Making rearranging videos-Revised.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making rearranging Videos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comes from December 7th 2017, (Thursday) Data Analysis Notebook, data from december 7th and december 8th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:53:38.446413Z",
     "start_time": "2018-02-08T03:53:38.440929Z"
    }
   },
   "outputs": [],
   "source": [
    "year=\"2017\"\n",
    "month=\"December\"\n",
    "day=\"8\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis Code Update Plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Better Documentation\n",
    "- Maintain coordinates\n",
    "- Better histogram fitting\n",
    "- option to just input guess for hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Miscellaneous Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:53:40.147752Z",
     "start_time": "2018-02-08T03:53:40.144742Z"
    }
   },
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:53:41.818480Z",
     "start_time": "2018-02-08T03:53:40.715992Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:53:47.040574Z",
     "start_time": "2018-02-08T03:53:42.364219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import init_notebook_mode\n",
    "init_notebook_mode(connected=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:53:48.468751Z",
     "start_time": "2018-02-08T03:53:48.241133Z"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "from matplotlib import style\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib notebook\n",
    "# inline\n",
    "# ## set matplotlib plot defaults :D\n",
    "# Style controls many default colors in matplotlib plots.\n",
    "# Change the following if you don't like dark backgrounds. Many other options.\n",
    "style.use(['seaborn-darkgrid'])\n",
    "#style.use(['seaborn-dark-palette'])\n",
    "\n",
    "### old solarized stuff\n",
    "# style.use(['dark_background'])\n",
    "# mpl.rcParams['axes.facecolor'] = '#0a0a0a'\n",
    "# the default cycling of colors in this mode isn't very good.\n",
    "# mpl.rcParams['axes.prop_cycle'] = cycler('color', ['r','c','g','#FFFFFF','y','m','b'])\n",
    "### General\n",
    "rcParams['figure.figsize'] = (18.0, 8.0)\n",
    "rcParams['axes.grid'] = True\n",
    "rcParams['axes.formatter.useoffset'] = False\n",
    "rcParams['grid.alpha'] = 0.3\n",
    "rcParams['axes.formatter.limits'] = (0,3)\n",
    "# jet is awful.\n",
    "rcParams['image.cmap'] = 'viridis'\n",
    "# to see all available options, decomment this line.\n",
    "# print(mpl.rcParams)\n",
    "rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ExpFile as exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ExpFile import setPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "setPath(day, month, year);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-28T17:06:46.067900Z",
     "start_time": "2018-02-28T17:06:31.753411Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PrintVersions;</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mainAnalysis:</th>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnalysisHelpers:</th>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bokehPlotters:</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fittingFunctions:</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarksConstants:</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarksFourierAnalysis:</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matplotlibPlotters:</th>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miscellaneous:</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plotlyPlotters:</th>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Version\n",
       "PrintVersions;            1.0\n",
       "mainAnalysis:             1.4\n",
       "AnalysisHelpers:          1.4\n",
       "bokehPlotters:            1.0\n",
       "fittingFunctions:         1.0\n",
       "MarksConstants:           1.0\n",
       "MarksFourierAnalysis:     1.0\n",
       "matplotlibPlotters:       1.1\n",
       "miscellaneous:            1.0\n",
       "plotlyPlotters:           1.6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ExpFile import setPath\n",
    "import ExpFile as exp\n",
    "from VersionManagement import getVersions\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",
    "\n",
    "from importlib import reload\n",
    "def reloadMyModules():\n",
    "    reload(exp)\n",
    "    reload(misc)\n",
    "    reload(ma)\n",
    "    reload(ah)\n",
    "    reload(pp)\n",
    "    reload(mp)\n",
    "    setPath(day,month,year)\n",
    "\n",
    "reloadMyModules()\n",
    "getVersions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Please run and freeze the \"versions\" output above before starting work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prelim defs for this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:23.244496Z",
     "start_time": "2018-02-08T03:54:23.241482Z"
    }
   },
   "outputs": [],
   "source": [
    "location = 'C:/Users/Mark-Brown/Documents/Analysis-Code/Videos/Inferno/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def savePureIm(imData, name):\n",
    "    fig = figure(frameon=False)\n",
    "    fig.set_size_inches(9,15)\n",
    "    ax = Axes(fig, [0., 0., 1., 1.])\n",
    "    ax.set_axis_off()\n",
    "    fig.add_axes(ax)\n",
    "    ax.imshow(imData, origin='bottom', cmap='inferno')\n",
    "    grid(False)\n",
    "    axis('off');\n",
    "    savefig(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:23.792877Z",
     "start_time": "2018-02-08T03:54:23.721187Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def findMatches(arrangement, runNum, threshold=210, lim=None, lowLim=0):\n",
    "    with exp.ExpFile(runNum, old=True) as f:\n",
    "        rawData = f.get_pics()\n",
    "    #rawData, keyName, hdf5Key, repetitions = loadHDF5(runNum)\n",
    "    locs = unpackAtomLocations([2,2,2,3,6])\n",
    "    pic1Data = arr([None] * len(locs))\n",
    "    pic1Atoms = [[] for _ in locs]\n",
    "    for i, loc, in enumerate(locs):\n",
    "        pic1Data[i] = ah.normalizeData_2(rawData, loc, 1, 1, True)\n",
    "        for pic in pic1Data[i]:\n",
    "            pic1Atoms[i].append(1 * (pic > threshold))\n",
    "    pic1Atoms = arr(pic1Atoms)\n",
    "    atomsArray = misc.transpose(pic1Atoms)\n",
    "    for i, elem in enumerate(atomsArray):\n",
    "        atomsArray[i] = np.transpose(arr(elem).reshape(3, 6))\n",
    "    atomsArray = arr(atomsArray)\n",
    "    if lim is None:\n",
    "        lim = len(atomsArray)\n",
    "    picsToReturn = []\n",
    "    exactPics = []\n",
    "    areAny = False\n",
    "    for i, atoms in enumerate(atomsArray[lowLim:lim]):\n",
    "        picsToReturn.append(rawData[i+lowLim])\n",
    "        exact = True\n",
    "        for atom in (atoms - arrangement).flatten():\n",
    "            if atom != 0:\n",
    "                exact = False\n",
    "        if exact:\n",
    "            print('exact', i+lowLim)\n",
    "            areAny = True\n",
    "            exactPics.append(rawData[i+lowLim])\n",
    "        elif np.sum(atoms * arrangement) == np.sum(arrangement):\n",
    "            print('filled @', i+lowLim, ', extra:', np.sum(atoms) - np.sum(arrangement))\n",
    "            areAny = True\n",
    "    if not areAny:\n",
    "        print('No Matches')\n",
    "    return picsToReturn, exactPics\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:25.767610Z",
     "start_time": "2018-02-08T03:54:24.359514Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import imageio\n",
    "import numpy as np\n",
    "from numpy import array as arr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm\n",
    "\n",
    "\n",
    "infernoMap = [mpl.cm.inferno(i)[:-1] for i in range(256)]\n",
    "viridisMap = [mpl.cm.viridis(i)[:-1] for i in range(256)]\n",
    "hotMap = [mpl.cm.hot(i)[:-1] for i in range(256)]\n",
    "\n",
    "def makeVid(pics, gifAddress, videoType, fileAddress=None, dur=1, lim=None, includeCount=True, lowLim=None, \n",
    "            finLabels=[], finTxt=\"Atom Reservoir Depleted\", vidMap=infernoMap):\n",
    "    # global count\n",
    "    # select subsection\n",
    "    if lim is None:\n",
    "        lim = len(pics)\n",
    "    if lowLim is None:\n",
    "        lowLim = 0\n",
    "    pics = pics[lowLim:lim]\n",
    "    # normalize to rgb scale\n",
    "    \n",
    "    pics = np.uint16(pics / max(pics.flatten()) * 350)\n",
    "    pics = arr([[[int(elem) for elem in row] for row in pic] for pic in pics])\n",
    "    pics = arr(pics-min(pics.flatten()) - 50)\n",
    "    pics = [[[vidMap[elem] if elem < 256 and elem >= 0 else vidMap[255] if elem >= 256 else vidMap[0] \n",
    "              for elem in row] for row in pic] for pic in pics]\n",
    "    images = []\n",
    "    sequenceCount = 1\n",
    "    offset = 0\n",
    "    for picCount, pic in enumerate(pics):\n",
    "        fig = figure()\n",
    "        fig.set_size_inches([9,15])\n",
    "        ax = Axes(fig, [0., 0., 1., 1.])\n",
    "        ax.set_axis_off()\n",
    "        fig.add_axes(ax)\n",
    "        grid('off')\n",
    "        ax.imshow(pic, aspect='equal')\n",
    "        ax.text(0,14,str(sequenceCount) + \".\" + str(picCount+1-offset), color='white', fontsize=40)\n",
    "        ax.plot([2,4],[1,1],color='w')\n",
    "        ax.plot([2,2],[0.9, 1.1], 'w')\n",
    "        ax.plot([4,4],[0.9, 1.1], 'w')\n",
    "        ax.text(2.5, 0.9,r\"$1.7 \\mu m $\", color='white', fontsize=20)\n",
    "        if picCount+1 in finLabels:\n",
    "            ax.text(1.5, 14, finTxt, color='r', fontsize=40)\n",
    "        name = \"temp\"+str(picCount+1)+\".png\"\n",
    "        savefig(name)\n",
    "        images.append(imageio.imread(name))\n",
    "        if picCount+1 in finLabels:\n",
    "            sequenceCount += 1\n",
    "            offset = picCount+1\n",
    "            for _ in range(4):\n",
    "                images.append(imageio.imread(name))\n",
    "        close('all')\n",
    "    # make bigger\n",
    "    pics = [np.repeat(np.repeat(pic,32, axis=0), 32, axis=1) for pic in pics]\n",
    "    imageio.mimsave(gifAddress, images, format=videoType, duration=dur)\n",
    "from IPython.display import Image, HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:25.976626Z",
     "start_time": "2018-02-08T03:54:25.973616Z"
    }
   },
   "outputs": [],
   "source": [
    "location = \"C:/Users/Mark-Brown/Documents/Analysis-Code/Videos/Inferno/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Videos of Rearranging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Line rearranging: 35 (5000 reps)  \n",
    "J rearranging: 36 (2000 reps)  \n",
    "I rearranging: 37  \n",
    "L Rearranging: 38  \n",
    "A rearranging: 45 (5000)  \n",
    " \n",
    "A -   \n",
    "T rearranging: 46 (5000 opps)  \n",
    "O rearranging: 48 (5000)  \n",
    "M rearranging: 54 (3000)  \n",
    "S rearranging: 49 (3000)  \n",
    " \n",
    "R rearranging: 55 (3000)  \n",
    "E rearranging: 56 (2000)  \n",
    "G rearranging: 58 (2000)  \n",
    "A -  \n",
    "L -  \n",
    "\n",
    "L -  \n",
    "A -  \n",
    "B rearranging: 57 (?)  \n",
    "S -  \n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'loadHDF5' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-933520e738c1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrawData\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadHDF5\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m35\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mgifData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrawData\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m36\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mgifData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgifData\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrawData\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m37\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m51\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      4\u001b[0m \u001b[0mgifData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgifData\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrawData\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m57\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m109\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      5\u001b[0m \u001b[0mendpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m27\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m27\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m27\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m27\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m27\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m27\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'loadHDF5' is not defined"
     ]
    }
   ],
   "source": [
    "rawData, _, _, _ = loadHDF5(35)\n",
    "gifData = rawData[9:36]\n",
    "gifData = np.concatenate((gifData, rawData[37:51]))\n",
    "gifData = np.concatenate((gifData, rawData[57:109]))\n",
    "endpoints = [27, 27+14, 27+14+27, 27+14+27+25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "makeVid(gifData, location+'niceLine.gif', 'gif', dur=0.15, finLabels=endpoints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from AnalysisHelpers import unpackAtomLocations, normalizeData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import array as arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## single line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'J:\\\\Data repository\\\\New Data Repository\\\\2017\\\\December\\\\December 7\\\\Raw Data\\\\'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "makeVid(rawData, location+'lineRearrange1.gif', 'gif', dur=0.3, lim=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "day = '7'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "reloadMyModules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 959\n",
      "exact 960\n",
      "exact 961\n",
      "exact 962\n",
      "exact 963\n",
      "exact 964\n",
      "exact 965\n",
      "exact 966\n",
      "exact 967\n",
      "exact 968\n",
      "exact 969\n",
      "exact 970\n",
      "exact 971\n",
      "exact 972\n",
      "exact 973\n",
      "exact 974\n",
      "exact 975\n",
      "exact 976\n",
      "exact 977\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [0, 0, 1],\n",
    "                    [0, 0, 1],\n",
    "                    [0, 0, 0]])\n",
    "pics, exactPics = findMatches(arrangement, 36, lowLim=955, lim=978);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgPic = np.zeros(exactPics[0].shape)\n",
    "for pic in exactPics:\n",
    "    avgPic += pic\n",
    "avgPic /= len(exactPics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savePureIm(avgPic, 'J.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning:\n",
      "\n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'J_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "day = '7'\n",
    "reloadMyModules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 570\n",
      "exact 571\n",
      "exact 572\n",
      "exact 573\n",
      "exact 574\n",
      "exact 575\n",
      "exact 576\n",
      "exact 577\n",
      "exact 578\n",
      "exact 579\n",
      "exact 580\n",
      "exact 581\n",
      "exact 582\n",
      "exact 583\n",
      "exact 584\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "# I_1 = lowLim=349, lim=383\n",
    "# best:\n",
    "# I_2 = lowLim=564, lim=585\n",
    "# might be more good ones\n",
    "pics, exactPics = findMatches(arrangement, 37, lowLim=564, lim=585);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgPic = np.zeros(exactPics[0].shape)\n",
    "for pic in exactPics:\n",
    "    avgPic += pic\n",
    "avgPic /= len(exactPics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savePureIm(avgPic, 'I.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'I_2.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 143\n",
      "exact 144\n",
      "exact 145\n",
      "exact 146\n",
      "exact 147\n",
      "exact 148\n",
      "exact 149\n",
      "exact 150\n",
      "exact 151\n",
      "exact 152\n",
      "exact 153\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 0, 0],\n",
    "                    [0, 0, 0]] )\n",
    "pics, exactPics = findMatches(arrangement, 38, lowLim=133, lim=154);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgPic = np.zeros(exactPics[0].shape)\n",
    "for pic in exactPics:\n",
    "    avgPic += pic\n",
    "avgPic /= len(exactPics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savePureIm(avgPic, 'L.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'L_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "day = '8'\n",
    "reloadMyModules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 842\n",
      "exact 843\n",
      "exact 844\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics, exactPics = findMatches(arrangement, 31, lowLim=840, lim=845);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgPic = np.zeros(exactPics[0].shape)\n",
    "for pic in exactPics:\n",
    "    avgPic += pic\n",
    "avgPic /= len(exactPics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savePureIm(avgPic, 'A.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'A_1.gif', 'GIF', dur=0.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 980\n",
      "exact 981\n",
      "exact 982\n",
      "exact 983\n",
      "exact 984\n",
      "exact 985\n",
      "exact 986\n",
      "exact 987\n",
      "exact 988\n",
      "exact 989\n",
      "exact 990\n",
      "exact 991\n",
      "exact 992\n",
      "exact 993\n",
      "exact 994\n",
      "exact 995\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 46, lowLim=968, lim=996);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'T_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:47.899874Z",
     "start_time": "2018-02-08T03:54:47.895352Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:48.924832Z",
     "start_time": "2018-02-08T03:54:48.115944Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading HDF5 File..."
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"Unable to open object (Object 'seq #1 variables' doesn't exist)\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-c0247eaa3f56>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfindMatches\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrangement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m48\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlowLim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m771\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m784\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-11-badaed2aaa21>\u001b[0m in \u001b[0;36mfindMatches\u001b[1;34m(arrangement, runNum, threshold, lim, lowLim)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfindMatches\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrangement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrunNum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m210\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlowLim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mrawData\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeyName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdf5Key\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrepetitions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadHDF5\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrunNum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munpackAtomLocations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mpic1Data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mpic1Atoms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlocs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Analysis-Code\\AnalysisHelpers.py\u001b[0m in \u001b[0;36mloadHDF5\u001b[1;34m(fileId, quiet)\u001b[0m\n\u001b[0;32m    312\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Loading HDF5 File...'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m     \u001b[0mfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopenHDF5\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileId\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 314\u001b[1;33m     \u001b[0mkeyName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetKeyFromHDF5\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    315\u001b[0m     \u001b[0mreps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Master-Parameters'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Repetitions'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m     \u001b[0mpics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetPicsFromHDF5\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Analysis-Code\\AnalysisHelpers.py\u001b[0m in \u001b[0;36mgetKeyFromHDF5\u001b[1;34m(file)\u001b[0m\n\u001b[0;32m    272\u001b[0m     \u001b[0mkeyValues\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    273\u001b[0m     \u001b[0mfoundOne\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 274\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mvar\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Master-Parameters'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Seq #1 Variables'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    275\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Master-Parameters'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Seq #1 Variables'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Constant'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m     \u001b[1;31m# to look at older files...\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\_hl\\group.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    167\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid HDF5 object reference\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m             \u001b[0moid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5o\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_e\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlapl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lapl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0motype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5i\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\h5o.pyx\u001b[0m in \u001b[0;36mh5py.h5o.open\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"Unable to open object (Object 'seq #1 variables' doesn't exist)\""
     ]
    }
   ],
   "source": [
    "pics = findMatches(arrangement, 48, lowLim=771, lim=784);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "makeVid(arr(flippedPics), location+'O_4.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 551\n",
      "exact 552\n",
      "exact 553\n",
      "exact 554\n",
      "exact 555\n",
      "exact 556\n"
     ]
    }
   ],
   "source": [
    "pics = findMatches(arrangement, 48, lowLim=544, lim=557);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "makeVid(arr(flippedPics), location+'O_3.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 183\n",
      "exact 184\n"
     ]
    }
   ],
   "source": [
    "pics = findMatches(arrangement, 48, lowLim=175, lim=185);\n",
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "makeVid(arr(flippedPics), location+'O_2.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 3343\n",
      "exact 3344\n",
      "exact 3345\n",
      "exact 3346\n",
      "exact 3347\n",
      "exact 3348\n",
      "exact 3349\n",
      "exact 3350\n",
      "exact 3351\n",
      "exact 3352\n",
      "exact 3353\n",
      "exact 3354\n",
      "exact 3355\n",
      "exact 3356\n",
      "exact 3357\n",
      "exact 3358\n",
      "exact 3359\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 48, lowLim=3339, lim=3360);\n",
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "makeVid(arr(flippedPics), location+'O_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## concatenated O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:55:02.291233Z",
     "start_time": "2018-02-08T03:55:02.283188Z"
    }
   },
   "outputs": [],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:59:42.961646Z",
     "start_time": "2018-02-08T03:59:42.943097Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'day' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-445e37962bf9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAnalysisHelpers\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAnalysisHelpers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msetPath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mday\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmonth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myear\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'day' is not defined"
     ]
    }
   ],
   "source": [
    "from importlib import reload\n",
    "import AnalysisHelpers\n",
    "reload(AnalysisHelpers)\n",
    "setPath(day, month, year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:59:57.023537Z",
     "start_time": "2018-02-08T03:59:57.019527Z"
    }
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:00:26.595769Z",
     "start_time": "2018-02-08T03:59:57.581260Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading HDF5 File...Loaded File Successfully.\n",
      "exact 778\n",
      "exact 779\n",
      "exact 780\n",
      "exact 781\n",
      "exact 782\n",
      "exact 783\n",
      "Loading HDF5 File...Loaded File Successfully.\n",
      "exact 551\n",
      "exact 552\n",
      "exact 553\n",
      "exact 554\n",
      "exact 555\n",
      "exact 556\n",
      "Loading HDF5 File...Loaded File Successfully.\n",
      "exact 183\n",
      "exact 184\n",
      "Loading HDF5 File...Loaded File Successfully.\n",
      "exact 3343\n",
      "exact 3344\n",
      "exact 3345\n",
      "exact 3346\n",
      "exact 3347\n",
      "exact 3348\n",
      "exact 3349\n",
      "exact 3350\n",
      "exact 3351\n",
      "exact 3352\n",
      "exact 3353\n",
      "exact 3354\n",
      "exact 3355\n",
      "exact 3356\n",
      "exact 3357\n",
      "exact 3358\n",
      "exact 3359\n"
     ]
    }
   ],
   "source": [
    "pics = findMatches(arrangement, 48, lowLim=771, lim=784);\n",
    "flippedPics4 = []\n",
    "for pic in pics:\n",
    "    flippedPics4.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics4[-1][-i-1] = row\n",
    "        \n",
    "pics = findMatches(arrangement, 48, lowLim=544, lim=557);\n",
    "flippedPics3 = []\n",
    "for pic in pics:\n",
    "    flippedPics3.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics3[-1][-i-1] = row\n",
    "\n",
    "pics = findMatches(arrangement, 48, lowLim=175, lim=185);\n",
    "flippedPics2 = []\n",
    "for pic in pics:\n",
    "    flippedPics2.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics2[-1][-i-1] = row\n",
    "        \n",
    "pics = findMatches(arrangement, 48, lowLim=3339, lim=3360);\n",
    "flippedPics1 = []\n",
    "for pic in pics:\n",
    "    flippedPics1.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics1[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:00:30.362788Z",
     "start_time": "2018-02-08T04:00:30.351759Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21, 15, 9)\n",
      "(31, 15, 9)\n",
      "(44, 15, 9)\n",
      "(57, 15, 9)\n"
     ]
    }
   ],
   "source": [
    "concData = arr(flippedPics1)\n",
    "print(concData.shape)\n",
    "concData = np.concatenate((concData, flippedPics2))\n",
    "print(concData.shape)\n",
    "concData = np.concatenate((concData, flippedPics3))\n",
    "print(concData.shape)\n",
    "concData = np.concatenate((concData, flippedPics4))\n",
    "print(concData.shape)\n",
    "finPts = [21, 31, 44, 57]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:10:46.442803Z",
     "start_time": "2018-02-08T04:10:46.212177Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import imageio\n",
    "import numpy as np\n",
    "from numpy import array as arr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm\n",
    "\n",
    "\n",
    "infernoMap = [mpl.cm.inferno(i)[:-1] for i in range(256)]\n",
    "viridisMap = [mpl.cm.viridis(i)[:-1] for i in range(256)]\n",
    "hotMap = [mpl.cm.hot(i)[:-1] for i in range(256)]\n",
    "\n",
    "def makeVid(pics, gifAddress, videoType, fileAddress=None, dur=1, lim=None, includeCount=True, lowLim=None, \n",
    "            finLabels=[], finTxt=\"Atom Reservoir Depleted\", vidMap=infernoMap):\n",
    "    # global count\n",
    "    # select subsection\n",
    "    if lim is None:\n",
    "        lim = len(pics)\n",
    "    if lowLim is None:\n",
    "        lowLim = 0\n",
    "    pics = pics[lowLim:lim]\n",
    "    # normalize to rgb scale\n",
    "    \n",
    "    pics = np.uint16(pics / max(pics.flatten()) * 350)\n",
    "    pics = arr([[[int(elem) for elem in row] for row in pic] for pic in pics])\n",
    "    pics = arr(pics-min(pics.flatten()) - 50)\n",
    "    pics = [[[vidMap[elem] if elem < 256 and elem >= 0 else vidMap[255] if elem >= 256 else vidMap[0] \n",
    "              for elem in row] for row in pic] for pic in pics]\n",
    "    images = []\n",
    "    sequenceCount = 1\n",
    "    offset = 0\n",
    "    for picCount, pic in enumerate(pics):\n",
    "        fig = figure()\n",
    "        fig.set_size_inches([9,15])\n",
    "        ax = Axes(fig, [0., 0., 1., 1.])\n",
    "        ax.set_axis_off()\n",
    "        fig.add_axes(ax)\n",
    "        grid('off')\n",
    "        ax.imshow(pic, aspect='equal')\n",
    "        ax.text(0,14,str(sequenceCount) + \".\" + str(picCount+1-offset), color='white', fontsize=40)\n",
    "        ax.plot([2,4],[1,1],color='w', linewidth=2)\n",
    "        ax.plot([2,2],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.plot([4,4],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.text(2.0, 0.8,r\"$1.7 \\mu m $\", color='white', fontsize=40)\n",
    "        if picCount+1 in finLabels:\n",
    "            ax.text(1.5, 14, finTxt, color='r', fontsize=40)\n",
    "        name = \"temp\"+str(picCount+1)+\".png\"\n",
    "        savefig(name, dpi=19)\n",
    "        images.append(imageio.imread(name))\n",
    "        if picCount+1 in finLabels:\n",
    "            sequenceCount += 1\n",
    "            offset = picCount+1\n",
    "            for _ in range(4):\n",
    "                images.append(imageio.imread(name))\n",
    "        close('all')\n",
    "    # make bigger\n",
    "    #pics = [np.repeat(np.repeat(pic,32, axis=0), 32, axis=1) for pic in pics]\n",
    "    imageio.mimsave(gifAddress, images, format=videoType, duration=dur)\n",
    "from IPython.display import Image, HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:11:00.271405Z",
     "start_time": "2018-02-08T04:10:46.655216Z"
    }
   },
   "outputs": [],
   "source": [
    "makeVid(concData, location+'Low-Res-Nice-O.gif', 'gif', dur=0.15, finLabels=finPts, finTxt=\"Fin.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import AnalysisHelpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'AnalysisHelpers' from 'C:\\\\Users\\\\Mark-Brown\\\\Documents\\\\Analysis-Code\\\\AnalysisHelpers.py'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from importlib import reload\n",
    "reload(AnalysisHelpers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 2975\n",
      "exact 2976\n",
      "exact 2977\n",
      "exact 2978\n",
      "exact 2979\n",
      "exact 2980\n",
      "exact 2981\n",
      "exact 2982\n",
      "exact 2983\n",
      "exact 2984\n",
      "exact 2985\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 54, lowLim=2971, lim=2986);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "makeVid(arr(flippedPics), location+'M_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 58\n",
      "exact 59\n",
      "exact 60\n",
      "exact 61\n",
      "exact 62\n",
      "exact 63\n",
      "exact 64\n",
      "exact 65\n",
      "exact 66\n",
      "exact 67\n",
      "exact 68\n",
      "exact 69\n",
      "exact 70\n",
      "exact 71\n",
      "exact 72\n",
      "exact 73\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [0, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 49, lowLim=50, lim=74);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'S_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    }
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 123\n",
      "exact 124\n",
      "exact 125\n",
      "exact 126\n",
      "exact 127\n",
      "exact 128\n",
      "exact 129\n",
      "exact 130\n",
      "exact 131\n",
      "exact 132\n",
      "exact 133\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 0, 1],\n",
    "                    [1, 1, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 55, lowLim=118, lim=134);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "makeVid(arr(flippedPics), location+'R_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 1531\n",
      "exact 1532\n",
      "exact 1533\n",
      "exact 1534\n",
      "exact 1535\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 56, lowLim=1525, lim=1536);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'E_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"8\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 27, lowLim = 1917, lim=1929);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "makeVid(arr(flippedPics), location+'G_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"7\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 2948\n",
      "exact 2949\n",
      "exact 2950\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 0],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 57, lowLim=2945, lim=2951);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'B_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"8\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 340\n",
      "exact 341\n",
      "exact 342\n",
      "exact 343\n",
      "exact 344\n",
      "exact 345\n",
      "exact 346\n",
      "exact 347\n",
      "exact 348\n",
      "exact 349\n",
      "exact 350\n",
      "exact 351\n",
      "exact 352\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 0, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 32, lowLim=331, lim=353);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'C_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 1587\n",
      "exact 1588\n",
      "exact 1589\n",
      "exact 1590\n",
      "exact 1591\n",
      "exact 1592\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 34, lowLim=1582, lim=1593);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'H_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 949\n",
      "exact 950\n",
      "exact 951\n",
      "exact 952\n",
      "exact 953\n",
      "exact 954\n",
      "exact 955\n",
      "exact 956\n",
      "exact 957\n",
      "exact 958\n",
      "exact 959\n",
      "exact 960\n",
      "exact 961\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 35, lowLim=941, lim=962);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'Y_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"8\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 810\n",
      "exact 811\n",
      "exact 812\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [0, 0, 0]] )\n",
    "# n_1 = lowLim=924, lim=932\n",
    "# n_2\n",
    "pics = findMatches(arrangement, 36, lowLim=803, lim=813);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'n_2.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"8\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 1005\n",
      "exact 1006\n",
      "exact 1007\n",
      "exact 1008\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 0],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 37, lowLim=999, lim=1009);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'K_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 310\n",
      "exact 311\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 38, lowLim=302, lim=312);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'U_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-16T10:37:29.329638Z",
     "start_time": "2017-11-16T10:37:29.323638Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "yearloc=\"2017\"\n",
    "monthloc=\"December\"\n",
    "dayloc=\"8\"\n",
    "setPath(dayloc, monthloc, yearloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 3\n",
      "exact 4\n",
      "exact 5\n",
      "exact 6\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 0, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 1, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 40, lowLim=0, lim=7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning:\n",
      "\n",
      "Lossy conversion from float64 to uint8, range [0, 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'W_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 1309\n",
      "exact 1310\n",
      "exact 1311\n",
      "exact 1312\n",
      "exact 1313\n",
      "exact 1314\n",
      "exact 1315\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[1, 1, 0],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 0, 1],\n",
    "                    [1, 1, 0],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 41, lowLim=1302, lim=1316);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'D_2.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact 396\n",
      "exact 397\n",
      "exact 398\n",
      "exact 399\n",
      "exact 400\n",
      "exact 401\n"
     ]
    }
   ],
   "source": [
    "arrangement = arr( [[0, 1, 0],\n",
    "                    [0, 0, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [0, 1, 0],\n",
    "                    [0, 0, 0]] )\n",
    "pics = findMatches(arrangement, 42, lowLim=391, lim=402);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flippedPics = []\n",
    "for pic in pics:\n",
    "    flippedPics.append(np.zeros((15, 9)))\n",
    "    for i, row in enumerate(pic):\n",
    "        flippedPics[-1][-i-1] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n"
     ]
    }
   ],
   "source": [
    "makeVid(arr(flippedPics), location+'!_1.gif', 'gif', dur=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "# Loading Gifs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3x3 loading (for Tobias Talk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=\"2018\"\n",
    "month=\"April\"\n",
    "day=\"15\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:59:42.961646Z",
     "start_time": "2018-02-08T03:59:42.943097Z"
    }
   },
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "import AnalysisHelpers\n",
    "reload(AnalysisHelpers)\n",
    "setPath(day, month, year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:10:46.442803Z",
     "start_time": "2018-02-08T04:10:46.212177Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import imageio\n",
    "import numpy as np\n",
    "from numpy import array as arr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm\n",
    "\n",
    "\n",
    "infernoMap = [mpl.cm.inferno(i)[:-1] for i in range(256)]\n",
    "viridisMap = [mpl.cm.viridis(i)[:-1] for i in range(256)]\n",
    "hotMap = [mpl.cm.hot(i)[:-1] for i in range(256)]\n",
    "\n",
    "def makeVid(pics, gifAddress, videoType, fileAddress=None, dur=1, lim=None, includeCount=True, lowLim=None, \n",
    "            finLabels=[], finTxt=\"Atom Reservoir Depleted\", vidMap=infernoMap):\n",
    "    # global count\n",
    "    # select subsection\n",
    "    if lim is None:\n",
    "        lim = len(pics)\n",
    "    if lowLim is None:\n",
    "        lowLim = 0\n",
    "    pics = pics[lowLim:lim]\n",
    "    # normalize to rgb scale    \n",
    "    pics = np.uint16(pics / max(pics.flatten()) * 350)\n",
    "    pics = arr([[[int(elem) for elem in row] for row in pic] for pic in pics])\n",
    "    pics = arr(pics-min(pics.flatten()) - 50)\n",
    "    pics = [[[vidMap[elem] if elem < 256 and elem >= 0 else vidMap[255] if elem >= 256 else vidMap[0] \n",
    "              for elem in row] for row in pic] for pic in pics]\n",
    "    images = []\n",
    "    sequenceCount = 1\n",
    "    offset = 0\n",
    "    for picCount, pic in enumerate(pics):\n",
    "        fig = figure()\n",
    "        fig.set_size_inches([9,15])\n",
    "        ax = Axes(fig, [0., 0., 1., 1.])\n",
    "        ax.set_axis_off()\n",
    "        fig.add_axes(ax)\n",
    "        grid('off')\n",
    "        ax.imshow(pic, aspect='equal')\n",
    "        if includeCount:\n",
    "            ax.text(0,14,str(sequenceCount) + \".\" + str(picCount+1-offset), color='white', fontsize=40)\n",
    "        ax.plot([2,4],[1,1],color='w', linewidth=2)\n",
    "        ax.plot([2,2],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.plot([4,4],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.text(2.02, 0.8,r\"$2.0 \\mu m $\", color='white', fontsize=30)\n",
    "        if picCount+1 in finLabels:\n",
    "            ax.text(1.5, 14, finTxt, color='r', fontsize=40)\n",
    "        name = \"temp\"+str(picCount+1)+\".png\"\n",
    "        margins(0,0)\n",
    "        gca().yaxis.set_major_locator(NullLocator())\n",
    "        gca().xaxis.set_major_locator(NullLocator())\n",
    "        savefig(name, dpi=40, bbox_inches='tight', pad_inches=0.0)\n",
    "        images.append(imageio.imread(name))\n",
    "        if picCount+1 in finLabels:\n",
    "            sequenceCount += 1\n",
    "            offset = picCount+1\n",
    "            for _ in range(4):\n",
    "                images.append(imageio.imread(name))\n",
    "        close('all')\n",
    "    # make bigger\n",
    "    #pics = [np.repeat(np.repeat(pic,32, axis=0), 32, axis=1) for pic in pics]\n",
    "    imageio.mimsave(gifAddress, images, format=videoType, duration=dur)\n",
    "from IPython.display import Image, HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import AnalysisHelpers as AH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "setPath(day, month, year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = AH.openHDF5(19)\n",
    "pics = f['Andor']['Pictures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "vidPics = pics[0:-2:2]\n",
    "vidPics = vidPics[:,6:15,6:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2c193758978>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c193098978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = subplots(1,5, figsize=(20,40))\n",
    "ax[0].imshow(vidPics[0])\n",
    "ax[1].imshow(vidPics[1])\n",
    "ax[2].imshow(vidPics[2])\n",
    "ax[3].imshow(vidPics[3])\n",
    "ax[4].imshow(vidPics[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:11:00.271405Z",
     "start_time": "2018-02-08T04:10:46.655216Z"
    }
   },
   "outputs": [],
   "source": [
    "makeVid(vidPics, location+'3x3-Loading.gif', 'gif', dur=0.15, includeCount=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10x10 loading for tobias talk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:23.244496Z",
     "start_time": "2018-02-08T03:54:23.241482Z"
    }
   },
   "outputs": [],
   "source": [
    "location = 'C:/Users/Mark-Brown/Documents/Analysis-Code/Videos/Inferno/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=\"2018\"\n",
    "month=\"April\"\n",
    "day=\"30\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "setPath(day, month, year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import AnalysisHelpers as AH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = AH.openHDF5(34)\n",
    "pics = f['Andor']['Pictures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "vidPics = pics[100:200:2]\n",
    "vidPics = vidPics[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:10:46.442803Z",
     "start_time": "2018-02-08T04:10:46.212177Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import imageio\n",
    "import numpy as np\n",
    "from numpy import array as arr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm\n",
    "\n",
    "\n",
    "infernoMap = [mpl.cm.inferno(i)[:-1] for i in range(256)]\n",
    "viridisMap = [mpl.cm.viridis(i)[:-1] for i in range(256)]\n",
    "hotMap = [mpl.cm.hot(i)[:-1] for i in range(256)]\n",
    "cividisMap = [mpl.cm.cividis(i)[:-1] for i in range(256)]\n",
    "\n",
    "def makeVid(pics, gifAddress, videoType, fileAddress=None, dur=1, lim=None, includeCount=True, lowLim=None, \n",
    "            finLabels=[], finTxt=\"Atom Reservoir Depleted\", vidMap=infernoMap, baseCutoff=50, multiplier=350):\n",
    "    # global count\n",
    "    # select subsection\n",
    "    if lim is None:\n",
    "        lim = len(pics)\n",
    "    if lowLim is None:\n",
    "        lowLim = 0\n",
    "    pics = pics[lowLim:lim]\n",
    "    # normalize to rgb scale    \n",
    "    pics = np.uint16(pics / max(pics.flatten()) * multiplier)\n",
    "    pics = arr([[[int(elem) for elem in row] for row in pic] for pic in pics])\n",
    "    pics = arr(pics-min(pics.flatten()) - baseCutoff)\n",
    "    pics = [[[vidMap[elem] if elem < 256 and elem >= 0 else vidMap[255] if elem >= 256 else vidMap[0] \n",
    "              for elem in row] for row in pic] for pic in pics]\n",
    "    images = []\n",
    "    sequenceCount = 1\n",
    "    offset = 0\n",
    "    for picCount, pic in enumerate(pics):\n",
    "        fig = figure()\n",
    "        fig.set_size_inches([9,15])\n",
    "        ax = Axes(fig, [0., 0., 1., 1.])\n",
    "        ax.set_axis_off()\n",
    "        fig.add_axes(ax)\n",
    "        grid(False)\n",
    "        ax.imshow(pic, aspect='equal')\n",
    "        if includeCount:\n",
    "            ax.text(0,14,str(sequenceCount) + \".\" + str(picCount+1-offset), color='white', fontsize=40)\n",
    "        ax.plot([2,4],[1,1],color='w', linewidth=2)\n",
    "        ax.plot([2,2],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.plot([4,4],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.text(2.02, 0.8,r\"$2.0 \\mu m $\", color='white', fontsize=30)\n",
    "        if picCount+1 in finLabels:\n",
    "            ax.text(1.5, 14, finTxt, color='r', fontsize=40)\n",
    "        name = \"temp\"+str(picCount+1)+\".png\"\n",
    "        margins(0,0)\n",
    "        gca().yaxis.set_major_locator(NullLocator())\n",
    "        gca().xaxis.set_major_locator(NullLocator())\n",
    "        savefig(name, dpi=40, bbox_inches='tight', pad_inches=0.0)\n",
    "        images.append(imageio.imread(name))\n",
    "        if picCount+1 in finLabels:\n",
    "            sequenceCount += 1\n",
    "            offset = picCount+1\n",
    "            for _ in range(4):\n",
    "                images.append(imageio.imread(name))\n",
    "        close('all')\n",
    "    # make bigger\n",
    "    #pics = [np.repeat(np.repeat(pic,32, axis=0), 32, axis=1) for pic in pics]\n",
    "    imageio.mimsave(gifAddress, images, format=videoType, duration=dur)\n",
    "from IPython.display import Image, HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b3800b99e8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = subplots(1,5, figsize=(20,40))\n",
    "ax[0].imshow(vidPics[0])\n",
    "ax[1].imshow(vidPics[1])\n",
    "ax[2].imshow(vidPics[2])\n",
    "ax[3].imshow(vidPics[3])\n",
    "ax[4].imshow(vidPics[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:23.244496Z",
     "start_time": "2018-02-08T03:54:23.241482Z"
    }
   },
   "outputs": [],
   "source": [
    "location = 'C:/Users/Mark-Brown/Documents/Analysis-Code/Videos/Viridis/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:11:00.271405Z",
     "start_time": "2018-02-08T04:10:46.655216Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning:\n",
      "\n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "makeVid(vidPics, location+'10x10-Loading.gif', 'gif', dur=0.15, vidMap=viridisMap, includeCount=False, baseCutoff=100, multiplier=550)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### updated 10x10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:23.244496Z",
     "start_time": "2018-02-08T03:54:23.241482Z"
    }
   },
   "outputs": [],
   "source": [
    "location = 'C:/Users/Mark-Brown/Documents/Analysis-Code/Videos/Inferno/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=\"2018\"\n",
    "month=\"May\"\n",
    "day=\"4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "setPath(day, month, year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import AnalysisHelpers as AH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = AH.openHDF5(18)\n",
    "pics = f['Andor']['Pictures']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "vidPics = pics[100:200:2]\n",
    "vidPics = vidPics[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:10:46.442803Z",
     "start_time": "2018-02-08T04:10:46.212177Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import imageio\n",
    "import numpy as np\n",
    "from numpy import array as arr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm\n",
    "\n",
    "\n",
    "infernoMap = [mpl.cm.inferno(i)[:-1] for i in range(256)]\n",
    "viridisMap = [mpl.cm.viridis(i)[:-1] for i in range(256)]\n",
    "hotMap = [mpl.cm.hot(i)[:-1] for i in range(256)]\n",
    "cividisMap = [mpl.cm.cividis(i)[:-1] for i in range(256)]\n",
    "jetMap = [mpl.cm.jet(i)[:-1] for i in range(256)]\n",
    "greyMap = [mpl.cm.Greys(i)[:-1] for i in range(256)]\n",
    "greysReversedMap = list(reversed(greyMap))\n",
    "bluesMap = [mpl.cm.Blues(i)[:-1] for i in range(256)]\n",
    "orangesMap = [mpl.cm.Oranges(i)[:-1] for i in range(256)]\n",
    "\n",
    "def makeVid(pics, gifAddress, videoType, fileAddress=None, dur=1, lim=None, includeCount=True, lowLim=None, \n",
    "            finLabels=[], finTxt=\"Atom Reservoir Depleted\", vidMap=infernoMap, baseCutoff=50, multiplier=350):\n",
    "    # global count\n",
    "    # select subsection\n",
    "    if lim is None:\n",
    "        lim = len(pics)\n",
    "    if lowLim is None:\n",
    "        lowLim = 0\n",
    "    pics = pics[lowLim:lim]\n",
    "    # normalize to rgb scale    \n",
    "    pics = np.uint16(pics / max(pics.flatten()) * multiplier)\n",
    "    pics = arr([[[int(elem) for elem in row] for row in pic] for pic in pics])\n",
    "    pics = arr(pics-min(pics.flatten()) - baseCutoff)\n",
    "    pics = [[[vidMap[elem] if elem < 256 and elem >= 0 else vidMap[255] if elem >= 256 else vidMap[0] \n",
    "              for elem in row] for row in pic] for pic in pics]\n",
    "    images = []\n",
    "    sequenceCount = 1\n",
    "    offset = 0\n",
    "    for picCount, pic in enumerate(pics):\n",
    "        fig = figure()\n",
    "        fig.set_size_inches([9,15])\n",
    "        ax = Axes(fig, [0., 0., 1., 1.])\n",
    "        ax.set_axis_off()\n",
    "        fig.add_axes(ax)\n",
    "        grid(False)\n",
    "        ax.imshow(pic, aspect='equal')\n",
    "        if includeCount:\n",
    "            ax.text(0,14,str(sequenceCount) + \".\" + str(picCount+1-offset), color='white', fontsize=40)\n",
    "        ax.plot([2,4],[1,1],color='w', linewidth=2)\n",
    "        ax.plot([2,2],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.plot([4,4],[0.8, 1.2], 'w', linewidth=2)\n",
    "        ax.text(2.02, 0.8,r\"$2.0 \\mu m $\", color='white', fontsize=30)\n",
    "        if picCount+1 in finLabels:\n",
    "            ax.text(1.5, 14, finTxt, color='r', fontsize=40)\n",
    "        name = \"temp\"+str(picCount+1)+\".png\"\n",
    "        margins(0,0)\n",
    "        gca().yaxis.set_major_locator(NullLocator())\n",
    "        gca().xaxis.set_major_locator(NullLocator())\n",
    "        savefig(name, dpi=40, bbox_inches='tight', pad_inches=0.0)\n",
    "        images.append(imageio.imread(name))\n",
    "        if picCount+1 in finLabels:\n",
    "            sequenceCount += 1\n",
    "            offset = picCount+1\n",
    "            for _ in range(4):\n",
    "                images.append(imageio.imread(name))\n",
    "        close('all')\n",
    "    # make bigger\n",
    "    #pics = [np.repeat(np.repeat(pic,32, axis=0), 32, axis=1) for pic in pics]\n",
    "    imageio.mimsave(gifAddress, images, format=videoType, duration=dur)\n",
    "from IPython.display import Image, HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ab86f04550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHkAAADbCAYAAAD5900uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XeUJGd9L/xvVXXunp6enHd3Zna3d5UDCAEmO5FsXuyX1yZcbIxsE4yxMVyMA77H1/c1JhgjDBiTbSGLYLCxjQkCYWQEkjaPdtWbc5wcOnfV/QPU1U/37O/Xmg1q9fl+zplzuurX81R11ZP6qeqnLM/zQERERERERERET272E70DRERERERERER06TjIQ0RERERERETUBjjIQ0RERERERETUBjjIQ0RERERERETUBjjIQ0RERERERETUBjjIQ0RERERERETUBgJXKuEXTrzNeDb7+JYhHHn0THXZm51X03Anx8R4JRFS0wjOrMjbiAaN5YlNAzh84Fx12V4uqNtY3totxuNHltQ07PNzyhvM8bjxrcM4su90dTm3dUjdRmTHETFuJTvEeCWVULdh7T9afT1x3RgOT50w4t7WcTUNZ3ZZjHsLi2J84fmb1G2ktvnnuD5vAoDbEVXTsBezYvzrh95nqYk8AX6++/Vm2bx2FEceOVld9jaMqGlYyzn5DTN6+bYiYfU9tcavGcGRvaeqy165ov5PeeOwshP6doOnH2fZrM9Puby6jdKGATEemJfzWqVTz6+BY+err+uPZdMCcpPhdst1SKEvpm4ifMH/rBObB3B4/zkjbq3ox9M9ekKMf7N4d0uWzZ/rfJ1RNuvrUDvVqabhFYti3Irr56DSI59HZ8Zs0+rzvHthRt2Gt3mdGM8P6vsZ+9Fh+Q3lcvVlfT0HAFYkom7D60qq7xH/P6R3s+wFv5+yanuU1I+FPSO3i/X1VL3lm5S6EkBovlR9PTHZj8OHzpvxoxfUNFAqieGvn/5wS5bNF657q9lu1vXB3F69bFrFshj3lHP04zTk4wfXNRZXy08aLyG3J4Vevb2J7JHrYG+ot/q6vt8NAFZB+ZwA8mPyMQ+flvuS5ZRe/q2Kf9onNvbj8EEzzwcW9fbIs+QsrZ3Txet7xTgAJHfVtO+rnPNKt95/D5yT+21fP/KBliub9d81gbrPn9e/w3kFpc1M6sfOnZ4V4/Vtd3394SX0Ot6Lyt95S03k58ADj4hxO2buR0O7aetZwBsbFOOVWFCMB/Yd17cxYvaZJzYP4vD+s9Vle0X5jgIAXkPWMcNxuZ7Lj8j9JACInDTb5fH0II5k/P0sN9F3tyquGP/WD//0oidlTYM86XTaBvARADcCKAB4fSaTOSj9T7yJL82tIJ7QC0kriCdb/3jGmsi8rSDe8eQ4581YU9l8EuQl4Mlznp4M+/lk2EcAiD1J6uNmrKVsxlg2L5snTz33JNnP+OMbpG9la+vTtn6eB54cfdrYk+VYxp8k+/kkOOfNatfvm0+a9uhJsp+xjidHe3S1y+Zaf671MgCRTCbzdADvBPD+y7dLRHQJWDaJWhPLJlFrYtkkak0sm0RrtNZBnp8C8J8AkMlkfgjgKZdtj4joUrBsErUmlk2i1sSySdSaWDaJ1mitc/IkASzULFfS6XQgk8lUf3A8vmXIuGVuvG7OGG9J/q0sAHjD/WK8EtV3X/u9rBc205jYZP7Oz8rJv9UEgNx6+Tf7kS55Pg0AsOfkeX1Wm5OnVmF9j7qNUEQ+XlZM/k2oq/xOGwDQ5/9mdOK6VeZUWq/PHWQvyr+l9Fbk47mydVTdRsL2P+v4lsZ9cmP6rX/2ysV/6zv1kDJXxJWjl81rR41bMCeuNY+XN9SnbsTKKr9zXtTLtxWSf5dbb/wac64gT/mdKgBURpTfsjfxC/PASJf8hrrfKDfkJ+X33gBQHpTLv7Ms12NuQs+vzqD/++H6Y9k0x5H3Q5k7pJnfjAfn/c86sbnxt93N1MneaPyisT33P6r+/xWils2J68aMn2jV16F24uKf6zFeWZ73o5m5sLTzWD8fWX2ed+f19ghj8jxUxW59P8OWcp2q4s/bVV/PAYAV0uf18zr0Yy7+f1AuMwBg15Tv+r4SALhN/DRKmyMOyrwguY36vB+BZT9vTUw29s+C/focBRDy59SPDun/f2Xo7ebWYeMnWvV9MLeZOQtL8jxyXhPzXWhp1M/JM7FK/0bjKf2fUqeeH0NKnvV6/DlK6vvdgD5/EQAU++WyGRyS+5LNzOtp1RzuiY2Ned7JNtEeKadVO6fZCX2+p1jQPxarnXO3iZ/dOML3kKkHW7NPW/9dE6irQ5U56gDAKyltpjI3CwC4C3L9aSfM+qG+/vCiepnywnKfuZn87Nha/8DsozW0m83MydMv92fdsPxd1Ek1cSz6zH55fV/RbqKfqM7Jo5yTYhNzk4UGzHZ5Im3uZ6WJtt1y9e86F7PWQZ5FALWtuV3bGAJYdaK32i++zU28fOkV9OOdeBkA9uw4Vn3d1MTLK1d/4mXA7BDltA4erv7EywAw9d/7jWVvVp9M75InXm5iLKp24mUAmHrIPDaXY+LlJ4heNusmHwWAqQcOVF97G/TP9URMvAyYeb65iZe1Lz76Nh/vxMtAXX5qauJlOc9f7omXgTV+obrKEy8DwNT2Y8by5Zh4+Qmils36ieoBYOoHfh3aqhMvA2aeb27iZfk8Xu6JlwGzngNac+JloPEiwVWZeLmol6vaiZcBYM9uM79ejomXnyB6u1kzSepjar/4turEywAw9bDc92vYj6s88TKwSj3fghMvA8CeXeaksFdl4uXC45t4GWg855dj4uUniFg2LzapeLUObdGJl4G6/uxVm3g5I8brJ14G6trNFp14GQCmttV8d2/RiZcBYGrb0erryzHxsmStP9f6bwAvAoB0On07gD1r3gMiupxYNolaE8smUWti2SRqTSybRGu01jt5vgLgZ9Lp9A/w42vhv17/Bi9mjih6kZCxrji+Ud1I+Kh8NdCZk0fhAKi3KNt1I2RWrmjcvZPbkFI3UYrKY2UXbtPT6N0h39Jd6DGPZ368B9makUqnqI/0nX7lFjEePyvfGbG4Xr/tfN1Zf2TTjsVg190dNJPWR8TjZ+Q7PLID8iix1cSgZ+1t0Z5lNdwmbc838XPCx/lzo6tELZsI1x3fUMhY18xnhzKy7K4yyt7grHzVtzJh3s5a6U+hPOb/lCxw/Hz9vzQInpavruRX+clBPUe5wlJJmsez3J1AadC/amPn9J97OMJP/wDAWpLv5LEi+l2Nbo9fD7mdCWMZALywXr41uWH5s8aO63c1GlezLbvh6radb+I23OvT+nuuPrVsWgPmFVurq9NY5yk/lwMAS7s6tdjEnaVNXK0T/79H+YkjALci72dsv343kKXcWeZ11Vw9TXUCfebPyDzlWAHA4jXynbrRaTk/Fjv0diJSc0dxpSeB8qBZNgMnptU0iuNyXWa58mctJvRrfolH/Lsag8MphE7V3eUY0POn16FfsX4CqGXT7THv6HI748Y6V/k5PAAE5uSy19RPA7Vb98N1bUEoaKwrDDZxV0JALv/N/KwMnfJVbvPuFqvhbpfikH4HXWBJvgOm3CNfJXcW9bbErvlpurOUQ2DWvOuu0nXp+bkcl897OdrE3RM1d195sXDD3VheQC/fpWFl2ognhlw2V+uLum51vTugfyZ3t/wT7kCXfpde/Xedel7SPMdeLGKsc5t46pKVk/N7U+XyGvm7t1dXv3gD3fDW+d+5vL36HeCVS3zSVeHmCfU9kSNmm2jnisbdO838/G21X/LUyg3J5TI8o98lZmXNO/2sfNFYF2zijiPt1yuSNQ3yZDIZF8Bvr3mrRHRFsGwStSaWTaLWxLJJ1JpYNonWbq0/1yIiIiIiIiIiohbCQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojYQuFIJVxJhY9mNBo114SPTehp9nWJ8ZTSmpnHbHz8kxrf96a3GcmGkA9n5rury73/wLnUbf/Dl14rxH7zqfWoaz/no28X49jf9jbFsh26DW3ywuvyM//UWdRt/9bt/L8bf/MXXi/FXveg+dRufXf+s6uuOoUHse0HZiL/35z6vpvFnn3i1GH/eK+Rz+m8P3KJuo+OeI9XX7lAElYNHjHjleXoagfun1Pe0pI64uRyNGOtKfUk1icB8Vox7QUdNw07ExXhgeslYdhayxrpieljdRiUoj2O775hR0yi8r1eMF39/1nx/VxbZOX8/E3+s11NW0RPjpTF5H6Zv0rcRnXarr7PrE1jMmvXr6efJ+wAAm/6xIMbDM3I8N5xQt3Hmp/xmqXcshiOTZn4c+W5ETSM0l1ff04qsQslcLpXNdSW53AGA58nn0YrreaWSksumfeS0uc2VLLyFRX95dEDfRjwkxq1QE3VIXM4LuRH/cxTGksgu9hjx4EKp/l8axN94SoznPyjXQ2efpn+OgYf8z1HoCiE3YH6uqNtT/y8NynG5O2dVlHyhF3+4MX+/3EjIWAaaq/efrMpJ87NWYiFjnZPT81J5ICXGAxcWxTgAeBG53BT7zLJbTkWMdUdepnf7U3vldnPuprIYB4DJu+X+uxuwqq9LnSEUe8zjGzm9VP8vjWmEg2K8kIyKcass/z8AZNf5xy63Lonl5W4jPvvaFTWN5Bc6xPj5p8r/373bkt8A4MCr/XYyNRLDgWvMdnP8a3LbDABOVj+vrcZbJQ94wYC/XmkPAcC+casYz/XrbWZkxzExXk6ZaVQSEWPd4p/o+Sj80W4xHjmXU9MoDMifpeePzO9C0WQn4osXqsvn33eTuo3zr5L3o/ceeR/iJ/R+TiVl9iXdRNRY51yYV9MoDg+K8VOvLIrx0bvkuhgAAotmHeRFw/Di/jo3pNfHjrP2dpV38hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQH9Ae1rZJVcc7nsGevKgyk1jexQVIznuvUxqgf+6jYxHimUjGWr5MEu+Pv5v//iteo2vK1y/Ol3/4GaRuc5T4w/80/fYizfsmkE2w/cXl0Or8j/DwD/8wN3iPGEK6fxxc8/V91GR95/HXNtdBx2jPif73+1mkZ0Sd6Pb3/1qWJ8/UNFdRvOtenqa3t8HZxZM7+6Zf14WukJ9T2tyItHzOVoyFgXmM/qaQQdMV7uDKtpWJ58jMuJkLFc6k2gOOzXG55tqdsopuQqbv47w2oasWF5PytfGTCWCxu7kDvoH8NYUD+e9lJejDtZOU8nj4bEOABYNR8jkHURWjLzfN+DTTQHlnzMC93yflhNlKv+bf5+pbIe+veZ+xmakY8VABQGYup7WpGXjJvL8aixzppf0hOJy+2mG9XziheQ21Z3w5D5/sEeuBuWq8tW2a3/lwaViFyHNFG8UeyOiHEn7++HXfSMZQCwm9jPxU+MytsIyXk6dlb/IIkD89XX0WTKWAaASlL+nAAQns6p75EEVsrqe6wLs/7r+W5jGQDsgF6HVIa6H//OtYL6ImGb66y8fvy8qUfFeOUp16lpODNyHeA5Zn7zbMtYN/JddRNYGpXzbOdUUE9jTM4LVk1/s9DlIDtgplmJ6t8RomfktjW0ILeb2ncMALBr2izL/fFfrdi/JNU0ohfk/Rj7ZhOVnSJ+1n/df10FY1NmfvSUthsA7GW9bW05zirtiGNX13shvU6y9h4W49H5Xn0/OuJiOHhuwVgOzC0b69wvDNX/S4PAipyPlibkfQCA1O5ZMX74rk3m+zeO4PBBP+90FvR6LvpfHWI8Mivns/q+/2pCs/XtnWd8p1i6dURNI35oUYx33tclxu1SSYwDAM7XHe+5bmOd3d2pJuGF9fr2YngnDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRGwhcqYTdqJm0G3aMdeVEUE2j/JvTYrzzfSk1jVO/VRTjXV+JG8uFlINcn79vD/zF36rbuO1/vUmMf+idehq/9kU5jac/+xFjeX1HCfml/dXlH917rbqNX3rx/WL8vr98hhhPHS6p26iVdEro2Wce//O3hNT/C8/L8Zf/8vfF+H9NPV3dxuF3+/vR3RvA4ZeY+zX+fwpqGm5C/yytqNwRNpYrsaCxrtDboaZx8vmWGB//l7Kaxplny+V3+Ntm+Q8sFhCczVaXD7y2R91G/zZPjJdvWVLTCH4xJsY33rHfWB7rqGDlGQeqy/O/2a9uo9Ilb6OYCovx4y/Ux+tH7/WPhRu0UAmb57D7fxxX03D/pE+Mxw8viHEvoO9n5vf8YzHSX8GpTWZeGvr3hJpG5yNz6ntakeeY58Sz69ZF5Xzw43+S83wlrtdb554aF+P19XopGUKxJ1pdPvJLcv0AACPflPPC4muyYhwAEndHxPj1b5vyt5eoILucMeJH3rpZ3Ub0gtzuBbJyXXf6+fI+AkBo0a/LFjcnMWOZdVv+5UqjCGDoL+TunBuUj7ezrLd5+/9gsvq6c2QY+3/KzGuTX1xW0/CCjvqeVmTnzPNsFyrGOst11TTcZ918yfuReZfcnmz8nLmfVsmFXfT3zfntc+o2Al8cEuPeL8yoaVT+VW6fF5+f81/3FTG7KWfEN9ypbkKVG5TL3smfletKAAif98vV7DoH52JmOfvpF21T08j83lYxbrly2Qwf0M+Zm/L7baHBHCKnzLLoxvTvXG7sSdindVY5drZdXe9ML6pJVK6dlONNlG3nnF5HG0ploOi3LYnT+vcrT+k+aW0VAKxMyv3uZ/zGdmN5Y2IFkZp2c/v79Dos/KLzYtzaKffhvJDeRsxvTRrLy+tjmM/565KvP6mmkf3rYTEeeblc7gJ/nhTjAIDuTnM5GTfWVTqj0JQv4bsm7+QhIiIiIiIiImoDHOQhIiIiIiIiImoDHOQhIiIiIiIiImoDHOQhIiIiIiIiImoDHOQhIiIiIiIiImoDHOQhIiIiIiIiImoDHOQhIiIiIiIiImoDgSuVcDFlPte9nAgY6+yyp6YRf0+nGA9Or6hp9N4jp5E4tmwuJ3LoPOCvu+HDb1a3MbZ9UYzf8Wk9jc4z8vHYu/9aYzmyaQR7DwSry+syOXUb9+5/phhPns6LcWe5qG6jEvf3yS5U4GTLRjy4FKr/lwadh+XPcv8f3i7Gc4OOuo0N73Grr4du9rBhh2vE7QU9b3nBpPqeVuSGzePjBR1jXXiupKax+dNyXnDml8U4AKTC/WJ8eVPKWM6NJrC86K/r2qtuApWgHE98I6GmEb1QEOPn/mTCWB64fhjn9lSqy4UtejXbcWhJeUdYjPb/yNK3sfts9XUsHEfH7nNGvPyuHjWN/KBcfp0O+YAHcmUxDgAbP+6/Z+TGCjbuMv/HLun1kBvT65lWVOyNG8vlzqixzi5FL3kbgUW5ngeA/u3y9Z/6tiA4X0Togl9vD323Q91GcFnOC7Evym03AEQvyHXVo39+ffV1+JpRPLrXzBexrF5P2SVXfoMnt93JR/X2qPOA397EYwV0Hsga8djH9Hqq0K33qSQlpewCwPi/+nln6IYixnebeclqYhecaf2Yt6JCX8RYLqVCxrrYil4vWa58gOyCXj9O3C2Xzfo07FLFXPc3feo2Eq68H84H9fIdWJLPs13y83Ricxip/TEjXuiu1P/LKmnIZdMuyce790G9bXaDfhrRqIf4KTPNBz98i5pGPCEfz+jhWTHuReX2HwDsOf97iL2UNZYBwPL0c2YtZ9X3tJpSd6xhXTkZ9tf3xRvi9ezv7RDjTlLv63sDvfIb5s0+nlcqwyv4dUZoVm+X3aDcllRien6OnJO/X+18z03miq2j2LnP73dEFvU6Kv9Z+Vh4QbmurIT1+08ic2b9EFpyjXULnxpV0wg5cv0R+qDcJ3ZW9O+JbtLss3mxsLHO2rVfTSO0dVJ9z8XwTh4iIiIiIiIiojaw5jt50un0DgALP1k8kslkfv3y7BIRXQqWTaLWxLJJ1JpYNolaE8sm0dqsaZAnnU5HACCTyTz3su4NEV0Slk2i1sSySdSaWDaJWhPLJtHarfVOnhsBxNLp9Dd/ksa7MpnMDy/fbhHRGrFsErUmlk2i1sSySdSaWDaJ1mitgzxZAO8D8AkAmwB8PZ1OpzOZTHVGpsnxPiRi/mRhk+PmZG9WRZ+hT5uc2VnQJ6rKjckTFka7zUmoJjYNGMsDG/RJu/pvTsnx9XoakYRyPOrCm0bN45mIyJPDAkB2SJ68LdahTKbbxKSplag/MdjEZOPEuguTkYZ19VKefF7LyuRiuZ4mJrp0uqqv6885ANiL+kTWbmfjhG+P2bPzuPr/V4heNif6EY/XlM0J8zxZFWWiUQDOipwX7GX9+OXXy+XGDZiTCW/cYOb5YkKfUsxW6plKSJ+wOBGXJ3f1LDON+uNZ6tDzY7RTnvCw2C2Xm2JSPxbJmkl7x7cMNcQr3Xo9VeiWJzS2i0qdXdAn07Rq6v2JjY11iF3W82dDhVljz44ncdls6rPLnGwTE+grE1fXtwX1dejKOn2C6OCSnBeKnXq5ifTKaVQifrmY3NA44Wy4T6+n4GhlS87zI5N6uUqV/DZvtTxfTjTRVVMmgL4cnLyf/1Zr351iE+U7e/G+ytTDR9e0X5eBXjY39CERv3ifNtyjT1pbSSj1Z0k/fmW1bJrt1cRGs2yWUk1MSq9UM808OMXJyu3m0ga/jtg0tkrZHNDrOrU+dOT2Pdel1zFezVs2jzbup60/pwKRPvm8hruVSeabKNtWzSS+41uHG5NIXLy/Wk0jd/G+99SDh9X/v0LEsjkx2Y94wvxuY9RNtt7Hs135O4cd1+twdCnnsG5S64lrRoxlb7ALGteR86sb0fOzVi7zA2Zfc+N6M88HVvQ6qqj0eSMD8veHSujxTxdcXx830ycO5JSJ8JWJ20PD+hhEff+h4ftmh54/sWFEf89FrHWQZz+Ag5lMxgOwP51OzwAYAnDisTccOnKh4Z92PXKy+rqZRsIuyhV4cFpvVJeWtadrNc6OXfsl4ESvPqv62A756VrHB/U0YsrTtVbrR24/cKr6uquJp2stLskd7+SRy/t0LQDYs+uEsTwNvaHp26V84U3KTwFZGdSzdc9u85zt2XHMWHZm9SeAVJrIG08AvWwePt/wT7v3+OepmS/RzuKlP11rZVl+upYbbKz8du3165B8Sm/QtHqmHNEr2NQheQDVW6UTUXs8C12X/nSt/KDcyWhmYLPnoTPG8tRDZqetPKA/zSg3KA82OXn5eDfzdC2r7okpe3aZgzLq046Aq/KFdw0uvWw289kVzTxdq5xUzvMqbUFtHbqY1Z/kEp6T80KuVy838TNyh7UcN8tFbf0BALETej3lKU800fLa2YjeTvTvMvsh9Xm+1NnEF/OrkOcDWbODv2e32b47eb18N3MB5Qmgl82jcp82dlTuBwJAOSX3wZp5ulYpKV+sC6xWNmsuOtU/JWxV2tiJ0jcHgMCS3EeYK5oXYHfsP2UsR6cv/elaXkBu31f6m3m6lrm8/aC5n45+fRXxc5f2dK2mBnmyZr0+9aNDZhKpJ+3TtcSyefhQY5sJ1Hz3UAb6AMD+/j4x3szTtfA4n64FAFM/PFh97a5vvNhc73I8XUsrlyvrGr+j7dzn13OhRb1c5pU+b+y8vA/lJgarVnuS4+4pfz+b6ROHluX6wynI5S5ytpmnMDfuh3ETwG796VrWrN4uXMxan671OgDvB4B0Oj0MIAngjPgfRHQ1sGwStSaWTaLWxLJJ1JpYNonWaK138nwSwGfS6fT9+PE9Jq+rva0VAGIPHDT+IQzLWGdpt7YBKClXlq2zjVdW6iWVEfBKh3lVxHNseAF/7Gv4fv2qk6VcvXrTr35NTeOjn3upGJ96y0fMFcGnAqWHqotP+59vULfxG3/4L2L8H/5I3odQUB8TXB71rzhmh8INdw99653vVdP41V99kxiPnpavvobn5Dt9ACBzh39nROdgBJmnmHdKbHm7/pMOe25efc8TQC2bXt2FDc8y15XierVw8s1ynu+5Z1BNY/oGOT91PWqW3XLEQile8z+vmFa3EX+/fAXm6Ov0K5LJE/IVgcCyeTeBXawYV7STu5SrdAC8mHx11nLleuzcT+mfIzrj3zmVX5dCdvHx/xQocl6+bHnwlfJV4s2f1a9IHPlF/wpv/1gYx0bMK0sb//aImgaiTVytvvrUshmsu8oWyJaMdfaCfqXVC8n1nz2j11vBklxuiv3mVfhKIohyp5+Hz/yMfp4nPi/n6dRe+e42APDCctkMzfjHK9ybRey4maY9q29j5brGnzbWcsPyleLgz+n1lPNdv16zc0U4S2Y5c5XPCQCBBblsHvsFuT818Q+n1W0cuMM/Fv2jIRwbNcvZ+LuamCqjsyXvgNX7tHV36oR7ssa6+p/trubMM+U7mfu36beFaFfi668cewHbWBea139fFDw9J8bzE8pdCwCKXXKb1vta/86/zs4Aep9u3k1d+HO53AHA8Z+X73CLnpPPye63fUSMA2bf2ikCgbqvBI7ykw4AcPLKz0pTcr4InNC/63iu3357lQq8cl0dXNDvxPfCTdwxePWJZTO4u/FnZIFEoLre6tF/BuVtnhTj1or+PfD0z8l93sH7zbrS60vBG/F/YpS5Q/+J86bPyOdw/q16exb9rHw8omfNOig0UDTW1X4/vpizz5LLxLp/19szTb7up5bFDttY99Nv/281jW+/75li/MIvy/Xxhs/o5+zwL5v72TkURuYW//+2fkD/KZbVxC8jLmZNgzyZTKYI4JVr3ioRXREsm0StiWWTqDWxbBK1JpZNorVb68+1iIiIiIiIiIiohXCQh4iIiIiIiIioDXCQh4iIiIiIiIioDXCQh4iIiIiIiIioDXCQh4iIiIiIiIioDXCQh4iIiIiIiIhx7VlaAAAgAElEQVSoDXCQh4iIiIiIiIioDQSuWMo9KXM5mTDWeZalJuHsOiC/YcOomoYbkT+iG3TMZcc21gWnl9VtQPksf3v3S9UkYhc8Mb75s28wlp86OoKHTj6lutxXkP8fAO789MvE+MBsXowHZ7PqNsLJmmOXdRFerBjxX/iDt6lpdC4vivHsWIcYD66U1W1svKtUfT1yQxkbd5eMuLd5nZoGChX9PS0oNGOe5+BC0VjnnJ1R0xjAmBgPz+bUNPJdcTHu1Q9B23XrvtCrbgNWUQyP3hNUkwisyGnkBiPGcqE7ZKxzUiF1G9CKr1Jd9v3Ikd8AILjol9/AShnBRfNzFXrCahq5Pvl4jdyrfBDXVbcxcp+/X73XlzGyx9zPymCPmoZ96IT6nlbknJ0zlu3ZLmOdl9PLlTc2KMbLY31qGpYrn0cna9aXdr5srFv3lai6jeDsihgvd+r5MTgtt0lW0d8nq1CGlTPzUmmsibxUkY9F5JTcblp/lxLjAOAFa46F48Cr65dkB/Q6pCMvt3sDD5XEeG6jXp9O3rNQfT18SwqT2xeMuHfrtWoa1oUF9T2tqP6ceAHbWFfu0PPr8H1LYjw3pJcbeHJesEtmHetZFjzbb0DcoH5tNxCS6/mg0lcEADcq970XPur3sVa29GPhUTN/Rl253QWAgQfl9qQYlz/rtXe+Ud1GQmmcLaV+aEYlJh/vQLiJPkTtexIxIJU0wlZBP55uQs/DLWdkoHFdb5e/fl4ucwCAgNx/8lLydw4AGLx/XoyXE+Y5rEQDxrqNn9e/t7hheT+jn+1S00g+MivGl9NmGqXOIAq9/n46eT2/9z6s1TFyufUcfXwgcbpgLEd7S8a6r9zzLDWNniX5mK+7S67DPEc/FhNfNL8nDl/vYmKPv662j3IxhXG9z3YxvJOHiIiIiIiIiKgNcJCHiIiIiIiIiKgNcJCHiIiIiIiIiKgNcJCHiIiIiIiIiKgNcJCHiIiIiIiIiKgNcJCHiIiIiIiIiKgNcJCHiIiIiIiIiKgNyA+BvwTeyTPm8vqksa5yc1pNw4mF5Tfk9efLe3+1IMYXPjdmLK8Mh7CwHKku3/aBveo2HvjELWL8na/6gprG+//uFWL8pT/7Q2N5Ir4Vg9fsqy7/aNtT1W307paPV+jUvBg/+opBdRvrvrlUfR3pKyB2Kme+wfPUNBa2JMV46uFzYnzmGfp+XnhBofq6p9/CodssI77lPXrewox8vFpVudMsV5VE0Fi3tGmDmsbZ2+V4z+64msbCRjk+cl/FWA4tuIjM+OuCS/o5Cp2aE+Phz2bVNI5/Tt7Rm+/YbSyvTxRRWN5fXd7x8RvUbcw+uyDGe78j14Wve8e/qtv420//YvX18HgEZxLmORr8+RNqGktfGhXjlZfMiPHAnSl1G8de4l976B9ycHTSbKbSH6/U/0ujwT79Pa2oXDaXKxVjnZXQy9X+1yfE+OQXimoazrL8Hjtr7qdVKMPO+uXx3FM71G2MLYTE+Pm35sQ4AIy82xLjXijovw46xjIAuCFH3caR/0+Ob/qUnIZTcNVtVBL+sahEA8YyAORfJddjAJD/ercYn7upLMZH/1M/Fid/pbP6uns0jkObO434xD2Lahqw5HPWqqxlMz9a2YKxzm4iL538ablcdO/T67Zzt8ltwch3loxlu1SBXfDPffC8Xq5Kg/J+2jk5LwFAvk/ez543Ha2+TiYd9CweNeJnPjOubiM2LR+vmLKfTkk/Z8EV13gdXjS3efoVej9k7B/kr1qBFeV42vr1+OMv66++7tvQheOjZt9m9F69bDrTTZTfVjO9Sj98YdlfH5bbGQDwlO+alYTyXRRA8KTc9wmdM/NJsDeO0KGz1eXipP69xYL8/Wnd7+0X4wBw4BNbxPgtb9hpLK9PFFFczlSXp96r92ezg3IdH8jJ5S61/by6jdyE2d5VQjbKET/dv339x9Q0/uwtvyHG7ZLcdlfCerk8+TNmn6N/JIBjo36e3PzpqJpGaOch9T0Xwzt5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaQOBKJVx+yhZjubJlDOWCVV0OHb2gJxKUd89NxtQklj/WI8bjS2VjOdJXQfycv27qz25Qt9EznxPj7/v4K9Q0uh8tifHvffRpxvLSphFsP5CsLvceXVK34QXkMb3cpHysgivqJjBzXaL6emEiiplywog7JU9No+NYQYwv3NwvxsMLFXUbmz/kb2Ps5hLmd5jb9EJ60bBSSfU9ragScczlkG2si53Oq2kM/CgixiNzcn4GALskH+Ng1iybTqFirAvOyeUOABZvHhTj5Y9ZYhwAEvNlMf7g5280lisTI9h2OFpd7jukH8/4OflYlOJyufmHd79E3YY9UvO6DNh1p2ju7lE1jf6dcj2zNKPUIYv6OZv4kp8Xh28oY2J33Y7aTVybUNqOVlUZ7TOW3b6Usc6e1yvhoe/JedrOyfkZAJzzC2Lci5nl3ypXYJX8dPt26nWwk5PriN4P6+17YUD+rKG5mno9FIAXDRpxN6CX/y0fko95sVffT00p4efXStQxlgHA+4ZcrgBg6N5zYjx2vleMR8/q9dTw90PV173XVjD8SF1eaqJoup1x/U0tyIuEzeVw0FhXiQfr/6VB4qRcj0fP6ecgdsoV485K0Vi282Vzna3nec9S6pC83r4Hl0Ji/MQXJqqv+yaHceKQeWyS03o9ZSv9Sc8Rw8j16BnWqzkU5aiFYsL8n9i2KHTy8XKD8n4U1nerW+jf4Z/jrlIJ/VNmPih3yOcDAKz8pddlV1t543DDuspIL8obswCAwHxWTyMp92dDB06rabi9XWLcXq7bj2AQCPvnxMnr+b2YCovxA5/cIsYBIDYtt827P2j2Z+30CHZn/OPTcVLvwwHy8YydLYrx/Lie3wN583M4RddY9ydvv0NNI352WX6D1tdsokyN3mee1/5rKxitaTfdsN5uOImE+p6L4Z08RERERERERERtgIM8RERERERERERtgIM8RERERERERERtgIM8RERERERERERtgIM8RERERERERERtgIM8RERERERERERtgIM8RERERERERERtIHClErbv32kuWwXY9++rLrs3XaOmkRuOi/HlYX33Z26piPF1X7eMZc+x4Ab9dT1vP6Ju49ydE2I8dbisplGJyuNtPbuXjeWknTPWLU0k1G2ceXFRjA9/Td6HQM5Tt9Fxwt9GLFU2lgHgrR+9W03j/b/3ajGezCyI8aXNneo2Au+frb52OrsQWJg14it/OaqmEf3BSfU9LcnzGpdr1hV6QmoS559iifHUo0E1jcJL5PPY+R6zfNslF3ahpjwXS+o2OnafF+Ob7zmhpvG9T90mxm/8lSljebKjiPJt+6vLh05vVbcRvSB/FicvH+9Sh14XWjWHznLNZQBwf8EsA6s5G+sR40u35cT42Of1vHXyef5n6RsJ4uhY2IhPflmvTwNzS+p7WpFVqjspFddYV+lpop5/tlxPj5Rjahqn3iifp3X3mG1FYagD2Ymu6nJ4Ti+b5Q55G895/wNqGl9/77PFuPf2C9XXxVQWhflFI774pWF1G90lV4wHVuTP6jn6tbRSh1lfWnWn8GW/eZ+axtfn5WPR8bpTYnz570fUbSyM+59lfkMAFxxzv0fm9Xo/eOyC+p5WVO6KGsuVRNhYF5yV6z4AmH9BWIxblagYB4DEKbkfV06Y5arUFUVhwK83Asvy/wNA6MyiGD/28n41jcisXA/97Ot/UH09Ht+K3pV9Rvw/7nqGuo2BbXkxXkzK7eK7fucudRt/8dFXVV8Xumzk+s3y3J3R6zqnIH8POfRKR4wP3SvHAWB2q79fw+uCON1p5oMNX5X7WwBgL62o72k1lWjjOXbDTnW9s6Qfu2MviojxwR+uV9OInpHLfyXZZSyXeztQGvbXHXiNXDcAwDX/57S8Dwf1tmbpxkExHv61s8ZyKBVF+On+unP/obeZXS+S99N9j9yP9Gy5vwsAlaD5Wd2AbayLH9fz8sLmDjE+fZO8H5P/JNeTABA+YeaLcDKJ2P4Zf4WtnzOvpNcxF8M7eYiIiIiIiIiI2kBTd/Kk0+mnAXhPJpN5bjqd3gjgMwA8AFMA3pTJZORLXUR0RbBsErUmlk2i1sSySdSaWDaJLh/1Tp50Ov0OAJ8A8Nj9bB8A8MeZTOZZACwAv3jldo+ILoZlk6g1sWwStSaWTaLWxLJJdHk183OtQwBeXrN8K4Dv/eT11wH89OXeKSJqCssmUWti2SRqTSybRK2JZZPoMlJ/rpXJZL6cTqc31KyyMpnMYzOqLQFYdZbbiRvWI94ZM5ZrWZv0iayKPfJkWLlefUKthSH5zr6BuvmfJzf0GcvJpL6Noa3yRFR2WZ+w2LPkCZ4iveYEcxMbB4zl3KB8rABgrF+esLR3q5wdCkl9Mqx4zN/G5ETj5Hw9kVvUNG68Vp70ONwrT6iVG9MnJ010+hNEjscbz1/uur6GdQ37gYuf16kf7L9o7HJZa9mcHO9DPB6uWTbPkxvSx35HRuT3dMjzDAIAij0pMd57k1n26vO8vaBPdNkwyXSdsbg8uTsALE3KE5JOdJgTWa6Lmfm3Y6s+iXd4SC6briOXvXJMr6dyff452zzc2xC3uuVJ6ADAm0iK8WxfQYz3X9tEfTriv2drX2M5HLlJnmwTAJyxrovGph48rP7/pVpzu7lpALGOiLFcywvpx69rSJ7AsVefBxwj/XIBHrjWzI+T4+Z5coqXfkf9+pg+qeHNablshlL+RLaTiaGGeHajPAEkAHSE5IlqbWVi5mYmkSzH/La3/lgCwPqY/qAK7VhEO+V8kd/SWCfUWxny65D0YOP7exf0shkYuHg909Jlc7LfaDcnJs1208nqExr3KH2TDlfvK0Z75Laivi9Z3w9zcvoknvaKXI/3bbh4/fqYULf8WcbjfkU0FGn8PnDruD4ReHdRPualuFxf9kVuVrdx64S/H5uHGvN8R1B/EICTl+uInkHlwStb9D7Z4jr/PVsGGvdz6Ga5vwXIEy+3atmcnDDL5WPrHhOY1/uJ3aNy36f7Gr09Cw/IZaZ+Av6JjWa57BrSJ63fcJvyPa+JSXyzG7vlJFJmn7i+3Vya0NuJjpTc1tjXyw/HcQN6m2lVzPqlvp4LNjHB/PKoPNH9wqi8HyM36/WglTPzxcSWun5IE/0DLGf191zEWp6uVZvbOwDMr/amw7uPNazb831/9nx7Sf9gl+XpWsq3zXV7G9ft2us/NalnsYmna+2TKwCn1MQgj3KiYycbT/Kencerr5c36F9Wz0wqT9faJ1cyuR69Auk8ZG5j9x7z6UXPz29X09j1iNyhjR2XO/9Ly/rTtVILx43lqYVDxvLKlFxhA0D0KgzkPE5Nlc1DRxqfbrJ7ys/zlYh+nk93y52n1AE1CRTS8tMehnc2lu/aPO/MLDfEGyiDPMUV/ela2w/JAxulpcZ8kKlZd2if/sVce7qW1ug183StpVFzP7YdMp+2Y0/MQOMeVp6u1as8XesRfT9P9pvveeiUuZ+zO/WnJgTOzKnvucqaazcPnGtYN7Xdb0vdiH78Dlwnd1xG9olhAMCpTfIXl3WPNNYRux7x65BArolRXqUL0JldpXGusyMjdzijt58xlnfPm19UFg/qgxLdj8rvsZUn56zl6Vq19TEAdF+GY9HxTPnpWiuP6m3eQsH8LA8fNdMc2aV3Rlvw6VrNlc1DjU9q3LPbbz8Ci3peOjwpf9Huyuh9Re3pWqv1JWv7Yc08XctRLqAcW6efZ+3pWt11T9M6Ure87Yj+BWpgz6U9XetZ+R3qNrYdvqZu2czzXfv1QbNAVnm61vXK07UebeLpWlZd2Txu7ueGHfrTtZxZfWD9KlPL5qHDqz9B9bE8Hzqv9xcObZHz2uBevT3Tnq7lBRrbgT27/P7sgRv0p2stPyg/tQpNtDVLRbns2s9rPJ675v3vwXOH9Xaia4u8n84euR/pBvXPYZUbv3fX1nOh+Sbas2V50H06IndSZnfo5cVebswXUw/XjCs0MTCH+bWXy7U8XWtHOp1+7k9evxDA99e8dSK6nFg2iVoTyyZRa2LZJGpNLJtEl2Atd/K8DcDfp9PpEIB9AL602pvc55i3Qbo3jMG1/VvN7O0H1Q3ZvZvFeHRGv4Vu/b/LVxNih82B4UhnJ+IH/avAs+/ZoG4jtfes/IaiPtJf6ZdHki/cZt5NsLAximnLH4UMLutXgNbfLV8NKMsXgBFo4o6xfK9/RbLY6RjLAPCBt7xKTSN6Vh55X9os36kTmdaPt/vL/tUs9/YS3B+aV7eiPfqdAOVrx9X3XGVNlc3oEfOzhXtSxrriiH4nVO9OeXxYu9oIAMcOyNtZ2Gjm6ZWRMBaW/Z+Ahpr4iWJ4Ts4LP/zrATEOAB3L8l0NB+40r/TF0yM4kPGr1nJCv2sxdkauy/ID8mfN9uvj9R2n/KtR0aRrLANA6Qv6ldPIrHw842dCYjw8q986vfEL/tXZ0ZtymNtp3rHlXNCvSKKJnz1cZU2VTatgHl+rWDbWOTm9XE18WW7WA01c4er/lnz7eqEuqxQ7bBS6/PYlPNPET+oebbzbt9Z3/vin1DRSJ5fE+Pkv+z/FXdnYg4W6O3eis3ofInhevmPQTchl8+ztMTEOAKlDfh3jORbcoFlnfPHzz1XTGD4qN9C5D8s/K089qt/JF73g90O6cyUM7jHzkrNTv4XTS+h3HV9lzfVp664uewHbWFeJyXUfAAz9t9yWWE3UW9pd35H9Zn80lIoZ6yqDej3vJuS7CqLTTdxxdFL+rN9/z+3V10tbRrDzUbM/MHhKbyu0n56VI3Kf90N/+CvqNgZO+/3RrlweA7vM/ml2WO+HBBflYzH5eeVOEU+/kyRxzH/PwI15rKvbT6vUxN2VrUctm6vdmebkSv76Jm5lGPuW3K7W18er0X5N4RTMtqaYCiLf55ezgfub+KnVtY0/OTa3oZ9jbfqQ3D+a2yhs7kFuv99uJpf1bVgfkKe7CJ2T2+3ZW/Q6yiman6P++2b0oH7HaEK5Mzo6I8ct5ZcCAFDpMe8Wcjujxrp8n15/xA+t5X6cH2tqkCeTyRwFcPtPXu8H8Jw1b5GILhuWTaLWxLJJ1JpYNolaE8sm0eWz9uEhIiIiIiIiIiJqGRzkISIiIiIiIiJqAxzkISIiIiIiIiJqAxzkISIiIiIiIiJqAxzkISIiIiIiIiJqAxzkISIiIiIiIiJqAxzkISIiIiIiIiJqA4ErlXBw70lzQ8mIsa5w22Y1DecPz4vxyl8PqWmU4o78Btczlz3PWBfffkLdhpeIifEzLx5R0+iZyovxL73rvcZyJHw78oUfVpdf+tF3qNsodMljeqmDJTGe69XHBJNH/M8R6SkiftL8XEvrI2oaoXk5W55+SVmMb76zoG6jMjFcfe0O96IykTXigVMzahroievvaUULS+byStZYV5noVpO48FQ5HplTyh2AyoB8nuxSyFi2yoBdk0VPvNRVt7HpE/J7Uo8siXEAyK6Xz/PAbx0xlruTNgYW/XUn7ppQt2HV10N1YueLYvwHf/0pdRtP+59vqL4uRy0U45d/jP/Mc+T4hq8G1TSi+85UX9tjKTin68piOARVXq8DWpF37JS5PJow1tldKTWNwHJUjDvn59Q05l6eFOPde1fJrzWrDr5F71pM/N2kGI8d18umZ8t5eP52v9ws95cx32uWo8B9el5a/qDc3pQ+I9cPu97xEXUb13z0jdXX8+sdXAiZxy9waxPn7IKcN5ZfuCzGI3+jt2ehab+dDCzkjWUAwKb1aho4dkZ/TwuKHDL7o6GehLGuuK5XTeP4i+T8uvFuuR8IAHaxIsbdVIe53BEz11mWug03LJffxResqGkkPyHX9Wdf6H+O2f4yzo6bZbPne3pfsXtKLpvhGfl4Lo0l1G04xXD1dakziEJv2IgnDsvl6seJyMfcmV4U467yHQMA7CX/nARGUwienjXiXkhve92uDvU9rSZQ9zkBwBlNVddXBvQ2M3LgnPwGR+8rnf21UTHeecBcLkctFBN+uueeI5drANjyIblNLKf0MhM5J/clw78/bywnOoNIPd3/HnzmP9ap2xj4kdz/cmNyXrzujXvUbRz+s63GcnDZRXjeP4bNlBmrIve7j/+sXA92T3Wq25h+ivkdpGsojAPX+X20zZ/R69JLwTt5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjaAAd5iIiIiIiIiIjagPwQ+EtgOeb4kWVbxrrI4Wk1jfmPDYvx2HxBTSO4LI9jlbvjxnKlI2qsy2/tVrfhOpYYH/z+nJqGZ8v7+cLPvMNYfsq6ETx8/NnV5e6jrroNp+iJcTcof47gsvz/ADB7bbT6emlDGLPlqBG3S2oSgLwb6P9WUP73clbdRKkzUn1diQZRToSMuFMuq2kEzi+q72lJqaS5nIgZ66JH9Py66a6oGHdDjppG371yGqFF8xwEV1yEFivV5cFv69VXJark+bC+n+E5OdNe+PAGY3l+6xAu7PP3M2zr5aaUDIlxy5XTuO1db1C3kRvwC1a+y0J2wCxozZTv+Bm5nhn7T7nwRk4vqdsoj/RUX1d6k8YyAAROz6ppeJ7+WVqRFTPLhBUOmevCcj4BACsn51cvFhHjABA/JccrIfM8uwHLWDf5Eb09CszJ9XSpJy7GAcApVMT4ui/47erAtRbWPWK2s06hqG4j9+kBMV4Jy3n+WW/+LXUbnWH/eMUDLjoPmsfPO9KpphG9IJ/34Jfl42l5eXUbVs4/XlahbCwDgFXS201Ewvp7WlBp2OwLlnuSxrrAXE5NY+Pn5XJhNVFv2UvKeapLwypVYBX9vFHuktvdZvR/UU+jnJA/y/hn/bI7dL2F8T1mOSp063WI5yjXqZW+ZMdJPb+G5vw8HlwoITxtfu+oKG03AFhl+VhYiZgYt1f0vFUY76u+Lg2ljGUACKzonW97fkV9T8uprNIGuG51vVXRy5QXleukSrfeFnUekPNreMGMB1c8Y93wt/R7Lryo/N1Haw8BoNgtt//ZT40ayyvpPixk/DKQWtbLjBdUvncr3w/2/9W16jZKPWbhLiZt5Hr8dO2Sfs5C03IfZPyrcgUSmNfbzM5DZl05fEMJk7v94+mc1b9vISSfdwnv5CEiIiIiIiIiagMc5CEiIiIiIiIiagMc5CEiIiIiIiIiagMc5CEiIiIiIiIiagMc5CEiIiIiIiIiagMc5CEiIiIiIiIiagMc5CEiIiIiIiIiagOBK5Ww53nmcv26SEhN4/3v+YgY/6M7flNNww3Iz7m36vczaMEL+WNf6T98RN3G9k/eIMbLkU41DcuV4wMPl43l7mwFA3v9deWI/DkB4K1/ebcY////6lVi3G0it0Rn/A8SWvIQmTU/WOfOC2oa088cEOPX//YeMf7wPfL5AIDcgH/eB0cDODFk5sdNR2NqGljO6u9pRTNz5vJCj7mubOa11dhujxgvj6TUNGZ/PifG+78SNtOM2Sgl/LLZ/cBpdRvlQXk/LryroKYRvrtLjIeWzTzuFDwE8n7+ilyQPycALL57RYzbn+oV41r9AQCx8zX7lPKMZQDIvXxeTWPly/LxHLnjoBhf/sMRdRvHf9Yve33rYjg20mHEJz8xV/8vbcNKmp/ViseMdZXuhJpGoScib8MTwwCAlVE5njxsLns24Dn+cm5A3gcAiOfkeqb0p3p+nPnnITHet9MvV8GlEsKzZnkv9ITr/6XBmefKhWvgfvlaWcfvnFC3MfPp9dXXnmPBDZrteTNl07krKcanf1Guh+wv6efs/Euj1dddoykcvKHfiI9+p6SmEdl+RH1PKwosmMfPWSkY6/Kj8vEHgBPPD4rx9f+pt0der1wHeHV93nJPDKUhf9+Ov7GibqP7q3L/J/Ybettb+KhcNgPLxeprJ1cylgEg1y8fK+DHZUVSiThKXL/OXY77Hd9K1DGWAeDs0/Q6pGu/fMzjyv8vbtW/Q5y73T8WfSMBHF1v9mk33l2s/5dVNrSsv6fVeKs0aJ5XXa/lEQA4/D/6xfj4V5fUNM49S24nRr9p7ocbsOCG/HWJY/r3iZVRuVxG3qyXy+XPyY37a97xH8bycPR6XJPzv3N9/LMvVreReN6MGPf+sU+MO3oz0vCdtxKyjHXHXqTXHxu+FhXj2vhBZVjvjx15uVnHdA8GcPB6f9/Sn+xW03BO6t+bL4Z38hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtQEO8hARERERERERtYHAlUrYHe0zlr2+lLHOzpfVNN7+tjeK8fj8spqGVfHEeDkRMle4HlDzP4f+aIu6jViHK8YD2Yqaxsx1ITEePGhuw7N//FeNr8j7AAD/+29eLcatoHysFtJyHACsvf5OFRMW8l3mOGL++QNqGh0n5Lyx6++vF+Pdp0rqNoon/f1KZT307TOP39IN/WoaHdtOq+9pSd0pcznZYaxzk1E1iZWxuBh3inp+7PpmWIyHF8zzGFyuILzgl6Xl6wfVbeS7HDFu/bv8OQBgboslxqPnzDy+0u9gccmvWith/XjiczExnO2Xx+OjM/rxDi777wktuYjMmvVS+DMdahpWWa7Lzn14UownSivqNoYe8M97z1IZQ1N15Tkgn1MAqJw4r76nJbl159H1jHV2Tq/b7JJcrpyC3h717ZSPsZM324JIn4vYeT/dSlguMwCwsFXOb5EPyBGeaBQAACAASURBVGUCANzN8nbyfZHq62JnyFgGALukt2lb331UjBe3jorxC59br24juugfu2DWRXjRPEfBf0iqaXjKJbuhu+R8YZf0fLH+3wrV1wM3FrB+V86Ih45eUNNwC0X1PS2pvu6ruMa68Gm9PzrxVbmf51l6ucmORMS4VVeFFJNB5Hv87ca/q1/bDS/KfTD3fXo/zlKaveX1fvnODUaMZQBIHMvV/8vjFj00Lca9zX1iHAAKXX5bXupwjGUASB3U297UAyfFeHGDvB+JE/qxiJ336+yhG0oY310w4vainoYVkeuIlhQMNq4LBKrrrX1H1CTWJdJi3GqibuxS2szI+bq6cqiIyHn/HGVH9PZO61ef+daYmkYoKrd5n/y7FxvLt06MYNvhddXloYf0Ppz7YKcYr0Tk41mK63VU/Jx5LCK9LuLn/HSjc3pdGliW2yJnVq7TvfAqea9O7496jeXOzTZ699fklbJefyCw9qGapv4znU4/DcB7MpnMc9Pp9C0AvgbgwE/CH81kMveseQ+IaM1YNolaE8smUWti2SRqTSybRJePOsiTTqffAeA1AB4bvrsFwAcymcz7r+SOEZGMZZOoNbFsErUmlk2i1sSySXR5NTMnzyEAL69ZvhXAi9Pp9H+l0+lPptNp/R5/IroSWDaJWhPLJlFrYtkkak0sm0SXkTrIk8lkvgygdiKABwG8PZPJPBvAYQDvvkL7RkQClk2i1sSySdSaWDaJWhPLJtHltZbZfL6SyWTmH3sN4M7V3jSxaQDxRMRYrmUV9YmssmMJMR7taWJiNleeZKoSNQ/BxKQ54a4b0if5LMe0SSr1z7o4Lk/glAiZaWysm6jNLusTSC4Pyafb8uQ0Vob1bSSK/mRXm0dXmUxOnwsLsaR8vHI98vGOdunHuxzzxzc3rm/cz2aOZ8y5+ASIUw8eVv//CmiqbI6nhxDvqCmb6SEj7sXlySEBINcvz6rolPTJxPIp5Tx2m5M/Tk7Ulc2gfiNisUN+T6WJeQZzvXKmDddVU5tHzInWoj36sagoNbH2OcJ9+jYCOT9PT64y0aOnV3WwlKKlTbgbTeXVbZSSfv6bHG/cz0hSbhcAoDJ68UkM93x/n/r/V0BzZXPLEOIdftka31pXNqN6hi11yWWzmQl2Cz1yHWAXzfqxPj+5Qb2iryhzFoaW9Dy9PCoXnHjU/6yr5XntwQwAEFYmVyyv0n7UWh7SJ2cM13zW+rYdADxbP57axMtOQf6sVhMTQNb2Zer7SgAQ7NcvtLsr2YvGpn6wX/3/K+DytJtBvQL1onJe8JroIBX6lPa57jTW92/yXfo24kofrH4bq6mE5QxZ296s1geL9Fz6BN2BsW4xnh+TJ4gFgHLc39HV9tNrok/bUZLPe2koJcbtJuqp2v7QamUzMHLxcvcYq3Txif1btU87vnXYKJePrXuMu6hPrl3ZIk9Y7CgT9ALAyEa5X5J0zImw689RMaX3u7V+9fwG/St9cFnOS17AzNCbh83+bKqo9+G8kNLv1uIRvW9f/9CEhj5IQC+Y2hiCNlm5F9SP9/xms47ZVPe9uNPqUtOwF/RJ/S9mLYM830in07+TyWQeBPACANtWe9PhA+ca1u3Zcaz6upmnay0tyRVf/PgVeLoWgD27TlRfVyJ6w13qkA9jU0/XglzAUwcbj9euvf6M/c08JWQhKzc0ljIgtuDo20gdNAvW9oOn6jaiJqE+XUsbrOo4peet+i/NO/eZTz9wmjieLfh0rabK5pHMmYZ1Uw/7TyC4Wk/XyvbJ5zFxurHDsXvKP09aJxLQn65VbuLBVytZ7elajeu2H/Lzffy8Xv4rypfifPflfboWAOx6xMzzXhOtgaUUrdrB09UkjupPZih2mx222nMOALGM/uSs8pFj6nuusubK5qOrlM2H/M61F9czbGFA7mw283St7JDyhK58Y/1Ym5+aebqW9p76p7+tZq4ot2mdR8wMW9tmAs21m9Edcl4qLsid3oUlfWAuOmd+1vr9dJ1LH+SpHeRdjd3EwHxgxTyetX0loMmnay0squ+5yi5Lu+mF9ME8d5X+Zq0r8XQtANhV079ZUZ7UCABJpQ+mDfYDQDkqb8eta2/q+2Dxk/qXSU3w7LwYzy7oAwDFpNmHqN/PZgZgux48Ica1p2vZTQzAumFzP/fsNrcZPKOXO6v1nnynls0j+1bvhz82KOXOzKobKSsfOzCv58VzYfn7as9U44BBbf2Z79PbCa1fPW3rdVBoXm4H3FBjft522O/P9u9q4ulaYbnfrX2vbubpWqtdtKjtg6z2OerFj8uf5XI8XetCsLdh3Y79/vHs2amXS2d6QX3PxaxlkOcNAD6cTqeLAM4C+M01b52ILieWTaLWxLJJ1JpYNolaE8sm0SVoapAnk8kcBXD7T15vB/AM7X+cc+YIuj3bbayrXJhWt2tP3CjHcxe/tbBK+QmSHTEPgV3xjFHzYM2VmoumcfOkGJ/bpI/Qdh6RL42U6q6QlyOWsS603MSdE0PysRi5Tx7Oft7rd6jb+OfIU6qvFwddzNbd9vvCW3eraXz73pvF+PqnnhTj0/8s33oJAMXn+yOjyz05zN9ojthueNuSmkYzP5240tZSNisHjxrL7lDEWOfdfp263dPPkkfJN/ybnh/tV8hXfVf+qe428+4AVgb9kfPka+V88OM0RsX405rI09vulPNj/d0AoWUXkTn/8wdW9GPxlr/5ghh/71++UowXOvWrFp17/SsG4d4VxI6bVxDKSfkKMaDf4XH+ZfIVr+wP9J9zrIz6x3N41MGp3rqf1E7raQS8dep7rrS1lM3G9soz1rkhvcnW7sgInNevHJ2+Q76FuPu/zTsS8j02Vob8q3Mzt+qX+nt/JF+tW1ynf1btzrLgkv+GQK5iLANAcE7/ybc7LF9ld7JyPyS0rF/t00Rmm+nryOGTz5PLd99O/ZydfL7/WfpGwzi6zryzbOxbg2oakaP6zxKutLWUTWvJvOprZXPGumJ6uP5fGhx7kVx/DjyotxWnXiSfp6FvmOXGDZh3ii4/R//ZTuAbF/+5KwBEZ5q4lUdpksILfhrB5YqxDADOUqH+Xxo34So/X3nqkBg//Tz9ePds9+upUsJCIWXWW9kBve21K3I/5MJNcl3Yu0vfz7ktfv07tD6MUymzbPZM6eUu+ZDep7rSHm/Z9BKNd7d60XB1vRXTy2U5qvwaY0a/4zP3Avmuj8WseYdtdiiMxSV/35d+Qf/OEfx+UoxPveUjahpP+ZM3iPG+7eZ+dOWzGNjpr3OW9Luayim5/rCVu4mP/ar+C5poxmzPFjY4mHb885gd19vM9MeU6Vy65buiiyn9O+DMzWbZXRz2MBP31yWP6f1u54J8R6KkmadrERERERERERFRi+MgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRG+AgDxERERERERFRGwhcqYQrfSlj2U11GOvsaFhNw3PkeLkrpqaRG5C3k/jGlLFsJR3Yew5Vl8+9+gZ1Gz1TOTEemffUNBL7F8T43E3m8YRtHp/QfEndxvhXs2I8OyIfz+986nZ9G5lC9fXQ9S7G91SM+O5v36imMXFkUYzn7h8S4925ghgHgMBD/vhm300WRnea4535yT41jfC2g+p7WlFgbNhYdvp7jXWVXFlNY+Kf82K8EmuiarmrVwwHiq6x7BQ9BHL+urP/OaZuov+wnBce/NjNahoIyuGuvcvGciKRQ+cBf11+IKpu4hO//v+I8URCLt+eY6nbWBlP+vs0FDeWASB2Wq7HACB6Qd6P/q8o9e2xJXUb2VN+PdS15GFgr5kPrKKeP1Eo6u9pQV7QLDee4xjrnLMzeiLDPWLYTer5sesHITEeXDHbNCfnGeuG79XzY3LfnBgvDsTVNGaukfNbvtcvvMXOgLEMAHahUv8vDZwVuQ6pJOVj1bnjvLoNt8PP85HuPGLHVox4uVPvLwUW5P0c+7bcD2mmDhn9rt/p6LuugtEpsyxGHtbbxPK14+p7WlKoriEIBox1gWW9ztn4ObkPhoB+3XXk3zvEeOys2TZHe4pInKxZ9y96+XcKrhjP9iqdcwCxablshWb89ia4WDSWAcCNKQ0vgGKXXC5SD50R4/FTXeo2anUEC+jea+5nzx75WAGAVZHLXmRG/qyhE3JdCQDJff6xGLi1A+u3zZr7UNS/I6Ci14etxppv7FNYy9nqerfuu+hqAstK/yqi92dHPiKfQ2elrp8YySH1qL/OchPqNmLn5TrmmW/9bTUNS2lWc0Nm/VDoCRvrAim9Laovy/XsBbkeHP5av7qN6FkzjZ6FPIZ3+esqD+vnzJlbEeOFdd1iPDug11Fb/9qsg9Y/NYalh876K8pXtszxTh4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojbAQR4iIiIiIiIiojagP0h+jZzpBWPZXlg21pXW9app5O+YE+OL3+hT0wiseGI8fFvaWK5sGUOpZFWXf+ut/6Ju459+90ViPPnovJpGuSsqxj/053cayx3h27BUeLC6/Cv/8WZ1G/te9nExfuudvyvGO4656jZy/cHq60LKMZYBoPirs2oaoTuTYvz4L1XEePcDIXUbr/nde6uvR6LXYzS3x4j/25ufr6aBcFh/TwsqnzhtLFc2JI11Vl+nmkbolFw2YVlyHMCx13SJ8Z57I8ZyOWKhFPfHpZ/yS3vq/6XBme+uF+PuLy6radj/2i3GV9bFjeVcf9RYVwnqx+L8r+fEeNc9cl5zXzetbiP1Tr9cRLqziB9dMuL5oYSaRuSMfLzmJ+X9jHToZfPcbf45nhu1cC5hXosILcTr/6VxO7NL6ntakeWa7ZXlecY6L9WhpnHwbUEx3nmf3NYAwMJz8mI8dZ9ZNitRC6W4n89nr5fbXQAIL8j57fxvy2UCAHo/KV+n6n3nkerr7qSFwcVDRvzw5zep2+jbJrd7wTOLcgJBvZvlhR3/dcg2lgHg0Gv1OmTjp+SyNbs1Isb7duh14bnb/DQWxgOYts28NnpySE0jMLuivufJyF4pqO9x43L9aOXLahqhBfk9+V4zHxQ7g8a6sz9fUrfR/y25Dhn/jf1qGic/LJetyrhf/nNDUSzPm/VB7LRcBwHAyRfI+9ndPyzGOw/rdQxq62S3bhlA4Jz5XWc15T65T3v6mUrZ3KV/14lc8I+XFw3CTZj5wN59XE3D2yT3l1qRl2xsR7x4tLreyuv5/cQvy33R0e/oZdsqy21eue58VKJBY13h/23ie+LX5P0cfs0RMQ4Ap+4aF+Mz15rt1eIGBzOWvy56Tm/brbfIbWLgrgExHszp3zXrv4MEh7uMdW6n3k8srJP79uHj8vfV4LRcbgFg/2+ZdVDnaA/23+KX1U2fvqCm4TXRh7gY3slDRPR/27v3GLmuuw7g3zvv2Xns++Vde/0+tvtIk9CXgDaIoNISKII/aStRAQJVECFEK0WJECIIAW0kStUCUaO0hUJfilAl+uBR2hRKmtibxI7tGzu2k8bPXe9jdmd3dmbuXP7Y9d45M+vfb7Ke3Z0ZfT9/zbln99yz997fOWeO7zkmIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJv/z9cVlalpOz3Xbx2LlkpqGf439ov5EcdXyyhlHTE/NxG30vmRKHK54NiXHvpl9RzpH54U8723HVLLmDmaEPN/+9MPWul794/h+IV3r6eHpirqOd77o98X851d8u9nXiuo5yingkcqPuIhOV228itf7VfLiM4tivmpsxkxPznjqef4yqO/uP75bUfG8PzZMSs/s7KsllE8Oq7+TCuKjI1a6fBAv3WsFAurZXhjvWJ+qKjfg11PxcT8rit5K51JFNB7Njh28U+PqOdwBuT81JMptYz0hXkxf+5o1kp7MaAcD9odLy63QQDQ8/W0mO8r0/HpP5djAgAWDgXt2vJ4CgsLdr19vZqIX5crkv2J3K6HVvTnYuD5oF3vLvgYOGu389FZvR1CLKr/TCsq1ly/Utk65qwU1SL2Pi4/C5Wo3vcmZuXYjOXsMpLZMjKvBceS03obEsmXxfzxP1OLAMJyOz33yJ71z4tvHcbci/b1GyguNXASrQ5KcE7P6UV4Qf8dyhUQnrHbvkOPy+0DAIRzK2J+6rocEyv98hgEAHZ/c2r989C9Kew+PmXl+2H9vsPTxyotyaltIB3rmB/Th9NL411ifvqc3NcAwPKgfB+TU3ZshgsVRPJBu3vkr+XxFQCUe5Ji/sXPH1bL6MrLbX11naI5D/EZuz1YmJDrAAA9Z+T87vNyfPsR/d+5/WjwM34shErcfsb9BvqayFROzB//T6UeDfTNjudbn6vTAODdrd+zSE270wmc2j51A4OTcpsUndH7iZUheSxZiYdq0g68ZHAs/tUe9Rw9Z+XnaGZuQi0jrHQlQyfs69VTLGPoVHBscZfezqX/Nivmx5XnbHlEj/2Fu4at9NLBXiwUg2N+XXtdT2tvVyb6xHynos9BjD1tt2uDb/Iw9lJwzE/o7UdoWu8Xbvu7m/5NIiIiIiIiIiJqGZzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJzkISIiIiIiIiLqAJEtK9nss9N7RoHp4nrSi+mnHv7IJTH/6j/vVcsYeuAnYv6Nb+620qW0g5UeZz09+J1X1XNUDk3I+fGwWkZ0yRfzH/jYD6z0RNcx7F46vZ7+8rffo54je+ymmB//134xP3p1Tj1HJBrc19hgDxKv2r/zG3/zQ7WML378V8T8/KGimJ+4GVXPsTwY3OOlkRByBXu+s+e0fA4AiMyW1Z9pRX7cvj5+NGIdCxU9tYxiT1w+R0SfPw4XKmL+zTelrHRubwI3S8Gxm2/X62kez4v5ySX9Ps+/uU/Mv3a//RzMDHu4Nh4c6zkRU88x974lMb/337rE/NiVBmJzMSgjmV1C5vyClf/aB3rUMnqeXRDzE/mCmL8yrp+jlApis5xwrDQAhOflewoAWJbr0bJiNW1XNGIdKw9k1CJm/lC+PvF/6VXLuP4uuT8a+rHdfxd7wlgeDI71PXNDPQdCchvhJ/W4WelLivmlVND3FjMRrPTa17frckk9RzgvtxGVLrme4UxKzAcAlIL2wil7cEp2m1KJ6WOIyLUpMT99Xe7/MaDHprO8EnxeKVlpAPBvzqplYHhA/5lWtFLzHJRK1rHQtD4eCI/Iz0KpV36eAeDae+V+s/d5+3lcHItgbjk4NjIpPycAgOxuOf/XlWcJQOEr8ngyWQ7amErUgRe324P0lZXaX6kTvbEo5pd75X7z6k/r17vralDP/HAUuQl77BNp4HnueVG+Xo4vt7cLe+S/AwCWB4LrN3agC9djdl8x/ExOLQOl9hvTOov1YydneWX9eGVWHxt5sV1ifrmBuKx8XL7HhS+MWOliOoRCT1X/lHVqf6VOqCDfn+SUPp7N7ZH/lr6PXLHSse4E4vPBsblvyN93ASBSkMfmflj+W3N79PmBvjN2+xDJVxDNBeed/gN5TA0A+GK3mH3zTXK/239K/w4y/Va7jJk9EVxPBuOQiSv6d6XiwWH1Z25HvJLGmCiAJwDsBRAH8CiA0wCeBOADOAXgY67ryr0OETUVY5OoNTE2iVoTY5OoNTE2iZpPm0L6EICbruv+LID3A/gMgMcAPLx2zAHwwa2tIhFtgLFJ1JoYm0StibFJ1JoYm0RNpk3yfA3AI1XpMoB7AXx/Lf0tAPdvQb2ISMbYJGpNjE2i1sTYJGpNjE2iJhOXa7muuwgAxpgMgK8DeBjAJ13XvbWAdAHAhova9h8eRlc6UZW21yMioq8xD3fL6+VGD46I+QCQ6ZbXy8/tH7LSh3fZ62sH36mvk6z0yvskeBl5/xIA1n4GG5noOmalRxJ7rfTbx8fUc6T65Ho6h7Jifu9CQswHAL/qvu43o3X5I0l5Tw8AuOvYuJg/Miy/rdl9SF/PWb3vkhmpX1M9dI98LQDAKd1+Peap45fU378TdxKb+46MIlUdm0fs++Rn9PtcSstxFfLkNeYA6tbg11rut9uIQ+ODVnp+RH9rd8/d8t4sTkHfkyO/X34Wxoft5+Bor92mpA/oe0QVBuV6ZozchnQX9XtWSVXd80P1a3yH9qbVMkbeLt93PyrHXmlQP0d+JCij9p4DQG9e/1vr9s+ocuqZV/TfvwN3HJtV8bevJja9bn1vhrk+ed1+1Oj3YFbeogC9xk4f2m3fp4zXwD40Iblvrd07bCMlZW+wcjJoQw5O1D9LCSXuAH0fBF/ZLye0sKyeA1XNZe09B4DSiN4fRdLK9XKUf9Pr1p8LZyVoL/cdra9nJaf3707v7cd1p567qP7+nbij2Dw2ZsfmsZoxl7LHFAAU9sn71ISVvSwAoG9Evs+Zg3Y9Do/Zz33fu/V+0xsfEvMXevX9ckJGfmbjVf3mgX31sRle0a9FZFaOLU8Zy4zu1fuSRCYIztp2DgDCRX2sk47KY+9KWm7Hlob17xAr3cF9PzxWP6btKzaw59bC7dvDVo3NfUd3IZVN1h27pbIg79sEAPkj8venxJA+TvR65OtbNPbejof22M9S7d6DG+nLyzFV7tafk/H98s8MdNv92f6UPRgYO6B/7+4Oye1DqCS3QbMH9D2QshH7nhyo+S4/36f37V1H5XFKbkJu07MVvS2d32OXcaRmD6+Ru/W49FKb3z5Z/U1jzG4ATwH4rOu6XzbG/FVVdgbAhrtaXXj5et2xUyeCTYz9BjZejsxPi/lXzzfQId7zuph/40J98B6/cHn98/gzF9RzVIbkjSxLyuaQALA4Jn95GqraZPmWV6uOPfu6vvlbNqts/nZOHoAMP3tVPUftl7xTz12y0mb5BbWMF07LG3u9dkgOrP5zb2zjZQB47tJlK73nhL6BpFPc2U3qNhubF8/W38fqDrzSo39B0zZeDpX1gU+5S/5ytDha30acOBfcp5s9evzPT8qb0IYa2Xh5Rd54+cpEfT1+fD1od3pe0TeQzY/K9ex15S/3Aw3EZiVrl1HdHgPAayN6Z7P3Wbk99Ws3Dq7RyMbLuQX7ek2es2NzqIG/dac3Xm5qbD4bxGYjGy9PPSA/03G3gY2Xe5WNl936AemkG9ynvue2aePlITkuqjdeBoAXTtvPb9dlfXNGdePlhDyWCc82sFF4zcarp561xx0rE3LfDACxU8p/EqFN8rzBjZeB+npWGth42dnhjZc3HZunL9cdsyaMG5jkWcrLkxKRvD6mOP8WOS56z9f3qyfOB3Uf+dE59Rzlw3L7OfuAPnEZcpWNl2s2qn7hJTs2o0v6tbjTjZevdOtjneqNlwFg8mX7OYgU9LGOtvGy1y1/R1iYeGMbLwPAiVfseg5P6hsvh+YaaKu20GZi8+KZK7WHAASx2cjGy/N98ng2dU2f1CzNyW1fwa2Pmeo+s5GNl0eOz8h1GNCfk+tR+Vnbday+Hzk5H7Tz11/Rx90Dp+T2IaRM4N4I6//gULvxMgC8eDL4j5amf07v27Nn5EmzmyFl4+XTDWy8vMELLc+9Ftz3icl5tQwvq4+FbkfslYwxwwC+C+ATrus+sXZ40hhz39rn9wN4etNnJ6JNYWwStSbGJlFrYmwStSbGJlHzaa/TPASgF8AjxphbayUfBPBpY0wMwBmsvlZHRNuLsUnUmhibRK2JsUnUmhibRE2m7cnzIFaDrNZ7tYKdK/ZSK2dPj3UslNKXMOX/Ql4nmQnrr3P6j9avoa02Ome/KtWf68Fo1etTfkZ/ndO5WP8ab7VYQV7fDAD9l+Q1n09fe5eVnn/Lbrx4MnjVbCyuX4vll+TXZ3tfkl/n1PbbAAA/ESzX8GMRKw0ATz6kb44f8uXXX0f/Xa5H9rz+WqqXDOrVnytg1wv2q32eskYaACIn5fu+le4kNv3X7SUh/kTWOhYu689rvCy/Tum8qi+p8d55UMwffM5+zba70o3BE8GxweP6661OXn7tfPGYvnQgdVku48CX7Ncxx97q48CLwZLCcEFfEx7+D/nVUk9ZPtNIbJZ6g/0HvHTMSgPA2Pf0V7Ur2uvtSuzGr+qx2V0JliSl0yV0X7Bfy/WTemxq+71spTuJzdKovWSm3J+2jjmevgY8+zn5FeTENX3flMyr8rI7L2E/84kBD6mrQR9WuSQv6wMAvPmQmK0tgwKA2JSy/GUhKCM+XETy2iaW8ZXkvjU0Lb+m7/c0sL9b9R5Svl8XR9oeBgDg7Zc3UgovyksOnIK+dNXa66pUEve+uq1pecnBVrqT2ES45rX7UMg65mf1sWListwXVJS97gBg/9fkJQJewn52UlkP2Z8Ez7CT1usZvVS/3UK17GP6HpDFbvmZjeWC9iKyVLbSAFDK6MvuI7X3pIZTluuw6/t6nxfOB3GTdbLon7TjXdqbMfghuT9ylOUrPZNT6il6wsHCjP75FEaP27/jR/V9UBtqA7bIZmPTX6rvA/zCyvrx0KC+1DX9mtyPhBtY1r/8D/X7HFrnmLbLSPaXkb4cHAtdaOA50vaAaeA/l++5IPdnub/fbaXzR4aQOxvEZv91vQ91inJFSsr+cUPH9TFz5FV7SXgsm0DiTPCdLPsFedsPAEjekPvEkUV5DBK/oS8JS5+zr8XIvRnsrV52F9GX+cZebmCLgtvQSyciIiIiIiIiopbHSR4iIiIiIiIiog7ASR4iIiIiIiIiog7ASR4iIiIiIiIiog7ASR4iK2D2JwAAB2ZJREFUIiIiIiIiog7ASR4iIiIiIiIiog7ASR4iIiIiIiIiog7ASR4iIiIiIiIiog4Q2aqCywd3WWlvbADlg0vr6VDJU8uIz6yI+aG5vFqG15sS8/2oPc/lRxzrmN8VV88RymbE/IUjfWoZfsgR8+OzJfucno9Q2a86UlHP0ffCnJjvdcXEfCeu37PQjdng82yflQaAzJJ8TwGg0t0l5++W76n2dwBAeKkYfF7xEF62r285o993RLcsfLaUk7Kvr5NMWMf8Bv4uLxkV80N7d4n5AOBUfDm/WLbTJc86Vkk1cI+S8rOQPj+vFuFH5LnwyLwde5G5AmJTQdvkzDRwjh65DYm8flMuwNNjM1LVloUXS4jM2bHopfW4qb0ndULytXJWSmI+AERngn4inCtYaQBARW/rcEO5Xi0qPF+w0qF80ToWml/Uy5iR48JXYgIAQkX5eQoV7PsYWSwiOldVz1RSPYfvXpTz7zmslrE8LvcF6ZPX1j9HxnsRvWr3R34krJ7DKcvXwl+R+zTPPa+eIzI6EiRKJWClaOdPLahlOEU5tvyk8lxMz6jnqCwH97gyPwSv5ncq7zimlhF+8RX1Z1qRX7Kvr+959jFf7s8AwOtOiPlOA+PiUFlu/xKnp+10Ko3U6RvraT8tj68A1D1/tSpRebwKANG8/LdEZoI+MpwrWGkAiMzq54DyzDtROb61MQgAeFXjjEoiZqUBwEvq46XExWkx308rY5kGxmTFwaAtLPd1oThsjymis8tqGZWBbvVnWo3TXT92clJd68f9vP531z57tUpD8vgMALKnZ8X8StweM4cXS4jMB32H08AYzlmW+5royRtiPgAs339EzO85abfp6VjWOubH9GfRWZbbj8iZKTHfOzKhnsMb6bfSlb5u61jmJTnmAP27ZuJGTi5gRv5ODQCV/WP2gVgEftX3p9B8zfh2ozJyev9/O3yTh4iIiIiIiIioA3CSh4iIiIiIiIioA3CSh4iIiIiIiIioA3CSh4iIiIiIiIioA3CSh4iIiIiIiIioA3CSh4iIiIiIiIioA3CSh4iIiIiIiIioAzi+7+90HYiIiIiIiIiI6A7xTR4iIiIiIiIiog7ASR4iIiIiIiIiog7ASR4iIiIiIiIiog4Q2eoTGGNCAD4L4C4AKwB+y3Xd81t93s0wxkwCmF9LXnRd9zd3sj7VjDHvBPCXruveZ4w5COBJAD6AUwA+5rpuZSfrd0tNPe8B8E0A59ayP+e67ld2rnaAMSYK4AkAewHEATwK4DRa9HpupXaJzVaOS4Cx2SyMzVXtEpcAY7NZGJvtgbHZPIzN5mBsrmJsNg9jszlaITa3fJIHwK8CSLiu+25jzLsAfArAB7fhvG+IMSYBAK7r3rfDValjjPk4gA8DyK8degzAw67r/rcx5u+wej2f2qn63bJBPe8B8Jjrup/auVrV+RCAm67rftgY0w9gEsDzaMHruQ1aPjZbOS4BxmaTMTZXtXxcAozNZmFsthXGZhMwNpuKsbmKsdkEjM2m2vHY3I7lWj8D4NsA4Lru/wH4qW0452bcBaDLGPNdY8x/rTUSreIVAL9Wlb4XwPfXPn8LwP3bXqONbVTPXzLG/MAY83ljTGaH6lXtawAeqUqX0brXc6u1Q2y2clwCjM1mYmyuaoe4BBibzcLYbB+MzeZgbDYPY3MVY7M5GJvNs+OxuR2TPFkEr6UBgGeM2Y43iN6oJQCfBPA+AL8L4J9apZ6u634DQKnqkOO6rr/2eQFA9/bXqt4G9fwxgD92Xfc9AC4A+JMdqVgV13UXXdddWGsAvg7gYbTo9dwG7RCbLRuXAGOzmRib69ohLgHGZlMwNtsKY7MJGJvNw9hcx9hsAsZm87RCbG7HJE8OQPWMWsh13fI2nPeNehnAP7qu67uu+zKAmwBGd7hOt1O9fi8DYG6nKqJ4ynXd47c+A7h7JytzizFmN4DvAfiS67pfRvtcz2Zrh9hsp7gE2udZYmy2rnaIS4CxuVUYm62Lsbk12uVZYmy2Lsbm1miXZ4mxuYHtmOT5HwAfAIC119JObsM5N+OjWF3DCWPMLqzOCl/d0Rrd3qQx5r61z+8H8PQO1kXyHWPMO9Y+/zyA49IPbwdjzDCA7wL4hOu6T6wdbpfr2WztEJvtFJdA+zxLjM3W1Q5xCTA2twpjs3UxNrdGuzxLjM3WxdjcGu3yLDE2N7Adr4g9BeAXjDH/C8AB0FK7iFf5PIAnjTE/xOqu1x9t0VlgAPgjAI8bY2IAzmD1NbBW9HsAPmOMKQK4BuB3drg+APAQgF4Ajxhjbq2VfBDAp9vgejZbO8RmO8UlwNi8E4zNVe0QlwBjc6swNlsXY3NrMDY3j7G5irG5NRibm7fjsen4vq//FBERERERERERtbTtWK5FRERERERERERbjJM8REREREREREQdgJM8REREREREREQdgJM8REREREREREQdgJM8REREREREREQdgJM8REREREREREQdgJM8REREREREREQdgJM8REREREREREQd4P8BXEJmj4YavnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x2880 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = subplots(1,5, figsize=(20,40))\n",
    "ax[0].imshow(vidPics[0])\n",
    "ax[1].imshow(vidPics[1])\n",
    "ax[2].imshow(vidPics[2])\n",
    "ax[3].imshow(vidPics[3])\n",
    "ax[4].imshow(vidPics[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T03:54:23.244496Z",
     "start_time": "2018-02-08T03:54:23.241482Z"
    }
   },
   "outputs": [],
   "source": [
    "location = 'C:/Users/Mark-Brown/Documents/Analysis-Code/Videos/oranges/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-08T04:11:00.271405Z",
     "start_time": "2018-02-08T04:10:46.655216Z"
    }
   },
   "outputs": [],
   "source": [
    "makeVid(vidPics, location+'10x10-Loading-updated.gif', 'gif', dur=0.15, vidMap=orangesMap, includeCount=False, baseCutoff=100, multiplier=550)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "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": "3.6.6"
  },
  "nav_menu": {},
  "notify_time": "10",
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": true,
   "toc_position": {
    "height": "780px",
    "left": "0px",
    "right": "1329.33px",
    "top": "107px",
    "width": "277px"
   },
   "toc_section_display": "none",
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}