nonlinearbaseline2025/punitexamplecell.py

306 lines
13 KiB
Python

import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from spectral import diag_projection, peak_size
from plotstyle import plot_style
from plotstyle import plot_chi2
example_cell = [['2020-10-27-ag-invivo-1', 0],
['2020-10-27-ag-invivo-1', 1]]
example_cells = [
#['2021-06-18-ae-invivo-1', 3], # 98Hz, 1%, ok
['2021-06-18-ae-invivo-1', 6], # 98Hz, 2: 10%, ok OR 6: 5%
#['2012-03-30-ah', 5], # 177Hz, 5%, 2.0, nice
##['2012-07-03-ak', 0], # 120Hz, 2.5%, 1.8, broader, the one model cell, nice triangle up to 1%!
##['2012-12-20-ac', 0], # 213Hz, 2.5%, 2.1, ok, model cell, weak triangle up to 1%!
['2017-07-18-ai-invivo-1', 2], # 78Hz, 5%, 2.3, weak, nice model cell with clear triangle up to 10%!
##['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
['2018-08-24-ak', 1], # 145Hz, 5%, no diagonal
['2018-08-14-ac', 1], # 239Hz, 0: 10%, no diagonal OR 1: 5%
##['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 = 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']
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, mode, cell_name, run=None):
data = np.load(path / f'{cell_name}-spectral-{mode}-s{run:02d}.npz')
contrast = float(data['contrast'])
fcutoff = float(data['fcutoff'])
freqs = data['freqs']
pss = data['pss']
prs = data['prs']
prss = data['prss']
nsegs = int(data['nsegs'])
gain = np.abs(prs)/pss
chi2 = np.abs(prss)*0.5/(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(0.6, 1.08, f'CV$_{{\\rm base}}$={cv:.2f}, $r={rate:.0f}$Hz',
transform=ax.transAxes)
def plot_isih_small(ax, s, contrast, rate, cv, isis, pdf):
ax.show_spines('b')
ax.fill_between(1000*isis, pdf, facecolor=s.cell_color1)
ax.set_xlim(0, 20)
ax.set_xticks_fixed([0, 5, 10, 15, 20], ['0', '5', '10', '15', '20\\,ms'])
xt = 1 if rate > 80 else 1.3
ax.text(xt, 1.05, f'CV$_{{\\rm base}}$={cv:.2f}', ha='right',
transform=ax.transAxes)
ax.text(xt, 0.6, f'$r={rate:.0f}$Hz', ha='right',
transform=ax.transAxes)
ax.text(xt, 0.15, f'$c={100*contrast:.0f}$\\%', ha='right',
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 > 30) & (freqs < 890)
ax.plot(freqs[mask], power_db[mask], **s.lsC1)
ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD)
ax.plot(freqs[rate_i], power_db[rate_i] + 2, **s.psF0)
ax.set_ylim(-25, 5)
#ax.plot(freqs[mask], 1e-3*prr[mask], **s.lsC1)
#ax.plot(freqs[eod_i], 1e-3*prr[eod_i] + 2, **s.psFEOD)
#ax.plot(freqs[rate_i], 1e-3*prr[rate_i] + 2, **s.psF0)
#ax.set_ylim(0, 30)
ax.set_xlim(0, 900)
ax.set_xticks_delta(300)
ax.set_xlabel('$f$', 'Hz')
ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$',
ha='center')
ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$',
ha='center')
ax.yscalebar(1.05, 0, 10, 'dB', ha='right')
#ax.text(freqs[eod_i], 1e-3*prr[eod_i] + 4, '$f_{\\rm EOD}$',
# ha='center')
#ax.text(freqs[rate_i], 1e-3*prr[rate_i] + 4, '$r$',
# ha='center')
#ax.yscalebar(1.05, 0, 5, 'kHz', ha='right')
def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1,
contrast2, spikes2, am=True):
t0 = 0.3
t1 = 0.4
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)
stim = contrast1*stimulus1
if am:
eod = np.sin(2*np.pi*eodf*time1) * (1 + stim)
else:
eod = np.sin(2*np.pi*eodf*time1) + stim
ax.plot(time1, 4*eod + 7, **s.lsEOD)
ax.plot(time1, 4*(1 + stim) + 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, ymax, dy):
ax.plot(freqs2, 1e-2*gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
ax.plot(freqs1, 1e-2*gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
ax.set_xlim(0, fcutoff)
ax.set_ylim(0, ymax)
ax.set_xticks_delta(fcutoff//3)
ax.set_yticks_delta(dy)
ax.set_xlabel('$f$', 'Hz')
ax.set_ylabel(r'$|\chi_1|$', r'Hz/\%')
def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21,
contrast2, freqs2, chi22, fcutoff, ymax, toffs):
diags = []
sis = []
sips = []
sifs = []
for contrast, freqs, chi2 in [[contrast1, freqs1, chi21],
[contrast2, freqs2, chi22]]:
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
diags.append([dfreqs, diag])
sinorm, sirel, sif = peak_size(dfreqs, diag, fbase, median=False)
sip = diag[np.argmin(np.abs(dfreqs - sif))]
sis.append(sinorm)
sips.append(sip)
sifs.append(sif)
print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}')
ax.plot(diags[1][0], 1e-4*diags[1][1], **s.lsC2)
ax.plot(diags[0][0], 1e-4*diags[0][1], **s.lsC1)
offs = 0.05*ymax
ax.plot(sifs[1], 1e-4*sips[1] + offs, clip_on=False, **s.psC2)
ax.plot(sifs[0], 1e-4*sips[0] + offs, clip_on=False, **s.psC1)
ax.set_xlim(0, 2*fcutoff)
ax.set_ylim(0, ymax)
ax.set_xticks_delta(fcutoff)
ax.set_yticks_delta(ymax//3)
ax.set_xlabel('$f_1 + f_2$', 'Hz')
ax.text(sifs[1] - 0.15*fcutoff, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
ha='right', color=s.cell_color2)
ax.text(sifs[1] + 0.25*fcutoff, 1e-4*sips[1], f'SI={sis[1]:.1f}')
ax.text(sifs[0] - 0.15*fcutoff, 1e-4*sips[0] + toffs, f'{100*contrast1:.0f}\\%',
ha='right', color=s.cell_color1)
ax.text(sifs[0] + 0.25*fcutoff, 1e-4*sips[0] + toffs, f'SI={sis[0]:.1f}')
if __name__ == '__main__':
"""
# find a nice example cell:
from thunderlab.tabledata import TableData
data = TableData('data/Apteronotus_leptorhynchus-Punit-data.csv')
data = data[(data['sinorm_nmax'] > 0) & (data['sinorm_nmax'] < 1.5), :]
data = data[(data['contrast'] > 0.04) & (data['contrast'] < 0.06), :]
#data = data[(data['respmod2'] > 150) & (data['respmod2'] < 200), :]
data = data[(data['cvbase'] > 0.4) & (data['cvbase'] < 0.8), :]
data = data[(data['ratebase'] > 220) & (data['ratebase'] < 300), :]
for k in range(data.rows()):
print(f'{data[k, "cell"]:<22s} s{data[k, "stimindex"]:02.0f}: '
f'{100*data[k, "contrast"]:3g}%, r={data[k, "ratebase"]:3.0f}Hz, '
f'CV={data[k, "cvbase"]:4.2f}, '
f'rmod={data[k, "respmod2"]:3.0f}Hz, '
f'SI={data[k, "sinorm_nmax"]:5.2f}')
print()
#exit()
"""
#mode = 'all'
mode = '100'
cell_name = example_cell[0][0]
print('Example P-unit:')
eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
print(f' {cell_name:<22s}: fbase={rate:3.0f}Hz, CV={cv:.2f}')
contrast1, time1, stimulus1, spikes1 = load_noise(data_path,
*example_cell[0])
contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
*example_cell[1])
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, mode,
*example_cell[0])
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')
print(f' duration1: {time1[-1]:4.1f}s duration2: {time2[-1]:4.1f}s')
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, 0.3, 4.7],
cmsize=(s.plot_width, 0.85*s.plot_width))
fig.subplots_adjust(leftm=8, rightm=2, topm=2, bottomm=3.5,
wspace=0.4, hspace=0.42)
axi, axp, axr = ax1.subplots(1, 3, width_ratios=[2, 3, 0, 10, 0.2])
axg, axc1, axc2, axd = ax2.subplots(1, 4, wspace=0.2,
width_ratios=[3.5, 0.5, 4, 4, 0.8, 3.5])
axs = ax3.subplots(2, 4, wspace=0.4, hspace=0.35,
width_ratios=[1, 1, 0.1, 1, 1, 0.1],
height_ratios=[1, 4])
axi.text(0, 1.08, 'P-unit:', transform=axi.transAxes,
color=s.cell_color1, fontsize='large')
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, am=True)
plot_gain(axg, s, contrast1, freqs1, gain1,
contrast2, freqs2, gain2, fcutoff1, ymax=10, dy=5)
axc = plot_chi2(axc1, s, freqs2, chi22, fcutoff2, None, 6)
axc.remove()
axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag)
axc1.set_title(f'$c$={100*contrast2:g}\\,\\%',
fontsize='medium', color=s.cell_color2)
plot_chi2(axc2, s, freqs1, chi21, fcutoff1, None, 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)
plot_diagonals(axd, s, rate, contrast1, freqs1, chi21,
contrast2, freqs2, chi22, fcutoff1, ymax=14, toffs=2.5)
fig.common_yticks(axc1, axc2)
fig.tag([axi, axp, axr], xoffs=-3, yoffs=0)
fig.tag([axg, axc1, axc2, axd], xoffs=-3, yoffs=2)
print()
print('Additional example cells:')
axs[0, 0].text(0, 1.6, 'P-units:', transform=axs[0, 0].transAxes,
color=s.cell_color1, fontsize='large')
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, mode,
cell, run)
print(f' {cell:<22s}: run={run:2d}, contrast={100*contrast:3.2g}%, '
f'fbase={rate:3.0f}Hz, CV={cv:.2f}')
plot_isih_small(axs[0, k], s, contrast, rate, cv, isis, pdf)
vmax = 20 if k < 2 else 30
axc = plot_chi2(axs[1, k], s, freqs, chi2, fcutoff, rate, vmax)
if k % 2 == 0:
axc.remove()
if k == 1:
axc.set_ylabel('')
fig.common_yticks(axs[1, :])
fig.tag([axs[0, :2], axs[0, 2:]], xoffs=-3, yoffs=1)
fig.savefig()
print()