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[noisesplit] almost done

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Jan Benda 2025-05-17 13:29:20 +02:00
parent 8119543dda
commit 8d8b02d581
4 changed files with 186 additions and 44 deletions
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4
modelsusceptovern.py
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@ -93,10 +93,10 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
ns = ns[indx]
stats = stats[indx]
ax.set_visible(True)
ax.plot(ns, stats[:, 7], '0.5', lw=1, zorder=50, label='99.8\\%')
ax.plot(ns, stats[:, 7], zorder=50, label='99.8\\%', **s.lsMax)
ax.fill_between(ns, stats[:, 2], stats[:, 6], fc='0.85', zorder=40, label='5--95\\%')
ax.fill_between(ns, stats[:, 3], stats[:, 5], fc='0.5', zorder=45, label='25-75\\%')
ax.plot(ns, stats[:, 4], zorder=50, label='median', **s.lsSpine)
ax.plot(ns, stats[:, 4], zorder=50, label='median', **s.lsMedian)
#ax.plot(ns, stats[:, 8], '0.0')
if title:
if 'noise_frac' in data:

214
noisesplit.py
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@ -1,6 +1,7 @@
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from spectral import whitenoise
from plotstyle import plot_style
@ -33,9 +34,11 @@ def plot_chi2(ax, s, freqs, chi2, nsegs):
for fac, delta in zip([1, 2, 3, 4, 6, 8, 10],
[0.5, 1, 1, 2, 3, 4, 5]):
if fac*ten >= vmax:
vmax = fac*ten
ten *= delta
vmax = prev_fac*ten
ten *= prev_delta
break
prev_fac = fac
prev_delta = delta
pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
rasterized=True)
ax.set_xlim(0, 300)
@ -54,11 +57,60 @@ def plot_chi2(ax, s, freqs, chi2, nsegs):
cb.outline.set_linewidth(0)
cax.set_ylabel(r'$|\chi_2|$ [Hz]')
cax.set_yticks_delta(ten)
return cax
def plot_chi2_contrast(ax1, ax2, s, cell_name, contrast, nsmall, nlarge):
data_files = sims_path.glob(f'chi2-noisen-{cell_name}-{1000*contrast:03.0f}-*.npz')
files, nums = sort_files(cell_name, data_files, 2)
def plot_overn(ax, s, files, nmax=1e6):
ns = []
stats = []
for fname in files:
data = np.load(fname)
n = data['n']
if nmax is not None and n > nmax:
continue
alpha = data['alpha']
freqs = data['freqs']
pss = data['pss']
dt_fix = 1 # 0.0005
chi2 = np.abs(data['prss'])/dt_fix*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
ns.append(n)
i0 = np.argmin(freqs < 0)
i1 = np.argmax(freqs > 300)
if i1 == 0:
i1 = len(freqs)
chi2 = chi2[i0:i1, i0:i1]
stats.append(np.quantile(chi2, [0, 0.001, 0.05, 0.25, 0.5,
0.75, 0.95, 0.998, 1.0]))
ns = np.array(ns)
stats = np.array(stats)
indx = np.argsort(ns)
ns = ns[indx]
stats = stats[indx]
ax.set_visible(True)
ax.plot(ns, stats[:, 7], zorder=50, label='99.8\\%', **s.lsMax)
ax.fill_between(ns, stats[:, 2], stats[:, 6], fc='0.85', zorder=40, label='5--95\\%')
ax.fill_between(ns, stats[:, 3], stats[:, 5], fc='0.5', zorder=45, label='25-75\\%')
ax.plot(ns, stats[:, 4], zorder=50, label='median', **s.lsMedian)
#ax.plot(ns, stats[:, 8], '0.0')
ax.set_xlim(1e2, nmax)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_yticks_log(numticks=5)
if nmax > 1e6:
ax.set_ylim(3e-1, 5e3)
ax.set_minor_yticks_log(numticks=5)
ax.set_xticks_log(numticks=4)
ax.set_minor_xticks_log(numticks=8)
else:
ax.set_ylim(1e0, 1.3e3)
#ax.set_minor_yticks_log(numticks=5)
ax.set_xticks_log(numticks=5)
#ax.set_minor_xticks_log(numticks=6)
ax.set_xlabel('segments')
ax.set_ylabel('$|\\chi_2|$ [Hz]')
def plot_chi2_contrast(ax1, ax2, s, files, nums, nsmall, nlarge):
for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
i = nums.index(n)
data = np.load(files[i])
@ -67,12 +119,11 @@ def plot_chi2_contrast(ax1, ax2, s, cell_name, contrast, nsmall, nlarge):
freqs = data['freqs']
pss = data['pss']
chi2 = np.abs(data['prss'])*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
plot_chi2(ax, s, freqs, chi2, n)
cax = plot_chi2(ax, s, freqs, chi2, n)
cax.set_ylabel('')
def plot_chi2_split(ax1, ax2, s, cell_name, nsmall, nlarge):
data_files = sims_path.glob(f'chi2-split-{cell_name}-*.npz')
files, nums = sort_files(cell_name, data_files, 1)
def plot_chi2_split(ax1, ax2, s, files, nums, nsmall, nlarge):
for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
i = nums.index(n)
data = np.load(files[i])
@ -82,11 +133,17 @@ def plot_chi2_split(ax1, ax2, s, cell_name, nsmall, nlarge):
freqs = data['freqs']
pss = data['pss']
chi2 = np.abs(data['prss'])*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
plot_chi2(ax, s, freqs, chi2, n)
cax = plot_chi2(ax, s, freqs, chi2, n)
cax.set_ylabel('')
return alpha, noise_frac
def plot_chi2_data(ax, s, cell_name, run):
data_file = data_path / f'{cell_name}-baseline.npz'
data = np.load(data_file)
eodf = float(data['eodf'])
ratebase = float(data['ratebase/Hz'])
cvbase = float(data['cvbase'])
data_file = data_path / f'{cell_name}-spectral-s{run:02d}.npz'
data = np.load(data_file)
n = data['n']
@ -94,57 +151,138 @@ def plot_chi2_data(ax, s, cell_name, run):
freqs = data['freqs']
pss = data['pss']
chi2 = np.abs(data['prss'])*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
print(f'Measured cell {data_file.name} at {100*alpha:.1f}% contrast')
print(f'Measured cell {data_file.name} at {100*alpha:.1f}% contrast: r={ratebase:3.0f}Hz, CV={cvbase:4.2f}')
plot_chi2(ax, s, freqs, chi2, n)
return alpha
return alpha, ratebase, eodf
def plot_noise_split(ax, contrast, noise_contrast, noise_frac):
axr, axs, axn = ax.subplots(3, 1, hspace=0.1)
def plot_ram(ax, contrast, eodf, wtime, wnoise):
tmax = 50
am = 1 + contrast*wnoise
eod = np.sin(2*np.pi*eodf*wtime)*am
ax.show_spines('l')
ax.plot(1e3*wtime, eod, clip_on=False, **s.lsEOD)
ax.plot(1e3*wtime, +am, clip_on=False, **s.lsAM)
ax.plot(1e3*wtime, -am, clip_on=False, **s.lsAM)
ax.set_xlim(0, tmax)
ax.set_ylim(-1.3, 1.3)
ax.set_yticks_delta(1)
ax.set_ylabel('EOD')
ax.text(1, 1, f'RAM ($c={100*contrast:.0f}$\\,\\%)', ha='right',
transform=ax.transAxes, color=s.lsAM['color'])
def plot_noise_split(ax, contrast, noise_contrast, noise_frac,
wtime, wnoise):
axr, axs, axn = ax.subplots(3, 1, hspace=0.2)
cmax = 26
cdelta = 20
tmax = 50
axr.show_spines('l')
axr.axhline(0, **s.lsGrid)
axr.plot(1e3*wtime, 100*contrast*wnoise, clip_on=False, **s.lsAM)
axr.set_xlim(0, tmax)
axr.set_ylim(-8, 8)
axr.set_yticks_delta(6)
axr.set_ylim(-cmax, cmax)
axr.set_yticks_delta(cdelta)
axr.set_ylabel('\\%')
axr.text(1, 1, f'RAM ($c={100*contrast:.0f}$\\,\\%)', ha='right',
transform=axr.transAxes, color=s.lsAM['color'])
axs.show_spines('l')
axs.axhline(0, **s.lsGrid)
axs.plot(1e3*wtime, 100*noise_contrast*wnoise, clip_on=False, **s.lsAMsplit)
axs.set_xlim(0, tmax)
axs.set_ylim(-8, 8)
axs.set_yticks_delta(6)
axs.set_ylim(-cmax, cmax)
axs.set_yticks_delta(cdelta)
axs.set_ylabel('\\%')
axn.set_ylim(-6, 6)
if noise_contrast > 0:
axs.text(1, 1, f'$s_{{\\xi}}(t)$ ($c={100*noise_contrast:.0f}$\\,\\%)',
ha='right', transform=axs.transAxes,
color=s.lsAMsplit['color'])
ntime = np.linspace(0, 1e-3*tmax, 800)
rng = np.random.default_rng(45432)
nnoise = rng.normal(size=len(ntime))
axn.show_spines('l')
axn.axhline(0, **s.lsGrid)
axn.plot(1e3*ntime, noise_frac*nnoise, clip_on=False, **s.lsNoise)
axn.set_ylim(-2, 2)
axn.set_xlim(0, tmax)
axn.set_yticks_delta(6)
axn.set_yticks_delta(5)
axn.set_yticks_blank()
axn.set_xticks_delta(25)
axn.set_xlabel('Time', 'ms')
#axn.set_xticks_delta(25)
#axn.set_xlabel('Time', 'ms')
y = 0.8 if noise_frac < 1 else 1.2
axn.text(1, y, f'Intrinsic noise (${100*noise_frac:.0f}$\\,\\%)',
ha='right', transform=axn.transAxes, color=s.lsNoise['color'])
if noise_frac < 1:
axn.xscalebar(1, 0, 10, 'ms', ha='right')
return axr
if __name__ == '__main__':
cell_name = '2012-07-03-ak-invivo-1'
#cell_name = ['2012-07-03-ak-invivo-1', 0]
cell_name = ['2017-07-18-ai-invivo-1', 1] # Take this! at 3% model, 5% data
nsmall = 100
nlarge = 1000000
contrast = 0.03
wdt = 0.0001
wnoise = whitenoise(0, 300, wdt, 0.05, rng=np.random.default_rng(51234))
wtime = np.arange(len(wnoise))*wdt
s = plot_style()
fig, axs = plt.subplots(2, 4, cmsize=(s.plot_width, 0.4*s.plot_width),
width_ratios=[1, 0, 1, 1, 1])
fig.subplots_adjust(leftm=7, rightm=8, topm=2, bottomm=3.5,
wspace=0.4, hspace=0.6)
axs[1, 0].set_visible(False)
data_contrast = plot_chi2_data(axs[0, 0], s, cell_name[:13], 0)
plot_noise_split(axs[0, 1], data_contrast, 0, 1)
plot_chi2_contrast(axs[0, 2], axs[0, 3], s, cell_name, contrast, nsmall, nlarge)
noise_contrast, noise_frac = plot_chi2_split(axs[1, 2], axs[1, 3], s,
cell_name, nsmall, nlarge)
plot_noise_split(axs[1, 1], contrast, noise_contrast, noise_frac)
fig, axs = plt.subplots(3, 4, cmsize=(s.plot_width, 0.7*s.plot_width),
width_ratios=[1, 0, 1, 1, 0.15, 1])
fig.subplots_adjust(leftm=8, rightm=1.5, topm=3, bottomm=4,
wspace=0.25, hspace=0.8)
axs[0, 2].set_visible(False)
axs[0, 3].set_visible(False)
# data:
axs[0, 1].text(-2.42, 1.2, 'P-unit data', fontsize='large',
transform=axs[0, 1].transAxes, color=s.punit_color1)
data_contrast, ratebase, eodf = plot_chi2_data(axs[0, 1], s, cell_name[0],
cell_name[1])
plot_ram(axs[0, 0], data_contrast, eodf, wtime, wnoise)
axs[0, 1].text(-1.5, 1.2, f'$r={ratebase:.0f}$\\,Hz',
transform=axs[0, 1].transAxes, fontsize='large')
# model:
data_files = sims_path.glob(f'chi2-noisen-{cell_name[0]}-{1000*contrast:03.0f}-*.npz')
files, nums = sort_files(cell_name[0], data_files, 2)
axs[1, 1].text(-2.42, 1.2, 'P-unit model', fontsize='large',
transform=axs[1, 1].transAxes, color=s.model_color1)
plot_chi2_contrast(axs[1, 1], axs[1, 2], s, files, nums, nsmall, nlarge)
axr1 = plot_noise_split(axs[1, 0], contrast, 0, 1, wtime, wnoise)
plot_overn(axs[1, 3], s, files, nmax=1e6)
axs[1, 3].legend(loc='lower center', bbox_to_anchor=(0.5, 1.1),
markerfirst=False, title='$|\\chi_2|$ percentiles')
# model noise split:
data_files = sims_path.glob(f'chi2-split-{cell_name[0]}-*.npz')
files, nums = sort_files(cell_name[0], data_files, 1)
axs[2, 1].text(-2.42, 1.2, 'P-unit model', fontsize='large',
transform=axs[2, 1].transAxes, color=s.model_color1)
axs[2, 1].text(-1.5, 1.2, f'(noise split)', fontsize='large',
transform=axs[2, 1].transAxes)
noise_contrast, noise_frac = plot_chi2_split(axs[2, 1], axs[2, 2], s,
files, nums, nsmall, nlarge)
axr2 = plot_noise_split(axs[2, 0], 0, noise_contrast, noise_frac,
wtime, wnoise)
plot_overn(axs[2, 3], s, files, nmax=1e6)
fig.common_xticks(axs[:, 1])
fig.common_xticks(axs[:, 2])
fig.common_xticks(axs[:, 3])
fig.common_yticks(axs[0, 2:])
fig.common_yticks(axs[1, 2:])
#fig.tag(axs, xoffs=-4.5, yoffs=1.8)
fig.common_yticks(axs[1, 1:3])
fig.common_yticks(axs[2, 1:3])
fig.tag([axs[0, :2],
[axr1] + axs[1, 1:].tolist(),
[axr2] + axs[2, 1:].tolist()],
xoffs=-4.5, yoffs=2)
fig.savefig()
print()

6
plotstyle.py
View File

@ -55,7 +55,11 @@ def plot_style():
pt.make_line_styles(ns, 'ls', 'Line', '', palette['black'], '-',
lwthin)
pt.make_line_styles(ns, 'ls', 'EOD', '', palette['gray'], '-', lwthin)
pt.make_line_styles(ns, 'ls', 'AM', '', palette['red'], '-', lwthick)
pt.make_line_styles(ns, 'ls', 'AM', '', palette['red'], '-', lwmid)
pt.make_line_styles(ns, 'ls', 'AMsplit', '', palette['orange'], '-', lwmid)
pt.make_line_styles(ns, 'ls', 'Noise', '', palette['gray'], '-', lwmid)
pt.make_line_styles(ns, 'ls', 'Median', '', palette['black'], '-', lwthick)
pt.make_line_styles(ns, 'ls', 'Max', '', palette['black'], '-', lwmid)
ns.lsStim = dict(color='gray', lw=ns.lwmid)
ns.lsRaster = dict(color='black', lw=ns.lwthin)

6
punitexamplecell.py
View File

@ -14,9 +14,9 @@ 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
##['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%!
##['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