In [1]:
%load_ext autoreload
%autoreload 2
In [2]:
import os
import expipe
import pathlib
import numpy as np
import spatial_maps.stats as stats
import septum_mec
import septum_mec.analysis.data_processing as dp
import septum_mec.analysis.registration
import head_direction.head as head
import spatial_maps as sp
import speed_cells.speed as spd
import re
import joblib
import multiprocessing
import shutil
import psutil
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from distutils.dir_util import copy_tree
from neo import SpikeTrain
import scipy
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
from tqdm._tqdm_notebook import tqdm_notebook
tqdm_notebook.pandas()

from spike_statistics.core import permutation_resampling

from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features

from septum_mec.analysis.plotting import violinplot
18:16:41 [I] klustakwik KlustaKwik2 version 0.2.6
In [3]:
%matplotlib inline
plt.rc('axes', titlesize=12)
plt.rcParams.update({
    'font.size': 12, 
    'figure.figsize': (6, 4), 
    'figure.dpi': 150
})

output_path = pathlib.Path("output") / "stimulus-lfp-response"
(output_path / "statistics").mkdir(exist_ok=True, parents=True)
(output_path / "figures").mkdir(exist_ok=True, parents=True)
output_path.mkdir(exist_ok=True)
In [4]:
data_loader = dp.Data()
actions = data_loader.actions
project = data_loader.project
In [5]:
identify_neurons = actions['identify-neurons']
sessions = pd.read_csv(identify_neurons.data_path('sessions'))
In [6]:
lfp_action = actions['stimulus-lfp-response']
lfp_results = pd.read_csv(lfp_action.data_path('results'))
In [15]:
lfp_results = pd.merge(sessions, lfp_results, how='left')
In [16]:
def action_group(row):
    a = int(row.channel_group in [0,1,2,3])
    return f'{row.action}-{a}'
lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)
In [17]:
lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']
In [18]:
lfp_results_hemisphere = lfp_results.sort_values(
    by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]
).drop_duplicates(subset='action_side_a', keep='first')
lfp_results_hemisphere.loc[:,['action_side_a','channel_group', 'signal_to_noise', 'stim_strength']].head()
Out[18]:
action_side_a channel_group signal_to_noise stim_strength
68 1833-010719-1-0 4 0.006686 NaN
66 1833-010719-1-1 2 0.034550 NaN
694 1833-010719-2-0 6 0.004609 7.173297
691 1833-010719-2-1 3 0.003974 6.446883
580 1833-020719-1-0 4 0.008427 NaN
In [19]:
colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']
labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']
queries = ['baseline and Hz11', 'frequency==11', 'baseline and Hz30', 'frequency==30']
In [20]:
density = True
cumulative = True
histtype = 'step'
lw = 2
bins = {
    'theta_energy': np.arange(0, .7, .03),
    'theta_peak': np.arange(0, .7, .03),
    'theta_freq': np.arange(4, 10, .5),
    'theta_half_width': np.arange(0, 15, .5)
}
xlabel = {
    'theta_energy': 'Theta energy (dB)',
    'theta_peak': 'Peak PSD (dB/Hz)',
    'theta_freq': '(Hz)',
    'theta_half_width': '(Hz)',
}
# key = 'theta_energy'
# key = 'theta_peak'
for key in bins:
    fig = plt.figure(figsize=(3.5,2))
    plt.suptitle(key)
    legend_lines = []
    for color, query, label in zip(colors, queries, labels):
        lfp_results_hemisphere.query(query)[key].hist(
            bins=bins[key], density=density, cumulative=cumulative, lw=lw, 
            histtype=histtype, color=color)
        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))
        
    plt.legend(
        handles=legend_lines,
        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)
    plt.tight_layout()
    plt.grid(False)
    plt.xlim(-0.05, bins[key].max() - bins[key].max()*0.025)
    sns.despine()
    plt.xlabel(xlabel[key])
    figname = f'lfp-psd-histogram-{key}'
    fig.savefig(
        output_path / 'figures' / f'{figname}.png', 
        bbox_inches='tight', transparent=True)
    fig.savefig(
        output_path / 'figures' / f'{figname}.svg', 
        bbox_inches='tight', transparent=True)
In [21]:
density = True
cumulative = True
histtype = 'step'
lw = 2
bins = {
    'stim_energy': np.arange(0, .7, .01),
    'stim_half_width': np.arange(0, 10, .5),
    'stim_p_max': np.arange(0, 4, .01),
    'stim_strength': np.arange(0, 160, 1)
}
xlabel = {
    'stim_energy': 'Energy (dB)',
    'stim_half_width': '(Hz)',
    'stim_p_max': 'Peak PSD (dB/Hz)',
    'stim_strength': 'Ratio',
}
# key = 'theta_energy'
# key = 'theta_peak'
for key in bins:
    fig = plt.figure(figsize=(3.2,2))
    plt.suptitle(key)
    legend_lines = []
    for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):
        lfp_results_hemisphere.query(query)[key].hist(
            bins=bins[key], density=density, cumulative=cumulative, lw=lw, 
            histtype=histtype, color=color)
        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))
        
    plt.legend(
        handles=legend_lines,
        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)
    plt.tight_layout()
    plt.grid(False)
    plt.xlim(-0.05, bins[key].max() - bins[key].max()*0.02)
    sns.despine()
    plt.xlabel(xlabel[key])
    figname = f'lfp-psd-histogram-{key}'
    fig.savefig(
        output_path / 'figures' / f'{figname}.png', 
        bbox_inches='tight', transparent=True)
    fig.savefig(
        output_path / 'figures' / f'{figname}.svg', 
        bbox_inches='tight', transparent=True)

Plot PSD

In [22]:
psd = pd.read_feather(output_path / 'data' / 'psd.feather')
freqs = pd.read_feather(output_path / 'data' / 'freqs.feather')
In [23]:
from septum_mec.analysis.plotting import plot_bootstrap_timeseries
In [24]:
freq = freqs.T.iloc[0].values

mask = (freq < 100)
In [25]:
fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))
axs = axs.repeat(2)
for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):
    selection = [
        f'{r.action}_{r.channel_group}' 
        for i, r in lfp_results_hemisphere.query(query).iterrows()]
    values = psd.loc[mask, selection].to_numpy()
    values = 10 * np.log10(values)
    plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])
#     ax.set_title(titles[i])
    ax.set_xlabel('Frequency Hz')
    ax.legend(frameon=False)
axs[0].set_ylabel('PSD (dB/Hz)')
    
sns.despine()

figname = 'lfp-psd'
fig.savefig(
    output_path / 'figures' / f'{figname}.png', 
    bbox_inches='tight', transparent=True)
fig.savefig(
    output_path / 'figures' / f'{figname}.svg', 
    bbox_inches='tight', transparent=True)

Store results in Expipe action

In [26]:
action = project.require_action("stimulus-lfp-response")
In [27]:
copy_tree(output_path, str(action.data_path()))
Out[27]:
['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/data/psd.feather',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/data/freqs.feather',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_energy.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_strength.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_peak.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_p_max.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_freq.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_energy.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_freq.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_half_width.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_half_width.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_half_width.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_energy.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_peak.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_p_max.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_half_width.png',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_energy.svg',
 '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_strength.svg']
In [28]:
septum_mec.analysis.registration.store_notebook(action, "20_stimulus-lfp-response.ipynb")
In [ ]: