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