Chromatography_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import math\n",
    "from pathlib import Path\n",
    "\n",
    "############################## SECTION FOR PEAK PARAMETERS, MUST RUN APQUANT IN PD 2.5 #######################################\n",
    "WRITE_OUTPUT = False\n",
    "url_base = None\n",
    "APPFOLDER = \"./\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_peaks_files(settings, data_files):\n",
    "    #load files\n",
    "    \n",
    "    files = []\n",
    "    for each_file in data_files:\n",
    "        currentFile = pd.read_table(each_file, sep=\"\\t\")\n",
    "        currentFile = currentFile[currentFile[\"Identified By\"] == \"MS/MS\"]\n",
    "        files.append(currentFile)\n",
    "    \n",
    "    #create groups\n",
    "    all_peaks = {}\n",
    "    in_filters = settings[\"filter_in\"].tolist()\n",
    "    out_filters = settings[\"filter_out\"].tolist()\n",
    "    i = 0\n",
    "    for eachGroup in settings[\"Group Name\"].tolist():\n",
    "        #check for which file\n",
    "        if len(str.split(in_filters[i],sep = \"@\")) == 2:\n",
    "            file_index = int(str.split(in_filters[i],sep = \"@\")[1])\n",
    "            filter_in = str.split(in_filters[i],sep = \"@\")[0]\n",
    "        elif len(str.split(in_filters[i],sep = \"@\")) == 1:\n",
    "            file_index = 0\n",
    "            filter_in = in_filters[i]\n",
    "        else:\n",
    "            print(\"error, multiple peak files per group not implemented\")\n",
    "            break\n",
    "        if isinstance(out_filters[i], str) and len(out_filters[i]) > 0:\n",
    "            filter_out = out_filters[i]\n",
    "        elif out_filters[i] == None or out_filters[i] == \"\" or math.isnan(out_filters[i]):\n",
    "            filter_out = \"M@di\"\n",
    "        else:\n",
    "            filter_out = out_filters[i]\n",
    "        #filter groups\n",
    "        currentGroupData = files[file_index][files[file_index][\"Spectrum File\"].str.contains(filter_in)]\n",
    "        currentGroupData = currentGroupData[~currentGroupData[\"Spectrum File\"].str.contains(filter_out)]\n",
    "        #group\n",
    "        all_peaks[eachGroup] = currentGroupData.groupby(\"Sequence\").agg({\n",
    "            \"Peak Apex\": \"median\",\n",
    "            \"FWHM\": \"mean\",\n",
    "            \"Apex Intensity\": \"mean\",\n",
    "            \"Area\": \"mean\"}).reset_index()\n",
    "        i = i + 1\n",
    "    return all_peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FWHM_Boxplot (peakdata_object, plot_options, settings, username = None):\n",
    "\n",
    "    if len(settings[\"Group Name\"].tolist()) > 1:\n",
    "        firstName = settings[\"Group Name\"].tolist()[0]\n",
    "        all_fwhm = pd.DataFrame({\"Sequence\":peakdata_object[firstName][\"Sequence\"],firstName:peakdata_object[firstName][\"FWHM\"]})\n",
    "        for eachGroup in settings[\"Group Name\"].tolist()[1:]:\n",
    "            current = pd.DataFrame({eachGroup:peakdata_object[eachGroup][\"FWHM\"],\"Sequence\":peakdata_object[eachGroup][\"Sequence\"]})\n",
    "            all_fwhm = pd.merge(current, all_fwhm, how=\"inner\")\n",
    "    else:\n",
    "        all_fwhm = peakdata_object[0]\n",
    "        \n",
    "    toPlotFWHM = all_fwhm.set_index(\"Sequence\").melt(ignore_index=False, var_name=\"Group Name\", value_name=\"FWHM\").reset_index()\n",
    "    toPlotFWHM = toPlotFWHM.merge(settings, how=\"inner\")\n",
    "    return plot_FWHM_boxplot(toPlotFWHM, plot_options, username)\n",
    "\n",
    "def plot_FWHM_boxplot(FWHMs, plot_options, username):\n",
    "    \n",
    "    plot_div = None\n",
    "    CSV_link = None\n",
    "    SVG_link = None\n",
    "\n",
    "    if plot_options[\"log10\"]:\n",
    "        FWHMs= FWHMs[FWHMs[\"FWHM\"]>0]\n",
    "\n",
    "    if not plot_options[\"ylimits\"] or plot_options[\"ylimits\"] == \"[]\" or \\\n",
    "            not isinstance(plot_options[\"ylimits\"], list):\n",
    "        ylimits = None\n",
    "    else:\n",
    "        ylimits = plot_options[\"ylimits\"]\n",
    "\n",
    "    #Remove outliers\n",
    "    if plot_options[\"outliers\"] == \"remove\":\n",
    "        q_low = FWHMs[\"FWHM\"].quantile(0.01)\n",
    "        q_hi  = FWHMs[\"FWHM\"].quantile(0.99)\n",
    "\n",
    "        FWHMs = FWHMs[(FWHMs[\"FWHM\"] < q_hi) & (FWHMs[\"FWHM\"] > q_low)]\n",
    "        plot_options[\"outliers\"] = False\n",
    "        \n",
    "    FWHM_summary = FWHMs.groupby([\"Group Name\"]).agg(\n",
    "        {'FWHM': ['median', 'mean']}).reset_index()\n",
    "    FWHM_summary.columns = [\"Group Name\", 'meds', 'CoVar']\n",
    "    # median label\n",
    "    if plot_options[\"median label\"] == \"True\" or \\\n",
    "            plot_options[\"median label\"] == True:\n",
    "        total_labels = [{\"x\": x, \"y\": total*1.15, \"text\": str(\n",
    "            round(total,1)), \"showarrow\": False} for x, total in zip(\n",
    "            FWHM_summary[\"Group Name\"], FWHM_summary[\"meds\"])]\n",
    "    else:\n",
    "        total_labels = []   # no median labels\n",
    "\n",
    "    if plot_options[\"FWHM mode\"] == \"grouped\":  \n",
    "            \n",
    "        #find out present categories\n",
    "        categories = FWHMs.groupby(plot_options[\"Group By Color\"]).first().reset_index()[plot_options[\"Group By Color\"]].tolist()\n",
    "        # create the plot\n",
    "        fig_data = []\n",
    "        i = 0\n",
    "        for eachCategory in categories:\n",
    "            fig_data.append(go.Box(name = eachCategory,\n",
    "                        x=FWHMs.loc[FWHMs[plot_options[\"Group By Color\"]]==eachCategory,plot_options[\"Group By X\"]].tolist(),\n",
    "                        y=FWHMs.loc[FWHMs[plot_options[\"Group By Color\"]]==eachCategory,\"FWHM\"].tolist(),\n",
    "                        fillcolor = plot_options[\"color\"][i],\n",
    "                        boxpoints=plot_options[\"outliers\"],\n",
    "                        marker=dict(opacity=0)\n",
    "                        ))\n",
    "            i = i + 1\n",
    "\n",
    "        fig = go.Figure(data = fig_data)\n",
    "        fig.update_layout(\n",
    "            boxmode = \"group\",\n",
    "            plot_bgcolor='white',\n",
    "            paper_bgcolor='white',\n",
    "            yaxis=dict(title=plot_options[\"Y Title\"],showline=True, linewidth=1, linecolor='black'),\n",
    "            xaxis=dict(title=plot_options[\"X Title\"],showline=True, linewidth=1, linecolor='black')\n",
    "            )\n",
    "        \n",
    "    else:\n",
    "    # create the interactive plot\n",
    "        fig = px.box(FWHMs,\n",
    "                        x=\"Group Name\",\n",
    "                        y='FWHM',\n",
    "                        color=\"Group Name\",\n",
    "                        color_discrete_sequence=plot_options[\"color\"],\n",
    "                        width=plot_options[\"width\"],\n",
    "                        height=plot_options[\"height\"],\n",
    "                        )\n",
    "\n",
    "        fig.update_layout(\n",
    "            yaxis=dict(title=plot_options[\"Y Title\"],\n",
    "                    range=ylimits, showline=True, linewidth=1, linecolor='black'),\n",
    "            font=plot_options[\"font\"],\n",
    "            xaxis=dict(title=plot_options[\"X Title\"],showline=True, linewidth=1, linecolor='black'),\n",
    "            showlegend=True,\n",
    "            annotations=total_labels,\n",
    "            boxmode = \"group\",\n",
    "            plot_bgcolor='white',\n",
    "            paper_bgcolor='white',\n",
    "        )\n",
    "    if WRITE_OUTPUT:        \n",
    "        # create the file for donwnload\n",
    "        img_dir = os.path.join(APPFOLDER, \"images/\")\n",
    "        if not os.path.exists(img_dir):\n",
    "            Path(img_dir).mkdir(parents=True)\n",
    "\n",
    "        fig.write_image(os.path.join(\n",
    "            img_dir, f\"{username}_FWHM_Boxplot.svg\"), format = \"svg\", validate = False, engine = \"kaleido\")\n",
    "        \n",
    "        # create the download CSV and its link\n",
    "        data_dir = os.path.join(APPFOLDER, \"csv/\")\n",
    "        if not os.path.exists(data_dir):\n",
    "            Path(data_dir).mkdir(parents=True)\n",
    "        FWHMs.to_csv(os.path.join(\n",
    "            data_dir, f\"{username}_all_FWHMs.csv\"), index=False)\n",
    "        FWHM_summary.to_csv(os.path.join(\n",
    "            data_dir, f\"{username}_FWHM_Summary.csv\"), index=False)\n",
    "        print(\"Downloading links...\")\n",
    "        CSV_link = f\"/files/{url_base}/csv/\" \\\n",
    "            f\"{username}_all_FWHMs.csv\"\n",
    "\n",
    "        # download SVG link\n",
    "        SVG_link = f\"/files/{url_base}/images/\" \\\n",
    "            f\"{username}_FWHM_boxplot.svg\"\n",
    "\n",
    "\n",
    "    return fig, CSV_link, SVG_link\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Intensity_Boxplot (peakdata_object, plot_options, settings, username = None):\n",
    "\n",
    "    if len(settings[\"Group Name\"].tolist()) > 1:\n",
    "        firstName = settings[\"Group Name\"].tolist()[0]\n",
    "        all_intensity = pd.DataFrame({\"Sequence\":peakdata_object[firstName][\"Sequence\"],firstName:peakdata_object[firstName][\"Apex Intensity\"]})\n",
    "        for eachGroup in settings[\"Group Name\"].tolist()[1:]:\n",
    "            current = pd.DataFrame({eachGroup:peakdata_object[eachGroup][\"Apex Intensity\"],\"Sequence\":peakdata_object[eachGroup][\"Sequence\"]})\n",
    "            all_intensity = pd.merge(current, all_intensity, how=\"inner\")\n",
    "    else:\n",
    "        all_intensity = peakdata_object[0]\n",
    "        \n",
    "    toPlotIntensity = all_intensity.set_index(\"Sequence\").melt(ignore_index=False, var_name=\"Group Name\", value_name=\"Intensity\").reset_index()\n",
    "    toPlotIntensity = toPlotIntensity.merge(settings, how=\"inner\")\n",
    "    return plot_Intensity_boxplot(toPlotIntensity, plot_options, username)\n",
    "\n",
    "def plot_Intensity_boxplot(Intensities, plot_options, username):\n",
    "    \n",
    "    plot_div = None\n",
    "    CSV_link = None\n",
    "    SVG_link = None\n",
    "\n",
    "    Intensities= Intensities[Intensities[\"Intensity\"]>0]\n",
    "\n",
    "    if not plot_options[\"ylimits\"] or plot_options[\"ylimits\"] == \"[]\" or \\\n",
    "            not isinstance(plot_options[\"ylimits\"], list):\n",
    "        ylimits = None\n",
    "    else:\n",
    "        ylimits = plot_options[\"ylimits\"]\n",
    "\n",
    "    if plot_options[\"log10\"]:\n",
    "        Intensities[\"Intensity\"] = np.log10(Intensities[\"Intensity\"])\n",
    "\n",
    "    #Remove outliers\n",
    "    if plot_options[\"outliers\"] == \"remove\":\n",
    "        q_low = Intensities[\"Intensity\"].quantile(0.01)\n",
    "        q_hi  = Intensities[\"Intensity\"].quantile(0.99)\n",
    "\n",
    "        Intensities = Intensities[(Intensities[\"Intensity\"] < q_hi) & (Intensities[\"Intensity\"] > q_low)]\n",
    "        plot_options[\"outliers\"] = False\n",
    "        \n",
    "    Intensity_summary = Intensities.groupby([\"Group Name\"]).agg(\n",
    "        {'Intensity': ['median', 'mean']}).reset_index()\n",
    "    Intensity_summary.columns = [\"Group Name\", 'meds', 'CoVar']\n",
    "    # median label\n",
    "    if plot_options[\"median label\"] == \"True\" or \\\n",
    "            plot_options[\"median label\"] == True:\n",
    "        total_labels = [{\"x\": x, \"y\": total*1.15, \"text\": str(\n",
    "            round(total,1)), \"showarrow\": False} for x, total in zip(\n",
    "            Intensity_summary[\"Group Name\"], Intensity_summary[\"meds\"])]\n",
    "    else:\n",
    "        total_labels = []   # no median labels\n",
    "    \n",
    "\n",
    "    if plot_options[\"Intensity mode\"] == \"grouped\":  \n",
    "            \n",
    "        #find out present categories\n",
    "        categories = Intensities.groupby(plot_options[\"Group By Color\"]).first().reset_index()[plot_options[\"Group By Color\"]].tolist()\n",
    "        # create the plot\n",
    "        fig_data = []\n",
    "        i = 0\n",
    "        for eachCategory in categories:\n",
    "            fig_data.append(go.Box(name = eachCategory,\n",
    "                        x=Intensities.loc[Intensities[plot_options[\"Group By Color\"]]==eachCategory,plot_options[\"Group By X\"]].tolist(),\n",
    "                        y=Intensities.loc[Intensities[plot_options[\"Group By Color\"]]==eachCategory,\"Intensity\"].tolist(),\n",
    "                        fillcolor = plot_options[\"color\"][i],\n",
    "                        boxpoints=plot_options[\"outliers\"],\n",
    "                        marker=dict(opacity=0)\n",
    "                        ))\n",
    "            i = i + 1\n",
    "\n",
    "        fig = go.Figure(data = fig_data)\n",
    "        fig.update_layout(\n",
    "            boxmode = \"group\",\n",
    "            plot_bgcolor='white',\n",
    "            paper_bgcolor='white',\n",
    "            yaxis=dict(title=plot_options[\"Y Title\"],showline=True, linewidth=1, linecolor='black'),\n",
    "            xaxis=dict(title=plot_options[\"X Title\"],showline=True, linewidth=1, linecolor='black'))\n",
    "        \n",
    "    else:\n",
    "    # create the interactive plot\n",
    "        fig = px.box(Intensities,\n",
    "                        x=\"Group Name\",\n",
    "                        y='Intensity',\n",
    "                        color=\"Group Name\",\n",
    "                        color_discrete_sequence=plot_options[\"color\"],\n",
    "                        width=plot_options[\"width\"],\n",
    "                        height=plot_options[\"height\"],\n",
    "                        )\n",
    "\n",
    "        fig.update_layout(\n",
    "            yaxis=dict(title=plot_options[\"Y Title\"],\n",
    "                    range=ylimits,showline=True, linewidth=1, linecolor='black'),\n",
    "            font=plot_options[\"font\"],\n",
    "            xaxis=dict(title=plot_options[\"X Title\"],showline=True, linewidth=1, linecolor='black'),\n",
    "            showlegend=True,\n",
    "            annotations=total_labels,\n",
    "            boxmode = \"group\",\n",
    "            plot_bgcolor='white',\n",
    "            paper_bgcolor='white',\n",
    "        )\n",
    "    if WRITE_OUTPUT:        \n",
    "        # create the file for donwnload\n",
    "        img_dir = os.path.join(APPFOLDER, \"images/\")\n",
    "        if not os.path.exists(img_dir):\n",
    "            Path(img_dir).mkdir(parents=True)\n",
    "\n",
    "        fig.write_image(os.path.join(\n",
    "            img_dir, f\"{username}_Intensity_Boxplot.svg\"), format = \"svg\", validate = False, engine = \"kaleido\")\n",
    "        \n",
    "        # create the download CSV and its link\n",
    "        data_dir = os.path.join(APPFOLDER, \"csv/\")\n",
    "        if not os.path.exists(data_dir):\n",
    "            Path(data_dir).mkdir(parents=True)\n",
    "        Intensities.to_csv(os.path.join(\n",
    "            data_dir, f\"{username}_all_Intensity.csv\"), index=False)\n",
    "        Intensity_summary.to_csv(os.path.join(\n",
    "            data_dir, f\"{username}_Intensity_Summary.csv\"), index=False)\n",
    "        print(\"Downloading links...\")\n",
    "        CSV_link = f\"/files/{url_base}/csv/\" \\\n",
    "            f\"{username}_all_Intensity.csv\"\n",
    "\n",
    "        # download SVG link\n",
    "        SVG_link = f\"/files/{url_base}/images/\" \\\n",
    "            f\"{username}_Intensity_boxplot.svg\"\n",
    "\n",
    "\n",
    "    return fig, CSV_link, SVG_link\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Area_Boxplot (peakdata_object, plot_options, settings, username = None):\n",
    "\n",
    "    if len(settings[\"Group Name\"].tolist()) > 1:\n",
    "        firstName = settings[\"Group Name\"].tolist()[0]\n",
    "        all_area = pd.DataFrame({\"Sequence\":peakdata_object[firstName][\"Sequence\"],firstName:peakdata_object[firstName][\"Area\"]})\n",
    "        for eachGroup in settings[\"Group Name\"].tolist()[1:]:\n",
    "            current = pd.DataFrame({eachGroup:peakdata_object[eachGroup][\"Area\"],\"Sequence\":peakdata_object[eachGroup][\"Sequence\"]})\n",
    "            all_area = pd.merge(current, all_area, how=\"inner\")\n",
    "    else:\n",
    "        all_area = peakdata_object[0]\n",
    "        \n",
    "    toPlotArea = all_area.set_index(\"Sequence\").melt(ignore_index=False, var_name=\"Group Name\", value_name=\"Area\").reset_index()\n",
    "    toPlotArea = toPlotArea.merge(settings, how=\"inner\")\n",
    "    return plot_Area_boxplot(toPlotArea, plot_options, username)\n",
    "\n",
    "def plot_Area_boxplot(Areas, plot_options, username):\n",
    "    \n",
    "    plot_div = None\n",
    "    CSV_link = None\n",
    "    SVG_link = None\n",
    "    Areas= Areas[Areas[\"Area\"]>0]\n",
    "    if plot_options[\"log10\"]:\n",
    "        Areas[\"Area\"] = np.log10(Areas[\"Area\"])\n",
    "\n",
    "    if not plot_options[\"ylimits\"] or plot_options[\"ylimits\"] == \"[]\" or \\\n",
    "            not isinstance(plot_options[\"ylimits\"], list):\n",
    "        ylimits = None\n",
    "    else:\n",
    "        ylimits = plot_options[\"ylimits\"]\n",
    "\n",
    "    #Remove outliers\n",
    "    if plot_options[\"outliers\"] == \"remove\":\n",
    "        q_low = Areas[\"Area\"].quantile(0.01)\n",
    "        q_hi  = Areas[\"Area\"].quantile(0.99)\n",
    "\n",
    "        Areas = Areas[(Areas[\"Area\"] < q_hi) & (Areas[\"Area\"] > q_low)]\n",
    "        plot_options[\"outliers\"] = False\n",
    "    \n",
    "    Area_summary = Areas.groupby([\"Group Name\"]).agg(\n",
    "        {'Area': ['median', 'mean']}).reset_index()\n",
    "    Area_summary.columns = [\"Group Name\", 'meds', 'CoVar']\n",
    "    # median label\n",
    "    if plot_options[\"median label\"] == \"True\" or \\\n",
    "            plot_options[\"median label\"] == True:\n",
    "        total_labels = [{\"x\": x, \"y\": total*1.15, \"text\": str(\n",
    "            round(total,1)), \"showarrow\": False} for x, total in zip(\n",
    "            Area_summary[\"Group Name\"], Area_summary[\"meds\"])]\n",
    "    else:\n",
    "        total_labels = []   # no median labels\n",
    "\n",
    "    if plot_options[\"Area mode\"] == \"grouped\":  \n",
    "            \n",
    "        #find out present categories\n",
    "        categories = Areas.groupby(plot_options[\"Group By Color\"]).first().reset_index()[plot_options[\"Group By Color\"]].tolist()\n",
    "        # create the plot\n",
    "        fig_data = []\n",
    "        i = 0\n",
    "        for eachCategory in categories:\n",
    "            fig_data.append(go.Box(name = eachCategory,\n",
    "                        x=Areas.loc[Areas[plot_options[\"Group By Color\"]]==eachCategory,plot_options[\"Group By X\"]].tolist(),\n",
    "                        y=Areas.loc[Areas[plot_options[\"Group By Color\"]]==eachCategory,\"Area\"].tolist(),\n",
    "                        fillcolor = plot_options[\"color\"][i],\n",
    "                        boxpoints=plot_options[\"outliers\"],\n",
    "                        marker=dict(opacity=0)\n",
    "                        ))\n",
    "            i = i + 1\n",
    "\n",
    "        fig = go.Figure(data = fig_data)\n",
    "        fig.update_layout(\n",
    "            boxmode = \"group\", \n",
    "            plot_bgcolor='white',\n",
    "            paper_bgcolor='white',\n",
    "            yaxis=dict(title=plot_options[\"Y Title\"],showline=True, linewidth=1, linecolor='black'),\n",
    "            xaxis=dict(title=plot_options[\"X Title\"], showline=True, linewidth=1, linecolor='black'),\n",
    "            legend=dict(itemclick=False, itemdoubleclick=False),\n",
    "            )\n",
    "        \n",
    "    else:\n",
    "    # create the interactive plot\n",
    "        fig = px.box(Areas,\n",
    "                        x=\"Group Name\",\n",
    "                        y='Area',\n",
    "                        color=\"Group Name\",\n",
    "                        color_discrete_sequence=plot_options[\"color\"],\n",
    "                        width=plot_options[\"width\"],\n",
    "                        height=plot_options[\"height\"],\n",
    "        )\n",
    "\n",
    "        fig.update_layout(\n",
    "            yaxis=dict(title=plot_options[\"Y Title\"],\n",
    "                    range=ylimits,showline=True, linewidth=1, linecolor='black'),\n",
    "            font=plot_options[\"font\"],\n",
    "            xaxis=dict(title=plot_options[\"X Title\"],showline=True, linewidth=1, linecolor='black'),\n",
    "            showlegend=True,\n",
    "            annotations=total_labels,\n",
    "            boxmode = \"group\",\n",
    "            plot_bgcolor='white',\n",
    "            paper_bgcolor='white',\n",
    "        \n",
    "        )\n",
    "        \n",
    "    if WRITE_OUTPUT:        \n",
    "        # create the file for donwnload\n",
    "        img_dir = os.path.join(APPFOLDER, \"images/\")\n",
    "        if not os.path.exists(img_dir):\n",
    "            Path(img_dir).mkdir(parents=True)\n",
    "\n",
    "        fig.write_image(os.path.join(\n",
    "            img_dir, f\"{username}_Area_Boxplot.svg\"), format = \"svg\", validate = False, engine = \"kaleido\")\n",
    "        \n",
    "        # create the download CSV and its link\n",
    "        data_dir = os.path.join(APPFOLDER, \"csv/\")\n",
    "        if not os.path.exists(data_dir):\n",
    "            Path(data_dir).mkdir(parents=True)\n",
    "        Areas.to_csv(os.path.join(\n",
    "            data_dir, f\"{username}_all_Areas.csv\"), index=False)\n",
    "        Area_summary.to_csv(os.path.join(\n",
    "            data_dir, f\"{username}_Area_Summary.csv\"), index=False)\n",
    "        print(\"Downloading links...\")\n",
    "        CSV_link = f\"/files/{url_base}/csv/\" \\\n",
    "            f\"{username}_all_Areas.csv\"\n",
    "\n",
    "        # download SVG link\n",
    "        SVG_link = f\"/files/{url_base}/images/\" \\\n",
    "            f\"{username}_Area_Violin_Plot.svg\"\n",
    "\n",
    "\n",
    "    return fig, CSV_link, SVG_link\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PEAKS_SETTINGS_FILE = \"peaks_settings.txt\"\n",
    "saved_peak_settings = pd.read_table(PEAKS_SETTINGS_FILE, sep=\"\\t\")\n",
    "PEAKS_DATA_FILES = [\"C12_apQuant_apQuantFeatures.txt\",\n",
    "                          \"50umC12_apQuantFeatures.txt\"]\n",
    "\n",
    "peakdata_obj = read_peaks_files(saved_peak_settings,PEAKS_DATA_FILES)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FWHM boxplot\n",
    "display(peakdata_obj)\n",
    "plot_options={    \n",
    "        \"median label\": \"True\",\n",
    "        \"X Title\": \"Conditions\",\n",
    "        \"Y Title\": \"FWHM (s)\",\n",
    "        \"color\": [\"blue\", \"cyan\", \"black\", \"gray\", \"purple\", \"pink\",\n",
    "                  \"orange\", \"brown\", \"pink\", \"gray\", \"olive\", \"cyan\"],\n",
    "        \"log10\": True,\n",
    "        \"width\": 700,\n",
    "        \"height\": 450,\n",
    "        \"font\": dict(size=16, family=\"Arial black\"),\n",
    "        \"outliers\": \"all\", #False, \"all\", \"outliers\", \"suspectedoutliers\", \"remove\"\n",
    "        \"ylimits\": [],\n",
    "        \"FWHM mode\": \"ungrouped\", #grouped, ungrouped\n",
    "        \"Group By X\": \"Sample Amount\",\n",
    "        \"Group By Color\": \"SPE Type\",\n",
    "        \"help for information only\": \\\n",
    "        \"median label options: True or False.\" \\\n",
    "        \"color: the first few colors will be used\"\\\n",
    "    }\n",
    "figure, _, _ =FWHM_Boxplot(peakdata_obj, plot_options, saved_peak_settings)\n",
    "figure.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intensity boxplot\n",
    "display()\n",
    "plot_options={    \n",
    "        \"median label\": \"True\", \n",
    "        \"X Title\": \"Conditions\",\n",
    "        \"Y Title\": \"Intensity\",\n",
    "        \"color\": [\"blue\", \"cyan\", \"black\", \"gray\", \"purple\", \"pink\",\n",
    "                  \"orange\", \"brown\", \"pink\", \"gray\", \"olive\", \"cyan\"],\n",
    "        \"log10\": False,\n",
    "        \"width\": 700,\n",
    "        \"height\": 450,\n",
    "        \"font\": dict(size=16, family=\"Arial black\"),\n",
    "        \"outliers\": \"all\", #False, \"all\", \"outliers\", \"suspectedoutliers\", \"remove\"\n",
    "        \"ylimits\": [],\n",
    "        \"Intensity mode\": \"grouped\", #grouped, ungrouped\n",
    "        \"Group By X\": \"Sample Amount\",\n",
    "        \"Group By Color\": \"SPE Type\",\n",
    "        \"help for information only\": \\\n",
    "        \"median labelel options: True or False.\" \\\n",
    "        \"color: the first few colors will be used\"\\\n",
    "    }\n",
    "figure, _, _ =Intensity_Boxplot(peakdata_obj, plot_options, saved_peak_settings)\n",
    "figure.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Area boxplot\n",
    "display()\n",
    "plot_options={    \n",
    "        \"median label\": \"True\",\n",
    "        \"X Title\": \"Conditions\",\n",
    "        \"Y Title\": \"Area\",\n",
    "        \"color\": [\"blue\", \"cyan\", \"black\", \"gray\", \"purple\", \"pink\",\n",
    "                  \"orange\", \"brown\", \"pink\", \"gray\", \"olive\", \"cyan\"],\n",
    "        \"width\": 700,\n",
    "        \"height\": 450,\n",
    "        \"font\": dict(size=16, family=\"Arial black\"),\n",
    "        \"outliers\": \"remove\", #False, \"all\", \"outliers\", \"suspectedoutliers\", \"remove\"\n",
    "        \"log10\": False,\n",
    "        \"ylimits\": [],\n",
    "        \"Area mode\": \"grouped\", #grouped, ungrouped\n",
    "        \"Group By X\": \"Sample Amount\",\n",
    "        \"Group By Color\": \"SPE Type\",\n",
    "        \"help for information only\": \\\n",
    "        \"median labelel options: True or False.\" \\\n",
    "        \"color: the first few colors will be used\"\\\n",
    "    }\n",
    "figure, _, _ =Area_Boxplot(peakdata_obj, plot_options, saved_peak_settings)\n",
    "figure.show()\n",
    "\n"
   ]
  }
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