142 lines
5.9 KiB
Python
142 lines
5.9 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 plotstyle import plot_chi2
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from punitexamplecell import load_baseline, load_noise, load_spectra
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from punitexamplecell import plot_response_spectrum, plot_response
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from punitexamplecell import plot_gain, plot_diagonals, plot_isih_small
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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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['2010-11-08-aa', 1],
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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(0.95, 1.08, f'CV$_{{\\rm base}}$={cv:.2f}, $r={rate:.0f}$Hz',
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transform=ax.transAxes)
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if __name__ == '__main__':
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"""
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# find a nice example cell:
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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['sinorm_nmax'] > 5) & (data['sinorm_nmax'] < 50), :]
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data = data[(data['contrast'] > 0.04) & (data['contrast'] < 0.06), :]
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data = data[(data['respmod2'] > 0) & (data['respmod2'] < 100), :]
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data = data[(data['cvbase'] > 0) & (data['cvbase'] < 0.2), :]
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data = data[(data['ratebase'] > 50) & (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}%, r={data[k, "ratebase"]:3.0f}Hz, '
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f'CV={data[k, "cvbase"]:4.2f}, '
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f'rmod={data[k, "respmod2"]:3.0f}Hz, '
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f'SI={data[k, "sinorm_nmax"]: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:')
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eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
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print(f' {cell_name:<22s}: fbase={rate:3.0f}Hz, 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, 0.3, 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=2, topm=2, bottomm=3.5,
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wspace=0.4, hspace=0.42)
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axi, axp, axr = ax1.subplots(1, 3, width_ratios=[2, 3, 0, 10, 0.2])
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axg, axc1, axc2, axd = ax2.subplots(1, 4, wspace=0.2,
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width_ratios=[3.5, 0.5, 4, 4, 0.8, 3.5])
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axs = ax3.subplots(2, 4, wspace=0.4, hspace=0.35,
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width_ratios=[1, 0.1, 1, 1, 1, 0.1],
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height_ratios=[1, 4])
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axi.text(0, 1.08, 'Ampullary:', transform=axi.transAxes,
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color=s.cell_color1, fontsize='large')
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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, am=False)
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plot_gain(axg, s, contrast1, freqs1, gain1,
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contrast2, freqs2, gain2, fcutoff1, ymax=12, dy=4)
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axg.axvline(rate, **s.lsGrid)
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axg.text(rate, 12.5, '$r$', ha='center')
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axc = plot_chi2(axc1, s, freqs2, chi22, fcutoff2, None, 12)
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axc.remove()
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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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plot_chi2(axc2, s, freqs1, chi21, fcutoff1, None, 12)
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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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plot_diagonals(axd, s, rate, contrast1, freqs1, chi21,
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contrast2, freqs2, chi22, fcutoff1, ymax=17, toffs=1)
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fig.common_yticks(axc1, axc2)
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fig.tag([axi, axp, axr], xoffs=-3, yoffs=0)
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fig.tag([axg, axc1, axc2, axd], xoffs=-3, yoffs=2)
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print()
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print('Additional example cells:')
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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, fontsize='large')
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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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print(f' {cell:<22s}: run={run:2d}, contrast={100*contrast:3.2g}%, '
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f'fbase={rate:3.0f}Hz, CV={cv:.2f}')
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plot_isih_small(axs[0, k], s, contrast, rate, cv, isis, pdf)
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vmax = 15 if k > 0 else 3
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axc = plot_chi2(axs[1, k], s, freqs, chi2, fcutoff, rate, vmax)
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if k % 3 != 0:
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axc.remove()
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if k == 0:
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axc.set_ylabel('')
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fig.common_yticks(axs[1, :])
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fig.tag([axs[0, :3]], xoffs=-3, yoffs=1)
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fig.tag(axs[0, 3:])
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fig.savefig()
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print()
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