started to updated example cells to new analysis with the right units
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@ -4,7 +4,7 @@ from pathlib import Path
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from spectral import diag_projection, peak_size
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from plotstyle import plot_style
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from punitexamplecell import load_baseline, load_noise, load_spectra
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from punitexamplecell import plot_colorbar
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from punitexamplecell import plot_chi2, plot_colorbar
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example_cell = [['2012-05-15-ac', 3],
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@ -51,18 +51,27 @@ def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
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power_db = 10*np.log10(prr/np.max(prr))
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ax.show_spines('b')
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mask = (freqs > 30) & (freqs < 890)
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ax.plot(freqs[mask], power_db[mask], **s.lsC1)
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ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD)
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ax.plot(freqs[rate_i], power_db[rate_i] + 2, **s.psF0)
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#ax.plot(freqs[mask], power_db[mask], **s.lsC1)
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#ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD)
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#ax.plot(freqs[rate_i], power_db[rate_i] + 2, **s.psF0)
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#ax.set_ylim(-25, 5)
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ax.plot(freqs[mask], 1e-3*prr[mask], **s.lsC1)
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ax.plot(freqs[eod_i], 1e-3*prr[eod_i] + 0.4, **s.psFEOD)
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ax.plot(freqs[rate_i], 1e-3*prr[rate_i] + 0.4, **s.psF0)
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ax.set_ylim(0, 6)
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ax.set_xlim(0, 900)
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ax.set_ylim(-25, 5)
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ax.set_xticks_delta(300)
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ax.set_xlabel('$f$', 'Hz')
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ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$',
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#ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$',
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# ha='center')
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#ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$',
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# ha='center')
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#ax.yscalebar(1.05, 0, 10, 'dB', ha='right')
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ax.text(freqs[eod_i], 1e-3*prr[eod_i] + 0.8, '$f_{\\rm EOD}$',
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ha='center')
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ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$',
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ax.text(freqs[rate_i], 1e-3*prr[rate_i] + 0.8, '$r$',
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ha='center')
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ax.yscalebar(1.05, 0, 10, 'dB', ha='right')
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ax.yscalebar(1.05, 0, 1, 'kHz', ha='right')
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def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2, spikes2):
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@ -91,30 +100,15 @@ def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2,
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def plot_gain(ax, s, fbase, contrast1, freqs1, gain1,
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contrast2, freqs2, gain2, fcutoff):
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ax.axvline(fbase, **s.lsGrid)
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ax.plot(freqs2, gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
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ax.plot(freqs1, gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
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ax.plot(freqs2, 1e-2*gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
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ax.plot(freqs1, 1e-2*gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
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ax.set_xlim(0, fcutoff)
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ax.set_ylim(0, 1500)
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ax.set_ylim(0, 12)
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ax.set_xticks_delta(50)
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ax.set_yticks_delta(4)
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ax.set_xlabel('$f$', 'Hz')
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ax.set_ylabel(r'$|\chi_1|$', 'Hz')
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ax.text(fbase, 1550, '$r$', ha='center')
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def plot_chi2(ax, s, contrast, freqs, chi2, fcutoff, vmax):
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ax.set_aspect('equal')
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if vmax is None:
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vmax = np.quantile(1e-3*chi2, 0.99)
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pc = ax.pcolormesh(freqs, freqs, 1e-3*chi2, vmin=0, vmax=vmax,
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rasterized=True, zorder=10)
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ax.set_xlim(0, fcutoff)
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ax.set_ylim(0, fcutoff)
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df = 100 if fcutoff == 300 else 50
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ax.set_xticks_delta(df)
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ax.set_yticks_delta(df)
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ax.set_xlabel('$f_1$', 'Hz')
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ax.set_ylabel('$f_2$', 'Hz')
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return pc
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ax.set_ylabel(r'$|\chi_1|$', r'Hz/\%')
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ax.text(fbase, 12.5, '$r$', ha='center')
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def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, chi22, fcutoff):
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@ -132,22 +126,22 @@ def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, ch
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sifs.append(sif)
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print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}')
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#ax.axvline(fbase, **s.lsGrid)
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ax.plot(diags[1][0], 1e-3*diags[1][1], **s.lsC2)
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ax.plot(diags[0][0], 1e-3*diags[0][1], **s.lsC1)
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ax.plot(sifs[1], 1e-3*sips[1], **s.psC2)
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ax.plot(sifs[0], 1e-3*sips[0], **s.psC1)
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ax.plot(diags[1][0], 1e-4*diags[1][1], **s.lsC2)
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ax.plot(diags[0][0], 1e-4*diags[0][1], **s.lsC1)
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ax.plot(sifs[1], 1e-4*sips[1] + 0.3, clip_on=False, **s.psC2)
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ax.plot(sifs[0], 1e-4*sips[0] + 0.3, clip_on=False, **s.psC1)
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ax.set_xlim(0, 2*fcutoff)
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ax.set_ylim(0, 1.7)
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ax.set_ylim(0, 17)
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ax.set_xticks_delta(100)
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ax.set_yticks_delta(1)
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ax.set_yticks_delta(5)
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ax.set_xlabel('$f_1 + f_2$', 'Hz')
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#ax.set_ylabel(r'$|\chi_2|$', 'kHz')
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ax.text(sifs[1] - 25, 1e-3*sips[1], f'{100*contrast2:.0f}\\%',
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#ax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
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ax.text(sifs[1] - 25, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
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ha='right')
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ax.text(sifs[1] + 35, 1e-3*sips[1], f'SI={sis[1]:.1f}')
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ax.text(sifs[0] - 25, 1e-3*sips[0], f'{100*contrast1:.0f}\\%',
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ax.text(sifs[1] + 35, 1e-4*sips[1], f'SI={sis[1]:.1f}')
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ax.text(sifs[0] - 25, 1e-4*sips[0] + 0.5, f'{100*contrast1:.0f}\\%',
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ha='right')
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ax.text(sifs[0] + 35, 1e-3*sips[0], f'SI={sis[0]:.1f}')
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ax.text(sifs[0] + 35, 1e-4*sips[0] + 0.5, f'SI={sis[0]:.1f}')
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#ax.text(fbase, 1.75, '$r$', ha='center')
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@ -168,6 +162,9 @@ if __name__ == '__main__':
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exit()
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"""
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#mode = 'all'
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mode = '100'
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cell_name = example_cell[0][0]
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print('Example Ampullary cell:', cell_name)
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eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
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@ -177,9 +174,9 @@ if __name__ == '__main__':
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*example_cell[0])
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contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
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*example_cell[1])
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fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path,
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fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, mode,
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*example_cell[0])
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fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path,
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fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, mode,
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*example_cell[1])
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print(f' contrast1: {100*contrast1:4.1f}% contrast2: {100*contrast2:4.1f}%')
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print(f' fcutoff1 : {fcutoff1:3.0f}Hz fcutoff2 : {fcutoff2:3.0f}Hz')
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@ -210,15 +207,15 @@ if __name__ == '__main__':
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plot_gain(axg, s, rate, contrast1, freqs1, gain1,
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contrast2, freqs2, gain2, fcutoff1)
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pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 1.7)
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pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 10)
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axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag)
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axc1.set_title(f'$c$={100*contrast2:g}\\,\\%',
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fontsize='medium', color=s.cell_color2)
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pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 1.7)
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pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 10)
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axc2.set_title(f'$c$={100*contrast1:g}\\,\\%',
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fontsize='medium', color=s.cell_color1)
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axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag)
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plot_colorbar(axc2, pc, 1)
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plot_colorbar(axc2, pc, 2)
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plot_diagonals(axd, s, rate, contrast1, freqs1, chi21,
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contrast2, freqs2, chi22, fcutoff1)
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@ -229,22 +226,20 @@ if __name__ == '__main__':
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print('Additional example cells:')
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for k, (cell, run) in enumerate(example_cells):
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eodf, rate, cv, isis, pdf, _, _ = load_baseline(data_path, cell)
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fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, cell, run)
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fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, mode,
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cell, run)
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dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
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sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
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print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={sinorm:3.1f}')
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plot_isih2(axs[0, k], s, rate, cv, isis, pdf)
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pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 1.2)
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#axs[k].set_title(f'$r={rate:.0f}$Hz, CV$_{{\\rm base}}$={cv:.2f}', fontsize='medium')
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pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 20)
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axs[1, k].text(0.95, 0.9, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
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color='white', fontsize='medium',
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transform=axs[1, k].transAxes)
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axs[0, 0].text(0, 1.6, 'Ampullary cells:', transform=axs[0, 0].transAxes,
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color=s.cell_color1,
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fontsize='large')
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#axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right',
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# transform=axs[0, -1].transAxes)
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plot_colorbar(axs[1, -1], pc, 0.4)
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plot_colorbar(axs[1, -1], pc, 5)
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fig.common_yticks(axs[1, :])
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fig.tag([axs[0, :]], xoffs=-3, yoffs=1)
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@ -248,7 +248,6 @@ if __name__ == '__main__':
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punit_model = TableData(data_path /
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'Apteronotus_leptorhynchus-Punit-models.csv',
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sep=';')
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print(punit_model.keys())
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punit_model = punit_model[punit_model['contrast'] > 1e-6, :]
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punit_data = TableData(data_path /
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'Apteronotus_leptorhynchus-Punit-data.csv',
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@ -568,7 +568,7 @@ Overall, observing \nli{} values greater than at least 1.6, even for a number of
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interspike intervals during stimulation with the white-noise
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stimulus (right column). Pearson's correlation coefficient $R$ and
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the number of data points $n$ are indicated; all correlations are
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significant at a level below $p=0.0002$. Kernel-density estimates
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significant at a level below $p=0.0006$. Kernel-density estimates
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of the distributions of the displayed quantities are plotted on
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top and right. Data points are color coded by CV$_{\text{base}}$
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or response modulation as indicated by the color bars. The
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@ -1,5 +1,3 @@
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import sys
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sys.path.insert(0, 'ephys') # for analysing data
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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@ -13,7 +11,7 @@ example_cell = [['2020-10-27-ag-invivo-1', 0],
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example_cells = [
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#['2021-06-18-ae-invivo-1', 3], # 98Hz, 1%, ok
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['2021-06-18-ae-invivo-1', 2], # 98Hz, 10%, ok
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['2012-03-30-ah', 2], # 177Hz, 2.5%, 2.0, nice
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['2012-03-30-ah', 5], # 177Hz, 5%, 2.0, nice
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##['2012-07-03-ak', 0], # 120Hz, 2.5%, 1.8, broader, the one model cell, nice triangle up to 1%!
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##['2012-12-20-ac', 0], # 213Hz, 2.5%, 2.1, ok, model cell, weak triangle up to 1%!
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#['2017-07-18-ai-invivo-1', 1], # 78Hz, 5%, 2.3, weak, nice model cell with clear triangle up to 10%!
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@ -51,8 +49,6 @@ def load_noise(path, cell_name, run):
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contrast = data['contrast']
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time = data['time']
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stimulus = data['stimulus']
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name = str(data['stimulus_name'])
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fcutoff = float(name.lower().replace('blwn', '').replace('inputarr_', '').replace('gwn', '').split('h')[0])
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spikes = []
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for k in range(1000):
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key = f'spikes_{k:03d}'
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@ -62,12 +58,8 @@ def load_noise(path, cell_name, run):
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return contrast, time, stimulus, spikes
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def load_spectra(path, cell_name, run=None):
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if run is None:
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data = np.load(cell_name)
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else:
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d = list(path.glob(f'{cell_name}-spectral*-s{run:02d}.npz'))
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data = np.load(d[0])
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def load_spectra(path, mode, cell_name, run=None):
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data = np.load(path / f'{cell_name}-spectral-{mode}-s{run:02d}.npz')
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contrast = float(data['alpha'])
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fcutoff = float(data['fcutoff'])
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freqs = data['freqs']
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@ -111,18 +103,27 @@ def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
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power_db = 10*np.log10(prr/np.max(prr))
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ax.show_spines('b')
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mask = freqs < 890
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ax.plot(freqs[mask], power_db[mask], **s.lsC1)
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ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD)
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ax.plot(freqs[rate_i], power_db[rate_i] + 2, **s.psF0)
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#ax.plot(freqs[mask], power_db[mask], **s.lsC1)
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#ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD)
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#ax.plot(freqs[rate_i], power_db[rate_i] + 2, **s.psF0)
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#ax.set_ylim(-25, 5)
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ax.plot(freqs[mask], 1e-3*prr[mask], **s.lsC1)
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ax.plot(freqs[eod_i], 1e-3*prr[eod_i] + 2, **s.psFEOD)
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ax.plot(freqs[rate_i], 1e-3*prr[rate_i] + 2, **s.psF0)
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ax.set_ylim(0, 30)
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ax.set_xlim(0, 900)
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ax.set_ylim(-25, 5)
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ax.set_xticks_delta(300)
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ax.set_xlabel('$f$', 'Hz')
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ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$',
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#ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$',
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# ha='center')
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#ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$',
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# ha='center')
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#ax.yscalebar(1.05, 0, 10, 'dB', ha='right')
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ax.text(freqs[eod_i], 1e-3*prr[eod_i] + 4, '$f_{\\rm EOD}$',
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ha='center')
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ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$',
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ax.text(freqs[rate_i], 1e-3*prr[rate_i] + 4, '$r$',
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ha='center')
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ax.yscalebar(1.05, 0, 10, 'dB', ha='right')
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ax.yscalebar(1.05, 0, 5, 'kHz', ha='right')
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def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2, spikes2):
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@ -150,30 +151,30 @@ def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2,
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def plot_gain(ax, s, contrast1, freqs1, gain1, contrast2, freqs2, gain2, fcutoff):
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ax.plot(freqs2, gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
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ax.plot(freqs1, gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
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ax.plot(freqs2, 1e-2*gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
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ax.plot(freqs1, 1e-2*gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
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ax.set_xlim(0, fcutoff)
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ax.set_ylim(0, 800)
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ax.set_ylim(0, 10)
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ax.set_xticks_delta(100)
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ax.set_xlabel('$f$', 'Hz')
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ax.set_ylabel(r'$|\chi_1|$', 'Hz')
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ax.set_ylabel(r'$|\chi_1|$', r'Hz/\%')
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def plot_colorbar(ax, pc, dc=None):
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cax = ax.inset_axes([1.04, 0, 0.05, 1])
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cax.set_spines_outward('lrbt', 0)
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cb = cax.get_figure().colorbar(pc, cax=cax, label=r'$|\chi_2|$ [kHz]')
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cb = cax.get_figure().colorbar(pc, cax=cax, label=r'$|\chi_2|$ [Hz/\%$^2$]')
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cb.outline.set_color('none')
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cb.outline.set_linewidth(0)
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if dc is not None:
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cax.set_yticks_delta(dc)
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def plot_chi2(ax, s, contrast, freqs, chi2, fcutoff, vmax):
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def plot_chi2(ax, s, contrast, freqs, chi2, fcutoff, vmax=None):
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ax.set_aspect('equal')
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if vmax is None:
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vmax = np.quantile(1e-3*chi2, 0.99)
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pc = ax.pcolormesh(freqs, freqs, 1e-3*chi2, vmin=0, vmax=vmax,
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vmax = np.quantile(1e-4*chi2, 0.998)
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pc = ax.pcolormesh(freqs, freqs, 1e-4*chi2, vmin=0, vmax=vmax,
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rasterized=True, zorder=10)
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ax.set_xlim(0, fcutoff)
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ax.set_ylim(0, fcutoff)
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@ -200,22 +201,22 @@ def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, ch
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sifs.append(sif)
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print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}')
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#ax.axvline(fbase, **s.lsGrid)
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ax.plot(diags[1][0], 1e-3*diags[1][1], **s.lsC2)
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ax.plot(diags[0][0], 1e-3*diags[0][1], **s.lsC1)
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ax.plot(sifs[1], 1e-3*sips[1], **s.psC2)
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ax.plot(sifs[0], 1e-3*sips[0], **s.psC1)
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ax.plot(diags[1][0], 1e-4*diags[1][1], **s.lsC2)
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ax.plot(diags[0][0], 1e-4*diags[0][1], **s.lsC1)
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ax.plot(sifs[1], 1e-4*sips[1] + 0.5, clip_on=False, **s.psC2)
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ax.plot(sifs[0], 1e-4*sips[0] + 0.5, clip_on=False, **s.psC1)
|
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ax.set_xlim(0, 2*fcutoff)
|
||||
ax.set_ylim(0, 4.2)
|
||||
ax.set_ylim(0, 14)
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||||
ax.set_xticks_delta(300)
|
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ax.set_yticks_delta(1)
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ax.set_yticks_delta(4)
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ax.set_xlabel('$f_1 + f_2$', 'Hz')
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#ax.set_ylabel(r'$|\chi_2|$', 'kHz')
|
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ax.text(sifs[1] - 50, 1e-3*sips[1], f'{100*contrast2:.0f}\\%',
|
||||
#ax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
|
||||
ax.text(sifs[1] - 50, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
|
||||
ha='right')
|
||||
ax.text(sifs[1] + 70, 1e-3*sips[1], f'SI={sis[1]:.1f}')
|
||||
ax.text(sifs[0] - 50, 1e-3*sips[0], f'{100*contrast1:.0f}\\%',
|
||||
ax.text(sifs[1] + 70, 1e-4*sips[1], f'SI={sis[1]:.1f}')
|
||||
ax.text(sifs[0] - 50, 1e-4*sips[0] + 2, f'{100*contrast1:.0f}\\%',
|
||||
ha='right')
|
||||
ax.text(sifs[0] + 70, 1e-3*sips[0], f'SI={sis[0]:.1f}')
|
||||
ax.text(sifs[0] + 70, 1e-4*sips[0] + 2, f'SI={sis[0]:.1f}')
|
||||
#ax.text(fbase, 4.3, '$r$', ha='center')
|
||||
|
||||
|
||||
@ -237,6 +238,9 @@ if __name__ == '__main__':
|
||||
#exit()
|
||||
"""
|
||||
|
||||
#mode = 'all'
|
||||
mode = '100'
|
||||
|
||||
cell_name = example_cell[0][0]
|
||||
print('Example P-unit:', cell_name)
|
||||
eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
|
||||
@ -246,9 +250,9 @@ if __name__ == '__main__':
|
||||
*example_cell[0])
|
||||
contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
|
||||
*example_cell[1])
|
||||
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path,
|
||||
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, mode,
|
||||
*example_cell[0])
|
||||
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path,
|
||||
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, mode,
|
||||
*example_cell[1])
|
||||
print(f' contrast1: {100*contrast1:4.1f}% contrast2: {100*contrast2:4.1f}%')
|
||||
print(f' fcutoff1 : {fcutoff1:3.0f}Hz fcutoff2 : {fcutoff2:3.0f}Hz')
|
||||
@ -279,11 +283,11 @@ if __name__ == '__main__':
|
||||
|
||||
plot_gain(axg, s, contrast1, freqs1, gain1,
|
||||
contrast2, freqs2, gain2, fcutoff1)
|
||||
pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 4)
|
||||
pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 6)
|
||||
axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag)
|
||||
axc1.set_title(f'$c$={100*contrast2:g}\\,\\%',
|
||||
fontsize='medium', color=s.cell_color2)
|
||||
pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 4)
|
||||
pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 6)
|
||||
axc2.set_title(f'$c$={100*contrast1:g}\\,\\%',
|
||||
fontsize='medium', color=s.cell_color1)
|
||||
axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag)
|
||||
@ -298,20 +302,19 @@ if __name__ == '__main__':
|
||||
print('Additional example cells:')
|
||||
for k, (cell, run) in enumerate(example_cells):
|
||||
eodf, rate, cv, isis, pdf, _, _ = load_baseline(data_path, cell)
|
||||
fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, cell, run)
|
||||
fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path,
|
||||
mode, cell, run)
|
||||
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
|
||||
sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
|
||||
print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={sinorm:3.1f}')
|
||||
plot_isih2(axs[0, k], s, rate, cv, isis, pdf)
|
||||
pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 1.0)
|
||||
pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 10)
|
||||
axs[1, k].text(0.95, 0.9, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
|
||||
color='white', fontsize='medium',
|
||||
transform=axs[1, k].transAxes)
|
||||
axs[0, 0].text(0, 1.6, 'P-units:', transform=axs[0, 0].transAxes,
|
||||
color=s.cell_color1,
|
||||
fontsize='large')
|
||||
#axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right',
|
||||
# transform=axs[0, -1].transAxes)
|
||||
plot_colorbar(axs[1, -1], pc)
|
||||
fig.common_yticks(axs[1, :])
|
||||
fig.tag([axs[0, :]], xoffs=-3, yoffs=1)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user