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started to updated example cells to new analysis with the right units

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Jan Benda 2025-05-25 00:47:27 +02:00
parent 223ad8bd4b
commit e785d51b18
4 changed files with 96 additions and 99 deletions
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99
ampullaryexamplecell.py
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@ -4,7 +4,7 @@ from pathlib import Path
from spectral import diag_projection, peak_size from spectral import diag_projection, peak_size
from plotstyle import plot_style from plotstyle import plot_style
from punitexamplecell import load_baseline, load_noise, load_spectra from punitexamplecell import load_baseline, load_noise, load_spectra
from punitexamplecell import plot_colorbar from punitexamplecell import plot_chi2, plot_colorbar
example_cell = [['2012-05-15-ac', 3], example_cell = [['2012-05-15-ac', 3],
@ -51,18 +51,27 @@ def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
power_db = 10*np.log10(prr/np.max(prr)) power_db = 10*np.log10(prr/np.max(prr))
ax.show_spines('b') ax.show_spines('b')
mask = (freqs > 30) & (freqs < 890) mask = (freqs > 30) & (freqs < 890)
ax.plot(freqs[mask], power_db[mask], **s.lsC1) #ax.plot(freqs[mask], power_db[mask], **s.lsC1)
ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD) #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.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] + 0.4, **s.psFEOD)
ax.plot(freqs[rate_i], 1e-3*prr[rate_i] + 0.4, **s.psF0)
ax.set_ylim(0, 6)
ax.set_xlim(0, 900) ax.set_xlim(0, 900)
ax.set_ylim(-25, 5)
ax.set_xticks_delta(300) ax.set_xticks_delta(300)
ax.set_xlabel('$f$', 'Hz') ax.set_xlabel('$f$', 'Hz')
ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$', #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] + 0.8, '$f_{\\rm EOD}$',
ha='center') ha='center')
ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$', ax.text(freqs[rate_i], 1e-3*prr[rate_i] + 0.8, '$r$',
ha='center') ha='center')
ax.yscalebar(1.05, 0, 10, 'dB', ha='right') ax.yscalebar(1.05, 0, 1, 'kHz', ha='right')
def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2, spikes2): def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2, spikes2):
@ -91,31 +100,16 @@ def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2,
def plot_gain(ax, s, fbase, contrast1, freqs1, gain1, def plot_gain(ax, s, fbase, contrast1, freqs1, gain1,
contrast2, freqs2, gain2, fcutoff): contrast2, freqs2, gain2, fcutoff):
ax.axvline(fbase, **s.lsGrid) ax.axvline(fbase, **s.lsGrid)
ax.plot(freqs2, gain2, label=f'{100*contrast2:.0f}', **s.lsC2) ax.plot(freqs2, 1e-2*gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
ax.plot(freqs1, gain1, label=f'{100*contrast1:.0f}', **s.lsC1) ax.plot(freqs1, 1e-2*gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
ax.set_xlim(0, fcutoff) ax.set_xlim(0, fcutoff)
ax.set_ylim(0, 1500) ax.set_ylim(0, 12)
ax.set_xticks_delta(50) ax.set_xticks_delta(50)
ax.set_yticks_delta(4)
ax.set_xlabel('$f$', 'Hz') ax.set_xlabel('$f$', 'Hz')
ax.set_ylabel(r'$|\chi_1|$', 'Hz') ax.set_ylabel(r'$|\chi_1|$', r'Hz/\%')
ax.text(fbase, 1550, '$r$', ha='center') ax.text(fbase, 12.5, '$r$', ha='center')
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,
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): def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, chi22, fcutoff):
diags = [] diags = []
@ -132,22 +126,22 @@ def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, ch
sifs.append(sif) sifs.append(sif)
print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}') print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}')
#ax.axvline(fbase, **s.lsGrid) #ax.axvline(fbase, **s.lsGrid)
ax.plot(diags[1][0], 1e-3*diags[1][1], **s.lsC2) ax.plot(diags[1][0], 1e-4*diags[1][1], **s.lsC2)
ax.plot(diags[0][0], 1e-3*diags[0][1], **s.lsC1) ax.plot(diags[0][0], 1e-4*diags[0][1], **s.lsC1)
ax.plot(sifs[1], 1e-3*sips[1], **s.psC2) ax.plot(sifs[1], 1e-4*sips[1] + 0.3, clip_on=False, **s.psC2)
ax.plot(sifs[0], 1e-3*sips[0], **s.psC1) ax.plot(sifs[0], 1e-4*sips[0] + 0.3, clip_on=False, **s.psC1)
ax.set_xlim(0, 2*fcutoff) ax.set_xlim(0, 2*fcutoff)
ax.set_ylim(0, 1.7) ax.set_ylim(0, 17)
ax.set_xticks_delta(100) ax.set_xticks_delta(100)
ax.set_yticks_delta(1) ax.set_yticks_delta(5)
ax.set_xlabel('$f_1 + f_2$', 'Hz') ax.set_xlabel('$f_1 + f_2$', 'Hz')
#ax.set_ylabel(r'$|\chi_2|$', 'kHz') #ax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
ax.text(sifs[1] - 25, 1e-3*sips[1], f'{100*contrast2:.0f}\\%', ax.text(sifs[1] - 25, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
ha='right') ha='right')
ax.text(sifs[1] + 35, 1e-3*sips[1], f'SI={sis[1]:.1f}') ax.text(sifs[1] + 35, 1e-4*sips[1], f'SI={sis[1]:.1f}')
ax.text(sifs[0] - 25, 1e-3*sips[0], f'{100*contrast1:.0f}\\%', ax.text(sifs[0] - 25, 1e-4*sips[0] + 0.5, f'{100*contrast1:.0f}\\%',
ha='right') ha='right')
ax.text(sifs[0] + 35, 1e-3*sips[0], f'SI={sis[0]:.1f}') ax.text(sifs[0] + 35, 1e-4*sips[0] + 0.5, f'SI={sis[0]:.1f}')
#ax.text(fbase, 1.75, '$r$', ha='center') #ax.text(fbase, 1.75, '$r$', ha='center')
@ -167,7 +161,10 @@ if __name__ == '__main__':
print() print()
exit() exit()
""" """
#mode = 'all'
mode = '100'
cell_name = example_cell[0][0] cell_name = example_cell[0][0]
print('Example Ampullary cell:', cell_name) print('Example Ampullary cell:', cell_name)
eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name) eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
@ -177,9 +174,9 @@ if __name__ == '__main__':
*example_cell[0]) *example_cell[0])
contrast2, time2, stimulus2, spikes2 = load_noise(data_path, contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
*example_cell[1]) *example_cell[1])
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, mode,
*example_cell[0]) *example_cell[0])
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, mode,
*example_cell[1]) *example_cell[1])
print(f' contrast1: {100*contrast1:4.1f}% contrast2: {100*contrast2:4.1f}%') 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' fcutoff1 : {fcutoff1:3.0f}Hz fcutoff2 : {fcutoff2:3.0f}Hz')
@ -210,15 +207,15 @@ if __name__ == '__main__':
plot_gain(axg, s, rate, contrast1, freqs1, gain1, plot_gain(axg, s, rate, contrast1, freqs1, gain1,
contrast2, freqs2, gain2, fcutoff1) contrast2, freqs2, gain2, fcutoff1)
pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 1.7) pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 10)
axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag) axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag)
axc1.set_title(f'$c$={100*contrast2:g}\\,\\%', axc1.set_title(f'$c$={100*contrast2:g}\\,\\%',
fontsize='medium', color=s.cell_color2) fontsize='medium', color=s.cell_color2)
pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 1.7) pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 10)
axc2.set_title(f'$c$={100*contrast1:g}\\,\\%', axc2.set_title(f'$c$={100*contrast1:g}\\,\\%',
fontsize='medium', color=s.cell_color1) fontsize='medium', color=s.cell_color1)
axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag) axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag)
plot_colorbar(axc2, pc, 1) plot_colorbar(axc2, pc, 2)
plot_diagonals(axd, s, rate, contrast1, freqs1, chi21, plot_diagonals(axd, s, rate, contrast1, freqs1, chi21,
contrast2, freqs2, chi22, fcutoff1) contrast2, freqs2, chi22, fcutoff1)
@ -229,22 +226,20 @@ if __name__ == '__main__':
print('Additional example cells:') print('Additional example cells:')
for k, (cell, run) in enumerate(example_cells): for k, (cell, run) in enumerate(example_cells):
eodf, rate, cv, isis, pdf, _, _ = load_baseline(data_path, cell) eodf, rate, cv, isis, pdf, _, _ = load_baseline(data_path, cell)
fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, cell, run) fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, mode,
cell, run)
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff) dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False) sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={sinorm:3.1f}') print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={sinorm:3.1f}')
plot_isih2(axs[0, k], s, rate, cv, isis, pdf) plot_isih2(axs[0, k], s, rate, cv, isis, pdf)
pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 1.2) pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 20)
#axs[k].set_title(f'$r={rate:.0f}$Hz, CV$_{{\\rm base}}$={cv:.2f}', fontsize='medium')
axs[1, k].text(0.95, 0.9, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50, axs[1, k].text(0.95, 0.9, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
color='white', fontsize='medium', color='white', fontsize='medium',
transform=axs[1, k].transAxes) transform=axs[1, k].transAxes)
axs[0, 0].text(0, 1.6, 'Ampullary cells:', transform=axs[0, 0].transAxes, axs[0, 0].text(0, 1.6, 'Ampullary cells:', transform=axs[0, 0].transAxes,
color=s.cell_color1, color=s.cell_color1,
fontsize='large') fontsize='large')
#axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right', plot_colorbar(axs[1, -1], pc, 5)
# transform=axs[0, -1].transAxes)
plot_colorbar(axs[1, -1], pc, 0.4)
fig.common_yticks(axs[1, :]) fig.common_yticks(axs[1, :])
fig.tag([axs[0, :]], xoffs=-3, yoffs=1) fig.tag([axs[0, :]], xoffs=-3, yoffs=1)

1
dataoverview.py
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@ -248,7 +248,6 @@ if __name__ == '__main__':
punit_model = TableData(data_path / punit_model = TableData(data_path /
'Apteronotus_leptorhynchus-Punit-models.csv', 'Apteronotus_leptorhynchus-Punit-models.csv',
sep=';') sep=';')
print(punit_model.keys())
punit_model = punit_model[punit_model['contrast'] > 1e-6, :] punit_model = punit_model[punit_model['contrast'] > 1e-6, :]
punit_data = TableData(data_path / punit_data = TableData(data_path /
'Apteronotus_leptorhynchus-Punit-data.csv', 'Apteronotus_leptorhynchus-Punit-data.csv',

2
nonlinearbaseline.tex
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@ -568,7 +568,7 @@ Overall, observing \nli{} values greater than at least 1.6, even for a number of
interspike intervals during stimulation with the white-noise interspike intervals during stimulation with the white-noise
stimulus (right column). Pearson's correlation coefficient $R$ and stimulus (right column). Pearson's correlation coefficient $R$ and
the number of data points $n$ are indicated; all correlations are the number of data points $n$ are indicated; all correlations are
significant at a level below $p=0.0002$. Kernel-density estimates significant at a level below $p=0.0006$. Kernel-density estimates
of the distributions of the displayed quantities are plotted on of the distributions of the displayed quantities are plotted on
top and right. Data points are color coded by CV$_{\text{base}}$ top and right. Data points are color coded by CV$_{\text{base}}$
or response modulation as indicated by the color bars. The or response modulation as indicated by the color bars. The

93
punitexamplecell.py
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@ -1,5 +1,3 @@
import sys
sys.path.insert(0, 'ephys') # for analysing data
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from pathlib import Path from pathlib import Path
@ -13,7 +11,7 @@ example_cell = [['2020-10-27-ag-invivo-1', 0],
example_cells = [ example_cells = [
#['2021-06-18-ae-invivo-1', 3], # 98Hz, 1%, ok #['2021-06-18-ae-invivo-1', 3], # 98Hz, 1%, ok
['2021-06-18-ae-invivo-1', 2], # 98Hz, 10%, ok ['2021-06-18-ae-invivo-1', 2], # 98Hz, 10%, ok
['2012-03-30-ah', 2], # 177Hz, 2.5%, 2.0, nice ['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%! ##['2012-07-03-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%! ##['2012-12-20-ac', 0], # 213Hz, 2.5%, 2.1, ok, model cell, weak triangle up to 1%!
#['2017-07-18-ai-invivo-1', 1], # 78Hz, 5%, 2.3, weak, nice model cell with clear triangle up to 10%! #['2017-07-18-ai-invivo-1', 1], # 78Hz, 5%, 2.3, weak, nice model cell with clear triangle up to 10%!
@ -51,8 +49,6 @@ def load_noise(path, cell_name, run):
contrast = data['contrast'] contrast = data['contrast']
time = data['time'] time = data['time']
stimulus = data['stimulus'] stimulus = data['stimulus']
name = str(data['stimulus_name'])
fcutoff = float(name.lower().replace('blwn', '').replace('inputarr_', '').replace('gwn', '').split('h')[0])
spikes = [] spikes = []
for k in range(1000): for k in range(1000):
key = f'spikes_{k:03d}' key = f'spikes_{k:03d}'
@ -62,12 +58,8 @@ def load_noise(path, cell_name, run):
return contrast, time, stimulus, spikes return contrast, time, stimulus, spikes
def load_spectra(path, cell_name, run=None): def load_spectra(path, mode, cell_name, run=None):
if run is None: data = np.load(path / f'{cell_name}-spectral-{mode}-s{run:02d}.npz')
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']) contrast = float(data['alpha'])
fcutoff = float(data['fcutoff']) fcutoff = float(data['fcutoff'])
freqs = data['freqs'] freqs = data['freqs']
@ -111,18 +103,27 @@ def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
power_db = 10*np.log10(prr/np.max(prr)) power_db = 10*np.log10(prr/np.max(prr))
ax.show_spines('b') ax.show_spines('b')
mask = freqs < 890 mask = freqs < 890
ax.plot(freqs[mask], power_db[mask], **s.lsC1) #ax.plot(freqs[mask], power_db[mask], **s.lsC1)
ax.plot(freqs[eod_i], power_db[eod_i] + 2, **s.psFEOD) #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.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_xlim(0, 900)
ax.set_ylim(-25, 5)
ax.set_xticks_delta(300) ax.set_xticks_delta(300)
ax.set_xlabel('$f$', 'Hz') ax.set_xlabel('$f$', 'Hz')
ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$', #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') ha='center')
ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$', ax.text(freqs[rate_i], 1e-3*prr[rate_i] + 4, '$r$',
ha='center') ha='center')
ax.yscalebar(1.05, 0, 10, 'dB', ha='right') ax.yscalebar(1.05, 0, 5, 'kHz', ha='right')
def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2, spikes2): def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2, spikes2):
@ -150,30 +151,30 @@ def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2,
def plot_gain(ax, s, contrast1, freqs1, gain1, contrast2, freqs2, gain2, fcutoff): 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(freqs2, 1e-2*gain2, label=f'{100*contrast2:.0f}', **s.lsC2)
ax.plot(freqs1, gain1, label=f'{100*contrast1:.0f}', **s.lsC1) ax.plot(freqs1, 1e-2*gain1, label=f'{100*contrast1:.0f}', **s.lsC1)
ax.set_xlim(0, fcutoff) ax.set_xlim(0, fcutoff)
ax.set_ylim(0, 800) ax.set_ylim(0, 10)
ax.set_xticks_delta(100) ax.set_xticks_delta(100)
ax.set_xlabel('$f$', 'Hz') ax.set_xlabel('$f$', 'Hz')
ax.set_ylabel(r'$|\chi_1|$', 'Hz') ax.set_ylabel(r'$|\chi_1|$', r'Hz/\%')
def plot_colorbar(ax, pc, dc=None): def plot_colorbar(ax, pc, dc=None):
cax = ax.inset_axes([1.04, 0, 0.05, 1]) cax = ax.inset_axes([1.04, 0, 0.05, 1])
cax.set_spines_outward('lrbt', 0) cax.set_spines_outward('lrbt', 0)
cb = cax.get_figure().colorbar(pc, cax=cax, label=r'$|\chi_2|$ [kHz]') cb = cax.get_figure().colorbar(pc, cax=cax, label=r'$|\chi_2|$ [Hz/\%$^2$]')
cb.outline.set_color('none') cb.outline.set_color('none')
cb.outline.set_linewidth(0) cb.outline.set_linewidth(0)
if dc is not None: if dc is not None:
cax.set_yticks_delta(dc) cax.set_yticks_delta(dc)
def plot_chi2(ax, s, contrast, freqs, chi2, fcutoff, vmax): def plot_chi2(ax, s, contrast, freqs, chi2, fcutoff, vmax=None):
ax.set_aspect('equal') ax.set_aspect('equal')
if vmax is None: if vmax is None:
vmax = np.quantile(1e-3*chi2, 0.99) vmax = np.quantile(1e-4*chi2, 0.998)
pc = ax.pcolormesh(freqs, freqs, 1e-3*chi2, vmin=0, vmax=vmax, pc = ax.pcolormesh(freqs, freqs, 1e-4*chi2, vmin=0, vmax=vmax,
rasterized=True, zorder=10) rasterized=True, zorder=10)
ax.set_xlim(0, fcutoff) ax.set_xlim(0, fcutoff)
ax.set_ylim(0, fcutoff) ax.set_ylim(0, fcutoff)
@ -200,22 +201,22 @@ def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, ch
sifs.append(sif) sifs.append(sif)
print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}') print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}')
#ax.axvline(fbase, **s.lsGrid) #ax.axvline(fbase, **s.lsGrid)
ax.plot(diags[1][0], 1e-3*diags[1][1], **s.lsC2) ax.plot(diags[1][0], 1e-4*diags[1][1], **s.lsC2)
ax.plot(diags[0][0], 1e-3*diags[0][1], **s.lsC1) ax.plot(diags[0][0], 1e-4*diags[0][1], **s.lsC1)
ax.plot(sifs[1], 1e-3*sips[1], **s.psC2) ax.plot(sifs[1], 1e-4*sips[1] + 0.5, clip_on=False, **s.psC2)
ax.plot(sifs[0], 1e-3*sips[0], **s.psC1) ax.plot(sifs[0], 1e-4*sips[0] + 0.5, clip_on=False, **s.psC1)
ax.set_xlim(0, 2*fcutoff) ax.set_xlim(0, 2*fcutoff)
ax.set_ylim(0, 4.2) ax.set_ylim(0, 14)
ax.set_xticks_delta(300) ax.set_xticks_delta(300)
ax.set_yticks_delta(1) ax.set_yticks_delta(4)
ax.set_xlabel('$f_1 + f_2$', 'Hz') ax.set_xlabel('$f_1 + f_2$', 'Hz')
#ax.set_ylabel(r'$|\chi_2|$', 'kHz') #ax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
ax.text(sifs[1] - 50, 1e-3*sips[1], f'{100*contrast2:.0f}\\%', ax.text(sifs[1] - 50, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
ha='right') ha='right')
ax.text(sifs[1] + 70, 1e-3*sips[1], f'SI={sis[1]:.1f}') ax.text(sifs[1] + 70, 1e-4*sips[1], f'SI={sis[1]:.1f}')
ax.text(sifs[0] - 50, 1e-3*sips[0], f'{100*contrast1:.0f}\\%', ax.text(sifs[0] - 50, 1e-4*sips[0] + 2, f'{100*contrast1:.0f}\\%',
ha='right') ha='right')
ax.text(sifs[0] + 70, 1e-3*sips[0], f'SI={sis[0]:.1f}') ax.text(sifs[0] + 70, 1e-4*sips[0] + 2, f'SI={sis[0]:.1f}')
#ax.text(fbase, 4.3, '$r$', ha='center') #ax.text(fbase, 4.3, '$r$', ha='center')
@ -237,6 +238,9 @@ if __name__ == '__main__':
#exit() #exit()
""" """
#mode = 'all'
mode = '100'
cell_name = example_cell[0][0] cell_name = example_cell[0][0]
print('Example P-unit:', cell_name) print('Example P-unit:', cell_name)
eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name) eodf, rate, cv, isis, pdf, freqs, prr = load_baseline(data_path, cell_name)
@ -246,9 +250,9 @@ if __name__ == '__main__':
*example_cell[0]) *example_cell[0])
contrast2, time2, stimulus2, spikes2 = load_noise(data_path, contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
*example_cell[1]) *example_cell[1])
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, mode,
*example_cell[0]) *example_cell[0])
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, mode,
*example_cell[1]) *example_cell[1])
print(f' contrast1: {100*contrast1:4.1f}% contrast2: {100*contrast2:4.1f}%') 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' fcutoff1 : {fcutoff1:3.0f}Hz fcutoff2 : {fcutoff2:3.0f}Hz')
@ -279,11 +283,11 @@ if __name__ == '__main__':
plot_gain(axg, s, contrast1, freqs1, gain1, plot_gain(axg, s, contrast1, freqs1, gain1,
contrast2, freqs2, gain2, fcutoff1) contrast2, freqs2, gain2, fcutoff1)
pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 4) pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 6)
axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag) axc1.plot([0, fcutoff2], [0, fcutoff2], zorder=20, **s.lsDiag)
axc1.set_title(f'$c$={100*contrast2:g}\\,\\%', axc1.set_title(f'$c$={100*contrast2:g}\\,\\%',
fontsize='medium', color=s.cell_color2) fontsize='medium', color=s.cell_color2)
pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 4) pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 6)
axc2.set_title(f'$c$={100*contrast1:g}\\,\\%', axc2.set_title(f'$c$={100*contrast1:g}\\,\\%',
fontsize='medium', color=s.cell_color1) fontsize='medium', color=s.cell_color1)
axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag) axc2.plot([0, fcutoff1], [0, fcutoff1], zorder=20, **s.lsDiag)
@ -298,20 +302,19 @@ if __name__ == '__main__':
print('Additional example cells:') print('Additional example cells:')
for k, (cell, run) in enumerate(example_cells): for k, (cell, run) in enumerate(example_cells):
eodf, rate, cv, isis, pdf, _, _ = load_baseline(data_path, cell) eodf, rate, cv, isis, pdf, _, _ = load_baseline(data_path, cell)
fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path, cell, run) fcutoff, contrast, freqs, gain, chi2 = load_spectra(data_path,
mode, cell, run)
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff) dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False) sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={sinorm:3.1f}') print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={sinorm:3.1f}')
plot_isih2(axs[0, k], s, rate, cv, isis, pdf) plot_isih2(axs[0, k], s, rate, cv, isis, pdf)
pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 1.0) pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 10)
axs[1, k].text(0.95, 0.9, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50, axs[1, k].text(0.95, 0.9, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
color='white', fontsize='medium', color='white', fontsize='medium',
transform=axs[1, k].transAxes) transform=axs[1, k].transAxes)
axs[0, 0].text(0, 1.6, 'P-units:', transform=axs[0, 0].transAxes, axs[0, 0].text(0, 1.6, 'P-units:', transform=axs[0, 0].transAxes,
color=s.cell_color1, color=s.cell_color1,
fontsize='large') fontsize='large')
#axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right',
# transform=axs[0, -1].transAxes)
plot_colorbar(axs[1, -1], pc) plot_colorbar(axs[1, -1], pc)
fig.common_yticks(axs[1, :]) fig.common_yticks(axs[1, :])
fig.tag([axs[0, :]], xoffs=-3, yoffs=1) fig.tag([axs[0, :]], xoffs=-3, yoffs=1)