nwb_conversion_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'NWB'...\n",
      "remote: Enumerating objects: 50, done.\u001b[K\n",
      "remote: Counting objects: 100% (50/50), done.\u001b[K\n",
      "remote: Compressing objects: 100% (36/36), done.\u001b[K\n",
      "remote: Total 211 (delta 24), reused 34 (delta 14), pack-reused 161\u001b[K\n",
      "Receiving objects: 100% (211/211), 38.87 MiB | 16.16 MiB/s, done.\n",
      "Resolving deltas: 100% (109/109), done.\n"
     ]
    }
   ],
   "source": [
    "! git clone https://github.com/stripathy/NWB.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import argparse\n",
    "import pandas as pd\n",
    "\n",
    "from NWB.ABF1Converter import ABF1Converter ### import the converter class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read final csv that has the ouput of metadata gathering process\n",
    "csv_meta_save_path = 'output_files/cell_final_raw_meta_df.csv'\n",
    "cell_final_raw_meta_df = pd.read_csv(open(csv_meta_save_path, 'rb'))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def abf_to_nwb(inputPath, outFolder, outputMetadata, acquisitionChannelName, stimulusChannelName, overwrite, \n",
    "               responseGain = 1.0, stimulusGain = 1.0, responseOffset = 0.0, clampMode = 1):\n",
    "    \"\"\"\n",
    "    Sample file handling script for NWB conversion.\n",
    "\n",
    "    Takes the path to the ABF v1 file as the first command line argument and writes the corresponding NWB file\n",
    "    to the folder specified by the second command line argument.\n",
    "\n",
    "    NWB Files organized by cell, with assumption that each abf file corresponds to each cell\n",
    "    \"\"\"\n",
    "\n",
    "    if not os.path.exists(inputPath):\n",
    "        raise ValueError(f\"The file or folder {inputPath} does not exist.\")\n",
    "\n",
    "    if not os.path.exists(outFolder):\n",
    "        raise ValueError(f\"The file or folder {outFolder} does not exist.\")\n",
    "\n",
    "    # Collect ABF files from the specified directory. \n",
    "\n",
    "    if os.path.isfile(inputPath):\n",
    "        files = [inputPath]\n",
    "    elif os.path.isdir(inputPath):\n",
    "        files = glob.glob(inputPath + \"/*.abf\")\n",
    "    else:\n",
    "        raise ValueError(f\"Invalid path {inputPath}: input must be a path to a file or a directory\")\n",
    "\n",
    "    if len(files) == 0:\n",
    "        raise ValueError(f\"Invalid path {inputPath} does not contain any ABF files.\")\n",
    "\n",
    "    for inputFile in files:\n",
    "\n",
    "        fileName = os.path.basename(inputFile)\n",
    "        root, _ = os.path.splitext(fileName)\n",
    "\n",
    "        print(f\"Converting {fileName}...\")\n",
    "\n",
    "        # Generate name for new NWB file\n",
    "\n",
    "        outFile = os.path.join(outFolder, root + \".nwb\")\n",
    "\n",
    "        if os.path.exists(outFile):\n",
    "            if overwrite:\n",
    "                os.unlink(outFile)\n",
    "            else:\n",
    "                raise ValueError(f\"The file {outFile} already exists.\")\n",
    "\n",
    "        # Enter each ABF file into the converter script. The additional arguments are meant for command line operations.\n",
    "\n",
    "        conv = ABF1Converter(inputFile, outFile, \n",
    "                             acquisitionChannelName=acquisitionChannelName, stimulusChannelName=stimulusChannelName, \n",
    "          responseGain = responseGain,\n",
    "          stimulusGain = stimulusGain,\n",
    "          responseOffset = responseOffset, clampMode = clampMode\n",
    "                            )\n",
    "        conv.convert()\n",
    "\n",
    "        if outputMetadata:\n",
    "            conv._outputMetadata()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/stripathy/Downloads/valiante_lab_ephys_mar_2020/L5/Homeira/Total5Homeira-Last-Version - without 2020 Data/19320030.abf\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "curr_file = '19320030.abf'\n",
    "\n",
    "\n",
    "\n",
    "#meta_dict = cell_final_raw_meta_df\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == curr_file]\n",
    "file_path = meta_row['full_path'].values[0]\n",
    "print(file_path)\n",
    "stim_file_path = meta_row['stim_path'].values[0]\n",
    "\n",
    "resp_abf = pyabf.ABF(file_path)\n",
    "stim_abf = pyabf.ABF(stim_file_path) # for some files we're using stim traces from a different file\n",
    "\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "\n",
    "stim_channel_num = int(meta_row['stim_chan'].values[0])\n",
    "response_chan_num = int(meta_row['resp_chan'].values[0])\n",
    "stim_gain = meta_row['stim_gain'].values[0]\n",
    "if stim_gain == 1000:\n",
    "    stim_gain = 1.0\n",
    "response_gain = meta_row['resp_gain'].values[0]\n",
    "\n",
    "start_time = meta_row['stim_start_time'].values[0]\n",
    "end_time = meta_row['stim_end_time'].values[0]\n",
    "resp_sampling_rate = meta_row['resp_sampling_rate'].values[0]\n",
    "stim_sampling_rate = meta_row['stim_sampling_rate'].values[0]\n",
    "resp_offset = meta_row['resp_offset'].values[0]\n",
    "stim_name = meta_row['stim_name'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iclamp\n",
      "Current_in\n"
     ]
    }
   ],
   "source": [
    "acq_channel_name = resp_abf.adcNames[response_chan_num]\n",
    "\n",
    "if stim_name == 'sweepC':\n",
    "    stim_chan_name = resp_abf.dacNames[stim_channel_num]\n",
    "else:                  \n",
    "    stim_chan_name = resp_abf.adcNames[stim_channel_num]\n",
    "    \n",
    "print(acq_channel_name)\n",
    "print(stim_chan_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/stripathy/Downloads/valiante_lab_ephys_mar_2020/L5/Homeira/Total5Homeira-Last-Version - without 2020 Data/19320030.abf\n",
      "Converting 19320030.abf...\n",
      "/Users/stripathy/Downloads/valiante_lab_ephys_mar_2020/L5/Homeira/Total5Homeira-Last-Version - without 2020 Data/19320030.abf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Date is missing timezone information. Updating to local timezone.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully converted to /Users/stripathy/Downloads/19320030.nwb.\n"
     ]
    }
   ],
   "source": [
    "abf_path = file_path\n",
    "print(abf_path)\n",
    "outFolder = '/Users/stripathy/Downloads/'\n",
    "outputMetadata = True\n",
    "\n",
    "#acq_channel_name = '200B IC'\n",
    "#stim_chan_name = 'Cmd 0 IC'\n",
    "responseGain = response_gain\n",
    "responseOffset = resp_offset\n",
    "stimulusGain = stim_gain\n",
    "clampMode = 1\n",
    "\n",
    "abf_to_nwb(inputPath=abf_path,\n",
    "           outFolder=outFolder,\n",
    "           outputMetadata=False,\n",
    "           acquisitionChannelName=acq_channel_name,\n",
    "           stimulusChannelName=stim_chan_name,\n",
    "           overwrite=True, \n",
    "          responseGain = responseGain,\n",
    "          stimulusGain = stimulusGain,\n",
    "          responseOffset = responseOffset, clampMode = clampMode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipfx.hbg_dataset import HBGDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-33-e4439fd6699c>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-33-e4439fd6699c>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    data_set = HBGDataSet(nwb_file=nwb_file_name, ontology = ) ##loads the NWB as an HBG dataset, Equal to the nwb1.0 -> AIBS dataset\u001b[0m\n\u001b[0m                                                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "nwb_file_name = outFolder + '2016_02_04_0018.nwb'\n",
    "print(nwb_file_name)\n",
    "data_set = HBGDataSet(nwb_file=nwb_file_name, ontology = ) ##loads the NWB as an HBG dataset, Equal to the nwb1.0 -> AIBS dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bridge_balance_mohm</th>\n",
       "      <th>leak_pa</th>\n",
       "      <th>stimulus_code</th>\n",
       "      <th>stimulus_code_ext</th>\n",
       "      <th>stimulus_scale_factor</th>\n",
       "      <th>stimulus_units</th>\n",
       "      <th>sweep_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Amps</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bridge_balance_mohm leak_pa stimulus_code stimulus_code_ext  \\\n",
       "0                 None    None            NA                NA   \n",
       "1                 None    None            NA                NA   \n",
       "2                 None    None            NA                NA   \n",
       "3                 None    None            NA                NA   \n",
       "4                 None    None            NA                NA   \n",
       "5                 None    None            NA                NA   \n",
       "6                 None    None            NA                NA   \n",
       "7                 None    None            NA                NA   \n",
       "8                 None    None            NA                NA   \n",
       "9                 None    None            NA                NA   \n",
       "10                None    None            NA                NA   \n",
       "11                None    None            NA                NA   \n",
       "12                None    None            NA                NA   \n",
       "13                None    None            NA                NA   \n",
       "14                None    None            NA                NA   \n",
       "15                None    None            NA                NA   \n",
       "16                None    None            NA                NA   \n",
       "17                None    None            NA                NA   \n",
       "18                None    None            NA                NA   \n",
       "19                None    None            NA                NA   \n",
       "20                None    None            NA                NA   \n",
       "21                None    None            NA                NA   \n",
       "22                None    None            NA                NA   \n",
       "23                None    None            NA                NA   \n",
       "24                None    None            NA                NA   \n",
       "\n",
       "    stimulus_scale_factor stimulus_units  sweep_number  \n",
       "0                    20.0           Amps             0  \n",
       "1                    20.0           Amps            10  \n",
       "2                    20.0           Amps            11  \n",
       "3                    20.0           Amps            12  \n",
       "4                    20.0           Amps            13  \n",
       "5                    20.0           Amps            14  \n",
       "6                    20.0           Amps            15  \n",
       "7                    20.0           Amps            16  \n",
       "8                    20.0           Amps            17  \n",
       "9                    20.0           Amps            18  \n",
       "10                   20.0           Amps            19  \n",
       "11                   20.0           Amps             1  \n",
       "12                   20.0           Amps            20  \n",
       "13                   20.0           Amps            21  \n",
       "14                   20.0           Amps            22  \n",
       "15                   20.0           Amps            23  \n",
       "16                   20.0           Amps            24  \n",
       "17                   20.0           Amps             2  \n",
       "18                   20.0           Amps             3  \n",
       "19                   20.0           Amps             4  \n",
       "20                   20.0           Amps             5  \n",
       "21                   20.0           Amps             6  \n",
       "22                   20.0           Amps             7  \n",
       "23                   20.0           Amps             8  \n",
       "24                   20.0           Amps             9  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data_set.extract_sweep_stim_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.sweep_set([0, 13]).sweeps[1].i[2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAD8CAYAAABdCyJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xl8VPW9//HXJwkJEMK+b4KKIu4aFZdWW20L1Ra9Vqq9Vmxt/XWztfb21u5WbWt3a2td61W7aNVq3VHBXRAElU1AdohsYYcACUm+vz/mzGQCWWbmnJlzZvJ+Ph6amTNzzvnMYc73c77L+Y455xAREclUUdgBiIhIflMiERERX5RIRETEFyUSERHxRYlERER8USIRERFflEhERMQXJRIREfFFiURERHwpCTuAIIwbN85Nnjw57DBERPKNBbGRgqiRbNq0KewQREQ6rIJIJCIiEh4lEhER8SXURGJmK81snpm9a2azvGW9zewFM1vi/e0VZoz57t+zq7jn9RUpvXfNlt2ccMMLrNmyO+39zFyxhUXrd6S9nnOO15ZU42cW6s27apm5YkvG66/aXONr/0HYtKuWbbvrQo1BJFNRqJF8xDl3nHOu0nt+LTDVOTcKmOo9lwx95+E5XP/Ueym99+HZVWypqeOR2VVp72fiHdMZd/Nraa/32Dsf8Pm/zuShWWvSXjfuotunM/GO6RmtO2P5Zs78zcu+9j95/npGXPs0O/buy3gblTdO4bjrX8h4/V8+u5DrnliQ8foA/5ixijE/mZxxUn19ySbG/mIqe+oafMVx60tL+dv0lb62AbBhx17ufSO1iyjxJwqJZH8TgPu8x/cB54cYS4dS5I3fyOW1+Qdb9wCwZsuejLexfFNNxusuq46t++6abRlv4y8vL43FUZ15HH7d8cpy7p220tc2fvyf+eyua6ChMbNvwI1Pv8f6HXtZvmmXrzh+89xifvy4v6QIcOX9s7juyfcyqmFLesJOJA543sxmm9mV3rIBzrl1AN7f/qFF18FYfCRgDpt5zNtlY0hNS4n9N/rZRmwjYTeP+ZX4HH7Xj8hh2LYnVkOszzAxSurCvo/kdOfcWjPrD7xgZotSXdFLPFcCDB8+PFvxdUi5PO38Fl7JnHOJ7aW8//i6PiKIbyPfy6vEscjwcwRyQ0KAigokweeDUGskzrm13t+NwGPAycAGMxsE4P3d2Mq6dzrnKp1zlf369ctVyAXNcl8hCXSfmWwjiP035a78LrASxyLDzxHG96cthZLg80FoicTMys2sIv4Y+DgwH3gCmOS9bRLweDgRdjxBXJ2nv894jcT/PjNpHmvaf+b8XslHReJYZFojiVqVJGrxFLAwm7YGAI95TRElwD+dc5PN7C3gITO7AlgNXBRijB1KUVHu27itKXv5ltEmAqmRBNc8l8+CvCgIQlMeiUY8hSy0ROKcWw4c28LyzcDZuY9I4nLaR+L9DaKzPZNtJNrRA+gjyfcaid+kGrmmLS8gNW1lX9ijtiRCOmQfSWLlzPfb9Bnyu8QKKqlH5SgUTILPA0okkhBG00QQfRRxvjrbfew3yM8QJr/HImrDoIOobUpqlEgkIcj+inT3GcSoqYZMOtuDqE1ErEknU02d7RmO2vL+RuUwBHGPkKRGiUQSguyvSHmfgfZRhDxqKzJFaGaabg71t518T6iSPiUSSQhj+GYQ7dh+Rk0FUXiGUZPLKt/Df6NxIIK4SJHUKJFIgt/7CDLaZwDJK4hk5Kdpq2D6SLy/Gd+QGF8/YgciavEUIiUSSQiqaSOtfRLfp4+C3Ec/RxD3gERt2Gum/M6VFbX7aXQ/Yu4okUiogpjoz09NKtDhv5EpQjPjN6lHrUYSuTvtC5gSiRwgp8N/gzjZfQxbDSIJWKFc+/oe/htYJIGKSmIrZEokkhDGNODBzr7rY9RWIDckZr6NKPBfI4nWfSSFUlPMB0okkhDKBWUA01j4KciDvIrO9+LKzG+VJPYnKlOShDF4pKNSIpGEMKb6CKZlK/NhnkG260flSjxTAeWRyIhqU1shUiKRhDDOuyDvbPdTI/EzaqwoYqOVMuW7aStiTUmFdntPlCmRSEIYwzeDaFf3V6sIbvhvvpdYvof/+u2tD1qi2TQqARUuJRJJCHP2X3/b8NG0FeTsw5EpQTMTXI1EOholEjlAbmf/9fbpf87EjDp5Axx9nPedun6TatRGrxVFLJ5CpkQiCaEM/w2iXT2AO9v9XEeHcdyyI5hesqjUzPQLibmjRCIJYTb1B1Ej8XNnu6/hx/H9Z76JSPA78KAoYgm1cBJ89CmRyAFyWyMJorPbxxQpgfTR4O0/v0usoJroonIUgrhIkNQokUhCvk4jH+evsz2IXyTJb0EMhYboJNRCSfD5QIlEEizERho/7ep+OlUD+WGrAhmt5PdYRG/232jFU8iUSCQhjJ8mDaJd3QK4XyCYPpr8LrL8XsFH7HetkgZhhBtGR6BEIglh3tkeBH+z/2auUKbi8NvMGN0726MRTyFTIpEDhDGNvJ+reV9NW4kaUQC/kJjn5VVTzS7D9b2/UTkOhTLjQD5QIpGEUO5sx//sv4lpTkIqwQqljyQu45/ajVjVrFDmQMsHSiSSEEbnZNhNW0He/RyVK/FM+b6zHX/rBy1qd9oXMiUSaRLiiRfIqClfo7b83NmO721Egd/hv0ENHw5avv+75AMlEkkIo3MymD6KmPBGbRVIH4nvzxHRpq08/3fJB0okkuD7F/J8CK1GEkQtrED6SAKbtDGYcAITtRpSIVIikYQwrieLmqpBGQvkFxID+M34vL+PxPubcWd7fP2IHIao3SBZyNpMJGZWmatAJDpC+WGrIPoofAQexA2R+c738N+I3ZFYKAk+H7RXI7nLzJaY2fVmNiYnEUloirxvQxgnXliz/7r9/uZ6/1Hit+CNWl+RRm3lTpuJxDl3PHAe0AA8Ymbvmtn3zOygbAdmZuPMbLGZLTWza7O9Pwln+G8Qw2/9/EJigq/9xzeR5yVWYtSVv81E5Sho9t/cabePxDm32Dn3M+fcGGAS0BN40czeyFZQZlYM3AqMB8YAl6hGlDt598NWnoxqJM7//gujYcv/D0E11WiDiMa/IEYESmpS7mw3syKgPzAAKAeqsxUUcDKw1Dm33DlXBzwITMji/oRwR934q5HE/oY2/DdCw0x9DaP2+TmC6O8KkmZIyZ12E4mZfcjM/gJUAd8FXgcOd86dn8W4hgBrkp5XecskQ+kUMLm9gguuOc3PNgqljySIvibfTVsROA6g3yPJpZK2XjSzNcBqYjWCnznnNuQkqpZbC5p9G8zsSuBKgOHDh+ciprzW6KC4nTaYMIZLBtEh6ufGs/jVs6/CJkL3TwRzP46/OVKicBwgWjXFQtdmIgHOcM6tMrOTgNvMbIS3jgHOOXdMluKqAoYlPR8KrE1+g3PuTuBOgMrKSn1V2hErHNrOJKFMI594FMTwXx9NWxmvmTzxZPhfw1T+nVvjdwLNqA23VWd77rSZSJxzq7yHfyfWrDUPyMXPHr0FjDKzkcAHwMXA53Kw34KVyrkUxrTbieG3QUya6GfdAPpoosBPoel34ENU76eJSp9NIWuvRhJX7Zx7IquRJHHO1ZvZN4DngGLgHufcglztvxClUlCG2VnqZ4++mrYSo7YyF6X78MIa/QbR6iuCpu+FaiTZl2oi+amZ3Q1MBWrjC51zj2Ylqti2nwGeydb2O5p0ml1Cmf035Ekb/XzoKM16G+bos6hVSNTZnjupJpIvAKOBTjQ1bTkga4lEci+MO4GDqBHsv6201glgv3FRKEiDGbXlc66tKFTNiMa/R0eRaiI51jl3dFYjkaxKpWwI4ua8TAUz+244ne2JbUSg/Azmd1UyXd9rSspFL2oKojQIotClekPim7qzPL+lU8Dk3Q9bxbfhYyN+ChsLZbxby4IYNJD5XFsRo7m2cibVGskZwCQzW0GsjyTbw38lYCnVSMK5pz32/5DuyA6y/TwK5VUQCTHjTUTtPhLvrzrbsy/VRDIuq1FI1qXV2Z7FOLIhiLb5YK7kM99GUAK5ITHj3yOJ1txWmmsrd1JKJEn3k0ieSudUyuWJl+iXCWSurQz2v18cfvYfBYF0tvvs4wjq2+Oc83Vviubayh39QmIHkVZneyh9JEE0yYTc2R6BIsvfVC/+psgJerit381o+G/uKJF0FCn1kcSEcYUdSNOSr/377+4Poi3eb6EXieG/AZXbfjfj50ZVSY8SSQeRSuEQL8Ryeh9JYt8BbMvHjSSBfOYANuJ3E74623329RT5rNHsz29SVWd77iiRdBBRPZeCTFp+Zv8NohYWxEfxe89DMMOo/cUQ1H0bvhNAofxyZR5QIukgUikcwqgVxAudQAryDOIP4ua5RGd/AJe+frfga5YYnzE0TcmfeQwtbS9TfmczltQpkXQQqZxLiYLAz37SXDnIczyTK+Eg9x+F1rEgOpYz3USQ0934iSN7G5LWKJF0EOmM2vIj3cI87BsCg9h/kJ3MYTZtxXedaQxN/V3RGLWV6PMLIBZpmxJJB5Gzpq103x9Ic1rm2wqi2aNQZv/13bQV8PBxv01b8X+PIJocpW1KJB1EKqdSQ7y/ws9+0jxnGxr995HE487kSjiIwj/IcspvPEFMpV8one3x9ZVGsk+JpINI5dyub4i9qaS9H3dvQ7qFyL6GWG93SVHmX8V6bxuZFBh19bF1S0sy3/8+bxtBFKC+m3N8rNuUkDNbv94buRBYjSSghKYKSfYpkXQQqTQT1NU3AOkX6n5O+Hgi6eQjee1ryLwAjCeSTsWZnwp1Df4K0OTj57uPxMf6e/f5S4i1+zJP6HHNj4WPDdH03dKd7dmnRNJBpHJS7suwRlJb3zSGNt1zNr6unxpJosDIoAir9ZJnqY9Ess9HjSgWQ9LxyzgKb30fG4gfi0y30bR+5kHU1DU0PfF5MHbX+fs8kjolkg4ilZN7c00dAF1LU50UOmbn3vrE43SvZnd463YtK05rvWRbvLgzuYLds89LJD6atrbv2eftP7MSa4e3PmRW6O1JKnz9FJo1tbHtZPo54uv7iWGr928J/jvb4/8uuiEx+5RIOohUTu512/dktO0de5MKwjTX3b47VnD4+XGo+sbMO4m31MRiL/eRyFZt3u3tP7P1tzdLJOlvZHNNbeJxxkN3nUsk5EyL3RWba7z1My+4NyclEr9NWyuqvXiUR7JOiaSApTvscd32vUD6hdl6bz1IvyBbES+EMyx8dtfVt/+mNqzZ6i8JNDQ6qrZmloDjlnkFXqZxNFs/wxg27WoqwDPZyJ66Bqp3xhKanwSwZMPOpjB8ZIDtu/exs7bedzySGiWSArZld1IzQTsnk3OO1RleWc+p2pbyfpI1NDrmeutmWmYsWt9U8GRyNb7gg+2+9r9my+6m/WdYYr27pun4NWQQyK8nL2qKIcMP8tbKLb62MW3ZpsTjBh8l96tLkrbjI5HMX7s98TgK9/cUOiWSApZ8pdzeybR04y7W79ib0nuTNTQ6/jljddOCNM7ZqQs3sG23v/6Fx97+oGnXaW5i0fodrPSSZ6aF1k3PNhXimWzBOcfzC9Ynnid3vKeiautuFqzdkXge7/hPx566Bm6e8j6dOxVltI3GRsdX//42fbuVAbB3X0M7a7SsautuJs9fR5dOsWbG+CiwdDU2On7/wvv06toJaBqZJ9mjRFLA5iXXFNp571Nz12EGZSVFaRUkT81dS9XWPZw8sjeQekKoq2/ktleWMahHZ0b2Lc+oAFy4bgd/e3MV5xzRP7HNVDnn+O1ziyktLmJ4764ZFX6zVm5h8oL1fOrYwRRZZgXWI7OrWL6phjMP6wekVwhvqanjjF+9BMAvLjgaaBqplKr6hka+/+hc3t+wi99PPA5oGoCQqs/fM4O6hkau+dhhdOlUnNGxXLx+J5fePYPS4iK+efaojOIAmFu1jYN/8AyzV23l2vGj6dKp2Hfzp7RPiaSAvbS4OvG4oY1pbues2cYfpy7hiIHdGdm3POXCaNbKLfzg0XmMHljBuUcPAlK/mv3aP2bzzuptfPtjh9G9c0lixE+qHnprDeP/+BoA3/n44UDqhejabXsY+f1nmLJwI98bP5phvbukXQCv3FTDtx58F4AfnXsE5aUl1KRZYD0zbx0/eGwep4zszSUnDwOaj8Bqy8wVWzjhhhcA+MZHDuWQfuVprQ+x4bpXPfAO/3l3Ld/52GF84siBQHrJ6O9vruKNpZspKTIuOXkYXUqL004AUxdu4DO3T6Nq6x7umlTJ4QO7pR3HwnU7GHHt03z6z28AcNKIXkysHEZ5WQm70vxuSfrSG+cpeWNrTR2vLanm4H7lLK+uafWknFe1nQm3xk6+my8+ju/9e267BcHuunrG/OQ5APpXlPF/XziJmStibew79tbTv3vbsT0yu4opCzcysXIoEyuH8djbH6R81bi7rp7L73mLmV6b/r1fOIlD+8cKnpra9rcxY/lmPnvnm4nnXzx9BNOXbWZLTWod5vsaGnluwXquf/I99jU08vQ3z2BA986Ul5WktH+I1Tque2IBD761hhMP6sWdl1Um+opSuZpP/gzf/OihXPPxw5lXFesTSLXwXbNlNx/6daw28+PzxnDFGSOB2DDoVJJRY6Pj+qfe495pKykymPWjczAzr0aS2sVE9c5aPv6HV9i6ex+j+nfjtktP5ND+3Zi+bLP3Wdo/ng2NjlumLuGPU5cklr32vx9hWO+uQGw0Xqr/LpI5JZIC9cSctexrcFx6ykFc/9R7LV7xT1u2ic/dNQOAp646g8MGVNC1tLjNwmj+B9s570+vJ57/40unMKhHF7p3jrVHJw8F3l9tfQPn3vI6SzfuYkD3Mm48P9YcU15WwtptbRfktfUNXHDrNN5b19Qf8MCXx3LqIX2AWAFY00bc+xoaufGp97hv+ioARvTpysvf/QgAXUuL2ZNCoTV71Ra++ve32bizlpF9y7n90hM5fGBFbBtlxSnVqp6au5Zv/PMdAI4d1pO/XXEyXUtL6Foa6xfY2Uah55zjpsmLuOOV5Rzct5w/f+4ExgyOZe1unWOncvJQ4pbU1TdyzUPv8tTcdQDcMOFIPn/qiMTr3TuXJPqtWvPy4o1c/n9vATB6YAX/+frpdPb6NbqUFrOzje9A/HOceOOUxHDjS04eHqvVlcU+Q48use/SlqShwPtrbHQ8OXdtolYI8L1xo/nqWYc0e9+gHp1Ztblm/9UlYEokBcg5x0+fWMDogRVUjugFHHh19+DM1Vz76Dy6dCrmyavOSFzVl5eWsGFHyyfe7FVbuPC26QCcMrI3D145FvNmW+zunfxbWzj5nXM8PW8d1/xrTmI6keevPjNxE2Dv8k68u2Zrq5/l/umr+OkTCxLLLjpxKL+56Nhm7+tTXsr6Vu6DeeX9aibdMxOAo4f04BcXHM3RQ3skXu9dXsrGnbU0NjqKiprfz+Kc4+FZVTw8ew1vrYzF+KsLj+a/ThjabFqVft3KWr0Pp7Ex9vmveuCdxLLbLz2RcUcNTDyPX0Gv3FQDhx+4jWXVu/jOQ3N4d802ThrRi79eflIieQMM7dWFTsXGko07D1yZ2HDY026amki2JUXGfV88mdMP7dvsfaP6V7Bo/Y6WNkH1zlpO+vmUxPMLTxjKbz5zTLNjNqJPOUs27Gpxfeccb63cyq8nL0okiaeuOoOjhvRo9r5D+pfTqdiY/8EOzjtmcLPXGhsdn79nBm8s3ZxYdsOEI7l07EGJ72Ky44f34q5Xl7N3X0Mi2UnwlEgK0HMLNgBw5uH96FcRG0nzw8fm8+HD+rF6y27O/t0rQGzG3Ue+emoiiUDsCvP59zawfc++xJVhQ6Pj2/96lyfmrKVfRRl//OxxnLZfAXTYgG6YwTurt3H2EQMSy99cvpmLvWaYg/p05fvjj2hWgAIcPrA7D82qYt32PQzq0SWx/O7XlnPj0wsTz6/71BguP31ki5/5yMHdmb+2eQFY39DIBX+ZxjxviO/4owbyl/8+4YAC5/CBFeyua+D9jTsZPbCpXW7mii1MvGN64vkZh/blNxcd0yzGuCMGdefeaSubHTfnHH95eRm/eW5x4n0XnjCUn356TLMkALFE1LdbGbNWbuULSZ9x774Grv33XP7z7loAbjj/KC49ZfgBn6FTcRFHD+nBG0s3NVteU1vPxDumNxvZ9ZUzD+Ha8aMP+AwAxwzrwV2vLmfb7jp6di1NLP+fh+fwyOwqAAb36Mzfv3QKB/frdsD6px3ShykLN7Bkw05GDahILH/07SqueWgOAH27lTKxcig3nn90izMKlJUUM2ZwD6Yu3MB3P3E4xV6iWl69i496312AX194DBOOH0xZSesJ4oThvahvdDwxZy0TK4e1+j7xxwphQrPKyko3a9assMOIjP99ZA4Pzapi8Y3jKCspZsS1Tx/wnvFHDeTmi4874CR8a+UWLrp9On3KS7n6Y4cxc8UWnpwTK8QGdC/j0a+dzpCeBxakAJf/30xeXlzNDecfxREDK/jcXTOob2yk0cGlY4fzk/OObLHgWLGpho/89mUuP20E1336yAMKjG+fcxhfPeuQNqcxueOVZfzy2UU8/JVTOWlEb/7wwvvN2s2nXPNhDu1f0eK6a7ft4bSbXmT0wAqeuuoMVm7ezaR7ZvJBUnPbwuvH0aW09QJr9qqtXHjbNADuubySZ+atTxS8EEva8677BN3KWr92++nj8/nnzNX840tjOXpID55/b32zpptHvnIqlSN6t7r+/dNX8pPHF/ChUX35/cTjuPu15dzx6vLE63+dVMlZh/dPFMwtWbB2O+fe8jqj+nfj2vGj+dOLS5vd53LfF09OjDBrSfXOWsb+cioNjY6fnDeGJ+eu5Z3VTeuff9xgfvFfR7c7Dc8z89bxtX+83eJrnx97ENdPOLLFGsj+9jU0MuqHzwJQUVZCbX0jHx3dn9s/f2K763YQAfzItRJJwWlodJz88ymcfmhfbrnkeAC27a7jwbfWsKK6hj7dSrn4pOEM79O11W386D/z+Pubq5st++LpI/nBJ0dT0sbkhis31XDWb19utuxjYwbwu4nHHnAFvr+fPD6f+73+i7gxg7rzqwuPadYM1Zptu+s4/9Y3EveFxF19ziiuPuewdtf/8X/m87c3Vx2w/E+XHM+njh3cwhoH+t4jc/nXrDXNln362MHccP5RiVpKW9Zv38uHf/PSAcOI776sknPGDGhlrSb1DY18+NcvsTZppoGD+5Xz2cphfPlDBx/QbNea8/70GvM/aF67+9bZo5h02gh6l5e2slaTX01exG0vL2u27NyjB/G7icem3LzknOMHj83jgZlNx/OQfuXceVklh7RQE2rL9GWbufL+Wc36n1bedG5a2yhgSiRxSiRN4v0Yt1xyPJ9OsQBsySOzq/ifh+dw+6Un8IkjB6Z09Rff/68mx+7PmHjSsJRjqK1voPLGKRSZMbRXF3583hjGHtwnrZjXb9/LxXdOZ+Xm3VR0LmHmD85psxaxvxufeo+7X19B5UG9+OmnjuSoId1T/txxv3xmIV1KixncowsXVQ5Ne/0NO/ZyxX1vMbRnVy477SDGjuyTcgIA7/6cl5cxe/VWvnTGSD40qm/aMUBsUMW901Zy9JAeXHLy8LQntVy6cSd/nLqUowZ357JTR6T175CspraeuvpGeqWQwNrjnOOmZxdx77SVLL5xvO/tFYj8TSRmdh3wZSB+o8MPnHPPeK99H7gCaAC+6Zx7rr3tKZE0uenZRdz92nJm//hjKV0Fi3QkN095n5unLGHpz8e3WbvuQAJJJGF2tv/BOffb5AVmNga4GDgSGAxMMbPDnHO6oyhFUxZu4JSDeyuJiLRgYPfOQGyC0vhIOfEvail5AvCgc67WObcCWAqcHHJMeWPzrlqWbtzVZmeoSEc2akCsf2VB0qSO4l+YieQbZjbXzO4xs17esiFAcm9llbfsAGZ2pZnNMrNZ1dXVLb2lw4l3NI9qZXSSSEd3zNCeVJSVMHn++vbfLCnLWiIxsylmNr+F/yYAtwGHAMcB64DfxVdrYVMtduI45+50zlU65yr79dMVOMRmTwUY1rvl4bkiHV2n4iI+e9IwnpizlsXrW755U9KXtUTinDvHOXdUC/897pzb4JxrcM41AnfR1HxVBSTfNTQUWJutGPOBc45Vm2tSmgxxlze8saKdobYiHdk3Pnoo3cpK+NSfX2/2ezKSuVA6281skHNunff0AmC+9/gJ4J9m9ntine2jgJntbW/Djr1ceNs0Kkf04qSDelNeVsLPnlxAo3P0Li/l1IP7UlwEvcpLWbNlD93KillWXcP4owayq7aexet30rlTMX+cuoQLjh/CY+98wLDeXThuWC827tjLWYf3p7a+gQ07Yr8At3DdDv73E7F5LD5394xEHD26dErMdVRcZIkf+Lnh/KMY2qsLXTsV8/x7G1hWvQuj+ey8qTh2aA+OG9aTfhVlHDGoO9U7a+nWuYSzRw9g+559id/m6JrhUEuRjqBn11J++V/H8PV/vs2Hfv0Sd11WyTlH9M9omHSybbvrcC4231hZSRG19Y10Ki5iefUuenYtpaJzCUVmFBcZO/fuS5QVKzfvpshiv3m/aVctxwztQf+Kzry6pJrqnbW8srg6MUlpLP5OdC4pZl9DY7OfJi4vLW5xvrmjhnRP3Bc0pGcXenbtxKL1O2lodIHdTxPW8N+/EWvWcsBK4P/FE4uZ/RD4IlAPXO2ce7a97ZUNGuUGTbo5ewGH7MzD+vHK+6knHQ1tFGnfP2es5udPv5cofD8+ZgDFRca67Xs554j+jD24D9OWbWbqoo3sq29kw469bK6p4/ABFSzeEG6z2OiBFWzbvS/xY3T7G9m3nBWbYnPmDe7RudlNqslW3nRu/t5HErQTT6x0D09+mb+9uYqNO2s59+hB1NTWs2DtDob07MKQXl3YsWcf9Y2OnXvrOaRfOcP7dKWuvpGSoiJq6uoZ2L0zVVv3MLJvOX26lbJg7Q4WrtvBvA+2c8HxQzhiUHc27thLv4oyHpi5mjVb9tCvoozRAys4aURvupQWU1xkzSbyA3h/w05eW7KJ8tJiZq7cwhdPH0mPLp1nPGMOAAAO50lEQVRYvH4nowdV0Lu8lNLiopQKfuccy6prmFu1jamLNvK0N4PrVR89FANueXEpv73oWD5z4tBsHGaRglNX38g5v3+F1Wk0cVWUlVDX0Njir1meeFAvZq+KTe55ysjezFixhfOOGcTgnl14fsF6ystKOPXgPgzp1YVuZSWYGXe+uoxLTh5Oz66d2LCjlifnrE3MjTb1O2cyrFdXOhVbSjWmeHne3nv3NTSyr6GRrqUlSiRxuiFRRPxqbHRs3FnLC++tZ/GGndTUNnDaIX04YlB3xgzqntYMA3kk729IFBGJjKIiY2CPzs1+n0VSo4Z0ERHxRYlERER8KYg+EjPbCSxu943h6wtsavdd4VOcwcqHOPMhRlCcQevsnDvK70YKpY9ksXOuMuwg2mNmsxRncBRncPIhRlCcQTOzQEYpqWlLRER8USIRERFfCiWR3Bl2AClSnMFSnMHJhxhBcQYtkDgLorNdRETCUyg1EhERCYkSiYiI+KJEIiIiviiRiIiIL0okIiLiixKJiIj4okQiIiK+KJGIiIgvSiQiIuKLEomIiPiiRCIiIr4okYiIiC9KJCIi4osSiYiI+FIQP7U7btw4N3ny5LDDEBHJNxbERgqiRrJp06awQxAR6bAKIpGIiEh4CqJpSzyN+2DDy9BYF3YkItHVfTRUHBJ2FAVFiaSQrH4Ypv132FGIRFuPMXDugrCjKChKJIWkvib298ynoHP/cGMRiaJ5P4Pt88OOouAokRSiXsdB1yFhRyESPWV9w46gIKmzvaC4sAMQiT6n8yRoSiQiIuKLEklBCuQeI5HCYzo3skGJpJCoyi6SAp0nQVMiERERX5RICpGq7yKt0LmRDUokBUVVdpH26TwJmhKJiIj4okRSkFR9F2mZzo1sUCIpKKqyi7RLoxsDp0QiIiK+KJEUJFXfRVqkEY1ZkfVEYmbDzOwlM1toZgvM7Fve8t5m9oKZLfH+9vKWm5ndYmZLzWyumZ2Q7RgLhqrsIinQeRK0XNRI6oHvOOeOAMYCXzezMcC1wFTn3ChgqvccYDwwyvvvSuC2HMQoIiIZynoicc6tc8697T3eCSwEhgATgPu8t90HnO89ngDc72LeBHqa2aBsx1lQVH0XaYXOjWzIaR+JmY0AjgdmAAOcc+sglmyA+C8xDQHWJK1W5S3bf1tXmtksM5tVXV2dzbDziKrsIu3TeRK0nCUSM+sG/Bu42jm3o623trDsgH9559ydzrlK51xlv379ggpTRETSlJNEYmadiCWRfzjnHvUWb4g3WXl/N3rLq4BhSasPBdbmIs7Coeq7SMt0bmRDLkZtGfBXYKFz7vdJLz0BTPIeTwIeT1p+mTd6ayywPd4EJu3QqC2R9uk8CVwufrP9dODzwDwze9db9gPgJuAhM7sCWA1c5L32DPBJYCmwG/hCDmIsMLrqEmmRBqJkRdYTiXPudVov2c5u4f0O+HpWgxIRkcDoznYREfFFiaSgeG2/qr6LtMLQ8N/gKZGIiIgvSiQiIuKLEklBiVfZ1bQl0jI1bWWDEomIiPiiRCIiIr4okRQSp1FbIm0y053tWaBEIiIiviiRiIiIL0okBUWjtkTaplFb2aBEIiIiviiRiIiIL0okBUVNWyJtU9NWNiiRiIiIL0okIiLiixJJIdENiSJt0w2JWZFxIjGzQ8zsR2Y2P8iAREQkv6SVSMxskJldbWYzgQVAMXBJViITEZG8kFIiMbMvm9mLwCtAX+BLwDrn3M+cc/OyGaCkQ6O2RNqmUVvZUJLi+24FpgOfc87NAjAz/WuIiEjKiWQwcBHwezMbADwEdMpaVCIikjdSatpyzm1yzt3mnPswcDawHdhoZgvN7BdZjVDSoKYtkbZp1FY2pFojAcDMOgMTgdOBVcCbQO8sxCUiInkirUQC3A/sBG7xnl8C9Ao0IhERySvpJpLDnXPHJj1/yczmBBmQ+KAbEkXaZhq1lQ3p3pD4jpmNjT8xs1OAN4INSURE8km6ieQUYJqZrTSzlcSGBJ9pZvPMbG5LK5jZPWa2MfkOeDPrbWYvmNkS728vb7mZ2S1mttTM5prZCRl+LhERyZF0m7bGZbCPe4E/E+tfibsWmOqcu8nMrvWefw8YD4zy/jsFuM37KynRqC2RtqlpKxvSSiTOuVXp7sA596qZjdhv8QTgLO/xfcDLxBLJBOB+55wD3jSznmY2yDm3Lt39iohIboQ1+++AeHLw/vb3lg8B1iS9r8pbdgAzu9LMZpnZrOrq6qwGKyIirYvaNPIttcm0WA91zt3pnKt0zlX269cvy2HlCzVtibRNNyRmQ1iJZIOZDYLYjMLARm95FTAs6X1DgbU5jk1ERNIQViJ5ApjkPZ4EPJ60/DJv9NZYYLv6R0REoi3dUVtpM7MHiHWs9zWzKuCnwE3AQ2Z2BbCa2ISQAM8AnwSWAruBL2Q7voKiGxJF2qYbErMi64nEOdfaD1+d3cJ7HfD17EYkIiJBilpnu4iI5BklkoKiUVsibVPTVjYokYiIiC9KJCIi4osSSUFR05ZIm0w3JGaDEomIiPiiRCIiIr4okRQi3ZAo0gqdG9mgRFJI1PYrkgKdJ0FTIhEREV+USAqSqu8iLdO5kQ1KJAVFVXaR9uk8CZoSiYiI+KJEUpBUfRdpkUY0ZoUSSSHRqC2R9uk8CZwSiYiI+KJEUohUfRdphc6NbFAiKSiqsou0T+dJ0JRIRETEFyWSgqTqu0jLdG5kgxJJQVGVXaR9Ok+CpkQiIiK+KJEUIo3aEmmZzo2sUCIpJLrRSqR9Ok8Cp0QiIiK+KJGISAeipq1sUCIpKKqyi7RP50nQIptIzGycmS02s6Vmdm3Y8YiISMsimUjMrBi4FRgPjAEuMbMx4UaVL1R1F2mdzo9sKAk7gFacDCx1zi0HMLMHgQnAey2+e+8GWPSH3EUXVZumhx2BSPS5BpUXcaO/HchmoppIhgBrkp5XAackv8HMrgSuBDhxJPD2NTkLLtLKR4QdgUh0dRsR+6vyIqbAE0lL9c9mPWTOuTuBOwEqTzze8ZmXcxBWHijpGnYEItF1yBUwfCK4xrAjKShRTSRVwLCk50OBta2+24qhtEe2YxKRQtCpIuwICk4kO9uBt4BRZjbSzEqBi4EnQo5JRERaEMkaiXOu3sy+ATwHFAP3OOcWhByWiIi0wFwBzDtjZjuBxWHHkYK+wKawg0iB4gxWPsSZDzGC4gxaZ+fcUX43EskaSQYWO+cqww6iPWY2S3EGR3EGJx9iBMUZNDObFcR2otpHIiIieUKJREREfCmURHJn2AGkSHEGS3EGJx9iBMUZtEDiLIjOdhERCU+h1EhERCQkkU8k7U0nb2ZlZvYv7/UZZjYi6bXve8sXm9knQo7zGjN7z8zmmtlUMzso6bUGM3vX+y9rN16mEOPlZladFMuXkl6bZGZLvP8mZSvGFOP8Q1KM75vZtqTXcnIsvX3dY2YbzWx+K6+bmd3ifY65ZnZC0ms5OZ4pxPjfXmxzzWyamR2b9NpKM5vnHctARvf4iPMsM9ue9G/7k6TXcvaTEynE+d2kGOd738fe3mu5PJ7DzOwlM1toZgvM7FstvCe476dzLrL/EbsZcRlwMFAKzAHG7PeerwG3e48vBv7lPR7jvb8MGOltpzjEOD8CdPUefzUep/d8V0SO5eXAn1tYtzew3Pvby3vcK6w493v/VcRuWM3ZsUza14eBE4D5rbz+SeBZYnPHjQVmhHA824vxtPi+if1sw4yk11YCfSNyLM8CnvL7fcl2nPu991PAiyEdz0HACd7jCuD9Fs73wL6fUa+RJKaTd87VAfHp5JNNAO7zHj8CnG1m5i1/0DlX65xbASz1thdKnM65l5xzu72nbxKbPyyXUjmWrfkE8IJzbotzbivwAjAuInFeAjyQpVja5Jx7FdjSxlsmAPe7mDeBnmY2iBwez/ZidM5N82KAcL6X8TjaO5at8fO9TluacYb53VznnHvbe7wTWEhsVvVkgX0/o55IWppOfv+DkXiPc64e2A70SXHdXMaZ7ApiVwJxnc1slpm9aWbnZyNAUo/xQq+a+4iZxSfOjOSx9JoHRwIvJi3OxbFMVWufJZfHMx37fy8d8LyZzbbYzzaE7VQzm2Nmz5rZkd6ySB5LM+tKrPD9d9LiUI6nxZr7jwdm7PdSYN/PqN/Z3u508m28J5V1g5LyvszsUqASODNp8XDn3FozOxh40czmOeeWhRDjk8ADzrlaM/sKsZreR1NcNyjp7Oti4BHnXEPSslwcy1RF4buZEjP7CLFEckbS4tO9Y9kfeMHMFnlX5GF4GzjIObfLzD4J/AcYRQSPpedTwBvOueTaS86Pp5l1I5bMrnbO7dj/5RZWyej7GfUaSSrTySfeY2YlQA9iVc/0pqLPfpyY2TnAD4FPO+dq48udc2u9v8uBl4ldPeQ8Rufc5qS47gJOTHXdXMaZ5GL2azrI0bFMVWufJZfHs11mdgxwNzDBObc5vjzpWG4EHiN7TcPtcs7tcM7t8h4/A3Qys75E7Fgmaeu7mZPjaWadiCWRfzjnHm3hLcF9P3PR8eOjw6iEWEfPSJo60o7c7z1fp3ln+0Pe4yNp3tm+nOx1tqcS5/HEOgVH7be8F1DmPe4LLCELnYUpxjgo6fEFwJuuqfNthRdrL+9x77COpfe+w4l1Xlquj+V+cYyg9Q7ic2nemTkz18czhRiHE+s/PG2/5eVARdLjacC4EI/lwPi/NbECeLV3XFP6vuQqTu/1+MVseVjH0zs29wM3t/GewL6fWTvYAR6QTxIbcbAM+KG37HpiV/UAnYGHvZNhJnBw0ro/9NZbDIwPOc4pwAbgXe+/J7zlpwHzvBNgHnBFiDH+EljgxfISMDpp3S96x3gp8IUwj6X3/Drgpv3Wy9mx9Pb3ALAO2EfsKu4K4CvAV7zXDbjV+xzzgMpcH88UYrwb2Jr0vZzlLT/YO45zvO/ED0M+lt9I+m6+SVLia+n7Elac3nsuJzbQJ3m9XB/PM4g1R81N+rf9ZLa+n7qzXUREfIl6H4mIiEScEomIiPiiRCIiIr4okYiIiC9KJCIi4osSiYiI+KJEIiIiviiRiIiIL/8fkN2SqCmQ4ToAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from ipfx.sweep import Sweep, SweepSet\n",
    "\n",
    "sweep_plot_index = 13\n",
    "curr_sweep = data_set.sweep_set([0, 13]).sweeps[1]\n",
    "t = curr_sweep.t\n",
    "v = curr_sweep.v\n",
    "i = curr_sweep.i\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, sharex=True)\n",
    "\n",
    "axes[0].plot(t, v)\n",
    "axes[0].set_xlim(0, 2)\n",
    "axes[0].set_ylabel(\"mV\")\n",
    "\n",
    "axes[1].plot(t, i, c=\"orange\")\n",
    "axes[1].set_ylabel(\"pA\")\n",
    "\n",
    "sns.despine()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}