{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageProcessor:\n",
    "    def __init__(self, flatmasks):\n",
    "        self.flatmasks = flatmasks\n",
    "        \n",
    "    def update_adjacency_value(self, adjacency_matrix, original, neighbor):\n",
    "            border = False\n",
    "\n",
    "            if original != 0 and original != neighbor:\n",
    "                border = True\n",
    "                if neighbor != 0:\n",
    "                    adjacency_matrix[int(original - 1), int(neighbor - 1)] += 1\n",
    "            return border\n",
    "\n",
    "    def update_adjacency_matrix(self, plane_mask_flattened, width, height, adjacency_matrix, index):\n",
    "            mod_value_width = index % width\n",
    "            origin_mask = plane_mask_flattened[index]\n",
    "            left, right, up, down = False, False, False, False\n",
    "\n",
    "            if (mod_value_width != 0):\n",
    "                left = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index-1])\n",
    "            if (mod_value_width != width - 1):\n",
    "                right = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index+1])\n",
    "            if (index >= width):\n",
    "                up = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index-width])\n",
    "            if (index <= len(plane_mask_flattened) - 1 - width):\n",
    "                down = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index+width])\n",
    "            \n",
    "            if (left or right or up or down):\n",
    "                adjacency_matrix[int(origin_mask - 1), int(origin_mask-1)] += 1\n",
    "\n",
    "    def compute_channel_means_sums_compensated(self, image):\n",
    "            height, width, n_channels = image.shape\n",
    "            mask_height, mask_width = self.flatmasks.shape\n",
    "            n_masks = len(np.unique(self.flatmasks)) - 1\n",
    "            channel_sums = np.zeros((n_masks, n_channels))\n",
    "            channel_counts = np.zeros((n_masks, n_channels))\n",
    "            if n_masks == 0:\n",
    "                return channel_sums, channel_sums, channel_counts\n",
    "\n",
    "            squashed_image = np.reshape(image, (height*width, n_channels))\n",
    "            \n",
    "            #masklocs = np.nonzero(self.flatmasks)\n",
    "            #plane_mask = np.zeros((mask_height, mask_width), dtype = np.uint32)\n",
    "            #plane_mask[masklocs[0], masklocs[1]] = masklocs[2] + 1\n",
    "            #plane_mask = plane_mask.flatten()\n",
    "            plane_mask = self.flatmasks.flatten()\n",
    "            \n",
    "            adjacency_matrix = np.zeros((n_masks, n_masks))\n",
    "            for i in range(len(plane_mask)):\n",
    "                self.update_adjacency_matrix(plane_mask, mask_width, mask_height, adjacency_matrix, i)\n",
    "                \n",
    "                mask_val = plane_mask[i] - 1\n",
    "                if mask_val != -1:\n",
    "                    channel_sums[mask_val.astype(np.int32)] += squashed_image[i]\n",
    "                    channel_counts[mask_val.astype(np.int32)] += 1\n",
    "            \n",
    "            \n",
    "            # Normalize adjacency matrix\n",
    "            for i in range(n_masks):\n",
    "                adjacency_matrix[i] = adjacency_matrix[i] / (max(adjacency_matrix[i, i], 1) * 2)\n",
    "                adjacency_matrix[i, i] = 1\n",
    "            \n",
    "            means = np.true_divide(channel_sums, channel_counts, out=np.zeros_like(channel_sums, dtype='float'), where=channel_counts!=0)\n",
    "            results = np.linalg.lstsq(adjacency_matrix, means, rcond=None)\n",
    "            compensated_means = np.maximum(results[0], np.zeros((1,1)))        \n",
    "\n",
    "            return compensated_means, means, channel_counts[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageProcessor:\n",
    "    def __init__(self, flatmasks):\n",
    "        self.flatmasks = flatmasks\n",
    "        \n",
    "    def update_adjacency_value(self, adjacency_matrix, original, neighbor):\n",
    "            border = False\n",
    "\n",
    "            if original != 0 and original != neighbor:\n",
    "                border = True\n",
    "                if neighbor != 0:\n",
    "                    adjacency_matrix[int(original - 1), int(neighbor - 1)] += 1\n",
    "            return border\n",
    "\n",
    "    def update_adjacency_matrix(self, plane_mask_flattened, width, height, adjacency_matrix, index):\n",
    "            mod_value_width = index % width\n",
    "            origin_mask = plane_mask_flattened[index]\n",
    "            left, right, up, down = False, False, False, False\n",
    "\n",
    "            if (mod_value_width != 0):\n",
    "                left = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index-1])\n",
    "            if (mod_value_width != width - 1):\n",
    "                right = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index+1])\n",
    "            if (index >= width):\n",
    "                up = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index-width])\n",
    "            if (index <= len(plane_mask_flattened) - 1 - width):\n",
    "                down = self.update_adjacency_value(adjacency_matrix, origin_mask, plane_mask_flattened[index+width])\n",
    "            \n",
    "            if (left or right or up or down):\n",
    "                adjacency_matrix[int(origin_mask - 1), int(origin_mask-1)] += 1\n",
    "\n",
    "    def compute_channel_means_sums_compensated(self, image):\n",
    "            height, width, n_channels = image.shape\n",
    "            mask_height, mask_width = self.flatmasks.shape\n",
    "            n_masks = len(np.unique(self.flatmasks)) - 1\n",
    "            channel_sums = np.zeros((n_masks, n_channels))\n",
    "            channel_counts = np.zeros((n_masks, n_channels))\n",
    "            if n_masks == 0:\n",
    "                return channel_sums, channel_sums, channel_counts\n",
    "\n",
    "            squashed_image = np.reshape(image, (height*width, n_channels))\n",
    "            \n",
    "            #masklocs = np.nonzero(self.flatmasks)\n",
    "            #plane_mask = np.zeros((mask_height, mask_width), dtype = np.uint32)\n",
    "            #plane_mask[masklocs[0], masklocs[1]] = masklocs[2] + 1\n",
    "            #plane_mask = plane_mask.flatten()\n",
    "            plane_mask = self.flatmasks.flatten()\n",
    "            \n",
    "            adjacency_matrix = np.zeros((n_masks, n_masks))\n",
    "            for i in range(len(plane_mask)):\n",
    "                self.update_adjacency_matrix(plane_mask, mask_width, mask_height, adjacency_matrix, i)\n",
    "                \n",
    "                mask_val = plane_mask[i] - 1\n",
    "                if mask_val != -1:\n",
    "                    channel_sums[mask_val.astype(np.int32)] += squashed_image[i]\n",
    "                    channel_counts[mask_val.astype(np.int32)] += 1\n",
    "            \n",
    "            \n",
    "            # Normalize adjacency matrix\n",
    "            for i in range(n_masks):\n",
    "                adjacency_matrix[i] = adjacency_matrix[i] / (max(adjacency_matrix[i, i], 1) * 2)\n",
    "                adjacency_matrix[i, i] = 1\n",
    "            \n",
    "            means = np.true_divide(channel_sums, channel_counts, out=np.zeros_like(channel_sums, dtype='float'), where=channel_counts!=0)\n",
    "            # Convert your numpy arrays to PyTorch tensors\n",
    "            adjacency_matrix_torch = torch.from_numpy(adjacency_matrix)\n",
    "            means_torch = torch.from_numpy(means)\n",
    "\n",
    "            # Solve the least squares problem\n",
    "            results_torch = torch.linalg.lstsq(adjacency_matrix_torch, means_torch).solution\n",
    "\n",
    "            # Convert the result back to a numpy array if needed\n",
    "            # Convert the result back to a numpy array if needed\n",
    "            results = results_torch.numpy()\n",
    "            compensated_means = np.maximum(results, np.zeros(results.shape))              \n",
    "\n",
    "            return compensated_means, means, channel_counts[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pickle file\n",
    "import pickle \n",
    "\n",
    "with open(\"/home/timkempchen/Downloads/seg_output_tonsil2.pickle\", 'rb') as f:\n",
    "    seg_output = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['img', 'masks', 'image_dict'])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_output.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get keys of image_dict\n",
    "channelnames = list(seg_output['image_dict'].keys())\n",
    "\n",
    "images = seg_output['image_dict']\n",
    "masks = seg_output['masks']\n",
    "masks = masks.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Assuming `images` is your dictionary of 2D numpy arrays\n",
    "# and `masks` is your 2D numpy array of masks\n",
    "\n",
    "# Create a list of the 2D numpy arrays in the dictionary\n",
    "image_list = [images[channel_name] for channel_name in images.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Stack the 2D numpy arrays along the third dimension to create a 3D numpy array\n",
    "image = np.stack(image_list, axis=-1)\n",
    "\n",
    "# Now you can use `image` as the input for the function\n",
    "processor = ImageProcessor(masks)\n",
    "compensated_means, means, channel_counts = processor.compute_channel_means_sums_compensated(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
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       "      <td>0.603485</td>\n",
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       "      <td>1506.226562</td>\n",
       "      <td>0.766017</td>\n",
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       "      <td>14.691870</td>\n",
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       "      <td>19.306122</td>\n",
       "      <td>16.571429</td>\n",
       "      <td>2.561224</td>\n",
       "      <td>1.010204</td>\n",
       "      <td>13.540816</td>\n",
       "      <td>...</td>\n",
       "      <td>9.806122</td>\n",
       "      <td>2525.234694</td>\n",
       "      <td>1420.387755</td>\n",
       "      <td>0.760933</td>\n",
       "      <td>35.556349</td>\n",
       "      <td>103.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>13.929985</td>\n",
       "      <td>9.038195</td>\n",
       "      <td>23330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23330</th>\n",
       "      <td>23331</td>\n",
       "      <td>11.522727</td>\n",
       "      <td>0.147727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.170455</td>\n",
       "      <td>1.238636</td>\n",
       "      <td>1.397727</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>4.488636</td>\n",
       "      <td>...</td>\n",
       "      <td>1.659091</td>\n",
       "      <td>2535.886364</td>\n",
       "      <td>231.329545</td>\n",
       "      <td>0.472365</td>\n",
       "      <td>31.556349</td>\n",
       "      <td>89.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>11.256538</td>\n",
       "      <td>9.921544</td>\n",
       "      <td>23331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23331 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        DAPI     FoxP3    HLA-DR      CD103       CHGA  \\\n",
       "0               1  105.993197  1.340136  0.557823  16.442177   8.278912   \n",
       "1               2  123.677686  0.619835  0.830579  17.223140  17.194215   \n",
       "2               3  107.203125  1.281250  0.671875  17.925781   9.699219   \n",
       "3               4   49.660959  0.136986  0.006849  39.623288  25.102740   \n",
       "4               5  148.702532  1.310127  1.563291  18.183544  33.227848   \n",
       "...           ...         ...       ...       ...        ...        ...   \n",
       "23326       23327   50.512346  0.956790  1.401235  23.746914  18.395062   \n",
       "23327       23328  160.015686  1.176471  3.137255  19.737255  34.219608   \n",
       "23328       23329   56.734177  0.759494  0.860759  16.835443  17.898734   \n",
       "23329       23330   86.326531  0.806122  1.316327  18.612245  19.306122   \n",
       "23330       23331   11.522727  0.147727  0.000000   1.170455   1.238636   \n",
       "\n",
       "            EGFR     CD206      GFAP       PD-1  ...      GATA3            x  \\\n",
       "0       6.183673  3.306122  1.068027  13.020408  ...  11.074830     4.986395   \n",
       "1       5.975207  3.623967  0.942149  15.983471  ...  11.909091     5.359504   \n",
       "2       6.589844  3.566406  1.023438  13.890625  ...  11.816406     5.710938   \n",
       "3       2.797945  0.989726  0.801370   8.965753  ...   6.722603     8.544521   \n",
       "4       7.981013  5.082278  0.943038  21.196203  ...  16.829114     9.006329   \n",
       "...          ...       ...       ...        ...  ...        ...          ...   \n",
       "23326  24.481481  2.932099  0.901235  12.617284  ...   6.654321  2523.888889   \n",
       "23327  11.309804  4.721569  0.976471  19.717647  ...  21.035294  2522.047059   \n",
       "23328  16.405063  2.063291  0.924051  10.569620  ...   6.506329  2523.924051   \n",
       "23329  16.571429  2.561224  1.010204  13.540816  ...   9.806122  2525.234694   \n",
       "23330   1.397727  0.136364  0.886364   4.488636  ...   1.659091  2535.886364   \n",
       "\n",
       "                 y  eccentricity  perimeter  convex_area   area  \\\n",
       "0      1472.238095      0.603485  44.142136        154.0  147.0   \n",
       "1      1322.851240      0.853893  63.248737        267.0  242.0   \n",
       "2      1506.226562      0.766017  61.798990        268.0  256.0   \n",
       "3       641.938356      0.645764  63.112698        306.0  292.0   \n",
       "4      1303.702532      0.766800  48.420310        172.0  158.0   \n",
       "...            ...           ...        ...          ...    ...   \n",
       "23326  1384.086420      0.761539  48.627417        176.0  162.0   \n",
       "23327  1438.568627      0.722578  62.077164        274.0  255.0   \n",
       "23328  1349.759494      0.707599  32.970563         86.0   79.0   \n",
       "23329  1420.387755      0.760933  35.556349        103.0   98.0   \n",
       "23330   231.329545      0.472365  31.556349         89.0   88.0   \n",
       "\n",
       "       axis_major_length  axis_minor_length  label  \n",
       "0              15.439633          12.311169      1  \n",
       "1              24.688741          12.849201      2  \n",
       "2              22.855322          14.691870      3  \n",
       "3              22.077563          16.857044      4  \n",
       "4              17.888621          11.482448      5  \n",
       "...                  ...                ...    ...  \n",
       "23326          17.973486          11.648970  23327  \n",
       "23327          21.866422          15.116015  23328  \n",
       "23328          12.134167           8.574169  23329  \n",
       "23329          13.929985           9.038195  23330  \n",
       "23330          11.256538           9.921544  23331  \n",
       "\n",
       "[23331 rows x 69 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open df\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"/home/timkempchen/Downloads/tonsil2_mesmer_result.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[73.4003194 ,  0.96314494,  0.        , ..., 17.3796773 ,\n",
       "         6.1359789 ,  4.10462704],\n",
       "       [62.18477628,  0.        ,  0.        , ...,  1.98561595,\n",
       "         3.43656937,  2.04505872],\n",
       "       [95.65718851,  1.13134308,  0.        , ..., 22.91852613,\n",
       "        10.15428826,  9.49503184],\n",
       "       ...,\n",
       "       [23.74777761,  0.40885537,  1.12656697, ...,  2.91814228,\n",
       "         2.94357561, 30.74130252],\n",
       "       [41.32538188,  0.15023363,  0.5936194 , ...,  2.48362393,\n",
       "         2.98513237, 47.74424314],\n",
       "       [11.52272727,  0.14772727,  0.        , ...,  2.59090909,\n",
       "         1.65909091,  1.88636364]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compensated_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the keys\n",
    "keys = list(images.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DAPI</th>\n",
       "      <th>FoxP3</th>\n",
       "      <th>HLA-DR</th>\n",
       "      <th>CD103</th>\n",
       "      <th>CHGA</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>CD206</th>\n",
       "      <th>GFAP</th>\n",
       "      <th>PD-1</th>\n",
       "      <th>...</th>\n",
       "      <th>GATA3</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>convex_area</th>\n",
       "      <th>area</th>\n",
       "      <th>axis_major_length</th>\n",
       "      <th>axis_minor_length</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>1.340136</td>\n",
       "      <td>0.557823</td>\n",
       "      <td>16.442177</td>\n",
       "      <td>8.278912</td>\n",
       "      <td>6.183673</td>\n",
       "      <td>3.306122</td>\n",
       "      <td>1.068027</td>\n",
       "      <td>13.020408</td>\n",
       "      <td>...</td>\n",
       "      <td>11.074830</td>\n",
       "      <td>4.986395</td>\n",
       "      <td>1472.238095</td>\n",
       "      <td>0.603485</td>\n",
       "      <td>44.142136</td>\n",
       "      <td>154.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>15.439633</td>\n",
       "      <td>12.311169</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>123.677686</td>\n",
       "      <td>0.619835</td>\n",
       "      <td>0.830579</td>\n",
       "      <td>17.223140</td>\n",
       "      <td>17.194215</td>\n",
       "      <td>5.975207</td>\n",
       "      <td>3.623967</td>\n",
       "      <td>0.942149</td>\n",
       "      <td>15.983471</td>\n",
       "      <td>...</td>\n",
       "      <td>11.909091</td>\n",
       "      <td>5.359504</td>\n",
       "      <td>1322.851240</td>\n",
       "      <td>0.853893</td>\n",
       "      <td>63.248737</td>\n",
       "      <td>267.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>24.688741</td>\n",
       "      <td>12.849201</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>107.203125</td>\n",
       "      <td>1.281250</td>\n",
       "      <td>0.671875</td>\n",
       "      <td>17.925781</td>\n",
       "      <td>9.699219</td>\n",
       "      <td>6.589844</td>\n",
       "      <td>3.566406</td>\n",
       "      <td>1.023438</td>\n",
       "      <td>13.890625</td>\n",
       "      <td>...</td>\n",
       "      <td>11.816406</td>\n",
       "      <td>5.710938</td>\n",
       "      <td>1506.226562</td>\n",
       "      <td>0.766017</td>\n",
       "      <td>61.798990</td>\n",
       "      <td>268.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>22.855322</td>\n",
       "      <td>14.691870</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>49.660959</td>\n",
       "      <td>0.136986</td>\n",
       "      <td>0.006849</td>\n",
       "      <td>39.623288</td>\n",
       "      <td>25.102740</td>\n",
       "      <td>2.797945</td>\n",
       "      <td>0.989726</td>\n",
       "      <td>0.801370</td>\n",
       "      <td>8.965753</td>\n",
       "      <td>...</td>\n",
       "      <td>6.722603</td>\n",
       "      <td>8.544521</td>\n",
       "      <td>641.938356</td>\n",
       "      <td>0.645764</td>\n",
       "      <td>63.112698</td>\n",
       "      <td>306.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>22.077563</td>\n",
       "      <td>16.857044</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>148.702532</td>\n",
       "      <td>1.310127</td>\n",
       "      <td>1.563291</td>\n",
       "      <td>18.183544</td>\n",
       "      <td>33.227848</td>\n",
       "      <td>7.981013</td>\n",
       "      <td>5.082278</td>\n",
       "      <td>0.943038</td>\n",
       "      <td>21.196203</td>\n",
       "      <td>...</td>\n",
       "      <td>16.829114</td>\n",
       "      <td>9.006329</td>\n",
       "      <td>1303.702532</td>\n",
       "      <td>0.766800</td>\n",
       "      <td>48.420310</td>\n",
       "      <td>172.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>17.888621</td>\n",
       "      <td>11.482448</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>23326</th>\n",
       "      <td>23327</td>\n",
       "      <td>50.512346</td>\n",
       "      <td>0.956790</td>\n",
       "      <td>1.401235</td>\n",
       "      <td>23.746914</td>\n",
       "      <td>18.395062</td>\n",
       "      <td>24.481481</td>\n",
       "      <td>2.932099</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>12.617284</td>\n",
       "      <td>...</td>\n",
       "      <td>6.654321</td>\n",
       "      <td>2523.888889</td>\n",
       "      <td>1384.086420</td>\n",
       "      <td>0.761539</td>\n",
       "      <td>48.627417</td>\n",
       "      <td>176.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>17.973486</td>\n",
       "      <td>11.648970</td>\n",
       "      <td>23327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23327</th>\n",
       "      <td>23328</td>\n",
       "      <td>160.015686</td>\n",
       "      <td>1.176471</td>\n",
       "      <td>3.137255</td>\n",
       "      <td>19.737255</td>\n",
       "      <td>34.219608</td>\n",
       "      <td>11.309804</td>\n",
       "      <td>4.721569</td>\n",
       "      <td>0.976471</td>\n",
       "      <td>19.717647</td>\n",
       "      <td>...</td>\n",
       "      <td>21.035294</td>\n",
       "      <td>2522.047059</td>\n",
       "      <td>1438.568627</td>\n",
       "      <td>0.722578</td>\n",
       "      <td>62.077164</td>\n",
       "      <td>274.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>21.866422</td>\n",
       "      <td>15.116015</td>\n",
       "      <td>23328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23328</th>\n",
       "      <td>23329</td>\n",
       "      <td>56.734177</td>\n",
       "      <td>0.759494</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>16.835443</td>\n",
       "      <td>17.898734</td>\n",
       "      <td>16.405063</td>\n",
       "      <td>2.063291</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>10.569620</td>\n",
       "      <td>...</td>\n",
       "      <td>6.506329</td>\n",
       "      <td>2523.924051</td>\n",
       "      <td>1349.759494</td>\n",
       "      <td>0.707599</td>\n",
       "      <td>32.970563</td>\n",
       "      <td>86.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.134167</td>\n",
       "      <td>8.574169</td>\n",
       "      <td>23329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23329</th>\n",
       "      <td>23330</td>\n",
       "      <td>86.326531</td>\n",
       "      <td>0.806122</td>\n",
       "      <td>1.316327</td>\n",
       "      <td>18.612245</td>\n",
       "      <td>19.306122</td>\n",
       "      <td>16.571429</td>\n",
       "      <td>2.561224</td>\n",
       "      <td>1.010204</td>\n",
       "      <td>13.540816</td>\n",
       "      <td>...</td>\n",
       "      <td>9.806122</td>\n",
       "      <td>2525.234694</td>\n",
       "      <td>1420.387755</td>\n",
       "      <td>0.760933</td>\n",
       "      <td>35.556349</td>\n",
       "      <td>103.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>13.929985</td>\n",
       "      <td>9.038195</td>\n",
       "      <td>23330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23330</th>\n",
       "      <td>23331</td>\n",
       "      <td>11.522727</td>\n",
       "      <td>0.147727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.170455</td>\n",
       "      <td>1.238636</td>\n",
       "      <td>1.397727</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>4.488636</td>\n",
       "      <td>...</td>\n",
       "      <td>1.659091</td>\n",
       "      <td>2535.886364</td>\n",
       "      <td>231.329545</td>\n",
       "      <td>0.472365</td>\n",
       "      <td>31.556349</td>\n",
       "      <td>89.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>11.256538</td>\n",
       "      <td>9.921544</td>\n",
       "      <td>23331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23331 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        DAPI     FoxP3    HLA-DR      CD103       CHGA  \\\n",
       "0               1  105.993197  1.340136  0.557823  16.442177   8.278912   \n",
       "1               2  123.677686  0.619835  0.830579  17.223140  17.194215   \n",
       "2               3  107.203125  1.281250  0.671875  17.925781   9.699219   \n",
       "3               4   49.660959  0.136986  0.006849  39.623288  25.102740   \n",
       "4               5  148.702532  1.310127  1.563291  18.183544  33.227848   \n",
       "...           ...         ...       ...       ...        ...        ...   \n",
       "23326       23327   50.512346  0.956790  1.401235  23.746914  18.395062   \n",
       "23327       23328  160.015686  1.176471  3.137255  19.737255  34.219608   \n",
       "23328       23329   56.734177  0.759494  0.860759  16.835443  17.898734   \n",
       "23329       23330   86.326531  0.806122  1.316327  18.612245  19.306122   \n",
       "23330       23331   11.522727  0.147727  0.000000   1.170455   1.238636   \n",
       "\n",
       "            EGFR     CD206      GFAP       PD-1  ...      GATA3            x  \\\n",
       "0       6.183673  3.306122  1.068027  13.020408  ...  11.074830     4.986395   \n",
       "1       5.975207  3.623967  0.942149  15.983471  ...  11.909091     5.359504   \n",
       "2       6.589844  3.566406  1.023438  13.890625  ...  11.816406     5.710938   \n",
       "3       2.797945  0.989726  0.801370   8.965753  ...   6.722603     8.544521   \n",
       "4       7.981013  5.082278  0.943038  21.196203  ...  16.829114     9.006329   \n",
       "...          ...       ...       ...        ...  ...        ...          ...   \n",
       "23326  24.481481  2.932099  0.901235  12.617284  ...   6.654321  2523.888889   \n",
       "23327  11.309804  4.721569  0.976471  19.717647  ...  21.035294  2522.047059   \n",
       "23328  16.405063  2.063291  0.924051  10.569620  ...   6.506329  2523.924051   \n",
       "23329  16.571429  2.561224  1.010204  13.540816  ...   9.806122  2525.234694   \n",
       "23330   1.397727  0.136364  0.886364   4.488636  ...   1.659091  2535.886364   \n",
       "\n",
       "                 y  eccentricity  perimeter  convex_area   area  \\\n",
       "0      1472.238095      0.603485  44.142136        154.0  147.0   \n",
       "1      1322.851240      0.853893  63.248737        267.0  242.0   \n",
       "2      1506.226562      0.766017  61.798990        268.0  256.0   \n",
       "3       641.938356      0.645764  63.112698        306.0  292.0   \n",
       "4      1303.702532      0.766800  48.420310        172.0  158.0   \n",
       "...            ...           ...        ...          ...    ...   \n",
       "23326  1384.086420      0.761539  48.627417        176.0  162.0   \n",
       "23327  1438.568627      0.722578  62.077164        274.0  255.0   \n",
       "23328  1349.759494      0.707599  32.970563         86.0   79.0   \n",
       "23329  1420.387755      0.760933  35.556349        103.0   98.0   \n",
       "23330   231.329545      0.472365  31.556349         89.0   88.0   \n",
       "\n",
       "       axis_major_length  axis_minor_length  label  \n",
       "0              15.439633          12.311169      1  \n",
       "1              24.688741          12.849201      2  \n",
       "2              22.855322          14.691870      3  \n",
       "3              22.077563          16.857044      4  \n",
       "4              17.888621          11.482448      5  \n",
       "...                  ...                ...    ...  \n",
       "23326          17.973486          11.648970  23327  \n",
       "23327          21.866422          15.116015  23328  \n",
       "23328          12.134167           8.574169  23329  \n",
       "23329          13.929985           9.038195  23330  \n",
       "23330          11.256538           9.921544  23331  \n",
       "\n",
       "[23331 rows x 69 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the keys\n",
    "keys = list(images.keys())\n",
    "\n",
    "# Cycle over the keys\n",
    "for i in range(len(keys)):\n",
    "    # Add the compensated_means to the DataFrame with column names from keys\n",
    "    df[keys[i]] = compensated_means[:, i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DAPI</th>\n",
       "      <th>FoxP3</th>\n",
       "      <th>HLA-DR</th>\n",
       "      <th>CD103</th>\n",
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       "      <td>0.613547</td>\n",
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       "      <td>5.509342</td>\n",
       "      <td>3.137370</td>\n",
       "      <td>0.875055</td>\n",
       "      <td>11.599501</td>\n",
       "      <td>...</td>\n",
       "      <td>5.710938</td>\n",
       "      <td>1506.226562</td>\n",
       "      <td>0.766017</td>\n",
       "      <td>61.798990</td>\n",
       "      <td>268.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>22.855322</td>\n",
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       "      <td>0.761539</td>\n",
       "      <td>48.627417</td>\n",
       "      <td>176.0</td>\n",
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       "      <th>23327</th>\n",
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       "      <td>2523.924051</td>\n",
       "      <td>1349.759494</td>\n",
       "      <td>0.707599</td>\n",
       "      <td>32.970563</td>\n",
       "      <td>86.0</td>\n",
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       "      <td>23329</td>\n",
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       "      <td>1420.387755</td>\n",
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       "      <td>23330</td>\n",
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       "    <tr>\n",
       "      <th>23330</th>\n",
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       "      <td>1.886364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23331 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        DAPI     FoxP3    HLA-DR      CD103       CHGA  \\\n",
       "0               1   73.400319  0.963145  0.000000  10.701006   0.000000   \n",
       "1               2   62.184776  0.000000  0.000000   8.109533   1.652147   \n",
       "2               3   95.657189  1.131343  0.000000  15.147517   5.491188   \n",
       "3               4   49.660959  0.136986  0.006849  39.623288  25.102740   \n",
       "4               5  114.610899  1.152759  0.888520  13.454905  26.300720   \n",
       "...           ...         ...       ...       ...        ...        ...   \n",
       "23326       23327   42.196426  0.832806  1.086360  21.067722  15.171849   \n",
       "23327       23328  131.577919  0.681751  2.114098  14.009404  27.114338   \n",
       "23328       23329   23.747778  0.408855  1.126567  10.593432  12.509655   \n",
       "23329       23330   41.325382  0.150234  0.593619  12.793476  10.459755   \n",
       "23330       23331   11.522727  0.147727  0.000000   1.170455   1.238636   \n",
       "\n",
       "            EGFR     CD206      GFAP       PD-1  ...            x  \\\n",
       "0       4.174474  1.792112  0.841941   8.013893  ...     4.986395   \n",
       "1       2.626056  1.173389  0.613547   7.322614  ...     5.359504   \n",
       "2       5.509342  3.137370  0.875055  11.599501  ...     5.710938   \n",
       "3       2.797945  0.989726  0.801370   8.965753  ...     8.544521   \n",
       "4       5.784891  3.961348  0.703600  16.377783  ...     9.006329   \n",
       "...          ...       ...       ...        ...  ...          ...   \n",
       "23326  22.810752  2.518938  0.782610  10.996428  ...  2523.888889   \n",
       "23327   7.196972  3.672131  0.731182  14.524425  ...  2522.047059   \n",
       "23328  11.727330  0.997654  0.661402   6.655545  ...  2523.924051   \n",
       "23329  14.349209  1.055391  0.741730   3.383721  ...  2525.234694   \n",
       "23330   1.397727  0.136364  0.886364   4.488636  ...  2535.886364   \n",
       "\n",
       "                 y  eccentricity  perimeter  convex_area   area  \\\n",
       "0      1472.238095      0.603485  44.142136        154.0  147.0   \n",
       "1      1322.851240      0.853893  63.248737        267.0  242.0   \n",
       "2      1506.226562      0.766017  61.798990        268.0  256.0   \n",
       "3       641.938356      0.645764  63.112698        306.0  292.0   \n",
       "4      1303.702532      0.766800  48.420310        172.0  158.0   \n",
       "...            ...           ...        ...          ...    ...   \n",
       "23326  1384.086420      0.761539  48.627417        176.0  162.0   \n",
       "23327  1438.568627      0.722578  62.077164        274.0  255.0   \n",
       "23328  1349.759494      0.707599  32.970563         86.0   79.0   \n",
       "23329  1420.387755      0.760933  35.556349        103.0   98.0   \n",
       "23330   231.329545      0.472365  31.556349         89.0   88.0   \n",
       "\n",
       "       axis_major_length  axis_minor_length  label  segmentation_channel  \n",
       "0              15.439633          12.311169      1              4.104627  \n",
       "1              24.688741          12.849201      2              2.045059  \n",
       "2              22.855322          14.691870      3              9.495032  \n",
       "3              22.077563          16.857044      4             17.931507  \n",
       "4              17.888621          11.482448      5             25.419915  \n",
       "...                  ...                ...    ...                   ...  \n",
       "23326          17.973486          11.648970  23327             39.302940  \n",
       "23327          21.866422          15.116015  23328             21.348579  \n",
       "23328          12.134167           8.574169  23329             30.741303  \n",
       "23329          13.929985           9.038195  23330             47.744243  \n",
       "23330          11.256538           9.921544  23331              1.886364  \n",
       "\n",
       "[23331 rows x 70 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save df to csv\n",
    "df.to_csv(\"/home/timkempchen/Downloads/tonsil2_mesmer_result_compensated.csv\", index=False)"
   ]
  }
 ],
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