324 lines
13 KiB
Python
324 lines
13 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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example_cell = [['2020-10-27-ag-invivo-1', 0],
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['2020-10-27-ag-invivo-1', 1]]
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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', 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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##['2019-06-28-ae', 0], # 477Hz, 10%, 2.6, weak
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##['2020-10-27-aa-invivo-1', 4], # 259Hz, 0.5%, 2.0, ok
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##['2020-10-27-ae-invivo-1', 4], # 375Hz, 0.5%, 4.3, nice, additional low freq line
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###['2020-10-27-ag-invivo-1', 2], # 405Hz, 5%, 3.9, strong, is already the example
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##['2021-08-03-ab-invivo-1', 1], # 140Hz, 0.5%, ok
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#['2020-10-29-ag-invivo-1', 2], # 164Hz, 5%, 1.6, no diagonal
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['2018-08-24-ak', 1], # 145Hz, 5%, no diagonal
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['2018-08-14-ac', 0], # 239Hz, 10%, no diagonal
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##['2010-08-31-ag', 1], # 269Hz, 5%, no diagonal
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##['2018-08-29-af', 1], # 383Hz, 5%, no diagonal
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]
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data_path = Path('data') / 'cells'
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def load_baseline(path, cell_name):
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d = path / f'{cell_name}-baseline.npz'
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data = np.load(d)
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eodf = float(data['eodf'])
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rate = float(data['ratebase/Hz'])
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cv = float(data['cvbase'])
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isis = data['isis']
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pdf = data['isih']
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freqs = data['freqs']
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prr = data['prr']
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return eodf, rate, cv, isis, pdf, freqs, prr
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def load_noise(path, cell_name, run):
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data = np.load(path / f'{cell_name}-spectral-data-s{run:02d}.npz')
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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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spikes = []
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for k in range(1000):
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key = f'spikes_{k:03d}'
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if not key in data.keys():
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break
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spikes.append(data[key])
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return contrast, time, stimulus, spikes
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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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pss = data['pss']
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prs = data['prs']
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prss = data['prss']
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nsegs = int(data['n'])
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gain = np.abs(prs)/pss
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chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
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return fcutoff, contrast, freqs, gain, chi2
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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, 8)
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ax.set_xticks_delta(2)
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ax.set_xlabel('ISI', 'ms')
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ax.text(0, 1.08, 'P-unit:', transform=ax.transAxes, color=s.cell_color1,
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fontsize='large')
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ax.text(0.6, 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 < 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] + 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_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] + 4, '$f_{\\rm EOD}$',
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ha='center')
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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, 5, '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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#print(len(spikes1), len(spikes2))
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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 = 1 + 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*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, contrast1, freqs1, gain1, contrast2, freqs2, gain2, fcutoff):
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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, 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'$|\chi_1|$', 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'$|\chi_2|$ [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=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-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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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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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] + 0.5, 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)
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ax.set_ylim(0, 14)
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ax.set_xticks_delta(300)
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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'$|\chi_2|$', r'Hz/\%$^2$')
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ax.text(sifs[1] - 50, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
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ha='right')
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ax.text(sifs[1] + 70, 1e-4*sips[1], f'SI={sis[1]:.1f}')
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ax.text(sifs[0] - 50, 1e-4*sips[0] + 2, f'{100*contrast1:.0f}\\%',
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ha='right')
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ax.text(sifs[0] + 70, 1e-4*sips[0] + 2, f'SI={sis[0]:.1f}')
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#ax.text(fbase, 4.3, '$r$', 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-Punit-data.csv')
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data = data[(data['nli'] > 0) & (data['nli'] <= 1.2), :]
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data = data[(data['respmod2'] > 150) & (data['respmod2'] < 200), :]
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data = data[(data['cvbase'] > 0.4) & (data['cvbase'] < 0.8), :]
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data = data[(data['ratebase'] > 300) & (data['ratebase'] < 400), :]
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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'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 P-unit:', 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.punit_color1
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s.cell_color2 = s.punit_color2
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s.lsC1 = s.lsP1
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s.lsC2 = s.lsP2
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s.psC1 = s.psP1
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s.psC2 = s.psP2
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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, contrast1, freqs1, gain1,
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contrast2, freqs2, gain2, fcutoff1)
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pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 6)
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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, 6)
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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)
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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,
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mode, 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, 10)
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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, 'P-units:', 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)
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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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