{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot regions coverage percentage in the spinal cord.\n",
    "\n",
    "This showcases that any brainglobe atlases should be supported.\n",
    "\n",
    "Here we're going to quantify the percentage of area of each spinal cord regions innervated by axons.\n",
    "\n",
    "The \"area µm^2\" measurement for each annotations can be created in QuPath with a pixel classifier, using the Measure button.\n",
    "\n",
    "We're going to consider that the \"area µm^2\" measurement generated by the pixel classifier is an object count.  \n",
    "`histoquant` computes a density, which is the count in each region divided by its aera.  \n",
    "Therefore, in  this case, it will be actually the fraction of area covered by fibers in a given color.\n",
    "\n",
    "The data was generated using QuPath with a pixel classifier on toy data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import cuisto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Full path to your configuration file, edited according to your need beforehand\n",
    "config_file = \"../../resources/demo_config_fibers.toml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# - Files\n",
    "# not important if only one animal\n",
    "animal = \"animalid1-SC\"\n",
    "# set the full path to the annotations tsv file from QuPath\n",
    "annotations_file = \"../../resources/fibers_measurements_annotations.tsv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get configuration\n",
    "cfg = cuisto.config.Config(config_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Image</th>\n",
       "      <th>Object type</th>\n",
       "      <th>Name</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Parent</th>\n",
       "      <th>ROI</th>\n",
       "      <th>Centroid X µm</th>\n",
       "      <th>Centroid Y µm</th>\n",
       "      <th>Fibers: EGFP area µm^2</th>\n",
       "      <th>Fibers: DsRed area µm^2</th>\n",
       "      <th>ID</th>\n",
       "      <th>Side</th>\n",
       "      <th>Parent ID</th>\n",
       "      <th>Area µm^2</th>\n",
       "      <th>Perimeter µm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Object ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dcfe5196-4e8d-4126-b255-a9ea393c383a</th>\n",
       "      <td>animalid1-SC_s1.ome.tiff</td>\n",
       "      <td>Annotation</td>\n",
       "      <td>Root</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Root object (Image)</td>\n",
       "      <td>Geometry</td>\n",
       "      <td>1353.70</td>\n",
       "      <td>1060.00</td>\n",
       "      <td>108993.1953</td>\n",
       "      <td>15533.3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3172474.0</td>\n",
       "      <td>9853.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc74bc0-3dd0-4b3e-86e3-e6c7b681d544</th>\n",
       "      <td>animalid1-SC_s1.ome.tiff</td>\n",
       "      <td>Annotation</td>\n",
       "      <td>root</td>\n",
       "      <td>Right: root</td>\n",
       "      <td>Root</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>864.44</td>\n",
       "      <td>989.95</td>\n",
       "      <td>39162.8906</td>\n",
       "      <td>5093.2798</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1603335.7</td>\n",
       "      <td>4844.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94571cf9-f22b-453f-860c-eb13d0e72440</th>\n",
       "      <td>animalid1-SC_s1.ome.tiff</td>\n",
       "      <td>Annotation</td>\n",
       "      <td>WM</td>\n",
       "      <td>Right: WM</td>\n",
       "      <td>root</td>\n",
       "      <td>Geometry</td>\n",
       "      <td>791.00</td>\n",
       "      <td>1094.60</td>\n",
       "      <td>20189.0469</td>\n",
       "      <td>2582.4824</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>884002.0</td>\n",
       "      <td>7927.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473d65fb-fda4-4721-ba6f-cc659efc1d5a</th>\n",
       "      <td>animalid1-SC_s1.ome.tiff</td>\n",
       "      <td>Annotation</td>\n",
       "      <td>vf</td>\n",
       "      <td>Right: vf</td>\n",
       "      <td>WM</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>984.31</td>\n",
       "      <td>1599.00</td>\n",
       "      <td>6298.3574</td>\n",
       "      <td>940.4100</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>281816.9</td>\n",
       "      <td>2719.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449e2cd1-eca2-4708-83fe-651f378c3a14</th>\n",
       "      <td>animalid1-SC_s1.ome.tiff</td>\n",
       "      <td>Annotation</td>\n",
       "      <td>df</td>\n",
       "      <td>Right: df</td>\n",
       "      <td>WM</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>1242.90</td>\n",
       "      <td>401.26</td>\n",
       "      <td>1545.0750</td>\n",
       "      <td>241.3800</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>152952.8</td>\n",
       "      <td>1694.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Image Object type  \\\n",
       "Object ID                                                                    \n",
       "dcfe5196-4e8d-4126-b255-a9ea393c383a  animalid1-SC_s1.ome.tiff  Annotation   \n",
       "acc74bc0-3dd0-4b3e-86e3-e6c7b681d544  animalid1-SC_s1.ome.tiff  Annotation   \n",
       "94571cf9-f22b-453f-860c-eb13d0e72440  animalid1-SC_s1.ome.tiff  Annotation   \n",
       "473d65fb-fda4-4721-ba6f-cc659efc1d5a  animalid1-SC_s1.ome.tiff  Annotation   \n",
       "449e2cd1-eca2-4708-83fe-651f378c3a14  animalid1-SC_s1.ome.tiff  Annotation   \n",
       "\n",
       "                                      Name Classification  \\\n",
       "Object ID                                                   \n",
       "dcfe5196-4e8d-4126-b255-a9ea393c383a  Root            NaN   \n",
       "acc74bc0-3dd0-4b3e-86e3-e6c7b681d544  root    Right: root   \n",
       "94571cf9-f22b-453f-860c-eb13d0e72440    WM      Right: WM   \n",
       "473d65fb-fda4-4721-ba6f-cc659efc1d5a    vf      Right: vf   \n",
       "449e2cd1-eca2-4708-83fe-651f378c3a14    df      Right: df   \n",
       "\n",
       "                                                   Parent       ROI  \\\n",
       "Object ID                                                             \n",
       "dcfe5196-4e8d-4126-b255-a9ea393c383a  Root object (Image)  Geometry   \n",
       "acc74bc0-3dd0-4b3e-86e3-e6c7b681d544                 Root   Polygon   \n",
       "94571cf9-f22b-453f-860c-eb13d0e72440                 root  Geometry   \n",
       "473d65fb-fda4-4721-ba6f-cc659efc1d5a                   WM   Polygon   \n",
       "449e2cd1-eca2-4708-83fe-651f378c3a14                   WM   Polygon   \n",
       "\n",
       "                                      Centroid X µm  Centroid Y µm  \\\n",
       "Object ID                                                            \n",
       "dcfe5196-4e8d-4126-b255-a9ea393c383a        1353.70        1060.00   \n",
       "acc74bc0-3dd0-4b3e-86e3-e6c7b681d544         864.44         989.95   \n",
       "94571cf9-f22b-453f-860c-eb13d0e72440         791.00        1094.60   \n",
       "473d65fb-fda4-4721-ba6f-cc659efc1d5a         984.31        1599.00   \n",
       "449e2cd1-eca2-4708-83fe-651f378c3a14        1242.90         401.26   \n",
       "\n",
       "                                      Fibers: EGFP area µm^2  \\\n",
       "Object ID                                                      \n",
       "dcfe5196-4e8d-4126-b255-a9ea393c383a             108993.1953   \n",
       "acc74bc0-3dd0-4b3e-86e3-e6c7b681d544              39162.8906   \n",
       "94571cf9-f22b-453f-860c-eb13d0e72440              20189.0469   \n",
       "473d65fb-fda4-4721-ba6f-cc659efc1d5a               6298.3574   \n",
       "449e2cd1-eca2-4708-83fe-651f378c3a14               1545.0750   \n",
       "\n",
       "                                      Fibers: DsRed area µm^2     ID  Side  \\\n",
       "Object ID                                                                    \n",
       "dcfe5196-4e8d-4126-b255-a9ea393c383a               15533.3701    NaN   NaN   \n",
       "acc74bc0-3dd0-4b3e-86e3-e6c7b681d544                5093.2798  250.0   0.0   \n",
       "94571cf9-f22b-453f-860c-eb13d0e72440                2582.4824  130.0   0.0   \n",
       "473d65fb-fda4-4721-ba6f-cc659efc1d5a                 940.4100   70.0   0.0   \n",
       "449e2cd1-eca2-4708-83fe-651f378c3a14                 241.3800   74.0   0.0   \n",
       "\n",
       "                                      Parent ID  Area µm^2  Perimeter µm  \n",
       "Object ID                                                                 \n",
       "dcfe5196-4e8d-4126-b255-a9ea393c383a        NaN  3172474.0        9853.3  \n",
       "acc74bc0-3dd0-4b3e-86e3-e6c7b681d544        NaN  1603335.7        4844.2  \n",
       "94571cf9-f22b-453f-860c-eb13d0e72440      250.0   884002.0        7927.8  \n",
       "473d65fb-fda4-4721-ba6f-cc659efc1d5a      130.0   281816.9        2719.5  \n",
       "449e2cd1-eca2-4708-83fe-651f378c3a14      130.0   152952.8        1694.4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read data\n",
    "df_annotations = pd.read_csv(annotations_file, index_col=\"Object ID\", sep=\"\\t\")\n",
    "df_detections = pd.DataFrame()  # empty DataFrame\n",
    "\n",
    "# remove annotations that are not brain regions\n",
    "df_annotations = df_annotations[df_annotations[\"Classification\"] != \"Region*\"]\n",
    "df_annotations = df_annotations[df_annotations[\"ROI\"] != \"Rectangle\"]\n",
    "\n",
    "# have a look\n",
    "display(df_annotations.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Name</th>\n",
       "      <th>hemisphere</th>\n",
       "      <th>Area µm^2</th>\n",
       "      <th>Area mm^2</th>\n",
       "      <th>area µm^2</th>\n",
       "      <th>area mm^2</th>\n",
       "      <th>density µm^-2</th>\n",
       "      <th>density mm^-2</th>\n",
       "      <th>coverage index</th>\n",
       "      <th>relative count</th>\n",
       "      <th>relative density</th>\n",
       "      <th>channel</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10Sp</td>\n",
       "      <td>Contra.</td>\n",
       "      <td>1749462.18</td>\n",
       "      <td>1.749462</td>\n",
       "      <td>53117.3701</td>\n",
       "      <td>53.11737</td>\n",
       "      <td>3.036211</td>\n",
       "      <td>30362.113973</td>\n",
       "      <td>1612.755645</td>\n",
       "      <td>0.036535</td>\n",
       "      <td>0.033062</td>\n",
       "      <td>Negative</td>\n",
       "      <td>animalid1-SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10Sp</td>\n",
       "      <td>Contra.</td>\n",
       "      <td>1749462.18</td>\n",
       "      <td>1.749462</td>\n",
       "      <td>5257.1025</td>\n",
       "      <td>5.257103</td>\n",
       "      <td>0.300498</td>\n",
       "      <td>3004.98208</td>\n",
       "      <td>15.797499</td>\n",
       "      <td>0.030766</td>\n",
       "      <td>0.02085</td>\n",
       "      <td>Positive</td>\n",
       "      <td>animalid1-SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10Sp</td>\n",
       "      <td>Ipsi.</td>\n",
       "      <td>1439105.93</td>\n",
       "      <td>1.439106</td>\n",
       "      <td>64182.9823</td>\n",
       "      <td>64.182982</td>\n",
       "      <td>4.459921</td>\n",
       "      <td>44599.206328</td>\n",
       "      <td>2862.51007</td>\n",
       "      <td>0.023524</td>\n",
       "      <td>0.023265</td>\n",
       "      <td>Negative</td>\n",
       "      <td>animalid1-SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10Sp</td>\n",
       "      <td>Ipsi.</td>\n",
       "      <td>1439105.93</td>\n",
       "      <td>1.439106</td>\n",
       "      <td>8046.3375</td>\n",
       "      <td>8.046337</td>\n",
       "      <td>0.559121</td>\n",
       "      <td>5591.205854</td>\n",
       "      <td>44.988729</td>\n",
       "      <td>0.028911</td>\n",
       "      <td>0.022984</td>\n",
       "      <td>Positive</td>\n",
       "      <td>animalid1-SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10Sp</td>\n",
       "      <td>both</td>\n",
       "      <td>3188568.11</td>\n",
       "      <td>3.188568</td>\n",
       "      <td>117300.3524</td>\n",
       "      <td>117.300352</td>\n",
       "      <td>3.678778</td>\n",
       "      <td>36787.783216</td>\n",
       "      <td>4315.219935</td>\n",
       "      <td>0.028047</td>\n",
       "      <td>0.025734</td>\n",
       "      <td>Negative</td>\n",
       "      <td>animalid1-SC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name hemisphere   Area µm^2  Area mm^2    area µm^2   area mm^2  \\\n",
       "0  10Sp    Contra.  1749462.18   1.749462   53117.3701    53.11737   \n",
       "0  10Sp    Contra.  1749462.18   1.749462    5257.1025    5.257103   \n",
       "1  10Sp      Ipsi.  1439105.93   1.439106   64182.9823   64.182982   \n",
       "1  10Sp      Ipsi.  1439105.93   1.439106    8046.3375    8.046337   \n",
       "2  10Sp       both  3188568.11   3.188568  117300.3524  117.300352   \n",
       "\n",
       "  density µm^-2 density mm^-2 coverage index relative count relative density  \\\n",
       "0      3.036211  30362.113973    1612.755645       0.036535         0.033062   \n",
       "0      0.300498    3004.98208      15.797499       0.030766          0.02085   \n",
       "1      4.459921  44599.206328     2862.51007       0.023524         0.023265   \n",
       "1      0.559121   5591.205854      44.988729       0.028911         0.022984   \n",
       "2      3.678778  36787.783216    4315.219935       0.028047         0.025734   \n",
       "\n",
       "    channel        animal  \n",
       "0  Negative  animalid1-SC  \n",
       "0  Positive  animalid1-SC  \n",
       "1  Negative  animalid1-SC  \n",
       "1  Positive  animalid1-SC  \n",
       "2  Negative  animalid1-SC  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get distributions per regions, spatial distributions and coordinates\n",
    "df_regions, dfs_distributions, df_coordinates = cuisto.process.process_animal(\n",
    "    animal, df_annotations, df_detections, cfg, compute_distributions=False\n",
    ")\n",
    "\n",
    "# convert the \"density µm^-2\" column, which is actually the coverage fraction, to a percentage\n",
    "df_regions[\"density µm^-2\"] = df_regions[\"density µm^-2\"] * 100\n",
    "\n",
    "# have a look\n",
    "display(df_regions.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distributions per regions\n",
    "fig_regions = cuisto.display.plot_regions(df_regions, cfg)\n",
    "# specify which regions to plot\n",
    "# fig_regions = hq.display.plot_regions(df_regions, cfg, names_list=[\"Rh9\", \"Sr9\", \"8Sp\"])\n",
    "\n",
    "# save as svg\n",
    "# fig_regions[0].savefig(r\"C:\\Users\\glegoc\\Downloads\\nice_figure.svg\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hq",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}