In [1]:
%load_ext autoreload
%autoreload 2
In [2]:
import matplotlib.pyplot as plt
%matplotlib inline
import spatial_maps as sp
import septum_mec.analysis.data_processing as dp
import septum_mec.analysis.registration
import expipe
import os
import pathlib
import numpy as np
import exdir
import pandas as pd
import optogenetics as og
import quantities as pq
import shutil
from distutils.dir_util import copy_tree

from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response

from tqdm import tqdm_notebook as tqdm
from tqdm._tqdm_notebook import tqdm_notebook
tqdm_notebook.pandas()

07:55:15 [I] klustakwik KlustaKwik2 version 0.2.6
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:20: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`
In [3]:
std_gaussian_kde = 0.04
window_size = 0.03
In [4]:
data_loader = dp.Data()
actions = data_loader.actions
project = data_loader.project
In [5]:
output = pathlib.Path('output/stimulus-response')
(output / 'data').mkdir(parents=True, exist_ok=True)
In [6]:
identify_neurons = actions['identify-neurons']
units = pd.read_csv(identify_neurons.data_path('units'))
In [7]:
def process(row):
    
    action_id = row['action']
    channel_id = int(row['channel_group'])
    unit_id = int(row['unit_name'])    
    
    spike_times = data_loader.spike_train(action_id, channel_id, unit_id)
    
    spike_times = np.array(spike_times)
    
    stim_times = data_loader.stim_times(action_id)
    
    nan_series = pd.Series({
            't_e_peak': np.nan,
            'p_e_peak': np.nan,
            't_i_peak': np.nan,
            'p_i_peak': np.nan
        })
    
    if stim_times is None:
        return nan_series
    
    stim_times = np.array(stim_times)
    
    times, spikes, kernel, p_e, p_i = stimulus_response_latency(
        spike_times, stim_times, window_size, std_gaussian_kde)
    
    # if no spikes detected after stimulus nan is returned
    if all(np.isnan([p_e, p_i])):
        return nan_series
        
    t_e_peak, p_e_peak, t_i_peak, p_i_peak = compute_response(
        spike_times, stim_times, times, kernel, p_e, p_i)

    return pd.Series({
        't_e_peak': t_e_peak,
        'p_e_peak': p_e_peak,
        't_i_peak': t_i_peak,
        'p_i_peak': p_i_peak
    })
In [8]:
results = units.merge(
    units.progress_apply(process, axis=1), 
    left_index=True, right_index=True)

/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/stimulus_response.py:33: RuntimeWarning: invalid value encountered in less
  if any(times[idxs_i] < te_peak):



















In [9]:



    


results.loc[:, ['t_e_peak', 't_i_peak', 'p_e_peak', 'p_i_peak']].hist()
plt.gcf().savefig(output / 'figures' / 'summary_histogram.png')



    















    


























In [8]:



    


psth, time = {}, {}
for i, row in tqdm(units.iterrows(), total=len(units)):
    action_id = row['action']
    channel_group = row['channel_group']
    unit_name = row['unit_name']
    name = f'{action_id}_{channel_group}_{unit_name}'
    spike_times = data_loader.spike_train(action_id, channel_group, unit_name)
    
    spike_times = np.array(spike_times)
    
    stim_times = data_loader.stim_times(action_id)
    
    if stim_times is None:
        continue
    
    stim_times = np.array(stim_times)
    
    _, spikes, kernel, p_e, p_i = stimulus_response_latency(
        spike_times, stim_times, window_size, std_gaussian_kde)
    
    if all(np.isnan([p_e, p_i])):
        continue
    times = np.arange(-0.005, window_size, 1e-4)
    psth.update({name: kernel(times)})
    time.update({name: times})



    















    






/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
  










    






 
 















    
























In [9]:



    


pd.DataFrame(psth).to_feather(output / 'data' / 'psth.feather')
pd.DataFrame(time).to_feather(output / 'data' / 'times.feather')



    















Save to expipe












In [27]:



    


action = project.require_action("stimulus-response")



    













In [11]:



    


action.modules['parameters'] = {
    'window_size': window_size,
    'std_gaussian_kde': std_gaussian_kde
}



    













In [12]:



    


action.data['results'] = 'results.csv'
results.to_csv(action.data_path('results'), index=False)



    













In [28]:



    


copy_tree(output, str(action.data_path()))



    















    
Out[28]:








['/media/storage/expipe/septum-mec/actions/stimulus-response/data/data/times.feather',
 '/media/storage/expipe/septum-mec/actions/stimulus-response/data/data/psth.feather',
 '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/summary_histogram.png']



















In [29]:



    


septum_mec.analysis.registration.store_notebook(action, "10-calculate-stimulus-response.ipynb")



    













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