334 lines
12 KiB
Python
334 lines
12 KiB
Python
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import argparse
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import pearsonr
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from ModelFit import get_best_fit
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from Baseline import BaselineModel
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from FiCurve import FICurveModel
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from CellData import CellData
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from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
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def main():
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# parser = argparse.ArgumentParser()
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# parser.add_argument("dir", help="folder containing the cell folders with the fit results")
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# args = parser.parse_args()
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dir_path = "results/invivo_results/" # args.dir
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dir_path = "results/test_data/"
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# if not os.path.isdir(dir_path):
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# print("Argument dir is not a directory.")
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# parser.print_usage()
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# exit(0)
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# sensitivity_analysis(dir_path, max_models=3)
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fits_info = get_fit_info(dir_path)
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errors = calculate_percent_errors(fits_info)
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create_boxplots(errors)
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labels, corr_values, corrected_p_values = behaviour_correlations(fits_info, model_values=False)
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create_correlation_plot(labels, corr_values, corrected_p_values)
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labels, corr_values, corrected_p_values = parameter_correlations(fits_info)
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create_correlation_plot(labels, corr_values, corrected_p_values)
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create_parameter_distributions(get_parameter_values(fits_info))
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cell_b, model_b = get_behaviour_values(fits_info)
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create_behaviour_distributions(cell_b, model_b)
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pass
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def get_fit_info(folder):
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fits_info = {}
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for item in os.listdir(folder):
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cell_folder = os.path.join(folder, item)
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results = get_best_fit(cell_folder, use_comparable_error=True)
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cell_behaviour, model_behaviour = results.get_behaviour_values()
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fits_info[item] = [results.get_final_parameters(), model_behaviour, cell_behaviour]
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return fits_info
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def calculate_percent_errors(fits_info):
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errors = {}
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for cell in sorted(fits_info.keys()):
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for behaviour in fits_info[cell][1].keys():
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if behaviour not in errors.keys():
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errors[behaviour] = []
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if fits_info[cell][2][behaviour] == 0:
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if fits_info[cell][1][behaviour] == 0:
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errors[behaviour].append(0)
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else:
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print("Cannot calc % error if reference is 0")
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continue
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errors[behaviour].append((fits_info[cell][1][behaviour] - fits_info[cell][2][behaviour]) / fits_info[cell][2][behaviour])
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return errors
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def get_parameter_values(fits_info):
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par_keys = sorted(["input_scaling", "delta_a", "mem_tau", "noise_strength",
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"refractory_period", "tau_a", "v_offset", "dend_tau"])
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parameter_values = {}
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for cell in sorted(fits_info.keys()):
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for par in par_keys:
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if par not in parameter_values.keys():
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parameter_values[par] = []
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parameter_values[par].append(fits_info[cell][0][par])
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return parameter_values
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def get_behaviour_values(fits_info):
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behaviour_values_cell = {}
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behaviour_values_model = {}
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for cell in sorted(fits_info.keys()):
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for behaviour in fits_info[cell][1].keys():
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if behaviour not in behaviour_values_cell.keys():
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behaviour_values_cell[behaviour] = []
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behaviour_values_model[behaviour] = []
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behaviour_values_model[behaviour].append(fits_info[cell][1][behaviour])
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behaviour_values_cell[behaviour].append(fits_info[cell][2][behaviour])
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return behaviour_values_cell, behaviour_values_model
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def behaviour_correlations(fits_info, model_values=True):
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bv_cell, bv_model = get_behaviour_values(fits_info)
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if model_values:
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behaviour_values = bv_model
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else:
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behaviour_values = bv_cell
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labels = sorted(behaviour_values.keys())
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corr_values = np.zeros((len(labels), len(labels)))
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p_values = np.ones((len(labels), len(labels)))
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for i in range(len(labels)):
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for j in range(len(labels)):
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c, p = pearsonr(behaviour_values[labels[i]], behaviour_values[labels[j]])
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corr_values[i, j] = c
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p_values[i, j] = p
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corrected_p_values = p_values * sum(range(len(labels)))
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return labels, corr_values, corrected_p_values
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def parameter_correlations(fits_info):
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parameter_values = get_parameter_values(fits_info)
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labels = sorted(parameter_values.keys())
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corr_values = np.zeros((len(labels), len(labels)))
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p_values = np.ones((len(labels), len(labels)))
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for i in range(len(labels)):
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for j in range(len(labels)):
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c, p = pearsonr(parameter_values[labels[i]], parameter_values[labels[j]])
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corr_values[i, j] = c
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p_values[i, j] = p
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corrected_p_values = p_values * sum(range(len(labels)))
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return labels, corr_values, corrected_p_values
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def create_correlation_plot(labels, correlations, p_values):
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cleaned_cors = np.zeros(correlations.shape)
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for i in range(correlations.shape[0]):
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for j in range(correlations.shape[1]):
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if abs(p_values[i, j]) < 0.05:
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cleaned_cors[i, j] = correlations[i, j]
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fig, ax = plt.subplots()
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im = ax.imshow(cleaned_cors, vmin=-1, vmax=1)
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cbar = ax.figure.colorbar(im, ax=ax)
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cbar.ax.set_ylabel("Correlation coefficient", rotation=-90, va="bottom")
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# We want to show all ticks...
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ax.set_xticks(np.arange(len(labels)))
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ax.set_yticks(np.arange(len(labels)))
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# ... and label them with the respective list entries
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ax.set_xticklabels(labels)
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ax.set_yticklabels(labels)
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# Rotate the tick labels and set their alignment.
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plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
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rotation_mode="anchor")
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# Loop over data dimensions and create text annotations.
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for i in range(len(labels)):
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for j in range(len(labels)):
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text = ax.text(j, i, "{:.2f}".format(correlations[i, j]),
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ha="center", va="center", color="w")
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fig.tight_layout()
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plt.show()
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def create_boxplots(errors):
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labels = ["{}_n:{}".format(k, len(errors[k])) for k in sorted(errors.keys())]
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for k in sorted(errors.keys()):
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print("{}: median %-error: {:.2f}".format(k, np.median(errors[k])))
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y_values = [errors[k] for k in sorted(errors.keys())]
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plt.boxplot(y_values)
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plt.xticks(np.arange(1, len(y_values)+1, 1), labels, rotation=45)
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plt.tight_layout()
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plt.show()
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plt.close()
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def create_parameter_distributions(par_values):
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fig, axes = plt.subplots(4, 2)
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if len(par_values.keys()) != 8:
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print("not eight parameters")
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labels = sorted(par_values.keys())
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axes_flat = axes.flatten()
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for i, l in enumerate(labels):
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min_v = min(par_values[l]) * 0.95
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max_v = max(par_values[l]) * 1.05
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step = (max_v - min_v) / 15
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bins = np.arange(min_v, max_v+step, step)
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axes_flat[i].hist(par_values[l], bins=bins)
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axes_flat[i].set_title(l)
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plt.tight_layout()
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plt.show()
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plt.close()
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def create_behaviour_distributions(cell_b_values, model_b_values):
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fig, axes = plt.subplots(4, 2)
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labels = sorted(cell_b_values.keys())
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axes_flat = axes.flatten()
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for i, l in enumerate(labels):
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min_v = min(min(cell_b_values[l]), min(model_b_values[l])) * 0.95
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max_v = max(max(cell_b_values[l]), max(model_b_values[l])) * 1.05
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limit = 50000
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if max_v > limit:
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print("For {} the max value was limited to {}, {} values were excluded!".format(l, limit, np.sum(np.array(cell_b_values[l]) > limit)))
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max_v = limit
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step = (max_v - min_v) / 15
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bins = np.arange(min_v, max_v + step, step)
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axes_flat[i].hist(cell_b_values[l], bins=bins, alpha=0.5)
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axes_flat[i].hist(model_b_values[l], bins=bins, alpha=0.5)
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axes_flat[i].set_title(l)
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plt.tight_layout()
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plt.show()
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plt.close()
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pass
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def sensitivity_analysis(dir_path, par_range=(0.5, 1.6, 0.1), contrast_range=(-0.3, 0.4, 0.1), parameters=None, behaviours=None, max_models=None):
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models = []
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eods = []
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base_freqs = []
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count = 0
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for item in sorted(os.listdir(dir_path)):
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count += 1
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if max_models is not None and count > max_models:
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break
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cell_folder = os.path.join(dir_path, item)
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results = get_best_fit(cell_folder)
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models.append(results.get_model())
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eods.append(CellData(results.get_cell_path()).get_eod_frequency())
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cell, model = results.get_behaviour_values()
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base_freqs.append(cell["baseline_frequency"])
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if parameters is None:
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parameters = ["input_scaling", "delta_a", "mem_tau", "noise_strength",
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"refractory_period", "tau_a", "dend_tau"]
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if behaviours is None:
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behaviours = ["burstiness", "coefficient_of_variation", "serial_correlation",
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"vector_strength", "f_inf_slope", "f_zero_slope", "f_zero_middle"]
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model_behaviour_responses = []
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contrasts = np.arange(contrast_range[0], contrast_range[1], contrast_range[2])
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factors = np.arange(par_range[0], par_range[1], par_range[2])
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for model, eod, base_freq in zip(models, eods, base_freqs):
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par_responses = {}
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for par in parameters:
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par_responses[par] = {}
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for b in behaviours:
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par_responses[par][b] = np.zeros(len(factors))
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for i, factor in enumerate(factors):
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model_copy = model.get_model_copy()
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model_copy.set_variable(par, model.get_parameters()[par] * factor)
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print("{} at {}, ({} of {})".format(par, model.get_parameters()[par] * factor, i+1, len(factors)))
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base_stimulus = SinusoidalStepStimulus(eod, 0)
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v_offset = model_copy.find_v_offset(base_freq, base_stimulus)
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model_copy.set_variable("v_offset", v_offset)
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baseline = BaselineModel(model_copy, eod, trials=3)
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print(baseline.get_baseline_frequency())
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if "burstiness" in behaviours:
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par_responses[par]["burstiness"][i] = baseline.get_burstiness()
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if "coefficient_of_variation" in behaviours:
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par_responses[par]["coefficient_of_variation"][i] = baseline.get_coefficient_of_variation()
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if "serial_correlation" in behaviours:
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par_responses[par]["serial_correlation"][i] = baseline.get_serial_correlation(1)[0]
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if "vector_strength" in behaviours:
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par_responses[par]["vector_strength"][i] = baseline.get_vector_strength()
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fi_curve = FICurveModel(model_copy, contrasts, eod, trials=20)
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if "f_inf_slope" in behaviours:
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par_responses[par]["f_inf_slope"][i] = fi_curve.get_f_inf_slope()
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if "f_zero_slope" in behaviours:
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par_responses[par]["f_zero_slope"][i] = fi_curve.get_f_zero_fit_slope_at_straight()
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if "f_zero_middle" in behaviours:
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par_responses[par]["f_zero_middle"][i] = fi_curve.f_zero_fit[3]
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model_behaviour_responses.append(par_responses)
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print("sensitivity analysis done!")
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plot_sensitivity_analysis(model_behaviour_responses, behaviours, parameters, factors)
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def plot_sensitivity_analysis(responses, behaviours, parameters, factors):
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fig, axes = plt.subplots(len(behaviours), len(parameters), sharex="all", sharey="row", figsize=(8, 8))
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for i, behaviour in enumerate(behaviours):
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for j, par in enumerate(parameters):
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for model in responses:
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axes[i, j].plot(factors, model[par][behaviour])
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if j == 0:
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axes[i, j].set_ylabel("{}".format(behaviour))
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if i == 0:
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axes[i, j].set_title("{}".format(par))
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plt.tight_layout()
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plt.show()
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plt.close()
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if __name__ == '__main__':
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main()
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