import numpy as np 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, peakedness 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' 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'] freqs = data['freqs'] pss = data['pss'] chi2 = np.abs(data['prss'])*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1)) ax.set_visible(True) ax.set_aspect('equal') i0 = np.argmin(freqs < -fcutoff) i0 = np.argmin(freqs < 0) i1 = np.argmax(freqs > fcutoff) if i1 == 0: i1 = len(freqs) freqs = freqs[i0:i1] chi2 = chi2[i0:i1, i0:i1] vmax = np.quantile(chi2, 0.996) ten = 10**np.floor(np.log10(vmax)) for fac, delta in zip([1, 2, 3, 4, 6, 8, 10], [0.5, 1, 1, 2, 3, 4, 5]): if fac*ten >= vmax: vmax = fac*ten ten *= delta break pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax, rasterized=True) ns = f'$N={n}$' if n <= 100 else f'$N=10^{np.log10(n):.0f}$' if 'noise_frac' in data: ax.set_title(f'$c$=0\\,\\%, {ns}', fontsize='medium') else: ax.set_title(f'$c$={100*alpha:g}\\,\\%, {ns}', fontsize='medium') ax.set_xlim(0, fcutoff) ax.set_ylim(0, fcutoff) ax.set_xticks_delta(100) ax.set_yticks_delta(100) ax.set_xlabel('$f_1$', 'Hz') ax.set_ylabel('$f_2$', 'Hz') dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff) nli, nlif = peakedness(dfreqs, diag, rate, median=False) ax.text(0.95, 0.88, f'SI($r$)={nli:.1f}', ha='right', zorder=50, color='white', fontsize='medium', transform=ax.transAxes) cax = ax.inset_axes([1.04, 0, 0.05, 1]) cax.set_spines_outward('lrbt', 0) if alpha == 0.1: cb = fig.colorbar(pc, cax=cax, label=r'$|\chi_2|$ [Hz]') else: cb = fig.colorbar(pc, cax=cax) cb.outline.set_color('none') cb.outline.set_linewidth(0) cax.set_yticks_delta(ten) def plot_chi2_contrasts(axs, s, cell_name, n=None): d = sims_path / f'baseline-{cell_name}.npz' data = np.load(d) 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) idx = -1 if n is None else nums.index(n) plot_chi2(axs[0], s, files[idx], 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) idx = -1 if n is None else nums.index(n) plot_chi2(axs[k + 1], s, files[idx], rate) 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'] nfp = np.sum((nliy > ythresh) & (nlix < xthresh)) ntp = np.sum((nliy > ythresh) & (nlix > xthresh)) ntn = np.sum((nliy < ythresh) & (nlix < xthresh)) nfn = np.sum((nliy < ythresh) & (nlix > xthresh)) print(f' {ntp:2d} ({100*ntp/len(nlix):2.0f}%) true positive') print(f' {nfp:2d} ({100*nfp/len(nlix):2.0f}%) false positive') print(f' {ntn:2d} ({100*ntn/len(nlix):2.0f}%) true negative') print(f' {nfn:2d} ({100*nfn/len(nlix):2.0f}%) false negative') r, p = pearsonr(nlix, nliy) l = linregress(nlix, nliy) x = np.linspace(0, 10, 10) ax.set_visible(True) ax.set_title(f'$c$={100*alphay:g}\\,\\%', fontsize='medium') ax.plot(x, x, **s.lsLine) ax.plot(x, l.slope*x + l.intercept, **s.lsGrid) ax.axhline(ythresh, **s.lsLine) ax.axvline(xthresh, 0, 0.5, **s.lsLine) if alphax == 0: mask = datax['triangle'] > 0.5 ax.plot(nlix[mask], nliy[mask], zorder=30, label='strong', **s.psA1m) 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 color = s.psB1m['color'] if alphax == 0: if datax[mask, 'border']: color = s.psA2m['color'] elif datax[mask, 'triangle']: color = s.psA1m['color'] ax.plot(nlix[mask], nliy[mask], zorder=40, marker='o', ms=s.psB1m['markersize'], mfc=color, mec='k', mew=0.8) box = dict(boxstyle='square,pad=0.1', fc='white', ec='none') ax.text(1.0, 0.0, f'{ntn}', ha='right', fontsize='small', bbox=box) ax.text(7.5, 0.0, f'{nfn}', ha='right', fontsize='small', bbox=box) ax.text(1.0, 3.7, f'{nfp}', ha='right', fontsize='small', bbox=box) ax.text(7.5, 3.7, f'{ntp}', ha='right', fontsize='small', bbox=box) ax.set_ylim(0, 9) ax.set_xlim(0, 9) n = datax[0, 'nsegs'] if alphax == 0: ax.set_xlabel(f'SI, $c=0$, $N=10^{np.log10(n):.0f}$') else: ax.set_xlabel(f'SI, $N=10^{np.log10(n):.0f}$') ax.set_ylabel('SI, $N=100$') ax.set_xticks_delta(4) ax.set_yticks_delta(4) ax.set_minor_xticks_delta(1) ax.set_minor_yticks_delta(1) ax.text(0, 0.9, f'$R={r:.2f}$', transform=ax.transAxes, fontsize='small') ax.text(0, 0.75, significance_str(p), transform=ax.transAxes, fontsize='small') if alphax == 0 and alphay == 0.01: ax.legend(loc='upper left', bbox_to_anchor=(-1.5, 1), title='triangle', handlelength=0.5, handletextpad=0.5, labelspacing=0.2) kde = gaussian_kde(nliy, 0.15/np.std(nliy, ddof=1)) nli = np.linspace(0, 8, 100) pdf = kde(nli) 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) def plot_summary_contrasts(axs, s, xthresh, ythresh, cell_name): print(f'against contrast with thresholds: x={xthresh} and y={ythresh}') data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv') for i, a in enumerate([0.01, 0.03, 0.1]): print(f'contrast {100*a:2g}%:') plot_nli_diags(axs[1 + i], s, data, a, a, xthresh, ythresh, cell_name) print() def plot_summary_diags(axs, s, xthresh, ythresh, cell_name): print(f'against split with thresholds: x={xthresh} and y={ythresh}') data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv') for i, a in enumerate([0.01, 0.03, 0.1]): print(f'contrast {100*a:2g}%:') plot_nli_diags(axs[1 + i], s, data, 0, a, xthresh, ythresh, cell_name) if __name__ == '__main__': nsmall = 100 xthresh = 1.2 ythresh = 1.8 s = plot_style() fig, axs = plt.subplots(4, 4, cmsize=(s.plot_width, 0.85*s.plot_width), height_ratios=[1, 1, 0, 1, 0, 1]) fig.subplots_adjust(leftm=7, rightm=9, topm=2, bottomm=4, wspace=1, hspace=0.8) for ax in axs.flat: ax.set_visible(False) print('Example cells:') plot_chi2_contrasts(axs[0], s, model_cell) plot_chi2_contrasts(axs[1], s, model_cell, nsmall) for k in range(2): fig.common_yticks(axs[k, :]) for k in range(4): fig.common_xticks(axs[:2, k]) print() plot_summary_contrasts(axs[2], s, xthresh, ythresh, model_cell) plot_summary_diags(axs[3], s, xthresh, ythresh, model_cell) fig.common_yticks(axs[2, 1:]) fig.common_yticks(axs[3, 1:]) fig.tag(axs, xoffs=-4.5, yoffs=1.8) axs[1, 0].set_visible(False) fig.savefig() print()