from models.LIFACnoise import LifacNoiseModel
from CellData import CellData, icelldata_of_dir
from FiCurve import FICurve
from AdaptionCurrent import Adaption
from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
import helperFunctions as hF
import numpy as np
from scipy.optimize import curve_fit, minimize
import functions as fu
import time
import matplotlib.pyplot as plt
def main():
#run_test_with_fixed_model()
#quit()
# fitter = Fitter()
# fmin, params = fitter.fit_model_to_values(700, 1400, [-0.3], 1, [0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3], [1370, 1380, 1390, 1400, 1410, 1420, 1430], 100, 0.02, 0.01)
# print("calculated parameters:")
# print(params)
run_with_real_data()
def run_with_real_data():
for celldata in icelldata_of_dir("./data/"):
start_time = time.time()
fitter = Fitter()
fmin, parameters = fitter.fit_model_to_data(celldata)
print(fmin)
print(parameters)
end_time = time.time()
print('Fitting of cell took function took {:.3f} s'.format((end_time - start_time)))
res_model = LifacNoiseModel(parameters)
m_bf, m_vs, m_sc = res_model.calculate_baseline_markers(celldata.get_eod_frequency())
m_f_values, m_f_slope = res_model.calculate_fi_markers(celldata.get_fi_contrasts(), celldata.get_eod_frequency())
c_bf = celldata.get_base_frequency()
c_vs = celldata.get_vector_strength()
c_sc = celldata.get_serial_correlation(1)
fi_curve = FICurve(celldata)
c_f_slope = fi_curve.get_f_infinity_slope()
c_f_values = fi_curve.f_infinities
print("bf: cell - {:.2f} vs model {:.2f}".format(c_bf, m_bf))
print("vs: cell - {:.2f} vs model {:.2f}".format(c_vs, m_vs))
print("sc: cell - {:.2f} vs model {:.2f}".format(c_sc[0], m_sc[0]))
print("f_slope: cell - {:.2f} vs model {:.2f}".format(c_f_slope, m_f_slope))
print("f values:\n cell -", c_f_values, "\n model -", m_f_values)
f_b, f_zero, f_inf = res_model.calculate_fi_curve(celldata.get_fi_contrasts(), celldata.get_eod_frequency())
fi_curve.plot_fi_curve(comp_f_baselines=f_b, comp_f_zeros=f_zero, comp_f_infs=f_inf)
break
pass
def run_test_with_fixed_model():
a_tau = 0.1
a_delta = 0.08
parameters = {"mem_tau": 0.015,
"v_base": 0,
"v_zero": 0,
"threshold": 1,
"v_offset": -10,
"input_scaling": 60,
"delta_a": a_delta,
"tau_a": a_tau,
"a_zero": 10,
"noise_strength": 0.05,
"step_size": 0.00005}
model = LifacNoiseModel(parameters)
eod_freq = 750
contrasts = np.arange(0.5, 1.51, 0.1)
baseline_freq, vector_strength, serial_correlation = model.calculate_baseline_markers(eod_freq)
f_infinities, f_infinities_slope = model.calculate_fi_markers(contrasts, eod_freq)
print("Baseline freq:{:.2f}\nVector strength: {:.3f}\nSerial cor:".format(baseline_freq, vector_strength), serial_correlation)
print("FI-Curve\nSlope: {:.2f}\nValues:".format(f_infinities_slope), f_infinities)
fitter = Fitter()
starting_conditions = [[0.05, 0.02, 50], [0.01, 0.08, 75], [0.02, 0.03, 70],
[0.02, 0.03, 70]]
for x0 in starting_conditions:
init_simplex = create_init_simples(x0)
fmin, fit_parameters = fitter.fit_model_to_values(eod_freq, baseline_freq, serial_correlation, vector_strength, contrasts, f_infinities, f_infinities_slope, a_delta, a_tau, x0, init_simplex)
print(x0)
print(fmin)
print("calculated parameters:")
print(fit_parameters)
print("ref parameters:")
print(parameters)
def create_init_simples(x0, search_scale=2):
dim = len(x0)
simplex = [[x0[0]/search_scale], [x0[0]*search_scale]]
for i in range(1, dim, 1):
for vertex in simplex:
vertex.append(x0[i]*search_scale)
new_vertex = list(x0[:i])
new_vertex.append(x0[i]/search_scale)
simplex.append(new_vertex)
return simplex
class Fitter:
def __init__(self, step_size=None):
if step_size is not None:
self.model = LifacNoiseModel({"step_size": step_size})
else:
self.model = LifacNoiseModel({"step_size": 0.00005})
# self.data = data
self.fi_contrasts = []
self.eod_freq = 0
self.sc_max_lag = 1
# expected values the model has to replicate
self.baseline_freq = 0
self.vector_strength = -1
self.serial_correlation = []
self.f_infinities = []
self.f_infinities_slope = 0
# fixed values needed to fit model
self.a_tau = 0
self.a_delta = 0
self.counter = 0
# mem_tau, (threshold?), (v_offset), noise_strength, input_scaling
def cost_function(self, X, tau_a=0.1, delta_a=0.08, error_scaling=()):
# set model parameters to the given ones:
self.model.set_variable("mem_tau", X[0])
self.model.set_variable("noise_strength", X[1])
self.model.set_variable("input_scaling", X[2])
self.model.set_variable("tau_a", X[3])
self.model.set_variable("delta_a", X[4])
#self.model.set_variable("tau_a", tau_a)
#self.model.set_variable("delta_a", delta_a)
# minimize the difference in baseline_freq first by fitting v_offset
# v_offset = self.__fit_v_offset_to_baseline_frequency__()
base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0)
test_model = self.model.get_model_copy()
test_model.set_variable("noise_strength", 0)
v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
self.model.set_variable("v_offset", v_offset)
baseline_freq, vector_strength, serial_correlation = self.model.calculate_baseline_markers(self.eod_freq, self.sc_max_lag)
f_infinities, f_infinities_slope = self.model.calculate_fi_markers(self.fi_contrasts, self.eod_freq)
error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq)
error_vs = abs((vector_strength - self.vector_strength) / self.vector_strength)
error_sc = abs((serial_correlation[0] - self.serial_correlation[0]) / self.serial_correlation[0])
error_f_inf_slope = abs((f_infinities_slope - self.f_infinities_slope) / self.f_infinities_slope) * 4
#print("vs:", vector_strength, self.vector_strength)
#print("sc", serial_correlation[0], self.serial_correlation[0])
#print("f slope:", f_infinities_slope, self.f_infinities_slope)
error_f_inf = 0
for i in range(len(f_infinities)):
f_inf = f_infinities[i]
if f_inf <= 0:
f_inf = 1
error_f_inf += abs((f_infinities[i] - self.f_infinities[i]) / f_inf)
error_f_inf = error_f_inf / len(f_infinities)
# print("mem_tau:", X[0], "noise:", X[0], "input_scaling:", X[2])
errors = [error_bf, error_vs, error_sc, error_f_inf_slope, error_f_inf]
self.counter += 1
if self.counter % 100 == 0:
print("\nCost function run times: {:}\n".format(self.counter),
"Total error: {:.4f}\n".format(sum(errors)),
"Baseline frequency - expected: {:.0f}, current: {:.0f}, error: {:.3f}\n".format(self.baseline_freq, baseline_freq, error_bf),
"Vector strength - expected: {:.2f}, current: {:.2f}, error: {:.3f}\n".format(self.vector_strength, vector_strength, error_vs),
"Serial correlation - expected: {:.2f}, current: {:.2f}, error: {:.3f}\n".format(self.serial_correlation[0], serial_correlation[0], error_sc),
"f-infinity slope - expected: {:.0f}, current: {:.0f}, error: {:.3f}\n".format(self.f_infinities_slope, f_infinities_slope, error_f_inf_slope),
"f-infinity values:\nexpected:", np.around(self.f_infinities), "\ncurrent: ", np.around(f_infinities), "\nerror: {:.3f}".format(error_f_inf))
return error_bf + error_vs + error_sc + error_f_inf_slope + error_f_inf
def fit_model_to_data(self, data: CellData):
self.calculate_needed_values_from_data(data)
return self.fit_model()
def calculate_needed_values_from_data(self, data: CellData):
self.eod_freq = data.get_eod_frequency()
self.baseline_freq = data.get_base_frequency()
self.vector_strength = data.get_vector_strength()
self.serial_correlation = data.get_serial_correlation(self.sc_max_lag)
fi_curve = FICurve(data, contrast=True)
self.fi_contrasts = fi_curve.stimulus_value
print("Fitter: fi-contrasts", self.fi_contrasts)
self.f_infinities = fi_curve.f_infinities
self.f_infinities_slope = fi_curve.get_f_infinity_slope()
f_zero_slope = fi_curve.get_fi_curve_slope_of_straight()
self.a_delta = (f_zero_slope / self.f_infinities_slope) / 1000
adaption = Adaption(data, fi_curve)
self.a_tau = adaption.get_tau_real()
print()
def fit_model_to_values(self, eod_freq, baseline_freq, sc, vs, fi_contrasts, fi_inf_values, fi_inf_slope, a_delta, a_tau, x0=None, init_simplex=None):
self.eod_freq = eod_freq
self.baseline_freq = baseline_freq
self.serial_correlation = sc
self.vector_strength = vs
self.fi_contrasts = fi_contrasts
self.f_infinities = fi_inf_values
self.f_infinities_slope = fi_inf_slope
self.a_delta = a_delta
self.a_tau = a_tau
return self.fit_model(x0, init_simplex)
def fit_model(self, x0=None, initial_simplex=None):
self.counter = 0
if x0 is None:
x0 = np.array([0.02, 0.03, 70])
if initial_simplex is None:
initial_simplex = create_init_simples(x0)
# fmin = minimize(fun=self.cost_function, x0=x0, args=(self.a_tau, self.a_delta), method="Nelder-Mead")
# else:
fmin = minimize(fun=self.cost_function, x0=x0, args=(self.a_tau, self.a_delta), method="Nelder-Mead", options={"initial_simplex": initial_simplex})
return fmin, self.model.get_parameters()
if __name__ == '__main__':
main()