started noisesplit figure
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noisesplit.py
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150
noisesplit.py
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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from plotstyle import plot_style
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base_path = Path('data')
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data_path = base_path / 'cells'
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sims_path = base_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, freqs, chi2, nsegs):
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ax.set_aspect('equal')
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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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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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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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ax.text(1, 1.1, f'$N=10^{{{np.log10(nsegs):.0f}}}$',
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ha='right', 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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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_ylabel(r'$|\chi_2|$ [Hz]')
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cax.set_yticks_delta(ten)
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def plot_chi2_contrast(ax1, ax2, s, cell_name, contrast, nsmall, nlarge):
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data_files = sims_path.glob(f'chi2-noisen-{cell_name}-{1000*contrast:03.0f}-*.npz')
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files, nums = sort_files(cell_name, data_files, 2)
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for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
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i = nums.index(n)
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data = np.load(files[i])
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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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chi2 = np.abs(data['prss'])*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
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plot_chi2(ax, s, freqs, chi2, n)
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def plot_chi2_split(ax1, ax2, s, cell_name, nsmall, nlarge):
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data_files = sims_path.glob(f'chi2-split-{cell_name}-*.npz')
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files, nums = sort_files(cell_name, data_files, 1)
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for ax, n in zip([ax1, ax2], [nsmall, nlarge]):
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i = nums.index(n)
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data = np.load(files[i])
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n = data['n']
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alpha = data['alpha']
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noise_frac = data['noise_frac']
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freqs = data['freqs']
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pss = data['pss']
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chi2 = np.abs(data['prss'])*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
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plot_chi2(ax, s, freqs, chi2, n)
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return alpha, noise_frac
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def plot_chi2_data(ax, s, cell_name, run):
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data_file = data_path / f'{cell_name}-spectral-s{run:02d}.npz'
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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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chi2 = np.abs(data['prss'])*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
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print(f'Measured cell {data_file.name} at {100*alpha:.1f}% contrast')
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plot_chi2(ax, s, freqs, chi2, n)
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return alpha
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def plot_noise_split(ax, contrast, noise_contrast, noise_frac):
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axr, axs, axn = ax.subplots(3, 1, hspace=0.1)
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tmax = 50
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axr.show_spines('l')
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axr.set_xlim(0, tmax)
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axr.set_ylim(-8, 8)
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axr.set_yticks_delta(6)
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axr.set_ylabel('\\%')
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axs.show_spines('l')
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axs.set_xlim(0, tmax)
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axs.set_ylim(-8, 8)
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axs.set_yticks_delta(6)
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axs.set_ylabel('\\%')
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axn.set_ylim(-6, 6)
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axn.set_xlim(0, tmax)
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axn.set_yticks_delta(6)
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axn.set_yticks_blank()
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axn.set_xticks_delta(25)
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axn.set_xlabel('Time', 'ms')
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if __name__ == '__main__':
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cell_name = '2012-07-03-ak-invivo-1'
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nsmall = 100
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nlarge = 1000000
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contrast = 0.03
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s = plot_style()
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fig, axs = plt.subplots(2, 4, cmsize=(s.plot_width, 0.4*s.plot_width),
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width_ratios=[1, 0, 1, 1, 1])
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fig.subplots_adjust(leftm=7, rightm=8, topm=2, bottomm=3.5,
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wspace=0.4, hspace=0.6)
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axs[1, 0].set_visible(False)
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data_contrast = plot_chi2_data(axs[0, 0], s, cell_name[:13], 0)
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plot_noise_split(axs[0, 1], data_contrast, 0, 1)
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plot_chi2_contrast(axs[0, 2], axs[0, 3], s, cell_name, contrast, nsmall, nlarge)
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noise_contrast, noise_frac = plot_chi2_split(axs[1, 2], axs[1, 3], s,
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cell_name, nsmall, nlarge)
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plot_noise_split(axs[1, 1], contrast, noise_contrast, noise_frac)
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fig.common_xticks(axs[:, 2])
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fig.common_xticks(axs[:, 3])
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fig.common_yticks(axs[0, 2:])
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fig.common_yticks(axs[1, 2:])
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#fig.tag(axs, xoffs=-4.5, yoffs=1.8)
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fig.savefig()
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print()
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@ -495,7 +495,7 @@ Electric fish possess an additional electrosensory system, the passive or ampull
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In the example recordings shown above (\figsrefb{fig:punit} and \fref{fig:ampullary}), we only observe nonlinear responses on the anti-diagonal of the second-order susceptibility, where the sum of the two stimulus frequencies matches the neuron's baseline firing rate, which is in line with theoretical expectations \citep{Voronenko2017,Franzen2023}. However, a pronounced nonlinear response at frequencies \foneb{} or \ftwob{}, although predicted by theory, cannot be observed. In the following, we investigate how these discrepancies can be understood.
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\begin{figure*}[t]
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%\includegraphics[width=\columnwidth]{noisesplit}
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\includegraphics[width=\columnwidth]{noisesplit}
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\notejb{This model in the next figure shows a triangle for 3\% contrast ...}
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\notejb{We cannot really make up this twist with the 3\% contrast not converging into a triangle.}
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\caption{\label{fig:noisesplit} Estimation of second-order susceptibilities in the limit of weak stimuli. \figitem{A} \suscept{} estimated from $N=11$ 0.5\,s long segments of an electrophysiological recording of another low-CV P-unit (cell 2012-07-03-ak, $\fbase=120$\,Hz, CV=0.20) driven with a weak RAM stimulus with contrast 2.5\,\%. Pink edges mark the baseline firing rate where enhanced nonlinear responses are expected. \figitem[i]{B} \textit{Standard condition} of model simulations with intrinsic noise (bottom) and a RAM stimulus (top). \figitem[ii]{B} \suscept{} estimated from simulations of the cell's LIF model counterpart (cell 2012-07-03-ak, table~\ref{modelparams}) based on the same number of FFT segments $N=11$ as in the electrophysiological recording. \figitem[iii]{B} Same as \panel[ii]{B} but using $10^6$ segments. \figitem[i-iii]{C} Same as in \panel[i-iii]{B} but in the \textit{noise split} condition: there is no external RAM signal driving the model. Instead, a large part (90\,\%) of the total intrinsic noise is treated as a signal and is presented as an equivalent amplitude modulation (\signalnoise, center), while the intrinsic noise is reduced to 10\,\% of its original strength (see methods for details). Simulating one million segments, this reveals the full expected trangular structure of the second-order susceptibility.}
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