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script for analysis includes/absorbed fit statistics code

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a.ott 2020-07-16 13:29:49 +02:00
parent a7fe56be66
commit c1228b1cba
2 changed files with 216 additions and 64 deletions
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216
analysis.py Normal file
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import argparse
import os
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
from ModelFit import get_best_fit
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
# if not os.path.isdir(dir_path):
# print("Argument dir is not a directory.")
# parser.print_usage()
# exit(0)
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))
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()
pass
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):
pass
if __name__ == '__main__':
main()

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fit_statistics.py
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from ModelFit import get_best_fit
import matplotlib.pyplot as plt
import os
import numpy as np
def get_all_cell_folders(dir):
paths = []
for item in os.listdir(dir):
cell_path = os.path.join(dir, item)
if os.path.isdir(cell_path):
paths.append(cell_path)
return paths
def main():
values_models = {}
values_cells = {}
for cell in get_all_cell_folders("results/invivo_results/"):
best_fit = get_best_fit(cell)
c_behaviour, m_behaviour = best_fit.get_behaviour_values()
for key in c_behaviour.keys():
if key not in values_cells.keys():
values_cells[key] = []
if key not in values_models.keys():
values_models[key] = []
values_cells[key].append(c_behaviour[key])
values_models[key].append(m_behaviour[key])
percentage_error_data = []
labels = []
for key in values_models.keys():
errors = []
for i in range(len(values_models[key])):
if values_cells[key][i] == 0:
if values_models[key][i] == 0:
errors.append(0)
else:
print("Cannot calc % error if reference is 0")
continue
errors.append((values_models[key][i] / values_cells[key][i]) -1)
percentage_error_data.append(errors)
labels.append(key)
plt.boxplot(percentage_error_data)
plt.xticks(range(1, len(percentage_error_data)+1, 1), labels=labels, rotation=45)
plt.show()
plt.close()
# plt.plot([np.median(x) for x in percentage_error_data], 'o')
# plt.xticks(range(len(percentage_error_data)), labels=labels, rotation=45)
# plt.show()
if __name__ == '__main__':
main()