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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
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@@ -3,8 +3,8 @@ import matplotlib.pyplot as plt
from scipy.stats import pearsonr, linregress, gaussian_kde
from thunderlab.tabledata import TableData
from pathlib import Path
from spectral import whitenoise, diag_projection, peak_size
from plotstyle import plot_style, labels_params, significance_str
from plotstyle import plot_style, labels_params
from plotstyle import noise_files, plot_chi2, significance_str
model_cells = ['2017-07-18-ai-invivo-1', # strong triangle
@@ -16,101 +16,55 @@ data_path = Path('data')
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, rate):
fcutoff = 300
data = np.load(data_file)
n = data['n']
alpha = data['alpha']
def load_chi2(file_path, cell_name, contrast=None, n=None):
files, nums = noise_files(sims_path, cell_name, contrast)
idx = -1 if n is None else nums.index(n)
data = np.load(files[idx])
n = data['nsegs']
fcutoff = data['fcutoff']
contrast = data['contrast']
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)
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)
return freqs, chi2, fcutoff, contrast, n
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)
rate = float(data['rate'])
cv = float(data['cv'])
print(f' {cell_name}: r={rate:3.0f}Hz, CV={cv:4.2f}')
files, nums = sort_files(cell_name,
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1)
plot_chi2(axs[0], s, files[-1], rate)
for k, alphastr in enumerate(['010', '030', '100']):
files, nums = sort_files(cell_name,
sims_path.glob(f'chi2-noisen-{cell_name}-{alphastr}-*.npz'), 2)
plot_chi2(axs[k + 1], s, files[-1], rate)
freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name)
cax = plot_chi2(axs[0], s, freqs, chi2, fcutoff, rate)
cax.set_ylabel('')
axs[0].set_title(r'$c$=0\,\%', fontsize='medium')
for k, alpha in enumerate([0.01, 0.03, 0.1]):
freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
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):
data = data[data['contrast'] == alpha, :]
r, p = pearsonr(data['cvbase'], data['dnli'])
l = linregress(data['cvbase'], data['dnli'])
r, p = pearsonr(data['cvbase'], data['dsinorm'])
l = linregress(data['cvbase'], data['dsinorm'])
x = np.linspace(0, 1, 10)
ax.set_visible(True)
ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
ax.axhline(1, **s.lsLine)
ax.plot(x, l.slope*x + l.intercept, **s.lsGrid)
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)
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)
ax.plot(data['cvbase'], data['dnli'], clip_on=False,
ax.plot(data['cvbase'], data['dsinorm'], clip_on=False,
zorder=10, label='none', **s.psB1m)
for cell_name in cells:
@@ -120,16 +74,16 @@ def plot_si_cv(ax, s, data, alpha, cells):
color = s.psA2m['color']
elif data[mask, 'triangle']:
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'],
mfc=color, mec='k', mew=0.8)
ax.set_ylim(0, 8)
ax.set_xlim(0, 1)
ax.set_ylim(0, 9)
ax.set_yticks_delta(3)
ax.set_minor_yticks_delta(1)
ax.set_xlabel('CV$_{\\rm base}$')
ax.set_ylabel('SI')
ax.set_yticks_delta(4)
ax.set_ylabel('SI($r$)')
ax.text(1, 0.9, f'$R={r:.2f}$', transform=ax.transAxes,
ha='right', fontsize='small')
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,
handletextpad=0.5, labelspacing=0.2)
kde = gaussian_kde(data['dnli'], 0.15/np.std(data['dnli'], ddof=1))
nli = np.linspace(0, 8, 100)
pdf = kde(nli)
kde = gaussian_kde(data['dsinorm'], 0.15/np.std(data['dsinorm'], ddof=1))
si = np.linspace(0, 8, 100)
pdf = kde(si)
dax = ax.inset_axes([1.04, 0, 0.3, 1])
dax.show_spines('')
dax.fill_betweenx(nli, pdf, **s.fsB1a)
dax.plot(pdf, nli, clip_on=False, **s.lsB1m)
dax.fill_betweenx(si, pdf, **s.fsB1a)
dax.plot(pdf, si, clip_on=False, **s.lsB1m)
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)
print('noise split:')
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}')
n = np.sum(nli_split > nli_thresh)
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()
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}%:')
cdata = data[data['contrast'] == a, :]
nli = cdata['dnli']
nli = cdata['dsinorm']
r, p = pearsonr(nli_split, nli)
print(f' correlation with split: r={r:.2f}, p={p:.1e}')
print(f' mean SI = {np.mean(nli):.2f}, stdev = {np.std(nli):.2f}')