save more data after Fit
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a.ott
parent
574f4a80f2
commit
222af65bd6
@ -4,11 +4,13 @@ from CellData import icelldata_of_dir, CellData
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from Baseline import get_baseline_class
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from FiCurve import get_fi_curve_class
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from Fitter import Fitter
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from ModelFit import ModelFit
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import time
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import os
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import copy
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import argparse
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import numpy as np
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import multiprocessing as mp
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@ -32,13 +34,16 @@ def main():
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# quit()
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start_parameters = [p for p in iget_start_parameters()]
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start_data = 8
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count = 0
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for cell_data in icelldata_of_dir("./invivo_data/"):
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count += 1
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if count < start_data:
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continue
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fit_cell_parallel(cell_data, start_parameters)
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# start_data = 8
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# count = 0
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# for cell_data in icelldata_of_dir("./invivo_data/"):
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# count += 1
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# if count < start_data:
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# continue
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# fit_cell_parallel(cell_data, start_parameters)
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cell_data = CellData("invivo_data/2012-04-20-ab-invivo-1/")
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fit_cell_parallel(cell_data, start_parameters)
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def test_single_cell(path):
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@ -94,13 +99,15 @@ def fit_all_cells_parallel_sync(cells, start_parameters, thread_pool, results_ba
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def fit_cell_parallel(cell_data, start_parameters):
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cell_path = os.path.basename(cell_data.get_data_path())
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save_directory = "./results/invivo_results/"
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save_path_cell = os.path.join(save_directory, cell_data.get_cell_name())
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print(cell_path)
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core_count = mp.cpu_count()
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pool = mp.Pool(core_count - 1)
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parameters = []
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for i, p in enumerate(start_parameters):
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parameters.append((cell_data, i, p, "./results/invivo_results/"))
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parameters.append((cell_data, i, p, save_directory))
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time1 = time.time()
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pool.map(fit_cell_base, parameters)
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@ -109,6 +116,17 @@ def fit_cell_parallel(cell_data, start_parameters):
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del pool
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del cell_data
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best_fit = None
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min_err = np.inf
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for fit in os.listdir(save_path_cell):
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cur_fit = ModelFit(os.path.join(save_path_cell, fit))
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if cur_fit.comparable_error() < min_err:
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min_err = cur_fit.comparable_error()
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best_fit = cur_fit
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best_fit.generate_master_plot("./results/invivo_best/singles/")
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def test_fit_routines():
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fitter = Fitter()
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@ -238,6 +256,9 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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if save_path is None:
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return
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with open(save_path + "parameters_info.txt", "w") as file:
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file.writelines(["start_parameters:\t" + str(start_parameters),
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"\nfinal_parameters:\t" + str(parameters)])
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@ -245,6 +266,24 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
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model = LifacNoiseModel(parameters)
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eod_frequency = cell_data.get_eod_frequency()
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data_baseline = get_baseline_class(cell_data)
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c_bf = data_baseline.get_baseline_frequency()
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c_vs = data_baseline.get_vector_strength()
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c_sc = data_baseline.get_serial_correlation(1)
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c_cv = data_baseline.get_coefficient_of_variation()
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c_burst = data_baseline.get_burstiness()
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data_fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
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c_f_inf_slope = data_fi_curve.get_f_inf_slope()
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c_f_inf_values = data_fi_curve.f_inf_frequencies
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c_f_zero_slope = data_fi_curve.get_f_zero_fit_slope_at_straight()
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c_f_zero_values = data_fi_curve.f_zero_frequencies
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if c_f_inf_slope < 0:
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contrasts = np.array(cell_data.get_fi_contrasts()) * -1
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else:
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contrasts = np.array(cell_data.get_fi_contrasts())
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model_baseline = get_baseline_class(model, eod_frequency)
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m_bf = model_baseline.get_baseline_frequency()
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m_vs = model_baseline.get_vector_strength()
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@ -258,19 +297,18 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
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m_f_infinities_slope = model_ficurve.get_f_inf_slope()
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m_f_zero_slope = model_ficurve.get_f_zero_fit_slope_at_straight()
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data_baseline = get_baseline_class(cell_data)
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c_bf = data_baseline.get_baseline_frequency()
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c_vs = data_baseline.get_vector_strength()
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c_sc = data_baseline.get_serial_correlation(1)
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c_cv = data_baseline.get_coefficient_of_variation()
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c_burst = data_baseline.get_burstiness()
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np.save(os.path.join(save_path, "model_fi_inf_values.npy"), np.array(m_f_infinities))
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np.save(os.path.join(save_path, "cell_fi_inf_values.npy"), np.array(c_f_inf_values))
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np.save(os.path.join(save_path, "model_fi_zero_values.npy"), np.array(m_f_zeros))
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np.save(os.path.join(save_path, "cell_fi_zero_values.npy"), np.array(c_f_zero_values))
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data_fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
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c_f_inf_slope = data_fi_curve.get_f_inf_slope()
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c_f_inf_values = data_fi_curve.f_inf_frequencies
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with open(os.path.join(save_path, "cell_data_path.txt"), "w") as f:
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path = cell_data.get_data_path() + "\n"
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f.write(path)
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if c_f_inf_slope < 0:
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model_ficurve.stimulus_values = contrasts * -1
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c_f_zero_slope = data_fi_curve.get_f_zero_fit_slope_at_straight()
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c_f_zero_values = data_fi_curve.f_zero_frequencies
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# print("EOD-frequency: {:.2f}".format(cell_data.get_eod_frequency()))
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# print("bf: cell - {:.2f} vs model {:.2f}".format(c_bf, m_bf))
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# print("vs: cell - {:.2f} vs model {:.2f}".format(c_vs, m_vs))
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