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 from plotstyle import noise_files, plot_chi2, significance_str from modelsusceptcontrasts import load_chi2 model_cell = '2012-12-21-ak-invivo-1' data_path = Path('data') sims_path = data_path / 'simulations' def plot_chi2_contrasts(axs, s, cell_name, nsegs=None, vmax=None): d = sims_path / f'{cell_name}-baseline.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}') freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name, None, nsegs) ns = f'$N={n}$' if n < 1000 else f'$N=10^{np.log10(n):.0f}$' cax = plot_chi2(axs[0], s, freqs, chi2, fcutoff, rate, vmax) cax.set_ylabel('') axs[0].set_title(f'$c$=0\\,\\%, {ns}', fontsize='medium') for k, alpha in enumerate([0.01, 0.03, 0.1]): freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name, alpha, nsegs) ns = f'$N={n}$' if n < 1000 else f'$N=10^{np.log10(n):.0f}$' cax = plot_chi2(axs[k + 1], s, freqs, chi2, fcutoff, rate, vmax) if n < 1000: vmax /= 10 else: vmax /= 4 if alpha < 0.1: cax.set_ylabel('') axs[k + 1].set_title(f'$c$={100*alpha:g}\\,\\%, {ns}', fontsize='medium') def plot_si_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name): datax = data[data['contrast'] == alphax, :] datay = data[data['contrast'] == alphay, :] six = datax['dsinorm'] siy = datay['dsinorm100'] nfp = np.sum((siy > ythresh) & (six < xthresh)) ntp = np.sum((siy > ythresh) & (six > xthresh)) ntn = np.sum((siy < ythresh) & (six < xthresh)) nfn = np.sum((siy < ythresh) & (six > xthresh)) print(f' {ntp:2d} ({100*ntp/len(six):2.0f}%) true positive') print(f' {nfp:2d} ({100*nfp/len(six):2.0f}%) false positive') print(f' {ntn:2d} ({100*ntn/len(six):2.0f}%) true negative') print(f' {nfn:2d} ({100*nfn/len(six):2.0f}%) false negative') r, p = pearsonr(six, siy) l = linregress(six, siy) 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(six[mask], siy[mask], zorder=30, label='strong', **s.psA1m) mask = datax['border'] > 0.5 ax.plot(siy[mask], siy[mask], zorder=20, label='weak', **s.psA2m) ax.plot(six, siy, 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(six[mask], siy[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(siy, 0.15/np.std(siy, ddof=1)) si = np.linspace(0, 8, 100) pdf = kde(si) dax = ax.inset_axes([1.04, 0, 0.3, 1]) dax.show_spines('') dax.fill_betweenx(si, pdf, **s.fsB1a) dax.plot(pdf, si, 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_si_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_si_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(3, 4, cmsize=(s.plot_width, 0.6*s.plot_width), height_ratios=[3, 3, 0, 2]) fig.subplots_adjust(leftm=7, rightm=9, topm=2, bottomm=4, wspace=1, hspace=0.6) for ax in axs.flat: ax.set_visible(False) print('Example cells:') plot_chi2_contrasts(axs[0], s, model_cell, None, 40) plot_chi2_contrasts(axs[1], s, model_cell, nsmall, 600) 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) fig.common_yticks(axs[2, 1:]) #plot_summary_diags(axs[3], s, xthresh, ythresh, model_cell) #fig.common_yticks(axs[3, 1:]) fig.tag(axs, xoffs=-4.5, yoffs=1.8) axs[1, 0].set_visible(False) fig.savefig()