updated model figures to new analysis with the right units
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@ -3,8 +3,8 @@ 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 spectral import whitenoise, diag_projection, peak_size
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from plotstyle import plot_style, labels_params, significance_str
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from plotstyle import plot_style, labels_params
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from plotstyle import noise_files, plot_chi2, significance_str
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model_cells = ['2017-07-18-ai-invivo-1', # strong triangle
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@ -16,101 +16,55 @@ 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, rate):
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fcutoff = 300
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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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def load_chi2(file_path, cell_name, contrast=None, n=None):
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files, nums = noise_files(sims_path, cell_name, contrast)
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idx = -1 if n is None else nums.index(n)
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data = np.load(files[idx])
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n = data['nsegs']
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fcutoff = data['fcutoff']
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contrast = data['contrast']
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freqs = data['freqs']
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pss = data['pss']
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prss = data['prss']
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chi2 = np.abs(prss)*0.5/(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 < -fcutoff)
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i0 = np.argmin(freqs < 0)
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i1 = np.argmax(freqs > fcutoff)
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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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chi2 = chi2[i0:i1, i0:i1]
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vmax = np.quantile(chi2, 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, chi2, vmin=0, vmax=vmax,
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rasterized=True)
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if 'noise_frac' in data:
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ax.set_title('$c$=0\\,\\%', fontsize='medium')
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else:
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ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
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ax.set_xlim(0, fcutoff)
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ax.set_ylim(0, fcutoff)
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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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dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
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sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
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ax.text(0.95, 0.88, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
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color='white', fontsize='medium', transform=ax.transAxes)
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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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return freqs, chi2, fcutoff, contrast, n
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def plot_chi2_contrasts(axs, s, cell_name):
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d = sims_path / f'baseline-{cell_name}.npz'
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d = sims_path / f'{cell_name}-baseline.npz'
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data = np.load(d)
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rate = float(data['rate'])
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cv = float(data['cv'])
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print(f' {cell_name}: r={rate:3.0f}Hz, CV={cv:4.2f}')
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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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plot_chi2(axs[0], s, files[-1], rate)
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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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plot_chi2(axs[k + 1], s, files[-1], rate)
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freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name)
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cax = plot_chi2(axs[0], s, freqs, chi2, fcutoff, rate)
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cax.set_ylabel('')
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axs[0].set_title(r'$c$=0\,\%', fontsize='medium')
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for k, alpha in enumerate([0.01, 0.03, 0.1]):
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freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
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alpha)
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cax = plot_chi2(axs[k + 1], s, freqs, chi2, fcutoff, rate)
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if alpha < 0.1:
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cax.set_ylabel('')
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axs[k + 1].set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
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def plot_si_cv(ax, s, data, alpha, cells):
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data = data[data['contrast'] == alpha, :]
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r, p = pearsonr(data['cvbase'], data['dnli'])
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l = linregress(data['cvbase'], data['dnli'])
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r, p = pearsonr(data['cvbase'], data['dsinorm'])
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l = linregress(data['cvbase'], data['dsinorm'])
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x = np.linspace(0, 1, 10)
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ax.set_visible(True)
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ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
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ax.axhline(1, **s.lsLine)
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ax.plot(x, l.slope*x + l.intercept, **s.lsGrid)
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mask = data['triangle'] > 0.5
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ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'],
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ax.plot(data[mask, 'cvbase'], data[mask, 'dsinorm'],
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clip_on=False, zorder=30, label='strong', **s.psA1m)
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mask = data['border'] > 0.5
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ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'],
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ax.plot(data[mask, 'cvbase'], data[mask, 'dsinorm'],
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zorder=20, label='weak', **s.psA2m)
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ax.plot(data['cvbase'], data['dnli'], clip_on=False,
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ax.plot(data['cvbase'], data['dsinorm'], clip_on=False,
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zorder=10, label='none', **s.psB1m)
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for cell_name in cells:
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@ -120,16 +74,16 @@ def plot_si_cv(ax, s, data, alpha, cells):
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color = s.psA2m['color']
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elif data[mask, 'triangle']:
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color = s.psA1m['color']
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ax.plot(data[mask, 'cvbase'], data[mask, 'dnli'],
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ax.plot(data[mask, 'cvbase'], data[mask, 'dsinorm'],
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zorder=40, marker='o', ms=s.psB1m['markersize'],
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mfc=color, mec='k', mew=0.8)
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ax.set_ylim(0, 8)
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 9)
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ax.set_yticks_delta(3)
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ax.set_minor_yticks_delta(1)
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ax.set_xlabel('CV$_{\\rm base}$')
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ax.set_ylabel('SI')
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ax.set_yticks_delta(4)
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ax.set_ylabel('SI($r$)')
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ax.text(1, 0.9, f'$R={r:.2f}$', transform=ax.transAxes,
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ha='right', fontsize='small')
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ax.text(1, 0.75, significance_str(p), transform=ax.transAxes,
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@ -139,13 +93,13 @@ def plot_si_cv(ax, s, data, alpha, cells):
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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(data['dnli'], 0.15/np.std(data['dnli'], ddof=1))
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nli = np.linspace(0, 8, 100)
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pdf = kde(nli)
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kde = gaussian_kde(data['dsinorm'], 0.15/np.std(data['dsinorm'], ddof=1))
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si = np.linspace(0, 8, 100)
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pdf = kde(si)
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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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dax.fill_betweenx(si, pdf, **s.fsB1a)
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dax.plot(pdf, si, clip_on=False, **s.lsB1m)
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def plot_summary_contrasts(axs, s, cells):
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@ -154,7 +108,7 @@ def plot_summary_contrasts(axs, s, cells):
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plot_si_cv(axs[0], s, data, 0, cells)
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print('noise split:')
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cdata = data[data['contrast'] == 0, :]
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nli_split = cdata['dnli']
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nli_split = cdata['dsinorm']
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print(f' mean SI = {np.mean(nli_split):.2f}, stdev = {np.std(nli_split):.2f}')
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n = np.sum(nli_split > nli_thresh)
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print(f' {n} cells ({100*n/len(nli_split):.1f}%) have SI > {nli_thresh:.1f}:')
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@ -163,10 +117,10 @@ def plot_summary_contrasts(axs, s, cells):
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print(f' triangle cells have SI >= {np.min(nli_split[cdata["triangle"] > 0.5]):.2f}')
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print()
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for i, a in enumerate([0.01, 0.03, 0.1]):
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plot_nli_cv(axs[1 + i], s, data, a, cells)
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plot_si_cv(axs[1 + i], s, data, a, cells)
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print(f'contrast {100*a:2g}%:')
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cdata = data[data['contrast'] == a, :]
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nli = cdata['dnli']
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nli = cdata['dsinorm']
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r, p = pearsonr(nli_split, nli)
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print(f' correlation with split: r={r:.2f}, p={p:.1e}')
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print(f' mean SI = {np.mean(nli):.2f}, stdev = {np.std(nli):.2f}')
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@ -3,8 +3,9 @@ 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 spectral import whitenoise, diag_projection, peak_size
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from plotstyle import plot_style, labels_params, significance_str
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from plotstyle import plot_style, labels_params
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from plotstyle import noise_files, plot_chi2, significance_str
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from modelsusceptcontrasts import load_chi2
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model_cell = '2012-12-21-ak-invivo-1'
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@ -13,103 +14,44 @@ 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, rate):
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fcutoff = 300
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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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prss = data['prss']
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chi2 = np.abs(prss)*0.5/(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 < -fcutoff)
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i0 = np.argmin(freqs < 0)
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i1 = np.argmax(freqs > fcutoff)
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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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chi2 = chi2[i0:i1, i0:i1]
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vmax = np.quantile(chi2, 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, chi2, 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, fcutoff)
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ax.set_ylim(0, fcutoff)
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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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dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
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sinorm, sirel, sif = peak_size(dfreqs, diag, rate, median=False)
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ax.text(0.95, 0.88, f'SI($r$)={sinorm:.1f}', ha='right', zorder=50,
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color='white', fontsize='medium', transform=ax.transAxes)
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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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d = sims_path / f'baseline-{cell_name}.npz'
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def plot_chi2_contrasts(axs, s, cell_name, nsegs=None):
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d = sims_path / f'{cell_name}-baseline.npz'
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data = np.load(d)
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rate = float(data['rate'])
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cv = float(data['cv'])
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print(f' {cell_name}: r={rate:3.0f}Hz, CV={cv:4.2f}')
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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], rate)
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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], rate)
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def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
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freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
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None, nsegs)
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ns = f'$N={n}$' if n < 1000 else f'$N=10^{np.log10(n):.0f}$'
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cax = plot_chi2(axs[0], s, freqs, chi2, fcutoff, rate)
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cax.set_ylabel('')
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axs[0].set_title(f'$c$=0\\,\\%, {ns}', fontsize='medium')
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for k, alpha in enumerate([0.01, 0.03, 0.1]):
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freqs, chi2, fcutoff, contrast, n = load_chi2(sims_path, cell_name,
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alpha, nsegs)
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ns = f'$N={n}$' if n < 1000 else f'$N=10^{np.log10(n):.0f}$'
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cax = plot_chi2(axs[k + 1], s, freqs, chi2, fcutoff, rate)
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if alpha < 0.1:
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cax.set_ylabel('')
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axs[k + 1].set_title(f'$c$={100*alpha:g}\\,\\%, {ns}',
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fontsize='medium')
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def plot_si_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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six = datax['dsinorm']
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siy = datay['dsinorm100']
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nfp = np.sum((siy > ythresh) & (six < xthresh))
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ntp = np.sum((siy > ythresh) & (six > xthresh))
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ntn = np.sum((siy < ythresh) & (six < xthresh))
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nfn = np.sum((siy < ythresh) & (six > xthresh))
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print(f' {ntp:2d} ({100*ntp/len(six):2.0f}%) true positive')
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print(f' {nfp:2d} ({100*nfp/len(six):2.0f}%) false positive')
|
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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')
|
||||
@ -119,10 +61,10 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
|
||||
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)
|
||||
ax.plot(six[mask], siy[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)
|
||||
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']
|
||||
@ -131,7 +73,7 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
|
||||
color = s.psA2m['color']
|
||||
elif datax[mask, 'triangle']:
|
||||
color = s.psA1m['color']
|
||||
ax.plot(nlix[mask], nliy[mask], zorder=40, marker='o',
|
||||
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')
|
||||
@ -159,13 +101,13 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
|
||||
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)
|
||||
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(nli, pdf, **s.fsB1a)
|
||||
dax.plot(pdf, nli, clip_on=False, **s.lsB1m)
|
||||
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):
|
||||
@ -173,7 +115,7 @@ def plot_summary_contrasts(axs, s, xthresh, ythresh, cell_name):
|
||||
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)
|
||||
plot_si_diags(axs[1 + i], s, data, a, a, xthresh, ythresh, cell_name)
|
||||
print()
|
||||
|
||||
|
||||
@ -182,7 +124,7 @@ def plot_summary_diags(axs, s, xthresh, ythresh, cell_name):
|
||||
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)
|
||||
plot_si_diags(axs[1 + i], s, data, 0, a, xthresh, ythresh, cell_name)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
@ -211,4 +153,3 @@ if __name__ == '__main__':
|
||||
fig.tag(axs, xoffs=-4.5, yoffs=1.8)
|
||||
axs[1, 0].set_visible(False)
|
||||
fig.savefig()
|
||||
print()
|
||||
|
||||
@ -3,78 +3,26 @@ from scipy.stats import linregress
|
||||
import matplotlib.pyplot as plt
|
||||
from pathlib import Path
|
||||
from plotstyle import plot_style, labels_params
|
||||
from plotstyle import noise_files, plot_chi2
|
||||
from modelsusceptcontrasts import load_chi2
|
||||
|
||||
|
||||
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):
|
||||
data = np.load(data_file)
|
||||
n = data['n']
|
||||
alpha = data['alpha']
|
||||
freqs = data['freqs']
|
||||
pss = data['pss']
|
||||
prss = data['prss']
|
||||
chi2 = np.abs(prss)/0.5/(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]
|
||||
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)
|
||||
ax.set_title(f'$N=10^{np.log10(n):.0f}$', fontsize='medium')
|
||||
ax.set_xlim(0, 300)
|
||||
ax.set_ylim(0, 300)
|
||||
ax.set_xticks_delta(300)
|
||||
ax.set_minor_xticks(3)
|
||||
ax.set_yticks_delta(300)
|
||||
ax.set_minor_yticks(3)
|
||||
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)
|
||||
cb = fig.colorbar(pc, cax=cax)
|
||||
cb.outline.set_color('none')
|
||||
cb.outline.set_linewidth(0)
|
||||
cax.set_yticks_delta(ten)
|
||||
|
||||
|
||||
def plot_overn(ax, s, files, nmax=1e6, title=False):
|
||||
ns = []
|
||||
stats = []
|
||||
for fname in files:
|
||||
data = np.load(fname)
|
||||
if not 'n' in data:
|
||||
if not 'nsegs' in data:
|
||||
return
|
||||
n = data['n']
|
||||
n = data['nsegs']
|
||||
if nmax is not None and n > nmax:
|
||||
continue
|
||||
alpha = data['alpha']
|
||||
noise_frac = data['noise_frac']
|
||||
alpha = data['contrast']
|
||||
freqs = data['freqs']
|
||||
pss = data['pss']
|
||||
prss = data['prss']
|
||||
@ -84,7 +32,7 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
|
||||
i1 = np.argmax(freqs > 300)
|
||||
if i1 == 0:
|
||||
i1 = len(freqs)
|
||||
chi2 = chi2[i0:i1, i0:i1]
|
||||
chi2 = 1e-4*chi2[i0:i1, i0:i1] # Hz/%^2
|
||||
stats.append(np.quantile(chi2, [0, 0.001, 0.05, 0.25, 0.5,
|
||||
0.75, 0.95, 0.998, 1.0]))
|
||||
ns = np.array(ns)
|
||||
@ -94,12 +42,14 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
|
||||
stats = stats[indx]
|
||||
ax.set_visible(True)
|
||||
ax.plot(ns, stats[:, 7], zorder=50, label='99.8\\%', **s.lsMax)
|
||||
ax.fill_between(ns, stats[:, 2], stats[:, 6], fc='0.85', zorder=40, label='5--95\\%')
|
||||
ax.fill_between(ns, stats[:, 3], stats[:, 5], fc='0.5', zorder=45, label='25-75\\%')
|
||||
ax.fill_between(ns, stats[:, 2], stats[:, 6], fc='0.85',
|
||||
zorder=40, label='5--95\\%')
|
||||
ax.fill_between(ns, stats[:, 3], stats[:, 5], fc='0.5',
|
||||
zorder=45, label='25-75\\%')
|
||||
ax.plot(ns, stats[:, 4], zorder=50, label='median', **s.lsMedian)
|
||||
#ax.plot(ns, stats[:, 8], '0.0')
|
||||
if title:
|
||||
if 'noise_frac' in data:
|
||||
if noise_frac < 1:
|
||||
ax.set_title('$c$=0\\,\\%', fontsize='medium')
|
||||
else:
|
||||
ax.set_title(f'$c$={100*alpha:g}\\,\\%', fontsize='medium')
|
||||
@ -107,14 +57,12 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
ax.set_yticks_log(numticks=3)
|
||||
if nmax > 1e6:
|
||||
ax.set_ylim(3e-1, 5e3)
|
||||
ax.set_ylim(1e-1, 3e3)
|
||||
ax.set_minor_yticks_log(numticks=5)
|
||||
if nmax > 1e6:
|
||||
ax.set_xticks_log(numticks=4)
|
||||
ax.set_minor_xticks_log(numticks=8)
|
||||
else:
|
||||
ax.set_ylim(5e0, 1e4)
|
||||
ax.set_minor_yticks_log(numticks=5)
|
||||
ax.set_xticks_log(numticks=3)
|
||||
ax.set_minor_xticks_log(numticks=6)
|
||||
ax.set_xlabel('segments')
|
||||
@ -126,10 +74,15 @@ def plot_overn(ax, s, files, nmax=1e6, title=False):
|
||||
|
||||
def plot_chi2_overn(axs, s, cell_name):
|
||||
print(cell_name)
|
||||
files, nums = sort_files(cell_name,
|
||||
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1)
|
||||
for k, n in enumerate([1e1, 1e2, 1e3, 1e6]):
|
||||
plot_chi2(axs[k], s, files[nums.index(int(n))])
|
||||
files, nums = noise_files(sims_path, cell_name)
|
||||
for k, nsegs in enumerate([1e1, 1e2, 1e3, 1e6]):
|
||||
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[k], s, freqs, chi2, fcutoff)
|
||||
if k < len(axs) - 2:
|
||||
cax.set_ylabel('')
|
||||
axs[k].set_title(ns, fontsize='medium')
|
||||
plot_overn(axs[-1], s, files)
|
||||
|
||||
|
||||
@ -149,12 +102,10 @@ if __name__ == '__main__':
|
||||
for k in range(len(cells)):
|
||||
plot_chi2_overn(axs[k], s, cells[k])
|
||||
cell_name = cells[0]
|
||||
files, nums = sort_files(cell_name,
|
||||
sims_path.glob(f'chi2-split-{cell_name}-*.npz'), 1)
|
||||
files, nums = noise_files(sims_path, cell_name)
|
||||
plot_overn(axs[-1, 0], s, files, 1e7, True)
|
||||
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)
|
||||
for k, alpha in enumerate([0.01, 0.03, 0.1]):
|
||||
files, nums = noise_files(sims_path, cell_name, alpha)
|
||||
plot_overn(axs[-1, k + 1], s, files, 1e7, True)
|
||||
for k in range(4):
|
||||
fig.common_yticks(axs[k, :4])
|
||||
|
||||
130
noisesplit.py
130
noisesplit.py
@ -1,8 +1,8 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from pathlib import Path
|
||||
from spectral import whitenoise, diag_projection, peak_size
|
||||
from plotstyle import plot_style
|
||||
from spectral import whitenoise
|
||||
from plotstyle import plot_style, noise_files, plot_chi2
|
||||
|
||||
|
||||
example_cell = ['2017-07-18-ai-invivo-1', 1]
|
||||
@ -13,78 +13,21 @@ data_path = base_path / 'cells'
|
||||
sims_path = base_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, freqs, chi2, nsegs, rate):
|
||||
fcutoff = 300
|
||||
ax.set_aspect('equal')
|
||||
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 = prev_fac*ten
|
||||
ten *= prev_delta
|
||||
break
|
||||
prev_fac = fac
|
||||
prev_delta = delta
|
||||
pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
|
||||
rasterized=True)
|
||||
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')
|
||||
if nsegs < 10000:
|
||||
ax.text(1, 1.1, f'$N={nsegs}$',
|
||||
ha='right', transform=ax.transAxes)
|
||||
else:
|
||||
ax.text(1, 1.1, f'$N=10^{{{np.log10(nsegs):.0f}}}$',
|
||||
ha='right', transform=ax.transAxes)
|
||||
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
|
||||
nli, nlirel, nlif = peak_size(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)
|
||||
cb = fig.colorbar(pc, cax=cax)
|
||||
cb.outline.set_color('none')
|
||||
cb.outline.set_linewidth(0)
|
||||
cax.set_ylabel(r'$|\chi_2|$ [Hz]')
|
||||
cax.set_yticks_delta(ten)
|
||||
return cax
|
||||
|
||||
|
||||
def plot_overn(ax, s, files, nmax=1e6):
|
||||
fcutoff = 300
|
||||
ns = []
|
||||
stats = []
|
||||
for fname in files:
|
||||
data = np.load(fname)
|
||||
n = data['n']
|
||||
fcutoff = data['fcutoff']
|
||||
n = data['nsegs']
|
||||
if nmax is not None and n > nmax:
|
||||
continue
|
||||
alpha = data['alpha']
|
||||
alpha = data['contrast']
|
||||
freqs = data['freqs']
|
||||
pss = data['pss']
|
||||
prss = data['prss']
|
||||
chi2 = np.abs(prss)/0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
|
||||
chi2 *= 1e-4 # Hz/%^2
|
||||
ns.append(n)
|
||||
i0 = np.argmin(freqs < 0)
|
||||
i1 = np.argmax(freqs > fcutoff)
|
||||
@ -114,26 +57,33 @@ def plot_overn(ax, s, files, nmax=1e6):
|
||||
ax.set_xticks_log(numticks=4)
|
||||
ax.set_minor_xticks_log(numticks=8)
|
||||
else:
|
||||
ax.set_ylim(4e0, 1.3e3)
|
||||
ax.set_ylim(1e-1, 2e3)
|
||||
#ax.set_minor_yticks_log(numticks=5)
|
||||
ax.set_minor_yticks_off()
|
||||
ax.set_xticks_log(numticks=5)
|
||||
ax.set_xticks_log(numticks=6)
|
||||
#ax.set_minor_xticks_log(numticks=6)
|
||||
ax.set_xlabel('segments')
|
||||
ax.set_ylabel('$|\\chi_2|$ [Hz]')
|
||||
ax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
|
||||
|
||||
|
||||
def plot_chi2_contrast(ax1, ax2, s, files, nums, nsmall, nlarge, rate):
|
||||
for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
|
||||
i = nums.index(n)
|
||||
data = np.load(files[i])
|
||||
n = data['n']
|
||||
alpha = data['alpha']
|
||||
nsegs = data['nsegs']
|
||||
fcutoff = data['fcutoff']
|
||||
alpha = data['contrast']
|
||||
freqs = data['freqs']
|
||||
pss = data['pss']
|
||||
prss = data['prss']
|
||||
if nsegs < 10000:
|
||||
ax.text(1, 1.1, f'$N={nsegs}$',
|
||||
ha='right', transform=ax.transAxes)
|
||||
else:
|
||||
ax.text(1, 1.1, f'$N=10^{{{np.log10(nsegs):.0f}}}$',
|
||||
ha='right', transform=ax.transAxes)
|
||||
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
|
||||
cax = plot_chi2(ax, s, freqs, chi2, n, rate)
|
||||
cax = plot_chi2(ax, s, freqs, chi2, fcutoff, rate)
|
||||
cax.set_ylabel('')
|
||||
print(f'Modeled cell {"-".join(files[i].name.split("-")[2:-2])} at {100*alpha:4.1f}% contrast: noise_frac={1:3.1f}, nsegs={n}')
|
||||
print()
|
||||
@ -143,14 +93,21 @@ def plot_chi2_split(ax1, ax2, s, files, nums, nsmall, nlarge, rate):
|
||||
for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
|
||||
i = nums.index(n)
|
||||
data = np.load(files[i])
|
||||
n = data['n']
|
||||
alpha = data['alpha']
|
||||
nsegs = data['nsegs']
|
||||
fcutoff = data['fcutoff']
|
||||
alpha = data['contrast']
|
||||
noise_frac = data['noise_frac']
|
||||
freqs = data['freqs']
|
||||
pss = data['pss']
|
||||
prss = data['prss']
|
||||
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
|
||||
cax = plot_chi2(ax, s, freqs, chi2, n, rate)
|
||||
if nsegs < 10000:
|
||||
ax.text(1, 1.1, f'$N={nsegs}$',
|
||||
ha='right', transform=ax.transAxes)
|
||||
else:
|
||||
ax.text(1, 1.1, f'$N=10^{{{np.log10(nsegs):.0f}}}$',
|
||||
ha='right', transform=ax.transAxes)
|
||||
cax = plot_chi2(ax, s, freqs, chi2, fcutoff, rate)
|
||||
cax.set_ylabel('')
|
||||
print(f'Modeled cell {"-".join(files[i].name.split("-")[2:-1])} at {100*alpha:4.1f}% contrast: noise_frac={noise_frac:3.1f}, nsegs={n}')
|
||||
print()
|
||||
@ -163,9 +120,10 @@ def plot_chi2_data(ax, s, cell_name, run):
|
||||
eodf = float(data['eodf'])
|
||||
ratebase = float(data['ratebase/Hz'])
|
||||
cvbase = float(data['cvbase'])
|
||||
data_file = data_path / f'{cell_name}-spectral-s{run:02d}.npz'
|
||||
data_file = data_path / f'{cell_name}-spectral-all-s{run:02d}.npz'
|
||||
data = np.load(data_file)
|
||||
n = data['n']
|
||||
nsegs = data['n']
|
||||
fcutoff = data['fcutoff']
|
||||
nfft = data['nfft']
|
||||
deltat = data['deltat']
|
||||
alpha = data['alpha']
|
||||
@ -173,9 +131,11 @@ def plot_chi2_data(ax, s, cell_name, run):
|
||||
pss = data['pss']
|
||||
prss = data['prss']
|
||||
chi2 = np.abs(prss)*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
|
||||
print(f'Measured cell {"-".join(data_file.name.split("-")[:-2])} at {100*alpha:4.1f}% contrast: r={ratebase:3.0f}Hz, CV={cvbase:4.2f}, dt={1000*deltat:4.2f}ms, nfft={nfft}, win={1000*deltat*nfft:6.1f}ms, nsegs={n}')
|
||||
print(f'Measured cell {"-".join(data_file.name.split("-")[:-2])} at {100*alpha:4.1f}% contrast: r={ratebase:3.0f}Hz, CV={cvbase:4.2f}, dt={1000*deltat:4.2f}ms, nfft={nfft}, win={1000*deltat*nfft:6.1f}ms, nsegs={nsegs}')
|
||||
print()
|
||||
plot_chi2(ax, s, freqs, chi2, n, ratebase)
|
||||
ax.text(1, 1.1, f'$N={nsegs}$',
|
||||
ha='right', transform=ax.transAxes)
|
||||
plot_chi2(ax, s, freqs, chi2, fcutoff, ratebase)
|
||||
return alpha, ratebase, eodf
|
||||
|
||||
|
||||
@ -277,11 +237,11 @@ if __name__ == '__main__':
|
||||
|
||||
# model 5%:
|
||||
axss = axs[1]
|
||||
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*data_contrast:03.0f}-*.npz')
|
||||
files, nums = sort_files(model_cell, data_files, 2)
|
||||
files, nums = noise_files(sims_path, model_cell, data_contrast)
|
||||
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
|
||||
transform=axs[1, 1].transAxes, color=s.model_color1)
|
||||
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase)
|
||||
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge,
|
||||
ratebase)
|
||||
axr1 = plot_noise_split(axss[0], data_contrast, 0, 1, wtime, wnoise)
|
||||
plot_overn(axss[3], s, files, nmax=1e6)
|
||||
axss[3].legend(loc='lower center', bbox_to_anchor=(0.5, 1.2),
|
||||
@ -289,22 +249,22 @@ if __name__ == '__main__':
|
||||
|
||||
# model 1%:
|
||||
axss = axs[2]
|
||||
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*contrast:03.0f}-*.npz')
|
||||
files, nums = sort_files(model_cell, data_files, 2)
|
||||
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase)
|
||||
files, nums = noise_files(sims_path, model_cell, contrast)
|
||||
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge,
|
||||
ratebase)
|
||||
axr2 = plot_noise_split(axss[0], contrast, 0, 1, wtime, wnoise)
|
||||
plot_overn(axss[3], s, files, nmax=1e6)
|
||||
|
||||
# model noise split:
|
||||
axss = axs[3]
|
||||
data_files = sims_path.glob(f'chi2-split-{model_cell}-*.npz')
|
||||
files, nums = sort_files(model_cell, data_files, 1)
|
||||
files, nums = noise_files(sims_path, model_cell)
|
||||
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
|
||||
transform=axss[1].transAxes, color=s.model_color1)
|
||||
axss[1].text(xt + 0.9, yt, f'(noise split)', fontsize='large',
|
||||
transform=axss[1].transAxes)
|
||||
noise_contrast, noise_frac = plot_chi2_split(axss[1], axss[2], s,
|
||||
files, nums, nsmall, nlarge, ratebase)
|
||||
files, nums, nsmall, nlarge,
|
||||
ratebase)
|
||||
axr3 = plot_noise_split(axss[0], 0, noise_contrast, noise_frac,
|
||||
wtime, wnoise)
|
||||
plot_overn(axss[3], s, files, nmax=1e6)
|
||||
|
||||
62
plotstyle.py
62
plotstyle.py
@ -1,5 +1,7 @@
|
||||
import numpy as np
|
||||
import matplotlib as mpl
|
||||
import plottools.plottools as pt
|
||||
from spectral import diag_projection, peak_size
|
||||
from plottools.spines import spines_params
|
||||
from plottools.labels import labels_params
|
||||
from plottools.colors import lighter, darker
|
||||
@ -16,6 +18,66 @@ def significance_str(p):
|
||||
return '$p<0.001$'
|
||||
|
||||
|
||||
def noise_files(data_path, cell_name, alpha=None):
|
||||
if alpha is None:
|
||||
file_pattern = f'{cell_name}-chi2-split-*.npz'
|
||||
else:
|
||||
file_pattern = f'{cell_name}-chi2-noise-{1000*alpha:03.0f}-*.npz'
|
||||
files = sorted(data_path.glob(file_pattern), key=lambda x: x.stem)
|
||||
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, freqs, chi2, fcutoff, rate=None):
|
||||
ax.set_visible(True)
|
||||
ax.set_aspect('equal')
|
||||
i0 = np.argmin(freqs < 0)
|
||||
i1 = np.argmax(freqs > fcutoff)
|
||||
if i1 == 0:
|
||||
i1 = len(freqs)
|
||||
freqs = freqs[i0:i1]
|
||||
chi2 = 1e-4*chi2[i0:i1, i0:i1] # Hz/%^2
|
||||
vquantile = 0.996
|
||||
vmax = np.quantile(chi2, vquantile)
|
||||
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 = prev_fac*ten
|
||||
#ten *= prev_delta
|
||||
vmax = fac*ten
|
||||
ten *= delta
|
||||
break
|
||||
prev_fac = fac
|
||||
prev_delta = delta
|
||||
pc = ax.pcolormesh(freqs, freqs, chi2, vmin=0, vmax=vmax,
|
||||
rasterized=True)
|
||||
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')
|
||||
if rate is not None:
|
||||
dfreqs, diag = diag_projection(freqs, chi2, 2*fcutoff)
|
||||
nli, nlirel, nlif = peak_size(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)
|
||||
cb = ax.get_figure().colorbar(pc, cax=cax)
|
||||
cb.outline.set_color('none')
|
||||
cb.outline.set_linewidth(0)
|
||||
cax.set_ylabel(r'$|\chi_2|$', r'Hz/\%$^2$')
|
||||
cax.set_yticks_delta(ten)
|
||||
return cax
|
||||
|
||||
|
||||
def plot_style():
|
||||
palette = pt.palettes['muted']
|
||||
lwthick = 1.6
|
||||
|
||||
Loading…
Reference in New Issue
Block a user