def plot_marker_cluster_avail(ax: matplotlib.axes.Axes, marker_data: np.ndarray, frame_nums: np.ndarray,
marker_names: Collection[str], vicon_endpts: np.ndarray, **kwargs) \
-> Tuple[List[matplotlib.lines.Line2D], List[matplotlib.lines.Line2D]]:
"""Create plot showing the presence of each marker in a marker cluster."""
marker_ints = np.arange(len(marker_names))
markers_present = ~np.any(np.isnan(marker_data), 2)
lines_present = []
lines_absent = []
for i in marker_ints:
marker_present = markers_present[i]
line_present = ax.plot(frame_nums[marker_present], np.ones(np.count_nonzero(marker_present)) * (i+1), 'gs',
ms=6, ls='', **kwargs)
line_absent = ax.plot(frame_nums[~marker_present], np.ones(np.count_nonzero(~marker_present)) * (i + 1), 'rs',
ms=6, ls='', **kwargs)
lines_present.append(line_present[0])
lines_absent.append(line_absent[0])
ax.set_yticks(marker_ints + 1)
ax.set_yticklabels(marker_names)
plot_utils.update_spines(ax)
plot_utils.update_xticks(ax, font_size=8)
plot_utils.update_yticks(ax, fontsize=8)
plot_utils.update_xlabel(ax, 'Frame Number', font_size=10)
ax.axvline(vicon_endpts[0] + 1)
ax.axvline(vicon_endpts[1])
return lines_present, lines_absent
def marker_diff_his_init(ax: np.ndarray, filtered_diff: np.ndarray, x_label: str, color: str) \
-> List[matplotlib.patches.Polygon]:
"""Histogram plot each column of filtered_diff ((n,3) numpy array) onto the axes provided in ax.
Additionally, visually format each Axes and include a x_label on 2nd Axes.
"""
polygons_filtered = []
for n in range(3):
current_filtered_diff = filtered_diff[:, n]
_, _, patches_filtered = ax[n].hist(
current_filtered_diff[~np.isnan(current_filtered_diff)],
bins=20,
histtype='step',
color=color)
polygons_filtered.append(patches_filtered[0])
plot_utils.update_spines(ax[n])
plot_utils.update_xticks(ax[n], font_size=8)
plot_utils.update_yticks(ax[n], fontsize=8)
if n == 1:
plot_utils.update_xlabel(ax[n], x_label, font_size=10)
elif n == 0:
plot_utils.update_ylabel(ax[n], 'Instances', font_size=10)
return polygons_filtered
def update_bp_xticks(ax,
num_bars,
tick_labels,
font_size=12,
font_weight='bold'):
ax.set_xticks(np.arange(num_bars))
ax.set_xticklabels(tick_labels)
plot_utils.update_xticks(ax, font_size=font_size, font_weight=font_weight)
ax.xaxis.set_tick_params(which='both', length=0)
def update_bp_xticks_groups(ax,
num_bars,
num_groups,
num_metrics,
tick_labels,
font_size=12,
font_weight='bold'):
first_tick = np.average(np.arange(num_metrics) + 1)
ax.set_xticks(np.arange(first_tick, num_bars + num_groups,
num_metrics + 1))
ax.set_xticklabels(tick_labels)
plot_utils.update_xticks(ax, font_size=font_size, font_weight=font_weight)
ax.xaxis.set_tick_params(which='both', length=0)
def marker_diff_graph(ax: matplotlib.axes.Axes, marker_data: np.ndarray,
y_label: str, x_label: Union[str, None],
x_data: np.ndarray, **kwargs) -> matplotlib.lines.Line2D:
"""Plot marker_data onto the axes provided in ax.
Additionally, visually format the axes and include a y_label. **kwargs passed to matplotlib plot().
"""
lines = ax.plot(x_data, marker_data, **kwargs)
plot_utils.update_spines(ax)
plot_utils.update_xticks(ax, font_size=8)
plot_utils.update_yticks(ax, fontsize=8)
if x_label:
plot_utils.update_xlabel(ax, x_label, font_size=10)
plot_utils.update_ylabel(ax, y_label, font_size=10)
return lines
def kine_graph_init(ax: matplotlib.axes.Axes, marker_data: np.ndarray, y_label: str, x_data: np.ndarray,
plot_args=None) -> List[matplotlib.lines.Line2D]:
"""Plot each column of marker_data ((n,3) numpy array) onto the axes provided in ax.
Additionally, visually format each Axes and include a y_label on 2nd Axes. **kwargs passed to matplotlib plot().
"""
if plot_args is None:
plot_args = [{}] * marker_data.shape[1]
lines = []
for dim in range(marker_data.shape[1]):
current_line, = ax.plot(x_data, marker_data[:, dim], **plot_args[dim])
lines.append(current_line)
plot_utils.update_spines(ax)
plot_utils.update_xticks(ax, font_size=8)
plot_utils.update_yticks(ax, fontsize=8)
ax.margins(x=0, y=0.05)
ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=4, integer=True))
plot_utils.update_xlabel(ax, 'Frame Number', font_size=10)
plot_utils.update_ylabel(ax, y_label, font_size=10)
return lines
def marker_graph_init(ax: np.ndarray, marker_data: np.ndarray, y_label: str, x_data: np.ndarray, **kwargs) \
-> List[matplotlib.lines.Line2D]:
"""Plot each column of marker_data ((n,3) numpy array) onto the axes provided in ax.
Additionally, visually format each Axes and include a y_label on 2nd Axes. **kwargs passed to matplotlib plot().
"""
lines = []
for n in range(3):
current_line, = ax[n].plot(x_data, marker_data[:, n], **kwargs)
lines.append(current_line)
plot_utils.update_spines(ax[n])
plot_utils.update_xticks(ax[n], font_size=8)
plot_utils.update_yticks(ax[n], fontsize=8)
ax[n].margins(x=0, y=0.05)
ax[n].yaxis.set_major_locator(ticker.MaxNLocator(nbins=4,
integer=True))
if n == 2:
plot_utils.update_xlabel(ax[n], 'Frame Number', font_size=10)
elif n == 1:
plot_utils.update_ylabel(ax[n], y_label, font_size=10)
return lines
def cov_trend_graph_init(ax: np.ndarray, variance_data: Any,
x_data: np.ndarray, y_labels: Sequence[str],
process_func: Callable,
**kwargs) -> List[List[matplotlib.lines.Line2D]]:
"""Plot each kinematic variable/dimension combination contained in variance_data (tuple of 3 kinematic variables,
each comprised of a (n, 3) numpy array) onto the 3 rows (kinematic variable) and 3 columns (dimension) contained in
ax.
Apply process_func to each kinematic variable/dimension before plotting. Additionally, visually format each axes
and include a y_label for the first column of each row. **kwargs passed to matplotlib plot().
"""
lines = []
# iterate over kinematic variable
for i in range(3):
# iterate over dimension
dim_lines = []
for j in range(3):
line, = ax[i, j].plot(x_data, process_func(variance_data[i][:, j]),
**kwargs)
dim_lines.append(line)
plot_utils.update_spines(ax[i, j])
plot_utils.update_xticks(ax[i, j], font_size=8)
plot_utils.update_yticks(ax[i, j], fontsize=8)
ax[i, j].margins(x=0, y=0.05)
ax[i, j].yaxis.set_major_locator(
ticker.MaxNLocator(nbins=4, integer=True))
if i == 2 and j == 1:
plot_utils.update_xlabel(ax[i, j],
'Frame Number',
font_size=10)
if j == 0:
plot_utils.update_ylabel(ax[i, j], y_labels[i], font_size=10)
lines.append(dim_lines)
return lines
