2_data_visualization.ipynb

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   "source": [
    "from pathlib import Path\n",
    "import os\n",
    "import pandas as pd\n",
    "import plotly.express as px"
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    "# Define your output directory (folder containing the results of the analysis in 1_image_analysis)\n",
    "OUTPUT_FOLDER = Path(\"./output\")\n",
    "\n",
    "# Define your username (subfolder name under the output directory)\n",
    "USERNAME = \"Andrew\"\n",
    "\n",
    "# Merge parent_folder and username into a Path\n",
    "results_directory = OUTPUT_FOLDER / USERNAME\n",
    "\n",
    "# Define summary stats path\n",
    "summary_stats_path = OUTPUT_FOLDER / USERNAME / \"summary_stats\"\n",
    "\n",
    "# Read final_df containing all summary statistics from disk\n",
    "summary_stats_csv_path = summary_stats_path /\"summary_stats.csv\"\n",
    "\n",
    "final_df = pd.read_csv(summary_stats_csv_path)"
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    "# Drop morphology and class columns\n",
    "ratios_df = final_df.drop(columns=['Class Name', 'Area', 'Area_filled', 'Perimeter', 'Circularity', 'Eccentricity', 'Solidity'])\n",
    "\n",
    "# Remove duplicates based on 'well_id' and 'plate_name'\n",
    "ratios_df = ratios_df.drop_duplicates(subset=['well_id', 'plate_name'])\n",
    "\n",
    "# Group by plate_name\n",
    "plate_names = ratios_df['plate_name'].unique()\n",
    "\n",
    "for plate_name in plate_names:\n",
    "    # Filter the dataframe for the current plate_name\n",
    "    df_filtered = ratios_df[ratios_df['plate_name'] == plate_name]\n",
    "    \n",
    "    # Plotting\n",
    "    fig = px.bar(df_filtered, x='well_id', y=['organoid_ratio','spheroid_ratio'], title=f'Organoid/Spheroid ratio per Well for Plate: {plate_name}')\n",
    "    \n",
    "    # Construct the file path for saving\n",
    "    # file_path = summary_stats_path / f\"{plate_name}_organoid_ratio.png\"\n",
    "    \n",
    "    # Save plot as PNG file\n",
    "    # fig.write_image(file_path.as_posix(), scale=2)  # Adjust scale as needed for resolution\n",
    "    \n",
    "    # Show plot\n",
    "    fig.show()"
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   "source": [
    "# Group by plate_name\n",
    "plate_names = final_df['plate_name'].unique()\n",
    "\n",
    "for plate_name in plate_names:\n",
    "    # Filter the dataframe for the current plate_name\n",
    "    df_filtered = final_df[final_df['plate_name'] == plate_name]\n",
    "    \n",
    "    # Plotting\n",
    "    fig = px.scatter(df_filtered, x='well_id', y='Area_filled', color='Class Name', title=f'Object Area per Class for Plate: {plate_name}')\n",
    "    \n",
    "    # Construct the file path for saving\n",
    "    # file_path = summary_stats_path / f\"{plate_name}_object_area.png\"\n",
    "    \n",
    "    # Save plot as PNG file\n",
    "    # fig.write_image(file_path.as_posix(), scale=2)  # Adjust scale as needed for resolution\n",
    "    \n",
    "    # Show plot\n",
    "    fig.show()"
   ]
  }
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