def save_analyzed_image(self,
filename,
guard_rails=True,
mlc_peaks=True,
overlay=True,
leaf_error_subplot=False,
interactive=False,
**kwargs):
"""Save the analyzed figure to a file. See :meth:`~pylinac.picketfence.PicketFence.plot_analyzed_image()` for
further parameter info.
interactive : bool
If False (default), saves the figure as a .png image.
If True, saves an html file, which can be opened in a browser, etc.
.. note:: mpld3 must be installed to use this feature.
"""
self.plot_analyzed_image(guard_rails,
mlc_peaks,
overlay,
leaf_error_subplot=leaf_error_subplot,
interactive=interactive,
show=False)
if interactive:
mpld3 = import_mpld3()
mpld3.save_html(plt.gcf(), filename)
else:
plt.savefig(filename, **kwargs)
if isinstance(filename, str):
print("Picket fence image saved to: {}".format(
osp.abspath(filename)))
def save_analyzed_subimage(self,
filename,
subimage='dmlc',
interactive=False,
**kwargs):
"""Save a subplot to file.
Parameters
----------
filename : str, file-object
Where to save the file to.
subimage : str
Which subplot to save.
interactive : bool
Only applicable for the profile subplot. If False, saves as a .png image, else saves as an interactive
HTML file.
kwargs
Passed to matplotlib.
"""
self.plot_analyzed_subimage(subimage, show=False)
if interactive and (subimage == PROFILE):
mpld3 = import_mpld3()
mpld3.save_html(plt.gcf(), filename)
else:
plt.savefig(filename, **kwargs)
if isinstance(filename, str):
print("VMAT subimage figure saved to {}".format(
osp.abspath(filename)))
def _add_leaf_error_subplot(self, ax, fig, interactive):
"""Add a bar subplot showing the leaf error."""
tol_line_height = [self.settings.tolerance, self.settings.tolerance]
tol_line_width = [0, max(self.image.shape)]
# make the new axis
divider = make_axes_locatable(ax)
if self.settings.orientation == orientations['UD']:
axtop = divider.append_axes('right', 2, pad=1, sharey=ax)
else:
axtop = divider.append_axes('bottom', 2, pad=1, sharex=ax)
# get leaf positions, errors, standard deviation, and leaf numbers
pos, vals, err, leaf_nums = self.pickets.error_hist()
# plot the leaf errors as a bar plot
if self.settings.orientation == orientations['UD']:
axtop.barh(pos,
vals,
xerr=err,
height=self.pickets[0].sample_width * 2,
alpha=0.4,
align='center')
# plot the tolerance line(s)
# TODO: replace .plot() calls with .axhline when mpld3 fixes funtionality
axtop.plot(tol_line_height, tol_line_width, 'r-', linewidth=3)
if self.settings.action_tolerance is not None:
axtop.plot(tol_line_height, tol_line_width, 'y-', linewidth=3)
# reset xlims to comfortably include the max error or tolerance value
axtop.set_xlim([0, max(max(vals), self.settings.tolerance) + 0.1])
else:
axtop.bar(pos,
vals,
yerr=err,
width=self.pickets[0].sample_width * 2,
alpha=0.4,
align='center')
axtop.plot(tol_line_width, tol_line_height, 'r-', linewidth=3)
if self.settings.action_tolerance is not None:
axtop.plot(tol_line_width, tol_line_height, 'y-', linewidth=3)
axtop.set_ylim([0, max(max(vals), self.settings.tolerance) + 0.1])
# add formatting to axis
axtop.grid('on')
axtop.set_title("Average Error (mm)")
# add tooltips if interactive
if interactive:
labels = [[
'Leaf pair {}/{}, Avg Error: {:3.3f}mm, Stdev: {:3.3f}mm'.
format(leaf_num[0], leaf_num[1], err, std)
] for leaf_num, err, std in zip(leaf_nums, vals, err)]
mpld3 = import_mpld3()
for num, patch in enumerate(axtop.axes.patches):
ttip = mpld3.plugins.PointLabelTooltip(patch,
labels[num],
location='top left')
mpld3.plugins.connect(fig, ttip)
def plot_analyzed_image(self, guard_rails=True, mlc_peaks=True, overlay=True, leaf_error_subplot=True, interactive=False, show=True):
"""Plot the analyzed image.
Parameters
----------
guard_rails : bool
Do/don't plot the picket "guard rails" around the ideal picket
mlc_peaks : bool
Do/don't plot the MLC positions.
overlay : bool
Do/don't plot the alpha overlay of the leaf status.
leaf_error_subplot : bool
.. versionadded:: 1.0
If True, plots a linked leaf error subplot adjacent to the PF image plotting the average and standard
deviation of leaf error.
interactive : bool
.. versionadded:: 1.0
.. note:: mpld3 must be installed to use this feature.
If False (default), plots a matplotlib figure.
If True, plots a MPLD3 local server image, which adds some tooltips.
"""
# plot the image
fig, ax = plt.subplots(figsize=self.settings.figure_size)
ax.imshow(self.image.array, cmap=plt.cm.Greys)
# generate a leaf error subplot if desired
if leaf_error_subplot:
self._add_leaf_error_subplot(ax, fig, interactive)
# plot guard rails and mlc peaks as desired
for p_num, picket in enumerate(self.pickets):
if guard_rails:
picket.add_guards_to_axes(ax.axes)
if mlc_peaks:
for idx, mlc_meas in enumerate(picket.mlc_meas):
mlc_meas.add_to_axes(ax.axes, width=1.5)
# plot the overlay if desired.
if overlay:
o = Overlay(self.image, self.settings, self.pickets)
o.add_to_axes(ax)
# tighten up the plot view
ax.set_xlim([0, self.image.shape[1]])
ax.set_ylim([0, self.image.shape[0]])
ax.axis('off')
if show:
if interactive:
mpld3 = import_mpld3()
mpld3.show()
else:
plt.show()
def _add_leaf_error_subplot(self, ax, fig, interactive):
"""Add a bar subplot showing the leaf error."""
tol_line_height = [self.settings.tolerance, self.settings.tolerance]
tol_line_width = [0, max(self.image.shape)]
# make the new axis
divider = make_axes_locatable(ax)
if self.settings.orientation == orientations['UD']:
axtop = divider.append_axes('right', 2, pad=1, sharey=ax)
else:
axtop = divider.append_axes('bottom', 2, pad=1, sharex=ax)
# get leaf positions, errors, standard deviation, and leaf numbers
pos, vals, err, leaf_nums = self.pickets.error_hist()
# plot the leaf errors as a bar plot
if self.settings.orientation == orientations['UD']:
axtop.barh(pos, vals, xerr=err, height=self.pickets[0].sample_width * 2, alpha=0.4, align='center')
# plot the tolerance line(s)
# TODO: replace .plot() calls with .axhline when mpld3 fixes funtionality
axtop.plot(tol_line_height, tol_line_width, 'r-', linewidth=3)
if self.settings.action_tolerance is not None:
axtop.plot(tol_line_height, tol_line_width, 'y-', linewidth=3)
# reset xlims to comfortably include the max error or tolerance value
axtop.set_xlim([0, max(max(vals), self.settings.tolerance) + 0.1])
else:
axtop.bar(pos, vals, yerr=err, width=self.pickets[0].sample_width * 2, alpha=0.4, align='center')
axtop.plot(tol_line_width, tol_line_height,
'r-', linewidth=3)
if self.settings.action_tolerance is not None:
axtop.plot(tol_line_width, tol_line_height, 'y-', linewidth=3)
axtop.set_ylim([0, max(max(vals), self.settings.tolerance) + 0.1])
# add formatting to axis
axtop.grid('on')
axtop.set_title("Average Error (mm)")
# add tooltips if interactive
if interactive:
labels = [['Leaf pair {}/{}, Avg Error: {:3.3f}mm, Stdev: {:3.3f}mm'.format(leaf_num[0], leaf_num[1], err, std)]
for leaf_num, err, std in zip(leaf_nums, vals, err)]
mpld3 = import_mpld3()
for num, patch in enumerate(axtop.axes.patches):
ttip = mpld3.plugins.PointLabelTooltip(patch, labels[num], location='top left')
mpld3.plugins.connect(fig, ttip)
def save_analyzed_image(self, filename, guard_rails=True, mlc_peaks=True, overlay=True, leaf_error_subplot=False, interactive=False, **kwargs):
"""Save the analyzed figure to a file. See :meth:`~pylinac.picketfence.PicketFence.plot_analyzed_image()` for
further parameter info.
interactive : bool
If False (default), saves the figure as a .png image.
If True, saves an html file, which can be opened in a browser, etc.
.. note:: mpld3 must be installed to use this feature.
"""
self.plot_analyzed_image(guard_rails, mlc_peaks, overlay, leaf_error_subplot=leaf_error_subplot,
interactive=interactive, show=False)
if interactive:
mpld3 = import_mpld3()
mpld3.save_html(plt.gcf(), filename)
else:
plt.savefig(filename, **kwargs)
if isinstance(filename, str):
print("Picket fence image saved to: {}".format(osp.abspath(filename)))
def save_analyzed_subimage(self, filename, subimage='dmlc', interactive=False, **kwargs):
"""Save a subplot to file.
Parameters
----------
filename : str, file-object
Where to save the file to.
subimage : str
Which subplot to save.
interactive : bool
Only applicable for the profile subplot. If False, saves as a .png image, else saves as an interactive
HTML file.
kwargs
Passed to matplotlib.
"""
self.plot_analyzed_subimage(subimage, show=False)
if interactive and (subimage == PROFILE):
mpld3 = import_mpld3()
mpld3.save_html(plt.gcf(), filename)
else:
plt.savefig(filename, **kwargs)
if isinstance(filename, str):
print("VMAT subimage figure saved to {}".format(osp.abspath(filename)))
def plot_analyzed_image(self,
guard_rails=True,
mlc_peaks=True,
overlay=True,
leaf_error_subplot=True,
interactive=False,
show=True):
"""Plot the analyzed image.
Parameters
----------
guard_rails : bool
Do/don't plot the picket "guard rails" around the ideal picket
mlc_peaks : bool
Do/don't plot the MLC positions.
overlay : bool
Do/don't plot the alpha overlay of the leaf status.
leaf_error_subplot : bool
.. versionadded:: 1.0
If True, plots a linked leaf error subplot adjacent to the PF image plotting the average and standard
deviation of leaf error.
interactive : bool
.. versionadded:: 1.0
.. note:: mpld3 must be installed to use this feature.
If False (default), plots a matplotlib figure.
If True, plots a MPLD3 local server image, which adds some tooltips.
"""
# plot the image
fig, ax = plt.subplots(figsize=self.settings.figure_size)
ax.imshow(self.image.array, cmap=plt.cm.Greys)
# generate a leaf error subplot if desired
if leaf_error_subplot:
self._add_leaf_error_subplot(ax, fig, interactive)
# plot guard rails and mlc peaks as desired
for p_num, picket in enumerate(self.pickets):
if guard_rails:
picket.add_guards_to_axes(ax.axes)
if mlc_peaks:
for idx, mlc_meas in enumerate(picket.mlc_meas):
mlc_meas.add_to_axes(ax.axes, width=1.5)
# plot the overlay if desired.
if overlay:
o = Overlay(self.image, self.settings, self.pickets)
o.add_to_axes(ax)
# tighten up the plot view
ax.set_xlim([0, self.image.shape[1]])
ax.set_ylim([0, self.image.shape[0]])
ax.axis('off')
if show:
if interactive:
mpld3 = import_mpld3()
mpld3.show()
else:
plt.show()
