248 lines
10 KiB
Python
248 lines
10 KiB
Python
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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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_chi2, plot_colorbar
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example_cell = [['2012-05-15-ac', 3],
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['2012-05-15-ac', 1]]
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example_cells = [
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['2010-11-26-an', 0],
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['2011-10-25-ac', 0],
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['2011-02-18-ab', 1],
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['2014-01-16-aj', 5],
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]
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data_path = Path('data') / 'cells'
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def plot_isih(ax, s, rate, cv, isis, pdf):
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ax.show_spines('b')
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ax.fill_between(1000*isis, pdf, facecolor=s.cell_color1)
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ax.set_xlim(0, 12)
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ax.set_xticks_delta(4)
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ax.set_xlabel('ISI', 'ms')
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ax.text(0, 1.08, 'Ampullary:', transform=ax.transAxes, color=s.cell_color1,
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fontsize='large')
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ax.text(0.95, 1.08, f'$r={rate:.0f}$Hz, CV$_{{\\rm base}}$={cv:.2f}',
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transform=ax.transAxes)
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def plot_isih2(ax, s, rate, cv, isis, pdf):
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ax.show_spines('b')
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ax.fill_between(1000*isis, pdf, facecolor=s.cell_color1)
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ax.set_xlim(0, 20)
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#ax.set_xticks_delta(5)
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#ax.set_xticks_blank()
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#ax.set_xticks_fixed([0, 5, 10, 15, 20], ['0', '', '', '', '20\\,ms'])
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ax.set_xticks_fixed([0, 5, 10, 15, 20], ['0', '5', '10', '15', '20\\,ms'])
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ax.text(1, 1.1, f'CV$_{{\\rm base}}$={cv:.2f}', ha='right',
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transform=ax.transAxes)
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ax.text(1, 0.6, f'$r={rate:.0f}$Hz', ha='right', transform=ax.transAxes)
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def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
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rate_i = np.argmax(prr[freqs < 0.7*eodf])
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eod_i = np.argmax(prr[freqs > 500]) + np.argmax(freqs > 500)
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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.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_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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# 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], 1e-3*prr[rate_i] + 0.8, '$r$',
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ha='center')
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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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t0 = 0.3
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t1 = 0.4
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maxtrials = 8
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trials = np.arange(maxtrials)
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ax.show_spines('')
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ax.eventplot(spikes1[2:2+maxtrials], lineoffsets=trials - maxtrials + 1,
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linelength=0.8, linewidths=1, color=s.cell_color1)
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ax.eventplot(spikes2[2:2+maxtrials], lineoffsets=trials - 2*maxtrials,
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linelength=0.8, linewidths=1, color=s.cell_color2)
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am = contrast1*stimulus1
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eod = np.sin(2*np.pi*eodf*time1) + am
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ax.plot(time1, 4*eod + 7, **s.lsEOD)
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ax.plot(time1, 4*(1 + am) + 7, **s.lsAM)
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ax.set_xlim(t0, t1)
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ax.set_ylim(-2*maxtrials - 0.5, 14)
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ax.xscalebar(1, -0.05, 0.01, None, '10\\,ms', ha='right')
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ax.text(t1 + 0.003, -0.5*maxtrials, f'${100*contrast1:.0f}$\\,\\%',
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va='center', color=s.cell_color1)
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ax.text(t1 + 0.003, -1.55*maxtrials, f'${100*contrast2:.0f}$\\,\\%',
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va='center', color=s.cell_color2)
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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, 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, 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'$|\chi_1|$', 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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diags = []
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sis = []
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sips = []
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sifs = []
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for contrast, freqs, chi2 in [[contrast1, freqs1, chi21], [contrast2, freqs2, chi22]]:
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dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
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diags.append([dfreqs, diag])
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sinorm, sirel, sif = peak_size(dfreqs, diag, fbase, median=False)
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sip = diag[np.argmin(np.abs(dfreqs - sif))]
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sis.append(sinorm)
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sips.append(sip)
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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-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, 17)
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ax.set_xticks_delta(100)
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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'$|\chi_2|$', 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-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-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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if __name__ == '__main__':
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"""
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from thunderlab.tabledata import TableData
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data = TableData('data/Apteronotus_leptorhynchus-Ampullary-data.csv')
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data = data[(data['fcutoff'] > 140) & (data['fcutoff'] < 160), :]
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data = data[(data['nli'] > 2) & (data['nli'] < 2.5), :]
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data = data[(data['respmod2'] > 20) & (data['respmod2'] < 100), :]
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data = data[(data['cvbase'] > 0.05) & (data['cvbase'] < 0.2), :]
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data = data[(data['ratebase'] > 100) & (data['ratebase'] < 180), :]
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for k in range(data.rows()):
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print(f'{data[k, "cell"]:<22s} s{data[k, "stimindex"]:02.0f}: '
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f'{100*data[k, "contrast"]:3g}%, {data[k, "respmod2"]:3.0f}Hz, '
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f'nli={data[k, "nli"]:5.2f}')
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print()
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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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print(f' baseline firing rate: {rate:.0f}Hz')
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print(f' baseline firing CV : {cv:.2f}')
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contrast1, time1, stimulus1, spikes1 = load_noise(data_path,
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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, mode,
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*example_cell[0])
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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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print(f' duration1: {time1[-1]:4.1f}s duration2: {time2[-1]:4.1f}s')
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s = plot_style()
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s.cell_color1 = s.ampul_color1
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s.cell_color2 = s.ampul_color2
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s.lsC1 = s.lsA1
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s.lsC2 = s.lsA2
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s.psC1 = s.psA1
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s.psC2 = s.psA2
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fig, (ax1, ax2, ax3) = \
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plt.subplots(3, 1, height_ratios=[3, 0, 3, 0.2, 4.7],
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cmsize=(s.plot_width, 0.85*s.plot_width))
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fig.subplots_adjust(leftm=8, rightm=9, topm=2, bottomm=4,
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wspace=0.4, hspace=0.4)
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axi, axp, axr = ax1.subplots(1, 3, width_ratios=[2, 3, 0, 10])
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axg, axc1, axc2, axd = ax2.subplots(1, 4, wspace=0.4)
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axg = axg.subplots(1, 1, width_ratios=[1, 0.1])
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axd = axd.subplots(1, 1, width_ratios=[0.2, 1])
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axs = ax3.subplots(2, 4, wspace=0.4, hspace=0.35, height_ratios=[1, 4])
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plot_isih(axi, s, rate, cv, isis, pdf)
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plot_response_spectrum(axp, s, eodf, rate, freqs, prr)
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plot_response(axr, s, eodf, time1, stimulus1, contrast1, spikes1,
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contrast2, spikes2)
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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, 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, 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, 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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fig.common_yticks(axc1, axc2)
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fig.tag([axi, axp, axr], xoffs=-3, yoffs=-1)
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fig.tag([axg, axc1, axc2, axd], xoffs=-3, yoffs=2)
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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, 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, 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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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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fig.savefig()
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print()
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