Alexander/P-unit_model
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mergin master

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a.ott 2020-07-23 10:50:23 +02:00
commit 5fda783dfd
3 changed files with 41 additions and 65 deletions
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53
Fitter.py
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@ -9,6 +9,7 @@ import numpy as np
from warnings import warn
from scipy.optimize import minimize
import time
from helperFunctions import plot_errors
import matplotlib.pyplot as plt
@ -53,6 +54,7 @@ class Fitter:
self.f_zero_curve_freq = np.array([])
self.f_zero_curve_time = np.array([])
self.errors = []
# self.tau_a = 0
@ -125,54 +127,9 @@ class Fitter:
error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1)
fmin = minimize(fun=self.cost_function_all,
args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 800})
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
print("best that was returned: {}".format(fmin.fun))
print("best that was visited: {}".format(self.smallest_error))
return fmin, self.base_model.get_parameters()
def fit_routine_const_ref_period(self, start_parameters):
self.counter = 0
# fit only v_offset, mem_tau, input_scaling, dend_tau
x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
start_parameters["input_scaling"], self.tau_a, start_parameters["delta_a"],
start_parameters["dend_tau"], start_parameters["refractory_period"]])
initial_simplex = create_init_simples(x0, search_scale=2)
self.base_model.set_variable("refractory_period", start_parameters["refractory_period"])
# error_list = [error_bf, error_vs, error_sc, error_cv,
# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
error_weights = (0, 1, 1, 1, 1, 1, 1, 1, 1)
fmin = minimize(fun=self.cost_function_without_ref_period,
args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 200, "maxiter": 400})
return fmin, self.base_model.get_parameters()
# similar results to fit routine 1
def fit_routine_2(self, start_parameters):
self.counter = 0
x0 = np.array([start_parameters["mem_tau"], start_parameters["input_scaling"], # mem_tau, input_scaling
start_parameters["delta_a"], start_parameters["dend_tau"]]) # delta_a, dend_tau
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (0, 1, 1, 1, 1, 3, 2, 3, 2)
fmin = minimize(fun=self.cost_function_only_adaption,
args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 100, "maxiter": 400})
best_pars = fmin.x
x0 = np.array([best_pars[0], start_parameters["noise_strength"],
best_pars[1], self.tau_a, best_pars[2],
best_pars[3]])
initial_simplex = create_init_simples(x0, search_scale=2)
# error_list = [error_bf, error_vs, error_sc, error_cv,
# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
error_weights = (0, 2, 2, 2, 2, 1, 1, 1, 1)
fmin = minimize(fun=self.cost_function_all,
args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 100, "maxiter": 400})
plot_errors(self.errors)
return fmin, self.base_model.get_parameters()
@ -206,7 +163,7 @@ class Fitter:
if sum(error_list) < self.smallest_error:
self.smallest_error = sum(error_list)
self.best_parameters_found = X
self.errors.append(error_list)
return sum(error_list)
def cost_function_without_ref_period(self, X, error_weights=None):

34
analysis.py
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@ -17,28 +17,28 @@ def main():
# 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
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)
# 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

19
helperFunctions.py
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@ -5,6 +5,25 @@ from scipy.optimize import curve_fit
import functions as fu
from numba import jit
import matplotlib.pyplot as plt
import time
def plot_errors(list_errors):
names = ["error_bf", "error_vs", "error_sc", "error_f_inf",
"error_f_inf_slope", "error_f_zero", "error_f_zero_s", "f_zero_curve"]
data = np.array(list_errors)
fig, axes = plt.subplots(2, 4, figsize=(10, 8))
for i in range(8):
col = i % 4
row = int(i/4.0)
axes[row, col].hist(data[:, i])
axes[row, col].set_title(names[i])
plt.savefig("figures/error_distributions/error_distribution_{}.png".format(time.strftime("%H:%M:%S")))
plt.close()
def fit_clipped_line(x, y):