Stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8bb4adb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f61510f-9038-47a0-91da-398511c9a451",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path(\"D://Estelle/PW166_split_sections_ABBA_June10/qupath/export2\")\n",
    "annotations_filename = data_dir / \"PW166-A09_Scan2_[5204,56299]_component_data_merged_Region 5.ome.tif__annotations.tsv\"\n",
    "detections_filename = data_dir / \"PW166-A09_Scan2_[5204,56299]_component_data_merged_Region 5.ome.tif__detections.tsv\"\n",
    "assert annotations_filename.exists() and detections_filename.exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6079ab03-7a2c-4e13-b606-18ffe3b51498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Name</th>\n",
       "      <th>Class</th>\n",
       "      <th>Parent</th>\n",
       "      <th>ROI</th>\n",
       "      <th>Centroid X µm</th>\n",
       "      <th>Centroid Y µm</th>\n",
       "      <th>Allen ID</th>\n",
       "      <th>Parent Allen ID</th>\n",
       "      <th>Side</th>\n",
       "      <th>...</th>\n",
       "      <th>Num Negative</th>\n",
       "      <th>Num Positive</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>Positive %</th>\n",
       "      <th>Num Positive per mm^2</th>\n",
       "      <th>Area µm^2</th>\n",
       "      <th>Perimeter µm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PW166-A09_Scan2_[5204,56299]_component_data_me...</td>\n",
       "      <td>Root</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Image</td>\n",
       "      <td>Geometry</td>\n",
       "      <td>3794.0</td>\n",
       "      <td>4991.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>56705</td>\n",
       "      <td>3547250</td>\n",
       "      <td>414645</td>\n",
       "      <td>62904</td>\n",
       "      <td>72642</td>\n",
       "      <td>99.98</td>\n",
       "      <td>1187.1</td>\n",
       "      <td>47765953.6</td>\n",
       "      <td>37871.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PW166-A09_Scan2_[5204,56299]_component_data_me...</td>\n",
       "      <td>root</td>\n",
       "      <td>Left: root</td>\n",
       "      <td>Root</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>3768.2</td>\n",
       "      <td>3049.6</td>\n",
       "      <td>997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>28175</td>\n",
       "      <td>1639299</td>\n",
       "      <td>212787</td>\n",
       "      <td>36741</td>\n",
       "      <td>41646</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1175.1</td>\n",
       "      <td>23976494.7</td>\n",
       "      <td>18962.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PW166-A09_Scan2_[5204,56299]_component_data_me...</td>\n",
       "      <td>Basic cell groups and regions</td>\n",
       "      <td>Left: grey</td>\n",
       "      <td>root</td>\n",
       "      <td>Geometry</td>\n",
       "      <td>3756.6</td>\n",
       "      <td>2956.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>27098</td>\n",
       "      <td>1581640</td>\n",
       "      <td>206944</td>\n",
       "      <td>33159</td>\n",
       "      <td>39989</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1244.5</td>\n",
       "      <td>21773971.1</td>\n",
       "      <td>40554.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PW166-A09_Scan2_[5204,56299]_component_data_me...</td>\n",
       "      <td>Cerebrum</td>\n",
       "      <td>Left: CH</td>\n",
       "      <td>Basic cell groups and regions</td>\n",
       "      <td>Geometry</td>\n",
       "      <td>3631.7</td>\n",
       "      <td>2864.2</td>\n",
       "      <td>567.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>25929</td>\n",
       "      <td>1522212</td>\n",
       "      <td>195821</td>\n",
       "      <td>21263</td>\n",
       "      <td>39044</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1261.9</td>\n",
       "      <td>20547841.5</td>\n",
       "      <td>40666.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PW166-A09_Scan2_[5204,56299]_component_data_me...</td>\n",
       "      <td>Cerebral nuclei</td>\n",
       "      <td>Left: CNU</td>\n",
       "      <td>Cerebrum</td>\n",
       "      <td>Geometry</td>\n",
       "      <td>4487.4</td>\n",
       "      <td>3305.6</td>\n",
       "      <td>623.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>11452</td>\n",
       "      <td>657026</td>\n",
       "      <td>53195</td>\n",
       "      <td>17784</td>\n",
       "      <td>17044</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1102.1</td>\n",
       "      <td>10390975.6</td>\n",
       "      <td>23860.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Image  \\\n",
       "0  PW166-A09_Scan2_[5204,56299]_component_data_me...   \n",
       "1  PW166-A09_Scan2_[5204,56299]_component_data_me...   \n",
       "2  PW166-A09_Scan2_[5204,56299]_component_data_me...   \n",
       "3  PW166-A09_Scan2_[5204,56299]_component_data_me...   \n",
       "4  PW166-A09_Scan2_[5204,56299]_component_data_me...   \n",
       "\n",
       "                            Name       Class                         Parent  \\\n",
       "0                           Root         NaN                          Image   \n",
       "1                           root  Left: root                           Root   \n",
       "2  Basic cell groups and regions  Left: grey                           root   \n",
       "3                       Cerebrum    Left: CH  Basic cell groups and regions   \n",
       "4                Cerebral nuclei   Left: CNU                       Cerebrum   \n",
       "\n",
       "        ROI  Centroid X µm  Centroid Y µm  Allen ID  Parent Allen ID  Side  \\\n",
       "0  Geometry         3794.0         4991.5       NaN              NaN   NaN   \n",
       "1   Polygon         3768.2         3049.6     997.0              0.0   0.0   \n",
       "2  Geometry         3756.6         2956.4       8.0            997.0   0.0   \n",
       "3  Geometry         3631.7         2864.2     567.0              8.0   0.0   \n",
       "4  Geometry         4487.4         3305.6     623.0            567.0   0.0   \n",
       "\n",
       "   ...  Num Negative  Num Positive        2       3      4      5  Positive %  \\\n",
       "0  ...            10         56705  3547250  414645  62904  72642       99.98   \n",
       "1  ...             0         28175  1639299  212787  36741  41646      100.00   \n",
       "2  ...             0         27098  1581640  206944  33159  39989      100.00   \n",
       "3  ...             0         25929  1522212  195821  21263  39044      100.00   \n",
       "4  ...             0         11452   657026   53195  17784  17044      100.00   \n",
       "\n",
       "   Num Positive per mm^2   Area µm^2  Perimeter µm  \n",
       "0                 1187.1  47765953.6       37871.2  \n",
       "1                 1175.1  23976494.7       18962.7  \n",
       "2                 1244.5  21773971.1       40554.1  \n",
       "3                 1261.9  20547841.5       40666.4  \n",
       "4                 1102.1  10390975.6       23860.7  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf = pd.read_csv(annotations_filename, sep=\"\\t\").rename(columns={\"Num Subcellular spot: Channel 2 object\": \"2\",  \"Num Subcellular spot: Channel 3 object\": \"3\",  \"Num Subcellular spot: Channel 4 object\": \"4\",  \"Num Subcellular spot: Channel 5 object\": \"5\"})\n",
    "adf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5ed6446-2d04-4aa9-92c1-727b05d52740",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Allen ID</th>\n",
       "      <th>Area µm^2</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Num Spots</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Root</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47765953.60</td>\n",
       "      <td>2</td>\n",
       "      <td>3547250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>root</td>\n",
       "      <td>997.0</td>\n",
       "      <td>23976494.70</td>\n",
       "      <td>2</td>\n",
       "      <td>1639299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic cell groups and regions</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21773971.10</td>\n",
       "      <td>2</td>\n",
       "      <td>1581640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cerebrum</td>\n",
       "      <td>567.0</td>\n",
       "      <td>20547841.50</td>\n",
       "      <td>2</td>\n",
       "      <td>1522212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cerebral nuclei</td>\n",
       "      <td>623.0</td>\n",
       "      <td>10390975.60</td>\n",
       "      <td>2</td>\n",
       "      <td>657026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>anterior commissure, temporal limb</td>\n",
       "      <td>908.0</td>\n",
       "      <td>164147.90</td>\n",
       "      <td>5</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>cingulum bundle</td>\n",
       "      <td>940.0</td>\n",
       "      <td>108159.70</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>hypothalamus related</td>\n",
       "      <td>824.0</td>\n",
       "      <td>496.53</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>medial forebrain bundle</td>\n",
       "      <td>54.0</td>\n",
       "      <td>496.53</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>supra-callosal cerebral white matter</td>\n",
       "      <td>484682512.0</td>\n",
       "      <td>108947.50</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1368 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Name     Allen ID    Area µm^2 Channel  \\\n",
       "0                                     Root          NaN  47765953.60       2   \n",
       "1                                     root        997.0  23976494.70       2   \n",
       "2            Basic cell groups and regions          8.0  21773971.10       2   \n",
       "3                                 Cerebrum        567.0  20547841.50       2   \n",
       "4                          Cerebral nuclei        623.0  10390975.60       2   \n",
       "...                                    ...          ...          ...     ...   \n",
       "1363    anterior commissure, temporal limb        908.0    164147.90       5   \n",
       "1364                       cingulum bundle        940.0    108159.70       5   \n",
       "1365                  hypothalamus related        824.0       496.53       5   \n",
       "1366               medial forebrain bundle         54.0       496.53       5   \n",
       "1367  supra-callosal cerebral white matter  484682512.0    108947.50       5   \n",
       "\n",
       "      Num Spots  \n",
       "0       3547250  \n",
       "1       1639299  \n",
       "2       1581640  \n",
       "3       1522212  \n",
       "4        657026  \n",
       "...         ...  \n",
       "1363        609  \n",
       "1364          0  \n",
       "1365          0  \n",
       "1366          0  \n",
       "1367         10  \n",
       "\n",
       "[1368 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfl = adf.melt(id_vars=[\"Name\", \"Allen ID\", \"Area µm^2\"], value_vars=[\"2\", \"3\", \"4\", \"5\"], value_name=\"Num Spots\", var_name=\"Channel\")\n",
    "dfl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e63ac5d-f4df-4bb3-aa1c-222e41db0db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Allen ID</th>\n",
       "      <th>Area µm^2</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Num Spots</th>\n",
       "      <th>Spots/um2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Root</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47765953.6</td>\n",
       "      <td>2</td>\n",
       "      <td>3547250</td>\n",
       "      <td>13.465629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>root</td>\n",
       "      <td>997.0</td>\n",
       "      <td>23976494.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1639299</td>\n",
       "      <td>14.626066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic cell groups and regions</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21773971.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1581640</td>\n",
       "      <td>13.766705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cerebrum</td>\n",
       "      <td>567.0</td>\n",
       "      <td>20547841.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1522212</td>\n",
       "      <td>13.498673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cerebral nuclei</td>\n",
       "      <td>623.0</td>\n",
       "      <td>10390975.6</td>\n",
       "      <td>2</td>\n",
       "      <td>657026</td>\n",
       "      <td>15.815167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Name  Allen ID   Area µm^2 Channel  Num Spots  \\\n",
       "0                           Root       NaN  47765953.6       2    3547250   \n",
       "1                           root     997.0  23976494.7       2    1639299   \n",
       "2  Basic cell groups and regions       8.0  21773971.1       2    1581640   \n",
       "3                       Cerebrum     567.0  20547841.5       2    1522212   \n",
       "4                Cerebral nuclei     623.0  10390975.6       2     657026   \n",
       "\n",
       "   Spots/um2  \n",
       "0  13.465629  \n",
       "1  14.626066  \n",
       "2  13.766705  \n",
       "3  13.498673  \n",
       "4  15.815167  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfl[\"Spots/um2\"] = dfl[\"Area µm^2\"] / dfl[\"Num Spots\"]\n",
    "dfl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "73cbc0bf-46c5-41d1-8401-122dbb93cd91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(adf[\"Area µm^2\"] == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "09d8692d-bb26-4727-8e5c-71b166f23424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(adf[\"Area µm^2\"].isnull()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aae4b052-e8a3-457f-a56d-b944332029b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 100000.0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(adf[\"Area µm^2\"], bins=2000)\n",
    "plt.xlim(0, 0.1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d07f22db-49d1-4148-a049-b36f9ece5692",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "autodetected range of [4.923872679045092, inf] is not finite",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-ccf5ed49a79b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfl\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Spots/um2\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, by, bins, **kwargs)\u001b[0m\n\u001b[0;32m   1294\u001b[0m             \u001b[1;33m>>\u001b[0m\u001b[1;33m>\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1295\u001b[0m         \"\"\"\n\u001b[1;32m-> 1296\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"hist\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw_method\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    953\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    954\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 955\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    956\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    957\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 277\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    278\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\hist.py\u001b[0m in \u001b[0;36m_args_adjust\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     35\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m             _, self.bins = np.histogram(\n\u001b[0m\u001b[0;32m     38\u001b[0m                 \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"range\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m             )\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mhistogram\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[1;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[0;32m    790\u001b[0m     \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_ravel_and_check_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 792\u001b[1;33m     \u001b[0mbin_edges\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muniform_bins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_bin_edges\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    793\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m     \u001b[1;31m# Histogram is an integer or a float array depending on the weights.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py\u001b[0m in \u001b[0;36m_get_bin_edges\u001b[1;34m(a, bins, range, weights)\u001b[0m\n\u001b[0;32m    424\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'`bins` must be positive, when an integer'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 426\u001b[1;33m         \u001b[0mfirst_edge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_edge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_outer_edges\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py\u001b[0m in \u001b[0;36m_get_outer_edges\u001b[1;34m(a, range)\u001b[0m\n\u001b[0;32m    321\u001b[0m         \u001b[0mfirst_edge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_edge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    322\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst_edge\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlast_edge\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 323\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m    324\u001b[0m                 \"autodetected range of [{}, {}] is not finite\".format(first_edge, last_edge))\n\u001b[0;32m    325\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: autodetected range of [4.923872679045092, inf] is not finite"
     ]
    }
   ],
   "source": [
    "dfl[\"Spots/um2\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90bc968e-9734-4581-83db-08d0c53f3c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=dfl[dfl[\"Channel\"] != \"2\"], x=\"Channel\", y=\"Spots/um2\", kind=\"bar\",\n",
    "#             col=\"Name\", col_wrap=5\n",
    "           );"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}