3029 lines
1.1 MiB
Plaintext
3029 lines
1.1 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import re\n",
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"import shutil\n",
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"import pandas as pd\n",
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"import scipy.stats\n",
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"from functools import reduce\n",
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"import statsmodels\n",
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"import seaborn as sns\n",
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"import exdir\n",
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"import expipe\n",
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"from distutils.dir_util import copy_tree\n",
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"import septum_mec\n",
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"import spatial_maps as sp\n",
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"import head_direction.head as head\n",
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"import septum_mec.analysis.data_processing as dp\n",
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"import septum_mec.analysis.registration\n",
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"from septum_mec.analysis.plotting import violinplot, savefig, despine\n",
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"from tqdm.notebook import tqdm\n",
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"tqdm.pandas()\n",
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"\n",
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"from septum_mec.analysis.statistics import (\n",
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" load_data_frames, \n",
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" make_paired_tables, \n",
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" make_statistics_table, \n",
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" estimate_power_lmm, \n",
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" estimate_power_lmm_paralell, \n",
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" LMM, \n",
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" estimate_sample_size_lmm, \n",
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" estimate_sample_size_lmm_paralell\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"project_path = dp.project_path()\n",
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"project = expipe.get_project(project_path)\n",
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"actions = project.actions\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / \"comparisons-power\"\n",
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"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
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"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
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"(output_path / \"data\").mkdir(exist_ok=True, parents=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Load cell statistics and shuffling quantiles"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of sessions above threshold 194\n",
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"Number of animals 4\n",
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"Number of individual gridcells 139\n",
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"Number of gridcell recordings 230\n"
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]
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}
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],
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"source": [
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"data, labels, colors, queries = load_data_frames()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Calculate statistics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"columns = [\n",
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" 'average_rate', \n",
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" 'gridness', \n",
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" 'information_specificity',\n",
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" 'max_rate', \n",
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" 'information_rate', \n",
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" 'in_field_mean_rate', \n",
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" 'out_field_mean_rate', \n",
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" 'speed_score', \n",
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" 'spacing', \n",
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" 'field_area'\n",
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"\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"results, labels = make_paired_tables(data, columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 151,
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"metadata": {},
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"outputs": [
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{
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|
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"data": {
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|
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"text/plain": [
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|
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f54b4eddcc0>"
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|
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]
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},
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|
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"execution_count": 151,
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|
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"metadata": {},
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|
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"output_type": "execute_result"
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|
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},
|
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|
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{
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"data": {
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"image/png": "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|
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"text/plain": [
|
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|
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"<Figure size 900x600 with 1 Axes>"
|
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]
|
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},
|
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|
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"metadata": {
|
||
|
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"needs_background": "light"
|
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},
|
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|
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"output_type": "display_data"
|
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|
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}
|
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|
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],
|
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"source": [
|
||
|
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"plt.rc('axes', titlesize=12)\n",
|
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|
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"plt.rcParams.update({\n",
|
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|
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" 'font.size': 12, \n",
|
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|
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" 'figure.figsize': (6, 4), \n",
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|
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" 'figure.dpi': 150\n",
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"})\n",
|
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"\n",
|
||
|
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"results['gridcell']['gridness'][labels].plot.density()"
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|
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]
|
||
|
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},
|
||
|
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{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 77,
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||
|
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"metadata": {},
|
||
|
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"outputs": [
|
||
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{
|
||
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"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
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" }\n",
|
||
|
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"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
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"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
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" }\n",
|
||
|
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"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
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" <tr style=\"text-align: right;\">\n",
|
||
|
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" <th></th>\n",
|
||
|
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" <th>Baseline I</th>\n",
|
||
|
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" <th>11 Hz</th>\n",
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||
|
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" <th>Baseline II</th>\n",
|
||
|
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" <th>30 Hz</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
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" <tr>\n",
|
||
|
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" <th>Average rate</th>\n",
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||
|
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" <td>9.040 ± 6.369</td>\n",
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||
|
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" <td>8.934 ± 6.545</td>\n",
|
||
|
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" <td>8.368 ± 6.154</td>\n",
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||
|
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" <td>7.553 ± 5.359</td>\n",
|
||
|
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" </tr>\n",
|
||
|
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" <tr>\n",
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||
|
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" <th>Gridness</th>\n",
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" <td>0.527 ± 0.353</td>\n",
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" <td>0.393 ± 0.369</td>\n",
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|
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" <td>0.579 ± 0.287</td>\n",
|
||
|
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" <td>0.479 ± 0.355</td>\n",
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|
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" </tr>\n",
|
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|
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" <tr>\n",
|
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|
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" <th>Information specificity</th>\n",
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" <td>0.245 ± 0.229</td>\n",
|
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|
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" <td>0.208 ± 0.264</td>\n",
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" <td>0.218 ± 0.177</td>\n",
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" <td>0.226 ± 0.209</td>\n",
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|
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" </tr>\n",
|
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|
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" <tr>\n",
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" <th>Max rate</th>\n",
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" <td>37.533 ± 15.081</td>\n",
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" <td>32.799 ± 14.292</td>\n",
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" <td>37.684 ± 16.860</td>\n",
|
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|
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" <td>34.584 ± 12.544</td>\n",
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||
|
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" </tr>\n",
|
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|
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" <tr>\n",
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" <th>Information rate</th>\n",
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|
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" <td>1.324 ± 0.617</td>\n",
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" <td>0.906 ± 0.543</td>\n",
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||
|
|
" <td>1.177 ± 0.640</td>\n",
|
||
|
|
" <td>0.983 ± 0.555</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>In field mean rate</th>\n",
|
||
|
|
" <td>14.973 ± 8.381</td>\n",
|
||
|
|
" <td>13.344 ± 8.073</td>\n",
|
||
|
|
" <td>14.126 ± 7.835</td>\n",
|
||
|
|
" <td>12.109 ± 6.060</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Out field mean rate</th>\n",
|
||
|
|
" <td>6.389 ± 5.358</td>\n",
|
||
|
|
" <td>6.712 ± 5.898</td>\n",
|
||
|
|
" <td>5.787 ± 5.225</td>\n",
|
||
|
|
" <td>5.265 ± 4.488</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Speed score</th>\n",
|
||
|
|
" <td>0.142 ± 0.081</td>\n",
|
||
|
|
" <td>0.105 ± 0.090</td>\n",
|
||
|
|
" <td>0.120 ± 0.060</td>\n",
|
||
|
|
" <td>0.104 ± 0.073</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Spacing</th>\n",
|
||
|
|
" <td>0.439 ± 0.121</td>\n",
|
||
|
|
" <td>0.456 ± 0.123</td>\n",
|
||
|
|
" <td>0.416 ± 0.093</td>\n",
|
||
|
|
" <td>0.424 ± 0.080</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Field area</th>\n",
|
||
|
|
" <td>0.431 ± 0.053</td>\n",
|
||
|
|
" <td>0.417 ± 0.051</td>\n",
|
||
|
|
" <td>0.423 ± 0.051</td>\n",
|
||
|
|
" <td>0.431 ± 0.052</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Baseline I 11 Hz Baseline II \\\n",
|
||
|
|
"Average rate 9.040 ± 6.369 8.934 ± 6.545 8.368 ± 6.154 \n",
|
||
|
|
"Gridness 0.527 ± 0.353 0.393 ± 0.369 0.579 ± 0.287 \n",
|
||
|
|
"Information specificity 0.245 ± 0.229 0.208 ± 0.264 0.218 ± 0.177 \n",
|
||
|
|
"Max rate 37.533 ± 15.081 32.799 ± 14.292 37.684 ± 16.860 \n",
|
||
|
|
"Information rate 1.324 ± 0.617 0.906 ± 0.543 1.177 ± 0.640 \n",
|
||
|
|
"In field mean rate 14.973 ± 8.381 13.344 ± 8.073 14.126 ± 7.835 \n",
|
||
|
|
"Out field mean rate 6.389 ± 5.358 6.712 ± 5.898 5.787 ± 5.225 \n",
|
||
|
|
"Speed score 0.142 ± 0.081 0.105 ± 0.090 0.120 ± 0.060 \n",
|
||
|
|
"Spacing 0.439 ± 0.121 0.456 ± 0.123 0.416 ± 0.093 \n",
|
||
|
|
"Field area 0.431 ± 0.053 0.417 ± 0.051 0.423 ± 0.051 \n",
|
||
|
|
"\n",
|
||
|
|
" 30 Hz \n",
|
||
|
|
"Average rate 7.553 ± 5.359 \n",
|
||
|
|
"Gridness 0.479 ± 0.355 \n",
|
||
|
|
"Information specificity 0.226 ± 0.209 \n",
|
||
|
|
"Max rate 34.584 ± 12.544 \n",
|
||
|
|
"Information rate 0.983 ± 0.555 \n",
|
||
|
|
"In field mean rate 12.109 ± 6.060 \n",
|
||
|
|
"Out field mean rate 5.265 ± 4.488 \n",
|
||
|
|
"Speed score 0.104 ± 0.073 \n",
|
||
|
|
"Spacing 0.424 ± 0.080 \n",
|
||
|
|
"Field area 0.431 ± 0.052 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 77,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"summary = pd.DataFrame()\n",
|
||
|
|
"for key, df in results['gridcell'].items():\n",
|
||
|
|
" Key = key.replace('_', ' ').capitalize()\n",
|
||
|
|
" for label in labels:\n",
|
||
|
|
" summary.loc[label, Key] = \"{:.3f} ± {:.3f}\".format(df[label].mean(), df[label].std())\n",
|
||
|
|
"summary.T"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 83,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
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"data": {
|
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|
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"text/html": [
|
||
|
|
"<table class=\"simpletable\">\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <td>Model:</td> <td>MixedLM</td> <td>Dependent Variable:</td> <td>val</td> \n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <td>No. Observations:</td> <td>119</td> <td>Method:</td> <td>REML</td> \n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <td>No. Groups:</td> <td>4</td> <td>Scale:</td> <td>0.0932</td> \n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <td>Min. group size:</td> <td>7</td> <td>Log-Likelihood:</td> <td>-49.4026</td>\n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <td>Max. group size:</td> <td>81</td> <td>Converged:</td> <td>Yes</td> \n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <td>Mean group size:</td> <td>29.8</td> <td></td> <td></td> \n",
|
||
|
|
"</tr>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"<table class=\"simpletable\">\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <td></td> <th>Coef.</th> <th>Std.Err.</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <th>Intercept</th> <td>0.409</td> <td>0.048</td> <td>8.488</td> <td>0.000</td> <td>0.315</td> <td>0.504</td>\n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <th>label[T.Baseline I]</th> <td>0.124</td> <td>0.062</td> <td>1.997</td> <td>0.046</td> <td>0.002</td> <td>0.246</td>\n",
|
||
|
|
"</tr>\n",
|
||
|
|
"<tr>\n",
|
||
|
|
" <th>unit_idnum Var</th> <td>0.036</td> <td>0.090</td> <td></td> <td></td> <td></td> <td></td> \n",
|
||
|
|
"</tr>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<class 'statsmodels.iolib.summary2.Summary'>\n",
|
||
|
|
"\"\"\"\n",
|
||
|
|
" Mixed Linear Model Regression Results\n",
|
||
|
|
"============================================================\n",
|
||
|
|
"Model: MixedLM Dependent Variable: val \n",
|
||
|
|
"No. Observations: 119 Method: REML \n",
|
||
|
|
"No. Groups: 4 Scale: 0.0932 \n",
|
||
|
|
"Min. group size: 7 Log-Likelihood: -49.4026\n",
|
||
|
|
"Max. group size: 81 Converged: Yes \n",
|
||
|
|
"Mean group size: 29.8 \n",
|
||
|
|
"------------------------------------------------------------\n",
|
||
|
|
" Coef. Std.Err. z P>|z| [0.025 0.975]\n",
|
||
|
|
"------------------------------------------------------------\n",
|
||
|
|
"Intercept 0.409 0.048 8.488 0.000 0.315 0.504\n",
|
||
|
|
"label[T.Baseline I] 0.124 0.062 1.997 0.046 0.002 0.246\n",
|
||
|
|
"unit_idnum Var 0.036 0.090 \n",
|
||
|
|
"============================================================\n",
|
||
|
|
"\n",
|
||
|
|
"\"\"\""
|
||
|
|
]
|
||
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},
|
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|
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"execution_count": 83,
|
||
|
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"metadata": {},
|
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|
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"output_type": "execute_result"
|
||
|
|
}
|
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|
|
],
|
||
|
|
"source": [
|
||
|
|
"mdf.summary()"
|
||
|
|
]
|
||
|
|
},
|
||
|
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{
|
||
|
|
"cell_type": "code",
|
||
|
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"execution_count": 10,
|
||
|
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"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
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"source": [
|
||
|
|
"vss = [\n",
|
||
|
|
" ('Baseline I', '11 Hz'),\n",
|
||
|
|
" ('Baseline I', 'Baseline II'),\n",
|
||
|
|
" ('Baseline II', '30 Hz'),\n",
|
||
|
|
"]"
|
||
|
|
]
|
||
|
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},
|
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{
|
||
|
|
"cell_type": "code",
|
||
|
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"execution_count": 92,
|
||
|
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"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"ps = pd.DataFrame()\n",
|
||
|
|
"ci = pd.DataFrame()\n",
|
||
|
|
"ef = pd.DataFrame()\n",
|
||
|
|
"mf = pd.DataFrame()\n",
|
||
|
|
"for key, df in results['gridcell'].items():\n",
|
||
|
|
" Key = key.replace('_', ' ').capitalize()\n",
|
||
|
|
" for vs in vss:\n",
|
||
|
|
" pval, low, high, mdf = LMM(df, *vs, key)\n",
|
||
|
|
" ps.loc[f'LMM {vs[0]} - {vs[1]}', key] = pval\n",
|
||
|
|
" ci.loc[f'LMM {vs[0]} - {vs[1]}', key] = f'{low}, {high}'\n",
|
||
|
|
" ef.loc[f'LMM {vs[0]} - {vs[1]}', key] = mdf.params[1]\n",
|
||
|
|
" mf.loc[f'LMM {vs[0]} - {vs[1]}', key] = abs(df[vs[0]].mean() - df[vs[1]].mean())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"cell_type": "code",
|
||
|
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"execution_count": 93,
|
||
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|
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|
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" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
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|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>average_rate</th>\n",
|
||
|
|
" <th>gridness</th>\n",
|
||
|
|
" <th>information_specificity</th>\n",
|
||
|
|
" <th>max_rate</th>\n",
|
||
|
|
" <th>information_rate</th>\n",
|
||
|
|
" <th>in_field_mean_rate</th>\n",
|
||
|
|
" <th>out_field_mean_rate</th>\n",
|
||
|
|
" <th>speed_score</th>\n",
|
||
|
|
" <th>spacing</th>\n",
|
||
|
|
" <th>field_area</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - 11 Hz</th>\n",
|
||
|
|
" <td>0.928209</td>\n",
|
||
|
|
" <td>0.054459</td>\n",
|
||
|
|
" <td>0.427792</td>\n",
|
||
|
|
" <td>0.088252</td>\n",
|
||
|
|
" <td>0.000080</td>\n",
|
||
|
|
" <td>0.285853</td>\n",
|
||
|
|
" <td>0.759764</td>\n",
|
||
|
|
" <td>0.016639</td>\n",
|
||
|
|
" <td>0.441497</td>\n",
|
||
|
|
" <td>0.112510</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - Baseline II</th>\n",
|
||
|
|
" <td>0.626481</td>\n",
|
||
|
|
" <td>0.587566</td>\n",
|
||
|
|
" <td>0.618199</td>\n",
|
||
|
|
" <td>0.955198</td>\n",
|
||
|
|
" <td>0.265741</td>\n",
|
||
|
|
" <td>0.595705</td>\n",
|
||
|
|
" <td>0.642428</td>\n",
|
||
|
|
" <td>0.040733</td>\n",
|
||
|
|
" <td>0.985474</td>\n",
|
||
|
|
" <td>0.535198</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline II - 30 Hz</th>\n",
|
||
|
|
" <td>0.556933</td>\n",
|
||
|
|
" <td>0.107195</td>\n",
|
||
|
|
" <td>0.839598</td>\n",
|
||
|
|
" <td>0.315473</td>\n",
|
||
|
|
" <td>0.081515</td>\n",
|
||
|
|
" <td>0.195477</td>\n",
|
||
|
|
" <td>0.665571</td>\n",
|
||
|
|
" <td>0.382314</td>\n",
|
||
|
|
" <td>0.603904</td>\n",
|
||
|
|
" <td>0.729193</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" average_rate gridness information_specificity \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.928209 0.054459 0.427792 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.626481 0.587566 0.618199 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.556933 0.107195 0.839598 \n",
|
||
|
|
"\n",
|
||
|
|
" max_rate information_rate in_field_mean_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.088252 0.000080 0.285853 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.955198 0.265741 0.595705 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.315473 0.081515 0.195477 \n",
|
||
|
|
"\n",
|
||
|
|
" out_field_mean_rate speed_score spacing \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.759764 0.016639 0.441497 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.642428 0.040733 0.985474 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.665571 0.382314 0.603904 \n",
|
||
|
|
"\n",
|
||
|
|
" field_area \n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.112510 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.535198 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.729193 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 93,
|
||
|
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|
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|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"ps"
|
||
|
|
]
|
||
|
|
},
|
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|
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{
|
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|
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|
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|
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|
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|
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|
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|
|
" 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>average_rate</th>\n",
|
||
|
|
" <th>gridness</th>\n",
|
||
|
|
" <th>information_specificity</th>\n",
|
||
|
|
" <th>max_rate</th>\n",
|
||
|
|
" <th>information_rate</th>\n",
|
||
|
|
" <th>in_field_mean_rate</th>\n",
|
||
|
|
" <th>out_field_mean_rate</th>\n",
|
||
|
|
" <th>speed_score</th>\n",
|
||
|
|
" <th>spacing</th>\n",
|
||
|
|
" <th>field_area</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - 11 Hz</th>\n",
|
||
|
|
" <td>-2.180247281054466, 2.390356722874524</td>\n",
|
||
|
|
" <td>-0.0023867695467641864, 0.25185586146463945</td>\n",
|
||
|
|
" <td>-0.051885367585011555, 0.12239826383625366</td>\n",
|
||
|
|
" <td>-0.6829644446775598, 9.804487199879677</td>\n",
|
||
|
|
" <td>0.19919191566537162, 0.5924315270050661</td>\n",
|
||
|
|
" <td>-1.331724110761481, 4.516001132500179</td>\n",
|
||
|
|
" <td>-2.2810214616259765, 1.6653183103649472</td>\n",
|
||
|
|
" <td>0.006403510267647752, 0.0641586683092662</td>\n",
|
||
|
|
" <td>-0.03396312071531312, 0.014810117866574444</td>\n",
|
||
|
|
" <td>-0.0034073546240015826, 0.03240571021475902</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - Baseline II</th>\n",
|
||
|
|
" <td>-2.9916621645393118, 1.8014641972655332</td>\n",
|
||
|
|
" <td>-0.08520533103529787, 0.15040363600350248</td>\n",
|
||
|
|
" <td>-0.09750576854071875, 0.05796956577487157</td>\n",
|
||
|
|
" <td>-5.89536982035741, 6.243311205778393</td>\n",
|
||
|
|
" <td>-0.33674475640623824, 0.09282359337229243</td>\n",
|
||
|
|
" <td>-3.975512425442468, 2.2816253105581294</td>\n",
|
||
|
|
" <td>-2.481648525103001, 1.531057021714961</td>\n",
|
||
|
|
" <td>-0.05295935481734079, -0.0011404936657067148</td>\n",
|
||
|
|
" <td>-0.01536441943997291, 0.015081590471976884</td>\n",
|
||
|
|
" <td>-0.025402212987792654, 0.013191871767799448</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline II - 30 Hz</th>\n",
|
||
|
|
" <td>-1.6613041417405623, 3.083251607547346</td>\n",
|
||
|
|
" <td>-0.02440548847993497, 0.2496890970945263</td>\n",
|
||
|
|
" <td>-0.06538802363578307, 0.08044872106578226</td>\n",
|
||
|
|
" <td>-3.155261296686419, 9.780244204335176</td>\n",
|
||
|
|
" <td>-0.02805504733315664, 0.47641353819177146</td>\n",
|
||
|
|
" <td>-1.0103474044666294, 4.941540621504646</td>\n",
|
||
|
|
" <td>-1.5577059549335808, 2.439136826914514</td>\n",
|
||
|
|
" <td>-0.015556136704263908, 0.04057715367485169</td>\n",
|
||
|
|
" <td>-0.01107815629347443, 0.019054030954896606</td>\n",
|
||
|
|
" <td>-0.023819756067117653, 0.016668142405318446</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" average_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -2.180247281054466, 2.390356722874524 \n",
|
||
|
|
"LMM Baseline I - Baseline II -2.9916621645393118, 1.8014641972655332 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -1.6613041417405623, 3.083251607547346 \n",
|
||
|
|
"\n",
|
||
|
|
" gridness \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -0.0023867695467641864, 0.25185586146463945 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.08520533103529787, 0.15040363600350248 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -0.02440548847993497, 0.2496890970945263 \n",
|
||
|
|
"\n",
|
||
|
|
" information_specificity \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -0.051885367585011555, 0.12239826383625366 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.09750576854071875, 0.05796956577487157 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -0.06538802363578307, 0.08044872106578226 \n",
|
||
|
|
"\n",
|
||
|
|
" max_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -0.6829644446775598, 9.804487199879677 \n",
|
||
|
|
"LMM Baseline I - Baseline II -5.89536982035741, 6.243311205778393 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -3.155261296686419, 9.780244204335176 \n",
|
||
|
|
"\n",
|
||
|
|
" information_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.19919191566537162, 0.5924315270050661 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.33674475640623824, 0.09282359337229243 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -0.02805504733315664, 0.47641353819177146 \n",
|
||
|
|
"\n",
|
||
|
|
" in_field_mean_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -1.331724110761481, 4.516001132500179 \n",
|
||
|
|
"LMM Baseline I - Baseline II -3.975512425442468, 2.2816253105581294 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -1.0103474044666294, 4.941540621504646 \n",
|
||
|
|
"\n",
|
||
|
|
" out_field_mean_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -2.2810214616259765, 1.6653183103649472 \n",
|
||
|
|
"LMM Baseline I - Baseline II -2.481648525103001, 1.531057021714961 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -1.5577059549335808, 2.439136826914514 \n",
|
||
|
|
"\n",
|
||
|
|
" speed_score \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.006403510267647752, 0.0641586683092662 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.05295935481734079, -0.0011404936657067148 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -0.015556136704263908, 0.04057715367485169 \n",
|
||
|
|
"\n",
|
||
|
|
" spacing \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -0.03396312071531312, 0.014810117866574444 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.01536441943997291, 0.015081590471976884 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -0.01107815629347443, 0.019054030954896606 \n",
|
||
|
|
"\n",
|
||
|
|
" field_area \n",
|
||
|
|
"LMM Baseline I - 11 Hz -0.0034073546240015826, 0.03240571021475902 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.025402212987792654, 0.013191871767799448 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -0.023819756067117653, 0.016668142405318446 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 94,
|
||
|
|
"metadata": {},
|
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|
|
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|
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|
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|
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|
|
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|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>average_rate</th>\n",
|
||
|
|
" <th>gridness</th>\n",
|
||
|
|
" <th>information_specificity</th>\n",
|
||
|
|
" <th>max_rate</th>\n",
|
||
|
|
" <th>information_rate</th>\n",
|
||
|
|
" <th>in_field_mean_rate</th>\n",
|
||
|
|
" <th>out_field_mean_rate</th>\n",
|
||
|
|
" <th>speed_score</th>\n",
|
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|
|
" <th>spacing</th>\n",
|
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|
|
" <th>field_area</th>\n",
|
||
|
|
" </tr>\n",
|
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|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - 11 Hz</th>\n",
|
||
|
|
" <td>0.105055</td>\n",
|
||
|
|
" <td>0.124735</td>\n",
|
||
|
|
" <td>0.035256</td>\n",
|
||
|
|
" <td>4.560761</td>\n",
|
||
|
|
" <td>0.395812</td>\n",
|
||
|
|
" <td>1.592139</td>\n",
|
||
|
|
" <td>-0.307852</td>\n",
|
||
|
|
" <td>0.035281</td>\n",
|
||
|
|
" <td>-0.009577</td>\n",
|
||
|
|
" <td>0.014499</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - Baseline II</th>\n",
|
||
|
|
" <td>-0.595099</td>\n",
|
||
|
|
" <td>0.032599</td>\n",
|
||
|
|
" <td>-0.019768</td>\n",
|
||
|
|
" <td>0.173971</td>\n",
|
||
|
|
" <td>-0.121961</td>\n",
|
||
|
|
" <td>-0.846944</td>\n",
|
||
|
|
" <td>-0.475296</td>\n",
|
||
|
|
" <td>-0.027050</td>\n",
|
||
|
|
" <td>-0.000141</td>\n",
|
||
|
|
" <td>-0.006105</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline II - 30 Hz</th>\n",
|
||
|
|
" <td>0.710974</td>\n",
|
||
|
|
" <td>0.112642</td>\n",
|
||
|
|
" <td>0.007530</td>\n",
|
||
|
|
" <td>3.312491</td>\n",
|
||
|
|
" <td>0.224179</td>\n",
|
||
|
|
" <td>1.965597</td>\n",
|
||
|
|
" <td>0.440715</td>\n",
|
||
|
|
" <td>0.012511</td>\n",
|
||
|
|
" <td>0.003988</td>\n",
|
||
|
|
" <td>-0.003576</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" average_rate gridness information_specificity \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.105055 0.124735 0.035256 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.595099 0.032599 -0.019768 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.710974 0.112642 0.007530 \n",
|
||
|
|
"\n",
|
||
|
|
" max_rate information_rate in_field_mean_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 4.560761 0.395812 1.592139 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.173971 -0.121961 -0.846944 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 3.312491 0.224179 1.965597 \n",
|
||
|
|
"\n",
|
||
|
|
" out_field_mean_rate speed_score spacing \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz -0.307852 0.035281 -0.009577 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.475296 -0.027050 -0.000141 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.440715 0.012511 0.003988 \n",
|
||
|
|
"\n",
|
||
|
|
" field_area \n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.014499 \n",
|
||
|
|
"LMM Baseline I - Baseline II -0.006105 \n",
|
||
|
|
"LMM Baseline II - 30 Hz -0.003576 "
|
||
|
|
]
|
||
|
|
},
|
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|
|
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|
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|
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|
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|
|
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|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>average_rate</th>\n",
|
||
|
|
" <th>gridness</th>\n",
|
||
|
|
" <th>information_specificity</th>\n",
|
||
|
|
" <th>max_rate</th>\n",
|
||
|
|
" <th>information_rate</th>\n",
|
||
|
|
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|
||
|
|
" <th>out_field_mean_rate</th>\n",
|
||
|
|
" <th>speed_score</th>\n",
|
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|
|
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|
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|
|
" <th>field_area</th>\n",
|
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|
|
" </tr>\n",
|
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|
|
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|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - 11 Hz</th>\n",
|
||
|
|
" <td>0.105449</td>\n",
|
||
|
|
" <td>0.133233</td>\n",
|
||
|
|
" <td>0.036166</td>\n",
|
||
|
|
" <td>4.734064</td>\n",
|
||
|
|
" <td>0.417936</td>\n",
|
||
|
|
" <td>1.629185</td>\n",
|
||
|
|
" <td>0.322805</td>\n",
|
||
|
|
" <td>0.037336</td>\n",
|
||
|
|
" <td>0.016343</td>\n",
|
||
|
|
" <td>0.014129</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline I - Baseline II</th>\n",
|
||
|
|
" <td>0.671719</td>\n",
|
||
|
|
" <td>0.052668</td>\n",
|
||
|
|
" <td>0.026500</td>\n",
|
||
|
|
" <td>0.151797</td>\n",
|
||
|
|
" <td>0.147302</td>\n",
|
||
|
|
" <td>0.847032</td>\n",
|
||
|
|
" <td>0.602720</td>\n",
|
||
|
|
" <td>0.021480</td>\n",
|
||
|
|
" <td>0.023525</td>\n",
|
||
|
|
" <td>0.008499</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>LMM Baseline II - 30 Hz</th>\n",
|
||
|
|
" <td>0.814797</td>\n",
|
||
|
|
" <td>0.100659</td>\n",
|
||
|
|
" <td>0.007509</td>\n",
|
||
|
|
" <td>3.100584</td>\n",
|
||
|
|
" <td>0.193361</td>\n",
|
||
|
|
" <td>2.016844</td>\n",
|
||
|
|
" <td>0.521574</td>\n",
|
||
|
|
" <td>0.016176</td>\n",
|
||
|
|
" <td>0.007847</td>\n",
|
||
|
|
" <td>0.008441</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" average_rate gridness information_specificity \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.105449 0.133233 0.036166 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.671719 0.052668 0.026500 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.814797 0.100659 0.007509 \n",
|
||
|
|
"\n",
|
||
|
|
" max_rate information_rate in_field_mean_rate \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 4.734064 0.417936 1.629185 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.151797 0.147302 0.847032 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 3.100584 0.193361 2.016844 \n",
|
||
|
|
"\n",
|
||
|
|
" out_field_mean_rate speed_score spacing \\\n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.322805 0.037336 0.016343 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.602720 0.021480 0.023525 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.521574 0.016176 0.007847 \n",
|
||
|
|
"\n",
|
||
|
|
" field_area \n",
|
||
|
|
"LMM Baseline I - 11 Hz 0.014129 \n",
|
||
|
|
"LMM Baseline I - Baseline II 0.008499 \n",
|
||
|
|
"LMM Baseline II - 30 Hz 0.008441 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 96,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mf"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 110,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"effect_ranges = {\n",
|
||
|
|
" 'gridness': (.01, .3, .01),\n",
|
||
|
|
" 'average_rate': (.5, 6, .2), \n",
|
||
|
|
" 'information_specificity': (.01,.2,.01),\n",
|
||
|
|
" 'max_rate': (1,15,.5), \n",
|
||
|
|
" 'information_rate': (.1,.4,.02), \n",
|
||
|
|
" 'in_field_mean_rate': (.5, 6, .2), \n",
|
||
|
|
" 'out_field_mean_rate': (.5, 6, .2), \n",
|
||
|
|
" 'speed_score': (.01,.1,.005), # if run again, change this to go to 0.1\n",
|
||
|
|
" 'spacing': (.01,.1,.005), # if run again, change this to go to 0.1\n",
|
||
|
|
" 'field_area': (.01,.1,.005),# if run again, change this to go to 0.1\n",
|
||
|
|
"}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 128,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "13f4f0aa63f84c6f8f32526954e607fd",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(IntProgress(value=0, description='Baseline I - 11 Hz', max=10, style=ProgressStyle(description_…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "c65251a34561404d910166151ed0b7e8",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(IntProgress(value=0, description='Baseline I - Baseline II', max=10, style=ProgressStyle(descri…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "7ae981b7b1f0402aad9c533789909c5e",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(IntProgress(value=0, description='Baseline II - 30 Hz', max=10, style=ProgressStyle(description…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"powers = {}\n",
|
||
|
|
"for vs in vss:\n",
|
||
|
|
" powers[vs] = {}\n",
|
||
|
|
" for key, df in tqdm(results['gridcell'].items(), desc=' - '.join(vs)):\n",
|
||
|
|
" power, effect_size = estimate_power_lmm_paralell(results['gridcell'][key], *vs, effect_range=effect_ranges[key])\n",
|
||
|
|
" powers[vs][key] = {'p': power, 'e': effect_size}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 129,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"plt.rc('axes', titlesize=12)\n",
|
||
|
|
"plt.rcParams.update({\n",
|
||
|
|
" 'font.size': 12, \n",
|
||
|
|
" 'figure.figsize': (3.7, 2.2), \n",
|
||
|
|
" 'figure.dpi': 150\n",
|
||
|
|
"})"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 146,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"jupyter": {
|
||
|
|
"outputs_hidden": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 555x330 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"metadata": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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||
|
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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||
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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||
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"text/plain": [
|
||
|
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"<Figure size 555x330 with 1 Axes>"
|
||
|
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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||
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 555x330 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
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