403 lines
18 KiB
Python
403 lines
18 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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from scipy.stats import pearsonr, gaussian_kde
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from scipy.stats import mannwhitneyu
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from thunderlab.tabledata import TableData
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from plotstyle import plot_style, lighter, significance_str
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data_path = Path('data')
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from noisesplit import model_cell as model_split_example
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from modelsusceptcontrasts import model_cells as model_contrast_examples
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from modelsusceptlown import model_cell as model_lown_example
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from punitexamplecell import example_cell as punit_example
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from punitexamplecell import example_cells as punit_examples
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from noisesplit import example_cell as punit_split_example
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from ampullaryexamplecell import example_cell as ampul_example
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from ampullaryexamplecell import example_cells as ampul_examples
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model_examples = ([[model_lown_example, 0.01],
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[model_lown_example, 0.03],
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[model_lown_example, 0.1]],
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[[model_split_example, 0.01]],
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[[m, a] for m in model_contrast_examples for a in [0.01, 0.03, 0.1]])
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punit_examples = (punit_example, [punit_split_example], punit_examples)
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ampul_examples = (ampul_example, [], ampul_examples)
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def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
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si_thresh, example=[], split_example=[], examples=[]):
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xdata = data[xcol]
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ydata = data[ycol]
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ax.axhline(si_thresh, color='k', ls=':', lw=0.5)
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xmax = ax.get_xlim()[1]
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ymax = ax.get_ylim()[1]
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mask = (xdata < xmax) & (ydata < ymax)
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if 'stimindex' in data:
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for cell, run in example + split_example + examples:
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mask &= ~((data['cell'] == cell) & (data['stimindex'] == run))
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else: # simulations
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for cell, alpha in example + split_example + examples:
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mask &= ~((data['cell'] == cell) & (data['contrast'] == alpha))
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sc = ax.scatter(xdata[mask], ydata[mask], c=data[mask, zcol],
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s=4, marker='o', linewidth=0, edgecolors='none',
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clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax, zorder=20)
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elw = 0.3
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if 'stimindex' in data:
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for cell, run in example:
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mask = (data['cell'] == cell) & (data['stimindex'] == run)
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ax.scatter(xdata[mask], ydata[mask], c=data[mask, zcol],
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s=6, marker='^', linewidth=elw, edgecolors='black',
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clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
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zorder=20)
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for cell, run in split_example:
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mask = (data['cell'] == cell) & (data['stimindex'] == run)
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ax.scatter(xdata[mask], ydata[mask], c=data[mask, zcol],
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s=6, marker='s', linewidth=elw, edgecolors='black',
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clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
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zorder=20)
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for cell, run in examples:
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mask = (data['cell'] == cell) & (data['stimindex'] == run)
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ax.scatter(xdata[mask], ydata[mask], c=data[mask, zcol],
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s=6, marker='o', linewidth=elw, edgecolors='black',
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clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
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zorder=20)
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else: # simulations
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for cell, alpha in example:
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mask = (data['cell'] == cell) & (data['contrast'] == alpha)
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ax.scatter(xdata[mask], ydata[mask], c=data[mask, zcol],
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s=6, marker='^', linewidth=elw, edgecolors='black',
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clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
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zorder=20)
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for cell, alpha in split_example:
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mask = (data['cell'] == cell) & (data['contrast'] == alpha)
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ax.scatter(xdata[mask], ydata[mask], c=data[mask, zcol],
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s=6, marker='s', linewidth=elw, edgecolors='black',
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clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
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zorder=20)
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for cell, alpha in examples:
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mask = (data['cell'] == cell) & (data['contrast'] == alpha)
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ax.scatter(xdata[mask], ydata[mask], c=data[mask, zcol],
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s=6, marker='o', linewidth=elw, edgecolors='black',
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clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
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zorder=20)
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# color bar:
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fig = ax.get_figure()
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cax = ax.inset_axes([1.3, 0, 0.04, 1])
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cax.set_spines_outward('lrbt', 0)
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cb = fig.colorbar(sc, cax=cax)
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cb.outline.set_color('none')
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cb.outline.set_linewidth(0)
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# pdf x-axis:
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kde = gaussian_kde(xdata, 0.02*xmax/np.std(xdata, ddof=1))
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xx = np.linspace(0, ax.get_xlim()[1], 400)
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pdf = kde(xx)
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xax = ax.inset_axes([0, 1.05, 1, 0.2])
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xax.show_spines('')
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xax.fill_between(xx, pdf, facecolor=color, edgecolor='none')
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#xax.plot(xx, np.zeros(len(xx)), clip_on=False, color=color, lw=0.5)
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xax.set_ylim(bottom=0)
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xax.set_ylim(0, xpdfmax)
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# pdf y-axis:
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kde = gaussian_kde(ydata, 0.02*ymax/np.std(ydata, ddof=1))
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xx = np.linspace(0, ax.get_ylim()[1], 400)
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pdf = kde(xx)
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yax = ax.inset_axes([1.05, 0, 0.2, 1])
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yax.show_spines('')
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yax.fill_betweenx(xx, pdf, facecolor=color, edgecolor='none')
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#yax.plot(np.zeros(len(xx)), xx, clip_on=False, color=color, lw=0.5)
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yax.set_xlim(left=0)
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# threshold:
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if 'cvbase' in xcol:
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ax.text(xmax, 0.4*ymax, f'{100*np.sum(ydata > si_thresh)/len(data):.0f}\\%',
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ha='right', va='bottom', fontsize='small')
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ax.text(xmax, 0.3, f'{100*np.sum(ydata < si_thresh)/len(data):.0f}\\%',
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ha='right', va='center', fontsize='small')
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# statistics:
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r, p = pearsonr(xdata, ydata)
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ax.text(1, 0.9, f'$R={r:.2f}$', ha='right',
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transform=ax.transAxes, fontsize='small')
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ax.text(1, 0.77, f'{significance_str(p)}', ha='right',
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transform=ax.transAxes, fontsize='small')
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if 'cvbase' in xcol:
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ax.text(1, 0.64, f'$n={data.rows()}$', ha='right',
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transform=ax.transAxes, fontsize='small')
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print(f' correlation {xcol:<8s} - {ycol}: r={r:5.2f}, p={p:.2g}')
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return cax
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def si_stats(title, data, sicol, si_thresh, nsegscol):
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print(title)
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sidata = data[sicol]
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cells = np.unique(data['cell'])
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ncells = len(cells)
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nrecs = len(data)
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print(f' cells: {ncells}')
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print(f' recordings: {nrecs}')
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print(f' SI threshold: {si_thresh:.1f}')
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hcells = np.unique(data[sidata > si_thresh, 'cell'])
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print(f' high SI cells: n={len(hcells):3d}, {100*len(hcells)/ncells:4.1f}%')
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print(f' high SI recordings: n={np.sum(sidata > si_thresh):3d}, '
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f'{100*np.sum(sidata > si_thresh)/nrecs:4.1f}%')
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nrecs = []
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for cell in cells:
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nrecs.append(len(data[data["cell"] == cell, :]))
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print(f' number of recordings per cell: {np.min(nrecs):4.0f} - {np.max(nrecs):4.0f}, median={np.median(nrecs):4.0f}, mean={np.mean(nrecs):4.0f}, std={np.std(nrecs):4.0f}')
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fcutoff = data['fcutoff']
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print(' cutoff frequencies:', ' '.join([f'{f:3.0f}Hz' for f in np.unique(fcutoff)]))
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print(' cutoff frequencies:', ' '.join([f'{np.sum(fcutoff == f):3d}' for f in np.unique(fcutoff)]))
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print(f' cutoff frequencies: {np.min(fcutoff):.0f}Hz - {np.max(fcutoff):.0f}Hz, median={np.median(fcutoff):.0f}Hz, mean={np.mean(fcutoff):.0f}Hz, std={np.std(fcutoff):.0f}Hz')
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print(' contrasts:')
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contrasts = data['contrast']
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for c in np.unique(contrasts):
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nc = np.sum(contrasts == c)
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nsi = np.sum(sidata[contrasts == c] > si_thresh)
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print(f' {100*c:3.2g}% n={nc:3d} ({100*nc/len(contrasts):4.1f}%):'
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f' n={nsi:3d} ({100*nsi/nc:5.1f}%) have SI > {si_thresh:.1f}')
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contrasts *= 100
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print(f' {np.min(contrasts):.2g}% - {np.max(contrasts):.2g}%, median={np.median(contrasts):.2g}%, mean={np.mean(contrasts):.2g}%, std={np.std(contrasts):.2g}%')
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nsegs = data[nsegscol]
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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}')
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nsegs = data['nsegs']
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print(f' available 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}')
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trials = data['trials']
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print(f' trials: {np.min(trials):.0f} - {np.max(trials):.0f}, median={np.median(trials):.0f}, mean={np.mean(trials):.0f}, std={np.std(trials):.0f}')
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duration = data['duration']
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print(f' duration: {np.min(duration):.1f}s - {np.max(duration):.1f}s, median={np.median(duration):.1f}s, mean={np.mean(duration):.1f}s, std={np.std(duration):.1f}s')
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duration *= trials
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print(f' total duration: {np.min(duration):.1f}s - {np.max(duration):.1f}s, median={np.median(duration):.1f}s, mean={np.mean(duration):.1f}s, std={np.std(duration):.1f}s')
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cols = ['cvbase', 'respmod2', 'ratebase', 'vsbase', 'serialcorr1', 'burstfrac', 'ratestim', 'cvstim']
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for i in range(len(cols)):
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for j in range(i + 1, len(cols)):
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xcol = cols[i]
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ycol = cols[j]
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if xcol not in data or ycol not in data:
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continue
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r, p = pearsonr(data[xcol], data[ycol])
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print(f' correlation {xcol:<11s} - {ycol:<11s}: r={r:5.2f}, p={p:.5f}')
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def plot_cvbase_si_punit(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('CV$_{\\rm base}$')
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ax.set_xlim(0, 1.5)
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ax.set_xticks_delta(0.5)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 6.5)
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ax.set_yticks_delta(2)
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examples = punit_examples if 'stimindex' in data else model_examples
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cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 250, 3,
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'coolwarm', color, si_thresh, *examples)
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cax.set_ylabel('Response mod.', 'Hz')
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def plot_cvstim_si_punit(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('CV$_{\\rm stim}$')
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ax.set_xlim(0, 1.6)
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ax.set_xticks_delta(0.5)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 6.5)
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ax.set_yticks_delta(2)
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examples = punit_examples if 'stimindex' in data else model_examples
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#cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 250, 2,
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# 'coolwarm', color, si_thresh, *examples)
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#cax.set_ylabel('Response mod.', 'Hz')
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cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 1.5, 2,
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'coolwarm', color, si_thresh, *examples)
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cax.set_ylabel('CV$_{\\rm base}$')
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#cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 50, 450, 2,
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# 'coolwarm', color, si_thresh, *examples)
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#cax.set_ylabel('$r$', 'Hz')
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#cax = plot_corr(ax, data, 'cvstim', ycol, 'serialcorr1', -0.6, 0, 2,
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# 'coolwarm', color, si_thresh, *examples)
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#cax.set_ylabel('$\\rho_1$')
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def plot_rmod_si_punit(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('Response modulation', 'Hz')
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ax.set_xlim(0, 250)
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ax.set_xticks_delta(100)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 6.5)
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ax.set_yticks_delta(2)
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examples = punit_examples if 'stimindex' in data else model_examples
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cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 1.5, 0.016,
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'coolwarm', color, si_thresh, *examples)
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cax.set_ylabel('CV$_{\\rm base}$')
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def plot_rate_si_punit(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('Baseline rate $r$', 'Hz')
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ax.set_xlim(0, 700)
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ax.set_xticks_delta(200)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 6.5)
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ax.set_yticks_delta(2)
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examples = punit_examples if 'stimindex' in data else model_examples
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cax = plot_corr(ax, data, 'ratebase', ycol, 'cvbase', 0, 1.5, 0.016,
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'coolwarm', color, si_thresh, *examples)
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cax.set_ylabel('CV$_{\\rm base}$')
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def plot_cvbase_si_ampul(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('CV$_{\\rm base}$')
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ax.set_xlim(0, 0.2)
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ax.set_xticks_delta(0.1)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 10)
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ax.set_yticks_delta(2)
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cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 80, 20,
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'coolwarm', color, si_thresh, *ampul_examples)
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cax.set_ylabel('Response mod.', 'Hz')
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def plot_cvstim_si_ampul(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('CV$_{\\rm stim}$')
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ax.set_xlim(0, 0.85)
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ax.set_xticks_delta(0.2)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 10)
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ax.set_yticks_delta(2)
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#cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 80, 6,
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# 'coolwarm', color, si_thresh, *ampul_examples)
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#cax.set_ylabel('Response mod.', 'Hz')
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cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 0.2, 6,
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'coolwarm', color, si_thresh, *ampul_examples)
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cax.set_ylabel('CV$_{\\rm base}$')
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cax.set_yticks_delta(0.1)
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#cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 90, 180, 6,
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# 'coolwarm', color, si_thresh, *ampul_examples)
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#cax.set_ylabel('$r$', 'Hz')
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#cax.set_yticks_delta(30)
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def plot_rmod_si_ampul(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('Response modulation', 'Hz')
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ax.set_xlim(0, 80)
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ax.set_xticks_delta(20)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 10)
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ax.set_yticks_delta(2)
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cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 0.2, 0.06,
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'coolwarm', color, si_thresh, *ampul_examples)
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cax.set_ylabel('CV$_{\\rm base}$')
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cax.set_yticks_delta(0.1)
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def plot_rate_si_ampul(ax, data, ycol, si_thresh, color):
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ax.set_xlabel('Baseline rate $r$', 'Hz')
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ax.set_xlim(50, 200)
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ax.set_xticks_delta(50)
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ax.set_ylabel('SI($r$)')
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ax.set_ylim(0, 10)
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ax.set_yticks_delta(2)
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cax = plot_corr(ax, data, 'ratebase', ycol, 'cvbase', 0, 0.2, 0.06,
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'coolwarm', color, si_thresh, *ampul_examples)
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cax.set_ylabel('CV$_{\\rm base}$')
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cax.set_yticks_delta(0.1)
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if __name__ == '__main__':
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punit_model = TableData(data_path /
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'Apteronotus_leptorhynchus-Punit-models.csv',
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sep=';')
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punit_model = punit_model[punit_model['contrast'] > 1e-6, :]
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punit_data = TableData(data_path /
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'Apteronotus_leptorhynchus-Punit-data.csv',
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sep=';')
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ampul_data = TableData(data_path /
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'Apteronotus_leptorhynchus-Ampullary-data.csv',
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sep=';')
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#si = ''
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si = '_nmax'
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si_thresh = 1.8
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cells = np.unique(list(punit_model['cell']) + list(punit_data['cell']) + list(ampul_data['cell']))
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specimen = np.unique(['-'.join(c.split('-')[:3]) for c in cells])
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print(f'Total of {len(cells)} cells recorded in {len(specimen)} specimens')
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print()
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cvmodel = punit_model['cvbase']
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cvdata = punit_data['cvbase']
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u, p = mannwhitneyu(cvmodel, cvdata)
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print('CV differs between P-unit models and data:')
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print(f' U={u:g}, p={p:.2g}')
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print(f' CV model: min={np.min(cvmodel):4.2f} max={np.max(cvmodel):4.2f} median={np.median(cvmodel):4.2f}')
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print(f' CV data: min={np.min(cvdata):4.2f} max={np.max(cvdata):.2f} median={np.median(cvdata):4.2f}')
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print()
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rmmodel = punit_model['respmod2']
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rmdata = punit_data['respmod2']
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u, p = mannwhitneyu(rmmodel, rmdata)
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print('Response modulation differs between P-unit models and data:')
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print(f' U={u:g}, p={p:.2g}')
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print(f' response modulation model: min={np.min(rmmodel):3.0f}Hz max={np.max(rmmodel):3.0f}Hz median={np.median(rmmodel):3.0f}Hz')
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print(f' response modulation data: min={np.min(rmdata):3.0f}Hz max={np.max(rmdata):3.0f}Hz median={np.median(rmdata):3.0f}Hz')
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print()
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simodel = punit_model['dsinorm100']
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sidata = punit_data['sinorm' + si]
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u, p = mannwhitneyu(simodel, sidata)
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print('SI does not differ between P-unit models and data:')
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print(f' U={u:g}, p={p:.2g}')
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print(f' SI model: min={np.min(simodel):4.1f} max={np.max(simodel):4.1f} median={np.median(simodel):4.1f}')
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print(f' SI data: min={np.min(sidata):4.1f} max={np.max(sidata):4.1f} median={np.median(sidata):4.1f}')
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print()
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s = plot_style()
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fig, axs = plt.subplots(3, 3, cmsize=(s.plot_width, 0.75*s.plot_width),
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height_ratios=[1, 0, 1, 0.3, 1])
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fig.subplots_adjust(leftm=6.5, rightm=13.5, topm=4.5, bottomm=4,
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wspace=1.1, hspace=0.6)
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si_stats('P-unit model:', punit_model, 'dsinorm100', si_thresh,
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'nsegs100')
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axs[0, 0].text(0, 1.35, 'P-unit models',
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transform=axs[0, 0].transAxes, color=s.model_color1)
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plot_cvbase_si_punit(axs[0, 0], punit_model, 'dsinorm100', si_thresh,
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s.model_color2)
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plot_rmod_si_punit(axs[0, 1], punit_model, 'dsinorm100', si_thresh,
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s.model_color2)
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#plot_cvstim_si_punit(axs[0, 2], punit_model, 'dsinorm100', si_thresh,
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# s.model_color2)
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plot_rate_si_punit(axs[0, 2], punit_model, 'dsinorm100', si_thresh,
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s.model_color2)
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print()
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|
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si_stats('P-unit data:', punit_data, 'sinorm' + si, si_thresh,
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'nsegs' + si)
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axs[1, 0].text(0, 1.35, 'P-unit data',
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transform=axs[1, 0].transAxes, color=s.punit_color1)
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plot_cvbase_si_punit(axs[1, 0], punit_data, 'sinorm' + si, si_thresh,
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|
s.punit_color2)
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plot_rmod_si_punit(axs[1, 1], punit_data, 'sinorm' + si, si_thresh,
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|
s.punit_color2)
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#plot_cvstim_si_punit(axs[1, 2], punit_data, 'sinorm' + si, si_thresh,
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# s.punit_color2)
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plot_rate_si_punit(axs[1, 2], punit_data, 'sinorm' + si, si_thresh,
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|
s.punit_color2)
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print()
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|
|
|
si_stats('Ampullary data:', ampul_data, 'sinorm' + si, si_thresh,
|
|
'nsegs' + si)
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|
axs[2, 0].text(0, 1.35, 'Ampullary data',
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|
transform=axs[2, 0].transAxes, color=s.ampul_color1)
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|
plot_cvbase_si_ampul(axs[2, 0], ampul_data, 'sinorm' + si, si_thresh,
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|
s.ampul_color2)
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plot_rmod_si_ampul(axs[2, 1], ampul_data, 'sinorm' + si, si_thresh,
|
|
s.ampul_color2)
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|
#plot_cvstim_si_ampul(axs[2, 2], ampul_data, 'sinorm' + si, si_thresh,
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# s.ampul_color2)
|
|
plot_rate_si_ampul(axs[2, 2], ampul_data, 'sinorm' + si, si_thresh,
|
|
s.ampul_color2)
|
|
print()
|
|
|
|
fig.common_xticks(axs[:2, 0])
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|
fig.common_xticks(axs[:2, 1])
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|
fig.common_xticks(axs[:2, 2])
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|
fig.common_yticks(axs[0, :])
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|
fig.common_yticks(axs[1, :])
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|
fig.common_yticks(axs[2, :])
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|
fig.tag(axs, xoffs=-3.5, yoffs=2)
|
|
fig.savefig()
|