Alexander/P-unit_model
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change a_delta to start_parameter

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a.ott 2020-05-20 15:22:13 +02:00
parent 92119373c1
commit e84ac53657
1 changed files with 25 additions and 21 deletions
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44
Fitter.py
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@ -24,20 +24,28 @@ def iget_start_parameters():
# expand by tau_a, delta_a ? # expand by tau_a, delta_a ?
mem_tau_list = [0.01] mem_tau_list = [0.01]
input_scaling_list = [40, 60, 80] input_scaling_list = [40, 60]
noise_strength_list = [0.03] # [0.02, 0.06] noise_strength_list = [0.03] # [0.02, 0.06]
dend_tau_list = [0.001, 0.002] dend_tau_list = [0.001, 0.002]
delta_a_list = [0.035, 0.065]
for mem_tau in mem_tau_list: for mem_tau in mem_tau_list:
for input_scaling in input_scaling_list: for input_scaling in input_scaling_list:
for noise_strength in noise_strength_list: for noise_strength in noise_strength_list:
for dend_tau in dend_tau_list: for dend_tau in dend_tau_list:
for delta_a in delta_a_list:
yield {"mem_tau": mem_tau, "input_scaling": input_scaling, yield {"mem_tau": mem_tau, "input_scaling": input_scaling,
"noise_strength": noise_strength, "dend_tau": dend_tau} "noise_strength": noise_strength, "dend_tau": dend_tau,
"delta_a": delta_a}
def run_with_real_data(): def run_with_real_data():
count = 0
for cell_data in icelldata_of_dir("./data/"): for cell_data in icelldata_of_dir("./data/"):
count += 1
if count < 7:
pass
#continue
print("cell:", cell_data.get_data_path()) print("cell:", cell_data.get_data_path())
trace = cell_data.get_base_traces(trace_type=cell_data.V1) trace = cell_data.get_base_traces(trace_type=cell_data.V1)
@ -181,7 +189,6 @@ class Fitter:
self.f_zero_fit = [] self.f_zero_fit = []
self.tau_a = 0 self.tau_a = 0
self.delta_a = 0
# counts how often the cost_function was called # counts how often the cost_function was called
self.counter = 0 self.counter = 0
@ -209,14 +216,9 @@ class Fitter:
# around 1/3 of the value at straight # around 1/3 of the value at straight
# self.f_zero_slope = fi_curve.get_fi_curve_slope_at(fi_curve.get_f_zero_and_f_inf_intersection()) # self.f_zero_slope = fi_curve.get_fi_curve_slope_at(fi_curve.get_f_zero_and_f_inf_intersection())
# seems to work if divided by 1000... adaption = Adaption(fi_curve)
self.delta_a = (fi_curve.get_f_zero_fit_slope_at_straight() / self.f_inf_slope) / 1000
adaption = Adaption(data, fi_curve)
self.tau_a = adaption.get_tau_real() self.tau_a = adaption.get_tau_real()
# print("delta_a: {:.3f}".format(self.delta_a), "tau_a: {:.3f}".format(self.tau_a))
return self.fit_routine_5(data, start_parameters) return self.fit_routine_5(data, start_parameters)
def fit_routine_5(self, cell_data=None, start_parameters=None): def fit_routine_5(self, cell_data=None, start_parameters=None):
@ -226,7 +228,8 @@ class Fitter:
x0 = np.array([0.02, 70, 0.001]) x0 = np.array([0.02, 70, 0.001])
else: else:
x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"], x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
start_parameters["input_scaling"], self.tau_a, self.delta_a, start_parameters["dend_tau"]]) start_parameters["input_scaling"], self.tau_a, start_parameters["delta_a"],
start_parameters["dend_tau"]])
initial_simplex = create_init_simples(x0, search_scale=2) initial_simplex = create_init_simples(x0, search_scale=2)
# error_list = [error_bf, error_vs, error_sc, error_cv, # error_list = [error_bf, error_vs, error_sc, error_cv,
@ -234,7 +237,7 @@ class Fitter:
error_weights = (0, 1, 1, 1, 1, 1, 1, 1) error_weights = (0, 1, 1, 1, 1, 1, 1, 1)
fmin = minimize(fun=self.cost_function_all, fmin = minimize(fun=self.cost_function_all,
args=(error_weights,), x0=x0, method="Nelder-Mead", args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 400, "maxiter": 400}) options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 200, "maxiter": 400})
if cell_data is not None: if cell_data is not None:
print("##### After step 1: (Everything)") print("##### After step 1: (Everything)")
@ -247,7 +250,7 @@ class Fitter:
if fit_adaption: if fit_adaption:
if x0 is None: if x0 is None:
x0 = np.array([0.02, 0.03, 70, self.tau_a, self.delta_a]) x0 = np.array([0.02, 0.03, 70, self.tau_a, 0.05])
if initial_simplex is None: if initial_simplex is None:
initial_simplex = create_init_simples(x0) initial_simplex = create_init_simples(x0)
@ -255,11 +258,11 @@ class Fitter:
method="Nelder-Mead", options={"initial_simplex": initial_simplex}) method="Nelder-Mead", options={"initial_simplex": initial_simplex})
else: else:
if x0 is None: if x0 is None:
x0 = np.array([0.02, 0.03, 70]) x0 = np.array([0.02, 0.03, 70, 0.05])
if initial_simplex is None: if initial_simplex is None:
initial_simplex = create_init_simples(x0) initial_simplex = create_init_simples(x0)
fmin = minimize(fun=self.cost_function_with_fixed_adaption, x0=x0, args=(self.tau_a, self.delta_a), fmin = minimize(fun=self.cost_function_with_fixed_adaption_tau, x0=x0, args=(self.tau_a,),
method="Nelder-Mead", options={"initial_simplex": initial_simplex}) method="Nelder-Mead", options={"initial_simplex": initial_simplex})
return fmin, self.base_model.get_parameters() return fmin, self.base_model.get_parameters()
@ -320,14 +323,15 @@ class Fitter:
return sum(error_list) return sum(error_list)
def cost_function_with_fixed_adaption(self, X, tau_a, delta_a, error_weights=None): def cost_function_with_fixed_adaption_tau(self, X, tau_a, error_weights=None):
# set model parameters: # set model parameters:
model = self.base_model model = self.base_model
model.set_variable("mem_tau", X[0]) model.set_variable("mem_tau", X[0])
model.set_variable("noise_strength", X[1]) model.set_variable("noise_strength", X[1])
model.set_variable("input_scaling", X[2]) model.set_variable("input_scaling", X[2])
model.set_variable("delta_a", X[3])
model.set_variable("tau_a", tau_a) model.set_variable("tau_a", tau_a)
model.set_variable("delta_a", delta_a)
base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0) base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0)
# find right v-offset # find right v-offset
@ -398,12 +402,12 @@ class Fitter:
# calculate errors with reference values # calculate errors with reference values
error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq) error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq)
error_vs = abs((vector_strength - self.vector_strength) / self.vector_strength) error_vs = abs((vector_strength - self.vector_strength) / 0.1)
error_cv = abs((coefficient_of_variation - self.coefficient_of_variation) / self.coefficient_of_variation) error_cv = abs((coefficient_of_variation - self.coefficient_of_variation) / 0.1)
error_sc = 0 error_sc = 0
for i in range(self.sc_max_lag): for i in range(self.sc_max_lag):
error_sc = abs((serial_correlation[i] - self.serial_correlation[i]) / (self.serial_correlation[i]/10)) error_sc = abs((serial_correlation[i] - self.serial_correlation[i]) / 0.1)
error_sc = error_sc / self.sc_max_lag error_sc = error_sc / self.sc_max_lag
error_f_inf_slope = abs((f_infinities_slope - self.f_inf_slope) / (self.f_inf_slope/20)) error_f_inf_slope = abs((f_infinities_slope - self.f_inf_slope) / (self.f_inf_slope/20))
@ -411,7 +415,7 @@ class Fitter:
# error_f_zero_slopes = calculate_list_error(f_zero_slopes, self.f_zero_slopes) # error_f_zero_slopes = calculate_list_error(f_zero_slopes, self.f_zero_slopes)
error_f_zero_slope_at_straight = abs(self.f_zero_slope_at_straight - f_zero_slope_at_straight) \ error_f_zero_slope_at_straight = abs(self.f_zero_slope_at_straight - f_zero_slope_at_straight) \
/ (self.f_zero_slope_at_straight / 20) / (self.f_zero_slope_at_straight / 10)
error_f_zero = calculate_list_error(f_zeros, self.f_zero_values) error_f_zero = calculate_list_error(f_zeros, self.f_zero_values)
error_list = [error_bf, error_vs, error_sc, error_cv, error_list = [error_bf, error_vs, error_sc, error_cv,