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 1/3 [00:54<01:48, 54.43s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_1.36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_2.82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_4.5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 2/3 [01:53<00:55, 55.76s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_1.36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_2.82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_4.5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [02:51<00:00, 57.16s/it]\n"
     ]
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "from tqdm import tqdm\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "cmap = plt.get_cmap('Reds')\n",
    "\n",
    "data_sn = pd.read_csv('../output/predicted_pathology_iSN.csv')\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "\n",
    "data_sn.head()\n",
    "\n",
    "# Matching with BrainRender\n",
    "for r in data_sn.index:\n",
    "    \n",
    "    names = reg_names[reg_names['ROInames'] == data_sn.loc[r, 'regions']]['BrainR']\n",
    "    \n",
    "    if len(names) > 1:\n",
    "         data_sn.loc[r, 'br_regions'] = list(names)[0]\n",
    "    elif len(names) == 1:    \n",
    "        data_sn.loc[r, 'br_regions'] = names.values\n",
    "        \n",
    "for r in data_sn.index:\n",
    "    \n",
    "    if data_sn.loc[r, 'regions'][0] == 'i':\n",
    "        data_sn.loc[r, 'hem'] = 'right'\n",
    "    elif data_sn.loc[r, 'regions'][0] == 'c':\n",
    "        data_sn.loc[r, 'hem'] = 'left' \n",
    "        \n",
    "# Normalization using Max and Min - Seed Pir\n",
    "max_SN= np.max(data_sn['MPI6'])\n",
    "min_SN = np.min(data_sn['MPI1'])\n",
    "\n",
    "data_sn['MPI6'] = (data_sn['MPI6'] - min_SN) / (max_SN - min_SN)\n",
    "data_sn['MPI3'] = (data_sn['MPI3'] - min_SN) / (max_SN - min_SN)\n",
    "data_sn['MPI1'] = (data_sn['MPI1'] - min_SN) / (max_SN - min_SN)\n",
    "data_sn = data_sn.dropna()\n",
    "\n",
    "data_sn.head()\n",
    "#list_coor = [-1700, 0, +2700, +3000, +4040] # Bregma corresponding coordinates\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "\n",
    "timepoints = [1,3,6]\n",
    "for t in tqdm(timepoints):\n",
    "    \n",
    "    for section in list_coor:\n",
    "        bregma = np.array([5400,  # AP    \n",
    "              0,  # DV    \n",
    "              5700,]) # ML\n",
    "\n",
    "        bregma[0] = bregma[0] + section\n",
    "\n",
    "        # Create a brainrender scene\n",
    "        scene = Scene(inset=False,screenshots_folder=\"../Screenshot_Brainrender/SN_MPIs\")\n",
    "        frontal_camera = {\n",
    "             'pos': (-19781, 3777, -5832),\n",
    "             'viewup': (0, -1, 0),\n",
    "             'clippingRange': (25465, 26836),\n",
    "            'focalPoint': (5705, 3991, -5691),\n",
    "             'distance': 25487}\n",
    "\n",
    "        # Add brain regions\n",
    "        for r in data_sn.index:\n",
    "\n",
    "            region_to_plot = data_sn.loc[r, \"br_regions\"]\n",
    "            color = cmap(data_sn.loc[r, 'MPI{}'.format(t)])[:3]\n",
    "            alpha =  cmap(data_sn.loc[r, 'MPI{}'.format(t)])[:3]\n",
    "            scene.add_brain_region(region_to_plot, color=color, alpha=1, hemisphere=data_sn.loc[r, 'hem'], silhouette=True) #alpha=data_cpu.loc[r, 'MPI{}'.format(t)]\n",
    "\n",
    "\n",
    "        plane_1 = scene.atlas.get_plane(pos=bregma+50, plane=\"frontal\", norm=(-1, 0, 0), alpha=1, color=\"white\") # Thickness: 150 um\n",
    "        plane_2 = scene.atlas.get_plane(pos=bregma-50, plane=\"frontal\", norm=(1, 0, 0), alpha=1, color=\"white\")\n",
    "        scene.slice(plane_1, close_actors=True)\n",
    "        scene.slice(plane_2, close_actors=True)\n",
    "\n",
    "\n",
    "        brainrender.settings.SHADER_STYLE = \"cartoon\" #[metallic, plastic, shiny, glossy]\n",
    "        brainrender.settings.ROOT_ALPHA = 0.17\n",
    "        brainrender.settings.LW =2.4\n",
    "        brainrender.settings.ROOT_COLOR  = [0.4, 0.4, 0.4]\n",
    "        brainrender.settings.OFFSCREEN = True\n",
    "        brainrender.settings.SHOW_AXES = False\n",
    "        brainrender.settings.BACKGROUND_COLOR = \"white\"\n",
    "        scene.render(atlas_name='allen_mouse_50um', camera= frontal_camera, zoom=1.5)\n",
    "        scene.screenshot(name=\"50um_SNc_MPI{}_Breg_{}_mm\".format(t,section/1000), scale = 3)\n",
    "        img_totranspose = Image.open(\"../Screenshot_Brainrender\\SN_MPIs/50um_SNc_MPI{}_Breg_{}_mm.png\".format(t,section/1000))\n",
    "        img_totranspose = img_totranspose.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "        img_totranspose.save(\"../Screenshot_Brainrender\\SN_MPIs/50um_SNc_MPI{}_Breg_{}_mm.png\".format(t,section/1000))\n",
    "#         title=\"-Coronal section Bregma {} mm -50um thickness- Seed = SN - MPI {}\".format(-section/1000,t),\n",
    "        scene.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for tp in timepoint:\n",
    "    for section in list_coor:\n",
    "        Cor = Image.open(\"../Screenshot_Brainrender/SN_MPIs/50um_SNc_MPI{}_Breg_{}_mm.png\".format(tp,section/1000))\n",
    "        image.append(Cor)\n",
    "        \n",
    "fig = plt.figure()\n",
    "\n",
    "G = gridspec.GridSpec(nrows=15, ncols=12, wspace=0.2)\n",
    "\n",
    "\n",
    "axes_1_1 = fig.add_subplot(G[0:3, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2_1 = fig.add_subplot(G[3:6, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3_1 = fig.add_subplot(G[6:9, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4_1 = fig.add_subplot(G[9:12, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[3])\n",
    "\n",
    "axes_5_1 = fig.add_subplot(G[12:15, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[4])\n",
    "\n",
    "\n",
    "###############\n",
    "\n",
    "\n",
    "axes_1_3 = fig.add_subplot(G[0:3, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.text(3000,-100,\"MPI 3\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[5])\n",
    "\n",
    "axes_2_3 = fig.add_subplot(G[3:6, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[6])\n",
    "\n",
    "axes_3_3 = fig.add_subplot(G[6:9, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[7])\n",
    "\n",
    "axes_4_3 = fig.add_subplot(G[9:12, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[8])\n",
    "\n",
    "axes_5_3 = fig.add_subplot(G[12:15, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[9])\n",
    "\n",
    "\n",
    "###############\n",
    "\n",
    "axes_1_6 = fig.add_subplot(G[0:3, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.text(3000,-100,\"MPI 6\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[10])\n",
    "\n",
    "axes_2_6 = fig.add_subplot(G[3:6, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[11])\n",
    "\n",
    "axes_3_6 = fig.add_subplot(G[6:9, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[12])\n",
    "\n",
    "axes_4_6 = fig.add_subplot(G[9:12, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[13])\n",
    "\n",
    "axes_5_6 = fig.add_subplot(G[12:15, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[14])\n",
    "\n",
    "plt.savefig('../Data_ReScience/Pir SN/SN.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/Pir SN/SN.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "cmap = plt.get_cmap('Reds')\n",
    "\n",
    "def grayscale_cmap(cmap):\n",
    "    \"\"\"Return a grayscale version of the given colormap\"\"\"\n",
    "    cmap = plt.cm.get_cmap(cmap)\n",
    "    colors = cmap(np.arange(cmap.N))\n",
    "    \n",
    "    # convert RGBA to perceived grayscale luminance\n",
    "    # cf. http://alienryderflex.com/hsp.html\n",
    "    RGB_weight = [0.299, 0.587, 0.114]\n",
    "    luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))\n",
    "    colors[:, :3] = luminance[:, np.newaxis]\n",
    "        \n",
    "    return LinearSegmentedColormap.from_list(cmap.name + \"_gray\", colors, cmap.N)\n",
    "    \n",
    "\n",
    "def view_colormap(cmap):\n",
    "    \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n",
    "    cmap = plt.cm.get_cmap(cmap)\n",
    "    colors = cmap(np.arange(cmap.N))\n",
    "    \n",
    "    cmap = grayscale_cmap(cmap)\n",
    "    grayscale = cmap(np.arange(cmap.N))\n",
    "    plt.figure(figsize=(15,3))\n",
    "    plt.imshow([colors], extent=[0, 10, 0, 1])\n",
    "    plt.xticks([0, 2.5, 5, 7.5 ,10], ['0', '0.25', '0.5','0.75','1'], fontsize=30)\n",
    "    plt.yticks([])\n",
    "    plt.text(-0.8,1.1,'Estimated percentage of area with \\u03B1-synuclein pathology', fontsize=35)\n",
    "    #plt.text(10.5,0.6,'Estimated percentage of area with \\u03B1-synuclein')\n",
    "    plt.savefig(\"../Screenshot_Brainrender\\SN_MPIs/colormap.png\",dpi=300)\n",
    "    plt.savefig(\"../Screenshot_Brainrender\\SN_MPIs/colormap.pdf\",dpi=300)\n",
    "    plt.savefig(\"../Screenshot_Brainrender\\PIR_MPIs/colormap.png\",dpi=300)\n",
    "    plt.savefig(\"../Screenshot_Brainrender\\PIR_MPIs/colormap.pdf\",dpi=300)\n",
    "      \n",
    "view_colormap(\"Reds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "Pir = Image.open('../Data_ReScience/Pir SN/Pir.png')\n",
    "SN = Image.open(\"../Data_ReScience/Pir SN/SN.png\")\n",
    "colormap = Image.open(\"../Screenshot_Brainrender/SN_MPIs/colormap.png\")\n",
    "                      \n",
    "fig = plt.figure()\n",
    "G = gridspec.GridSpec(nrows=20, ncols=20, wspace=0.000001, hspace=1)              \n",
    "axes_1_1 = fig.add_subplot(G[0:20, 0:12])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "#plt.text(795,0,\"Piriform In Silico injection\", ha=\"center\", fontsize=7)\n",
    "plt.annotate(\"A\",(150,100), fontsize=10, color=\"black\")\n",
    "plt.annotate(\"*\",(455,653), fontsize=4, color=\"blue\")\n",
    "plt.annotate(\"*\",(807,653), fontsize=4, color=\"blue\")\n",
    "plt.annotate(\"*\",(1159,653), fontsize=4, color=\"blue\")\n",
    "\n",
    "plt.imshow(Pir)\n",
    "\n",
    "axes_1_2 = fig.add_subplot(G[0:20, 8:20])\n",
    "xticks([]), yticks([])\n",
    "#plt.text(710,0,\"SN In Silico injection\", ha=\"center\", fontsize=7)\n",
    "plt.annotate(\"B\",(150,100), fontsize=10, color=\"black\")\n",
    "plt.annotate(\"*\",(412,810), fontsize=4, color=\"blue\")\n",
    "plt.annotate(\"*\",(764,810), fontsize=4, color=\"blue\")\n",
    "plt.annotate(\"*\",(1118,810), fontsize=4, color=\"blue\")\n",
    "\n",
    "\n",
    "\n",
    "#plt.annotate(\"B\",(150,1050), fontsize=7, color=\"black\")\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "#plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(SN)\n",
    "\n",
    "plt.savefig('../Data_ReScience/Pir SN/Fig5_A_B.png', dpi=600)\n",
    "plt.savefig('../Data_ReScience/Pir SN/Fig5_A_B.pdf', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axes_1_3 = fig.add_subplot(G[6:7, 4:7])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(colormap)\n",
    "\n",
    "plt.savefig('../Data_ReScience/Pir SN/Fig5.png', dpi=600)\n",
    "plt.savefig('../Data_ReScience/Pir SN/Fig5.pdf', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Figure 2 (Residuals, Snca gene expression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) MPI 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1_a) Vulnerability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5826647387422237\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_2.1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:10<00:42, 10.74s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_1.08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:21<00:32, 10.69s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [00:32<00:21, 10.79s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [00:43<00:10, 10.92s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI1_Breg_-<span style=\"color: #000080; font-weight: bold\">4.</span>5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:54<00:00, 10.88s/it]\n"
     ]
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "resi_cpu = pd.read_csv('../output/model_output_MPI1_iCPu.csv')\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "resi_cpu.head()\n",
    "resi_cpu = resi_cpu.rename(columns={'Unnamed: 0': 'region'})\n",
    "\n",
    "for r in resi_cpu.index:\n",
    "    \n",
    "    names = reg_names[reg_names['ROInames'] == resi_cpu.loc[r, 'region']]['BrainR']\n",
    "    \n",
    "    if len(names) > 1:\n",
    "         resi_cpu.loc[r, 'br_regions'] = list(names)[0]\n",
    "    elif len(names) == 1:    \n",
    "        resi_cpu.loc[r, 'br_regions'] = names.values\n",
    "        \n",
    "for r in resi_cpu.index:\n",
    "    \n",
    "    if resi_cpu.loc[r, 'region'][0] == 'i':\n",
    "        resi_cpu.loc[r, 'hem'] = 'right'\n",
    "    elif resi_cpu.loc[r, 'region'][0] == 'c':\n",
    "        resi_cpu.loc[r, 'hem'] = 'left' \n",
    "        \n",
    "\n",
    "max_resi_cpu = np.max(resi_cpu['residual'])\n",
    "min_resi_cpu = np.min(resi_cpu['residual'])\n",
    "mid_pt= 1 - (max_resi_cpu) / (max_resi_cpu + np.abs(min_resi_cpu))\n",
    "print(mid_pt)\n",
    "cmap=shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "\n",
    "\n",
    "resi_cpu['residual']= (resi_cpu['residual']- min_resi_cpu)/(max_resi_cpu-min_resi_cpu)\n",
    "resi_cpu['residual']\n",
    "resi_cpu= resi_cpu.dropna() #Dropping the br_regions overlapping in brain render\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoints = [1,3,6]\n",
    "for section in tqdm(list_coor):\n",
    "    bregma = np.array([5400,  # AP    \n",
    "          0,  # DV    \n",
    "          5700,]) # ML\n",
    "\n",
    "    bregma[0] = bregma[0] + section\n",
    "\n",
    "    # Create a brainrender scene\n",
    "    scene = Scene(inset=False,screenshots_folder=\"../Screenshot_Brainrender/residuals/MPI1\")\n",
    "    frontal_camera = {\n",
    "         'pos': (-19781, 3777, -5832),\n",
    "         'viewup': (0, -1, 0),\n",
    "         'clippingRange': (26465, 26836),\n",
    "        'focalPoint': (5705, 3991, -5691),\n",
    "         'distance': 25487}\n",
    "\n",
    "   # Add brain regions\n",
    "    for r in resi_cpu.index:\n",
    "\n",
    "        region_to_plot = resi_cpu.loc[r, \"br_regions\"]\n",
    "        color = cmap(resi_cpu.loc[r, 'residual'])[:3]\n",
    "\n",
    "        scene.add_brain_region(region_to_plot, color=color, alpha=1, hemisphere=resi_cpu.loc[r, 'hem'], silhouette=True) #alpha=data_cpu.loc[r, 'MPI{}'.format(t)]\n",
    "\n",
    "\n",
    "    plane_1 = scene.atlas.get_plane(pos=bregma+50, plane=\"frontal\", norm=(-1, 0, 0), alpha=1, color=\"white\") # Thickness: 150 um\n",
    "    plane_2 = scene.atlas.get_plane(pos=bregma-50, plane=\"frontal\", norm=(1, 0, 0), alpha=1, color=\"white\")\n",
    "    plane_3 = scene.atlas.get_plane(plane=\"sagittal\", norm=(0,0,-1), alpha=1, color=\"white\")\n",
    "    scene.slice(plane_1, close_actors=True)\n",
    "    scene.slice(plane_2, close_actors=True)\n",
    "    scene.slice(plane_3, close_actors=True)\n",
    "\n",
    "\n",
    "    brainrender.settings.SHADER_STYLE = \"cartoon\" #[metallic, plastic, shiny, glossy]\n",
    "    brainrender.settings.ROOT_ALPHA = 0.17\n",
    "    brainrender.settings.LW =2.4\n",
    "    brainrender.settings.ROOT_COLOR  = [0.4, 0.4, 0.4]\n",
    "    brainrender.settings.OFFSCREEN = True\n",
    "    brainrender.settings.SHOW_AXES = False\n",
    "    brainrender.settings.BACKGROUND_COLOR = \"white\"\n",
    "    scene.render(atlas_name='allen_mouse_50um', camera= frontal_camera, zoom=1.5)\n",
    "    scene.screenshot(name=\"50um_MPI1_Breg_{}_mm\".format(-section/1000), scale = 3)\n",
    "    img_totranspose = Image.open(\"../Screenshot_Brainrender/residuals/MPI1/50um_MPI1_Breg_{}_mm.png\".format(-section/1000))\n",
    "    img_totranspose = img_totranspose.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "    img_totranspose.save(\"../Screenshot_Brainrender/residuals/MPI1/50um_MPI1_Breg_{}_mm.png\".format(-section/1000))\n",
    "    #title=\"-Coronal section Bregma {} mm -50um thickness- Seed = SN - MPI {}\".format(-section/1000,t),\n",
    "    scene.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for section in list_coor:\n",
    "    Cor = Image.open(\"../Screenshot_Brainrender/residuals/MPI1/50um_MPI1_Breg_{}_mm.png\".format(-section/1000))\n",
    "    image.append(Cor)\n",
    "        \n",
    "fig = plt.figure(figsize=(10,1))\n",
    "\n",
    "G = gridspec.GridSpec(nrows=4, ncols=15)\n",
    "\n",
    "\n",
    "axes_1_1 = fig.add_subplot(G[0:4, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "#plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2_1 = fig.add_subplot(G[0:4, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3_1 = fig.add_subplot(G[0:4, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4_1 = fig.add_subplot(G[0:4, 9:12])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[3])\n",
    "\n",
    "axes_5_1 = fig.add_subplot(G[0:4, 12:15])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[4])\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/MPI1/vulnerabilitympi1.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI1/vulnerabilitympi1.pdf', dpi=300)\n",
    "\n",
    "vul = Image.open('../Data_ReScience/residuals/MPI1/vulnerabilitympi1.png')\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "plt.annotate(\"A\",(400,50), fontsize=20, color=\"black\")\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(vul)\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/MPI1/vulnerabilitympi1.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI1/vulnerabilitympi1.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5826647387422237\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "def view_colormap(cmap):\n",
    "    \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n",
    "    cmap = plt.cm.get_cmap(cmap)\n",
    "    colors = cmap(np.arange(cmap.N))\n",
    "    plt.figure(figsize=(15,3))\n",
    "    plt.imshow([colors], extent=[0, 10, 0, 1])\n",
    "    plt.xticks([0, 2.5, 5, 7.5 ,10], ['0', '0.25', '0.5','0.75','1'], fontsize=30)\n",
    "    plt.yticks([])\n",
    "    plt.text(2.9,1.1,'Relative vulnerability', fontsize=35)\n",
    "    #plt.text(10.5,0.6,'Estimated percentage of area with \\u03B1-synuclein')\n",
    "\n",
    "print(mid_pt)\n",
    "cmap=shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "view_colormap(cmap)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI1/colorbar_mpi1.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI1/colorbar_mpi1.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1_b) SNCA application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Regions</th>\n",
       "      <th>SNCA_val</th>\n",
       "      <th>br_regions</th>\n",
       "      <th>hem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iM2</td>\n",
       "      <td>0.985962</td>\n",
       "      <td>MOs</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iM1</td>\n",
       "      <td>0.703961</td>\n",
       "      <td>MOp</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iAI</td>\n",
       "      <td>0.725033</td>\n",
       "      <td>AId</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iPrL</td>\n",
       "      <td>0.868442</td>\n",
       "      <td>PL</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iMO</td>\n",
       "      <td>0.636978</td>\n",
       "      <td>ORBm</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>cEnt</td>\n",
       "      <td>0.985708</td>\n",
       "      <td>ENT</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>cPS</td>\n",
       "      <td>0.366010</td>\n",
       "      <td>SUB</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>cSuC</td>\n",
       "      <td>0.106475</td>\n",
       "      <td>SCs</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>cIC</td>\n",
       "      <td>0.105516</td>\n",
       "      <td>IC</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>cPnO</td>\n",
       "      <td>0.190870</td>\n",
       "      <td>PRNr</td>\n",
       "      <td>left</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 4 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "os.getcwd()\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "snca_exp = pd.read_csv('Data83018/SncaExpression.csv', header=None, )\n",
    "snca_exp.head()\n",
    "snca_exp.columns= [\"Regions\",\"SNCA_val\"]\n",
    "snca_exp.head()\n",
    "\n",
    "\n",
    "for r in snca_exp.index:\n",
    "    \n",
    "    names = reg_names[reg_names['ROInames'] == snca_exp.loc[r, 'Regions']]['BrainR']\n",
    "    \n",
    "    if len(names) > 1:\n",
    "         snca_exp.loc[r, 'br_regions'] = list(names)[0]\n",
    "    elif len(names) == 1:    \n",
    "        snca_exp.loc[r, 'br_regions'] = names.values   \n",
    "        \n",
    "for r in snca_exp.index:\n",
    "    \n",
    "    if snca_exp.loc[r, 'Regions'][0] == 'i':\n",
    "        snca_exp.loc[r, 'hem'] = 'right'\n",
    "    elif snca_exp.loc[r, 'Regions'][0] == 'c':\n",
    "        snca_exp.loc[r, 'hem'] = 'left'\n",
    "        \n",
    "snca_exp= snca_exp.dropna() #Dropping the br_regions overlapping in brain render\n",
    "max_val= np.max(snca_exp[\"SNCA_val\"].values)\n",
    "min_val= np.min(snca_exp[\"SNCA_val\"].values)\n",
    "\n",
    "#no need for midpoint as continous\n",
    "cmap=plt.get_cmap(\"YlOrRd\")\n",
    "#normalisation\n",
    "snca_exp[\"SNCA_val\"]=(snca_exp[\"SNCA_val\"] - min_val)/(max_val-min_val)\n",
    "snca_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at SNCA_exp_Breg2.<span style=\"color: #000080; font-weight: bold\">1.</span>png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:16<01:04, 16.09s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at SNCA_exp_Breg1.<span style=\"color: #000080; font-weight: bold\">08.</span>png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:32<00:48, 16.05s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at SNCA_exp_Breg-<span style=\"color: #000080; font-weight: bold\">1.36</span>.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [00:48<00:32, 16.26s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at SNCA_exp_Breg-<span style=\"color: #000080; font-weight: bold\">2.82</span>.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [01:07<00:16, 16.90s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at SNCA_exp_Breg-<span style=\"color: #000080; font-weight: bold\">4.5</span>.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [01:30<00:00, 18.03s/it]\n"
     ]
    }
   ],
   "source": [
    "# Saving BrainRender SNCA expression\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoints = [1,3,6]\n",
    "for section in tqdm(list_coor):\n",
    "    bregma = np.array([5400,  # AP    \n",
    "          0,  # DV    \n",
    "          5700,]) # ML\n",
    "\n",
    "    bregma[0] = bregma[0] + section\n",
    "\n",
    "    # Create a brainrender scene\n",
    "    scene = Scene(inset=False,screenshots_folder=\"../Screenshot_Brainrender/SNCA_exp\")\n",
    "    frontal_camera = {\n",
    "         'pos': (-19781, 3777, -5832),\n",
    "         'viewup': (0, -1, 0),\n",
    "         'clippingRange': (26465, 26836),\n",
    "        'focalPoint': (5705, 3991, -5691),\n",
    "         'distance': 25487}\n",
    "\n",
    "   # Add brain regions\n",
    "    for r in snca_exp.index:\n",
    "\n",
    "        region_to_plot = snca_exp.loc[r, \"br_regions\"]\n",
    "        color = cmap(snca_exp.loc[r, 'SNCA_val'])[:3]\n",
    "\n",
    "        scene.add_brain_region(region_to_plot, color=color, alpha=1, hemisphere=snca_exp.loc[r, 'hem'], silhouette=True) #alpha=snca_exp.loc[r, 'SNCA_val'])\n",
    "\n",
    "\n",
    "    plane_1 = scene.atlas.get_plane(pos=bregma+50, plane=\"frontal\", norm=(-1, 0, 0), alpha=1, color=\"white\") # Thickness: 150 um\n",
    "    plane_2 = scene.atlas.get_plane(pos=bregma-50, plane=\"frontal\", norm=(1, 0, 0), alpha=1, color=\"white\")\n",
    "    plane_3 = scene.atlas.get_plane(plane=\"sagittal\", norm=(0,0,-1), alpha=1, color=\"white\")\n",
    "    scene.slice(plane_1, close_actors=True)\n",
    "    scene.slice(plane_2, close_actors=True)\n",
    "    scene.slice(plane_3, close_actors=True)\n",
    "\n",
    "\n",
    "    brainrender.settings.SHADER_STYLE = \"cartoon\" #[metallic, plastic, shiny, glossy]\n",
    "    brainrender.settings.ROOT_ALPHA = 0.17\n",
    "    brainrender.settings.LW = 2.4\n",
    "    brainrender.settings.ROOT_COLOR  = [0.4, 0.4, 0.4]\n",
    "    brainrender.settings.OFFSCREEN = True\n",
    "    brainrender.settings.SHOW_AXES = False\n",
    "    brainrender.settings.BACKGROUND_COLOR = \"white\"\n",
    "    scene.render(atlas_name='allen_mouse_50um', camera= frontal_camera, zoom=1.5)\n",
    "    scene.screenshot(name=\"SNCA_exp_Breg{}.png\".format(-section/1000), scale = 3)\n",
    "    img_totranspose = Image.open(\"../Screenshot_Brainrender/SNCA_exp/SNCA_exp_Breg{}.png\".format(-section/1000))\n",
    "    img_totranspose = img_totranspose.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "    img_totranspose.save(\"../Screenshot_Brainrender/SNCA_exp/SNCA_exp_Breg{}.png\".format(-section/1000))\n",
    "    #title=\"-Coronal section Bregma {} mm -50um thickness- Seed = SN - MPI {}\".format(-section/1000,t),\n",
    "    scene.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "def view_colormap(cmap):\n",
    "    \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n",
    "    cmap = plt.cm.get_cmap(cmap)\n",
    "    colors = cmap(np.arange(cmap.N))\n",
    "    \n",
    "    plt.figure(figsize=(15,3))\n",
    "    plt.imshow([colors], extent=[0, 10, 0, 0.5])\n",
    "    plt.xticks([0, 2.5, 5, 7.5 ,10], [\"0\", '0.25', '0.5','0.75','1'],fontsize=30)\n",
    "    plt.yticks([])\n",
    "    plt.text(3.1,0.6,'SNCA Expression',fontsize=35)\n",
    "    #plt.text(10.5,0.6,'Estimated percentage of area with \\u03B1-synuclein')\n",
    "    plt.savefig('../Screenshot_Brainrender/SNCA_exp/scale.png', dpi=300)\n",
    "    plt.savefig('../Screenshot_Brainrender/SNCA_exp/scale.pdf', dpi=300)\n",
    "      \n",
    "view_colormap(\"YlOrRd\")\n",
    "plt.savefig('../Data_ReScience/residuals/colorbar_snca.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/colorbar_snca.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAABECAYAAACbKN+sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqg0lEQVR4nO2deZhdRZn/P1VnuXvv3dlXkkAgCcGwq2yCbAqCIzgsjoKIyCjqD4UBZFDUUdHBkWFGUWFExxGG0VHUQVBACCIksgQIS0gICemk0+m9+y7nnKr6/XG6b9JZWITu2923Ps+TJ8m953TXe99bdb711ltvCWMMFovFYrFYLOMRWekGWCwWi8Visfy1WCFjsVgsFotl3GKFjMVisVgslnGLFTIWi8VisVjGLVbIWCwWi8ViGbe4r/G+3dJUecSbvN/6sPJYH45/rA/HN2/Wf2B9WGn26EMbkbFYLBaLxTJusULGYrFYLBbLuMUKGYvFYrFYLOMWK2QsFsuoYozBVhS3WCxvFa+V7GuxWCxvCVprSqUSSikASj2vkMg0kMjUI4VAOg4AQrwVeZmWkWBIhGqtAdAqxHE86ztLRRGvMTOy06bKY3dLjH+q3odRFNHX11d+0AVBgFr+aZTwCNIfIPrNHYgp00kvWkp28dvITJ2G4/lj6cFY9T40xtDf308UReXXev54JaHMsO35JhpmzKJp2TJy06bhpVJjyXdgdy1NBPboQytkxj5VP4AOYYxBRREqUkhH4jgOQsqxNmDujqr2oVKKzvYt+Kls+bWHl9/L4uLtAJS++1y81DToR+W46EnTSBxxLA3HnkxuxkykdCrS9h2oah8CrH/qfmqm7Ifj+eXX+n//cQBWfmI1NRlJviRwmuuZf85h1B51EjULl+ClM2Ohj1ohM/7Zow+rcmnJGDMWOpblDTDQ08vd132XDQ+soNDbj5tKkpnUxJyjD2HpSUdSN2UyiUzW+nUM8tCD9zG35+foAy8lmW0EIJ2pwSnBlo4CNTuIGABHRTitL6N/9kPa/vtHbFm0jPqzz6dxyQE4TlUOWRWnv78ff/WN9G6YR/agS/ASKQBcKTDG4AQQGYUsGtRAO/7yu8gv/x092VoSbz+WulPOIjN1hu2flhGhaiIyxhh6t3Wyec06SgMFcs0NNE6bQra+DumM6Vl91c8ES4Ui1y05kWhbd/xhDPnKGIwQJJKCk/ZN4R9yOC0fPJu6hfshHWcs+bSqfXjBexdz/UcX0BlkSB/5dbxEms7OTnKrruCeG1ZxsPCH37CTsAHQQhAuWMTkT3yO+oWLKuHbqvbhfXffycLuH6EMhInppA/+LH66hud/93Wmu+tZ9annCEsGqQyhAwe+w4tvNCbeUSIdSoccQ8v5nyXT1Dwe/Qfj3IevhjEGrRQ6CnFcDzG2xs8hqntpSUUR/33ZV3nhjrtR/XkEYITAySRpWrSA/d5/PItPPJr6Sc0IOeY2clX1AFosFvn2kWdQemHj8Ifbjt9bbTh/Th5PKwzQv/e+JM//OC1HHoOXTI6FDlnVPjztHTP5/qcPJtTQLyfRctxXCcOQ6IFP0v9vz+7iHzEoUHeHlhKOOpFZn74CP5UejeaXm/Um7x/XPvzhNz7Fufu+wra8xBgInDrEPn9HdtLeDDx3B2vPvon+AUBA0z6GGU2xkBHGIDBoBAjBVjzm3/o7stkscnTHWitk9oAxhvaNqwmv/wpOdydhbQPewqWk3nk82fn7Il13LIyhUM2VfY0x3HT6hTx38y8wfXmkAWFAaoPpK9D+pye479JvcMPBp/GjCz7PusefQg/uqrBUntVPP03Ti+sRmFi8GAN6+x+hDUiBazSOMXjGUPfcMziXXcKG953A2jtuo5TP2+2+FSQIFYGKXZZWbQC4rkspty8mV7uLaPGMHi5Ud0Bqjbz3N7x43un0vrJhxNtuiZk8az8cCXVJgxTgq27Equ/gej61i/6Wpi9fTuKQRciGDNObvLL/DJRFjDGG2llzUGHImlv+lVJ/X2WNqkKM0RSL/QSlQnnnWVtbG8UVX0M2biXoake+9Bz6tz+j/4rzWP+Zs2l/4B60il7jJ1eWCR2RMcbw1J2/45d/dxlavEayoDGxsa7DlHccwCnfupLJc2aNBSVa1TPBB++9j94zP01Ba14KNAWceFAUAjE0WErJxTN6yh+UYfuH1u0n6Jw5h8bzLmD6cSfgJSoSoalqH15w4l5c8+EDAAgSU2g4+ssYY4iCEqWtWwnWPY96ciX6qZXI7k48pVCDPt4tg36PPJ9J37mVuvn7jIYZVe3Dl156ifonLiWXMPSVBEEk0AbU264k2zx30J8BxW3biNY+TbTyQeTzT+IW4wg4xtAbKSb94Ld03fQNvMceROXqmXTdf5CdMn00TKj6iEyx0EfbhvsQqgODwMha6icdSq52Ei898xDJl35Mfm0riUe2IAtmu7FCEM3bj6ZPXUN2xuxKPhOrc2lJa83/NS4hMoancYjkHhIFd7MmL5I+x//L5Sx738m4njcKrd0jVT2APnjvfeTf/0mSxZCC5/CklBTxEEohNIBBKM3H5xd2+0EZoDuRoOC6RHOm03zJlUw75O2j3Rmr2ofXXnYhHzmgkyg5mexhV+D4STY9+Cgb7/g/or48qVlTqV24F7V7TcPr7yC851eknn+SYE+TD2Pi5WHAOA7NN91B7aw5I21GVfuwra2N6P6LyfqGbMLQVZBIYegPfWpP+C4QTxx71r9C38uvkKjL4add1JpVqPt+jbt1ExKDPuZU1O//l4QTLwYo16P++v+idsbskTah6oXMhtU34ziaMHIRwuC5CiEkMnMwdY3z6WrfRP75X+Bv+hP6gU247QFAua+Fjk/iwn9g0rtPqZSYqU4ho5Ti3oYlhJ6LKAb8xfNRyF1Ey+6ETPwanPLu2Sz8/q0kc7nRa/hwqnoAXX3/g2x5z8eIXIfQc1DAYyWFGxhU0sMpRYhQceHC0h4/qFBIehM+bq1PwWjCU89mycWXlot4jQJV7cONGzei21aQmXkYqqR5+p9/QNutv0Tq7WZpIRC1GZqOfyfzLzgDt3UthVv+FdnbVb5GAAKDNAZnUMxoIcD1aPn5AyQymZE0o6p9uHXrVvoe+jKpoJXapCHhGjryklBB6l03UuhYB/k2Hn7v11HFAOG5pPaew/QPn0bLIYuJNq5B/fG3BKseJetsH4O1MfQEhr1uvpPMlGkjaYIVMmt+g5OYiePVYYwiLHUjo1ayqW76zaE0NE6mWCzQ37oK5/lb8O9fQ3p9HyXhUBAuWoj4Azj1Q8z48CfHVGR7wguZu5v2R7sOXqgYcCWrQmDnLZxDn8FuxMw5c4sUGpqYc/uvyNTXj0q7d6KqB9Dn73uQ9ad/gsgdTOcSggGleC4QiEjjBBEq6XHhXnmcPXyXNfEDb1NjmrqUh440vfsfxP5f+y7O6IiZqvZha2sr6VSKbatf5JmLr6G0fhNm50TPwcmEMQZdn2PJNy+nef8F9H/vn/EeeyhOACZOHpWAM5gTJTEoBI8xhePvvWckk/Wr2oednZ0M9G7F+csXcXREQ1qTjyTtPRFu7RycwisIo3noE2uH3WekJDlvJvMuu4DJb19Gx3WXkV7zFAiBMoZ8SVFjDMLzaf7ZvfjZEZswVr2Q6erqQghRFiBxlWZNGAzQuv7PtExbTLZmalyBu78D/ZevEv7falKtBYo7RkeFQB//AWZ+/PNjJrI94ZN9uxEUQkWfK6nNl1jgsWvi5x6coTCko5CG9i08+zcnExQLo9Biy44YIYaJGIyhxoCWAU4pjB+Ig/kyexplhrw7HQUGhCNpaF3Fn//fhTYJeJR48ss38vQJHyV6ceOec1+IS9w73f08ccFVbP7jSuo//yXUmR8dnA2Kcl8d+gkCaOvWlB5r5/7PfmnkDalSHMdB5VdSmn8q+QUfYducT9C392dwE1nc/EaE0RR6QqTSSG3K+WvCGEprXubpC77AU1/6V+ovuZbSoccRGMNAISSFIBQSHYZs+uj7bH8cQXbZHSgEUjokkjU01fVS7LyfbW3PUiwWSeaacA66is3TalFi137n3HU7m35605jx14QXMilcPFyMlnQ7LgljqBHhrhfuJhrzCt30+T6hlEzuaOexsz8wZhxXTTgqzq7HGKQ2OEqzzPcBgdAaWQyR7Hm6ZACDwHcMWU/hZiSmqJny0uM88ZMfjo4RVU7/9T+BMMJIsceJw454BlZd+nUcz6P+A+eiP3QxDgbHaMAQCYERsKVf8OKGJEJI+m75Bd1bt428MVVKquEd1M14N/WzDqfgaoq6jfysIxiYdgzR/A/x5DdbkWpwJ+HOc0Vj2PaTO3niI1dS++FL2Hzc35KWceI+QhAJQaJrGxu+cVVljKsCOltX0LX5Cfq6W3d5jkkBxVKAKD0OJkQpRSLTQMtxV1LAwQxGP5NGlVcw5O3fY/Ndv6iEKbsw4YVMshhgpMBF4EoXrR2mIVBS7HGLJwDacHAzREKSdz0Krsfkl9bw2I3fGb3GWwDKM3hhYhHjKE3C83nn965m6rsPw5/WHO9iIl5G2h0Sg2zwyeTA1RpCDULg//RGwnA3wtby1iLisgflLfQ7slOOmjEGZQz+4BKwMYb6U87AfORTlBwXg8Azmo4ew9o1Lm5kyt+N5QefPopGVQ/GGJKpeqSMC6Xl6haRyMyhfso0pu87BdEwGRMmEZhBsbr7n1N48C88et4/sN/Z5xM43rDvQygkmd//koGuzlG0rHrIlh7C67sPtt5O58YHh4kZYwx1tRkSvkf35rsoFApxv9tnMeKsiykeeCS9TTMgUgjiEgmOMajvfZXOp5+omE1DTHghEyS27zjyQkUq1CSVZFrKRYR7qBdj4tokc5NeOWM7kpKC6+Hd+n36u7p2f5/lLUcZjZbxqDgUshbGsPdvbmbxB07ivbdexwfvuYXi1V8hyOVQUu4SmdFCII3Br3GIBjQpFZFUEYSGdM7jrs9dPPqGVRnTf/xPg74b/roxBuF70JBDTG3CmTuNzOFLmXr+33DgHfGkoXflf5B/5TFyJ78fee5FuBi29AnWrnUHl5so/2y3rYPObTYq81bT272OQn67wPATaXK1s0nkDqY/WIAvN3PQ/96IaK7HlNf9dlUzkefQ/8gqVv3LzfjX3DBM72gg77i8cuH7R9SWakUbgRDgSk06WEl368Pl9za8sr3P5DKCVCpFsVhECEHd8afRfPFV/GGNyx1PCVasDymFiowJcYwm+sL5qKiydWYmvJAZeghCPNgliwF+qJjeMcA+55+GFuwxMvPL9j5crXHN9jVfF3j4zFPtEtMoUfQ9vFDhhQpncP09dcaJ1M2Na/y4vkfNpCayx56Ac/PPKL3ndIqJ5C4/py1SBL2KoAiuY8gWSxAqEDBz3QrrzxFm1klHU3fmCQgzWLnXGEQuRfKYBcz81t8y74ZzmP/tD7P0h1/g8Fu+wdIrP0Hz0n0BSLXeS+7Jf2bboz+m5r1nEH7yKta95BE5blzpl8EkYKVxIs0f3vvRSpo6IUk463H0KgqF7vJrQggcxyORnoqXPYzczKkcdPfNuNMm7T4PyhgiL04a3fIvPyHRNJlirm6YmFEIUr1dtj+OALrxfQCoOBhNsvBI+XNOp7YfEyKEYO1zvyc/WEhUCIGQko6n1tHW5/DUOsH/rFRs6FK4WiGV4uVrPlkJk8pMeCFTZqiImjZIQpirOfTDizn8giMpmSguO7pj5zGGMJ9DGlPe9TL07tTuTnpsVGZ0SHiDAiZeNHLeeSB7f+sLAGgVUezeTN+mZ+h/5UmUUyB10UWI73yPYKe6Io+ogEKHwk2AI0GEmlQYQaSpn5Ri07q1u/xqy1uHEILF376K2V/7DM0nHUnixEXM/86HWPaNDzDznbOZsm8TLfOSuOkNKBMN232kNETakGq9h/5HbyJ39AkYMZQAHv8ldZw/VUz5eC/Yir9vNX09Ea5rSLvPEEXBsPeEEHheAtf10Il+lv7s6ySX7beLGBlKzB/igVMupOZrNw2PoAqBQvD4T28ZQWuqk0zdbIaSlyIFQhg2rFsFQBgNlwKT6nvZunUrxWIRgFc2bIzfEGBcSW/o8/jLiqfWF8nnQ5qeXD5aZuyWCS9kkm9biLdkAYmDFuEdewj9e9Uy/bAkCz+3GK/7fg7MPsmxJ6RxhEIMlUYf7GxTZZaS48S1KqCcmCaEYPln/r6CVlUPBnixJYfnutSe+R4W3/JNpOOgwhJdD16P+ePlBA99DfXnb6IfvIboj1cR9DxMdPU/UDz07WUReuqUWpysg/QEji/K67x+qDAlzX0XfajClk58pOsw99zTWHrD1YSnHkT9vvU47vZtnUIIfN9h3drfD7sv9OoomQSehMaOP9K97hEmXX5B/DO1Ke+UkVqjBmf8AwMDo2dYNdD9LO3rVpNMCHo7V+zxMik249VsYeH1l+Atnj/sPbNTkEZs2orBxQjBzgvC9T+54S1rumU7UoqylowUqLZfAjBj/mkUQw9DnAMVeQfS3NxMMhlHt9s2b0aoHTIQBTzd7zJZG15pVfy5tbLH+uyh1O3E4cCfXB9nXA/mTnRd8F56Ex6T5zZRDA1ZEXDgogS+0gw83s+z/S7dQXxI1tBy0o6nLQNIY6h76vHKGFRl+L7PiXf9J/T0kZs7q1zErv33X6ahtBZHCpS7/XCCpO6htvN+8lsfoP/0d1EKQ9wnHyfT5KO6AtxJPtIXRL7ECTVOpNAYlnRvqZyRVYJScVK1CoscfcwH2bTxbloa4xw2YwxKuYQqxYyZS4fdlzrmy6hCN+rBqyhEkvpnvkPNR35E+1e+F5+9pGIxEyS88kTkTz++jeM+ft5omzhhSfoKR0a0d08llXhpj9dp5gMd+FmXI277Dr/d70SS7DARHGLw339470dZcv5p1N7z82HvS7u0NCIM5XwO0dKUBSCZrsNx342KSmhVoCE7GYDa2lo6OjpYtHR/HhpKzB6c0CdNgpeLgnpH8cLzmuMGl6EqwYSPyHipJH46hZtM0HP7D/BNSM05SzBAx0vtFI3EE5qFS9LU1sKRLZp9akMcud1hQLks+lARrtzoVYWtempbmqmdP7csYowxTI7W4jtxx/SkKX+RQy2IjKDG10zp+h3m5DnI+gbcnIOrFNIYRFJCnY9jDCbQmKJmTn3CrsuPIP0dL9P/xL/T//i/U1z5VTzPJ5FoIIxShCqLlvNJ5Q6ioXkZ2VzDsHv9TB2pptnUePGOCSGgd/n1eHvNRCqDo+OlxyCxfV5WvPT6UbZwYiOEwHUdMrlZCP+APV5XW9tMTe0C6urn4KdTLPn2Fa/ar/zNncw67+/LxRCNMWhjCHcO31jeEvROrtjat70AoeenSKbrSOemlAvnDQmTRCKBcR2GbUcTgs2Rx/rAZ1qDw3NPPz0KFuyeCS9khohKRXK/u418XYLauhRBBDUb2skrB08YfKGpP6YBH8O+NYYjJoXs01BC77hFDVBCEAlJZ4WztKuZl19cjS8NKVeTcDRJx5BwDK4ErUFpgTLgOYLJpccpXHIOazsCnIyD6Q4J8gaZlHE4O+GgS5qH1ko6OjoqbdqExV99HX7vatze1ThRFy+ve4HmSQeRrTuQXN3byNXMwE+kkXLPEwQhIONpPGmYkn+ct/3vv+NEKl5Scpy4fskg2rUTjbeaIIw/32Sq7lWvE0KWH4BzTjmOLrPnsXLoYflUm+KBlxW/f1Hx6+c1v36usksVE5WhoIoxUFQ5Zi557SV1z/O2i9Gd6kB1l2Btn0Ob9vjD+ZeOVLNfk6oQMsYYgms/hTSGpr9bRMKDwkAeT4IC8tohLTUz6wQ99XH+RL0H+2QFQWDIRyoOx+0QodkcWiFTKR544I9owBWQGBQxCVeTdDSeY1AGokExI6Wk+PKvaT3kdCLXwW3xwRicwV35KoJkraTU6VMqlSpq10TGlxHKlIszs2L5/+F5Pq7r4Tju6wpJF5Uk0PEavxSG9o3PYKREKkMh5RFhKBhN3mg6te2fI0F3+xtLihdCUC9e5bBeoPX5NTy6TrBmi8OGbZL2HodtvVaIjgSeKwh0iih3JHV7nYPrJV7znpqaGoIg2O12+tZi3K/bC4L+dW0j0eTXxYQXMsYYBp54BHfdsyghMJ5PqKDjoRdISI0nDAMqzp/xhWHhMfWEs2YBIIXAQ+BEgkKgiHQc+kQI9t5/aUXtqmYKpQilBaGOd5J50pCQsaBJOgZPxmJGaUHCMXguHPDRi3n0ccWTaxWbejQdA5oO7eDWuwxp0goeTz/hCQY/47gaguB9Z138hpfy4t1LgkhDIZKkn7qB9oYMndLQoxR92hAoQ2QEvpnwQ1tFCFvv+OtufBVfP3L5N5AYEhgcDG4Ybc9PtLyllNJHkp19LnVT3obrp17XPXESvr/b93LCoy4BSReaMpUbPyd8sm//8rtJfPcraBGf6ZEYLDo5J+XhonGFQQvBgHbISkWNZ6i78jIGPvEpGFw+coXANZIo0AxIQyKdouXar1fWsCrmqGOOI/XSn1BGEKrtM3RXGBzH4AgRP/CMwDWGmpRLFEaUNmnaX1FsdiUmIVAe1G4NGAgUeeORSr2+jm154xSVg0QhBZS8yWRch+7ul8lmp+J5ux8kd0aZOAFfRZKSFiQ9GOgbIClcUoEmTDgIY4hcseuhlJY3hTECJdL40055w/d2mJAW46Ilw2f1BhAw6di34z2+hvhsc3C0wVu271vUcsuO5CYd8FdN2IZ2L+2MEILFLfGKRagqJ2QmfG/3v/dVSkISCElex30niCCSPpERaAMOBmMgGkwwa7nvWmq/+30kg6lNg1EYV0jSWpL300ybPedVfqtlJFmw9z7lJF9/MAIjiB90BnAHX3eEQRmBVhH5H3yPbE1EKekQJH1kSeDkJb0bDdHWOImtvjKnm1cFzuLPovwW+rJvI3fQpfT0vIjvtzMw8PoTBLWJk7lLevsBdg0lg3YlUmm8UljOZpRqT4dVWP4aRPPJpGedQ23TXm/ovkJXN43Ci6tr75RpOhR1Ofhj5+AikIAXKaKEy2l33fpWNd2yA29UxAxFTV9tkvdsp6B9QNCdTb+ptr0ZJnxEZmCvRZjOrTipND3T5pFwpxD5tSTPuJDeJ35L6YYfoXMumZlJmJok6cC2xWdQv2BvipdfSenrXx0WFpVCoI1A2hlfxRBCUFQSKUyce0b8tyu3+2noNQO094Y03f6fzJos8aSkpz8i7NUEkYtxXIwUdGFzKkaKMMyTrZuOf9BlOF6S3t4XSST60Bo8T5Wrh74W8VFN8QZSAfgyPqAwSHloKXBChV8MifzYp5a3jkz9/NfnI62HFTO859QL8cWgz7RBaF2OlonBHUpSSvIoHASeK3EAYf03oqgopDDQjtERyWwLnrf7iEtfXx/pdBopJXJyPXrLroVg1/VFdA54nPnL7490s/fIhBcytVd8C1UsIF2PmmQKMCAkUkoyx11I67W3ITf20fViQMfUIi0H1lL39jMAaDnxJNryecIbvl1Wpn1KM+8HtupkpXEH82Bgew72jkPf8Ai2RBzbTOH+XnKOIFsPuk7SG2h68hH9eUnjOaeOavuriRr1AD3tzdRPPQIA329C6xyFQgfJZIGurheoq5v/hiYHBmhPzCOVXoMWASrhELgSrxTnWMi500fImupkRxETFvtwE9ldhE1UKtC5/FZa3nUhAFopnA1t5Q6qpMSNFJE3WFjUgJo7lR9/7hqSaegqGqSBqTnNihUrOPTQQ0fVxmohDAr0vXInrt4GwtDfWUv9Xufs9lqtNT09PTiOwxm3f4efHXHuLtfUCA+nNsv8fSu3HDjhwwpOIolfW4+bySIdB+m45QFTSEn9f/8X4fRpCCOgNaL1dz30Pvzn8ixx0vv/htwXr8XN5ZBA3veZstcbC69a3gS72boZBUXyATi7mbTtWIQZIB8J8hv7EA1JoiG/C4EjBPUJwax6w/xJIe/7pytHyoKqp0/NpablsPL/0+lG0ukpNDTsB8xFiF56ep7d7b07JgSLYf8SRLnj2MsvsY9fZK9kiVm5kGl1EY31msW3fGWkzKlqSqUCpVXX0f7So0RhCa3jiJpWim3/cyWZRceXr/3TFd/Age37fQcd6JWiuC6XMSy66mP03XkfoXDox6FPOiTSDgcffHBF7KsG/vCr6/BpR8o4au2Jnj1eG5+nFVf7XbDfboSKMbiZFBf+6Y6KbpaY8BGZ1yI9ZQqT//NWWv/2HJyt7VCKKFz09wxcdCGTzvswju9Te+TRJPZdRPcf7mHhYYfb3S2jiNHhsP+HxTxm+Zfp7QuRWUj47rBQTPnIrMGcp66iw7z6FMVQYQ6ajdaNsHYtor8fqVQc3nYESZvoO2K4mQU4rjfstaH6IdlsE0GQpVjcfQ2frq2rqamfh+N6BFOPRPRtjHeqzT2OrtM/R6OUSCFwHYFxHIQjybiShcv2XLTN8tezbdOL7JXsY6DjNno6/4SfbqA/uQzx3N0kF59MZtJsIBag3bfdhWsEgsFTz43BCIGrFTrSeCe/kw1XXk1LyqXeU/SWHISALQW7dD+SNE+aDfS97uuHnndCCJoPXcLWR1YhELjZNC1LFnDav11L45RJI9PY10nVCxmAbX9cTvbLX6JwwYUoHPp9D/mDH9K+ciU1136R1OTJJJubaXz/B2hqaqp0c6uKbCZRjo4ZY1D3/yOJ/EZm10sipckXQxIJF9fZvh3bGNAICpHguU19zDr4YkTbKqZcdAGOlyLo6SHY2kbp5ZcprXmeUmNLpc2sanw/iedN3eX13u7NBFtX0PnEbZCbQWqvY0g1TAMDjpdAG8VKo5iXSOLp+EwthCD7xSvsZGOEmDp7PwqPCTIJSOiNvLx2HW7hWTKHnkNmxn7l65648Ue4xOu9BoERIDUIrdFS4tVlqZ/h0Pe0om1AMuA5hAoOn6bxrrq6ghZOfOpz2+s5vV6GhOV5//Nd/nL7r6mfMZkpC+dT09yI41ZeRlS+BWOAnmuuIZizkIYhxwqBBsyjK9ly1rmk/uEymo4+itraWjtAjjID+RK0vUjT5Pn0rl9JS7AB48T1SHxXoo2mWIwo+QkEcRRGCnCEIdQCb32J7l/9G+rdh9B8So6oVMKrqSHZ2Aj77oc54UQW1tRU2syqRamQUrGfdGb4jjGtNcWex2jY+CBBGEJxPUHbw2xyz6J+3iwmLZxPY43HD3o72bsmRRQpAqHJnfkBlpz9wQpZM3FRUYCQDkJIuqeeT2nrrxCJRrKLDiUzbf6wmiT9W7ex+Vs/Qu54vIuJk32NEJgF09n3X69m80fO44U+n+Z0XJVbaVjdKfnUmadXyMrqoLEmQuuhU5fEYAL9q5PNDp7JlEpy+IfeD4ytultWyAA9JYdsUw5eAl8pAtcFIcgnfEx/P/1XXY38/ndpOuKISje16sikEwRtv4TJn2bK098kMJBytk8lPEeQcCWrthRpaUjjyng79kAoMQacu3vwUj2I393JxnQNwcqVeFLi7L03/rIDyBx+uN12XSH6+zoIBp4m4RYgc8Kw9/L5AYJ1a0iKEkrEvly9vJ21P/8KruNw1pp7SOUEZ9bk8KXAdSSRA3tfddmYGmAnCn1P34QRaZzGxdRMWYpuvATH9Uk4wx8hpb5+lp98QZx8OTjlF3qwPEIuhbP/3jSfdCQvf/5qsgOG5kRIna95uV8yv06w7ZAjrf9GmNTUMwiKHcSxMgnytes47eiTsegfK2SAppPejX/vXSCIT9MdDH+awfhbTxTR0tRc6WZWJflCRLZxKPHPkBysXD4UFg0iQ2AcGjIJDHGkJlACg6A3H5Fs9CmqkO6OkJYf/wSMIZsvIB5Zgfrxf9K07oVKmGUBKD5Cyh88QiQo4O8wq89kssjexyhIiSsNrtC8eFsXrpCIQkBn62aCpgwztgbx0iNx1e1MJlNBgyYuyZZadBgwsPVewrYVNBzw8WHbrCEWMQ+97yJo7wLPgzCKS9w11tJ46jHMPusU7jn3c2y+5kZ6RR/HTG9hn3ycGzUppwkNHHPjP1XCvKrCT2bxk9nXfb3WY78mkxUygDn1eFJ33UnoOChH4mhNKARaCALfY/K5H2HGwn0q3cyqxAif+qnxjhdtBGbwEHpjBIEW9IcuGnAc6OgpMaXOo6gkkUyQe+cldC6/kWDFM7T4sQJKB2Ec5gbWH3YI8/ZQetsy8ghhGBoj853L8Scft8N7goFAU5cSOMLwqx9uwY8MoJCRYt3ZF1JnAoZSiF2l6PvSP466DdWCl44ffInaBnq2tLJx7RPMmHdA+cRqYwxPXnQNwaY2soctZe/LPkbnX56CRIKpxx5OpqWJrsf+AJ6Low3vuOErzD7kAFrfc0q89AS4nkvOLvOOKd7oMSKVwgoZ4JFLPs3hqWS8gwUIpCRx+GEkjz6KxoMOomWfvcdkOK1aGPrs40q9cR7MQCgJdbwNd4iGtEfvgKIYRaSPuZzCM6+QeHotKXd7GMdTEUYItjY1ctRPf1IBayxD5Asl0qn40DrX2fXAzoFFnyb74o30+3Pp/fN6PKXRjqTkGGRbGxlfoQUEros0hsPOOmu0TagafM8hCBVRpMk0TyaXW4QxhmQyWT6H54j/+jYDW7ch63JooGn/hQBEQYFSzxZEyuWoGz+FCrJMPXAJQgiSX7iK8JovYQRMvtXW5xpLGGPwfX9cHN1ihQxwwJe+iPritWghENOm0XLZ58gt3Z9cbS3JZNKKmDFCn/FIExGo7SXqd5wvCCFI+g4JD7rzAcVrvoTcYUaRCkO0kPROncyi3/4a1xu+JdgyuiRrD0GET2AMBGbXIz9m7bOMLb/X+MXnmH+ky9r7QTuC2VmBcAJA4CmDkpqX5s5mb9tPR4yCmo4CRCKLn2omV1tfri8yhJPNkMhmMMZQKpXo7eog/+yvYctj+KV2siY+QTl617fL9017z0m0XXU10dw5TF22rELWWXZGa00qldrjGUtjDStkgP1OPokNz6zGmzmD2uOOJdPURC6Xs7UMxhjqoHez5YG78WV8Ntbug54CIQzTFixl3Q4ixgDhtKnoY49l30s/SzL7+teILSNDNjeV/m3PEOkM9S27Ftvq27aFmQNbyBuPdy1NsPK+AjUu+K5giiNwB89TcrXmHb/831FufXXRNP2oYf9/tcmdEIJkMskLa5+gcd2dxAeGGJQAT2i23vl5Zp9zc/la8/WvMv/UU+yEcYygtaaurm5c+WNCCxkpJWEY4r2OmffUz1yC4zikMxl83x9XTqwa/DSFecsQax/DQTP8UILttIscNe2rcQ49hOiRR1G+T+7iT1B/3LE0z5yBOwbqHljih1ii5u2k3d1HPXv/4zKmCtDEp+tuJuCd2RShH2FUnMOWiCLaFsxjX5tbMaL8NePh4refxPr2h/E7ngQEyhjQgqZEqZx7obVm7mnvKy9PWSrLeBQxMMGFjBCChoYGent7d+sYYwxKKYQQ1NTW4nneuHPgRKehoaFcEK9/IKJuxnSiTILCc6vwgoFhS0dKOBTTddQvOZigsJaZ37qO3idX4TY3kZ0509YBGoP4iQxKqfLOiB2joLlzrqXnjquoKbRTMg5Z14Njj6LuoXsJMXGRNeNw0G/urFTzLa+CEIJZp36RTQ/eDGt+g0QRaUGxYTHpwT5dU1NjJxajhOM4KKUAygnaO4+H41HEAIjXyEoeHynLr0EURfT29pYHSa01xhgcxyGZTJJIJMay895sw8a1D40xFAoFgiCgY9srJMKHkRJQAVFfD1F+gFIxxHEkbk0dIl2DcadTO+kwXDee5SmlqK+vr6SPq9qHra2te9wWbYwhm83iOA5hGNLf34+UsuyroK8L89xyoiji2RU9LL3obDYdcnh8AKHW5M8/j0OuuHw0zKhqH74ZjDEMdGxkYOMq3FwLtbPfhuPECfij2Cffil80rn2447PeGFN+DsLwM5XGMHtsXFUIGYjFTKkUhzRd18XzvGED5hjGDqBAb28vWmvy/dswRmF0EaP6cBggioo4jkeoknipKaSzk8uiNQxDGhsbK+3nqvbhzkJmaMyRUpLL5Yb5Jooi+vr6ymcxDV1f6tpEon4aQgg2nXcB0RNPUqrJceBjK0bLt1Xtw7eC3UUARpGqFzITACtkhqhwZ/prsAMog4fQdXeXBUr5e2sMBoMgPmdnR98aY0ilUiQSiUo0eUeq2oebNm0inU6XlxAcx8HzPFzX3eOSb7FYpFAolCcb3T//GHWn3wRAVCqx4YNnMf+O28k2NIyWGVXtwwmAFTLjHytkxjF2AB3EGENPT3zk/GuJUWMMUkpqxkYSaFX7sFAo4Pt+WYS+3omE1ppSqUSpVKK79QXqpi4oT0SSrjvaO8+q2ocTACtkxj9WyIxj7AC6A0M5M2EYlhNEd34waq3xPI9sNjtWom/Wh2+CofV8rXUl1/KtD8c3VsiMf6yQGcfYAXQ3DGXdDz3goihCa43jOOXZ/xgRMWB9OBGwPhzfWCEz/rFCZhxjB9DXyRjOf7I+HP9YH45vrJAZ/+zRh7Z0rWXCMEZFjMVisVhGECtkLBaLxWKxjFuskLFYLBaLxTJusULGYrFYLBbLuOW1kn0tFovFYrFYxiw2ImOxWCwWi2XcYoWMxWKxWCyWcYsVMhaLxWKxWMYtVshYLBaLxWIZt1ghY7FYLBaLZdxihYzFYrFYLJZxy/8Ht9KmO9UfdEoAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for tp in timepoint:\n",
    "    for section in list_coor:\n",
    "        Cor = Image.open(\"../Screenshot_Brainrender/SNCA_exp/SNCA_exp_Breg{}.png\".format(-section/1000))\n",
    "        image.append(Cor)\n",
    "        \n",
    "fig = plt.figure(figsize=(10,1))\n",
    "\n",
    "G = gridspec.GridSpec(nrows=4, ncols=15)\n",
    "\n",
    "\n",
    "axes_1_1 = fig.add_subplot(G[0:4, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "#plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2_1 = fig.add_subplot(G[0:4, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3_1 = fig.add_subplot(G[0:4, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4_1 = fig.add_subplot(G[0:4, 9:12])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[3])\n",
    "\n",
    "axes_5_1 = fig.add_subplot(G[0:4, 12:15])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[4])\n",
    "\n",
    "plt.savefig('../Data_ReScience/SNCA_exp/SNCA_exp_summary.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/SNCA_exp/SNCA_exp_summary.pdf', dpi=300)\n",
    "\n",
    "vul = Image.open('../Data_ReScience/SNCA_exp/SNCA_exp_summary.png')\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "plt.annotate(\"B\",(400,50), fontsize=20, color=\"black\")\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(vul)\n",
    "\n",
    "plt.savefig('../Data_ReScience/SNCA_exp/SNCA_exp_summary.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/SNCA_exp/SNCA_exp_summary.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) MPI 3 vulnerability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5415458915059606\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_2.1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:11<00:46, 11.69s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_1.08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:23<00:35, 11.69s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [00:35<00:23, 11.79s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [00:47<00:11, 11.98s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI3_Breg_-<span style=\"color: #000080; font-weight: bold\">4.</span>5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [01:00<00:00, 12.06s/it]\n"
     ]
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "resi_cpu = pd.read_csv('../output/model_output_MPI3_iCPu.csv')\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "resi_cpu.head()\n",
    "resi_cpu = resi_cpu.rename(columns={'Unnamed: 0': 'region'})\n",
    "\n",
    "for r in resi_cpu.index:\n",
    "    \n",
    "    names = reg_names[reg_names['ROInames'] == resi_cpu.loc[r, 'region']]['BrainR']\n",
    "    \n",
    "    if len(names) > 1:\n",
    "         resi_cpu.loc[r, 'br_regions'] = list(names)[0]\n",
    "    elif len(names) == 1:    \n",
    "        resi_cpu.loc[r, 'br_regions'] = names.values\n",
    "        \n",
    "for r in resi_cpu.index:\n",
    "    \n",
    "    if resi_cpu.loc[r, 'region'][0] == 'i':\n",
    "        resi_cpu.loc[r, 'hem'] = 'right'\n",
    "    elif resi_cpu.loc[r, 'region'][0] == 'c':\n",
    "        resi_cpu.loc[r, 'hem'] = 'left' \n",
    "        \n",
    "\n",
    "max_resi_cpu = np.max(resi_cpu['residual'])\n",
    "min_resi_cpu = np.min(resi_cpu['residual'])\n",
    "mid_pt= 1 - (max_resi_cpu) / (max_resi_cpu + np.abs(min_resi_cpu))\n",
    "print(mid_pt)\n",
    "cmap=shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "\n",
    "\n",
    "resi_cpu['residual']= (resi_cpu['residual']- min_resi_cpu)/(max_resi_cpu-min_resi_cpu)\n",
    "resi_cpu['residual']\n",
    "resi_cpu= resi_cpu.dropna() #Dropping the br_regions overlapping in brain render\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoints = [1,3,6]\n",
    "for section in tqdm(list_coor):\n",
    "    bregma = np.array([5400,  # AP    \n",
    "          0,  # DV    \n",
    "          5700,]) # ML\n",
    "\n",
    "    bregma[0] = bregma[0] + section\n",
    "\n",
    "    # Create a brainrender scene\n",
    "    scene = Scene(inset=False,screenshots_folder=\"../Screenshot_Brainrender/residuals/MPI3\")\n",
    "    frontal_camera = {\n",
    "         'pos': (-19781, 3777, -5832),\n",
    "         'viewup': (0, -1, 0),\n",
    "         'clippingRange': (26465, 26836),\n",
    "        'focalPoint': (5705, 3991, -5691),\n",
    "         'distance': 25487}\n",
    "\n",
    "   # Add brain regions\n",
    "    for r in resi_cpu.index:\n",
    "\n",
    "        region_to_plot = resi_cpu.loc[r, \"br_regions\"]\n",
    "        color = cmap(resi_cpu.loc[r, 'residual'])[:3]\n",
    "\n",
    "        scene.add_brain_region(region_to_plot, color=color, alpha=1, hemisphere=resi_cpu.loc[r, 'hem'], silhouette=True) #alpha=data_cpu.loc[r, 'MPI{}'.format(t)]\n",
    "\n",
    "\n",
    "    plane_1 = scene.atlas.get_plane(pos=bregma+50, plane=\"frontal\", norm=(-1, 0, 0), alpha=1, color=\"white\") # Thickness: 150 um\n",
    "    plane_2 = scene.atlas.get_plane(pos=bregma-50, plane=\"frontal\", norm=(1, 0, 0), alpha=1, color=\"white\")\n",
    "    plane_3 = scene.atlas.get_plane(plane=\"sagittal\", norm=(0,0,-1), alpha=1, color=\"white\")\n",
    "    scene.slice(plane_1, close_actors=True)\n",
    "    scene.slice(plane_2, close_actors=True)\n",
    "    scene.slice(plane_3, close_actors=True)\n",
    "\n",
    "\n",
    "    brainrender.settings.SHADER_STYLE = \"cartoon\" #[metallic, plastic, shiny, glossy]\n",
    "    brainrender.settings.ROOT_ALPHA = 0.17\n",
    "    brainrender.settings.LW =2.4\n",
    "    brainrender.settings.ROOT_COLOR  = [0.4, 0.4, 0.4]\n",
    "    brainrender.settings.OFFSCREEN = True\n",
    "    brainrender.settings.SHOW_AXES = False\n",
    "    brainrender.settings.BACKGROUND_COLOR = \"white\"\n",
    "    scene.render(atlas_name='allen_mouse_50um', camera= frontal_camera, zoom=1.5)\n",
    "    scene.screenshot(name=\"50um_MPI3_Breg_{}_mm\".format(-section/1000), scale = 3)\n",
    "    img_totranspose = Image.open(\"../Screenshot_Brainrender/residuals/MPI3/50um_MPI3_Breg_{}_mm.png\".format(-section/1000))\n",
    "    img_totranspose = img_totranspose.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "    img_totranspose.save(\"../Screenshot_Brainrender/residuals/MPI3/50um_MPI3_Breg_{}_mm.png\".format(-section/1000))\n",
    "    #title=\"-Coronal section Bregma {} mm -50um thickness- Seed = SN - MPI {}\".format(-section/1000,t),\n",
    "    scene.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for section in list_coor:\n",
    "    Cor = Image.open(\"../Screenshot_Brainrender/residuals/MPI3/50um_MPI3_Breg_{}_mm.png\".format(-section/1000))\n",
    "    image.append(Cor)\n",
    "\n",
    "fig = plt.figure(figsize=(10,1))\n",
    "\n",
    "G = gridspec.GridSpec(nrows=4, ncols=15)\n",
    "\n",
    "\n",
    "axes_1_1 = fig.add_subplot(G[0:4, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "#plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2_1 = fig.add_subplot(G[0:4, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3_1 = fig.add_subplot(G[0:4, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4_1 = fig.add_subplot(G[0:4, 9:12])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[3])\n",
    "\n",
    "axes_5_1 = fig.add_subplot(G[0:4, 12:15])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[4])\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/MPI3/vulnerabilitympi3.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI3/vulnerabilitympi3.pdf', dpi=300)\n",
    "\n",
    "vul = Image.open('../Data_ReScience/residuals/MPI3/vulnerabilitympi3.png')\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "plt.annotate(\"A\",(400,50), fontsize=20, color=\"black\")\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(vul)\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/MPI3/vulnerabilitympi3.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI3/vulnerabilitympi3.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5415458915059606\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "def view_colormap(cmap):\n",
    "    \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n",
    "    cmap = plt.cm.get_cmap(cmap)\n",
    "    colors = cmap(np.arange(cmap.N))\n",
    "    plt.figure(figsize=(15,3))\n",
    "    plt.imshow([colors], extent=[0, 10, 0, 1])\n",
    "    plt.xticks([0, 2.5, 5, 7.5 ,10], ['0', '0.25', '0.5','0.75','1'], fontsize=30)\n",
    "    plt.yticks([])\n",
    "    plt.text(2.9,1.1,'Relative vulnerability', fontsize=35)\n",
    "    #plt.text(10.5,0.6,'Estimated percentage of area with \\u03B1-synuclein')\n",
    "\n",
    "print(mid_pt)\n",
    "cmap=shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "view_colormap(cmap)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI3/colorbar_mpi3.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI3/colorbar_mpi3.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) MPI 6 vulnerability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    region  experimental_data  ndm_data  linreg_data  residual\n",
      "0      iCg          -1.463974 -0.457169    -1.962573  0.498599\n",
      "1     iAcb          -2.312397 -0.895875    -2.903617  0.591219\n",
      "2      iTC          -3.404267 -1.002679    -3.132717 -0.271550\n",
      "3      iAI          -1.631181 -0.429272    -1.902733  0.271553\n",
      "4    iAI-b          -1.679294 -0.497654    -2.049416  0.370122\n",
      "..     ...                ...       ...          ...       ...\n",
      "108   cTeA          -1.984738 -0.628589    -2.330275  0.345537\n",
      "109   cIns          -1.829568 -0.340096    -1.711447 -0.118122\n",
      "110     cV          -2.586285 -0.741784    -2.573084 -0.013201\n",
      "111   cV-b          -2.577062 -0.787912    -2.672031  0.094969\n",
      "112   cVTA          -4.449998 -0.924025    -2.964000 -1.485998\n",
      "\n",
      "[113 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "resi_cpu = pd.read_csv('../output/model_output_MPI6_iCPu.csv')\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "resi_cpu.head()\n",
    "resi_cpu = resi_cpu.rename(columns={'Unnamed: 0': 'region'})\n",
    "print(resi_cpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6037200634022687\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_2.1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:11<00:45, 11.34s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_1.08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:22<00:33, 11.22s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [00:33<00:22, 11.13s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [00:44<00:11, 11.18s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_SNc_MPI6_Breg_-<span style=\"color: #000080; font-weight: bold\">4.</span>5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:55<00:00, 11.15s/it]\n"
     ]
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "resi_cpu = pd.read_csv('../output/model_output_MPI6_iCPu.csv')\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "resi_cpu.head()\n",
    "resi_cpu = resi_cpu.rename(columns={'Unnamed: 0': 'region'})\n",
    "\n",
    "for r in resi_cpu.index:\n",
    "    \n",
    "    names = reg_names[reg_names['ROInames'] == resi_cpu.loc[r, 'region']]['BrainR']\n",
    "    \n",
    "    if len(names) > 1:\n",
    "         resi_cpu.loc[r, 'br_regions'] = list(names)[0]\n",
    "    elif len(names) == 1:    \n",
    "        resi_cpu.loc[r, 'br_regions'] = names.values\n",
    "        \n",
    "for r in resi_cpu.index:\n",
    "    \n",
    "    if resi_cpu.loc[r, 'region'][0] == 'i':\n",
    "        resi_cpu.loc[r, 'hem'] = 'right'\n",
    "    elif resi_cpu.loc[r, 'region'][0] == 'c':\n",
    "        resi_cpu.loc[r, 'hem'] = 'left' \n",
    "        \n",
    "\n",
    "max_resi_cpu = np.max(resi_cpu['residual'])\n",
    "min_resi_cpu = np.min(resi_cpu['residual'])\n",
    "mid_pt= 1 - (max_resi_cpu) / (max_resi_cpu + np.abs(min_resi_cpu))\n",
    "print(mid_pt)\n",
    "cmap=shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "\n",
    "\n",
    "resi_cpu['residual']= (resi_cpu['residual']- min_resi_cpu)/(max_resi_cpu-min_resi_cpu)\n",
    "resi_cpu['residual']\n",
    "resi_cpu= resi_cpu.dropna() #Dropping the br_regions overlapping in brain render\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoints = [1,3,6]\n",
    "for section in tqdm(list_coor):\n",
    "    bregma = np.array([5400,  # AP    \n",
    "          0,  # DV    \n",
    "          5700,]) # ML\n",
    "\n",
    "    bregma[0] = bregma[0] + section\n",
    "\n",
    "    # Create a brainrender scene\n",
    "    scene = Scene(inset=False,screenshots_folder=\"../Screenshot_Brainrender/residuals/MPI6\")\n",
    "    frontal_camera = {\n",
    "         'pos': (-19781, 3777, -5832),\n",
    "         'viewup': (0, -1, 0),\n",
    "         'clippingRange': (26465, 26836),\n",
    "        'focalPoint': (5705, 3991, -5691),\n",
    "         'distance': 25487}\n",
    "\n",
    "   # Add brain regions\n",
    "    for r in resi_cpu.index:\n",
    "\n",
    "        region_to_plot = resi_cpu.loc[r, \"br_regions\"]\n",
    "        color = cmap(resi_cpu.loc[r, 'residual'])[:3]\n",
    "\n",
    "        scene.add_brain_region(region_to_plot, color=color, alpha=1, hemisphere=resi_cpu.loc[r, 'hem'], silhouette=True) #alpha=data_cpu.loc[r, 'MPI{}'.format(t)]\n",
    "\n",
    "\n",
    "    plane_1 = scene.atlas.get_plane(pos=bregma+50, plane=\"frontal\", norm=(-1, 0, 0), alpha=1, color=\"white\") # Thickness: 150 um\n",
    "    plane_2 = scene.atlas.get_plane(pos=bregma-50, plane=\"frontal\", norm=(1, 0, 0), alpha=1, color=\"white\")\n",
    "    plane_3 = scene.atlas.get_plane(plane=\"sagittal\", norm=(0,0,-1), alpha=1, color=\"white\")\n",
    "    scene.slice(plane_1, close_actors=True)\n",
    "    scene.slice(plane_2, close_actors=True)\n",
    "    scene.slice(plane_3, close_actors=True)\n",
    "\n",
    "\n",
    "    brainrender.settings.SHADER_STYLE = \"cartoon\" #[metallic, plastic, shiny, glossy]\n",
    "    brainrender.settings.ROOT_ALPHA = 0.17\n",
    "    brainrender.settings.LW =2.4\n",
    "    brainrender.settings.ROOT_COLOR  = [0.4, 0.4, 0.4]\n",
    "    brainrender.settings.OFFSCREEN = True\n",
    "    brainrender.settings.SHOW_AXES = False\n",
    "    brainrender.settings.BACKGROUND_COLOR = \"white\"\n",
    "    scene.render(atlas_name='allen_mouse_50um', camera= frontal_camera, zoom=1.5)\n",
    "    scene.screenshot(name=\"50um_MPI6_Breg_{}_mm\".format(-section/1000), scale = 3)\n",
    "    img_totranspose = Image.open(\"../Screenshot_Brainrender/residuals/MPI6/50um_MPI6_Breg_{}_mm.png\".format(-section/1000))\n",
    "    img_totranspose = img_totranspose.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "    img_totranspose.save(\"../Screenshot_Brainrender/residuals/MPI6/50um_MPI6_Breg_{}_mm.png\".format(-section/1000))\n",
    "    #title=\"-Coronal section Bregma {} mm -50um thickness- Seed = SN - MPI {}\".format(-section/1000,t),\n",
    "    scene.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for section in list_coor:\n",
    "    Cor = Image.open(\"../Screenshot_Brainrender/residuals/MPI6/50um_MPI6_Breg_{}_mm.png\".format(-section/1000))\n",
    "    image.append(Cor)\n",
    "\n",
    "fig = plt.figure(figsize=(10,1))\n",
    "\n",
    "G = gridspec.GridSpec(nrows=4, ncols=15)\n",
    "\n",
    "\n",
    "axes_1_1 = fig.add_subplot(G[0:4, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "#plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2_1 = fig.add_subplot(G[0:4, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3_1 = fig.add_subplot(G[0:4, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4_1 = fig.add_subplot(G[0:4, 9:12])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[3])\n",
    "\n",
    "axes_5_1 = fig.add_subplot(G[0:4, 12:15])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[4])\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/MPI6/vulnerabilitympi6.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI6/vulnerabilitympi6.pdf', dpi=300)\n",
    "\n",
    "vul = Image.open('../Data_ReScience/residuals/MPI6/vulnerabilitympi6.png')\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "plt.annotate(\"A\",(400,50), fontsize=20, color=\"black\")\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(vul)\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/MPI6/vulnerabilitympi6.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI6/vulnerabilitympi6.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6037200634022687\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "def view_colormap(cmap):\n",
    "    \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n",
    "    cmap = plt.cm.get_cmap(cmap)\n",
    "    colors = cmap(np.arange(cmap.N))\n",
    "    plt.figure(figsize=(15,3))\n",
    "    plt.imshow([colors], extent=[0, 10, 0, 1])\n",
    "    plt.xticks([0, 2.5, 5, 7.5 ,10], ['0', '0.25', '0.5','0.75','1'], fontsize=30)\n",
    "    plt.yticks([])\n",
    "    plt.text(2.9,1.1,'Relative vulnerability', fontsize=35)\n",
    "    #plt.text(10.5,0.6,'Estimated percentage of area with \\u03B1-synuclein')\n",
    "\n",
    "print(mid_pt)\n",
    "cmap=shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "view_colormap(cmap)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI6/colorbar_mpi6.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/MPI6/colorbar_mpi6.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean over 3 MPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Mean MPI Residuals\n",
      "0              0.572580\n",
      "1              0.466618\n",
      "2              0.690671\n",
      "3              0.449743\n",
      "4             -0.498268\n",
      "..                  ...\n",
      "110           -0.609307\n",
      "111           -0.560718\n",
      "112            0.267988\n",
      "113            0.625915\n",
      "114            0.278136\n",
      "\n",
      "[115 rows x 1 columns]\n",
      "      region\n",
      "0        cAI\n",
      "1      cAI-b\n",
      "2       cAIP\n",
      "3       cAcb\n",
      "4        cAu\n",
      "..       ...\n",
      "110  iVLThal\n",
      "111  iVMThal\n",
      "112      iVO\n",
      "113     iVTA\n",
      "114      iot\n",
      "\n",
      "[115 rows x 1 columns]\n",
      "      region  Mean MPI Residuals\n",
      "0        cAI            0.572580\n",
      "1      cAI-b            0.466618\n",
      "2       cAIP            0.690671\n",
      "3       cAcb            0.449743\n",
      "4        cAu           -0.498268\n",
      "..       ...                 ...\n",
      "110  iVLThal           -0.609307\n",
      "111  iVMThal           -0.560718\n",
      "112      iVO            0.267988\n",
      "113     iVTA            0.625915\n",
      "114      iot            0.278136\n",
      "\n",
      "[115 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "resi_cpu1 = pd.read_csv('../output/model_output_MPI1_iCPu.csv')\n",
    "resi_cpu3 = pd.read_csv('../output/model_output_MPI3_iCPu.csv')\n",
    "resi_cpu6 = pd.read_csv('../output/model_output_MPI6_iCPu.csv')\n",
    "\n",
    "res1=pd.DataFrame(resi_cpu1[\"residual\"].values, index=[resi_cpu1[\"Unnamed: 0\"].values], columns=[\"MPI 1 residual\"])\n",
    "res3=pd.DataFrame(resi_cpu3[\"residual\"].values, index=[resi_cpu3[\"Unnamed: 0\"].values], columns=[\"MPI 3 residual\"])\n",
    "res6=pd.DataFrame(resi_cpu6[\"residual\"].values, index=[resi_cpu6[\"Unnamed: 0\"].values], columns=[\"MPI 6 residual\"])\n",
    "\n",
    "res1_3= pd.merge(res1,res3, right_index=True, left_index=True, how=\"outer\")\n",
    "\n",
    "residuals = pd.merge(res1_3, res6, right_index=True, left_index=True, how=\"outer\")\n",
    "residuals.to_excel(\"Test_residuals.xlsx\")\n",
    "residuals\n",
    "mean_res= pd.DataFrame(np.nanmean(residuals.values, axis=1), columns=[\"Mean MPI Residuals\"])\n",
    "reg_res= pd.DataFrame([i[0] for i in residuals.index.values], columns=[\"region\"])\n",
    "residuals = pd.merge(reg_res,mean_res, right_index=True, left_index=True, how=\"outer\")\n",
    "print(mean_res)\n",
    "print(reg_res)\n",
    "print(residuals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Mean MPI Residuals\n",
      "0              0.572580\n",
      "1              0.466618\n",
      "2              0.690671\n",
      "3              0.449743\n",
      "4             -0.498268\n",
      "..                  ...\n",
      "110           -0.609307\n",
      "111           -0.560718\n",
      "112            0.267988\n",
      "113            0.625915\n",
      "114            0.278136\n",
      "\n",
      "[115 rows x 1 columns]\n",
      "      region\n",
      "0        cAI\n",
      "1      cAI-b\n",
      "2       cAIP\n",
      "3       cAcb\n",
      "4        cAu\n",
      "..       ...\n",
      "110  iVLThal\n",
      "111  iVMThal\n",
      "112      iVO\n",
      "113     iVTA\n",
      "114      iot\n",
      "\n",
      "[115 rows x 1 columns]\n",
      "      region  Mean MPI Residuals\n",
      "0        cAI            0.572580\n",
      "1      cAI-b            0.466618\n",
      "2       cAIP            0.690671\n",
      "3       cAcb            0.449743\n",
      "4        cAu           -0.498268\n",
      "..       ...                 ...\n",
      "110  iVLThal           -0.609307\n",
      "111  iVMThal           -0.560718\n",
      "112      iVO            0.267988\n",
      "113     iVTA            0.625915\n",
      "114      iot            0.278136\n",
      "\n",
      "[115 rows x 2 columns]\n",
      "mid_pt is 0.5402614359754954\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Mean_MPI_Breg_2.1_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:16<01:07, 16.77s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Mean_MPI_Breg_1.08_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:33<00:50, 16.75s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Mean_MPI_Breg_-<span style=\"color: #000080; font-weight: bold\">1.</span>36_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [00:51<00:34, 17.11s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Mean_MPI_Breg_-<span style=\"color: #000080; font-weight: bold\">2.</span>82_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [01:08<00:17, 17.19s/it]"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "Saving new screenshot at 50um_Mean_MPI_Breg_-<span style=\"color: #000080; font-weight: bold\">4.</span>5_mm.png\n",
       "\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [01:25<00:00, 17.16s/it]\n"
     ]
    }
   ],
   "source": [
    "from vedo import embedWindow\n",
    "embedWindow(None)\n",
    "\n",
    "from brainrender import Scene\n",
    "import brainrender\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "reg_names = pd.read_excel('./Data83018/BrainR_ROInames.xlsx')\n",
    "resi_cpu1 = pd.read_csv('../output/model_output_MPI1_iCPu.csv')\n",
    "resi_cpu3 = pd.read_csv('../output/model_output_MPI3_iCPu.csv')\n",
    "resi_cpu6 = pd.read_csv('../output/model_output_MPI6_iCPu.csv')\n",
    "\n",
    "res1=pd.DataFrame(resi_cpu1[\"residual\"].values, index=[resi_cpu1[\"Unnamed: 0\"].values], columns=[\"MPI 1 residual\"])\n",
    "res3=pd.DataFrame(resi_cpu3[\"residual\"].values, index=[resi_cpu3[\"Unnamed: 0\"].values], columns=[\"MPI 3 residual\"])\n",
    "res6=pd.DataFrame(resi_cpu6[\"residual\"].values, index=[resi_cpu6[\"Unnamed: 0\"].values], columns=[\"MPI 6 residual\"])\n",
    "\n",
    "res1_3= pd.merge(res1,res3, right_index=True, left_index=True, how=\"outer\")\n",
    "\n",
    "residuals = pd.merge(res1_3, res6, right_index=True, left_index=True, how=\"outer\")\n",
    "residuals.to_excel(\"Test_residuals.xlsx\")\n",
    "residuals\n",
    "mean_res= pd.DataFrame(np.nanmean(residuals.values, axis=1), columns=[\"Mean MPI Residuals\"])\n",
    "reg_res= pd.DataFrame([i[0] for i in residuals.index.values], columns=[\"region\"])\n",
    "residuals = pd.merge(reg_res,mean_res, right_index=True, left_index=True, how=\"outer\")\n",
    "# print(mean_res)\n",
    "# print(reg_res)\n",
    "# print(residuals)\n",
    "\n",
    "for r in residuals.index:\n",
    "    \n",
    "    names = reg_names[reg_names['ROInames'] == residuals.loc[r, 'region']]['BrainR']\n",
    "    \n",
    "    if len(names) > 1:\n",
    "        residuals.loc[r, 'br_regions'] = list(names)[0]\n",
    "    elif len(names) == 1:    \n",
    "        residuals.loc[r, 'br_regions'] = names.values\n",
    "        \n",
    "for r in residuals.index:\n",
    "    \n",
    "    if residuals.loc[r, 'region'][0] == 'i':\n",
    "        residuals.loc[r, 'hem'] = 'right'\n",
    "    elif residuals.loc[r, 'region'][0] == 'c':\n",
    "        residuals.loc[r, 'hem'] = 'left' \n",
    "        \n",
    "\n",
    "max_resi_cpu = np.max(residuals[\"Mean MPI Residuals\"])\n",
    "min_resi_cpu = np.min(residuals[\"Mean MPI Residuals\"])\n",
    "mid_pt= 1 - (max_resi_cpu) / (max_resi_cpu + np.abs(min_resi_cpu))\n",
    "print(\"mid_pt is\",mid_pt)\n",
    "\n",
    "cmap = shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "\n",
    "\n",
    "residuals[\"Mean MPI Residuals\"]= (residuals[\"Mean MPI Residuals\"]- min_resi_cpu)/(max_resi_cpu-min_resi_cpu)\n",
    "residuals[\"Mean MPI Residuals\"]\n",
    "residuals = residuals.dropna() #Dropping the br_regions overlapping in brain render\n",
    "residuals\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "for section in tqdm(list_coor):\n",
    "    bregma = np.array([5400,  # AP    \n",
    "          0,  # DV    \n",
    "          5700,]) # ML\n",
    "\n",
    "    bregma[0] = bregma[0] + section\n",
    "\n",
    "    # Create a brainrender scene\n",
    "    scene = Scene(inset=False,screenshots_folder=\"../Screenshot_Brainrender/residuals/Mean_MPI\")\n",
    "    frontal_camera = {\n",
    "         'pos': (-19781, 3777, -5832),\n",
    "         'viewup': (0, -1, 0),\n",
    "         'clippingRange': (26465, 26836),\n",
    "        'focalPoint': (5705, 3991, -5691),\n",
    "         'distance': 25487}\n",
    "\n",
    "   # Add brain regions\n",
    "    for r in residuals.index:\n",
    "\n",
    "        region_to_plot = residuals.loc[r, \"br_regions\"]\n",
    "        color = cmap(residuals.loc[r, \"Mean MPI Residuals\"])[:3]\n",
    "\n",
    "        scene.add_brain_region(region_to_plot, color=color, alpha=1, hemisphere=residuals.loc[r, 'hem'], silhouette=True) \n",
    "\n",
    "\n",
    "    plane_1 = scene.atlas.get_plane(pos=bregma+50, plane=\"frontal\", norm=(-1, 0, 0), alpha=1, color=\"white\") # Thickness: 150 um\n",
    "    plane_2 = scene.atlas.get_plane(pos=bregma-50, plane=\"frontal\", norm=(1, 0, 0), alpha=1, color=\"white\")\n",
    "    plane_3 = scene.atlas.get_plane(plane=\"sagittal\", norm=(0,0,-1), alpha=1, color=\"white\")\n",
    "    scene.slice(plane_1, close_actors=True)\n",
    "    scene.slice(plane_2, close_actors=True)\n",
    "    scene.slice(plane_3, close_actors=True)\n",
    "\n",
    "\n",
    "    brainrender.settings.SHADER_STYLE = \"cartoon\" #[metallic, plastic, shiny, glossy]\n",
    "    brainrender.settings.ROOT_ALPHA = 0.17\n",
    "    brainrender.settings.LW =2.4\n",
    "    brainrender.settings.ROOT_COLOR  = [0.4, 0.4, 0.4]\n",
    "    brainrender.settings.OFFSCREEN = True\n",
    "    brainrender.settings.SHOW_AXES = False\n",
    "    brainrender.settings.BACKGROUND_COLOR = \"white\"\n",
    "    scene.render(atlas_name='allen_mouse_50um', camera= frontal_camera, zoom=1.5)\n",
    "    scene.screenshot(name=\"50um_Mean_MPI_Breg_{}_mm\".format(-section/1000), scale = 3)\n",
    "    img_totranspose = Image.open(\"../Screenshot_Brainrender/residuals/Mean_MPI/50um_Mean_MPI_Breg_{}_mm.png\".format(-section/1000))\n",
    "    img_totranspose = img_totranspose.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "    img_totranspose.save(\"../Screenshot_Brainrender/residuals/Mean_MPI/50um_Mean_MPI_Breg_{}_mm.png\".format(-section/1000))\n",
    "    #title=\"-Coronal section Bregma {} mm -50um thickness- Seed = SN - MPI {}\".format(-section/1000,t),\n",
    "    scene.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "list_coor = [-2100, -1080, +1360, +2820, +4500] # Bregma corresponding coordinates\n",
    "timepoint=[1,3,6]\n",
    "image=[]\n",
    "for section in list_coor:\n",
    "    Cor = Image.open(\"../Screenshot_Brainrender/residuals/Mean_MPI/50um_Mean_MPI_Breg_{}_mm.png\".format(-section/1000))\n",
    "    image.append(Cor)\n",
    "\n",
    "fig = plt.figure(figsize=(10,1))\n",
    "\n",
    "G = gridspec.GridSpec(nrows=4, ncols=15)\n",
    "\n",
    "\n",
    "axes_1_1 = fig.add_subplot(G[0:4, 0:3])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "#plt.text(3250,-100,\"MPI 1\", ha=\"right\")\n",
    "# plt.annotate(\"A\",(50,150), fontsize=7, color=\"black\")\n",
    "plt.imshow(image[0])\n",
    "\n",
    "axes_2_1 = fig.add_subplot(G[0:4, 3:6])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[1])\n",
    "\n",
    "axes_3_1 = fig.add_subplot(G[0:4, 6:9])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[2])\n",
    "\n",
    "axes_4_1 = fig.add_subplot(G[0:4, 9:12])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[3])\n",
    "\n",
    "axes_5_1 = fig.add_subplot(G[0:4, 12:15])\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(image[4])\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/Mean_MPI/vulnerabilityMean_MPI.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/Mean_MPI/vulnerabilityMean_MPI.pdf', dpi=300)\n",
    "\n",
    "vul = Image.open('../Data_ReScience/residuals/Mean_MPI/vulnerabilityMean_MPI.png')\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "plt.annotate(\"A\",(400,50), fontsize=20, color=\"black\")\n",
    "xticks([]), yticks([])\n",
    "for pos in ['right','top','bottom','left']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.imshow(vul)\n",
    "\n",
    "plt.savefig('../Data_ReScience/residuals/Mean_MPI/vulnerabilityMean_MPI.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/Mean_MPI/vulnerabilityMean_MPI.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5402614359754954\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2YAAACpCAYAAABAgy4CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgn0lEQVR4nO3debgkRZ3u8fdlkUUQml1QaEFkV8QNVEZAVLxeRUEBbRFEQHF3roijODQKruOIy1VxgdYLAgoO4gIOsgmKF9lEwAuiNAqNIM0qzdr9u39ElhWnupas5VRUd38/z1PPycqMjIw6lVFZv4rICEeEAAAAAADlLFe6AAAAAACwrCMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMACzxbM+xHdVjduny1GH7wqzMB5YuD9qzPTd7n3YpXZ6lUfb/DdszR5hvzzpme2Z+/C55zc7SzRlVGQEgR2AGYDEtgU67x2O277L9e9vfsb2P7SeULjcAAMCSisAMwCBWkLS2pG0lvUXSaZJusr170VIVwC/pABpoCQcwjBVKFwDAxHtY0kUt61aUtIGkLdX8geepks62vWdE/GyM5QMAAFjiEZgB6OWOiNij3Qbb60s6StJh1aoVJM2xvWlE/GNcBVwSRcQupcsALM1GWcciYrak2aPKDwDaoSsjgIFFxB0R8U5JX8lWryvpzYWKBAAAsEQiMAMwCkdLWpQ9f2mpggAAACyJCMwADC0i7pL0/7JVT6uzn+01bL/T9k9s32x7ge37bd9o+9vTMZiI7XVtv8X2CbYvtz2/GmXyftt/sn2a7QNsr9gjn7nV8NpHZasP6DKS5cyW/TsOEmB7i5Z9t+vj9T0/22+R7U16pN/U9sdsX2L7NtuPVP+Tq2x/1vaWdY9do2yXZmX7fJ/7Xp/t+7GWbbtk2+bWzO/AbJ8L+ylL3bxsb237i7avq86vB6rX8cVBhoUfVX1pd+7ZXtH2frZ/VNWDBdX297fZ/xm232v79Or13FfVofm2r7X9jWHrru31bH/Y9mW277T9UFWuE2zvOOjrHKI8HQf5cTbkvqSXZJtO7PBZMLfabw3bD2brX91HeTar6ndj3+cN8/oATAbuMQMwKvdky2v0Smz7IEmfkbROm82rS9pc0kG2z5E0KyLuHraAto+T9C61/+xbvXpsKmkfSbNt7xMRvx32uP2KiBtsXy7pudWqWZI+XHP3WdnyJRFxS7tETtMbfErSe5QGc8mtVT22l/SB6v92REQs0nBOktT4Ur2f7cPr5Gn72ZK2ylZ9b8hyTDvbh0v6pBY/17aqHofa3j8iTq+Z37TVF9ubSTpVzfOtW9rLJT2nw+bGebONpENsXyBpv4i4s25ZqmO8VNIpSt2ic5tWjwNtf1nSB0ZwThYTEffZPk3SW6tVh0j6cc3d3ybJ1fLvSnxOARg9AjMAo7J2tvxAt4S2Py3piJbVt1SP5ZVGe2zkt4eki23vPILgbFs1P/eiOt7tkhYofbndqvorSTMlXWT7hRFxdZu8LpK0vqSnS9qsWjdP0u87HPuhPst6kppflN9k+98iouMEuJJke3lJ+7bk0S7dapLO1NQup4uUWj3vkLSapO0kraz0//qgpKdIemOfr6HVaZK+oBQIbihpV0nn1dgvDzYvjYg/DVmOaWX7SEmfqJ4+KOk6pXPs6Ur/Ryn9b0+x/aeIuKpHftNZX9ZSeg8aLau3SvpzVb4t2qTfPlt+TNJNkuZLelQpaNxKzUB/V0mX2H5ORHT9TGjJ/1RJK1XP/6B0Tm4o6RnVOkt6r1JdPahmvtPpIUk/r5afL2lGtXytpNvapL8jWz5ezcDsf9jeMCLmdTtYVc8PyFZ9s+8SA5hMEcGDBw8eUx6S5igFLiFpbo3060tamO3znS5pD8nShdKXsC1a0iwnaW+lLzCNdGfULO/sLunOlvT9Ku8ntdm+vKRXS7oxy+86Se6S5+ws7Zw+/scXZvsd2Gb7epIez9K8pEaee2TpH5E0o0O6k7J0jyrdI7h2S5pVJX1E6ct3I+17RnBu/TjL78Qa6ZdT+nLb2OedbdLs0s/5Wu1zYLbPhV3Szc3S7VIjr/lKQe591bm+Ukva10i6P0t/QY9yTkd9yc+9RlmukLRTS7qVJW3Usu4uSV+s/udPaJP3apLeXb3+xjG+2uM15q/v79Xf8yRt3pJuW0mXtqSfNWgdq9LMzPPrktfsLF3Hel7nmG32uTrb5yM10r8mS79A0pqD1kcePHhM1oN7zACMwtGaes9q2+5Ztp8s6bhs1eyI2C8ibsjTRcSiiDhD0s5KXxwlaS/bLxqynK+PiH0i4oyIuL91Y0QsjIgfS9pJ0s3V6q0lvXLI4/YtUvevc7NVszql7ZDmpxFxT2sC23tm6R6T9KqIOCoi5rccf0FEfFLS/tnqj9t+Yq0X0FneireX7ZV7pN9VqbWkUd7Thjz+dFtLKSh+aUR8MyIeyTdGxFlK3dAadrHd9p7MMdWX1SVdpRT4X9qS78MR0dris0lEvC8iLoyIR1szi4h/RMRXJL1M6YcFSXqr7bVb03awjqQLJO0REX9syftaSbtLujJb/fmqW+6S7Phs+SDb7pgyOThbPj0i7h19kQCUQGAGYGC2N7D9dUlvz1b/UtJPOuzyHqWWGEn6VUQc3S3/iLhR0jHZqncOWtYqvwdrppsv6dhs1Z7DHHcIJ2fLr+/2BdT2qpJem61q241RU7vEfTYizu2QTpIUEadKakwYvqakN3VLX8NZanZ1fZJSC2U3ebB5TmsAOaE+HRGXd9l+uqS/ZM9f2CHduOrLoVFz3sE+6tBlSq17Ump5e3nNsjwm6ZCIeKzL8RutiFJqrX9dzbwn1clKXV6l1C16104Jq2A9/6GIbozAUoTADEAv69s+p+XxC9uN+yfyoOwKSW+IiE73QuWtL1+oefw8OBnnMPy/yZZLjXj2X2p+YZsh6VVd0r5WqRuZJN0r6aetCapBHnaqni6U9KWa5cgH2xjqPYiIhyT9MFvVcc67qjVt72zVyZ3STpjju22s6sevslVbdUg6jvpyVY8gchiD1KGfRY97CCPiSk39/+3Vb8EmSdV6f2q26uBOaZXuR2vcJ3tDRFw8bQUDMHYM/gGgl5UlvaJHmtuURqD7Zqdfup2GB39Ktur8OgePiHm271VqrVm/zs3xddjeQdKLlboqrqUU1OQ/Vq2SLeflHpuIeND2mWq2Gs1SCtbayVuWTm/tQlfZOVu+NuqPlndttrxDzX26OUnNwQteaXutaD9QxauVWtWk1Mp21giOPd1ujoi/1Uh3a7Y8o3XjGOvLJXXybVO+FZRadp6rNCjHk5Ra9/JueBtly3Xr0Dk1052tVH+lcj+cjNLxanZx3atdnai6OOaDnXxrXIUDMB4EZgBGYUNJz1Kze1E7+Vxci5RGpKubf34f0rpKox8OxPYrJH1eaUjvunoO/z+NTlIz6PqftteIiPvyBLbX0dSuYp26MebvwUbV0Op15EFq6xDmgzhf6T3cUGkEvzeofStTHmyeUbW2Tbo6QZmUBm1oWLXN9nHVl75GuKxGBHy30sAw6/Wxa906dG3vJIule5rtFSLi8Y6pJ1xE/Nb2VZKerTQi5f5Kg6zkdlVzBNjHJH1nfCUEMA4EZgB6uSUiZjae2F5O0pOVWpreo9SqYUmHKv1q3mlI9fzm/+XUuxWuk4GDJNsfkPSfA+xacnCBc5VG21tf6Qvb3pJOaEmzr5qf539Rus+vnfw9WEeDvQdDB6kRscj2KZL+V7VqlloCM9szNPVemk7B5qRZbECMGtpFXOOqL3WHsW+0kp2qqd1L61qpdxJJaVTLQdKt0ce+k+p4SV+vlg/W4oFZPmjMjyLi72MpFYCx4R4zAH2pRoC7LSLOjYjXSDoy27yf7UM77DrsaH4NA31u2X6BUktZw18kfUxp2O+NlboyLh8RjghLajtS3rhFxEJNHYmw3T1Z+brvdbnHbxTvQe1mmx7ye6FebHuTlu37qBkQz1MaqW9ZMq760s8Ezf+qqUHZZUoTtj9P6YeDVSUtl9Whty6eRU91A9vWrrp1A79J9j1JjUFYtq0+syT984eK/F46Bv0AlkIEZgCGEhHHKt3v0fBZ22u1SZp3v/tb48vbAI8LByzqh9UMKi6TtG1EHBMRF0XEXyPiwYjIv6SuvngWxeStRS+x/c97d2xvKmnHbHu3ATLy9+DUQd+DUbygSJMqX9d4GVp8tMe8G+MpLe/NKCw/4vxGrXR9maLqwvihbNVXJe0YEV+NiMsj4s6IeKjlR4FB6lDdfVrTLTb9xZIm0iTc+UA7+SAgb1azi+pcSb8YU7EAjBGBGYBReK+acxatIenf2qS5I1vewPbYAp/qpvmXZauOqL4EdVNkwI92IuK3khpzVy2nqd1F8wDmd9VcT53k78HmIyreMPIg8p+vw/bGag7sIPXuxpi3sqxY89hr1kxXSrH60sEOanavXCDp8C4tsw2D1KG6LdWbZssL6g73vwTIu/TuZ7sx0mrejfGEafihAsAEIDADMLSIuEnSnGzVu2xv0JLsMqUh2ht2m+5yZdbS1K5hv62xT6e5pVrlX5BG1c2vnTyIybsu5oFZrwAmH758+z4m/Z0uJ6s5YMw2trevlmep+b+8LiKu7pFPHmTPqDFBrzR1cI1JVLK+tLNxtnx9RCzomLKpbh3KPX+AdFcNcJzpMtTnQTUVwBXV09Uk7Wv7eUqDK0npnDhxqBICmFgEZgBG5VNqtpqtIunwfGM1kuD/zVYNNVl0n+q2okj6Z7et/XsmTPIJd1fpmGp4edD1LNvb2H6upC2qdYskndIjj4skPVwtL680YEsxEfEXSfk8TLNa/kr1Bv3IJ2teRdLTuyWuJup+Zbc0pRWuL+3kdahXS5lsby7pRQMc5w3VAEPd8l5JUyeVnqS5vEbxeZC3mh2iqV0az46IWwVgqURgBmAkIuLPmnp/xDtstw6nnU+S+3LbrfcVTZf5SsNLN7y4U8LK4ZJaB6PoJB8evWtAMIyIuFnSpdmqWZoawFwQEbf1yON+Sd/OVn3Y9pajK+VA8pbAN9p+tppTGYSmnlNtVUHMn7NV+/bY5f3qb6j3UkrVl3Zuz5a3s91xtMeqxfJLGqwFeTNNnaurnQ9o6rQNcwY4znQZxefBKWq2Ar9A0luybcxdBizFCMwAjNIn1ezKs6paWs0knaGpv26faPvQXl3PbM+0/VnbHxmkUNWk17/KVn3O9podjnWwpGP7yP7KbHl727v2X8La8tajN0naL3vebdCP3CclNYbZfpKk82z3ClRl+/m2T7P9sl5p+/R9NUfY20jS17JtF1etanX8MFs+3PbW7RLZfqOkY/ouZRlF6ksHl0lqzCO3sqQvtmvZqlojj5e0xxDH+pLttl03bb9W0ieyVT+MiBvapS0k/zzYx/aG/WZQ3S+X1+fGoB+3S/rpEGUDMOGYxwzAyETEDbZ/oGaLxWG2P9uYbyciwvY+Sl/ynqo0HPrxkt5r+/tK94rco9QFaD2l+ypeoub9JJ8ZonjHKQ2NL6X7i66x/RWl+zkWKQ2G8abqeJL0DdXo6hcRf7B9taTtlVoIzrd9jaS/qtm1U5IOjYg7hyi/lIbNP06pW1neovew0pf4niJinu19JZ2j9P/fUNLFtn8h6ceSblQasnt1pcEbdlCaQ6sxKMNI72+JiHtt/0zNrmkvyDb3M3fZ/1Yaun0VpYDzUttfUgpsHlUaLGIfpdeyUKklrmQLVE+F60trWR6yfbxSa6MkHSBpS9vfUjpnVlGqAwcrtRQ9Lun/qP8h809T+vw41/bJks6SdKfS3Il7K01G3nCP0sBDk+SHSi2dKyn90PBn21dKukvNH63ujIheny3HS3pHy7oTl+RJtAH0RmAGYNSOUfoCbKUBNz4o6YjGxoj4m+0dJf2Xml8gt5F09HQWKiJ+VH2xfHu16qnq/MX1e5I+rfr3YB2iNBH0mtXzZ1aP3PvrlrWTiJhv+xylSb1zZ1XdFOvmc0HVInGG0vxTkrR79SjhJE29Z0hKrWg/qJtBRMy1/T6lL7RWCs6ObJN0kdKX+QWa8MBMKldfOjhS0r8oBetSCqJf0CbdQknvU/of9xuYHak0WM/LlO7z7HSv5wOS9ujVfXfcIuIO2+9VavldTilA26kl2S018rna9mVqvuehqd2QASyF6MoIYKSq4drPzFa9y/Y6LWnmKQ0M8DZJf+iR5SNKc/Ycov66GLZzmFL3yns7bL9N0rsjYpZqDHDQEBGXS9q2Kt9vJN2tqa1lo9SuFamfliVJUkT8StKWSt3CerXk3a3UkvFqpQB01H6q1PoxZV1E3NtPJhHxTaUfBTp9Wb9e0ssj4qt9l7CggvWltRwPKrXIfVudz++rJe0+xP/4caWBWT6pznOT/VzS9hFx2YDHmFYR8Q2lgOobkq5Veh2DDG9/TbZ8fnUfL4ClmHtPQwIA06uat2pHpdabNZR+ab9Lae6uayLioS67D3K8Jyp1a3yG0i/ad0j6o6RfL4vzA1X3LD1LqYvnukrd0v4haZ5SIHD9kvR/sb2CUiCzrVLL2R2Sfl/NB7fEG3d96VCG9SXtqjSM/iKl+5+uiojrR3iMVZWmCdhEqWvtPEm/jIi5ozrGpLK9stLrnVGt2i8iTitYJABjQGAGAAAwQWzvL+m71dO7JG0UEY922QXAUoCujAAAABOiGu3yg9mqbxGUAcsGAjMAAIAJUE1u/wk1Bw96SGlOOADLAEZlBAAAKMT2fkpzEq4kaWul+/YaPh8Rt7fdEcBSh8AMAACgnC0l7dlm/cVaciZDBzACdGUEAACYDI8oDbH/UUkvi4hHCpcHwBgxKiMAAAAAFDaSroz2OiHNrJGubn7DpRnlcbql4zjlyjMJZViay7MkHWdJ/Fzh3J7M44y0DN1+9Kz7g2indIPkPWnlmc68hz3mJOY9XeXhvZv+vJeU8owr76Xt9bS4QrorItbte8fKiO4xmynpckmLX7CWW66/5W7b8rzr7jOuvLvt0ynvUbyGUebduk/pvAc5ziDn33TmPcj5N515j+vcns68l4Z6M+hroN6M75pQ/zU0L9zOL+KLWubjzp8vXFgvXZ3lbvl122dJLE/dsuX71y3DslqeQd5vylN//7r7UJ7ByzYJ5clYuqXjxhq4xwwAAAAACiMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMAAAAAKAwAjMAAAAAKIzADAAAAAAKIzADAAAAgMIIzAAAAACgMAIzAAAAACiMwAwAAAAACiMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMAAAAAKAwAjMAAAAAKIzADAAAAAAKIzADAAAAgMIIzAAAAACgMAIzAAAAACiMwAwAAAAACiMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMAAAAAKAwAjMAAAAAKIzADAAAAAAKIzADAAAAgMIIzAAAAACgMAIzAAAAACiMwAwAAAAACiMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMAAAAAKAwAjMAAAAAKIzADAAAAAAKIzADAAAAgMIIzAAAAACgMAIzAAAAACiMwAwAAAAACiMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMAAAAAKAwAjMAAAAAKIzADAAAAAAKIzADAAAAgMIIzAAAAACgMAIzAAAAACiMwAwAAAAACiMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMAAAAAKAwAjMAAAAAKIzADAAAAAAKIzADAAAAgMIIzAAAAACgMAIzAAAAACiMwAwAAAAACiMwAwAAAIDCCMwAAAAAoDACMwAAAAAojMAMAAAAAAojMAMAAACAwgjMAAAAAKAwAjMAAAAAKIzADAAAAAAKIzADAAAAgMIcEcNnYj8g6YbhiwMss9aRdFfpQgBLMOoQMDzqETCcLSJi9UF3XmFEhbghIp47oryAZY7ty6lDwOCoQ8DwqEfAcGxfPsz+dGUEAAAAgMIIzAAAAACgsFEFZt8YUT7Asoo6BAyHOgQMj3oEDGeoOjSSwT8AAAAAAIOjKyMAAAAAFEZgBgAAAACF9R2YOdnX9k9s32r7Edu32z7P9sG2RzUEP1DMOM9z2xvbfrvtk21fa/t+24/a/rvtS2x/3PbGNfOaaztqPi4c1WsABjHmela3XoTtOaM6LjCocdUP2xf2WT8ajwM75Md1CEs928vb3tb2gba/bPtS2wuyc3v2IPn2Valtz5B0uqTdWjZtUD12k3SY7ddFxF8GKRBQ2jjPc9tnSnqNJLfZvE71eJGkD9k+MiL+Y5jjAZOC6wnQ2RJSP/5c6LjAJPi+pL1GnWntwMz2EyT9SNLO1aq/Ko08cpOkp0g6SNJWknaQdLbtnSLi/tEWF5heBc7zbdUMyn4r6QJJN0q6vzre3kqB2UqSPmd7pYg4tka+f5d0aI80dw1UYmBIha8n10k6skcaAkEUU6B+HKn0I2AvL5L0wWr5T5Iu7pGe6xCWZsu3PL9b0nxJmw+Tae1RGW2/T9Jx1dMrJe0eEfdk21eWdKakV1Sr/iMiDh+mcMC4jfs8t32tpAslfTkibuiQ5v2SvlA9fVzSNhFxY4e0cyVtIumWiJg5aLmA6VTiemK7cbG7KCJ2GSYvYDpN6vct26dK2rd6emSnHwm5DmFZYPsjklaXdIWkKyLi5qp774lVkqMjYnbf+dYJzKp+zPMkrSspJG0XEde1SbeeUtP2EyU9ImmjiJjfb6GAEkqc57Zn5BfcLulOV2o9k6SPR8RRHdLNFRdETLBS1xMCMywJJvX7VtW18nal3huLJG0SEbd2SDtXXIewDBpFYFZ38I/dlD4kJOm8dh8SkhQRd0o6tXq6kqQ9+y0QUNDYz/M6QVnlB9nydoMeD5gAXE+Azia1frypOo4kndspKAMwnLqB2cuz5XN6pM2379FfcYCiJvk8fyBbXmUMxwOmyyTXM6C0Sa0fB2XLJ0zzsYBlVt3AbNts+YoeaS/vsB8w6Sb5PM+PcUuN9Gvb/oXtO90cev8y25+xvdl0FRKooXQ928JpGor5Vd24w/YvbR9le4MRHQMYVOn6sRjbz1QaaERKAxz8qOauXIeAPtUNzJ6RLc/tkfZWSQur5c1ttxsGHJhEE3meV/ccvDVb9dMau60m6aVKXWJWVBpx63mSPiTpBtufst06ohAwDqXr2QZKo8utpVQ31lMa/W62pLnVwAtAKaXrRzt5a9nJEfFIzf24DgF9qjtc/prZctehTSPicdv3S5pR5f9ESf8YqHTAeK2ZLU/Sef5BSVtWy79X78BsnlIXl6sl3SHpCZKerjTfxnZKQ7x+WNKTJR048tIC3a2ZLY+7nv1J0n9LukZpWONVJG0j6fWSNlW6h+Y422t1GmAHmGZrZsvFr0O2V5Q0K1tVtxsj1yFgAHUDs9Wy5YdrpH9I6YNCSkNJEphhSTBx57ntXSV9onr6uKS3R8SiLru8WdKvO6SZbfswSV9WuigeYPvciDh5pIUGuitVz14SEb9st6Ea9ni2mvOb/XtVNy4Z8FjAoCbtOvQaNec4uyoirq6xD9chYEB1uzICGDPbWyqNxtj4AeUjEXFpt30i4pJugVtEfE3Sv2erek20CywVOgVl1baFEfExSd/MVn90+ksFTLy8G32t1jKuQ8Dg6gZm+S8wK9dIn48a90DHVMBkmZjz3PbTJJ0rae1q1XER8bkRZf+fku6rlre0vemI8gXqmJh61sZspbmjJGlX24yAinGbmPph+8lqjvb4iKRRtmpxHQLaqBuY3Zstr90pkfTPgQqeVD19XNKD/RcLKOLebLnYeW77qZLOl/SUatXXI+IDo8o/Ih6W9Jts1Rajyhuo4d5seaKuJxExT9KN1dOVJM2czuMBbdybLZeuH29R6m4oSWf2Me9mT1yHgPbqBmY3Zssze6R9ipoV+Y8REd0SAxOk+Hlue0OloKxx/BMkvXMUebeYny3P6JgKGL3i9awH6gZKmqT60Xc3xj5R14AWdQOza7Pl5/ZIm2+/tmMqYPIUPc+rOZTOVxq5SkrdRg6Zpi+j+S+x905D/kAnk349oW6gpImoH7ZfqGYr1l8l/WKU+Veoa0CLuoHZz7PlV/RIm88+32vWemCSFDvPba8r6Tw1L4SnSTqgxwiMgx5rJUk7Zqtu7JQWmAYTez2p7qlpzCP1qOpN5g6M0qTUj3zusjmjvhZxHQLaqxuYXSDp79Xy7ra3aZfI9nqS9quePqz6s8MDk6DIeW57LaVfI7euVv1Q0psjYmHnvYbyr5LWqJb/GBE3TdNxgHYm+XoyW1Jjkt6LIoJ7pDFuxeuH7VUl7VM9DUknjirvDNchoI1agVlEPC7p2OqpJX3X9pT+wLZXlvQdpQkOJekrEZH3HwYm2qjPc9tzbEf1mN0hzRpKE94+s1p1lqT9qrL0xfZHbW/VI8071JwXTWq+XmAsCtWzT9neuFOZbC9v++OSDs1WUzcwdiXqRxtvUJoTTZIujIib65af6xAwnLoTTEvS1yTtLWlnSTtI+p3t4yXdpHQD6tskNSrj9ZKOGWE5gXEZ93l+tqTnVMvzlO4re5XtzntICyLiv9usf4OkY2xfJekiSX+QdI+kJyjdt7aXmgGgJJ0k6btDlR4YzLjr2WGSjrD9a0m/Uuo2dZ/SUONbK7UO5MN1HxsRFw15TGBQpb9vDTPoB9chLBOqaY3e1rI6P7d3q0ZOzZ0REVd1y7d2YBYRj9reU9LpknaT9FS1/zC4UtLrIuK+NtuAiVbgPN8pW95Q6d6yXm5R99G6nl09Onlc0qclHc2oqSih0PXEkl5UPTpZIOmIiPjKCI4HDKTk9y3bm0n6l+rpfZLOGDArrkNY2m0i6aNdtu9cPXI3SRpNYCZJEXGP7d2Vfl3cX6nSraP0a8h1kk6VdOIg3bCASbEEn+f7K11Qd5K0jVKZ11bqsny30i+rF0k6ISJuK1VIQBp7PXuFUkC2k9LgHusq1Y2FSkN2X6M0+M6ciLh7BMcDhlLwOnSgmvdZnhoRD/W5P9chYAjmhwoAAAAAKKvuqIwAAAAAgGlCYAYAAAAAhRGYAQAAAEBhBGYAAAAAUBiBGQAAAAAURmAGAAAAAIURmAEAAABAYQRmAAAAAFAYgRkAAAAAFEZgBgAAAACFEZgBAAAAQGEEZgAAAABQ2P8HF2+FLZLmlRsAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "def view_colormap(cmap):\n",
    "    \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n",
    "    cmap = plt.cm.get_cmap(cmap)\n",
    "    colors = cmap(np.arange(cmap.N))\n",
    "    plt.figure(figsize=(15,3))\n",
    "    plt.imshow([colors], extent=[0, 10, 0, 1])\n",
    "    plt.xticks([0, 2.5, 5, 7.5 ,10], ['0', '0.25', '0.5','0.75','1'], fontsize=30)\n",
    "    plt.yticks([])\n",
    "    plt.text(2.9,1.1,'Relative vulnerability', fontsize=35)\n",
    "    #plt.text(10.5,0.6,'Estimated percentage of area with \\u03B1-synuclein')\n",
    "\n",
    "print(mid_pt)\n",
    "cmap=shiftedColorMap(cmap=plt.get_cmap(\"bwr\"), start=0.0, midpoint=mid_pt, stop=1.0, name=\"ternary_centered\")\n",
    "view_colormap(cmap)\n",
    "plt.savefig('../Data_ReScience/residuals/Mean_MPI/colorbar_Mean_MPI.png', dpi=300)\n",
    "plt.savefig('../Data_ReScience/residuals/Mean_MPI/colorbar_Mean_MPI.pdf', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "render",
   "language": "python",
   "name": "render"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}