WIP

glue_jupyter/bqplot/scatter/scatter_density_mark.py

@@ -5,6 +5,8 @@
 from bqplot_image_gl import ImageGL
 from bqplot_image_gl.viewlistener import ViewListener
 
+from mpl_scatter_density.fixed_data_density_helper import FixedDataDensityHelper
+
 from ...utils import debounced
 
 __all__ = ['GenericDensityMark']
@@ -147,3 +149,73 @@ def update(self, *args, **kwargs):
 
     def invalidate_cache(self):
         self.update()
+
+
+class ScatterDensityMark(GenericDensityMark):
+    """
+    Bqplot artist to make a density plot of (x, y) scatter data.
+
+    Parameters
+    ----------
+    figure : bqplot.figure
+        The figure
+    x, y : iterable
+        The data to plot.
+    c : iterable
+        Values to use for color-encoding. This is meant to be the same as
+        the argument with the same name in :meth:`~matplotlib.axes.Axes.scatter`
+        although for now only 1D iterables of values are accepted. Note that
+        values are averaged inside each pixel of the density map *before*
+        applying the colormap, which in some cases will be different from what
+        the average color of markers would have been inside each pixel.
+    dpi : int or `None`
+        The number of dots per inch to include in the density map. To use
+        the native resolution of the drawing device, set this to None.
+    downres_factor : int
+        For interactive devices, when panning, the density map will
+        automatically be made at a lower resolution and including only a
+        subset of the points. The new dpi of the figure when panning will
+        then be dpi / downres_factor, and the number of elements in the
+        arrays will be reduced by downres_factor**2.
+    cmap : `matplotlib.colors.Colormap`
+        The colormap to use for the density map.
+    color : str or tuple
+        The color to use for the density map. This can be any valid
+        Matplotlib color. If specified, this takes precedence over the
+        colormap.
+    alpha : float
+        Overall transparency of the density map.
+    norm : `matplotlib.colors.Normalize`
+        The normalization class for the density map.
+    vmin, vmax : float or func
+        The lower and upper levels used for scaling the density map. These can
+        optionally be functions that take the density array and returns a single
+        value (e.g. a function that returns the 5% percentile, or the minimum).
+        This is useful since when zooming in/out, the optimal limits change.
+    update_while_panning : bool, optional
+        Whether to compute histograms on-the-fly while panning.
+    kwargs
+        Any additional keyword arguments are passed to AxesImage.
+    """
+
+    def __init__(self, figure, x, y, downres_factor=4, c=None, **kwargs):
+        self.histogram2d_helper = FixedDataDensityHelper(figure, x, y, c=c,
+                                                         downres_factor=downres_factor)
+        super().__init__(figure=figure, histogram2d_func=self.histogram2d_helper,
+                         **kwargs)
+
+    def set_xy(self, x, y):
+        self.histogram2d_helper.set_xy(x, y)
+
+    def set_c(self, c):
+        self.histogram2d_helper.set_c(c)
+
+    # def on_press(self, event=None, force=False):
+    #     if not force:
+    #         if self._update_while_panning and self.histogram2d_helper._downres_factor == 1:
+    #             return
+    #     self.histogram2d_helper.downres()
+    #     return super().on_press(force=force)
+
+    # def on_release(self, event=None):
+    #     self.histogra).on_release()

notebooks/Experimental/bqplot_scatter_density.ipynb

@@ -9,7 +9,7 @@
    "source": [
     "import numpy as np\n",
     "from bqplot import Figure, LinearScale, Axis, ColorScale\n",
-    "from glue_jupyter.bqplot.scatter.scatter_density_mark import GenericDensityMark"
+    "from glue_jupyter.bqplot.scatter.scatter_density_mark import ScatterDensityMark"
    ]
   },
   {
@@ -19,15 +19,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from fast_histogram import histogram2d\n",
-    "\n",
     "N = 1_000_000\n",
     "x = np.random.normal(1, 0.1, N)\n",
-    "y = np.random.normal(0, 0.2, N)\n",
-    "\n",
-    "def density(*, bins, range):\n",
-    "    print(bins, range)\n",
-    "    return histogram2d(y, x, bins=bins, range=range)"
+    "y = np.random.normal(0, 0.2, N)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6af0f4b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CustomFigure(Figure):\n",
+    "    def get_xscale(self):\n",
+    "        return 'linear'\n",
+    "    def get_yscale(self):\n",
+    "        return 'linear'"
    ]
   },
   {
@@ -44,19 +52,29 @@
     "axis_x = Axis(scale=scale_x, label='x')\n",
     "axis_y = Axis(scale=scale_y, label='y', orientation='vertical')\n",
     "\n",
-    "figure = Figure(scales=scales, axes=[axis_x, axis_y])\n",
+    "figure = CustomFigure(scales=scales, axes=[axis_x, axis_y])\n",
     "\n",
     "scales_image = {'x': scale_x,\n",
     "                'y': scale_y,\n",
     "                'image': ColorScale(min=0, max=1)}\n",
     "\n",
-    "image = GenericDensityMark(figure=figure, histogram2d_func=density)\n",
+    "image = ScatterDensityMark(figure=figure, x=x, y=y)\n",
     "\n",
     "figure.marks = (image,)\n",
     "\n",
     "figure"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd8b0344",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: FixedDataDensityHelper uses self._ax in one place, see if we can avoid that"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,