{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# milliPillar calcium tissue batch analysis\n",
    "#### Author: Youngbin Kim\n",
    "#### Last updated: 06/23/21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:15:08.410199Z",
     "start_time": "2021-05-10T19:15:01.833601Z"
    }
   },
   "outputs": [],
   "source": [
    "import millipillar as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os.path\n",
    "import glob\n",
    "import tkinter as tk\n",
    "from tkinter import filedialog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select folder containing videos to be analyzed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For ND2 files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:25:42.190157Z",
     "start_time": "2021-05-10T19:25:35.659733Z"
    }
   },
   "outputs": [],
   "source": [
    "# asks for file path of your video\n",
    "root = tk.Tk()\n",
    "root.withdraw()\n",
    "root.call('wm', 'attributes', '.', '-topmost', True)\n",
    "folder_path = filedialog.askdirectory()\n",
    "file_paths = glob.glob(os.path.join(folder_path, \"**/*.nd2\"), recursive=True)\n",
    "print(len(file_paths), \"files detected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For tif files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T14:46:36.211484Z",
     "start_time": "2021-04-29T14:46:31.350743Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# asks for file path of your video\n",
    "root = tk.Tk()\n",
    "root.withdraw()\n",
    "root.call('wm', 'attributes', '.', '-topmost', True)\n",
    "folder_path = filedialog.askdirectory()\n",
    "file_paths = glob.glob(os.path.join(folder_path, \"**/*.tif\"), recursive=True)\n",
    "print(len(file_paths), \"files detected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze all the videos in the folder and output a summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for nd2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:25:59.992248Z",
     "start_time": "2021-05-10T19:25:46.935537Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#%%timeit\n",
    "total_summary = pd.DataFrame()\n",
    "traces = []\n",
    "for file_path in file_paths:\n",
    "    video = mp.Video(file_path, filetype=\"nd2\")\n",
    "    name = os.path.splitext(file_path.replace(folder_path+\"\\\\\", \"\"))[0]\n",
    "    trace = mp.Trace(data=video.trace, sampling_rate=video.frame_rate, name=name)\n",
    "    trace.analyze(baseline_fit=\"exp\")\n",
    "    traces.append(trace)\n",
    "    trace_summary = trace.summary()\n",
    "    trace_summary[\"trace\"] = trace\n",
    "    total_summary = total_summary.append(trace_summary)\n",
    "    print(\"Processed\", len(traces), \"out of\", len(file_paths), \"videos.\")\n",
    "total_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for tif videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T14:46:58.769891Z",
     "start_time": "2021-04-29T14:46:56.219619Z"
    }
   },
   "outputs": [],
   "source": [
    "#%%timeit\n",
    "total_summary = pd.DataFrame()\n",
    "traces = []\n",
    "for file_path in file_paths:\n",
    "    video = mp.Video(file_path, filetype=\"tif\", frame_rate=100)\n",
    "    name = os.path.splitext(file_path.replace(folder_path+\"\\\\\", \"\"))[0]\n",
    "    trace = mp.Trace(data=video.trace, sampling_rate=video.frame_rate, name=name)\n",
    "    trace.analyze(baseline_fit=\"exp\")\n",
    "    traces.append(trace)\n",
    "    trace_summary = trace.summary()\n",
    "    trace_summary[\"trace\"] = trace\n",
    "    total_summary = total_summary.append(trace_summary)\n",
    "    print(\"Processed\", len(traces), \"out of\", len(file_paths), \"videos.\")\n",
    "total_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-29T15:12:12.111171Z",
     "start_time": "2021-04-29T15:12:11.835286Z"
    }
   },
   "outputs": [],
   "source": [
    "#looking at a sample peak_summary for one of the traces\n",
    "df_f0 = pd.DataFrame([t.df_f0 for t in total_summary[\"trace\"]])\n",
    "df_f0.index = total_summary.index\n",
    "total_summary[\"max df_f0\"] = df_f0.max(axis=1)\n",
    "total_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save these results to csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:22:04.799599Z",
     "start_time": "2021-05-10T19:22:04.782607Z"
    }
   },
   "outputs": [],
   "source": [
    "file_name = \"summary.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-16T04:05:48.484548Z",
     "start_time": "2021-02-16T04:05:48.481619Z"
    }
   },
   "source": [
    "#### save general summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:22:05.840437Z",
     "start_time": "2021-05-10T19:22:05.780472Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "total_summary.to_csv(os.path.join(folder_path, file_name))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### save df/f0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:22:07.881867Z",
     "start_time": "2021-05-10T19:22:07.525435Z"
    }
   },
   "outputs": [],
   "source": [
    "#looking at a sample peak_summary for one of the traces\n",
    "df_f0 = pd.DataFrame([t.df_f0 for t in total_summary[\"trace\"]])\n",
    "df_f0.index = total_summary.index\n",
    "df_f0.to_csv(os.path.join(folder_path, \"df_f0 trace.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### save summary for each individual video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-16T04:06:20.303898Z",
     "start_time": "2021-02-16T04:06:20.299991Z"
    }
   },
   "outputs": [],
   "source": [
    "total_summary['trace'].apply(lambda row: row.peak_summary().to_csv(os.path.join(folder_path, row.name+\".csv\")));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More detailed information for each video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-16T15:19:17.937266Z",
     "start_time": "2021-02-16T15:19:17.922617Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "trace = total_summary.iloc[0]['trace']\n",
    "trace.peak_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-16T13:42:52.196331Z",
     "start_time": "2021-02-16T13:42:52.049843Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.scatter(trace.peaks, trace.df_f0[trace.peaks], label=\"peaks\")\n",
    "plt.plot(trace.df_f0, label=\"df_f0\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calculating df/f0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-16T07:35:45.222953Z",
     "start_time": "2021-02-16T07:35:45.074476Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot(trace.data, label=\"raw\")\n",
    "plt.plot(trace.baseline, label='f0')\n",
    "plt.scatter(trace.peaks, trace.data[trace.peaks], label='peaks')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### accounting for fluorescent decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-16T07:31:51.680431Z",
     "start_time": "2021-02-16T07:31:51.633458Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.plot(trace.df_f0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-16T07:33:01.650178Z",
     "start_time": "2021-02-16T07:33:01.632094Z"
    }
   },
   "outputs": [],
   "source": [
    "trace.summary().iloc[[0],15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}