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lfp speed results
2019-12-18 14:03:01 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"14:40:25 [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 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",
"execution_count": 5,
"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",
"execution_count": 6,
"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",
"execution_count": 8,
"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",
"execution_count": 9,
"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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" .dataframe thead th {\n",
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"<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",
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" <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",
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" <td>[0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000...</td>\n",
" <td>[6.399999999999999, 6.399999999999999, 6.39999...</td>\n",
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" </tr>\n",
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],
"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... "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"results = results.merge(sessions, on='action')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frequency score"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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_base_combined = 'baseline and Hz11 or Hz30'\n",
"query_stim_combined = 'stimulated and Hz11 or Hz30'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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",
"U-test: U value 150719.0 p value 5.562570660595682e-21\n"
]
},
{
"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": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAASAAAAGeCAYAAAA0Q9yFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOy9eZQk13Wf+b3Ycs+sDb2gF3Q3GohuAAQIgCBAggQhgLtEidJYlOwzljW2j+2RRpRlH9kacWxxaEtzZjyekXxIHdkeaWSNJEu0JZGUaIqkQIIQCJAiSCwCAQSxo3d0d1XlnhnLe/NHZBYKjarqWjIjcnnfOThJVmZG3Oqq+uW9991FKKXQaDSaNDDSNkCj0UwvWoA0Gk1qaAHSaDSpoQVIo9GkhhYgjUaTGlqANBpNamgB0mg0qaEFSKPRpIYWII1GkxpagDQaTWpoAdJoNKmhBUij0aSGFiCNRpMaWoA0Gk1qaAHSJI7rul9yXfeL6zz3H1zX9TZ47y+6rtvd4Pm/5bqucl137yBs1QwXK20DNNOB67oC+H7gz4GLgHJd1wbeB3wBeC9wf++5i67rGsDdwHc8z1t2Xfce4Nv953vXvB047XneCdd17wSe618bWHRd93pAep73dGLfqGZLaA9IkxS3A38KvAK8CbgBeLn3tR8DPg+cBt4N7AY84D7gw67rOsBngDPA3wZM13UfBb4B/L3e9X8TOAn8AtAFvgg8CfzzBL43zTbRAqRJim8SC8//BRwHrgd+A3g78IeAC/w8cB1whFiQ7gV+3/M8v/f6vwscBXYB3yX2qP6P3vXvAj4C7AGyQBv4EeAfD/9b02wXLUCaRPA8TxF7Ne8nFqNHiMOuRz3PizzPexZ4B3AO+CzwAeDxnvjged4JYuHKAv8v8B7gGc/zWr3nXwVmiQXqk8SCdNrzvOXEvknNltECpEmSe4lF5meAfwDcCnwIwHXdG4k9mJ8Dfgq4AvjJ/htd190N/DTwS733N1jl3biumwV+kVh8fhZ4HPjYkL8fzQ4Reii9Jklc193red6Z3v++0vO805t5rve1PcA5z/PU6teuen4G6Hqe13Zddxew6HleOPRvSrNttABpNJrU0CGYRqNJDS1AGo0mNbQArYPrup9zXfdzaduh0UwyuhJ6fa4+evTodcRVtRqN5jXEoC6kPSCNRpMaWoA0Gk1qaAHSaDSpoQVIo9GkhhYgjUaTGlqANBpNamgB0mg0qaEFSKPRpIYWII1GkxpagDQaTWpoAdJoNKmhBUij0aSGbkadAlqtFr7vp23G1OM4Dvl8Pm0zRgotQBPOT/3UT/Ebv/Eb6MmX6SOE4B/9o3/Er//6r6dtysigR7Kug+u63z169Oh1n//859M2Zdu0Wi2KxaIWnxFCCEGj0Rh3T0iP49BcHt/3tfiMGEopHQ6vQodgU8RLL71EpVJJ24ypo1qtcujQobTNGEm0AE0RlUqFmZmZtM3QaFbQIZhGo0kNLUAajSY1tABpNJrU0AKk0WhSQyehJwylFH99usZiy6fTbmEYBlJKDNPkG69UyV8IuW5PiYViJm1TpwKlFOrCuZWfg2mamH43bbNGBu0BTRgvXmzxqb98gd/6xsv8/uPnufbdH0EYJtfe+6P8/uPn+a1vvMxvf/OVtM2cGhqP/RVLv/MpfuLWGzCF4G/fcj1Lv/MplIzSNm0k0B7QhHGq2qYTRCy3AzKmwQ0f+cfc8JF/DEDLj/AjyelqG6UUQgysoFWzDv6rZ4laTX7h1mP8wluvRwUBYaOGbLcxC8W0zUsdLUATxoWGTygVedtkLu+87jmpFCeW27SDiKYfUczoH/+wke0WSIlRKGGVyvhnT4GUyHZLCxA6BJs4LjZjAbKMN3o3hhCYQhBKxcWmbgdIAtluoaREGL0/NWGgpCRqN9M1bETQAjRhLLZ8wkiuKUAAlhEL0GJLC1ASRK0GyAj6AmQYsQfU0gIEWoAmjr4HZBpr/2gtQxBG2gNKiks9INEToKjdStmy0UAL0AQRRJJaJyBaJwQDME1BpEOwxIh6OaDVHpCSkqjZSNewEUEL0ASx2PKRvekb6+gPlmEQSsmSDsGGjuqFWrEHZAKxB6RkpEOwHlqAJoilVkAo4/zPekfsryWhg4Stmz5ku4VSEpRa5QGZcQimBQjQAjRRXGz6hJHCMtf/sVq9EEx7QMMnajYgivM//Q8EsRKC1VO2bjTQAjRBLLV6CegNCgz7p2BNP6QT6GrcYRI163HF86oDAWGaEEVInQMCUi5EdF33J4CfBVygBXwJ+JjneS9v41oCuA/4PuCw53kvDdDUseBi048T0Ob6AmQIgSFiL2ix5XNlJZeghdNF1KjBqvwPAIaJkhFhQ3tAkKIH5LruLwP/CcgCnyIWjx8HHnFd9/A2LvlRYvGZWi5sUIS4mr4XdKGhw7BhEjXqqCgC8zUBEmbvGL5R0/O6SckDcl33JuAXgQeBez3P83tf/zTwx8CvAT+4heu5wP82BFPHirgGSGIZG/9Y+wKkj+KHS1iromS0hgckUVGIbDWnvh0jLQ/oo73HT/TFB8DzvD8BHgB+wHXdfZu5kOu6JvA7wKvAE4M2dFzww/hoPYwU1jpFiH36xYivNvRYiGES1ZYhiuK8Tw8hRFyMGEWEteUUrRsN0hKge4CQWGwu5T7ivUObDaf+Z+A24O8BUxtYX2h2iaRCiPVrgPpYpkEgJee1AA2VsLb8hhAMANNCRVEsUFNO4gLkuq4DXAWc8Dxvrb+AF3qPxzZxrZuBfwn8hud59w3OyvHjXL1LIGPv53JjNuyeB3SupgVomITVZVQUvs4DgvgkTEURYVULUBoe0Byxh7O4zvPV3uOG+2Nc180Qh14ngX82MOvGlFPLHYJQ4mxQA9THNg2CSHKh2dVH8UNChQFRvYqKIoT5+pycMC1UFBIsXUzJutEhDQHqD6lZ7+O3//XsZa7zr4Drgf/B87ypL6o4U+sQSIW9wRF8H9OIj+JDqThb6yRg3fQRez89cb8kJycsE8KQcHm9z+DpIQ0BavcenXWe7w8rXldUXNd9B/BPgU96nve1Ado2tpxYauGHEnsTHhDEXpAfSk4sty//Ys2WCRbP98Iv6w0h8YoHdPF8StaNDmkIUBWQrB9iVVa97g24rlsAfps4V/QLgzZuHGn6IecbXYJocyEYQMYy8CPJy4t6LMQwCC6cR4UBwnpjSYSwbFQYEi5eQEmZgnWjQ+J1QJ7n+a7rvgAcdF3X9jzv0q7Iq3uPT61zidtWvaYZlwC9gRd7X5+KiuiXL7bwI4VpCMzLHYH1cExBrRvx4kUtQMMgWDyPCsM1BQgzrgWSgU9YXcKenU/ewBEhrVaM+4G/D9zZ+9+ruRdQwNfXee9LwP+6znN/FzhAXMi43Ptv4nn2fINuEJGxNu/QZiwTv+lzutqm0Q31fOgBE5w/iwoDjGz+Dc8JIXpeUEBw4VUtQCnwW8QC9Cuu697reV4bwHXdHwbeCXzW87yTa72x59F8fK3nXNd9N7EA/eo0eD59vne+QSeU5G3z8i/uYRoCyzTohpJnzze4ef+Gh46aLaCUwj93BhUEiJK95muEbaOCAP/VM+SvOZ6whaNDKgLked7Drut+Cvhp4HHXdT8D7Ac+ApwD/kn/ta7r3g3cDTzmed5nkrd2tGn6IS9ebNENI+Zya/+yr0fWMugEkqfP1rUADZCoUSdqNeMjeGsdAep7QK+eSdi60SLNcRw/0/uvS9ya8S7gD4C3e573wqrX3Q38EvDhpA0cB757pk47iDANY8M5QGuRs03aQcSTZ3Rj5CDxz55aSUCvVxRq9D2gs6cTtm60SC3w9zxPAZ/s/bfR6z7OOiHXGq99x44NGzOeOFWl7Ufk7a1/lmQtgzCSXGh0eWWpzVVzb8xXaLaOf+YkKvAR9nqVJnEIJgMf//xZZBBg2FvzXicFPZBsjOkEEU+crtIKInL21j9LhBBkbZOWH/HIK0tDsHA66Z49iQoCjA0ECMNECIEKfPxz0+sFaQEaYx4/VaXeDTGIj9W3Q8GxaPoR33plCanDsIH
"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",
"base = results.query(query_base_combined)[stuff].to_numpy()\n",
"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",
"execution_count": 14,
"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",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"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",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"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",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"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",
" query_base_combined, query_stim_combined, 'mean_freq', \n",
" colors[4:], labels[4:], filename='lfp_speed_freq_combined')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Power Familiar"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"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",
" query_base_i, query_stim_11, 'mean_power', \n",
" colors, labels, filename='lfp_speed_power_11')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"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",
" query_base_ii, query_stim_30, 'mean_power', \n",
" colors[2:], labels[2:], filename='lfp_speed_power_30')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"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",
" query_base_combined, query_stim_combined, 'mean_power', \n",
" colors[4:], labels[4:], filename='lfp_speed_power_combined')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Speed"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 103872.0 p value 0.07377152599659564\n"
]
},
{
"ename": "NameError",
"evalue": "name 'control_color' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-26-80d1c5c9dd12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mstim\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\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;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery_stim_combined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstuff\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[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\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[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'control'\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[0mcontrol_color\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 21\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[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'base'\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[0mbase_color\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 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'control_color' is not defined"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 600x450 with 0 Axes>"
]
},
"metadata": {},
"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",
"base = np.array([f(a) for a in results.query(query_base_combined)[stuff]])\n",
"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",
"base = np.array([np.histogram(a, bins)[0] for a in results.query(query_base_combined)[stuff]])\n",
"stim = np.array([np.histogram(a, bins)[0] for a in results.query(query_stim_combined)[stuff]])\n",
"\n",
"plt.bar(bins[1:], stim.mean(axis=0), width=np.diff(bins)[0], label='control', alpha=.5, color=control_color);\n",
"plt.bar(bins[1:], base.mean(axis=0), width=np.diff(bins)[0], label='base', alpha=.5, color=base_color);\n",
"\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",
"execution_count": 35,
"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",
"summary_i['Baseline'] = results.query('not {}'.format(query_base_i))[columns].agg(summarize)\n",
"summary_i['11 Hz'] = results.query('{}'.format(query_stim_11))[columns].agg(summarize)\n",
"\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",
"summary_ii['Baseline'] = results.query('not {}'.format(query_base_ii))[columns].agg(summarize)\n",
"summary_ii['11 Hz'] = results.query('{}'.format(query_stim_30))[columns].agg(summarize)\n",
"\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",
"summary_combined['Baseline'] = results.query('not {}'.format(query_base_combined))[columns].agg(summarize)\n",
"summary_combined['11 Hz'] = results.query('{}'.format(query_stim_combined))[columns].agg(summarize)\n",
"\n",
"summary_combined['MWU'] = list(map(lambda x: MWU(x, query_base_combined, query_stim_combined), columns))\n",
"summary_combined['PRS'] = list(map(lambda x: PRS(x, query_base_combined, query_stim_combined), columns))\n",
"\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",
"execution_count": 32,
"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>Baseline</th>\n",
" <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": [
" Baseline 11 Hz MWU \\\n",
"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 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_i"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>Baseline</th>\n",
" <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>0.04 ± 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": [
" Baseline 11 Hz MWU \\\n",
"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 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_ii"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>Baseline</th>\n",
" <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 (480)</td>\n",
" <td>0.03 ± 0.01 (480)</td>\n",
" <td>144593.00, 0.000</td>\n",
" <td>0.05, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_score</th>\n",
" <td>0.06 ± 0.01 (480)</td>\n",
" <td>0.06 ± 0.01 (480)</td>\n",
" <td>150719.00, 0.000</td>\n",
" <td>0.12, 0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline 11 Hz MWU \\\n",
"power_score 0.03 ± 0.01 (480) 0.03 ± 0.01 (480) 144593.00, 0.000 \n",
"freq_score 0.06 ± 0.01 (480) 0.06 ± 0.01 (480) 150719.00, 0.000 \n",
"\n",
" PRS \n",
"power_score 0.05, 0.000 \n",
"freq_score 0.12, 0.000 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_combined"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register in expipe"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Project' object has no attribute 'get_action'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-36-351529841fbe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"lfp_speed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Project' object has no attribute 'get_action'"
]
}
],
"source": [
"action = project.g_action(\"lfp_speed\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"pnnmec.registration.store_notebook(action, \"20_power_spectrum_density.ipynb\")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING: Code repo dirty. Please commit your changes.\n",
"WARNING: Data repo dirty. Please commit your changes.\n"
]
}
],
"source": [
"action.modules[\"code_version\"] = vc.create_code_version_module()\n",
"action.modules[\"data_version\"] = vc.create_data_version_module(project_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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