import sys
sys.path.insert(0, 'ephys') # for analysing data
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from spectral import diag_projection, peakedness
from plotstyle import plot_style
cell_name = '2020-10-27-ag-invivo-1'
run1 = 0
run2 = 1
example_cells = [
['2021-06-18-ae-invivo-1', 3], # 98Hz, 1%, ok
['2012-03-30-ah', 2], # 177Hz, 2.5%, 2.0, nice
##['2012-07-03-ak', 0], # 120Hz, 2.5%, 1.8, broader
##['2012-12-20-ac', 0], # 213Hz, 2.5%, 2.1, ok
#['2017-07-18-ai-invivo-1', 1], # 78Hz, 5%, 2.3, weak
##['2019-06-28-ae', 0], # 477Hz, 10%, 2.6, weak
##['2020-10-27-aa-invivo-1', 4], # 259Hz, 0.5%, 2.0, ok
##['2020-10-27-ae-invivo-1', 4], # 375Hz, 0.5%, 4.3, nice, additional low freq line
###['2020-10-27-ag-invivo-1', 2], # 405Hz, 5%, 3.9, strong, is already the example
##['2021-08-03-ab-invivo-1', 1], # 140Hz, 0.5%, ok
['2020-10-29-ag-invivo-1', 2], # 164Hz, 5%, 1.6, no diagonal
##['2010-08-31-ag', 1], # 269Hz, 5%, no diagonal
['2018-08-24-ak', 1], # 145Hz, 5%, no diagonal
##['2018-08-29-af', 1], # 383Hz, 5%, no diagonal
]
data_path = Path('data') / 'cells'
def load_baseline(path, cell_name):
d = path / f'{cell_name}-baseline.npz'
data = np.load(d)
['eodf', 'isis', 'isih', 'lags', 'corrs', 'freqs', 'prr']
eodf = float(data['eodf'])
rate = float(data['ratebase/Hz'])
cv = float(data['cvbase'])
isis = data['isis']
pdf = data['isih']
freqs = data['freqs']
prr = data['prr']
return eodf, rate, cv, isis, pdf, freqs, prr
def load_noise(path, cell_name, run):
data = np.load(path / f'{cell_name}-spectral-data-s{run:02d}.npz')
contrast = data['contrast']
time = data['time']
stimulus = data['stimulus']
name = str(data['stimulus_name'])
fcutoff = float(name.lower().replace('blwn', '').replace('inputarr_', '').replace('gwn', '').split('h')[0])
spikes = []
for k in range(1000):
key = f'spikes_{k:03d}'
if not key in data.keys():
break
spikes.append(data[key])
return contrast, time, stimulus, spikes
def load_spectra(path, cell_name, run=None):
if run is None:
data = np.load(cell_name)
else:
d = list(path.glob(f'{cell_name}-spectral*-s{run:02d}.npz'))
data = np.load(d[0])
contrast = float(data['alpha'])
fcutoff = float(data['fcutoff'])
freqs = data['freqs']
pss = data['pss']
prs = data['prs']
prss = data['prss']
nsegs = int(data['n'])
gain = np.abs(prs)/pss
chi2 = np.abs(prss)*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
return fcutoff, contrast, freqs, gain, chi2
def plot_isih(ax, s, rate, cv, isis, pdf):
ax.show_spines('b')
ax.fill_between(1000*isis, pdf, facecolor=s.cell_color1)
ax.set_xlim(0, 8)
ax.set_xticks_delta(2)
ax.set_xlabel('ISI', 'ms')
ax.text(0, 1.08, 'P-unit:', transform=ax.transAxes, color=s.cell_color1,
fontsize='large')
ax.text(0.6, 1.08, f'$r={rate:.0f}$Hz, CV$_{{\\rm base}}$={cv:.2f}',
transform=ax.transAxes)
def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
rate_i = np.argmax(prr[freqs < 0.7*eodf])
eod_i = np.argmax(prr[freqs > 500]) + np.argmax(freqs > 500)
power_db = 10*np.log10(prr/np.max(prr))
ax.show_spines('b')
mask = freqs < 890
ax.plot(freqs[mask], power_db[mask], **s.lsC1)
ax.plot(freqs[eod_i], power_db[eod_i], **s.psA1c)
ax.plot(freqs[rate_i], power_db[rate_i], **s.psA2c)
ax.set_xlim(0, 900)
ax.set_ylim(-25, 5)
ax.set_xticks_delta(300)
ax.set_xlabel('$f$', 'Hz')
ax.text(freqs[eod_i], power_db[eod_i] + 2, '$f_{\\rm EOD}$',
ha='center')
ax.text(freqs[rate_i], power_db[rate_i] + 2, '$r$',
ha='center')
ax.yscalebar(1.05, 0, 10, 'dB', ha='right')
def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2, spikes2):
t0 = 0.3
t1 = 0.4
#print(len(spikes1), len(spikes2))
maxtrials = 8
trials = np.arange(maxtrials)
ax.show_spines('')
ax.eventplot(spikes1[2:2+maxtrials], lineoffsets=trials - maxtrials + 1,
linelength=0.8, linewidths=1, color=s.cell_color1)
ax.eventplot(spikes2[2:2+maxtrials], lineoffsets=trials - 2*maxtrials,
linelength=0.8, linewidths=1, color=s.cell_color2)
am = 1 + contrast1*stimulus1
eod = np.sin(2*np.pi*eodf*time1) * am
ax.plot(time1, 4*eod + 7, **s.lsEOD)
ax.plot(time1, 4*am + 7, **s.lsAM)
ax.set_xlim(t0, t1)
ax.set_ylim(-2*maxtrials - 0.5, 14)
ax.xscalebar(1, -0.05, 0.01, None, '10\\,ms', ha='right')
ax.text(t1 + 0.003, -0.5*maxtrials, f'${100*contrast1:.0f}$\\,\\%',
va='center', color=s.cell_color1)
ax.text(t1 + 0.003, -1.55*maxtrials, f'${100*contrast2:.0f}$\\,\\%',
va='center', color=s.cell_color2)
def plot_gain(ax, s, contrast1, freqs1, gain1, contrast2, freqs2, gain2, fcutoff):
ax.plot(freqs2, gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
ax.plot(freqs1, gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
ax.set_xlim(0, fcutoff)
ax.set_ylim(0, 800)
ax.set_xticks_delta(100)
ax.set_xlabel('$f$', 'Hz')
ax.set_ylabel(r'$|\chi_1|$', 'Hz')
def plot_colorbar(ax, pc, dc=None):
cax = ax.inset_axes([1.04, 0, 0.05, 1])
cax.set_spines_outward('lrbt', 0)
cb = cax.get_figure().colorbar(pc, cax=cax, label=r'$|\chi_2|$ [kHz]')
cb.outline.set_color('none')
cb.outline.set_linewidth(0)
if dc is not None:
cax.set_yticks_delta(dc)
def plot_chi2(ax, s, contrast, freqs, chi2, fcutoff, vmax):
ax.set_aspect('equal')
if vmax is None:
vmax = np.quantile(1e-3*chi2, 0.99)
pc = ax.pcolormesh(freqs, freqs, 1e-3*chi2, vmin=0, vmax=vmax,
cmap='viridis', rasterized=True, zorder=10)
ax.set_xlim(0, fcutoff)
ax.set_ylim(0, fcutoff)
df = 100 if fcutoff == 300 else 50
ax.set_xticks_delta(df)
ax.set_yticks_delta(df)
ax.set_xlabel('$f_1$', 'Hz')
ax.set_ylabel('$f_2$', 'Hz')
return pc
def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, chi22, fcutoff):
diags = []
nlis = []
nlips = []
nlifs = []
for contrast, freqs, chi2 in [[contrast1, freqs1, chi21], [contrast2, freqs2, chi22]]:
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
diags.append([dfreqs, diag])
nli, nlif = peakedness(dfreqs, diag, fbase, median=False)
nlip = diag[np.argmin(np.abs(dfreqs - nlif))]
nlis.append(nli)
nlips.append(nlip)
nlifs.append(nlif)
print(f' SI at {100*contrast:.1f}% contrast: {nli:.2f}')
ax.axvline(fbase, **s.lsGrid)
ax.plot(diags[1][0], 1e-3*diags[1][1], **s.lsC2)
ax.plot(diags[0][0], 1e-3*diags[0][1], **s.lsC1)
ax.plot(nlifs[1], 1e-3*nlips[1], **s.psC2)
ax.plot(nlifs[0], 1e-3*nlips[0], **s.psC1)
ax.set_xlim(0, 2*fcutoff)
ax.set_ylim(0, 4.2)
ax.set_xticks_delta(300)
ax.set_yticks_delta(1)
ax.set_xlabel('$f_1 + f_2$', 'Hz')
#ax.set_ylabel(r'$|\chi_2|$', 'kHz')
ax.text(nlifs[1] - 50, 1e-3*nlips[1], f'{100*contrast2:.0f}\\%',
ha='right')
ax.text(nlifs[1] + 70, 1e-3*nlips[1], f'SI={nlis[1]:.1f}')
ax.text(nlifs[0] - 50, 1e-3*nlips[0], f'{100*contrast1:.0f}\\%',
ha='right')
ax.text(nlifs[0] + 70, 1e-3*nlips[0], f'SI={nlis[0]:.1f}')
ax.text(fbase, 4.3, '$r$', ha='center')
if __name__ == '__main__':
print('Example P-unit:', cell_name)
eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
print(f' baseline firing rate: {rate:.0f}Hz')
print(f' baseline firing CV : {cv:.2f}')
contrast1, time1, stimulus1, spikes1 = load_noise(data_path, cell_name, run1)
contrast2, time2, stimulus2, spikes2 = load_noise(data_path, cell_name, run2)
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, cell_name, run1)
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, cell_name, run2)
s = plot_style()
s.cell_color1 = s.punit_color1
s.cell_color2 = s.punit_color2
s.lsC1 = s.lsP1
s.lsC2 = s.lsP2
s.psC1 = s.psP1
s.psC2 = s.psP2
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, height_ratios=[3, 0, 3, 0.5, 3],
cmsize=(s.plot_width, 0.8*s.plot_width))
fig.subplots_adjust(leftm=8, rightm=9, topm=2, bottomm=4,
wspace=0.4, hspace=0.5)
axi, axp, axr = ax1.subplots(1, 3, width_ratios=[2, 3, 0, 10])
axg, axc1, axc2, axd = ax2.subplots(1, 4, wspace=0.4)
axg = axg.subplots(1, 1, width_ratios=[1, 0.1])
axd = axd.subplots(1, 1, width_ratios=[0.2, 1])
axs = ax3.subplots(1, 4, wspace=0.4)
plot_isih(axi, s, rate, cv, isis, pdf)
plot_response_spectrum(axp, s, eodf, rate, freqs, prr)
plot_response(axr, s, eodf, time1, stimulus1, contrast1, spikes1,
contrast2, spikes2)
plot_gain(axg, s, contrast1, freqs1, gain1,
contrast2, freqs2, gain2, fcutoff1)
pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 4)
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)
axc2.set_title(f'$c$={100*contrast1:g}\\,\\%',
fontsize='medium', color=s.cell_color1)
axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag)
plot_colorbar(axc2, pc)
plot_diagonals(axd, s, rate, contrast1, freqs1, chi21,
contrast2, freqs2, chi22, fcutoff1)
fig.common_yticks(axc1, axc2)
fig.tag([axi, axp, axr], xoffs=-3, yoffs=-1)
fig.tag([axg, axc1, axc2, axd], xoffs=-3, yoffs=2)
print('Additional example cells:')
for k, (cell, run) in enumerate(example_cells):
eodf, rate, cv, _, _, _, _ = load_baseline(data_path, cell)
fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, cell, run)
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
nli, nlif = peakedness(dfreqs, diag, rate, median=False)
print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={nli:3.1f}')
pc = plot_chi2(axs[k], s, contrast, freqs, chi2, fcutoff, 1.3)
axs[k].set_title(f'$r={rate:.0f}$Hz, CV$_{{\\rm base}}$={cv:.2f}', fontsize='medium')
axs[k].text(0.95, 0.9, f'SI($r$)={nli:.1f}', ha='right', zorder=50,
color='white', fontsize='medium',
transform=axs[k].transAxes)
plot_colorbar(axs[-1], pc)
fig.common_yticks(axs)
fig.tag(axs, xoffs=-3, yoffs=2)
fig.savefig()