204 lines
7.6 KiB
Python
204 lines
7.6 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import pearsonr, linregress, gaussian_kde
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from thunderlab.tabledata import TableData
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from pathlib import Path
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from plotstyle import plot_style, labels_params, significance_str
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model_cell = '2012-12-21-ak-invivo-1'
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data_path = Path('data')
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sims_path = data_path / 'simulations'
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def sort_files(cell_name, all_files, n):
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files = [fn for fn in all_files if '-'.join(fn.stem.split('-')[2:-n]) == cell_name]
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if len(files) == 0:
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return None, 0
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nums = [int(fn.stem.split('-')[-1]) for fn in files]
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idxs = np.argsort(nums)
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files = [files[i] for i in idxs]
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nums = [nums[i] for i in idxs]
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return files, nums
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def plot_chi2(ax, s, data_file):
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data = np.load(data_file)
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n = data['n']
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alpha = data['alpha']
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freqs = data['freqs']
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pss = data['pss']
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dt_fix = 1 # 0.0005
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prss = np.abs(data['prss'])/dt_fix*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
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ax.set_visible(True)
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ax.set_aspect('equal')
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i0 = np.argmin(freqs < -300)
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i0 = np.argmin(freqs < 0)
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i1 = np.argmax(freqs > 300)
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if i1 == 0:
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i1 = len(freqs)
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freqs = freqs[i0:i1]
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prss = prss[i0:i1, i0:i1]
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vmax = np.quantile(prss, 0.996)
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ten = 10**np.floor(np.log10(vmax))
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for fac, delta in zip([1, 2, 3, 4, 6, 8, 10],
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[0.5, 1, 1, 2, 3, 4, 5]):
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if fac*ten >= vmax:
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vmax = fac*ten
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ten *= delta
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break
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pc = ax.pcolormesh(freqs, freqs, prss, vmin=0, vmax=vmax,
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rasterized=True)
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ns = f'$N={n}$' if n <= 100 else f'$N=10^{np.log10(n):.0f}$'
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if 'noise_frac' in data:
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ax.set_title(f'$c$=0\\,\\%, {ns}', fontsize='medium')
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else:
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ax.set_title(f'$c$={100*alpha:g}\\,\\%, {ns}', fontsize='medium')
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ax.set_xlim(0, 300)
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ax.set_ylim(0, 300)
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ax.set_xticks_delta(100)
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ax.set_yticks_delta(100)
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ax.set_xlabel('$f_1$', 'Hz')
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ax.set_ylabel('$f_2$', 'Hz')
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cax = ax.inset_axes([1.04, 0, 0.05, 1])
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cax.set_spines_outward('lrbt', 0)
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if alpha == 0.1:
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cb = fig.colorbar(pc, cax=cax, label=r'$|\chi_2|$ [Hz]')
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else:
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cb = fig.colorbar(pc, cax=cax)
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cb.outline.set_color('none')
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cb.outline.set_linewidth(0)
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cax.set_yticks_delta(ten)
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def plot_chi2_contrasts(axs, s, cell_name, n=None):
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print(f' {cell_name}')
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files, nums = sort_files(cell_name,
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sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1)
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idx = -1 if n is None else nums.index(n)
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plot_chi2(axs[0], s, files[idx])
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for k, alphastr in enumerate(['010', '030', '100']):
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files, nums = sort_files(cell_name,
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sims_path.glob(f'chi2-noisen-{cell_name}-{alphastr}-*.npz'), 2)
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idx = -1 if n is None else nums.index(n)
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plot_chi2(axs[k + 1], s, files[idx])
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def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
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datax = data[data['contrast'] == alphax, :]
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datay = data[data['contrast'] == alphay, :]
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nlix = datax['dnli']
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nliy = datay['dnli100']
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nfp = np.sum((nliy > ythresh) & (nlix < xthresh))
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ntp = np.sum((nliy > ythresh) & (nlix > xthresh))
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ntn = np.sum((nliy < ythresh) & (nlix < xthresh))
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nfn = np.sum((nliy < ythresh) & (nlix > xthresh))
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print(f' {ntp:2d} ({100*ntp/len(nlix):2.0f}%) true positive')
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print(f' {nfp:2d} ({100*nfp/len(nlix):2.0f}%) false positive')
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print(f' {ntn:2d} ({100*ntn/len(nlix):2.0f}%) true negative')
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print(f' {nfn:2d} ({100*nfn/len(nlix):2.0f}%) false negative')
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r, p = pearsonr(nlix, nliy)
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l = linregress(nlix, nliy)
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x = np.linspace(0, 10, 10)
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ax.set_visible(True)
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ax.set_title(f'$c$={100*alphay:g}\\,\\%', fontsize='medium')
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ax.plot(x, x, **s.lsLine)
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ax.plot(x, l.slope*x + l.intercept, **s.lsGrid)
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ax.axhline(ythresh, **s.lsLine)
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ax.axvline(xthresh, 0, 0.5, **s.lsLine)
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if alphax == 0:
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mask = datax['triangle'] > 0.5
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ax.plot(nlix[mask], nliy[mask], zorder=30, label='strong', **s.psA1m)
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mask = datax['border'] > 0.5
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ax.plot(nliy[mask], nliy[mask], zorder=20, label='weak', **s.psA2m)
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ax.plot(nlix, nliy, zorder=10, label='none', **s.psB1m)
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# mark cell:
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mask = datax['cell'] == cell_name
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color = s.psB1m['color']
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if alphax == 0:
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if datax[mask, 'border']:
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color = s.psA2m['color']
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elif datax[mask, 'triangle']:
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color = s.psA1m['color']
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ax.plot(nlix[mask], nliy[mask], zorder=40, marker='o',
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ms=s.psB1m['markersize'], mfc=color, mec='k', mew=0.8)
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box = dict(boxstyle='square,pad=0.1', fc='white', ec='none')
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ax.text(1.0, 0.0, f'{ntn}', ha='right', fontsize='small', bbox=box)
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ax.text(7.5, 0.0, f'{nfn}', ha='right', fontsize='small', bbox=box)
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ax.text(1.0, 3.7, f'{nfp}', ha='right', fontsize='small', bbox=box)
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ax.text(7.5, 3.7, f'{ntp}', ha='right', fontsize='small', bbox=box)
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ax.set_ylim(0, 9)
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ax.set_xlim(0, 9)
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n = datax[0, 'nsegs']
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if alphax == 0:
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ax.set_xlabel(f'SI, $c=0$, $N=10^{np.log10(n):.0f}$')
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else:
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ax.set_xlabel(f'SI, $N=10^{np.log10(n):.0f}$')
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ax.set_ylabel('SI, $N=100$')
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ax.set_xticks_delta(4)
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ax.set_yticks_delta(4)
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ax.set_minor_xticks_delta(1)
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ax.set_minor_yticks_delta(1)
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ax.text(0, 0.9, f'$R={r:.2f}$', transform=ax.transAxes, fontsize='small')
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ax.text(0, 0.75, significance_str(p), transform=ax.transAxes,
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fontsize='small')
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if alphax == 0 and alphay == 0.01:
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ax.legend(loc='upper left', bbox_to_anchor=(-1.5, 1),
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title='triangle', handlelength=0.5,
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handletextpad=0.5, labelspacing=0.2)
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kde = gaussian_kde(nliy, 0.15/np.std(nliy, ddof=1))
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nli = np.linspace(0, 8, 100)
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pdf = kde(nli)
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dax = ax.inset_axes([1.04, 0, 0.3, 1])
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dax.show_spines('')
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dax.fill_betweenx(nli, pdf, **s.fsB1a)
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dax.plot(pdf, nli, clip_on=False, **s.lsB1m)
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def plot_summary_contrasts(axs, s, xthresh, ythresh, cell_name):
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print(f'against contrast with thresholds: x={xthresh} and y={ythresh}')
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data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv')
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for i, a in enumerate([0.01, 0.03, 0.1]):
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print(f'contrast {100*a:2g}%:')
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plot_nli_diags(axs[1 + i], s, data, a, a, xthresh, ythresh, cell_name)
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print()
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def plot_summary_diags(axs, s, xthresh, ythresh, cell_name):
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print(f'against split with thresholds: x={xthresh} and y={ythresh}')
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data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv')
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for i, a in enumerate([0.01, 0.03, 0.1]):
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print(f'contrast {100*a:2g}%:')
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plot_nli_diags(axs[1 + i], s, data, 0, a, xthresh, ythresh, cell_name)
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if __name__ == '__main__':
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xthresh = 1.2
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ythresh = 1.8
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s = plot_style()
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fig, axs = plt.subplots(6, 4, cmsize=(s.plot_width, 0.85*s.plot_width),
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height_ratios=[1, 1, 0, 1, 0, 1])
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fig.subplots_adjust(leftm=7, rightm=9, topm=2, bottomm=4,
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wspace=1, hspace=1)
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for ax in axs.flat:
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ax.set_visible(False)
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print('Example cells:')
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plot_chi2_contrasts(axs[0], s, model_cell)
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plot_chi2_contrasts(axs[1], s, model_cell, 10)
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for k in range(2):
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fig.common_yticks(axs[k, :])
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for k in range(4):
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fig.common_xticks(axs[:2, k])
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print()
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plot_summary_contrasts(axs[3], s, xthresh, ythresh, model_cell)
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plot_summary_diags(axs[5], s, xthresh, ythresh, model_cell)
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fig.common_yticks(axs[3, 1:])
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fig.common_yticks(axs[5, 1:])
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fig.tag(axs, xoffs=-4.5, yoffs=1.8)
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axs[1, 0].set_visible(False)
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fig.savefig()
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print()
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