283 lines
11 KiB
Python
283 lines
11 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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from FiCurve import FICurveModel, get_fi_curve_class
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from Baseline import get_baseline_class
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from AdaptionCurrent import Adaption
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from stimuli.StepStimulus import StepStimulus
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from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
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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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def test_fi_curve_class():
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model_parameters = {'v_offset': -15.234375, 'input_scaling': 64.94152780134829, 'step_size': 5e-05, 'a_zero': 2,
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'threshold': 1, 'v_base': 0, 'delta_a': 0.04763179657857666, 'tau_a': 0.07891848949732623,
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'mem_tau': 0.004828473985707999, 'noise_strength': 0.017132801387559883,
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'v_zero': 0, 'dend_tau': 0.0015230454266819539}
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model = LifacNoiseModel(model_parameters)
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contrasts = np.arange(-0.4, 0.4, 0.05)
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ficurve = get_fi_curve_class(model, contrasts, 700)
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ficurve.plot_mean_frequency_curves()
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return
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for cell_data in icelldata_of_dir("../data/"):
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fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
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fi_curve.plot_mean_frequency_curves()
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# fi_curve.plot_f_point_detections()
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pass
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def test_adaption_class():
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model_parameters = {'v_offset': -15.234375, 'input_scaling': 64.94152780134829, 'step_size': 5e-05, 'a_zero': 2,
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'threshold': 1, 'v_base': 0, 'delta_a': 0.04763179657857666, 'tau_a': 0.07891848949732623,
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'mem_tau': 0.004828473985707999, 'noise_strength': 0.017132801387559883,
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'v_zero': 0, 'dend_tau': 0.0015230454266819539}
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model = LifacNoiseModel(model_parameters)
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contrasts = np.arange(-0.4, 1, 0.05)
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for delta_a in np.arange(0.1, 1.5, 0.1):
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model.set_variable("delta_a", delta_a)
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# for tau_a in np.arange(0.01, 0.1, 0.01):
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# model.set_variable("tau_a", tau_a)
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fi_curve = FICurveModel(model, contrasts, 750, 10)
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adaption = Adaption(fi_curve)
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# adaption.plot_exponential_fits()
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m_tau = model.get_parameters()["tau_a"]
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approx_tau = adaption.get_tau_real()
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m_delta_a = model.get_parameters()["delta_a"]
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approx_delta_a = adaption.get_delta_a()
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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))
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# print("model tau_a \t: {:.4f} vs {:.4f} adaption estimate, error: {:.2}".format(m_tau, approx_tau, (approx_tau / m_tau)))
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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)))
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print(fi_curve.f_zero_fit[3])
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quit()
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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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fi_curve = FICurve(cell_data)
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adaption = Adaption(fi_curve)
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adaption.plot_exponential_fits()
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print("tau_effs:", adaption.get_tau_effs())
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print("tau_real:", adaption.get_tau_real())
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fi_curve.plot_fi_curve()
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def test_parameters():
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parameters = {'mem_tau': 21., 'delta_a': 0.1, 'input_scaling': 400.,
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'v_offset': 85.25, 'threshold': 0.1, 'v_base': 0, 'step_size': 0.00005, 'tau_a': 0.01,
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'a_zero': 0, 'v_zero': 0, 'noise_strength': 3}
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model = LifacNoiseModel(parameters)
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base_stimulus_freq = 350
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stimulus = SinusAmplitudeModulationStimulus(base_stimulus_freq, 1.2, 5, 5, 20)
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plot_model_during_stimulus(model, stimulus, 30)
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bf, vs, sc = model.calculate_baseline_markers(base_stimulus_freq)
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contrasts = [0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3]
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modulation_frequency = 1
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f_infs, f_inf_slope = model.calculate_fi_markers(contrasts, base_stimulus_freq, modulation_frequency)
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print("Baseline frequency: {:.2f}".format(bf))
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print("Vector strength: {:.2f}".format(vs))
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print("serial correlation: {:.2f}".format(sc[0]))
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print("f infinity slope: {:.2f}".format(f_inf_slope))
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print("f infinities: \n", f_infs)
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def test_vector_strength_calculation():
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model = LifacNoiseModel({"noise_strength": 0})
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bf, vs1, sc = model.calculate_baseline_markers(600)
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base_stim = SinusAmplitudeModulationStimulus(600, 0, 0)
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_, spiketimes = model.simulate_fast(base_stim, 30)
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stimulus_trace = base_stim.as_array(0, 30, model.get_sampling_interval())
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time_trace = np.arange(0, 30, model.get_sampling_interval())
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vs2 = hf.calculate_vector_strength_from_spiketimes(time_trace, stimulus_trace, spiketimes, model.get_sampling_interval())
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print("with assumed eod durations vs: {:.3f}".format(vs1))
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print("with detected eod durations vs: {:.3f}".format(vs2))
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def test_baseline_polar_plot():
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model_parameter = {'v_offset': -15.234375, 'input_scaling': 64.94152780134829, 'step_size': 5e-05, 'a_zero': 2,
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'threshold': 1, 'v_base': 0, 'delta_a': 0.04763179657857666, 'tau_a': 0.07891848949732623,
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'mem_tau': 0.004828473985707999, 'noise_strength': 0.017132801387559883,
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'v_zero': 0, 'dend_tau': 0.0015230454266819539}
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baseline = get_baseline_class(LifacNoiseModel(model_parameter), 700)
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baseline.plot_polar_vector_strength()
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# for data in icelldata_of_dir("../data/"):
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# trace = data.get_base_traces(trace_type=data.V1)
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# if len(trace) == 0:
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# print("NO V1 TRACE FOUND")
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# continue
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#
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# baseline = get_baseline_class(data)
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# baseline.plot_polar_vector_strength()
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def plot_model_during_stimulus(model: LifacNoiseModel, stimulus:SinusAmplitudeModulationStimulus, total_time):
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_, spiketimes = model.simulate_fast(stimulus, total_time)
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time = np.arange(0, total_time, model.get_sampling_interval())
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fig, axes = plt.subplots(5, 1, figsize=(9, 4*2), sharex="all")
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stimulus_array = stimulus.as_array(0, total_time, model.get_sampling_interval())
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axes[0].plot(time, stimulus_array)
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axes[0].set_title("Stimulus")
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axes[1].plot(time, rectify_stimulus_array(stimulus_array))
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axes[1].set_title("rectified Stimulus")
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axes[2].plot(time, model.get_voltage_trace())
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axes[2].set_title("Voltage")
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axes[3].plot(time, model.get_adaption_trace())
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axes[3].set_title("Adaption")
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f_time, f = hf.calculate_time_and_frequency_trace(spiketimes, model.get_sampling_interval())
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axes[4].plot(f_time, f)
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axes[4].set_title("Frequency")
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plt.show()
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def rectify_stimulus_array(stimulus_array: np.ndarray):
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return np.array([x if x > 0 else 0 for x in stimulus_array])
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if __name__ == '__main__':
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model_parameters = {'v_offset': -15.234375, 'input_scaling': 64.94152780134829, 'step_size': 5e-05, 'a_zero': 2,
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'threshold': 1, 'v_base': 0, 'delta_a': 0.04763179657857666, 'tau_a': 0.07891848949732623,
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'mem_tau': 0.004828473985707999, 'noise_strength': 0.017132801387559883,
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'v_zero': 0, 'dend_tau': 0.0015230454266819539}
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model = LifacNoiseModel(model_parameters)
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base = get_baseline_class(model, 650)
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base.plot_baseline()
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fi = get_fi_curve_class(model, np.arange(0, 2, 0.2), 650)
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fi.plot_fi_curve()
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quit()
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# test_baseline_polar_plot()
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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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# test_parameters()
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test_fi_curve_class()
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# test_adaption_class()
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# test_vector_strength_calculation()
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pass
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