Running MOT Calibration Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Running Calibration Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "day = '9'\n",
    "month = 'September'\n",
    "year = '2018'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import ExpFile as exp\n",
    "import AnalysisHelpers as ah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline\n",
    "style.use('dark_background')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false",
    "ExecuteTime": {
     "end_time": "2018-02-28T17:06:46.067900Z",
     "start_time": "2018-02-28T17:06:31.753411Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from ExpFile import setPath\n",
    "import ExpFile as exp\n",
    "import PlotlyPlotters as pp\n",
    "import MatplotlibPlotters as mp\n",
    "import MainAnalysis as ma\n",
    "import AnalysisHelpers as ah\n",
    "import Miscellaneous as misc\n",
    "import MarksConstants as mc\n",
    "\n",
    "from importlib import reload\n",
    "def reloadMyModules():\n",
    "    reload(exp)\n",
    "    setPath(day,month,year)\n",
    "    reload(misc)\n",
    "    reload(ma)\n",
    "    reload(ah)\n",
    "    reload(pp)\n",
    "    reload(mp)\n",
    "\n",
    "reloadMyModules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "year = '2018'\n",
    "import numpy as np\n",
    "from importlib import reload\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## MOT Fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "reloadMyModules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "date_pbs_added = datetime.datetime.strptime('2019:April:18', '%Y:%B:%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan 2018\n",
      "Feb 2018\n",
      "Mar 2018\n",
      "Apr 2018\n",
      "May 2018\n",
      "Jun 2018\n",
      "Jul 2018\n",
      "Aug 2018."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:787: OptimizeWarning:\n",
      "\n",
      "Covariance of the parameters could not be estimated\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...........\n",
      "Sep 2018....................\n",
      "Oct 2018......\n",
      "Nov 2018...............\n",
      "Dec 2018..................\n",
      "Jan 2019..................\n",
      "Feb 2019.....................\n",
      "Mar 2019...................\n",
      "Apr 2019........................\n",
      "May 2019................\n",
      "Jun 2019.................\n",
      "Jul 2019..................\n",
      "Aug 2019\n",
      "Sep 2019\n",
      "Oct 2019\n",
      "Nov 2019\n",
      "Dec 2019\n"
     ]
    }
   ],
   "source": [
    "motNums, times, loadTimes = [[], [], []]\n",
    "for year in ['2018', '2019']:\n",
    "    for month in ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']:\n",
    "        print(month[:3], year, end='')\n",
    "        for d in range(1,31):\n",
    "            day = str(d)\n",
    "            exp.setPath(day,month,year)\n",
    "            reloadMyModules()\n",
    "            reloadMyModules()\n",
    "            try:\n",
    "                try:\n",
    "                    date = datetime.datetime.strptime(year+':'+month+':'+day, '%Y:%B:%d')\n",
    "                except ValueError: \n",
    "                    pass\n",
    "                res = ah.motFillAnalysis( 'MOT_NUMBER', loadType='basler', motKey = np.arange(0,5,0.1), exposureTime=1e-3, dataRange=(1,-1), imagingLoss= (0.8 if date < date_pbs_added else 0.4))\n",
    "                rawData, intRawData, motNum, fitParams, fluoresence, motKey = res\n",
    "                print('.',end='')\n",
    "                times.append(date)\n",
    "                motNums.append(motNum)\n",
    "                loadTimes.append(fitParams[1])\n",
    "            except FileNotFoundError:\n",
    "                pass\n",
    "            except OSError:\n",
    "                pass\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 5)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = subplots(1,1,figsize=(30,10))\n",
    "title('Mot load fit parameters over time');\n",
    "ax.plot(times, motNums,'oc', label='Mot-Number (relative)')\n",
    "legend(loc='upper right')\n",
    "ax.tick_params(axis='y', colors='c')\n",
    "ax.set_ylabel('Mot Number (Relative)',color='c')\n",
    "ax.set_xlabel('Day (At most 1 measurement per day');\n",
    "ax.set_ylim(0,1.5e6);\n",
    "ax.grid(True, color='c', alpha=0.2)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(times, loadTimes,'or',label='Fill-Time')\n",
    "legend(loc='upper left')\n",
    "ax2.tick_params(axis='y', colors='r')\n",
    "ax2.set_ylabel('Fill Time (s)', color='r');\n",
    "ax2.grid(True,color='r', alpha=0.2)\n",
    "ax2.set_ylim(0,5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Temperatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### mot temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-72fc193d04c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m                 \u001b[0mkn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m             temp, fitVals, fitCov, times, waists, rawData, pictureFitParams = ah.temperatureAnalysis(pics, 3, key=key, accumulations=5,\n\u001b[1;32m---> 14\u001b[1;33m                                                                                                     fitWidthGuess=20, window=[50,200,50,250])\n\u001b[0m\u001b[0;32m     15\u001b[0m             \u001b[0mdates\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'2018:'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mmonth\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m':'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mday\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'%Y:%B:%d'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[0mtemps\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 7)"
     ]
    }
   ],
   "source": [
    "dates = []\n",
    "temps = []\n",
    "for month in ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August']:\n",
    "    for d in range(1,31):\n",
    "        day = str(d)\n",
    "        exp.setPath(day,month,year)\n",
    "        reload(ah)\n",
    "        try:\n",
    "            with exp.ExpFile() as f:\n",
    "                f.open_hdf5('MOT_TEMPERATURE', True)\n",
    "                pics = f.get_basler_pics()\n",
    "                kn, key = f.get_key()\n",
    "            temp, fitVals, fitCov, times, waists, rawData, pictureFitParams = ah.temperatureAnalysis(pics, 3, key=key, accumulations=5,\n",
    "                                                                                                    fitWidthGuess=20, window=[50,200,50,250])\n",
    "            dates.append(datetime.datetime.strptime('2018:'+month+':'+day, '%Y:%B:%d'))\n",
    "            temps.append(temp)\n",
    "        except FileNotFoundError:\n",
    "            pass\n",
    "        except OSError:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "plot(dates, np.array(temps)*1e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### red pgc temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assuming input is a picture or array of pictures.\n",
      "Data Loaded.\n",
      "fitting raw data.\n",
      "fitting picture Number...\n",
      "0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,Assuming input is a picture or array of pictures.\n",
      "Data Loaded.\n",
      "fitting raw data.\n",
      "fitting picture Number...\n",
      "0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,Assuming input is a picture or array of pictures.\n",
      "Data Loaded.\n",
      "fitting raw data.\n",
      "fitting picture Number...\n",
      "0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,"
     ]
    }
   ],
   "source": [
    "dates = []\n",
    "temps = []\n",
    "for month in ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August']:\n",
    "    for d in range(1,31):\n",
    "        day = str(d)\n",
    "        exp.setPath(day,month,year)\n",
    "        reload(ah)\n",
    "        try:\n",
    "            with exp.ExpFile() as f:\n",
    "                f.open_hdf5('RED_PGC_TEMPERATURE', True)\n",
    "                pics = f.get_basler_pics()\n",
    "                kn, key = f.get_key()\n",
    "            temp, fitVals, fitCov, times, waists, rawData, pictureFitParams = ah.temperatureAnalysis(pics, 3, key=key, accumulations=5,\n",
    "                                                                                                    fitWidthGuess=20, window=[50,200,50,250])\n",
    "            dates.append(datetime.datetime.strptime('2018:'+month+':'+day, '%Y:%B:%d'))\n",
    "            temps.append(temp)\n",
    "        except FileNotFoundError:\n",
    "            pass\n",
    "        except OSError:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28cf04daeb8>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WVgAQZmZmYtu2bWLx4sVq+7aBg4ODQ9tHbQtzsfDoz2LhkZ9FbQvzaj2XWqdc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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(dates, np.array(temps)*1e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### grey molasses Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assuming input is a picture or array of pictures.\n",
      "Data Loaded.\n",
      "fitting raw data.\n",
      "fitting picture Number...\n",
      "0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Regal-Lab\\code\\Data-Analysis-Code\\fitters\\LargeBeamMotExpansion.py:23: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in sqrt\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assuming input is a picture or array of pictures.\n",
      "Data Loaded.\n",
      "fitting raw data.\n",
      "fitting picture Number...\n",
      "0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,Assuming input is a picture or array of pictures.\n",
      "Data Loaded.\n",
      "fitting raw data.\n",
      "fitting picture Number...\n",
      "0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,"
     ]
    }
   ],
   "source": [
    "dates = []\n",
    "temps = []\n",
    "for month in ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August']:\n",
    "    for d in range(1,31):\n",
    "        day = str(d)\n",
    "        exp.setPath(day,month,year)\n",
    "        reload(ah)\n",
    "        try:\n",
    "            with exp.ExpFile() as f:\n",
    "                f.open_hdf5('GREY_MOLASSES_TEMPERATURE', True)\n",
    "                pics = f.get_basler_pics()\n",
    "                kn, key = f.get_key()\n",
    "            temp, fitVals, fitCov, times, waists, rawData, pictureFitParams = ah.temperatureAnalysis(pics, 3, key=key, accumulations=5,\n",
    "                                                                                                    fitWidthGuess=20, window=[50,200,50,250])\n",
    "            dates.append(datetime.datetime.strptime('2018:'+month+':'+day, '%Y:%B:%d'))\n",
    "            temps.append(temp)\n",
    "        except FileNotFoundError:\n",
    "            pass\n",
    "        except OSError:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28cf0b915c0>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0srKy0lyQ7t69OwGgqKioxy5If/bZZ1q7qMLV+GrozCkUknSGbIc4Cc/SUEre7UVaevY4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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(dates, np.array(temps)*1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}