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 plotstyle import plot_style, labels_params, significance_str data_path = Path('newdata3') 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): data = np.load(data_file) n = data['n'] alpha = data['alpha'] freqs = data['freqs'] pss = data['pss'] dt_fix = 1 # 0.0005 prss = np.abs(data['prss'])/dt_fix*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1)) ax.set_visible(True) ax.set_aspect('equal') i0 = np.argmin(freqs < -300) i0 = np.argmin(freqs < 0) i1 = np.argmax(freqs > 300) if i1 == 0: i1 = len(freqs) freqs = freqs[i0:i1] prss = prss[i0:i1, i0:i1] vmax = np.quantile(prss, 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, prss, vmin=0, vmax=vmax, cmap='viridis', rasterized=True) if 'noise_frac' in data: ax.set_title('$c$=0\\,\\%', fontsize='medium') else: ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium') ax.set_xlim(0, 300) ax.set_ylim(0, 300) ax.set_xticks_delta(100) ax.set_yticks_delta(100) ax.set_xlabel('$f_1$', 'Hz') ax.set_ylabel('$f_2$', 'Hz') 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): print(cell_name) files, nums = sort_files(cell_name, data_path.glob(f'chi2-split-{cell_name}-*.npz'), 1) plot_chi2(axs[0], s, files[-1]) for k, alphastr in enumerate(['010', '030', '100']): files, nums = sort_files(cell_name, data_path.glob(f'chi2-noisen-{cell_name}-{alphastr}-*.npz'), 2) plot_chi2(axs[k + 1], s, files[-1]) 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']) x = np.linspace(0, 1, 10) ax.set_visible(True) ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium') ax.axhline(1, **s.lsLine) ax.plot(x, l.slope*x + l.intercept, **s.lsGrid) mask = data('triangle') > 0.5 ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'], clip_on=False, zorder=30, label='strong', **s.psA1m) 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, zorder=10, label='none', **s.psB1m) for cell_name in cells: mask = data[:, 'cell'] == cell_name color = s.psB1m['color'] if data[mask, 'border']: color = s.psA2m['color'] elif data[mask, 'triangle']: color = s.psA1m['color'] ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'], zorder=40, marker='o', ms=s.psB1m['markersize'], mfc=color, mec='k', mew=0.8) ax.set_ylim(0, 8) ax.set_xlim(0, 1) ax.set_minor_yticks_delta(1) ax.set_xlabel('CV$_{\\rm base}$') ax.set_ylabel('SI') ax.set_yticks_delta(4) ax.text(1, 0.9, f'$r={r:.2f}$', transform=ax.transAxes, ha='right', fontsize='small') ax.text(1, 0.7, significance_str(p), transform=ax.transAxes, ha='right', fontsize='small') if alpha == 0: ax.legend(loc='upper left', bbox_to_anchor=(1.15, 1.05), title='triangle', handlelength=0.5, handletextpad=0.5, labelspacing=0.2) kde = gaussian_kde(data('dnli'), 0.15/np.std(data('dnli'), ddof=1)) nli = np.linspace(0, 8, 100) pdf = kde(nli) 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, cells): nli_thresh = 1.2 data = TableData('summarychi2noise.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}') 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() 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') 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}') 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() print('lowest baseline CV:', np.unique(data('cvbase'))[:3]) 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), height_ratios=[1, 1, 1, 1, 0, 1]) fig.subplots_adjust(leftm=7, rightm=8, topm=2, bottomm=3.5, 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]) for k in range(4): fig.common_yticks(axs[k, :]) fig.common_xticks(axs[:4, k]) plot_summary_contrasts(axs[5], s, cells) fig.common_yticks(axs[5, :]) fig.tag(axs, xoffs=-4.5, yoffs=1.8) fig.savefig()