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added firing rate to regimes

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This commit is contained in:
Jan Benda
2026-01-29 12:03:32 +01:00
parent 195f419a90
commit b211b42c3f
7 changed files with 142 additions and 78 deletions
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203
regimes.py
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@@ -1,16 +1,38 @@
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from scipy.stats import linregress
from numba import jit
from spectral import rate
from plotstyle import plot_style, lighter, darker
model_cell = '2018-05-08-ad-invivo-1' # 228Hz, CV=0.67
alphas = [0.002, 0.01, 0.03, 0.06]
rmax = 500
amax = 60
cthresh1 = 1.2
cthresh2 = 3.5
model_cell = '2018-05-08-ab-invivo-1' # 116, CV=0.68
alphas = [0.002, 0.008, 0.025, 0.05]
rmax = 400
amax = 50
cthresh1 = 1.2
cthresh2 = 3.0
data_path = Path('data')
sims_path = data_path / 'simulations'
trials = 1000
spec_trials = 100 # set to zero to only recompute firng rates
sigma = 0.002
nfft = 2**18
recompute = False
def load_data(file_path):
data = np.load(file_path)
@@ -127,13 +149,13 @@ def punit_spikes(parameter, alpha, beatf1, beatf2, tmax, trials):
return spikes
def plot_am(ax, s, alpha, beatf1, beatf2, tmax):
time = np.arange(0, tmax, 0.0001)
def plot_am(ax, s, alpha, beatf1, beatf2, tmin, tmax):
time = np.arange(tmin, tmax, 0.0001)
am = alpha*np.sin(2*np.pi*beatf1*time)
am += alpha*np.sin(2*np.pi*beatf2*time)
ax.show_spines('l')
ax.plot(1000*time, -100*am, **s.lsAM)
ax.set_xlim(0, 1000*tmax)
ax.plot(1000*(time - tmin), -100*am, **s.lsAM)
ax.set_xlim(0, 1000*(tmax - tmin))
ax.set_ylim(-13, 13)
ax.set_yticks_delta(10)
#ax.set_xlabel('Time', 'ms')
@@ -142,22 +164,47 @@ def plot_am(ax, s, alpha, beatf1, beatf2, tmax):
transform=ax.transAxes, ha='right')
def plot_raster(ax, s, spikes, tmax):
spikes_ms = [1000*s[s<tmax] for s in spikes[:16]]
def plot_raster(ax, s, spikes, tmin, tmax):
spikes_ms = [1000*(s[(s >= tmin) & (s <= tmax)] - tmin)
for s in spikes[:16]]
ax.show_spines('')
ax.eventplot(spikes_ms, linelengths=0.9, **s.lsRaster)
ax.set_xlim(0, 1000*tmax)
ax.set_xlim(0, 1000*(tmax - tmin))
#ax.set_xlabel('Time', 'ms')
#ax.set_ylabel('Trials')
def plot_rate(ax, s, path, spikes, tmin, tmax, sigma=0.002):
if recompute or not path.is_file():
print(' compute firing rate')
time = np.arange(0, tmin + tmax, sigma/4)
r, rsd = rate(time, spikes, sigma)
np.savez(path, time=time, rate=r, ratesd=rsd,
sigma=sigma, trials=len(spikes))
else:
print(f' load firing rate from {path}')
data = np.load(path)
time = data['time']
r = data['rate']
rsd = data['ratesd']
mask = (time >= tmin) &(time <= tmax)
time = time[mask] - tmin
r = r[mask]
ax.show_spines('l')
ax.plot(1000*time, r, clip_on=False, **s.lsRate)
ax.set_xlim(0, 1000*(tmax - tmin))
ax.set_ylim(0, rmax)
ax.set_ylabel('Rate', 'Hz')
ax.set_yticks_delta(200)
def compute_power(path, contrast, spikes, nfft, dt):
if not path.exists():
print(f'Compute power spectrum for contrast = {100*contrast:4.1f}%')
def compute_power(path, spikes, nfft, dt):
if spec_trials > 0 and (recompute or not path.is_file()):
print(' compute power spectrum')
psds = []
time = np.arange(nfft + 1)*dt
tmax = nfft*dt
for s in spikes:
for s in spikes[:spec_trials]:
b, _ = np.histogram(s, time)
b = b / dt
fourier = np.fft.rfft(b - np.mean(b))
@@ -165,9 +212,9 @@ def compute_power(path, contrast, spikes, nfft, dt):
freqs = np.fft.rfftfreq(nfft, dt)
prr = np.mean(psds, 0)*dt/nfft
np.savez(path, nfft=nfft, deltat=dt, nsegs=len(spikes),
freqs=freqs, prr=prr)
freqs=freqs, prr=prr, trials=len(spikes))
else:
print(f'Load power spectrum for contrast = {100*contrast:4.1f}%')
print(f' load power spectrum from {path}')
data = np.load(path)
freqs = data['freqs']
prr = data['prr']
@@ -175,10 +222,12 @@ def compute_power(path, contrast, spikes, nfft, dt):
def decibel(x):
return 10*np.log10(x/1e8)
return 10*np.log10(x/1e8 + 1e-12)
def peak_ampl(freqs, psd, f, df=2):
if f < 0:
f = 5
psd_snippet = psd[(freqs > f - df) & (freqs < f + df)]
return np.max(psd_snippet)
@@ -186,67 +235,77 @@ def peak_ampl(freqs, psd, f, df=2):
def plot_psd(ax, s, path, contrast, spikes, nfft, dt, beatf1, beatf2, eodf):
offs = 5
offsm = 3
freqs, psd = compute_power(path, contrast, spikes, nfft, dt)
freqs, psd = compute_power(path, spikes, nfft, dt)
psd /= freqs[1]
ax.plot(freqs, decibel(psd), **s.lsPower)
# mark frequencies:
ax.plot(eodf, decibel(peak_ampl(freqs, psd, eodf)) + offs,
label=r'$f_{EOD}$', clip_on=False, **s.psFEOD)
label=r'$f_{EOD}$', clip_on=False, zorder=50, **s.psFEOD)
ax.plot(beatf2, decibel(peak_ampl(freqs, psd, beatf2)) + offs,
label=r'$r$', clip_on=False, **s.psF0)
label=r'$r$', clip_on=False, zorder=50, **s.psF0)
ax.plot(beatf1, decibel(peak_ampl(freqs, psd, beatf1)) + offs,
label=r'$\Delta f_1$', clip_on=False, **s.psF01)
label=r'$\Delta f_1$', clip_on=False, zorder=50, **s.psF01)
ax.plot(beatf2, decibel(peak_ampl(freqs, psd, beatf2)) + 2*offs + 2,
label=r'$\Delta f_2$', clip_on=False, **s.psF02)
label=r'$\Delta f_2$', clip_on=False, zorder=50, **s.psF02)
ax.plot(beatf2 - beatf1, decibel(peak_ampl(freqs, psd, beatf2 - beatf1)) + offs,
label=r'$\Delta f_2 - \Delta f_1$', clip_on=False, **s.psF01_2)
label=r'$\Delta f_2 - \Delta f_1$', clip_on=False, zorder=50, **s.psF01_2)
ax.plot(beatf1 + beatf2, decibel(peak_ampl(freqs, psd, beatf1 + beatf2)) + offs,
label=r'$\Delta f_1 + \Delta f_2$', clip_on=False, **s.psF012)
label=r'$\Delta f_1 + \Delta f_2$', clip_on=False, zorder=50, **s.psF012)
ax.plot(eodf + beatf1, decibel(peak_ampl(freqs, psd, eodf + beatf1)) + offsm,
label=r'$f_{EOD} \pm k \Delta f_1$', **s.psFEODm)
label=r'$f_{EOD} \pm k \Delta f_1$', zorder=40, **s.psFEODm)
ax.plot(eodf - beatf1, decibel(peak_ampl(freqs, psd, eodf - beatf1)) + offsm, **s.psFEODm)
if contrast > 0.008:
if contrast >= alphas[1]:
ax.plot(eodf - beatf2, decibel(peak_ampl(freqs, psd, eodf - beatf2)) + offsm,
label=r'$f_{EOD} - k \Delta f_2$', **s.psF0m)
if contrast > 0.02:
label=r'$f_{EOD} - k \Delta f_2$', zorder=40, **s.psF0m)
if contrast >= alphas[2]:
ax.plot(2*beatf1, decibel(peak_ampl(freqs, psd, 2*beatf1)) + offsm,
label=r'$k\Delta f_1$', **s.psF01m)
ax.plot(eodf + 2*beatf1, decibel(peak_ampl(freqs, psd, eodf + 2*beatf1)) + offsm, **s.psFEODm)
label=r'$k\Delta f_1$', zorder=40, **s.psF01m)
ax.plot(eodf + 2*beatf1, decibel(peak_ampl(freqs, psd, eodf + 2*beatf1)) + offsm, zorder=40, **s.psFEODm)
ax.plot(eodf - beatf2 + beatf1, decibel(peak_ampl(freqs, psd, eodf - beatf2 + beatf1)) + offsm,
label=r'$f_{EOD} - \Delta f_2 \pm k\Delta f_1$', **s.psF02m)
ax.plot(eodf - beatf2 - beatf1, decibel(peak_ampl(freqs, psd, eodf - beatf2 - beatf1)) + offsm, **s.psF02m)
if contrast > 0.05:
label=r'$f_{EOD} - \Delta f_2 \pm k\Delta f_1$', zorder=40, **s.psF02m)
ax.plot(eodf - beatf2 - beatf1, decibel(peak_ampl(freqs, psd, eodf - beatf2 - beatf1)) + offsm, zorder=40, **s.psF02m)
if contrast >= alphas[3]:
ax.plot(beatf2 + 2*beatf1, decibel(peak_ampl(freqs, psd, beatf2 + 2*beatf1)) + offsm,
label=r'$\Delta f_2 \pm k\Delta f_1$', **s.psF012m)
ax.plot(beatf2 + 3*beatf1, decibel(peak_ampl(freqs, psd, beatf2 + 3*beatf1)) + offsm, **s.psF012m)
ax.plot(beatf2 - 2*beatf1, decibel(peak_ampl(freqs, psd, beatf2 - 2*beatf1)) + offsm, **s.psF012m)
ax.plot(beatf2 - 3*beatf1, decibel(peak_ampl(freqs, psd, beatf2 - 3*beatf1)) + offsm, **s.psF012m)
ax.plot(3*beatf1, decibel(peak_ampl(freqs, psd, 3*beatf1)) + offsm, **s.psF01m)
ax.plot(4*beatf1, decibel(peak_ampl(freqs, psd, 4*beatf1)) + offsm, **s.psF01m)
ax.plot(eodf - 2*beatf1, decibel(peak_ampl(freqs, psd, eodf - 2*beatf1)) + offsm, **s.psFEODm)
ax.plot(eodf - 3*beatf1, decibel(peak_ampl(freqs, psd, eodf - 3*beatf1)) + offsm, **s.psFEODm)
ax.plot(eodf - 4*beatf1, decibel(peak_ampl(freqs, psd, eodf - 4*beatf1)) + offsm, **s.psFEODm)
ax.plot(eodf - 2*beatf2, decibel(peak_ampl(freqs, psd, eodf - 2*beatf2)) + offsm, **s.psF0m)
label=r'$\Delta f_2 \pm k\Delta f_1$', zorder=40, **s.psF012m)
ax.plot(beatf2 + 3*beatf1, decibel(peak_ampl(freqs, psd, beatf2 + 3*beatf1)) + offsm, zorder=40, **s.psF012m)
ax.plot(beatf2 - 2*beatf1, decibel(peak_ampl(freqs, psd, beatf2 - 2*beatf1)) + offsm, zorder=40, **s.psF012m)
ax.plot(beatf2 - 3*beatf1, decibel(peak_ampl(freqs, psd, beatf2 - 3*beatf1)) + offsm, zorder=40, **s.psF012m)
ax.plot(3*beatf1, decibel(peak_ampl(freqs, psd, 3*beatf1)) + offsm, zorder=40, **s.psF01m)
ax.plot(4*beatf1, decibel(peak_ampl(freqs, psd, 4*beatf1)) + offsm, zorder=40, **s.psF01m)
ax.plot(eodf - 2*beatf1, decibel(peak_ampl(freqs, psd, eodf - 2*beatf1)) + offsm, zorder=40, **s.psFEODm)
ax.plot(eodf - 3*beatf1, decibel(peak_ampl(freqs, psd, eodf - 3*beatf1)) + offsm, zorder=40, **s.psFEODm)
ax.plot(eodf - 4*beatf1, decibel(peak_ampl(freqs, psd, eodf - 4*beatf1)) + offsm, zorder=40, **s.psFEODm)
ax.plot(eodf - 2*beatf2, decibel(peak_ampl(freqs, psd, eodf - 2*beatf2)) + offsm, zorder=40, **s.psF0m)
ax.plot(eodf - beatf2 + 2*beatf1, decibel(peak_ampl(freqs, psd, eodf - beatf2 + 2*beatf1)) + offsm,
**s.psF02m)
zorder=40, **s.psF02m)
ax.plot(eodf - beatf2 + 3*beatf1, decibel(peak_ampl(freqs, psd, eodf - beatf2 + 2*beatf1)) + offsm,
**s.psF02m)
zorder=40, **s.psF02m)
ax.set_xlim(0, 750)
ax.set_ylim(-60, 0)
ax.set_xticks_delta(200)
ax.set_yticks_delta(20)
ax.set_xlabel('Frequency', 'Hz')
ax.set_ylabel('Power [dB]')
def plot_example(axs, axr, axp, s, path, cell, alpha, beatf1, beatf2, eodf,
def plot_example(axs, axr, axf, axp, s, path, cell, alpha, beatf1, beatf2, eodf,
nfft, trials):
spec_path = path.with_name(path.stem + f'-contrastspectrum-{1000*alpha:03.0f}.npz')
rate_path = path.with_name(path.stem + f'-contrastrates-{1000*alpha:03.0f}.npz')
dt = 0.0001
tmax = nfft*dt
t1 = 0.1
t0 = 0.112
t1 = 0.212
if not recompute and spec_path.is_file() and rate_path.is_file():
tmax = t0 + t1
trials = 20
else:
print(' compute spike response')
spikes = punit_spikes(cell, alpha, beatf1, beatf2, tmax, trials)
plot_am(axs, s, alpha, beatf1, beatf2, t1)
plot_raster(axr, s, spikes, t1)
plot_psd(axp, s, path, alpha, spikes, nfft, dt, beatf1, beatf2, eodf)
plot_am(axs, s, alpha, beatf1, beatf2, t0, t1)
plot_raster(axr, s, spikes, t0, t1)
plot_rate(axf, s, rate_path, spikes, t0, t1, sigma)
plot_psd(axp, s, spec_path, alpha, spikes, nfft, dt, beatf1, beatf2, eodf)
def amplitude(power):
@@ -279,13 +338,13 @@ def plot_peaks(ax, s, alphas, contrasts, powerf1, powerf2, powerfsum,
powerfdiff):
cmax = 10
contrasts *= 100
ax.plot(contrasts, amplitude_linearfit(contrasts, powerf1, 4),
ax.plot(contrasts, amplitude_linearfit(contrasts, powerf1, cthresh1),
**s.lsF01m)
ax.plot(contrasts, amplitude_linearfit(contrasts, powerf2, 2),
ax.plot(contrasts, amplitude_linearfit(contrasts, powerf2, cthresh1),
**s.lsF02m)
ax.plot(contrasts, amplitude_squarefit(contrasts, powerfsum, 4),
ax.plot(contrasts, amplitude_squarefit(contrasts, powerfsum, cthresh2),
**s.lsF012m)
ax.plot(contrasts, amplitude_squarefit(contrasts, powerfdiff, 4),
ax.plot(contrasts, amplitude_squarefit(contrasts, powerfdiff, cthresh2),
**s.lsF01_2m)
ax.plot(contrasts, amplitude(powerf1), **s.lsF01)
ax.plot(contrasts, amplitude(powerf2), **s.lsF02)
@@ -294,27 +353,28 @@ def plot_peaks(ax, s, alphas, contrasts, powerf1, powerf2, powerfsum,
clip_on=False, **s.lsF012)
ax.plot(contrasts[mask], amplitude(powerfdiff)[mask],
clip_on=False, **s.lsF01_2)
ymax = 60
for alpha, tag in zip(alphas, ['A', 'B', 'C', 'D']):
ax.plot(100*alpha, ymax*0.95, 'vk', ms=4, clip_on=False)
ax.text(100*alpha, ymax, tag, ha='center')
ax.plot(100*alpha, 1.05*amax, 'vk', ms=4, clip_on=False)
ax.text(100*alpha, 1.1*amax, tag, ha='center')
#ax.axvline(contrast, **s.lsGrid)
#ax.text(contrast, 630, tag, ha='center')
cthresh1 = 1.2
cthresh2 = 3.5
print(f'Linear regime ends at a contrast of about {cthresh1:4.1f}%')
print(f'Weakly non-linear regime ends at a contrast of about {cthresh2:4.1f}%')
ax.axvline(cthresh1, **s.lsLine)
ax.axvline(cthresh2, **s.lsLine)
yoffs = 35
yoffs = 35 if amax == 60 else 31
ax.text(cthresh1/2, yoffs, 'linear\nregime',
ha='center', va='center')
ax.text((cthresh1 + cthresh2)/2, yoffs, 'weakly\nnonlinear\nregime',
ha='center', va='center')
ax.text(5.5, yoffs, 'strongly\nnonlinear\nregime',
ha='center', va='center')
if amax == 60:
ax.text(5.5, yoffs, 'strongly\nnonlinear\nregime',
ha='center', va='center')
else:
ax.text(5.5, 6, 'strongly\nnonlinear\nregime',
ha='center', va='bottom')
ax.set_xlim(0, cmax)
ax.set_ylim(0, ymax)
ax.set_ylim(0, amax)
ax.set_xticks_delta(2)
ax.set_yticks_delta(20)
ax.set_xlabel('Contrast', r'\%')
@@ -325,12 +385,10 @@ if __name__ == '__main__':
ratebase, cvbase, beatf1, beatf2, \
contrasts, powerf1, powerf2, powerfsum, powerfdiff = \
load_data(sims_path / f'{model_cell}-contrastpeaks.npz')
alphas = [0.002, 0.01, 0.03, 0.06]
parameters = load_models(data_path / 'punitmodels.csv')
cell = cell_parameters(parameters, model_cell)
eodf = cell['EODf']
nfft = 2**18
print(f'Loaded data for cell {model_cell}: ')
print(f' baseline rate = {ratebase:.0f}Hz, CV = {cvbase:.2f}')
@@ -338,23 +396,26 @@ if __name__ == '__main__':
print()
s = plot_style()
fig, (axes, axa) = plt.subplots(2, 1, height_ratios=[4, 3],
cmsize=(s.plot_width, 0.65*s.plot_width))
fig, (axes, axa) = plt.subplots(2, 1, height_ratios=[5, 3],
cmsize=(s.plot_width, 0.7*s.plot_width))
fig.subplots_adjust(leftm=8, rightm=2, topm=2, bottomm=3.5, hspace=0.6)
axe = axes.subplots(3, 4, wspace=0.4, hspace=0.2,
height_ratios=[1, 2, 0.6, 3])
axe = axes.subplots(4, 4, wspace=0.4, hspace=0.2,
height_ratios=[1, 2, 2, 0.6, 3])
fig.show_spines('lb')
# example power spectra:
for c, alpha in enumerate(alphas):
path = sims_path / f'{model_cell}-contrastspectrum-{1000*alpha:03.0f}.npz'
plot_example(axe[0, c], axe[1, c], axe[2, c], s, path,
cell, alpha, beatf1, beatf2, eodf, nfft, 100)
axe[1, 0].xscalebar(1, -0.1, 20, 'ms', ha='right')
axe[2, 3].legend(loc='center right', bbox_to_anchor=(1.05, -0.8),
path = sims_path / f'{model_cell}'
print(f'Example response for contrast {100*alpha:4.1f}%:')
plot_example(axe[0, c], axe[1, c], axe[2, c], axe[3, c], s, path,
cell, alpha, beatf1, beatf2, eodf, nfft, trials)
print()
axe[2, 0].xscalebar(1, -0.1, 20, 'ms', ha='right')
axe[3, 3].legend(loc='center right', bbox_to_anchor=(1.05, -0.9),
ncol=11, columnspacing=0.6, handletextpad=0)
fig.common_yspines(axe[0, :])
fig.common_yticks(axe[2, :])
fig.common_yticks(axe[3, :])
fig.tag(axe[0, :], xoffs=-3, yoffs=1.6)
# contrast dependence: