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updated model figures to new analysis with the right units

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Jan Benda 2025-05-25 12:29:46 +02:00
parent e785d51b18
commit e87d63c46b
5 changed files with 219 additions and 351 deletions
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126
modelsusceptcontrasts.py
View File

@ -3,8 +3,8 @@ import matplotlib.pyplot as plt
from scipy.stats import pearsonr, linregress, gaussian_kde from scipy.stats import pearsonr, linregress, gaussian_kde
from thunderlab.tabledata import TableData from thunderlab.tabledata import TableData
from pathlib import Path from pathlib import Path
from spectral import whitenoise, diag_projection, peak_size from plotstyle import plot_style, labels_params
from plotstyle import plot_style, labels_params, significance_str from plotstyle import noise_files, plot_chi2, significance_str
model_cells = ['2017-07-18-ai-invivo-1', # strong triangle model_cells = ['2017-07-18-ai-invivo-1', # strong triangle
@ -16,101 +16,55 @@ data_path = Path('data')
sims_path = data_path / 'simulations' sims_path = data_path / 'simulations'
def sort_files(cell_name, all_files, n): def load_chi2(file_path, cell_name, contrast=None, n=None):
files = [fn for fn in all_files if '-'.join(fn.stem.split('-')[2:-n]) == cell_name] files, nums = noise_files(sims_path, cell_name, contrast)
if len(files) == 0: idx = -1 if n is None else nums.index(n)
return None, 0 data = np.load(files[idx])
nums = [int(fn.stem.split('-')[-1]) for fn in files] n = data['nsegs']
idxs = np.argsort(nums) fcutoff = data['fcutoff']
files = [files[i] for i in idxs] contrast = data['contrast']
nums = [nums[i] for i in idxs]
return files, nums
def plot_chi2(ax, s, data_file, rate):
fcutoff = 300
data = np.load(data_file)
n = data['n']
alpha = data['alpha']
freqs = data['freqs'] freqs = data['freqs']
pss = data['pss'] pss = data['pss']
prss = data['prss'] prss = data['prss']
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1)) chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
ax.set_visible(True) return freqs, chi2, fcutoff, contrast, n
ax.set_aspect('equal')
i0 = np.argmin(freqs < -fcutoff)
i0 = np.argmin(freqs < 0)
i1 = np.argmax(freqs > fcutoff)
if i1 == 0:
i1 = len(freqs)
freqs = freqs[i0:i1]
chi2 = chi2[i0:i1, i0:i1]
vmax = np.quantile(chi2, 0.996)
ten = 10**np.floor(np.log10(vmax))
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
break
pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
rasterized=True)
if 'noise_frac' in data:
ax.set_title('$c$=0\\,\\%', fontsize='medium')
else:
ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
ax.set_xlim(0, fcutoff)
ax.set_ylim(0, fcutoff)
ax.set_xticks_delta(100)
ax.set_yticks_delta(100)
ax.set_xlabel('$f_1$', 'Hz')
ax.set_ylabel('$f_2$', 'Hz')
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
ax.text(0.95, 0.88, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
color='white', fontsize='medium', transform=ax.transAxes)
cax = ax.inset_axes([1.04, 0, 0.05, 1])
cax.set_spines_outward('lrbt', 0)
if alpha == 0.1:
cb = fig.colorbar(pc, cax=cax, label=r'$|\chi_2|$ [Hz]')
else:
cb = fig.colorbar(pc, cax=cax)
cb.outline.set_color('none')
cb.outline.set_linewidth(0)
cax.set_yticks_delta(ten)
def plot_chi2_contrasts(axs, s, cell_name): def plot_chi2_contrasts(axs, s, cell_name):
d = sims_path / f'baseline-{cell_name}.npz' d = sims_path / f'{cell_name}-baseline.npz'
data = np.load(d) data = np.load(d)
rate = float(data['rate']) rate = float(data['rate'])
cv = float(data['cv']) cv = float(data['cv'])
print(f' {cell_name}: r={rate:3.0f}Hz, CV={cv:4.2f}') print(f' {cell_name}: r={rate:3.0f}Hz, CV={cv:4.2f}')
files, nums = sort_files(cell_name, freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name)
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1) cax = plot_chi2(axs[0], s, freqs, chi2, fcutoff, rate)
plot_chi2(axs[0], s, files[-1], rate) cax.set_ylabel('')
for k, alphastr in enumerate(['010', '030', '100']): axs[0].set_title(r'$c$=0\,\%', fontsize='medium')
files, nums = sort_files(cell_name, for k, alpha in enumerate([0.01, 0.03, 0.1]):
sims_path.glob(f'chi2-noisen-{cell_name}-{alphastr}-*.npz'), 2) freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
plot_chi2(axs[k + 1], s, files[-1], rate) alpha)
cax = plot_chi2(axs[k + 1], s, freqs, chi2, fcutoff, rate)
if alpha < 0.1:
cax.set_ylabel('')
axs[k + 1].set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
def plot_si_cv(ax, s, data, alpha, cells): def plot_si_cv(ax, s, data, alpha, cells):
data = data[data['contrast'] == alpha, :] data = data[data['contrast'] == alpha, :]
r, p = pearsonr(data['cvbase'], data['dnli']) r, p = pearsonr(data['cvbase'], data['dsinorm'])
l = linregress(data['cvbase'], data['dnli']) l = linregress(data['cvbase'], data['dsinorm'])
x = np.linspace(0, 1, 10) x = np.linspace(0, 1, 10)
ax.set_visible(True) ax.set_visible(True)
ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium') ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
ax.axhline(1, **s.lsLine) ax.axhline(1, **s.lsLine)
ax.plot(x, l.slope*x + l.intercept, **s.lsGrid) ax.plot(x, l.slope*x + l.intercept, **s.lsGrid)
mask = data['triangle'] > 0.5 mask = data['triangle'] > 0.5
ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'], ax.plot(data[mask, 'cvbase'], data[mask, 'dsinorm'],
clip_on=False, zorder=30, label='strong', **s.psA1m) clip_on=False, zorder=30, label='strong', **s.psA1m)
mask = data['border'] > 0.5 mask = data['border'] > 0.5
ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'], ax.plot(data[mask, 'cvbase'], data[mask, 'dsinorm'],
zorder=20, label='weak', **s.psA2m) zorder=20, label='weak', **s.psA2m)
ax.plot(data['cvbase'], data['dnli'], clip_on=False, ax.plot(data['cvbase'], data['dsinorm'], clip_on=False,
zorder=10, label='none', **s.psB1m) zorder=10, label='none', **s.psB1m)
for cell_name in cells: for cell_name in cells:
@ -120,16 +74,16 @@ def plot_si_cv(ax, s, data, alpha, cells):
color = s.psA2m['color'] color = s.psA2m['color']
elif data[mask, 'triangle']: elif data[mask, 'triangle']:
color = s.psA1m['color'] color = s.psA1m['color']
ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'], ax.plot(data[mask, 'cvbase'], data[mask, 'dsinorm'],
zorder=40, marker='o', ms=s.psB1m['markersize'], zorder=40, marker='o', ms=s.psB1m['markersize'],
mfc=color, mec='k', mew=0.8) mfc=color, mec='k', mew=0.8)
ax.set_ylim(0, 8)
ax.set_xlim(0, 1) ax.set_xlim(0, 1)
ax.set_ylim(0, 9)
ax.set_yticks_delta(3)
ax.set_minor_yticks_delta(1) ax.set_minor_yticks_delta(1)
ax.set_xlabel('CV$_{\\rm base}$') ax.set_xlabel('CV$_{\\rm base}$')
ax.set_ylabel('SI') ax.set_ylabel('SI($r$)')
ax.set_yticks_delta(4)
ax.text(1, 0.9, f'$R={r:.2f}$', transform=ax.transAxes, ax.text(1, 0.9, f'$R={r:.2f}$', transform=ax.transAxes,
ha='right', fontsize='small') ha='right', fontsize='small')
ax.text(1, 0.75, significance_str(p), transform=ax.transAxes, ax.text(1, 0.75, significance_str(p), transform=ax.transAxes,
@ -139,13 +93,13 @@ def plot_si_cv(ax, s, data, alpha, cells):
title='triangle', handlelength=0.5, title='triangle', handlelength=0.5,
handletextpad=0.5, labelspacing=0.2) handletextpad=0.5, labelspacing=0.2)
kde = gaussian_kde(data['dnli'], 0.15/np.std(data['dnli'], ddof=1)) kde = gaussian_kde(data['dsinorm'], 0.15/np.std(data['dsinorm'], ddof=1))
nli = np.linspace(0, 8, 100) si = np.linspace(0, 8, 100)
pdf = kde(nli) pdf = kde(si)
dax = ax.inset_axes([1.04, 0, 0.3, 1]) dax = ax.inset_axes([1.04, 0, 0.3, 1])
dax.show_spines('') dax.show_spines('')
dax.fill_betweenx(nli, pdf, **s.fsB1a) dax.fill_betweenx(si, pdf, **s.fsB1a)
dax.plot(pdf, nli, clip_on=False, **s.lsB1m) dax.plot(pdf, si, clip_on=False, **s.lsB1m)
def plot_summary_contrasts(axs, s, cells): def plot_summary_contrasts(axs, s, cells):
@ -154,7 +108,7 @@ def plot_summary_contrasts(axs, s, cells):
plot_si_cv(axs[0], s, data, 0, cells) plot_si_cv(axs[0], s, data, 0, cells)
print('noise split:') print('noise split:')
cdata = data[data['contrast'] == 0, :] cdata = data[data['contrast'] == 0, :]
nli_split = cdata['dnli'] nli_split = cdata['dsinorm']
print(f' mean SI = {np.mean(nli_split):.2f}, stdev = {np.std(nli_split):.2f}') print(f' mean SI = {np.mean(nli_split):.2f}, stdev = {np.std(nli_split):.2f}')
n = np.sum(nli_split > nli_thresh) n = np.sum(nli_split > nli_thresh)
print(f' {n} cells ({100*n/len(nli_split):.1f}%) have SI > {nli_thresh:.1f}:') print(f' {n} cells ({100*n/len(nli_split):.1f}%) have SI > {nli_thresh:.1f}:')
@ -163,10 +117,10 @@ def plot_summary_contrasts(axs, s, cells):
print(f' triangle cells have SI >= {np.min(nli_split[cdata["triangle"] > 0.5]):.2f}') print(f' triangle cells have SI >= {np.min(nli_split[cdata["triangle"] > 0.5]):.2f}')
print() print()
for i, a in enumerate([0.01, 0.03, 0.1]): for i, a in enumerate([0.01, 0.03, 0.1]):
plot_nli_cv(axs[1 + i], s, data, a, cells) plot_si_cv(axs[1 + i], s, data, a, cells)
print(f'contrast {100*a:2g}%:') print(f'contrast {100*a:2g}%:')
cdata = data[data['contrast'] == a, :] cdata = data[data['contrast'] == a, :]
nli = cdata['dnli'] nli = cdata['dsinorm']
r, p = pearsonr(nli_split, nli) r, p = pearsonr(nli_split, nli)
print(f' correlation with split: r={r:.2f}, p={p:.1e}') print(f' correlation with split: r={r:.2f}, p={p:.1e}')
print(f' mean SI = {np.mean(nli):.2f}, stdev = {np.std(nli):.2f}') print(f' mean SI = {np.mean(nli):.2f}, stdev = {np.std(nli):.2f}')

149
modelsusceptlown.py
View File

@ -3,8 +3,9 @@ import matplotlib.pyplot as plt
from scipy.stats import pearsonr, linregress, gaussian_kde from scipy.stats import pearsonr, linregress, gaussian_kde
from thunderlab.tabledata import TableData from thunderlab.tabledata import TableData
from pathlib import Path from pathlib import Path
from spectral import whitenoise, diag_projection, peak_size from plotstyle import plot_style, labels_params
from plotstyle import plot_style, labels_params, significance_str from plotstyle import noise_files, plot_chi2, significance_str
from modelsusceptcontrasts import load_chi2
model_cell = '2012-12-21-ak-invivo-1' model_cell = '2012-12-21-ak-invivo-1'
@ -13,103 +14,44 @@ data_path = Path('data')
sims_path = data_path / 'simulations' sims_path = data_path / 'simulations'
def sort_files(cell_name, all_files, n): def plot_chi2_contrasts(axs, s, cell_name, nsegs=None):
files = [fn for fn in all_files if '-'.join(fn.stem.split('-')[2:-n]) == cell_name] d = sims_path / f'{cell_name}-baseline.npz'
if len(files) == 0:
return None, 0
nums = [int(fn.stem.split('-')[-1]) for fn in files]
idxs = np.argsort(nums)
files = [files[i] for i in idxs]
nums = [nums[i] for i in idxs]
return files, nums
def plot_chi2(ax, s, data_file, rate):
fcutoff = 300
data = np.load(data_file)
n = data['n']
alpha = data['alpha']
freqs = data['freqs']
pss = data['pss']
prss = data['prss']
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
ax.set_visible(True)
ax.set_aspect('equal')
i0 = np.argmin(freqs < -fcutoff)
i0 = np.argmin(freqs < 0)
i1 = np.argmax(freqs > fcutoff)
if i1 == 0:
i1 = len(freqs)
freqs = freqs[i0:i1]
chi2 = chi2[i0:i1, i0:i1]
vmax = np.quantile(chi2, 0.996)
ten = 10**np.floor(np.log10(vmax))
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
break
pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
rasterized=True)
ns = f'$N={n}$' if n <= 100 else f'$N=10^{np.log10(n):.0f}$'
if 'noise_frac' in data:
ax.set_title(f'$c$=0\\,\\%, {ns}', fontsize='medium')
else:
ax.set_title(f'$c$={100*alpha:g}\\,\\%, {ns}', fontsize='medium')
ax.set_xlim(0, fcutoff)
ax.set_ylim(0, fcutoff)
ax.set_xticks_delta(100)
ax.set_yticks_delta(100)
ax.set_xlabel('$f_1$', 'Hz')
ax.set_ylabel('$f_2$', 'Hz')
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
ax.text(0.95, 0.88, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
color='white', fontsize='medium', transform=ax.transAxes)
cax = ax.inset_axes([1.04, 0, 0.05, 1])
cax.set_spines_outward('lrbt', 0)
if alpha == 0.1:
cb = fig.colorbar(pc, cax=cax, label=r'$|\chi_2|$ [Hz]')
else:
cb = fig.colorbar(pc, cax=cax)
cb.outline.set_color('none')
cb.outline.set_linewidth(0)
cax.set_yticks_delta(ten)
def plot_chi2_contrasts(axs, s, cell_name, n=None):
d = sims_path / f'baseline-{cell_name}.npz'
data = np.load(d) data = np.load(d)
rate = float(data['rate']) rate = float(data['rate'])
cv = float(data['cv']) cv = float(data['cv'])
print(f' {cell_name}: r={rate:3.0f}Hz, CV={cv:4.2f}') print(f' {cell_name}: r={rate:3.0f}Hz, CV={cv:4.2f}')
files, nums = sort_files(cell_name, freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1) None, nsegs)
idx = -1 if n is None else nums.index(n) ns = f'$N={n}$' if n < 1000 else f'$N=10^{np.log10(n):.0f}$'
plot_chi2(axs[0], s, files[idx], rate) cax = plot_chi2(axs[0], s, freqs, chi2, fcutoff, rate)
for k, alphastr in enumerate(['010', '030', '100']): cax.set_ylabel('')
files, nums = sort_files(cell_name, axs[0].set_title(f'$c$=0\\,\\%, {ns}', fontsize='medium')
sims_path.glob(f'chi2-noisen-{cell_name}-{alphastr}-*.npz'), 2) for k, alpha in enumerate([0.01, 0.03, 0.1]):
idx = -1 if n is None else nums.index(n) freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
plot_chi2(axs[k + 1], s, files[idx], rate) alpha, nsegs)
ns = f'$N={n}$' if n < 1000 else f'$N=10^{np.log10(n):.0f}$'
cax = plot_chi2(axs[k + 1], s, freqs, chi2, fcutoff, rate)
if alpha < 0.1:
cax.set_ylabel('')
axs[k + 1].set_title(f'$c$={100*alpha:g}\\,\\%, {ns}',
fontsize='medium')
def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
def plot_si_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
datax = data[data['contrast'] == alphax, :] datax = data[data['contrast'] == alphax, :]
datay = data[data['contrast'] == alphay, :] datay = data[data['contrast'] == alphay, :]
nlix = datax['dnli'] six = datax['dsinorm']
nliy = datay['dnli100'] siy = datay['dsinorm100']
nfp = np.sum((nliy > ythresh) & (nlix < xthresh)) nfp = np.sum((siy > ythresh) & (six < xthresh))
ntp = np.sum((nliy > ythresh) & (nlix > xthresh)) ntp = np.sum((siy > ythresh) & (six > xthresh))
ntn = np.sum((nliy < ythresh) & (nlix < xthresh)) ntn = np.sum((siy < ythresh) & (six < xthresh))
nfn = np.sum((nliy < ythresh) & (nlix > xthresh)) nfn = np.sum((siy < ythresh) & (six > xthresh))
print(f' {ntp:2d} ({100*ntp/len(nlix):2.0f}%) true positive') print(f' {ntp:2d} ({100*ntp/len(six):2.0f}%) true positive')
print(f' {nfp:2d} ({100*nfp/len(nlix):2.0f}%) false positive') print(f' {nfp:2d} ({100*nfp/len(six):2.0f}%) false positive')
print(f' {ntn:2d} ({100*ntn/len(nlix):2.0f}%) true negative') print(f' {ntn:2d} ({100*ntn/len(six):2.0f}%) true negative')
print(f' {nfn:2d} ({100*nfn/len(nlix):2.0f}%) false negative') print(f' {nfn:2d} ({100*nfn/len(six):2.0f}%) false negative')
r, p = pearsonr(nlix, nliy) r, p = pearsonr(six, siy)
l = linregress(nlix, nliy) l = linregress(six, siy)
x = np.linspace(0, 10, 10) x = np.linspace(0, 10, 10)
ax.set_visible(True) ax.set_visible(True)
ax.set_title(f'$c$={100*alphay:g}\\,\\%', fontsize='medium') ax.set_title(f'$c$={100*alphay:g}\\,\\%', fontsize='medium')
@ -119,10 +61,10 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
ax.axvline(xthresh, 0, 0.5, **s.lsLine) ax.axvline(xthresh, 0, 0.5, **s.lsLine)
if alphax == 0: if alphax == 0:
mask = datax['triangle'] > 0.5 mask = datax['triangle'] > 0.5
ax.plot(nlix[mask], nliy[mask], zorder=30, label='strong', **s.psA1m) ax.plot(six[mask], siy[mask], zorder=30, label='strong', **s.psA1m)
mask = datax['border'] > 0.5 mask = datax['border'] > 0.5
ax.plot(nliy[mask], nliy[mask], zorder=20, label='weak', **s.psA2m) ax.plot(siy[mask], siy[mask], zorder=20, label='weak', **s.psA2m)
ax.plot(nlix, nliy, zorder=10, label='none', **s.psB1m) ax.plot(six, siy, zorder=10, label='none', **s.psB1m)
# mark cell: # mark cell:
mask = datax['cell'] == cell_name mask = datax['cell'] == cell_name
color = s.psB1m['color'] color = s.psB1m['color']
@ -131,7 +73,7 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
color = s.psA2m['color'] color = s.psA2m['color']
elif datax[mask, 'triangle']: elif datax[mask, 'triangle']:
color = s.psA1m['color'] color = s.psA1m['color']
ax.plot(nlix[mask], nliy[mask], zorder=40, marker='o', ax.plot(six[mask], siy[mask], zorder=40, marker='o',
ms=s.psB1m['markersize'], mfc=color, mec='k', mew=0.8) ms=s.psB1m['markersize'], mfc=color, mec='k', mew=0.8)
box = dict(boxstyle='square,pad=0.1', fc='white', ec='none') box = dict(boxstyle='square,pad=0.1', fc='white', ec='none')
@ -159,13 +101,13 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
title='triangle', handlelength=0.5, title='triangle', handlelength=0.5,
handletextpad=0.5, labelspacing=0.2) handletextpad=0.5, labelspacing=0.2)
kde = gaussian_kde(nliy, 0.15/np.std(nliy, ddof=1)) kde = gaussian_kde(siy, 0.15/np.std(siy, ddof=1))
nli = np.linspace(0, 8, 100) si = np.linspace(0, 8, 100)
pdf = kde(nli) pdf = kde(si)
dax = ax.inset_axes([1.04, 0, 0.3, 1]) dax = ax.inset_axes([1.04, 0, 0.3, 1])
dax.show_spines('') dax.show_spines('')
dax.fill_betweenx(nli, pdf, **s.fsB1a) dax.fill_betweenx(si, pdf, **s.fsB1a)
dax.plot(pdf, nli, clip_on=False, **s.lsB1m) dax.plot(pdf, si, clip_on=False, **s.lsB1m)
def plot_summary_contrasts(axs, s, xthresh, ythresh, cell_name): def plot_summary_contrasts(axs, s, xthresh, ythresh, cell_name):
@ -173,7 +115,7 @@ def plot_summary_contrasts(axs, s, xthresh, ythresh, cell_name):
data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv') data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv')
for i, a in enumerate([0.01, 0.03, 0.1]): for i, a in enumerate([0.01, 0.03, 0.1]):
print(f'contrast {100*a:2g}%:') print(f'contrast {100*a:2g}%:')
plot_nli_diags(axs[1 + i], s, data, a, a, xthresh, ythresh, cell_name) plot_si_diags(axs[1 + i], s, data, a, a, xthresh, ythresh, cell_name)
print() print()
@ -182,7 +124,7 @@ def plot_summary_diags(axs, s, xthresh, ythresh, cell_name):
data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv') data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv')
for i, a in enumerate([0.01, 0.03, 0.1]): for i, a in enumerate([0.01, 0.03, 0.1]):
print(f'contrast {100*a:2g}%:') print(f'contrast {100*a:2g}%:')
plot_nli_diags(axs[1 + i], s, data, 0, a, xthresh, ythresh, cell_name) plot_si_diags(axs[1 + i], s, data, 0, a, xthresh, ythresh, cell_name)
if __name__ == '__main__': if __name__ == '__main__':
@ -211,4 +153,3 @@ if __name__ == '__main__':
fig.tag(axs, xoffs=-4.5, yoffs=1.8) fig.tag(axs, xoffs=-4.5, yoffs=1.8)
axs[1, 0].set_visible(False) axs[1, 0].set_visible(False)
fig.savefig() fig.savefig()
print()

101
modelsusceptovern.py
View File

@ -3,78 +3,26 @@ from scipy.stats import linregress
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from pathlib import Path from pathlib import Path
from plotstyle import plot_style, labels_params from plotstyle import plot_style, labels_params
from plotstyle import noise_files, plot_chi2
from modelsusceptcontrasts import load_chi2
data_path = Path('data') data_path = Path('data')
sims_path = data_path / 'simulations' sims_path = data_path / 'simulations'
def sort_files(cell_name, all_files, n):
files = [fn for fn in all_files if '-'.join(fn.stem.split('-')[2:-n]) == cell_name]
if len(files) == 0:
return None, 0
nums = [int(fn.stem.split('-')[-1]) for fn in files]
idxs = np.argsort(nums)
files = [files[i] for i in idxs]
nums = [nums[i] for i in idxs]
return files, nums
def plot_chi2(ax, s, data_file):
data = np.load(data_file)
n = data['n']
alpha = data['alpha']
freqs = data['freqs']
pss = data['pss']
prss = data['prss']
chi2 = np.abs(prss)/0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
ax.set_visible(True)
ax.set_aspect('equal')
i0 = np.argmin(freqs < -300)
i0 = np.argmin(freqs < 0)
i1 = np.argmax(freqs > 300)
if i1 == 0:
i1 = len(freqs)
freqs = freqs[i0:i1]
chi2 = chi2[i0:i1, i0:i1]
vmax = np.quantile(chi2, 0.996)
ten = 10**np.floor(np.log10(vmax))
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
break
pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
rasterized=True)
ax.set_title(f'$N=10^{np.log10(n):.0f}$', fontsize='medium')
ax.set_xlim(0, 300)
ax.set_ylim(0, 300)
ax.set_xticks_delta(300)
ax.set_minor_xticks(3)
ax.set_yticks_delta(300)
ax.set_minor_yticks(3)
ax.set_xlabel('$f_1$', 'Hz')
ax.set_ylabel('$f_2$', 'Hz')
cax = ax.inset_axes([1.04, 0, 0.05, 1])
cax.set_spines_outward('lrbt', 0)
cb = fig.colorbar(pc, cax=cax)
cb.outline.set_color('none')
cb.outline.set_linewidth(0)
cax.set_yticks_delta(ten)
def plot_overn(ax, s, files, nmax=1e6, title=False): def plot_overn(ax, s, files, nmax=1e6, title=False):
ns = [] ns = []
stats = [] stats = []
for fname in files: for fname in files:
data = np.load(fname) data = np.load(fname)
if not 'n' in data: if not 'nsegs' in data:
return return
n = data['n'] n = data['nsegs']
if nmax is not None and n > nmax: if nmax is not None and n > nmax:
continue continue
alpha = data['alpha'] noise_frac = data['noise_frac']
alpha = data['contrast']
freqs = data['freqs'] freqs = data['freqs']
pss = data['pss'] pss = data['pss']
prss = data['prss'] prss = data['prss']
@ -84,7 +32,7 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
i1 = np.argmax(freqs > 300) i1 = np.argmax(freqs > 300)
if i1 == 0: if i1 == 0:
i1 = len(freqs) i1 = len(freqs)
chi2 = chi2[i0:i1, i0:i1] chi2 = 1e-4*chi2[i0:i1, i0:i1] # Hz/%^2
stats.append(np.quantile(chi2, [0, 0.001, 0.05, 0.25, 0.5, stats.append(np.quantile(chi2, [0, 0.001, 0.05, 0.25, 0.5,
0.75, 0.95, 0.998, 1.0])) 0.75, 0.95, 0.998, 1.0]))
ns = np.array(ns) ns = np.array(ns)
@ -94,12 +42,14 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
stats = stats[indx] stats = stats[indx]
ax.set_visible(True) ax.set_visible(True)
ax.plot(ns, stats[:, 7], zorder=50, label='99.8\\%', **s.lsMax) 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[:, 2], stats[:, 6], fc='0.85',
ax.fill_between(ns, stats[:, 3], stats[:, 5], fc='0.5', zorder=45, label='25-75\\%') 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[:, 4], zorder=50, label='median', **s.lsMedian)
#ax.plot(ns, stats[:, 8], '0.0') #ax.plot(ns, stats[:, 8], '0.0')
if title: if title:
if 'noise_frac' in data: if noise_frac < 1:
ax.set_title('$c$=0\\,\\%', fontsize='medium') ax.set_title('$c$=0\\,\\%', fontsize='medium')
else: else:
ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium') ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
@ -107,14 +57,12 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
ax.set_xscale('log') ax.set_xscale('log')
ax.set_yscale('log') ax.set_yscale('log')
ax.set_yticks_log(numticks=3) ax.set_yticks_log(numticks=3)
ax.set_ylim(1e-1, 3e3)
ax.set_minor_yticks_log(numticks=5)
if nmax > 1e6: if nmax > 1e6:
ax.set_ylim(3e-1, 5e3)
ax.set_minor_yticks_log(numticks=5)
ax.set_xticks_log(numticks=4) ax.set_xticks_log(numticks=4)
ax.set_minor_xticks_log(numticks=8) ax.set_minor_xticks_log(numticks=8)
else: else:
ax.set_ylim(5e0, 1e4)
ax.set_minor_yticks_log(numticks=5)
ax.set_xticks_log(numticks=3) ax.set_xticks_log(numticks=3)
ax.set_minor_xticks_log(numticks=6) ax.set_minor_xticks_log(numticks=6)
ax.set_xlabel('segments') ax.set_xlabel('segments')
@ -126,10 +74,15 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
def plot_chi2_overn(axs, s, cell_name): def plot_chi2_overn(axs, s, cell_name):
print(cell_name) print(cell_name)
files, nums = sort_files(cell_name, files, nums = noise_files(sims_path, cell_name)
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1) for k, nsegs in enumerate([1e1, 1e2, 1e3, 1e6]):
for k, n in enumerate([1e1, 1e2, 1e3, 1e6]): freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
plot_chi2(axs[k], s, files[nums.index(int(n))]) None, nsegs)
ns = f'$N={n}$' if n < 1000 else f'$N=10^{np.log10(n):.0f}$'
cax = plot_chi2(axs[k], s, freqs, chi2, fcutoff)
if k < len(axs) - 2:
cax.set_ylabel('')
axs[k].set_title(ns, fontsize='medium')
plot_overn(axs[-1], s, files) plot_overn(axs[-1], s, files)
@ -149,12 +102,10 @@ if __name__ == '__main__':
for k in range(len(cells)): for k in range(len(cells)):
plot_chi2_overn(axs[k], s, cells[k]) plot_chi2_overn(axs[k], s, cells[k])
cell_name = cells[0] cell_name = cells[0]
files, nums = sort_files(cell_name, files, nums = noise_files(sims_path, cell_name)
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1)
plot_overn(axs[-1, 0], s, files, 1e7, True) plot_overn(axs[-1, 0], s, files, 1e7, True)
for k, alphastr in enumerate(['010', '030', '100']): for k, alpha in enumerate([0.01, 0.03, 0.1]):
files, nums = sort_files(cell_name, files, nums = noise_files(sims_path, cell_name, alpha)
sims_path.glob(f'chi2-noisen-{cell_name}-{alphastr}-*.npz'), 2)
plot_overn(axs[-1, k + 1], s, files, 1e7, True) plot_overn(axs[-1, k + 1], s, files, 1e7, True)
for k in range(4): for k in range(4):
fig.common_yticks(axs[k, :4]) fig.common_yticks(axs[k, :4])

132
noisesplit.py
View File

@ -1,8 +1,8 @@
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
from spectral import whitenoise, diag_projection, peak_size from spectral import whitenoise
from plotstyle import plot_style from plotstyle import plot_style, noise_files, plot_chi2
example_cell = ['2017-07-18-ai-invivo-1', 1] example_cell = ['2017-07-18-ai-invivo-1', 1]
@ -13,78 +13,21 @@ data_path = base_path / 'cells'
sims_path = base_path / 'simulations' sims_path = base_path / 'simulations'
def sort_files(cell_name, all_files, n):
files = [fn for fn in all_files if '-'.join(fn.stem.split('-')[2:-n]) == cell_name]
if len(files) == 0:
return None, 0
nums = [int(fn.stem.split('-')[-1]) for fn in files]
idxs = np.argsort(nums)
files = [files[i] for i in idxs]
nums = [nums[i] for i in idxs]
return files, nums
def plot_chi2(ax, s, freqs, chi2, nsegs, rate):
fcutoff = 300
ax.set_aspect('equal')
i0 = np.argmin(freqs < 0)
i1 = np.argmax(freqs > fcutoff)
if i1 == 0:
i1 = len(freqs)
freqs = freqs[i0:i1]
chi2 = chi2[i0:i1, i0:i1]
vmax = np.quantile(chi2, 0.996)
ten = 10**np.floor(np.log10(vmax))
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 = 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, fcutoff)
ax.set_ylim(0, fcutoff)
ax.set_xticks_delta(100)
ax.set_yticks_delta(100)
ax.set_xlabel('$f_1$', 'Hz')
ax.set_ylabel('$f_2$', 'Hz')
if nsegs < 10000:
ax.text(1, 1.1, f'$N={nsegs}$',
ha='right', transform=ax.transAxes)
else:
ax.text(1, 1.1, f'$N=10^{{{np.log10(nsegs):.0f}}}$',
ha='right', transform=ax.transAxes)
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
nli, nlirel, nlif = peak_size(dfreqs, diag, rate, median=False)
ax.text(0.95, 0.88, f'SI($r$)={nli:.1f}', ha='right', zorder=50,
color='white', fontsize='medium', transform=ax.transAxes)
cax = ax.inset_axes([1.04, 0, 0.05, 1])
cax.set_spines_outward('lrbt', 0)
cb = fig.colorbar(pc, cax=cax)
cb.outline.set_color('none')
cb.outline.set_linewidth(0)
cax.set_ylabel(r'$|\chi_2|$ [Hz]')
cax.set_yticks_delta(ten)
return cax
def plot_overn(ax, s, files, nmax=1e6): def plot_overn(ax, s, files, nmax=1e6):
fcutoff = 300
ns = [] ns = []
stats = [] stats = []
for fname in files: for fname in files:
data = np.load(fname) data = np.load(fname)
n = data['n'] fcutoff = data['fcutoff']
n = data['nsegs']
if nmax is not None and n > nmax: if nmax is not None and n > nmax:
continue continue
alpha = data['alpha'] alpha = data['contrast']
freqs = data['freqs'] freqs = data['freqs']
pss = data['pss'] pss = data['pss']
prss = data['prss'] prss = data['prss']
chi2 = np.abs(prss)/0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1)) chi2 = np.abs(prss)/0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
chi2 *= 1e-4 # Hz/%^2
ns.append(n) ns.append(n)
i0 = np.argmin(freqs < 0) i0 = np.argmin(freqs < 0)
i1 = np.argmax(freqs > fcutoff) i1 = np.argmax(freqs > fcutoff)
@ -114,26 +57,33 @@ def plot_overn(ax, s, files, nmax=1e6):
ax.set_xticks_log(numticks=4) ax.set_xticks_log(numticks=4)
ax.set_minor_xticks_log(numticks=8) ax.set_minor_xticks_log(numticks=8)
else: else:
ax.set_ylim(4e0, 1.3e3) ax.set_ylim(1e-1, 2e3)
#ax.set_minor_yticks_log(numticks=5) #ax.set_minor_yticks_log(numticks=5)
ax.set_minor_yticks_off() ax.set_minor_yticks_off()
ax.set_xticks_log(numticks=5) ax.set_xticks_log(numticks=6)
#ax.set_minor_xticks_log(numticks=6) #ax.set_minor_xticks_log(numticks=6)
ax.set_xlabel('segments') ax.set_xlabel('segments')
ax.set_ylabel('$|\\chi_2|$ [Hz]') ax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
def plot_chi2_contrast(ax1, ax2, s, files, nums, nsmall, nlarge, rate): def plot_chi2_contrast(ax1, ax2, s, files, nums, nsmall, nlarge, rate):
for ax, n in zip([ax1, ax2], [nsmall, nlarge]): for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
i = nums.index(n) i = nums.index(n)
data = np.load(files[i]) data = np.load(files[i])
n = data['n'] nsegs = data['nsegs']
alpha = data['alpha'] fcutoff = data['fcutoff']
alpha = data['contrast']
freqs = data['freqs'] freqs = data['freqs']
pss = data['pss'] pss = data['pss']
prss = data['prss'] prss = data['prss']
if nsegs < 10000:
ax.text(1, 1.1, f'$N={nsegs}$',
ha='right', transform=ax.transAxes)
else:
ax.text(1, 1.1, f'$N=10^{{{np.log10(nsegs):.0f}}}$',
ha='right', transform=ax.transAxes)
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1)) chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
cax = plot_chi2(ax, s, freqs, chi2, n, rate) cax = plot_chi2(ax, s, freqs, chi2, fcutoff, rate)
cax.set_ylabel('') cax.set_ylabel('')
print(f'Modeled cell {"-".join(files[i].name.split("-")[2:-2])} at {100*alpha:4.1f}% contrast: noise_frac={1:3.1f}, nsegs={n}') print(f'Modeled cell {"-".join(files[i].name.split("-")[2:-2])} at {100*alpha:4.1f}% contrast: noise_frac={1:3.1f}, nsegs={n}')
print() print()
@ -143,14 +93,21 @@ def plot_chi2_split(ax1, ax2, s, files, nums, nsmall, nlarge, rate):
for ax, n in zip([ax1, ax2], [nsmall, nlarge]): for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
i = nums.index(n) i = nums.index(n)
data = np.load(files[i]) data = np.load(files[i])
n = data['n'] nsegs = data['nsegs']
alpha = data['alpha'] fcutoff = data['fcutoff']
alpha = data['contrast']
noise_frac = data['noise_frac'] noise_frac = data['noise_frac']
freqs = data['freqs'] freqs = data['freqs']
pss = data['pss'] pss = data['pss']
prss = data['prss'] prss = data['prss']
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1)) chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
cax = plot_chi2(ax, s, freqs, chi2, n, rate) if nsegs < 10000:
ax.text(1, 1.1, f'$N={nsegs}$',
ha='right', transform=ax.transAxes)
else:
ax.text(1, 1.1, f'$N=10^{{{np.log10(nsegs):.0f}}}$',
ha='right', transform=ax.transAxes)
cax = plot_chi2(ax, s, freqs, chi2, fcutoff, rate)
cax.set_ylabel('') cax.set_ylabel('')
print(f'Modeled cell {"-".join(files[i].name.split("-")[2:-1])} at {100*alpha:4.1f}% contrast: noise_frac={noise_frac:3.1f}, nsegs={n}') print(f'Modeled cell {"-".join(files[i].name.split("-")[2:-1])} at {100*alpha:4.1f}% contrast: noise_frac={noise_frac:3.1f}, nsegs={n}')
print() print()
@ -163,9 +120,10 @@ def plot_chi2_data(ax, s, cell_name, run):
eodf = float(data['eodf']) eodf = float(data['eodf'])
ratebase = float(data['ratebase/Hz']) ratebase = float(data['ratebase/Hz'])
cvbase = float(data['cvbase']) cvbase = float(data['cvbase'])
data_file = data_path / f'{cell_name}-spectral-s{run:02d}.npz' data_file = data_path / f'{cell_name}-spectral-all-s{run:02d}.npz'
data = np.load(data_file) data = np.load(data_file)
n = data['n'] nsegs = data['n']
fcutoff = data['fcutoff']
nfft = data['nfft'] nfft = data['nfft']
deltat = data['deltat'] deltat = data['deltat']
alpha = data['alpha'] alpha = data['alpha']
@ -173,9 +131,11 @@ def plot_chi2_data(ax, s, cell_name, run):
pss = data['pss'] pss = data['pss']
prss = data['prss'] prss = data['prss']
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1)) chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
print(f'Measured cell {"-".join(data_file.name.split("-")[:-2])} at {100*alpha:4.1f}% contrast: r={ratebase:3.0f}Hz, CV={cvbase:4.2f}, dt={1000*deltat:4.2f}ms, nfft={nfft}, win={1000*deltat*nfft:6.1f}ms, nsegs={n}') print(f'Measured cell {"-".join(data_file.name.split("-")[:-2])} at {100*alpha:4.1f}% contrast: r={ratebase:3.0f}Hz, CV={cvbase:4.2f}, dt={1000*deltat:4.2f}ms, nfft={nfft}, win={1000*deltat*nfft:6.1f}ms, nsegs={nsegs}')
print() print()
plot_chi2(ax, s, freqs, chi2, n, ratebase) ax.text(1, 1.1, f'$N={nsegs}$',
ha='right', transform=ax.transAxes)
plot_chi2(ax, s, freqs, chi2, fcutoff, ratebase)
return alpha, ratebase, eodf return alpha, ratebase, eodf
@ -277,34 +237,34 @@ if __name__ == '__main__':
# model 5%: # model 5%:
axss = axs[1] axss = axs[1]
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*data_contrast:03.0f}-*.npz') files, nums = noise_files(sims_path, model_cell, data_contrast)
files, nums = sort_files(model_cell, data_files, 2)
axss[1].text(xt, yt, 'P-unit model', fontsize='large', axss[1].text(xt, yt, 'P-unit model', fontsize='large',
transform=axs[1, 1].transAxes, color=s.model_color1) transform=axs[1, 1].transAxes, color=s.model_color1)
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase) plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge,
ratebase)
axr1 = plot_noise_split(axss[0], data_contrast, 0, 1, wtime, wnoise) axr1 = plot_noise_split(axss[0], data_contrast, 0, 1, wtime, wnoise)
plot_overn(axss[3], s, files, nmax=1e6) plot_overn(axss[3], s, files, nmax=1e6)
axss[3].legend(loc='lower center', bbox_to_anchor=(0.5, 1.2), axss[3].legend(loc='lower center', bbox_to_anchor=(0.5, 1.2),
markerfirst=False, title='$|\\chi_2|$ percentiles') markerfirst=False, title='$|\\chi_2|$ percentiles')
# model 1%: # model 1%:
axss = axs[2] axss = axs[2]
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*contrast:03.0f}-*.npz') files, nums = noise_files(sims_path, model_cell, contrast)
files, nums = sort_files(model_cell, data_files, 2) plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge,
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase) ratebase)
axr2 = plot_noise_split(axss[0], contrast, 0, 1, wtime, wnoise) axr2 = plot_noise_split(axss[0], contrast, 0, 1, wtime, wnoise)
plot_overn(axss[3], s, files, nmax=1e6) plot_overn(axss[3], s, files, nmax=1e6)
# model noise split: # model noise split:
axss = axs[3] axss = axs[3]
data_files = sims_path.glob(f'chi2-split-{model_cell}-*.npz') files, nums = noise_files(sims_path, model_cell)
files, nums = sort_files(model_cell, data_files, 1)
axss[1].text(xt, yt, 'P-unit model', fontsize='large', axss[1].text(xt, yt, 'P-unit model', fontsize='large',
transform=axss[1].transAxes, color=s.model_color1) transform=axss[1].transAxes, color=s.model_color1)
axss[1].text(xt + 0.9, yt, f'(noise split)', fontsize='large', axss[1].text(xt + 0.9, yt, f'(noise split)', fontsize='large',
transform=axss[1].transAxes) transform=axss[1].transAxes)
noise_contrast, noise_frac = plot_chi2_split(axss[1], axss[2], s, noise_contrast, noise_frac = plot_chi2_split(axss[1], axss[2], s,
files, nums, nsmall, nlarge, ratebase) files, nums, nsmall, nlarge,
ratebase)
axr3 = plot_noise_split(axss[0], 0, noise_contrast, noise_frac, axr3 = plot_noise_split(axss[0], 0, noise_contrast, noise_frac,
wtime, wnoise) wtime, wnoise)
plot_overn(axss[3], s, files, nmax=1e6) plot_overn(axss[3], s, files, nmax=1e6)

62
plotstyle.py
View File

@ -1,5 +1,7 @@
import numpy as np
import matplotlib as mpl import matplotlib as mpl
import plottools.plottools as pt import plottools.plottools as pt
from spectral import diag_projection, peak_size
from plottools.spines import spines_params from plottools.spines import spines_params
from plottools.labels import labels_params from plottools.labels import labels_params
from plottools.colors import lighter, darker from plottools.colors import lighter, darker
@ -16,6 +18,66 @@ def significance_str(p):
return '$p<0.001$' return '$p<0.001$'
def noise_files(data_path, cell_name, alpha=None):
if alpha is None:
file_pattern = f'{cell_name}-chi2-split-*.npz'
else:
file_pattern = f'{cell_name}-chi2-noise-{1000*alpha:03.0f}-*.npz'
files = sorted(data_path.glob(file_pattern), key=lambda x: x.stem)
if len(files) == 0:
return None, 0
nums = [int(fn.stem.split('-')[-1]) for fn in files]
idxs = np.argsort(nums)
files = [files[i] for i in idxs]
nums = [nums[i] for i in idxs]
return files, nums
def plot_chi2(ax, s, freqs, chi2, fcutoff, rate=None):
ax.set_visible(True)
ax.set_aspect('equal')
i0 = np.argmin(freqs < 0)
i1 = np.argmax(freqs > fcutoff)
if i1 == 0:
i1 = len(freqs)
freqs = freqs[i0:i1]
chi2 = 1e-4*chi2[i0:i1, i0:i1] # Hz/%^2
vquantile = 0.996
vmax = np.quantile(chi2, vquantile)
ten = 10**np.floor(np.log10(vmax))
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 = prev_fac*ten
#ten *= prev_delta
vmax = fac*ten
ten *= delta
break
prev_fac = fac
prev_delta = delta
pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
rasterized=True)
ax.set_xlim(0, fcutoff)
ax.set_ylim(0, fcutoff)
ax.set_xticks_delta(100)
ax.set_yticks_delta(100)
ax.set_xlabel('$f_1$', 'Hz')
ax.set_ylabel('$f_2$', 'Hz')
if rate is not None:
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
nli, nlirel, nlif = peak_size(dfreqs, diag, rate, median=False)
ax.text(0.95, 0.88, f'SI($r$)={nli:.1f}', ha='right', zorder=50,
color='white', fontsize='medium', transform=ax.transAxes)
cax = ax.inset_axes([1.04, 0, 0.05, 1])
cax.set_spines_outward('lrbt', 0)
cb = ax.get_figure().colorbar(pc, cax=cax)
cb.outline.set_color('none')
cb.outline.set_linewidth(0)
cax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
cax.set_yticks_delta(ten)
return cax
def plot_style(): def plot_style():
palette = pt.palettes['muted'] palette = pt.palettes['muted']
lwthick = 1.6 lwthick = 1.6