tau calculation and more
This commit is contained in:
alexanderott
@@ -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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