5. Unsupervised Clustering.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (C) 2023 Glencoe Software, Inc. All Rights Reserved.\n",
    "\n",
    "This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.\n",
    "\n",
    "This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n",
    "\n",
    "You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised Clustering for Whole Slide Image with omero2pandas"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to retrieve analytical data stored as an [OMERO.table](https://docs.openmicroscopy.org/omero-blitz/5.5.8/slice2html/omero/grid/Table.html) from the [OMERO.server](https://www.glencoesoftware.com/products/omeroplus/) using [omero2pandas](https://github.com/glencoesoftware/omero2pandas) and build unsupervised clustering model to profile the cells.\n",
    "\n",
    "The example below uses the output of the image processing performed using [OMERO-Segmentation Connector for WSI](https://www.glencoesoftware.com/solutions/digital-pathology/) which utilizes [StarDist](https://github.com/stardist/stardist) as a segmenter. Derived features (parameters describing cells' shape, intensity and neighborhood) are then used to build a clustering model using [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html). Finally the cluster ids are assigned to each object and saved to [OMERO.tables](https://docs.openmicroscopy.org/omero-blitz/5.5.8/slice2html/omero/grid/Table.html) using [omero2pandas](https://github.com/glencoesoftware/omero2pandas). The stored data can be visualized remotely with [PathViewer](https://www.glencoesoftware.com/products/pathviewer/) and [Pageant](https://www.glencoesoftware.com/products/pageant/)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Workflow\n",
    " * Load OMERO.table to Pandas DataFrame using omero2pandas\n",
    " * Pre-process the data (replace missing values, normalize, etc.)\n",
    " * Remove strongly correlated features using Spearman correlation\n",
    " * Run dimensionality reduction using UMAP\n",
    " * Build an unsupervised clustering model and classify the cells\n",
    " * Visualize the results using Datashader, Bokeh, and HoloViews\n",
    " * Save the result to OMERO.tables for visualization with [PathViewer](https://www.glencoesoftware.com/products/pathviewer/) and [Pageant](https://www.glencoesoftware.com/products/pageant/), or other downstream workflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "numba_logger = logging.getLogger('numba')\n",
    "numba_logger.setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "from umap import UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import omero2pandas\n",
    "import numpy as np\n",
    "from scipy.stats import spearmanr\n",
    "from sklearn.cluster import Birch\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import calinski_harabasz_score, davies_bouldin_score\n",
    "from umap import UMAP\n",
    "from sklearn.decomposition import PCA\n",
    "import holoviews as hv\n",
    "import holoviews.operation.datashader as hd\n",
    "from bokeh.models import HoverTool\n",
    "from holoviews import opts\n",
    "import datashader as ds\n",
    "from matplotlib.colors import ListedColormap\n",
    "from bokeh.palettes import Viridis256\n",
    "from bokeh.plotting import show\n",
    "\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import skimage.exposure\n",
    "import shapely\n",
    "import shapely.wkt\n",
    "import math\n",
    "import shapely.affinity\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define variables and OMERO IDs that will be used for the next steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the image and table IDs\n",
    "# Image ID will be used to link the uploaded table to the image\n",
    "IMAGE_ID = 1912\n",
    "# Table ID\n",
    "TABLE_FILE_ID = 14510\n",
    "# Analysis ID - distinct name for the local files and the table\n",
    "# Can also use uuid lib to generate a random ID\n",
    "ANALYSIS_ID = \"95a5420c-9732-4a49-961c-cf43264a7b50\"\n",
    "\n",
    "# Where to store the downloaded table?\n",
    "table_path = f\"./objects_{ANALYSIS_ID}.csv\"\n",
    "# The name will be used as OMERO.table name\n",
    "output_table_name = f\"{ANALYSIS_ID}_unsupervised_clustering\"\n",
    "# Store outputs locally\n",
    "output_table_path = f\"{output_table_name}.csv\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the table from OMERO and save it locally for future us"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If [omero-user-token](https://github.com/glencoesoftware/omero-user-token) is installed and valid [omero2pandas](https://github.com/glencoesoftware/omero2pandas) will use it automatically for the OMERO.server connection otherwise it will ask for the connection details."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Without omero-user-token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to apps.dev.omero-plus.io\n"
     ]
    }
   ],
   "source": [
    "# Initialize Connector\n",
    "connector = omero2pandas.connect_to_omero(\n",
    "    server=\"where.is.your.omero.server\",\n",
    "    username=\"what.is.your.username\",\n",
    "    password=\"what.is.your.password\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5507d54e27a4473b56102c39eea705e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading table from OMERO:   0%|          | 0/1201949 rows, 00:00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the data\n",
    "omero2pandas.download_table(\n",
    "    table_path, file_id=TABLE_FILE_ID, chunk_size=10000,\n",
    "    omero_connector=connector)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### With omero-user-token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to apps.dev.omero-plus.io\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading table from OMERO: 100%|██████████| 1201949/1201949 rows, 2:33:11 \n"
     ]
    }
   ],
   "source": [
    "# Load the data, auth and connection is handled via omero-user-token\n",
    "omero2pandas.download_table(\n",
    "    table_path, file_id=TABLE_FILE_ID, chunk_size=10000)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the table from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skip columns that are not needed for clustering\n",
    "columns_to_skip = [\n",
    "    \"prob\", \"polygon\", \"centroid\", \"bbox_min_x\", \"bbox_min_y\", \"bbox_max_x\", \"bbox_max_y\",\n",
    "    \"prob_thresh\", \"nms_thresh\", \"polygon_centroid_x\", \"polygon_centroid_y\", \"polygon_bbox\",\n",
    "    \"polygon_min_rot_rect\", \"raster_centroid_y\", \"raster_centroid_x\", \"tile_index\",\n",
    "    \"nearest_object\", \"roi\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From dist, needs to be switched to omero2pandas\n",
    "df = pd.read_csv(\n",
    "    table_path,\n",
    "    usecols=lambda column: column not in columns_to_skip\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess the data (replace missing values, normalize, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape raw: (500000, 307)\n"
     ]
    }
   ],
   "source": [
    "df.fillna(df.mean(), inplace=True)\n",
    "scaler = StandardScaler()\n",
    "df_scaled = scaler.fit_transform(df)\n",
    "print(f\"Shape raw: {df_scaled.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove strongly correlated features using Spearman correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 0 to remove\n",
      "Shape spearmaned: (500000, 82)\n"
     ]
    }
   ],
   "source": [
    "corr_matrix, _ = spearmanr(df_scaled)\n",
    "corr_threshold = 0.9\n",
    "\n",
    "corr_matrix = np.abs(corr_matrix)\n",
    "np.fill_diagonal(corr_matrix, 0)\n",
    "cols_to_remove = []\n",
    "for i in range(corr_matrix.shape[0]):\n",
    "    for j in range(i+1, corr_matrix.shape[1]):\n",
    "        if corr_matrix[i, j] > corr_threshold:\n",
    "            cols_to_remove.append(j)\n",
    "\n",
    "# Remove correlated columns\n",
    "print(f\"Found {len(set(cols_to_remove))} to remove\")\n",
    "df_scaled = df_scaled[:, list(set(range(df_scaled.shape[1])) - set(cols_to_remove))]\n",
    "print(f\"Shape spearmaned: {df_scaled.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dimensionality reduction using UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# pca = PCA(n_components=2)\n",
    "# df_reduced = pca.fit_transform(df_scaled)\n",
    "\n",
    "reducer = UMAP(n_components=2, low_memory=True)\n",
    "df_reduced = reducer.fit_transform(df_scaled)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find optimum number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "CH: [8548.830968561631, 162211.8353836989, 148992.0861990337, 112811.98844055133, 94847.49221796962, 87051.16909739483, 74766.08259322333, 221475.5876233015, 197432.91026139422, 177856.44290296585, 183383.389271722, 168758.47089394837, 156408.5393031664, 145593.9235561664, 159068.59359019483, 149182.27681486125, 140533.80766651587]\n",
      "DB: [0.49892720615123287, 0.9217888985503627, 0.8411822051366689, 0.6707548957074821, 0.6397796808260114, 0.6537660028222555, 0.6895218943334774, 0.6652803098474886, 0.6546078678670433, 0.5686382541344497, 0.602495089992296, 0.6646700112893077, 0.6763088591226946, 0.692985699833145, 0.7258272236991318, 0.6675323651614432, 0.6706590058235556]\n",
      "Optimal CH: 9, Optimal DB: 2\n",
      "Number of clusters identified: 2\n"
     ]
    }
   ],
   "source": [
    "# Define the range of clusters to try\n",
    "n_clusters_range = range(2, 19)\n",
    "ch_scores = []\n",
    "db_scores = []\n",
    "\n",
    "for n_clusters in n_clusters_range:\n",
    "    print(n_clusters)\n",
    "    birch = Birch(n_clusters=n_clusters, branching_factor=50, threshold=0.5)\n",
    "    clusters = birch.fit_predict(df_reduced)\n",
    "    ch_score = calinski_harabasz_score(df_reduced, clusters)\n",
    "    ch_scores.append(ch_score)\n",
    "    db_score = davies_bouldin_score(df_reduced, clusters)\n",
    "    db_scores.append(db_score)\n",
    "\n",
    "# Find the optimal number of clusters using the elbow method\n",
    "optimal_clusters_ch = n_clusters_range[np.argmax(ch_scores)]\n",
    "optimal_clusters_db = n_clusters_range[np.argmin(db_scores)]\n",
    "print(f\"CH: {ch_scores}\")\n",
    "print(f\"DB: {db_scores}\")\n",
    "print(f\"Optimal CH: {optimal_clusters_ch}, Optimal DB: {optimal_clusters_db}\")\n",
    "\n",
    "n_clusters = min([optimal_clusters_ch, optimal_clusters_db])\n",
    "\n",
    "# Calculate clusters\n",
    "\n",
    "birch = Birch(n_clusters=n_clusters, branching_factor=50, threshold=0.5)\n",
    "clusters = birch.fit_predict(df_reduced)\n",
    "print(f'Number of clusters identified: {len(np.unique(clusters))}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Or use a fixed number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters identified: 5\n"
     ]
    }
   ],
   "source": [
    "n_clusters = 5\n",
    "birch = Birch(n_clusters=5, branching_factor=50, threshold=0.5)\n",
    "clusters = birch.fit_predict(df_reduced)\n",
    "print(f'Number of clusters identified: {len(np.unique(clusters))}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the results using Datashader, Bokeh, and HoloViews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#FFA500', '#FF00FF', '#00FFFF', '#FFFF00', '#FF0000']\n",
    "cmap = ListedColormap(colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>UMAP1</th>\n",
       "      <th>UMAP2</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.559985</td>\n",
       "      <td>-0.156121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.789196</td>\n",
       "      <td>0.583535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.038660</td>\n",
       "      <td>0.362964</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.465014</td>\n",
       "      <td>0.335364</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.910857</td>\n",
       "      <td>0.645336</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       UMAP1     UMAP2  Cluster\n",
       "0  12.559985 -0.156121        1\n",
       "1  10.789196  0.583535        1\n",
       "2  10.038660  0.362964        1\n",
       "3  10.465014  0.335364        1\n",
       "4   9.910857  0.645336        1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced_labels = pd.DataFrame(df_reduced, columns=['UMAP1', 'UMAP2'])\n",
    "df_reduced_labels['Cluster'] = clusters\n",
    "df_reduced_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#FFA500', '#FF00FF', '#00FFFF', '#FFFF00', '#FF0000']\n",
    "cmap = ListedColormap(colors)\n",
    "\n",
    "df_reduced_labels = pd.DataFrame(df_reduced, columns=['UMAP1', 'UMAP2'])\n",
    "df_reduced_labels['Cluster'] = clusters\n",
    "points = hv.Points(df_reduced_labels, kdims=['UMAP1', 'UMAP2'], vdims=['Cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.plotting.figure.Figure\">Figure</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'1006', <span id=\"1067\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">above&nbsp;=&nbsp;[],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">align&nbsp;=&nbsp;'start',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">aspect_ratio&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">aspect_scale&nbsp;=&nbsp;1,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">background&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">background_fill_alpha&nbsp;=&nbsp;1.0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">background_fill_color&nbsp;=&nbsp;'#ffffff',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">below&nbsp;=&nbsp;[LinearAxis(id='1015', ...)],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">border_fill_alpha&nbsp;=&nbsp;1.0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">border_fill_color&nbsp;=&nbsp;'#ffffff',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">center&nbsp;=&nbsp;[Grid(id='1018', ...), Grid(id='1022', ...)],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">css_classes&nbsp;=&nbsp;[],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">disabled&nbsp;=&nbsp;False,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_x_ranges&nbsp;=&nbsp;{},</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_x_scales&nbsp;=&nbsp;{},</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_y_ranges&nbsp;=&nbsp;{},</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_y_scales&nbsp;=&nbsp;{},</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">frame_height&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">frame_width&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">height&nbsp;=&nbsp;800,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">height_policy&nbsp;=&nbsp;'auto',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hidpi&nbsp;=&nbsp;True,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">inner_height&nbsp;=&nbsp;0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">inner_width&nbsp;=&nbsp;0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{'rangesupdate': [CustomJS(id='1051', ...)]},</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">left&nbsp;=&nbsp;[LinearAxis(id='1019', ...)],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_factor&nbsp;=&nbsp;10,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_interval&nbsp;=&nbsp;300,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_threshold&nbsp;=&nbsp;2000,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_timeout&nbsp;=&nbsp;500,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">margin&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">match_aspect&nbsp;=&nbsp;False,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">max_height&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">max_width&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border&nbsp;=&nbsp;5,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_bottom&nbsp;=&nbsp;10,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_left&nbsp;=&nbsp;10,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_right&nbsp;=&nbsp;10,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_top&nbsp;=&nbsp;10,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_height&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_width&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outer_height&nbsp;=&nbsp;0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outer_width&nbsp;=&nbsp;0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_alpha&nbsp;=&nbsp;1.0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_cap&nbsp;=&nbsp;'butt',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_color&nbsp;=&nbsp;'#e5e5e5',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_dash&nbsp;=&nbsp;[],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_dash_offset&nbsp;=&nbsp;0,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_join&nbsp;=&nbsp;'bevel',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_width&nbsp;=&nbsp;1,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">output_backend&nbsp;=&nbsp;'webgl',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">renderers&nbsp;=&nbsp;[GlyphRenderer(id='1042', ...)],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">reset_policy&nbsp;=&nbsp;'standard',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">right&nbsp;=&nbsp;[],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">sizing_mode&nbsp;=&nbsp;'fixed',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;['rangesupdate'],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">syncable&nbsp;=&nbsp;True,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">title&nbsp;=&nbsp;Title(id='1007', ...),</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">title_location&nbsp;=&nbsp;'above',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar&nbsp;=&nbsp;Toolbar(id='1029', ...),</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_location&nbsp;=&nbsp;'right',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_sticky&nbsp;=&nbsp;True,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible&nbsp;=&nbsp;True,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width&nbsp;=&nbsp;800,</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width_policy&nbsp;=&nbsp;'auto',</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range&nbsp;=&nbsp;Range1d(id='1003', ...),</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_scale&nbsp;=&nbsp;LinearScale(id='1011', ...),</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range&nbsp;=&nbsp;Range1d(id='1004', ...),</div></div><div class=\"1066\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_scale&nbsp;=&nbsp;LinearScale(id='1013', ...))</div></div></div>\n",
       "<script>\n",
       "(function() {\n",
       "  let expanded = false;\n",
       "  const ellipsis = document.getElementById(\"1067\");\n",
       "  ellipsis.addEventListener(\"click\", function() {\n",
       "    const rows = document.getElementsByClassName(\"1066\");\n",
       "    for (let i = 0; i < rows.length; i++) {\n",
       "      const el = rows[i];\n",
       "      el.style.display = expanded ? \"none\" : \"table-row\";\n",
       "    }\n",
       "    ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n",
       "    expanded = !expanded;\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "Figure(id='1006', ...)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datashaded = hd.datashade(points, aggregator=ds.count_cat('Cluster'), color_key=cmap)\n",
    "hover = HoverTool(tooltips=[('Cluster', '@Cluster')])\n",
    "opts.defaults(opts.RGB(tools=[hover]))\n",
    "plot = datashaded.opts(width=800, height=800)\n",
    "hv.render(plot)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show example cell for each label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the original dataset with the new DataFrame on index\n",
    "df_original = pd.read_csv(table_path, usecols=['object'] + columns_to_skip)\n",
    "df_labeled = df_original.merge(df_reduced_labels, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>object</th>\n",
       "      <th>prob</th>\n",
       "      <th>polygon</th>\n",
       "      <th>centroid</th>\n",
       "      <th>bbox_min_x</th>\n",
       "      <th>bbox_min_y</th>\n",
       "      <th>bbox_max_x</th>\n",
       "      <th>bbox_max_y</th>\n",
       "      <th>prob_thresh</th>\n",
       "      <th>nms_thresh</th>\n",
       "      <th>...</th>\n",
       "      <th>polygon_bbox</th>\n",
       "      <th>polygon_min_rot_rect</th>\n",
       "      <th>raster_centroid_y</th>\n",
       "      <th>raster_centroid_x</th>\n",
       "      <th>tile_index</th>\n",
       "      <th>nearest_object</th>\n",
       "      <th>roi</th>\n",
       "      <th>UMAP1</th>\n",
       "      <th>UMAP2</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.483748</td>\n",
       "      <td>POLYGON ((746.704345703125 10160, 746.33465576...</td>\n",
       "      <td>POINT (742.8420755280428 10160.00173679036)</td>\n",
       "      <td>739.195618</td>\n",
       "      <td>10156.444336</td>\n",
       "      <td>746.704346</td>\n",
       "      <td>10163.665039</td>\n",
       "      <td>0.479071</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>POLYGON ((739.1956176757812 10156.4443359375, ...</td>\n",
       "      <td>POLYGON ((746.9015607102662 10163.37895624251,...</td>\n",
       "      <td>10159.550000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>11</td>\n",
       "      <td>-1</td>\n",
       "      <td>2038</td>\n",
       "      <td>12.559985</td>\n",
       "      <td>-0.156121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.912714</td>\n",
       "      <td>POLYGON ((901.2435302734375 27912, 900.5768432...</td>\n",
       "      <td>POINT (889.8568234644157 27912.035999986587)</td>\n",
       "      <td>878.947937</td>\n",
       "      <td>27898.771484</td>\n",
       "      <td>901.243530</td>\n",
       "      <td>27925.275391</td>\n",
       "      <td>0.479071</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>POLYGON ((878.9479370117188 27898.771484375, 9...</td>\n",
       "      <td>POLYGON ((876.4823036524576 27922.94341846238,...</td>\n",
       "      <td>27911.561555</td>\n",
       "      <td>889.360691</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>2038</td>\n",
       "      <td>10.789196</td>\n",
       "      <td>0.583535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.911478</td>\n",
       "      <td>POLYGON ((620.9896850585938 28112, 620.2854614...</td>\n",
       "      <td>POINT (610.0222623486634 28111.0383113105)</td>\n",
       "      <td>598.603149</td>\n",
       "      <td>28099.779297</td>\n",
       "      <td>620.989685</td>\n",
       "      <td>28122.238281</td>\n",
       "      <td>0.479071</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>POLYGON ((598.6031494140625 28099.779296875, 6...</td>\n",
       "      <td>POLYGON ((598.7960617217437 28122.4452065584, ...</td>\n",
       "      <td>28110.545455</td>\n",
       "      <td>609.540404</td>\n",
       "      <td>30</td>\n",
       "      <td>-1</td>\n",
       "      <td>2038</td>\n",
       "      <td>10.038660</td>\n",
       "      <td>0.362964</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.903430</td>\n",
       "      <td>POLYGON ((939.1497802734375 27838, 938.3896484...</td>\n",
       "      <td>POINT (926.5268208001696 27838.0755411473)</td>\n",
       "      <td>914.584717</td>\n",
       "      <td>27826.935547</td>\n",
       "      <td>939.149780</td>\n",
       "      <td>27849.691406</td>\n",
       "      <td>0.479071</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>POLYGON ((914.584716796875 27826.935546875, 93...</td>\n",
       "      <td>POLYGON ((940.6016025786357 27847.447551637953...</td>\n",
       "      <td>27671.543578</td>\n",
       "      <td>12.018349</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>2038</td>\n",
       "      <td>10.465014</td>\n",
       "      <td>0.335364</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.891712</td>\n",
       "      <td>POLYGON ((891.408203125 27892, 886.57360839843...</td>\n",
       "      <td>POINT (875.5334526718246 27890.43671011221)</td>\n",
       "      <td>857.480347</td>\n",
       "      <td>27878.464844</td>\n",
       "      <td>895.363708</td>\n",
       "      <td>27903.857422</td>\n",
       "      <td>0.479071</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>POLYGON ((857.4803466796875 27878.46484375, 89...</td>\n",
       "      <td>POLYGON ((899.093139403205 27896.741274306383,...</td>\n",
       "      <td>27671.986068</td>\n",
       "      <td>18.086687</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>2038</td>\n",
       "      <td>9.910857</td>\n",
       "      <td>0.645336</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   object      prob                                            polygon   \n",
       "0       1  0.483748  POLYGON ((746.704345703125 10160, 746.33465576...  \\\n",
       "1       2  0.912714  POLYGON ((901.2435302734375 27912, 900.5768432...   \n",
       "2       3  0.911478  POLYGON ((620.9896850585938 28112, 620.2854614...   \n",
       "3       4  0.903430  POLYGON ((939.1497802734375 27838, 938.3896484...   \n",
       "4       5  0.891712  POLYGON ((891.408203125 27892, 886.57360839843...   \n",
       "\n",
       "                                       centroid  bbox_min_x    bbox_min_y   \n",
       "0   POINT (742.8420755280428 10160.00173679036)  739.195618  10156.444336  \\\n",
       "1  POINT (889.8568234644157 27912.035999986587)  878.947937  27898.771484   \n",
       "2    POINT (610.0222623486634 28111.0383113105)  598.603149  28099.779297   \n",
       "3    POINT (926.5268208001696 27838.0755411473)  914.584717  27826.935547   \n",
       "4   POINT (875.5334526718246 27890.43671011221)  857.480347  27878.464844   \n",
       "\n",
       "   bbox_max_x    bbox_max_y  prob_thresh  nms_thresh  ...   \n",
       "0  746.704346  10163.665039     0.479071         0.3  ...  \\\n",
       "1  901.243530  27925.275391     0.479071         0.3  ...   \n",
       "2  620.989685  28122.238281     0.479071         0.3  ...   \n",
       "3  939.149780  27849.691406     0.479071         0.3  ...   \n",
       "4  895.363708  27903.857422     0.479071         0.3  ...   \n",
       "\n",
       "                                        polygon_bbox   \n",
       "0  POLYGON ((739.1956176757812 10156.4443359375, ...  \\\n",
       "1  POLYGON ((878.9479370117188 27898.771484375, 9...   \n",
       "2  POLYGON ((598.6031494140625 28099.779296875, 6...   \n",
       "3  POLYGON ((914.584716796875 27826.935546875, 93...   \n",
       "4  POLYGON ((857.4803466796875 27878.46484375, 89...   \n",
       "\n",
       "                                polygon_min_rot_rect raster_centroid_y   \n",
       "0  POLYGON ((746.9015607102662 10163.37895624251,...      10159.550000  \\\n",
       "1  POLYGON ((876.4823036524576 27922.94341846238,...      27911.561555   \n",
       "2  POLYGON ((598.7960617217437 28122.4452065584, ...      28110.545455   \n",
       "3  POLYGON ((940.6016025786357 27847.447551637953...      27671.543578   \n",
       "4  POLYGON ((899.093139403205 27896.741274306383,...      27671.986068   \n",
       "\n",
       "  raster_centroid_x  tile_index  nearest_object   roi      UMAP1     UMAP2   \n",
       "0        742.250000          11              -1  2038  12.559985 -0.156121  \\\n",
       "1        889.360691          30               7  2038  10.789196  0.583535   \n",
       "2        609.540404          30              -1  2038  10.038660  0.362964   \n",
       "3         12.018349          30              10  2038  10.465014  0.335364   \n",
       "4         18.086687          30              15  2038   9.910857  0.645336   \n",
       "\n",
       "   Cluster  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labeled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_grab = []\n",
    "for label in range(5):\n",
    "    poly = shapely.wkt.loads(df_labeled[df_labeled[\"Cluster\"] == label][\"polygon\"].iloc[5])\n",
    "    x1, y1, x2, y2 = poly.buffer(10).bounds\n",
    "    x1 = math.floor(x1)\n",
    "    y1 = math.floor(y1)\n",
    "    x2 = math.ceil(x2)\n",
    "    y2 = math.ceil(y2)\n",
    "    to_grab.append((label, poly, x1, y1, x2 - x1, y2 - y1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims = []\n",
    "\n",
    "red_channel = 12\n",
    "green_channel = 7\n",
    "blue_channel = 0\n",
    "\n",
    "colors = (red_channel, green_channel, blue_channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to apps.dev.omero-plus.io\n",
      "Gateway open\n",
      "Done  0\n",
      "Done  1\n",
      "Done  2\n",
      "Done  3\n",
      "Done  4\n",
      "Image data downloaded\n"
     ]
    }
   ],
   "source": [
    "max_h = max_w = 0\n",
    "for _, _, _, _, w, h in to_grab:\n",
    "    max_h = max(max_h, h)\n",
    "    max_w = max(max_w, w)\n",
    "    \n",
    "max_h = (int(math.ceil(max_h / 10.0)) * 10) + 1\n",
    "max_w = (int(math.ceil(max_w / 10.0)) * 10) + 1\n",
    "moved_polys = []\n",
    "\n",
    "ims = []\n",
    "with omero2pandas.OMEROConnection(\n",
    "        server=\"where.is.your.omero.server\",\n",
    "        username=\"what.is.your.username\",\n",
    "        password=\"what.is.your.password\") as connector:\n",
    "    conn = connector.get_gateway()\n",
    "    conn.SERVICE_OPTS.setOmeroGroup('-1')\n",
    "    print(\"Gateway open\")\n",
    "    img = conn.getObject(\"Image\", 1912)\n",
    "    pixel_fetcher = img.getPrimaryPixels()\n",
    "    for cluster_id, poly, x, y, _, _ in to_grab:\n",
    "        moved_poly = shapely.affinity.translate(poly, xoff=-x, yoff=-y)\n",
    "        moved_polys.append(moved_poly)\n",
    "        want = [(0, c, 0, (x, y, max_w, max_h)) for c in colors]\n",
    "        data = pixel_fetcher.getTiles(want)\n",
    "        ims.append(list(data))\n",
    "        print(\"Done \", cluster_id)\n",
    "print(\"Image data downloaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate display bounds\n",
    "blue_max = df['raster_01_Hoechst_intensity_max'].mean() + (df['raster_01_Hoechst_intensity_max'].std() * 2)\n",
    "red_max = df['raster_13_E-Cadherin_Argo730_intensity_max'].mean() + (df['raster_13_E-Cadherin_Argo730_intensity_max'].std() * 2)\n",
    "green_max = df['raster_08_CD4_Argo572_intensity_max'].mean() + (df['raster_08_CD4_Argo572_intensity_max'].std() * 2)\n",
    "\n",
    "fig, axs = plt.subplots(1, 5, sharex=True, sharey=True)\n",
    "fig.set_figheight(4)\n",
    "fig.set_figwidth(15)\n",
    "fig.suptitle(\"Representative Cells\")\n",
    "px = 0\n",
    "py = 0\n",
    "\n",
    "for i, img_data in enumerate(ims):\n",
    "    red, green, blue = img_data.copy()\n",
    "    poly = moved_polys[i]\n",
    "    red = skimage.exposure.rescale_intensity(red, in_range=(0, red_max), out_range=(0, 1))\n",
    "    green = skimage.exposure.rescale_intensity(green, in_range=(0, green_max), out_range=(0, 1))\n",
    "    blue = skimage.exposure.rescale_intensity(blue, in_range=(0, blue_max), out_range=(0, 1))\n",
    "    preview = np.dstack((red, green, blue))\n",
    "    \n",
    "    axs[px].imshow(preview)\n",
    "    axs[px].plot(*poly.exterior.xy, color=cmap(i))\n",
    "    axs[px].set_title(f\"Cluster {i}\")\n",
    "\n",
    "    px += 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to apps.dev.omero-plus.io\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Uploading table to OMERO: 100%|██████████| 1201949/1201949 rows, 53:44 \n"
     ]
    }
   ],
   "source": [
    "# Upload the table to OMERO\n",
    "ann_id = omero2pandas.upload_table(\n",
    "    df_labeled, output_table_name, IMAGE_ID, \"Image\", chunk_size=10000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "notebooks.venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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