def plot2axes(self,
axes: plt.Axes = None,
edgecolor: str = 'black',
fill: bool = False,
plot_peaks: bool = True):
"""Plot the circle to an axes.
Parameters
----------
axes : matplotlib.Axes, None
The axes to plot on. If None, will create a new figure of the image array.
edgecolor : str
Color of the Circle; must be a valid matplotlib color.
fill : bool
Whether to fill the circle. matplotlib keyword.
plot_peaks : bool
If True, plots the found peaks as well.
"""
if axes is None:
fig, axes = plt.subplots()
axes.imshow(self.image_array)
axes.add_patch(
mpl_Circle((self.center.x, self.center.y),
edgecolor=edgecolor,
radius=self.radius,
fill=fill))
if plot_peaks:
x_locs = [peak.x for peak in self.peaks]
y_locs = [peak.y for peak in self.peaks]
axes.autoscale(enable=False)
axes.scatter(x_locs, y_locs, s=40, marker='x', c=edgecolor)
def plot2axes(self,
axes: plt.Axes = None,
edgecolor: str = 'black',
fill: bool = False,
plot_peaks: bool = True):
"""Add 2 circles to the axes: one at the maximum and minimum radius of the ROI.
See Also
--------
:meth:`~pylinac.core.profile.CircleProfile.plot2axes` : Further parameter info.
"""
if axes is None:
fig, axes = plt.subplots()
axes.imshow(self.image_array)
axes.add_patch(
mpl_Circle((self.center.x, self.center.y),
edgecolor=edgecolor,
radius=self.radius * (1 + self.width_ratio),
fill=fill))
axes.add_patch(
mpl_Circle((self.center.x, self.center.y),
edgecolor=edgecolor,
radius=self.radius * (1 - self.width_ratio),
fill=fill))
if plot_peaks:
x_locs = [peak.x for peak in self.peaks]
y_locs = [peak.y for peak in self.peaks]
axes.autoscale(enable=False)
axes.scatter(x_locs, y_locs, s=20, marker='x', c=edgecolor)
def plot_analyzed_subimage(self, subimage: str='wobble', ax: plt.Axes=None, show: bool=True):
"""Plot a subimage of the starshot analysis. Current options are the zoomed out image and the zoomed in image.
Parameters
----------
subimage : str
If 'wobble', will show a zoomed in plot of the wobble circle.
Any other string will show the zoomed out plot.
ax : None, matplotlib Axes
If None (default), will create a new figure to plot on, otherwise plot to the passed axes.
"""
if ax is None:
fig, ax = plt.subplots()
# show analyzed image
ax.imshow(self.image.array, cmap=get_dicom_cmap())
self.lines.plot(ax)
self.wobble.plot2axes(ax, edgecolor='green')
self.circle_profile.plot2axes(ax, edgecolor='green')
ax.autoscale(tight=True)
ax.axis('off')
# zoom in if wobble plot
if subimage == 'wobble':
xlims = [self.wobble.center.x + self.wobble.diameter, self.wobble.center.x - self.wobble.diameter]
ylims = [self.wobble.center.y + self.wobble.diameter, self.wobble.center.y - self.wobble.diameter]
ax.set_xlim(xlims)
ax.set_ylim(ylims)
ax.axis('on')
if show:
plt.show()
def generate_correlation_plot(axes: plt.Axes, window_metrics: pd.DataFrame):
"""
Creates plot that demonstrates correlation between fixation counts and average fixation duration.
:param axes: the axes to plot on
:param window_metrics: the pre-cleaned data
:return: None
"""
plt.sca(axes)
min_fix_dur = window_metrics[AVERAGE_FIX_DUR].min() # left
max_fix_dur = window_metrics[AVERAGE_FIX_DUR].max() # right
min_fix_count = window_metrics[FIXATION_COUNTS].min() # bottom
max_fix_count = window_metrics[FIXATION_COUNTS].max() # top
axes.set_title("Overview of Participant Visual Effort")
x_mid = (max_fix_dur + min_fix_dur) // 2
y_mid = (max_fix_count + min_fix_count) // 2
# Background quadrant colors
bars = axes.bar(x=(x_mid, min_fix_dur, min_fix_dur, x_mid),
height=y_mid - min_fix_count,
bottom=(y_mid, y_mid, min_fix_count, min_fix_count),
width=x_mid - min_fix_dur,
color=get_quadrant_color_map().values(),
align='edge',
alpha=.3,
zorder=1)
# Vertical line for quadrants
axes.plot([x_mid, x_mid], [min_fix_count, max_fix_count],
color="black",
zorder=2)
# Horizontal line for quadrants
axes.plot([min_fix_dur, max_fix_dur], [y_mid, y_mid],
color="black",
zorder=3)
legend = plt.legend(
bars,
("Slow Comprehension\nComplexity\nImportance\nConfusion",
"Fast Comprehension\nSimplicity\nImportance\nPossible confusion",
"Fast Comprehension\nSimplicity\nUnimportance",
"Slow Comprehension\nComplexity\nUnimportance"),
loc='center left',
bbox_to_anchor=(1, 0.5))
for lh in legend.legendHandles:
lh.set_alpha(1)
axes.scatter(window_metrics[AVERAGE_FIX_DUR],
window_metrics[FIXATION_COUNTS],
zorder=4)
axes.set_xlabel("Mean Fixation Duration (ms)", fontsize="large")
axes.set_ylabel("Fixation Count", fontsize="large")
axes.autoscale(tight=True)
def set_windowed_x_axis(axes: plt.Axes):
"""
A helper function for showing markers on windowed data.
In particular, we show two-minute segments with the major
indicator and the proper window size with the minor indicator
:param axes:
:return:
"""
seconds = int(WINDOW[:-1])
axes.xaxis.set_major_locator(MultipleLocator(2))
axes.xaxis.set_minor_locator(MultipleLocator(seconds / 60))
axes.autoscale(tight=True, axis="x")
def plot_scenario(ex, ax: plt.Axes = None, *, figsize=(12, 8)):
"""
Plot the source position, sensor position and room dimensions from an
SMS-WSJ example in the (x, y) plane, i.e. the z-axis is ignored.
Args:
ex: An example from SMS-WSJ
ax:
Returns:
"""
if ax is None:
ax = plt.subplots(1, figsize=figsize)[1]
speaker_id_to_source_position = {}
for i, source_position in enumerate(np.array(ex['source_position']).T):
x, y, z = source_position
speaker_id = ex["speaker_id"][i]
if speaker_id in speaker_id_to_source_position:
np.testing.assert_equal(
source_position, speaker_id_to_source_position[speaker_id])
else:
speaker_id_to_source_position[speaker_id] = source_position
ax.scatter(x, y, label=f'Speaker {speaker_id}')
xs, ys, zs = np.array(ex['sensor_position'])
ax.scatter(xs, ys, label=f'Microphones')
room_dimensions = ex['room_dimensions']
(x,), (y,), (z,) = room_dimensions
# Draw the wall
w = 0.30
ax.add_patch(matplotlib.patches.Polygon(np.array([
(0, 0), (x, 0), (x, y), (0, y), (0, -w), (-w, -w), (-w, y+w),
(x+w, y+w), (x+w, -w), (0, -w)
]), fill=False, hatch='/', linewidth=0))
ax.add_patch(matplotlib.patches.Rectangle((0, 0), x, y, fill=None))
ax.set(title=f'Dataset: {ex["dataset"]!r}, ExID: {ex["example_id"]!r}, RT60: {ex["sound_decay_time"]}')
ax.autoscale(tight=True)
ax.set_aspect('equal')
ax.legend()
return ax
def _plot_analyzed_subimage(self,
subimage: str,
show: bool = True,
ax: plt.Axes = None):
"""Plot an individual piece of the VMAT analysis.
Parameters
----------
subimage : str
Specifies which image to plot.
show : bool
Whether to actually plot the image.
ax : matplotlib Axes, None
If None (default), creates a new figure to plot to, otherwise plots to the given axes.
"""
plt.ioff()
if ax is None:
fig, ax = plt.subplots()
# plot DMLC or OPEN image
if subimage in (DMLC, OPEN):
if subimage == DMLC:
img = self.dmlc_image
elif subimage == OPEN:
img = self.open_image
ax.imshow(img, cmap=get_dicom_cmap())
self._draw_segments(ax)
plt.sca(ax)
plt.axis('off')
plt.tight_layout()
# plot profile
elif subimage == PROFILE:
dmlc_prof, open_prof = self._median_profiles(
(self.dmlc_image, self.open_image))
ax.plot(dmlc_prof.values, label='DMLC')
ax.plot(open_prof.values, label='Open')
ax.autoscale(axis='x', tight=True)
ax.legend(loc=8, fontsize='large')
ax.grid()
if show:
plt.show()
def plot2axes(self, axes: plt.Axes=None, edgecolor: str='black', fill: bool=False, plot_peaks: bool=True):
"""Add 2 circles to the axes: one at the maximum and minimum radius of the ROI.
See Also
--------
:meth:`~pylinac.core.profile.CircleProfile.plot2axes` : Further parameter info.
"""
if axes is None:
fig, axes = plt.subplots()
axes.imshow(self.image_array)
axes.add_patch(mpl_Circle((self.center.x, self.center.y), edgecolor=edgecolor, radius=self.radius*(1+self.width_ratio),
fill=fill))
axes.add_patch(mpl_Circle((self.center.x, self.center.y), edgecolor=edgecolor, radius=self.radius*(1-self.width_ratio),
fill=fill))
if plot_peaks:
x_locs = [peak.x for peak in self.peaks]
y_locs = [peak.y for peak in self.peaks]
axes.autoscale(enable=False)
axes.scatter(x_locs, y_locs, s=20, marker='x', c=edgecolor)
def plot_analyzed_subimage(self,
subimage: str = 'wobble',
ax: plt.Axes = None,
show: bool = True):
"""Plot a subimage of the starshot analysis. Current options are the zoomed out image and the zoomed in image.
Parameters
----------
subimage : str
If 'wobble', will show a zoomed in plot of the wobble circle.
Any other string will show the zoomed out plot.
ax : None, matplotlib Axes
If None (default), will create a new figure to plot on, otherwise plot to the passed axes.
"""
if ax is None:
fig, ax = plt.subplots()
# show analyzed image
ax.imshow(self.image.array, cmap=get_dicom_cmap())
self.lines.plot(ax)
self.wobble.plot2axes(ax, edgecolor='green')
self.circle_profile.plot2axes(ax, edgecolor='green')
ax.autoscale(tight=True)
ax.axis('off')
# zoom in if wobble plot
if subimage == 'wobble':
xlims = [
self.wobble.center.x + self.wobble.diameter,
self.wobble.center.x - self.wobble.diameter
]
ylims = [
self.wobble.center.y + self.wobble.diameter,
self.wobble.center.y - self.wobble.diameter
]
ax.set_xlim(xlims)
ax.set_ylim(ylims)
ax.axis('on')
if show:
plt.show()
def _plot_analyzed_subimage(self, subimage: str, show: bool=True, ax: plt.Axes=None):
"""Plot an individual piece of the VMAT analysis.
Parameters
----------
subimage : str
Specifies which image to plot.
show : bool
Whether to actually plot the image.
ax : matplotlib Axes, None
If None (default), creates a new figure to plot to, otherwise plots to the given axes.
"""
plt.ioff()
if ax is None:
fig, ax = plt.subplots()
# plot DMLC or OPEN image
if subimage in (DMLC, OPEN):
if subimage == DMLC:
img = self.dmlc_image
elif subimage == OPEN:
img = self.open_image
ax.imshow(img, cmap=get_dicom_cmap())
self._draw_segments(ax)
plt.sca(ax)
plt.axis('off')
plt.tight_layout()
# plot profile
elif subimage == PROFILE:
dmlc_prof, open_prof = self._median_profiles((self.dmlc_image, self.open_image))
ax.plot(dmlc_prof.values, label='DMLC')
ax.plot(open_prof.values, label='Open')
ax.autoscale(axis='x', tight=True)
ax.legend(loc=8, fontsize='large')
ax.grid()
if show:
plt.show()
def plot2axes(self, axes: plt.Axes=None, edgecolor: str='black', fill: bool=False, plot_peaks: bool=True):
"""Plot the circle to an axes.
Parameters
----------
axes : matplotlib.Axes, None
The axes to plot on. If None, will create a new figure of the image array.
edgecolor : str
Color of the Circle; must be a valid matplotlib color.
fill : bool
Whether to fill the circle. matplotlib keyword.
plot_peaks : bool
If True, plots the found peaks as well.
"""
if axes is None:
fig, axes = plt.subplots()
axes.imshow(self.image_array)
axes.add_patch(
mpl_Circle((self.center.x, self.center.y), edgecolor=edgecolor, radius=self.radius, fill=fill))
if plot_peaks:
x_locs = [peak.x for peak in self.peaks]
y_locs = [peak.y for peak in self.peaks]
axes.autoscale(enable=False)
axes.scatter(x_locs, y_locs, s=40, marker='x', c=edgecolor)
