276 lines
12 KiB
Python
276 lines
12 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 punitexamplecell import example_cell as punit_example
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from punitexamplecell import example_cells as punit_examples
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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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def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
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nli_thresh, example=[], examples=[]):
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ax.axhline(nli_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 = (data[xcol] < xmax) & (data[ycol] < ymax)
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sc = ax.scatter(data[mask, xcol], data[mask, ycol], 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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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(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
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s=6, marker='^', linewidth=0.5, 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(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
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s=5, marker='o', linewidth=0.5, 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(data[xcol], 0.02*xmax/np.std(data[xcol], 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(data[ycol], 0.02*ymax/np.std(data[ycol], 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(data[ycol] > nli_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(data[ycol] < nli_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(data[xcol], data[ycol])
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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, 0.77, 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 nli_stats(title, data, column, nli_thresh):
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print(title)
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print(f' nli threshold: {nli_thresh:.1f}')
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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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hcells = np.unique(data[data(column) > nli_thresh, 'cell'])
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print(f' high nli cells: n={len(hcells):3d}, {100*len(hcells)/ncells:4.1f}%')
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print(f' high nli recordings: n={np.sum(data(column) > nli_thresh):3d}, '
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f'{100*np.sum(data(column) > nli_thresh)/nrecs:4.1f}%')
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nsegs = data['nsegs']
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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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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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def plot_cvbase_nli_punit(ax, data, ycol, nli_thresh, color):
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ax.set_xlabel('CV$_{\\rm base}$')
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ax.set_ylabel('SI($r$)')
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ax.set_xlim(0, 1.5)
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ax.set_ylim(0, 7.2)
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ax.set_yticks_delta(2)
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cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 250, 3,
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'coolwarm', color, nli_thresh,
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punit_example, punit_examples)
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cax.set_ylabel('Response mod.', 'Hz')
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def plot_cvstim_nli_punit(ax, data, ycol, nli_thresh, color):
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ax.set_xlabel('CV$_{\\rm stim}$')
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ax.set_ylabel('SI($r$)')
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ax.set_xlim(0, 1.6)
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ax.set_ylim(0, 7.2)
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ax.set_xticks_delta(0.5)
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ax.set_yticks_delta(2)
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#cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 250, 2,
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# 'coolwarm', color, nli_thresh,
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# punit_example, punit_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, nli_thresh,
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punit_example, punit_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, nli_thresh,
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# punit_example, punit_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, nli_thresh,
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# punit_example, punit_examples)
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#cax.set_ylabel('$\\rho_1$')
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def plot_mod_nli_punit(ax, data, ycol, nli_thresh, color):
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ax.set_xlabel('Response modulation', 'Hz')
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ax.set_ylabel('SI($r$)')
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ax.set_xlim(0, 250)
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ax.set_ylim(0, 7.2)
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ax.set_yticks_delta(2)
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cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 1.5, 0.016,
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'coolwarm', color, nli_thresh,
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punit_example, punit_examples)
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cax.set_ylabel('CV$_{\\rm base}$')
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def plot_cvbase_nli_ampul(ax, data, ycol, nli_thresh, color):
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ax.set_xlabel('CV$_{\\rm base}$')
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ax.set_ylabel('SI($r$)')
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ax.set_xlim(0, 0.2)
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ax.set_ylim(0, 15)
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ax.set_xticks_delta(0.1)
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ax.set_yticks_delta(5)
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cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 80, 20,
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'coolwarm', color, nli_thresh,
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ampul_example, ampul_examples)
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cax.set_ylabel('Response mod.', 'Hz')
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def plot_cvstim_nli_ampul(ax, data, ycol, nli_thresh, color):
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ax.set_xlabel('CV$_{\\rm stim}$')
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ax.set_ylabel('SI($r$)')
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ax.set_xlim(0, 0.85)
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ax.set_ylim(0, 15)
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ax.set_xticks_delta(0.2)
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ax.set_yticks_delta(5)
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#cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 80, 6,
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# 'coolwarm', color, nli_thresh,
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# ampul_example, 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, nli_thresh,
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ampul_example, 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, nli_thresh,
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# ampul_example, 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_mod_nli_ampul(ax, data, ycol, nli_thresh, color):
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ax.set_xlabel('Response modulation', 'Hz')
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ax.set_ylabel('SI($r$)')
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ax.set_xlim(0, 80)
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ax.set_ylim(0, 15)
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ax.set_xticks_delta(20)
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ax.set_yticks_delta(5)
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cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 0.2, 0.06,
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'coolwarm', color, nli_thresh,
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ampul_example, 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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nli_thresh = 1.8
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u, p = mannwhitneyu(punit_model['cvbase'], punit_data['cvbase'])
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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' median model: {np.median(punit_model["cvbase"]):.2f}')
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print(f' median data: {np.median(punit_data["cvbase"]):.2f}')
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print()
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u, p = mannwhitneyu(punit_model['respmod2'], punit_data['respmod2'])
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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' median model: {np.median(punit_model["respmod2"]):.2f}')
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print(f' median data: {np.median(punit_data["respmod2"]):.2f}')
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print()
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u, p = mannwhitneyu(punit_model['dnli100'], punit_data['nli'])
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print('NLI 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' median model: {np.median(punit_model["dnli100"]):.1f}')
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print(f' median data: {np.median(punit_data["nli"]):.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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nli_stats('P-unit model:', punit_model, 'dnli100', nli_thresh)
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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_nli_punit(axs[0, 0], punit_model, 'dnli100', nli_thresh, s.model_color2)
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plot_mod_nli_punit(axs[0, 1], punit_model, 'dnli100', nli_thresh, s.model_color2)
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plot_cvstim_nli_punit(axs[0, 2], punit_model, 'dnli100', nli_thresh, s.model_color2)
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print()
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nli_stats('P-unit data:', punit_data, 'nli', nli_thresh)
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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_nli_punit(axs[1, 0], punit_data, 'nli', nli_thresh, s.punit_color2)
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plot_mod_nli_punit(axs[1, 1], punit_data, 'nli', nli_thresh, s.punit_color2)
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plot_cvstim_nli_punit(axs[1, 2], punit_data, 'nli', nli_thresh, s.punit_color2)
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print()
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nli_stats('Ampullary data:', ampul_data, 'nli', nli_thresh)
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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_nli_ampul(axs[2, 0], ampul_data, 'nli', nli_thresh, s.ampul_color2)
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plot_mod_nli_ampul(axs[2, 1], ampul_data, 'nli', nli_thresh, s.ampul_color2)
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plot_cvstim_nli_ampul(axs[2, 2], ampul_data, 'nli', nli_thresh, s.ampul_color2)
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print()
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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(xoffs=-3.5, yoffs=2)
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fig.savefig()
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print()
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