Merge remote-tracking branch 'origin/master' into link_selections_ref…

…actor

.travis.yml

@@ -12,6 +12,9 @@ os:
 
 dist: xenial
 
+addons:
+  firefox: latest
+
 notifications:
   email:
     on_failure: change # [always|never|change] default: always
@@ -23,6 +26,7 @@ env:
     - CHANS="-c pyviz"
     - MPLBACKEND="Agg"
     - PYTHON_VERSION=3.7
+    - MOZ_HEADLESS=1
 
 stages:
   - test
@@ -66,6 +70,7 @@ jobs:
         - source activate test-environment
         - travis_wait 30 doit develop_install $CHANS_DEV -o $HV_REQUIREMENTS
         - doit env_capture
+        - hash -r
       script:
         - doit test_all_recommended
       after_success: coveralls
@@ -88,10 +93,11 @@ jobs:
       <<: *default
       stage: docs_dev
       env: DESC="docs" HV_DOC_GALLERY="false" HV_DOC_REF_GALLERY="true" HV_REQUIREMENTS="doc" PANEL_EMBED="true" PANEL_EMBED_JSON="true" PANEL_EMBED_JSON_PREFIX="json" CHANS_DEV="-c pyviz/label/dev -c bokeh -c conda-forge"
+      before-script:
+        - conda install -c conda-forge geckodriver selenium
       script:
-        - conda install $CHANS_DEV firefox geckodriver mpl_sample_data
         - bokeh sampledata
-        - nbsite generate-rst --org pyviz --project-name holoviews --skip ^reference
+        - nbsite generate-rst --org holoviz --project-name holoviews --skip ^reference
         - python ./doc/generate_modules.py holoviews -d ./doc/reference_manual -n holoviews -e tests
         - nbsite build --what=html --output=builtdocs
       after_success:
@@ -102,7 +108,6 @@ jobs:
       stage: gallery_dev
       env: DESC="gallery" HV_DOC_GALLERY="true" HV_DOC_REF_GALLERY="false" BUCKET="dev." HV_REQUIREMENTS="doc" PANEL_EMBED="true" PANEL_EMBED_JSON="true" PANEL_EMBED_JSON_PREFIX="json" CHANS_DEV="-c pyviz/label/dev -c bokeh -c conda-forge"
       script:
-        - conda install $CHANS_DEV firefox geckodriver mpl_sample_data
         - bokeh sampledata
         - aws s3 sync --quiet s3://holoviews-doc-builds/$TRAVIS_BUILD_NUMBER ./
         - git reset --hard --recurse-submodule

doc/FAQ.rst

@@ -62,7 +62,7 @@ set the ``xlim`` and ``ylim`` plot options:
 
 .. code:: python
 
-  hv.Curve(df, 'x_col', 'y_col').options(xlim=(0, None), ylim=(0, 10))
+  hv.Curve(df, 'x_col', 'y_col').opts(xlim=(0, None), ylim=(0, 10))
 
 This approach allows you to customize objects easily as a final step, but note that the values won't be applied to the underlying data, and thus won't be inherited if this object is subsequently used in an operation or data selection command.
 
@@ -118,10 +118,10 @@ determine the appropriate axis range yourself and set that, e.g. with
 .. code:: python
 
   # for matplotlib:
-  hv_obj = hv_obj.options(fig_size=500)
+  hv_obj = hv_obj.opts(fig_size=500)
 
   # for bokeh:
-  hv_obj = hv_obj.options(width=1000, height=500)
+  hv_obj = hv_obj.opts(width=1000, height=500)
 
 
 **Q: How do I get a legend on my overlay figure?**
@@ -184,7 +184,7 @@ plotted, instead consider that it is possible to write so called ``hooks``:
 	# artist, axis, legend and in bokeh x_range, y_range, glyph, cds etc.
 	plot.handles
 
-  hv.Curve(df, 'x_col', 'y_col').options(hooks=[hook])
+  hv.Curve(df, 'x_col', 'y_col').opts(hooks=[hook])
 
 These hooks can modify the backend specific representation, e.g. the
 matplotlib figure, before it is displayed, allowing arbitrary customizations to be
@@ -229,7 +229,7 @@ for that HoloViews object:
       b.axis[0].ticker = FixedTicker(ticks=list(range(0, 10)))
 
   h = hv.Curve([1,2,7], 'x_col', 'y_col')
-  h = h.options(hooks=[update_axis])
+  h = h.opts(hooks=[update_axis])
   h
 
 Here, you've wrapped your Bokeh-API calls into a function, then
@@ -360,7 +360,7 @@ they don't get applied until the object is returned
 (during IPython's "display hooks" processing). So to make sure that
 options get applied, (a) return the object from a cell, and then (b)
 access it (e.g. for exporting) after the object has been returned.
-To avoid confusion, you may prefer to use .options() directly on the
+To avoid confusion, you may prefer to use .opts() directly on the
 object to ensure that the options have been applied before exporting.
 Example code below:
 
@@ -451,7 +451,7 @@ element:
   hv.extension('matplotlib')
   Store.add_style_opts(hv.Image, ['filternorm'], backend='matplotlib')
 
-Now you can freely use ``'filternorm'`` in ``.options()`` and in the
+Now you can freely use ``'filternorm'`` in ``.opts()`` and in the
 ``%opts`` line/cell magic, including tab-completion!
 
 

doc/conf.py

@@ -4,8 +4,8 @@
 
 # Declare information specific to this project.
 project = u'HoloViews'
-authors = u'PyViz developers'
-copyright = u'2019 ' + authors
+authors = u'HoloViz developers'
+copyright = u'2020 ' + authors
 description = 'Stop plotting your data - annotate your data and let it visualize itself.'
 
 import param
@@ -16,6 +16,7 @@
 import holoviews
 version = release = holoviews.__version__
 
+holoviews.extension.inline = False
 
 html_theme = 'sphinx_holoviz_theme'
 html_static_path += ['_static']

examples/gallery/demos/bokeh/lesmis_example.ipynb

@@ -78,10 +78,9 @@
     "\n",
     "combined = hv.Overlay([o.opts(cmap=cm).sort() for o, cm in zip(overlaid, cmaps)], label='LesMis Occurences')\n",
     "styled = combined.opts(\n",
-    "    opts.HeatMap(logz=True, clipping_colors={'NaN':(1,1,1,0.)},\n",
-    "                xaxis='top', xrotation=90,\n",
+    "    opts.HeatMap(logz=True, clim=(0.1, None), clipping_colors={'NaN':(1,1,1,0.)}, xaxis='top', xrotation=90,\n",
     "                 fontsize={'ticks': '7pt', 'title': '18pt'}, invert_xaxis=True, tools=['hover'],\n",
-    "                 labelled=[],),\n",
+    "                 labelled=[], axiswise=True),\n",
     "    opts.Overlay(height=800, width=800)\n",
     ")\n",
     "styled"

examples/reference/elements/bokeh/Spread.ipynb

@@ -33,7 +33,7 @@
    "source": [
     "``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show an average trend along with a corresponding spread of values; see the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for examples. \n",
     "\n",
-    "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less that one, making it partially transparent. \n",
+    "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less than one, making it partially transparent. \n",
     "\n",
     "\n",
     "##### Symmetric"

examples/reference/elements/matplotlib/Spread.ipynb

@@ -33,7 +33,7 @@
    "source": [
     "``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show an average trend along with a corresponding spread of values; see the [Tabular Datasets](../../../user_guide/07-Tabular_Datasets.ipynb) user guide for examples.\n",
     "\n",
-    "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less that one, making it partially transparent. \n",
+    "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less than one, making it partially transparent. \n",
     "\n",
     "##### Symmetric"
    ]

examples/reference/elements/plotly/Spread.ipynb

@@ -30,7 +30,7 @@
    "source": [
     "``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show an average trend along with a corresponding spread of values; see the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for examples. \n",
     "\n",
-    "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less that one, making it partially transparent. \n",
+    "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less than one, making it partially transparent. \n",
     "\n",
     "\n",
     "##### Symmetric"

examples/user_guide/Continuous_Coordinates.ipynb

@@ -53,7 +53,7 @@
     "|||\n",
     "|:--------------:|:----------------|\n",
     "| **``f(x,y)``** | a simple function that accepts a location in a 2D plane specified in millimeters (mm) |\n",
-    "| **``region``** | a 1mm&times;1mm square region of this 2D plane, centered at the origin, and</dd> |\n",
+    "| **``region``** | a 1mm&times;1mm square region of this 2D plane, centered at the origin, and |\n",
     "| **``coords``** | a function returning a square (s&times;s) grid of (x,y) coordinates regularly sampling the region in the given bounds, at the centers of each grid cell |\n",
     "||||\n",
     "\n"
@@ -332,6 +332,8 @@
     "\n",
     "They also work the same for the n-dimensional coordinates and slicing supported by the [container](Containers) types ``HoloMap``, ``NdLayout``, and ``NdOverlay``, implemented in ``holoviews.core.dimension.Dimensioned`` and again allowing arbitrary irregular spacing.  \n",
     "\n",
+    "``QuadMesh`` elements are similar but allow more general types of mapping between the underlying array and the continuous space, with arbitrary spacing along each of the axes or even over the entire array. See the ``QuadMesh`` element for more details.\n",
+    "\n",
     "Together, these powerful continuous-coordinate indexing and slicing operations allow you to work naturally and simply in the full *n*-dimensional space that characterizes your data and parameter values."
    ]
   },

holoviews/core/data/cudf.py

@@ -163,7 +163,7 @@ def groupby(cls, dataset, dimensions, container_type, group_type, **kwargs):
         group_kwargs['dataset'] = dataset.dataset
 
         # Find all the keys along supplied dimensions
-        keys = product(*(dataset.data[dimensions[0]].unique() for d in dimensions))
+        keys = product(*(dataset.data[dimensions[0]].unique().values_host for d in dimensions))
 
         # Iterate over the unique entries applying selection masks
         grouped_data = []

holoviews/core/util.py

@@ -1544,10 +1544,16 @@ def resolve_dependent_value(value):
        A new dictionary where any parameter dependencies have been
        resolved.
     """
+    range_widget = False
     if 'panel' in sys.modules:
-        from panel.widgets.base import Widget
-        if isinstance(value, Widget):
-            value = value.param.value
+        from panel.widgets import RangeSlider, Widget
+        range_widget = isinstance(value, RangeSlider)
+        try:
+            from panel.depends import param_value_if_widget
+            value = param_value_if_widget(value)
+        except Exception:
+            if isinstance(value, Widget):
+                value = value.param.value
     if is_param_method(value, has_deps=True):
         value = value()
     elif isinstance(value, param.Parameter) and isinstance(value.owner, param.Parameterized):
@@ -1557,6 +1563,8 @@ def resolve_dependent_value(value):
         args = (getattr(p.owner, p.name) for p in deps.get('dependencies', []))
         kwargs = {k: getattr(p.owner, p.name) for k, p in deps.get('kw', {}).items()}
         value = value(*args, **kwargs)
+    if isinstance(value, tuple) and range_widget:
+        value = slice(*value)
     return value
 
 

holoviews/element/raster.py

@@ -291,9 +291,10 @@ def __init__(self, data, kdims=None, vdims=None, bounds=None, extents=None,
 
         Dataset.__init__(self, data, kdims=kdims, vdims=vdims, extents=extents, **params)
         if not self.interface.gridded:
-            raise DataError("%s type expects gridded data, %s is columnar."
+            raise DataError("%s type expects gridded data, %s is columnar. "
                             "To display columnar data as gridded use the HeatMap "
-                            "element or aggregate the data." %
+                            "element or aggregate the data (e.g. using rasterize "
+                            "or np.histogram2d)." %
                             (type(self).__name__, self.interface.__name__))
 
         dim2, dim1 = self.interface.shape(self, gridded=True)[:2]
@@ -783,9 +784,10 @@ def __init__(self, data, kdims=None, vdims=None, **params):
             data = ([], [], np.zeros((0, 0)))
         super(QuadMesh, self).__init__(data, kdims, vdims, **params)
         if not self.interface.gridded:
-            raise DataError("%s type expects gridded data, %s is columnar."
+            raise DataError("%s type expects gridded data, %s is columnar. "
                             "To display columnar data as gridded use the HeatMap "
-                            "element or aggregate the data." %
+                            "element or aggregate the data (e.g. using "
+                            "np.histogram2d)." %
                             (type(self).__name__, self.interface.__name__))
 
 

holoviews/plotting/bokeh/callbacks.py

@@ -111,6 +111,8 @@ def reset(self):
         if self.handle_ids:
             handles = self._init_plot_handles()
             for handle_name in self.models:
+                if not (handle_name in handles):
+                    continue
                 handle = handles[handle_name]
                 cb_hash = (id(handle), id(type(self)))
                 self._callbacks.pop(cb_hash, None)
@@ -1198,8 +1200,14 @@ def _process_msg(self, msg):
                 new_values = []
                 for vals in values:
                     if isinstance(vals, dict):
+
+                        shape = vals.pop('shape', None)
+                        dtype = vals.pop('dtype', None)
+                        vals.pop('dimension', None)
                         vals = sorted([(int(k), v) for k, v in vals.items()])
                         vals = [v for k, v in vals]
+                        if dtype is not None:
+                            vals = np.array(vals, dtype=dtype).reshape(shape)
                     new_values.append(vals)
                 values = new_values
             elif any(isinstance(v, (int, float)) for v in values):

holoviews/plotting/bokeh/element.py

@@ -1380,6 +1380,9 @@ def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
         for cb in self.callbacks:
             cb.initialize()
 
+        if self.top_level:
+            self.init_links()
+
         if not self.overlaid:
             self._set_active_tools(plot)
             self._process_legend()

holoviews/plotting/bokeh/stats.py

@@ -548,6 +548,8 @@ def get_data(self, element, ranges, style):
         kde_data, line_data, seg_data, bar_data, scatter_data = (defaultdict(list) for i in range(5))
         for i, (key, g) in enumerate(groups.items()):
             key = decode_bytes(key)
+            if element.kdims:
+                key = tuple(d.pprint_value(k) for d, k in zip(element.kdims, key))
             kde, line, segs, bars, scatter = self._kde_data(element, g, key, split_dim, split_cats, **kwargs)
             for k, v in segs.items():
                 seg_data[k] += v

holoviews/plotting/plotly/renderer.py

@@ -159,6 +159,8 @@ def load_nb(cls, inline=True):
         """
         import panel.models.plotly # noqa
         cls._loaded = True
+        if 'plotly' not in getattr(pn.extension, '_loaded_extensions', ['plotly']):
+            pn.extension._loaded_extensions.append('plotly')
 
 
 def _activate_plotly_backend(renderer):