THN20181212_GEMZPM_PSDdisturbance_triplicates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compile triplicates for negative disturbance (PSD)\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/THN20181212_GEMZPM_PSDdisturbance_triplicates/\")+os.sep\n",
    " \n",
    "allfiles = {}\n",
    "date1 = '20180806'\n",
    "date2 = '20180820'\n",
    "date3 = '20181211'\n",
    "\n",
    "experiment = [date1, date2, date3]\n",
    "date = '20180806'\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] = \"THN20180806_summaryfile.csv\"\n",
    "paths[date2] = \"THN20180820_summaryfile.csv\"\n",
    "paths[date3] = \"THN20181211_summaryfile.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(0,49))\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: no disturbance',\n",
    "          'CL: 1.56',\n",
    "          'CL: 3.13',\n",
    "          'CL: 6.25',\n",
    "          'OL: no disturbance',\n",
    "          'OL: 1.56',\n",
    "          'OL: 3.13',\n",
    "          'OL: 6.25'];"
   ]
  },
  {
   "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 < 49:\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*samp_pd)/60.0 \n",
    "t_cut = t[0:49]\n",
    "t_half = t[20:50]-10.0\n"
   ]
  },
  {
   "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(\"PuBu_r\", 4)))\n",
    "    return(CL_color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot norm data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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",
    "mCherry = {}\n",
    "for exp in experiment:\n",
    "    FITC[exp] = pd.Panel({'time':empty, 'raw':empty, 'med':empty, 'sd':empty, 'ravg':empty})\n",
    "    mCherry[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",
    "        \n",
    "            data, raw_data, time, yerr = get_norm_data(exp, reactor_num, timepoint, 'mCherry-H')\n",
    "            mCherry[exp].time.set_value(reactor_num, timepoint, time)\n",
    "            mCherry[exp].raw.set_value(reactor_num, timepoint, raw_data)\n",
    "            mCherry[exp].med.set_value(reactor_num, timepoint, data)\n",
    "            mCherry[exp].sd.set_value(reactor_num, timepoint, yerr)\n",
    "            mCherry[exp].ravg.set_value(reactor_num, timepoint, yerr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
    "reactors_to_plot = {}\n",
    "reactors_to_plot[date1] = [2, 6, 4, 8];\n",
    "reactors_to_plot[date2] = [1, 4, 5, 8];\n",
    "reactors_to_plot[date3] = [1, 2, 5, 6];\n",
    "\n",
    "for i in range(4):\n",
    "    for timepoint in tot_timepoints:\n",
    "        \n",
    "        d1 = FITC[date1].med.loc[reactors_to_plot[date1][i], timepoint]\n",
    "        d2 = FITC[date2].med.loc[reactors_to_plot[date2][i], timepoint]\n",
    "        d3 = FITC[date3].med.loc[reactors_to_plot[date3][i], timepoint]\n",
    "        \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(i+1, timepoint, davg)\n",
    "        FITC_all.sd.set_value(i+1, timepoint, dyerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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)-1\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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot on log10 yscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qQuA5YLare2utb27lnilAChDakdfTU1JSUrj44ov581NPcdgvnoWXzurtKvWo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\nzezJKWN6ykC38510dfb4rupo4saOnr+j5V1Jmi7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+0+Ty2Ox+c9GRgoICwEp0QBPn+DIB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x44986080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on log 10 scale output over time\n",
    "# rep 2\n",
    "fig, ax = plt.subplots(1, 2, sharex = True, sharey = 'row', figsize = (13, 3.5))\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = get_YFP_color()\n",
    "\n",
    "for k in range (1):\n",
    "    c_YFP = next(YFP_color)\n",
    "    c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "ms = 5\n",
    "lw = 1.5\n",
    "mew = 1\n",
    "cs = 2\n",
    "ct = 1\n",
    "\n",
    "for reactor_num in tot_reactors: \n",
    "    if reactor_num == 2:\n",
    "        ax[0].errorbar(t[1:49], FITC_all.med.loc[reactor_num, 1:49], FITC_all.sd.loc[reactor_num, 1:49], elinewidth=1, linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = 'grey')\n",
    "        ax[0].plot(t[1:49], FITC_all.ravg.loc[reactor_num, 1:49], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 1:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax[0].errorbar(t[1:49], FITC_all.med.loc[reactor_num, 1:49], FITC_all.sd.loc[reactor_num, 1:49], elinewidth=1, linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax[0].plot(t[1:49], FITC_all.ravg.loc[reactor_num, 1:49], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 4:\n",
    "        ax[1].errorbar(t[1:49], FITC_all.med.loc[reactor_num, 1:49], FITC_all.sd.loc[reactor_num, 1:49], elinewidth=1, linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize = 5, markeredgewidth=1, markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax[1].plot(t[1:49], FITC_all.ravg.loc[reactor_num, 1:49], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 3: \n",
    "        c_YFP_OL = next(YFP_color_OL)\n",
    "        ax[1].errorbar(t[1:49], FITC_all.med.loc[reactor_num, 1:49], FITC_all.sd.loc[reactor_num, 1:49], elinewidth=1, linestyle='none', marker='o', capsize=cs, capthick=ct, color = c_YFP_OL, markersize = 5, markeredgewidth=1, markerfacecolor = 'none', markeredgecolor=c_YFP_OL)\n",
    "        ax[1].plot(t[1:49], FITC_all.ravg.loc[reactor_num, 1:49], label='_nolegend_', linewidth = lw, color=c_YFP_OL)\n",
    "\n",
    "ax[0].set_ylabel('Normalized YFP')\n",
    "ax[0].set_title('Closed loop') \n",
    "ax[1].set_title('Open loop') \n",
    "ax[0].set_ylim([0.12, .9])\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_yscale('log')\n",
    "    ax[i].legend(['light','control'],loc='upper left')\n",
    "\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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44slWRwjV82QItJ6xb62J3ds+mxv+tAUl2nZmZL9Ybk6JZ/3hsziBj746g8MZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b03d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on log 10 scale output over time\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, sharex = True, sharey = True, figsize = (2, 3.))\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = get_YFP_color()\n",
    "\n",
    "for k in range (1):\n",
    "    c_YFP = next(YFP_color)\n",
    "    c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "ms = 3; lw = 1. ; mew = 1; cs = 1.5; ct = 1;\n",
    "\n",
    "for reactor_num in tot_reactors: \n",
    "    if reactor_num == 2:\n",
    "        ax[0].errorbar(t_half, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50], elinewidth=1, \n",
    "                       linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = 'grey')\n",
    "        ax[0].plot(t_half, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 1:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax[0].errorbar(t_half, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50], elinewidth=1, \n",
    "                       linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax[0].plot(t_half, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 4:\n",
    "        ax[1].errorbar(t_half, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50], elinewidth=1, \n",
    "                       linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize = ms, \n",
    "                       markeredgewidth=mew, markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax[1].plot(t_half, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 3: \n",
    "        c_YFP_OL = next(YFP_color_OL)\n",
    "        ax[1].errorbar(t_half, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50], elinewidth=1, \n",
    "                       linestyle='none', marker='o', capsize=cs, capthick=ct, color = c_YFP_OL, markersize = ms, \n",
    "                       markeredgewidth=mew, markerfacecolor = 'none', markeredgecolor=c_YFP_OL)\n",
    "        ax[1].plot(t_half, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color=c_YFP_OL)\n",
    "\n",
    "#ax[0].set_ylabel('Normalized YFP')\n",
    "#ax[0].set_title('Closed loop') \n",
    "#ax[1].set_title('Open loop') \n",
    "#ax[0].set_ylim([0.16, .65])\n",
    "\n",
    "ax[1].set_xticks(np.arange(-2, 12, step=2))\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_ylim([.15, .6])\n",
    "    ax[i].set_yscale('log')\n",
    "    #ax[i].legend(['light','control'],loc='upper left')\n",
    "\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    #ax[i].set_xlabel('Time (hrs)')\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_log10scaleoutput_cropped'\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get mean and sd data\n",
    "ssFITC = {}\n",
    "empty = pd.DataFrame(index = range(1,4), columns = range(1,4)) # columns are triplicates\n",
    "ssFITC = pd.Panel({'avg':empty, 'precision':empty})\n",
    "\n",
    "reactors_to_plot[date1] = [2, 6, 4, 8];\n",
    "reactors_to_plot[date2] = [1, 4, 5, 8];\n",
    "reactors_to_plot[date3] = [1, 2, 5, 6];\n",
    "\n",
    "for i in range(4):\n",
    "        \n",
    "    d1 = np.mean(FITC[date1].med.loc[reactors_to_plot[date1][i], 43:49]);\n",
    "    d2 = np.mean(FITC[date2].med.loc[reactors_to_plot[date2][i], 43:49]);\n",
    "    d3 = np.mean(FITC[date3].med.loc[reactors_to_plot[date3][i], 43:49]);\n",
    "\n",
    "    ssFITC.avg.set_value(i+1, 1, d1)\n",
    "    ssFITC.avg.set_value(i+1, 2, d2)\n",
    "    ssFITC.avg.set_value(i+1, 3, d3)\n",
    "\n",
    "\n",
    "# rows\n",
    "# 1 Cl\n",
    "# 2 CL ctrl\n",
    "# 3 OL\n",
    "# 4 OL ctrl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tayng\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:18: RuntimeWarning: divide by zero encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "# get precision measurements\n",
    "\n",
    "for j in range(1,4): # iterate through triplicates\n",
    "    for reactor_num in range(1,5): #iterate through reactors \n",
    "    \n",
    "        # no disturbance value\n",
    "        CL_NoDisturbance = ssFITC.avg.loc[2, j]; \n",
    "        OL_NoDisturbance = ssFITC.avg.loc[4, j];\n",
    "    \n",
    "        disturbance = ssFITC.avg.loc[reactor_num, j];\n",
    "\n",
    "        if reactor_num == 1 or reactor_num == 2: \n",
    "            error = np.abs((disturbance-CL_NoDisturbance)/CL_NoDisturbance);\n",
    "        \n",
    "        elif reactor_num == 3 or reactor_num == 4:\n",
    "            error = np.abs((disturbance-OL_NoDisturbance)/OL_NoDisturbance);\n",
    "        \n",
    "        precision = (error)**-1;\n",
    "        ssFITC.precision.set_value(reactor_num, j, precision)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tayng\\Anaconda2\\lib\\site-packages\\numpy\\core\\_methods.py:112: RuntimeWarning: invalid value encountered in subtract\n",
      "  x = asanyarray(arr - arrmean)\n"
     ]
    }
   ],
   "source": [
    "ssFITCprecision = {}\n",
    "empty = pd.DataFrame(index = rows, columns = [0]) # columns are triplicates\n",
    "ssFITCprecision = pd.Panel({'avg':empty, 'sd':empty})\n",
    "\n",
    "for reactor_num in range(1,5):\n",
    "        \n",
    "    d1 = ssFITC.precision.loc[reactor_num, 1];\n",
    "    d2 = ssFITC.precision.loc[reactor_num, 2];\n",
    "    d3 = ssFITC.precision.loc[reactor_num, 3];\n",
    "    \n",
    "    davg = np.mean([d1, d2, d3], dtype=np.float64)\n",
    "    dyerr = np.std([d1, d2, d3], dtype=np.float64)\n",
    "\n",
    "    ssFITCprecision.avg.set_value(reactor_num, 0, davg)\n",
    "    ssFITCprecision.sd.set_value(reactor_num, 0, dyerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0220570754565\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAD0AAABrCAYAAADEtYoDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZ3vjnOPpj2kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3a886b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot precision\n",
    "fig, ax = plt.subplots(figsize = (.4, 1.2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "index = np.arange(1)\n",
    "bar_width = 1.5;\n",
    "cs = 1;\n",
    "ct = 1;\n",
    "space = np.array([0.2,0.5,0.8]);\n",
    "lwidth = 0.5;\n",
    "mwidth = 0.3;\n",
    "size = 4;\n",
    "\n",
    "CL_barlist = ax.bar(index, ssFITCprecision.avg.loc[1, 0], bar_width, label='Closed loop', \n",
    "                    yerr = ssFITCprecision.sd.loc[1, 0], linewidth = lwidth, \n",
    "                    error_kw=dict(lw=lwidth, capsize = cs, capthick = ct), color='y', fill=False)\n",
    "OL_barlist = ax.bar(index + bar_width, ssFITCprecision.avg.loc[3, 0], bar_width, label='Open loop',\n",
    "                    yerr = ssFITCprecision.sd.loc[3, 0], linewidth = lwidth,\n",
    "                    error_kw=dict(lw=lwidth, capsize = cs, capthick = ct), color='y', fill=False)\n",
    "\n",
    "# add the individual data points\n",
    "idx=1;\n",
    "for i in range(3): # closed loop reactors\n",
    "    x = space[i]*bar_width-bar_width/2\n",
    "    CL_scatter = ax.scatter(x, ssFITC.precision.loc[1, i+1], \n",
    "                            color='none', edgecolor = 'black', linewidth = mwidth, s=size)\n",
    "for i in range(3): # open loop reactors\n",
    "    x = space[i]*bar_width+bar_width/2\n",
    "    OL_scatter = ax.scatter(x, ssFITC.precision.loc[3, i+1], \n",
    "                               color='none', edgecolor = 'black', linewidth = mwidth, s =size)\n",
    "\n",
    "# two sided t-test\n",
    "t1 = ssFITC.avg.loc[1, 1:3];\n",
    "t2 = ssFITC.avg.loc[3, 1:3];\n",
    "t, p = stats.ttest_ind(t1, t2)\n",
    "print(p)\n",
    "\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = get_YFP_color()\n",
    "c_YFP = next(YFP_color)\n",
    "c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "for i in range(1):\n",
    "    c_YFP = next(YFP_color)\n",
    "    CL_barlist[i].set_color(c_YFP)\n",
    "    OL_barlist[i].set_color(c_YFP)\n",
    "    OL_barlist[i].set_hatch('/')\n",
    "\n",
    "ax.set_ylim([0, 5.0])\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "#ax.legend()\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_precision'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plot on linear scale output over time\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, sharex = True, sharey = True, figsize = (2, 3.))\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = get_YFP_color()\n",
    "\n",
    "for k in range (1):\n",
    "    c_YFP = next(YFP_color)\n",
    "    c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "ms = 5\n",
    "lw = 1.5\n",
    "mew = 1\n",
    "cs = 2\n",
    "ct = 1\n",
    "\n",
    "for reactor_num in tot_reactors: \n",
    "    if reactor_num == 2:\n",
    "        ax[0].errorbar(t_half[0:28], FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = 'grey')\n",
    "        ax[0].plot(t_half[0:28], FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 1:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax[0].errorbar(t_half[0:28], FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax[0].plot(t_half[0:28], FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 4:\n",
    "        ax[1].errorbar(t_half[0:28], FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize = 5, markeredgewidth=1, markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax[1].plot(t_half[0:28], FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 3: \n",
    "        c_YFP_OL = next(YFP_color_OL)\n",
    "        ax[1].errorbar(t_half[0:28], FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, linestyle='none', marker='o', capsize=cs, capthick=ct, color = c_YFP_OL, markersize = 5, markeredgewidth=1, markerfacecolor = 'none', markeredgecolor=c_YFP_OL)\n",
    "        ax[1].plot(t_half[0:28], FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color=c_YFP_OL)\n",
    "\n",
    "ax[0].set_ylabel('Normalized YFP')\n",
    "ax[0].set_title('Closed loop') \n",
    "ax[1].set_title('Open loop') \n",
    "ax[0].set_ylim([0.16, .5])\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].legend(['light','control'],loc='upper left')\n",
    "\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].set_xlabel('Time (hrs)')\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_linear_cropped'\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ehL/L7c0u7nxpK89t3c8tf9/E0uvOGpRJDSEGW6IByIWapj2ZtJYco1pcHhpa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\nIwbEmRPHcKixDYNeF9nEc7iJ78+lJXU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kDA7FaeWfnzrNSU+Q7W3VXNvu4suPTBkoX75p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rGc/VZz7tRZ7zIKew2WHqWaIRoU5vvvv4LXb27GYhJ4/eZmNjRPpVgqwL8/37to92ZgsFIwDJAi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5eda630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# individual experiments\n",
    "# plot on log 10 scale output over time\n",
    "# rep 2\n",
    "fig, ax = plt.subplots(1, 3, sharex = True, sharey = 'row', figsize = (13, 3.5))\n",
    "\n",
    "ms = 5; lw = 1.5; mew = 1; cs = 2; ct = 1;\n",
    "\n",
    "reactors_to_plot[date1] = [2, 6, 4, 8];\n",
    "reactors_to_plot[date2] = [1, 4, 5, 8];\n",
    "reactors_to_plot[date3] = [1, 2, 5, 6];\n",
    "\n",
    "\n",
    "for i, exp in enumerate(experiment):\n",
    "    YFP_color = get_YFP_color()\n",
    "\n",
    "    for j in range(4):\n",
    "        reactor_num = reactors_to_plot[exp][j]\n",
    "        ax[i].plot(t_cut[0:48], FITC[exp].med.loc[reactor_num, 1:48], marker='o', markersize=ms, color = c_YFP)\n",
    "        c_YFP = next(YFP_color)\n",
    "\n",
    "ax[0].set_ylabel('Normalized YFP')\n",
    "ax[0].set_title('Closed loop') \n",
    "ax[1].set_title('Open loop') \n",
    "ax[0].set_ylim([0.12, .9])\n",
    "\n",
    "for i in range(3):\n",
    "    ax[i].set_yscale('log')\n",
    "    ax[i].legend(['light','control'],loc='upper left')\n",
    "\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].set_xlabel('Time (hrs)')\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_log10scaleoutput_individual'\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
}