Source code for allensdk.brain_observatory.receptive_field_analysis.fit_parameters

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from .fitgaussian2D import fitgaussian2D, GaussianFitError, gaussian2D
import numpy as np
import pandas as pd
import collections
import sys
import warnings

[docs]def add_to_fit_parameters_dict_single(fit_parameters_dict, p): fit_parameters_dict['height'].append(p[0]) fit_parameters_dict['center_y'].append(p[1]) fit_parameters_dict['center_x'].append(p[2]) fit_parameters_dict['width_y'].append(p[3]) fit_parameters_dict['width_x'].append(p[4]) fit_parameters_dict['rotation'].append(p[5]) if (p[3] is None) or (p[4] is None): fit_parameters_dict['area'].append(None) else: fit_parameters_dict['area'].append(np.pi * (3./2) ** 2 * np.abs(p[3]) * np.abs(p[4]))
[docs]def get_gaussian_fit_single_channel(rf, fit_parameters_dict): try: p_fit = fitgaussian2D(rf) add_to_fit_parameters_dict_single(fit_parameters_dict, p_fit) data_fitted_on = gaussian2D(*p_fit)(*np.indices(rf.shape)) fit_parameters_dict['data'].append(data_fitted_on) except GaussianFitError: warnings.warn('GaussianFitError (on subfield) caught') add_to_fit_parameters_dict_single(fit_parameters_dict, [None]*6) fit_parameters_dict['data'].append(np.zeros_like(rf))
[docs]def compute_distance(center_on, center_off): center_x_on, center_y_on = center_on center_x_off, center_y_off = center_off if (center_x_on is None) or (center_y_on is None) or (center_x_off is None) or (center_y_off is None): return None else: return np.sqrt((center_x_off-center_x_on)**2+(center_y_off-center_y_on)**2)
[docs]def compute_overlap(data_fitted_on, data_fitted_off): on_bin = np.where(data_fitted_on > 0.001, 1, 0) off_bin = np.where(data_fitted_off > 0.001, 1, 0) return float((np.multiply(on_bin, off_bin)).sum()) / (np.sqrt(on_bin.sum()) * np.sqrt(off_bin.sum()))