tau calculation and more
This commit is contained in:
alexanderott
parent
2a351795ad
commit
ccca6e030b
@ -6,7 +6,7 @@ import Figure_constants as Fc
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import my_util.save_load as sl
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fit_folder = "results/sam_cells" # kraken fit
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fit_folder = "results/sam_cells_only_best" # kraken fit
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# fit_folder = "results/final_sam_dend_noise_test/" # noise in input fit
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# fit_folder = "results/final_sam2/" # noise in input fit
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@ -1,7 +1,7 @@
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import plottools.colors as ptc
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from plottools.axes import labelaxes_params
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#from plottools.axes import labelaxes_params
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from plottools.axes import axes_params
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SAVE_FOLDER = "./thesis/figures/"
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@ -35,5 +35,5 @@ finf_marker = (4, 0, 0) # "s"
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def set_figure_labels(xoffset="auto", yoffset='auto'):
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labelaxes_params(xoffs=xoffset, yoffs=yoffset, labels='A',
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axes_params(xoffs=xoffset, yoffs=yoffset, label='A',
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font=dict(size=16, family='serif'))
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@ -318,7 +318,7 @@ def create_parameter_distributions(par_values, prefix=""):
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plt.tight_layout()
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consts.set_figure_labels(xoffset=-2.5, yoffset=1.5)
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fig.label_axes()
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# fig.label_axes()
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plt.savefig(consts.SAVE_FOLDER + prefix + "parameter_distributions.pdf")
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plt.close()
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46
download_fits.py
Normal file
46
download_fits.py
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@ -0,0 +1,46 @@
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import os
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def main():
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file_path = "./results/folders.txt"
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cells = {}
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with open(file_path) as f:
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for l in f:
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parts = l.strip().split("/")
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cell_folder = parts[0]
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start_para = parts[1]
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score = float(start_para.split('_')[-1])
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if cell_folder not in cells:
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cells[cell_folder] = [score, start_para]
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else:
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if cells[cell_folder][0] > score:
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cells[cell_folder] = [score, start_para]
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for k in sorted(cells.keys()):
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print(k, cells[k][1])
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remotehost = "alex@kraken.am28.uni-tuebingen.de"
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remote_base = "P-unit_model/results/sam_cells/"
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folders_to_copy = [remote_base + k + "/" + cells[k][1] + "/ " for k in sorted(cells.keys())]
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remote_files = ""
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for i in range(len(folders_to_copy)):
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remote_files += folders_to_copy[i]
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local_base = "./results/sam_cells_only_best/"
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# os.system('scp -r "%s:%s" "%s"' % (remotehost, remote_files, local_base))
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# create folders
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for k in sorted(cells.keys()):
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cell_folder = "./results/sam_cells_only_best/" + k + "/"
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os.makedirs(cell_folder)
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os.rename("./results/sam_cells_only_best/best/" + cells[k][1], cell_folder + cells[k][1])
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def read_file(path):
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pass
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if __name__ == '__main__':
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main()
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@ -6,6 +6,7 @@ import numpy as np
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import matplotlib.pyplot as plt
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from warnings import warn
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from my_util import helperFunctions as hF, functions as fu
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from scipy.optimize import curve_fit
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from os.path import join, exists
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import pickle
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from sys import stderr
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@ -23,7 +24,7 @@ class FICurve:
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self.f_inf_frequencies = []
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self.indices_f_zero = []
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self.f_zero_frequencies = []
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self.taus = []
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# increase, offset
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self.f_inf_fit = []
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# f_max, f_min, k, x_zero
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@ -45,9 +46,59 @@ class FICurve:
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self.f_inf_fit = hF.fit_clipped_line(self.stimulus_values, self.f_inf_frequencies)
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self.f_zero_fit = hF.fit_boltzmann(self.stimulus_values, self.f_zero_frequencies)
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def __calculate_time_constant_internal__(self, contrast, mean_frequency, baseline_freq, sampling_interval, pre_duration, plot=False):
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time_constant_fit_length = 0.05
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if contrast > 0:
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maximum_idx = np.argmax(mean_frequency)
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maximum = mean_frequency[maximum_idx]
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start_fit_idx = maximum_idx
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while (mean_frequency[start_fit_idx]) > 0.80 * (maximum - baseline_freq) + baseline_freq:
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start_fit_idx += 1
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else:
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minimum_idx = np.argmin(mean_frequency)
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minimum = mean_frequency[minimum_idx]
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start_fit_idx = minimum_idx
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# print("Border: ", baseline_freq - (0.80 * (baseline_freq - minimum)))
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while (mean_frequency[start_fit_idx]) < baseline_freq - (0.80 * (baseline_freq - minimum)):
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start_fit_idx += 1
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# print("start:", start_fit_idx * sampling_interval - pre_duration)
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end_fit_idx = start_fit_idx + int(time_constant_fit_length / sampling_interval)
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x_values = np.arange(end_fit_idx - start_fit_idx) * sampling_interval
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y_values = mean_frequency[start_fit_idx:end_fit_idx]
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try:
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popt, pcov = curve_fit(fu.exponential_function, x_values, y_values,
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p0=(1 / (np.power(1, 10)), 5, 50), maxfev=100000)
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# print(popt)
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if plot:
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if contrast > 0:
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plt.title("c: {:.2f} Base_f: {:.2f}, f_zero: {:.2f}".format(contrast, baseline_freq, maximum))
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else:
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plt.title("c: {:.2f} Base_f: {:.2f}, f_zero: {:.2f}".format(contrast, baseline_freq, minimum))
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plt.plot(np.arange(len(mean_frequency)) * sampling_interval - pre_duration, mean_frequency,
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'.')
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plt.plot(np.arange(start_fit_idx, end_fit_idx, 1) * sampling_interval - pre_duration, y_values,
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color="darkgreen")
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plt.plot(np.arange(start_fit_idx, end_fit_idx, 1) * sampling_interval - pre_duration,
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fu.exponential_function(x_values, popt[0], popt[1], popt[2]), color="orange")
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plt.show()
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plt.close()
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return popt, pcov
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except RuntimeError:
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print("RuntimeError happened in fit_exponential.")
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return [], []
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def calculate_all_frequency_points(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def calculate_time_constant(self, contrast_idx, plot=False):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_f_baseline_frequencies(self):
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return self.f_baseline_frequencies
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@ -143,7 +194,7 @@ class FICurve:
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plt.savefig(save_path + "mean_frequency_contrast_{:.2f}.png".format(sv))
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plt.close()
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def plot_fi_curve(self, save_path=None):
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def plot_fi_curve(self, save_path=None, title=""):
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min_x = min(self.stimulus_values)
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max_x = max(self.stimulus_values)
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step = (max_x - min_x) / 5000
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@ -161,6 +212,7 @@ class FICurve:
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color='red', label='f_0_fit')
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plt.legend()
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plt.title(title)
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plt.ylabel("Frequency [Hz]")
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plt.xlabel("Stimulus value")
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@ -231,6 +283,33 @@ class FICurve:
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plt.savefig(save_path + "fi_curve_comparision.png")
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plt.close()
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def write_detection_data_to_csv(self, save_path, name=""):
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steady_state = self.get_f_inf_frequencies()
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onset = self.get_f_zero_frequencies()
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baseline = self.get_f_baseline_frequencies()
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contrasts = self.stimulus_values
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headers = ["contrasts", "f_baseline", "f_steady_state", "f_onset"]
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if len(name) is not 0:
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file_name = name
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else:
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file_name = "fi_data.csv"
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with open(save_path + file_name, 'w') as f:
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for i in range(len(headers)):
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if i == 0:
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f.write(headers[i])
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else:
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f.write("," + headers[i])
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f.write("\n")
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for i in range(len(contrasts)):
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f.write(str(contrasts[i]) + ",")
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f.write(str(baseline[i]) + ",")
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f.write(str(steady_state[i]) + ",")
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f.write(str(onset[i]) + "\n")
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def plot_f_point_detections(self, save_path=None):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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@ -243,6 +322,11 @@ class FICurve:
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values["f_zero_frequencies"] = self.f_zero_frequencies
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values["f_inf_fit"] = self.f_inf_fit
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values["f_zero_fit"] = self.f_zero_fit
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taus = []
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for i in range(len(self.stimulus_values)):
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taus.append(self.calculate_time_constant(i))
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values["time_constants"] = taus
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with open(join(save_directory, self.save_file_name), "wb") as file:
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pickle.dump(values, file)
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@ -267,6 +351,7 @@ class FICurve:
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self.f_zero_frequencies = values["f_zero_frequencies"]
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self.f_inf_fit = values["f_inf_fit"]
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self.f_zero_fit = values["f_zero_fit"]
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self.taus = values["time_constants"]
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print("Fi-Curve: Values loaded!")
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return True
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@ -277,6 +362,21 @@ class FICurveCellData(FICurve):
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self.cell_data = cell_data
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super().__init__(stimulus_values, save_dir, recalculate)
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def calculate_time_constant(self, contrast_idx, plot=False):
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if len(self.taus) > 0:
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return self.taus[contrast_idx]
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mean_frequency = self.cell_data.get_mean_fi_curve_isi_frequencies()[contrast_idx]
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baseline_freq = self.get_f_baseline_frequencies()[contrast_idx]
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pre_duration = -1*self.cell_data.get_recording_times()[0]
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sampling_interval = self.cell_data.get_sampling_interval()
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# __calculate_time_constant_internal__(self, contrast, mean_frequency, baseline_freq, sampling_interval, pre_duration, plot=False):
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popt, pcov = super().__calculate_time_constant_internal__(self.stimulus_values[contrast_idx], mean_frequency,
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baseline_freq, sampling_interval, pre_duration, plot=plot)
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return popt[1]
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def calculate_all_frequency_points(self):
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mean_frequencies = self.cell_data.get_mean_fi_curve_isi_frequencies()
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time_axes = self.cell_data.get_time_axes_fi_curve_mean_frequencies()
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@ -458,6 +558,22 @@ class FICurveModel(FICurve):
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self.f_baseline_frequencies.append(f_baseline)
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self.indices_f_baseline.append(f_base_idx)
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def calculate_time_constant(self, contrast_idx, plot=False):
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if len(self.taus) > 0:
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return self.taus[contrast_idx]
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mean_frequency = self.mean_frequency_traces[contrast_idx]
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baseline_freq = self.get_f_baseline_frequencies()[contrast_idx]
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pre_duration = 0
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sampling_interval = self.model.get_sampling_interval()
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popt, pcov = super().__calculate_time_constant_internal__(self.stimulus_values[contrast_idx], mean_frequency,
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baseline_freq, sampling_interval, pre_duration, plot=plot)
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if len(popt) > 0:
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return popt[1]
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else:
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return -1
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def get_mean_time_and_freq_traces(self):
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return self.mean_time_traces, self.mean_frequency_traces
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27
find_ram_stimulus_files.py
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27
find_ram_stimulus_files.py
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import os
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import numpy as np
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import pyrelacs.DataLoader as Dl
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def main():
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folder = "data/final/"
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stim_files = []
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for cell in sorted(os.listdir(folder)):
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base_path = folder + cell
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for info, key, time, x in Dl.iload_traces(base_path, repro="FileStimulus", before=0, after=0):
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print(cell)
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if len(info) == 2 and "file" in info[1].keys():
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stim_files.append(info[1]["file"])
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break
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for file in np.unique(stim_files):
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print(file)
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if __name__ == '__main__':
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main()
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@ -187,7 +187,7 @@ class DatParser(AbstractParser):
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return self.fi_recording_times
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def get_baseline_traces(self):
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return self.__get_traces__("BaselineActivity")
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return self.get_traces("BaselineActivity")
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def get_baseline_spiketimes(self):
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# TODO change: reading from file -> detect from v1 trace
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@ -201,7 +201,7 @@ class DatParser(AbstractParser):
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return spiketimes
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def get_fi_curve_traces(self):
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return self.__get_traces__("FICurve")
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return self.get_traces("FICurve")
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def get_fi_frequency_traces(self):
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raise NotImplementedError("Not possible in .dat data type.\n"
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@ -290,7 +290,7 @@ class DatParser(AbstractParser):
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return trans_amplitudes, intensities, spiketimes
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def get_sam_traces(self):
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return self.__get_traces__("SAM")
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return self.get_traces("SAM")
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def get_sam_info(self):
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contrasts = []
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@ -351,7 +351,8 @@ class DatParser(AbstractParser):
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return spiketimes, contrasts, delta_fs, eod_freqs, durations, trans_amplitudes
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def __get_traces__(self, repro):
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def get_traces(self, repro, before=0, after=0):
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time_traces = []
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v1_traces = []
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eod_traces = []
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@ -360,7 +361,7 @@ class DatParser(AbstractParser):
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nothing = True
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for info, key, time, x in Dl.iload_traces(self.base_path, repro=repro):
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for info, key, time, x in Dl.iload_traces(self.base_path, repro=repro, before=before, after=after):
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nothing = False
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time_traces.append(time)
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v1_traces.append(x[0])
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@ -75,7 +75,7 @@ def plot_traces_with_spiketimes():
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modelfit = get_best_fit("results/final_2/2011-10-25-ad-invivo-1/")
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cell_data = modelfit.get_cell_data()
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traces = cell_data.parser.__get_traces__("SAM")
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traces = cell_data.parser.get_traces("SAM")
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# [time_traces, v1_traces, eod_traces, local_eod_traces, stimulus_traces]
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sam_spiketimes = cell_data.get_sam_spiketimes()
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for i in range(len(traces[0])):
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@ -5,14 +5,14 @@ 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/lab_rotation/"
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SAVE_DIR = "results/sam_cells_only_best/"
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def main():
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res_folder = "results/final_2/"
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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_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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94
test.py
94
test.py
@ -6,34 +6,88 @@ from fitting.ModelFit import ModelFit, get_best_fit
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# from plottools.axes import labelaxes_params
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import matplotlib.pyplot as plt
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from run_Fitter import iget_start_parameters
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from experiments.FiCurve import FICurve, FICurveCellData, FICurveModel
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colors = ["black", "red", "blue", "orange", "green"]
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def main():
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fit = get_best_fit("results/kraken_fit/2011-10-25-ad-invivo-1/")
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print(fit.get_fit_routine_error())
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quit()
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sam_tests()
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# cells = 40
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# number = len([i for i in iget_start_parameters()])
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# single_core = number * 1400 / 60 / 60
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# print("start parameters:", number)
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# print("single core time:", single_core, "h")
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# print("single core time:", single_core/24, "days")
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# results_dir = "data/final/"
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# for folder in sorted(os.listdir(results_dir)):
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# folder_path = os.path.join(results_dir, folder)
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#
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# if not os.path.isdir(folder_path):
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# continue
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#
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# cores = 16
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# cells = 40
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# cell_data = CellData(folder_path)
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# cell_name = cell_data.get_cell_name()
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#
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# print(cores, "core time:", single_core/cores, "h")
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# print(cores, "core time:", single_core / 24 / cores, "days")
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# print(cores, "core time all", cells, "cells:", single_core / 24 / cores * cells, "days")
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# fi_cell = FICurveCellData(cell_data, cell_data.get_fi_contrasts(), cell_data.data_path)
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#
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# print("left over:", number%cores)
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# fi_cell.plot_fi_curve(title=cell_name, save_path="temp/cell_fi_curves_images/" + cell_name + "_")
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#
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# steady_state = fi_cell.get_f_inf_frequencies()
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# onset = fi_cell.get_f_zero_frequencies()
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# baseline = fi_cell.get_f_baseline_frequencies()
|
||||
# contrasts = fi_cell.stimulus_values
|
||||
#
|
||||
# headers = ["contrasts", "f_baseline", "f_steady_state", "f_onset"]
|
||||
# with open("temp/cell_fi_curves_csvs/" + cell_name + ".csv", 'w') as f:
|
||||
# for i in range(len(headers)):
|
||||
# if i == 0:
|
||||
# f.write(headers[i])
|
||||
# else:
|
||||
# f.write("," + headers[i])
|
||||
# f.write("\n")
|
||||
#
|
||||
# for i in range(len(contrasts)):
|
||||
# f.write(str(contrasts[i]) + ",")
|
||||
# f.write(str(baseline[i]) + ",")
|
||||
# f.write(str(steady_state[i]) + ",")
|
||||
# f.write(str(onset[i]) + "\n")
|
||||
# quit()
|
||||
cell_taus = []
|
||||
model_taus = []
|
||||
|
||||
results_dir = "results/sam_cells_only_best/"
|
||||
for folder in sorted(os.listdir(results_dir)):
|
||||
folder_path = os.path.join(results_dir, folder)
|
||||
if not os.path.isdir(folder_path):
|
||||
continue
|
||||
|
||||
# fit = get_best_fit("results/final_sam2/2012-12-20-ae-invivo-1/")
|
||||
# fit.generate_master_plot()
|
||||
fit = get_best_fit(folder_path)
|
||||
print(fit.get_fit_routine_error())
|
||||
model = fit.get_model()
|
||||
cell_data = fit.get_cell_data()
|
||||
|
||||
fi_model = FICurveModel(model, cell_data.get_fi_contrasts(), cell_data.get_eod_frequency())
|
||||
tau_model = fi_model.calculate_time_constant(-2)
|
||||
model_taus.append(tau_model)
|
||||
fi_cell = FICurveCellData(cell_data, cell_data.get_fi_contrasts(), cell_data.data_path)
|
||||
|
||||
tau_cell = fi_cell.calculate_time_constant(-2)
|
||||
cell_taus.append(tau_cell)
|
||||
# model_taus = [0.008227050473746214, 339.82706244279075, 0.010807838358313856, 0.01115826226335211, 0.007413613528371537, 0.013213123673467943, 0.010808781901437248, 0.0014254019917934319, 0.015448860984264491, 0.014413888046967265, 0.029301687421672096, 255.82969629640462, 0.00457130444591641, 0.009463250852321902, 0.007755615618900141, 0.009110183466482135, 0.007225102891006319, 0.0024319255218167336, 0.017420779742227246, 0.027195130905873905, 0.00934661249103802, 0.07158177921097474, 0.004866423936911278, 0.0008792730042370866, 0.00820470663372859, 0.05135988132772797, -945.8805502129879, -625.3981095962032, 0.00045249542468299257, 0.10198296886109447, 0.02992101543230009, 715.8802825637086, 0.0074281010613263775, 0.002038042609377947, 0.0055331475878047445, 0.010965819934792512, 0.00916015878530846, -123.0502556160885, 0.013734214511572751, 0.004193114169578979, 0.011103783836162914, 0.018070119202374276]
|
||||
# cell_taus = [0.0035588022114672975, 0.005541599918212267, 0.007848670525682807, 0.008147461940299978, 0.005948699597158819, 0.0024739217090879104, 0.0038303906688137847, 0.00300889313116284, 0.014167509501882801, 0.009459132581703281, 0.005226151863380407, 772.607757547133, 0.0016936075127979523, 0.008768601246126134, 0.0036987681597240958, 0.009306705661392982, 0.004808427175831087, 0.005419130192821167, 0.0028735071877832733, 0.005983916198767454, 0.004369124640159074, 0.020115307489662095, 468.1810372271939, 0.0012946259647070454, 0.0021810924044437753, 259.6701021041893, 2891.7659169677813, -2155.469810882238, 0.0027895996432137117, 0.01503608591999554, 1138.5941497875147, -0.009831620851536924, 0.004657794528111363, -0.007131468820451661, -0.0221455330638256, -589.1530734507537, -506.6077728634018, -0.0028166760486066605, 359.3395355603788, -0.003053762369811596, 0.00465946355831796, 0.01675427242298042]
|
||||
|
||||
model_taus_c = [v for v in model_taus if np.abs(v) < 0.15]
|
||||
cell_taus_c = [v for v in cell_taus if np.abs(v) < 0.15]
|
||||
print("model removed:", len(model_taus) - len(model_taus_c))
|
||||
print("cell removed:", len(cell_taus) - len(cell_taus_c))
|
||||
|
||||
fig, axes = plt.subplots(1, 2, sharey="all", sharex="all")
|
||||
|
||||
axes[0].hist(model_taus_c)
|
||||
axes[0].set_title("Model taus")
|
||||
|
||||
axes[1].hist(cell_taus_c)
|
||||
axes[1].set_title("Cell taus")
|
||||
|
||||
plt.show()
|
||||
plt.close()
|
||||
print(model_taus)
|
||||
print(cell_taus)
|
||||
# sam_tests()
|
||||
|
||||
|
||||
def sam_tests():
|
||||
|
||||
Loading…
Reference in New Issue
Block a user