import helperFunctions as hf
from CellData import icelldata_of_dir
import functions as fu
import numpy as np
import time
import matplotlib.pyplot as plt
import os
from scipy.signal import argrelmax
from thunderfish.eventdetection import detect_peaks
from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus
from models.LIFACnoise import LifacNoiseModel
from FiCurve import FICurveModel, get_fi_curve_class
from Baseline import get_baseline_class
from AdaptionCurrent import Adaption
from stimuli.StepStimulus import StepStimulus
from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
def time_test_function():
for n in [1000]: # number of calls
print("calls:", n)
start = time.time()
for i in range(n):
data = np.random.normal(size=10000)
y = [fu.rectify(x) for x in data]
end = time.time()
print("time:", end - start)
def test_cell_data():
for cell_data in icelldata_of_dir("../data/"):
#if "2012-12-20-ad" not in cell_data.get_data_path():
# continue
print()
print(cell_data.get_data_path())
if len(cell_data.get_base_traces(cell_data.TIME)) != 0:
# print("works!")
#print("VS:", cell_data.get_vector_strength())
#print("SC:", cell_data.get_serial_correlation(5))
print("Eod freq:", cell_data.get_eod_frequency())
else:
pass
#print("NNNOOOOOOOOOO!")
#print("spiketimes:", len(cell_data.get_base_spikes()))
#print("Times:", len(cell_data.get_base_traces(cell_data.TIME)))
#print("EOD:", len(cell_data.get_base_traces(cell_data.EOD)))
def test_peak_detection():
for cell_data in icelldata_of_dir("../data/"):
print()
print(cell_data.get_data_path())
times = cell_data.get_base_traces(cell_data.TIME)
eod = cell_data.get_base_traces(cell_data.EOD)
v1 = cell_data.get_base_traces(cell_data.V1)
for i in range(len(v1)):
pieces = 20
v1_trace = v1[i]
total = len(v1_trace)
all_peaks = []
plt.plot(times[i], v1[i])
for n in range(pieces):
length = int(total/pieces)
first_index = n*length
last_index = (n+1)*length
std = np.std(v1_trace[first_index:last_index])
peaks, _ = detect_peaks(v1_trace[first_index:last_index], std * 3)
peaks = peaks + first_index
all_peaks.extend(peaks)
plt.plot(times[i][first_index], v1_trace[first_index], 'o', color="green")
all_peaks = np.array(all_peaks)
plt.plot(times[i][all_peaks], v1[i][all_peaks], 'o', color='red')
plt.show()
def test_simulation_speed():
parameters = {'mem_tau': 21.348990483539083, 'delta_a': 20.41809814660199, 'input_scaling': 3.0391541280864196, 'v_offset': 26.25, 'threshold': 1, 'v_base': 0, 'step_size': 0.00005, 'tau_a': 158.0404259501454, 'a_zero': 0, 'v_zero': 0, 'noise_strength': 2.87718460648148}
model = LifacNoiseModel(parameters)
repetitions = 1
seconds = 10
stimulus = SinusAmplitudeModulationStimulus(750, 1, 10, 1, 8)
time_start = 0
t_start = time.time()
for i in range(repetitions):
v, spikes = model.simulate(stimulus, seconds, time_start)
print(len(v))
print(len(spikes))
#time_v = np.arange(time_start, seconds, model.get_sampling_interval())
#plt.plot(time_v, v, '.')
#plt.show()
#freq = hf.mean_freq_of_spiketimes_after_time_x(spikes, parameters["step_size"], 0)
#print(freq)
t_end = time.time()
#print("baseline markers:", model.calculate_baseline_markers(750, 3))
print("took:", round((t_end-t_start)/repetitions, 5), "seconds for " + str(seconds) + "s simulation", "step size:", parameters["step_size"]*1000, "ms")
def test_fi_curve_class():
model_parameters = {'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}
model = LifacNoiseModel(model_parameters)
contrasts = np.arange(-0.4, 0.4, 0.05)
ficurve = get_fi_curve_class(model, contrasts, 700)
ficurve.plot_mean_frequency_curves()
return
for cell_data in icelldata_of_dir("../data/"):
fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
fi_curve.plot_mean_frequency_curves()
# fi_curve.plot_f_point_detections()
pass
def test_adaption_class():
model_parameters = {'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}
model = LifacNoiseModel(model_parameters)
contrasts = np.arange(-0.4, 1, 0.05)
for delta_a in np.arange(0.1, 1.5, 0.1):
model.set_variable("delta_a", delta_a)
# for tau_a in np.arange(0.01, 0.1, 0.01):
# model.set_variable("tau_a", tau_a)
fi_curve = FICurveModel(model, contrasts, 750, 10)
adaption = Adaption(fi_curve)
# adaption.plot_exponential_fits()
m_tau = model.get_parameters()["tau_a"]
approx_tau = adaption.get_tau_real()
m_delta_a = model.get_parameters()["delta_a"]
approx_delta_a = adaption.get_delta_a()
fi_curve.plot_fi_curve("../figures/error_plots/adaption_test_{:.2f}_delta_a_with_{:.2f}_error.png".format(delta_a, approx_delta_a/ m_delta_a))
# print("model tau_a \t: {:.4f} vs {:.4f} adaption estimate, error: {:.2}".format(m_tau, approx_tau, (approx_tau / m_tau)))
print("model delta_a\t: {:.4f} vs {:.4f} adaption estimate, error: {:.2}".format(m_delta_a, approx_delta_a, (approx_delta_a / m_delta_a)))
print(fi_curve.f_zero_fit[3])
quit()
for cell_data in icelldata_of_dir("../data/"):
print()
print(cell_data.get_data_path())
fi_curve = FICurve(cell_data)
adaption = Adaption(fi_curve)
adaption.plot_exponential_fits()
print("tau_effs:", adaption.get_tau_effs())
print("tau_real:", adaption.get_tau_real())
fi_curve.plot_fi_curve()
def test_parameters():
parameters = {'mem_tau': 21., 'delta_a': 0.1, 'input_scaling': 400.,
'v_offset': 85.25, 'threshold': 0.1, 'v_base': 0, 'step_size': 0.00005, 'tau_a': 0.01,
'a_zero': 0, 'v_zero': 0, 'noise_strength': 3}
model = LifacNoiseModel(parameters)
base_stimulus_freq = 350
stimulus = SinusAmplitudeModulationStimulus(base_stimulus_freq, 1.2, 5, 5, 20)
plot_model_during_stimulus(model, stimulus, 30)
bf, vs, sc = model.calculate_baseline_markers(base_stimulus_freq)
contrasts = [0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3]
modulation_frequency = 1
f_infs, f_inf_slope = model.calculate_fi_markers(contrasts, base_stimulus_freq, modulation_frequency)
print("Baseline frequency: {:.2f}".format(bf))
print("Vector strength: {:.2f}".format(vs))
print("serial correlation: {:.2f}".format(sc[0]))
print("f infinity slope: {:.2f}".format(f_inf_slope))
print("f infinities: \n", f_infs)
def test_vector_strength_calculation():
model = LifacNoiseModel({"noise_strength": 0})
bf, vs1, sc = model.calculate_baseline_markers(600)
base_stim = SinusAmplitudeModulationStimulus(600, 0, 0)
_, spiketimes = model.simulate(base_stim, 30)
stimulus_trace = base_stim.as_array(0, 30, model.get_sampling_interval())
time_trace = np.arange(0, 30, model.get_sampling_interval())
vs2 = hf.calculate_vector_strength_from_spiketimes(time_trace, stimulus_trace, spiketimes, model.get_sampling_interval())
print("with assumed eod durations vs: {:.3f}".format(vs1))
print("with detected eod durations vs: {:.3f}".format(vs2))
def test_baseline_polar_plot():
model_parameter = {'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}
baseline = get_baseline_class(LifacNoiseModel(model_parameter), 700)
baseline.plot_polar_vector_strength()
# for data in icelldata_of_dir("../data/"):
# trace = data.get_base_traces(trace_type=data.V1)
# if len(trace) == 0:
# print("NO V1 TRACE FOUND")
# continue
#
# baseline = get_baseline_class(data)
# baseline.plot_polar_vector_strength()
def plot_model_during_stimulus(model: LifacNoiseModel, stimulus:SinusAmplitudeModulationStimulus, total_time):
_, spiketimes = model.simulate(stimulus, total_time)
time = np.arange(0, total_time, model.get_sampling_interval())
fig, axes = plt.subplots(5, 1, figsize=(9, 4*2), sharex="all")
stimulus_array = stimulus.as_array(0, total_time, model.get_sampling_interval())
axes[0].plot(time, stimulus_array)
axes[0].set_title("Stimulus")
axes[1].plot(time, rectify_stimulus_array(stimulus_array))
axes[1].set_title("rectified Stimulus")
axes[2].plot(time, model.get_voltage_trace())
axes[2].set_title("Voltage")
axes[3].plot(time, model.get_adaption_trace())
axes[3].set_title("Adaption")
f_time, f = hf.calculate_time_and_frequency_trace(spiketimes, model.get_sampling_interval())
axes[4].plot(f_time, f)
axes[4].set_title("Frequency")
plt.show()
def rectify_stimulus_array(stimulus_array: np.ndarray):
return np.array([x if x > 0 else 0 for x in stimulus_array])
if __name__ == '__main__':
model_parameters = {'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}
model = LifacNoiseModel(model_parameters)
base = get_baseline_class(model, 650)
base.plot_baseline()
fi = get_fi_curve_class(model, np.arange(0, 2, 0.2), 650)
fi.plot_fi_curve()
quit()
# test_baseline_polar_plot()
# time_test_function()
test_cell_data()
# test_peak_detection()
# test_simulation_speed()
# test_parameters()
test_fi_curve_class()
# test_adaption_class()
# test_vector_strength_calculation()
pass