switched position of functions
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@ -54,6 +54,40 @@ def binary_spikes(spike_times, duration, dt):
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binary[spike_indices] = 1 # put the indices into binary
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return binary
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def calculate_integral(freq, power, point, delta):
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"""
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Calculate the integral around a single specified point.
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Parameters
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----------
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frequency : np.array
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An array of frequencies corresponding to the power values.
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power : np.array
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An array of power spectral density values.
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point : float
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The harmonic frequency at which to calculate the integral.
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delta : float
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Radius of the range for integration around the point.
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Returns
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-------
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integral : float
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The calculated integral around the point.
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local_mean : float
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The local mean value (adjacent integrals).
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"""
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indices = (freq >= point - delta) & (freq <= point + delta)
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integral = np.trapz(power[indices], freq[indices])
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left_indices = (freq >= point - 5 * delta) & (freq < point - delta)
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right_indices = (freq > point + delta) & (freq <= point + 5 * delta)
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l_integral = np.trapz(power[left_indices], freq[left_indices])
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r_integral = np.trapz(power[right_indices], freq[right_indices])
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local_mean = np.mean([l_integral, r_integral])
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return integral, local_mean
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def extract_stim_data(stimulus):
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'''
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extracts all necessary metadata for each stimulus
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@ -252,39 +286,6 @@ def spike_times(stim):
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dt = ti.sampling_interval
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return spikes, stim_dur, dt # se changed spike_times to spikes so its not the same as name of function
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def calculate_integral(freq, power, point, delta):
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"""
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Calculate the integral around a single specified point.
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Parameters
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----------
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frequency : np.array
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An array of frequencies corresponding to the power values.
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power : np.array
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An array of power spectral density values.
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point : float
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The harmonic frequency at which to calculate the integral.
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delta : float
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Half-width of the range for integration around the point.
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Returns
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-------
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integral : float
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The calculated integral around the point.
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local_mean : float
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The local mean value (adjacent integrals).
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"""
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indices = (freq >= point - delta) & (freq <= point + delta)
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integral = np.trapz(power[indices], freq[indices])
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left_indices = (freq >= point - 5 * delta) & (freq < point - delta)
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right_indices = (freq > point + delta) & (freq <= point + 5 * delta)
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l_integral = np.trapz(power[left_indices], freq[left_indices])
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r_integral = np.trapz(power[right_indices], freq[right_indices])
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local_mean = np.mean([l_integral, r_integral])
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return integral, local_mean
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def valid_integrals(integral, local_mean, threshold, point):
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"""
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