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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
2019-12-19 12:58:01 +00:00
"12:54:11 [I] klustakwik KlustaKwik2 version 0.2.6\n",
2019-12-18 14:03:01 +00:00
"/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 septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"from septum_mec.analysis.plotting import violinplot, despine, plot_bootstrap_timeseries\n",
"from phase_precession import cl_corr\n",
"from spike_statistics.core import permutation_resampling\n",
"import matplotlib.mlab as mlab\n",
"import scipy.signal as ss\n",
"from scipy.interpolate import interp1d\n",
"from septum_mec.analysis.plotting import regplot\n",
"from skimage import measure\n",
"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
"import scipy.signal as ss\n",
"\n",
"\n",
"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
"\n",
"import pycwt"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
"colors = ['#1b9e77','#d95f02','#7570b3','#e7298a', '#4393c3', '#d6604d']\n",
"labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz', 'Baseline', 'Stimulated']\n",
"plt.rcParams['figure.dpi'] = 150\n",
"figsize_violin = (1.7, 3)\n",
"figsize_speed = (4, 3)\n",
"plt.rc('axes', titlesize=10)"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [],
"source": [
"project_path = dp.project_path()\n",
"project = expipe.get_project(project_path)\n",
"actions = project.actions\n",
"\n",
"output_path = pathlib.Path(\"output\") / \"lfp_speed\"\n",
"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"data_action = actions['lfp_speed']\n",
"output = exdir.File(\n",
" data_action.data_path('results'),\n",
" plugins = [exdir.plugins.git_lfs, exdir.plugins.quantities])\n",
"\n",
"ignore = ['wavelet_power', 'wavelet_freqs', 'signal']\n",
"results = []\n",
"for group in output.values():\n",
" d = group.attrs.to_dict()\n",
" d.update({k: np.array(v.data) for k, v in group.items() if k not in ignore})\n",
" results.append(d)\n",
"results = pd.DataFrame(results)"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>freq_score</th>\n",
" <th>sample_rate</th>\n",
" <th>power_score</th>\n",
" <th>action</th>\n",
" <th>channel_group</th>\n",
" <th>max_speed</th>\n",
" <th>min_speed</th>\n",
" <th>position_low_pass_frequency</th>\n",
" <th>mean_freq</th>\n",
" <th>mean_power</th>\n",
" <th>speed</th>\n",
" <th>speed_bins</th>\n",
" <th>theta_freq</th>\n",
" <th>theta_power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.191729</td>\n",
" <td>1000.0</td>\n",
" <td>0.432532</td>\n",
" <td>1833-010719-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.02</td>\n",
" <td>6</td>\n",
" <td>[7.154332133229601, 7.106500202042717, 7.13862...</td>\n",
" <td>[18.005621200653046, 18.66435212100411, 20.504...</td>\n",
" <td>[0.02795137493203615, 0.0283076211590443, 0.02...</td>\n",
" <td>[0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000...</td>\n",
" <td>[6.799999999999997, 6.799999999999997, 6.79999...</td>\n",
" <td>[3.990633076071412, 3.992883430179942, 3.99513...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.255882</td>\n",
" <td>1000.0</td>\n",
" <td>0.434938</td>\n",
" <td>1833-010719-1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.02</td>\n",
" <td>6</td>\n",
" <td>[7.035831237674811, 7.05973079549096, 7.120455...</td>\n",
" <td>[16.966011451769536, 17.60417640800431, 19.452...</td>\n",
" <td>[0.02795137493203615, 0.0283076211590443, 0.02...</td>\n",
" <td>[0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000...</td>\n",
" <td>[6.799999999999997, 6.799999999999997, 6.79999...</td>\n",
" <td>[3.649171825378523, 3.6511305369806806, 3.6530...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.169116</td>\n",
" <td>1000.0</td>\n",
" <td>0.338942</td>\n",
" <td>1833-010719-1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0.02</td>\n",
" <td>6</td>\n",
" <td>[7.156957284750235, 7.121730043055997, 7.17760...</td>\n",
" <td>[14.747162413722597, 15.548073560884317, 16.81...</td>\n",
" <td>[0.02795137493203615, 0.0283076211590443, 0.02...</td>\n",
" <td>[0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000...</td>\n",
" <td>[6.799999999999997, 6.799999999999997, 6.79999...</td>\n",
" <td>[3.069575227276876, 3.0713927350182493, 3.0732...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.071480</td>\n",
" <td>1000.0</td>\n",
" <td>0.141405</td>\n",
" <td>1833-010719-1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.02</td>\n",
" <td>6</td>\n",
" <td>[7.256682286107137, 7.237350035531646, 7.27254...</td>\n",
" <td>[13.017027147293039, 12.651121743582284, 13.91...</td>\n",
" <td>[0.02795137493203615, 0.0283076211590443, 0.02...</td>\n",
" <td>[0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000...</td>\n",
" <td>[6.399999999999999, 6.399999999999999, 6.39999...</td>\n",
" <td>[1.9508693636836856, 1.9523977795413874, 1.953...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.216792</td>\n",
" <td>1000.0</td>\n",
" <td>-0.012191</td>\n",
" <td>1833-010719-1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.02</td>\n",
" <td>6</td>\n",
" <td>[7.095817125902336, 7.050223640391819, 7.12869...</td>\n",
" <td>[32.456068185302364, 23.01562486642484, 21.395...</td>\n",
" <td>[0.02795137493203615, 0.0283076211590443, 0.02...</td>\n",
" <td>[0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000...</td>\n",
" <td>[6.399999999999999, 6.399999999999999, 6.39999...</td>\n",
" <td>[1.2545438245339104, 1.2553897239251606, 1.256...</td>\n",
" </tr>\n",
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"</div>"
],
"text/plain": [
" freq_score sample_rate power_score action channel_group \\\n",
"0 0.191729 1000.0 0.432532 1833-010719-1 0 \n",
"1 0.255882 1000.0 0.434938 1833-010719-1 1 \n",
"2 0.169116 1000.0 0.338942 1833-010719-1 2 \n",
"3 0.071480 1000.0 0.141405 1833-010719-1 3 \n",
"4 0.216792 1000.0 -0.012191 1833-010719-1 4 \n",
"\n",
" max_speed min_speed position_low_pass_frequency \\\n",
"0 1 0.02 6 \n",
"1 1 0.02 6 \n",
"2 1 0.02 6 \n",
"3 1 0.02 6 \n",
"4 1 0.02 6 \n",
"\n",
" mean_freq \\\n",
"0 [7.154332133229601, 7.106500202042717, 7.13862... \n",
"1 [7.035831237674811, 7.05973079549096, 7.120455... \n",
"2 [7.156957284750235, 7.121730043055997, 7.17760... \n",
"3 [7.256682286107137, 7.237350035531646, 7.27254... \n",
"4 [7.095817125902336, 7.050223640391819, 7.12869... \n",
"\n",
" mean_power \\\n",
"0 [18.005621200653046, 18.66435212100411, 20.504... \n",
"1 [16.966011451769536, 17.60417640800431, 19.452... \n",
"2 [14.747162413722597, 15.548073560884317, 16.81... \n",
"3 [13.017027147293039, 12.651121743582284, 13.91... \n",
"4 [32.456068185302364, 23.01562486642484, 21.395... \n",
"\n",
" speed \\\n",
"0 [0.02795137493203615, 0.0283076211590443, 0.02... \n",
"1 [0.02795137493203615, 0.0283076211590443, 0.02... \n",
"2 [0.02795137493203615, 0.0283076211590443, 0.02... \n",
"3 [0.02795137493203615, 0.0283076211590443, 0.02... \n",
"4 [0.02795137493203615, 0.0283076211590443, 0.02... \n",
"\n",
" speed_bins \\\n",
"0 [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... \n",
"1 [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... \n",
"2 [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... \n",
"3 [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... \n",
"4 [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... \n",
"\n",
" theta_freq \\\n",
"0 [6.799999999999997, 6.799999999999997, 6.79999... \n",
"1 [6.799999999999997, 6.799999999999997, 6.79999... \n",
"2 [6.799999999999997, 6.799999999999997, 6.79999... \n",
"3 [6.399999999999999, 6.399999999999999, 6.39999... \n",
"4 [6.399999999999999, 6.399999999999999, 6.39999... \n",
"\n",
" theta_power \n",
"0 [3.990633076071412, 3.992883430179942, 3.99513... \n",
"1 [3.649171825378523, 3.6511305369806806, 3.6530... \n",
"2 [3.069575227276876, 3.0713927350182493, 3.0732... \n",
"3 [1.9508693636836856, 1.9523977795413874, 1.953... \n",
"4 [1.2545438245339104, 1.2553897239251606, 1.256... "
]
},
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"execution_count": 6,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results.head()"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [],
"source": [
"results = results.merge(sessions, on='action')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frequency score"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [],
"source": [
"query_base_i = 'baseline and Hz11'\n",
"query_stim_11 = 'stimulated and Hz11'\n",
"\n",
"query_base_ii = 'baseline and Hz30'\n",
"query_stim_30 = 'stimulated and Hz30'\n",
"\n",
"query_stim_combined = 'stimulated and Hz11 or Hz30'"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 37221.0 p value 1.9679601390031718e-50\n",
"U-test: U value 16950.0 p value 1.642880876541107e-32\n",
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"U-test: U value 72140.0 p value 2.0364061218728567e-30\n"
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]
},
{
"data": {
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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-19 12:58:01 +00:00
"image/png": "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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"stuff = 'freq_score'\n",
"base = results.query(query_base_i)[stuff].to_numpy()\n",
"stim = results.query(query_stim_11)[stuff].to_numpy()\n",
"plt.figure(figsize=figsize_violin)\n",
"violinplot(base, stim, colors=colors)\n",
"# plt.ylim(-0.02, 0.5)\n",
"plt.savefig(output_path / \"figures\" / \"frequency_score_11.svg\", bbox_inches=\"tight\", transparent=True)\n",
"plt.savefig(output_path / \"figures\" / \"frequency_score_11.png\", bbox_inches=\"tight\", transparent=True)\n",
"\n",
"base = results.query(query_base_ii)[stuff].to_numpy()\n",
"stim = results.query(query_stim_30)[stuff].to_numpy()\n",
"plt.figure(figsize=figsize_violin)\n",
"violinplot(base, stim, colors=colors[2:])\n",
"# plt.ylim(-0.02, 0.5)\n",
"plt.savefig(output_path / \"figures\" / \"frequency_score_30.svg\", bbox_inches=\"tight\", transparent=True)\n",
"plt.savefig(output_path / \"figures\" / \"frequency_score_30.png\", bbox_inches=\"tight\", transparent=True)\n",
"\n",
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"base = results.query(query_base_i)[stuff].to_numpy()\n",
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"stim = results.query(query_stim_combined)[stuff].to_numpy()\n",
"plt.figure(figsize=figsize_violin)\n",
"violinplot(base, stim, colors=colors[4:])\n",
"# plt.ylim(-0.02, 0.5)\n",
"plt.savefig(output_path / \"figures\" / \"frequency_score_combined.svg\", bbox_inches=\"tight\", transparent=True)\n",
"plt.savefig(output_path / \"figures\" / \"frequency_score_combined.png\", bbox_inches=\"tight\", transparent=True)"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [],
"source": [
"def plot_speed(query_base, query_stim, stuff, colors, labels, filename=None, show_raw=False):\n",
" base = np.array([s for s in results.query(query_base)[stuff]])\n",
" base_bins = results.query(query_base)['speed_bins'].to_numpy()\n",
"\n",
" stim = np.array([s for s in results.query(query_stim)[stuff]])\n",
" stim_bins = results.query(query_stim)['speed_bins'].to_numpy()\n",
" if show_raw:\n",
" fig, axs = plt.subplots(1, 2, sharey=True, figsize=figsize_speed)\n",
"\n",
" for b, h in zip(base_bins, base):\n",
" axs[1].plot(b, h)\n",
" axs[1].set_xlim(0.1,1)\n",
" axs[1].set_title(labels[0])\n",
"\n",
" for b, h in zip(stim_bins, stim):\n",
" axs[0].plot(b, h)\n",
" axs[0].set_xlim(0.1,1)\n",
" axs[0].set_title(labels[1]) \n",
"\n",
" fig, ax = plt.subplots(1, 1, figsize=figsize_speed)\n",
" plot_bootstrap_timeseries(base_bins[0], base.T, ax=ax, label=labels[0], color=colors[0])\n",
" plot_bootstrap_timeseries(stim_bins[0], stim.T, ax=ax, label=labels[1], color=colors[1])\n",
"\n",
" plt.xlim(0, 0.9)\n",
" plt.gca().spines['top'].set_visible(False)\n",
" plt.gca().spines['right'].set_visible(False)\n",
" plt.legend(frameon=False)\n",
" \n",
" if filename is not None:\n",
" plt.savefig(output_path / \"figures\" / f\"{filename}.svg\", bbox_inches=\"tight\", transparent=True)\n",
" plt.savefig(output_path / \"figures\" / f\"{filename}.png\", bbox_inches=\"tight\", transparent=True)"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 600x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_speed(query_base_i, query_stim_11, 'mean_freq', \n",
" colors, labels, filename='lfp_speed_freq_11')"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 600x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_speed(query_base_ii, query_stim_30, 'mean_freq', \n",
" colors[2:], labels[2:], filename='lfp_speed_freq_30')"
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 600x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_speed(\n",
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" query_base_i, query_stim_combined, 'mean_freq', \n",
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" colors[4:], labels[4:], filename='lfp_speed_freq_combined')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Power Familiar"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
2019-12-18 14:03:01 +00:00
"text/plain": [
"<Figure size 600x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_speed(\n",
" query_base_i, query_stim_11, 'mean_power', \n",
" colors, labels, filename='lfp_speed_power_11')"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [
{
"data": {
2019-12-19 12:58:01 +00:00
"image/png": "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
2019-12-18 14:03:01 +00:00
"text/plain": [
"<Figure size 600x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_speed(\n",
" query_base_ii, query_stim_30, 'mean_power', \n",
" colors[2:], labels[2:], filename='lfp_speed_power_30')"
]
},
{
"cell_type": "code",
2019-12-19 12:58:01 +00:00
"execution_count": 17,
2019-12-18 14:03:01 +00:00
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 600x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_speed(\n",
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" query_base_i, query_stim_combined, 'mean_power', \n",
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" colors[4:], labels[4:], filename='lfp_speed_power_combined')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Speed"
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 31872.0 p value 4.112172727188835e-10\n"
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]
},
{
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"ename": "IndexError",
"evalue": "list index out of range",
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"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-ea7e4968e5a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\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[0mstim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Stimulated'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\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---> 21\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\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[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Baseline'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\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[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 600x450 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
}
],
"source": [
"stuff = 'speed'\n",
"min_speed = results['min_speed'].iloc[0]\n",
"max_speed = results['max_speed'].iloc[0]\n",
"\n",
"f = np.median\n",
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"base = np.array([f(a) for a in results.query(query_base_i)[stuff]])\n",
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"stim = np.array([f(a).mean() for a in results.query(query_stim_combined)[stuff]])\n",
"plt.figure(figsize=figsize_violin)\n",
"violinplot(stim, base)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"speed.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"speed.png\", bbox_inches=\"tight\", transparent=True)\n",
"\n",
"plt.figure(figsize=figsize_speed)\n",
"binsize = 0.02\n",
"bins = np.arange(min_speed, max_speed + binsize, binsize)\n",
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"base = np.array([np.histogram(a, bins)[0] for a in results.query(query_base_i)[stuff]])\n",
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"stim = np.array([np.histogram(a, bins)[0] for a in results.query(query_stim_combined)[stuff]])\n",
"\n",
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"plt.bar(bins[1:], stim.mean(axis=0), width=np.diff(bins)[0], label='Stimulated', alpha=.5, color=colors[5]);\n",
"plt.bar(bins[1:], base.mean(axis=0), width=np.diff(bins)[0], label='Baseline', alpha=.5, color=colors[6]);\n",
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"\n",
"plt.legend(frameon=False)\n",
"\n",
"plt.savefig(output_path / \"figures\" / \"speed_histogram.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / \"speed_histogram.png\", bbox_inches=\"tight\", transparent=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [],
"source": [
"columns = [\n",
" 'power_score',\n",
" 'freq_score'\n",
"]\n",
"\n",
"\n",
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"def MWU(column, query1, query2):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" results.query(query1)[column].dropna(), \n",
" results.query(query2)[column].dropna(),\n",
" alternative='two-sided')\n",
" \n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(column, query1, query2):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" results.query(query1)[column].dropna(), \n",
" results.query(query2)[column].dropna(), statistic=np.median)\n",
" \n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
"\n",
"summary_i = pd.DataFrame()\n",
"\n",
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"summary_i['Baseline I'] = results.query(query_base_i)[columns].agg(summarize)\n",
"summary_i['11 Hz'] = results.query(query_stim_11)[columns].agg(summarize)\n",
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"\n",
"summary_i['MWU'] = list(map(lambda x: MWU(x, query_base_i, query_stim_11), columns))\n",
"summary_i['PRS'] = list(map(lambda x: PRS(x, query_base_i, query_stim_11), columns))\n",
"\n",
"summary_ii = pd.DataFrame()\n",
"\n",
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"summary_ii['Baseline II'] = results.query(query_base_ii)[columns].agg(summarize)\n",
"summary_ii['30 Hz'] = results.query(query_stim_30)[columns].agg(summarize)\n",
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"\n",
"summary_ii['MWU'] = list(map(lambda x: MWU(x, query_base_ii, query_stim_30), columns))\n",
"summary_ii['PRS'] = list(map(lambda x: PRS(x, query_base_ii, query_stim_30), columns))\n",
"\n",
"summary_combined = pd.DataFrame()\n",
"\n",
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"summary_combined['Baseline I'] = results.query('not {}'.format(query_base_i))[columns].agg(summarize)\n",
"summary_combined['Combined'] = results.query('{}'.format(query_stim_combined))[columns].agg(summarize)\n",
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"\n",
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"summary_combined['MWU'] = list(map(lambda x: MWU(x, query_base_i, query_stim_combined), columns))\n",
"summary_combined['PRS'] = list(map(lambda x: PRS(x, query_base_i, query_stim_combined), columns))\n",
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"\n",
"summary_i.to_latex(output_path / \"statistics\" / \"power_freq_score_summary_i.tex\")\n",
"summary_i.to_csv(output_path / \"statistics\" / \"power_freq_score_summary_i.csv\")\n",
"\n",
"summary_ii.to_latex(output_path / \"statistics\" / \"power_freq_score_summary_ii.tex\")\n",
"summary_ii.to_csv(output_path / \"statistics\" / \"power_freq_score_summary_ii.csv\")\n",
"\n",
"summary_combined.to_latex(output_path / \"statistics\" / \"power_freq_score_summary_combined.tex\")\n",
"summary_combined.to_csv(output_path / \"statistics\" / \"power_freq_score_summary_combined.csv\")"
]
},
{
"cell_type": "code",
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"execution_count": 20,
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"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",
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" <th>Baseline I</th>\n",
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" <th>11 Hz</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>power_score</th>\n",
" <td>-0.03 ± 0.01 (208)</td>\n",
" <td>-0.03 ± 0.01 (208)</td>\n",
" <td>32624.00, 0.000</td>\n",
" <td>0.18, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_score</th>\n",
" <td>-0.01 ± 0.01 (208)</td>\n",
" <td>-0.01 ± 0.01 (208)</td>\n",
" <td>37221.00, 0.000</td>\n",
" <td>0.21, 0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline I 11 Hz MWU \\\n",
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"power_score -0.03 ± 0.01 (208) -0.03 ± 0.01 (208) 32624.00, 0.000 \n",
"freq_score -0.01 ± 0.01 (208) -0.01 ± 0.01 (208) 37221.00, 0.000 \n",
"\n",
" PRS \n",
"power_score 0.18, 0.000 \n",
"freq_score 0.21, 0.000 "
]
},
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"execution_count": 20,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_i"
]
},
{
"cell_type": "code",
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"execution_count": 21,
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"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",
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" <th>Baseline II</th>\n",
" <th>30 Hz</th>\n",
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" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>power_score</th>\n",
" <td>0.04 ± 0.01 (136)</td>\n",
" <td>0.04 ± 0.01 (136)</td>\n",
" <td>12586.00, 0.000</td>\n",
" <td>0.08, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_score</th>\n",
" <td>0.01 ± 0.01 (136)</td>\n",
" <td>0.01 ± 0.01 (136)</td>\n",
" <td>16950.00, 0.000</td>\n",
" <td>0.22, 0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline II 30 Hz MWU \\\n",
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"power_score 0.04 ± 0.01 (136) 0.04 ± 0.01 (136) 12586.00, 0.000 \n",
"freq_score 0.01 ± 0.01 (136) 0.01 ± 0.01 (136) 16950.00, 0.000 \n",
"\n",
" PRS \n",
"power_score 0.08, 0.000 \n",
"freq_score 0.22, 0.000 "
]
},
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"execution_count": 21,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_ii"
]
},
{
"cell_type": "code",
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"execution_count": 22,
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"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",
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" <th>Baseline I</th>\n",
" <th>Combined</th>\n",
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" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>power_score</th>\n",
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" <td>-0.03 ± 0.01 (208)</td>\n",
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" <td>0.03 ± 0.01 (480)</td>\n",
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" <td>67804.00, 0.000</td>\n",
" <td>0.13, 0.000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>freq_score</th>\n",
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" <td>-0.01 ± 0.01 (208)</td>\n",
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" <td>0.06 ± 0.01 (480)</td>\n",
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" <td>72140.00, 0.000</td>\n",
" <td>0.17, 0.000</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline I Combined MWU \\\n",
"power_score -0.03 ± 0.01 (208) 0.03 ± 0.01 (480) 67804.00, 0.000 \n",
"freq_score -0.01 ± 0.01 (208) 0.06 ± 0.01 (480) 72140.00, 0.000 \n",
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"\n",
" PRS \n",
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"power_score 0.13, 0.000 \n",
"freq_score 0.17, 0.000 "
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]
},
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"execution_count": 22,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_combined"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register in expipe"
]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"action = project.actions[\"lfp_speed\"]"
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]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [],
"source": [
"outdata = {\n",
" \"figures\": \"figures\",\n",
" \"statistics\": \"statistics\"\n",
"}\n",
"\n",
"for key, value in outdata.items():\n",
" action.data[key] = value\n",
" data_path = action.data_path(key)\n",
" data_path.parent.mkdir(exist_ok=True, parents=True)\n",
" source = output_path / value\n",
" if source.is_file():\n",
" shutil.copy(source, data_path)\n",
" else:\n",
" copy_tree(str(source), str(data_path))"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [],
"source": [
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"septum_mec.analysis.registration.store_notebook(action, \"20_lfp_speed.ipynb\")"
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]
},
{
"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
}
