{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1af1704b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# analyze features\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "from imageio import volread as imread\n",
    "from skimage.filters import threshold_otsu\n",
    "\n",
    "from skimage import measure\n",
    "from scipy import stats\n",
    "import umap\n",
    "from sklearn.decomposition import PCA\n",
    "import math\n",
    "from scipy import stats\n",
    "\n",
    "import tifffile\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a35d17b6",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Feature Data\n",
    "MP_DIR = 'mp_score_max'\n",
    "DATA_DIR = 'max'\n",
    "\n",
    "Compartment = 'Soma'\n",
    "DATA_TYPE = 'max_clean'\n",
    "META_DIR = 'metadata'\n",
    "\n",
    "_allFOVs = sorted(glob.glob(f'{MP_DIR}/*'))\n",
    "allFOVs = [x.split('F')[-1][:3] for x in _allFOVs]\n",
    "allFOVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "096ec14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "FLAG_IMAGE = pd.DataFrame(index=allFOVs)\n",
    "for fov in allFOVs:\n",
    "    # load image\n",
    "    img = imread(f'max/F{fov}_max.tif')\n",
    "    # count 0's\n",
    "    count0 = np.count_nonzero(img==0, axis=(1,2), keepdims=True) \n",
    "    # count below 10's\n",
    "    count10 = np.count_nonzero(img<10, axis=(1,2), keepdims=True)\n",
    "    # count below 20's\n",
    "    count20 = np.count_nonzero(img<20, axis=(1,2), keepdims=True)\n",
    "    # count above 60k\n",
    "    count60K = np.count_nonzero(img>60000, axis=(1,2), keepdims=True)\n",
    "    # count above 65K\n",
    "    count65K = np.count_nonzero(img>65000, axis=(1,2), keepdims=True)\n",
    "    \n",
    "    FLAG_IMAGE.loc[fov,'count_0'] = np.count_nonzero(count0>0)\n",
    "    FLAG_IMAGE.loc[fov,'count_10'] = np.count_nonzero(count10>0)\n",
    "    FLAG_IMAGE.loc[fov,'count_20'] = np.count_nonzero(count20>0)\n",
    "    FLAG_IMAGE.loc[fov,'count_60K'] = np.count_nonzero(count60K>0)\n",
    "    FLAG_IMAGE.loc[fov,'count_65K'] = np.count_nonzero(count65K>0)\n",
    "FLAG_IMAGE.to_csv('QC_Coverslip2_max_ExtremeValues.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7c55d50",
   "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",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_0</th>\n",
       "      <th>count_10</th>\n",
       "      <th>count_20</th>\n",
       "      <th>count_60K</th>\n",
       "      <th>count_65K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>220</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 5 columns</p>\n",
       "</div>"
      ],
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       "     count_0  count_10  count_20  count_60K  count_65K\n",
       "000        0         0         0          1          1\n",
       "001        0         0         0          1          1\n",
       "002        0         0         0          1          1\n",
       "003        0         0         0          1          1\n",
       "004        0         0         0          1          1\n",
       "..       ...       ...       ...        ...        ...\n",
       "220        0         0         0          1          1\n",
       "221        0         0         0          1          1\n",
       "222        0         0         0          1          1\n",
       "223        0         0         0          1          1\n",
       "224        0         0         0          1          1\n",
       "\n",
       "[194 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FLAG_IMAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "382401bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fov='224'\n",
    "img = imread(f'max/F{fov}_max.tif')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "265d7241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[   0]],\n",
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "count60K = np.count_nonzero(img>60000, axis=(1,2), keepdims=True)\n",
    "count60K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d1ff66b6",
   "metadata": {},
   "outputs": [
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       "0"
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     "execution_count": 18,
     "metadata": {},
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   "source": [
    "np.count_nonzero(count60K>0)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8faaff8a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 5000x5000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(5,5,figsize=(50,50))\n",
    "axs=axs.ravel()\n",
    "for i in range(img.shape[0]):\n",
    "    axs[i].matshow(img[i]>60000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0998b3c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2cb660cd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 617.143x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(img[12,800:1500, 300:1200]>50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75a8bde9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1701"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(img[12,800:1500, 300:1200]>50000).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2007aae1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}