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analysis update
2019-12-13 10:43:57 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"18:00:49 [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"
]
}
],
"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",
" which_field, compute_crossings)\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()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# %matplotlib notebook\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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\") / \"phase-precession\"\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",
"execution_count": 5,
"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>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",
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" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
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" <td>baseline ii</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",
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" <tr>\n",
" <th>2</th>\n",
" <td>1849-060319-3</td>\n",
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" <td>NaN</td>\n",
" <td>False</td>\n",
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" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <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",
" <td>0.101911</td>\n",
" <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",
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" <td>0.000000</td>\n",
" </tr>\n",
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"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]"
]
},
"execution_count": 5,
"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",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"statistics['unit_day'] = statistics.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"statistics = pd.merge(statistics, stim_response_results, how='left')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"N cells: 1284\n"
]
}
],
"source": [
"print('N cells:',statistics.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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" <td>0.331942</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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"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",
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"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 "
]
},
"execution_count": 10,
"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",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
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" <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",
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" <td>0.344280</td>\n",
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" <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 × 51 columns</p>\n",
"</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]"
]
},
"execution_count": 11,
"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",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulated\n",
"False 624\n",
"True 660\n",
"Name: action, dtype: int64"
]
},
"execution_count": 12,
"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",
"execution_count": 13,
"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",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"once_a_gridcell = statistics[statistics.unit_day.isin(sessions_above_threshold.unit_day.values)]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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",
"execution_count": 16,
"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",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"baseline_ids = baseline_i.unit_idnum.unique()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 30, 31, 32, 78, 79, 150, 205, 243, 263, 265, 45, 46, 47,\n",
" 49, 96, 118, 121, 185, 186, 106, 168, 231, 232, 233, 379, 609,\n",
" 658, 615, 616, 666, 667, 179, 214, 278, 279, 317, 613, 661, 361,\n",
" 362, 851, 357, 358, 359, 332, 338, 655, 715, 8, 56, 57, 58,\n",
" 129, 130, 132, 23, 174, 250, 251, 252, 253, 304, 932])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"baseline_ids"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"stimulated_11_sub = stimulated_11[stimulated_11.unit_idnum.isin(baseline_ids)]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"baseline_ids_11 = stimulated_11_sub.unit_idnum.unique()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"baseline_i_sub = baseline_i[baseline_i.unit_idnum.isin(baseline_ids_11)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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",
"execution_count": 23,
"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",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"from scipy.signal import butter, filtfilt\n",
"\n",
"def butter_bandpass(lowcut, highcut, fs, order=5):\n",
" nyq = 0.5 * fs\n",
" low = lowcut / nyq\n",
" high = highcut / nyq\n",
" b, a = butter(order, [low, high], btype='band')\n",
" return b, a\n",
"\n",
"\n",
"def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
" b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
" y = filtfilt(b, a, data)\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def signaltonoise(a, axis=0, ddof=0):\n",
" a = np.asanyarray(a)\n",
" m = a.mean(axis)\n",
" sd = a.std(axis=axis, ddof=ddof)\n",
" return np.where(sd == 0, 0, m / sd)\n",
"\n",
"\n",
"def remove_artifacts(anas, spikes=None, width=500, threshold=2, sampling_rate=None, fillval=0):\n",
" sampling_rate = sampling_rate or anas.sampling_rate.magnitude\n",
" times = np.arange(anas.shape[0]) / sampling_rate\n",
" anas = np.array(anas)\n",
" if anas.ndim == 1:\n",
" anas = np.reshape(anas, (anas.size, 1))\n",
" assert len(times) == anas.shape[0]\n",
" nchan = anas.shape[1]\n",
" if spikes is not None:\n",
" spikes = np.array(spikes)\n",
" for ch in range(nchan):\n",
" idxs, = np.where(abs(anas[:, ch]) > threshold)\n",
" for idx in idxs:\n",
" if spikes is not None:\n",
" t0 = times[idx-width]\n",
" stop = idx+width\n",
" if stop > len(times) - 1:\n",
" stop = len(times) - 1 \n",
" t1 = times[stop]\n",
" mask = (spikes > t0) & (spikes < t1)\n",
" spikes = spikes[~mask]\n",
" anas[idx-width:idx+width, ch] = fillval\n",
" if spikes is not None:\n",
" spikes = spikes[spikes <= times[-1]]\n",
" return anas, times, spikes\n",
" else:\n",
" return anas, times"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def find_theta_peak(p, f, f1, f2):\n",
" if np.all(np.isnan(p)):\n",
" return np.nan, np.nan\n",
" mask = (f > f1) & (f < f2)\n",
" p_m = p[mask]\n",
" f_m = f[mask]\n",
" peaks = find_peaks(p_m)\n",
" idx = np.argmax(p_m[peaks])\n",
" return f_m[peaks[idx]], p_m[peaks[idx]]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def compute_spike_phase(lfp, times, return_degrees=False):\n",
" x_a = ss.hilbert(lfp)\n",
" x_phase = np.angle(x_a)\n",
" if return_degrees:\n",
" x_phase = x_phase * 180 / np.pi\n",
" return interp1d(times, x_phase)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def normalize(a):\n",
" _a = a - a.min()\n",
" return _a / _a.max()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"def distance(x, y):\n",
" _x = x - x.min()\n",
" _y = y - y.min()\n",
" dx, dy = np.diff(x), np.diff(y)\n",
" s = np.sqrt(dx**2 + dy**2)\n",
" distance = np.cumsum(s) \n",
" # first index is distance from first point, \n",
" # to match len(x) we put a zero as first index to initialize distance 0\n",
" return np.concatenate(([0], distance))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def model(x, slope, phi0):\n",
" return 2 * np.pi * slope * x + phi0"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def compute_data(row, flim=[6,10]):\n",
" lfp = data_loader.lfp(row.action, row.channel_group)\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",
" \n",
" spikes = np.array(spikes)\n",
" spikes = spikes[(spikes > pos_t.min()) & (spikes < pos_t.max())]\n",
"\n",
" cleaned_lfp_, times_ = remove_artifacts(lfp)\n",
" peak_amp = {}\n",
" for i, ch in enumerate(cleaned_lfp_.T):\n",
" pxx, freqs = mlab.psd(ch, Fs=lfp.sampling_rate.magnitude, NFFT=4000)\n",
" f, p = find_theta_peak(pxx, freqs, *flim)\n",
" peak_amp[i] = p\n",
"\n",
" theta_channel = max(peak_amp, key=lambda x: peak_amp[x])\n",
" filtered_lfp = butter_bandpass_filter(\n",
" lfp.magnitude[:,theta_channel], *flim, fs=lfp.sampling_rate.magnitude, order=3)\n",
" \n",
" cleaned_lfp, times, cleaned_spikes = remove_artifacts(\n",
" filtered_lfp, spikes, threshold=2, sampling_rate=lfp.sampling_rate.magnitude, fillval=0)\n",
" \n",
" cleaned_lfp = cleaned_lfp.ravel()\n",
"\n",
" spike_phase_func = compute_spike_phase(cleaned_lfp, times)\n",
" \n",
" fields = find_grid_fields(rate_map, sigma=3, seed=2.5)\n",
" \n",
" return spike_phase_func, cleaned_spikes, pos_x, pos_y, pos_t, rate_map, fields"
]
},
{
"cell_type": "code",
"execution_count": 386,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def compute_phase_precession(row, flim=[6, 10], return_runs=False, field_num=None, \n",
" plot=False, plot_grid=False, plot_lines=True, save=False):\n",
" spike_phase_func, cleaned_spikes, pos_x, pos_y, pos_t, rate_map, fields = compute_data(row, flim)\n",
" \n",
" if field_num is not None:\n",
" fields = np.where(fields == field_num, fields, 0)\n",
" \n",
" in_field_indices = which_field(pos_x, pos_y, fields, box_size)\n",
" in_field_enter, in_field_exit = compute_crossings(in_field_indices)\n",
"\n",
" if plot:\n",
" if plot_grid:\n",
" fig, axs = plt.subplots(2, 2)\n",
" plt.suptitle(f'{row.action} {row.channel_group} {row.unit_idnum}')\n",
" else:\n",
" fig, ax = plt.subplots(1, 1)\n",
" axs = [[ax]]\n",
" ax.set_title(f'{row.action} {row.channel_group} {row.unit_idnum}')\n",
" dot_size = 1\n",
"\n",
" in_field_spikes, in_field_dur, in_field_dist, spike_phase = [], [], [], []\n",
"\n",
" sx, sy = interp1d(pos_t, pos_x), interp1d(pos_t, pos_y)\n",
" results = []\n",
" for en, ex in zip(in_field_enter, in_field_exit):\n",
" x, y, t = pos_x[en:ex+1], pos_y[en:ex+1], pos_t[en:ex+1]\n",
"\n",
" s = cleaned_spikes[(cleaned_spikes > t[0]) & (cleaned_spikes < t[-1])]\n",
" if len(s) < 5:\n",
" continue\n",
"\n",
" in_field_spikes.append(s)\n",
" \n",
" dist = distance(x, y)\n",
" t_to_dist_norm = interp1d(t, normalize(dist))\n",
" t_to_dist = interp1d(t, dist)\n",
" in_field_dist.append(t_to_dist_norm(s))\n",
" \n",
" t_to_dur = interp1d(t, t)\n",
" t_to_dur_norm = interp1d(t, normalize(t))\n",
" in_field_dur.append(t_to_dur_norm(s))\n",
" \n",
" spike_phase.append(spike_phase_func(s))\n",
" if return_runs:\n",
" circ_lin_corr_dist, pval_dist, slope_dist, phi0_dist, RR_dist = cl_corr(\n",
" t_to_dist(s), spike_phase_func(s), -100, 100, return_pval=True)\n",
" circ_lin_corr_dur, pval_dur, slope_dur, phi0_dur, RR_dur = cl_corr(\n",
" s - t[0], spike_phase_func(s), -100, 100, return_pval=True)\n",
" result_run = {\n",
" 'action': row.action, \n",
" 'channel_group': row.channel_group, \n",
" 'unit_name': row.unit_name,\n",
" 'circ_lin_corr_dist': circ_lin_corr_dist, \n",
" 'pval_dist': pval_dist, \n",
" 'slope_dist': slope_dist, \n",
" 'phi0_dist': phi0_dist, \n",
" 'RR_dist': RR_dist,\n",
" 'circ_lin_corr_dur': circ_lin_corr_dur, \n",
" 'pval_dur': pval_dur, \n",
" 'slope_dur': slope_dur, \n",
" 'phi0_dur': phi0_dur, \n",
" 'RR_dur': RR_dur\n",
" }\n",
" results.append(result_run)\n",
" if plot:\n",
" p = axs[0][0].scatter(t_to_dist(s), spike_phase_func(s), s=dot_size)\n",
" axs[0][0].scatter(\n",
" t_to_dist(s), spike_phase_func(s) + 2 * np.pi, \n",
" s=dot_size, color=p.get_facecolor()[0])\n",
" axs[0][0].set_yticks([-np.pi, np.pi, 3*np.pi])\n",
" axs[0][0].set_yticklabels([r'$-\\pi$', r'$\\pi$', r'$3\\pi$'])\n",
" if plot_lines:\n",
" line_fit = model(np.array([0, .4]), slope_dist, phi0_dist)\n",
" axs[0][0].plot([0, .4], line_fit, lw=2, label=\n",
" f'corr = {circ_lin_corr_dist:.3f}, '\n",
" f'pvalue = {pval_dist:.3f}, '\n",
" f'R = {RR_dist:.3f}')\n",
" \n",
" if plot and plot_grid:\n",
" axs[0][1].plot(x, y)\n",
" axs[0][1].scatter(sx(s), sy(s), s=dot_size, color='r', zorder=100000)\n",
" \n",
" dist = np.array([d for di in in_field_dist for d in di])\n",
" dur = np.array([d for di in in_field_dur for d in di])\n",
" phase = np.array([d for di in spike_phase for d in di])\n",
" if not return_runs:\n",
" circ_lin_corr_dist, pval_dist, slope_dist, phi0_dist, RR_dist = cl_corr(\n",
" dist, phase, -2, 2, return_pval=True)\n",
" circ_lin_corr_dur, pval_dur, slope_dur, phi0_dur, RR_dur = cl_corr(\n",
" dur, phase, -2, 2, return_pval=True)\n",
"\n",
" results = {\n",
" 'action': row.action, \n",
" 'channel_group': row.channel_group, \n",
" 'unit_name': row.unit_name,\n",
" 'circ_lin_corr_dist': circ_lin_corr_dist, \n",
" 'pval_dist': pval_dist, \n",
" 'slope_dist': slope_dist, \n",
" 'phi0_dist': phi0_dist, \n",
" 'RR_dist': RR_dist,\n",
" 'circ_lin_corr_dur': circ_lin_corr_dur, \n",
" 'pval_dur': pval_dur, \n",
" 'slope_dur': slope_dur, \n",
" 'phi0_dur': phi0_dur, \n",
" 'RR_dur': RR_dur\n",
" }\n",
" if plot:\n",
" axs[0][0].scatter(dist, phase, s=dot_size, color='k')\n",
" axs[0][0].scatter(dist, phase + 2 * np.pi, s=dot_size, color='k')\n",
" axs[0][0].set_yticks([-np.pi, np.pi, 3*np.pi])\n",
" axs[0][0].set_yticklabels([r'$-\\pi$', r'$\\pi$', r'$3\\pi$'])\n",
" if plot_lines:\n",
" line_fit = model(np.array([0, 1]), slope_dist, phi0_dist)\n",
" axs[0][0].plot([0, 1], line_fit, lw=2, color='k')\n",
" axs[0][0].set_title(\n",
" f'corr = {circ_lin_corr_dist:.3f}, '\n",
" f'pvalue = {pval_dist:.3f}, '\n",
" f'R = {RR_dist:.3f}')\n",
" if plot and plot_grid:\n",
" contours = measure.find_contours(fields, 0.8)\n",
"\n",
" # Display the image and plot all contours found\n",
" axs[1][0].imshow(rate_map.T, extent=[0, box_size[0], 0, box_size[1]], origin='lower')\n",
" axs[1][1].plot(pos_x, pos_y, color='k', alpha=.2, zorder=1000)\n",
" axs[1][1].scatter(\n",
" interp1d(pos_t, pos_x)(cleaned_spikes), interp1d(pos_t, pos_y)(cleaned_spikes), \n",
" s=1, zorder=10001)\n",
"\n",
" for ax in axs.ravel()[1:]:\n",
" for n, contour in enumerate(contours):\n",
" ax.plot(contour[:, 0] * bin_size, contour[:, 1] * bin_size, linewidth=2)\n",
" \n",
" for ax in axs.ravel()[1:]:\n",
" ax.axis('image')\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" \n",
" axs[0][0].set_aspect(1 / (4*np.pi))\n",
" if plot:\n",
" despine()\n",
" if plot_lines:\n",
" plt.legend()\n",
" if plot and save:\n",
" figname = f'{row.action}_{row.channel_group}_{row.unit_idnum}_f{flim[0]}-{flim[1]}'\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",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 370,
"metadata": {},
"outputs": [],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (2, 2), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 371,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'action': '1833-260619-2',\n",
" 'channel_group': 0,\n",
" 'unit_name': 174,\n",
" 'circ_lin_corr_dist': 0.015035045939466871,\n",
" 'pval_dist': 0.58032151303154,\n",
" 'slope_dist': 0.31943787776044974,\n",
" 'phi0_dist': 4.843627836303633,\n",
" 'RR_dist': 0.03720602786152166,\n",
" 'circ_lin_corr_dur': 0.008726766011604742,\n",
" 'pval_dur': 0.7501129594935525,\n",
" 'slope_dur': 0.12178247278219806,\n",
" 'phi0_dur': 5.408259850880937,\n",
" 'RR_dur': 0.034326500937119704}"
]
},
"execution_count": 371,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"compute_phase_precession(\n",
" data.query('action==\"1833-010719-1\" and unit_idnum==121').iloc[0], \n",
" plot=True, save=True)\n",
"compute_phase_precession(\n",
" data.query('action==\"1833-120619-1\" and unit_idnum==168').iloc[0], \n",
" plot=True, save=True)\n",
"compute_phase_precession(\n",
" data.query('action==\"1833-260619-1\" and unit_idnum==32').iloc[0], \n",
" plot=True, save=True)\n",
"compute_phase_precession(\n",
" data.query('action==\"1833-010719-2\" and unit_idnum==121').iloc[0], \n",
" plot=True, save=True)\n",
"compute_phase_precession(\n",
" data.query('action==\"1833-120619-2\" and unit_idnum==168').iloc[0], \n",
" plot=True, save=True)\n",
"compute_phase_precession(\n",
" data.query('action==\"1833-260619-2\" and unit_idnum==32').iloc[0], \n",
" plot=True, save=True)"
]
},
{
"cell_type": "code",
"execution_count": 372,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUMAAAFECAYAAAC9Nly/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO19eZxlRXn2w4CCy6CgOKwCzcypuEK73A5GPoltEpc7rUnQNi4RMbh8idoT4/JFG82MYlAjo8ZIxBU0Zowa5V41xrSIqOAYHVBHLUFFDEZlMTBENpn7/VHndJ+uruWt5Zx7b/f7/H71u91nqe1UPfUutewzGAzAYDAYax3rhp0BBoPBGAUwGTIYDAaYDBkMBgMAkyGDwWAAYDJkMBgMAEyGDAaDAYDJkMFgMAAwGTIYDAYAJkMGg8EAwGTIYDAYAJgMGQwGAwCTIYPBYABgMmQwGAwATIYMBoMBANhv2BkYZQghXgDgHACnSynf43juPgBeCeDJAI4GcDuA7wD4AID3SCn3Wt57OIBXAPg/AO4D4JcAvgjgbVLKrzvSmwYwB+BEAOsB/AzAvwN4q5RSBhXSASHEGwG8CsDvSSn/w/HcbwF4GYDHAjgCwB0ArgDwCaiy7LG8dx8Ar4GqtyMB/ArAVwC8SUp5qSO9dQCeC+A5AB4M4B4AfgLgUwDOlFL+ili+jwB4OoBNUsorHc89uizf70DV91UAPg3gbCnlNZS0LPFG1VtgGusBfBuqXf6ulPKLGeIktYvy2T8GcDqAhwO4F1Rb/TcAr5dS/pflnfsD+H8AHg9VL/8L4JsA3iWl/Fhq/m1gydACIcQjAbyZ8NzRUB/q5QAmAPwQqlOfCOAfAXxKCHEXw3vPBfA1AE8DcHcAuwHsD+CZAC4RQrzYkt4ZAP4DQLe8tBvAQQCeD+CysvElQwjRBfBXhOeeCuAyAH8GRWhXArgewAkAtgH4TyHEkYb3NkCVfw7ABgDfAjAA8IcAviyEOM2S3j0AfB7AewCcBOBaKHI6rszvN03pGeJ5IRQR+p57JYAvAXgKgLtBDXIHQZHYd4QQJ/visMQbVW8R2A5FhFkQ0C72E0J8CMDHAPwBFKH9AIrcXgDgciHEQw3vPQyqXl4I4DAA3wdwK9SA8S9CiHMyFWUFmAwNKBv456CkAB/eB+D+UKT0QCnlg6SURwOYgfqIXSjprx7/A6Akzn2hGusGKeUkFCm8vLpeEnL9vWkAf1P++/LyvYeV720HcACAD6V2IiHE06AasVNzEEJMADgfisTPA3A/KeWDpZTHAHgYgO8BKAB81PD6DigC+zyAI6WUjwBwOJTEsS+Ac8p60vEuqI7xMwBTUkohpRRQJHIFgGOgBiFXvucA/IPrmfK5zQD+FsA+AM4FcKiU8pFlPl8O4N4APiOEONYXlxZvSr2FpPMkAMZBJTI+UrsocQbUwH4TgCdJKY+RUj4Y6pvvBHAwgA8LIfapxb8vgI9ADTZfAnCMlPKhUsrDoAb7AYAXCCGematMdTAZ1iCEOEAI8TooyesgwvNHQXVMAHi+lPKK6p6UsgfgTeW/z9NefSmAuwL4KoC/lFLeWr6zV0r5Fig1Yh1UA6jj5eXvR6SUb5FS3lm+dxuAv4TqRAdAqY/BEELcWwjxD1BEtT/hlZeUz10G4DQp5Y3VDSnlZQD+CMCdAE4sVc0qnZMBPAbAzQCeUam1ZfnPAvAhAHcB8Gotfx0Azy7jfLyUcmctvW9DSRwA8AQhxBGG8h0mhPgYgLOhCM6HauD5dynl86WU/1vL51sA/DOUtPhWQlx1RNVbCIQQB0MR+K9j3tfiCmoXZd2/svz36VLKz1T3pJRXA3gGFLE9GMBv1149EWoQAFS7+HntvXOhBhBgZX/KAibDEkKIjVBi/GvLS6+BskO5UJfALjfcr+x+R2nXvwXg4wDOkVKazl34VvmrqzdfAXABlDS6DGU837a854UQ4kQoVe1FAG4BTaL43fL3oxUxa3n6PpSaAwB1KffU8vdTUsrrDPFWqtBThBB3q12vSP6DJfnp+CLUd3sJFJksQgjxh1CS4x9DmTH+3PB+/flDAUyW/77J8tj28ndzST5UxNZbCN4JpWb+deT7AKLbxTOgBvsvSCk/q9+UUv4QyjwyB6D+/at+cp3FFlv1p/vTch8GdqAs4Uioj3EpgL+QUn5DCHG6552ra39PAviydr+yiSwjVSnlP8Ctpj2i/L2iflFKuc32QqliVJ33CttzDggoJ85nAbxESnmlEGIF6Wp4KYBjoWx/NlQS2L61ayeWv3p9VdgJ4DdQjpFHALi4vP575e8nTC+VA8IbLHEeD2Wb/TCUzesAR56B5QPKNyzPVM6qfct8/rsnzgqx9UaCEOIUKHvolwC8HUukHYOYduH8TgAgpXy74XLVn+4rhDjS4GCp+tNVnvSjwGS4hP+Csm18xvtkCSnlNUKIT0F5Q98lhHhKOepBCPFYKI8YQFSjhBCHQUk2j4VSIU0NxvTecVC2rU0Afg6D5EjAdwA8Wkr5FeoLpWfyi458PQjAA8t/d5fX1kE5mgDlbDLFe4cQ4hooQioAXCyEuDuUvQkAdpde0mdB1dVBUAPOR6WUn7Nk50sAjq8kSiHEMZ7i1SX2OyzP1B1jvvgWEVNvVJSOqXdBOSyeK6UcCCFCotAR3C6wRFq7hRB3hSLmx0PZtv8byuv/MYNW9FUo08EJAM4XQjxdSvkLABBCPB1KKh0gjdytYDIsUU6tsE6vcOBZAN4L4KkAvi+E+AGUHelYAP8DYK6UBK0QQvwFgL+A6uz7AfgugOf5pskIIc4EMAvVEdcBuATKBnV9aCGklP8Z+o4nb/tiSfq9BsAXyr8PwlK7u9YRxfVQZHjf8v+jsGTWORLARVipLp0mhNgB4DmlHXURUsovIAw/rv19ApSJQseDan97bcwUOOqNin+EqrMXSyl/lJqf0HYhhNgfivQAJYl/A8o2WMczAVwohPgjKeX/1NIaCCGeAGUbfByAnwghJJSz5UgoIv1LKWU/qjAesM0wHQMoe+ENUJ38gVBECCgypBiwT4JSRyqS2ADgyaYpORqmoaSs6jsehaUpN0ND6SE8B2r+JAC8rEZOd689eqsjmlu05+ue/U9A1ftToFTp+0INJrdADQ7JkoOU8lqowQUAXlP3etZQd/DcNTVNT71R3n8OlJZyIZTNcBiof6cPQHnenwPleb8XlPBwPZTd9COG938D4D+h+s3+UFJmZZu/DnYpPRlMhgkQQhwINXK/AUpNeyyUVHhfqImm9wbw7tIT58IryveOBrAFqhG8Cv6pFU+Hsn1tgpqbtgHAm4UQZ8eUJwdKyeZcqPlzAPAOKeWO2iMrHAYeVKpU3ZGyP4DHSCk/JaX8tZTyeinlO7HkFHm+SNQNS/w1gL1QKt6/CiEeLIS4ixBikxDiPKjvXU3wTuqkhHrzvX8kgLdBmVdOszjm2kD9Ox0M4A+klOdJKW+UUt4kpfwwlBYFAI8XQlT2xaoMl0C1/a8C6EC178Oh+kgB4GNCiGVT1XKByTANL4f6YD8DMC2lvFBKeWvZOd8D4PehOv+LXJNzpZQ/Kd+7Wkq5HUvS3VM87/1YSnmblPJKKeUZWOpILy7tiK2inBD9CSxNfXg/lMewjptrf7ucGFWn+rX2CwDvl1KaPP0fgBqU1gHYTMiyE6Vt7zSoFUVPhvLW3w416+CpUAPeD8rHb4pNh1hvPrwXSvJ6hZTyqti8ZED9O/VNaraU8kIoGy6g6rXCGwFshNK0niil/HrZvv9bSvlmKC81AJxZzv7ICrYZpqEa4d5Wt31UkFJ+XQjRh/rgz4DDaK69d5EQ4qsAHgXg5ID3zhNqqdThUKr3D4UQfw3giZZXTqnP5UqBEOJwAH0sebTPhlLzdAnlZgC3QUl393FEWdkKf1n+1uv3MtMLpc1pN5SEPWF6JhRSyg8KIS6GIr5JqMFtF4D3Sil/LISYLx+9BgBC6zug3qwQQrwIauBdwNK0pGHhJihpeh0s36nEd6DMARPAoonglPLemVLKFZK2lPITQojLoWYGPA3
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAFECAYAAABf6kfGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO29f5xlR1km/uQHhEnSQwIMPRNAQpM5pQGJjXB7QedLpF0XtfsGFbaRoGDcBvyuSs8q6K65o5tBXFCcUVfJOggKoo4/WJnbotFtECNCJgshmgAVyBCiIQkQfjWSACZ3/zineurWfd+33jrn3NvdmXo+n/p033Pq1KmqU/XUW+/7VtVpg8EAGRkZGRnA6ZudgYyMjIytgkyIGRkZGRUyIWZkZGRUyISYkZGRUSETYkZGRkaFTIgZGRkZFTIhZmRkZFTIhJiRkZFRIRNiRkZGRoVMiBkZGRkVMiFmZGRkVMiEmJGRkVEhE2JGRkZGhUyIGRkZGRXO3OwMbFcYY14G4GoAy9baNwrxHgngZwBcBuDxAL4G4CYAvwvgjdbaB5jnvhXAqwD8fwAeCeDTAP4WwK9Za68X3jcPYAXAMwBMAfgUgL8G8KvWWptUyOF0vxHATwF4NoDHAPg6gI8BeHuVp3Xh2R8AsAzgWwE8vMrTXwF4tbX2X5hnHgngSpT19lgAnwfwXgCvs9a+X3jX6QB+BMCLATwZwDkAPgngHQBeY639vLK8fwjgBQD2Wms/LsT7dpT18m0o6/s2AH8B4JC19g7Nu5h0a9e3kGatOo2kWau9GWO+C8D/D2AOZfv+MoAbAbwZwFuttZuyL+FpeT/EdBhjng5gDWUDYAnRGPN4AH8H4BsA/BuAWwCcW/0GgFUA32+t/Xrw3I8AOALgDABfAnACZQN+FID7Aey31v4G8b4DAP579fMeALcDmEFJQvcBeJG19s9qlPf5AN4K4CyUhP4xrxynVeWaD8nNGHMmSuK/vLr0SZQN36AcjD8H4Dustf8YPDeNsqM+EcBXAHykKv90Vf6XWmvfROTzHADHUJIIqnwBwEUoZ0O3AdjHkbCXzssBvKH6yRKiMeZnAPxSVQdfBvBRAI+r8vkFAN9nrf1b6V1MurXqO5JmrTqNpFmrvRljXg/gv1Q/1wHcipL0d1XXjgF4XtgvJoE8ZU6EMeZSANegJMMY3oSyEd8M4GJr7ZOstY8H0EXZYBZQSoF++t+EUvI8A8BhANPW2lmUDfeV7npFyv5z8zjZOF9ZPffU6rnDAB4G4PeNMY9NLO8MTnbOtwB4tLX2ydbaCwE8FWXHKgD8MfH4AZRk+CUA32utvdBa+2SUnfI4gEcAeJsx5rTguaNVnL8B8Fhr7dMAXADgZ6vyX13VU4g3oCTDTwGYs9Yaa60B8C0oSeVCAP8rUt4VAL8lxaniLQL4HygJ6giA3dbap1f5fCWA8wC80xjzhFhaQbpN6ltC3Trl8lmrvRljLkdJhvdXf8+z1s5aax8N4PkoCbIL4KrE8rWCTIhKGGMeZoz5BQD/B8D5iviPw0lJ5aXW2o+5e9baPoDXVT9/NHj0FQAeCuAfAPwXa+191TMPWGt/BeVU83QALw2ee2X19w+ttb9irb2/eu6rKBveR1A20hfHSzuEn0TZOT8E4Apr7Re9cnwIwPejbNzPqKaPrvyPQakqAIAXWGvf6T13O4AXAhignNb+O++5SwE8C6XE9UI3xa3K/1oAvw/gIQB+zs+kMaYD4IeqvDzHWnvce98/AXhZ9fO7q7wheH6PMeZPARxCSXIxODL4a2vtS621/+rl81cA/BGAHQB+VZGWj1r1LaFunUZQt725537LWnvIVxlZa/8UJyXHnzDGnJWQn1aQCVEBY8xFKKcpP19duhLl9E+CPzLeSNx3esDHBdf/EcCfAbia0aO46eXjg+vvRTnVGJn2VOn8E/NcDN9R/f1j1+iDtD+KcqoIAL7U+kKUxP4ua+1fEs/dilL3tALgs96tl1R/32Gt/Wz4HErpGQCea4zZ4V13He/3KgIM8bcov9tPoiSUDRhjvg+lBPkDKPVq/5l43o+/G8Bs9fN1TLTD1d9FY8wjpPQC1K1vCS+p/qbWqYTk9lbVwyXVzz9k0v3z6u85AC5W5qU1ZKOKDo9FSVzvB/Dj1toPGGOWI8/c7v0/C+Dvg/tPqf4OEau19rcgT9meVv39mH/RWnuQe8AYcwZOduCPcfEYvALAEwBcJ8RxEtUZ3rV/X/19O/eQtfbXicvPqP6G9eVwHKU+9hyUdXGt5n1VJ/1FJs1LAJwN4G0AfhqlZCPBH1Q+wMRxBoUzqnz+dSRNh7r1LaFunbKo2d7uA7CIsj/dxDzuS+fa8rWGTIg6/AtKHdg7ozErWGvvMMa8A6VF7w3GmOdWUhGMMc8G8F+rqKoplTFmD0oJ59kopz4UmVDPPRGlrmsvgLtAjOiRcvwtSumKS/9JODmS3+zdcoR/szHmoSgtts9BqWO6E6XV9099KbiyEM9UP29l8vN1Y8wdKEmpAHCtMeZslPox974pAC9CWVfnoxx0/thaew1TjL8DcImTLI0xF3LlreBL7pzi/yHe/7H0NtCgvrn4tepUm1/ifWx7s9Z+BaUhUcLzq7/Oqj5RZEJUoLIysq4XAl4E4HdQfuSPGmNuQalXegJKK+RKJRGyMMb8OIAfR9nhzwTwYQA/GnOhMca8BsASys54OoD3odRJ3VOjHNw7zsBJafYOAO+qrp+FkviAUvL6AEpdoY/LAbzbGPP91tovVNfOx8k2+Rnh1feg7LyPqn4/DifVP48F8B6ctOQ7XGGMOQrgxZWeawPW2ncJ76LwCe//b0E5fQzxJO//qM5ZA66+I6hbp6l5a9zeqkHf6Wbf4etPJ4WsQxwvBij1h59D2SgvRkmGQEmIX1GksQ8n3VSAkmguM8Y8hH8EADCPUjJw3/hxKK3araCyDF+N0k8SAH7KIxrfAv+7KK2ZL0ZpeX04yoHiHpT6Ml+XdLb3/33C6+8N4vvvezvKen8uyingo1AOKPei7LCH0RDW2s+g7PAAcCVhJQeGDRQPbfrOSH1LqFunqWjU3owxD0cpPT4K5Qzov8pPjAeZEMcEY8xOlCP4L6Kcsj0bpXT4KJROyucB+G1jTMzF41XVc48HsB+lBfJnEXe7eAFKXdheAAdREukvG2MO1SmPj0pSOQLgP1WXfsNae9SL4ivmHwHgP1hr32Kt/aK19kvW2rfh5NToOcYYp/8bMSJE4Kau/vvOAvAsa+07rLVfsdbeY639TZw0lLzUGGMS30PhvwF4AKUa4H8bY55sjHmIMWavMeYtKL+3cwJv5E+nqG8Jdes0FbXbmzHmUSi9N55avf9HJGf4cSIT4vjwSgAdlD5x89bad1tr76s66BsBfBfKxvpjlVsECWvtJ6vnbrfWHsbJUfe5kec+Ya39qrX249baAzjZmX6i0vPUQuX8/HacdBd6M0pLsQ9f8l211v5fIn/vRqm7A0o9K1BKBg6SYcMR4FeCvwDwZmst5QHwuygHptNRKvYbodL1XYHScfoylFbVr6H0Rng+ykHPOYZ/qe57lPUtoW6dJqFue6s8ON6L0pjzAMqFDn9aJw9tIOsQxwcnAf2apyPbgLX2emPMKsrO9EIIivTgufcYY/4BwDMBXJrw3FuMMb+Ecvq6D8Ctxpj/BuB7mEeeZ629y79gjLkA5bTGWRAPoZy6hVLFl1A27tNR+tNxuAnlFNAp/b8M4KsopbxHCs85Pdenq79+/ZLvs9YOjDE3o5S0Z6g4qbDW/p4x5lqU5DeLcoC7AcDvWGs/YYzpVVHvAIAx1reEunXaCFR7C+NUfpR/XuXrawB+yFqb6nDeKjIhjg/ONeOjQpwPoyTEjdUMxphHV79vttZ+mXnukygJcXf1zGkA9lTPvc8y66Or5y5wz6G0KH4bE3dImjDGfDOAd6I0WjyAcvkgaemurJa3opw+Sc61/1b9/Wr13APGGIvSQn0h9UClO72g+ukksNtQ6scelvK+NmCtPQFC31WtG76w+ul88sZS35H81a1TFg3am5/
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"compute_phase_precession(\n",
" data.query('action==\"1833-010719-2\" and unit_idnum==121').iloc[0], \n",
" plot=True, save=True, flim=[10,12])\n",
"compute_phase_precession(\n",
" data.query('action==\"1833-120619-2\" and unit_idnum==168').iloc[0], \n",
" plot=True, save=True, flim=[10,12])\n",
"compute_phase_precession(\n",
" data.query('action==\"1833-260619-2\" and unit_idnum==32').iloc[0], \n",
" plot=True, save=True, flim=[10,12]);"
]
},
{
"cell_type": "code",
"execution_count": 373,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAElCAYAAABpvBCJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeXwU5fnAv7u5TxIghIQ7hgz3KUHQKIh3Q2jFFhAvtNFqq4Jae2iwJfbyVwW0hy22akUt3iWpBwoiKJagIArKGzDckHCGnOTc3x8zk52dndmd3WySje7388lnszPvvPPuzs4zz/s8z/s8NofDQYgQIUKEMMbe1QMIESJEiGAmJCRDhAgRwgMhIRkiRIgQHggJyRAhQoTwQEhIhggRIoQHQkIyRIgQITwQEpIhQoQI4YGQkAwRIkQID4SEZIgQIUJ4ICQkQ4QIEcIDISEZIkSIEB4ICckQIUKE8EBISIYIESKEB0JCMkSIECE8EN7VAwg0kiRFAluBkcAUIcT/LBwzBFgIXAYMBGzAQeA9YJkQYreX48OA64DrgXFAAlAOlAD/FEK8ZWEMacDdwHeADKAFOAAUA38TQuz11odVJEkaCvwYuBgYBEQDJ5TxPgv8RwjhMYee8plvAuYAo4FewCmlj79a/MxTkb/384EU4CSwHfiHEOJli59lEnAbMB1IA5oBAbwK/EkIUWOlH01/mcDngF0IEW3xmFnAzcBkoCfy97AZeEoIUeTL+X1FkqQHgIeRfyM/ClCfo4B7cX6nVcBXwL+Ap4UQLe3sPxD3SzLydc8FhgOJwBngC+A15O++vj3jVLF90/JJSpL0KHCP8tarkJQk6fvAM0CsSZMG4FYhxL9Mju8JFAFTPZzmVeAGIUSdSR9XAC8CSSbH1wE/EUI87eEclpAk6W7g/4AID83eAuYKIapM+uiP/JnHeejjH8jfW6tJHw8BDyE/kIx4QxlDg8nxNuTPcY+HPvYAVwghvvYwTm2f0cBa5GvZ4E1ISpIUD7wAzPTQ7A3g2kDdsLrzTwI2AlEESEhKknQL8FfMfx+bgFwhxGk/+w/E/TINWAX08dDHHmCWEOJLf8ap5Rs13ZYk6Rc4BaSV9tnA88gCsgV4ArgKuBT4HdCI/AN8WpKkSw2OtwOrcV7wj5GfkBcgPyU/UbbPBv5pMoaxyDdSkmYMucha3i+Rn46xwFOSJF1l9bOZnOtGYBnyDVAJ/BpZWzgPWRParjS9EnhFEUT6PpKAD3AKyLeAq4EpwI+QNXCAW4ACk3H8EPgVsnDbo5z7PGStdLPS7LvAXzx8nEeRtR1V678T+Xufiax9A2QCxZIkRXnoRx1TBPAynm9ebXs7ssaiCsjjwP3IWvGlyIKmRfkc7yn9BwxF23sL+fcZqD4vA1Yg/z4qkL/TKcAs4B2l2VTgZaPfhoX+A3G/jEQWsn2Qv9+nkGdf2ci/wzeUppnAGkmSUn0dp55vhCapTLGXI9+kWjxqkpIkrUH+QQP8QD/FkyTpImAd8sPkCyHEGN3+mwBVu3sZmKOdpio/ijeBy5VN5wshNun6WA9cpLzN00/PJEnKQjYfxCFPI4d7mwqbfNZ4YB/ytPgE8nezR9cmEngF540/Xwjxgq7Nk8jTHIBHhBA/0+1PBT4D+gJngXSt1qFoEl8jPxR2A5N1+8ORNYk8ZdNkIUSJ7hxTgI+QBeQO4GIhxHFdm38CC5S3dwgh/urhu0lDvn7nazZ71CQVQb9CeVsKTBdCHNG1mYl809qBnwoh/mjWny8o/T4H9NBsbpcmqQjxr4BzkH8f5woh9uva/Bm4Q3k7Rwjxko/nuIn23y9vK/sdwGwhxOsG5/kF8Fvl7QohxK2+jFNPt9ckFW3wI5wC0pK9RNEupitvS4xsYEKIDwD1IoyWJGmQrskPldcm4Ha98FKmmr/UbPqBbgyZOAXka0b2KyFEKc6nqgSM9fS5PJCHLCABFusFpHKuRiAf+fMA3KAbb39lP8AGvYBU+qjA+QONRtaKtSzAaVb4uX7aJoRoBm5FNjEA/NTgs/wKWUA2I98oxw3a3Kf5HNcY7AdAkqS5yA8hVUBatbfdpbw6kM0CR/QNlOv5d+XtYuVB5TeSJCVLkrQc+A+ygGyXbVBHHrKABPidXkAq3AOon9PounijvfdLf2S/AcDLRgJS4ffIdmWAucrD32+6tZCUJOn3wP+Ac5VN/0GeTlqhJ07HVamHdjs0/6fp9n0BbAHWCCFOmhz/leb/gbp98chTtj04hbGvfVglR/P/arNGipD7QnmrF8hzcP5mfuHhXK8iazpLgUO6fVcrr2eQr5fZGP6rvL1KkqQ2e7Giqc5Q3j6tPESM+jiFbDL5C7J24oYkSR8j24L7Aq3INtIPPXwu9bgUZGcVyA+LbR6aq5pTArIpxy8UJ9ceZOFsA44C1/rbnwHqdXEgXzs3FPvw88rbcyVJGuzjOdp7v5yP0/7s6TfsQHa6gvy9D/FxnC50d+/2echf2ingfiHEPyRJ+pXFY48jayLhwDAP7TI1/7toC0KI2y2cR6t9HtUd/xmy/cXvPnzgHWQvZRq6z2GA+kPUTzevVF4P6qdBWhSt6gb9dmVKN0l5+6EXL+kG4PvI9tjzkM0eIJtHwpT/V3k4HiHEQ572K/2CfGPmCyE+kiTpYi/HgOvNu9m0lYzWcTAF8GmKqiEL+cHuQPYyL8J1ut1eVE36SxPNXGUDTi3yYkxsh0a0935BNs8UAv1w2s/N0NpMLUUpmNHdheRp4A/AH3z1tgkhmiVJehN5mnGuJEnf06vvkiSNR75RQZ6SH/DlHEqoQ6Fmk8eb2qSPITinKXuAT33tA0AI8QZOo7an8/XCqSXpp1zq9k+0GyVJSgDSgWqjaaeGTJxeU49hVch2S5XhOIXkaM32tnEotsz+yL/pg2ZecR1lwCPIIUfNFtqraKdv1V7aNmn+z/LhHHpakTXiXwkhtgBIkhQQIalo6qrg9/W6BAxv94sQYiuyacQK05RXB05nol90dyE52yzExCI/RfaK9QVekiTpCWANctjPBcDPkG+Ik8h2Mq8oFzod+cl8HzBR2fU3IcQGC8fbkJ98Gcg2mTuBZGVM+e38vFa4D+fvQvVoqsJTDbnYr2y7GtnDPFXT7iDyFHepgaDqp/nf2wNH+8PWHjdCea0UQpxRpnxLkKeLccq+ekmSVgMPeAn/Gern93lC839/L20HaP7v68e5VFaahaEFgHScmpe/18Uv2nu/mPR5OTBeebtFMb34Tbe2SbZXYCj2rPOAlchTuEXIYRXrkG+8OGT72iQhhDf1vq1b5B/ai8gX/Czyxb/D00Eazkd2WuwAFiMLyC+BaUKI9Rb78AvF7nWv8rYB+JNmd2/N/5WKl/tV3ENmBiDbAtcpAb9aemr+96aB1Wr+18aPquOoVMKyvkAOH4nTtIlBtp9ukyTpErMTtOP3swf5wQnwHS/hPdoYyjjTVl7o4IdjIK6Lv7T3fnFBkqTewN80mx5t7wC7tZAMEFORV+eYxX3lALOV8ASPKFrgAN3maGSNUO/lNUPvQQdZq7xDkiR/nTZeUUKNXsc5HS4UQmi1Bq1n9mbkMKAyZGGUjCwAZiA70kD+XlfqTqON6TvrZUja4Gvtceo4kpCFdDTyipNzlHZZyDeGA9lo/6oSRRAwFMeA6sDoj9Ob74ISDaH11gY0VjKAWL4uymdXZwjtitEM0P2i7S8O2aSk3kNrkEON2sW3WkhKkvRb5BUT45FXElyKfBPGI9/wHyBPMf8P+IcFQRmFMzD6EmR7Vx3ylP4NSZJ+bGFYO5G9oJORfyxvIf9wrgc+UoRZQJEkaRiy9qxOp9/E/cbXrkgaiGybyhZCvCSEqBRC1Akh1iHbglQP8VW6AHito8aXWE9tW3UcScjX6QdCiAIhRJkQolEIsVsIcR/wE6VdosFnCQS/RV5KB3CfJEmrJEmaKElSlCRJvSRJugH5N5WsadfYAeMIBIG4Lv4QiPsFaLOLF+N0QB0ArvMnplhPd7dJ+o0kSXk4w1jeRg7k1hrZ10mS9AGyNjQXeZ3yZuBJsz6
"text/plain": [
"<Figure size 300x300 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
" compute_phase_precession(baseline_i.sort_values('gridness', ascending=False).iloc[18], plot=True, plot_grid=True);"
]
},
{
"cell_type": "code",
"execution_count": 388,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.4129467750189508,\n",
" 'pval_dist': 0.28464111660579006,\n",
" 'slope_dist': 28.093945031625527,\n",
" 'phi0_dist': 0.16742503048943053,\n",
" 'RR_dist': 0.8263134195104782,\n",
" 'circ_lin_corr_dur': 0.45019436077125674,\n",
" 'pval_dur': 0.23466570092972083,\n",
" 'slope_dur': 7.60530119306991,\n",
" 'phi0_dur': 2.559041546574352,\n",
" 'RR_dur': 0.9151777474060984},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.9172178573980904,\n",
" 'pval_dist': 0.04558526041179123,\n",
" 'slope_dist': 20.643552625318872,\n",
" 'phi0_dist': 3.7770417048542133,\n",
" 'RR_dist': 0.9494493337637299,\n",
" 'circ_lin_corr_dur': -0.7812146888876957,\n",
" 'pval_dur': 0.060057201741145594,\n",
" 'slope_dur': -68.05547576848763,\n",
" 'phi0_dur': 1.8139821995133372,\n",
" 'RR_dur': 0.6676959218271774},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': -0.265840449081054,\n",
" 'pval_dist': 0.4625226050621034,\n",
" 'slope_dist': -23.301088851788954,\n",
" 'phi0_dist': 2.566564620765877,\n",
" 'RR_dist': 0.5693840101370895,\n",
" 'circ_lin_corr_dur': -0.4630967615992036,\n",
" 'pval_dur': 0.2042202329379157,\n",
" 'slope_dur': -23.34510043249202,\n",
" 'phi0_dur': 4.516455459791437,\n",
" 'RR_dur': 0.5965273552272874},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.30367944771269895,\n",
" 'pval_dist': 0.026135127305834782,\n",
" 'slope_dist': 14.149754716354465,\n",
" 'phi0_dist': 1.1693565535871226,\n",
" 'RR_dist': 0.3314870454477721,\n",
" 'circ_lin_corr_dur': 0.4618892719151249,\n",
" 'pval_dur': 0.0006395229268481017,\n",
" 'slope_dur': 9.021986257398721,\n",
" 'phi0_dur': 2.783446591667775,\n",
" 'RR_dur': 0.39308123478746143},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.9645237355164202,\n",
" 'pval_dist': 0.001823411910458983,\n",
" 'slope_dist': 26.70323060703037,\n",
" 'phi0_dist': 1.3158184207558894,\n",
" 'RR_dist': 0.9629331089459956,\n",
" 'circ_lin_corr_dur': 0.6072925403004289,\n",
" 'pval_dur': 0.009703337725364802,\n",
" 'slope_dur': -23.62288151655366,\n",
" 'phi0_dur': 2.6856436154554024,\n",
" 'RR_dur': 0.40446535590794314},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.9911167344823372,\n",
" 'pval_dist': 0.050122642544521145,\n",
" 'slope_dist': 48.001098030230565,\n",
" 'phi0_dist': 0.8814362136350085,\n",
" 'RR_dist': 0.912890955119381,\n",
" 'circ_lin_corr_dur': -0.1333551438932498,\n",
" 'pval_dur': 0.7443122589388493,\n",
" 'slope_dur': -24.93268402473139,\n",
" 'phi0_dur': 6.274996051779313,\n",
" 'RR_dur': 0.6249452967636183},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.16547965733786388,\n",
" 'pval_dist': 0.14124946518803294,\n",
" 'slope_dist': -12.455079310491033,\n",
" 'phi0_dist': 6.207897911524018,\n",
" 'RR_dist': 0.27009312976403244,\n",
" 'circ_lin_corr_dur': -0.11643443056432957,\n",
" 'pval_dur': 0.31967716933659585,\n",
" 'slope_dur': -23.47550060316967,\n",
" 'phi0_dur': 2.5651006117534303,\n",
" 'RR_dur': 0.13813365449174708},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.746534745210693,\n",
" 'pval_dist': 0.00023103425988657555,\n",
" 'slope_dist': 28.759505218226185,\n",
" 'phi0_dist': 1.5684112725295802,\n",
" 'RR_dist': 0.9347072516993947,\n",
" 'circ_lin_corr_dur': -0.23985224003814648,\n",
" 'pval_dur': 0.3070938529180911,\n",
" 'slope_dur': -22.425631682208603,\n",
" 'phi0_dur': 3.973167364069665,\n",
" 'RR_dur': 0.27989097932985585},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.9193528711271779,\n",
" 'pval_dist': 0.010992921886874285,\n",
" 'slope_dist': 25.82566687705631,\n",
" 'phi0_dist': 0.03756099795784066,\n",
" 'RR_dist': 0.8000390143854937,\n",
" 'circ_lin_corr_dur': 0.9957206567761857,\n",
" 'pval_dur': 0.00823766690802552,\n",
" 'slope_dur': 7.232062114847552,\n",
" 'phi0_dur': 1.3430528701524695,\n",
" 'RR_dur': 0.9937332903207565},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': 0.712693653840821,\n",
" 'pval_dist': 0.04188142051134802,\n",
" 'slope_dist': 25.702044480941627,\n",
" 'phi0_dist': 2.460403243905358,\n",
" 'RR_dist': 0.6969993151612308,\n",
" 'circ_lin_corr_dur': -0.1574673579760977,\n",
" 'pval_dur': 0.6654107468230355,\n",
" 'slope_dur': -14.70173580978851,\n",
" 'phi0_dur': 4.151621446378472,\n",
" 'RR_dur': 0.505881569847836},\n",
" {'action': '1833-260619-1',\n",
" 'channel_group': 0,\n",
" 'unit_name': 132,\n",
" 'circ_lin_corr_dist': -0.3272773950469095,\n",
" 'pval_dist': 0.19157360520736888,\n",
" 'slope_dist': -4.56009392702431,\n",
" 'phi0_dist': 4.533929895678337,\n",
" 'RR_dist': 0.5532784366113003,\n",
" 'circ_lin_corr_dur': 0.27336783776006485,\n",
" 'pval_dur': 0.23109078232440483,\n",
" 'slope_dur': 55.84599943412176,\n",
" 'phi0_dur': 3.077875813963241,\n",
" 'RR_dur': 0.44463970728712504}]"
]
},
"execution_count": 388,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATsAAAFECAYAAABYqJnTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO2de3wV5Z3/3yRcAwghoYGYKIHgY7gEAcVwEwiVYq1dum21bLvS1tZW1263u9t23d2fdd1u3fbX1v6qi12tWihdV3tZrcVSuiSCgBEFJaDx4dIgwUDMhUsgXAO/P2YmmTOZmTMzZ845k5zn/XrxOsyZ6zmZ+Zzn+V77Xbp0CYVCoejrZKX7AhQKhSIVKLFTKBQZgRI7hUKRESixUygUGYESO4VCkREosVMoFBmBEjuFQpERKLFTKBQZgRI7hUKRESixUygUGYESO4VCkREosVMoFBmBEjuFQpERKLFTKBQZQf90X0BvRQjxJeAnwBellD912S4P+CbwZ8CVwDlgN/Az4KdSyosO+80EvgHcAOQB7wMvAf9PSvmay/kWA38DzAaGA43AeuCHUkrp60PGHvdq4O+ASuBy4DywF/iNfk3tLvt+HPgiMBMYoV/TOuDbUspDDvvkAf+M9r0VAUeBLcD3pJQ1LufKAj4HrACmAEOBd4Hnge9IKY96/LxPA58CJkop97lsNw/te5mL9n0fANYCD0kp3/NyLo/X8yDwD8CNUsr/DXiMQN9pnGMGut+EEEuAu4Hr0e7vk8BO4Cng51LK0GvP9VP17PwjhLgO2ID2x3UUOyHElcAm4ArgArAHGKYvA/wO+HMp5XnLfp8DHgeygRPAn9BuznygE/ialPJhm/PdB/yLvtgKHATGownMGeAzUspfB/i8nwR+DgxCE+u9ps/RT/9ci63CJYTojybqn9bfehftphZoP7RtwCIpZa1lvwK0h3AC0AHU6Z+/QP/8d0opn7S5zqHAb9EEGf26AErRZjEHgPlOAms6zpeBR/VFR7ETQnwTeFD/Dk4C7wDF+nUeAz4mpXzJ7VxeEEJ8BPgftO8skNgF/U7jHDPQ/SaE+AHwt/piO7Af7Qd0tP7eb4FPWJ+LRFHTWJ8IIRYCf0ATung8iSYIbwGTpJSTpZRXAh9Fuxk+gjZ6Mx+/DG3EmA38CCiQUk5Huym/bryvC655v8V033hf1/eboe/3I2AwsEYIUeTz846nW+hWAx+QUk6RUo4DZqA9NFcBz9rsfh+a0J0AbpZSjpNSTkF74LYBo4BfCCH6WfZ7Rt/mj0CRlPJaoBBtZJMN/ET/nqw8iiZ0jcD1UkohpRTANWgCPQ74zzif92+AlW7b6NvdAvw7mtA9DoyRUl6nX+fXgZHAi0KIknjHinOeW4FfkfgsLOh36nRdge43IcSn0YSuU38dKaWcLqX8APBJNPH7KPBA0A/qhBI7jwghBgsh7gf+F8j1sH0x3SOMO6WUe411UsoXgO/pi3dYdv0qMBDYCvytlPKMvs9FKeX30aZ/WcCdlv2+rr8+LaX8vpSyU9/vLNpNVYd2A66I/2lj+Gs0oXsT+LyU8rjpc7wJ/DnajTtbn9IZn/9ytOk7wKeklC+a9jsI/AVwCW2qWWHabyGwAG2k9BfGtFP//N8F1gADgH8yX6QQYhbwl/q1LJVSbjOdbxfwJX3xJv3asOw/VgjxK+AhNAGLh/Ggr5dS3imlPGW6zu8D/w0MAX7o4Vg9EEKMFEKsRBOpQUGOYTrWQgJ8p3EIer8Z+62UUj5kNuNIKX9F94jvK0KIhD63FSV2HhBClKJNib6lv/XPaFMyN8y/aDtt1ht2t2LL+7XAr4GfONgtjCnflZb3t6AN/3tMRfTj7HLYLx6L9NdnjRvacux30KZvAObR5l+giXaVlPL3NvvtR7P1/A3QYlr1Wf31eSlli3U/tFEvwDIhxBDT+8ZDtUoXNysvof3d/hpNELsQQnwMbeT3cTQ71l/Z7G/efgwwXV/8nsNmP9JfbxFCjHI7ns3xZwP7gLuA08Dn/exvw2f1V7/fqRu+7zf9e5imLz7tcNzn9NehwCSP1+IJ5aDwRhGaKNUA90gptwshvhhnn4Om/08HNlvWl+uvMaIppVyJ+zTqWv11r/lNKeW/Ou0ghMim++Hc67SdA18FSoBXXbYxRkLZpvdu1F9/47STlPLHNm/P1l+t35fBNjT751C07+JlL+fTH8B/czjmNCAH+AXw92gjEjfMPxjbHbYxjPPZ+nWuj3NMMwLNaP974K+llPuEEL7saRaCfqeOBLzfzgC3oD1Pux12N4+qsx22CYQSO28cQrM5vRh3Sx0p5XtCiOfRPF+PCiGW6aMZhBCVwL36pp6mOUKIsWgjk0q06YidUNjtNwHNtjQROILNL3Gcz/ES2qjI6fiT6f4Ffsu0yhDzt4QQA9E8m0vRbDqH0byjvzKPXnVP6nh9cb/D9ZwXQryHJjhXAS8LIXLQ7FHG+YYDn0H7rnLRflCelVL+weFjbAKmGSNCIcQ4p8+rYx5xOxnRB5j+H+94VnYD86SUW3zu14Og32kC53O836SUHWhOOTc+qb8a3v7QUGLnAd0b5xh+4MJngCfQ/oDvCCH2oNlxStC8dX+jj+QcEULcA9yD9jD3B94G7ogXRiKE+A5wG9qDlgW8gmZzaw3wOZzOkU33KPQ9oEp/fxCaqIE2YtqOZpsz82mgWgjx51LKY/p7uXTfk80up25FezDz9eViuk0yRcBGuj3eBp8XQjwDrNDtSl1IKatczmVHven/16BN6axMNv0/ro3Xcj2v+7weN4J+p74I437Tf9ANW+jzZvtwGCibXXK5hGava0O74SahCR1oYtfh4Rjz6Q7VAE1E/kwIMcB5FwAWo/2iG3/jYjTvbyjoHtSfoMUBAvydSUTMnuqfoXn9VqB5KEeg/Qi0otkDzbabHNP/z7ic/rRle/P5foP2vS9Dm5blo/1YnEZ7GH9Egkgpm9EeZoB/tvEmQ6yxf2Ci50yAoN+pXxK634QQI9BGffloM5d73ffwjxK7JCGEuAxtpPNvaNOoSrRRXT5agO1I4DHd4+bGN/T9rgS+huaZ+wfsQz3MfArN9jQR+Fc0kfy/QoiHgnweM/qI7nHgC/pbD0spnzFtYjZyjwI+JKVcLaU8LqU8IaX8Bd3TlaVCCMPe1sMBEgdjOmk+3yBggZTyeSllh5SyVUr5H3Q7He4UQgif57HjH4GLaFPz/xFCTBFCDBBCTBRCrEb7exsBzKHGi/kk6Hfql8D3mxAiHy3KYYZ+/s+5BXIHRYld8vg6MAst5muxlLJaSnlGf/h+CixBuxHv0kMDbJFSvqvvd1BK+SO6fy2XxdmvXkp5Vkq5T0p5H93C9BXdrhIIPXD3N3SHzDyF5lE1Yx6x/s5uWialrEazlYFm1wTtF93AzUlgiFuH5RXgKSmlnaf8Z2g/OlloRvKE0G2Zn0cLsv4zNO/jOTSv/SfRftCMoOYTiZ4vAYJ+p74Ier/pkQ5b0BwjF9GC9H8V5BrioWx2ycMYufw/k02qCynla0KI36E9KH+BixPAst9GIcRWYA6w0Md+q/WUo0K0qfF+IcQ/Ah922OUTUsoj5jeEEIVoUw3D0/YQ2vTVOho4gXbjZqHF5zmxG20abBjQTwJn0UZneS77GXal9/VX8/drez4p5SUhxFtoI+Txdtv4RUq5SgjxMpqwTUf78XoDeEJKWS+E+D/6pu8B+P2+QyLod5oQdvebdRs9LvM5/brOAX8ppYw3YwmMErvkYYQnvOOyzdtoYtcVZS+E+IC+/JaU8qTDfu+iid0YfZ9+wFh9v1ekQ76tvl+hsR+a522uw7YxowAhxFTgRTQHwEW0lDVbj7Du3duPNqVxCwy9oL+e1fe7KISQaJ7ccXY76LbKQn3RGDkdQLNHDfZzvjCQUv4JG/uSnoc6Tl80Ys48f99hkcB36kgC95v5GLcBq9D+Xm1oqXWbrNuFiRK75HEC7QYe67LNB0zbGrmkB9FugNtwtssZQmokmhfRHdd3PVrcVNz9pJSfpTvg1BEhxCQ0+2M+miF7uZT
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
" compute_phase_precession(\n",
" baseline_i.sort_values('gridness', ascending=False).iloc[18], \n",
" plot=True, field_num=1, return_runs=True, plot_lines=False)"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:11: 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",
" # This is added back by InteractiveShellApp.init_path()\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAAI4CAYAAACybtLXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOyde5gUxbn/vz0zO7MssEtWVlCUVVe3MK4BXRMMuWlMwKyRCG6uavRnTDAHjzEm0WTJycWI0ZgYT4KJHM3B4yUc77A5QSFqjIkX8AIIKuUNEFDucl32MjP1++Ptoqtrunt6dpdd0PfzPPXszkx3dVV1dXW99b71vo5SCgzDMAzDMAzDMEw0if4uAMMwDMMwDMMwzIEAC08MwzAMwzAMwzAxYOGJYRiGYRiGYRgmBiw8MQzDMAzDMAzDxICFJ4ZhGIZhGIZhmBiw8MQwDMMwDMMwDBODVH8XgGEYhnl/IoRwAMwCsFxK+Wv3uySAGQA+5R42D8APpJRKCHEqgOsBlAHYA+BSKeUiN59fAJjsnvMsgG9LKdsCrlkB4FYAJ4AWEK+UUs5xf/t3ANMArHcP3yml/IT724UAfgB6bz7iXrvLLe9/AJgIYKBb3sullMq45oUAJkkpzzTq/QsAXwawG8BT7jntEW01GsDDUspDQn4vWg73uAEAbgLwYbf+CwFMlVLuEUJ8CMAf3fMVgBYp5UPueee69VcA2tz6PxdWXoZhmPcqrHliGIZh+hwhxLEAHgXwJeun8wAIAMcDGA0SopqFEGkAdwP4ppRyNICrAdzhnjMJwHgAYwAcB6ACwHdCLv0zALuklMcC+CyAPwghDnN/GwcSOMa4SQtODQB+DuCTbtmGAPiue853AJwC4GMAPgTgoyChCEKIaiHEzQB+D8AxynABgM8D+LCUcgyAd9z6BLVTSgjxXQALAAwOqVNkOSymgQTA0e5xAwD8yP3tTgDXu2U6D8A9Qoi0EEKAhNbT3d+uBvBARFkYhmHes7DwxDAMw/QHU0Fap3us75MgzUfGTWkA7VLKTgAjpJSLXc3NUQC2AICU8gEAH3OPGQzgYP1bAJMA3OKe9xZIKNEC3DgAXxNCLBZCzBdCHO9+/wUArVLKTVLKPICZAM51f/s6gKullHuklB0AzgYJhXDzfQfA960yNAKYI6Xc5n5+AEBzSHlPBAk5Yb9rosph8oR7XF5KmQOwGECtca257v91ALYByAHoAHCRlPId97fnAAx3BVqGYZj3FWy2xzAMw/Q5UspLAEAIcZr1020AvghgHegdtUBK+Rf3nC4hxDAALwAYCkOz4v52CUgrsg7AgyGXPhzAGuPzWgCHCSEGAlgB4Bop5VNCiC8BeEgIMco9Z5V9jvt/PYAPCiF+BKAGQCuAn7plutmt4wVWGRYC+K4QYgaArSDBJ9AcT0q5CMAiIcQRIfXRhJbDym+B/l8IUQvgMgDfcn/LCiEcIcQbAI4A8B1XwFql6+8KrjeAhMnOImViGIZ5z8GaJ4ZhGGZ/4qcANgEYBhJQqoUQ39M/Sik3SClHgMzSZgkh6o3fZgD4AEhwui8k/6D3Xk5KuVtKOUFK+ZSb1z0A3oW3N6jgHPdvGYCTATSBTOY+DuDfoyoopbwDwL0AHgPwJEho66kgUlI5hBCNAP4JYIaU8v+MsikpZR2AowH8UAjxaeOcgSBN4dEALupheRmGYQ5IWHhiGIZh9icmA/hvKWWnlHI7gP8BcKoQokoIMUkfJKV8AcBSAMcLIUYLIU5wv1cghxAnCiEOFUIsMdKhAN6CX8szAsBaIUSt6zDCxAHQFXaO+//bAP5XStkhpdwJEoo+GlVBIUQ1gD9LKT8kpfwogJcBvB5S3rjELocQ4isA/gbgh1LKa9zv0kKIrwghEgAgpVwJcoxxgvv7SJBjixyAUw2TQ4ZhmPcVbLbHMAzD7E+8ANor9HchRBnIe9wzoEn7fwshNkopnxRCHAdgFMgE7lQA3xNCjHM97H0dwGNSyrdBTiT2IoSYCzJT+7brKOJ0kKnfbgBXCyEWuh78mkCOJxYB2AFgrhBiOkgr9i0Ac9ws7wNwrhDi/0D7tT6P4L1GJicBuFYIMRbkve5HAO4KKm8JxCqHEKIZwO8AjDe95UkpO4UQV4MWVf/sCm6nApjhCnv/AHCblPLn3SwfwzDMewIWnhiGYZj9ie8C+L0QYgVIYHoUwHXunqazANzoClUdAL4mpVwL4A4hxNEAnhNCZAG8BOAbIfn/FMAfhRAvgYSMH0gp3wAAd5/TTNcRwg6Qe/FOAC8KIa4CmdmVgQS269z8fuz+vxz0Tv0bgBujKiilXCCE+BSAF0HCyhwAvy2plQqJW45fgjRqt5ITPQDAk1LKqSBnGjcJIa4AkAe1zXNCiGkARgKYZGr/AJwmpQxzzMEwDPOexFFKFT+KYRiGYRiGYRjmfQ7veWIYhmEYhmEYhokBC08MwzAMwzAMwzAxYOGJYRiGYRiGYRgmBiw8MQzDMAzDMAzDxICFJ4ZhGIZhGIZhmBiw8MQwDMMwDMMwDBMDFp4YhmEYhmEYhmFiwMITwzAMwzAMwzBMDFh4YhiGYRiGYRiGiQELTwzDMAzDMAzDMDFg4YlhGIZhGIZhGCYGLDwxDMMwDMMwDMPEgIUnhmEYhmEYhmGYGLDwxDAMwzAMwzAMEwMWnhiGYRiGYRiGYWLAwhPDMAzDMAzDMEwMWHhiGIZhGIZhGIaJAQtPDMMwDMMwDMMwMWDhiWEYhmEYhmEYJgYsPDEMwzAMwzAMw8SAhSeGYRiGYRiGYZgYsPDEMAzDMAzDMAwTAxaeGIZhGIZhGIZhYsDCE8MwDMMwDMMwTAxYeGIYhmEYhmEYhokBC08MwzAMwzAMwzAxYOGJYRiGYRiGYRgmBiw8MQzDMAzDMAzDxICFJ4ZhGIZhGIZhmBiw8MQwDMMwDMMwDBMDFp4YhmEYhmEYhmFiwMITwzAMwzAMwzBMDFh4YhiGYRiGYRiGiQELTwzDMAzDMAzDMDFg4YlhGIZhGIZhGCYGLDwxDMMwDMMwDMPEgIUnhmEYhmEYhmGYGLDwxDAMwzAMwzAMEwMWnhiGYRiGYRiGYWLAwhPDMAzDMAzDMEwMWHhiGIZhGIZhGIaJAQtPDMMwDMMwDMMwMUj1dwEYQAhxEoAfSimbeyk/BaAGwDgAn5FSXtob+e5PCCHGArgJwEAAbwM4V0r5TtzjhBAJANcCOANAHsBrAKZIKTcJIWoAzARwNOgZ+SuAK6WU+SJl+hKAnwLIAlgL4N+klKvd354HMABAp3v4XVLK6wPyKAcwDcDnATgAkgDuBPArKaUSQlwAoFlK+flYDRVcztsAfBbAX6SUFwf8fiGAH4Dq/giAS6WUXXGPi2o/IcSpAK4HUAZgj3vOom7UoQXA19387wTwcymlinucEOJoAH8EPSdpAH+SUv7GOvcqANVSykuEEEkAz7t1ukBKeV+R8ikAywHkACgAFQB2APi2lPK5Uutr5FsB4FYAJ4AWv66UUs4p5bgi/fRsAC0AMgBWA/i6lHJLd8vLMAzDMO81WPO0HyClfK63BCcr39b3qOCUBnAfgO9IKY91//9TicddCKARwIlSyuMBvA5AT55/C+BlKeWHAJwIYCyAC4qU6RiQwPAVKeVoANe514MQYiCAOgCjpZRj3BQkODkA5gCoAvBRKeUYAKcAmATgquItUxK/DRGcGgD8HMAnAQgAQwB8t8TjAtvPvR93A/im20ZXA7ij1IILIZoAfBF0/xoAnOp+LuW42wDc7bbxRwFMEUJ82j3vMCHEfQC+r/OSUubcY0sRfE517/UJUkoBqvvvS6lrAD8DsMvtz58F8AchxGFxjyvST08CMAPA2VLKBgCvApjew/IyDMMwzHsK1jz1Me5q/fdAK9KbAZwPmljPkFI2uFqBave7/wPwC9CE62OgleI5AKYFrbIHXOsCuFoKIcTjAJ528xkJ4J8Azo/SpgghUgB+BdKCZAE8BeDfQCvpNwA4za3HQgDflVLuFEKscj9/CLSC/Vvzs5TyQSP/HwL4SsClTyuy2v1
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for row in baseline_i.sort_values('gridness', ascending=False).itertuples():\n",
" compute_phase_precession(row, plot=True)"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:11: 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",
" # This is added back by InteractiveShellApp.init_path()\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAAI4CAYAAACybtLXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZgUxfnHv3PsLCzCKocgCIjolgpGECKGxKgBkYBZxSAmMSbxCiYQY7xdEvPzwCsYUUkM0YhnNB7orjeC4i3KqSCWisilIpezwLLnvL8/amqnpqa6p2dv5P08Tz27011dV1fP1NvvUSEiAsMwDMMwDMMwDONPuLUbwDAMwzAMwzAMszvAwhPDMAzDMAzDMEwAWHhiGIZhGIZhGIYJAAtPDMMwDMMwDMMwAWDhiWEYhmEYhmEYJgAsPDEMwzAMwzAMwwQg2toNYBiGYRghRAjALADLpZTTksciAGYAODaZ7TkAl0opSQhxPIC/AcgDsAvABVLKd5PlXAvg1OQ17wH4nZSywlFnNwAzARwE9Xv4LIDLpZQJIURnAHcAOAxAewBTpZQPJK8bC+AGAPkA3gdwjpSyPHnu9wDOTV6zKHmuyqhzFICbpZSDjGN/ADA52Y+VACZJKbc62tsewD8AfBfq5eeCZN5djrwlAH6V7NeDAK6WUpKVJ5bs4zHJQ88DuExKWSeEOAjAnQC6AYgB+I+U8ha7HoZhmD0N1jwxDMMwrYoQ4lAA8wBMsE6dCUAAOBzAEVBC1Pjkov9/AM6TUh4B4DoADySvGQdgFIBBAAYAKADwR4+qbwXwoZTyOwCOBDAMwG+S5+4FsF5KORjASAC3CyH2TwpcswD8VEopAHwG4MZkP04F8Idk/gFQAtSfkufaCyGuA/AojBeXSSHwcgAjkgLVcwD+7dHeKclrjwDwnWT5V9qZhBBjAJwGYAiAgQCOT362mQwlHA1MljccqXtwL4D/Jdv0PQAThRA/8mgXwzDMHgMLTwzDMExrMwlKIHnUOh4B0AFKw5MPpQGplFJWA+glpVyS1DQdCGALAEgpZwP4fjJPRwD76nMOnoTSbEFKWQlgOYC+Sa3TCQCuTp5bDyVYbYUSzN6TUn6SLONOAGck2/ErALdIKbdKKRMAzkdKqDsx2ZezrTYMATA3WQcAzAbwk6SAaPMagOuklAkpZR2AJQD6OvKNA/BfKeXOZL9mAfilnUlK+XcApyfb2gXA3sk+AsB/APw3mS8O4FOPuhiGYfYo2GyPYRiGaVWklJMBQAgxwjp1L5TGZAPU79UcKeXTyWtqhBDdASwG0BXA6UZ5NUKIyVAaqQ1QQpKr3if0/0KIwQB+AeA4KDO+LwFcJIT4MZTgNk1K+bEQojeAdUYx6wF0ghLUigDsK4R4AUBPAK8DuCxZ11MAnhJCHGc1410AFwgh+kop1wA4C0pI7JJsg9neOUZ7+wK4EMBvHV3rDaXJM9u4v8cY1AghboTSQi1MthlSyllGXaOhtFLnuMpgGIbZk2DNE8MwDNNW+SuATQC6Qy3+OwshLtYnpZQbpZS9oMzKZgkhioxzMwDsAyU4Pe5XiRDiRABzAPxBSrkUyo+qH4ByKeX3AfwMwK1CiCHw/t2sS153ApTp21AAnQFM9atbSvkalIbrSSHEQgAJKO1PtU97h0AJOTOklM84srjaWOfThiugxupzKE2aWdevoXymxkspv8y8mmEYZs+ChSeGYRimrXIqgHuklNVJ07H7ABwvhCgUQozTmaSUiwEsA3C4EOKIpBYJyQAJdwM4UgjRUwix1Eg9AUAIcRGUad3PdUAIAF8k/96bLOdTAG8AOArAWgD7GW3sBWCblHJn8ronpZTlSbPBB6EEO0+EEB0BvCqlPFJKORSA1oZttdo7NJn/ZwBeAnCFlPJ6j2JdbVxvZxJCfF8LnFLKmmR/j0yeCwkhbgFwDYCRUsq5fv1gGIbZU2CzPYZhGKatshhKi/OKECIPQDGAd6C0KPcIIb6WUr4phBgA4BCo6HPHA7hYCDE8GWHvVwBellJ+ARVEop6k4DQJwNFSys/0cSnlaiHEYgC/BnBH0jxwOICbAawBcIsQ4uCk39P5AEqTlz4OYIIQ4i4AlQBOgYr250dPAPOEEIclI/b9BcDDScHPbu94ALcDGCWlXOhTZimAvwoh/g2gFioIxr2OfD8CcLQQ4mQojdcZAF5OnrsNSvAbKqXclKUPDMMwewwsPDEMwzBtlT9BCS8fQQlM8wDclPTTOQXA9KRQVQXgF8mgCw8kw2wvFELUAlgBh69OMiDDtQC+ATBbCKFPPSalnAoVdOEfQojzoaw0rpFSvpe89iwAjyfLWAUloAHAP6FM9RZBBbtYDKDezNCFlFImfY4WCCHCUBquyR7ZbwAQAnC30d43pZSTrDKfFkIcDuVPFYMSpu53lHcTgOlQWrtEsu4rk35dk6EExZeMum4zfaEYhmH2REJElD0XwzAMwzAMwzDMHg77PDEMwzAMwzAMwwSAhSeGYRiGYRiGYZgAsPDEMAzDMAzDMAwTABaeGIZhGIZhGIZhAsDCE8MwDMMwDMMwTABYeGIYhmEYhmEYhgkAC08MwzAMwzAMwzABYOGJYRiGYRiGYRgmACw8MQzDMAzDMAzDBICFJ4ZhGIZhGIZhmACw8MQwDMMwDMMwDBMAFp4YhmEYhmEYhmECwMITwzAMwzAMwzBMAFh4YhiGYRiGYRiGCQALTwzDMAzDMAzDMAFg4YlhGIZhGIZhGCYALDwxDMMwDMMwDMMEgIUnhmEYhmEYhmGYALDwxDAMwzAMwzAMEwAWnhiGYRiGYRiGYQLAwhPDMAzDMAzDMEwAWHhiGIZhGIZhGIYJAAtPDMMwDMMwDMMwAWDhiWEYhmEYhmEYJgAsPDEMwzAMwzAMwwSAhSeGYRiGYRiGYZgAsPDEMAzDMAzDMAwTABaeGIZhGIZhGIZhAsDCE8MwDMMwDMMwTABYeGIYhmEYhmEYhgkAC08MwzAMwzAMwzABYOGJYRiGYRiGYRgmACw8MQzDMAzDMAzDBICFJ4ZhGIZhGIZhmACw8MQwDMMwDMMwDBMAFp4YhmEYhmEYhmECwMITwzAMwzAMwzBMAFh4YhiGYRiGYRiGCQALTwzDMAzDMAzDMAFg4YlhGIZhGIZhGCYALDwxDMMwDMMwDMMEgIUnhmEYhmEYhmGYAERbuwGMN0KIoQCukFKOb6LyCEA3AMMBjJRSXtAU5bYmQohhAP4BoAOALwD8Ukr5Za75hBB7A3gNwNlSyoXJY4cDeBvAp0ZRp0spZZY2/QHAZAC7AKwEMElKuTV5bhOADUb2v0kpH3KUsTeAawAcByABgADMkFL+J3n+/wB0lVJO9mtLlnbOB9AXwCwp5TVCiBIAv4L6XngQwNVSSnJc55tPCDEKwM1SykHGsbEAbgCQD+B9AOdIKcuztK8AwN0ABkO96LlcSvlUQ/IJIa4B0FmPlxAiBuAOAMckszwP4DIAxQCmAugjpdwrS/t+A+A2AKuTh0IAOgF4HcBvpZSVftdnKbtR89qnfx0BzLeKORzApVLKvze0vQzDMAyzp8CapzaMlHJhUwlOVrll3xLBKQbgcQB/lFIemvz/P7nmE0KMAfAugEOsS4cD+K+UcpCRsglOxwO4HMCIpPDwHIB/J88JANus8lyCUzsArwJYD+DIZDmnALhSCHFO9pHJiUuTgtMYAKcBGAJgIIDjk5/ttnnmE0K0F0JcB+BRGC9mhBDdAMwC8FMppQDwGYAbA7Tt/wDsSN6zEwD8Uwixfy75hBD7CyEeB3CJdc1kqBcJAwF8B+peT5BSPglgTIC2aV437uURAA4FcBiAX+dQRhpNNK+9+veNOf+S+d+AErQYhmEYhskCa57aCEKIswFcDKAOwGaoxVd/KG3DQCHEvQA6J489A+BaqAXP9wHUAngKwBSXpsBR128AjJdSnpTUPrydLKcP1FvzX0spEz7XRwHcDOCkZN1vAfg9lHbk7wBGJPuxAMCfpJTbhRCfJz9/B0AJgFvNz8lFqy7/CgA/c1Q9Qkq5xfj8XQDlUso
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAI4CAYAAACLCWOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZhcRdX/v7NkJhlDgkgECTBItAtkWAdEoyIKBN+g4QeMyOoCKmqCoijByasoEhQMiyxCXsBEAYOyzhAWYwIxrGELkAAp2ZOwhBAgC5PM0n1+f9StdHV1Vd17e7pnIefzPPXMdN9761bVXfp861SdqiIiMAzDMAzDMAzDMMmo7u8CMAzDMAzDMAzDDCZYRDEMwzAMwzAMw6SARRTDMAzDMAzDMEwKWEQxDMMwDMMwDMOkgEUUwzAMwzAMwzBMClhEMQzDMAzDMAzDpKC2vwvAMAzDbJ4IIaoAzACwREo5LfquBsBlAL4Y7XYngF9IKUkI8SUAfwQwBMAGAD+WUj4S5fM7AEdGxzwK4IdSyg7HOUcBmA7gE1C/gXcAmCylzAkhtgJwKYBPARgGYKqU8trouMMA/B5APYCnAZwspVwbbfsRgO9Gxzwebes0zjkOwPlSyr2M704FMCmqx3MAJkop33GUdxiAywHsB9XxuTDad4Nj36MAtEZlfBXAN6WUqx37nQ7gJAA9AFYBOEVK+aK1zy0AXpdSToo+7wHgCgAfAkAAWqWUd9l5MwzDbC6wJ4phGIbpc4QQuwKYB+Boa9OJAASA3QHsCSWmWoQQdQD+AeB7Uso9AZwD4NromCMAjAOwF4DdADQA+Inn1BcBeFZKuQeAfQDsD+Db0baZAFZIKfcGcDCAS4QQ20fCawaAo6SUAsBLAP4Q1eNIAKdG++8GJaR+Gm0bJoQ4B8A/YXRaRmJwMoCDImF1J4D/85R3SnTsngD2iPL/pb2TEGJfKPF5lJSyCcB/AUx17HcwgJMBfDZqx1uiupn7nAHgC9ah1wH4Y1TeEwH8M7omDMMwmyUsohiGYZj+YCKU8f5P6/saKG9HfZTqAGyUUnYBGC2lXBR5nnYGsBoApJS3APhctM8WAD6qtzm4FUpsQEq5EcASAI2RF+oQAL+Ntq2AEljvQAm0R6WUz0d5XAHg+Kgc3wRwgZTyHSllDsAPkBd3h0Z1OckqQzOAudE5ACVkvuYRJQsAnCOlzEkpswAWAWh07HcCgGuklK9En38D4HzHfm9CeenWRp8fM/OLBN5XAFxpHbcPgLbo/zEA3gOQdeTPMAyzWcDD+RiGYZg+xxgmdpC1aSaArwN4Deo3ao6U8vbomG4hxDYAngCwNYBvGPl1CyEmQXmoXoMSS67z3qz/F0LsDeA4AAdCDe97A8DPhBD/AyXgpkkp/yuE2AHAciObFQBGQAm2DICPCiHuBrAdgPsAnBGd6zYAtwkhDrSK8QiAHwshGqWUrwL4DpRY/EhUBrO8c4zyNgI4DcD3HVXLAHhaCNEGYCcAixF5xKz8lhj51UN51G6MPm8H4E9Q4u8U67geIUSVEOLFKP+fRKKOYRhms4Q9UQzDMMxA4iyoeTrbANgewFbRHB4AgJRypZRyNIDPApghhMgY2y4D8GEoAXVT6CRCiEMBzAFwqpTySah5Vh8HsFZK+TkAxwC4SAjRDP9vZTY67hCoYYn7AtgKjmF0JlLKBVAer1uFEI8ByEF5vLoC5W2GEmiXSSlnO3YZAuBrUOJnbyiP01WB/EZB1X89gFYhxBAANwA4TUr5husYKSVJKcdACc4zhRBfDtWTYRjmgwyLKIZhGGYgcSSAv0gpu6SUawD8FcCXhBAjhRBH6J2klE8AeArA7kKIPSOvEqSUBOBqAPsIIbYTQjxppO0AQAjxM6ghd8fqwBEAXo/+zozyeQHA/QA+DWAZgI8ZZRwN4F0p5fvRcbdKKddGwwmvgxJ4XoQQWwD4j5RyHynlvgC0d+wdq7z7RvsfA+DfAM6UUp7ryfZ1AP+SUr4ZDSuc4StHFCTiUSiP3hFRufeFEpEXCiGehBqW+A0hxNVCiDohxDFCiOqobV4GMBdKrDEMw2yW8HA+hmEYZiDxBJRX597IOzIBwMNQXp+/CCHeklI+IITYDcAuUNHqvgTgdCHE2Cgi3zcB3COlfB0q2MQmIgE1EcBnpJQv6e+llC8LIZ4A8C0Al0bDBsdCzSt6FcAFQohPRvOifoD8/KCbABwthLgKwEYA/w9KoITYDsA8IcSnorlJvwIwKxKAdnlbAFwCYJyU8rFAnjcBmCaEmBpF5DvSVQ4hxCcA3AsV8fAvRv0fArCDsd9vAGxtDLs8B6rj9e+RGP0SorllDMMwmyMsohiGYZiBxE+hRMxSKOE0D8B50Zyn/wfg4khcdQI4LgrOcG0kDh4TQvQAeAYqAl0BUeCG30EFRbhFCKE33SilnAoV5e9yIcQPoATD2VLKR6NjvwPgpiiPF6GEGgD8GWoI3+NQQTGeALBp+KELKaUUQvwBwMLIu3M/VLhzF78HUAXgaqO8D0gpJ1p53i6E2B7Af6I8X3W1AVRUwAaoOVk/jr7rlFLuHyoz8m1zBtTww1/EiDqGYZgPNFVE1N9lYBiGYRiGYRiGGTTwnCiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhkkBiyiGYRiGYRiGYZgUsIhiGIZhGIZhGIZJAYsohmEYhmEYhmGYFLCIYhiGYRiGYRiGSQGLKIZhGIZhGIZhmBSwiGIYhmEYhmEYhklBbX8XgAGEEPsCOFNK2VKm/AjAKABjARwspfxxOfIdSAgh9gdwOYAPAXgdwAlSyjeS7ieEqAbwBwCHAcgBeB7AKVLKVcaxdQAWALhJSjktQZmOBnAWgB4AKwD8SEr5arTtcQDDAHRFu18vpfyjI4+hAKYA+CqAKgA1AK4DcL6UkoQQ3wbQIqX8alx5AuWcCeAQALdLKX/g2H4SgF9AvR/mAvixlLI77X5CiD0B3C2l/Fj0+WAAZjsOA5ABsK+U8vEU5a8BcCGAQ6NzT5NSXlnKflEdjpBSfi36XAXgdwCOjHZ5FMAPAewOYDqAPQGMklK+HSjfgQDuAiCNr7cA8CyAb0spVyetqyPvTwL4C4CPAFgP4JtSyqVp9hNCnA7gJKj7dBXUff+iEGIrAFcA2AvA+wBmSCkvLbWsDMMwDPNBhj1RAwAp5WPlElBWvu0fUAFVB+AmAD+RUu4a/X9Nyv1OAtAMYB8p5e4AXgBwgZXFxQDGJCzTJ6GM7GOklHsCOC86H4QQH4ry2VNKuVeUXAKqCsBtAEYC+KyUci8ABwI4AsDZScqRgos8AqoJwG8BHABAANgSwE/T7CeEqBVC/BTAHCjxAACQUs416r8XgMUA/pBGQEWcAuCTAJoA7AfgNCHEp9PsJ4TYSghxJYBLocSq5ggA46CExG4AGqDun4VRmZPyolXXDIAsgJ+nyMPF9QCukFJ+Ckqw3xzdN4n2i4TsyVD3154AbgE
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for row in stimulated_11_sub.sort_values('gridness', ascending=False).itertuples():\n",
" compute_phase_precession(row, plot=True, flim=[6,10])"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:11: 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",
" # This is added back by InteractiveShellApp.init_path()\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAAI4CAYAAACybtLXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZwUxfn/Pz0zuwuLgCCIirpRcAsFvNCoGCMGg4rJKrJE1HhEE8GABuMv0UC+ridBNF7BKAqCmnggiLveBBBvwAOQQwoERG45F917Z57fH9W9U1PT3dMzeyLP+/Wq1253193HPE89VU9ZRASGYRiGYRiGYRjGn1BzV4BhGIZhGIZhGGZfgJUnhmEYhmEYhmGYALDyxDAMwzAMwzAMEwBWnhiGYRiGYRiGYQLAyhPDMAzDMAzDMEwAWHliGIZhGIZhGIYJQKS5K8AwDMMwQggLwBQAy6SUD9jnwgAmADjbjvYmgL9IKUkIcQ6A+wFkAagAcJOUcqGdz90ALrHTfArgBilluUuZnQFMBNAd6vfwDQC3SiljQoiOAP4F4DgArQHcK6V8zk53IYB/AMgB8CWA66SUe+1rfwTwezvN5/a1Kq3MAQDGSylP1M7dCGCk3Y6vAIyQUu5yqW9rAI8BOBVq8HOBHbfCo08PBPA+gGullJ+5XH8UwM+1U10BbJFSHi+EOAHAvwG0B7AXwN+llHPdymEYhtmfYMsTwzAM06wIIY4FMAfAb4xLVwIQAHoDOAFKiSoUQmQDeAnAH6SUJwC4B8BzdppBAAYAOBFATwC5AP7kUfRDAFZIKY8HcDKA0wBcY1+bCmCjlPIkAOcCeFQIcbitcE0BMFhKKQCsBTDObsclAG604/eEUqButq+1FkLcA2AatIFLWwm8FUB/W6F6E8CTHvUdY6c9AcDxdv5/c4sohBgIYCGAHh55QUp5k5TyRLvciwFUArjKvlwMYJKUsheUIvq4EOIQr7wYhmH2F1h5YhiGYZqbEVAKyTTjfBhAGygLTw6AbACVUspqAF2llItsS9PRAHYCgJTyFQBn2nHaAjjYuebCTCjLFqSUlQCWAcizrU6/BHCnfW0jlGK1C0ox+1RKudrO43EAV9j1uArAP6WUu6SUMQDDEVfqzrPbcq1Rhz4AZttlAMArAH5tK4gm7wO4R0oZk1JGASwCkOfRtpsAXA1gs8d1k6cAPCilXCyE6ATgCADP2u3fCmVhOz9gXgzDMD9aWHliGIZhmhUp5UhnSpzBVAC7AWwCsAXA11LK1+w0NUKILgA2Qk3fG6/lVyOEGAngWwCdoJQkt3Jn2IoBhBAnAbjcjtvdLu/PQoiPhBCfATjZnvp3BIANWjYbAbSDUtTyARwshHhbCPElgDsA7LHLelVKeTOUAqazEMAvhBCOEvQ7KCXxIJf6zpJSrrLrmwdgFICXPdp2vpTyE7drJkKIC+x2PWqn3QFgHZTyBSHE0QDOAnBokPwYhmF+zLDyxDAMw7RUigBsB9AFwOEAOgohbnEuSim3SSm7AjgDwBQhRL52bQKADlDK0HS/QoQQ5wGYBeBGKeViqHVURwHYK6U8E8BQAA8JIfrA+3czaqf7JdT0w1MAdARwr1/ZUsr3oSxcM20lLQalYFX71LcPgA8ATJBSvu6Xf0BuBjDOtmY5FEBNkVwK4C6o9WCedWIYhtlfYOWJYRiGaalcAuBpKWW1lLIUwDMAzhFCtBdCDHIiSSm/ALAEQG8hxAm2FQlSSgIwCcDJQojDhBCLtXAYAAgh/gw1te4yzfrlTHWbaufzNYAPAfwUypqlW2C6AtgtpSyz082UUu61pw3+B0qx80QI0RbAe1LKk6WUpwCYYV/aZdT3FDv+UAD/A3CblHJsGn3pVX5nqCmJpgUrBKBAStlbSvlbAIcB+Lq+5TEMw+zrsLc9hmEYpqXyBZQV510hRBaUNWQ+lJXnaSHEd1LKj4QQPaEcIywAcA6AW4QQfe1pdlcBmCul3AzlRKIOW3EaAeB0KeVa57yUcp0Q4guoaWv/sqcH9oWaGrgewD+FEMfY656GQzlXAJSF6zdCiKegnC9cDOXtz4/DAMwRQhxne+z7PwAv2IqfWd9CqKl1A9y852XImVBruMqM808CeBDAdCFEXwC9AMxuoDIZhmH2WVh5YhiGYVoqN0MpLyuhFKY5AO6z1zRdDOBhW6mqAnC57XThOSFEdwCfCSFqASwHcJ2Zse2Q4W6oNUmvCCGcSy9LKe+F8tr3mBBiOJQV5i4p5ad22t9BKRXZANYg7qHu31BT9T6HcnbxBYC6aYZuSCmlEGIcgAVCiBCUhWukR/R/ALAATNLq+5GUcoRfGSk4BsA3Luevt8spAvADgItdFCyGYZj9DouImrsODMMwDMMwDMMwLR5e88QwDMMwDMMwDBMAVp4YhmEYhmEYhmECwMoTwzAMwzAMwzBMAFh5YhiGYRiGYRiGCQArTwzDMAzDMAzDMAFg5YlhGIZhGIZhGCYArDwxDMMwDMMwDMMEgJUnhmEYhmEYhmGYALDyxDAMwzAMwzAMEwBWnhiGYRiGYRiGYQLAyhPDMAzDMAzDMEwAWHliGIZhGIZhGIYJACtPDMMwDMMwDMMwAWDliWEYhmEYhmEYJgCsPDEMwzAMwzAMwwSAlSeGYRiGYRiGYZgAsPLEMAzDMAzDMAwTAFaeGIZhGIZhGIZhAsDKE8MwDMMwDMMwTABYeWIYhmEYhmEYhgkAK08MwzAMwzAMwzABYOWJYRiGYRiGYRgmAKw8MQzDMAzDMAzDBICVJ4ZhGIZhGIZhmACw8sQwDMMwDMMwDBMAVp4YhmEYhmEYhmECwMoTwzAMwzAMwzBMAFh5YhiGYRiGYRiGCQArTwzDMAzDMAzDMAFg5YlhGIZhGIZhGCYArDwxDMMwDMMwDMMEgJUnhmEYhmEYhmGYALDyxDAMwzAMwzAMEwBWnhiGYRiGYRiGYQLAyhPDMAzDMAzDMEwAWHliGIZhGIZhGIYJACtPDMMwDMMwDMMwAWDliWEYhmEYhmEYJgCsPDEMwzAMwzAMwwSAlSeGYRiGYRiGYZgAsPLEMAzDMAzDMAwTAFaeGIZhGIZhGIZhAhBp7gow3gghTgFwm5SysIHyIwCdAfQFcK6U8qaGyLc5EUKcBuAxAG0AbAbwWynllnTjCSEOBPA+gGullJ/Z53oD+ATA11pWl0opZYo63QhgJIAKAF8BGCGl3GVf2w5gkxb9finlf13yOBDAXQD6AYgBIAATpJST7et3AOgkpRzpV5cU9ZwHIA/AFCnlXUKI0QCugvou/AfAnVJKcknnG08IMQDAeCnlidq53wL4i92OcgA3Of3sU78wgAcBnGeX9YCU8olM4gkhrgUwSEr5a+3czwGMB9AaQCmAawDUAHgNwHEA+vrVUQjxEwBrACzVTh8AYCPUc7TWr31+CCE6A3gW6v7EAFwvpfzYJV53AI9DvdfZACZLKf9pxLkLQEfnWRFChACMA3ChnfdqAMOklNszrS/DMAzD7C+w5akFI6X8rKEUJyPfkh+J4pQNYDqAP0kpj7X/n5xuPCHEQAALAfQwkvYF8LyU8kQtpFKczgFwK4D+tvLwJoAn7WsCwG4jPzfFqRWA96CE8JPtfC4G8DchxHWpeyYt/mIrTgMBDAHQB0AvAOfYx2bdPOMJIVoLIe4BMA3awIzd7vsBnG+35R4ArwSo2zAAx9jlnApglBDip+nEE0J0FEI8AeBfACytTocDmAngj1LKEwDMAPBvKeUGu46bA9QPACr0+2nXYymAewOm9+IxAB9IKY8D8FsALwshcl3iTQXwkl32GQCGCSF+Aag2CiGmA/h/Rpproe7fyVLK3lCDA/8EwzAMwzApYctTC8EeGb8FQBTADgBXA+gGZW3oJYSYCqCjfe51AHdDCYRnAqgF8CqAMW6WApeyrgFQKKX8lW19+MTO50gAHwC4WkoZ80kfgRqx/5Vd9scA/ghlVXgQQH+7HQsA3Cyl/F4I8Y19fDyA0QAe0o+llDO1/G8DMNSl6P5Syp3a8akA9kopP7KPJwN4WAhxUJr
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAI4CAYAAACLCWOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeXzUxf3/X7sJCYkciiJWxKjoZ6wEr+CFR/FCG9t4xfto1dYLtdT+FA1tVSpWreJFv5XiQa03CiQiagRBxftC0cqIiHKogBwBBJLs7vz+mM+ws7PzuTabS97Px2Meye7n85nPzHyOnfe8r5gQAgRBEARBEARBEEQ44u3dAIIgCIIgCIIgiM4ECVEEQRAEQRAEQRARICGKIAiCIAiCIAgiAiREEQRBEARBEARBRICEKIIgCIIgCIIgiAiQEEUQBEEQBEEQBBGBwvZuAEEQBLFlwhiLAXgYwKec8zvc7woAjAXwC3e3aQCu4ZwLxtiRAP4BoAuAjQCu4py/69bzNwCnuMe8B+AyzvkGyzl7AxgHYHfI38DnAYzgnKcYY70A3AdgLwAlAEZzzv/rHncCgL8DKAbwCYCLOOdr3W2XA/ide8wH7rZG7ZxDAdzOOd9X++5KAFe4/fgcwDDO+SpLe0sA/BPAAZALn++4+2607HsqgBq3jd8AOJ9zvtKy358AXAggAWAFgEs45wuMfSYB+JZzfoX7eW8A/wKwFQABoIZz/oJZN0EQxJYCaaIIgiCINocx9nMAMwCcbmw6DwADMBDAPpDCVDVjrAjAUwB+zznfB8DNAP7rHnMygKEA9gUwAEApgD94nPouAP/jnO8NYH8ABwH4rbttAoAlnPP9ABwD4F7G2E6u4PUwgFM55wzAVwBudftxCoAr3f0HQApSf3S3lTDGbgbwNLRFS1cYHAHgaFewmgbg3x7tHekeuw+Avd36rzd3YowNghQ+T+WclwP4AsBoy37HALgIwCHuOE5y+6bvcy2Aw41DHwXwD7e95wF42r0mBEEQWyQkRBEEQRDtwTDIyfvTxvcFkNqOYrcUAdjEOW8C0Jdz/pGredoNwEoA4JxPAnCou093ANurbRYmQwob4JxvAvApgDJXC3UsgJvcbUsgBaxVkALae5zz+W4d/wJwjtuO8wHcyTlfxTlPAbgUaeHuOLcvFxptqAAw3T0HIAWZX3sIJa8BuJlznuKcJwF8BKDMst+5AB7knH/tfr4RwO2W/b6H1NKtdT+/r9fnCnjHA7jfOG5/ALXu//0BrAGQtNRPEASxRUDmfARBEESbo5mJHW1smgDgNABLIX+j6jnnz7nHNDPG+gD4EMB2AM7Q6mtmjF0BqaFaCiks2c77rPqfMbYfgLMBDIE07/sOwNWMsV9CCnB3cM6/YIz1A7BYq2YJgB6QApsDYHvG2IsAdgTwOoBr3XNNATCFMTbEaMa7AK5ijJVxzr8BcAGksLit2wa9vfVae8sADAdwsaVrDoBPGGO1AHYBMBeuRsyo71OtvmJIjdpE9/OOAO6BFP4uMY5LMMZijLEFbv1/cIU6giCILRLSRBEEQRAdiRsg/XT6ANgJQC/XhwcAwDlfxjnvC+AQAA8zxhxt21gA20AKUM/4nYQxdhyAegBXcs7nQPpZ7QpgLef8UABnAriLMVYB79/KpHvcsZBmiYMA9ILFjE6Hc/4apMZrMmPsfQApSI1Xk097KyAFtLGc86mWXboA+DWk8LMfpMZpvE99vSH7vx5ADWOsC4AnAQznnH9nO4ZzLjjn/SEFzusYY0f59ZMgCOKnDAlRBEEQREfiFAAPcc6bOOcNAP4D4EjGWE/G2MlqJ875hwA+BjCQMbaPq1UC51wAeADA/oyxHRljc7SyIwAwxq6GNLk7SwWOAPCt+3eCW8+XAGYDOBDAIgA/09rYF8BqzvmP7nGTOedrXXPCRyEFPE8YY90BvMo5359zPgiA0o6tMto7yN3/TAAvA7iOc36LR7XfAniJc/69a1b4sFc73CAR70Fq9E522z0IUogcwxibA2mWeAZj7AHGWBFj7EzGWNwdm4UApkMKawRBEFskZM5HEARBdCQ+hNTqzHS1I1UA3obU+jzEGFvOOX+DMTYAwJ6Q0eqOBPAnxthgNyLf+QBe4Zx/CxlsYjOuADUMwMGc86/U95zzhYyxDwH8BsB9rtngYEi/om8A3MkY28P1i7oUaf+gZwCczhgbD2ATgJMgBRQ/dgQwgzG2l+ub9BcAT7gCoNneagD3AhjKOX/fp85nANzBGBvtRuQ7xdYOxtjuAGZCRjx8SOv/WwD6afvdCGA7zezyZsiF18ddYfRIuL5lBEEQWyIkRBEEQRAdiT9CCjHzIAWnGQBuc32eTgJwtytcNQI42w3O8F9XOHifMZYA8BlkBLoM3MANf4MMijCJMaY2TeScj4aM8vdPxtilkALDKM75e+6xFwB4xq1jAaSgBgD/B2nC9wFkUIwPAWw2P7TBOeeMsVsBvONqd2ZDhju38XcAMQAPaO19g3M+zKjzOcbYTgBedev8xjYGkFEBSyF9sq5yv2vknB/k12akx+ZaSPPDawKEOoIgiJ80MSFEe7eBIAiCIAiCIAii00A+UQRBEARBEARBEBEgIYogCIIgCIIgCCICJEQRBEEQBEEQBEFEgIQogiAIgiAIgiCICJAQRRAEQRAEQRAEEQESogiCIAiCIAiCICJAQhRBEARBEARBEEQESIgiCIIgCIIgCIKIAAlRBEEQBEEQBEEQESAhiiAIgiAIgiAIIgIkRBEEQRAEQRAEQUSAhCiCIAiCIAiCIIgIkBBFEARBEARBEAQRARKiCIIgCIIgCIIgIkBCFEEQBEEQBEEQRARIiCIIgiAIgiAIgogACVEEQRAEQRAEQRARICGKIAiCIAiCIAgiAiREEQRBEARBEARBRICEKIIgCIIgCIIgiAiQEEUQBEEQBEEQBBEBEqIIgiAIgiAIgiAiQEIUQRAEQRAEQRBEBEiIIgiCIAiCIAiCiAAJUQRBEARBEARBEBEgIYogCIIgCIIgCCICJEQRBEEQBEEQBEFEgIQogiAIgiAIgiCICJAQRRAEQRAEQRAEEQESogiCIAiCIAiCICJAQhRBEARBEARBEEQESIgiCIIgCIIgCIKIAAlRBEEQBEEQBEEQESAhiiAIgiAIgiAIIgIkRBEEQRAEQRAEQUSAhCiCIAiCIAiCIIgIkBBFEARBEARBEAQRARKiCIIgCIIgCIIgIkBCFEEQBEEQBEEQRARIiCIIgiAIgiAIgogACVEEQRAEQRAEQRARICGKIAiCIAiCIAgiAoXt3QACYIwNAnAd57w6T/UJAL0BDAZwDOf8qnzU25FgjB0E4J8AtgLwLYBzOeffRdmPMVYD4HzI5+BRADdxzgVjbHcA/4IcwyIAD3LO7wzRpisBXAFgI4DPAQzjnK9yt60AsFTb/R+c88csdWwNYBSAIQBSAASAsZzzB93tNwLYjnN+RVB7fNo5C0AZgIc556Ms263jEnY/xlhvAI+450gBuJhz/qZx7EkAHuGc98ih/QUAxgA4zj33HZzz+6PsxxjbA8BDALYFsB7A+Zzzee62CwFc4x4zHcBVAPYHMA7APgB6c85/8GnfEAAvAODa190B/A/AbznnK6P2Wavbs93GfgcAuBvyvi8AcBvn/FF32xUALoO8txYA+D3nfLl2bD8AbwPYx6+fBEEQBLElQ5qoDgDn/P18CVBGvXU/UQGqCMAzAP7AOf+5+/+DUfZjjFUCOA1ABYByAEe6nwFgAoCnOOf7AjgEwCWMsaMC2nQkgBEAjnaPmwbg3+42BmA153xfrdgEqK4AXgWwBMD+bj0nAbieMXZR2PEJyTUeApTfuITd758AXuec7wXgXAATGWOl2rF7ALgDub9/LgGwh3veAwAMZ4wdGHG/xwD8y23jDQCeZYzFGGPlAG4CcAQABmBrAH/knL/jXo+wLNCvNwAHQBLA/4vaWQNru/Ud3M/PArjBPfcvAYxhjO3BGKtw2zCYc14OYD6Av2nHng/gdQA7trCdBEEQBPGThjRRbYy7yv0
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for row in stimulated_11_sub.sort_values('gridness', ascending=False).itertuples():\n",
" compute_phase_precession(row, plot=True, flim=[9,12])"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "ValueError",
"evalue": "The length of x is too small: len(x) < 2.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-198-e9ca3d18003a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbaseline_ii\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gridness'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitertuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcompute_phase_precession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-194-7839614a8a2f>\u001b[0m in \u001b[0;36mcompute_phase_precession\u001b[0;34m(row, flim, field_num, plot)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mphase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mspike_phase\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mcirc_lin_corr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_corr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mline_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_corr\u001b[0;34m(x, phase, min_slope, max_slope, ci, bootstrap_iter)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0mgoodness\u001b[0m \u001b[0mof\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m '''\n\u001b[0;32m--> 139\u001b[0;31m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_regression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_slope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fit line to data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0mcirc_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# convert linear variable to circular one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_regression\u001b[0;34m(x, phase, min_slope, max_slope)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The length of x is too small: len(x) < 2.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The length of x is too small: len(x) < 2."
]
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAAI4CAYAAACybtLXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdf5wUdeHH8dfeHXeAwB2goIIi+OOTqeGvtB+a5g8s1PNHYj+00voWFqZkmQmWaaJlZlhqIf7WUtFUVsUfiWJqmgoagvpREhGoVMTjN3fc3X7/mF1ub9ndm92d2ZnZfT8fj3vc7e7szGdmZ+c+7/l85jOxRCKBiIiIiIiI5FcTdAFERERERESiQOFJRERERETEBYUnERERERERFxSeREREREREXFB4EhERERERcUHhSURERERExIW6oAsgIiICYIyJATcBC6y1VySfqwWuBg5JTjYLONdamzDGfB74DdAL2ACcZa19ITmfXwInJt/zIvA9a+36LMvcBpgG7ILzP/Eh4DxrbacxZhDwB+DjQB9girX2tuT7jgYuAxqA+cC3rbWrk699H/i/5HvmJl9rTVvmGOBya+3eac/9ADgzuR6vAxOstSuzlLcRuAH4GM4J0Fustb/Os013AJ4HRltrV2R5veDtm2tZIiLVQC1PIiISOGPM7sBs4OSMl74OGGAvYDROJf8kY0w9cBfwHWvtaOAS4Lbke04AxgB7A3sAfYGzcyz6d8Br1tpPAPsCBwKnJV+7GVhmrd0HOAL4vTFmeDJw3QR8yVprgLeBXyXX40TgB8np98AJUD9MvtbHGHMJMIO0k5fJkHIecHgyUM0CrstR3l8my7Qn8Enge8aYT2eb0BjzDeBpYPsc84Litq+ISNVSeBIRkTCYgBNIZmQ8XwtshdPC0wDUAxuttW3AMGvty8mWplHAhwDW2nuBzyan6Q8MSb2WxX04LS9YazcCC4ARyVanI4GLkq8twwlWK3GC2YvW2reS8/gjcEqyHN8AfmutXWmt7QTOoCt0HJVcl29llGE/4PHkMgDuBY5NBphMZwM/Tv69XXKbrMqcyBizPXA8MDbHeqcUvH1FRKqZuu2JiEjgrLVnAhhjDs946WZgHLAc53/WY9baB5Lv2WSMGQrMA7YGvpw2v03GmDNxWkyW44SkbMv9a+pvY8w+wNeAQ3G68f0XOMcY80WcYHGFtfbNZFe4pWmzWQYMwAlquwFDjDGP4LT4PA38JLms+4H7jTGHZhTjBeAsY8wIa+0S4HScEDM4WYb08iaAdmPM7cBJyfWyWdbrPyS7LRpjsq16ys0UsX1FRKqVWp5ERCTMLgQ+AIYCw4FBxpgfpV601r5nrR0GfBq4yRizW9prVwMDcQLGPfkWYow5CngM+IG19hWc63xGAquttZ8FvgL8zhizH7n/d3Yk33ckTvfD/YFBwJR8y7bW/h2nhes+Y8xLQCdOC1dbnvecihNoBgE/zzf/HhS9fUVEqpHCk4iIhNmJwI3W2jZr7SrgFuDzxphGY8wJqYmstfOAfwF7GWNGJ1uRUi011wP7GmO2N8a8kvazPYAx5hycrnVfTQ0IAfwn+fvm5HwWAc8ABwDv4nSZSxkGfGStXZd8333W2tXJrm+34wSPnIwx/YGnrLX7Wmv3B1KtYSszyru/MeaoVLmttWuBO3Cu1SpWwdu3hGWJiESewpOIiITZPJKDSBhjegHNOKPHdQA3GmM+m3xtD5wR6P4JfAKnlaRvch7fAJ6w1v7HWrt32s9/ksFpAvApa+3jqYVaaxcnl/3N5PyHAp8BXsJpofqUMWbX5ORnADOTf98DjEsODhHDue7oxR7WcXtgjjFmQPLxz4A7rLWJjPK+lNwWFxpjYsaYhuTjJ1xvzS0Vs31FRKqWwpOIiITZD4FGY8wbwCs41xf9Otnqcjww1RjzCnAj8DVr7bJk69FM4CVjzHxgd+DbmTNODsjwS6A3cG9aC8/k5CQnAGOMMQuBOcDF1toXrbXv41yXdI8x5nWc1phUV7drgcdxhih/A+gHTMq3gtZaizNa3z+NMRZnhL5zc0z+I6AReBUnyM0Frso3/x4UvH1LWJaISOTFEolE0GUQEREREREJPbU8iYiIiIiIuKDwJCIiIiIi4oLCk4iIiIiIiAsKTyIiIiIiIi4oPImIiIiIiLig8CQiIiIiIuKCwpOIiIiIiIgLCk8iIiIiIiIuKDyJiIiIiIi4oPAkIiIiIiLigsKTiIiIiIiICwpPIiIiIiIiLig8iYiIiIiIuKDwJCIiIiIi4oLCk4iIiIiIiAsKTyIiIiIiIi4oPImIiIiIiLig8CQiIiIiIuKCwpOIiIiIiIgLCk8iIiIiIiIuKDyJiIiIiIi4oPAkIiIiIiLigsKTiIiIiIiICwpPIiIiIiIiLig8iYiIiIiIuKDwJCIiIiIi4oLCk4iIiIiIiAsKTyIiIiIiIi4oPImIiIiIiLig8CQiIiIiIuKCwpOIiIiIiIgLCk8iIiIiIiIuKDyJiIiIiIi4oPAkIiIiIiLigsKTiIiIiIiICwpPIiIiIiIiLig8iYiIiIiIuKDwJCIiIiIi4oLCk4iIiIiIiAsKTyIiIiIiIi4oPImIiIiIiLig8CQiIiIiIuJCXdAFkNyMMfsDP7XWnuTR/BLANsBngCOstWd5Md8gGWMOBK4BtgL+A5xqrf2v2+mMMTXAr4CjgU7gLWC8tfaDtPeOBh6x1m7nskw/AM4ENgCvAxOstSuTr30ALE+b/DfW2j9nmUcTcDFwaLJcCeBqa+0Nydd/AWxtrT3TTZlylHMOMAK4yVp7sTFmEvANnOPC7cBF1tpElvdlnc4Ysw1wa3KencB3rbX/MMZ8AzgnbRaNwHBguLX2vTzl6wtcD+yDc6LnPGvt/cVMZ4y5GBiU2l7GmAbg98BhwFrgAeAXwHHAFGBHa22/XGVLzuM04CpgcfKpGDAAeDq57hvzvb+HeZe6X2ddP2ttpzFme+AmYFuc7fVra+3txZZVRESkmqjlKcSstS95FZwy5huvkOBUD9wDnG2t3T359w0FTvctYD9gX2vtXsAi4LfJ99UZY34IPAb0d1mmzwPnAYdba/cGZgHXJV8zwEfW2r3TfrIFp97AU8CyZLn2Bo4HzjfGfNtNOQpwbjI4jQXG4WyLPYHPJx9nli3fdNcAT1trPw6cCtxtjOlrrb01tb7AJ4H/AWfmC05JvwDWJj+zI4FrjTHDC5nOGDPcGHMP8OOM90zCCXl7JddlO+D71tr7gLE9lCvd02mf5Whgd+DjwDcLmEc3Hu3XWdcv+dqlwD+T5f0C8EdjzLbFlldERKSaqOUpJIwx3wJ+BHQAK3AqXzvjtDbsaYy5GRiUfO5B4JfAH4DPAu3A/cDkbC0FWZZ1GnCStfaYZOvDc8n57Ihz1vyb1trOPO+vAy4Hjkku+x84FbMEcCVweHI9/gn80Fq7xhjzTvLxJ3Aqdr9Lf5ystKbm/1PgK1kWfbi19sO0x58EVltrn00+vgGYaowZ7HY6YCFOgGhNvvYSMCH5977J8p0EPJxre2TYD3jcWrss+fhe4PpkRfczQIcx5klgME5ld4q1tiNjHl/GCQOXp56w1i4xxpwM1OdbuDHmCOCKLC+dZ619NM9bTwD+Yq1dl5zPTTgBaIab6Ywx9+LsDxOS5X3FGPMWTuX83vRyAO9ba6flW4+0ZX0tOb93jTGPASfj7GNup/s2zj79OjAw7T37AXemWoeMMfcD5wJXuyhXPoNxWtZWZr5Q5v063/rVAo3GmBjQF+c7nPP7LiIiIl0UnkIg2S3s1zitDEuNMROBycCdGZP2tdbukXzPlUBvnDPdtcDfgEOAOUUUYWec7mFbAW8k5/Nknum/j1M5Gw20AnfgVPh3AbZPPt+B05XqN8AZyfctsNZ+OVn+36U/Tmet/RVOV7qe7AAsTXtfW7Jb3DDgQzfTWWufSz1vjBkI/Bz4U3K6F4AXjDE7uShLygvAWcaYEdbaJcDpOIFnMM737W84ldg+wEPAamBqxjz2B57NeA5r7byeFm6tfRzYu4DypuwAzE5
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for row in baseline_ii.sort_values('gridness', ascending=False).itertuples():\n",
" compute_phase_precession(row, plot=True)"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "ValueError",
"evalue": "The length of x is too small: len(x) < 2.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-198-e9ca3d18003a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbaseline_ii\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gridness'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitertuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcompute_phase_precession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-194-7839614a8a2f>\u001b[0m in \u001b[0;36mcompute_phase_precession\u001b[0;34m(row, flim, field_num, plot)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mphase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mspike_phase\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mcirc_lin_corr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_corr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mline_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphi0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_corr\u001b[0;34m(x, phase, min_slope, max_slope, ci, bootstrap_iter)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0mgoodness\u001b[0m \u001b[0mof\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m '''\n\u001b[0;32m--> 139\u001b[0;31m \u001b[0mphi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_regression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_slope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fit line to data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0mcirc_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslope\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# convert linear variable to circular one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/apps/expipe-project/phase-precession/phase_precession/core.py\u001b[0m in \u001b[0;36mcl_regression\u001b[0;34m(x, phase, min_slope, max_slope)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The length of x is too small: len(x) < 2.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_slope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The length of x is too small: len(x) < 2."
]
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
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"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1AAAAI4CAYAAABkywiyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOyde3wU1dnHv5uEhAQwkICgqHgrxwteKrb4Yq22IlZsY1WstrZqtYoW2tL6qi30rdWWtlZbbcVWqlbbarGKWFKvCIr34l1E5XhD5SaXBEIgkOu8f5wdMhl2dmd2Z3dnN8/385lPsjOz5zZnZs9vnvM8J2ZZFoIgCIIgCIIgCEJqSvJdAEEQBEEQBEEQhEJBBJQgCIIgCIIgCIJPREAJgiAIgiAIgiD4RASUIAiCIAiCIAiCT0RACYIgCIIgCIIg+EQElCAIgiAIgiAIgk/K8l0AQRAEoXeilIoBtwNLtdbXxfeVAjOBY+OnPQRcprW2lFJfAK4F+gDbgO9rrV+Ip/ML4LT4d14ELtFatyTJ+zDgEa31bo59JwIzML+NXcBPtNaPxo+dD1wWP7Ygnnd7vLz/B9QB/eLl/ZHW2nKkez5wqtb6K456/wI4E9gKPBf/zvYE5dwTuA0YCpQC12qt/5bgvJTliJ9XCdwEfAbzEnUxMFlrvc1xziDgZeByrfWc+L4x8e/1A1YD39Rar/FqX0EQhGJGLFCCIAhCzlFKHQgsBL7mOvQtQAGHAIdhhNREpVQ58C/gQq31YcAvgX/Ev3MqMB44HDgYqAJ+4JFvmVLqh8B8YIBjfzXwT+BcrfXhwHnAv5RSA5RSo4CrgM/HyzYQ+GH8qz8AjgOOBg4F/gcjjFBK1SilbgZuBGKOYpwHfBn4TDyvNfH6JOIm4KF4nY8HblRK7ZHgPM9yuJiOEYGHxc+rBH7iaIcY8Heg2rGvHJgD/EBrfWD8/9s8yisIglD0iIASBEEQ8sFkjPXpHtf+UoyVoyK+lQPbtdZtwHCt9avxQf6+QAOA1noucHT8nAHArvaxBByBEQ4TXfv7AN/VWr8Z//wWRvQMBk4B6rXW67XWXcAs4Jvx884Bfqm13qa1bgVOxwhDMOJwDfC/rrxGA//WWm+Kf56boDw2X8UIMIC9gA6M9c1NsnI4eSp+XpfWuhN4FRjhOP5TYAnwhmPfZ4DNWutn459vA45XStV6lFkQBKGokSl8giAIQs7RWk8BUEod7zp0B3AGsArzGzVfa/2f+HfalVJDgVcwwuZMR3rtSqkpGEvOKuB+j3xfAF5QSu3t2r8BY+GyuRp4R2u9PD6N7kPHsZWAbQUaCRyklPoJMASoB66Mp3lzvI7nuYqxGPihUmom0IgRP7uRgLhgQym1CPgc8HutdSJx6FkOV3rz7f+VUiOAqcBF8c/jMRa/E+kpvvYEVjjSaFNKrQeG4y1UBUEQihaxQAmCIAhR4kpgPcbnZw+gRil1qX1Qa71Waz0cM0XtdqXUSMexmcAgjHiak07m8Sl+f8SIuNPjuxP9VnbG//YBjgImYKbPfQ74XrI8tNb/AO4FHgeeBZYBbSm+cxxGZI1XSn07wSmByqGUGg08DczUWj+glNoL+B3Gt6nTdbrXWMF9niAIQq9ALFCCIAhClDgN+F58Ol6bUupvGB+oW4Evaq3vB9Bav6KUeh04JB4YoURr/Wo82MStwA+UUrtjginYTNBar/bKOB48YQ5m6t5RDkvPx/S0EA3HWKHABFS4Oz5trlUpdS/GV+qGJPnUAP/UWv86/nkM8F6i8gJjgUe11s1a6/VKqX9jpiHe7krWdzmUUmcBfwKmaK3/Gd99BsZ37BGlFMD+wLVKqcHAUmf9lVJ9MBbAVV51FARBKGZEQAmCIAhR4hWM79AT8YF6HfBfjLXjr0qpdVrrZ5VSBwMHYKbDfQG4VCk1Nh557xzg8bhYOtxPpkqpCkxgideBi7XWHY7D9cA8pdQMjHXsIuDf8WNzgG8qpR7A+G99mcS+R06OBH4TF04WJojDXYnKq5S6BDM971fxQBenYKYXuvFVDqXUROCPwHit9Uv2fq317zAWKPu8RRjr1Jz4daiNt+9zwPnA8w4fLkEQhF6FCChBEAQhSvwQE2luGUY0LQSuifs4fRW4IT6gbwW+obVeCfxDKbU/8JJSqgN4E7ggYL4TMcKmbzwde/+3tNZLlFJXY6bc9cGItmvix38a/38p5jf1MZJYn8D4ISmljsUEayjBiLHrPU4/D5illFoS/3yLbYVz4bccv8ZY2G511PFZrfXkJOVtV0qdBsxUSvXD+D2d411DQRCE4iZmWVbqswRBEARBEARBEAQJIiEIgiAIgiAIguAXEVCCIAiCIAiCIAg+EQElCIIgCIIgCILgExFQgiAIgiAIgiAIPhEBJQiCIAiCIAiC4BMRUIIgCIIgCIIgCD4RASUIgiAIgiAIguATEVCCIAiCIAiCIAg+EQElCIIgCIIgCILgExFQgiAIgiAIgiAIPhEBJQiCIAiCIAiC4BMRUIIgCIIgCIIgCD4RASUIgiAIgiAIguATEVCCIAiCIAiCIAg+EQElCIIgCIIgCILgExFQgiAIgiAIgiAIPhEBJQiCIAiCIAiC4BMRUIIgCIIgCIIgCD4RASUIgiAIgiAIguATEVCCIAiCIAiCIAg+EQElCIIgCIIgCILgExFQgiAIgiAIgiAIPhEBJQiCIAiCIAiC4BMRUIIgCIIgCIIgCD4RASUIgiAIgiAIguATEVCCIAiCIAiCIAg+EQElCIIgCIIgCILgExFQgiAIgiAIgiAIPhEBJQiCIAiCIAiC4BMRUIIgCIIgCIIgCD4RASUIgiAIgiAIguATEVCCIAiCIAiCIAg+EQElCIIgCIIgCILgExFQgiAIgiAIgiAIPhEBJQiCIAiCIAiC4BMRUIIgCIIgCIIgCD4RASUIgiAIgiAIguATEVCCIAiCIAiCIAg+EQElCIIgCIIgCILgExFQgiAIgiAIgiAIPhEBJQiCIAiCIAiC4BMRUIIgCIIgCIIgCD4py3cBBFBKHQn8WGs9MaT0LGAIMBYYp7X+fhjpRgml1BjgJqAfsBr4ptZ6TZDzlFKnA9OACuAj4BytdYNSam/gZmAEsAW4Vmt9j48yfQ+YAmwD3gYma60b48fWA6scp1+rtb4rQRoDgauB44AuwAJmaq1vix//OTBYaz0lVXmSlHNRvG63a62vTnB8GnAO5vlwJ3CV1trye55S6hDgeeA9x+lnaq21UupEYEb8O13AT7TWj6ZRh4zK6Dg+CHgZuFxrPcf13c8CTwPDtdYblFKPAp8BfqW1vi5F+T4EWjF9wcL0sS7gf7XWjwStryPdUuD3wInxOl2ntb45yHlKqRrgRuAgoBKYobX+R/zYqcBV8bJuBL6jtX4/3fIKgiAIQjEiFqgIoLV+KSzx5Eq3vkjFUzkwB/iB1vrA+P+3BTkvLlpnAqdrrUcB72AG9gB/A/4b/84XgcuVUoelKNMXgCuA47XWhwMPAX+JH1PARq314Y4tkXjqCzwJrASOiKfzVeAnSqkL/LeQLy7zEE8TgDOA0cAo4Avxz0HOGwv801VfrZSqBv4JnBuv23nAv5RSA4IUPKQyopSKAX8HqhN8dzDwZ6Dc3qe1PhGoD1DUs+N1/7TW+iDg58DtAb6fiEnApzD1+QwwNS70gpx3B7BSa/1pYBzwR6XUHkqpSozIPC1+feqBP2ZYXkEQBEEoOsQClWOUUucDlwKdwAbgXGA/jJVhlFLqDqAmvu8B4BeYt8VHAx3Av4Hpid62J8jrPGCi1vrLcavD8/F09sK8WT9Xa92V5PtlwG+BL8fzfg74LuaN+u+B4+P1WAz8UGvdHH/zvhg4FGPdud75WWt9vyP9HwNnJcj6eK11Q5KqfQbYrLV+Nv75NuAGpVSt63ue5wHfBG7TWn8YP/ZzoDb+/2jMdSFepyeAU4HXk5RpNLBAa70y/nkucGtcxI0FOuPp1GKE3AytdacrjTOBLVrr39o7tNYfKaW+hmMgnwil1DggkVXkioAWnlMx4mdrPN3bMW3ltsA
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for row in stimulated_30.sort_values('gridness', ascending=False).itertuples():\n",
" compute_phase_precession(row, plot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis"
]
},
{
"cell_type": "code",
"execution_count": 390,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "45ec19625a034ec79e6f979e7f5d6241",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=40), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"baseline_i_pp = []\n",
"for row in tqdm(baseline_i.itertuples(), total=len(baseline_i)):\n",
" result_cell = compute_phase_precession(row)\n",
" baseline_i_pp.append(result_cell)\n",
"\n",
"stimulated_11_pp = []\n",
"for row in tqdm(stimulated_11.itertuples(), total=len(stimulated_11)):\n",
" result_cell = compute_phase_precession(row)\n",
" stimulated_11_pp.append(result_cell)\n",
"\n",
"stimulated_11_stim_pp = []\n",
"for row in tqdm(stimulated_11.itertuples(), total=len(stimulated_11)):\n",
" result_cell = compute_phase_precession(row, flim=[9,12])\n",
" stimulated_11_stim_pp.append(result_cell)\n",
"\n",
"baseline_ii_pp = []\n",
"for row in tqdm(baseline_ii.itertuples(), total=len(baseline_ii)):\n",
" result_cell = compute_phase_precession(row)\n",
" baseline_ii_pp.append(result_cell)\n",
" \n",
"stimulated_30_pp = []\n",
"for row in tqdm(stimulated_30.itertuples(), total=len(stimulated_30)):\n",
" result_cell = compute_phase_precession(row, flim=[6,10])\n",
" stimulated_30_pp.append(result_cell)\n",
" \n",
"stimulated_stim_30_pp = []\n",
"for row in tqdm(stimulated_30.itertuples(), total=len(stimulated_30)):\n",
" result_cell = compute_phase_precession(row, flim=[29,31])\n",
" stimulated_stim_30_pp.append(result_cell)"
]
},
{
"cell_type": "code",
"execution_count": 391,
"metadata": {},
"outputs": [],
"source": [
"baseline_i_pp = pd.DataFrame(baseline_i_pp)\n",
"\n",
"stimulated_11_pp = pd.DataFrame(stimulated_11_pp)\n",
"\n",
"stimulated_11_stim_pp = pd.DataFrame(stimulated_11_stim_pp)\n",
"\n",
"baseline_ii_pp = pd.DataFrame(baseline_ii_pp)\n",
"\n",
"stimulated_30_pp = pd.DataFrame(stimulated_30_pp)\n",
"\n",
"stimulated_stim_30_pp = pd.DataFrame(stimulated_stim_30_pp)"
]
},
{
"cell_type": "code",
"execution_count": 392,
"metadata": {},
"outputs": [],
"source": [
"stuff = ['action', 'channel_group', 'unit_name', 'unit_idnum', 'unit_id']\n",
"baseline_i_pp = baseline_i_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
"\n",
"stimulated_11_pp = stimulated_11_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
"\n",
"stimulated_11_stim_pp = stimulated_11_stim_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
"\n",
"baseline_ii_pp = baseline_ii_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
"\n",
"stimulated_30_pp = stimulated_30_pp.merge(data.loc[:,stuff], on=stuff[:3])\n",
"\n",
"stimulated_stim_30_pp = stimulated_stim_30_pp.merge(data.loc[:,stuff], on=stuff[:3])"
]
},
{
"cell_type": "code",
"execution_count": 393,
"metadata": {},
"outputs": [],
"source": [
"baseline_i_pp['baseline_i'] = True\n",
"\n",
"stimulated_11_pp['stimulated_11'] = True\n",
"\n",
"stimulated_11_stim_pp['stimulated_11_stim'] = True\n",
"\n",
"baseline_ii_pp['baseline_ii'] = True\n",
"\n",
"stimulated_30_pp['stimulated_30'] = True\n",
"\n",
"stimulated_stim_30_pp['stimulated_stim_30'] = True"
]
},
{
"cell_type": "code",
"execution_count": 394,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n",
" import sys\n"
]
}
],
"source": [
"results = pd.concat([\n",
" baseline_i_pp,\n",
" stimulated_11_pp,\n",
" stimulated_11_stim_pp,\n",
" baseline_ii_pp,\n",
" stimulated_30_pp,\n",
" stimulated_stim_30_pp,\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 396,
"metadata": {},
"outputs": [],
"source": [
"results.reset_index(drop=True).to_feather(output_path / 'results.feather')"
]
},
{
"cell_type": "code",
"execution_count": 397,
"metadata": {},
"outputs": [],
"source": [
"def compute_date_idnum(row):\n",
" return '-'.join(row.action.split('-')[:2]) + '_' + str(row.unit_idnum)"
]
},
{
"cell_type": "code",
"execution_count": 398,
"metadata": {},
"outputs": [],
"source": [
"baseline_i_pp['date_idnum'] = baseline_i_pp.apply(compute_date_idnum, axis=1)\n",
"\n",
"stimulated_11_pp['date_idnum'] = stimulated_11_pp.apply(compute_date_idnum, axis=1)\n",
"\n",
"stimulated_11_stim_pp['date_idnum'] = stimulated_11_stim_pp.apply(compute_date_idnum, axis=1)\n",
"\n",
"baseline_ii_pp['date_idnum'] = baseline_ii_pp.apply(compute_date_idnum, axis=1)\n",
"\n",
"stimulated_30_pp['date_idnum'] = stimulated_30_pp.apply(compute_date_idnum, axis=1)\n",
"\n",
"stimulated_stim_30_pp['date_idnum'] = stimulated_stim_30_pp.apply(compute_date_idnum, axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# barplot"
]
},
{
"cell_type": "code",
"execution_count": 474,
"metadata": {},
"outputs": [],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (2, 2), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 475,
"metadata": {},
"outputs": [],
"source": [
"query = 'pval_dist < 0.01 and RR_dist > .1'"
]
},
{
"cell_type": "code",
"execution_count": 476,
"metadata": {},
"outputs": [],
"source": [
"precess_i = sum(baseline_i_pp.query(query).circ_lin_corr_dist < 0) / len(baseline_i_pp) * 100\n",
"recess_i = sum(baseline_i_pp.query(query).circ_lin_corr_dist > 0) / len(baseline_i_pp) * 100\n",
"stim_11 = len(stimulated_11_pp.query(query).circ_lin_corr_dist)\n",
"precess_ii = sum(baseline_ii_pp.query(query).circ_lin_corr_dist < 0) / len(baseline_ii_pp) * 100\n",
"recess_ii = sum(baseline_ii_pp.query(query).circ_lin_corr_dist > 0) / len(baseline_ii_pp) * 100\n",
"stim_30 = len(stimulated_30_pp.query(query).circ_lin_corr_dist)"
]
},
{
"cell_type": "code",
"execution_count": 479,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"sns.barplot(data=[\n",
" [precess_i], \n",
" [recess_i], \n",
" [stim_11],\n",
" [stim_11],\n",
" [precess_ii], \n",
" [recess_ii], \n",
" [stim_30],\n",
" [stim_30]\n",
"], color='k')\n",
"plt.xticks(\n",
" range(8),\n",
" [\n",
" 'Baseline I precession', \n",
" 'Baseline I recession', \n",
" '11 Hz precession',\n",
" '11 Hz recession',\n",
" 'Baseline II precession', \n",
" 'Baseline II recession', \n",
" '30 Hz precession',\n",
" '30 Hz recession'\n",
" ], rotation=90)\n",
"plt.ylabel('Percentage')\n",
"despine()\n",
"figname = f'phase-precession-quantification'\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": "markdown",
"metadata": {},
"source": [
"# hist"
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd10cb721d0>"
]
},
"execution_count": 448,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bins = np.arange(-2, 2, .1)\n",
"\n",
"baseline_i_pp.query('pval_dist < 0.01').slope_dist.hist(density=False, bins=bins)\n",
"stimulated_11_pp.query('pval_dist < 0.01').slope_dist.hist(\n",
" density=False, bins=bins, alpha=.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 447,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd10cd4c0f0>"
]
},
"execution_count": 447,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bins = np.arange(-.25, .25, .01)\n",
"density=False\n",
"baseline_i_pp.query('pval_dist < 0.01 and RR_dist > .1').circ_lin_corr_dist.hist(\n",
" density=density, bins=bins)\n",
"stimulated_11_pp.query('pval_dist < 0.01 and RR_dist > .1').circ_lin_corr_dist.hist(\n",
" density=density, bins=bins, alpha=.5)"
]
},
{
"cell_type": "code",
"execution_count": 408,
"metadata": {},
"outputs": [],
"source": [
"baseline_i_pp['slope_R_dist'] = baseline_i_pp.slope_dist * baseline_i_pp.RR_dist\n",
"stimulated_11_pp['slope_R_dist'] = stimulated_11_pp.slope_dist * stimulated_11_pp.RR_dist"
]
},
{
"cell_type": "code",
"execution_count": 409,
"metadata": {},
"outputs": [],
"source": [
"baseline_i_pp['r_R_dist'] = baseline_i_pp.circ_lin_corr_dist * baseline_i_pp.RR_dist\n",
"stimulated_11_pp['r_R_dist'] = stimulated_11_pp.circ_lin_corr_dist * stimulated_11_pp.RR_dist"
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12d465a58>"
]
},
"execution_count": 410,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bins = np.arange(-2, 2, .1)\n",
"baseline_i_pp_sig = baseline_i_pp.query('pval_dist < 0.05')\n",
"baseline_i_pp_sig_date_idnum = baseline_i_pp_sig.date_idnum.unique()\n",
"stimulated_11_pp_sig = stimulated_11_pp.query('date_idnum in @baseline_i_pp_sig_date_idnum')\n",
"stimulated_11_pp_sig_date_idnum = stimulated_11_pp_sig.date_idnum.unique()\n",
"baseline_i_pp_sig = baseline_i_pp_sig.query('date_idnum in @stimulated_11_pp_sig_date_idnum')\n",
"\n",
"baseline_i_pp_sig.slope_dist.hist(density=False, bins=bins)\n",
"stimulated_11_pp_sig.slope_dist.hist(density=False, bins=bins, alpha=.5)"
]
},
{
"cell_type": "code",
"execution_count": 411,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12e3665c0>"
]
},
"execution_count": 411,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bins = np.arange(-.25, .25, .01)\n",
"baseline_i_pp_sig.circ_lin_corr_dist.hist(density=False, bins=bins)\n",
"stimulated_11_pp_sig.circ_lin_corr_dist.hist(density=False, bins=bins, alpha=.5)"
]
},
{
"cell_type": "code",
"execution_count": 412,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12d5567b8>"
]
},
"execution_count": 412,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"baseline_i_pp_sig.RR_dist.hist(density=False, bins=bins)\n",
"stimulated_11_pp_sig.RR_dist.hist(density=False, bins=bins, alpha=.5)"
]
},
{
"cell_type": "code",
"execution_count": 413,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12db50e10>"
]
},
"execution_count": 413,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"baseline_i_pp_sig.slope_R_dist.hist(density=False, bins=bins)\n",
"stimulated_11_pp_sig.slope_R_dist.hist(density=False, bins=bins, alpha=.5)"
]
},
{
"cell_type": "code",
"execution_count": 414,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd12dbea400>"
]
},
"execution_count": 414,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bins = np.arange(-.25, .25, .01)\n",
"baseline_i_pp.r_R_dist.hist(density=False, bins=bins)\n",
"stimulated_11_pp.r_R_dist.hist(density=False, bins=bins, alpha=.5)"
]
},
{
"cell_type": "code",
"execution_count": 415,
"metadata": {},
"outputs": [],
"source": [
"baseline_i_pp_stimulated_11_pp = baseline_i_pp.merge(stimulated_11_pp, on='date_idnum')"
]
},
{
"cell_type": "code",
"execution_count": 452,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fd10241c7f0>]"
]
},
"execution_count": 452,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(\n",
" baseline_i_pp_stimulated_11_pp.circ_lin_corr_dist_x, \n",
" baseline_i_pp_stimulated_11_pp.circ_lin_corr_dist_y,\n",
")\n",
"plt.plot([-.25, 0.25], [-.25, 0.25], '--k')"
]
},
{
"cell_type": "code",
"execution_count": 446,
"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>RR_dist_x</th>\n",
" <th>circ_lin_corr_dist_x</th>\n",
" <th>RR_dist_y</th>\n",
" <th>circ_lin_corr_dist_y</th>\n",
" <th>action_y</th>\n",
" <th>unit_idnum_y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.083627</td>\n",
" <td>0.074713</td>\n",
" <td>0.050477</td>\n",
" <td>0.093518</td>\n",
" <td>1833-120619-2</td>\n",
" <td>233</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RR_dist_x circ_lin_corr_dist_x RR_dist_y circ_lin_corr_dist_y \\\n",
"13 0.083627 0.074713 0.050477 0.093518 \n",
"\n",
" action_y unit_idnum_y \n",
"13 1833-120619-2 233 "
]
},
"execution_count": 446,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"baseline_i_pp_stimulated_11_pp.query('pval_dist_y < 0.01').loc[:, [\n",
" 'RR_dist_x', 'circ_lin_corr_dist_x', \n",
" 'RR_dist_y', 'circ_lin_corr_dist_y', \n",
" 'action_y', 'unit_idnum_y']]"
]
},
{
"cell_type": "code",
"execution_count": 445,
"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>RR_dist</th>\n",
" <th>circ_lin_corr_dist</th>\n",
" <th>pval_dist</th>\n",
" <th>action</th>\n",
" <th>unit_idnum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [RR_dist, circ_lin_corr_dist, pval_dist, action, unit_idnum]\n",
"Index: []"
]
},
"execution_count": 445,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stimulated_11_pp.query('pval_dist < 0.01 and RR_dist > 0.1').loc[:, [\n",
" 'RR_dist', 'circ_lin_corr_dist', 'pval_dist',\n",
" 'action', 'unit_idnum'\n",
"]]"
]
},
{
"cell_type": "code",
"execution_count": 427,
"metadata": {},
"outputs": [],
"source": [
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (6, 6), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 444,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'action': '1839-120619-2',\n",
" 'channel_group': 6,\n",
" 'unit_name': 106,\n",
" 'circ_lin_corr_dist': -0.11445218085660203,\n",
" 'pval_dist': 0.03558411784503357,\n",
" 'slope_dist': -0.8698732948412091,\n",
" 'phi0_dist': 3.89838512340452,\n",
" 'RR_dist': 0.15160195974199475,\n",
" 'circ_lin_corr_dur': -0.1699451973400952,\n",
" 'pval_dur': 0.0012656020106374122,\n",
" 'slope_dur': -0.8051851328081773,\n",
" 'phi0_dur': 3.7556339993235173,\n",
" 'RR_dur': 0.16494067381412736}"
]
},
"execution_count": 444,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"compute_phase_precession(\n",
"# stimulated_11.query('action==\"1834-220319-2\" and unit_idnum==358').iloc[0],\n",
" stimulated_11.query('action==\"1839-120619-2\" and unit_idnum==629').iloc[0],\n",
" plot=True, plot_grid=True, flim=[6,10]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 450,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAw4AAAMmCAYAAABcvdYWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydaZgU1dWA32HfEWV3H2GOuI24DLjFBbfoiLu4axIxamJEs6hfBA0YoyYRTGKikhiNkrgSccZdFHdEI4LrAUXABRCUZdhhpr8ft2q6urqqq3sWGOC8z9NPd1Xde+tW1e3uc+49S1EqlcIwDMMwDMMwDCMXzTZ2BwzDMAzDMAzDaPqY4mAYhmEYhmEYRiKmOBiGYRiGYRiGkYgpDoZhGIZhGIZhJGKKg2EYhmEYhmEYiZjiYBiGYRiGYRhGIqY4GIZhGIZhGIaRiCkOhmEYhmEYhmEkYoqDYRiGYRiGYRiJmOJgGIZhGIZhGEYipjgYhmEYhmEYhpGIKQ6GYRiGYRiGYSRiioNhGIZhGIZhGImY4mAYhmEYhmEYRiItNnYHDMPYshCRVsC7wO7AAao6OY86OwPDgKOBHYAi4AvgBWCMqs5MqN8cOBc4D9gb6AjMB6YA96jq03n0oRdwBXA8UAxUA3OBSuAuVf08qY18EZH2wM+A0wDxdn/hnet2Vf0yoX4RMBg4H9gf6A6s8/o7EfiTqn6aRz+OAy4FBgBbAd/g7tnfVPX5mDqTgEOT2o5gZ1WdnUefzgEeAJ5V1WPzKN8a+DFwBrAb0AH4EngJdx+m1aGveSMizwFHAWep6oONdI6xwEXAtap6cz3bagFUAW3yKP6yqh4WqHsvcEEdTnu4qk6qQz3DMDYwtuJgGMaG5nc4pSEvROR04AOcIL0r0A5oC5QAlwHvi8j5OepvDbwC3AsMArYBWuEUkNOAp0TkURFpl6ONY4GPgKuBPbw+dPSu42rgAxH5Qb7XlAsRKQbeA24C9gHae69dgV/grveYHPW3Ap4BHgdOAbYHWuME5t2Ay4GPROSyHG0084TRJ4FyoBvQEtgWOBl4TkTu8BSUhmJtUgFPgbw93wZFpASY7tU5COiCu46dgR8C74rIr+vU2/zOfzlOaWg0RGQwTmloKPqRn9LQkCQ+e8Mwmga24mAYxgZDRK4FriqgfBkwDifsVQN/BZ7GzZ4fAfwcJxT/U0TmhWfBRaQZ8ARwoLfrTeAOYDZOeLwC2A84FVgPnBnRh1KcEN460IdngZXAQJzi0Bn4u4gsUNWn8r2+iHO1B54C+gApYCzwME6wOganOGwFPCoiZar6cah+EfAY7t4AvONd78c4ZWuQd80dgTtEZJmqPhDRlZGkhdH/AX8APscJlVfjlJjLcKs2o0J1L8IpKUn8Gqe4AYxS1a9zFRaRbXGrJdvk0TYi0gO3qtDb2zUNGIO7F72Bi4FjgRtFpLOq/iqfdvPFWxkZ05BtRpzjCOChBm5278DnwbiVrjiWh7ZHkN81/wA3EQBwv6q+kX/3DMPYmJjiYBhGo+OZJ90OXFJg1RtxSgM4U49HAsdeEJFngRdxq6d/BPYK1T8fN9MM8AgwRFVT3vbrIvJvnKB+DDBERP4UIcTcjlMaAE5W1YrAsZdE5DGc6VV74DYReTpwjkL5FWnTpMtV9Y7AsVe9630eJ5j/ETguVH8IaaXhEeBsVV0fOD5JRP4DvIabfR8jIhNUtcov4M3SX+1tvg4coar+jPBbIvIITiDfH/i1iNyrqrXCZZ4mUN/HKWsAzwHXJ5Q/wLuebZPaDvBH0krDf3HPfl3g+H9F5Fbgl8AvRORRVZ1SQPtxfS0ChuOuqdFW9UVkKPBn0mOzofAVhxXAk6pak29FVZ2LM4eLxVPEf+xtvo9T4AzD2EQwUyXDMBoVb9XgddJKQ3We9VoDh3ubU0JKAwCq+jJOKATYU0R2DBXxZ83XAZeGBXpPKPq/wK4zQn3oQ9pef3xIafDbmAHc41cBSnNdVxyecvUTb3MabmUjfK5XcSsIAN8XkbDJ1w+991W4610fOo6qfoRTyMDN3h8fKnI56UmlnwWUBr/+Ctx9TeGE1p9RACLSBfg7zk/lO+CCOEVLRNqKyAjgZZzSkO/Y6UZ69egr4PyQ0uBzDfCh15dbCrmOmPMKbjXqN7j/17z6W+A5tvUU3rtJr4I1JL7i8H4hSkM+iEhL4D5cv9fiJgNWN+Q5DMNoXExxMAyj0RCRm4HJOHMggAnkb76xNWkBdkaOch8EPvcKHXsfeBt4TlW/jakfNPfZIXSsAzAe+JS0glJoG/lyKGkznAdyrFr8I/D5dP+DN9Ptr668nuN6wa1a+IQVnZO99w9V9d2oyqo6HWcGBWlzo3y5ifRKwFWqOj+qkKe0KU4IbwksA07M8xyHAs29z39X1bBJDVCrOP7LryMiPfNsP6q/P8WNRd+n4Q0yldJ6IyKn4L4LZ3m7PqHwVbwkfMVhagO3C85M0R9vN6nqh41wDsMwGhFTHAzDaEwGkp5ZvkhVTyLbLjqOhTi/A3A29XH0CXzOsJNX1UtVtUxVy3PUD65SzAvVf09VT1XVvjG+AIltFMBBgc+T4gp5KwaLvM0jAoda4/wN/kJuJQfcM/GpdYQVkZ1ImwPF9sHjFe99J8+hOxER2Zu0acprqnpfjuLb4Ry7wTlp766qT+ZzHjKfx1sJZT/y3otw0aPqyn44RXclcC3wPVwUqoZkL5xj/nqcKdY+OKW2QRCRHXAKOzSw4uBFJRvubc4C6hX9yTCMjYP5OBiG0ZgsxpmA3KKqiwupqKrrReQpnIPmfiJysqpmCMQi0p/0rPsUz8Y6b7wwrUHn3oIdTb1IP75J1Kc4Z+K60C/wOWd4WZzg1TVYxzP5yFcYOyzweU4d+/BZqN6sPM77O9yEVQq4MqFsCmfi9pu40K85aBX4XBVbyhE0YSop8DxBVgJ3EnD0dpZLDco64EHgBlXVRjhH0DH6cxG5BrcCtRtuBWc2Ton7Y9xKUQ5G4PyAAH6pqmvq2VfDMDYCpjgYhtGYnFpPO+lfAmVAT+BhEfkzzpl2DXAwzom3FfAteTpZespCb9wM/y+Afb1Dd6nqK7EV0/WLcLP0xTifiMtxjsZrgKH1uF5/pn+Zqi5NKPsF7r50FZFWYT+EXIhIG1xODJ9nI/oACU6uZEbbSXRa9nxd/LwLT6jqO7nKA6+q6sFJ7cawKPB5u4Sy2wc+19lUCfhpQ/sERPC7Rj5HUHF4DOgUOt7Pe/1YRM7KdwXIi4j1I2/zPVUdX++eGoaxUTBTJcMwGo36Cjme4/FAXMKv5rhZ6qdxkZRG4mYwHwP2LyCRl+KE4v/glIbVOAUiNq9BiINws8sf4GZRu+DMXQ6rZxIr30QkaYYcXMQbn60KPM8fcKFoAZ4OhXTdOvA5qR+F9iG4wpDoiFzPsRM0Tzo5tpTjhMDn9rGlEtgASsOGOEf/wOcOwP3ASbjv4Km4yFbgwvk+LiL5KnY/JR0d7dYG6KdhGBsJUxwMw2jqHIhLtBaXbOwQ4FQvZ0NOvNWC7UO72+BWDnL5QQQJR24Ct/pwmWcjXlf8sJr5RJlZFVEvEREZRjpy03KyzYWCbSX1I+8+eDPOvhP1K6r6ZkLb9cJz3vYVydNF5ISociJSTqbi0DKq3BaEv+KwFjhOVc9X1Qmq+paqjlfVM3A5GMBZLNzrZZqORUTakl4NnIXLS2IYxiaKKQ6GYTRZROQm4N+4mdA3cBFrOnivQbgwnd2B3wP/yEN5aI0LWToQOBI3+7kSZ/bzuIj8JEddnw9x+RMG4BSOp3HKx3m43BB1tZP3w2oWmgMir/JeFuPbAnWG+nbyEX0otB9JZS8jbRpb77CneXIVUINTOB8TkVEiUiwiLUVkRxG5Drda9Q3p697SMxgPxK2ofU9Vn40qoKr34pIyAuxCssJ9LumVrD+qaoOHqDUMY8NhPg6GYTRJRGQwLjoNwDPA4FAs/hdF5GWcGdOZwIU4E5U749r0HIjHBXZN9BKiTcJlfx4jIi9ECNTBNt4D3vM2pwCPiMgNuIRf2+FyFHwvv6v
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x900 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for row in baseline_i_pp.query('pval_dist < 0.01 and RR_dist > .1').itertuples():\n",
" compute_phase_precession(\n",
" row, plot=True, plot_grid=True, flim=[6,10]\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The inverted grid"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1152x648 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(1, 2, figsize=(16,9))\n",
"row = baseline_i.sort_values('gridness', ascending=False).iloc[9]\n",
"lfp = data_loader.lfp(row.action, row.channel_group)\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",
"spikes = np.array(spikes)\n",
"spikes = spikes[(spikes > pos_t.min()) & (spikes < pos_t.max())]\n",
"\n",
"axs[0].imshow(rate_map.T, extent=[0, box_size[0], 0, box_size[1]], origin='lower')\n",
"axs[1].plot(pos_x, pos_y, color='k', alpha=.2, zorder=1000)\n",
"axs[1].scatter(interp1d(pos_t, pos_x)(spikes), interp1d(pos_t, pos_y)(spikes), s=10, zorder=10001)\n",
"\n",
"for ax in axs:\n",
" ax.axis('image')\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n"
]
},
{
"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
}