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updated data and figures to new analysis with the right units

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Jan Benda
2025-05-25 23:12:11 +02:00
parent e87d63c46b
commit d043a8c47c
423 changed files with 607 additions and 923 deletions
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punitexamplecell.py
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@@ -3,6 +3,7 @@ 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],
@@ -10,11 +11,11 @@ example_cell = [['2020-10-27-ag-invivo-1', 0],
example_cells = [
#['2021-06-18-ae-invivo-1', 3], # 98Hz, 1%, ok
['2021-06-18-ae-invivo-1', 2], # 98Hz, 10%, ok
['2012-03-30-ah', 5], # 177Hz, 5%, 2.0, nice
['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', 1], # 78Hz, 5%, 2.3, weak, nice model cell with clear triangle up to 10%!
['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
@@ -22,8 +23,7 @@ example_cells = [
##['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', 0], # 239Hz, 10%, no diagonal
##['2010-08-31-ag', 1], # 269Hz, 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
]
@@ -60,13 +60,13 @@ def load_noise(path, cell_name, run):
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['alpha'])
contrast = float(data['contrast'])
fcutoff = float(data['fcutoff'])
freqs = data['freqs']
pss = data['pss']
prs = data['prs']
prss = data['prss']
nsegs = int(data['n'])
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
@@ -78,23 +78,22 @@ def plot_isih(ax, s, rate, cv, isis, pdf):
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}',
ax.text(0.6, 1.08, f'CV$_{{\\rm base}}$={cv:.2f}, $r={rate:.0f}$Hz',
transform=ax.transAxes)
def plot_isih2(ax, s, rate, cv, isis, pdf):
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_delta(5)
#ax.set_xticks_blank()
#ax.set_xticks_fixed([0, 5, 10, 15, 20], ['0', '', '', '', '20\\,ms'])
ax.set_xticks_fixed([0, 5, 10, 15, 20], ['0', '5', '10', '15', '20\\,ms'])
ax.text(1, 1.1, f'CV$_{{\\rm base}}$={cv:.2f}', ha='right',
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)
ax.text(1, 0.6, f'$r={rate:.0f}$Hz', ha='right', transform=ax.transAxes)
def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
@@ -102,34 +101,34 @@ def plot_response_spectrum(ax, s, eodf, rate, freqs, prr):
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] + 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)
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, '$f_{\\rm EOD}$',
ax.text(freqs[eod_i], power_db[eod_i] + 4, '$f_{\\rm EOD}$',
ha='center')
ax.text(freqs[rate_i], 1e-3*prr[rate_i] + 4, '$r$',
ax.text(freqs[rate_i], power_db[rate_i] + 4, '$r$',
ha='center')
ax.yscalebar(1.05, 0, 5, 'kHz', ha='right')
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):
def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1,
contrast2, spikes2, am=True):
t0 = 0.3
t1 = 0.4
#print(len(spikes1), len(spikes2))
maxtrials = 8
trials = np.arange(maxtrials)
ax.show_spines('')
@@ -137,10 +136,13 @@ def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2,
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
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*am + 7, **s.lsAM)
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')
@@ -150,48 +152,26 @@ def plot_response(ax, s, eodf, time1, stimulus1, contrast1, spikes1, contrast2,
va='center', color=s.cell_color2)
def plot_gain(ax, s, contrast1, freqs1, gain1, contrast2, freqs2, gain2, fcutoff):
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, 10)
ax.set_xticks_delta(100)
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_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'$|\chi_2|$ [Hz/\%$^2$]')
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=None):
ax.set_aspect('equal')
if vmax is None:
vmax = np.quantile(1e-4*chi2, 0.998)
pc = ax.pcolormesh(freqs, freqs, 1e-4*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, ymax, toffs):
diags = []
sis = []
sips = []
sifs = []
for contrast, freqs, chi2 in [[contrast1, freqs1, chi21], [contrast2, freqs2, chi22]]:
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)
@@ -200,52 +180,51 @@ def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, ch
sips.append(sip)
sifs.append(sif)
print(f' SI at {100*contrast:.1f}% contrast: {sinorm:.2f}')
#ax.axvline(fbase, **s.lsGrid)
ax.plot(diags[1][0], 1e-4*diags[1][1], **s.lsC2)
ax.plot(diags[0][0], 1e-4*diags[0][1], **s.lsC1)
ax.plot(sifs[1], 1e-4*sips[1] + 0.5, clip_on=False, **s.psC2)
ax.plot(sifs[0], 1e-4*sips[0] + 0.5, clip_on=False, **s.psC1)
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, 14)
ax.set_xticks_delta(300)
ax.set_yticks_delta(4)
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.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
ax.text(sifs[1] - 50, 1e-4*sips[1], f'{100*contrast2:.0f}\\%',
ha='right')
ax.text(sifs[1] + 70, 1e-4*sips[1], f'SI={sis[1]:.1f}')
ax.text(sifs[0] - 50, 1e-4*sips[0] + 2, f'{100*contrast1:.0f}\\%',
ha='right')
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(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['nli'] > 0) & (data['nli'] <= 1.2), :]
data = data[(data['respmod2'] > 150) & (data['respmod2'] < 200), :]
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'] > 300) & (data['ratebase'] < 400), :]
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'nli={data[k, "nli"]:5.2f}')
f'SI={data[k, "sinorm_nmax"]:5.2f}')
print()
#exit()
"""
#mode = 'all'
mode = '100'
cell_name = example_cell[0][0]
print('Example P-unit:', cell_name)
print('Example P-unit:')
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}')
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,
@@ -266,56 +245,59 @@ if __name__ == '__main__':
s.psC1 = s.psP1
s.psC2 = s.psP2
fig, (ax1, ax2, ax3) = \
plt.subplots(3, 1, height_ratios=[3, 0, 3, 0.2, 4.7],
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=9, topm=2, bottomm=4,
wspace=0.4, hspace=0.4)
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(2, 4, wspace=0.4, hspace=0.35, height_ratios=[1, 4])
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)
contrast2, spikes2, am=True)
plot_gain(axg, s, contrast1, freqs1, gain1,
contrast2, freqs2, gain2, fcutoff1)
pc = plot_chi2(axc1, s, contrast2, freqs2, chi22, fcutoff2, 6)
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)
pc = plot_chi2(axc2, s, contrast1, freqs1, chi21, fcutoff1, 6)
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_colorbar(axc2, pc)
plot_diagonals(axd, s, rate, contrast1, freqs1, chi21,
contrast2, freqs2, chi22, fcutoff1)
contrast2, freqs2, chi22, fcutoff1, ymax=14, toffs=2.5)
fig.common_yticks(axc1, axc2)
fig.tag([axi, axp, axr], xoffs=-3, yoffs=-1)
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)
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
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}')
plot_isih2(axs[0, k], s, rate, cv, isis, pdf)
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,
color='white', fontsize='medium',
transform=axs[1, k].transAxes)
axs[0, 0].text(0, 1.6, 'P-units:', transform=axs[0, 0].transAxes,
color=s.cell_color1,
fontsize='large')
plot_colorbar(axs[1, -1], pc)
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, :]], xoffs=-3, yoffs=1)