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
from ModelFit import ModelFit, get_best_fit
import os
import shutil
def main():
run_sam_analysis_for_all_cells("results/final_2")
# sam_analysis("results/final_2/2011-10-25-ad-invivo-1/")
# plot_traces_with_spiketimes()
# plot_mean_of_cuts()
quit()
modelfit = get_best_fit("results/final_2/2011-10-25-ad-invivo-1/")
cell_data = CellData(modelfit.get_cell_path())
eod_freq = cell_data.get_eod_frequency()
model = modelfit.get_model()
test_model_response(model, eod_freq, 0.1, np.arange(5, 2500, 5))
def run_sam_analysis_for_all_cells(folder):
count = 0
for item in os.listdir(folder):
cell_folder = os.path.join(folder, item)
fit = get_best_fit(cell_folder, use_comparable_error=False)
cell_data = fit.get_cell_data()
if cell_data.has_sam_recordings():
count += 1
# print("Fit quality:", fit.get_fit_routine_error())
sam_analysis(cell_folder)
print(count)
def test_model_response(model: LifacNoiseModel, eod_freq, contrast, modulation_frequencies):
stds = []
for m_freq in modulation_frequencies:
if (1/m_freq) / 10 <= model.parameters["step_size"]:
model.parameters["step_size"] = (1/m_freq) / 10
step_size = model.parameters["step_size"]
print("mode_freq:", m_freq, "- step size:", step_size)
stimulus = SAM(eod_freq, contrast / 100, m_freq)
duration = 30
v1, spikes_model = model.simulate(stimulus, duration)
prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size, kernel_width=0.005)
fig, ax = plt.subplots(1, 1)
ax.plot(prob_density_function_model)
ax.set_title("pdf with m_freq: {}".format(int(m_freq)))
plt.savefig("figures/sam/pdf_mfreq_{}.png".format(m_freq))
plt.close()
stds.append(np.std(prob_density_function_model))
plt.plot((np.array(modulation_frequencies)) / eod_freq, stds)
plt.show()
plt.close()
def plot_traces_with_spiketimes():
modelfit = get_best_fit("results/final_2/2011-10-25-ad-invivo-1/")
cell_data = modelfit.get_cell_data()
traces = cell_data.parser.__get_traces__("SAM")
# [time_traces, v1_traces, eod_traces, local_eod_traces, stimulus_traces]
sam_spiketimes = cell_data.get_sam_spiketimes()
for i in range(len(traces[0])):
fig, axes = plt.subplots(2, 1, sharex=True)
axes[0].plot(traces[0][i], traces[1][i])
axes[0].plot(list(sam_spiketimes[i]), list([max(traces[1][i])] * len(sam_spiketimes[i])), 'o')
axes[1].plot(traces[0][i], traces[3][i])
plt.show()
plt.close()
def plot_mean_of_cuts():
modelfit = get_best_fit("results/final_2/2018-05-08-ac-invivo-1/")
if not os.path.exists(os.path.join(modelfit.get_cell_path(), "samallspikes1.dat")):
print("Cell: {} \n Has no measured sam stimuli.")
return
cell_data = CellData(modelfit.get_cell_path())
eod_freq = cell_data.get_eod_frequency()
model = modelfit.get_model()
durations = cell_data.get_sam_durations()
u_durations = np.unique(durations)
mean_duration = np.mean(durations)
contrasts = cell_data.get_sam_contrasts()
u_contrasts = np.unique(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)):
print("meep")
continue
stimulus = SAM(eod_freq, contrast / 100, m_freq)
v1, spikes_model = model.simulate(stimulus, 4)
prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size)
fig, axes = plt.subplots(1, 4)
start_idx = int(2 / step_size)
cuts = cut_pdf_into_periods(prob_density_function_model[start_idx:], 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))
if len(means_cell) == 0:
print("means cell length zero")
continue
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 sam_analysis(fit_path):
modelfit = get_best_fit(fit_path)
if not os.path.exists(os.path.join(modelfit.get_cell_path(), "samallspikes1.dat")):
print("Cell: {} \n Has no measured sam stimuli.")
return
cell_data = CellData(modelfit.get_cell_path())
model = modelfit.get_model()
# 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}
# model = LifacNoiseModel(parameters)
# cell_data = CellData("./data/test_data/2012-12-13-an-invivo-1/")
eod_freq = cell_data.get_eod_frequency()
step_size = cell_data.get_sampling_interval()
durations = cell_data.get_sam_durations()
u_durations = np.unique(durations)
contrasts = cell_data.get_sam_contrasts()
u_contrasts = np.unique(contrasts)
spiketimes = cell_data.get_sam_spiketimes()
delta_freqs = cell_data.get_sam_delta_frequencies()
u_delta_freqs = np.unique(delta_freqs)
cell_stds = []
model_stds = []
approx_offset = approximate_axon_delay_in_idx(cell_data, model)
print("Approx offset idx:", approx_offset)
print("Approx offset ms:", (approx_offset * step_size) * 1000)
for mod_freq in sorted(u_delta_freqs):
# TODO problem of cutting the pdf as in some cases the pdf is shorter than 1 modulation frequency period!
# length info wrong ? always at least one period?
# if 1/mod_freq > durations[0] / 4:
# print("skipped mod_freq: {}".format(mod_freq))
# print("Duration: {} while mod_freq period: {:.2f}".format(durations[0], 1/mod_freq))
# print("Maybe long enough duration? unique durations:", u_durations)
# continue
mfreq_data = {}
cell_means = []
model_means = []
for c in u_contrasts:
mfreq_data[c] = []
for i in range(len(delta_freqs)):
if delta_freqs[i] != mod_freq:
continue
if len(spiketimes[i]) == 0:
print("No spiketimes found at index!")
continue
if len(spiketimes[i]) > 1:
print("There are more spiketimes in one 'point'! Only the first was used! ")
spikes = spiketimes[i][0]
cell_pdf = spiketimes_calculate_pdf(spikes, step_size)
cell_cuts = cut_pdf_into_periods(cell_pdf, 1/mod_freq, step_size)
cell_mean = np.mean(cell_cuts, axis=0)
cell_means.append(cell_mean)
stimulus = SAM(eod_freq, contrasts[i] / 100, mod_freq)
v1, spikes_model = model.simulate(stimulus, 10)
model_pdf = spiketimes_calculate_pdf(spikes_model, step_size)
model_cuts = cut_pdf_into_periods(model_pdf, 1/mod_freq, step_size)
model_mean = np.mean(model_cuts, axis=0)
model_means.append(model_mean)
min_length = min(min([len(cm) for cm in cell_means]), min([len(mm) for mm in model_means]))
for i in range(len(cell_means)):
cell_means[i] = cell_means[i][:min_length]
model_means[i] = model_means[i][:min_length]
final_cell_mean = np.mean(cell_means, axis=0)
final_model_mean = np.mean(model_means, axis=0)
cell_stds.append(np.std(final_cell_mean))
model_stds.append(np.std(final_model_mean))
# offset, final_model_mean_phase_corrected = correct_phase(final_cell_mean, final_model_mean, step_size)
# print("Offset:", offset)
# print("modfreq:", mod_freq)
final_model_mean_phase_corrected = np.roll(final_model_mean, approx_offset)
# PLOT EVERY MOD FREQ
# fig, axes = plt.subplots(1, 5, figsize=(15, 5), sharex=True)
# for c in cell_means:
# axes[0].plot(c, color="grey", alpha=0.2)
# axes[0].plot(np.mean(cell_means, axis=0), color="black")
# axes[0].set_title("Cell response")
# axis_cell = axes[0].axis()
#
# for m in model_means:
# axes[1].plot(m, color="grey", alpha=0.2)
# axes[1].plot(np.mean(model_means, axis=0), color="black")
# axes[1].set_title("Model response")
# axis_model = axes[1].axis()
# ylim_top = max(axis_cell[3], axis_model[3])
# axes[1].set_ylim(0, ylim_top)
# axes[0].set_ylim(0, ylim_top)
# axes[2].set_ylim(0, ylim_top)
#
# axes[2].plot(final_cell_mean, label="cell")
# axes[2].plot(final_model_mean, label="model")
# axes[2].plot(final_model_mean_phase_corrected, label="model p-cor")
# axes[2].legend()
# axes[2].set_title("cell-model overlapped")
# axes[3].plot((final_model_mean - final_cell_mean) / final_cell_mean, label="normal")
# axes[3].plot((final_model_mean_phase_corrected- final_cell_mean) / final_cell_mean, label="phase cor")
# axes[3].set_title("rel. error")
# axes[3].legend()
# axes[4].plot(final_model_mean - final_cell_mean, label="normal")
# axes[4].plot(final_model_mean_phase_corrected - final_cell_mean, label="phase cor")
# axes[4].set_title("abs. error (Hz)")
# axes[4].legend()
#
# fig.suptitle("modulation frequency: {}".format(mod_freq))
#
# # plt.tight_layout()
# # plt.show()
# plt.close()
fig, ax = plt.subplots(1, 1)
ax.plot(u_delta_freqs[-len(cell_stds):], cell_stds, label="cell stds")
ax.plot(u_delta_freqs[-len(model_stds):], model_stds, label="model stds")
ax.set_title("response modulation depth")
ax.set_xlabel("Modulation frequency")
ax.set_ylabel("STD")
ax.legend()
plt.savefig("figures/sam/" + cell_data.get_cell_name() + ".png")
# plt.show()
plt.close()
def correct_phase(cell_mean, model_mean, step_size):
# test for every 0.2 ms roll in the total time:
lowest_err = np.inf
roll_idx = 0
for i in range(int(len(cell_mean) * step_size * 1000) * 5):
roll_by = int((i / 5 / 1000) / step_size)
rolled = np.roll(model_mean, roll_by)
# rms = np.sqrt(np.mean(np.power((cell_mean - rolled), 2)))
abs = np.sum(np.abs(cell_mean-rolled))
if abs < lowest_err:
lowest_err = abs
roll_idx = roll_by
return roll_idx, np.roll(model_mean, roll_idx)
def approximate_axon_delay_in_idx(cell_data, model):
lowest_mod_freq = 80
highest_mod_freq = 150
eod_freq = cell_data.get_eod_frequency()
step_size = cell_data.get_sampling_interval()
durations = cell_data.get_sam_durations()
contrasts = cell_data.get_sam_contrasts()
u_contrasts = np.unique(contrasts)
spiketimes = cell_data.get_sam_spiketimes()
delta_freqs = cell_data.get_sam_delta_frequencies()
u_delta_freqs = np.unique(delta_freqs)
used_mod_freqs = []
axon_delays = []
for mod_freq in sorted(u_delta_freqs):
# Only use "stable" mod_freqs to approximate the axon delay
if not lowest_mod_freq <= mod_freq <= highest_mod_freq:
continue
if 1/mod_freq > durations[0] / 4:
print("skipped mod_freq: {}".format(mod_freq))
print("Duration: {} while mod_freq period: {:.2f}".format(durations[0], 1/mod_freq))
continue
mfreq_data = {}
cell_means = []
model_means = []
for c in u_contrasts:
mfreq_data[c] = []
for i in range(len(delta_freqs)):
if delta_freqs[i] != mod_freq:
continue
if len(spiketimes[i]) > 1:
print("There are more spiketimes in one 'point'! Only the first was used! ")
spikes = spiketimes[i][0]
cell_pdf = spiketimes_calculate_pdf(spikes, step_size)
cell_cuts = cut_pdf_into_periods(cell_pdf, 1/mod_freq, step_size)
if len(cell_cuts) == 0:
continue
cell_mean = np.mean(cell_cuts, axis=0)
cell_means.append(cell_mean)
stimulus = SAM(eod_freq, contrasts[i] / 100, mod_freq)
v1, spikes_model = model.simulate(stimulus, durations[i] * 4)
model_pdf = spiketimes_calculate_pdf(spikes_model, step_size)
model_cuts = cut_pdf_into_periods(model_pdf, 1/mod_freq, step_size)
model_mean = np.mean(model_cuts, axis=0)
model_means.append(model_mean)
final_cell_mean = np.mean(cell_means, axis=0)
final_model_mean = np.mean(model_means, axis=0)
offset, final_model_mean_phase_corrected = correct_phase(final_cell_mean, final_model_mean, step_size)
used_mod_freqs.append(mod_freq)
axon_delays.append(offset)
mean_delay = np.mean(axon_delays)
if np.isnan(mean_delay):
return 0
else:
return int(round(mean_delay))
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_slow(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=0.001):
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=0.0):
if period < 0:
# print("cut_pdf_into_periods(): Period was negative! Absolute value taken to continue")
period = abs(period)
idx_period_length = int(period / float(step_size))
offset_per_step = period / float(step_size) - idx_period_length
cut_length = idx_period_length + int(factor * idx_period_length)
num_of_cuts = int(len(pdf) / (idx_period_length + offset_per_step))
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 idx_period_length * 0.9 > len(pdf):
return []
# raise RuntimeError("SAM stimulus is too short for the given mod freq period.")
if cut_length > len(pdf) or num_of_cuts < 1:
return [pdf]
cuts = np.zeros((num_of_cuts-1, cut_length))
for i in np.arange(1, 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
cut = np.array(pdf[start_idx: end_idx])
cuts[i-1] = cut
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()