166 lines
6.7 KiB
Python
166 lines
6.7 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import pearsonr, linregress, gaussian_kde
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from thunderlab.tabledata import TableData
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from pathlib import Path
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from plotstyle import plot_style, labels_params
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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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'2012-12-13-ao-invivo-1', # triangle
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'2012-12-20-ac-invivo-1', # weak border triangle
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'2013-01-08-ab-invivo-1'] # no triangle
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data_path = Path('data')
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sims_path = data_path / 'simulations'
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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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return freqs, chi2, fcutoff, contrast, n
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def plot_chi2_contrasts(axs, s, cell_name, vmax):
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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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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, vmax)
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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, vmax)
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vmax /= 2
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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['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, '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, 'dsinorm'],
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zorder=20, label='weak', **s.psA2m)
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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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mask = data[:, 'cell'] == cell_name
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color = s.psB1m['color']
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if data[mask, 'border']:
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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, '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_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($r$)')
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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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ha='right', fontsize='small')
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if alpha == 0:
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ax.legend(loc='upper left', bbox_to_anchor=(1.15, 1.05),
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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['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(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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nli_thresh = 1.2
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data = TableData(data_path / 'Apteronotus_leptorhynchus-Punit-models.csv')
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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['dsinorm']
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print(f' mean SI = {np.mean(nli_split):.2f}, stdev = {np.std(nli_split):.2f}')
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print(f' triangle cells have SI >= {np.min(nli_split[cdata["triangle"] > 0.5]):.2f}')
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ntriangle = np.sum(cdata['triangle'] > 0.5)
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print(f' triangle cells: {ntriangle:2d} ({100*ntriangle/len(cdata):.1f}%)')
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nborder = np.sum(cdata['border'] > 0.5)
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print(f' border cells: {nborder:2d} ({100*nborder/len(cdata):.1f}%)')
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nother = len(cdata) - ntriangle - nborder
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print(f' other cells: {nother:2d} ({100*nother/len(cdata):.1f}%)')
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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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for name, cv in cdata[nli_split > nli_thresh, ['cell', 'cvbase']].row_data():
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print(f' {name:<22} CV={cv:4.2f}')
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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_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['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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n = np.sum(nli > nli_thresh)
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print(f' triangle cells have SI >= {np.min(nli[cdata["triangle"] > 0.5]):.2f}')
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print(f' {n} cells ({100*n/len(nli):.1f}%) have SI > {nli_thresh:.1f}:')
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for name, cv, respmod in cdata[nli > nli_thresh,
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['cell', 'cvbase', 'respmod2']].row_data():
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print(f' {name:<22} CV={cv:4.2f} '
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f'response modulation={respmod:4.0f}Hz')
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print()
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print('overall lowest baseline CV:',
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' '.join([f'{cv:.2f}' for cv in np.unique(data['cvbase'])[:5]]))
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if __name__ == '__main__':
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s = plot_style()
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#labels_params(xlabelloc='right', ylabelloc='top')
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fig, axs = plt.subplots(5, 4, cmsize=(s.plot_width, 0.95*s.plot_width),
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height_ratios=[3, 3, 3, 3, 0, 2])
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fig.subplots_adjust(leftm=7, rightm=9, topm=2, bottomm=3.5,
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wspace=1, hspace=0.5)
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print('Example cells:')
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vmax = [2, 6, 8, 40]
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for k in range(len(model_cells)):
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plot_chi2_contrasts(axs[k], s, model_cells[k], vmax[k])
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for k in range(4):
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fig.common_yticks(axs[k, :])
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fig.common_xticks(axs[:4, k])
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print()
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plot_summary_contrasts(axs[4], s, model_cells)
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fig.common_yticks(axs[4, :])
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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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