Colorbar on histogram

CHANGES.rst

@@ -13,6 +13,8 @@ New Features
 
 - User can now remove data from the app completely after removing it from viewers. [#2409, #2531]
 
+- Colorbar now shown on top of the histogram in Plot Options for image viewers. [#2517]
+
 Cubeviz
 ^^^^^^^
 
@@ -25,7 +27,6 @@ Imviz
 
 - Aperture photometry (previously "Imviz Simple Aperture Photometry") now supports batch mode. [#2465]
 
-
 - Expose sky regions in get_subsets. If 'include_sky_region' is True, a sky Region will be returned (in addition to a pixel Region) for spatial subsets with parent data that was a WCS. [#2496]
 
 Mosviz

docs/imviz/displayimages.rst

@@ -372,7 +372,7 @@ can be accessed with ``plot_options.stretch_function.choices``:
 
 A histogram is displayed showing the distribution of pixel values, with
 vertical lines representing the ``stretch_vmin`` and ``stretch_vmax``
-values. A stretch "curve" can be plotted under the histogram to represent
+values, and a colorbar on top. A stretch "curve" can be plotted under the histogram to represent
 how pixel values are mapped to the colorbar. This feature can be toggled
 on from the API with:
 

jdaviz/configs/default/plugins/plot_options/plot_options.py

@@ -1,17 +1,20 @@
 import os
+import matplotlib
 import numpy as np
 
 from astropy.visualization import (
     ManualInterval, ContrastBiasStretch, PercentileInterval
 )
+from echo import delay_callback
 from traitlets import Any, Dict, Float, Bool, Int, List, Unicode, observe
 
 from glue.core.subset_group import GroupedSubset
-from glue.config import stretches
+from glue.config import colormaps, stretches
 from glue.viewers.scatter.state import ScatterViewerState
 from glue.viewers.profile.state import ProfileViewerState, ProfileLayerState
 from glue.viewers.image.state import ImageSubsetLayerState
 from glue.viewers.scatter.state import ScatterLayerState as BqplotScatterLayerState
+from glue.viewers.image.composite_array import COLOR_CONVERTER
 from glue_jupyter.bqplot.image.state import BqplotImageLayerState
 from glue_jupyter.common.toolbar_vuetify import read_icon
 
@@ -473,12 +476,14 @@
         self.stretch_histogram._add_data('histogram', x=[0, 1])
 
         self.stretch_histogram.add_line('vmin', x=[0, 0], y=[0, 1], ynorm=True, color='#c75d2c')
-        self.stretch_histogram.add_line('vmax', x=[0, 0], y=[0, 1], ynorm=True, color='#c75d2c')
-        self.stretch_histogram.figure.axes[0].label = 'pixel value'
-        self.stretch_histogram.figure.axes[0].num_ticks = 3
-        self.stretch_histogram.figure.axes[0].tick_format = '0.1e'
-        self.stretch_histogram.figure.axes[1].label = 'density'
-        self.stretch_histogram.figure.axes[1].num_ticks = 2
+        self.stretch_histogram.add_line('vmax', x=[0, 0], y=[0, 1], ynorm='vmin', color='#c75d2c')
+        self.stretch_histogram.add_scatter('colorbar', x=[], y=[], ynorm='vmin', marker='square', stroke_width=33)  # noqa: E501
+        with self.stretch_histogram.figure.hold_sync():
+            self.stretch_histogram.figure.axes[0].label = 'pixel value'
+            self.stretch_histogram.figure.axes[0].num_ticks = 3
+            self.stretch_histogram.figure.axes[0].tick_format = '0.1e'
+            self.stretch_histogram.figure.axes[1].label = 'density'
+            self.stretch_histogram.figure.axes[1].num_ticks = 2
         self.stretch_histogram_widget = f'IPY_MODEL_{self.stretch_histogram.model_id}'
 
         self.subset_visible = PlotOptionsSyncState(self, self.viewer, self.layer, 'visible',
@@ -713,14 +718,83 @@
             stretch_vstep = (hist_lims[1] - hist_lims[0]) / 100.
             self.stretch_vstep = np.round(stretch_vstep, decimals=-int(np.log10(stretch_vstep))+1)  # noqa
 
-            self.stretch_histogram.viewer.state.hist_x_min = hist_lims[0]
-            self.stretch_histogram.viewer.state.hist_x_max = hist_lims[1]
+            with delay_callback(self.stretch_histogram.viewer.state, 'hist_x_min', 'hist_x_max'):
+                self.stretch_histogram.viewer.state.hist_x_min = hist_lims[0]
+                self.stretch_histogram.viewer.state.hist_x_max = hist_lims[1]
 
         self.stretch_histogram.figure.title = f"{len(sub_data)} pixels"
 
         # update the n_bins since this may be a new layer
         self._histogram_nbins_changed()
 
+    @observe('is_active', 'image_color_mode_value', 'image_color_value', 'image_colormap_value',
+             'image_contrast_value', 'image_bias_value',
+             'stretch_function_value', 'stretch_vmin_value', 'stretch_vmax_value',
+             'stretch_hist_nbins', 'stretch_hist_zoom_limits')
+    @skip_if_no_updates_since_last_active()
+    def _update_stretch_histogram_colorbar(self, msg={}):
+        """Renders a colorbar on top of the histogram."""
+        if not self._viewer_is_image_viewer() or not hasattr(self, 'stretch_histogram'):
+            # don't update histogram if selected viewer is not an image viewer,
+            # or the stretch histogram hasn't been initialized:
+            return
+
+        if self.multiselect:
+            with self.stretch_histogram.hold_sync():
+                self.stretch_histogram._marks["colorbar"].x = []
+                self.stretch_histogram._marks["colorbar"].y = []
+            return
+
+        if len(self.layer.selected_obj):
+            layer = self.layer.selected_obj[0]
+        else:
+            # skip further updates if no data are available:
+            return
+
+        if isinstance(layer.layer, GroupedSubset):
+            # don't update histogram for subsets:
+            return
+
+        # Cannot use layer.state because it can be out-of-sync
+        with self.stretch_histogram.hold_sync():
+            color_mode = self.image_color_mode_value
+            interval = ManualInterval(self.stretch_vmin.value, self.stretch_vmax.value)
+            contrast_bias = ContrastBiasStretch(self.image_contrast_value, self.image_bias_value)
+            stretch = stretches.members[self.stretch_function_value]
+
+            # NOTE: Index 0 in marks is assumed to be the bin centers.
+            x = self.stretch_histogram.figure.marks[0].x
+            y = np.ones_like(x)
+
+            # Copied from the __call__ internals of glue/viewers/image/composite_array.py
+            data = interval(x)
+            data = contrast_bias(data, out=data)
+            data = stretch(data, out=data)
+
+            if color_mode == 'Colormaps':
+                cmap = colormaps[self.image_colormap.text]
+                if hasattr(cmap, "get_bad"):
+                    bad_color = cmap.get_bad().tolist()[:3]
+                    layer_cmap = cmap.with_extremes(bad=bad_color + [self.image_opacity_value])
+                else:
+                    layer_cmap = cmap
+
+                # Compute colormapped image
+                plane = layer_cmap(data)
+
+            else:  # Monochromatic
+                # Get color
+                color = COLOR_CONVERTER.to_rgba_array(self.image_color_value)[0]
+                plane = data[:, np.newaxis] * color
+                plane[:, 3] = 1
+
+            plane = np.clip(plane, 0, 1, out=plane)
+            ipycolors = [matplotlib.colors.rgb2hex(p, keep_alpha=False) for p in plane]
+
+            self.stretch_histogram._marks["colorbar"].x = x
+            self.stretch_histogram._marks["colorbar"].y = y
+            self.stretch_histogram._marks["colorbar"].colors = ipycolors
+
     @observe('is_active', 'stretch_vmin_value', 'stretch_vmax_value', 'layer_selected',
              'stretch_hist_nbins', 'image_contrast_value', 'image_bias_value',
              'stretch_curve_visible')
@@ -745,6 +819,10 @@
             return
 
         for layer in self.layer.selected_obj:
+            if isinstance(layer.layer, GroupedSubset):
+                # don't update histogram for subsets:
+                continue
+
             # clear old mark, if it exists:
             mark_label = f'{mark_label_prefix}{layer.label}'
             mark_exists = mark_label in self.stretch_histogram.marks

jdaviz/configs/default/plugins/plot_options/tests/test_plot_options.py

@@ -1,10 +1,9 @@
 import pytest
-import numpy as np
-from numpy import allclose
+from astropy import units as u
+from astropy.nddata import NDData
 from numpy.testing import assert_allclose
 from photutils.datasets import make_4gaussians_image
-from astropy.nddata import NDData
-from astropy import units as u
+
 from jdaviz.configs.default.plugins.plot_options.plot_options import SplineStretch
 
 
@@ -77,12 +76,24 @@ def test_stretch_histogram(cubeviz_helper, spectrum1d_cube_with_uncerts):
 
     assert po.stretch_histogram is not None
 
+    # check the colorbar
+    cb = po.stretch_histogram._marks["colorbar"]
+    assert_allclose(cb.x, po.stretch_histogram.figure.marks[0].x)
+    assert_allclose(cb.y, 1)
+    assert cb.colors == [  # Gray scale, linear
+        '#050505', '#0f0f0f', '#191919', '#232323', '#2e2e2e',
+        '#383838', '#424242', '#4c4c4c', '#575757', '#616161',
+        '#6b6b6b', '#757575', '#808080', '#8a8a8a', '#949494',
+        '#9e9e9e', '#a8a8a8', '#b3b3b3', '#bdbdbd', '#c7c7c7',
+        '#d1d1d1', '#dcdcdc', '#e6e6e6', '#f0f0f0', '#fafafa']
+
     hist_lyr = po.stretch_histogram.layers['histogram']
     flux_cube_sample = hist_lyr.layer.data['x']
 
     # changing viewer should change results
     po.viewer.selected = 'uncert-viewer'
-    assert not allclose(hist_lyr.layer.data['x'], flux_cube_sample)
+    with pytest.raises(AssertionError):
+        assert_allclose(hist_lyr.layer.data['x'], flux_cube_sample)
 
     po.viewer.selected = 'flux-viewer'
     assert_allclose(hist_lyr.layer.data['x'], flux_cube_sample)
@@ -202,8 +213,8 @@ def test_stretch_spline(imviz_helper):
     expected_y = [0., 0.05, 0.3, 0.9, 1.]
 
     # Validate if the generated knots match the expected knots
-    np.testing.assert_allclose(knots_x, expected_x)
-    np.testing.assert_allclose(knots_y, expected_y)
+    assert_allclose(knots_x, expected_x)
+    assert_allclose(knots_y, expected_y)
 
     # Update the stretch options to new values and verify the knots update correctly
     with po.as_active():
@@ -219,8 +230,8 @@ def test_stretch_spline(imviz_helper):
     expected_y = [0., 0.05, 0.3, 0.9, 1.]
 
     # Validate if the generated knots for updated settings match the expected values
-    np.testing.assert_allclose(knots_x, expected_x)
-    np.testing.assert_allclose(knots_y, expected_y)
+    assert_allclose(knots_x, expected_x)
+    assert_allclose(knots_y, expected_y)
 
     # Disable the stretch curve and ensure no knots or stretches are visible
     with po.as_active():

jdaviz/core/template_mixin.py

@@ -3420,10 +3420,28 @@ def clear_all_marks(self):
         self.clear_marks(*self.marks.keys())
 
     def _add_mark(self, cls, label, xnorm=False, ynorm=False, **kwargs):
+        """
+        Parameters
+        ----------
+        xnorm : bool or str
+            If True, axes will be normalized.  If a string of an existing mark, this mark will
+            share that same x-axis scale.
+        ynorm : bool or str
+            If True, axes will be normalized.  If a string of an existing mark, this mark will
+            share that same y-axis scale.
+        """
         if label in self._marks:
             raise ValueError(f"mark with label '{label}' already exists")
-        mark = cls(scales={'x': bqplot.LinearScale() if xnorm else self.figure.axes[0].scale,
-                           'y': bqplot.LinearScale() if ynorm else self.figure.axes[1].scale},
+        scales = {}
+        for dim, norm in zip(('x', 'y'), (xnorm, ynorm)):
+            if isinstance(norm, str) and norm in self._marks.keys():
+                # point to an existing marks scales
+                scales[dim] = self._marks[norm].scales[dim]
+            elif norm:
+                scales[dim] = bqplot.LinearScale()
+            else:
+                scales[dim] = self.figure.axes[0].scale
+        mark = cls(scales=scales,
                    labels=[label],
                    **kwargs)
         self.figure.marks = self.figure.marks + [mark]