582 lines
25 KiB
Python
582 lines
25 KiB
Python
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from models.LIFACnoise import LifacNoiseModel
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from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
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from parser.CellData import CellData
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from experiments.Baseline import get_baseline_class
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from experiments.FiCurve import get_fi_curve_class
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import numpy as np
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from warnings import warn
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from scipy.optimize import minimize
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class Fitter:
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def __init__(self):
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self.base_model = LifacNoiseModel({"step_size": 0.00005})
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self.best_parameters_found = []
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self.smallest_error = np.inf
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#
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self.fi_contrasts = []
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self.recording_times = []
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self.eod_freq = 0
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self.data_sampling_interval = -1
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self.sc_max_lag = 2
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# values to be replicated:
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self.isi_bins = np.array(0)
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self.baseline_freq = 0
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self.vector_strength = -1
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self.serial_correlation = []
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self.coefficient_of_variation = 0
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self.burstiness = -1
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self.f_inf_values = []
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self.f_inf_slope = 0
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self.f_zero_values = []
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# self.f_zero_slopes = []
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self.f_zero_slope_at_straight = 0
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self.f_zero_straight_contrast = 0
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self.f_zero_fit = []
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self.f_zero_curve_contrast = 0
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self.f_zero_curve_contrast_idx = -1
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self.f_zero_curve_freq = np.array([])
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self.f_zero_curve_time = np.array([])
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self.errors = []
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# counts how often the cost_function was called
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self.counter = 0
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def set_data_reference_values(self, cell_data: CellData):
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self.eod_freq = cell_data.get_eod_frequency()
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self.data_sampling_interval = cell_data.get_sampling_interval()
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self.recording_times = cell_data.get_recording_times()
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data_baseline = get_baseline_class(cell_data)
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data_baseline.load_values(cell_data.get_data_path())
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self.baseline_freq = data_baseline.get_baseline_frequency()
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self.isi_bins = calculate_histogram_bins(data_baseline.get_interspike_intervals())
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# plt.close()
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# plt.plot(self.isi_bins)
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# plt.show()
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# plt.close()
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self.vector_strength = data_baseline.get_vector_strength()
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self.serial_correlation = data_baseline.get_serial_correlation(self.sc_max_lag)
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self.coefficient_of_variation = data_baseline.get_coefficient_of_variation()
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self.burstiness = data_baseline.get_burstiness()
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contrasts = np.array(cell_data.get_fi_contrasts())
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fi_curve = get_fi_curve_class(cell_data, contrasts, save_dir=cell_data.get_data_path())
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self.f_inf_slope = fi_curve.get_f_inf_slope()
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if self.f_inf_slope < 0:
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contrasts = contrasts * -1
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# print("old contrasts:", cell_data.get_fi_contrasts())
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# print("new contrasts:", contrasts)
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fi_curve = get_fi_curve_class(cell_data, contrasts, save_dir=cell_data.get_data_path())
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self.fi_contrasts = fi_curve.stimulus_values
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self.f_inf_values = fi_curve.f_inf_frequencies
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self.f_inf_slope = fi_curve.get_f_inf_slope()
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self.f_zero_values = fi_curve.f_zero_frequencies
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self.f_zero_fit = fi_curve.f_zero_fit
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# self.f_zero_slopes = [fi_curve.get_f_zero_fit_slope_at_stimulus_value(c) for c in self.fi_contrasts]
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self.f_zero_slope_at_straight = fi_curve.get_f_zero_fit_slope_at_straight()
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self.f_zero_slope_at_zero = fi_curve.get_f_zero_fit_slope_at_stimulus_value(0)
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self.f_zero_straight_contrast = self.f_zero_fit[3]
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max_contrast = max(contrasts)
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test_contrast = 0.5 * max_contrast
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diff_contrasts = np.abs(contrasts - test_contrast)
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self.f_zero_curve_contrast_idx = np.argmin(diff_contrasts)
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self.f_zero_curve_contrast = contrasts[self.f_zero_curve_contrast_idx]
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times, freqs = fi_curve.get_mean_time_and_freq_traces()
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self.f_zero_curve_freq = freqs[self.f_zero_curve_contrast_idx]
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self.f_zero_curve_time = times[self.f_zero_curve_contrast_idx]
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# around 1/3 of the value at straight
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# self.f_zero_slope = fi_curve.get_fi_curve_slope_at(fi_curve.get_f_zero_and_f_inf_intersection())
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# adaption = Adaption(fi_curve)
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# self.tau_a = adaption.get_tau_real()
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def fit_model_to_data(self, data: CellData, start_parameters, fit_routine_func: callable):
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self.set_data_reference_values(data)
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return fit_routine_func(start_parameters)
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def fit_routine(self, start_parameters, error_weights=None):
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self.counter = 0
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# fit only v_offset, mem_tau, input_scaling, dend_tau
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x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
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start_parameters["input_scaling"], start_parameters["tau_a"], start_parameters["delta_a"],
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start_parameters["dend_tau"], start_parameters["refractory_period"]])
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initial_simplex = create_init_simples(x0, search_scale=3)
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# error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty,
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# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
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fmin = minimize(fun=self.cost_function_all,
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args=(error_weights,), x0=x0, method="Nelder-Mead",
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options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
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return fmin, self.base_model.get_parameters()
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def cost_function_all(self, X, error_weights=None):
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# tau mins:
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tau_min = 0.001
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for i in (0, 3, 5):
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if X[i] < tau_min:
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print("tried too small tau value")
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return 1000 + abs(X[i] - tau_min) * 10000
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for i in (1, 2, 4, 6):
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if X[i] < 0:
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print("tried negative parameter value")
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return 1000 + abs(X[i]) * 10000
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if X[6] > 1.05/self.eod_freq: # refractory period shouldn't be larger than one eod period
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print("tried too large ref period")
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return 1000 + abs(X[6]) * 10000
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self.base_model.set_variable("mem_tau", X[0])
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self.base_model.set_variable("noise_strength", X[1])
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self.base_model.set_variable("input_scaling", X[2])
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self.base_model.set_variable("tau_a", X[3])
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self.base_model.set_variable("delta_a", X[4])
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self.base_model.set_variable("dend_tau", X[5])
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self.base_model.set_variable("refractory_period", X[6])
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base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0)
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# find right v-offset
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test_model = self.base_model.get_model_copy()
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test_model.set_variable("noise_strength", 0)
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# time1 = time.time()
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v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
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self.base_model.set_variable("v_offset", v_offset)
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# time2 = time.time()
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# print("time taken for finding v_offset: {:.2f}s".format(time2-time1))
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error_list = self.calculate_errors(error_weights)
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# print("sum: {:.2f}, ".format(sum(error_list)))
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if sum(error_list) < self.smallest_error:
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self.smallest_error = sum(error_list)
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self.best_parameters_found = X
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return sum(error_list)
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def fit_routine_no_dend_tau(self, start_parameters, error_weights=None):
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self.counter = 0
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# fit all except dend_tau
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self.base_model.parameters["dend_tau"] = 0
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x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
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start_parameters["input_scaling"], start_parameters["tau_a"],
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start_parameters["delta_a"], start_parameters["refractory_period"]])
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initial_simplex = create_init_simples(x0, search_scale=3)
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# error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty,
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# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
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fmin = minimize(fun=self.cost_function_no_dend_tau,
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args=(error_weights,), x0=x0, method="Nelder-Mead",
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options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
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return fmin, self.base_model.get_parameters()
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def cost_function_no_dend_tau(self, X, error_weights=None):
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# tau mins:
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tau_min = 0.001
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for i in (0, 3):
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if X[i] < tau_min:
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print("tried too small tau value")
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return 1000 + abs(X[i] - tau_min) * 10000
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for i in (1, 2, 4, 5):
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if X[i] < 0:
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print("tried negative parameter value")
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return 1000 + abs(X[i]) * 10000
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if X[5] > 1.05/self.eod_freq: # refractory period shouldn't be larger than one eod period
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print("tried too large ref period")
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return 1000 + abs(X[5]) * 10000
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self.base_model.set_variable("mem_tau", X[0])
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self.base_model.set_variable("noise_strength", X[1])
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self.base_model.set_variable("input_scaling", X[2])
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self.base_model.set_variable("tau_a", X[3])
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self.base_model.set_variable("delta_a", X[4])
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self.base_model.set_variable("refractory_period", X[5])
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base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0)
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# find right v-offset
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test_model = self.base_model.get_model_copy()
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test_model.set_variable("noise_strength", 0)
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# time1 = time.time()
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v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
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self.base_model.set_variable("v_offset", v_offset)
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# time2 = time.time()
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# print("time taken for finding v_offset: {:.2f}s".format(time2-time1))
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error_list = self.calculate_errors(error_weights)
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# print("sum: {:.2f}, ".format(sum(error_list)))
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if sum(error_list) < self.smallest_error:
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self.smallest_error = sum(error_list)
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self.best_parameters_found = X
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return sum(error_list)
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def fit_routine_no_ref_period(self, start_parameters, error_weights=None):
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self.counter = 0
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# fit all except ref_period
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self.base_model.set_variable("refractory_period", 0)
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x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
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start_parameters["input_scaling"], start_parameters["tau_a"], start_parameters["delta_a"],
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start_parameters["dend_tau"]])
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initial_simplex = create_init_simples(x0, search_scale=3)
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# error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty,
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# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
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fmin = minimize(fun=self.cost_function_no_ref_period,
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args=(error_weights,), x0=x0, method="Nelder-Mead",
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options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
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return fmin, self.base_model.get_parameters()
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def cost_function_no_ref_period(self, X, error_weights=None):
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# tau mins:
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tau_min = 0.001
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for i in (0, 3, 5):
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if X[i] < tau_min:
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print("tried too small tau value")
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return 1000 + abs(X[i] - tau_min) * 10000
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for i in (1, 2, 4):
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if X[i] < 0:
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print("tried negative parameter value")
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return 1000 + abs(X[i]) * 10000
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self.base_model.set_variable("mem_tau", X[0])
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self.base_model.set_variable("noise_strength", X[1])
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self.base_model.set_variable("input_scaling", X[2])
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self.base_model.set_variable("tau_a", X[3])
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self.base_model.set_variable("delta_a", X[4])
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self.base_model.set_variable("dend_tau", X[5])
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base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0)
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# find right v-offset
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test_model = self.base_model.get_model_copy()
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test_model.set_variable("noise_strength", 0)
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# time1 = time.time()
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v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
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self.base_model.set_variable("v_offset", v_offset)
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# time2 = time.time()
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# print("time taken for finding v_offset: {:.2f}s".format(time2-time1))
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error_list = self.calculate_errors(error_weights)
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# print("sum: {:.2f}, ".format(sum(error_list)))
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if sum(error_list) < self.smallest_error:
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self.smallest_error = sum(error_list)
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self.best_parameters_found = X
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return sum(error_list)
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def fit_routine_no_dend_tau_and_no_ref_period(self, start_parameters, error_weights=None):
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self.counter = 0
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# fit all except dend_tau and ref_period
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self.base_model.parameters["refractory_period"] = 0
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self.base_model.parameters["dend_tau"] = 0
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x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
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start_parameters["input_scaling"], start_parameters["tau_a"], start_parameters["delta_a"]])
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initial_simplex = create_init_simples(x0, search_scale=3)
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# error_list = [error_bf, error_vs, error_sc, error_cv, error_bursty,
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# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
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fmin = minimize(fun=self.cost_function_no_dend_tau_and_no_ref_period,
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args=(error_weights,), x0=x0, method="Nelder-Mead",
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options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
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return fmin, self.base_model.get_parameters()
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def cost_function_no_dend_tau_and_no_ref_period(self, X, error_weights=None):
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# tau mins:
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tau_min = 0.001
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for i in (0, 3):
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if X[i] < tau_min:
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print("tried too small tau value")
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return 1000 + abs(X[i] - tau_min) * 10000
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for i in (1, 2, 4):
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if X[i] < 0:
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print("tried negative parameter value")
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return 1000 + abs(X[i]) * 10000
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self.base_model.set_variable("mem_tau", X[0])
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self.base_model.set_variable("noise_strength", X[1])
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self.base_model.set_variable("input_scaling", X[2])
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self.base_model.set_variable("tau_a", X[3])
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self.base_model.set_variable("delta_a", X[4])
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base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0)
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# find right v-offset
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test_model = self.base_model.get_model_copy()
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test_model.set_variable("noise_strength", 0)
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# time1 = time.time()
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v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
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self.base_model.set_variable("v_offset", v_offset)
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# time2 = time.time()
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# print("time taken for finding v_offset: {:.2f}s".format(time2-time1))
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print(self.base_model.parameters)
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error_list = self.calculate_errors(error_weights)
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# print("sum: {:.2f}, ".format(sum(error_list)))
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if sum(error_list) < self.smallest_error:
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self.smallest_error = sum(error_list)
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self.best_parameters_found = X
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return sum(error_list)
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def calculate_errors(self, error_weights=None, model=None):
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if model is None:
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model = self.base_model
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# time1 = time.time()
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model_baseline = get_baseline_class(model, self.eod_freq, trials=3)
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baseline_freq = model_baseline.get_baseline_frequency()
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vector_strength = model_baseline.get_vector_strength()
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serial_correlation = model_baseline.get_serial_correlation(self.sc_max_lag)
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coefficient_of_variation = model_baseline.get_coefficient_of_variation()
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burstiness = model_baseline.get_burstiness()
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# time2 = time.time()
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isi_bins = calculate_histogram_bins(model_baseline.get_interspike_intervals())
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# print("Time taken for all baseline parameters: {:.2f}".format(time2-time1))
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# time1 = time.time()
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fi_curve_model = get_fi_curve_class(model, self.fi_contrasts, self.eod_freq, trials=8)
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f_zeros = fi_curve_model.get_f_zero_frequencies()
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f_infinities = fi_curve_model.get_f_inf_frequencies()
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f_infinities_slope = fi_curve_model.get_f_inf_slope()
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# f_zero_slopes = [fi_curve_model.get_f_zero_fit_slope_at_stimulus_value(x) for x in self.fi_contrasts]
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f_zero_slope_at_straight = fi_curve_model.get_f_zero_fit_slope_at_stimulus_value(self.f_zero_straight_contrast)
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# time2 = time.time()
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# print("Time taken for all fi-curve parameters: {:.2f}".format(time2 - time1))
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# calculate errors with reference values
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error_bf = abs((baseline_freq - self.baseline_freq) / self.baseline_freq)
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error_vs = abs((vector_strength - self.vector_strength) / 0.01)
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error_cv = abs((coefficient_of_variation - self.coefficient_of_variation) / 0.05)
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error_bursty = (abs(burstiness - self.burstiness) / 0.2)
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error_hist = np.sqrt(np.mean((isi_bins - self.isi_bins) ** 2)) / 10
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# print("error hist: {:.2f}".format(error_hist))
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# print("Burstiness: cell {:.2f}, model: {:.2f}, error: {:.2f}".format(self.burstiness, burstiness, error_bursty))
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error_sc = 0
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for i in range(self.sc_max_lag):
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error_sc += abs((serial_correlation[i] - self.serial_correlation[i]) / 0.1)
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# error_sc = error_sc / self.sc_max_lag
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error_f_inf_slope = abs((f_infinities_slope - self.f_inf_slope) / abs(self.f_inf_slope+1)) * 25
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error_f_inf = calculate_list_error(f_infinities, self.f_inf_values)
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# error_f_zero_slopes = calculate_list_error(f_zero_slopes, self.f_zero_slopes)
|
|
error_f_zero_slope_at_straight = abs(self.f_zero_slope_at_straight - f_zero_slope_at_straight) \
|
|
/ abs(self.f_zero_slope_at_straight+1)
|
|
error_f_zero = calculate_list_error(f_zeros, self.f_zero_values) / 10
|
|
|
|
error_f0_curve = self.calculate_f0_curve_error(model, fi_curve_model) / 20
|
|
|
|
error_list = [error_vs, error_sc, error_cv, error_hist, error_bursty,
|
|
error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope_at_straight, error_f0_curve]
|
|
|
|
self.errors.append(error_list)
|
|
if error_weights is not None and len(error_weights) == len(error_list):
|
|
for i in range(len(error_weights)):
|
|
error_list[i] = error_list[i] * error_weights[i]
|
|
elif error_weights is not None:
|
|
warn("Error: weights had different length than errors and were ignored!")
|
|
if sum(error_list) < 0:
|
|
print("Error negative: ", error_list)
|
|
if np.isnan(sum(error_list)):
|
|
print("--------SOME ERROR VALUE(S) IS/ARE NaN:")
|
|
print(error_list)
|
|
return [50 for e in error_list]
|
|
# raise ValueError("Some error value(s) is/are NaN!")
|
|
return error_list
|
|
|
|
def calculate_f0_curve_error(self, model, fi_curve_model, dendritic_delay=0.005):
|
|
buffer = 0.00
|
|
test_duration = 0.05
|
|
|
|
# prepare model frequency curve:
|
|
times, freqs = fi_curve_model.get_mean_time_and_freq_traces()
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|
freq_prediction = np.array(freqs[self.f_zero_curve_contrast_idx])
|
|
time_prediction = np.array(times[self.f_zero_curve_contrast_idx])
|
|
|
|
if len(time_prediction) == 0:
|
|
return 200
|
|
stimulus_start = fi_curve_model.get_stimulus_start() - time_prediction[0]
|
|
|
|
model_start_idx = int((stimulus_start - buffer) / fi_curve_model.get_sampling_interval())
|
|
model_end_idx = int((stimulus_start + buffer + test_duration) / model.get_sampling_interval())
|
|
|
|
idx_offset = int(dendritic_delay / model.get_sampling_interval())
|
|
|
|
model_start_idx += idx_offset
|
|
model_end_idx += idx_offset
|
|
|
|
if len(time_prediction) == 0 or len(time_prediction) < model_end_idx \
|
|
or time_prediction[0] > fi_curve_model.get_stimulus_start():
|
|
error_f0_curve = 200
|
|
return error_f0_curve
|
|
|
|
model_curve = freq_prediction[model_start_idx:model_end_idx]
|
|
|
|
# prepare cell frequency_curve:
|
|
|
|
stimulus_start = self.recording_times[1] - self.f_zero_curve_time[0]
|
|
cell_start_idx = int((stimulus_start - buffer) / self.data_sampling_interval)
|
|
cell_end_idx = int((stimulus_start + buffer + test_duration) / self.data_sampling_interval)
|
|
|
|
if round(model.get_sampling_interval() % self.data_sampling_interval, 4) == 0:
|
|
step_cell = int(round(model.get_sampling_interval() / self.data_sampling_interval))
|
|
else:
|
|
raise ValueError("Model sampling interval is not a multiple of data sampling interval.")
|
|
|
|
cell_curve = self.f_zero_curve_freq[cell_start_idx:cell_end_idx:step_cell]
|
|
# plt.close()
|
|
# plt.plot(cell_curve)
|
|
# plt.plot(model_curve)
|
|
# plt.savefig("./figures/f_zero_curve_error_{}.png".format(time.strftime("%H:%M:%S")))
|
|
# plt.close()
|
|
|
|
if len(cell_curve) < len(model_curve):
|
|
model_curve = model_curve[:len(cell_curve)]
|
|
elif len(model_curve) < len(cell_curve):
|
|
cell_curve = cell_curve[:len(model_curve)]
|
|
|
|
error_f0_curve = np.sqrt(np.mean((model_curve - cell_curve) ** 2))
|
|
|
|
return error_f0_curve
|
|
|
|
# def calculate_f0_curve_error_new(self, model, fi_curve_model):
|
|
# buffer = 0.05
|
|
# test_duration = 0.05
|
|
#
|
|
# times, freqs = fi_curve_model.get_mean_time_and_freq_traces()
|
|
# freq_prediction = np.array(freqs[self.f_zero_curve_contrast_idx])
|
|
# time_prediction = np.array(times[self.f_zero_curve_contrast_idx])
|
|
#
|
|
# if len(time_prediction) == 0:
|
|
# return 200
|
|
# stimulus_start = fi_curve_model.get_stimulus_start() - time_prediction[0]
|
|
#
|
|
# model_start_idx = int((stimulus_start - buffer) / model.get_sampling_interval())
|
|
# model_end_idx = int((stimulus_start + buffer + test_duration) / model.get_sampling_interval())
|
|
#
|
|
# if len(time_prediction) == 0 or len(time_prediction) < model_end_idx \
|
|
# or time_prediction[0] > fi_curve_model.get_stimulus_start():
|
|
# error_f0_curve = 200
|
|
# return error_f0_curve
|
|
#
|
|
# model_curve = np.array(freq_prediction[model_start_idx:model_end_idx])
|
|
#
|
|
# # prepare cell frequency_curve:
|
|
#
|
|
# stimulus_start = self.recording_times[1] - self.f_zero_curve_time[0]
|
|
# cell_start_idx = int((stimulus_start - buffer) / self.data_sampling_interval)
|
|
# cell_end_idx = int((stimulus_start - buffer + test_duration) / self.data_sampling_interval)
|
|
#
|
|
# if round(model.get_sampling_interval() % self.data_sampling_interval, 4) == 0:
|
|
# step_cell = int(round(model.get_sampling_interval() / self.data_sampling_interval))
|
|
# else:
|
|
# raise ValueError("Model sampling interval is not a multiple of data sampling interval.")
|
|
#
|
|
# cell_curve = self.f_zero_curve_freq[cell_start_idx:cell_end_idx:step_cell]
|
|
# cell_time = self.f_zero_curve_time[cell_start_idx:cell_end_idx:step_cell]
|
|
# cell_curve_std = np.std(self.f_zero_curve_freq)
|
|
# model_curve_std = np.std(freq_prediction)
|
|
#
|
|
# model_limit = self.baseline_freq + model_curve_std
|
|
# cell_limit = self.baseline_freq + cell_curve_std
|
|
#
|
|
# cell_full_precicion = np.array(self.f_zero_curve_freq[cell_start_idx:cell_end_idx])
|
|
# cell_points_above = cell_full_precicion > cell_limit
|
|
# cell_area_above = sum(cell_full_precicion[cell_points_above]) * self.data_sampling_interval
|
|
#
|
|
# model_points_above = model_curve > model_limit
|
|
# model_area_above = sum(model_curve[model_points_above]) * model.get_sampling_interval()
|
|
#
|
|
# # plt.close()
|
|
# # plt.plot(cell_time, cell_curve, color="blue")
|
|
# # plt.plot((cell_time[0], cell_time[-1]), (cell_limit, cell_limit),
|
|
# # color="lightblue", label="area: {:.2f}".format(cell_area_above))
|
|
# #
|
|
# # plt.plot(time_prediction[model_start_idx:model_end_idx], model_curve, color="orange")
|
|
# # plt.plot((time_prediction[model_start_idx], time_prediction[model_end_idx]), (model_limit, model_limit),
|
|
# # color="red", label="area: {:.2f}".format(model_area_above))
|
|
# # plt.legend()
|
|
# # plt.title("Error: {:.2f}".format(abs(model_area_above - cell_area_above) / 0.02))
|
|
# # plt.savefig("./figures/f_zero_curve_error_{}.png".format(time.strftime("%H:%M:%S")))
|
|
# # plt.close()
|
|
#
|
|
# return abs(model_area_above - cell_area_above)
|
|
|
|
|
|
def calculate_list_error(fit, reference):
|
|
error = 0
|
|
for i in range(len(reference)):
|
|
# error += abs_freq_error(fit[i] - reference[i])
|
|
error += normed_quadratic_freq_error(fit[i], reference[i])
|
|
norm_error = error / len(reference)
|
|
|
|
return norm_error
|
|
|
|
|
|
def calculate_histogram_bins(isis):
|
|
isis = np.array(isis) * 1000
|
|
step = 0.1
|
|
bins = np.arange(0, 50, step)
|
|
|
|
counts = np.array([np.sum((isis >= b) & (isis < b+0.1)) for b in bins])
|
|
return counts
|
|
|
|
|
|
def normed_quadratic_freq_error(fit, ref, factor=2):
|
|
return (abs(fit-ref)/factor)**2 / ref
|
|
|
|
|
|
def abs_freq_error(diff, factor=10):
|
|
return abs(diff) / factor
|
|
|
|
|
|
def create_init_simples(x0, search_scale=3.):
|
|
dim = len(x0)
|
|
simplex = [[x0[0]/search_scale], [x0[0]*search_scale]]
|
|
for i in range(1, dim, 1):
|
|
for vertex in simplex:
|
|
vertex.append(x0[i]*search_scale)
|
|
new_vertex = list(x0[:i])
|
|
new_vertex.append(x0[i]/search_scale)
|
|
simplex.append(new_vertex)
|
|
|
|
return simplex
|
|
|
|
|
|
if __name__ == '__main__':
|
|
print("use run_fitter.py to run the Fitter.")
|