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‎docs/example_notebooks/detailed_workflow.ipynb

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{
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"cells": [
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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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"# Prepare"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'plot_data_and_posterior' from 'bayes_window.visualization' (/home/m/mmy/bayes-window/bayes_window/visualization.py)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-1-f066f5cd42ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbulwark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mck\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerative_models\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgenerate_fake_spikes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfake_spikes_explore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_data_and_posterior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfit_numpyro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'plot_data_and_posterior' from 'bayes_window.visualization' (/home/m/mmy/bayes-window/bayes_window/visualization.py)"
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]
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}
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],
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"source": [
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"#!pip install -e ..\n",
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"from altair.vegalite.v4.api import FacetChart, Chart, LayerChart\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"import bulwark.checks as ck\n",
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"from bayes_window.generative_models import generate_fake_spikes\n",
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"from bayes_window.visualization import fake_spikes_explore, plot_data_and_posterior\n",
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"from bayes_window import models\n",
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"from bayes_window.fitting import fit_numpyro\n",
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"from bayes_window.utils import add_data_to_posterior\n",
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"\n",
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"trans = LabelEncoder().fit_transform\n",
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"\n"
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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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"# Make some data\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df, df_monster, index_cols, firing_rates = generate_fake_spikes(n_trials=2,\n",
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" n_neurons=8,\n",
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" n_mice=4,\n",
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" dur=7, )"
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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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"import numpy as np\n",
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"df['log_isi']=np.log10(df['isi'])"
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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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"scrolled": false
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},
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"outputs": [],
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"source": [
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"import altair as alt\n",
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"from bayes_window import visualization,utils\n",
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"from importlib import reload\n",
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"reload(visualization)\n",
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"reload(utils)\n",
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"y='log_isi'\n",
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"df['neuron']=df['neuron'].astype(int)\n",
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"ddf, dy = utils.make_fold_change(df,\n",
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" y=y,\n",
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" index_cols=('stim', 'mouse_code', 'neuron'),\n",
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" condition_name='stim',\n",
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" do_take_mean=True)\n",
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"\n",
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"visualization.plot_data(x='neuron',y=dy, color='mouse_code',add_box=True,df=ddf)"
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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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"# Estimate model"
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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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"#y = list(set(df.columns) - set(index_cols))[0]\n",
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"trace = fit_numpyro(y=df[y].values,\n",
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" stim_on=(df['stim']).astype(int).values,\n",
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" treat=trans(df['neuron']),\n",
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" subject=trans(df['mouse']),\n",
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" progress_bar=True,\n",
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" model=models.model_hier_normal_stim,\n",
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" n_draws=100, num_chains=1, )"
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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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"# Add data back"
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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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"reload(utils)\n",
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"df_both = utils.add_data_to_posterior(df,\n",
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" trace=trace,\n",
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" y=y,\n",
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" index_cols=['neuron', 'stim', 'mouse_code', ],\n",
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" condition_name='stim',\n",
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" b_name='b_stim_per_condition', # for posterior\n",
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" group_name='neuron' # for posterior\n",
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" )"
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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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"# Plot data and posterior"
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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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"scrolled": false
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},
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"outputs": [],
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"source": [
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"from bayes_window.workflow import BayesWindow\n",
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"#BayesWindow.plot_posteriors_slopes(df_both, y=f'{y} diff', x='neuron',color='mouse_code',title=y,hold_for_facet=False,add_box=False)\n",
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"\n",
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"\n",
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"chart_d = visualization.plot_data(df=df_both,x='neuron', y=f'{y} diff',)\n",
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"chart_d.display()\n",
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"\n",
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"chart_p = visualization.plot_posterior(df=df_both, title=f'd_{y}', x='neuron',)\n",
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"chart_p.display()"
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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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"(chart_d+chart_p).display()"
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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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"(chart_d+chart_p).facet(column='neuron')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "PyCharm (jup)",
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"language": "python",
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"name": "pycharm-d5912792"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎docs/example_notebooks/quickstart.ipynb

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{
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"cells": [
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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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"# Generate some data"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'plot_data_and_posterior' from 'bayes_window.visualization' (/home/m/mmy/bayes-window/bayes_window/visualization.py)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-1-540448e9e811>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerative_models\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgenerate_fake_spikes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfake_spikes_explore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_data_and_posterior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfit_numpyro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbayes_window\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0madd_data_to_posterior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'plot_data_and_posterior' from 'bayes_window.visualization' (/home/m/mmy/bayes-window/bayes_window/visualization.py)"
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]
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}
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],
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"source": [
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"from bayes_window.generative_models import generate_fake_spikes\n",
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"from bayes_window.visualization import fake_spikes_explore, plot_data_and_posterior\n",
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"from bayes_window import models\n",
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"from bayes_window.fitting import fit_numpyro\n",
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"from bayes_window.utils import add_data_to_posterior"
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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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"\n",
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"df, df_monster, index_cols, firing_rates = generate_fake_spikes(n_trials=10,\n",
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" n_neurons=7,\n",
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" n_mice=6,\n",
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" dur=7,\n",
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" mouse_response_slope=16)"
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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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"# Exploratory plot without any fitting"
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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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"scrolled": true
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},
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"outputs": [],
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"source": [
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"\n",
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"from bayes_window.visualization import fake_spikes_explore\n",
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"charts=fake_spikes_explore(df,df_monster,index_cols)\n",
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"[chart.display() for chart in charts];\n",
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"#fig_mice, fig_select, fig_neurons, fig_trials, fig_isi + fig_overlay, bar, box, fig_raster, bar_combined"
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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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"# Estimate with mouse>neuron"
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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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"from bayes_window import workflow\n",
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"from importlib import reload\n",
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"reload(workflow)\n",
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"bw=workflow.BayesWindow(df,y='isi', levels=('stim', 'mouse', 'neuron'))\n",
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"bw.fit_slopes(add_data=True, model=models.model_hier_normal_stim,)\n",
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"\n",
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"bw.plot()\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "PyCharm (jup)",
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"language": "python",
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"name": "pycharm-d5912792"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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