Alexander/P-unit_model
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add second set of parameters add additional variable to check

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a.ott 2020-05-20 15:21:30 +02:00
parent e92197d0ba
commit 92119373c1
1 changed files with 43 additions and 28 deletions
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71
variableEffect.py
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@ -8,31 +8,35 @@ import copy
import os
SEARCH_WIDTH = 3
SEARCH_PRECISION = 30
SEARCH_WIDTH = 1.1
SEARCH_PRECISION = 1
CONTRASTS = np.arange(-0.4, 0.45, 0.1)
def main():
model_parameters = {'threshold': 1,
'step_size': 5e-05,
'a_zero': 2,
'delta_a': 0.2032269898801589,
'mem_tau': 0.011314027210564803,
'noise_strength': 0.056724809998220195,
'v_zero': 0,
'v_base': 0,
'tau_a': 0.05958195972016753,
'input_scaling': 119.81500448274554,
'dend_tau': 0.0027746086464721723,
'v_offset': -24.21875}
def main():
model_parameters1 = {'threshold': 1,
'step_size': 5e-05,
'a_zero': 2,
'delta_a': 0.2032269898801589,
'mem_tau': 0.011314027210564803,
'noise_strength': 0.056724809998220195,
'v_zero': 0,
'v_base': 0,
'tau_a': 0.05958195972016753,
'input_scaling': 119.81500448274554,
'dend_tau': 0.0027746086464721723,
'v_offset': -24.21875}
model_parameters2 = {'v_offset': -15.234375, 'input_scaling': 64.94152780134829, 'step_size': 5e-05, 'a_zero': 2,
'threshold': 1, 'v_base': 0, 'delta_a': 0.04763179657857666, 'tau_a': 0.07891848949732623,
'mem_tau': 0.004828473985707999, 'noise_strength': 0.017132801387559883,
'v_zero': 0, 'dend_tau': 0.0015230454266819539}
parameters_to_test = ["input_scaling", "dend_tau", "mem_tau", "noise_strength", "v_offset", "delta_a", "tau_a"]
effect_data = []
for p in parameters_to_test:
print("Working on parameter " + p)
effect_data.append(test_parameter_effect(model_parameters, p))
effect_data.append(test_parameter_effect(model_parameters2, p))
plot_effects(effect_data, "./figures/variable_effect/")
@ -55,13 +59,15 @@ def test_parameter_effect(model_parameters, test_parameter):
f_inf_v = []
f_zero_s = []
f_zero_v = []
fi_curves = []
broken_i = []
for i in range(len(values)):
model_parameters[test_parameter] = values[i]
model = LifacNoiseModel(model_parameters)
fi_curve = FICurveModel(model, CONTRASTS, 600, trials=50)
fi_curve = FICurveModel(model, CONTRASTS, 600, trials=1)
fi_curves.append(fi_curve)
f_inf_s.append(fi_curve.get_f_inf_slope())
f_inf_v.append(fi_curve.get_f_inf_frequencies())
f_zero_s.append(fi_curve.get_f_zero_fit_slope_at_stimulus_value(0.1))
@ -76,8 +82,7 @@ def test_parameter_effect(model_parameters, test_parameter):
fi_curve.plot_f_point_detections(detection_save_path)
baseline = BaselineModel(model, 600, trials=10)
baseline = BaselineModel(model, 600, trials=1)
bf.append(baseline.get_baseline_frequency())
vs.append(baseline.get_vector_strength())
sc.append(baseline.get_serial_correlation(2))
@ -89,28 +94,33 @@ def test_parameter_effect(model_parameters, test_parameter):
for i in broken_i:
del values[i]
return ParameterEffectData(values, test_parameter, bf, vs, sc, cv, f_inf_s, f_inf_v, f_zero_s, f_zero_v)
return ParameterEffectData(fi_curves, values, test_parameter, bf, vs, sc, cv, f_inf_s, f_inf_v, f_zero_s, f_zero_v)
# plot_effects(values, test_parameter, bf, vs, sc, cv, f_inf_s, f_inf_v, f_zero_s, f_zero_v)
def plot_effects(par_effect_data_list, save_path=None):
fig, axes = plt.subplots(8, len(par_effect_data_list), figsize=(32, 4*len(par_effect_data_list)), sharex="col")
names = ("bf", "vs", "sc", "cv", "f_inf_s", "f_inf_v", "f_zero_s", "f_zero_v", "f_zero_fit_x_0")
names = ("bf", "vs", "sc", "cv", "f_inf_s", "f_inf_v", "f_zero_s", "f_zero_v")
fig, axes = plt.subplots(len(names), len(par_effect_data_list), figsize=(32, 4*len(par_effect_data_list)), sharex="col")
for j in range(len(par_effect_data_list)):
ped = par_effect_data_list[j]
ranges = ((0, max(ped.get_data("bf")) * 1.1), (0, 1), (-1, 1), (0, 1),
(0, max(ped.get_data("f_inf_s")) * 1.1), (0, 800),
(0, max(ped.get_data("f_zero_s")) * 1.1), (0, 3000))
(0, max(ped.get_data("f_zero_s")) * 1.1), (0, 10000), (-0.5, max(ped.get_data("f_zero_fit_x_0"))))
values = ped.values
for i in range(len(names)):
y_data = ped.get_data(names[i])
axes[i, j].plot(values, y_data)
axes[i, j].set_ylim(ranges[i])
if names[i] == "f_zero_v":
axes[i, j].set_yscale('log')
axes[i, j].set_ylim(ranges[i])
else:
axes[i, j].set_ylim(ranges[i])
if j == 0:
axes[i, j].set_ylabel(names[i])
@ -127,9 +137,10 @@ def plot_effects(par_effect_data_list, save_path=None):
class ParameterEffectData:
data_names = ("bf", "vs", "sc", "cv", "f_inf_s", "f_inf_v" "f_zero_s", "f_zero_v")
data_names = ("bf", "vs", "sc", "cv", "f_inf_s", "f_inf_v" "f_zero_s", "f_zero_v", "f_zero_fit_x_0")
def __init__(self, values, test_parameter, bf, vs, sc, cv, f_inf_s, f_inf_v, f_zero_s, f_zero_v):
def __init__(self, fi_curves, values, test_parameter, bf, vs, sc, cv, f_inf_s, f_inf_v, f_zero_s, f_zero_v):
self.fi_curves = fi_curves
self.values = values
self.test_parameter = test_parameter
self.bf = bf
@ -158,6 +169,10 @@ class ParameterEffectData:
return self.f_zero_s
elif name == "f_zero_v":
return self.f_zero_v
elif name == "f_zero_fit_x_0":
fits = [fi.f_zero_fit for fi in self.fi_curves]
x_zeros = [fit[3] for fit in fits]
return x_zeros
else:
raise ValueError("Unknown attribute name!")