from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus as SAM
from Baseline import get_baseline_class
from FiCurve import FICurveModel
from models.LIFACnoise import LifacNoiseModel
import numpy as np
import matplotlib.pyplot as plt
import helperFunctions as hF
from CellData import CellData
def main():
# 2012-07-12-ag-invivo-1 fit and eod frequency:
# parameters = {'refractory_period': 0.00080122694889117, 'v_base': 0, 'v_zero': 0, 'a_zero': 20, 'step_size': 5e-05,
# 'delta_a': 0.23628384937392385, 'threshold': 1, 'input_scaling': 100.66894113671654,
# 'mem_tau': 0.012388673630113763, 'tau_a': 0.09106579031822526, 'v_offset': -6.25,
# 'noise_strength': 0.0404417932620334, 'dend_tau': 0.00122153436141022}
# cell_data = CellData("./data/2012-07-12-ag-invivo-1/")
parameters = {'delta_a': 0.08820130374685671, 'refractory_period': 0.0006, 'a_zero': 15, 'step_size': 5e-05,
'v_base': 0, 'noise_strength': 0.03622523883042496, 'v_zero': 0, 'threshold': 1,
'input_scaling': 77.75367190909581, 'tau_a': 0.07623731247799118, 'v_offset': -10.546875,
'mem_tau': 0.008741976196676469, 'dend_tau': 0.0012058986118892773}
cell_data = CellData("./data/2012-12-13-an-invivo-1/")
eod_freq = cell_data.get_eod_frequency()
model = LifacNoiseModel(parameters)
# base_cell = get_baseline_class(cell_data)
# base_model = get_baseline_class(model, cell_data.get_eod_frequency())
# isis_cell = np.array(base_cell.get_interspike_intervals()) * 1000
# isi_model = np.array(base_model.get_interspike_intervals()) * 1000
# bins = np.arange(0, 20, 0.1)
# plt.hist(isi_model, bins=bins, alpha=0.5)
# plt.hist(isis_cell, bins=bins, alpha=0.5)
# plt.show()
# plt.close()
# ficurve = FICurveModel(model, np.arange(-1, 1.1, 0.1), eod_freq)
#
# ficurve.plot_fi_curve()
durations = cell_data.get_sam_durations()
u_durations = np.unique(durations)
mean_duration = np.mean(durations)
contrasts = cell_data.get_sam_contrasts()
contrast = contrasts[0] # are all the same in this test case
spiketimes = cell_data.get_sam_spiketimes()
delta_freqs = cell_data.get_sam_delta_frequencies()
step_size = cell_data.get_sampling_interval()
spikes_dictionary = {}
for i, m_freq in enumerate(delta_freqs):
if m_freq in spikes_dictionary:
spikes_dictionary[m_freq].append(spiketimes[i])
else:
spikes_dictionary[m_freq] = [spiketimes[i]]
for m_freq in sorted(spikes_dictionary.keys()):
if mean_duration < 2*1/float(m_freq):
continue
stimulus = SAM(eod_freq, contrast/100, m_freq)
v1, spikes_model = model.simulate_fast(stimulus, mean_duration*4)
prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size)
# plt.plot(prob_density_function_model)
# plt.show()
# plt.close()
fig, axes = plt.subplots(1, 4)
cuts = cut_pdf_into_periods(prob_density_function_model, 1/float(m_freq), step_size)
for c in cuts:
axes[0].plot(c, color="gray", alpha=0.2)
axes[0].set_title("model")
mean_model = np.mean(cuts, axis=0)
axes[0].plot(mean_model, color="black")
means_cell = []
for spikes_cell in spikes_dictionary[m_freq]:
prob_density_cell = spiketimes_calculate_pdf(spikes_cell[0], step_size)
cuts_cell = cut_pdf_into_periods(prob_density_cell, 1/float(m_freq), step_size)
for c in cuts_cell:
axes[1].plot(c, color="gray", alpha=0.15)
print(cuts_cell.shape)
means_cell.append(np.mean(cuts_cell, axis=0))
means_cell = np.array(means_cell)
total_mean_cell = np.mean(means_cell, axis=0)
axes[1].set_title("cell")
axes[1].plot(total_mean_cell, color="black")
axes[2].set_title("difference")
diff = [(total_mean_cell[i]-mean_model[i]) for i in range(len(total_mean_cell))]
axes[2].plot(diff)
axes[3].plot(total_mean_cell)
axes[3].plot(mean_model)
plt.show()
plt.close()
def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005):
trials_rate_list = []
step_size = model.get_parameters()["step_size"]
for _ in range(trials):
v1, spikes = model.simulate(stimulus, total_time_s=sim_length)
binary = np.zeros(int(sim_length/step_size))
spikes = [int(s / step_size) for s in spikes]
for s_idx in spikes:
binary[s_idx] = 1
kernel = gaussian_kernel(kernel_width, step_size)
rate = np.convolve(binary, kernel, mode='same')
trials_rate_list.append(rate)
times = [np.arange(0, sim_length, step_size) for _ in range(trials)]
t, mean_rate = hF.calculate_mean_of_frequency_traces(times, trials_rate_list, step_size)
return mean_rate
def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.005):
length = int(spikes[len(spikes)-1] / step_size)+1
binary = np.zeros(length)
spikes = [int(s / step_size) for s in spikes]
for s_idx in spikes:
binary[s_idx] = 1
kernel = gaussian_kernel(kernel_width, step_size)
rate = np.convolve(binary, kernel, mode='same')
return rate
def cut_pdf_into_periods(pdf, period, step_size, factor=1.5):
idx_period_length = int(period/float(step_size))
offset_per_step = period/float(step_size) - idx_period_length
cut_length = int(period / float(step_size) * factor)
cuts = []
num_of_cuts = int(len(pdf) / idx_period_length)
if len(pdf) - (num_of_cuts * idx_period_length + (num_of_cuts * offset_per_step)) < cut_length - idx_period_length:
num_of_cuts -= 1
if num_of_cuts <= 0:
raise RuntimeError("Probability density function to short to cut.")
for i in np.arange(0, num_of_cuts, 1):
offset_correction = int(offset_per_step * i)
start_idx = i*idx_period_length + offset_correction
end_idx = (i*idx_period_length)+cut_length + offset_correction
cuts.append(np.array(pdf[start_idx: end_idx]))
cuts = np.array(cuts)
if len(cuts.shape) < 2:
print("Fishy....")
return cuts
def gaussian_kernel(sigma, dt):
x = np.arange(-4. * sigma, 4. * sigma, dt)
y = np.exp(-0.5 * (x / sigma) ** 2) / np.sqrt(2. * np.pi) / sigma
return y
if __name__ == '__main__':
main()