469 lines
17 KiB
Python
469 lines
17 KiB
Python
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from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus as SAM
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from Baseline import get_baseline_class
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from FiCurve import FICurveModel
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from models.LIFACnoise import LifacNoiseModel
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import numpy as np
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import matplotlib.pyplot as plt
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import helperFunctions as hF
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from CellData import CellData
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from ModelFit import ModelFit, get_best_fit
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import os
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import shutil
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def main():
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run_sam_analysis_for_all_cells("results/final_2")
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# sam_analysis("results/final_2/2011-10-25-ad-invivo-1/")
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# plot_traces_with_spiketimes()
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# plot_mean_of_cuts()
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quit()
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modelfit = get_best_fit("results/final_2/2011-10-25-ad-invivo-1/")
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cell_data = CellData(modelfit.get_cell_path())
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eod_freq = cell_data.get_eod_frequency()
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model = modelfit.get_model()
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test_model_response(model, eod_freq, 0.1, np.arange(5, 2500, 5))
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def run_sam_analysis_for_all_cells(folder):
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count = 0
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for item in os.listdir(folder):
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cell_folder = os.path.join(folder, item)
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fit = get_best_fit(cell_folder, use_comparable_error=False)
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cell_data = fit.get_cell_data()
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if cell_data.has_sam_recordings():
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count += 1
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# print("Fit quality:", fit.get_fit_routine_error())
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sam_analysis(cell_folder)
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print(count)
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def test_model_response(model: LifacNoiseModel, eod_freq, contrast, modulation_frequencies):
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stds = []
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for m_freq in modulation_frequencies:
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if (1/m_freq) / 10 <= model.parameters["step_size"]:
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model.parameters["step_size"] = (1/m_freq) / 10
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step_size = model.parameters["step_size"]
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print("mode_freq:", m_freq, "- step size:", step_size)
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stimulus = SAM(eod_freq, contrast / 100, m_freq)
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duration = 30
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v1, spikes_model = model.simulate(stimulus, duration)
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prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size, kernel_width=0.005)
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fig, ax = plt.subplots(1, 1)
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ax.plot(prob_density_function_model)
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ax.set_title("pdf with m_freq: {}".format(int(m_freq)))
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plt.savefig("figures/sam/pdf_mfreq_{}.png".format(m_freq))
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plt.close()
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stds.append(np.std(prob_density_function_model))
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plt.plot((np.array(modulation_frequencies)) / eod_freq, stds)
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plt.show()
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plt.close()
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def plot_traces_with_spiketimes():
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modelfit = get_best_fit("results/final_2/2011-10-25-ad-invivo-1/")
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cell_data = modelfit.get_cell_data()
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traces = cell_data.parser.__get_traces__("SAM")
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# [time_traces, v1_traces, eod_traces, local_eod_traces, stimulus_traces]
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sam_spiketimes = cell_data.get_sam_spiketimes()
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for i in range(len(traces[0])):
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fig, axes = plt.subplots(2, 1, sharex=True)
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axes[0].plot(traces[0][i], traces[1][i])
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axes[0].plot(list(sam_spiketimes[i]), list([max(traces[1][i])] * len(sam_spiketimes[i])), 'o')
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axes[1].plot(traces[0][i], traces[3][i])
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plt.show()
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plt.close()
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def plot_mean_of_cuts():
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modelfit = get_best_fit("results/final_2/2018-05-08-ac-invivo-1/")
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if not os.path.exists(os.path.join(modelfit.get_cell_path(), "samallspikes1.dat")):
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print("Cell: {} \n Has no measured sam stimuli.")
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return
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cell_data = CellData(modelfit.get_cell_path())
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eod_freq = cell_data.get_eod_frequency()
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model = modelfit.get_model()
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durations = cell_data.get_sam_durations()
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u_durations = np.unique(durations)
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mean_duration = np.mean(durations)
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contrasts = cell_data.get_sam_contrasts()
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u_contrasts = np.unique(contrasts)
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contrast = contrasts[0] # are all the same in this test case
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spiketimes = cell_data.get_sam_spiketimes()
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delta_freqs = cell_data.get_sam_delta_frequencies()
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step_size = cell_data.get_sampling_interval()
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spikes_dictionary = {}
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for i, m_freq in enumerate(delta_freqs):
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if m_freq in spikes_dictionary:
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spikes_dictionary[m_freq].append(spiketimes[i])
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else:
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spikes_dictionary[m_freq] = [spiketimes[i]]
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for m_freq in sorted(spikes_dictionary.keys()):
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if mean_duration < 2 * (1 / float(m_freq)):
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print("meep")
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continue
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stimulus = SAM(eod_freq, contrast / 100, m_freq)
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v1, spikes_model = model.simulate(stimulus, 4)
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prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size)
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fig, axes = plt.subplots(1, 4)
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start_idx = int(2 / step_size)
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cuts = cut_pdf_into_periods(prob_density_function_model[start_idx:], 1 / float(m_freq), step_size)
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for c in cuts:
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axes[0].plot(c, color="gray", alpha=0.2)
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axes[0].set_title("model")
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mean_model = np.mean(cuts, axis=0)
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axes[0].plot(mean_model, color="black")
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means_cell = []
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for spikes_cell in spikes_dictionary[m_freq]:
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prob_density_cell = spiketimes_calculate_pdf(spikes_cell[0], step_size)
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cuts_cell = cut_pdf_into_periods(prob_density_cell, 1 / float(m_freq), step_size)
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for c in cuts_cell:
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axes[1].plot(c, color="gray", alpha=0.15)
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print(cuts_cell.shape)
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means_cell.append(np.mean(cuts_cell, axis=0))
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if len(means_cell) == 0:
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print("means cell length zero")
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continue
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means_cell = np.array(means_cell)
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total_mean_cell = np.mean(means_cell, axis=0)
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axes[1].set_title("cell")
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axes[1].plot(total_mean_cell, color="black")
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axes[2].set_title("difference")
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diff = [(total_mean_cell[i] - mean_model[i]) for i in range(len(total_mean_cell))]
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axes[2].plot(diff)
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axes[3].plot(total_mean_cell)
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axes[3].plot(mean_model)
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plt.show()
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plt.close()
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def sam_analysis(fit_path):
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modelfit = get_best_fit(fit_path)
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if not os.path.exists(os.path.join(modelfit.get_cell_path(), "samallspikes1.dat")):
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print("Cell: {} \n Has no measured sam stimuli.")
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return
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cell_data = CellData(modelfit.get_cell_path())
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model = modelfit.get_model()
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# parameters = {'delta_a': 0.08820130374685671, 'refractory_period': 0.0006, 'a_zero': 15, 'step_size': 5e-05,
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# 'v_base': 0, 'noise_strength': 0.03622523883042496, 'v_zero': 0, 'threshold': 1,
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# 'input_scaling': 77.75367190909581, 'tau_a': 0.07623731247799118, 'v_offset': -10.546875,
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# 'mem_tau': 0.008741976196676469, 'dend_tau': 0.0012058986118892773}
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# model = LifacNoiseModel(parameters)
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# cell_data = CellData("./data/test_data/2012-12-13-an-invivo-1/")
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eod_freq = cell_data.get_eod_frequency()
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step_size = cell_data.get_sampling_interval()
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durations = cell_data.get_sam_durations()
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u_durations = np.unique(durations)
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contrasts = cell_data.get_sam_contrasts()
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u_contrasts = np.unique(contrasts)
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spiketimes = cell_data.get_sam_spiketimes()
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delta_freqs = cell_data.get_sam_delta_frequencies()
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u_delta_freqs = np.unique(delta_freqs)
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cell_stds = []
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model_stds = []
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approx_offset = approximate_axon_delay_in_idx(cell_data, model)
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print("Approx offset idx:", approx_offset)
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print("Approx offset ms:", (approx_offset * step_size) * 1000)
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for mod_freq in sorted(u_delta_freqs):
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# TODO problem of cutting the pdf as in some cases the pdf is shorter than 1 modulation frequency period!
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# length info wrong ? always at least one period?
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# if 1/mod_freq > durations[0] / 4:
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# print("skipped mod_freq: {}".format(mod_freq))
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# print("Duration: {} while mod_freq period: {:.2f}".format(durations[0], 1/mod_freq))
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# print("Maybe long enough duration? unique durations:", u_durations)
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# continue
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mfreq_data = {}
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cell_means = []
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model_means = []
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for c in u_contrasts:
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mfreq_data[c] = []
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for i in range(len(delta_freqs)):
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if delta_freqs[i] != mod_freq:
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continue
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if len(spiketimes[i]) == 0:
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print("No spiketimes found at index!")
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continue
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if len(spiketimes[i]) > 1:
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print("There are more spiketimes in one 'point'! Only the first was used! ")
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spikes = spiketimes[i][0]
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cell_pdf = spiketimes_calculate_pdf(spikes, step_size)
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cell_cuts = cut_pdf_into_periods(cell_pdf, 1/mod_freq, step_size)
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cell_mean = np.mean(cell_cuts, axis=0)
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cell_means.append(cell_mean)
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stimulus = SAM(eod_freq, contrasts[i] / 100, mod_freq)
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v1, spikes_model = model.simulate(stimulus, 10)
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model_pdf = spiketimes_calculate_pdf(spikes_model, step_size)
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model_cuts = cut_pdf_into_periods(model_pdf, 1/mod_freq, step_size)
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model_mean = np.mean(model_cuts, axis=0)
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model_means.append(model_mean)
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min_length = min(min([len(cm) for cm in cell_means]), min([len(mm) for mm in model_means]))
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for i in range(len(cell_means)):
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cell_means[i] = cell_means[i][:min_length]
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model_means[i] = model_means[i][:min_length]
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final_cell_mean = np.mean(cell_means, axis=0)
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final_model_mean = np.mean(model_means, axis=0)
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cell_stds.append(np.std(final_cell_mean))
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model_stds.append(np.std(final_model_mean))
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# offset, final_model_mean_phase_corrected = correct_phase(final_cell_mean, final_model_mean, step_size)
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# print("Offset:", offset)
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# print("modfreq:", mod_freq)
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final_model_mean_phase_corrected = np.roll(final_model_mean, approx_offset)
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# PLOT EVERY MOD FREQ
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# fig, axes = plt.subplots(1, 5, figsize=(15, 5), sharex=True)
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# for c in cell_means:
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# axes[0].plot(c, color="grey", alpha=0.2)
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# axes[0].plot(np.mean(cell_means, axis=0), color="black")
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# axes[0].set_title("Cell response")
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# axis_cell = axes[0].axis()
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#
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# for m in model_means:
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# axes[1].plot(m, color="grey", alpha=0.2)
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# axes[1].plot(np.mean(model_means, axis=0), color="black")
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# axes[1].set_title("Model response")
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# axis_model = axes[1].axis()
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# ylim_top = max(axis_cell[3], axis_model[3])
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# axes[1].set_ylim(0, ylim_top)
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# axes[0].set_ylim(0, ylim_top)
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# axes[2].set_ylim(0, ylim_top)
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#
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# axes[2].plot(final_cell_mean, label="cell")
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# axes[2].plot(final_model_mean, label="model")
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# axes[2].plot(final_model_mean_phase_corrected, label="model p-cor")
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# axes[2].legend()
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# axes[2].set_title("cell-model overlapped")
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# axes[3].plot((final_model_mean - final_cell_mean) / final_cell_mean, label="normal")
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# axes[3].plot((final_model_mean_phase_corrected- final_cell_mean) / final_cell_mean, label="phase cor")
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# axes[3].set_title("rel. error")
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# axes[3].legend()
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# axes[4].plot(final_model_mean - final_cell_mean, label="normal")
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# axes[4].plot(final_model_mean_phase_corrected - final_cell_mean, label="phase cor")
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# axes[4].set_title("abs. error (Hz)")
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# axes[4].legend()
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#
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# fig.suptitle("modulation frequency: {}".format(mod_freq))
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#
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# # plt.tight_layout()
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# # plt.show()
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# plt.close()
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fig, ax = plt.subplots(1, 1)
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ax.plot(u_delta_freqs[-len(cell_stds):], cell_stds, label="cell stds")
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ax.plot(u_delta_freqs[-len(model_stds):], model_stds, label="model stds")
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ax.set_title("response modulation depth")
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ax.set_xlabel("Modulation frequency")
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ax.set_ylabel("STD")
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ax.legend()
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plt.savefig("figures/sam/" + cell_data.get_cell_name() + ".png")
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# plt.show()
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plt.close()
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def correct_phase(cell_mean, model_mean, step_size):
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# test for every 0.2 ms roll in the total time:
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lowest_err = np.inf
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roll_idx = 0
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for i in range(int(len(cell_mean) * step_size * 1000) * 5):
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roll_by = int((i / 5 / 1000) / step_size)
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rolled = np.roll(model_mean, roll_by)
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# rms = np.sqrt(np.mean(np.power((cell_mean - rolled), 2)))
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abs = np.sum(np.abs(cell_mean-rolled))
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if abs < lowest_err:
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lowest_err = abs
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roll_idx = roll_by
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return roll_idx, np.roll(model_mean, roll_idx)
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def approximate_axon_delay_in_idx(cell_data, model):
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lowest_mod_freq = 80
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highest_mod_freq = 150
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eod_freq = cell_data.get_eod_frequency()
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step_size = cell_data.get_sampling_interval()
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durations = cell_data.get_sam_durations()
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contrasts = cell_data.get_sam_contrasts()
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u_contrasts = np.unique(contrasts)
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spiketimes = cell_data.get_sam_spiketimes()
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delta_freqs = cell_data.get_sam_delta_frequencies()
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u_delta_freqs = np.unique(delta_freqs)
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used_mod_freqs = []
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axon_delays = []
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for mod_freq in sorted(u_delta_freqs):
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# Only use "stable" mod_freqs to approximate the axon delay
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if not lowest_mod_freq <= mod_freq <= highest_mod_freq:
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continue
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if 1/mod_freq > durations[0] / 4:
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print("skipped mod_freq: {}".format(mod_freq))
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print("Duration: {} while mod_freq period: {:.2f}".format(durations[0], 1/mod_freq))
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continue
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mfreq_data = {}
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cell_means = []
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model_means = []
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for c in u_contrasts:
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mfreq_data[c] = []
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for i in range(len(delta_freqs)):
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if delta_freqs[i] != mod_freq:
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continue
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if len(spiketimes[i]) > 1:
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print("There are more spiketimes in one 'point'! Only the first was used! ")
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spikes = spiketimes[i][0]
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cell_pdf = spiketimes_calculate_pdf(spikes, step_size)
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cell_cuts = cut_pdf_into_periods(cell_pdf, 1/mod_freq, step_size)
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if len(cell_cuts) == 0:
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continue
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cell_mean = np.mean(cell_cuts, axis=0)
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cell_means.append(cell_mean)
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stimulus = SAM(eod_freq, contrasts[i] / 100, mod_freq)
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v1, spikes_model = model.simulate(stimulus, durations[i] * 4)
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model_pdf = spiketimes_calculate_pdf(spikes_model, step_size)
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model_cuts = cut_pdf_into_periods(model_pdf, 1/mod_freq, step_size)
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model_mean = np.mean(model_cuts, axis=0)
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model_means.append(model_mean)
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final_cell_mean = np.mean(cell_means, axis=0)
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final_model_mean = np.mean(model_means, axis=0)
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offset, final_model_mean_phase_corrected = correct_phase(final_cell_mean, final_model_mean, step_size)
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used_mod_freqs.append(mod_freq)
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axon_delays.append(offset)
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mean_delay = np.mean(axon_delays)
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if np.isnan(mean_delay):
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return 0
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else:
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return int(round(mean_delay))
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def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005):
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trials_rate_list = []
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step_size = model.get_parameters()["step_size"]
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for _ in range(trials):
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v1, spikes = model.simulate_slow(stimulus, total_time_s=sim_length)
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binary = np.zeros(int(sim_length/step_size))
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spikes = [int(s / step_size) for s in spikes]
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for s_idx in spikes:
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binary[s_idx] = 1
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kernel = gaussian_kernel(kernel_width, step_size)
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rate = np.convolve(binary, kernel, mode='same')
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trials_rate_list.append(rate)
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times = [np.arange(0, sim_length, step_size) for _ in range(trials)]
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t, mean_rate = hF.calculate_mean_of_frequency_traces(times, trials_rate_list, step_size)
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return mean_rate
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def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.001):
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length = int(spikes[len(spikes)-1] / step_size)+1
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binary = np.zeros(length)
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spikes = [int(s / step_size) for s in spikes]
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for s_idx in spikes:
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binary[s_idx] = 1
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kernel = gaussian_kernel(kernel_width, step_size)
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rate = np.convolve(binary, kernel, mode='same')
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return rate
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def cut_pdf_into_periods(pdf, period, step_size, factor=0.0):
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if period < 0:
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# print("cut_pdf_into_periods(): Period was negative! Absolute value taken to continue")
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period = abs(period)
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idx_period_length = int(period / float(step_size))
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offset_per_step = period / float(step_size) - idx_period_length
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cut_length = idx_period_length + int(factor * idx_period_length)
|
|
num_of_cuts = int(len(pdf) / (idx_period_length + offset_per_step))
|
|
|
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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()
|