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": [
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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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"<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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"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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||
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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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"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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"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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"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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"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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||
|
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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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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 555x330 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for vs, vals in powers.items():\n",
|
||
|
|
" for key, val in vals.items():\n",
|
||
|
|
" plt.figure()\n",
|
||
|
|
" plt.plot(val['e'], val['p'])\n",
|
||
|
|
" plt.title(' - '.join(vs) + ', ' + key.replace('_', ' ').capitalize())\n",
|
||
|
|
" savefig(output_path / 'figures' / f\"{'-'.join(vs)}_{key}\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Store data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 135,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"np.savez(output_path / 'data' / 'powers.npz', powers)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Create nice table"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from scipy.interpolate import interp1d"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 143,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"effect_size_power_80p = pd.DataFrame()\n",
|
||
|
|
"for vs, vals in powers.items():\n",
|
||
|
|
" for key, val in vals.items():\n",
|
||
|
|
" e = interp1d(val['p'], val['e'])(0.8)\n",
|
||
|
|
" effect_size_power_80p.loc[key.replace('_', ' ').capitalize(), ' - '.join(vs)] = e"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 144,
|
||
|
|
"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 I - 11 Hz</th>\n",
|
||
|
|
" <th>Baseline I - Baseline II</th>\n",
|
||
|
|
" <th>Baseline II - 30 Hz</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Average rate</th>\n",
|
||
|
|
" <td>2.669697</td>\n",
|
||
|
|
" <td>2.775000</td>\n",
|
||
|
|
" <td>2.814286</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Gridness</th>\n",
|
||
|
|
" <td>0.144211</td>\n",
|
||
|
|
" <td>0.136500</td>\n",
|
||
|
|
" <td>0.154615</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Information specificity</th>\n",
|
||
|
|
" <td>0.105789</td>\n",
|
||
|
|
" <td>0.091765</td>\n",
|
||
|
|
" <td>0.087297</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Max rate</th>\n",
|
||
|
|
" <td>6.108696</td>\n",
|
||
|
|
" <td>6.951613</td>\n",
|
||
|
|
" <td>7.593750</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Information rate</th>\n",
|
||
|
|
" <td>0.231034</td>\n",
|
||
|
|
" <td>0.253158</td>\n",
|
||
|
|
" <td>0.285000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>In field mean rate</th>\n",
|
||
|
|
" <td>3.410345</td>\n",
|
||
|
|
" <td>3.646667</td>\n",
|
||
|
|
" <td>3.468000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Out field mean rate</th>\n",
|
||
|
|
" <td>2.285000</td>\n",
|
||
|
|
" <td>2.340000</td>\n",
|
||
|
|
" <td>2.404762</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Speed score</th>\n",
|
||
|
|
" <td>0.365714</td>\n",
|
||
|
|
" <td>0.029091</td>\n",
|
||
|
|
" <td>0.036545</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Spacing</th>\n",
|
||
|
|
" <td>0.029259</td>\n",
|
||
|
|
" <td>0.018495</td>\n",
|
||
|
|
" <td>0.018404</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Field area</th>\n",
|
||
|
|
" <td>0.260000</td>\n",
|
||
|
|
" <td>0.358824</td>\n",
|
||
|
|
" <td>0.378481</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Baseline I - 11 Hz Baseline I - Baseline II \\\n",
|
||
|
|
"Average rate 2.669697 2.775000 \n",
|
||
|
|
"Gridness 0.144211 0.136500 \n",
|
||
|
|
"Information specificity 0.105789 0.091765 \n",
|
||
|
|
"Max rate 6.108696 6.951613 \n",
|
||
|
|
"Information rate 0.231034 0.253158 \n",
|
||
|
|
"In field mean rate 3.410345 3.646667 \n",
|
||
|
|
"Out field mean rate 2.285000 2.340000 \n",
|
||
|
|
"Speed score 0.365714 0.029091 \n",
|
||
|
|
"Spacing 0.029259 0.018495 \n",
|
||
|
|
"Field area 0.260000 0.358824 \n",
|
||
|
|
"\n",
|
||
|
|
" Baseline II - 30 Hz \n",
|
||
|
|
"Average rate 2.814286 \n",
|
||
|
|
"Gridness 0.154615 \n",
|
||
|
|
"Information specificity 0.087297 \n",
|
||
|
|
"Max rate 7.593750 \n",
|
||
|
|
"Information rate 0.285000 \n",
|
||
|
|
"In field mean rate 3.468000 \n",
|
||
|
|
"Out field mean rate 2.404762 \n",
|
||
|
|
"Speed score 0.036545 \n",
|
||
|
|
"Spacing 0.018404 \n",
|
||
|
|
"Field area 0.378481 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 144,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"effect_size_power_80p"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 147,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"effect_size_power_80p.to_latex(output_path / \"statistics\" / \"effect_size_power_80p.tex\")\n",
|
||
|
|
"effect_size_power_80p.to_csv(output_path / \"statistics\" / \"effect_size_power_80p.csv\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Estimate required sample size to detect effect size of 0.1 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"effect_sizes = {\n",
|
||
|
|
" 'gridness': .1,\n",
|
||
|
|
" 'average_rate': 1, \n",
|
||
|
|
" 'information_specificity': 0.1,\n",
|
||
|
|
" 'max_rate': 5, \n",
|
||
|
|
" 'information_rate': 0.2, \n",
|
||
|
|
" 'in_field_mean_rate': 3, \n",
|
||
|
|
" 'out_field_mean_rate': 2, \n",
|
||
|
|
" 'speed_score': 0.1,\n",
|
||
|
|
" 'spacing': 0.05,\n",
|
||
|
|
" 'field_area': 0.05\n",
|
||
|
|
"}\n",
|
||
|
|
"\n",
|
||
|
|
"n_samples_ranges = {\n",
|
||
|
|
" 'gridness': (10, 300, 10),\n",
|
||
|
|
" 'average_rate': (10, 300, 10), \n",
|
||
|
|
" 'information_specificity': (10, 120, 10),\n",
|
||
|
|
" 'max_rate': (10, 120, 10), \n",
|
||
|
|
" 'information_rate': (10, 120, 10), \n",
|
||
|
|
" 'in_field_mean_rate': (10, 120, 10), \n",
|
||
|
|
" 'out_field_mean_rate': (10, 120, 10), \n",
|
||
|
|
" 'speed_score': (5, 60, 5),\n",
|
||
|
|
" 'spacing': (5, 60, 5),\n",
|
||
|
|
" 'field_area': (5, 60, 5)\n",
|
||
|
|
"}"
|
||
|
|
]
|
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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": 11,
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"metadata": {},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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|
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"\n"
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{
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|
|
"name": "stdout",
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|
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"output_type": "stream",
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|
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"text": [
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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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|
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{
|
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|
|
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||
|
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"text": [
|
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|
|
"\n"
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|
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|
"\n"
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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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{
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|
|
"name": "stdout",
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|
"output_type": "stream",
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|
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"text": [
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|
|
"\n"
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|
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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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"HBox(children=(IntProgress(value=0, description='Field area', max=11, style=ProgressStyle(description_width='i…"
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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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|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n",
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|
|
"\n"
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|
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]
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|
|
}
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|
|
],
|
||
|
|
"source": [
|
||
|
|
"powers_sample_size = {}\n",
|
||
|
|
"for vs in vss:\n",
|
||
|
|
" powers_sample_size[vs] = {}\n",
|
||
|
|
" for key, df in tqdm(results['gridcell'].items(), desc=' - '.join(vs)):\n",
|
||
|
|
" power, sample_size, effective_sample_size = estimate_sample_size_lmm(\n",
|
||
|
|
" results['gridcell'][key], *vs, effect_size=effect_sizes[key], n_samples_range=n_samples_ranges[key], key=key)\n",
|
||
|
|
" powers_sample_size[vs][key] = {'p': power, 's': sample_size, 'es': effective_sample_size}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
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|
|
"data": {
|
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"text/plain": [
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"<Figure size 432x288 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 432x288 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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"<Figure size 432x288 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 432x288 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 432x288 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 432x288 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 432x288 with 1 Axes>"
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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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"<Figure size 432x288 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 432x288 with 1 Axes>"
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"metadata": {
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"text/plain": [
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"<Figure size 432x288 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 432x288 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 432x288 with 1 Axes>"
|
||
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]
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|
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},
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"metadata": {
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||
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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 432x288 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 432x288 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 432x288 with 1 Axes>"
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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 432x288 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 432x288 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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"<Figure size 432x288 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 432x288 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 432x288 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 432x288 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 432x288 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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"<Figure size 432x288 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 432x288 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 432x288 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 432x288 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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"<Figure size 432x288 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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"<Figure size 432x288 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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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for vs, vals in powers_sample_size.items():\n",
|
||
|
|
" for key, val in vals.items():\n",
|
||
|
|
" plt.figure()\n",
|
||
|
|
" plt.plot(val['es'], val['p'])\n",
|
||
|
|
" try:\n",
|
||
|
|
" e = interp1d(val['p'], val['es'])(0.8)\n",
|
||
|
|
" plt.axvline(e, label=f'N={e:.0f}', color='k')\n",
|
||
|
|
" plt.axhline(0.8, color='k', alpha=0.1)\n",
|
||
|
|
" plt.legend()\n",
|
||
|
|
" except:\n",
|
||
|
|
" pass\n",
|
||
|
|
" n1, n2 = results['gridcell'][key][list(vs)].count().values\n",
|
||
|
|
" plt.title(f\"{vs[0]} ({n1}), {vs[1]} ({n2}), {key.replace('_', ' ').capitalize()} ({effect_sizes[key]})\")\n",
|
||
|
|
" plt.ylabel('Power p(~H0|H1)')\n",
|
||
|
|
" plt.xlabel('N neurons')\n",
|
||
|
|
" savefig(output_path / 'figures' / f\"sample_size_{'-'.join(vs)}_{key}\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "NameError",
|
||
|
|
"evalue": "name 'interp1d' 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-13-7929fdf41f49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvals\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpowers_sample_size\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterp1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'p'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'es'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.8\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 5\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{key.replace('_', ' ').capitalize()}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0msample_size_power_80p\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' - '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[0;31mNameError\u001b[0m: name 'interp1d' is not defined"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sample_size_power_80p = pd.DataFrame()\n",
|
||
|
|
"for vs, vals in powers_sample_size.items():\n",
|
||
|
|
" for key, val in vals.items():\n",
|
||
|
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" e = interp1d(val['p'], val['es'])(0.8)\n",
|
||
|
|
" name = f\"{key.replace('_', ' ').capitalize()}\"\n",
|
||
|
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" sample_size_power_80p.loc[name, ' - '.join(vs)] = e"
|
||
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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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"sample_size_power_80p"
|
||
|
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]
|
||
|
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},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Store data"
|
||
|
|
]
|
||
|
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},
|
||
|
|
{
|
||
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|
"cell_type": "code",
|
||
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|
"execution_count": null,
|
||
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|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"sample_size_power_80p.to_latex(output_path / \"statistics\" / f\"sample_size_power.tex\")\n",
|
||
|
|
"sample_size_power_80p.to_csv(output_path / \"statistics\" / f\"sample_size_power.csv\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"np.savez(output_path / 'data' / 'powers_sample_size.npz', powers_sample_size)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Register in Expipe"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 148,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"action = project.require_action(\"comparisons-power\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 149,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"['/media/storage/expipe/septum-mec/actions/comparisons-power/data/statistics/effect_size_power_80p.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/statistics/effect_size_power_80p.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/data/powers.npz',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_field_area.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_in_field_mean_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_gridness.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_field_area.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_spacing.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_information_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_speed_score.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_information_specificity.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_average_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_spacing.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_speed_score.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_field_area.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_speed_score.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_out_field_mean_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_average_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_field_area.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_out_field_mean_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_out_field_mean_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_in_field_mean_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_in_field_mean_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_max_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_information_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_max_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_information_specificity.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_information_specificity.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_max_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_gridness.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_information_specificity.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_gridness.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_field_area.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_gridness.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_in_field_mean_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_information_specificity.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_average_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_out_field_mean_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_out_field_mean_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_max_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_information_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_field_area.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_information_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_speed_score.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_max_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_average_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_in_field_mean_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_spacing.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_information_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_spacing.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_speed_score.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_information_specificity.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_in_field_mean_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_spacing.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_gridness.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-Baseline II_out_field_mean_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_average_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_gridness.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_information_rate.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_max_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_speed_score.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline II-30 Hz_average_rate.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/comparisons-power/data/figures/Baseline I-11 Hz_spacing.png']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 149,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 150,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20_comparisons_power.ipynb\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Testing"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 213,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"vs, key = vss[0], 'gridness'"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"power, sample_size, effective_sample_size = estimate_sample_size_lmm(\n",
|
||
|
|
" results['gridcell'][key], *vs, effect_size=effect_sizes[key], n_samples_range=(10, 300, 10), key=key)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 216,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "e280fa482c0b4d91aa6463176e88a5e5",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(IntProgress(value=0, description='Gridness', max=19, style=ProgressStyle(description_width='ini…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"power, sample_size, effective_sample_size =estimate_sample_size_lmm(\n",
|
||
|
|
" results['gridcell'][key], *vs, effect_size=effect_sizes[key], n_samples_range=(10, 200, 10), n_repeats=100, key=key)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 198,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"CPU times: user 992 ms, sys: 740 ms, total: 1.73 s\n",
|
||
|
|
"Wall time: 7min 45s\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%%time\n",
|
||
|
|
"power, sample_size, effective_sample_size = estimate_sample_size_lmm_paralell(\n",
|
||
|
|
" results['gridcell'][key], *vs, effect_size=effect_sizes[key], n_samples_range=(10, 130, 10), n_repeats=100, key=key)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 223,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 226,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Text(0.5, 0, 'N neurons')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 226,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 900x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(effective_sample_size, power)\n",
|
||
|
|
"estimated_sample_size = interp1d(power, effective_sample_size)(0.8)\n",
|
||
|
|
"plt.axvline(estimated_sample_size, label=f'N={estimated_sample_size:.0f}', color='k')\n",
|
||
|
|
"plt.axhline(0.8, color='k', alpha=0.1)\n",
|
||
|
|
"n1, n2 = results['gridcell'][key][list(vs)].count().values\n",
|
||
|
|
"plt.title(f\"{vs[0]} ({n1}), {vs[1]} ({n2}), {key.replace('_', ' ').capitalize()} ({effect_sizes[key]})\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.ylabel('Power p(~H0|H1)')\n",
|
||
|
|
"plt.xlabel('N neurons')\n",
|
||
|
|
"# plt.xlim(0,200)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.6.8"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|