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()