106 lines
3.8 KiB
Python
106 lines
3.8 KiB
Python
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import helperFunctions as hf
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from CellData import icelldata_of_dir
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import functions as fu
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import numpy as np
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import time
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import matplotlib.pyplot as plt
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import os
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from scipy.signal import argrelmax
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from thunderfish.eventdetection import detect_peaks
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from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus
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from models.LIFACnoise import LifacNoiseModel
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def time_test_function():
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for n in [1000]: # number of calls
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print("calls:", n)
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start = time.time()
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for i in range(n):
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data = np.random.normal(size=10000)
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y = [fu.rectify(x) for x in data]
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end = time.time()
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print("time:", end - start)
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def test_cell_data():
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for cell_data in icelldata_of_dir("./data/"):
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#if "2012-12-20-ad" not in cell_data.get_data_path():
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# continue
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print()
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print(cell_data.get_data_path())
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if len(cell_data.get_base_traces(cell_data.TIME)) != 0:
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# print("works!")
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#print("VS:", cell_data.get_vector_strength())
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#print("SC:", cell_data.get_serial_correlation(5))
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print("Eod freq:", cell_data.get_eod_frequency())
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else:
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pass
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#print("NNNOOOOOOOOOO!")
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#print("spiketimes:", len(cell_data.get_base_spikes()))
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#print("Times:", len(cell_data.get_base_traces(cell_data.TIME)))
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#print("EOD:", len(cell_data.get_base_traces(cell_data.EOD)))
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def test_peak_detection():
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for cell_data in icelldata_of_dir("./data/"):
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print()
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print(cell_data.get_data_path())
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times = cell_data.get_base_traces(cell_data.TIME)
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eod = cell_data.get_base_traces(cell_data.EOD)
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v1 = cell_data.get_base_traces(cell_data.V1)
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for i in range(len(v1)):
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pieces = 20
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v1_trace = v1[i]
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total = len(v1_trace)
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all_peaks = []
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plt.plot(times[i], v1[i])
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for n in range(pieces):
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length = int(total/pieces)
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first_index = n*length
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last_index = (n+1)*length
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std = np.std(v1_trace[first_index:last_index])
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peaks, _ = detect_peaks(v1_trace[first_index:last_index], std * 3)
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peaks = peaks + first_index
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all_peaks.extend(peaks)
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plt.plot(times[i][first_index], v1_trace[first_index], 'o', color="green")
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all_peaks = np.array(all_peaks)
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plt.plot(times[i][all_peaks], v1[i][all_peaks], 'o', color='red')
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plt.show()
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def test_simulation_speed():
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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}
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model = LifacNoiseModel(parameters)
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repetitions = 1
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seconds = 10
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stimulus = SinusAmplitudeModulationStimulus(750, 1, 10, 1, 8)
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time_start = 0
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t_start = time.time()
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for i in range(repetitions):
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v, spikes = model.simulate_fast(stimulus, seconds, time_start)
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print(len(v))
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print(len(spikes))
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#time_v = np.arange(time_start, seconds, model.get_sampling_interval())
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#plt.plot(time_v, v, '.')
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#plt.show()
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#freq = hf.mean_freq_of_spiketimes_after_time_x(spikes, parameters["step_size"], 0)
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#print(freq)
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t_end = time.time()
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#print("baseline markers:", model.calculate_baseline_markers(750, 3))
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print("took:", round((t_end-t_start)/repetitions, 5), "seconds for " + str(seconds) + "s simulation", "step size:", parameters["step_size"]*1000, "ms")
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if __name__ == '__main__':
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# time_test_function()
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# test_cell_data()
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# test_peak_detection()
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test_simulation_speed()
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pass
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