133 lines
5.0 KiB
Python
133 lines
5.0 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from itertools import product
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def prepare_fig(nrows, ncols, width=8, height=None, rheight=2,
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left=0.01, right=0.95, bottom=0.01, top=0.95,
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wspace=0.4, hspace=0.4):
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if height is None:
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height = rheight * nrows
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fig = plt.figure(figsize=(width, height))
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grid = fig.add_gridspec(nrows=nrows, ncols=ncols, wspace=wspace, hspace=hspace,
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left=left, right=right, top=top, bottom=bottom)
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axes = np.zeros((nrows, ncols), dtype=object)
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for i, j in product(range(nrows), range(ncols)):
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axes[i, j] = fig.add_subplot(grid[i, j])
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axes[i, j].set_facecolor('none')
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return fig, axes
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def xlimits(ax, time, minval=None, maxval=None, pad=0.05):
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limits = [minval, maxval]
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if minval is None:
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limits[0] = time[0]
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if maxval is None:
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limits[1] = time[-1]
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if pad is not None and minval is None:
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limits[0] -= (limits[1] - limits[0]) * pad
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if pad is not None and maxval is None:
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limits[1] += (limits[1] - limits[0]) * pad
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return ax.set_xlim(limits)
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def ylimits(ax, signal, minval=None, maxval=None, pad=0.05):
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limits = [minval, maxval]
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if minval is None:
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limits[0] = signal.min()
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if maxval is None:
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limits[1] = signal.max()
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if pad is not None and minval is None:
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limits[0] -= (limits[1] - limits[0]) * pad
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if pad is not None and maxval is None:
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limits[1] += (limits[1] - limits[0]) * pad
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return ax.set_ylim(limits)
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def ylabel(ax, label, x=-0.23, fontsize=20):
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ax.set_ylabel(label, fontsize=fontsize, rotation=0, ha='left', va='center')
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ax.yaxis.set_label_coords(x, 0.5)
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return None
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def super_xlabel(label, fig, high_ax, low_ax, y=0.005, **kwargs):
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x = (low_ax.get_position().x0 + high_ax.get_position().x1) / 2
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fig.supxlabel(label, x=x, y=y, **kwargs)
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return None
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def super_ylabel(label, fig, high_ax, low_ax, x=0.005, **kwargs):
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y = (low_ax.get_position().y0 + high_ax.get_position().y1) / 2
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fig.supylabel(label, x=x, y=y, **kwargs)
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return None
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def hide_axis(ax, side='bottom'):
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ax.spines[side].set_visible(False)
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params = {side: False, 'label' + side: False}
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ax.tick_params(axis='x' if side in ['top', 'bottom'] else 'y',
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which='both', **params)
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return None
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def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None,
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xpad=None, ypad=0.05, yloc=None, **kwargs):
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handles = ax.plot(time, signal, **kwargs)
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xlimits(ax, time, minval=xmin, maxval=xmax, pad=xpad)
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ylimits(ax, signal, minval=ymin, maxval=ymax, pad=ypad)
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
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return handles
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def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs):
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if xmin is None:
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xmin = time[0]
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if xmax is None:
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xmax = time[-1]
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lower, upper, handles = 0, 1, []
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for i in range(binary.shape[1]):
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h = ax.fill_between(time, lower, upper, where=binary[:, i], **kwargs)
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handles.append(h)
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if i < binary.shape[1] - 1:
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lower += offset + 1
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upper += offset + 1
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xlimits(ax, time, minval=xmin, maxval=xmax)
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ax.set_ylim(0, upper)
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hide_axis(ax, 'bottom')
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hide_axis(ax, 'left')
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return handles
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def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs):
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y0 = low_ax.get_position().y0
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y1 = high_ax.get_position().y1
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transform = low_ax.transData + fig.transFigure.inverted()
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x0 = transform.transform((zoom_abs[0], 0))[0]
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x1 = transform.transform((zoom_abs[1], 0))[0]
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rect = plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
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transform=fig.transFigure, **kwargs)
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fig.add_artist(rect)
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return None
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def assign_colors(handles, types, colors):
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for handle, type_id in zip(handles, types):
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handle.set_color(colors[str(int(type_id))])
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return None
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def reorder_traces(handles, signal, zlow=2, zhigh=2.5):
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inds = np.argsort(signal.std(axis=0))
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zorders = np.linspace(zlow, zhigh, len(inds))[::-1]
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for ind, z in zip(inds, zorders):
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handles[ind].set_zorder(z)
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return None
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def choose_kernels(kern_specs, features, kern_types, per_type=2, thresh=0.01):
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mean_feat = features.mean(axis=0)
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feat_diff = np.abs(mean_feat[:, None] - mean_feat[None, :])
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feat_diff[features.max(axis=0) < thresh, :] = np.nan
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feat_diff = np.nanmean(feat_diff, axis=0)
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ranking = np.argsort(feat_diff)
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kern_inds = []
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for type_id in kern_types:
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type_inds = np.nonzero(kern_specs[:, 0] == type_id)[0]
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rank_inds = np.nonzero(np.isin(ranking, type_inds))[0][-per_type:]
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kern_inds.extend(ranking[rank_inds])
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return np.array(kern_inds)
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def letter_subplots(axes, labels='abcd', x=0.02, y=1, ha='left', va='bottom',
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fontsize=16, fontweight='bold', **kwargs):
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for ax, label in zip(axes, labels):
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ax.text(x, y, label, transform=ax.transAxes, ha=ha, va=va,
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fontsize=fontsize, fontweight=fontweight, **kwargs)
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return None |