def do_plot(fig, file):
img = ctx.open_file(file)
ax = fig.subplots()
if align:
ctx.align(img)
if preprocess:
ctx.pre_process(img)
prj = ctx.get_projection(img)
plotutils.imshow_image(ax, img, projection=prj, **kwargs)
if not align:
core_offset = ctx.get_core_offset()
if core_offset is not None:
x, y = core_offset.get(img.get_epoch())
x, y = prj.s2p([x, y])
# print core_offset, x, y
ax.scatter(x, y, marker='*', s=40, c=plotutils.black)
# if not preprocess:
# bg_mask = imgutils.Image(np.zeros_like(img.data, dtype=np.int8))
# bg = ctx.get_bg(bg_mask)
# if bg is not None and isinstance(bg, np.ndarray):
# bg.fill(1)
if ctx.get_mask() is not None and show_mask is True:
mask = ctx.get_mask()
ctx.pre_process(mask)
ax.contour(mask.data, [0.5])
for region in show_regions:
plotutils.plot_region(ax, region, prj, text=True)
def do_plot(fig):
ax = fig.subplots()
plotutils.imshow_image(ax, stack_img, projection=ctx.get_projection(stack_img),
intensity_colorbar=intensity_colorbar, **kwargs)
if ctx.get_mask() is not None and show_mask is True:
ax.contour(ctx.get_mask().data, [0.5])
for region in show_regions:
plotutils.plot_region(ax, region, ctx.get_projection(stack_img), text=False, fill=True)
def do_plot(fig):
ax_all = fig.subplots()
plotutils.imshow_image(ax_all, ref_img, projection=projection, **img_kargs)
if region_list is not None:
for region, gdata in data.df.groupby('region'):
features = wds.DatedFeaturesGroupScale(0, features=gdata.features.values)
wiseutils.plot_features(ax_all, features, mode='com', c=region.get_color(), label=region.get_name())
plotutils.plot_region(ax_all, region, projection=projection, text=False,
color=region.get_color(), fill=True)
else:
features = wds.DatedFeaturesGroupScale(0, features=data.df.features.values)
wiseutils.plot_features(ax_all, features, mode='com')
if legend:
ax_all.legend(loc='best')
def preview_detection_stack(ctx, stack_detection_name, count_threshold=0, ms_set=None,
date_filter=None, show_regions=[]):
''' Plot detection in stack'''
stack = plotutils.FigureStack()
stack_img, img_snr, img_count = load_detection_stack_image(ctx, stack_detection_name, preprocess=True)
img_snr.data[img_count.data < count_threshold] = 0
img_count.data[img_count.data < count_threshold] = 0
prj = ctx.get_projection(stack_img)
fig, ax = stack.add_subplots("Stack image")
plotutils.imshow_image(ax, stack_img, projection=prj)
for region in show_regions:
plotutils.plot_region(ax, region, prj, text=True)
fig, ax1 = stack.add_subplots("Stack detection SNR")
plotutils.imshow_image(ax1, img_snr, projection=prj, cmap=plotutils.get_cmap('jet'))
for region in show_regions:
plotutils.plot_region(ax1, region, prj, text=True)
fig, ax2 = stack.add_subplots("Stack detection count")
plotutils.imshow_image(ax2, img_count, projection=prj, cmap=plotutils.get_cmap('jet'))
for region in show_regions:
plotutils.plot_region(ax2, region, prj, text=True)
if ms_set is not None:
colorbar_setting = plotutils.ColorbarSetting(cmap='jet', ticks_locator=mdates.AutoDateLocator(),
ticks_formatter=mdates.DateFormatter('%m/%y'))
def feature_filter(feature):
if img_snr.data[tuple(feature.get_coord())] == 0:
return False
if date_filter is not None and not date_filter(feature.get_epoch()):
return False
return True
ms_set = wds.MultiScaleImageSet.from_file(os.path.join(ctx.get_data_dir(), j),
feature_filter=feature_filter)
plot_ms_set_map(ax1, None, ms_set, prj, colorbar_setting=colorbar_setting)
plot_ms_set_map(ax2, None, ms_set, prj, colorbar_setting=colorbar_setting)
add_features_tooltip(stack, ax1, ms_set.features_iter(), projection=prj, epoch=True, tol=1)
add_features_tooltip(stack, ax2, ms_set.features_iter(), projection=prj, epoch=True, tol=1)
stack.show()
