import numpy as np
from matplotlib import pyplot as plt
from matplotlib.axes import Axes
from matplotlib.colors import Normalize
from neuralib.plot import plot_figure
from neuralib.plot.colormap import get_customized_cmap
from neuralib.tracking.facemap import FaceMapResult
from neuralib.typing import ArrayLikeStr, PathLike
__all__ = ['plot_facemap_keypoints',
'plot_cmap_time_series']
[docs]
def plot_facemap_keypoints(fmap: FaceMapResult,
frame_interval: tuple[int, int],
keypoints: str | ArrayLikeStr | None = None,
outlier_filter: bool = True,
output: PathLike | None = None) -> None:
"""
Plot all the keypoints
:param fmap: :class:`~neuralib.wrapper.facemap.core.FaceMapResult`
:param frame_interval: frame interval (start/end) for the plot
:param keypoints: a keypoint name or multiple keypoints. If None, then show all the keypoints
:param outlier_filter: remove jump and do the interpolation
:param output: output file. Show the fig if None
"""
if keypoints is None:
kps = fmap.keypoints
else:
kps = list(keypoints)
mx = np.arange(*frame_interval)
colors = get_customized_cmap('jet', (0, 1), len(kps))
with plot_figure(output) as ax:
df = fmap.get(*kps)
if outlier_filter:
df = df.with_outlier_filter()
for i, it in enumerate(df.partition_by('keypoint')):
name = it['keypoint'].unique().item()
ax.plot(mx, it['x'][mx], '-', color=colors[i], label=f'{name}_x')
ax.plot(mx, it['y'][mx], '--', color=colors[i], label=f'{name}_y')
ax.set(xlabel='frame', ylabel='keypoint coordinates')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
[docs]
def plot_cmap_time_series(x: np.ndarray,
y: np.ndarray, *,
cmap: str = 'viridis',
ax: Axes | None = None,
with_color_bar: bool = True,
color_bar_label: str = 'frames',
output: PathLike | None = None,
**kwargs) -> None:
"""
Plots a scatter plot with a colorbar
:param x: X-axis values. `Array[float, T]`
:param y: Y-axis values. `Array[float, T]`
:param cmap: Colormap to use
:param ax: ``matplotlib.axes.Axes``
:param with_color_bar: if show the colorbar
:param color_bar_label: color bar label
:param output: Output file path to save the plot
:param kwargs: pass through ``ax.scatter()``
"""
if x.size != y.size:
raise ValueError('size xy inconsistent')
colors = get_customized_cmap(cmap, value=(0, 1), numbers=len(x))
norm = Normalize(vmin=0, vmax=len(x))
def _plot(ax: Axes):
ax.scatter(x, y, c=colors, alpha=0.5, **kwargs)
if with_color_bar:
cbar = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax)
cbar.set_label(color_bar_label)
ax.set(xlabel='x', ylabel='y')
#
if ax is None:
with plot_figure(output) as ax:
_plot(ax)
else:
_plot(ax)
if output is not None:
plt.savefig(output)