127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
'''This script contains all functions for various plots that could be relevant
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for the presentation or protocol of the Grewe GP 2024'''
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import rlxnix as rlx
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from useful_functions import power_spectrum
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'''IMPORT DATA'''
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datafolder = '../data' #./ wo ich gerade bin; ../ eine ebene höher; ../../ zwei ebenen höher
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example_file = os.path.join('..', 'data', '2024-10-16-ac-invivo-1.nix')
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'''EXTRACT DATA'''
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dataset = rlx.Dataset(example_file)
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# get sams
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sams = dataset.repro_runs('SAM')
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sam = sams[2]
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# get stims
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stimulus = sam.stimuli[-1]
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stim_count = sam.stimulus_count
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'''PLOTS'''
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# create colormap
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colors = plt.cm.prism(np.linspace(0, 1, stim_count))
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# plot timeline of whole rec
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dataset.plot_timeline()
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# plot voltage over time for whole trace
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def plot_vt_spikes(t, v, spike_t):
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fig = plt.figure(figsize=(5, 2.5))
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# alternative to ax = axs[0]
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ax = fig.add_subplot()
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# plot vt diagram
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ax.plot(t[t<0.1], v[t<0.1])
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# plot spikes into vt diagram, at max V
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ax.scatter(spike_t[spike_t<0.1], np.ones_like(spike_t[spike_t<0.1]) * np.max(v))
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plt.show()
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# plot scatter plot for one sam with all 3 stims
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def scatter_plot(colormap, stimuli_list, stimulus_count):
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fig = plt.figure()
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ax = fig.add_subplot()
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ax.eventplot(stimuli_list, colors=colormap)
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ax.set_xlabel('Spike Times [ms]')
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ax.set_ylabel('Loop #')
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ax.set_yticks(range(stimulus_count))
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ax.set_title('Spikes of SAM 3')
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plt.show()
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# calculate power spectrum
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freq, power = power_spectrum(stimulus)
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# plot power spectrum
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def power_spectrum_plot(f, p):
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# plot power spectrum
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fig = plt.figure()
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ax = fig.add_subplot()
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ax.plot(freq, power)
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ax.set_xlabel('Frequency [Hz]')
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ax.set_ylabel('Power [1/Hz]')
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ax.set_xlim(0, 1000)
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plt.show()
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####### ADD DIANAS POWER SPECTRUM PLOT
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def plot_highlighted_integrals(frequency, power, exceeding_points, delta, threshold, color_mapping, points_categories):
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"""
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Plot the power spectrum and highlight integrals that exceed the threshold.
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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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exceeding_points : list
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A list of harmonic frequencies that exceed the threshold.
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delta : float
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Half-width of the range for integration around each point.
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threshold : float
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Threshold value to compare integrals with local mean.
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color_mapping : dict
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A dictionary mapping each category to its color.
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points_categories : dict
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A mapping of categories to lists of points.
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Returns
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-------
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fig : matplotlib.figure.Figure
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The created figure object with highlighted integrals.
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"""
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fig, ax = plt.subplots()
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ax.plot(frequency, power) # Plot power spectrum
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for point in exceeding_points:
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integral, local_mean = calculate_integral(frequency, power, point, delta)
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valid, _ = valid_integrals(integral, local_mean, threshold, point)
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if valid:
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# Define color based on the category of the point
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color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
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# Shade the region around the point where the integral was calculated
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ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
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print(f"Integral around {point:.2f} Hz: {integral:.5e}")
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# Define left and right boundaries of adjacent regions
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left_boundary = frequency[np.where((frequency >= point - 5 * delta) & (frequency < point - delta))[0][0]]
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right_boundary = frequency[np.where((frequency > point + delta) & (frequency <= point + 5 * delta))[0][-1]]
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# Add vertical dashed lines at the boundaries of the adjacent regions
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ax.axvline(x=left_boundary, color="k", linestyle="--")
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ax.axvline(x=right_boundary, color="k", linestyle="--")
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ax.set_xlim([0, 1200])
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ax.set_xlabel('Frequency (Hz)')
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ax.set_ylabel('Power')
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ax.set_title('Power Spectrum with Highlighted Integrals')
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ax.legend()
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return fig
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