THN20181011_GEMZPM_syntuning_triplicates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compile triplicates\n",
    "# synthetic feedback tuning\n",
    "# dilution, sample every 25 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from FlowCytometryTools import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import pickle as pkl\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy import stats\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "path = \"C:/Users/tayng/Documents/PythonScripts/cytometer\"\n",
    "\n",
    "if not path in sys.path:\n",
    "    sys.path.insert(1, path)\n",
    "    os.chdir(path)\n",
    "    from AHN_FlowTools import *\n",
    "del path\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks')\n",
    "%matplotlib inline\n",
    "\n",
    "os.chdir(\"C:/Users/tayng/Documents/PythonScripts/cytometer/CAGEKEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# update data directory\n",
    "datadir = os.path.normpath(\"C:/Users/tayng/Documents/Data/cytometer/CAGEKEY/THN20181011_GEMZPM_syntuning_triplicates/\")+os.sep\n",
    " \n",
    "allfiles = {}\n",
    "date1 = '20180904'\n",
    "date2 = '20181010'\n",
    "experiment = [date1, date2]\n",
    "date = '20180904'\n",
    "\n",
    "for idx, exp in enumerate(experiment): \n",
    "    filepath = datadir + os.sep +'*.fcs'\n",
    "    files = glob.glob(filepath)\n",
    "    assert len(files)>0, \"No filenames recovered - check file path: \" + filepath\n",
    "    allfiles[exp] = [files[idx]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "allexp = {}\n",
    "\n",
    "for exp in experiment: # iterate through experiments (fcs files)\n",
    "    for f in allfiles[exp]:\n",
    "        allexp[exp] = FCMeasurement(ID = exp, datafile = f)\n",
    "        \n",
    "    allexp[exp].data = allexp[exp].data.loc[:, ['Time','SSC-H','FITC-H','mCherry-H','FSC-H']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# settings\n",
    "og_num = 1;        # number of outgrowth timepoints\n",
    "samp_pd = 25;      # length of time b/w samples in min\n",
    "summary_idx  = 1   # check summaryfile from labview\n",
    "tot_reactors = pd.Series(range(1,9)) \n",
    "\n",
    "# read summary file\n",
    "summaryfilepath = {}\n",
    "summaryfile = {}\n",
    "paths = {}\n",
    "tot_timepoints = {}\n",
    "paths[date1] = \"THN20180904_summaryfile_edited.csv\"\n",
    "paths[date2] = \"THN20181010_summaryfile_edited.csv\"\n",
    "\n",
    "for exp in experiment:\n",
    "    summaryfilepath[exp] = os.path.normpath(datadir + paths[exp])\n",
    "    summaryfile[exp] = pd.read_csv(summaryfilepath[exp], names=['timepoint','valve','start','end'])\n",
    "    summaryfile[exp]['valve'] = summaryfile[exp]['valve'].astype(int)\n",
    "    summaryfile[exp]['timepoint'] = summaryfile[exp]['timepoint'].astype(int)\n",
    "\n",
    "tot_timepoints = pd.Series(range(0,32))\n",
    "\n",
    "# create sample_order (valve # : order #)\n",
    "valves = [5, 4, 7, 6, 11, 12, 2, 1]\n",
    "order = [1, 2, 3, 4, 5, 6, 7, 8] # update\n",
    "sample_order = {}\n",
    "\n",
    "for i in range(len(valves)):\n",
    "    sample_order[valves[i]] = order[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# legend \n",
    "labels = ['CL 6x/full 6.5nM',\n",
    "          'CL 6x/full 3.13.nM',\n",
    "          'CL 3x/full 6.5nM',\n",
    "          'CL 3x/full 3.13nM',\n",
    "          'CL 6x/m12 6.5nM',\n",
    "          'CL 6x/m12 3.13nM',\n",
    "          'OL 6.5nM',\n",
    "          'OL 3.13nM'];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create binned_data\n",
    "binned_data = {}\n",
    "for exp in experiment:\n",
    "    binned_data[exp] = pd.DataFrame(index=tot_reactors,columns=tot_timepoints)\n",
    "\n",
    "for exp in experiment:\n",
    "    for i, row in enumerate(summaryfile[exp].values): # iterate through summary file\n",
    "        d = allexp[exp].data[allexp[exp].data.Time.between(summaryfile[exp].iloc[i,2],summaryfile[exp].iloc[i,3])]\n",
    "        timepoint = summaryfile[exp].iloc[i,0] - summary_idx \n",
    "        if timepoint < 32:\n",
    "            reactor_num = sample_order[summaryfile[exp].iloc[i,1]] # sample order gives the order of reactor (indexed at valve num)\n",
    "            binned_data[exp].set_value(reactor_num, timepoint, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot some things!!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# delete outgrowth timepoint(s)\n",
    "t = ((tot_timepoints[:-(1)])*samp_pd)/60.0 \n",
    "t_cut = t[0:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_color():\n",
    "    color=iter(sns.color_palette('hls',8))\n",
    "    return(color)\n",
    "\n",
    "def get_YFP_color():\n",
    "    CL_color = iter(sns.color_palette(sns.color_palette(\"YlOrRd\", 8)))\n",
    "    return(CL_color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot norm data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_norm_data(exp, reactor_num, timepoint, datatype):\n",
    "    tp_data = binned_data[exp].loc[reactor_num, timepoint]\n",
    "    tp_data = tp_data[tp_data[datatype] > 0] # filter out cells that have no FITC output\n",
    "    tp_data = tp_data[tp_data['SSC-H'] > 1000]\n",
    "    \n",
    "    tot_events = tp_data.size\n",
    "    time = np.median(tp_data.loc[:,'Time'])\n",
    "    reading = tp_data.loc[:, datatype]\n",
    "    \n",
    "    raw_data = np.median(reading)\n",
    "    ssc = tp_data.loc[:,'SSC-H']\n",
    "    d = pd.Series(reading/ssc)\n",
    "            \n",
    "    d.dropna(how='any')    #to drop if all values in the row are nan\n",
    "    yerr = np.std(d)\n",
    "    d = np.median(d)\n",
    "    return(d, raw_data, time, yerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get normalized data data\n",
    "rows = tot_reactors\n",
    "cols = tot_timepoints\n",
    "empty = pd.DataFrame(index = rows, columns = cols)\n",
    "\n",
    "FITC = {}\n",
    "for exp in experiment:\n",
    "    FITC[exp] = pd.Panel({'time':empty, 'raw':empty, 'med':empty, 'sd':empty, 'ravg':empty})\n",
    "\n",
    "    for reactor_num in tot_reactors:    \n",
    "        for timepoint in tot_timepoints:\n",
    "        \n",
    "            data, raw_data, time, yerr = get_norm_data(exp, reactor_num, timepoint, 'FITC-H')\n",
    "            FITC[exp].time.set_value(reactor_num, timepoint, time)\n",
    "            FITC[exp].raw.set_value(reactor_num, timepoint, raw_data)\n",
    "            FITC[exp].med.set_value(reactor_num, timepoint, data)\n",
    "            FITC[exp].sd.set_value(reactor_num, timepoint, yerr)\n",
    "            FITC[exp].ravg.set_value(reactor_num, timepoint, yerr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get mean and sd data\n",
    "FITC_all = {}\n",
    "FITC_all = pd.Panel({'time':empty, 'med':empty, 'sd':empty, 'ravg':empty})\n",
    "\n",
    "for reactor_num in [1,2,3,4]:\n",
    "    for timepoint in tot_timepoints:\n",
    "        d1 = FITC[date1].med.loc[reactor_num*2, timepoint]\n",
    "        d2 = FITC[date2].med.loc[((reactor_num*2)-1), timepoint]\n",
    "        d3 = FITC[date2].med.loc[reactor_num*2, timepoint]\n",
    "        davg = np.mean([d1, d2, d3], dtype=np.float64)\n",
    "        dyerr = np.std([d1, d2, d3], dtype=np.float64)\n",
    "\n",
    "        FITC_all.med.set_value(reactor_num, timepoint, davg)\n",
    "        FITC_all.sd.set_value(reactor_num, timepoint, dyerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tayng\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=3,center=True).mean()\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "def rolling_mean(readout, reactor_num, N):\n",
    "    t_last = len(t)\n",
    "\n",
    "    if readout == 'FITC-H':\n",
    "        ravg = pd.rolling_mean(FITC_all.med.loc[reactor_num, :], N, center=True)\n",
    "        ravg[0] = (FITC_all.med.loc[reactor_num, 0]+FITC_all.med.loc[reactor_num, 1])/2\n",
    "        ravg[t_last] = (FITC_all.med.loc[reactor_num, t_last-1]+FITC_all.med.loc[reactor_num, t_last])/2\n",
    "        \n",
    "    return(ravg)\n",
    "\n",
    "window = 3;\n",
    "for reactor_num in range(1,9):\n",
    "    FITC_all.ravg.loc[reactor_num, :] = rolling_mean('FITC-H',reactor_num, window)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot on log10 yscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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LM1bozCQWkoZXeW2Ff9Q26U8hEAigWvdhBf3eqNZ9HDl8mO1bP+Tgvs9JcbWw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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x242bee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on log 10 scale output over time\n",
    "# rep 2\n",
    "fig, ax = plt.subplots(figsize = (6, 4))\n",
    "YFP_color = get_YFP_color()\n",
    "\n",
    "ms = 5\n",
    "lw = 1.5\n",
    "mew = 1\n",
    "cs = 2\n",
    "ct = 1\n",
    "\n",
    "reactors_to_plot = [4,2,3,1];\n",
    "for reactor_num in reactors_to_plot: \n",
    "\n",
    "    if reactor_num == 1 or reactor_num == 2 or reactor_num == 3:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 1:30], FITC_all.sd.loc[reactor_num, 1:30], elinewidth=1, \n",
    "                    linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax.plot(t_cut, FITC_all.ravg.loc[reactor_num, 1:30], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "        \n",
    "    if reactor_num == 4: \n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 1:30], FITC_all.sd.loc[reactor_num, 1:30], elinewidth=1, \n",
    "                    linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize = 5, markeredgewidth=1, \n",
    "                    markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax.plot(t_cut, FITC_all.ravg.loc[reactor_num, 1:30], label='_nolegend_', linewidth = lw, color='grey')\n",
    "        \n",
    "ax.set_yscale('log')\n",
    "ax.legend(['no FB pREV1','FB 3x/full key','FB 6x/m12key','FB 6x/full key'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "ax.set_title('Synthetic tuning') \n",
    "ax.set_ylabel('Normalized YFP')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.set_xlabel('Time (hrs)')\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_log10scaleoutput_compareDose'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}