307 lines
11 KiB
Python
307 lines
11 KiB
Python
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 spectral import whitenoise
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from plotstyle import plot_style
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#example_cell = ['2012-07-03-ak-invivo-1', 0]
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example_cell = ['2017-07-18-ai-invivo-1', 1] # Take this! at 3% model, 5% data
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model_cell = example_cell
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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 = prev_fac*ten
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ten *= prev_delta
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break
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prev_fac = fac
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prev_delta = delta
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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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return cax
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def plot_overn(ax, s, files, nmax=1e6):
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ns = []
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stats = []
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for fname in files:
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data = np.load(fname)
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n = data['n']
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if nmax is not None and n > nmax:
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continue
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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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chi2 = np.abs(data['prss'])/dt_fix*0.5/np.sqrt(pss.reshape(1, -1)*pss.reshape(-1, 1))
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ns.append(n)
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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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chi2 = chi2[i0:i1, i0:i1]
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stats.append(np.quantile(chi2, [0, 0.001, 0.05, 0.25, 0.5,
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0.75, 0.95, 0.998, 1.0]))
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ns = np.array(ns)
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stats = np.array(stats)
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indx = np.argsort(ns)
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ns = ns[indx]
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stats = stats[indx]
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ax.set_visible(True)
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ax.plot(ns, stats[:, 7], zorder=50, label='99.8\\%', **s.lsMax)
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ax.fill_between(ns, stats[:, 2], stats[:, 6], fc='0.85', zorder=40, label='5--95\\%')
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ax.fill_between(ns, stats[:, 3], stats[:, 5], fc='0.5', zorder=45, label='25-75\\%')
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ax.plot(ns, stats[:, 4], zorder=50, label='median', **s.lsMedian)
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#ax.plot(ns, stats[:, 8], '0.0')
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ax.set_xlim(1e2, nmax)
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ax.set_xscale('log')
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ax.set_yscale('log')
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ax.set_yticks_log(numticks=5)
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if nmax > 1e6:
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ax.set_ylim(3e-1, 5e3)
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ax.set_minor_yticks_log(numticks=5)
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ax.set_xticks_log(numticks=4)
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ax.set_minor_xticks_log(numticks=8)
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else:
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ax.set_ylim(4e0, 1.3e3)
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#ax.set_minor_yticks_log(numticks=5)
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ax.set_minor_yticks_off()
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ax.set_xticks_log(numticks=5)
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#ax.set_minor_xticks_log(numticks=6)
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ax.set_xlabel('segments')
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ax.set_ylabel('$|\\chi_2|$ [Hz]')
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def plot_chi2_contrast(ax1, ax2, s, files, nums, nsmall, nlarge):
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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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cax = plot_chi2(ax, s, freqs, chi2, n)
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cax.set_ylabel('')
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def plot_chi2_split(ax1, ax2, s, files, nums, nsmall, nlarge):
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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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cax = plot_chi2(ax, s, freqs, chi2, n)
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cax.set_ylabel('')
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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}-baseline.npz'
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data = np.load(data_file)
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eodf = float(data['eodf'])
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ratebase = float(data['ratebase/Hz'])
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cvbase = float(data['cvbase'])
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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: r={ratebase:3.0f}Hz, CV={cvbase:4.2f}')
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plot_chi2(ax, s, freqs, chi2, n)
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return alpha, ratebase, eodf
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def plot_ram(ax, contrast, eodf, wtime, wnoise):
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tmax = 50
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am = 1 + contrast*wnoise
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eod = np.sin(2*np.pi*eodf*wtime)*am
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ax.show_spines('l')
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ax.plot(1e3*wtime, eod, clip_on=False, **s.lsEOD)
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ax.plot(1e3*wtime, +am, clip_on=False, **s.lsAM)
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ax.plot(1e3*wtime, -am, clip_on=False, **s.lsAM)
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ax.set_xlim(0, tmax)
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ax.set_ylim(-1.3, 1.3)
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ax.set_yticks_delta(1)
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ax.set_ylabel('EOD')
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ax.text(1, 1, f'RAM ($c={100*contrast:.0f}$\\,\\%)', ha='right',
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transform=ax.transAxes, color=s.lsAM['color'])
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def plot_noise_split(ax, contrast, noise_contrast, noise_frac,
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wtime, wnoise):
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axr, axs, axn = ax.subplots(3, 1, hspace=0.2)
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cmax = 26
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cdelta = 20
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tmax = 50
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axr.show_spines('l')
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axr.axhline(0, **s.lsGrid)
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axr.plot(1e3*wtime, 100*contrast*wnoise, clip_on=False, **s.lsAM)
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axr.set_xlim(0, tmax)
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axr.set_ylim(-cmax, cmax)
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axr.set_yticks_delta(cdelta)
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axr.set_ylabel('\\%')
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axr.text(1, 1, f'RAM ($c={100*contrast:.0f}$\\,\\%)', ha='right',
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transform=axr.transAxes, color=s.lsAM['color'])
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axs.show_spines('l')
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axs.axhline(0, **s.lsGrid)
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axs.plot(1e3*wtime, 100*noise_contrast*wnoise, clip_on=False, **s.lsAMsplit)
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axs.set_xlim(0, tmax)
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axs.set_ylim(-cmax, cmax)
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axs.set_yticks_delta(cdelta)
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axs.set_ylabel('\\%')
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if noise_contrast > 0:
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axs.text(1, 1, f'$s_{{\\xi}}(t)$ ($c={100*noise_contrast:.0f}$\\,\\%)',
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ha='right', transform=axs.transAxes,
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color=s.lsAMsplit['color'])
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ntime = np.linspace(0, 1e-3*tmax, 800)
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rng = np.random.default_rng(45432)
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nnoise = rng.normal(size=len(ntime))
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axn.show_spines('l')
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axn.axhline(0, **s.lsGrid)
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axn.plot(1e3*ntime, noise_frac*nnoise, clip_on=False, **s.lsNoise)
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axn.set_ylim(-2, 2)
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axn.set_xlim(0, tmax)
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axn.set_yticks_delta(5)
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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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y = 0.8 if noise_frac < 1 else 1.2
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axn.text(1, y, f'Intrinsic noise (${100*noise_frac:.0f}$\\,\\%)',
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ha='right', transform=axn.transAxes, color=s.lsNoise['color'])
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if noise_frac < 1:
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axn.xscalebar(1, -0.1, 10, 'ms', ha='right')
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return axr
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if __name__ == '__main__':
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nsmall = 100
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nlarge = 1000000
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contrast = 0.01
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wdt = 0.0001
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wnoise = whitenoise(0, 300, wdt, 0.05, rng=np.random.default_rng(51234))
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wtime = np.arange(len(wnoise))*wdt
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s = plot_style()
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fig, axs = plt.subplots(4, 4, cmsize=(s.plot_width, 0.83*s.plot_width),
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width_ratios=[1, 0, 1, 1, 0.15, 1])
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fig.subplots_adjust(leftm=8, rightm=1.5, topm=3.5, bottomm=4,
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wspace=0.25, hspace=0.6)
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axs[0, 2].set_visible(False)
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axs[0, 3].set_visible(False)
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xt = -2.25
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yt = 1.25
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# data:
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axss = axs[0]
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axss[1].text(xt, yt, 'P-unit data', fontsize='large',
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transform=axss[1].transAxes, color=s.punit_color1)
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data_contrast, ratebase, eodf = plot_chi2_data(axss[1], s, example_cell[0],
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example_cell[1])
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plot_ram(axss[0], data_contrast, eodf, wtime, wnoise)
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axss[1].text(xt + 0.9, yt, f'$r={ratebase:.0f}$\\,Hz',
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transform=axss[1].transAxes, fontsize='large')
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# model 5%:
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axss = axs[1]
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data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*data_contrast:03.0f}-*.npz')
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files, nums = sort_files(example_cell[0], data_files, 2)
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axss[1].text(xt, yt, 'P-unit model', fontsize='large',
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transform=axs[1, 1].transAxes, color=s.model_color1)
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plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge)
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axr1 = plot_noise_split(axss[0], data_contrast, 0, 1, wtime, wnoise)
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plot_overn(axss[3], s, files, nmax=1e6)
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axss[3].legend(loc='lower center', bbox_to_anchor=(0.5, 1.2),
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markerfirst=False, title='$|\\chi_2|$ percentiles')
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# model 1%:
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axss = axs[2]
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data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*contrast:03.0f}-*.npz')
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files, nums = sort_files(example_cell[0], data_files, 2)
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plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge)
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axr2 = plot_noise_split(axss[0], contrast, 0, 1, wtime, wnoise)
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plot_overn(axss[3], s, files, nmax=1e6)
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# model noise split:
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axss = axs[3]
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data_files = sims_path.glob(f'chi2-split-{example_cell[0]}-*.npz')
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files, nums = sort_files(example_cell[0], data_files, 1)
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axss[1].text(xt, yt, 'P-unit model', fontsize='large',
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transform=axss[1].transAxes, color=s.model_color1)
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axss[1].text(xt + 0.9, yt, f'(noise split)', fontsize='large',
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transform=axss[1].transAxes)
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noise_contrast, noise_frac = plot_chi2_split(axss[1], axss[2], s,
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files, nums, nsmall, nlarge)
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axr3 = plot_noise_split(axss[0], 0, noise_contrast, noise_frac,
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wtime, wnoise)
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plot_overn(axss[3], s, files, nmax=1e6)
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fig.common_xticks(axs[:, 1])
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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[1, 1:3])
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fig.common_yticks(axs[2, 1:3])
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fig.common_yticks(axs[3, 1:3])
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fig.tag([axs[0, :2],
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[axr1] + axs[1, 1:].tolist(),
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[axr2] + axs[2, 1:].tolist(),
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[axr3] + axs[3, 1:].tolist()],
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xoffs=[-4.5, 1, 1, -4.5], yoffs=2)
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fig.savefig()
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print()
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