116 lines
3.2 KiB
Python
116 lines
3.2 KiB
Python
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from fitting.ModelFit import get_best_fit
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import os
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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SAVE_DIR = "results/sam_cells_only_best/"
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def main():
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res_folder = "results/sam_cells_only_best/"
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save_model_parameters(res_folder)
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# save_cell_info(res_folder)
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# test_save_cell_info()
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def save_model_parameters(res_folder):
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cells = []
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eod_freqs = []
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parameters = []
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for cell in sorted(os.listdir(res_folder)):
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cell_dir = res_folder + cell
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model = get_best_fit(cell_dir, use_comparable_error=False)
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cells.append(cell)
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eod_freqs.append(model.get_cell_data().get_eod_frequency())
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parameters.append(model.get_final_parameters())
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save_csv(SAVE_DIR + "models.csv", cells, eod_freqs, parameters)
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def test_save_cell_info():
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for cell in sorted(os.listdir(SAVE_DIR)):
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cell_dir = SAVE_DIR + cell + "/"
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if not os.path.isdir(cell_dir):
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continue
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fi_frame = pd.read_csv(cell_dir + "fi_curve_info.csv")
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plt.plot(fi_frame["contrast"], fi_frame["f_inf"], 'o')
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plt.plot(fi_frame["contrast"], fi_frame["f_zero"], '+')
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plt.show()
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plt.close()
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count = 1
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spike_file = "baseline_spikes_trial_{}.npy".format(count)
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while os.path.exists(cell_dir + spike_file):
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spiketimes = np.load(cell_dir + spike_file) * 1000
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plt.hist(np.diff(spiketimes), bins=np.arange(0, 50, 0.1))
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plt.show()
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plt.close()
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count += 1
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spike_file = "baseline_spikes_trial_{}.npy".format(count)
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def save_cell_info(res_folder):
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for cell in sorted(os.listdir(res_folder)):
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cell_dir = res_folder + cell
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fit = get_best_fit(cell_dir, use_comparable_error=False)
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save_path = SAVE_DIR + cell + "/"
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if not os.path.exists(save_path):
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os.mkdir(save_path)
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# fi-curve
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cell_data = fit.get_cell_data()
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f_zeros = fit.get_cell_f_zero_values()
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f_infs = fit.get_cell_f_inf_values()
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contrasts = cell_data.get_fi_contrasts()
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data_array = np.array([contrasts, f_infs, f_zeros]).T
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fi_frame = pd.DataFrame(data_array, columns=["contrast", "f_inf", "f_zero"])
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fi_frame.to_csv(save_path + "fi_curve_info.csv")
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spikes = cell_data.get_base_spikes()
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for i, spike_list in enumerate(spikes):
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spike_array = np.array(spike_list)
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np.save(save_path + "baseline_spikes_trial_{}.npy".format(i+1), spike_array)
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def save_csv(file, cells, eod_freqs, parameters):
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keys = sorted(parameters[0].keys())
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with open(file, "w") as file:
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header = "cell,EODf"
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for k in keys:
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if k == "refractory_period":
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header += ",ref_period"
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elif k == "step_size":
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header += ",deltat"
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else:
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header += ",{}".format(k)
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file.write(header + "\n")
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for i in range(len(cells)):
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line = "{},{:.2f}".format(cells[i], eod_freqs[i])
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for k in keys:
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line += ",{}".format(parameters[i][k])
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file.write(line + "\n")
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if __name__ == '__main__':
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main()
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