change start parameters, clean up old code
This commit is contained in:
a.ott
parent
05c808e90b
commit
90ccb4459d
168
run_Fitter.py
168
run_Fitter.py
@ -29,21 +29,10 @@ def main():
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fit_cell_parallel(cell_data, start_parameters)
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fit_cell_parallel(cell_data, start_parameters)
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quit()
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quit()
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# test_single_cell("invivo_data/2012-01-17-ap/")
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test_single_cell("data/invivo/2010-11-08-al-invivo-1/")
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#
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# quit()
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start_parameters = [p for p in iget_start_parameters()]
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# start_parameters = [p for p in iget_start_parameters()]
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# start_data = 8
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# cell_data = CellData("data/invivo/2013-04-17-ac-invivo-1/")
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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("data/invivo/2013-04-17-ac-invivo-1/")
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test_single_cell("data/invivo/2015-01-20-ad-invivo-1/")
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# fit_cell_parallel(cell_data, start_parameters)
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# fit_cell_parallel(cell_data, start_parameters)
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@ -59,7 +48,7 @@ def test_single_cell(path):
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cell_path = os.path.basename(cell_data.get_data_path())
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cell_path = os.path.basename(cell_data.get_data_path())
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error = fitter.calculate_errors(model=LifacNoiseModel(res_par))
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error = fitter.calculate_errors(model=LifacNoiseModel(res_par))
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save_path = "results/invivo_results/" + cell_path + "/start_parameter_{:}_err_{:.2f}/".format(i, sum(error))
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save_path = "results/invivo_bursty_results/" + cell_path + "/start_parameter_{:}_err_{:.2f}/".format(i, sum(error))
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save_fitting_run_info(cell_data, res_par, p, plot=True, save_path=save_path)
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save_fitting_run_info(cell_data, res_par, p, plot=True, save_path=save_path)
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print("Done with start parameters {}".format(str(i)))
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print("Done with start parameters {}".format(str(i)))
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@ -86,18 +75,6 @@ def fit_cell_base(parameters):
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"\n error: {:.2f}".format(sum(error)))
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"\n error: {:.2f}".format(sum(error)))
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def fit_all_cells_parallel_sync(cells, start_parameters, thread_pool, results_base_folder):
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parameter = []
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for cell in cells:
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for i, s_pars in enumerate(start_parameters):
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parameter.append((cell, i, s_pars, results_base_folder))
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time1 = time.time()
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thread_pool.map(fit_cell_base, parameter)
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time2 = time.time()
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print("Time taken for all ({:}): {:.2f}s".format(len(parameter)*len(cells), time2 - time1))
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def fit_cell_parallel(cell_data, start_parameters):
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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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cell_path = os.path.basename(cell_data.get_data_path())
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save_directory = "./results/invivo_results/"
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save_directory = "./results/invivo_results/"
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@ -125,49 +102,7 @@ def fit_cell_parallel(cell_data, start_parameters):
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min_err = cur_fit.comparable_error()
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min_err = cur_fit.comparable_error()
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best_fit = cur_fit
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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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best_fit.generate_master_plot("./results/invivo_best/")
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def test_fit_routines():
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fitter = Fitter()
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names = ("routine_1", "routine_2")
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global FIT_ROUTINE
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for i, routine in enumerate([fitter.fit_routine_1, fitter.fit_routine_2]):
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FIT_ROUTINE = names[i]
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run_with_real_data(fitter, routine)
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best = []
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cells = sorted(os.listdir("test_routines/" + names[0] + "/"))
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for name in names:
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save_path = "test_routines/" + name + "/"
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cell_best = []
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for directory in sorted(os.listdir(save_path)):
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path = os.path.join(save_path, directory)
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if os.path.isdir(path):
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cell_best.append(find_best_run(path))
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best.append(cell_best)
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with open("test_routines/comparision.csv", "w") as res_file:
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res_file.write("routine")
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for cell in cells:
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res_file.write("," + cell)
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for i, routine_results in enumerate(best):
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res_file.write(names[i])
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for cell_best in routine_results:
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res_file.write("," + str(cell_best))
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def find_best_run(cell_path):
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values = []
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for directory in sorted(os.listdir(cell_path)):
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start_par_path = os.path.join(cell_path, directory)
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if os.path.isdir(start_par_path):
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values.append(float(start_par_path.split("_")[-1]))
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return min(values)
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def iget_start_parameters():
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def iget_start_parameters():
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@ -175,12 +110,12 @@ def iget_start_parameters():
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# expand by tau_a, delta_a ?
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# expand by tau_a, delta_a ?
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mem_tau_list = [0.01]
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mem_tau_list = [0.01]
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input_scaling_list = [60]
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input_scaling_list = [100]
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noise_strength_list = [0.03] # [0.02, 0.06]
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noise_strength_list = [0.03] # [0.02, 0.06]
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dend_tau_list = [0.001, 0.002]
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dend_tau_list = [0.0015]
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delta_a_list = [0.035, 0.065]
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delta_a_list = [0.035, 0.065, 0.1]
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tau_a_list = [0.1, 0.4]
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tau_a_list = [0.1, 0.4]
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ref_time_list = [0.00065]
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ref_time_list = [0.00065, 0.0012]
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for mem_tau in mem_tau_list:
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for mem_tau in mem_tau_list:
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for input_scaling in input_scaling_list:
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for input_scaling in input_scaling_list:
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@ -194,64 +129,6 @@ def iget_start_parameters():
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"delta_a": delta_a, "tau_a": tau_a, "refractory_period": ref_time}
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"delta_a": delta_a, "tau_a": tau_a, "refractory_period": ref_time}
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def run_with_real_data(fitter, fit_routine_func, parallel=False):
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count = 0
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for cell_data in icelldata_of_dir("./data/"):
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count += 1
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if count < 7:
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pass
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#continue
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print("cell:", cell_data.get_data_path())
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trace = cell_data.get_base_traces(trace_type=cell_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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global FIT_ROUTINE
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# results_path = "results/" + os.path.split(cell_data.get_data_path())[-1] + "/"
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results_path = "test_routines/" + FIT_ROUTINE + "/" + os.path.split(cell_data.get_data_path())[-1] + "/"
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print("results at:", results_path)
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if not os.path.exists(results_path):
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os.makedirs(results_path)
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# plot cell images:
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cell_save_path = results_path + "cell/"
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if not os.path.exists(cell_save_path):
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os.makedirs(cell_save_path)
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data_baseline = get_baseline_class(cell_data)
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data_baseline.plot_baseline(cell_save_path)
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data_baseline.plot_interspike_interval_histogram(cell_save_path)
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data_baseline.plot_serial_correlation(6, cell_save_path)
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data_fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
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data_fi_curve.plot_fi_curve(cell_save_path)
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start_par_count = 0
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for start_parameters in iget_start_parameters():
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start_par_count += 1
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print("START PARAMETERS:", start_par_count)
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start_time = time.time()
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# fitter = Fitter()
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fmin, parameters = fitter.fit_model_to_data(cell_data, start_parameters, fit_routine_func)
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print(fmin)
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print(parameters)
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end_time = time.time()
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parameter_set_path = results_path + "start_par_set_{}_fmin_{:.2f}".format(start_par_count, fmin["fun"]) + "/"
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print('Fitting of cell took function took {:.3f} s'.format((end_time - start_time)))
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# print(results_path)
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save_fitting_run_info(cell_data, parameters, start_parameters,
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plot=True, save_path=parameter_set_path)
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# from Sounds import play_finished_sound
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# play_finished_sound()
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pass
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def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, save_path=None):
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def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, save_path=None):
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if save_path is not None:
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if save_path is not None:
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if not os.path.exists(save_path):
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if not os.path.exists(save_path):
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@ -343,32 +220,5 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
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model_ficurve.plot_fi_curve_comparision(data_fi_curve, model_ficurve, save_path)
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model_ficurve.plot_fi_curve_comparision(data_fi_curve, model_ficurve, save_path)
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def test_effect_of_refractory_period():
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ref_periods = [0.0006, 0.001, 0.0015]
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counter = 0
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core_count = mp.cpu_count()
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for cell in icelldata_of_dir("./data/"):
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pool = mp.Pool(core_count - 1)
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counter += 1
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if counter < 10:
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continue
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elif counter >= 14:
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return
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start_parameters_base = [p for p in iget_start_parameters()]
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for ref_period in ref_periods:
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print(cell.get_data_path())
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print("ref period: {:.4f}".format(ref_period))
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results_base_folder = "./test_routines/ref_period_{:.4f}/".format(ref_period)
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all_start_parameters = copy.deepcopy(start_parameters_base)
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for par_set in all_start_parameters:
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par_set["refractory_period"] = ref_period
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fit_all_cells_parallel_sync([cell], all_start_parameters, pool, results_base_folder)
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del cell
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del pool
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if __name__ == '__main__':
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if __name__ == '__main__':
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main()
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main()
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