Postpatch_vs_resIndex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from scipy import stats\n",
    "import math\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set(style='whitegrid', color_codes = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ps = pd.read_csv('patch_seq_log_mouse.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pass    1301\n",
       "fail     358\n",
       "na        16\n",
       "Name: SM_QC_PF, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ps[['Date', 'User', 'Post patch?', 'Notes', 'PCR cycles', 'SM_QC_PF', 'Resolution index', 'Bad dates']]\n",
    "ps = ps[ps['PCR cycles'] == 21]\n",
    "ps['Date'] = pd.to_datetime(ps['Date'], format = '%y%m%d')\n",
    "ps = ps[ps['Bad dates'] != 'x']\n",
    "ps = ps.drop('Bad dates', 1)\n",
    "ps = ps[ps['SM_QC_PF'] != 'cnt']\n",
    "ps.dropna(subset = ['Post patch?', 'SM_QC_PF'], inplace = True)\n",
    "ps['Post patch?'] = ps['Post patch?'].astype('category')\n",
    "ps['SM_QC_PF'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total # of experiments since April 20 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = ps[\"SM_QC_PF\"].count()\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### # of experiments passing smqc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "709"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passing = ps[ps['SM_QC_PF'] == 'pass']\n",
    "passed = passing['SM_QC_PF'].count()\n",
    "passed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### # of experiments with Resolution index >= 0.8 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_res = ps[ps['Resolution index'] >= 0.8]\n",
    "hi_res = high_res['Resolution index'].count()\n",
    "hi_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### percentage of experiments with Resolution index >= 0.8 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of total samples with Resolution index >= 0.8 = 0.152637485971\n",
      "percentage of smqc pass samples with Resolution index >= 0.8 = 0.191819464034\n"
     ]
    }
   ],
   "source": [
    "pct_ttl = float(hi_res) / total\n",
    "print 'percentage of total samples with Resolution index >= 0.8 =', pct_ttl\n",
    "pct_pass = float(hi_res) / passed\n",
    "print 'percentage of smqc pass samples with Resolution index >= 0.8 =', pct_pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering to only experiments that have Resolution index data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Post patch?</th>\n",
       "      <th>Notes</th>\n",
       "      <th>PCR cycles</th>\n",
       "      <th>SM_QC_PF</th>\n",
       "      <th>Resolution index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5433</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed,Cell Shrun...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.660082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5434</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed,Cell Shrun...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.556320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5435</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.556320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5436</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>Entire-Cell</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed,Cell Shrun...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.860881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5440</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette Large Bubbles Pretty s...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.660082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date User      Post patch?  \\\n",
       "5433 2017-06-01   P1          No-Seal   \n",
       "5434 2017-06-01   P1  Partial-Nucleus   \n",
       "5435 2017-06-01   P1        Nucleated   \n",
       "5436 2017-06-01   P1      Entire-Cell   \n",
       "5440 2017-06-01   P2          No-Seal   \n",
       "\n",
       "                                                  Notes  PCR cycles SM_QC_PF  \\\n",
       "5433  Fluorescence in Pipette,Cell Dimmed,Cell Shrun...        21.0     pass   \n",
       "5434  Fluorescence in Pipette,Cell Dimmed,Cell Shrun...        21.0     pass   \n",
       "5435  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5436  Fluorescence in Pipette,Cell Dimmed,Cell Shrun...        21.0     pass   \n",
       "5440  Fluorescence in Pipette Large Bubbles Pretty s...        21.0     pass   \n",
       "\n",
       "      Resolution index  \n",
       "5433          0.660082  \n",
       "5434          0.556320  \n",
       "5435          0.556320  \n",
       "5436          0.860881  \n",
       "5440          0.660082  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.dropna(subset = ['Resolution index'], inplace = True)\n",
    "ps.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### swarm-plot with Seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa1a7390>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5RV0ymD0ujmB/55S/canhjIl0/v9RkcPJHBbhNObn7U7yrInOBWUyGWmTR3U7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\nYmAcfirnfamMDFhxzs3Asb3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MzbqdRZgsVhZOHsTE9Cia2wx8uDWfmqYOMs/MJbNZqfrv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\ncSAiIgUWByIiUvgH/SdVbdbsj+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x34cf438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='Post patch?', y='Resolution index', hue='User', data=ps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### box-plot with Seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xacd55c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb020e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Post patch?', y='Resolution index', data=ps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### violin-plot with Seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb36f240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i4lxS9u7Z4+PQ54ESGlTsh0CWZRw8eBAMZwCfXhpvcwDAF114//3342xJZAiC\n4BPriOGSO3t87ty5AICzQvzF/pxbgEQILrvssnibEhWsViu4EDtOKq/TUtEqADAYPFn4blE9tRe9\nYyljUxIPKvZDcPbsWXR1dYGzFINRMyY2BKzeAtaQha+++iopC+14xH64nj3ju1YyMmLECEyYMAEN\nojvivfHR4pTgmTAtWLAgrnZEC7vdHlJyHqDdvfZms6eYjlNQL6TuFDx5HyZT/Bo7UYYmMRQsQamq\nqgIA8Ob8qFwvWmuDnDkPbrcb1dXVUbmeWsiy7JmgpLjYA8CiRYtAAFQJ8QshO2QZtW4BZWVlGD16\ndNzsiBZutxtutzuk5DwAvnC/1jz7rKwsAIDDoZ7Y271jKWNTEg8q9kOglH5kDcP7AEvOThC3AxAd\nsJ55A5JzeHtRFXuSrVqVEolghrHtDoBvspDMYn/FFVeA4ziccLniliB2SnBBBnD11VfHPR8lGvRu\nuwsxjK9Rz14R3FCr30UDm3es7Oxs1cakhAcV+yFobW0FADC6tGFdx9HwIZSi70TogbNheMl1rNce\nxb5kwbfsMFzP3jtZSMZlDIWMjAzMmzcPHbKE1gib2AyXk4ITLMviqquuisv40Ubx0LkgBXUUeI2u\n2efneyKR3Vb1JsPKWMrYlMSDiv0QdHZ2AgwLJsSw4GDIogNE8G9SIgs9kMXIS70yvKc0ZUdHR8TX\niAdR8+w1IPYA8M1vfhMAcFJQP9GwXRLRJkm49NJLNeONKaLNp7hnr9RL6O5RUex7qNgnOlTsh6Cz\nsxMMZxheiDNQpbRhVFBjOI/YJ1tpyqh59pw2xH7mzJnISE/HGUGArHIo/7R3gqEVrx7oE8YPNRtf\no2v2BQUFAIDObvUmkcpYytiUxIOKfQAIIejo6PB50YkEwxsAMNSzT3Kx53keCy6/HA4i47yo7u9y\nRhBgNBgwZ84cVceNJb4wfqgJehoN45vNZmRkZKgu9pmZGb6dAJTEg4p9ADo7OyEIgm99PJFgGBaM\nzoTm5sH7vScqPnEeptgzDAMwyS/2QO+e+7oIfhe9Xo/i4mLo9eEtM3VJErpkCdNnzIDRmHiT2Ujp\nLZUbelGdvudpiaKiInRbBV+xm1giywTdVgFFRcUxH4sSOVTsA1BTUwMAYPUZ8TUkAKw+A+3t7ejp\n6Qn+4gQhap49PFX0tCD2U6dOBc/zqA/Ts9fr9bjzzjuxefNm3HnnnWEJvlK5b9asWWGNmeiE7dlr\nNIwPeMRNeLR9AAAgAElEQVReEeFYo0wqioqKYj4WJXJUrW0oyzJWr16NkydPQq/XY82aNSgrKwPg\nySy/++67fa/9+uuvcc8992D58uW4/vrrYbF4GtCUlpZi3bp1Mbe1srISAMCZErMTGGfKhWRrQmVl\nJebPnx9vc0IiWp695xra8OyNRiPGjh2L01VVkAgBF2J+SF5eHpYsWQIAWLJkCbZv3w5YQ0v6bPXW\nL580aVJkRicoYa/Zs9r17AsLCwF4wutZGbGtaqcsFyhjUhITVcV+7969EAQBW7duRUVFBdavX4+n\nnnoKgCeLs7y8HADwxRdf4JFHHsH3v/99uLz7kJXn1IAQgo8++ghgOHDmxOwExluKILQdw0cffZR0\nYh+NYoQMy2im6ca4ceNQVVWFC5KE/BBri7e1teGdd97BkiVL8M4776CtrQ0whrbk1C5J0Ol0GDly\n5HDMTjh6s/FDraCnXc9eSZRTIyO/q4cm5yUDqobxjxw5goULFwIAZsyY4fOe+0IIwUMPPYTVq1d7\nio6cOAGHw4Hbb78dK1euREVFRczt/Oqrr1BfXw8+vQRMiOt/asMac8HqLfjwww+TJivfJ/bDzMYH\nALCMJjx7oNcjsoaxQ0MQBDz99NP46U9/iqeffjqsAkM9sowRI0ZormlJOL3sAU/uC8dqs6e9T+yt\nsU/SUyYUWmiRrGWCftvtdvuADMuGhgaUlJSEPZjVavWF4wGA4ziIouh309m3bx/Gjx+PsWPHAvCE\nOe+44w7cfPPNqKmpwY9//GO89dZbId2ojhw5EraNhBC8+OKLAAB9zoSwz1cLhmGgy54IV/MRPPnk\nk7jmmmvibVJQTp486XkQlTA+A4fDEdHfONHo6uoCANjCbFMmCAIaGxvDOkcmBA4ig+d5Tbx3fWlo\naAAQehgfAFiWx4ULFzT3XrS3twMAemyxnxBbvWOcP38+qataap2ginn99dfjT3/6E2bMmAEAeOml\nl/Dkk0/igw8+CHswi8XiN4uWZXmAaO/atQsrV670/TxmzBiUlZWBYRiMGTMGWVlZaG1tDSkZZPbs\n2WHb+N5776G2thacpRicKS/s89VElzUW7gsn8OmhQ/jhD3/oy39IVBwOz5pyVBL0WAByZH/jREOJ\nUEgqbLVXYgf5+fmaeO/6snPnTgChh/GV14qiqLn3wul0YtOmTT4hjiVKWd4rrrhCU7s7kpGhJq1B\nw/h//OMfcd9992HDhg3493//dxw8eBDbtm2LyJBZs2bhwIEDAICKigpMmDDQc66srPTLEt6+fTvW\nr18PAGhubobVao1Zlab29nZsfuYZMCwHY8HMmIwRTRiWg6FgFmRJwiOPPJrwYe3oJuhpJ4zPcRwA\nQIYK26S8YyhjagmbzQaW4cCyof9uPKeH3W6PW3+CWGE0GmEwGOBwxj6vxe50w2g0UKFPcIKK/Zw5\nc7BixQq89NJLOH36NH72s5+huDiy/ZRLliyBXq/HsmXLsG7dOtx3333YvXs3tm7dCgC4cOECLBaL\nX8W6m266CT09PVi+fDnuuusurF27NiZrjZIk4eGHH4a1pwf6/Blg9elRHyMW8Okl4DPH4MyZ077l\nh0TFl1AXFc9eOwl6LpdnXZVH7JvRKGMoY2oJT3vb8HJsOE4PWZbhdMav+2CsyMzMhN0Z+wmxwyki\nIyMz5uNQhkdQ1VyxYgU4jsPu3bvR0NCAe+65B4sWLcK9994b9mAsy+LBBx/0OzZu3Djf45ycHLz2\n2mt+z+v1emzcuDHsscLlhRdeQGVlJfj0UuiyL4r5eNHEWDgLdmc7du3ahfHjxydsCVRFnKMRxgfL\nQJIkEEKSvmObsrSlV+H34BgGHBhNJqVZrdaQk/MU+u6111ovdrPZjK6u9piPIwgy8vITr/gYxZ+g\nnv0111yDLVu2oLS0FPPmzcOOHTs05xXs3bsXu3btAmvIgLFoXtKJB8PqYCq5HAynw2OPP46qqqp4\nmzQovWH84V9LmTBowbtXyh6bWXU2x5jY5Cu1HAo2my2s9Xqgd33farXGwqS4YjKZIAhSTJcoCCEQ\n3BIN4ScBQe8ut956K44cOYKXX34ZgiDg+PHj+O///m81bFOF48eP44knngDD6WEqXZiwW+2CwRoy\nYCyeD9EtYs2aNb5s3EQi2p5932smM0qrYotKYm9hWLS3t0OSIm/GlGgIggBRFMPKxAcAXsOFdfR6\nPQgB5PA2eYSFLBMQgrBLNlPUJ+jdZcuWLXj00Ufx4osvwmazYdWqVXj++efVsC3mtLe3Y+3adZAk\nGcaSbyTNOn0geEsxDCOmo6OjA2vXrk24BDafuEQjcuK9hBbEvqmpCQBgCSOxbDhkcBwIIWhpaVFl\nPDXorYsfXrU4JeyvxcI6vsTPGNbHl7zX1lrNBi0SVOxfffVVPP/88zCZTMjOzsb27dvxyiuvqGFb\nTJEkCRs2bEBXVycMBTPAp2mj1KMuZyL4jDJUVVUlXMJeND17LYXxGxoaYGFZ6FRaPsr0TirC3aOf\nyChheBrG78Un9jEN43v+Z1WKSlEiJ+hfiGVZvxCNwWDQxLad1157DceOHfMm5CVu8ZxwYRgGxqJL\nwRoysGvXLnz55ZfxNsmHz7OPxn3BK/bJHop2Op1ob2/3CbAaZHm/v0oRGi3QWz0v/Gz8vudrCTVz\nj5ItzykVCXrbnTt3Lv70pz/B4XBg7969+I//+A9cdtllatgWM9rb2/H3l14CwxthLLxUlQ9qpO1I\nI4FheRiLLgPA4KmnnkoYQYxmGF+5hBzLBUkVOH/+PIBeAVYDZWKhRbEP5NkHSlLTcpvbSBLzIr1P\naa1OgRYJKva/+c1vUFZWhokTJ2Lnzp248sor8dvf/lYN22LGzp07Ibhc0OdNA8PHtiMUMLx2pJHC\nmXKgyxqDhoaGiKodxoJesY/CxZh+10xSlFB6PDx7JVdACwSqi293dkBw2yGIdnxxYifsTv9dCJyG\nw/jKd4MNcXIdyX1KK5PuVCCg2Dc2NqKxsRFNTU244oorcO+99+J3v/sdFi9enNSJPbIsY//+/WA4\nA3RZo1UZs3870rw8dcrw6nM9LUz37dunynjBUG4IUYmkeK+R7DeZ5uZmAEC6imueOoaBkWF9Y2sB\nn2fP+gvUyZr3QbxVA51CN07Wvu/3PK/hBD3luxHqRyuS+xSrkeW0VCBgCuWKFSvAMAxcLhfa29sx\ncuRIsCyLc+fOYeTIkdizZ4+adkaNpqYmdHV1gc8oA8Oo400N1o5Up0J3UVafDlafjq+/PpEQxWd8\nob4oevbJLvbKFkm1tt0pWFgG7W1tqo4ZSxTPvK9nL7gdcArdfq9zurohuB3Q6zwFdJQ2t5r27ENM\niB20bTJyhjyHY7Ux6U4FAoq94g3edddd+OEPf4g5c+YAAI4ePYrnnntOHetigNIOltWZg7wySrCc\nrx3p9u3b0dbWBkEQoFMpbMvozHDYmuF2u+O+Fza663rauMl0d3vEyMSoK/YmhkWbywWn06mJgii9\na/a9CXoyGdzb7Huc0/A+e1EUwTChR9IGu08FQ7m2FnbFaJ2gmyPPnDnjE3oAuOSSS1BdXR1To2KJ\n0mKXiOrUwmZ5Exh9OgShx7c+y+rTwfLqlOYkogt6vV6z+2DjHa0YLkonQDVK5fZFGc/hcGhC7JUw\nfLjlclmWA8twmhR7WZZDXq9XiKRtMssyST/pTgWCuhOFhYV47LHHcOrUKZw8eRIbNmzA6NGjVTAt\nNpSUlCAtLQ2irQkkzP7hkWIqWQDFE2X16TCWLFBlXNltg+zqxIQJE+g+2AQl3CSqaMFoJDKiECwb\nfyg4TqdJsVdr6Y5haDZ+MhBUATZs2IDu7m7cfffd+PWvfw1RFLFu3To1bIsJHMdh0aJFIKID7s6z\n6oxpzAKjMwG8CWnjloIzZqkyrtB2DABw9dVXqzJeMKJ74yExuKb6qFH4ZDCUpDWtTAJ71+zDL3fN\nsXpYrdoUezVgQD37ZCBobDczMxMPPPCAGraoxs0334y9774LV8uX4M0jwBoyVBlXTWESexrg7jyL\nsrKyhOmC5xOWaNyDNFK5S1lWchGC2G8C7cXpFQJl/GTHbreDAQOWCX+5ytPTvjMGVsUXlmV9k7pY\nQghJ+u9hKhD0L7Rjxw7MmzcPkyZNwqRJk3DxxRdj0qRJatgWM3JycvCz//xPENkNR/0ByG5HvE2K\nKpKjHc7Gj8HrdLj77rsTZr1euSFExeMg2vBMs7OzAQBWlT0jqyzDYrFAp0vOxk/9sdls4Dh9RBNq\njtNBFMWQEtKSCZZlY1oXH/B8l2Uq9klBUBX461//ivLyckyYoJ2SsgBw1VVXoa6uDtu2bYPj3D6Y\nRl0FVpf8PZlFeyuc9QcAIuHX9/wWY8eOjbdJPnyTjijompJukSgTmUgZOdKzB/OCLKIY6givSAi6\nZQmTRo1SZTw1sNvtEYXwAf+SufHesRJNlK53kkTAcbGJKipd7wwGNeNSlEgIOh0rKCjQnNArrFix\nAjfeeCNkoQeOmr2QHInXFjYc3N3n4Dz3Hhgi4e6778aCBeokAoZKr9hHwdvQSLetMWPGAABaVNy6\n1CaJIEBSJ9r2x263h93eVkE5T9kZoRWUiYsoxi5qJIrEbyxK4hL0TjllyhT88pe/xIIFC/xmb9dd\nd11MDVMDhmHwb//2b8jOzsbzzz8PR+0+6AtmQZc1NqkSvwiR4Wr5Eu4LJ2E0mXDvb3+L2bNnx9us\nASghYzKE2Ov1euTl5QXd56tcI9nD0GVlZUhPT0e9zaZa9nS9t/XxJZdcEvOx1ECWZTidTljMkbWo\nVvbma62KXlqaJ1LpEiQYDLGp6+EUPJNUs1mluiWUiAkq9larFWlpaaioqPA7rgWxV/jXf/1XlJSU\n4OGHN8LW9BkkewuMhbPBRLCNR21kwQpH48eQHe0oLi7G7373O5SVlcXbrEFRZv9EGtzTUGpzKxW8\nnn766cAXkzxin+zhQ5ZlMXPmTBw4cABtkoR8FSIVNW4BLMti+vTpMR9LDZxOJwghEXv2rEY9eyX5\n0uESkZEem3uZ0+XZOpqeHtlEa7i88MIL+PDDD+MydrxYsGABbr/99rDPC3pnSeZtduEwZ84cPP74\nY/jzn/+MkydPwu5og6F4HnjziKhcn0+P7vooIQRiVw1czZ+DyG5cccUV+NnPfpbQM2yfMEuDe/b9\na3Nv374dgXwtIslgGCbpw/gAsHDhQhw4cACnBCfy+eDZ8XwA7z/Q8b50SCJaJRFz5szRTCa+ItKR\nh/F5v+tohawszxZfu90dszFs3mtnZmbGbAxKdAh4p/zpT3+KzZs3Y/HixYOGFt99992YGhYPRowY\ngfXr1+Mf//iHJ3Gvdh90ORfDkD8NzDDL2xoLZkTJSkAWnXA1HYbYUw+jyYT/uPPnWLRoUcIvPZhM\nnqqBRBxc7AerzW1GwaCvJSKB0WRM+N85FGbPno309HSctNkwl5Cgom1mWWSyHLrk3rKvWSwHcwgZ\n0V+7PJUjFy1aNDyjEwif2HORTfw4jYbxc3NzAQA9ttiJfY/Ns9SmVnOv/tx+++0RebmpSMBvx0MP\nPQQAKC8vV82YRIDneaxYsQKzZ8/GX/7yFzQ1nYBka4Sx6DJwpqGbQqiBu7sOrqbDIJILU6ZMwV13\n3YWCgsEFMdHoFfvBw/iD1eYOFKcgbhlmU3xCh9FGp9Phmmuuwfbt23FKcGGSIXj52mss6dje3QkZ\nHqH/F0vw90IgMr4WXMjOysL8+fOjYHli4HR6JjDDTdBzuVxRsykRyM/PBwB09QT/vThu8IlioOMK\n3T2C31iUxCWg2I8Y4Qlfl5SUqGZMIjFp0iRs2rQJL774It544w3Ya96BPm8y9HlTwKjctAQAiCTA\n2XQEYnctdDodVv7bHfje976XVPtblSUG4g6cHRxqbW4ikoResgiXpUuX4tVXX0WF04GJekPQ8rm5\nHI80lgUhBMszs0Ma45jLCYEQfP+73036xMa+KGLPspF59sp5ynW0QnFxMQCgoyu42FvMOmRnGvxe\nm5NlgMU89OdEeb0yFiVxSR6liANGoxF33nkn1qxZg7y8PAhtx2CveQeSq0tVO0TredjO/h/E7lqM\nHz8Bjz32GK677rqkEnqgN2FIFoa3FYgQAuKWNLPmDHjCoIsXL0anLOGMO/TiLqEuY7gJwZcuJ8wm\nE5YuXRqpmQlJr2cfYRhfo2Kfm5sLg8GAC52h/V7/+i/joHTDzcky4HtLxgU950KnEwaDATk58Y96\nUoYmudQiTkyfPh1PPLEJ3/zmNyE7O+CofhvChaqY154msgRn0+dw1L0PlghYsWIFNmz4s68QS7Lh\n6zjoHrz1aKgQkQCkd2uRVvj+978PlmVx2GGPeq38SpcDDlnGtd/7nqYmSQB8WzQj9+w5v+toBZZl\nUVZWhvZOJ6QAO2D6kp9jgiVNj/Q0He64ZSryc4buzClJMto7nRg9enTSOR6pSNBvhyiK+OCDD3x9\n4BW0tPUuFNLS0vBf//VfmDt3LjZt2oSe5s8h2ppgKp4Hhov+9i/Z1Q1Hw0eQXZ0oLS3Fr3/9a4wb\nF3ymnchkZHh6EBDXMD1773Yf5XpaobCwEEuWLMGePXtwMsS1+1BwyTK+cDphSUvT5PdWWWtnmciS\naJV6+lpbswc8RZuqqqrQ1uFEQV5oy16hRovaOpyQZaKp4kxaJqjY33PPPWhsbMS4ceP8PgRavGmE\nwvz58zFhwgT85S9/wdGjR2GvfhvGkgVRTd5zd5+D6/whEFnENddcgx/96Eea6Dmu0+lgMpkgCMPz\n7GXv+Vrc7rN8+XLs27cPh512jNcbQtpOF4wKlwMuImPZTTdpzqsH+nr2EYq9Rj17ABg/fjz27NmD\nphZbyGIfKudbbL4xKIlPULE/efIk3nrrLTVsSRpyc3Px4IMPYtu2bXj55ZfhqH0XhqJLocscPazr\nEkIgtH4Fof04DAYDfvnLu3DFFVdEx+gEITMzEy2drcO6BnF6xF7ZR6wlcnNzce2112LHjh045nJi\nunHoUGowbLKMoy4nsrOz8d3vfjdKViYWbm9FwMg9e87vOlpi4sSJAIDGZhumT45uxnxjs0fsL774\n4qhelxIbgi60jBs3Di0tLWrYklRwHIfly5dj1apVMBr1cDZ+AldrZcTr+ESW4Gz4CEL7cRQWFmLj\nxo2aE3rA0+VNdknDyneQvWF8pWOc1rjpppuQZk7D5y4HBDK8JY/PnXaIhGD58uWaiA4NhiLSTIRi\nr+yu0aLYjxw5EmazGfVN1qhfu77JU121tLQ06temRJ+gYu90OvGtb30Ly5Ytw8qVK33/KB7mzJmD\njRs3Ij8/H0JbJVwtX4QtZEQW4ag/ALGnDpMnT8bGjRsTtuTtcMnJyQFI77p7JMhObYt9eno6rr/h\nejhlGUeHkSHeI0k47nL6cgG0iuhtIsREmCSmJJeJKjYjUguO4zBlyhR0drvQbY3eMkW3VUBXt6fW\nB8fFpu4+JboEDeP/9Kc/VcOOpGbkyJHYsGEDVq1ahXPnqgAAhhEzQ0p0IbIER/1BSLZmzJ07F7/5\nzW+Svt77UChVvWSHBNYYWfa07BD9rqVFrr32Wux67TV8abVimtEIQwS1HY447ZAB/OAHP9BEWeFA\nKCLNRri5iIF2xR7wNDz67LPPcK6hB1MnRuc7c66hGwAwbdq0qFyPEnuCfjvmzp0Lh8OB/fv34513\n3kF3dzfmzp2rhm1JRW5uLv74xz+itLQU7gtVENpPBD2HEAJn4yeQbM2YN28e7r33Xk0LPdBX7CO/\nsaaC2JvNZlx3/fUQCEFlBN69VZZwUnChuKhIk8tBfZEkb5QowmRGJYzvu47GmDHDU6q7pr47ates\nqe8BAMycOTNq16TElqBi/+yzz+KJJ55AUVERSktL8fTTTw/djWwIZFnGqlWrcMstt+DWW29FbW2t\n3/Mvvvgili5diltvvRW33norzp49G/ScRCIrKwtr1qxBbm4uhNYvIfYMXQlOaD/uC93/5je/0VRV\ns0AoZTWl4Yi9XYTZbNZUBb3BWLp0KcxmM74SnBDDXBo66nRABnDTzTdrPsyqLJsFiqTp9XoUFxcH\n7rnuPS/WdTPiRVlZGbKzs1Fb3x2V35EQgtr6bmRnZ2PUqOg2+KLEjqBiv2vXLpSXl2PlypW47bbb\nUF5ejtdeey2iwfbu3QtBELB161bcc889WL9+vd/zlZWV+NOf/oTy8nKUl5dj7NixQc9JNHJzc/HA\nAw9Ap9PBef5TyO7BO2mJ9lYIrZXIy8vH7373u8A3Io2hiL1sj0zsCSGQHZKvnLOWMZvN+Pa3vw2H\nLOO0EPoecDch+FpwISsrC1dddVXsDEwQZNmTxMhgoNgrbZM3b96MO++8c9DvmXKech2twTAMZs+e\nDbtTRHPb8Jv9NLXaYXd6OidqoRFVqhBU7Akhflm8BoMh4vW/I0eOYOHChQA8oaXKykq/548dO4Zn\nnnkGy5cvx+bNm0M6JxEZN24cbr/9dhDJBVfz5wOeJ7IE1/lDYBjg//2/X2tyv3ggFJGOWOwFGUSU\nU0LsAeA73/kOWJbFV67Q269WCZ4a+N/5zndSIlrU660OFJ7+bZOH6s6mVc8e8CQSA8CZ2uGX+j57\nznON2bNnD/taFPUIqtqXXXYZfvGLX+D6668HAOzcuRPz5s2LaDCr1epX1IPjOIii6Js8LF26FD/4\nwQ9gsVjw85//HPv37w96zlAcOXIkIjujQUFBAUaOHIm6ujqItmbwab2d6dwdpyALPb58iHjaqTaE\nEPA8DynCtpvKJIFhmJR538aPH4+TJ0+iTRSRF8Ln/oTLBYZhUFhYmBLvUVNTU8DnBmubXBCg8GJ3\nd7em3y+WZXH2XBcWzBle05qz57p8Oxi0/H5pjaB3jt///vd4+eWXsXPnThBCcNlll+GWW26JaDCL\nxQKbzeb7WZZln2gTQnDbbbchPd3TqvPKK6/E8ePHhzwnGPGeeWZmZuKee+6B0FbpE3siixDav4bZ\nbMavfvUr3++bShQVFaG+qSGic5VJwiWXXBL3v69aCIKAtWvXokpwIo8fugJehySiRRIxe/ZsTfWs\nH4qhon2DtU0ORGZmpqY/U1OnTsXRo0dhtblhSYss4mO1udHUasf06dOxYMGCKFtIGS5DTb4ChvFb\nWz1Vzs6fP4+rrroK9957L+677z4sWrQo4iI7s2bNwoEDBwAAFRUVmDBhgu85q9WK7373u7DZbCCE\n4NNPP8XUqVOHPCfRmTBhAmbNmgXJ3grJ2QEAcHfVgkguLF26NCWFHvBEPYhb9pW9DQfZ5vHsCwsL\no21WwjJnzhykpaXhjFsIGmo+4xWzVFirV1DWjQkGf2+UtsmBhF45T+vrz5deeikA4Gxd5KF8JYSv\nXIuSPAR0ke+//35s3rwZK1as8PsSEELAMAzefffdsAdbsmQJPvzwQyxbtgyEEKxduxa7d++G3W7H\nLbfcgrvuugsrV66EXq/H/PnzceWVV0KW5QHnJBNLly7F559/DndnNbjCbIhd1WAYBt/+9rfjbVrc\nUHpfy1Y32JzwMsUlq8ezLyoqirpdiYpOp8PcuXOxf/9+tEgiCvjAXtlZtws8z6fUzdjXcS3SNXfv\neVrv3DZ37lw8//zzOFPbiUsuDpy7MBRnajt916IkFwHFXkmQ27Fjx4Aa5PX19RENxrIsHnzwQb9j\nfTu5XXfddQMa7Ax2TjIxa9YspKWlwdZRBbGrGkR2Y+rUqb6s9FRE8colmwg+zP5Bks0NhmFQUFAQ\n/MUaYt68edi/fz/q3O6AYm+TJbRLEmbOnKm59r9DoWwtJIgsm17x7LW+RbG4uBglJSWorT8PUZTB\n8+FNbtyijJqGHpSWlqbUZFsrBPxrnz9/Ho2NjVixYoXvcWNjI+rq6vCjH/1ITRuTGp7n8f3vfx8j\nR45EaUkhRo8e7Ut2TFUUz17x0sNBtrqRl5eXMlsVFaZPnw6GYVDnDrzmXOet7T5r1iy1zEoIfGIf\nYR8B5Tytiz3g8cjdooxzjT1hn1vX2ANRlKlXn6QE9Owff/xxfPrpp2hpacEPf/jD3hN4PqXWA6PB\nDTfcgBtuuCHeZiQMkYo9EWXITgnFE4eXTZyMWCwWXHTRRThz6hREQgZtfXte7E1eTCWUhF15mGKv\n5ZLCCpdeeileffVVnKntwthR4W35VbbtpdISkZYI+Olet24dAOCZZ57BT37yE9UMomifESNGgOM4\nyGGKvTI5UCYLqcakSZNw6tQptIgiigfZP98kijCbTJptohQI346eCIviyCkk9pMmTYLZbMbZc12+\n/KtQIITg7LkupKWlYdKkSTG2khILgn66BUHAE088MeD4z3/+85gYRNE+HMehqKgIDc2NYd1wFLFP\n1ZaaF198MXbt2oVWaaDYC0RGpyzhkvHjUyIc3Reln4RMIqttL8ueHR6psDTE8zxmzZqFDz74AO2d\nTuRlm0I6r73DiW6rgMsvn5tyny+tEFaGhtvtxr59+9De3h4reygpQklJCYhbBhFC98ZS3bMfO3Ys\nAKBNGlh9sE30CF3fhNdUQakSKMuRir3nvFQQe6C3/kh1XeiNcZTXKpX4KMlHUM++vwf/s5/9DLff\nfnvMDKKkBiUlJQA8As4aQvMUpB6337mpRlFREfQ6HS4MIvYdXu909OjRKlsVf3o9+8hKMCsRgVQR\neyWBs6auG5deEtqulup6z3o97XKXvIS9sdRms6GxcehubhRKMHxJej2Bs8v7I1vd4DguZeri94dl\nWRSXlKBTlgcU1+n0tmdNxYmQT+zlCMXee17fHiBaJicnB2VlZahvskIUg0fWRFFGw3kbysrKkJMT\n5l5ZSsIQ1LNfvHhxb4UqQtDd3U09e8qwUdbdQ83IJ4RAsrpRWlyS0muGRUVFqKmpgaOf2Hd7k9NS\ncf+zItJShGIvpZjYA56tnLW1tWhotqKsJECzAC8NzVaIkowZM2aoZB0lFgQV+/Lyct9jhmGQkZHh\n197L7x4AACAASURBVJiGQokEXxi/J0Sxd8kgbjklPde+KFENa7/1aasswWgwpGQJZkWkqWcfOtOn\nT8euXbtQ1xhc7Ou8e/JTbUun1ggo9jt37hzyxP6V7iiUcMjMzITZbIYrRM9eiQCkutjn5uYCAGz9\ntpnZZILcvDzN13cfDLPZDACQpMg6KUqS4HedVGDy5MmeIk0hFNc512gFwzCYMmWKCpZRYkVAsf/0\n00+HPJGKPWU4MAyDkpISnDpzOqQ+4pLVc0NO1Ux8hcxMTyGUvmF8mRA4iDygrHWqYDJ5to9JcoRi\n7/XsleukAhaLBWPHjkVN9dkhS+e6RRnnW2wYN25cSpVg1iJBi+oAni131dXVkCQJ48ePT4niE5TY\nU1JSglOnTvl61A9Fqm+7U8jI8IRcnX2qxQle4U/FED4QDbF3+10nVZg8eTLOnDmDpjY7SgsHX5pt\nbrVDlgkmT56ssnWUaBNUtSsrK/HLX/4SWVlZkGUZbW1t+Otf/4rp06erYR9Fw/Qtm8tZhu6vLdMw\nPoDeULPQx7NXHqdSGLovBoMBLMtCpGH8sJg0aRJ2796NhiZrQLFvaLYC8BR0oiQ3QcV+zZo1eOSR\nR3ziXlFRgYceegjbt2+PuXEUbaNkjsshiL1kFWE0GlM2VK2geJ/uPmLv9nZtSzXPVIFhGJjNZkhD\nNAkaCmWSkGph6okTJwIAmlptAV/T1GLzey0leQm6z95ut/t58TNmzIDL5YqpUZTUQBF7yTa0R0YI\ngWxzo7i4OCUT0PqiFH6R+oi96H2cKkVhBsNisUCSIxN7xbNPNbHPz89HZmYmmlrsAV9zvtWOzMzM\nlG7JrRWCin1mZib27t3r+3nv3r0p711RooNP7K1Dr9kTpwQiERQWFqphVkKjlIbtu/FO8up+KufS\npKWlRRzGF2UBDMOkXGSEYRhcdNFF6LYKcDgHfgftDhE9VgHjx49P+Um2Fggq9g899BA2b96MefPm\nYe7cuXj66afxhz/8QQ3bKBonPT0dZrMZchDPXrJ5bkSpWDCmP0pBIbnPBgbiDeOnutjLshhRfXxJ\nEmA2m8GyYRcUTXqUXgrNbQO9+5Z2u99rKMlN0LvD6NGj8c9//hN2ux2yd28vLapDiQYMw6CwsBDV\ntdVDbr+T7Z7JQEFBaHW8tYxP7NH7fimPUlGsFJR7kiQLYNnwPHRREpCdmZo7GZTmSq3tDowu9S+u\no4j9mDFjVLeLEn2C3h3279+PDRs2gBCCm2++GVdffTX+/ve/q2EbJQUoKCgAkciQ3e8Uz56KPXpL\nV/c5prxzqVxGWBF7UQx/3V6UhJR1YEaNGgUAaLvgGPBc2wUnAKCsrExVmyixIajYP/HEE7jhhhvw\n5ptv4pJLLsG+ffvwyiuvqGEbJQVQBFx2BF63V8L8VOx7Bb2v2CuPU3ld1Sf2UnhiL8sSZFlMWbEv\nLi4Gz/No6xgo9u0dDuh0Orp8phFCivuNGzcO7733HhYvXoy0tDS43ZElwlAo/VGyfGVn4LVWyTsR\noBnBvaF6Av8KekBqr9lHKvbK61NV7DmOQ0lJCS50uvyW0gghuNDpRHFxcUpHjLREULHPy8vDQw89\nhMrKSixcuBDr169P+SpmlOjhE/uhPHu7iKysrJTeWqYwWIKeEsana/aAKIW3LVhKcbEHPB0oBbcE\nq73XibPa3BDcsq87JSX5CXp32LhxI6ZNm4by8nKYzWaMHDkSGzduVMM2SgoQzLMnhEB2SMjLy1PT\nrIRl0K13Xi9feS4V8SXoUc8+bBTnrbOrd6LU0e3ye46S/AQVe4vFApZl8corr8DhcCAtLS2lvxiU\n6KJ0cSOBxN5NAJlQsffCsiw4jvMV0gF6vfxUjnwoBXHCD+O7/M5PRRRB7+gj9orwU7HXDkHF/uGH\nH8aBAwfw9ttvQ5IkvPLKK1i/fr0atlFSgMzMTHAcB1kIJPae4zk5OWqaldCYjEaIhGCszoCxOoOv\ndG4q9WPvD12zjxwl8bWrx4WJY7MxcWw2Or2ePS1kpR2Civ0HH3yADRs2wGAwwGKx4H/+539w4MAB\nNWyjpAAsyyIrKwtEkMH2q4/PWnS+VHMq9r0YTSYIIPiGOQ3fMKf5GuGkWgW4vkS6Zk/F3l/sr5pf\niqvml6Krx/M+jhgxIp6mUaJIULFXkn6UbT2CIKR0IhAl+mRnZ0N2SrDMGwF4d4+xFh3S5xX41vJp\nieZeLBaLX9c7l/dxKgtW75p9eDuFUrUufl9yc3PBsiy6enqjIt1WARzH+ZbZKMlP0L063/rWt/Cr\nX/0KXV1dePHFF7Fr1y4sXbpUDdsoKUJWVhYgE3BmHVgTD0IIsv9lJADA3eyp4pWdnR1PExOK9PR0\nCIRAIgQcw/h626ey2Ee+Zk/FXhH1Hmun71i3VUBubi7ddqchgor9T37yExw8eBDFxcU4f/48fvGL\nX2DRokVq2EZJETIyPGU6icvjxfctDiN7jymvofRGOZxERhrDweEtY53K0Q+O42AwGMPufKe8PpUn\nSoBnV8zXX7dC9mZ72uxujCqjSbFaYkixP3v2LNLS0rBw4UIsXLgQANDe3o4HHngADz30kCoGUrSP\nIuSDJekpZXSp2PeiRDnsMkEaC9iJDL1en9Jr9gCQlmaGzRquZ+8J+5vN5liYlDTk5uaCEMDmcAME\nIAR0B4zGCLj4vmnTJtx444341re+hY8++giSJOGZZ57BkiVL0NjYqKaNFI2Tnu5pQkLcA+vjK9n4\nqe559UVJVrR5O7zZZILc3NyULpcLeELxdM0+MpTPlNXm9hXXoUmx2iKgZ79z507s2bMHLS0tePzx\nx/Hss8+ira0Njz32mM/Lp1CigSLkgzXDkb3HUv1m3Bclacoqy5AIgYPIGEcTqWA2myHJYYq97AbH\ncSldowDoM4HsU0WP5sloi4Bin5aWhhEjRmDEiBE4evQorrvuOjz33HPDStiQZRmrV6/GyZMnodfr\nsWbNGr+OSq+//jq2bNkCjuMwYcIErF69GizL4vrrr/cJQmlpKdatWxexDZTEQwk/E3EQz16UYTAY\naKJQH5Sqg1Yiw+Zdr6d9AzyfI0JkyLIElg3t8yJJ7pRf/gB68z2o2GuXgGLfd3tddnY27r333mEP\ntnfvXgiCgK1bt6KiogLr16/HU089BQBwOp149NFHsXv3bphMJtx9993Yv38/Lr/8chBCUF5ePuzx\nKYnJ0GJPYDKl9npqf3xiL8vo8Yby6fpq7+dIkt2hi73sRrqZLhEpYm93ilB2daZywqcWCbhm33f9\nL1qVuY4cOeJbApgxYwYqKyt9z+n1evzjH//wfWFFUYTBYMCJEyfgcDhw++23Y+XKlaioqIiKLZTE\noVfsycAnRZl6Xv1Q1ud7ZAlWr2dPi5/0FfvATZX6I8si/Xyhj9g73HA43X7HKNogoGd/6tQpXH31\n1QCA5uZm32NCCBiGwbvvvhv2YFar1S/RiuM4iKIInufBsqzPOykvL4fdbseCBQtQVVWFO+64Azff\nfDNqamrw4x//GG+99VZI7TyPHDkSto0U9Tl37pzngTRQ7IlEIEkS/Vv2Iz09HVar1Sf2nZ2dKf8e\n9fT0AAivsI4kixBFMeXfu66uLgCAo49nX1NTgwsXLsTRKko0CaiYe/bsifpgFosFNpvN97Msy36i\nLcsyNmzYgOrqamzatAkMw2DMmDEoKyvzPc7KykJrayuKioqCjjd79uyo/w6U6JOZmQkAIPLAMD5k\ngoyMDPq37EdJSQlOfP01ur1h/G984xsYNWpUnK2KL0ePHsVnn30GmQzeZ6E/MpFBiIzc3NyU/3y5\nXC488sgjcDglEG+N6gULFqR84mKyMdSkNaDYl5SURN2QWbNmYf/+/fjOd76DiooKTJgwwe/5VatW\nQa/X48knn/TlDGzfvh1VVVVYvXo1mpubYbVaaTKSxvAl3w2i9UQmIUVxUg1PEZSv0Sx6QtZ0zR4w\nGAwAPKH5UFBel8oNhBQMBgP0eh0cTs97otfrqdBrDFXvokuWLMGHH36IZcuWgRCCtWvXYvfu3bDb\n7Zg6dSq2b9+OOXPm4LbbbgMArFy5EjfddBPuu+8+LF++HAzDYO3atfTmrzEUsSdkkDV7ApqJPwjK\n9rsOWYLZZEr5ojBAJGLviQBQUfOQlmaBS/CUp6Z1LbSHqqrJsiwefPBBv2Pjxo3zPT5x4sSg5/3/\n9u48Oooq4QL47TVLZyELBBBQEoagIFtwFJBV58hyWFS2IEGRMYgTPgQMkUUIoiCbOaOIoAwyKCLJ\nISAIOIILeEAgoKigrEoMgpBAAmTrrd73R6cq3Ul3EiBJh8r9nTNjupbu6uqibr1Xr95btmxZjW4X\neVdlncHU985i3HEeoCSUz9gDKA3tqlfjl5ZiyRHwOdmOdg/hwezXQm1YRKY6z22Jv55zfgaaPZ05\nKGEvVTHsWbJ34e/vD3NJl9WsKVIfhj15XWmYuy/BM+zLkxs1lv27PjMYDACqXrIXJaMFyuvVd35+\nfspAOHwcUX04MD15nd1eMtqdu6NRq1HmUymGfXlyWx45xCsjXxQw7B2cGyoy7NWHYU9eZ7WWPBet\nLV+y12g1pfNJIQ8eVPbv+kwJ+ypW48sXBWzw6+Ac9nJjR1IPhj15nRzmGndhr2PYu+M8MBDvrzoo\nYY+q3fZh2LtybrvAsFcfhj15ndlsBgBo9G7u2es0ynwq5Xwy5oiADqWPcFatGl9uC8JHOx2cw563\nNtSHYU9eV1xc7PhDV/5w1Og0KCoqquUtqvtqYuyKO12F/TW4IV8UOA/6VZ8513CwtkN9eJST1xUW\nOjry0BjchL1Bi8LCQrbIrwCrXB3k0K5yNT5YsnfmvB8Y9urDsCevU8Je7ybs9VrY7XZYLJba3qw7\nBqtcHeTajipfGJYsx06bHJzDnrUd6sNflLwuPz8fAKA1ugn7kmnOAyiRK4a9Q2lA3VzJnsHm4HzR\nw32iPvxFyevkoUndV+PrXJah8ljl6qCEFW/53BLngGdth/ow7Mnrrl+/DgDQGMvfO5VL+wx7z1gK\nc1Cq8au6gnBdr75juxh141mCvE4Oe62Pm5K9j+MC4Nq1a7W6TXcShpVD6X6oamjxnr0z57CXpKo9\nvkh3DoY9ed21a9eg0WuhcfPonbYk7PPy8mp7s6ieYNg7OAc8S/nqw7Anr8vNy4PGTakeKA17luw9\n44mZqoNz2HM8CvVh2JNXSZKE69euKaFeloYle6JaYbPZ3P5N6sCwJ6+6ceMGJEmC1td92MvTc3Nz\na3Oz7ii8v0rVwTngWbJXH4Y9edXVq1cBABpf94+PaYxaQMOwrwhPzFQdnAec4uBT6sOwJ6+SQ9xj\nNb5GA62vjmFfAVa5UnVg2Ksbw568Sr4X76kaHwA0Pnrk5uayIZoHDHuqDs5dUrN7avVh2JNXKSX7\nCsJe66uD1WpV+tAnVwz7W8NLR1cMe3Vj2JNXySV7jYdqfKC0ip9V+e6xypWqA8Ne3Rj25FVVqcbn\ns/YV44nZPa3GwxMeHqbXdxaLBRoNoNEAZrPZ25tD1YxhT16lhH0FJXuNL5+1L8u5/QJL9g5le8Iz\nGvzgawxymebrEwSjwa/kFSvynZnNZhj0Wuj1Wl5AqhDDnrzq+vXrHrvKlcmD4ch96JPrfXqemB3c\njWcffU8vaOCY7usThOi7e5WuULIYBxJysFgs0Ou00Ou0LNmrEMfGJK+6fv26Mma9J/L9fIZ9KeeA\nZ8newd149v6+ITAa/CGEQKfooS7LCw6E48JisUCv10JA8AJShRj25FU3btyAxrfisNcaOaZ9WWxM\nVV5FQ9y6D3THkizZO1gsFuh0GgAaHlMqxKOcvMZms8FsNldesjc45vPRu1LsAKU8nc5xUShE1boP\nlpeT16vv5JK9XqflMaVCDHvyGjm8tYaqhX1+fn6Nb9OdgiX78pQSehU7X5Lv7bNk72C1WqHXaaDT\naWC18phSG1bjk9cUFxc7/tBXEvZ6RxUsGw2Vci55sVMdB73ecTq72ZK9vF59JoSAzWaDTusDAcBq\n5TGlNrykJa8pKioCAGh0lTSQ0mpclifXgGeVq4Mc2hLD/qZJkgQhBLRaDXRaDYQQHGBJZRj25DVy\nSFX02B3gaFyl0WkYak6cT8Qs2TsYDAYAgBBVCymJYa+QjyGtVgNtycU1w15dajXsJUnCnDlzMHLk\nSMTFxSEzM9Nl/ldffYUnn3wSI0eORGpqapXWoTuXElJVOQq1GoaaE449Xp4c9lUt2UslFwVGo7HG\ntulOIR9DjrB3nUbqUKthv3v3blgsFmzcuBHTpk3DG2+8ocyzWq1YuHAh1qxZgw8//BAbN25ETk5O\nhevQnU0+mVTlOWeNRsOTjxNJktz+XZ/JoS1JVbsoFBLDXiY3VnR0l+v498jjSl1qtf7qyJEj6NGj\nBwCgY8eOOHbsmDLv7NmzaNGiBYKDgwEAMTExyMjIwNGjRz2uQ1RfOfcSx4sgB4PBAI1Go5TYK2Nn\nyV5RGvYayP0PcEhpdanVsM/Pz0dAQIDyWqfTwWazQa/XIz8/H4GBgco8k8mE/Pz8CtepzJEjR6r3\nC1C1cndLxniXye2yAo7W+/xNHX777Tfl77y8PO6XEnq9vlzJPjT4brfLSpKjDciZM2fq/bgL8mOw\njqh3lOx/+OEH+Pv7e3GrqDrVatgHBASgoKBAeS1JkhLaZecVFBQgMDCwwnUqExMTU01bTjXBZHIE\nu3MJwnR/mPuFhYDJZOJvWsL530BISAj3Swk/P39YzK5hf0/TLm6XtZdcFMTExCA8PLzGt60uk/uw\nEE7/36lTJ5eCFtV9FV301+o9+86dO2Pv3r0AgKNHj6J169bKvKioKGRmZiIvLw8WiwWHDx9Gp06d\nKlyH7mxKYElVqC6UhNIAi1yxU5hS/v5+Vb5nL9kdy/n5+VWyZD0ihNInEccMUJdaLdn/4x//wL59\n+zBq1CgIIbBgwQJs27YNhYWFGDlyJF5++WWMHz8eQgg8+eSTiIiIcLsOqYN8r1TYKw57IQSEnWHv\nzDngeVIu5e/vj+zLV6q0rL2kGt/X17cmN+mOIHcZ7LjuFi7TSB1qNey1Wi1effVVl2lRUVHK3337\n9kXfvn0rXYfUQS5RCVslJfuSiwHePyzlfCLmSbmUn58f7JIVQohKL4Lsdit8fHy5/+AU9k61bKwx\nUhf+muQ1SthbK37ER57P6tZSzvfs2SlMKfkes91eed/uNrsFAQHuG4TWN85hb5fY2ZAaMezJa/z9\n/aHRaCoNe6lkvtygjxj2nsjHiE2qPOztksXlCaD6TKfTQavVwi5JsNuF8prUg78meY1Wq0VgYCCE\npeLnouX5QUFBtbFZdwTn9gtsy1BKLtnbbBWHvRASbHYLLyCdGI0G2GwCdruAwcALSLVh2JNXBQUF\nQZgrKdmXzGcprJRzwLNTmFLyBaHNXvEIibaSan5eQJYyGIyw2yXY7BKMRh9vbw5VM4Y9eVVISAgk\nix2igsfvRLHjEanQ0NDa2qw6jyV79+TwttqKK1zOZjO7LE+Aj48RVpsEm02C0chjSm0Y9uRVISEh\nAACp2HNVvjxPXpZcS/MM+1JVLdlb7cUuyxPg4+MLm13AZhPw8eHjiGrDsCevCgtz9JgnFXvuCEUq\nsrksS65hz2r8Ug0aNABQecleni8vT4CPjw+sVglWmx0+PqzGVxuGPXmV3E2pVOg57O0lYV/fuzR1\nxpK9e/JAWlZrUYXLyWEvL08lYW+zw2KV2NGQCjHsyasaNmwIoOKwlwptCA4OZgnWiXOHMSyFlZJv\n9VRasi+5GOCtoVK+vr5KV7kMe/Vh2JNXNW7cGABg9xD2QghIRXZlOSqPJftS/v7+MBgMsNgqLtnL\n8xn2pZwDnmGvPgx78qqIiAgAgFRgdTtfKrIDklCWo/JY41FKo9EgNDQUVlthhcuxZF+ecw+VDHv1\nYdiTV/n7+yMwMBD2Avcle/kigCV7z1iydxUaGgqLtRhCeO6/wWIrhNFoZKc6TpxvBzHs1YdhT17X\npEkTSIU2l3HtZfZ8q7IMucewd+V4akNUeN/eYi1CWFgYRwx04lyy5zgU6sOwJ69r0qQJIAm3jfTs\nLNlXiqO2uZIf0TRb3VflS0KC1VbETprKcC7Ns9Gn+jDsyeuaNm0KoLQU70wqmSYvQ+WxZO9KfkTT\n4iHsrSXT5SdByME57FmyVx+GPXmdHOSSm7C359vg4+vDhlQV4Kh3ruSSvcVa4Ha+fBHATppcsWSv\nbgx78jqlZF+mRb4QAlKBFXc1vYv3VivAoUhdVVayN5dcBDDsXbGBnrrxLEFep4T9Ddewl4rsEHbB\nKnwPunbtCoBdvpYlh725kpI9e2R0xZK9urH+j7wuICAAQUFByC9w7QhFfuyOYe9eUlISCgsLOfRv\nGaGhodBqtbBY3Ie9uWQ679m7Ysle3ViypzqhSZMmkAqsLkPd2tk4r0I6nY5B74ZOp0NISIjH1vhy\niZ9h74qDK6kbw57qhKZNmwLCtY98PmNPt6pRo0awWgvddqxjsRbCYDBweNsynEv2DHv1YdhTnSAH\nunMjPbkan2FPNys8PBwCAhY3o99ZrAVo2LAhG32W4RzwvGevPgx7qhPkQHd+/M5e4Hjsjg3Q6GbJ\nVfRlW+TbJRustmJW4bvBYZPVjWFPdULZ0e8cj93Z0KRxE5bA6KaVtsjPd5nOlvieOffXwLBXH4Y9\n1QllR78TFgnCJnG0O7olnkr2ckc7DPvynAOeHTWpD8Oe6oQGDRrAaDQqo9/Joc+wp1uhlOzLPH5n\ntrCrXE+cw54le/Vh2FOdoNFo0KhRI8f49QDsRY7QZ9jTrZDDvGzHOizZe8ZqfHVjXQ3VGQ0bNsT5\n8+chbJLyCB5LYHQrgoKCYDAYylXjmxn2Hvn6+iI+Ph4AW+OrEcOe6gw52KUimxL2PCnTrdBoNAgL\nC8OVnGsu09lAr2KDBg3y9iZQDWE1PtUZ8glYKrJDKra7TCO6WeHh4bDaiiA5daxjsRbC19cXJpPJ\ni1tGVPsY9lRnhIaGAnDcr5eKbNDpdAgODvbyVtGdSh7VzupUlW+xFvICkuolhj3VGXLYi2JHyb5B\ngwYcvpVuWem49o6wlyQ7bHYzh7aleqlW79kXFxcjMTERV65cgclkwqJFi5QTvGzt2rXYvn07AKBX\nr15ISEiAEAI9e/bEPffcAwDo2LEjpk2bVpubTrUgJCQEACAV2yDMdoQ0DfHyFtGdrGzYW2yFLtOJ\n6pNaDfsNGzagdevWmDRpErZv344VK1Zg9uzZyvysrCxs3boVaWlp0Gq1iI2NxaOPPgo/Pz+0bdsW\nK1eurM3NpVomd4trL7BB2AW7yaXbIhckLDZH//hyP/nyRSVRfVKrdaRHjhxBjx49AAA9e/bEd999\n5zK/cePGWL16NXQ6HTQaDWw2G3x8fHD8+HFcunQJcXFxeO655/Dbb7/V5mZTLZHvz9uvW1xeE90K\nJexLQt5a8t+ytYlE9UGNlezT0tLw3//+12VaWFiYMv62yWTCjRs3XOYbDAaEhoZCCIHFixfjvvvu\nQ8uWLZGTk4P4+Hj0798fhw8fRmJiIjZt2lTpNhw5cqT6vhDVCqPRCEuhI+yLior4G9Itu3r1KgDA\nanOtxs/NzeVxRfVOjYX98OHDMXz4cJdpCQkJKChwdGpRUFDgdjxps9mMmTNnwmQyYe7cuQCAdu3a\nQafTAQC6dOmCy5cvQwhR6QApMTEx1fFVqBY1aNAAly9fBgC0atWKvyHdsqKiIrz11luwWIsBAFab\n478xMTFo166dNzeNqEZUdBFbq9X4nTt3xp49ewAAe/fuLXciF0LghRdeQHR0NF599VUl4JcvX67U\nEpw4cQJNmnAkNLWSa37K/k10s/z8/ODj4wurzbUan/fsqT6q1QZ6sbGxSEpKQmxsLAwGA5YtWwYA\n+OCDD9CiRQtIkoRDhw7BYrHg22+/BQBMnToV8fHxSExMxJ49e6DT6bBw4cLa3GyqRT179sT169eV\nRplEtyMkpIHSi55csmdbEKqPNEII4e2NqAlHjhxhFTBRPffSSy/h1KnTeLDdUzh2dieKzLnYvHkz\nawZJlSrKPfZYQkSq1aBBAwghwW63wGorRnBwMIOe6iWGPRGpltwI2Go3w2Yzswqf6i2GPRGpltzI\n02otgl2yun0CiKg+YNgTkWrJYV9kvu7ymqi+YdgTkWrJ4V5sYdhT/cawJyLVCggIAACYLfkur4nq\nG4Y9EamWHO7FDHuq5xj2RKRaJpMJQGnJXn5NVN8w7IlIteRwt9nNAAB/f39vbg6R1zDsiUi1yoY7\nS/ZUXzHsiUi1yoY9S/ZUXzHsiUi1DAYDfHx8lNcs2VN9Vauj3hER1bapU6fi5MmTCAsLQ7Nmzby9\nOURewbAnIlXr1q0bunXr5u3NIPIqVuMTERGpHMOeiIhI5Rj2REREKsewJyIiUjmGPRERkcox7ImI\niFSOYU9ERKRyDHsiIiKVY9gTERGpHMOeiIhI5Rj2REREKqfqvvGPHDni7U0gIiLyOo0QQnh7I4iI\niKjmsBqfiIhI5Rj2REREKsewJyIiUjmGPRERkcox7ImIiFSOYV/GwYMH0bVrV8TFxSn/+7//+z+P\ny2/cuBFWqxW//vorli9ffsufu3HjRjz11FOIi4vDqFGjcPDgQY/Lnj9/HiNGjAAA9O3bF2az+ZY/\nt7odPHgQMTExuHjxojJt6dKlSE9Pr9L6e/bswdNPP42xY8dixIgR2Lp1601vQ3p6OpYuXXrT69Wk\n290vAPD2229jw4YNN/3Z8jFaFRs2bMDbb799059xq7KysjBp0iTluE9OTkZ+fr7H5Xft2oVLly55\nnJ+QkFBu2s1+p6tXryIpKQlxcXEYPXo0pk2bhuzs7ArXycjIwIkTJ6r8Ge6UPfeMGDECH374YZXW\nPXnyJDIyMgAAU6ZMgcVi8bhs9+7d3X727R6fwK0fo3WNu+OoIunp6fjyyy9raGuqh6qfs79VUo4j\nIQAAEPBJREFUDz30EFJSUqq07KpVqzB06FDce++9uPfee2/p87Zv3459+/Zh7dq1MBgMyMrKwpgx\nY7B582aEhobe0nt6k9FoxIwZM/DBBx9Ao9Hc1Lpz587F1q1bERQUhPz8fAwZMgTdu3dHWFhYDW1t\n7bmd/XI75GO0rikuLsYLL7yA1157DR06dAAAbN68GdOmTcOqVavcrrNu3TokJycjIiLC7fzbueAG\nACEEEhIS8Oyzz+LRRx8FAOzfvx8TJkxAWloadDqd2/U2bdqEAQMGoE2bNrf1+c7nHovFgn79+mHI\nkCEICgqqcL0vvvgC4eHheOCBB6p87irLW8dnXXSzx9ETTzxRQ1tSfRj2VRQXF4c2bdrg9OnTyM/P\nx7///W/s378f2dnZmDJlCp5++ml88sknSElJQZ8+fRAZGYmoqCiMGzcOr7zyCsxmM3x8fDB//nw0\nadLE5b0/+eQTzJgxAwaDAQDQvHlzbNmyBSEhIbh48WK59eu6hx56CJIkYf369RgzZowyfc2aNdi+\nfTv0ej26dOmCxMTEcusGBgZi3bp1eOyxx9CqVSvs3LkTRqMRN27cwKxZs5CbmwsAmD17NqKjo/HR\nRx/hiy++QFFREUJCQm77ZF+TPO2XESNGIDU1Vfn7zTffhL+/P5KSknDjxg0IIbBo0SKX91q2bBkO\nHz4MSZLwzDPPoH///jh06BCWL18OIQQKCgqUZeRjdMWKFW7XO3z4MBYsWICgoCDodDp07NixVvbH\nN998gwceeEAJegB4/PHHsWHDBiQlJWHgwIHo2bMn9u7dix07dqBfv3749ddfkZSUhLVr1+Kll15C\nfn4+ioqKMGXKFDz88MPo3r079u3b5/E7ffjhh/jss8+g0WgwYMAAjB071mWbjh07hsDAQCXoAaBb\nt25o0aIFMjIykJGRgfDwcMTGxuLs2bNITk5GUlISvv32Wxw/fhytWrVC06ZNq2X/5OfnQ6vV4sSJ\nE+V+V4PBgIkTJ6JBgwZ48MEHsXnzZhgMBrRt2xYvvvgidu7ciczMTLzxxhuw2+3Izc1FcnIyOnfu\n7PHzPB2f58+fx9SpU6vlGI2Li0NycjKioqKwYcMG5OTkID4+HpMnTy73W9ak9PR0fP311yguLkZ2\ndjbGjh2LL7/8EqdPn8b06dMxd+5c7Nu3D+vXr8eWLVug1Wpx//33Y/bs2fjiiy/w/vvvQ6/Xo1Gj\nRkhJScE777yD8PBwREZG4v3334fBYMD58+cxYMAATJw4EZmZmXj55Zeh1+tx11134c8//6xyrU11\nYdi7ceDAAcTFxSmve/XqBQBo3749Zs2ahZSUFGzfvh3x8fF49913kZKSgqNHjyrLX7x4Eenp6QgJ\nCcGLL76IuLg49OrVC9999x2WLl2KZcuWuXze5cuX0bx5c5dpISEhAIBFixaVW3/KlCk19dWrTXJy\nMoYPH44ePXoAAAoKCrBz50588skn0Ov1mDRpEr7++mv06dPHZb01a9Zg7dq1mDp1Kq5evYpRo0Yh\nISEBK1euxEMPPYTRo0fj3LlzmDFjBtavX4+8vDysXbsWWq0W48ePx88//+yNr1tlZfeLJytWrEDf\nvn0RGxuL77//Hj/99JMyb8+ePTh//jw2bNgAs9mMESNGoHv37jh9+jSWLFmCiIgIrFy5Ep9//jkm\nTpyoHKOe1ps3bx7eeusttGzZEnPnzq3pXaDIyspCixYtyk1v1qwZMjIyMHDgQJfpvXv3xr333ovk\n5GRcvHgReXl5WL16Na5cuYJz5865LOvuO505cwY7duzAxx9/DAAYN24cHn74YURGRrpsU9l/i4Dj\nAvzChQtuv0e7du3Qo0cPDBgw4LaDXj73aDQaGAwGvPLKK25/10GDBiE7OxubNm2C0WiEEALh4eFo\n37698l5nzpxBUlISoqOjsW3bNqSnp1cY9kDVj0/g1o5Rd/74448Kf8uaUlBQoBRA1q5di9TUVBw8\neBDr1q1TlklPT8fcuXPRvn17fPzxx7DZbPjss88wfvx49OvXD1u2bCl32+nChQvYunUrLBYLevTo\ngYkTJ2Lx4sV4/vnn0atXL6SmpuLPP/+sle/ojGHvhrtq/D179uC+++4DADRu3Bg5OTke1w8JCVHC\n+tSpU1i1ahVWr14NIQT0ej0+//xzrF+/HgCQlJSEu+66CxcvXkRgYKDyHt9++y2io6Pdrn8nCAkJ\nwcyZM5GUlITOnTvDbDajQ4cOSu1Fly5dcPr0aezevRt//PEHQkJCMH/+fFy4cAGJiYlITEzEpUuX\nMGnSJLRt2xanTp3CgQMHsHPnTgDAtWvXoNVqYTAYMHXqVPj7++Ovv/6CzWbz5teuVNn9UpbcoeXv\nv/+OYcOGAQA6d+6Mzp07K/edT506hePHjysXpDabDX/++SciIiLw+uuvw9/fH5cuXSr3/p7Wy8nJ\nQcuWLZXP+uOPP2rmy5cRERHhEhCyzMxMdOnSRXntrpPPv/3tbxg5ciSmTp0Km83mcnEOwO13OnXq\nFC5cuIBnnnkGgOMYyszMxKJFi1BYWIjWrVtjwIABbk/EmZmZ6NatW42fpN2de3bv3u32d23WrBmM\nRqPH92rUqBFWrFgBX19fFBQUICAgQJlXUFCA559/HoCj5kJ+z8qOT+D2jlF371PZb1lT5NuugYGB\niIqKgkajQXBwsEsbqIULF2LNmjVYvHgxOnbsCCEEZsyYgVWrVuGjjz5CZGSkSy0QALRu3Rp6vR56\nvR6+vr4AgLNnz6JTp04AgJiYGGzbtq1WvqOzOyM56jCNRgNJklymabWl7R4jIyPx7LPPonPnzjh7\n9iwyMjLQr18/9OvXT1nmySefxIoVK7B06VLo9Xr8/vvvmD17NtLT092uf6fo27cvdu3ahc2bN+OF\nF17ATz/9BJvNBp1Oh4yMDAwdOhTx8fHK8nJ1c2pqKsLDw9GwYUOEh4fDaDQiMjISgwcPxqBBg3Dl\nyhWkpaXhxIkT2L17N9LS0lBUVIQnnnjCbTDUNc77Zdy4cbhy5QrsdjsKCgpw/vx5AEBUVBR+/vln\ntGnTBhkZGfjmm2+UE0dkZCQefPBBzJ8/H5IkYcWKFWjevDmeffZZ7Nq1CwEBAUhKSlL2hXyMelov\nIiICZ8+eVT4zODi4VvbDI488gpUrV+Knn35SSqRpaWkICQmBr6+v0ijul19+UdbRaDQQQuDkyZMo\nKCjAe++9h8uXL2PUqFEutUTuvlNkZCRatWqF1atXQ6PRYO3atYiOjnZpHyCEQE5ODr766iv07dsX\nALB3715kZmbi73//O37++Wdlu44fP15uu2rCK6+84vZ3dT7PuDsPvf7661i6dCmioqLw1ltvuYSt\nyWRyqUZ2bhDsfHwmJibCx8en2o5Ro9GI7OxsREVF4ZdffkFERESlv2VNqUq7hNTUVMybNw8+Pj4Y\nP348fvjhB+zfvx+TJk1CWFgY5syZg127dlX6vq1bt8YPP/yAXr164ccff6y273AzGPZulK3GBxyN\nidzp0qUL4uPj8a9//cvt/KSkJCQnJ8NsNqO4uBizZs0qt8zAgQORnZ2N0aNHw2AwwG63Y8mSJQgL\nC6vS+nXZrFmzcODAAZhMJvTv3x+xsbGQJAkxMTHlrogbNmyIWbNmYcKECdDr9bDb7ejduzcefvhh\ntG3bFrNmzUJqairy8/ORkJCAu+++G35+fhg1apSy/uXLl73xNW+avF/Cw8PRvXt3DBs2DM2bN8fd\nd98NAHj++ecxc+ZM5WmEBQsWYMuWLQAcJ+NDhw5h9OjRKCwsxKOPPoqAgAAMHjwYTz31FPz8/BAe\nHq7sC/kYXbdundv1Xn31VUyfPh0BAQEwmUy1FvYmkwkrV67EggULkJeXB7vdjujoaLz55pvIzMzE\nzJkzsW3bNtxzzz3KOp06dcL06dPx7rvv4tChQ9i5cyckSSr3xIy779SmTRt07doVsbGxsFgsaN++\nfbmGfhqNRtkm+SKgcePGeO+996DT6dC/f3+8+OKLyMjIQNu2bZX1OnTogKVLl6JZs2aIioqq1v3k\n6Xd11q5dOyxevNjlswcPHozJkycjKCgIjRs3Vtq7VIV8fAKOf1fVdYyOHTsW8+bNQ9OmTdGoUSMA\nwD333IN33nnH42/pTdHR0Rg9ejRMJhMiIiLQoUMH5OfnY8KECTCZTPD390fv3r3x0UcfVfg+L730\nEmbOnIk1a9YgMDDQKzW0HAiHiIioBm3duhUdOnTA3XffjbS0NHz//fdYuHBhrW4DS/ZEREQ1qEmT\nJpgyZQr8/Pyg1WqxYMGCWt8GluyJiIhUjj3oERERqRzDnoiISOUY9kRERCrHsCdSsfPnz6Ndu3YY\nMmQIhg4dioEDB2LcuHH466+/bvq9srKyMHPmzGrZrhkzZlTYQU16ejpefvllt/PWrFmDAQMGYODA\ngZgzZ06VB/khqs8Y9kQq16hRI3z66afYsmULtm/fjnbt2t3SGAsXLlxAVlZWtWzTwYMHb6kTmu+/\n/x5paWlIS0vDp59+ipycHKX7WyLyjGFPVM906dJF6X/86NGjGD58OAYPHoynn34amZmZAIAPPvgA\ngwcPxtChQzFnzhwAwGuvvYZjx45h3rx5Lu938OBBjBkzBs888wwee+wxJCYmKkOspqSkYMSIEXjs\nsccwatQoZGdnKz2lxcfHIzc3F/v371d6R5wwYYLS13hmZibi4uLwyCOPYPbs2QAcXZsmJyfDZDIp\nAyrVVve+RHc0QUSqlZWVJfr06aO8tlgsIikpScyePVuYzWbRp08f8eOPPwohhNixY4d44oknhNVq\nFQ8++KCwWCzCbreLOXPmiL/++kscOHBAjBkzptxnHDhwQNx///3i7NmzQpIkMWnSJLFmzRpx7tw5\nkZCQIOx2uxBCiMTERPGf//xHCCFEnz59RFZWljCbzaJr167il19+EUIIsWzZMrFu3TqxadMm0atX\nL5GbmyvMZrPo0aOHOHXqlMvnnjlzRvTp00ccO3asRvYdkZqwUx0ilbt8+TKGDBkCAEo3sdOmTcO5\nc+cQFBSk9Evfv39/zJkzB0VFRejUqROGDRuGRx55BE899RQiIiIqHI3sgQceUEaPGzJkCFJTUzFu\n3DgkJSUhLS0Nv//+O44ePVpulLuTJ08iIiJCGZRk6tSpABz37Lt06YIGDRoAAFq0aOHS3WteXh6e\ne+45zJs3z6XbWiJyj2FPpHLyPfuy3DXSE0LAbrdjxYoVOHr0KPbu3Yt//vOfWLp0aYWfodPpXN5D\np9Ph2LFjmDZtmlK9r9Vqy92nl0dBlN24cQMFBQUA4NJ/eNmBZvbs2YNu3bpVaShWIuI9e6J6KzIy\nEnl5ecowszt27EDTpk0hSRL69++P1q1bY/LkyejevTtOnjwJnU7ncQjhI0eO4NKlS5AkCVu2bEHP\nnj2RkZGBv//974iNjUWrVq2wb98+2O12AI6LA7vdjpYtW+Lq1as4c+YMAGD16tXYsGFDpdveqVMn\njBs3rpr2BJH6sWRPVE8ZjUakpKRg/vz5KCoqQnBwMFJSUhAaGopRo0Zh2LBh8PPzQ5MmTfD444/D\narXixo0bSExMxJIlS1zeq1GjRpg+fTouXbqE7t27Y/jw4cjJyUFCQgIGDRoEg8GA6OhoZXjU3r17\nIz4+HqtXr8aSJUswffp0WK1WtGjRAosXL8b//ve/Crd93759uHz5MiZPnlxj+4dITdg3PhHdloMH\nD2L58uUu46MTUd3CanwiIiKVY8meiIhI5ViyJyIiUjmGPRERkcox7ImIiFSOYU9ERKRyDHsiIiKV\nY9gTERGp3P8DVEVpWwhN3BEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1634a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Post patch?', y='Resolution index', data=ps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### violin-plot and swarm-plot with Seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbf5be10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6NPF4grVt5UiShKbbqfC05zM3bI4qyiqyXRJ1axkWq4tUIkw4MHTdQg/Z2hdr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\n7z4CMR5CRd1qo3dAJNSDE9v/iIsvvhjvfve78/IeiKkzptjrAXK//e1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qGccQqcjzqMrg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\n+z8hA0qNnnPakwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc0924a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Post patch?', y='Resolution index', data=ps, inner=None)\n",
    "sns.swarmplot(x='Post patch?', y='Resolution index', data=ps, color='w', alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Post patch?</th>\n",
       "      <th>Notes</th>\n",
       "      <th>PCR cycles</th>\n",
       "      <th>SM_QC_PF</th>\n",
       "      <th>Resolution index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4775</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4803</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4838</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>no bubbles during expulsion.</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4920</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>P2</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>no bubbles during expulsion.</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4946</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>P6</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date User      Post patch?                         Notes  \\\n",
       "4775 2017-04-21   P2        Nucleated       Fluorescence in Pipette   \n",
       "4803 2017-04-24   P2          No-Seal                           NaN   \n",
       "4838 2017-04-25   P2        Nucleated  no bubbles during expulsion.   \n",
       "4920 2017-04-28   P2  Partial-Nucleus  no bubbles during expulsion.   \n",
       "4946 2017-05-01   P6          No-Seal                           NaN   \n",
       "\n",
       "      PCR cycles SM_QC_PF  Resolution index  \n",
       "4775        21.0     pass               1.0  \n",
       "4803        21.0     pass               1.0  \n",
       "4838        21.0     pass               1.0  \n",
       "4920        21.0     pass               1.0  \n",
       "4946        21.0     pass               1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_1 = ps[ps['Resolution index'] == 1]\n",
    "res_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### first attempt at plotting with Plotly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~rustymann/25.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "table = ff.create_table(ps)\n",
    "#py.iplot(table, filename='table1')\n",
    "dots = go.Scatter(x=ps['Post patch?'], y=ps['Resolution index'], mode='markers', name='dots')\n",
    "data = [dots]\n",
    "py.iplot(data, name='scatter')\n",
    "#py.iplot(plotly, x='Post patch?', y='Resolution index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### violin and rug plots with Plotly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~rustymann/11.embed\" height=\"500px\" width=\"800px\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plotly.graph_objs import graph_objs\n",
    "df = ps[['Post patch?','Resolution index']]\n",
    "df = df[df['Post patch?'] != 'missing'] #there was only one data point for \"missing\" which was causing an error with violin plot\n",
    "fig = ff.create_violin(df, data_header='Resolution index', group_header='Post patch?', height=500, width=800)\n",
    "py.iplot(fig, filename='violin plot')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### using regex to search for and extract exact strings to get bubble conditions from Notes field.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:6: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Post patch?</th>\n",
       "      <th>Notes</th>\n",
       "      <th>PCR cycles</th>\n",
       "      <th>SM_QC_PF</th>\n",
       "      <th>Resolution index</th>\n",
       "      <th>Bubbles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5215</th>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>P2</td>\n",
       "      <td>Outside-Out</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5216</th>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.601033</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5217</th>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.875031</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5228</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.825809</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5229</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P1</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5230</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.858195</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.900094</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.695597</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5235</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Large Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.787397</td>\n",
       "      <td>Large Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5236</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5240</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>No Bubbles got nucleus and gigaohm seal but th...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.601033</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5241</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5242</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Outside-Out</td>\n",
       "      <td>No Bubbles lost seal on cell during extraction...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5243</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Large Bubbles,Solution in Pipette Shank</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.601033</td>\n",
       "      <td>Large Bubbles,Solution in Pipette Shank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5244</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.601033</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5246</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.875031</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5247</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.630983</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5248</th>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.773439</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5260</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.927767</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5261</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.927767</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5262</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P1</td>\n",
       "      <td>Outside-Out</td>\n",
       "      <td>Cell Dimmed   Small Bubbles,Medium Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.836284</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5263</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P1</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette   Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.729438</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5269</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.836284</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5271</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5272</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.469928</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5273</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.322631</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5274</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.836284</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5275</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.836284</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5276</th>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.836284</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5389</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles,Medium B...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette Medium Bubbles may hav...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.763111</td>\n",
       "      <td>Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5391</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.860881</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5392</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles,Large Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.746661</td>\n",
       "      <td>Small Bubbles,Large Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5393</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Medium Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.469928</td>\n",
       "      <td>Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5394</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.781350</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5396</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles,Medium B...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.688154</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5403</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.688154</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5404</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Cell Dimmed   No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5405</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.890764</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5407</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.864362</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5408</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.927767</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5409</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.890764</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5410</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.660082</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5412</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Cell Dimmed   Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.688154</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5415</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.885860</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5416</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.890764</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5417</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.888508</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5418</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.765569</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5420</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.842398</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5421</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5422</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles might ha...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.521240</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5423</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette No Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.322631</td>\n",
       "      <td>No Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5424</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>P2</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette Small Bubbles</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.836284</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5432</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed,Cell Shrun...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.835709</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5433</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed,Cell Shrun...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.660082</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5434</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>Partial-Nucleus</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed,Cell Shrun...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.556320</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5435</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>Nucleated</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed   Small Bu...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.556320</td>\n",
       "      <td>Small Bubbles,Medium Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5436</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P1</td>\n",
       "      <td>Entire-Cell</td>\n",
       "      <td>Fluorescence in Pipette,Cell Dimmed,Cell Shrun...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.860881</td>\n",
       "      <td>Small Bubbles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5440</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>P2</td>\n",
       "      <td>No-Seal</td>\n",
       "      <td>Fluorescence in Pipette Large Bubbles Pretty s...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>0.660082</td>\n",
       "      <td>Large Bubbles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date User      Post patch?  \\\n",
       "5211 2017-05-16   P1        Nucleated   \n",
       "5215 2017-05-16   P2      Outside-Out   \n",
       "5216 2017-05-16   P2        Nucleated   \n",
       "5217 2017-05-16   P2        Nucleated   \n",
       "5228 2017-05-17   P1        Nucleated   \n",
       "5229 2017-05-17   P1  Partial-Nucleus   \n",
       "5230 2017-05-17   P1        Nucleated   \n",
       "5231 2017-05-17   P1        Nucleated   \n",
       "5234 2017-05-17   P1        Nucleated   \n",
       "5235 2017-05-17   P1        Nucleated   \n",
       "5236 2017-05-17   P1        Nucleated   \n",
       "5240 2017-05-17   P2  Partial-Nucleus   \n",
       "5241 2017-05-17   P2  Partial-Nucleus   \n",
       "5242 2017-05-17   P2      Outside-Out   \n",
       "5243 2017-05-17   P2        Nucleated   \n",
       "5244 2017-05-17   P2        Nucleated   \n",
       "5246 2017-05-17   P2        Nucleated   \n",
       "5247 2017-05-17   P2        Nucleated   \n",
       "5248 2017-05-17   P2        Nucleated   \n",
       "5260 2017-05-18   P1        Nucleated   \n",
       "5261 2017-05-18   P1        Nucleated   \n",
       "5262 2017-05-18   P1      Outside-Out   \n",
       "5263 2017-05-18   P1  Partial-Nucleus   \n",
       "5269 2017-05-18   P2  Partial-Nucleus   \n",
       "5271 2017-05-18   P2          No-Seal   \n",
       "5272 2017-05-18   P2        Nucleated   \n",
       "5273 2017-05-18   P2        Nucleated   \n",
       "5274 2017-05-18   P2        Nucleated   \n",
       "5275 2017-05-18   P2        Nucleated   \n",
       "5276 2017-05-18   P2        Nucleated   \n",
       "...         ...  ...              ...   \n",
       "5389 2017-05-25   P2          No-Seal   \n",
       "5390 2017-05-25   P2          No-Seal   \n",
       "5391 2017-05-25   P2  Partial-Nucleus   \n",
       "5392 2017-05-25   P2        Nucleated   \n",
       "5393 2017-05-25   P2        Nucleated   \n",
       "5394 2017-05-25   P2        Nucleated   \n",
       "5396 2017-05-25   P2        Nucleated   \n",
       "5403 2017-05-26   P1        Nucleated   \n",
       "5404 2017-05-26   P1        Nucleated   \n",
       "5405 2017-05-26   P1  Partial-Nucleus   \n",
       "5407 2017-05-26   P1  Partial-Nucleus   \n",
       "5408 2017-05-26   P1        Nucleated   \n",
       "5409 2017-05-26   P1        Nucleated   \n",
       "5410 2017-05-26   P1        Nucleated   \n",
       "5412 2017-05-26   P1        Nucleated   \n",
       "5415 2017-05-26   P2          No-Seal   \n",
       "5416 2017-05-26   P2        Nucleated   \n",
       "5417 2017-05-26   P2  Partial-Nucleus   \n",
       "5418 2017-05-26   P2        Nucleated   \n",
       "5420 2017-05-26   P2  Partial-Nucleus   \n",
       "5421 2017-05-26   P2          No-Seal   \n",
       "5422 2017-05-26   P2          No-Seal   \n",
       "5423 2017-05-26   P2        Nucleated   \n",
       "5424 2017-05-26   P2        Nucleated   \n",
       "5432 2017-06-01   P1          No-Seal   \n",
       "5433 2017-06-01   P1          No-Seal   \n",
       "5434 2017-06-01   P1  Partial-Nucleus   \n",
       "5435 2017-06-01   P1        Nucleated   \n",
       "5436 2017-06-01   P1      Entire-Cell   \n",
       "5440 2017-06-01   P2          No-Seal   \n",
       "\n",
       "                                                  Notes  PCR cycles SM_QC_PF  \\\n",
       "5211                                      Small Bubbles        21.0     pass   \n",
       "5215                                      Small Bubbles        21.0     pass   \n",
       "5216                                         No Bubbles        21.0     pass   \n",
       "5217                                         No Bubbles        21.0     pass   \n",
       "5228                       Small Bubbles,Medium Bubbles        21.0     pass   \n",
       "5229                                      Small Bubbles        21.0     pass   \n",
       "5230                                      Small Bubbles        21.0     pass   \n",
       "5231                                      Small Bubbles        21.0     pass   \n",
       "5234                                      Small Bubbles        21.0     pass   \n",
       "5235                                      Large Bubbles        21.0     pass   \n",
       "5236                       Small Bubbles,Medium Bubbles        21.0     pass   \n",
       "5240  No Bubbles got nucleus and gigaohm seal but th...        21.0     pass   \n",
       "5241                                      Small Bubbles        21.0     pass   \n",
       "5242  No Bubbles lost seal on cell during extraction...        21.0     pass   \n",
       "5243            Large Bubbles,Solution in Pipette Shank        21.0     pass   \n",
       "5244                                         No Bubbles        21.0     pass   \n",
       "5246                                         No Bubbles        21.0     pass   \n",
       "5247                                         No Bubbles        21.0     pass   \n",
       "5248                                         No Bubbles        21.0     pass   \n",
       "5260  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5261  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5262         Cell Dimmed   Small Bubbles,Medium Bubbles        21.0     pass   \n",
       "5263            Fluorescence in Pipette   Small Bubbles        21.0     pass   \n",
       "5269                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5271                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5272              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "5273                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5274                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5275              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "5276              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "...                                                 ...         ...      ...   \n",
       "5389  Fluorescence in Pipette Small Bubbles,Medium B...        21.0     pass   \n",
       "5390  Fluorescence in Pipette Medium Bubbles may hav...        21.0     pass   \n",
       "5391              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "5392  Fluorescence in Pipette Small Bubbles,Large Bu...        21.0     pass   \n",
       "5393             Fluorescence in Pipette Medium Bubbles        21.0     pass   \n",
       "5394                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5396  Fluorescence in Pipette Small Bubbles,Medium B...        21.0     pass   \n",
       "5403   Fluorescence in Pipette,Cell Dimmed   No Bubbles        21.0     pass   \n",
       "5404                           Cell Dimmed   No Bubbles        21.0     pass   \n",
       "5405  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5407  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5408  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5409  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5410  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5412                        Cell Dimmed   Small Bubbles        21.0     pass   \n",
       "5415                                      Small Bubbles        21.0     pass   \n",
       "5416                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5417              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "5418              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "5420              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "5421                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5422  Fluorescence in Pipette Small Bubbles might ha...        21.0     pass   \n",
       "5423                 Fluorescence in Pipette No Bubbles        21.0     pass   \n",
       "5424              Fluorescence in Pipette Small Bubbles        21.0     pass   \n",
       "5432  Fluorescence in Pipette,Cell Dimmed,Cell Shrun...        21.0     pass   \n",
       "5433  Fluorescence in Pipette,Cell Dimmed,Cell Shrun...        21.0     pass   \n",
       "5434  Fluorescence in Pipette,Cell Dimmed,Cell Shrun...        21.0     pass   \n",
       "5435  Fluorescence in Pipette,Cell Dimmed   Small Bu...        21.0     pass   \n",
       "5436  Fluorescence in Pipette,Cell Dimmed,Cell Shrun...        21.0     pass   \n",
       "5440  Fluorescence in Pipette Large Bubbles Pretty s...        21.0     pass   \n",
       "\n",
       "      Resolution index                                  Bubbles  \n",
       "5211          1.000000                            Small Bubbles  \n",
       "5215          1.000000                            Small Bubbles  \n",
       "5216          0.601033                               No Bubbles  \n",
       "5217          0.875031                               No Bubbles  \n",
       "5228          0.825809             Small Bubbles,Medium Bubbles  \n",
       "5229          0.000000                            Small Bubbles  \n",
       "5230          0.858195                            Small Bubbles  \n",
       "5231          0.900094                            Small Bubbles  \n",
       "5234          0.695597                            Small Bubbles  \n",
       "5235          0.787397                            Large Bubbles  \n",
       "5236          1.000000             Small Bubbles,Medium Bubbles  \n",
       "5240          0.601033                               No Bubbles  \n",
       "5241          1.000000                            Small Bubbles  \n",
       "5242          1.000000                               No Bubbles  \n",
       "5243          0.601033  Large Bubbles,Solution in Pipette Shank  \n",
       "5244          0.601033                               No Bubbles  \n",
       "5246          0.875031                               No Bubbles  \n",
       "5247          0.630983                               No Bubbles  \n",
       "5248          0.773439                               No Bubbles  \n",
       "5260          0.927767                            Small Bubbles  \n",
       "5261          0.927767                            Small Bubbles  \n",
       "5262          0.836284             Small Bubbles,Medium Bubbles  \n",
       "5263          0.729438                            Small Bubbles  \n",
       "5269          0.836284                               No Bubbles  \n",
       "5271          1.000000                               No Bubbles  \n",
       "5272          0.469928                            Small Bubbles  \n",
       "5273          0.322631                               No Bubbles  \n",
       "5274          0.836284                               No Bubbles  \n",
       "5275          0.836284                            Small Bubbles  \n",
       "5276          0.836284                            Small Bubbles  \n",
       "...                ...                                      ...  \n",
       "5389          0.000000             Small Bubbles,Medium Bubbles  \n",
       "5390          0.763111                           Medium Bubbles  \n",
       "5391          0.860881                            Small Bubbles  \n",
       "5392          0.746661              Small Bubbles,Large Bubbles  \n",
       "5393          0.469928                           Medium Bubbles  \n",
       "5394          0.781350                               No Bubbles  \n",
       "5396          0.688154             Small Bubbles,Medium Bubbles  \n",
       "5403          0.688154                               No Bubbles  \n",
       "5404          1.000000                               No Bubbles  \n",
       "5405          0.890764                            Small Bubbles  \n",
       "5407          0.864362                            Small Bubbles  \n",
       "5408          0.927767                            Small Bubbles  \n",
       "5409          0.890764                            Small Bubbles  \n",
       "5410          0.660082                            Small Bubbles  \n",
       "5412          0.688154                            Small Bubbles  \n",
       "5415          0.885860                            Small Bubbles  \n",
       "5416          0.890764                               No Bubbles  \n",
       "5417          0.888508                            Small Bubbles  \n",
       "5418          0.765569                            Small Bubbles  \n",
       "5420          0.842398                            Small Bubbles  \n",
       "5421          1.000000                               No Bubbles  \n",
       "5422          0.521240                            Small Bubbles  \n",
       "5423          0.322631                               No Bubbles  \n",
       "5424          0.836284                            Small Bubbles  \n",
       "5432          0.835709                            Small Bubbles  \n",
       "5433          0.660082                            Small Bubbles  \n",
       "5434          0.556320             Small Bubbles,Medium Bubbles  \n",
       "5435          0.556320             Small Bubbles,Medium Bubbles  \n",
       "5436          0.860881                            Small Bubbles  \n",
       "5440          0.660082                            Large Bubbles  \n",
       "\n",
       "[118 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.dropna(subset = ['Notes'], inplace = True)\n",
    "ps = ps[ps['Notes'].str.contains('Bubbles')]\n",
    "a_regex = '(No Bubbles|Small Bubbles,Medium Bubbles|Small Bubbles,Large Bubbles|Small Bubbles|Medium Bubbles,Large Bubbles|Medium Bubbles|Large Bubbles,Solution in Pipette Shank|Large Bubbles)'\n",
    "#a_regex = '(Large Bubbles,Solution in Pipette Shank|Large Bubbles|No Bubbles|Small Bubbles,Medium Bubbles|Small Bubbles,Large Bubbles|Small Bubbles||Medium Bubbles,Large Bubbles|Medium Bubbles)'\n",
    "#replacements = {'Large Bubbles,Solution in Pipette Shank':'Solution in Pipette Shank', 'Large Bubbles':'Large Bubbles', 'No Bubbles':'No Bubbles', 'Small Bubbles,Medium Bubbles':'Medium Bubbles', 'Small Bubbles,Large Bubbles':'Large Bubbles', 'Small Bubbles':'Small Bubbles', 'Medium Bubbles, Large Bubbles':'Large Bubbles', 'Medium Bubbles':'Medium Bubbles'}\n",
    "ps['Bubbles'] = ps['Notes'].str.extract(a_regex).fillna('')\n",
    "#ps['Bubbles'] = ps['Bubbles'].apply(lambda x: replacements.get(x,''))\n",
    "ps\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### swarm-plot of post patch vs resolution index by bubble condition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xd16c588>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ITVXzjlILimzvzZ9jq5Rjp5EbeKHrJkbmCYJwb6pVzx9cXV0pKiqyvy4uLsbd/cYWLI6N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\nbwAAhYg3AIBCHtdMsacmIXa2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5c749b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='Post patch?', y='Resolution index', hue='Bubbles', data=ps)\n",
    "plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bubb_filter(x):\n",
    "    df = ps[ps['Bubbles'] == x]\n",
    "    return df\n",
    "\n",
    "def pp_filter(y):\n",
    "    df2 = ps[ps['Post patch?'] == y]\n",
    "    return df2\n",
    "\n",
    "def hires_filter(z):\n",
    "    df3 = z[z['Resolution index'] >= 0.8]\n",
    "    return df3\n",
    "\n",
    "def get_count(v):\n",
    "    count = v['Bubbles'].count()\n",
    "    return count\n",
    "\n",
    "def calc_pct(passing, total):\n",
    "    pct = float(get_count(passing)) / get_count(total)\n",
    "    return pct\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### percentage with resolution index >=0.8 by bubble and post patch condition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no bubbles hires pct = 0.447368421053\n"
     ]
    }
   ],
   "source": [
    "nb = bubb_filter('No Bubbles')\n",
    "nbhr = hires_filter(bubb_filter('No Bubbles'))\n",
    "nbhrpp = calc_pct(nbhr,nb)\n",
    "print 'no bubbles hi-res pct =', nbhrpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "small bubbles hires pct = 0.584905660377\n"
     ]
    }
   ],
   "source": [
    "sb = bubb_filter('Small Bubbles')\n",
    "sbhr = hires_filter(bubb_filter('Small Bubbles'))\n",
    "sbhrpp = calc_pct(sbhr,sb)\n",
    "print 'small bubbles hi-res pct =', sbhrpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nucleated patch hires pct = 0.415384615385\n"
     ]
    }
   ],
   "source": [
    "nu = pp_filter('Nucleated')\n",
    "nuhr = hires_filter(pp_filter('Nucleated'))\n",
    "nuhrpp = calc_pct(nuhr,nu)\n",
    "print 'nucleated patch hi-res pct =', nuhrpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no-seal hires pct = 0.51724137931\n"
     ]
    }
   ],
   "source": [
    "ns = pp_filter('No-Seal')\n",
    "nshr = hires_filter(pp_filter('No-Seal'))\n",
    "nshrpp = calc_pct(nshr,ns) \n",
    "print 'no-seal hi-res pct =', nshrpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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