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changed example punit

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Jan Benda 2025-05-20 00:12:23 +02:00
parent 2e3d373100
commit ee2b8f98b7
18 changed files with 232 additions and 149 deletions
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2
Makefile
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@ -9,7 +9,7 @@ TXTFILE=$(TEXBASE).txt
PDFFIGURES=$(shell sed -n -e '/^[^%].*includegraphics/{s/^.*includegraphics.*{\([^}]*\)}.*/\1.pdf/;p}' $(TEXFILE))
PT=$(wildcard *.py)
PYTHONFILES=$(filter-out plotstyle.py spectral.py, $(PT))
PYTHONFILES=$(filter-out plotstyle.py spectral.py examplecells.py, $(PT))
PYTHONPDFFILES=$(PYTHONFILES:.py=.pdf)
REVISION=e3814a1be539f9424c17b7bd7ef45a8826a9f1e2

51
ampullaryexamplecell.py
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@ -7,9 +7,8 @@ from punitexamplecell import load_baseline, load_noise, load_spectra
from punitexamplecell import plot_colorbar
cell_name = '2012-05-15-ac'
run1 = 3 # 4
run2 = 1
example_cell = [['2012-05-15-ac', 3],
['2012-05-15-ac', 1]]
example_cells = [
['2010-11-26-an', 0],
@ -37,9 +36,10 @@ def plot_isih2(ax, s, 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_xlabel('ISI', 'ms')
#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',
transform=ax.transAxes)
ax.text(1, 0.6, f'$r={rate:.0f}$Hz', ha='right', transform=ax.transAxes)
@ -154,26 +154,33 @@ def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, ch
if __name__ == '__main__':
"""
from thunderlab.tabledata import TableData
data = TableData('Apteronotus_leptorhynchus-Ampullary-data.csv')
data = data[(data('fcutoff') > 140) & (data('fcutoff') < 160), :]
data = data[(data('nli') > 2) & (data('nli') < 2.5), :]
data = data[(data('respmod2') > 20) & (data('respmod2') < 100), :]
data = data[(data('cvbase') > 0.05) & (data('cvbase') < 0.2), :]
data = data[(data('ratebase') > 100) & (data('ratebase') < 180), :]
data = TableData('data/Apteronotus_leptorhynchus-Ampullary-data.csv')
data = data[(data['fcutoff'] > 140) & (data['fcutoff'] < 160), :]
data = data[(data['nli'] > 2) & (data['nli'] < 2.5), :]
data = data[(data['respmod2'] > 20) & (data['respmod2'] < 100), :]
data = data[(data['cvbase'] > 0.05) & (data['cvbase'] < 0.2), :]
data = data[(data['ratebase'] > 100) & (data['ratebase'] < 180), :]
for k in range(data.rows()):
print(f'{data[k, "cell"]:<22s} s{data[k, "stimindex"]:02.0f}: {100*data[k, "contrast"]:3g}%, {data[k, "respmod2"]:3.0f}Hz, nli={data[k, "nli"]:5.2f}')
print(f'{data[k, "cell"]:<22s} s{data[k, "stimindex"]:02.0f}: '
f'{100*data[k, "contrast"]:3g}%, {data[k, "respmod2"]:3.0f}Hz, '
f'nli={data[k, "nli"]:5.2f}')
print()
#exit()
exit()
"""
cell_name = example_cell[0][0]
print('Example Ampullary cell:', cell_name)
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}')
contrast1, time1, stimulus1, spikes1 = load_noise(data_path, cell_name, run1)
contrast2, time2, stimulus2, spikes2 = load_noise(data_path, cell_name, run2)
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, cell_name, run1)
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, cell_name, run2)
contrast1, time1, stimulus1, spikes1 = load_noise(data_path,
*example_cell[0])
contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
*example_cell[1])
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path,
*example_cell[0])
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path,
*example_cell[1])
s = plot_style()
s.cell_color1 = s.ampul_color1
@ -183,7 +190,7 @@ if __name__ == '__main__':
s.psC1 = s.psA1
s.psC2 = s.psA2
fig, (ax1, ax2, ax3) = \
plt.subplots(3, 1, height_ratios=[3, 0, 3, 0.2, 4.5],
plt.subplots(3, 1, height_ratios=[3, 0, 3, 0.2, 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)
@ -191,7 +198,7 @@ if __name__ == '__main__':
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=0.2, height_ratios=[1, 4])
axs = ax3.subplots(2, 4, wspace=0.4, hspace=0.35, height_ratios=[1, 4])
plot_isih(axi, s, rate, cv, isis, pdf)
plot_response_spectrum(axp, s, eodf, rate, freqs, prr)
@ -232,8 +239,8 @@ if __name__ == '__main__':
axs[0, 0].text(0, 1.6, 'Ampullary cells:', transform=axs[0, 0].transAxes,
color=s.cell_color1,
fontsize='large')
axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right',
transform=axs[0, -1].transAxes)
#axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right',
# transform=axs[0, -1].transAxes)
plot_colorbar(axs[1, -1], pc, 0.4)
fig.common_yticks(axs[1, :])
fig.tag([axs[0, :]], xoffs=-3, yoffs=1)

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93
dataoverview.py
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@ -10,41 +10,30 @@ from plotstyle import plot_style, lighter, significance_str
data_path = Path('data')
punit_example = [['2020-10-27-ag-invivo-1', 0],
['2020-10-27-ag-invivo-1', 1]]
ampul_example = [['2012-05-15-ac', 3],
['2012-05-15-ac', 1]]
from punitexamplecell import example_cell as punit_example
from punitexamplecell import example_cells as punit_examples
from ampullaryexamplecell import example_cell as ampul_example
from ampullaryexamplecell import example_cells as ampul_examples
def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
nli_thresh, example=[], examples=[]):
ax.axhline(nli_thresh, color='k', ls=':', lw=0.5)
"""
for c in np.unique(data('cell')):
xdata = data[data('cell') == c, xcol]
ydata = data[data('cell') == c, ycol]
contrasts = data[data('cell') == c, 'contrast']
idx = np.argsort(contrasts)
if len(idx) > 1:
ax.plot(xdata[idx], ydata[idx], '-k', alpha=0.2, zorder=10)
"""
xmax = ax.get_xlim()[1]
ymax = ax.get_ylim()[1]
mask = (data(xcol) < xmax) & (data(ycol) < ymax)
mask = (data[xcol] < xmax) & (data[ycol] < ymax)
sc = ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
s=4, marker='o', linewidth=0, edgecolors='none',
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax, zorder=20)
if 'stimindex' in data:
for cell, run in example:
mask = (data('cell') == cell) & (data('stimindex') == run)
mask = (data['cell'] == cell) & (data['stimindex'] == run)
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
s=6, marker='^', linewidth=0.5, edgecolors='black',
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
zorder=20)
for cell, run in examples:
mask = (data('cell') == cell) & (data('stimindex') == run)
mask = (data['cell'] == cell) & (data['stimindex'] == run)
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
s=5, marker='o', linewidth=0.5, edgecolors='black',
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
@ -57,7 +46,7 @@ def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
cb.outline.set_color('none')
cb.outline.set_linewidth(0)
# pdf x-axis:
kde = gaussian_kde(data(xcol), 0.02*xmax/np.std(data(xcol), ddof=1))
kde = gaussian_kde(data[xcol], 0.02*xmax/np.std(data[xcol], ddof=1))
xx = np.linspace(0, ax.get_xlim()[1], 400)
pdf = kde(xx)
xax = ax.inset_axes([0, 1.05, 1, 0.2])
@ -67,7 +56,7 @@ def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
xax.set_ylim(bottom=0)
xax.set_ylim(0, xpdfmax)
# pdf y-axis:
kde = gaussian_kde(data(ycol), 0.02*ymax/np.std(data(ycol), ddof=1))
kde = gaussian_kde(data[ycol], 0.02*ymax/np.std(data[ycol], ddof=1))
xx = np.linspace(0, ax.get_ylim()[1], 400)
pdf = kde(xx)
yax = ax.inset_axes([1.05, 0, 0.2, 1])
@ -77,12 +66,12 @@ def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
yax.set_xlim(left=0)
# threshold:
if 'cvbase' in xcol:
ax.text(xmax, 0.4*ymax, f'{100*np.sum(data(ycol) > nli_thresh)/data.rows():.0f}\\%',
ax.text(xmax, 0.4*ymax, f'{100*np.sum(data[ycol] > nli_thresh)/len(data):.0f}\\%',
ha='right', va='bottom', fontsize='small')
ax.text(xmax, 0.3, f'{100*np.sum(data(ycol) < nli_thresh)/data.rows():.0f}\\%',
ax.text(xmax, 0.3, f'{100*np.sum(data[ycol] < nli_thresh)/len(data):.0f}\\%',
ha='right', va='center', fontsize='small')
# statistics:
r, p = pearsonr(data(xcol), data(ycol))
r, p = pearsonr(data[xcol], data[ycol])
ax.text(1, 0.9, f'$R={r:.2f}$ **', ha='right',
transform=ax.transAxes, fontsize='small')
#ax.text(1, 0.77, f'{significance_str(p)}', ha='right',
@ -98,14 +87,14 @@ def nli_stats(title, data, column, nli_thresh):
print(title)
print(f' nli threshold: {nli_thresh:.1f}')
nrecs = data.rows()
ncells = len(np.unique(data('cell')))
ncells = len(np.unique(data['cell']))
print(f' cells: {ncells}')
print(f' recordings: {nrecs}')
hcells = np.unique(data[data(column) > nli_thresh, 'cell'])
print(f' high nli cells: n={len(hcells):3d}, {100*len(hcells)/ncells:4.1f}%')
print(f' high nli recordings: n={np.sum(data(column) > nli_thresh):3d}, '
f'{100*np.sum(data(column) > nli_thresh)/nrecs:4.1f}%')
nsegs = data('nsegs')
nsegs = data['nsegs']
print(f' number of segments: {np.min(nsegs):4.0f} - {np.max(nsegs):4.0f}, median={np.median(nsegs):4.0f}, mean={np.mean(nsegs):4.0f}, std={np.std(nsegs):4.0f}')
@ -132,14 +121,18 @@ def plot_cvstim_nli_punit(ax, data, ycol, nli_thresh, color):
# 'coolwarm', color, nli_thresh,
# punit_example, punit_examples)
#cax.set_ylabel('Response mod.', 'Hz')
#cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 1.5, 2,
# 'coolwarm', color, nli_thresh,
# punit_example, punit_examples)
#cax.set_ylabel('CV$_{\\rm base}$')
cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 50, 450, 2,
cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 1.5, 2,
'coolwarm', color, nli_thresh,
punit_example, punit_examples)
cax.set_ylabel('$r$', 'Hz')
cax.set_ylabel('CV$_{\\rm base}$')
#cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 50, 450, 2,
# 'coolwarm', color, nli_thresh,
# punit_example, punit_examples)
#cax.set_ylabel('$r$', 'Hz')
#cax = plot_corr(ax, data, 'cvstim', ycol, 'serialcorr1', -0.6, 0, 2,
# 'coolwarm', color, nli_thresh,
# punit_example, punit_examples)
#cax.set_ylabel('$\\rho_1$')
def plot_mod_nli_punit(ax, data, ycol, nli_thresh, color):
@ -178,16 +171,16 @@ def plot_cvstim_nli_ampul(ax, data, ycol, nli_thresh, color):
# 'coolwarm', color, nli_thresh,
# ampul_example, ampul_examples)
#cax.set_ylabel('Response mod.', 'Hz')
#cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 0.2, 6,
# 'coolwarm', color, nli_thresh,
# ampul_example, ampul_examples)
#cax.set_ylabel('CV$_{\\rm base}$')
#cax.set_yticks_delta(0.1)
cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 90, 180, 6,
cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 0.2, 6,
'coolwarm', color, nli_thresh,
ampul_example, ampul_examples)
cax.set_ylabel('$r$', 'Hz')
cax.set_yticks_delta(30)
cax.set_ylabel('CV$_{\\rm base}$')
cax.set_yticks_delta(0.1)
#cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 90, 180, 6,
# 'coolwarm', color, nli_thresh,
# ampul_example, ampul_examples)
#cax.set_ylabel('$r$', 'Hz')
#cax.set_yticks_delta(30)
def plot_mod_nli_ampul(ax, data, ycol, nli_thresh, color):
@ -208,7 +201,7 @@ if __name__ == '__main__':
punit_model = TableData(data_path /
'Apteronotus_leptorhynchus-Punit-models.csv',
sep=';')
punit_model = punit_model[punit_model('contrast') > 1e-6, :]
punit_model = punit_model[punit_model['contrast'] > 1e-6, :]
punit_data = TableData(data_path /
'Apteronotus_leptorhynchus-Punit-data.csv',
sep=';')
@ -217,23 +210,23 @@ if __name__ == '__main__':
sep=';')
nli_thresh = 1.8
u, p = mannwhitneyu(punit_model('cvbase'), punit_data('cvbase'))
u, p = mannwhitneyu(punit_model['cvbase'], punit_data['cvbase'])
print('CV differs between P-unit models and data:')
print(f' U={u:g}, p={p:g}')
print(f' median model: {np.median(punit_model("cvbase")):.2f}')
print(f' median data: {np.median(punit_data("cvbase")):.2f}')
print(f' U={u:g}, p={p:.2g}')
print(f' median model: {np.median(punit_model["cvbase"]):.2f}')
print(f' median data: {np.median(punit_data["cvbase"]):.2f}')
print()
u, p = mannwhitneyu(punit_model('respmod2'), punit_data('respmod2'))
u, p = mannwhitneyu(punit_model['respmod2'], punit_data['respmod2'])
print('Response modulation differs between P-unit models and data:')
print(f' U={u:g}, p={p:g}')
print(f' median model: {np.median(punit_model("respmod2")):.2f}')
print(f' median data: {np.median(punit_data("respmod2")):.2f}')
print(f' U={u:g}, p={p:.2g}')
print(f' median model: {np.median(punit_model["respmod2"]):.2f}')
print(f' median data: {np.median(punit_data["respmod2"]):.2f}')
print()
u, p = mannwhitneyu(punit_model('dnli100'), punit_data('nli'))
u, p = mannwhitneyu(punit_model['dnli100'], punit_data['nli'])
print('NLI does not differ between P-unit models and data:')
print(f' U={u:g}, p={p:g}')
print(f' median model: {np.median(punit_model("dnli100")):.1f}')
print(f' median data: {np.median(punit_data("nli")):.1f}')
print(f' U={u:g}, p={p:.2g}')
print(f' median model: {np.median(punit_model["dnli100"]):.1f}')
print(f' median data: {np.median(punit_data["nli"]):.1f}')
print()
s = plot_style()

46
examplecells.py Normal file
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@ -0,0 +1,46 @@
import numpy as np
from pathlib import Path
from importlib import import_module
exclude = ['examplecells.py', 'plotstyle.py', 'spectral.py']
data_cells = []
model_cells = []
for pf in sorted(Path('.').glob('*.py'), key=lambda x: x.stem):
if pf.name in exclude:
continue
print(pf.name)
figure = import_module(pf.stem)
if hasattr(figure, 'example_cell'):
name = figure.example_cell
while isinstance(name, list):
name = name[0]
print(f' found example_cell: {name}')
data_cells.append(name)
if hasattr(figure, 'example_cells'):
for name in figure.example_cells:
while isinstance(name, list):
name = name[0]
print(f' found example_cells: {name}')
data_cells.append(name)
if hasattr(figure, 'model_cell'):
name = figure.model_cell
while isinstance(name, list):
name = name[0]
print(f' found model_cell: {name}')
model_cells.append(name)
if hasattr(figure, 'model_cells'):
for name in figure.model_cells:
while isinstance(name, list):
name = name[0]
print(f' found model_cells: {name}')
model_cells.append(name)
print()
print('The following cell data are used in the plots:')
for cell in np.unique(data_cells):
print(cell)
print()
print('The following cell models are used in the plots:')
for cell in np.unique(model_cells):
print(cell)

62
modelsusceptcontrasts.py
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@ -6,6 +6,11 @@ from pathlib import Path
from plotstyle import plot_style, labels_params, significance_str
model_cells = ['2017-07-18-ai-invivo-1', # strong triangle
'2012-12-13-ao-invivo-1', # triangle
'2012-12-20-ac-invivo-1', # weak border triangle
'2013-01-08-ab-invivo-1'] # no triangle
data_path = Path('data')
sims_path = data_path / 'simulations'
@ -70,7 +75,7 @@ def plot_chi2(ax, s, data_file):
def plot_chi2_contrasts(axs, s, cell_name):
print(cell_name)
print(f' {cell_name}')
files, nums = sort_files(cell_name,
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1)
plot_chi2(axs[0], s, files[-1])
@ -81,21 +86,21 @@ def plot_chi2_contrasts(axs, s, cell_name):
def plot_nli_cv(ax, s, data, alpha, cells):
data = data[data('contrast') == alpha, :]
r, p = pearsonr(data('cvbase'), data[:, 'dnli'])
l = linregress(data('cvbase'), data[:, 'dnli'])
data = data[data['contrast'] == alpha, :]
r, p = pearsonr(data['cvbase'], data['dnli'])
l = linregress(data['cvbase'], data['dnli'])
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
mask = data['triangle'] > 0.5
ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'],
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'],
zorder=20, label='weak', **s.psA2m)
ax.plot(data[:, 'cvbase'], data[:, 'dnli'], clip_on=False,
ax.plot(data['cvbase'], data['dnli'], clip_on=False,
zorder=10, label='none', **s.psB1m)
for cell_name in cells:
@ -124,7 +129,7 @@ def plot_nli_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))
kde = gaussian_kde(data['dnli'], 0.15/np.std(data['dnli'], ddof=1))
nli = np.linspace(0, 8, 100)
pdf = kde(nli)
dax = ax.inset_axes([1.04, 0, 0.3, 1])
@ -137,34 +142,35 @@ def plot_summary_contrasts(axs, s, cells):
nli_thresh = 1.2
data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv')
plot_nli_cv(axs[0], s, data, 0, cells)
print('split:')
nli_split = data[data('contrast') == 0, 'dnli']
print(f' mean NLI = {np.mean(nli_split):.2f}, stdev = {np.std(nli_split):.2f}')
print('noise split:')
cdata = data[data['contrast'] == 0, :]
nli_split = cdata['dnli']
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 NLI > {nli_thresh:.1f}')
print(f' triangle cells have nli >= {np.min(nli_split[data[data("contrast") == 0, "triangle"] > 0.5])}')
print(f' {n} cells ({100*n/len(nli_split):.1f}%) have SI > {nli_thresh:.1f}:')
for name, cv in cdata[nli_split > nli_thresh, ['cell', 'cvbase']].row_data():
print(f' {name:<22} CV={cv:4.2f}')
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)
print(f'contrast {100*a:2g}%:')
cdata = data[data('contrast') == a, :]
nli = cdata('dnli')
cdata = data[data['contrast'] == a, :]
nli = cdata['dnli']
r, p = pearsonr(nli_split, nli)
print(f' correlation with split: r={r:.2f}, p={p:.1e}')
print(f' mean NLI = {np.mean(nli):.2f}, stdev = {np.std(nli):.2f}')
print(f' mean SI = {np.mean(nli):.2f}, stdev = {np.std(nli):.2f}')
n = np.sum(nli > nli_thresh)
print(f' {n} cells ({100*n/len(nli):.1f}%) have NLI > {nli_thresh:.1f}')
print( ' CVs:', cdata[nli > nli_thresh, 'cvbase'])
print( ' names:', cdata[nli > nli_thresh, 'cell'])
print(f' {n} cells ({100*n/len(nli):.1f}%) have SI > {nli_thresh:.1f}:')
for name, cv in cdata[nli > nli_thresh, ['cell', 'cvbase']].row_data():
print(f' {name:<22} CV={cv:4.2f}')
print(f' triangle cells have SI >= {np.min(nli[cdata["triangle"] > 0.5]):.2f}')
print()
print('lowest baseline CV:', np.unique(data('cvbase'))[:3])
print('overall lowest baseline CV:',
' '.join([f'{cv:.2f}' for cv in np.unique(data['cvbase'])[:5]]))
if __name__ == '__main__':
cells = ['2017-07-18-ai-invivo-1', # strong triangle
'2012-12-13-ao-invivo-1', # triangle
'2012-12-20-ac-invivo-1', # weak border triangle
'2013-01-08-ab-invivo-1'] # no triangle
s = plot_style()
#labels_params(xlabelloc='right', ylabelloc='top')
fig, axs = plt.subplots(6, 4, cmsize=(s.plot_width, 0.95*s.plot_width),
@ -173,12 +179,14 @@ if __name__ == '__main__':
wspace=1, hspace=0.7)
for ax in axs.flat:
ax.set_visible(False)
for k in range(len(cells)):
plot_chi2_contrasts(axs[k], s, cells[k])
print('Example cells:')
for k in range(len(model_cells)):
plot_chi2_contrasts(axs[k], s, model_cells[k])
for k in range(4):
fig.common_yticks(axs[k, :])
fig.common_xticks(axs[:4, k])
plot_summary_contrasts(axs[5], s, cells)
print()
plot_summary_contrasts(axs[5], s, model_cells)
fig.common_yticks(axs[5, :])
fig.tag(axs, xoffs=-4.5, yoffs=1.8)
fig.savefig()

29
modelsusceptlown.py
View File

@ -6,6 +6,8 @@ from pathlib import Path
from plotstyle import plot_style, labels_params, significance_str
model_cell = '2012-12-21-ak-invivo-1'
data_path = Path('data')
sims_path = data_path / 'simulations'
@ -71,7 +73,7 @@ def plot_chi2(ax, s, data_file):
def plot_chi2_contrasts(axs, s, cell_name, n=None):
print(cell_name)
print(f' {cell_name}')
files, nums = sort_files(cell_name,
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1)
idx = -1 if n is None else nums.index(n)
@ -84,10 +86,10 @@ def plot_chi2_contrasts(axs, s, cell_name, n=None):
def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
datax = data[data('contrast') == alphax, :]
datay = data[data('contrast') == alphay, :]
nlix = datax('dnli')
nliy = datay('dnli100')
datax = data[data['contrast'] == alphax, :]
datay = data[data['contrast'] == alphay, :]
nlix = datax['dnli']
nliy = datay['dnli100']
nfp = np.sum((nliy > ythresh) & (nlix < xthresh))
ntp = np.sum((nliy > ythresh) & (nlix > xthresh))
ntn = np.sum((nliy < ythresh) & (nlix < xthresh))
@ -106,13 +108,13 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
ax.axhline(ythresh, **s.lsLine)
ax.axvline(xthresh, 0, 0.5, **s.lsLine)
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)
mask = datax('border') > 0.5
mask = datax['border'] > 0.5
ax.plot(nliy[mask], nliy[mask], zorder=20, label='weak', **s.psA2m)
ax.plot(nlix, nliy, zorder=10, label='none', **s.psB1m)
# mark cell:
mask = datax('cell') == cell_name
mask = datax['cell'] == cell_name
color = s.psB1m['color']
if alphax == 0:
if datax[mask, 'border']:
@ -183,15 +185,16 @@ if __name__ == '__main__':
wspace=1, hspace=1)
for ax in axs.flat:
ax.set_visible(False)
cell_name = '2012-12-21-ak-invivo-1'
plot_chi2_contrasts(axs[0], s, cell_name)
plot_chi2_contrasts(axs[1], s, cell_name, 10)
print('Example cells:')
plot_chi2_contrasts(axs[0], s, model_cell)
plot_chi2_contrasts(axs[1], s, model_cell, 10)
for k in range(2):
fig.common_yticks(axs[k, :])
for k in range(4):
fig.common_xticks(axs[:2, k])
plot_summary_contrasts(axs[3], s, xthresh, ythresh, cell_name)
plot_summary_diags(axs[5], s, xthresh, ythresh, cell_name)
print()
plot_summary_contrasts(axs[3], s, xthresh, ythresh, model_cell)
plot_summary_diags(axs[5], s, xthresh, ythresh, model_cell)
fig.common_yticks(axs[3, 1:])
fig.common_yticks(axs[5, 1:])
fig.tag(axs, xoffs=-4.5, yoffs=1.8)

22
noisesplit.py
View File

@ -5,6 +5,10 @@ from spectral import whitenoise
from plotstyle import plot_style
#example_cell = ['2012-07-03-ak-invivo-1', 0]
example_cell = ['2017-07-18-ai-invivo-1', 1] # Take this! at 3% model, 5% data
model_cell = example_cell
base_path = Path('data')
data_path = base_path / 'cells'
sims_path = base_path / 'simulations'
@ -225,8 +229,6 @@ def plot_noise_split(ax, contrast, noise_contrast, noise_frac,
if __name__ == '__main__':
#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 = 0.01
@ -249,16 +251,16 @@ if __name__ == '__main__':
axss = axs[0]
axss[1].text(xt, yt, 'P-unit data', fontsize='large',
transform=axss[1].transAxes, color=s.punit_color1)
data_contrast, ratebase, eodf = plot_chi2_data(axss[1], s, cell_name[0],
cell_name[1])
data_contrast, ratebase, eodf = plot_chi2_data(axss[1], s, example_cell[0],
example_cell[1])
plot_ram(axss[0], data_contrast, eodf, wtime, wnoise)
axss[1].text(xt + 0.9, yt, f'$r={ratebase:.0f}$\\,Hz',
transform=axss[1].transAxes, fontsize='large')
# model 5%:
axss = axs[1]
data_files = sims_path.glob(f'chi2-noisen-{cell_name[0]}-{1000*data_contrast:03.0f}-*.npz')
files, nums = sort_files(cell_name[0], data_files, 2)
data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*data_contrast:03.0f}-*.npz')
files, nums = sort_files(example_cell[0], data_files, 2)
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
transform=axs[1, 1].transAxes, color=s.model_color1)
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge)
@ -269,16 +271,16 @@ if __name__ == '__main__':
# model 1%:
axss = axs[2]
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)
data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*contrast:03.0f}-*.npz')
files, nums = sort_files(example_cell[0], data_files, 2)
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge)
axr2 = plot_noise_split(axss[0], contrast, 0, 1, wtime, wnoise)
plot_overn(axss[3], s, files, nmax=1e6)
# model noise split:
axss = axs[3]
data_files = sims_path.glob(f'chi2-split-{cell_name[0]}-*.npz')
files, nums = sort_files(cell_name[0], data_files, 1)
data_files = sims_path.glob(f'chi2-split-{example_cell[0]}-*.npz')
files, nums = sort_files(example_cell[0], data_files, 1)
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
transform=axss[1].transAxes, color=s.model_color1)
axss[1].text(xt + 0.9, yt, f'(noise split)', fontsize='large',

59
punitexamplecell.py
View File

@ -7,9 +7,8 @@ from spectral import diag_projection, peakedness
from plotstyle import plot_style
cell_name = '2020-10-27-ag-invivo-1'
run1 = 0
run2 = 1
example_cell = [['2020-10-27-ag-invivo-1', 0],
['2020-10-27-ag-invivo-1', 1]]
example_cells = [
['2021-06-18-ae-invivo-1', 3], # 98Hz, 1%, ok
@ -22,10 +21,12 @@ example_cells = [
##['2020-10-27-ae-invivo-1', 4], # 375Hz, 0.5%, 4.3, nice, additional low freq line
###['2020-10-27-ag-invivo-1', 2], # 405Hz, 5%, 3.9, strong, is already the example
##['2021-08-03-ab-invivo-1', 1], # 140Hz, 0.5%, ok
['2020-10-29-ag-invivo-1', 2], # 164Hz, 5%, 1.6, no diagonal
##['2010-08-31-ag', 1], # 269Hz, 5%, no diagonal
#['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
##['2018-08-29-af', 1], # 383Hz, 5%, no diagonal
]
data_path = Path('data') / 'cells'
@ -95,9 +96,10 @@ def plot_isih2(ax, s, 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_xlabel('ISI', 'ms')
#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',
transform=ax.transAxes)
ax.text(1, 0.6, f'$r={rate:.0f}$Hz', ha='right', transform=ax.transAxes)
@ -218,14 +220,36 @@ def plot_diagonals(ax, s, fbase, contrast1, freqs1, chi21, contrast2, freqs2, ch
if __name__ == '__main__':
"""
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['cvbase'] > 0.4) & (data['cvbase'] < 0.8), :]
data = data[(data['ratebase'] > 300) & (data['ratebase'] < 400), :]
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}')
print()
#exit()
"""
cell_name = example_cell[0][0]
print('Example P-unit:', cell_name)
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}')
contrast1, time1, stimulus1, spikes1 = load_noise(data_path, cell_name, run1)
contrast2, time2, stimulus2, spikes2 = load_noise(data_path, cell_name, run2)
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path, cell_name, run1)
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path, cell_name, run2)
contrast1, time1, stimulus1, spikes1 = load_noise(data_path,
*example_cell[0])
contrast2, time2, stimulus2, spikes2 = load_noise(data_path,
*example_cell[1])
fcutoff1, contrast1, freqs1, gain1, chi21 = load_spectra(data_path,
*example_cell[0])
fcutoff2, contrast2, freqs2, gain2, chi22 = load_spectra(data_path,
*example_cell[1])
s = plot_style()
s.cell_color1 = s.punit_color1
@ -235,7 +259,7 @@ 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, 4.5],
plt.subplots(3, 1, height_ratios=[3, 0, 3, 0.2, 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)
@ -243,7 +267,7 @@ if __name__ == '__main__':
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=0.2, height_ratios=[1, 4])
axs = ax3.subplots(2, 4, wspace=0.4, hspace=0.35, height_ratios=[1, 4])
plot_isih(axi, s, rate, cv, isis, pdf)
plot_response_spectrum(axp, s, eodf, rate, freqs, prr)
@ -276,16 +300,15 @@ if __name__ == '__main__':
nli, nlif = peakedness(dfreqs, diag, rate, median=False)
print(f' {cell:<22s}: run={run:2d}, fbase={rate:3.0f}Hz, CV={cv:.2f}, SI={nli:3.1f}')
plot_isih2(axs[0, k], s, rate, cv, isis, pdf)
pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 1.3)
#axs[k].set_title(f'$r={rate:.0f}$Hz, CV$_{{\\rm base}}$={cv:.2f}', fontsize='medium')
pc = plot_chi2(axs[1, k], s, contrast, freqs, chi2, fcutoff, 1.0)
axs[1, k].text(0.95, 0.9, f'SI($r$)={nli:.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')
axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right',
transform=axs[0, -1].transAxes)
#axs[0, -1].text(0.97, -0.45, '5\\,ms', ha='right',
# transform=axs[0, -1].transAxes)
plot_colorbar(axs[1, -1], pc)
fig.common_yticks(axs[1, :])
fig.tag([axs[0, :]], xoffs=-3, yoffs=1)

17
regimes.py
View File

@ -3,10 +3,11 @@ import matplotlib.pyplot as plt
from pathlib import Path
from scipy.stats import linregress
from numba import jit
from thunderlab.tabledata import TableData
from plotstyle import plot_style, lighter, darker
model_cell = '2018-05-08-ad-invivo-1' # 228Hz, CV=0.67
data_path = Path('data')
sims_path = data_path / 'simulations'
@ -200,14 +201,13 @@ def plot_psd(ax, s, path, contrast, spikes, nfft, dt, beatf1, beatf2):
def plot_example(axs, axr, axp, s, path, cell, alpha, beatf1, beatf2,
nfft, trials):
sim_path = path / f'{cell_name}-contrastspectrum-{1000*alpha:03.0f}.npz'
dt = 0.0001
tmax = nfft*dt
t1 = 0.1
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, sim_path, alpha, spikes, nfft, dt, beatf1, beatf2)
plot_psd(axp, s, path, alpha, spikes, nfft, dt, beatf1, beatf2)
def peak_ampl(freqs, psd, f):
@ -285,17 +285,16 @@ def plot_peaks(ax, s, alphas, contrasts, powerf1, powerf2, powerfsum,
if __name__ == '__main__':
cell_name = '2018-05-08-ad-invivo-1' # 228Hz, CV=0.67
ratebase, cvbase, beatf1, beatf2, \
contrasts, powerf1, powerf2, powerfsum, powerfdiff = \
load_data(sims_path / f'{cell_name}-contrastpeaks.npz')
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, cell_name)
cell = cell_parameters(parameters, model_cell)
nfft = 2**18
print(f'Loaded data for cell {cell_name}: '
print(f'Loaded data for cell {model_cell}: '
f'baseline rate = {ratebase:.0f}Hz, CV = {cvbase:.2f}')
s = plot_style()
@ -308,7 +307,8 @@ if __name__ == '__main__':
# example power spectra:
for c, alpha in enumerate(alphas):
plot_example(axe[0, c], axe[1, c], axe[2, c], s, sims_path,
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, nfft, 100)
axe[1, 0].xscalebar(1, -0.1, 20, 'ms', ha='right')
axe[2, 0].legend(loc='center left', bbox_to_anchor=(0, -0.8),
@ -322,3 +322,4 @@ if __name__ == '__main__':
powerfsum, powerfdiff)
fig.tag(axa, yoffs=2)
fig.savefig()
print()