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{
"cells": [
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"13:18:46 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n"
]
}
],
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"source": [
"import os\n",
"import pathlib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import colors\n",
"import seaborn as sns\n",
"import re\n",
"import shutil\n",
"import pandas as pd\n",
"import scipy.stats\n",
"\n",
"import exdir\n",
"import expipe\n",
"from distutils.dir_util import copy_tree\n",
"import septum_mec\n",
"import spatial_maps as sp\n",
"import head_direction.head as head\n",
"import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"from septum_mec.analysis.plotting import violinplot, despine\n",
"from spatial_maps.fields import (\n",
" find_peaks, calculate_field_centers, separate_fields_by_laplace, \n",
" map_pass_to_unit_circle, calculate_field_centers, distance_to_edge_function, \n",
" compute_crossings, which_field)\n",
"from phase_precession import cl_corr\n",
"from spike_statistics.core import permutation_resampling\n",
"import matplotlib.mlab as mlab\n",
"import scipy.signal as ss\n",
"from scipy.interpolate import interp1d\n",
"from septum_mec.analysis.plotting import regplot\n",
"from skimage import measure\n",
"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
"\n",
"from scipy.stats import wilcoxon"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [],
"source": [
"# %matplotlib notebook\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"project_path = dp.project_path()\n",
"project = expipe.get_project(project_path)\n",
"actions = project.actions\n",
"\n",
"output_path = pathlib.Path(\"output\") / \"spikes-in-field\"\n",
"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load cell statistics and shuffling quantiles"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>action</th>\n",
" <th>baseline</th>\n",
" <th>entity</th>\n",
" <th>frequency</th>\n",
" <th>i</th>\n",
" <th>ii</th>\n",
" <th>session</th>\n",
" <th>stim_location</th>\n",
" <th>stimulated</th>\n",
" <th>tag</th>\n",
" <th>...</th>\n",
" <th>burst_event_ratio</th>\n",
" <th>bursty_spike_ratio</th>\n",
" <th>gridness</th>\n",
" <th>border_score</th>\n",
" <th>information_rate</th>\n",
" <th>information_specificity</th>\n",
" <th>head_mean_ang</th>\n",
" <th>head_mean_vec_len</th>\n",
" <th>spacing</th>\n",
" <th>orientation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1849-060319-3</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>0.138014</td>\n",
" <td>0.263173</td>\n",
" <td>-0.666792</td>\n",
" <td>0.308146</td>\n",
" <td>0.192524</td>\n",
" <td>0.033447</td>\n",
" <td>1.951740</td>\n",
" <td>0.017289</td>\n",
" <td>0.789388</td>\n",
" <td>27.897271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
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" <td>baseline ii</td>\n",
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" <td>0.373986</td>\n",
" <td>0.659259</td>\n",
" <td>-0.572566</td>\n",
" <td>0.143252</td>\n",
" <td>4.745836</td>\n",
" <td>0.393704</td>\n",
" <td>4.439721</td>\n",
" <td>0.124731</td>\n",
" <td>0.555402</td>\n",
" <td>28.810794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>0.087413</td>\n",
" <td>0.179245</td>\n",
" <td>-0.437492</td>\n",
" <td>0.268948</td>\n",
" <td>0.157394</td>\n",
" <td>0.073553</td>\n",
" <td>6.215195</td>\n",
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" <td>0.492250</td>\n",
" <td>9.462322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>0.248771</td>\n",
" <td>0.463596</td>\n",
" <td>-0.085938</td>\n",
" <td>0.218744</td>\n",
" <td>0.519153</td>\n",
" <td>0.032683</td>\n",
" <td>1.531481</td>\n",
" <td>0.053810</td>\n",
" <td>0.559905</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows ×
"</div>"
],
"text/plain": [
" action baseline entity frequency i ii session \\\n",
"0 1849-060319-3 True 1849 NaN False True 3 \n",
"1 1849-060319-3 True 1849 NaN False True 3 \n",
"2 1849-060319-3 True 1849 NaN False True 3 \n",
"3 1849-060319-3 True 1849 NaN False True 3 \n",
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... burst_event_ratio \\\n",
"0 NaN False baseline ii ... 0.398230 \n",
"1 NaN False baseline ii ... 0.138014 \n",
"2 NaN False baseline ii ... 0.373986 \n",
"3 NaN False baseline ii ... 0.087413 \n",
"4 NaN False baseline ii ... 0.248771 \n",
"\n",
" bursty_spike_ratio gridness border_score information_rate \\\n",
"0 0.678064 -0.466923 0.029328 1.009215 \n",
"1 0.263173 -0.666792 0.308146 0.192524 \n",
"2 0.659259 -0.572566 0.143252 4.745836 \n",
"3 0.179245 -0.437492 0.268948 0.157394 \n",
"4 0.463596 -0.085938 0.218744 0.519153 \n",
"\n",
" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
"0 0.317256 5.438033 0.040874 0.628784 \n",
"1 0.033447 1.951740 0.017289 0.789388 \n",
"2 0.393704 4.439721 0.124731 0.555402 \n",
"3 0.073553 6.215195 0.101911 0.492250 \n",
"4 0.032683 1.531481 0.053810 0.559905 \n",
"\n",
" orientation \n",
"0 20.224859 \n",
"1 27.897271 \n",
"2 28.810794 \n",
"3 9.462322 \n",
"4 0.000000 \n",
"\n",
"[5 rows x 39 columns]"
]
},
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"execution_count": 6,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"statistics_action = actions['calculate-statistics']\n",
"identification_action = actions['identify-neurons']\n",
"sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
"units = pd.read_csv(identification_action.data_path('units'))\n",
"session_units = pd.merge(sessions, units, on='action')\n",
"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
"statistics = pd.merge(session_units, statistics_results, how='left')\n",
"statistics.head()"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"statistics['unit_day'] = statistics.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [],
"source": [
"stim_response_action = actions['stimulus-response']\n",
"stim_response_results = pd.read_csv(stim_response_action.data_path('results'))"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [],
"source": [
"statistics = pd.merge(statistics, stim_response_results, how='left')"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"N cells: 1284\n"
]
}
],
"source": [
"print('N cells:',statistics.shape[0])"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
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" <td>223.0</td>\n",
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" <th>4</th>\n",
" <td>0.325842</td>\n",
" <td>0.180495</td>\n",
" <td>5.262721</td>\n",
" <td>0.103584</td>\n",
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" <td>1833-010719-1</td>\n",
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" <td>225.0</td>\n",
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],
"text/plain": [
" border_score gridness head_mean_ang head_mean_vec_len information_rate \\\n",
"0 0.348023 0.275109 3.012689 0.086792 0.707197 \n",
"1 0.362380 0.166475 3.133138 0.037271 0.482486 \n",
"2 0.367498 0.266865 5.586395 0.182843 0.271188 \n",
"3 0.331942 0.312155 5.955767 0.090786 0.354018 \n",
"4 0.325842 0.180495 5.262721 0.103584 0.210427 \n",
"\n",
" speed_score action channel_group unit_name \n",
"0 0.149071 1833-010719-1 0.0 127.0 \n",
"1 0.132212 1833-010719-1 0.0 161.0 \n",
"2 0.062821 1833-010719-1 0.0 191.0 \n",
"3 0.052009 1833-010719-1 0.0 223.0 \n",
"4 0.094041 1833-010719-1 0.0 225.0 "
]
},
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"execution_count": 11,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shuffling = actions['shuffling']\n",
"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
"quantiles_95.head()"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
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" <td>1849-060319-3</td>\n",
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" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.264610</td>\n",
" <td>-0.121081</td>\n",
" <td>5.759406</td>\n",
" <td>0.150827</td>\n",
" <td>4.964984</td>\n",
" <td>0.027120</td>\n",
" <td>0.400770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.344280</td>\n",
" <td>0.215829</td>\n",
" <td>6.033364</td>\n",
" <td>0.110495</td>\n",
" <td>0.239996</td>\n",
" <td>0.054074</td>\n",
" <td>0.269461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.342799</td>\n",
" <td>0.218967</td>\n",
" <td>5.768170</td>\n",
" <td>0.054762</td>\n",
" <td>0.524990</td>\n",
" <td>0.144702</td>\n",
" <td>0.133410</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows ×
"</div>"
],
"text/plain": [
" action baseline entity frequency i ii session \\\n",
"0 1849-060319-3 True 1849 NaN False True 3 \n",
"1 1849-060319-3 True 1849 NaN False True 3 \n",
"2 1849-060319-3 True 1849 NaN False True 3 \n",
"3 1849-060319-3 True 1849 NaN False True 3 \n",
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... p_e_peak t_i_peak p_i_peak \\\n",
"0 NaN False baseline ii ... NaN NaN NaN \n",
"1 NaN False baseline ii ... NaN NaN NaN \n",
"2 NaN False baseline ii ... NaN NaN NaN \n",
"3 NaN False baseline ii ... NaN NaN NaN \n",
"4 NaN False baseline ii ... NaN NaN NaN \n",
"\n",
" border_score_threshold gridness_threshold head_mean_ang_threshold \\\n",
"0 0.332548 0.229073 6.029431 \n",
"1 0.354830 0.089333 6.120055 \n",
"2 0.264610 -0.121081 5.759406 \n",
"3 0.344280 0.215829 6.033364 \n",
"4 0.342799 0.218967 5.768170 \n",
"\n",
" head_mean_vec_len_threshold information_rate_threshold \\\n",
"0 0.205362 1.115825 \n",
"1 0.073566 0.223237 \n",
"2 0.150827 4.964984 \n",
"3 0.110495 0.239996 \n",
"4 0.054762 0.524990 \n",
"\n",
" speed_score_threshold specificity \n",
"0 0.066736 0.451741 \n",
"1 0.052594 0.098517 \n",
"2 0.027120 0.400770 \n",
"3 0.054074 0.269461 \n",
"4 0.144702 0.133410 \n",
"\n",
"[5 rows x 51 columns]"
]
},
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"action_columns = ['action', 'channel_group', 'unit_name']\n",
"data = pd.merge(statistics, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))\n",
"\n",
"data['specificity'] = np.log10(data['in_field_mean_rate'] / data['out_field_mean_rate'])\n",
"\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statistics about all cell-sessions"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 13,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulated\n",
"False 624\n",
"True 660\n",
"Name: action, dtype: int64"
]
},
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"execution_count": 13,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('stimulated').count()['action']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find all cells with gridness above threshold"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 14,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of sessions above threshold 194\n",
"Number of animals 4\n"
]
}
],
"source": [
"query = (\n",
" 'gridness > gridness_threshold and '\n",
" 'information_rate > information_rate_threshold and '\n",
" 'gridness > .2 and '\n",
" 'average_rate < 25'\n",
")\n",
"sessions_above_threshold = data.query(query)\n",
"print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## select neurons that have been characterized as a grid cell on the same day"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
"outputs": [],
"source": [
"once_a_gridcell = statistics[statistics.unit_day.isin(sessions_above_threshold.unit_day.values)]"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 18,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells 139\n",
"Number of gridcell recordings 231\n",
"Number of animals 4\n"
]
}
],
"source": [
"print(\"Number of gridcells\", once_a_gridcell.unit_idnum.nunique())\n",
"print(\"Number of gridcell recordings\", len(once_a_gridcell))\n",
"print(\"Number of animals\", len(once_a_gridcell.groupby(['entity'])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# divide into stim not stim"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells in baseline i sessions 66\n",
"Number of gridcells in stimulated 11Hz ms sessions 61\n",
"Number of gridcells in baseline ii sessions 56\n",
"Number of gridcells in stimulated 30Hz ms sessions 40\n"
]
}
],
"source": [
"baseline_i = once_a_gridcell.query('baseline and Hz11')\n",
"stimulated_11 = once_a_gridcell.query('stimulated and frequency==11 and stim_location==\"ms\"')\n",
"\n",
"baseline_ii = once_a_gridcell.query('baseline and Hz30')\n",
"stimulated_30 = once_a_gridcell.query('stimulated and frequency==30 and stim_location==\"ms\"')\n",
"\n",
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
"\n",
"print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
"outputs": [],
"source": [
"baseline_ids = baseline_i.unit_day.unique()"
]
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['30_260619', '31_260619', '32_260619', '78_260619', '79_260619',\n",
" '150_260619', '205_260619', '243_260619', '263_260619',\n",
" '265_260619', '45_010719', '46_010719', '47_010719', '49_010719',\n",
" '96_010719', '118_010719', '121_010719', '185_010719',\n",
" '186_010719', '106_050619', '168_050619', '231_050619',\n",
" '232_050619', '233_050619', '379_150319', '609_120619',\n",
" '658_120619', '615_290519', '616_290519', '666_290519',\n",
" '667_290519', '179_290519', '214_290519', '278_290519',\n",
" '279_290519', '317_290519', '613_200619', '661_200619',\n",
" '361_010319', '362_010319', '168_120619', '233_120619',\n",
" '243_120619', '851_060319', '357_220319', '358_220319',\n",
" '359_220319', '332_060319', '338_060319', '655_060619',\n",
" '715_110319', '8_020719', '56_020719', '57_020719', '58_020719',\n",
" '129_020719', '130_020719', '132_020719', '23_200619',\n",
" '174_200619', '250_200619', '251_200619', '252_200619',\n",
" '253_200619', '304_200619', '932_280219'], dtype=object)"
]
},
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"execution_count": 21,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"baseline_ids"
]
},
{
"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
"outputs": [],
"source": [
"stimulated_11_sub = stimulated_11[stimulated_11.unit_day.isin(baseline_ids)]"
]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
"outputs": [],
"source": [
"baseline_ids_11 = stimulated_11_sub.unit_day.unique()"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 24,
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"metadata": {},
"outputs": [],
"source": [
"baseline_i_sub = baseline_i[baseline_i.unit_day.isin(baseline_ids_11)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [],
"source": [
"max_speed = .5 # m/s only used for speed score\n",
"min_speed = 0.02 # m/s only used for speed score\n",
"position_sampling_rate = 100 # for interpolation\n",
"position_low_pass_frequency = 6 # for low pass filtering of position\n",
"\n",
"box_size = [1.0, 1.0]\n",
"bin_size = 0.02\n",
"smoothing_low = 0.03\n",
"smoothing_high = 0.06\n",
"\n",
"speed_binsize = 0.02\n",
"\n",
"stim_mask = True\n",
"baseline_duration = 600"
]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [],
"source": [
"data_loader = dp.Data(\n",
" position_sampling_rate=position_sampling_rate, \n",
" position_low_pass_frequency=position_low_pass_frequency,\n",
" box_size=box_size, bin_size=bin_size, \n",
" stim_mask=stim_mask, baseline_duration=baseline_duration\n",
")"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 27,
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"metadata": {},
"outputs": [],
"source": [
"def find_grid_fields(rate_map, sigma=3, seed=2.5):\n",
" # find fields with laplace\n",
" fields_laplace = sp.fields.separate_fields_by_dilation(rate_map, sigma=sigma, seed=seed)\n",
" fields = fields_laplace.copy() # to be cleaned by Ismakov\n",
" fields_areas = scipy.ndimage.measurements.sum(\n",
" np.ones_like(fields), fields, index=np.arange(fields.max() + 1))\n",
" fields_area = fields_areas[fields]\n",
" fields[fields_area < 9.0] = 0\n",
"\n",
" # find fields with Ismakov-method\n",
" fields_ismakov, radius = sp.separate_fields_by_distance(rate_map)\n",
" fields_ismakov_real = fields_ismakov * bin_size\n",
" approved_fields = []\n",
"\n",
" # remove fields not found by both methods\n",
" for point in fields_ismakov:\n",
" field_id = fields[tuple(point)]\n",
" approved_fields.append(field_id)\n",
"\n",
" for field_id in np.arange(1, fields.max() + 1):\n",
" if not field_id in approved_fields:\n",
" fields[fields == field_id] = 0\n",
" \n",
" return fields"
]
},
{
"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
"outputs": [],
"source": [
"def get_data(row):\n",
" spikes = data_loader.spike_train(row.action, row.channel_group, row.unit_name)\n",
" rate_map = data_loader.rate_map(row.action, row.channel_group, row.unit_name, smoothing=0.04)\n",
" pos_x, pos_y, pos_t, pos_speed = map(data_loader.tracking(row.action).get, ['x', 'y', 't', 'v'])\n",
" stim_times = data_loader.stim_times(row.action)\n",
" if stim_times is not None:\n",
" stim_times = np.array(stim_times)\n",
" spikes = np.array(spikes)\n",
" spikes = spikes[(spikes > pos_t.min()) & (spikes < pos_t.max())]\n",
"# sx, sy = rate_map.shape\n",
"# dx = box_size[0] / sx\n",
"# dy = box_size[1] / sy\n",
"# x_bins = np.arange(0, box_size[0], dx)\n",
"# y_bins = np.arange(0, box_size[1], dy)\n",
"# f = interp2d(x_bins, y_bins, rate_map.T)\n",
"# x_new = np.arange(0, box_size[0], dx / 3)\n",
"# y_new = np.arange(0, box_size[1], dy / 3)\n",
"# rate_map = f(x_new, y_new).T\n",
" fields = find_grid_fields(rate_map)\n",
" \n",
" return spikes, pos_x, pos_y, pos_t, rate_map, fields, stim_times"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 29,
2019-12-13 10:43:57 +00:00
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def compute_field_spikes(row, plot=False, z1=5e-3, z2=11e-3, surrogate_fields=None):\n",
" spikes, pos_x, pos_y, pos_t, rate_map, fields, stim_times = get_data(row)\n",
" if surrogate_fields is not None:\n",
" fields = surrogate_fields\n",
"# if field_num is not None:\n",
"# fields = np.where(fields == field_num, fields, 0)\n",
" \n",
" if plot:\n",
" fig, axs = plt.subplots(1, 3, figsize=(16,9))\n",
" axs[1].set_title(f'{row.action} {row.channel_group} {row.unit_idnum}, G={row.gridness:.3f}')\n",
" dot_size = 10\n",
" \n",
" sx, sy = interp1d(pos_t, pos_x), interp1d(pos_t, pos_y)\n",
" \n",
" stim_spikes = []\n",
" stim_in_field_indices = []\n",
" if stim_times is not None:\n",
" for t in stim_times:\n",
" idx = np.searchsorted(spikes, [t + z1, t + z2], side='right')\n",
" tmp_spikes = spikes[idx[0]: idx[1]].tolist()\n",
" stim_spikes.extend(tmp_spikes)\n",
" stim_spikes_x = sx(stim_spikes)\n",
" stim_spikes_y = sy(stim_spikes)\n",
" stim_in_field_indices = which_field(stim_spikes_x, stim_spikes_y, fields, box_size).astype(bool)\n",
" \n",
"# stim_ids_ = []\n",
"# stim_spikes_ = []\n",
"# for i, t in enumerate(stim_times):\n",
"# idx = np.searchsorted(spikes, [t, t + 30e-3], side='right')\n",
"# tmp_spikes = (spikes[idx[0]: idx[1]] - t).tolist()\n",
"# stim_ids_.extend([i] * len(tmp_spikes))\n",
"# stim_spikes_.extend(tmp_spikes)\n",
" \n",
"# plt.scatter(stim_spikes_, stim_ids_, s=1)\n",
"# plt.axvspan(z1, z2, color='r', alpha=.3)\n",
"\n",
" spikes_x = sx(spikes)\n",
" spikes_y = sy(spikes)\n",
" in_field_indices = which_field(spikes_x, spikes_y, fields, box_size).astype(bool) \n",
" \n",
" if plot:\n",
" axs[0].imshow(\n",
" fields.T.astype(bool), extent=[0, box_size[0], 0, box_size[1]], \n",
" origin='lower', cmap=plt.cm.Greys, zorder=0)\n",
" axs[0].scatter(\n",
" spikes_x[in_field_indices], spikes_y[in_field_indices], \n",
" s=dot_size, color='r', zorder=1)\n",
" axs[0].scatter(\n",
" spikes_x[~in_field_indices], spikes_y[~in_field_indices], \n",
" s=dot_size, color='b', zorder=1)\n",
" if stim_times is not None:\n",
" axs[0].scatter(\n",
" stim_spikes_x, stim_spikes_y,\n",
" s=dot_size, color='orange', zorder=1)\n",
" # Display the image and plot all contours found\n",
" contours = measure.find_contours(fields, 0.0)\n",
" axs[1].imshow(rate_map.T, extent=[0, box_size[0], 0, box_size[1]], origin='lower')\n",
"\n",
" axs[2].plot(pos_x, pos_y, color='k', alpha=.2, zorder=0)\n",
" axs[2].scatter(\n",
" interp1d(pos_t, pos_x)(spikes), interp1d(pos_t, pos_y)(spikes), \n",
" s=dot_size, zorder=1)\n",
"\n",
" for ax in axs.ravel()[1:]:\n",
" for n, contour in enumerate(contours):\n",
" ax.plot(\n",
" contour[:, 0] * bin_size, contour[:, 1] * bin_size, \n",
" lw=4, color='y', zorder=3)\n",
"\n",
" for ax in axs.ravel():\n",
" ax.axis('image')\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" return fields, in_field_indices, stim_in_field_indices"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 30,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [],
"source": [
"def plot_stim_field_spikes(row, t1=0, t2=30, z1_base=0, z2_base=5, z1_stim=5, z2_stim=11, colors=['k','r']):\n",
" spikes, pos_x, pos_y, pos_t, rate_map, fields, stim_times = get_data(row)\n",
" spikes = np.array(spikes) * 1000\n",
" pos_t = np.array(pos_t) * 1000\n",
" stim_times = np.array(stim_times) * 1000\n",
" \n",
" fig, axs = plt.subplots(1, 2)\n",
" dot_size = 2\n",
" \n",
" sx, sy = interp1d(pos_t, pos_x), interp1d(pos_t, pos_y)\n",
" \n",
" stim_spikes_base = []\n",
" stim_spikes_base_plot = []\n",
" stim_ids_base = []\n",
" stim_spikes_stim = []\n",
" stim_spikes_stim_plot = []\n",
" stim_ids_stim = []\n",
" stim_ids_all = []\n",
" stim_spikes_all = []\n",
" for i, t in enumerate(stim_times):\n",
" idx = np.searchsorted(spikes, [t + z1_base, t + z2_base], side='right')\n",
" tmp_spikes = spikes[idx[0]: idx[1]] - t\n",
" stim_ids_base.extend([i] * len(tmp_spikes))\n",
" stim_spikes_base_plot.extend(tmp_spikes)\n",
" stim_spikes_base.extend(spikes[idx[0]: idx[1]].tolist())\n",
" \n",
" idx = np.searchsorted(spikes, [t + z1_stim, t + z2_stim], side='right')\n",
" tmp_spikes = spikes[idx[0]: idx[1]] - t\n",
" stim_ids_stim.extend([i] * len(tmp_spikes))\n",
" stim_spikes_stim_plot.extend(tmp_spikes)\n",
" stim_spikes_stim.extend(spikes[idx[0]: idx[1]].tolist())\n",
" \n",
" idx = np.searchsorted(spikes, [t + t1, t + t2], side='right')\n",
" tmp_spikes = (spikes[idx[0]: idx[1]] - t).tolist()\n",
" stim_ids_all.extend([i] * len(tmp_spikes))\n",
" stim_spikes_all.extend(tmp_spikes)\n",
" \n",
" \n",
" stim_spikes_base_x = sx(stim_spikes_base)\n",
" stim_spikes_base_y = sy(stim_spikes_base)\n",
"# stim_in_field_indices_base = which_field(\n",
"# stim_spikes_base_x, stim_spikes_base_y, fields, box_size).astype(bool)\n",
" \n",
" stim_spikes_stim_x = sx(stim_spikes_stim)\n",
" stim_spikes_stim_y = sy(stim_spikes_stim)\n",
"# stim_in_field_indices_stim = which_field(\n",
"# stim_spikes_stim_x, stim_spikes_stim_y, fields, box_size).astype(bool)\n",
"\n",
"\n",
" axs[0].scatter(stim_spikes_all, stim_ids_all, s=dot_size, color='k', alpha=.5)\n",
" axs[0].scatter(stim_spikes_base_plot, stim_ids_base, s=dot_size, color=colors[0], alpha=.8)\n",
" axs[0].scatter(stim_spikes_stim_plot, stim_ids_stim, s=dot_size, color=colors[1], alpha=.8)\n",
" \n",
" times = np.arange(t1, t2, .1)\n",
" from scipy.stats import gaussian_kde\n",
" kernel = gaussian_kde(stim_spikes_all, 0.1)\n",
" pdf = kernel(times)\n",
" m = max(stim_ids_all)\n",
" pdf = (pdf - pdf.min()) / (pdf - pdf.min()).max() * m\n",
" axs[0].plot(times, pdf, 'k', lw=1)\n",
" axs[0].set_xlim(t1, t2)\n",
"# ax.plot(0, len(trials) * 1.1, ls='none', marker='v', color='k', markersize=5)\n",
"# axs[0].axvspan(0, 5, color='#43a2ca', alpha=.3, zorder=-5)\n",
"\n",
" contours = measure.find_contours(fields, 0.0)\n",
"\n",
" axs[1].scatter(\n",
" stim_spikes_base_x, stim_spikes_base_y,\n",
" s=dot_size, color=colors[0], zorder=1, alpha=.8)\n",
" \n",
" axs[1].scatter(\n",
" stim_spikes_stim_x, stim_spikes_stim_y,\n",
" s=dot_size, color=colors[1], zorder=1, alpha=.8)\n",
"\n",
" axs[1].plot(pos_x, pos_y, color='k', alpha=.2, zorder=0)\n",
"\n",
" for n, contour in enumerate(contours):\n",
" axs[1].plot(\n",
" contour[:, 0] * bin_size, contour[:, 1] * bin_size, \n",
" lw=1, color='k', zorder=3)\n",
" axs[0].set_aspect((t2 - t1) / len(stim_times))\n",
" axs[1].axis('image')\n",
" axs[1].set_xticks([])\n",
" axs[1].set_yticks([])\n",
" despine(axs[0])\n",
" despine(axs[1], left=True, bottom=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([[9, 9, 9, ..., 6, 6, 6],\n",
" [9, 9, 9, ..., 6, 6, 6],\n",
" [0, 0, 0, ..., 6, 6, 6],\n",
" ...,\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]], dtype=int32),\n",
" array([False, False, False, ..., False, False, False]),\n",
" [])"
]
},
"execution_count": 307,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxkeV3v/1dtSWWp7OkknU6m093Th+kZUAYGATdGFhlGLj2obCoCsyk/8YoLOIDAVWAE9Yr6E5kNUBRkrkLDlQHZRcUBnBEcZrq/md6ydHfWyr5Uajn3j3PqdFVSa1LZOu/n49GPkzrrt06dqnTqfT7fr8+2bUREREREREREREREREREpHL8290AEREREREREREREREREZErjYJYEREREREREREREREREZEKUxArIiIiIiIiIiIiIiIiIlJhCmJFRERERERERERERERERCpMQayIiIiIiIiIiIiIiIiISIUpiBURERERERERERERERERqTAFsSIiIiIiIiIiIiIiIiIiFaYgVkRERERERERERERERESkwhTEioiIiIiIiIiIiIiIiIhUmIJYEREREREREREREREREZEKUxArIiIiIiIiIiIiIiIiIlJhCmJFRERERERERERERERERCpMQayIiIiIiIiIiIiIiIiISIUpiBURERERERERERERERERqbDgdjdARERERER2D8uy7gQ+DNxujLm/wHqtwFuBlwFXASvAD4CPAfcbY1J5tnsG8BbgJ4BWYAz4BvBnxpjvFjje84HfAJ4DRICLwJeA/22MMWU9yez9PgX4LeCngG4gDjwJfNpt01yBbX8WuB14BtDotumLwHuMMcN5tmkF3oFz3g4AU8C/Ax8wxjxc4Fh+4PXALwPXAXXAAPBZ4H3GmKkSn+8ngVcBVxtjThdY78dwzsuP4pzv88DngT81xlwo5Vgltudu4HeBFxpjvrLOfazrnBbZ57quN8uyXgS8EfgRnOt7Hvg+8FHg48YYez3tKaG9bcBrgRfjXB+tOO/JMeBh4ATwj/nelxU4fi/wTvf4+4Bx4KvA3caYk2Xs53U456pUNxpjvmFZ1ruBd5WxXZ8x5rxlWR/DeU+VxBjjK+MYIiIiIiKyB/hse1P+zhMRERERkSuMZVk34IQnEQoEsZZlXQV8E+gFEkA/UO8+Bvgn4OXGmPiq7V4P3AcEgFngLE5w1gYkgTcbY/4ix/HeCfwv9+EkMAgcwgk/l4FfNMb84zqe788DHweqcUKrJzOeh899Xs9fHapalhXECZx/wZ01gBO4WTg3w0ZxAqL/XrVdB05AeBhYBE66z7/Dff53GGM+kqOddcDncMJi3HYBHMHpBek88OP5wt+M/fwK8Ffuw7xBrGVZbwXuds/BPHAK6HHbOQ3cYoz5RqFjlcKyrJ8BPoNzztYVxK73nBbZ57quN8uy/gT4TffhHHAGJ9xvd+d9Dvi51e+LjbAsywf8NvBuoNadPQ6kw/I+t90Aj+G8L/MG8Otsg4XzGrQCMzjvo0NAC875Om6M+ecS93UT8PYiqx0DmoEl4GnGmNOWZb0BeEOR7a4HaoAJ4IgxZsayrLcBLymy3XNw3menjTFXF3sOIiIiIiKytyiIFRERERGRoizLeh5OFWizO6tQEPtVnFDwcZxQ7kl3/kuBB4Ew8A5jzHsztrkG+B5QBXwQuMsYs+xWev4m8EdACnh2ZmWsW5mYDuh+B6ciM2lZVjXwhzhVi8s4wWLBIHLVczgEPIETwv4N8OvGmBl32Q8DnwCuAf7DGPPcVdv+PvB7OGHyq40xD7nze4H/AzwLpzr4aZkVkJZlfQP4SeDLwCuNMVPu8/8d97nEgR9aXUFoWdbfAL+EU5V5izHmO+78pwL/CFwNPGSMubnA8/0N4H/jhKuQJ4h1X8PPuQ/vwwnHF1a9TkvAtcaYc/mOV4xlWa/AOe/V7qz1BrHfYB3ntMD+1nW9WZb1C8Df4oS/v4NTTZ1yl/0c8BGcGxz+0BhzV7nPs0B77wNucx/+vbv/72csD+JUqf4R8BRgFOe6HKvQ8YM44fcRnJsa7jTGLFmWVQX8CfBrOOH9EWPMZAWO93TgP3Cum1caYx4scbuX4NwgkgSeZ4z59xK3eyPwlzjv9WemP+tERERERETSNEasiIiIiIjkZVlW2O3W8ytcDmELrd/D5crMOzKDCWPM/wU+4D68ddWm/xMnhP0W8JvGmGV3m5Qx5o9xuvT1A3es2u533OknjTF/bIxJutvFcILBkzjBb8ndi7p+HSfM+R7whnQI6+77e8DLcUKb57jd9KaffzdOl8wAr0qHsO52g8BrABune9hnZ2z3PJzAcB54TborYff5vx8nxAuxqhrQsqxn4YSwSeDF6RDW3fYx4E734U1u21i1fZdlWf8A/CmXQ9hC0pWgXzLG3GGMWcho5x/jhH01OKFu2SzLarIs60PAp7gcwq7Les9pEeu93tLbfcgY86eZXQAbY/6By5Wyb3JD3Q2zLOsOLoew/9MY8+rMENY9dsIY80/Ac3EqqTtwAuVK+UWcEHYQuM0Ys+QedwXnPfavQBPw5o0eyK0M/3uc6+bDZYSwXcBf41z/7ygjhH0qTpgMqz7rRERERERE0hTEioiIiIhITpZlHcEJZ9JjK74Dp5vdQg5k/Pz9HMvT1aw9q+b/N0715ofzjJOZ7sb3qlXz/x2nQnNN97Lufh7Ls10xN7rTB9Nh26p9n8LpkhfghoxFr8EJlL9mjPlCju3O4FRN/gZOF6hpr3OnnzXGTKzeDmdcXoDjlmXVZMxPB35/7Qavq30D53X7dZyw1mNZ1i043cT+LM64qf9fju0z1+8Enu4+/ECe1T7oTl9qWVZLof3l2P9zgNPAr+JU1RbrSraY17nTcs9pIWVfb+55+CH34Sfz7PeEO63D6Vp3QyzLqgXelz6mMebPC63vhtTpsPhVlmU1bLQNrte504+74WvmMW3gHvfhqytwrP8FHMXpivu3y9juL3C6P/82TmVwUW6Xzw/ghO7/YIz5VFktFRERERGRPSO43Q0QEREREZEd6wBOYPow8GvGmEcsy7q9yDaDGT8/Hfi3Vcuf5k6zAl1jzIeADxXY7zPdaVbVmTHmD/JtYFlWgMvBYbnVav8TZ/zMbxdYJ11BGsiY90J3+ul8G+UJxZ7jTlefr7Tv4Iy3W4dzLv61lOO5Ydd7cy3DCQdrgb/DCa7C+drsygyzH8mzjnGnAbedXyqyz0wWzjiiX8DpCvq0ZVlljd+6ynrPaV7rvN6WgZfivJ9+kGfzzGrkQJ51yvEqnHMJ8PslbvN5nGD0GzhdNgPgVsS/K/cmOb3eGPMxtwvoZ7nz8r0G6erTQ5Zl9Rhjhso4jseyrKuBN7kPfz1dqV3Cdj+JcyNCCviVzErlIl6LcwPGPM5nhYiIiIiISE4KYkVEREREJJ9h4ObM7nWLMcZcsCzrs8DLgL+yLOu4WwWKZVk/BaTHvyyp61q329B34HR3PA8UrOzL2O4wTherVwMj5KhgLPI8voETSOXb/7Vcrlx8PGNROmh+3B0H81U4Y3B2AJeAz+JU0GWODesHDrkPz+RpT9yyrAs4YehR4F/dqsfDGceL4HQF+1M43UgP4FT0/nOep/FNnPFRH3PbcTDf83VlVirH86wTyvi52P5W+wHwY6V2DVvIes/pBo6X93ozxizijD9ayM+70zjl3zSQy8+40363ersot/L7V3IsGuRyYFqKUXfajdNNNeR5DYAhnErtAM5rsK4gFqeStQr4qtsFelFuVWu6gvujbpfjpWxXC9ztPny/MeZiuY0VEREREZG9Q0GsiIiIiIjkZIw5jdNVbLl+Eafbzp8HTlmW1Y8TyPQB08BvuBWweVmW9WvAr+EEjUHgCeBWY4wpst37gFfihIB+4D9wxnidXMfzyHeMAJerdy8AX3PnV+MEruBUmj6CMxZspl8Avm5Z1suNMdPuvGYu/202XuDQkzihYZv7uIfLw80cAP4F6F21zRssy/oU8MvuOKYeY8zXChwrl3MZP/8wucO5azN+Ljqm8Kr2/GeZ7Sl
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
" compute_field_spikes(baseline_i.sort_values('gridness', ascending=False).iloc[18], plot=True)"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeZxU1Z338U9V7w3dIPvWgAgeBMGFuGTM5r7gRHBJzGLUCGomkxkzzxOVmMkk84whmmTGbOMCRCbLmGUMmhhxwZgYjQlEHVkMBxCkG5B964beq54/zr3d1dW13FtVDQ39fb9e/aruqnPvPbfq1u3u+t7fOZF4PI6IiIiIiIiIiIiIiIiIiBRO9Gh3QERERERERERERERERETkeKMgVkRERERERERERERERESkwBTEioiIiIiIiIiIiIiIiIgUmIJYEREREREREREREREREZECUxArIiIiIiIiIiIiIiIiIlJgCmJFRERERERERERERERERApMQayIiIiIiIiIiIiIiIiISIEpiBURERERERERERERERERKTAFsSIiIiIiIiIiIiIiIiIiBaYgVkRERERERERERERERESkwBTEioiIiIiIiIiIiIiIiIgUmIJYEREREREREREREREREZECUxArIiIiIiIiIiIiIiIiIlJgCmJFRERERERERERERERERAqs+Gh3QEREREREjh3GmNuAh4C51tqFGdoNBu4CrgLGAS3AamAxsNBaG0uz3AzgTuADwGBgJ/A74NvW2hUZtnchcAfwXqAK2AY8B/y7tdaG2snu+/Elbz/GAPuAV4D7rbV/CrGex4DrgUnW2g0Z2o0FvgxcBgwDdgEvAPOttX9Navsh4MUQu3OztXZxhm1XAiuBcmvtmAztIsANwBzgNO/u9cAPca/t4RB9SssYEwVeAs4DSqy1bTmu5xLg74BzcMdUA/Am8CjwI2ttPId1/i3w98BZQCmwEfgF8D1r7b4My1UD/weYDUwAIsBfce+LB6217WH7ErC/NcBNwEXAZOAEoBF4F3c8/9xa+2xPbNvb/qm499H5wEBvu08D91prt+awvjLgH4CPAwZoA9YBP8W9Bk0h1vUfuHPHf1lrb8rQ7lPAZ4BpQBywuGPowXTnMxERERERkUg8Hvp/ThERERER6YOMMWfhQsEqMgSxxphxuABtLJ0BSX/vZ4CngKutta1Jy90MLACKgIO4cGsMMARoBz5vrf1uiu19Gfiq9+MeoBYXcg0AmoBPWmsfz2F/h+NCqpOAw7jAbAww3OvPrdbaHwRYz+3Ag96PaYNYY4zxtjcYOIALNycAg7z9mJUYlhljzgC6PR9JTgJGADHgQ9baP6TZdhT4ES7Y2pouiDXGlOACx6u8u7YDW4BTgH7AKuAKa+2WLP3KyhjzdVyYDzkGscaYbwH/5P1YD7wNjAaGevf9Crg2+VjMss7v44JdgL244/QkXLi5BbjcWrs6xXInA8uAGtyFCWtxx/Yor8kzwIfD9CVAX0uAe4HP03kh9jZcEFqGO74qvft/D3zEWruzUNv3+vB+3EUR5cBuYDMuPO2Pu7DhAmvt/4ZY3wnA88AMXCC6Efd8TsLt41u4Y31XgHV9AHehR4QMQawx5ru44B3c69YOTPV+fg64spCvm4iIiIiIHD80NLGIiIiIiGTlVV8+iwths/kBLnRdA0yx1k611o4DPowLFK/EVb0mrv8UXKVtEfAAMNxaewYu9PyCf78XBicudyGdIewXvOXO9JZ7ABf+/NgYk7bCM4Of4QK254Ex1tr34EKzu73+POT1Oy1jzB3Af2bbkDGmGBdQD8YFoiOttWcBI4HvefvxU69CFwBr7RvW2vel+8JV4JZ6zedlCGEr6Axhs/k6LoRtw1UHjvL6OQr3fE0Dlnr7kxNjTJEx5ht0hrC5rucTuBC23bsdaK09w1o7DLgOF8x+GPjXEOv8HJ0h7L8CIxJepwdwQf0yY8zApOXKca9vDe4ihQnW2tOstaOBa3BB/2W4ysyC8F6DX+HeF1HccTjJWjvaWvsea+003PH2SWAr8EHgd15fC9WHQcCTuOP3Ptxx7b+PHseF148bY0rTr6Wb7+BC2C3A2dbaidbaKbiLAVYBU3DnoGx964+rRI5kaXcTLoQ9ALzfWnuKtfZU4D24QPsSXBW7iIiIiIhINwpiRUREREQkLWNMuTHmK7hKvhMCtK8BLvB+vNVau95/zFr7a+B+78dbkhb9R1xo+Efgn/yhRa21MWvtN3HVglHg1qTlvuDdPmat/aY/tKu1thkXvv0VFwLdmH1vu+zHh3DBVAPwcX+4Wa8/9wE/BkqAe9IsP9IY8z/Af5Al6PF8EpiIq+adY61t9LbXghuC9Q+4IV0/H7D/RcBPcNW0S4FvpGk3A/gzAUJYY8xQOqsCv2ytfcgf1tdaexD3HL8DnAp8Nkg/U2xjEq7q+v/msnwS/9j4T2vtfyQOH2ut/R86K2U/5w11m61vxcA/ez8+Yq39F78K0lrbbK39PPAq7iKAf0la/HO4is13cBWzHcPxWmt/CXzT+3FOiP3L5l9x4W47rur3s8nV2NbaJmvtT3DDP+/GhZl3dltT7v4Bd974k7X2br+q2VpbjzvmNuKqcj8VZGXGmFF0Hqu3WGv/4j/m7dtc78crA1x88S3gRFwInm57RbghlQHusta+nLC913BDdAN83hgzIMg+iIiIiIhI36IgVkREREREUjLGTMQNK+yHSl/CDSuaSWL48WaKx/15XmuS7l+Jq5DrCPdSPA5uvtlEr+Cq/rpVwHnrWZVmuWxu8m6ftNbuTvH4Q97tLK+itIMxZjZuWOFrcEOvBgkl/e39yAtfO3j78bD348cCrAtcteoHcFV8c1M9p97QvytwVaxrcEPYZnIhLixvxlUlduGF3/7zEir49vrzWa8fHwTqyCMQ9Cox/flrH0vT7Anvth+uijKb99A5pPH9adp827u9wZtL13eTd/vlNHPoLsKFvN9KWi4n3gUR/8fvq7V2Sab21trNwP/zfpzrDVVdCDd5t4tSbLOFzvdt0OO6EvdeWIq7OCTZyoTvx6Z4HABjzKW4izr+AmR6bt6Pq4pvwVWNd2GtfQHYgDuGPpyl7yIiIiIi0gflPFyUiIiIiIgc98bgAtM/AX9vrX3NGDM3yzK1Cd+fAbyc9Ph077ZLoGut/U8yD+H7Hu92feKd1tr/l6It0FHNdkaq5QJ4r3eb3H/fctzwvP28viUO+3saLjD6Ca6yM+NQr17odXaW7b3i3U4wxtRYa+syrG8QncM1/3Ni9WWSc3HVgN8E5pM9DPPD7LXW2kNp2ljvdroxpsKv7A3IH3b6u7jQ/8wQyyZrAv4Wdwx3m6/Vkxh4FgVYp7//B6y1b6dp4+//YFwF7DqvinMKbp7eJ1IuZG0t8G8B+hDUXDpD8/sCLvMj3LynT5Pw3BhjFhMuWD/fWvs7Y8xIOp+zbMf1ecaYkmzzrHpVr3+XoYl/nojhqm278YaNXoR7bm4kc+DvnwfeSBOgg9uHicCHSBHWioiIiIhI36YgVkRERERE0tkCzLTWPh10AWvtVmPMk7h5RB80xszyQytjzAXAPK/pvwdZnxfmfAk33HEDKSox0yx3Em4+00nAdgLMGZmwbBQ3XCpAysDNWttqjNmKC5pOpmsQ+xJwmrV2lbe+8Vk2ORrwq2rTBXx1uCFmi7ztpQ1icfNVDsKFgg9maPcQ8KK1dofXzyzdxK+qzRSWlXi3RbgQf122lSZ4HPiqtXZTwP6k5YVmT2Vpdp1320qwoD7M/gOMx+2/f/FBnbW23hgzDlcpOgMow1UBL7LWrgnQh6Cu9G5/b609EGQBb/jt21I8tI7OwDQIf3sTvds4sClN23e82zJcBWu64z8jr4r4Cjov5lhkrd2epvl3ce+5u621b2U5zvx9yNSvd7zbk4P1VkRERERE+hIFsSIiIiIikpJXfbYha8PuPomrOLsOWGuMWYcLGk8E9gN3eBWwaRlj/h43H+lJuP9b3sLNCWmzLPc14KO4ECyKm7Pz09baPSH6fwKd/yvtytB
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeZgcV33v/3d1z67ZtK+jDdlHXiQweAVsC9uAhQKWxWYCJBgthnsJASchNhBCcoMVY67jJD8SZEm2uUCMiZFEMDYQbAsTQN7AtizZxxYaabTvs2893fX741TP9Mz0Uj2rpPm8nkdPq6tOVZ+qrmpp5tPfczzf9xERERERERERERERERERkaETGe0OiIiIiIiIiIiIiIiIiIicbRTEioiIiIiIiIiIiIiIiIgMMQWxIiIiIiIiIiIiIiIiIiJDTEGsiIiIiIiIiIiIiIiIiMgQUxArIiIiIiIiIiIiIiIiIjLEFMSKiIiIiIiIiIiIiIiIiAwxBbEiIiIiIiIiIiIiIiIiIkNMQayIiIiIiIiIiIiIiIiIyBBTECsiIiIiIiIiIiIiIiIiMsQUxIqIiIiIiIiIiIiIiIiIDDEFsSIiIiIiIiIiIiIiIiIiQ0xBrIiIiIiIiIiIiIiIiIjIEFMQKyIiIiIiIiIiIiIiIiIyxBTEioiIiIiIiIiIiIiIiIgMsYLR7oCIiIiIiJy+jDG3AN8CVltrN2RpNxH4a+AGYA7QCbwMPABssNYmMmz3FuALwFXAROAosBX4Z2vts1le71rgc8AVQAVwEPg5cLe11uZ1kL33uxD4C+AaYCYQA14HNgV9asqy7fuB1cBbgKqgTz8F/sFauz/DNhOBL+PO2yzgFPBr4OvW2m1ZXisC3Az8KXAhMA7YC/wIuMNaeyrk8T4I3AScY63dlaXd23Hn5W24870H+AnwT9baA2FeK8N+B3y+83iNtcBtwDuttb/Ic1s/j+bfttZ+os/2w3LecjHGTAL+BLged31MxN2TR4FtwBbgh5nuy+FmjCnD3fc3AfOAJuB54B5r7WND9Bphr+0LcNfHNcBkoAF4GvcePZ5luwtx9+47gGrgEPAo8LXhfG9FREREROTM4vl+Pj9XioiIiIjIWGGMuQR4HBcgZQxijTFzgKeA2UAX8BpQHjwHeARYYa2N9dnuZmA9EAUagd24MHISEAc+b6391zSv9xXg74KnJ4A6YD4u/GwHPmat/eEAjveDwHeAYlxo9XrKcXjBcV3bN1Q1xhTgAuePBov2As2AwX359STwDmvtS322m4oLXd8AtAKvBMc/NTj+Ndba+9L0cxzwX7jgiKBfAAtwox7tAa7MFP6m7OdTwL8HTzOGVcaYvwbWBuegGXgVqAn6WQ/caK3dmu21Mux3QOc7z9f4I2Az7n0YSBD7PzmaTMK9zwBfttZ+LWXbYTlvOfrrAX8JfBUoCxYfA5LB4DzcfQKwHXdfZgwph0Nw/T4OXIYL3l/GBcXJz4uvWmv/LsPmYV8j7LW9FBf6l+Duwddx9+DEoMkXrbVr02x3Je6LHyXAcdw9b3DX7yngGmvtC4M5BhEREREROTtoaGIREREREenHGLME+BkuhM3lPlyIsgM431p7gbV2DvA+XDD6R7jqt9T9n4ertI0C9wBTrbUX4UKqv0ouD8Lg1O2upSeE/atguzcH292DC0a+a4yZlefxzqcnFPx/wBRr7YXW2rnAm3Eh6bnAD9Js/hVcCNsILLPWzrXWXogLWJ8BJgDfC0KyVA8Fbf4bmGWtvRiYgavOiwLfCs5TX/+OC2EPApdZa4211gBvwgVJc4F1OY73c8C/ZWsTtHsv8I+4MHE9MM1ae0nQz7/CVQI+aoyZl2tfffY7mPMd9jU+BDzMIEaCsta+PdMfYAnuiwDgqly7A7vhOm8h3At8HRfCfh94k7V2irX2ouD+mgS8FxcKLwL+xxgzZYj7kMs3cSHsC8AbrLVvDj4v/gT3RY6vGmOuG+jO87i2q4Hv4j4zHgZmWGvfhPss+fug2R1BVXPqdhNwleclwJ3A9JR794fAeOCHxpiigR6DiIiIiIicPRTEioiIiIhIN2NMiTHmq8AvcIFCrvY19FRmrrHWvp5cZ639MS4UAljZZ9M/B4qA3wC3Wmvbg20S1tpv4Ib0jQBr+mz3V8Hjg9bab1hr48F2HcCtuACvBDdkbz4+iwsFXwA+aa1tSDmOF4AVuCrVK1KDGWPMTNyQzAA3WWsfTdmuDvhjwMcND3t5ynZLgKtxlZJ/nBxKODj+O3EBUSHwpdROGmMuBT4e9OV6a+0zKa+3HbgleLo06Bt9tp9ujHkY+CdcSJhLMvT+ubV2jbW2JaWf38CFfaXA3SH2lWpA5zsMY0y1MebfcEF3cZ79ysffAW/FVZv+SZ9hfofrvGVkjFkDrAqe/rm19iPW2hdT21hru6y1jwT9fg0XOv7jUPUhRB/fAHwMSAAftdbuS+nbd1L68tUB7Dvfa/uPcF+SqAf+NHkNWmvj1tq/xVX5A3yyz3afxX02brPW3mat7Qq2a8Ld77txFfp/ku8xiIiIiIjI2UdBrIiIiIiIAGCMWYALZ/42WPRl3JCb2aRWnr6YZn1ynteaPstfwlWPfctam26+lOQwvnP6LP81bljefkP2BvvZnmG7XN4RPP4gGe722feruCpCgNQq3T/GBcpPpJvb0lr7B9xctp/DDWGa9Ing8UfW2uN9t8NVCwMsN8aUpixPBszfDoLXvrbi3rfP4oLMbsaYG3EVs+/HDZ/6v9Nsn9p+GnBR8PTrGZrdEzy+N6gUDGug5zsrY8wVwC7g00Ab/UO0IWGMeSOuchngFmvtyZR1w3neMvWnDLgjePqgtfZfsrUPgv/klxpuMsZUDrYPIX0cV+39W2vtzjTrk9f924wxs9OsTyvfazuQ/EzaZa1tTbM++dnVtx+fCB439t3AWttJz2fTR0L0QUREREREznIDHqJJRERERETOOrNw4cQ24DPW2ueNMatzbFOX8veLgL5zai4OHnsFutbafyP78KEXB4+vpy601v6fTBsYY6L0BGCvZ2qXwZ/j5s98OkubZJVdNGXZO4PHTZk2yhCKXRE8ZpqD9BncMK3jcOfiV2FeLwijv5ZuHfBG3JC138PNI1qSqc+B1DD7+QxtbPAYDfr58xz7TBro+c7F4Ob3fAz4rLV2lzGmX2g/BO7BfbF5k7X2J33WDed5y+QmeuY1/ftsDVP8BDeE9VbcXK0ABBXxf5t+k7RuttY+ELJt1uveWnvAGLMXdw6vxg1fHUa+1zb0fHadY4wZl6xaTpH87NqTXGCMmU7P+5vp3v118Pg2Y0xh37mxRURERERkbFEQKyIiIiIiSftxc5w+mrNlIAhOfgTcAPy7MWZ5UAWKMeYa4PagaaghWIOg48u44Y6bgayVfSnbvQE3rOk5wGHSVMzmOI6tuEAq0/4vAM4Pnu5IWZUMa3YEc0LeBFyPG/L1EG4uyYdTq36NMRHc0KUAf8jQn5gx5gAu9DkX+FVQ9fiGlNerwA3zeg1uqNS9uArTn2U4jKeANyYraY0xczMdbyC1UjlTmFSY8vdc++s2iPOdy8vA2621v87ZcoCMMctx88PGcKFfX8N23rL4o+DxtaCaOKegEvlTaVbV0RMmhnEkj7YLgse0131gDz3XfVj5XtsAW3DzLM8A7jPGrLbWNgZzOd+K+9JDJ25O277994HaLP0HNyz2bLIfq4iIiIiInOUUxIqIiIiICADW2l24YV3z9THcMJ0fBF41xryGm/9yHm7+xc8FFbAZGWM+A3wGFzQWADuBldZam2O7O4AP48KsCPBb3JyjJwZwHJleI0pP9e4B4IlgeTEucAVXjfc8bi7YVB8FnjTGrLDW1gfLxtPzs9ixLC99AhdITQqe19Azvcws4Jf0Hzb1k8aYh3BzXnakrrDWPpHltdJJDZreRPpw7oKUv+ecUziMTOc7DGvtc0PRhxy+GDx+x1qbLowbjfP25uAxW4VxKNba+8jziwx5mBI85rruoee6z2kA1zbW2hZjzLW4+Zg/BCwzxryOC2a
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxU9b3/8ddkJewYZE8At6+KYLG4tS4RWxVzWwNY7WZdCND1Vr29KrXLvf1VUWpdrretQOLWFReIvQq1C0WtVhG1iChfNyARFTHskJBl5vfH95xkMjkzcyYJJuD7+XjwmMyZ7/me7zlz5oTM53w+30gsFkNERERERERERERERERERLpOVncPQERERERERERERERERETkYKNArIiIiIiIiIiIiIiIiIhIF1MgVkRERERERERERERERESkiykQKyIiIiIiIiIiIiIiIiLSxRSIFRERERERERERERERERHpYgrEioiIiIiIiIiIiIiIiIh0MQViRURERERERERERERERES6mAKxIiIiIiIiIiIiIiIiIiJdTIFYEREREREREREREREREZEupkCsiIiIiIiIiIiIiIiIiEgXUyBWRERERERERERERERERKSLKRArIiIiIiIiIiIiIiIiItLFFIgVEREREREREREREREREeliCsSKiIiIiIiIiIiIiIiIiHSxnO4egIiIiIiI9CzGmNnAXcBMa21FinaFwLXABcBooAF4BbgXqLDWRpOs90ngGuAMoBD4AFgB3GGtfT7F9k4D/gP4NNAP2AA8Btxmrd2UyT4G7McPvP0YBWwDngbmWWufTbFeFnA5cClwHNAH2Ag8Atxord2WZL1i4EfAecAQYAvwN2Cutfa1FNvLB74NXAwYIA94C/gD8HNrbV2Ifc0CnsQdw1xrbVOKtp/ztneit623gQeB/022b2EYY84GrgROxb2P7wJ/Bm611toM+ikB/p7Bpi+31t6bQf/9cefbVOAwIAK8hju/f2WtbQ5Y5zLgnjRd32GtvTJhvf8CfpxmvaustbeHGXsmjDEnAV8BTgMOx53H24Ea3Hl5r7V2bVdvN277n8OdD5OAXNx59nvcOV3fgf4OxV1fPo+7Ln0IvADcbq0Nfb6kuw4aYzZ4/YfxhLW2JOy2RURERETk4BGJxWLdPQYREREREekhjDEn4oIv/UgRiDXGjMYF9IqBJuB1oK/3HOBRYJq1tjFhvcuBhUA2sBMXdBkFDAaaccGmOwO2dy0wFxcM2w2sA4qAobig0VRr7YoO7O9QXND1cGAvLtA2yuu3GZhlrb07YL0+wB+Byd6i173HI3CVhzYAp1tr30lYz3jbKwR2AG/ggnyHAPVAmbX28STj/DMwwRvXOlzAbIzX5EVgsrV2R5r9vQkXPIcUgVhjzC+Ab3pPt+Lep8OBQcA7wBRr7SuptpWk3x8B/+09rQWqcfs/ALf/X7XWPhyyr4lAu3MlweHAMCAKlFhrnwrZ91HAX3HnWAPueA8GRnhN/gR8PuD8vg0XVFyPCzAHecBa+z8J6y0Byrzt1CZZ7w5r7YNhxh+GMWYY7rP4b96iKG7cW3Hvx+G4z2kUF5D8bqrAfQfH8D3gZ97T9bjPxHhvu/8CzrTW7sygv+OBZcBwIAa8irtmHOs1uRX4nrU25RchYa6DxpgHve0k0xc43vu5wlo7M+x+iIiIiIjIwUMZsSIiIiIiArRkGC7GBR/SuRsXdF2LC4K+4fXxOeABXHDnGuCGuP6PwQV0soHbgTnW2novS/NqXEDmdmPMs/GZsV6fN3lPF+KCtXsS1ltqjBlnrV2f4W4vwgWc/gJcbK3d5vX7n9427zLG/DMgU/VXuCDsu97+r/TGOh54GDgSmA+Uxu1HDi5AXQj8Gphtra0zxuQBP8dln/7BGHOEtbY2br2Id0wn4AJLU621r3uvnYHLUj0BuBH4VtBOGmOyvf35XroDYoz5Dq1B2J8AP7XWNnoZuTfhAo1/NcYcba3dnq6/uH7PpjUI+5+4TObmhH5/Y4x5LjGAHcRa+xIuizPZ9kYBq72nczIIwvbCvU9FuJsNvuxnXBtjpuHeu/O88f4sYXU/8DbHWrsozPYS1rvMWvtcBut1iDFmJO6GgNG4Gxl+Ctxnrf0wrs1g4OvA9bjzoTcuA7yrxnAWMA8X6P6StXaxt/wIXFb5J4BfAJeE7M+/OWI47saI6f7NAsaYE7w+r8ZloN+Uop8SQlwHrbVfSDOeRbj39UXg38Psg4iIiIiIHHw0R6yIiIiIyMecMaaXVxr1r7iMx3Tti2jNBJ3lB2EBrLX/hwuuAMxIWPW7uBK3zwBX+2VHrbVRa+0tuCzDLGBWwnp+8O7P1tpZ1to9Cev9ASjAZbuF5gVczsRl2H7ZL7fr9Xsz8BtcqdTrE9Y7CRccagbO84Ow3rprgNne0ylewMv3VVzGbDVQ7pcSttY24AI1TwEDgasShnohrozzTuBsPwjrrfskcJ339FJjTG7Afh6Jy+4LE4TNAX7oPV1grf2xn/Vprd1nrb0K+CcuYzhdKd1E/+k9/t5ae4tf2tdauw8XIHsN6IUr9dwpXuD5t7hM42W0D5im8h1cIH0DLvO3pey1Fyy8xXtaHrCuH1Bdk8FY++Mym2O4Gxv2Ky+wvwgXhH0fOMVa+/P4ICyAtfZDa+1PcecfwGVe4L+r/BiXrXqrH4T1tvsmMA33+fqKd/6GMQN3c0gdCRnb1toXaX2/fmiMaZfJmul1MBVjzEzgIty15aIwZcNFREREROTgpECsiIiIiMjHmJd99jqtQbUf4OY5TWVU3M+rA173s1mLEpa/jMsWvStJadCXvceWeRe98qkTvafz2q3h+PNmfs4Yc0iyQQe4zHt8JDEI5bnLeywzxhTELfcDhfd5gddEK3DH8d9xwaTE7f3aC7628I7HfO/pl5KM8xZr7fsB23sY9/5dA+THv2CM+RYuuHcmbs7PawLWjzcJONT7OdnxvsN7vMQL6oX1NC5jsV2pZ2///WMZdt7NVL6BC17vwJWWzWROnsu8xx9Za/cGvF6JC1b/PH7/vQzcQ3AZnq8HrJfMBFxAcoO1dncG63XURbg5gsFl4Kacl9da+xiwxHs6O1XbsIwxY3DnJLjjmbhNi5v/NwJ8MWS353uPD1tr3w7o83Fc6efeQJts1g5eBwN5QV7/ppDrrLVvdaQfERERERE5OKg0sYiIiIjIx9soXMD0WeDb1toXvGyuVKrjfp4I/CPh9QneY5tAhrX2l8AvU/Q7yXt8I25ZfFDuhSTr+YGkbK+PP6fYRrxTvcfE8ftW4ua/7eP165e2/az3uDhoJS/od0P8Mq/c8Ulptve093iYMabIWlvjZXb62cfJtrcTV0I4yIne45244NIJSdr5/OO9I0UAyT/ehbjM0VBBR2vt/0v2mreffsD9jWTtwvCC8X4W9Q/jM1pDrDsCN59oFKgKamOtrcaV8k3kZ8Ouy3Au1YyzaDvJL1/9XNB8xEncjnvfH4lfaIzZQAaBc2utH7j2P3ubvQzYIE8DnwFKgKTnThx/HMmuE+D24WjgFCB+nt6OXAeTuRE3P+wqUl/vRERERETkY0CBWBERERGRj7d3gFJr7dKwK1hrNxljHgEuAH5ljCnzg3bGmMnAHK9pqFLBXgbZD3ABx920DZDEZzI2JukivhzvmJDbzAIO854GBhy9eVE34QI8RwFPGWN64+aUBVhrjOmHKzk8GVfOdCPwQECAaySufHLS7eEyVptxAeWjvOdH4sr1NgPrjDGH4jJyP40L9rwB3G+tfTZJnw8D/+3PnWuMSdKshX+8kx1raH+8M8n+bMcYczhuzs4jcaVy22XMZuhHuMxUi5vLNxP+TQQ11tpdxpjRuAzZT+KyjdcCldbaoBLCfkD1Fa/s9UW497EeeAm4O8kcxv56a40xn8d9rsbgPgvPARXW2g8y3I9AxphBtGbDPpKqbTyvBPaTAS89j7uGZOoI7zFVtugG7/GokH1mcu6OSVie8XUwiDcXrZ8xf1WGmdgiIiIiInIQUiBWRERERORjzMtGS5aRlspXcSV
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJgCAYAAABYyaOvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxU9b3/8dckJBB2DLInCipfFcGiiFo3ilbFtBrQ1rbXtloC1La3vd0U7K92uVXUem17294rS7S32rq0QmwpdlHEfUNcAPULKJIoiwiyJ2Q7vz++55DJZJZzkgkh8H4+HjwmM3P2OTMk8z6fzzfmeR4iIiIiIiIiIiIiIiIiIpI9OR29ASIiIiIiIiIiIiIiIiIihxoFsSIiIiIiIiIiIiIiIiIiWaYgVkREREREREREREREREQkyxTEioiIiIiIiIiIiIiIiIhkmYJYEREREREREREREREREZEsUxArIiIiIiIiIiIiIiIiIpJlCmJFRERERERERERERERERLJMQayIiIiIiIiIiIiIiIiISJYpiBURERERERERERERERERyTIFsSIiIiIiIiIiIiIiIiIiWaYgVkREREREREREREREREQkyxTEioiIiIiIiIiIiIiIiIhkmYJYEREREREREREREREREZEsUxArIiIiIiIiIiIiIiIiIpJlXTp6A0RERERE5OBijJkB3AlMs9bOTzNdIXA9cBlwFFALrAR+B8y31jammO9U4DrgXKAQ+ABYCvzKWvtSmvWdD/wHcCbQC9gA/BO4w1prI+1k8+UeD3wXmAgMBeqANcACf5t2pZivEPh/uP0fBnwEPAPcZq19Ps36ioEbgYuBAcAW4DFgtrX2zTTzdQW+AVwJGCAfeBu4H/gva211iH3NAZ4EzgLyrLX1aab9tL++0/x1vQP8CfiNtfajTOtKs9zxwLeAc4BBQDXwBnAfcKe1tjbkco4G1kVY9U+stT+OsJ1XANcCpwJdgSpgEXC7tXZDmvnOAmYCHwd6+PMtwL2+2yOs/1vAL4EnrLUTws4XlTHmBOBLwARgJNAb2AW8DzwB/MFa+1w7rj8rxytueb1xny9TgBHAXmAV8HugPNXnUoplLQAmk+bcMcYsBc7LsKix1tpXw65XREREREQOHTHP8zp6G0RERERE5CBhjDkNFwr2Ik0Qa4w5ChfoFQP1wGqgp38fXGA1xVpblzDfNcA8IBfYiQv3hgH9gQbg29baXydZ343AT/y7W4FKXMjSB6gBrrLWPtSK/f0McA8uaKvFBbDBfsT8/TrfWvtewnwDcaHrMbig501/Pwb6+zHdWntXkvUZf75CYIe/vhHAEf5+lFpr/5FkvoG40HmMv/y3cKHV0f4ky4GJ1todGfb3Flx4DmmCWGPMb4Gv+Xe34V6nY4B+wHvAJGvtynTrSrHcbwF34LozVeP2vz8wxJ/kBeBCa+3OEMsaBPw5w2RDaTpGV1lr/xByO+cDU/27m/x/x+GO+XbgU9baZ5LM91lcoJyDCzI3A6Nw51clcLa1tirE+kcCrwIFtFMQa4zpBfwaF8LG/IfX4y4M6IF7vfP9x/8MXG2t3ZPlbcjK8Ypb3lHA48Bw3PtkDe4C9BH+Op4ELg550cIXceEtpA9it+HeFy/gPguTudpauzbsfoiIiIiIyKFDrYlFRERERAQAY8wE4B+4EDaTu3Bh5SrgRGvtKGvtUcCluEDxU7iqtPjln4CrtM3FVfoNtNaOxYWX3w8e98Pg+PnOpymE/b4/3yn+fL8EugH3GmOGRdzfETSFsL8HBlhrT7LWHg2cggtXRwIPJpn9AVxQ9S9gmLV2HC5MnOnvx53+/savrwsuoC701zvYWnsaMBj4jb8f9/uVtvHzxfxtGIOrHD3R387huEq8D/ztvTnNvuYaY35OUwib7rj8O00h7E+BQXHb+Utc4PyoMaZvpmUlLPcs4Be4v0NvA/pZa0+21g7FVSNvAE4H5oRZnrV2k7X27FT/gEm4MA7gfyOEsFNxIWw9Lrwd7J+nQ3Cve19ggTGme8J8Bve65gD/DhRZa0/FVYs/hXu//DHE+nOB/8OFsO3CD2GfAL4M7AN+5m/v0dba06y1J+IC8m/ggucrgL/452K2tiErxyvBvbgQ9nXgBGvtCdba43BV3e/jqvBvC7FtQ4H/DjFdMS6E3QucmeZ8VAgrIiIiInKYUhArIiIiInKYM8Z0M8b8GHgUFypkmr4IF5yBq/xcEzxnrf0rTUHH1IRZv4WrsHsW+I61tsafp9Faezvwd9zfKNMT5vu+f3uftfZ2a22DP98+4Du4wLQbLlSK4pu4EPZV4Cvx1aR+G9EpuCDvTGPM2XH7PwEXgO4GvhC06fX341ZcGJQH/CBhfVcBx+Iq/cqCqjy/Fe83ceFTX+DbCfNdgQuQduKqc1fHbeeTuPAX4MvGmLzEnTTGHIercv5epgPih8U/9O/Otdb+KKhqttbus9Z+G3gOF4L/KNPyEnwfV3n5V2vt9f7rF+zH4zS9fp/zz7G2uhMXlr9Ky2OaaTsBfh4f3vpVutfgWlAPwLWsjTcLd37fb639jbXW8+fbDJTiKqDPNsZckGH91wFn4MK99jIHGAvswZ1TP0ys+rbW7rLW/hY4HxfWTgS+mMVtyNbxAsAYcwYQvE8/m/C5tBzXfhxgqn+ep1OOey9meg1O9m9XBdsvIiIiIiIST0GsiIiIiMhhzBhzLK79bhCq/T9ce9J04itPX0vyfDDOa2KY9jrwEG4c0GShxev+7VEJjz8D/AVXhduMv5wVKebL5BP+7YNBuJuw7LdwLYDBVdQFrvZvH7bWfphkuXf6t6XGmPiqxmC+exLHQfX3I6gE/XzC8oL5brfWbkqyvodwr991uGB5P2PM13FVy+fhxt68rsXczY0DjvR/TlU5+Cv/9osRKySD431fiucfw41NGmxHqxljPgV8AVfVek186JthvnzcufYI0KKC1g/Pg4DvqLj5uuHG7gUX4iXOtw03vi60fH3j1z8a+DEurL8z1XRtYYw5PW4bvmOtfTbd9H6I+T/+3RlZ2oasHK8EDcB83Psr2ZjRwedLAS5IT7VtM4CLgAqaPstSCYLYyG26RURERETk8JDpKlARERERETm0DcMFps8D37DWvmyMmZZhnsq4n8cCTyc8P8a/bRboWmv/h6ZAJ5kgfFsT/6C19j9TzeC3cR2bbL4QvoVrY/pCmmmCoDE37rEz/dvE/Q68iAsAe+D26SljTA4wPsN8wZijI4wxRdbaKn//gurjBclm8is1f5pimUGA/GtcyH5KiukCQbi4w1r7doppgpCrEDdu6uoU0+3n7/+VuHPtqRSTxYe6uSmmycivCv4v/+5/+9XNofgBecqw2m/pa/y78efbWFxVtkfT65joGaAMmJBmu3+Pq6aeSlN1Z7Z93b/dQJIQNIU5uNdkUfyDxpiluJA/rOHW2nfJwvFKZK19ifTBafD5shM3Fm0LxpjhwO24cai/imtFnU4QxK5IO5WIiIiIiBy2FMSKiIiIiBze3gNKrLWLw85grX3fGPMwcBnwv8aY0iC0M8ZMxLUcBbgjzPKMMYNxIeFEXLvfjGMz+vMdA9yCCwM3kaRiNsN+LAWWpln+KOBE/+4q/7EcYIT/WNKg0lpbZ4x5HxdqjsQFj0NpGvMzVcBZhavqy/Xnq8LtWzf/8beMMUfiWvieBfTEhYG/t9Y+n2KZDwE/sdau87c/1e4GgkrlujTTxLc/PpoQQay1thHXejqdi2kan3hVpmWm8XXc8dtG6oA6MmPMybgxcvvgtq8i7ulj/duNQcvpJN71b482xuQFLZ/j3Ah8DFcx/mh8O+xs8SuYS/y7i5JVgifjV5h+K8lTK4j2vUKNf5uN4xWK34b4SpoquW9Ntt/+sbkb974qs9ZuDvF+CYLYdcaY6bg2zkfixm3+F3Bv2GpsERERERE5NCmIFRERERE5jFlr1wJrWzHrVbhqus/gAsLVuKBxOLAd+A+/AjYlY8w3gG/gxvHsArwBTE3RVjR+vptxwcrRuOFWnsON8bq1FfuRah25NFX
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxU9b3/8ddkIQk7giBIwlLrt4JgqVG7qRHr1tQaaKvd79Ww2Nt7b8VaK93bW4v2+lN729vKEm1rN3sVYqtYrcWotVWMCyLIF1E0UfYICUv2Ob8/vmeSyeTMzJkQSID38/HgcTLnfM8533MyMyR5z+f7jXieh4iIiIiIiIiIiIiIiIiI9J6svu6AiIiIiIiIiIiIiIiIiMjRRkGsiIiIiIiIiIiIiIiIiEgvUxArIiIiIiIiIiIiIiIiItLLFMSKiIiIiIiIiIiIiIiIiPQyBbEiIiIiIiIiIiIiIiIiIr1MQayIiIiIiIiIiIiIiIiISC9TECsiIiIiIiIiIiIiIiIi0ssUxIqIiIiIiIiIiIiIiIiI9DIFsSIiIiIiIiIiIiIiIiIivUxBrIiIiIiIiIiIiIiIiIhIL1MQKyIiIiIiIiIiIiIiIiLSyxTEioiIiIiIiIiIiIiIiIj0MgWxIiIiIiIiIiIiIiIiIiK9TEGsiIiIiIiIiIiIiIiIiEgvy+nrDoiIiIiISP9ijJkP3AHMtdYuS9FuJPB14DJgAtACvAz8ElhmrY0m2e904HrgHGAksAOoAn5irX02xfnOB64BPgAMAbYAjwC3WmttRheZgjFmEXADcIG19tEU7d4DfBWYCZwItAKvAstx17I3yX4jgW/h7tt4YDfwFPBja+3TKc6XBVwJ/AtwKjAIeBO4H/iRtXZ3yOv7PfBp4N3W2k0p2n3Yv74P4e73G8CDwG3W2rfDnCvJcXt030Ic9xPAXOB0YBju+fEX4IfW2rcOVz/7+/M74LyjgC8CF+OeVyNxr+UdwNNAJXBfstdzL5y/CPiOf/7RwE7gb8Aia+0rPTheFlCOe61Mw30A/XXc9+1Wa219Bsf6CnA78Li1tuRQ7yciIiIiIkefiOd5fd0HERERERHpJ4wxZ+BCkCGkCGKNMROAJ4AioA3YCAz2HwM8AMy21rYm7HclsBTIBhpwAcl4YBTQDiyw1v404HzfAb7vP6wDaoDJuMCtCfi8tfa+nl11l/N8DFiB+9Bq0iDWGPMp4G4gDxdavUrn9Udw9+P8xADQGDMGF7q+CzgAvIK7/jG4659nrb0z4HyDgD/hQkH84wOchAua3gDOThc4GmOuBn7hP0waxBpjvg4s8q9lH7ABKPT7uQeYZa2tSnWuJMft0X1Lc8wcXPj/OX/Vm36fDe77+A5wnrX2pUPdz/7+/E44ZwS4DvgeMNBfvROIheyT/PMDrMW9npMG9z3sg8G9HkYC9bj7PBk4DnfdZdbahzM43gBccHyJv6rGP+7JuO/l28BMa+3G4CN0OdbJwItAARkEqj3dT0REREREjk4amlhERERERAAwxpQAD+NC2HTuxIVS64Ap1tqp1toJwMdxAcrHcFWB8cc/BVdpm42rFhtjrZ2BC/e+Flvvh8Hx+51PZ0j1NX+/9/n73Q7kA78xxozP9JoTznM5cC9pRg4yxkymM6T7NTDaWnuqtXYi8D5cuHoy8MeA3e/BhbB/BcZba4uBcbgK3GzgDv8+JfoFLoTdApxlrTXWWgO8FxdeTQQWp+n3NcDPU7Xx210K3IQLHJcCJ1hrz/D7+TVgOLDSGDMp3bESjnsw9y2V7+BC2Aag1Fo70Vp7Ku4+r8aFer/1g8dD1s/+/vwOsAT4MS6E/QPwXmvtaGvtDL/fo4BLcSH8NODvxpjRvXVyP0B/ABfC3g2M9Z9nY4Gf4a77D34FeVjfxoWwDcCF1toJ1trpuNfHY7jK5nvTPReMMdnAr3BhaibX1KP9RERERETk6KUgVkRERETkGGeMyTfGfA94FBgRon0hnZWZ86y1r8a2WWv/jAt3wA0PGu8rwADgH8C11tomf5+otfYW3DCyWcC8hP2+5i9/b629xVrb7u/XDFyLC8bycUP2ZswYM9wY83NcSJoXYpf/9Nu9CFwVP9SptfZFYDau+vED/vC+sfOUAOfiqjU/GxtK2L/+m4HfALnANxP6dybwBf+YF1trV8edby0w3394iTHmxIDrG2uMuRe4DReuphMLBR+x1s6z1u6P6+ctuNCuALg1xLHi9ei+peJf79f9h5+21q6MO2YN8FnAww25+/5D3M9++fwOYoyZB8yJ9dta+xlr7Zr4NtbaNmvtA8AHcRXAY3ABfW/5PK6iuwaYY61t9M/bgvsePIkL/ReEOZhfDfuf/sOvWWv/Gttmrd1G53NhGu6aUrke93w5EPZiDnI/ERERERE5SimIFRERERE5hhljTsKFLN/1V30LN7RrKvGVeWsCtsfmwSxMWP8ScB9wh7U2aI6U2NCxExLWP4UblrfbkL3+cdYm2S8tY8wHgE3Al4BG4KoQu53nL/8YC80S+rQBV0UIEF/9+K/+8n5r7a6A497hL8uMMfEVdbEA7ld+8JqoCvd9+09cQNjBGDMLVzH7CdxctF8O2D++/QnADP/hj5M0u91fXmqMOS7V8RL09L6l8llc+LnKWvtQwDFfw827eg0QdM97s5/97vkdxBgzEPiR//D31tr/SdXe/8BALCz+tDFmaG/0g87Xw91++Bp/To/OCu/PhDzeMOAu3AdK7k3c6IexO/2HSe+lMWYabrjmGjpfk2n1dD8RERERETm6pRxyS0REREREjnrjcYHp08C/W2ufM8bMTbNPTdzXM4C/J2yf7i+7BLrW2p+TemjcYn/5avxKa+1/JdvBHwo0Fhy+mqxdCgY3NOpDwH9aazcZY7oFYgm+gps/85kUbWKVp9lx6z7gLxPvV8xq3Hy7g3D34kl//QX+cnnQTn5odWOSY56GG3r2t7j5QPNT9Bm6BlTPJWlj/WW2389H0hwzpqf3LZWU9wYgXdAYoEf97KfP7yCfxj3nAX4Qcp8HccFoFdAx77NfSf/d4F0CXWmt/aUxJgs401+X7PXwlL+cbIwptNbWpjqwtXYnLnAPZIyZiBtuGZLcS2NMLm446lxcRX/Yyuwe7SciIiIiIkc/BbEiIiIiIse2t3Dzaq5M29JnrX3bGHM/cBnwC2NMmV95iDFmJrDQbxpq6FpjzFhcRedM3LC9oYIzY8y7cEOlvhvYRkBFYQgvAx+21j6VtqXPWluFC6SS9WsqMMV/uM5flwVM9te9luS4rcaYt3Fh6MnAk3714rtixzLGDMEN6ToTN4z0m7jKzYeTdOcJ4LRYJa0fRqUSX8nZmqRNbtzX6Y7XoSf3LYRY6L/OH5r208DFuGF0twL3A/cmqVA9LP3s4+d3kI/5y41+dW9afmXw1QGbaugMTMPY7i9PpHMe1cDXA1CLq/DOxr0eUgaxqfhDSP8vblSwv1prn03S9Du4eZfvsNY+GnaI7IPYT0REREREjnIKYkVEREREjmHW2k24oXkz9XmgAvgUsMEYsxEXrEwC9gDX+BWCSRlj/h34d1zQmAOsB8qttTbNfj8CrsCFgFnAP3FzedZlehHW2upM90nTt2w6qyLfBlb5X4+g8/evnYn7xanDBbGxyr1COqeUGQ88DhQl7HOVMeYe4F/8eUU7WGtXkZnNcV+/l+CQbWrc12nnFA4jxX1LtU8eLnAFV/X7HG4u2HifAx4zxsy21u45nP3sD8/vJN7nL1NV/IZirb2TngXEo+O+Dnw9WGvbjTH1wHF0vh4yYox5BDcn7An+ql/jvidBbc8AbgDeoHMo5jDn6NF+IiIiIiJybNAcsSIiIiIi0hMebn7Yd3Ah0xRcCAsuiD0Q4hhn44YGjgWUY4DL/GE+UzkfV10a+32mkM4qvz5jjIng5oY8x1/11bhgdGBc06YUh2lMaD8kbtty3H0vww1fPAoXKjXigrvbOUj+8K7/9B9+y7+mRN+M+3rAwZ4zzX1LJf7e/BIYh5tPdzhuvtDP44Lt84Df90E/++v
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxU9b3/8ddMdiAEZZMlAVQ4CIhS49K6pWgXS1sD9dbW2/aqYdF72171ulGtvddWcava9nd7ixD1Wq21rRB7KWpbEZe2LnFBBPkqCoR935OQZc7vj++ZZDKZ5ZxsBPJ+Ph48DnO2+c7MOTOT+ZzP5xNyXRcREREREREREREREREREek44cM9ABERERERERERERERERGRo40CsSIiIiIiIiIiIiIiIiIiHUyBWBERERERERERERERERGRDqZArIiIiIiIiIiIiIiIiIhIB1MgVkRERERERERERERERESkgykQKyIiIiIiIiIiIiIiIiLSwRSIFRERERERERERERERERHpYArEioiIiIiIiIiIiIiIiIh0MAViRUREREREREREREREREQ6mAKxIiIiIiIiIiIiIiIiIiIdTIFYEREREREREREREREREZEOpkCsiIiIiIiIiIiIiIiIiEgHUyBWRERERERERERERERERKSDKRArIiIiIiIiIiIiIiIiItLBMg/3AERERERE5PByHGcW8CtghjFmfor1+gM3ARcDI4A64H3gUWC+MSaSZLvTgBuB84D+wDZgKfAzY8ybKe7vAuAa4NNAPrAJ+DNwvzHGBHqQrR/Hrd7jGA7sBv4G3GOMeS3JNiOBNWl2vcwYc2qCbSd49/dZoB+wGVgM3GGM2ZhinH2B/wCmAscDIeAD7PP9P8aYxjTjwXGcXsB7QK4xZniK9ULAt4HpwCne7I+Ax7CvbXW6+0qx788D/wqciX39DwDLgEeAXxtj3DbsMwf4LnAp4ADZwMfAb4GfGmNqumqcjuNcAlwNnAbkAOuBRcB9xphNPu/b1+vUERzHKQQuBy4ExgLHADXY4/JvwO+MMc935hjSjC/w+eljn4HPwaDnhOM4/wn8KMCwRhlj1gZYX0REREREjkAh1w38N6+IiIiIiBwlHMc5HXgBG+hMGoh1HGcE8DJQBDQAHwJ9vNtgA0/TjDH1cdtdAcwDMoB9wCfY4MoAoBG41hjziwT3dxvwX97NnUAVNhhZANQC3zLGPN2GxzsYG9Q5AajGBjaHA4O98cw0xjycYLuLgQpgl7dNIh8ZY66I2+5cbPA4F9gBrMMGDvtgA0yTjTHvJri/McBfgUJswHsV9jkb6q3yHPDV+Oc7bh9h4NfAZcDGZAE+x3GygN9jA18AW4ANwElAb2A58CVjzIZk95ViDD8FrvNu7scGS4cBA715fwQuSfU4EuxzMPY5nYh9zVZ54xzprfI29nnd29njdBxnPlDm3dzi/RvtjWcP8GVjzN/S3Lev16m9vNf5DuBami/K3oQNSuZgz69e3vyXgK8bY7Z1xlhSjLFN52eafQY+B9tyTjiOcyVwZZrhfArI88ZxYpBjVEREREREjkwqTSwiIiIi0kM5jlMCPI8NwqbzMDbougIYZ4wZb4wZAXwVGxj9MjbrNXb/J2EzbTOAB4HBxphJ2KDKDdH5XjA4drsLaA7C3uBt9ylvuwexAZXHHcdpS8DqKWyQ5y/AcGNMMTa4ebM3nl95444XzYj7nTHmnCT/4oOwxwLPeOO9GxgSc39PYzMRn3YcJztuu1xsYLsQG/w+3hhzijFmGPA1bIDqi9hs4YQcx8mjObiXzl3YgFMDNrNzqDHmdG+cTwEnA886jhOoopLjOP+MDW42etN+xphJxphBwD9hA55fBW4PsM8Q8DtsEHYl9licYIwZBZyPzbb+FHBnZ4/TcZwybBC2AXthwBDv+I4+b/2ABV62a7L7DvI6tZn32v0Rez6FgV8Co40xw4wxxcaYk7FZwN8CNmKfy6XesdiV2np+JtTWc5A2nBPGmIdTvDecgz0mc719lioIKyIiIiLSMygQKyIiIiLSwziOk+uV0fwrNhCRbv1CYLJ3c6Yx5qPoMmPM/wH3eDfL4jb9d2zJ2L8D1xljar1tIsaY+7BZnWFgZtx2N3jTJ40x90VL8BpjDmEDZR9gAxr/kv7RtngcJdgA0wHgMmPM7pjx3A08DmQBtyTYPBqIXR7gLr+PfX5fM8bcbIxp8O5vPzbw9gk2C/E7cdt9D5tVuRa4KLZ0qjFmAXCfd3N6ksd5GvA6PoJ7juMMxJb4BbjNGPOraAleY8w+7HO8FpgA/Fu6/cWJvo6/NMY8EFu62hjzB5ozUL/nlRr24xJsiet9wAXGmA9j9vkyNmAH8C9eVmNnjjO63b3GmCdittkHXIHNthyELS3dSpDXqQPcjg3eN2Ize//NGLM6dgVjTK33OM7GZmyeRNzFFZ2pnednMoHPwc44JxzHGQL8L7a8+K3psqRFREREROTooUCsiIiIiEgP4jjOidiywtFehrdiS3WmEpt5uizB8mif18K4+e9hs86aAhkJloPtNxvrb9jsvVYlSL39RIOh8dulc7k3fcYYsyPB8l9501IvUzFWNBD7fhvurzx+gTGmjubH980k292WpDdrOfBD4KdehmgTx3Huwr4eJ2Ozl+9IM8YLsMHyQ8DPE4zzEM3Pi+/At5eJGH3OnkyyWoU37Q2M87nry73pfcaYLQmWP409tm/EltvtlHF6GZR/BJ4FnojfwOtRG71godVx2obXqc28Cyn+w7t5jzFmYar1jTHrgB97N2d4pZO7wuXetC3nZ7p9BjkHO+Oc+AW2tPjrwL0+txERERERkaNAoNJSIiIiIiJyxBuODZi+BnzXGPOW4zgz0mxTFfP/ScCrccsnetMWAV1jzC+xJVCTKfamH8XONMb8OMG6ADiOk+GNodV2Pnzam8aPP+oNbNnQ3t7YXvHusw82aw58ZsR6GXDRAFyy+4tmxZ3tOE6WMabecZyh2GBfhOYAYAvGmCrgJ0n2eRa2dPF9wBxaB3njRce4yhhzMMk6xptOdBwnzwsyplMLfAV7vCULXscGkTPS7dB77aOZ2QsSDtRmLPoudUwbx+kF8ZJmizqOk4/tQwqJj9Ogr1N7zKA5sHi3z21+DYwHFhPz+B3HeZRgmeifNcYs9blum87PZNp6DtLB54TjOOdjS4pHgKtiM65FREREROTop0CsiIiIiEjPsgGYYoxZ7HcDY8xGx3GewfZM/B/HcUqNMR8DOI4zGZjtrXq/n/15AZJbsUG1AyTIOkuy3QnY3o2jgS0kyJhNsW2Y5mDqx4nW8QKhG7GBmDE0B3omYoNRm4CBjuNcjw0GZ2Kzi59MUGr0RG/qAmuSDGutN83B9t/9mOag9npjzH7HcUZgs/pO89ZbAZQbY1Yk2eevgBeNMVu9x51ktSbRTOX6FOtES/xmYIP4H6ZYFwAvk3dRmtX+Kea+/QTVR2NLUjcCq7wSsv+CLaXbx9vHY8aY13zsq9PG6TjOKdhexgXY1ytRQD3o69QeX/amL/ntS+qVBZ6VYNGHNAcv/fB1f+08P5Np6znYYeeEl7H+oHfzEWPMu2nGLCIiIiIiRxkFYkVEREREehCvL+TqtCu29i1sec9/wgbBPgTygFHAHuAaLwM2KcdxvovtvXgC9m+RlUCZMcak2e5O4FJgJLa9yj+AK40xOwOM/xia//7ZnmK9ndhAz4CYedHStcd4Y47N3vwc8G+O4zyMzXaLBm8GedN9XinTZPcVNQAbBIpm4213HOdbwFygV8x6nwe+7zjOjcaYVoFvY8xvUzy2RD7xpmMdx8mN9vGNMz7m/2l7CvvhBeP/y7v5jM8AYfS52Y0N4j8J9I9ZfiFwteM4DwD/kaQcdqeN03GcR7xxFXmzFgPTY46JJm14ndrE65M7wbv5env3Z4y5E7izvftJoD3nZzJtPQc78pz4EnAqNpO308pPi4iIiIhI96UesSIiIiIi4oeL7Q+7CxswGYcNwoINxCbqZRrvXGy51mjAZTBwsRcsSuUCbLZc9O+XQpq
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iter_base = baseline_i_sub.sort_values('unit_day', ascending=False).itertuples()\n",
"iter_stim = stimulated_11_sub.sort_values('unit_day', ascending=False).itertuples()\n",
"for row_base, row_stim in zip(iter_base, iter_stim):\n",
" fields,_,_ = compute_field_spikes(row_base, plot=True)\n",
" compute_field_spikes(row_stim, plot=True)#, surrogate_fields=fields)"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeZxU1Z338U9V7w3dIPvWgAgeBMGFuGTM5r7gRHBJzGLUCGomkxkzzxOVmMkk84whmmTGbOMCRCbLmGUMmhhxwZgYjQlEHVkMBxCkG5B964beq54/zr3d1dW13FtVDQ39fb9e/aruqnPvPbfq1u3u+t7fOZF4PI6IiIiIiIiIiIiIiIiIiBRO9Gh3QERERERERERERERERETkeKMgVkRERERERERERERERESkwBTEioiIiIiIiIiIiIiIiIgUmIJYEREREREREREREREREZECUxArIiIiIiIiIiIiIiIiIlJgCmJFRERERERERERERERERApMQayIiIiIiIiIiIiIiIiISIEpiBURERERERERERERERERKTAFsSIiIiIiIiIiIiIiIiIiBaYgVkRERERERERERERERESkwBTEioiIiIiIiIiIiIiIiIgUmIJYEREREREREREREREREZECUxArIiIiIiIiIiIiIiIiIlJgCmJFRERERERERERERERERAqs+Gh3QEREREREjh3GmNuAh4C51tqFGdoNBu4CrgLGAS3AamAxsNBaG0uz3AzgTuADwGBgJ/A74NvW2hUZtnchcAfwXqAK2AY8B/y7tdaG2snu+/Elbz/GAPuAV4D7rbV/CrGex4DrgUnW2g0Z2o0FvgxcBgwDdgEvAPOttX9Navsh4MUQu3OztXZxhm1XAiuBcmvtmAztIsANwBzgNO/u9cAPca/t4RB9SssYEwVeAs4DSqy1bTmu5xLg74BzcMdUA/Am8CjwI2ttPId1/i3w98BZQCmwEfgF8D1r7b4My1UD/weYDUwAIsBfce+LB6217WH7ErC/NcBNwEXAZOAEoBF4F3c8/9xa+2xPbNvb/qm499H5wEBvu08D91prt+awvjLgH4CPAwZoA9YBP8W9Bk0h1vUfuHPHf1lrb8rQ7lPAZ4BpQBywuGPowXTnMxERERERkUg8Hvp/ThERERER6YOMMWfhQsEqMgSxxphxuABtLJ0BSX/vZ4CngKutta1Jy90MLACKgIO4cGsMMARoBz5vrf1uiu19Gfiq9+MeoBYXcg0AmoBPWmsfz2F/h+NCqpOAw7jAbAww3OvPrdbaHwRYz+3Ag96PaYNYY4zxtjcYOIALNycAg7z9mJUYlhljzgC6PR9JTgJGADHgQ9baP6TZdhT4ES7Y2pouiDXGlOACx6u8u7YDW4BTgH7AKuAKa+2WLP3KyhjzdVyYDzkGscaYbwH/5P1YD7wNjAaGevf9Crg2+VjMss7v44JdgL244/QkXLi5BbjcWrs6xXInA8uAGtyFCWtxx/Yor8kzwIfD9CVAX0uAe4HP03kh9jZcEFqGO74qvft/D3zEWruzUNv3+vB+3EUR5cBuYDMuPO2Pu7DhAmvt/4ZY3wnA88AMXCC6Efd8TsLt41u4Y31XgHV9AHehR4QMQawx5ru44B3c69YOTPV+fg64spCvm4iIiIiIHD80NLGIiIiIiGTlVV8+iwths/kBLnRdA0yx1k611o4DPowLFK/EVb0mrv8UXKVtEfAAMNxaewYu9PyCf78XBicudyGdIewXvOXO9JZ7ABf+/NgYk7bCM4Of4QK254Ex1tr34EKzu73+POT1Oy1jzB3Af2bbkDGmGBdQD8YFoiOttWcBI4HvefvxU69CFwBr7RvW2vel+8JV4JZ6zedlCGEr6Axhs/k6LoRtw1UHjvL6OQr3fE0Dlnr7kxNjTJEx5ht0hrC5rucTuBC23bsdaK09w1o7DLgOF8x+GPjXEOv8HJ0h7L8CIxJepwdwQf0yY8zApOXKca9vDe4ihQnW2tOstaOBa3BB/2W4ysyC8F6DX+HeF1HccTjJWjvaWvsea+003PH2SWAr8EHgd15fC9WHQcCTuOP3Ptxx7b+PHseF148bY0rTr6Wb7+BC2C3A2dbaidbaKbiLAVYBU3DnoGx964+rRI5kaXcTLoQ9ALzfWnuKtfZU4D24QPsSXBW7iIiIiIhINwpiRUREREQkLWNMuTHmK7hKvhMCtK8BLvB+vNVau95/zFr7a+B+78dbkhb9R1xo+Efgn/yhRa21MWvtN3HVglHg1qTlvuDdPmat/aY/tKu1thkXvv0VFwLdmH1vu+zHh3DBVAPwcX+4Wa8/9wE/BkqAe9IsP9IY8z/Af5Al6PF8EpiIq+adY61t9LbXghuC9Q+4IV0/H7D/RcBPcNW0S4FvpGk3A/gzAUJYY8xQOqsCv2ytfcgf1tdaexD3HL8DnAp8Nkg/U2xjEq7q+v/msnwS/9j4T2vtfyQOH2ut/R86K2U/5w11m61vxcA/ez8+Yq39F78K0lrbbK39PPAq7iKAf0la/HO4is13cBWzHcPxWmt/CXzT+3FOiP3L5l9x4W47rur3s8nV2NbaJmvtT3DDP+/GhZl3dltT7v4Bd974k7X2br+q2VpbjzvmNuKqcj8VZGXGmFF0Hqu3WGv/4j/m7dtc78crA1x88S3gRFwInm57RbghlQHusta+nLC913BDdAN83hgzIMg+iIiIiIhI36IgVkREREREUjLGTMQNK+yHSl/CDSuaSWL48WaKx/15XmuS7l+Jq5DrCPdSPA5uvtlEr+Cq/rpVwHnrWZVmuWxu8m6ftNbuTvH4Q97tLK+itIMxZjZuWOFrcEOvBgkl/e39yAtfO3j78bD348cCrAtcteoHcFV8c1M9p97QvytwVaxrcEPYZnIhLixvxlUlduGF3/7zEir49vrzWa8fHwTqyCMQ9Cox/flrH0vT7Anvth+uijKb99A5pPH9adp827u9wZtL13eTd/vlNHPoLsKFvN9KWi4n3gUR/8fvq7V2Sab21trNwP/zfpzrDVVdCDd5t4tSbLOFzvdt0OO6EvdeWIq7OCTZyoTvx6Z4HABjzKW4izr+AmR6bt6Pq4pvwVWNd2GtfQHYgDuGPpyl7yIiIiIi0gflPFyUiIiIiIgc98bgAtM/AX9vrX3NGDM3yzK1Cd+fAbyc9Ph077ZLoGut/U8yD+H7Hu92feKd1tr/l6It0FHNdkaq5QJ4r3eb3H/fctzwvP28viUO+3saLjD6Ca6yM+NQr17odXaW7b3i3U4wxtRYa+syrG8QncM1/3Ni9WWSc3HVgN8E5pM9DPPD7LXW2kNp2ljvdroxpsKv7A3IH3b6u7jQ/8wQyyZrAv4Wdwx3m6/Vkxh4FgVYp7//B6y1b6dp4+//YFwF7DqvinMKbp7eJ1IuZG0t8G8B+hDUXDpD8/sCLvMj3LynT5Pw3BhjFhMuWD/fWvs7Y8xIOp+zbMf1ecaYkmzzrHpVr3+XoYl/nojhqm278YaNXoR7bm4kc+DvnwfeSBOgg9uHicCHSBHWioiIiIhI36YgVkRERERE0tkCzLTWPh10AWvtVmPMk7h5RB80xszyQytjzAXAPK/pvwdZnxfmfAk33HEDKSox0yx3Em4+00nAdgLMGZmwbBQ3XCpAysDNWttqjNmKC5pOpmsQ+xJwmrV2lbe+8Vk2ORrwq2rTBXx1uCFmi7ztpQ1icfNVDsKFgg9maPcQ8KK1dofXzyzdxK+qzRSWlXi3RbgQf122lSZ4HPiqtXZTwP6k5YVmT2Vpdp1320qwoD7M/gOMx+2/f/FBnbW23hgzDlcpOgMow1UBL7LWrgnQh6Cu9G5/b609EGQBb/jt21I8tI7OwDQIf3sTvds4sClN23e82zJcBWu64z8jr4r4Cjov5lhkrd2epvl3ce+5u621b2U5zvx9yNSvd7zbk4P1VkRERERE+hIFsSIiIiIikpJXfbYha8PuPomrOLsOWGuMWYcLGk8E9gN3eBWwaRlj/h43H+lJuP9b3sLNCWmzLPc14KO4ECyKm7Pz09baPSH6fwKd/yvtytB
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd3hc1Z3/8fdIllVsyb1hS+4+xjYtpgQCgdDBEGxDEgjZLGAbyCa7S3Y3CSW/LdkkBEh2STa7CdgGNglJSBZsdgk9lBQgmBawjY9tXGTcLRfJ6tLM749zrzUaTbl3NKr+vJ5Hz0gzt5yZOfdKms/9nhOJxWKIiIiIiIiIiIiIiIiIiEju5PV0A0RERERERERERERERERE+hsFsSIiIiIiIiIiIiIiIiIiOaYgVkREREREREREREREREQkxxTEioiIiIiIiIiIiIiIiIjkmIJYEREREREREREREREREZEcUxArIiIiIiIiIiIiIiIiIpJjCmJFRERERERERERERERERHJMQayIiIiIiIiIiIiIiIiISI4piBURERERERERERERERERyTEFsSIiIiIiIiIiIiIiIiIiOaYgVkREREREREREREREREQkxxTEioiIiIiIiIiIiIiIiIjkmIJYEREREREREREREREREZEcUxArIiIiIiIiIiIiIiIiIpJjA3q6ASIiIiIi0rsYY24CfgwssdYuS7PcCOBrwBXARKAJWA08BCyz1kZTrDcX+CrwcWAEsAd4Cfi+tXZVmv2dB9wCnA6UAjuAZ4F/s9baUE+y/XZnAn8PnAuMB5qBDcBjXptq0qx7JbAEmAsM8dr0NPBNa+2HKdYZAXwd97pNAA4AfwTutta+lmZfecD1wF8Cc4BBwFbgceDb1toDAZ/vL4CrgenW2o1pljsT97p8DPd6bwF+A/y7tXZ7kH0FbM+dwK3ABdba57PcRlZ9KuQ+Mr5uxphJwOYMm/qztfbEJOvOwfWLTwBDgZ3Ak8C3cvl6J9lvOXAdcD4wExgG1Hv7/yPwK2vtM124/5w+b2NMPrAIuBY4DhgM7ANewfWH36dYbyBwM/AXwLFAI/A+sBT4ubW2Oc3+/gr4vLdePrAJWAHcY609FPY5iIiIiIhI/xGJxWI93QYREREREekljDGnAL/FBW8pg1hjzETgd0AF0AKsxwUeFd4iTwALE8MLY8z1uGAjH6jGBRYTgJFAK/Bla+1/JNnfPwL/4v1YBVQCU3DhZwPwOWvto1k8308BPwUKcUHyhrjnEfGe13mJoaoxZgAucL7Wu2srcBgwuAte9wOfsNa+m7DeGFy4NRWowwU9E4Ax3vO/0Vr7QJJ2DgL+FxcW47ULYBpupKMtwFmpwt+47dwM/Mj7MV2g+DXgTu81OAysA8q9dh4EFlhrX0q3ryCMMZfhAqsBZBnEZtunQu4j6Ot2BbAS9/6/n2JzG6y11yesdxbuooIiXGi4FdeXBuOC+nOtte905jkkaWsB8C3gy7RdpL0DF4QW4o6vEu/+l4FPW2v35LgNOX3e3nHyJC6QBxfI78AdJ4OBGHCbtfauhPWG4i4yOMO7qxLYiwtyB+JC/QXW2oMJ6w3Enesu8O7aijuuZ+D64wfA2V0ZpIuIiIiISO+moYlFRERERAQAY8w5wDO4EDaTB3Bh5RpglrV2trV2IvBJXDB6Ga5CMX77x+IqbfOBe4Ex1tqTcOHeV/z7vTA4fr3zaAthv+Kt9xFvvXtxIc7PjDETQj7fKbSFsD8BRltr51hrJwEfwQVpM4BfJVn9H3EhbDUwz1o7yVo7Bxewvg4MBx42xkQS1nvEW+Y5YIK19mTgGFxFaD7wY+91SvQjXAi7AzjNWmustQY4ERceTwLuy/B8bwH+K90y3nKXA9/BhbBLgbHW2lO8dn4FV7X4pDFmcqZtZdjPp4H/oRMjNWXbp0LuI9Dr5jnBu/2VtfbMFF+JIexwXFVzEXAXMC6uXzyKq1B91Av9csK7kOB/ca9RHu75TbfWjrfWnmytPQ5XWfw5YDtwNvCSMaYoh23oiud9Ly6E3QNcYq31+8MI3DkkAnzHGHNRwnrLcSFsLe4CkoleWyqAF4FzgF8m2d/XcCFsLXCRdx6YhQtwN+KO9YdCtF9ERERERPoZBbEiIiIiIkc5Y0yRMeafgedx4Uem5ctpq8y80Vq7wX/MWvt/wN3ej4sSVv1bXHXZK8DfWWsbvHWi1trv4ob0zQNuTFjvK97tL6y137XWtnrrNQJ/hwtMi3BD9obxN7gQ9h3ghvghRL0qvIW4isrTvWF6/ec/HhfAAFxtrX0ybr1K4LO4yrs5wEfj1jsHF2gdBj7rDyXsPf+7gJ8BBcAd8Y00xpyKGy61FbjYWvt63P7eA27yfrzEaxsJ648zxvwP8O+4ICoTP/R+1lp7o7W2Nq6d38UFUsXAvwXYVgfGmKHGmP/ChdKF2WwjTrZ9Kkg7w75u0BbEvhdiV3+DO+5es9beaq1tAfCGxP4srsJ3Cm7o21z5BnAxrk9dZa39YmKVr7W2wVr7MG5o6n24YXe/2mFL2cvp8zbGjMQNsQzwBWvt0/5j1toma+0/Aw97d30lbr0TcMc6uFEAVsSttxs3HHU1cJFX8RzPP+d821r7bNx679PW5843xlQgIiIiIiJHJQWxIiIiIiJHMWPMNNwwt//k3fV13PCa6cRXnv45yeP+nJzlCfe/i6t0+7G1NtkcKf4wvhMT7v8jrnqvw5C93nb80CtxvUw+4d3+yg93E7a9DjckL0B8ReVnceHfC9bap5Ks9wFuLttbcAGW7zrv9nFr7b7E9XCVnQDzjTHFcff7Yc9/e8Fropdw79vf4IK1I4wxC3AVs1fihnr9YpL145cfC5zk/Xh3isXu9W4v96oaAzPGnI6rFPwCbh7SG8Ksn0S2fSqtsK9bHD+IXR1id9d5t8sTH7DWNtHW768Jsc2UvAsp/t778e744DEZa+1W4F+9H5d4cxXnwnXeba6e91m46uoG3PDQyfj3xx/Pl3i3lSSpevWGY/6592PixR7+OS7deRDahmwXEREREZGjTNZDQImIiIiISL8wARcmvAZ8yVr7pjFmSYZ1KuO+Pwn4Q8Ljx3u37QJda+1/kX6I15O92w3xd1pr/zXJsgAYY/JpCw43pFouhb8FJgN/SrOMXwmZH3efPx/kY6lWstb+IMndp3u3ia+X73XcfLuDcK/F74Pszwsgv5Vimyfg5vl8GPgHXOVwOvGB5ZsplrHebb7XzmdTLJeMwQ0T+xTwN9bajcaYDgF7UNn2qQDCvm4YYwbjKjghYEWsMWYcba95qn7xR+/2Y8aYgsR5l7OwBHchQSNuSOAgfgrMxs2/eqQ62BjzEOEq0T9hrX2pi573n4CrgEHW2miKZZIdz3473k4R5kNbn/9owv2VuPlnT8LNMRvv+LjvM13cIiIiIiIi/ZSCWBERERGRo9uHuDlOn8y4pMdau90Y8zhwBfAjY8x8rwoUY8y5wG3eooGGrvVCma/jhjs+DCQLMZOtNxU3l+l0YBdJKmYzPI+XcNWkqbY/G5jl/bgm7iE/YFnjzV95NW6Y1zHATty8l/8TH+p4VYR+SPdBivY0G2O244KhGcDvjTEluHkm/f2V4ubtPBc3rOtWXEXvMymexu+AE/xKWmPMpFTP1xMfRKUKvgrivs+0vUSrgTOttX/MuGQnZNun4oR93cD1iwhuHt9Rxph/wAV0A3BV579I8rynebcxYHOK7W7xbgtxlZVJ+08Il3m3L8cPx52ON4z2TUkeWk9bYBqEv7+cP29r7Q5cdXQ6n/Ju449nv8+nC3r9Pj/OGFPoDYsO8J+4oau/Zox51Vr7Wzgy/7Q/Z/OvrbXbMrRLRERERET6KQWxIiIiIiJHMW9eyI0ZF+zoc7ghRT8FrDPGrMfNGzoZOAjc4lUrpmSM+RLwJVzQOABYCyyy1toM630b+AwuBMwDXsXN8VqVxfNItY982iottwMvePcX4gJXcBWTb+Lmgo13LfCiMWahtfagd98w2v7/2ptm11W4IHak93M5bVPKTABepuMwpzc
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB2IAAAJfCAYAAACt5RbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd3xcV5338c/MaEajbslFLpJ7fFIJgRDaUgIsJASIHWpom5A4IRB2syVLAjw8z8ImhLILu8BCYpvQ2wJxICQEwgK71MASSHF83G1FliVbvWva88edO5qRptyRRtXf9+ul10hzz733zMy9I2m+93eOL5FIICIiIiIiIiIiIiIiIiIipeOf6w6IiIiIiIiIiIiIiIiIiCw2CmJFREREREREREREREREREpMQayIiIiIiIiIiIiIiIiISIkpiBURERERERERERERERERKTEFsSIiIiIiIiIiIiIiIiIiJaYgVkRERERERERERERERESkxBTEioiIiIiIiIiIiIiIiIiUmIJYEREREREREREREREREZESUxArIiIiIiIiIiIiIiIiIlJiCmJFREREREREREREREREREpMQayIiIiIiIiIiIiIiIiISIkpiBURERERERERERERERERKTEFsSIiIiIiIiIiIiIiIiIiJaYgVkRERERERERERERERESkxMrmugMiIiIiIjK/GGOuBz4PbLfW7szTbinwXuByYB0wBjwOfBHYaa2N51jvmcA/Ai8ElgIdwM+Bf7PW/j7P/l4K3AQ8F6gBjgM/Bv7VWmuLepCTH8cHko+jCegGfgV8zFr72yK28w3gTcAZ1toDedqtBT4IXAKsAE4CPwU+Yq19ckLbFwM/K+LhXG2t/WKefVcCjwJha21TnnY+4G3AtcD5ybv3A1/GeW2HiuhTTsYYP/DfwPOBoLU2OsXtvA64AXgmUA60APcBn7DWHi9BPz09b8m2bwCuAZ4BLAF6gN8Dn7PW/qDU+5suY0wzcBXwMuBMoB4YBtpwzoNvW2sfnMH9n4tz/l2M83y1AfcDt1lrW6ewvRDwTpzj9yxgFHgS2AF83VobKWJbn8R5z/mStfYqj+ucC/wR53l7a3G9FxERERGRxUYVsSIiIiIikmKMeRbwcQ/t1uGEDTcDG4GDOAHmc4E7gXuNMcEs610N/A54A1AJPIETnL0F+I0x5j059vdB4CHgVcm7nsAJjK4D/mSMea33R5mx3cZkf24CGnHCrwSwDfilMeYdHrfzTpwQtlA7g/O8XQNUA38Gwjih0R+NMa+YsEovThiW7+tEsm0c53XItW8/Thi1qUAfg8A9wJeAFwBDwD6ckO7fgN8aY0oVDt6OE8JOmTFmJ/CfwEtwAsS9wGrgb4EnjDHT3b7X5y1gjPkW8C3g5UAA58KEMuBS4PvGmM+Wan/TZYwJGmM+BhwCPoRzYUQU5xw4BjQD7wB+ZIz5uTFmxQz04QU4IfUbcT6feAzn4owbgMeMMU8vcntLcC5c+DfgQqATOAw8C+cCkR8n23jZ1guBvyly/0uBbwKT3vtEREREROT0pCBWRERERESAVPXlgzjVpoV8AViLE4ieba09x1q7DngNMIITmP7jhO2fhVNpGwA+BTRaay/ACUBvdu9PhsHp670U+Kfkjzcn13tGcr1P4QSZX51iOPgtnMDrJ0CTtfZCnBDvlmR/Pp/sd07GmJuA/yi0I2NMGU6V5lLgK8Aqa+2zgFXAZ5KP45vJMAcAa+0j1tq/yPWFE/6Gks1vtdb+T459VyT3+eZC/QTuwKkOjuIEYquT/VyN83ydBzyQfDxTkgwtP45TUT1lxphrcELtKPBWa+2q5DHl9nUJ8L1khelUtl/M83YLzgUGg8DbrbUNyb404DyPUeBdyYrzUuxvypKv3fdxzic/zvF7hrV2jbX2QmvteTjH6VuBVuBFwM+NMeES9qEBuBfnuP8ozvngnn/fxbnQ4rvJClevdgHPw3kNrrDWrktucy1OQPtinKC0UN+qcYJbXxGPZy1OZfs5RfRXREREREQWOQWxIiIiIiKnOWNM2Bjz/3AqTus9tG/GqT4EuM5au99dlhx69WPJH6+ZsOrf4ISGvwb+zlo7klwnbq39BPAjnP9Rrpuw3s3J229Yaz9hrY0l1xsF/g5n2NEw8FeFH23G43gxTsA0ALzZWtud1p+PAl/FqWx7f471VxljvgN8Em+BzVuBzTjVhtdaa4eT+xsD/hr4H5zg8G899j8AfA0n6HuAHJXMyaGgf4eHcM8Ysxy4MfnjB621n7fWJpL97MN5jo8A5wLv9tLPLPs4Ayew+oeprD+Be2x83Fr7NffOZF+vxqnSXoFT4VxsP4t53spwqqrBed6+ktaXhLX28zhhY3qfp7y/EvgQztDYMeB11tp3TxxO21o7knxOnw+cwhnm9x8nbWnq/hrn/ea31tpb3GGprbX9OM/BIZxq+7d72Zgx5nzgiuSP262197jLrLXtOBct9AGvMMZcXmBz/wJswKkG97LvNwKPMD6Mt4iIiIiICKAgVkRERETktGaM2Ywz7Oz/Td71AeBogdXSK0//nGW5O89r84T7H8WpdEuFe1mWgzPfbLpf4VTvfWHiCsntPJZjvUKuSt7ea609lWX555O3W5OViinGmG04c6a+Fifs8xJKuvv7SjJ8TUk+jjuTP17pYVvgVFm+EGf44u3ZnlNjzB04r8d5ONXLtxXY5ktxwvJR4N8nLkyG3+7zUlTwnezPu5P9eBHOPK5TDvaSlZLfxwmhvzZxeTLodi8SKOrYmMLzdh6wLPn9N3K02Z283WSMybjgYQr7m7LkhRR/n/zxY+mBZTbW2qPAh5M/bk8OnVwKVyVvd2XZ5xjj57vX8+HS5O0xslS9Wms7gK8nf8x57CaHB78O+APOEN15GWN+ndxfA87723c99ldERERERE4DUx5KSkREREREFoUmnMD0t8CN1tr/NcZsL7DOsbTvLwB+OWH505K3GYGutfY/yD+E74XJ2/3pd1prP5ylLZCqCr0g23oePDd5O7H/rodxhpOtSvYtfdjf83HmuP0aTmVn3iFbk+HVRQX296vk7UZjTLO1tiXP9hoYH675/1hrW3M0fQ5OVd8ngI9QONRyA8u91trBHG1s8vZpxpgKt7LXI3fY6U/jhP7PKGLdzE44YV3OINcYUwOY5I/FHhvFPm9HgK04wzi35WiTXjUdmOb+pmM742H7Rwu0dX0FZ8jd+0l7HMaYL1JcIH+xtfbnxphVjB9rhc6H5xtjgtbaSIFtu9t7JMeFHjB+7D4n28Lk/LG7cJ6bv8LbhQLPxbmo4L3W2m8knxMRERERERFAQayIiIiIyOnuKeAya+39Xlew1rYaY+7FmUf0c8aYrdbagwDGmJcAtyab/quX7SVDmQ/gDHc8QJZKzBzrbcKZz/QM4ARZKmbzrOvHGfYU4GC2NtbaiDGmFSfg2UJmEPvfwPnW2seS21tfYJdrALeqNuv+cMKcGE5ItyX5cy4fxKnAs8Dn8rT7PPCz5NCsGGPyNAXADbDyhV7B5G0AJ8TfV2ijab4L/JO19rDH/kxJcpjaTwF1OBWmu/OvMUlRz1tyWOt7C2zz9cnbdqBzOvubplclb39hre31skLy8WWb23Yf44GpF+7+NidvE8DhHG2PJG/LceZ4zXXeuIo5dlcZY8qTFd7pPo1zrt5ird3j8XV4J/Ald6h1ERERERGRdApiRUREREROY8l5IQ8UbDjZW3Eqx14P7DXG7MMJGjcAPcBNyQrYnIwxN+LMR7oJ53+TPcA11lpbYL3bgTcC63GmW/kN8A5r7cRwK596xv8fOpmnXSdOELss/U5r7X8VsS9w5il1Zd2ftTZmjOnFCViXZWsDYIxZxvg8ure5c2vm2OakIVoLOJS8PdMYE84RLp2T9n3BOYUn9OcHRfanKMaYu3EC/bXJu+7HmY+3UDVlhik8b4X6dS7OnKjgDE2dUbFZ6v3l6UcQZ35fcOajnRZr7e3A7VNY1T0f+rKEoa7083kZhYNY99h9ep426cfuEpxQHEgNN/5WnOflEwX
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2400x1350 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iter_base = baseline_i_sub.sort_values('unit_day', ascending=False).itertuples()\n",
"iter_stim = stimulated_11_sub.sort_values('unit_day', ascending=False).itertuples()\n",
"for row_base, row_stim in zip(iter_base, iter_stim):\n",
" fields,_,_ = compute_field_spikes(row_base, plot=True)\n",
" compute_field_spikes(row_stim, plot=True)#, surrogate_fields=fields)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# analysis stim vs stim"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 31,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (5, 2), \n",
" 'figure.dpi': 150\n",
"})\n",
"\n",
"plot_stim_field_spikes(\n",
" stimulated_11.sort_values('gridness', ascending=False).iloc[18],\n",
" colors=['#2166ac', '#b2182b']#['#1b9e77','#d95f02']\n",
")\n",
"fig = plt.gcf()\n",
"figname = 'stim_field_spikes_example'\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 32,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [],
"source": [
"results_stim_stim_11 = []\n",
"z1_stim=5e-3\n",
"z2_stim=11e-3\n",
"z1_base=0\n",
"z2_base=5e-3\n",
"for row_stim in stimulated_11.itertuples():\n",
" _, _, base_in_field = compute_field_spikes(\n",
" row_stim, z1=z1_base, z2=z2_base)\n",
" _, _, stim_in_field = compute_field_spikes(\n",
" row_stim, z1=z1_stim, z2=z2_stim)\n",
" \n",
" results_stim_stim_11.append({\n",
" 'base_in_field': 1 - base_in_field.mean(),\n",
" 'stim_in_field': 1 - stim_in_field.mean()\n",
" })\n",
"# break"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 33,
2019-12-13 10:43:57 +00:00
"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>base_in_field</th>\n",
" <th>stim_in_field</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.615038</td>\n",
" <td>0.678867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.620690</td>\n",
" <td>0.727273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.662100</td>\n",
" <td>0.739712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.437500</td>\n",
" <td>0.699267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.728571</td>\n",
" <td>0.741722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>0.283505</td>\n",
" <td>0.316288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>0.596752</td>\n",
" <td>0.638956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>0.311111</td>\n",
" <td>0.359195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>0.558739</td>\n",
" <td>0.601518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>0.552529</td>\n",
" <td>0.629842</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>61 rows ×
"</div>"
],
"text/plain": [
" base_in_field stim_in_field\n",
"0 0.615038 0.678867\n",
"1 0.620690 0.727273\n",
"2 0.662100 0.739712\n",
"3 0.437500 0.699267\n",
"4 0.728571 0.741722\n",
".. ... ...\n",
"56 0.283505 0.316288\n",
"57 0.596752 0.638956\n",
"58 0.311111 0.359195\n",
"59 0.558739 0.601518\n",
"60 0.552529 0.629842\n",
"\n",
"[61 rows x 2 columns]"
]
},
2019-12-16 15:16:33 +00:00
"execution_count": 33,
2019-12-13 10:43:57 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_stim_stim_11 = pd.DataFrame(results_stim_stim_11)\n",
"results_stim_stim_11"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 34,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [],
"source": [
"results_stim_stim_30 = []\n",
"z1_stim=5e-3\n",
"z2_stim=11e-3\n",
"z1_base=0\n",
"z2_base=5e-3\n",
"for row_stim in stimulated_30.itertuples():\n",
" _, _, base_in_field = compute_field_spikes(\n",
" row_stim, z1=z1_base, z2=z2_base)\n",
" _, _, stim_in_field = compute_field_spikes(\n",
" row_stim, z1=z1_stim, z2=z2_stim)\n",
" \n",
" results_stim_stim_30.append({\n",
" 'base_in_field': 1 - base_in_field.mean(),\n",
" 'stim_in_field': 1 - stim_in_field.mean()\n",
" })\n",
"# break"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 35,
2019-12-13 10:43:57 +00:00
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>base_in_field</th>\n",
" <th>stim_in_field</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>0.321023</td>\n",
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" <th>2</th>\n",
" <td>0.511504</td>\n",
" <td>0.579270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.640244</td>\n",
" <td>0.636975</td>\n",
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" <th>4</th>\n",
" <td>0.370968</td>\n",
" <td>0.378876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.687144</td>\n",
" <td>0.675090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.509735</td>\n",
" <td>0.528025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.535645</td>\n",
" <td>0.536465</td>\n",
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" <th>8</th>\n",
" <td>0.391549</td>\n",
" <td>0.433108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.524590</td>\n",
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" <td>0.543882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.574074</td>\n",
" <td>0.648464</td>\n",
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" <td>0.522109</td>\n",
" <td>0.571429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.555556</td>\n",
" <td>0.599616</td>\n",
" </tr>\n",
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" <th>14</th>\n",
" <td>0.188235</td>\n",
" <td>0.310062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.325301</td>\n",
" <td>0.510780</td>\n",
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" <th>16</th>\n",
" <td>0.685393</td>\n",
" <td>0.736201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.565046</td>\n",
" <td>0.621489</td>\n",
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" <th>18</th>\n",
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" <td>0.553879</td>\n",
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" <tr>\n",
" <th>19</th>\n",
" <td>0.485961</td>\n",
" <td>0.522303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.402010</td>\n",
" <td>0.466478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.329670</td>\n",
" <td>0.273570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.455128</td>\n",
" <td>0.426690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.421053</td>\n",
" <td>0.421569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.484375</td>\n",
" <td>0.461538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.736328</td>\n",
" <td>0.723063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.602041</td>\n",
" <td>0.472362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.570312</td>\n",
" <td>0.699342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.292683</td>\n",
" <td>0.357735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.517162</td>\n",
" <td>0.671468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.187500</td>\n",
" <td>0.328277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.562500</td>\n",
" <td>0.560521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>0.540453</td>\n",
" <td>0.692541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>0.612903</td>\n",
" <td>0.649031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.537879</td>\n",
" <td>0.569832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>0.525164</td>\n",
" <td>0.579110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>0.317073</td>\n",
" <td>0.383289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>0.354167</td>\n",
" <td>0.363636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>0.458333</td>\n",
" <td>0.446855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>0.526316</td>\n",
" <td>0.660650</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" base_in_field stim_in_field\n",
"0 0.444444 0.400000\n",
"1 0.220588 0.321023\n",
"2 0.511504 0.579270\n",
"3 0.640244 0.636975\n",
"4 0.370968 0.378876\n",
"5 0.687144 0.675090\n",
"6 0.509735 0.528025\n",
"7 0.535645 0.536465\n",
"8 0.391549 0.433108\n",
"9 0.524590 0.655920\n",
"10 0.568627 0.543882\n",
"11 0.574074 0.648464\n",
"12 0.522109 0.571429\n",
"13 0.555556 0.599616\n",
"14 0.188235 0.310062\n",
"15 0.325301 0.510780\n",
"16 0.685393 0.736201\n",
"17 0.565046 0.621489\n",
"18 0.457364 0.553879\n",
"19 0.485961 0.522303\n",
"20 0.402010 0.466478\n",
"21 0.329670 0.273570\n",
"22 0.455128 0.426690\n",
"23 0.421053 0.421569\n",
"24 0.484375 0.461538\n",
"25 0.736328 0.723063\n",
"26 0.602041 0.472362\n",
"27 0.570312 0.699342\n",
"28 0.292683 0.357735\n",
"29 0.517162 0.671468\n",
"30 0.187500 0.328277\n",
"31 0.562500 0.560521\n",
"32 0.540453 0.692541\n",
"33 0.612903 0.649031\n",
"34 0.537879 0.569832\n",
"35 0.525164 0.579110\n",
"36 0.317073 0.383289\n",
"37 0.354167 0.363636\n",
"38 0.458333 0.446855\n",
"39 0.526316 0.660650"
]
},
2019-12-16 15:16:33 +00:00
"execution_count": 35,
2019-12-13 10:43:57 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_stim_stim_30 = pd.DataFrame(results_stim_stim_30)\n",
"results_stim_stim_30"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 36,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [],
"source": [
"results_stim_stim_all = pd.concat([results_stim_stim_11, results_stim_stim_30])"
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 37,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAASUAAAGmCAYAAAA6Q5IBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOy9eZQkV3Xn/3mx5lp7V+97t55aUktCEkhCLJJYBbaxBy8cG7DHM2MzGC+/44X5Db/zQ94Y28ce45kfHuzjYxtjg1kMBhuwjRAGhIR2CaEl1C11t3qReqnuqso9MyLe74/IarWK6qqsqsiMyKz30dGJzorIF7cqM79533333SuUUmg0Gk1aMJI2QKPRaC5Ei5JGo0kVWpQ0Gk2q0KKk0WhShRYljUaTKrQoaTSaVKFFSaPRpAotShqNJlVoUdJoNKlCi5JGo0kVWpQ0Gk2q0KKk0WhShRYljUaTKrQoaTSaVKFFSRMbUspNUkolpfzzRa75yfY1P7nEWH/evm7TEtc9LaV8eqU2a9KHFiXNqpFS5tv/PNs+Trd/bkgps/OumZp3TU5KKdr/zkgpzYtcN/f8l/y7fd30Rc5p+hAtSppV0RaRg1LKfwPeRCQQoZTyfwBHgV+RUu4HTkkpPwZsbD91XfvxSeCG9s/+C3Cs/VzRHusN7bGPSind9nV/I6V8TEr5PmAGOCul/AUp5SPAP3T9l9Z0FStpAzR9zyRwL3Aj8I/tn72/fTwChMA4cB/wduDd7XN/3T73XWC0/bMAOAH8Gi++N/8RaAB3A5uAQ8Bp4Ergf19gx5uAc8CTUkrheZ4uqdqnCF0OVxMXUspPAz9KJCJv9DzvW/PODwMPALuJxOWVnuedXGCca4G7ABf4IvCjnuf5C1z3XuAj7Ye/7nneH8b462gSQk/fNLEgpfwA8GPALxN5O5+RUu684LwF/D2wA/gJYGv7GnfeOBuBz7XH+GXgbcCHFrjfLcAfA38HfBL4PSnlD8b+i2l6jhYlzaqRUq4jEpD7iDyXnyea1v3KBZfdDLwe+BPP8z4D/D5wEzBfSH4O2NYe7/8Dvgn83IUC1+b/BWpEU8X/iyj+dPsFgXJNn6Knb5pYkFJuAHKe5z3bfnyT53nfnnfNNcATnufVpZQ28DLP8+5bYKzzz5VSbgPwPO+5edc4wJWe5z1wwdhPep5X68Kvp+khWpQ0Gk2q0NM3jUaTKrQoaTSaVKFFSaPRpAotShqNJlVoUdJoNKliTYuSlPKLUsovJm2HRqN5kbW+9233nj17LgN0XoRG81JEUjde056SRqNJH1qUNBpNqtCipNFoUoUWJY1Gkyq0KGk0mlShRUmj0aQKLUoajSZVaFHSaDSpQouSRqNJFVqUNBpNqtCipNFoUoUWJY1GkyrW+obcgaFardJsNpM2Y83jOA65XC5pM/oaLUoDwHvf+14++tGPoptAJI8Qgve85z386Z/+adKm9C1rupuJlPLxPXv2XPalL30paVNWTLVapVAoaEFKEUIIyuVyv3tMunSJZmU0m00tSClDKaWn0qtAT98GjMOHDzM8PJy0GWuOmZkZduzYkbQZA4EWpQFjeHiYkZGRpM3QaFaMnr5pNJpUEZunJKXMAb8BvAPYCZSAB4EPe573lRWOeTnw34BbgXXADHAv8Mee530tDrs1Gk26iMVTklLmgTuBDwK7gMeBCvBG4MtSyg+uYMzbgAeAdwIjwBNEKwJvBe6QUv7fcdiu0WjSRVzTt48A1wOPALs9z7vG87ztwLsBH7hdSvn6TgeTUo4AfwtkgM8CmzzPuxpYD/xW+7IPSSlfFZP9fYvjOBhG9DKaponjOAlbtDbRr0N8rFqUpJS7ibyZEPgpz/OOzp3zPO/jwO+1H96+jGF/ABgDpoGf9jxvpj1e4HneB4Fvtq/72dVZ3//kcjl+8Rd/EdM0ed/73tfvuTF9i34d4iMOT+ldgAnc43neEwuc/2j7eJOUcluHY25tHw96nldd4Pz97WOn4w00H/7wh/F9nw9/+MNJm7Km0a9DPMQhSje2j3ctdNLzvOPAkfbD13Y45nPt4952vGo+V7aPhzscT6PR9AlxiNKe9vGZRa453D5e0uGY/wicAIaBv5RSDgFIKYWU8leBNwBNoliWRqMZIOIQpcn28fQi10y1jxOdDOh5XgV4HVFKwY8DJ6SUDwMvAH8IHADe4nnewyuyWKPRpJY4RGkuoldf5JravGs7oQZ8BwiAPHA1LwrgKaLAukajGTDiSJ4M6FzcOto5KqW8EriDKGHyU8BvE3lHm4D3AL8OfFVK+S7P8z65bIs1mkVovvA001//M5Tfu021AkFu/5soXP3Wnt0zrcQhSmVglCin6GJk28eFVtIW4iNEgvRlz/PeccHPDwP/TUp5Cvgj4P9IKb/ied708kzuL6bqZY6VV/8rFu0Mu4Y7mkGvac5+6Q+oPnknqrmY8x8zQlA//CDZPTdiFsZ6d98UEoconSESpfFFrpn7JJxaajAp5QZgLinyNy9y2Z8A/719z7cAn+jI0j6k5jd5z9c/wVS9vOqxbMPkQzf+CNdO6kyKi1E/8jD1ww8SlM5gDm+gV2WFwsoUQek0pXs/xcjr/mtP7plW4hClJ4G9wI5Frpk793QH422/4N9PLXSB53mBlPJponSEnR2M2bc8euYYZ2pljlemsQ1zxeMEKmTYyXLPC89oUVqE2W/9NWH5LEZ2GDPXu2oLQhgEpVOU7v0UQze9CyNT6Nm900YconQv8EO8mK/0EqSUW3gxyfHuDsabveDfG+c9vpC5oPfFzg8Ej5w+RtVvMuRkmMwWVzxOudXgXKPKw6ePLn3xGqX5vEft6W8RVKex1/X2u05kClA6jT97ktL9n2X41T/T0/uniThW3z7TPt4spZQLnH9P+/gNz/MOdzDeU0Q5SgA/t9AFUsrXArvbD+/s0M6+5OEzR6n5TXLW6vZS5SybetDiWHmaF6oDreMrZuaujxFUzmFkCohV/r2XixACozBOUDpD6TufJGw1enr/NLFqUfI87wBRTMcEPielnEumREr5TuD97Ye/M/+5UsrdUspLpZQbLxhP8WIs6VeklL8hpXQveM7NRCtyAH/ved7jq/0d0sqZWpkjs1PUA5+saa9qLEMYZEybmt/k4dPPLf2ENUZr6jmq3/sqQeUsRmGx8Gj3MLJDEAa0zh6j8vAXErEhDcRVJeCXgMeAy4CnpJQPSykPAx8nmiJ+wPO8OxZ43teIYlL/48Ifep7358AftO37feCklPJ+KeUh4OtE1QLuBP5zTPankodPP0fNb+KaJqax+pcqZzlU/SYPndJTuPnM3vWxSJCcHIa92EJy95jzlsLSGWbv+huU30rEjqSJRZQ8z5siiin9JlEwex/Rytg3gLd7nvehFYz5fuAW4B+IEimvJtp28u9E1QHe2M78HlgePn2Uqt8kG9NUImc5VFtNHj1zlFDp3NM5/OkXKD/yz4TlKYxCsikTRm6Y0G/QOnOYymMrqo3Y98RWebItELezjBIlnuftWOL8vxOJ0JojVCEPnzlKxW+uKsB9IRnTIlAhZxtVDkyfRo6uj2Xcfmf27r8lLJ8F28Vwsks/oYsIYWAWxgnKU8ze9THyV/0AIgYvuZ9YW79tH3FodooztTJBGK46njSHEILs3BROx5UACMpnKT/4eYLyFGbCXtIcRm6UsFmj+cIBqk8O9DrOgmhRSikPnXquPXWzESK+BL58ewr34KkjS1+8Bpj9zicJSqfBMBFOOgqzCcPAzI9G3tK3/mrN9fXTopRSHjwdidJqUwHmk7Mcan6Tp869QLW1thsmhrUSpfs+HXlJxYlYxX+1GPkxwnqZxrHHqR/4dtLm9BQtSimk5jd5fOoE1Vb8omSbJqYwqLSaPHJmba/Cle77NMHMSUAg3HRlUAvDxMiNEJSnmFlj3pIWpRTy3TPHqfgNDGGsamvJxcjZOq4UNmvR1K08hVkYT5WXNIdZGCeszVI//BCNI2undJgWpRTy4OnnqLSa5G2nKx+WfDvY/eCp59bUN/CFlB/6R/xzJyAMENm
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (1.7, 3), \n",
" 'figure.dpi': 150\n",
"})\n",
"\n",
"fig = plt.figure()\n",
"violinplot(\n",
" results_stim_stim_all.base_in_field, \n",
" results_stim_stim_all.stim_in_field, \n",
" colors=None,\n",
" test='wilcoxon'\n",
")\n",
"figname = 'stim_field_spikes_combined'\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)\n",
"\n",
"# 11\n",
"fig = plt.figure()\n",
"violinplot(\n",
" results_stim_stim_11.base_in_field, \n",
" results_stim_stim_11.stim_in_field, \n",
" colors=['#1b9e77','#d95f02'],\n",
" test='wilcoxon'\n",
")\n",
"figname = 'stim_field_spikes_11'\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)\n",
" \n",
"# 30\n",
"fig = plt.figure()\n",
"violinplot(\n",
" results_stim_stim_30.base_in_field, \n",
" results_stim_stim_30.stim_in_field, \n",
" colors=['#7570b3', '#e7298a'],\n",
" test='wilcoxon'\n",
")\n",
"figname = 'stim_field_spikes_30'\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)\n",
" "
]
},
{
"cell_type": "code",
2019-12-16 15:16:33 +00:00
"execution_count": 38,
2019-12-13 10:43:57 +00:00
"metadata": {},
"outputs": [],
2019-12-16 15:16:33 +00:00
"source": [
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"def Wilcoxon(df, keys):\n",
" '''\n",
" Wilcoxon\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.wilcoxon(\n",
" df[keys[0]].dropna(), \n",
" df[keys[1]].dropna(),\n",
" alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.48 ± 0.02 (101)'"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_stim_stim_all.base_in_field.agg(summarize)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>Combined</th>\n",
" <th>11 Hz</th>\n",
" <th>30 Hz</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>base_in_field</th>\n",
" <td>0.48 ± 0.02 (101)</td>\n",
" <td>0.47 ± 0.02 (61)</td>\n",
" <td>0.48 ± 0.02 (40)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stim_in_field</th>\n",
" <td>0.54 ± 0.01 (101)</td>\n",
" <td>0.54 ± 0.02 (61)</td>\n",
" <td>0.52 ± 0.02 (40)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU</th>\n",
" <td>380.00, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Combined 11 Hz 30 Hz\n",
"base_in_field 0.48 ± 0.02 (101) 0.47 ± 0.02 (61) 0.48 ± 0.02 (40)\n",
"stim_in_field 0.54 ± 0.01 (101) 0.54 ± 0.02 (61) 0.52 ± 0.02 (40)\n",
"MWU 380.00, 0.000 NaN NaN"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stat = pd.DataFrame()\n",
"\n",
"stat['Combined'] = results_stim_stim_all.agg(summarize)\n",
"stat['11 Hz'] = results_stim_stim_11.agg(summarize)\n",
"stat['30 Hz'] = results_stim_stim_30.agg(summarize)\n",
"\n",
"stat.loc['W', 'Combined'] = Wilcoxon(results_stim_stim_all, ['base_in_field', 'stim_in_field'])\n",
"\n",
"stat\n",
" \n"
]
2019-12-13 10:43:57 +00:00
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# more in baseline than response"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=35.0, pvalue=6.155288245645252e-11)"
]
},
"execution_count": 247,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results_stim_stim_11.base_in_field - results_stim_stim_11.stim_in_field)"
]
},
{
"cell_type": "code",
"execution_count": 248,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=35.0, pvalue=3.077644122822626e-11)"
]
},
"execution_count": 248,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results_stim_stim_11.base_in_field - results_stim_stim_11.stim_in_field, alternative='less')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbf3ae7fb70>"
]
},
"execution_count": 249,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAREAAAGXCAYAAACUfWRLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAYhElEQVR4nO3df5RfdX3n8eeQmAmJKDQsCyskQChvSZC4tMhmAcXWUrt4qKilUoJla/3RrfUselR2xSUWdW3tD6T1lFbr2kVElwP9cSRKpbZY5UfRRRYSfYdIEyLgCqgVmDDJkNk/7v2S2WHmOzPfz/3OnZk8H+fkfOb7vfd+5p3v9+aV++Nz7x0YHR1Fknp1QNsFSJrfDBFJRQwRSUUMEUlFDBFJRQwRSUUMEUlFDBFJRQwRSUUMEUlFDBFJRQwRSUUMEUlFFrddQL9FxPeAZcDOtmuR5pCjgKHMPLy0owUfIsCyJUuWHLRy5co1bRcyXwwPDwMwODjYciWC/nwfDzzwALt3726kr/0hRHauXLlyzY033th2HfPG5s2bAVi7dm3LlQj6832cffbZbNu2rZGtc4+JSCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKrI/3E9EmhXrrr2coZHebvSzbPES7j7/fQ1XNDsMEakhQyO72TWyp+0yZp27M5KKGCKSihgikooYIpKKNHpgNSKOBN4JvBJYVb/9z8Dngd/PzO9PsMwy4N3A64FjgMeBbwBXZOYXmqxPUvMa2xKJiDOAe4D/DPwk8ADwIBBUIXF3RJw0bpnlwJeBy4Bjgc3Ak8BZwKaIuKyp+iT1RyMhEhEHA9cDBwNfBI7KzBdm5k8CxwNfAw4H/ioilo5Z9GPAqcA3gdWZeXJmrgLeAIwAGyPiFU3UKKk/mtoSuQj4V8BDwHmZ+XBnQmbeD5wL/JBqd+V1ABGxGtgA7AUuyMydY5a5Gvhw/XJjQzVK6oOmQuTldfv5zHx8/MTMfAS4tX55St1eCCwCbsvMLRP0eVXdnhYRKxuqU1LDmgqRD1DtgnyyyzwDdbuobtfX7VcnmjkzHwR21C9fVlqgpP5o5OxMZt4J3DnZ9Ig4FDizfrm5bo+r2+906Xo71Vme48sqlNQvs3XtzEeBZcAQ1QFYgMPq9pEuyz1Wt4eW/PLh4eFnHoqsqXWeQu9nNrWBgQHWrFnTaJ9btmxhdHT0mdf9+D46fTah74PNIuJS4Ffql789ZqzIsrp9qsviu8bNK2mO6euWSD3OY2P98q+B3x0z+WmmH2KjU88yucHBQdauXVvSxX6l8z+en1k7xm/Z9OP7GBwcbKyvvoRIRCymGgPy5vqtm4DXZ+bYMHgCOARYyuQOrNuhxouU1IjGd2ci4nnAJvYFyOeAczJz/G7Lo3W7okt3nWMhzxouL2luaDRE6mtnvgb8XP3WR4DzM3Oi2z19q26P7tJlZ9rWJuqT1LzGdmci4t8A/wCspjre8bbMvKrLIncA57BvvMj4/o4EOoPMbp1oHknta+ramSXA31AFyG7gl6YIEIDr6vbMiIgJpr+1bm/JzO1N1CmpeU3tzrwH+Kn659/MzL+caoHMvA/4DNUI1hsiojP4jIjYUPcJ1WhYSXNU8e5MvRVycf1yBLgoIi7qssimzPxQ/fPbgRfVf74dEfdQnbHp3IvkvZl5c2mNkvqniWMiL6L6h9/p77Qp5t/W+SEzH4uI9cC7gPOAE4A9wC3AlZl5QwP1Seqj4hDJzG+w7+K6XpZ/kmpA2sbSWiTNPu+xKqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqUhjj9GU5qt1117O0MhEj4ue2orB5dx+3iUNVzS/GCLa7w2N7GbXyJ7ell3cW/gsJO7OSCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqYohIKmKISCpiiEgqsrifnUfEW4CrgDdl5icmmWc7sGqKrg7JzB81W52kJvQtRCLiFOAjU8xzMFWAPA3c3mXWkQZLk9SgvoRIRJwJ3AAcNMWsJ9XtfZl5ej9qkdRfjYZIRCwFLgEuBRZNY5F1dXtPk3VImj2NHViNiOOArcBl9VuXAjumWKwTIvc2VYek2dXk2ZkjgaOojm2cmpkfnMYybolI81yTuzPfBc7OzE3TmTkiFgEn1i8fioiLgTOA5wMPAjcC12Xm3gZrlNSwxkIkM7cB22awyPHA0vrnL/Hsg7AXAu+IiFdn5sMltQ0PD7N58+aSLvYrw8PDAAv6MxsYGGDNmjVtlzGhLVu2MDo6+szrfnwfnT6b0OZgs3Vjfr4TeCmwHFgBbAC+B7wE2BQRS2a/PEnT0dfBZlPYAVwJ7AXeOWa3ZQi4JiLuBO4CXgy8EfiTXn/R4OAga9euLSx3/9H5H8/PrB3jt5D68X0MDg421ldrIZKZtwG3dZm+NSKuAd4EnEtBiEjqn7l+7cxddXtMq1VImlSrIRIRB0xxvKNT357ZqEfSzLUWIhHxFWA38HtdZju5brf0vyJJvWhzS+ReqqHxr4mIZ11jExGrgF+uX352NguTNH1thsgfAsPAC4DPRcThnQkRsQ64ieqU71eoLuaTNAe1eXbmvoi4APg08AvAjojYWtf0wnq2rwPnOmpVmrtaPbCamddTjQP5OPAQ1SjWI6hO/f4WsD4zf9BehZKm0tctkcw8ehrzJPDmftYhqX/m+jgRSXOcISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqmIISKpiCEiqYghIqnI4n52HhFvAa4C3pSZn5hknmXAu4HXA8cAjwPfAK7IzC/0sz5J5fq2JRIRpwAfmWKe5cCXgcuAY4HNwJPAWcCmiLisX/VJakZfQiQizgRuAg6aYtaPAacC3wRWZ+bJmbkKeAMwAmyMiFf0o0ZJzWg0RCJiaURsBG4GDpli3tXABmAvcEFm7uxMy8yrgQ/XLzc2WaOkZjUWIhFxHLCVatcE4FJgR5dFLgQWAbdl5pYJpl9Vt6dFxMqm6pTUrCa3RI4EjgJuB07NzA9OMf/6uv3qRBMz80H2hdDLGqlQUuOaPDvzXeDszNw0zfmPq9vvdJlnO7AKOL6gLkl91FiIZOY2YNsMFjmsbh/pMs9jdXtoT0XVhoeH2bx5c0kX+5Xh4WGABf2ZDQwMsGbNmrbLmNCWLVsYHR195nU/vo9On01oc7DZsrp9qss8u8bNK2mO6etgsyk8zfRDbHTqWSY3ODjI2rVrS7rYr3T+x/Mza8f4LaR+fB+Dg4ON9dXmlsgTdbu0yzwH1u1Qn2uR1KM2Q+TRul3RZZ7OsZDv97kWST1qM0S+VbdHd5mnM21rXyuR1LM2Q+SOul0/0cSIOBLoDDK7dVYqkjRjbYbIdXV7ZkTEBNPfWre3ZOb22SlJ0ky1FiKZeR/wGaqh7zfUw+YBiIgNwHvqlx9ooTxJ09TmKV6AtwMvqv98OyLuobpwb1U9/b2ZeXNbxUmaWqt3NsvMx6iOibyf6uDpCVRna24BXpuZH2qxPEnT0Nctkcw8ehrzPEl1uf/GftYiqT+8x6qkIoaIpCKGiKQibZ+dkTSFgYGBtkv
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(results_stim_stim_11.base_in_field - results_stim_stim_11.stim_in_field).hist()"
]
},
{
"cell_type": "code",
"execution_count": 251,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.0628016121809225"
]
},
"execution_count": 251,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(results_stim_stim_11.base_in_field - results_stim_stim_11.stim_in_field).median()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# analysis baseline stim"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [],
"source": [
"iter_base = baseline_i_sub.sort_values('unit_day', ascending=False).itertuples()\n",
"iter_stim = stimulated_11_sub.sort_values('unit_day', ascending=False).itertuples()\n",
"results = []\n",
"z1=5e-3\n",
"z2=11e-3\n",
"# z1=0\n",
"# z2=5e-3\n",
"for row_base, row_stim in zip(iter_base, iter_stim):\n",
" base_fields, base_in_field, _ = compute_field_spikes(\n",
" row_base, z1=z1, z2=z2)\n",
" stim_fields, stim_in_field, stim_stim_in_field = compute_field_spikes(\n",
" row_stim, z1=z1, z2=z2)\n",
" results.append({\n",
" 'base_in_field': base_in_field.mean(),\n",
" 'stim_in_field': stim_in_field.mean(), \n",
" 'stim_stim_in_field': stim_stim_in_field.mean(),\n",
" 'gridness_base': row_base.gridness,\n",
" 'gridness_stim': row_stim.gridness,\n",
" 'action_base': row_base.action,\n",
" 'action_stim': row_stim.action\n",
" })\n",
"# break"
]
},
{
"cell_type": "code",
"execution_count": 189,
"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>base_in_field</th>\n",
" <th>stim_in_field</th>\n",
" <th>stim_stim_in_field</th>\n",
" <th>gridness_base</th>\n",
" <th>gridness_stim</th>\n",
" <th>action_base</th>\n",
" <th>action_stim</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.414166</td>\n",
" <td>0.428529</td>\n",
" <td>0.361044</td>\n",
" <td>0.339934</td>\n",
" <td>-0.045053</td>\n",
" <td>1833-010719-1</td>\n",
" <td>1833-010719-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.404667</td>\n",
" <td>0.477324</td>\n",
" <td>0.419231</td>\n",
" <td>0.401503</td>\n",
" <td>-0.179613</td>\n",
" <td>1833-260619-1</td>\n",
" <td>1833-260619-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.280399</td>\n",
" <td>0.384092</td>\n",
" <td>0.321133</td>\n",
" <td>0.557819</td>\n",
" <td>0.917221</td>\n",
" <td>1839-060619-1</td>\n",
" <td>1839-060619-3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.815385</td>\n",
" <td>0.745888</td>\n",
" <td>0.657431</td>\n",
" <td>0.532037</td>\n",
" <td>0.397104</td>\n",
" <td>1833-010719-1</td>\n",
" <td>1833-010719-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.481623</td>\n",
" <td>0.399033</td>\n",
" <td>0.300733</td>\n",
" <td>0.282903</td>\n",
" <td>0.001879</td>\n",
" <td>1834-220319-1</td>\n",
" <td>1834-220319-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.734889</td>\n",
" <td>0.681776</td>\n",
" <td>0.611208</td>\n",
" <td>0.776775</td>\n",
" <td>0.869214</td>\n",
" <td>1833-260619-1</td>\n",
" <td>1833-260619-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.280964</td>\n",
" <td>0.449841</td>\n",
" <td>0.416287</td>\n",
" <td>0.317565</td>\n",
" <td>0.344708</td>\n",
" <td>1833-200619-1</td>\n",
" <td>1833-200619-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.739768</td>\n",
" <td>0.729892</td>\n",
" <td>0.676768</td>\n",
" <td>1.101447</td>\n",
" <td>0.976654</td>\n",
" <td>1833-200619-1</td>\n",
" <td>1833-200619-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.599141</td>\n",
" <td>0.549244</td>\n",
" <td>0.487360</td>\n",
" <td>0.215937</td>\n",
" <td>-0.201297</td>\n",
" <td>1833-260619-1</td>\n",
" <td>1833-260619-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.826862</td>\n",
" <td>0.732639</td>\n",
" <td>0.663934</td>\n",
" <td>0.855641</td>\n",
" <td>0.554912</td>\n",
" <td>1833-120619-1</td>\n",
" <td>1833-120619-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.477416</td>\n",
" <td>0.392704</td>\n",
" <td>0.317717</td>\n",
" <td>0.298222</td>\n",
" <td>-0.087079</td>\n",
" <td>1833-260619-1</td>\n",
" <td>1833-260619-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.560374</td>\n",
" <td>0.543610</td>\n",
" <td>0.511349</td>\n",
" <td>0.977155</td>\n",
" <td>-0.058795</td>\n",
" <td>1833-020719-1</td>\n",
" <td>1833-020719-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.858028</td>\n",
" <td>0.624060</td>\n",
" <td>0.640805</td>\n",
" <td>0.941494</td>\n",
" <td>0.342802</td>\n",
" <td>1833-010719-1</td>\n",
" <td>1833-010719-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.767867</td>\n",
" <td>0.687664</td>\n",
" <td>0.667808</td>\n",
" <td>0.487981</td>\n",
" <td>0.199693</td>\n",
" <td>1833-050619-1</td>\n",
" <td>1833-050619-2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" base_in_field stim_in_field stim_stim_in_field gridness_base \\\n",
"0 0.414166 0.428529 0.361044 0.339934 \n",
"1 0.404667 0.477324 0.419231 0.401503 \n",
"2 0.280399 0.384092 0.321133 0.557819 \n",
"3 0.815385 0.745888 0.657431 0.532037 \n",
"4 0.481623 0.399033 0.300733 0.282903 \n",
"5 0.734889 0.681776 0.611208 0.776775 \n",
"6 0.280964 0.449841 0.416287 0.317565 \n",
"7 0.739768 0.729892 0.676768 1.101447 \n",
"8 0.599141 0.549244 0.487360 0.215937 \n",
"9 0.826862 0.732639 0.663934 0.855641 \n",
"10 0.477416 0.392704 0.317717 0.298222 \n",
"11 0.560374 0.543610 0.511349 0.977155 \n",
"12 0.858028 0.624060 0.640805 0.941494 \n",
"13 0.767867 0.687664 0.667808 0.487981 \n",
"\n",
" gridness_stim action_base action_stim \n",
"0 -0.045053 1833-010719-1 1833-010719-2 \n",
"1 -0.179613 1833-260619-1 1833-260619-2 \n",
"2 0.917221 1839-060619-1 1839-060619-3 \n",
"3 0.397104 1833-010719-1 1833-010719-2 \n",
"4 0.001879 1834-220319-1 1834-220319-2 \n",
"5 0.869214 1833-260619-1 1833-260619-2 \n",
"6 0.344708 1833-200619-1 1833-200619-2 \n",
"7 0.976654 1833-200619-1 1833-200619-2 \n",
"8 -0.201297 1833-260619-1 1833-260619-2 \n",
"9 0.554912 1833-120619-1 1833-120619-2 \n",
"10 -0.087079 1833-260619-1 1833-260619-2 \n",
"11 -0.058795 1833-020719-1 1833-020719-2 \n",
"12 0.342802 1833-010719-1 1833-010719-2 \n",
"13 0.199693 1833-050619-1 1833-050619-2 "
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = pd.DataFrame(results)\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# more in baseline than response"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=12.0, pvalue=0.011007912955186742)"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results.base_in_field - results.stim_stim_in_field)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=93.0, pvalue=0.005503956477593371)"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results.base_in_field - results.stim_stim_in_field, alternative='greater')"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbf3a9ee780>"
]
},
"execution_count": 192,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(results.base_in_field - results.stim_stim_in_field).hist()"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.09670222033391301"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(results.base_in_field - results.stim_stim_in_field).median()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# more in baseline than total of stim"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=96.0, pvalue=0.32207523283525596)"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results.base_in_field - results.stim_in_field)"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=157.0, pvalue=0.16103761641762798)"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results.base_in_field - results.stim_in_field, alternative='greater')"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbf40cfb710>"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(results.base_in_field - results.stim_in_field).hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# greater than chance"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=160.0, pvalue=0.13838602372323838)"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results.base_in_field - 0.5, alternative='greater') # most fall within the field"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbf3bb414a8>"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(results.base_in_field - 0.5).hist()"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WilcoxonResult(statistic=101.0, pvalue=0.7961290925328811)"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wilcoxon(results.stim_stim_in_field - 0.5, alternative='greater') # most fall within the field"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbf3aa22c88>"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(results.stim_stim_in_field - 0.5).hist()"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (3.5, 3), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x450 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1)\n",
"sc = ax.scatter(\n",
" results.base_in_field, results.stim_in_field,\n",
" c=results.gridness_base\n",
"# c=results.gridness_stim\n",
")\n",
"ax.plot([0, 1], [0,1], 'k--')\n",
"plt.xlabel('Baseline percentage in field')\n",
"plt.ylabel('11 Hz percentage in field')\n",
"cb = plt.colorbar(mappable=sc, cax=None, ax=ax)\n",
"cb.ax.yaxis.set_ticks_position('right')\n",
"cb.set_label('Baseline gridness')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>action_base</th>\n",
" <th>baseline_base</th>\n",
" <th>entity_base</th>\n",
" <th>frequency_base</th>\n",
" <th>i_base</th>\n",
" <th>ii_base</th>\n",
" <th>session_base</th>\n",
" <th>stim_location_base</th>\n",
" <th>stimulated_base</th>\n",
" <th>tag_base</th>\n",
" <th>...</th>\n",
" <th>information_rate_stim</th>\n",
" <th>information_specificity_stim</th>\n",
" <th>head_mean_ang_stim</th>\n",
" <th>head_mean_vec_len_stim</th>\n",
" <th>spacing_stim</th>\n",
" <th>orientation_stim</th>\n",
" <th>t_e_peak_stim</th>\n",
" <th>p_e_peak_stim</th>\n",
" <th>t_i_peak_stim</th>\n",
" <th>p_i_peak_stim</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1833-260619-1</td>\n",
" <td>True</td>\n",
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" <td>...</td>\n",
" <td>1.066774</td>\n",
" <td>0.302404</td>\n",
" <td>5.539069</td>\n",
" <td>0.103552</td>\n",
" <td>0.342142</td>\n",
" <td>10.007980</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>True</td>\n",
" <td>1833</td>\n",
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" <td>True</td>\n",
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" <td>baseline i</td>\n",
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" <td>0.553675</td>\n",
" <td>0.106860</td>\n",
" <td>5.284125</td>\n",
" <td>0.151640</td>\n",
" <td>0.361430</td>\n",
" <td>14.743563</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1833-260619-1</td>\n",
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" <td>True</td>\n",
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" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.914806</td>\n",
" <td>0.085579</td>\n",
" <td>5.650586</td>\n",
" <td>0.102010</td>\n",
" <td>0.477803</td>\n",
" <td>51.952957</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1833-260619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.681461</td>\n",
" <td>0.154506</td>\n",
" <td>2.601550</td>\n",
" <td>0.015570</td>\n",
" <td>0.343784</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1833-010719-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>1.021932</td>\n",
" <td>0.349152</td>\n",
" <td>1.196446</td>\n",
" <td>0.137136</td>\n",
" <td>0.344403</td>\n",
" <td>13.240520</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1833-010719-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.735341</td>\n",
" <td>0.043588</td>\n",
" <td>3.418123</td>\n",
" <td>0.055051</td>\n",
" <td>0.405348</td>\n",
" <td>46.468801</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1833-010719-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
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" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
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" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.857698</td>\n",
" <td>0.269769</td>\n",
" <td>2.340344</td>\n",
" <td>0.137602</td>\n",
" <td>0.351311</td>\n",
" <td>13.240520</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1833-050619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>1.442780</td>\n",
" <td>0.585854</td>\n",
" <td>2.321438</td>\n",
" <td>0.220139</td>\n",
" <td>0.387257</td>\n",
" <td>5.440332</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1833-050619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.862973</td>\n",
" <td>0.183897</td>\n",
" <td>0.069331</td>\n",
" <td>0.091981</td>\n",
" <td>0.366146</td>\n",
" <td>9.462322</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1833-050619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.873561</td>\n",
" <td>0.103053</td>\n",
" <td>3.668189</td>\n",
" <td>0.069408</td>\n",
" <td>0.434762</td>\n",
" <td>70.559965</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1833-050619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.678798</td>\n",
" <td>0.054802</td>\n",
" <td>3.726041</td>\n",
" <td>0.014519</td>\n",
" <td>0.355665</td>\n",
" <td>6.009006</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1834-010319-1</td>\n",
" <td>True</td>\n",
" <td>1834</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.858656</td>\n",
" <td>1.359164</td>\n",
" <td>4.808760</td>\n",
" <td>0.175971</td>\n",
" <td>0.547521</td>\n",
" <td>39.093859</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1833-120619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.802834</td>\n",
" <td>0.424748</td>\n",
" <td>5.035940</td>\n",
" <td>0.132587</td>\n",
" <td>0.384357</td>\n",
" <td>8.972627</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1833-120619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.928796</td>\n",
" <td>0.151146</td>\n",
" <td>3.473257</td>\n",
" <td>0.042733</td>\n",
" <td>0.392784</td>\n",
" <td>11.309932</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1834-220319-1</td>\n",
" <td>True</td>\n",
" <td>1834</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>1.174019</td>\n",
" <td>0.059398</td>\n",
" <td>4.815305</td>\n",
" <td>0.031014</td>\n",
" <td>0.590135</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1834-220319-1</td>\n",
" <td>True</td>\n",
" <td>1834</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>1.918954</td>\n",
" <td>0.627402</td>\n",
" <td>4.241643</td>\n",
" <td>0.074006</td>\n",
" <td>0.707647</td>\n",
" <td>29.054604</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1839-060619-1</td>\n",
" <td>True</td>\n",
" <td>1839</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.897083</td>\n",
" <td>0.060246</td>\n",
" <td>0.891795</td>\n",
" <td>0.030190</td>\n",
" <td>0.491483</td>\n",
" <td>48.576334</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1849-110319-1</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.398943</td>\n",
" <td>0.146648</td>\n",
" <td>0.222076</td>\n",
" <td>0.102539</td>\n",
" <td>0.643598</td>\n",
" <td>26.565051</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1833-020719-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.876495</td>\n",
" <td>0.115399</td>\n",
" <td>1.012156</td>\n",
" <td>0.003392</td>\n",
" <td>0.356249</td>\n",
" <td>12.528808</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1833-020719-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>1.429178</td>\n",
" <td>0.107937</td>\n",
" <td>5.369824</td>\n",
" <td>0.095342</td>\n",
" <td>0.344101</td>\n",
" <td>9.462322</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1833-200619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>1.192882</td>\n",
" <td>0.427188</td>\n",
" <td>0.039146</td>\n",
" <td>0.199068</td>\n",
" <td>0.390419</td>\n",
" <td>6.009006</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1833-200619-1</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>...</td>\n",
" <td>0.411013</td>\n",
" <td>0.037815</td>\n",
" <td>5.107094</td>\n",
" <td>0.029690</td>\n",
" <td>0.374918</td>\n",
" <td>6.009006</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22 rows ×
"</div>"
],
"text/plain": [
" action_base baseline_base entity_base frequency_base i_base \\\n",
"0 1833-260619-1 True 1833 NaN True \n",
"1 1833-260619-1 True 1833 NaN True \n",
"2 1833-260619-1 True 1833 NaN True \n",
"3 1833-260619-1 True 1833 NaN True \n",
"4 1833-010719-1 True 1833 NaN True \n",
"5 1833-010719-1 True 1833 NaN True \n",
"6 1833-010719-1 True 1833 NaN True \n",
"7 1833-050619-1 True 1833 NaN True \n",
"8 1833-050619-1 True 1833 NaN True \n",
"9 1833-050619-1 True 1833 NaN True \n",
"10 1833-050619-1 True 1833 NaN True \n",
"11 1834-010319-1 True 1834 NaN True \n",
"12 1833-120619-1 True 1833 NaN True \n",
"13 1833-120619-1 True 1833 NaN True \n",
"14 1834-220319-1 True 1834 NaN True \n",
"15 1834-220319-1 True 1834 NaN True \n",
"16 1839-060619-1 True 1839 NaN True \n",
"17 1849-110319-1 True 1849 NaN True \n",
"18 1833-020719-1 True 1833 NaN True \n",
"19 1833-020719-1 True 1833 NaN True \n",
"20 1833-200619-1 True 1833 NaN True \n",
"21 1833-200619-1 True 1833 NaN True \n",
"\n",
" ii_base session_base stim_location_base stimulated_base tag_base \\\n",
"0 False 1 NaN False baseline i \n",
"1 False 1 NaN False baseline i \n",
"2 False 1 NaN False baseline i \n",
"3 False 1 NaN False baseline i \n",
"4 False 1 NaN False baseline i \n",
"5 False 1 NaN False baseline i \n",
"6 False 1 NaN False baseline i \n",
"7 False 1 NaN False baseline i \n",
"8 False 1 NaN False baseline i \n",
"9 False 1 NaN False baseline i \n",
"10 False 1 NaN False baseline i \n",
"11 False 1 NaN False baseline i \n",
"12 False 1 NaN False baseline i \n",
"13 False 1 NaN False baseline i \n",
"14 False 1 NaN False baseline i \n",
"15 False 1 NaN False baseline i \n",
"16 False 1 NaN False baseline i \n",
"17 False 1 NaN False baseline i \n",
"18 False 1 NaN False baseline i \n",
"19 False 1 NaN False baseline i \n",
"20 False 1 NaN False baseline i \n",
"21 False 1 NaN False baseline i \n",
"\n",
" ... information_rate_stim information_specificity_stim \\\n",
"0 ... 1.066774 0.302404 \n",
"1 ... 0.553675 0.106860 \n",
"2 ... 0.914806 0.085579 \n",
"3 ... 0.681461 0.154506 \n",
"4 ... 1.021932 0.349152 \n",
"5 ... 0.735341 0.043588 \n",
"6 ... 0.857698 0.269769 \n",
"7 ... 1.442780 0.585854 \n",
"8 ... 0.862973 0.183897 \n",
"9 ... 0.873561 0.103053 \n",
"10 ... 0.678798 0.054802 \n",
"11 ... 0.858656 1.359164 \n",
"12 ... 0.802834 0.424748 \n",
"13 ... 0.928796 0.151146 \n",
"14 ... 1.174019 0.059398 \n",
"15 ... 1.918954 0.627402 \n",
"16 ... 0.897083 0.060246 \n",
"17 ... 0.398943 0.146648 \n",
"18 ... 0.876495 0.115399 \n",
"19 ... 1.429178 0.107937 \n",
"20 ... 1.192882 0.427188 \n",
"21 ... 0.411013 0.037815 \n",
"\n",
" head_mean_ang_stim head_mean_vec_len_stim spacing_stim \\\n",
"0 5.539069 0.103552 0.342142 \n",
"1 5.284125 0.151640 0.361430 \n",
"2 5.650586 0.102010 0.477803 \n",
"3 2.601550 0.015570 0.343784 \n",
"4 1.196446 0.137136 0.344403 \n",
"5 3.418123 0.055051 0.405348 \n",
"6 2.340344 0.137602 0.351311 \n",
"7 2.321438 0.220139 0.387257 \n",
"8 0.069331 0.091981 0.366146 \n",
"9 3.668189 0.069408 0.434762 \n",
"10 3.726041 0.014519 0.355665 \n",
"11 4.808760 0.175971 0.547521 \n",
"12 5.035940 0.132587 0.384357 \n",
"13 3.473257 0.042733 0.392784 \n",
"14 4.815305 0.031014 0.590135 \n",
"15 4.241643 0.074006 0.707647 \n",
"16 0.891795 0.030190 0.491483 \n",
"17 0.222076 0.102539 0.643598 \n",
"18 1.012156 0.003392 0.356249 \n",
"19 5.369824 0.095342 0.344101 \n",
"20 0.039146 0.199068 0.390419 \n",
"21 5.107094 0.029690 0.374918 \n",
"\n",
" orientation_stim t_e_peak_stim p_e_peak_stim t_i_peak_stim \\\n",
"0 10.007980 NaN NaN NaN \n",
"1 14.743563 NaN NaN NaN \n",
"2 51.952957 NaN NaN NaN \n",
"3 0.000000 NaN NaN NaN \n",
"4 13.240520 NaN NaN NaN \n",
"5 46.468801 NaN NaN NaN \n",
"6 13.240520 NaN NaN NaN \n",
"7 5.440332 NaN NaN NaN \n",
"8 9.462322 NaN NaN NaN \n",
"9 70.559965 NaN NaN NaN \n",
"10 6.009006 NaN NaN NaN \n",
"11 39.093859 NaN NaN NaN \n",
"12 8.972627 NaN NaN NaN \n",
"13 11.309932 NaN NaN NaN \n",
"14 0.000000 NaN NaN NaN \n",
"15 29.054604 NaN NaN NaN \n",
"16 48.576334 NaN NaN NaN \n",
"17 26.565051 NaN NaN NaN \n",
"18 12.528808 NaN NaN NaN \n",
"19 9.462322 NaN NaN NaN \n",
"20 6.009006 NaN NaN NaN \n",
"21 6.009006 NaN NaN NaN \n",
"\n",
" p_i_peak_stim \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"\n",
"[22 rows x 87 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"baseline_i.merge(stimulated_11, on='unit_day', suffixes=['_base', '_stim'])"
]
},
2019-12-16 15:16:33 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# save to expipe"
]
},
{
"cell_type": "code",
"execution_count": 344,
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"spikes-in-field\")"
]
},
{
"cell_type": "code",
"execution_count": 345,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_example.svg',\n",
" '/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_example.png',\n",
" '/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_combined.svg',\n",
" '/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_combined.png',\n",
" '/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/spikes-in-field/data/figures/stim_field_spikes_11.png']"
]
},
"execution_count": 345,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
"execution_count": 346,
"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_spikes_in_field.ipynb\")"
]
},
2019-12-13 10:43:57 +00:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
