Source code for allensdk.brain_observatory.receptive_field_analysis.postprocessing

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from .fit_parameters import get_gaussian_fit_single_channel, compute_distance, compute_overlap
from .chisquarerf import chi_square_binary, get_peak_significance, pvalue_to_NLL
from .utilities import upsample_image_to_degrees
import collections
import numpy as np
import sys

[docs]def get_gaussian_fit(rf): fit_parameters_dict_combined = {'on':collections.defaultdict(list), 'off':collections.defaultdict(list)} counter = {'on':0, 'off':0} for on_off_key in ['on', 'off']: fit_parameters_dict = fit_parameters_dict_combined[on_off_key] for ci in range(rf[on_off_key]['fdr_mask']['attrs']['number_of_components']): curr_component_mask = upsample_image_to_degrees(np.logical_not(rf[on_off_key]['fdr_mask']['data'][ci,:,:])) > .5 rf_response = upsample_image_to_degrees(rf[on_off_key]['rts_convolution']['data'].copy()) rf_response[curr_component_mask] = 0 if rf_response.sum() > 0: get_gaussian_fit_single_channel(rf_response, fit_parameters_dict) counter[on_off_key] += 1 for ii_off in range(counter['on']): fit_parameters_dict_combined['on']['distance'].append([None]*counter['off']) fit_parameters_dict_combined['on']['overlap'].append([None] * counter['off']) for ii_off in range(counter['off']): fit_parameters_dict_combined['off']['distance'].append([None] * counter['on']) fit_parameters_dict_combined['off']['overlap'].append([None]*counter['on']) for ii_on in range(counter['on']): for ii_off in range(counter['off']): center_on = fit_parameters_dict_combined['on']['center_x'][ii_on], fit_parameters_dict_combined['on']['center_y'][ii_on] center_off = fit_parameters_dict_combined['off']['center_x'][ii_off], fit_parameters_dict_combined['off']['center_y'][ii_off] curr_distance = compute_distance(center_on, center_off) fit_parameters_dict_combined['on']['distance'][ii_on][ii_off] = curr_distance fit_parameters_dict_combined['off']['distance'][ii_off][ii_on] = curr_distance data_on = fit_parameters_dict_combined['on']['data'][ii_on] data_off = fit_parameters_dict_combined['off']['data'][ii_off] curr_overlap = compute_overlap(data_on, data_off) fit_parameters_dict_combined['on']['overlap'][ii_on][ii_off] = curr_overlap fit_parameters_dict_combined['off']['overlap'][ii_off][ii_on] = curr_overlap return fit_parameters_dict_combined, counter
[docs]def run_postprocessing(data, rf): stimulus = rf['attrs']['stimulus'] # Gaussian fit postprocessing: fit_parameters_dict_combined, counter = get_gaussian_fit(rf) for on_off_key in ['on', 'off']: if counter[on_off_key] > 0: rf[on_off_key]['gaussian_fit'] = {} rf[on_off_key]['gaussian_fit']['attrs'] = {} fit_parameters_dict = fit_parameters_dict_combined[on_off_key] for key, val in fit_parameters_dict.items(): if key == 'data': rf[on_off_key]['gaussian_fit']['data'] = np.array(val) else: rf[on_off_key]['gaussian_fit']['attrs'][key] = np.array(val) # Chi squared test statistic postprocessing: cell_index = rf['attrs']['cell_index'] locally_sparse_noise_template = data.get_stimulus_template(stimulus) event_array = np.zeros((rf['event_vector']['data'].shape[0], 1), dtype=np.bool) event_array[:,0] = rf['event_vector']['data'] chi_squared_grid = chi_square_binary(event_array, locally_sparse_noise_template) alpha = rf['on']['fdr_mask']['attrs']['alpha'] assert rf['off']['fdr_mask']['attrs']['alpha'] == alpha chi_square_grid_NLL = pvalue_to_NLL(chi_squared_grid) peak_significance = get_peak_significance(chi_square_grid_NLL, locally_sparse_noise_template, alpha=alpha) significant = peak_significance[0][0] min_p = peak_significance[1][0] pvalues_chi_square = peak_significance[2][0] best_exclusion_region_mask = peak_significance[3][0] chi_squared_grid_dict = { 'best_exclusion_region_mask':{'data':best_exclusion_region_mask}, 'attrs':{'significant':significant, 'alpha': alpha, 'min_p':min_p}, 'pvalues':{'data':pvalues_chi_square} } rf['chi_squared_analysis'] = chi_squared_grid_dict return rf
if __name__ == "__main__": # csid = 517472416 # triple! csid = 517526760 # two ON # csid = 539917553 # csid = 540988186