updated model figures to new analysis with the right units
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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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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_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
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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')
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print(f' {nfn:2d} ({100*nfn/len(six):2.0f}%) false negative')
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r, p = pearsonr(six, siy)
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l = linregress(six, siy)
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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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@@ -119,10 +61,10 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
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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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ax.plot(six[mask], siy[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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ax.plot(siy[mask], siy[mask], zorder=20, label='weak', **s.psA2m)
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ax.plot(six, siy, 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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@@ -131,7 +73,7 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
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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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ax.plot(six[mask], siy[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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@@ -159,13 +101,13 @@ def plot_nli_diags(ax, s, data, alphax, alphay, xthresh, ythresh, cell_name):
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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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kde = gaussian_kde(siy, 0.15/np.std(siy, 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, xthresh, ythresh, cell_name):
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@@ -173,7 +115,7 @@ def plot_summary_contrasts(axs, s, xthresh, ythresh, cell_name):
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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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plot_si_diags(axs[1 + i], s, data, a, a, xthresh, ythresh, cell_name)
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print()
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@@ -182,7 +124,7 @@ def plot_summary_diags(axs, s, xthresh, ythresh, cell_name):
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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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plot_si_diags(axs[1 + i], s, data, 0, a, xthresh, ythresh, cell_name)
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if __name__ == '__main__':
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@@ -211,4 +153,3 @@ if __name__ == '__main__':
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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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