Update Ruff in pre-commit and report panel comms in show_versions

.pre-commit-config.yaml

@@ -17,7 +17,7 @@ repos:
         exclude: \.min\.js$
       - id: trailing-whitespace
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.276
+    rev: v0.0.284
     hooks:
       - id: ruff
         args: [holoviews]

holoviews/core/data/__init__.py

@@ -454,7 +454,7 @@ def closest(self, coords=[], **kwargs):
             if len(kwargs) > 1:
                 raise NotImplementedError("Closest method currently only "
                                           "supports 1D indexes")
-            samples = list(kwargs.values())[0]
+            samples = next(iter(kwargs.values()))
             coords = samples if isinstance(samples, list) else [samples]
 
         xs = self.dimension_values(0)

holoviews/core/data/dictionary.py

@@ -141,7 +141,7 @@ def unpack_scalar(cls, dataset, data):
         """
         if len(data) != 1:
             return data
-        key = list(data.keys())[0]
+        key = next(iter(data.keys()))
 
         if len(data[key]) == 1 and key in dataset.vdims:
             scalar = data[key][0]
@@ -331,7 +331,7 @@ def select(cls, dataset, selection_mask=None, **selection):
                 data[k] = v
             else:
                 data[k] = v[selection_mask]
-        if indexed and len(list(data.values())[0]) == 1 and len(dataset.vdims) == 1:
+        if indexed and len(next(iter(data.values()))) == 1 and len(dataset.vdims) == 1:
             value = data[dataset.vdims[0].name]
             return value if isscalar(value) else value[0]
         return data

holoviews/core/data/grid.py

@@ -132,9 +132,9 @@ def init(cls, eltype, data, kdims, vdims):
                             'by the key dimensions. Expected %d-D array, '
                             'found %d-D array.' % (vdim, len(expected), len(shape)))
             elif any((s!=e and (s+1)!=e) for s, e in zip(shape, valid_shape)):
-                raise error('Key dimension values and value array {} '
-                            'shapes do not match. Expected shape {}, '
-                            'actual shape: {}'.format(vdim, valid_shape, shape), cls)
+                raise error(f'Key dimension values and value array {vdim} '
+                            f'shapes do not match. Expected shape {valid_shape}, '
+                            f'actual shape: {shape}', cls)
         return data, {'kdims':kdims, 'vdims':vdims}, {}
 
 
@@ -557,17 +557,17 @@ def select(cls, dataset, selection_mask=None, **selection):
             if irregular:
                 if np.isscalar(ind) or isinstance(ind, (set, list)):
                     raise IndexError("Indexing not supported for irregularly "
-                                     "sampled data. {} value along {} dimension."
-                                     "must be a slice or 2D boolean mask.".format(ind, dim))
+                                     f"sampled data. {ind} value along {dim} dimension."
+                                     "must be a slice or 2D boolean mask.")
                 mask = mask.max(axis=i)
             elif dataset._binned:
                 edges = cls.coords(dataset, dim, False, edges=True)
                 inds = np.argwhere(mask)
                 if np.isscalar(ind):
                     emin, emax = edges.min(), edges.max()
                     if ind < emin:
-                        raise IndexError("Index {} less than lower bound "
-                                         "of {} for {} dimension.".format(ind, emin, dim))
+                        raise IndexError(f"Index {ind} less than lower bound "
+                                         f"of {emin} for {dim} dimension.")
                     elif ind >= emax:
                         raise IndexError("Index {} more than or equal to upper bound "
                                          "of {} for {} dimension.".format(ind, emax, dim))

holoviews/core/data/image.py

@@ -55,9 +55,9 @@ def init(cls, eltype, data, kdims, vdims):
             shape = data.shape[:2]
             error = DataError if len(shape) > 1 and not eltype._binned else ValueError
             if shape != expected and not (not expected and shape == (1,)):
-                raise error('Key dimension values and value array {} '
-                            'shapes do not match. Expected shape {}, '
-                            'actual shape: {}'.format(vdims[0], expected, shape), cls)
+                raise error(f'Key dimension values and value array {vdims[0]} '
+                            f'shapes do not match. Expected shape {expected}, '
+                            f'actual shape: {shape}', cls)
 
         if not isinstance(data, np.ndarray) or data.ndim not in [2, 3]:
             raise ValueError('ImageInterface expects a 2D array.')
@@ -297,7 +297,7 @@ def unpack_scalar(cls, dataset, data):
         """
         if np.isscalar(data) or len(data) != 1:
             return data
-        key = list(data.keys())[0]
+        key = next(iter(data.keys()))
 
         if len(data[key]) == 1 and key in dataset.vdims:
             return data[key][0]

holoviews/core/data/multipath.py

@@ -59,8 +59,8 @@ def init(cls, eltype, data, kdims, vdims):
                                 if hasattr(Interface.interfaces.get(dt), 'has_holes')]
                 geom_type = d.get('geom_type')
                 if geom_type is not None and geom_type not in cls.geom_types:
-                    raise DataError("Geometry type '{}' not recognized, "
-                                    "must be one of {}.".format(geom_type, cls.geom_types))
+                    raise DataError(f"Geometry type '{geom_type}' not recognized, "
+                                    f"must be one of {cls.geom_types}.")
                 else:
                     datatype = [dt for dt in datatype
                                 if hasattr(Interface.interfaces.get(dt), 'geom_type')]

holoviews/core/data/xarray.py

@@ -229,13 +229,13 @@ def retrieve_unit_and_label(dim):
                 undeclared.append(c)
             if undeclared and eltype.param.kdims.bounds[1] not in (0, None):
                 raise DataError(
-                    'The coordinates on the {!r} DataArray do not match the '
+                    f'The coordinates on the {vdim.name!r} DataArray do not match the '
                     'provided key dimensions (kdims). The following coords '
-                    'were left unspecified: {!r}. If you are requesting a '
+                    f'were left unspecified: {undeclared!r}. If you are requesting a '
                     'lower dimensional view such as a histogram cast '
                     'the xarray to a columnar format using the .to_dataframe '
                     'or .to_dask_dataframe methods before providing it to '
-                    'HoloViews.'.format(vdim.name, undeclared))
+                    'HoloViews.')
         return data, {'kdims': kdims, 'vdims': vdims}, {}
 
 

holoviews/core/dimension.py

@@ -997,7 +997,7 @@ def get_dimension_index(self, dimension):
         dim = dimension_name(dimension)
         try:
             dimensions = self.kdims+self.vdims
-            return [i for i, d in enumerate(dimensions) if d == dim][0]
+            return next(i for i, d in enumerate(dimensions) if d == dim)
         except IndexError:
             raise Exception(f"Dimension {dim} not found in {self.__class__.__name__}.")
 

holoviews/core/ndmapping.py

@@ -170,8 +170,8 @@ def _add_item(self, dim_vals, data, sort=True, update=True):
 
         for dim, val in valid_vals:
             if dim.values and val is not None and val not in dim.values:
-                raise KeyError('{} dimension value {} not in'
-                               ' specified dimension values.'.format(dim, repr(val)))
+                raise KeyError(f'{dim} dimension value {val!r} not in'
+                               ' specified dimension values.')
 
         # Updates nested data structures rather than simply overriding them.
         if (update and (dim_vals in self.data)

holoviews/core/options.py

@@ -1292,7 +1292,7 @@ def lookup(cls, backend, obj):
             idlist = ",".join([str(el) for el in sorted(ids)])
             raise Exception("Object contains elements combined across "
                             "multiple custom trees (ids %s)" % idlist)
-        return cls._custom_options[backend][list(ids)[0]]
+        return cls._custom_options[backend][next(iter(ids))]
 
     @classmethod
     def transfer_options(cls, obj, new_obj, backend=None, names=None, level=3):

holoviews/core/overlay.py

@@ -239,8 +239,8 @@ def group(self):
         values = {el.group for el in elements}
         types = {type(el) for el in elements}
         if values:
-            group = list(values)[0]
-            vtype = list(types)[0].__name__
+            group = next(iter(values))
+            vtype = next(iter(types)).__name__
         else:
             group, vtype = [], ''
         if len(values) == 1 and group != vtype:
@@ -262,7 +262,7 @@ def label(self):
         labels = {el.label for el in self
                   if not el._auxiliary_component}
         if len(labels) == 1:
-            return list(labels)[0]
+            return next(iter(labels))
         else:
             return ''
 

holoviews/core/pprint.py

@@ -146,7 +146,7 @@ def info(cls, obj, ansi=False, backend='matplotlib', visualization=True,
         plot_class = backend_registry.get(obj if isclass else type(obj), None)
         # Special case to handle PlotSelectors
         if hasattr(plot_class, 'plot_classes'):
-            plot_class =  list(plot_class.plot_classes.values())[0]
+            plot_class =  next(iter(plot_class.plot_classes.values()))
 
 
         if visualization is False or plot_class is None:
@@ -189,13 +189,13 @@ def target_info(cls, obj, ansi=False):
 
         element_info = None
         if len(element_set) == 1:
-            element_info = f'Element: {list(element_set)[0]}'
+            element_info = f'Element: {next(iter(element_set))}'
         elif len(element_set) > 1:
             element_info = 'Elements:\n   %s'  % '\n   '.join(sorted(element_set))
 
         container_info = None
         if len(container_set) == 1:
-            container_info = f'Container: {list(container_set)[0]}'
+            container_info = f'Container: {next(iter(container_set))}'
         elif len(container_set) > 1:
             container_info = 'Containers:\n   %s'  % '\n   '.join(sorted(container_set))
         heading = cls.heading('Target Specifications', ansi=ansi, char="-")

holoviews/core/util.py

@@ -2024,7 +2024,7 @@ def bound_range(vals, density, time_unit='us'):
             full_precision_density = compute_density(low, high, len(vals)-1)
             with np.errstate(over='ignore'):
                 density = round(full_precision_density, sys.float_info.dig)
-        if density == 0 or density == np.inf:
+        if density in (0, np.inf):
             density = full_precision_density
     if density == 0:
         raise ValueError('Could not determine Image density, ensure it has a non-zero range.')

holoviews/element/chart.py

@@ -4,7 +4,7 @@
 from ..core import util
 from ..core import Dimension, Dataset, Element2D, NdOverlay, Overlay
 from ..core.dimension import process_dimensions
-from .geom import Rectangles, Points, VectorField # noqa: backward compatible import
+from .geom import Rectangles, Points, VectorField # noqa: F401 backward compatible import
 from .selection import Selection1DExpr
 
 

holoviews/element/raster.py

@@ -126,7 +126,7 @@ def sample(self, samples=[], bounds=None, **sample_values):
             params['kdims'] = self.kdims
             return Table(table_data, **params)
         else:
-            dimension, sample_coord = list(sample_values.items())[0]
+            dimension, sample_coord = next(iter(sample_values.items()))
             if isinstance(sample_coord, slice):
                 raise ValueError(
                     'Raster sampling requires coordinates not slices,'

holoviews/operation/datashader.py

@@ -101,8 +101,8 @@ def _get_aggregator(cls, element, agg, add_field=True):
         if isinstance(agg, str):
             if agg not in cls._agg_methods:
                 agg_methods = sorted(cls._agg_methods)
-                raise ValueError("Aggregation method '{!r}' is not known; "
-                                 "aggregator must be one of: {!r}".format(agg, agg_methods))
+                raise ValueError(f"Aggregation method '{agg!r}' is not known; "
+                                 f"aggregator must be one of: {agg_methods!r}")
             if agg == 'count_cat':
                 agg = cls._agg_methods[agg]('__temp__')
             else:
@@ -968,10 +968,10 @@ def _precompute(self, element, agg):
                 else:
                     p, n = 'vertices', 'simplexes'
                 self.param.warning(
-                    "TriMesh {} were provided as dask DataFrame but {} "
+                    f"TriMesh {p} were provided as dask DataFrame but {n} "
                     "were not. Datashader will not use dask to parallelize "
                     "rasterization unless both are provided as dask "
-                    "DataFrames.".format(p, n))
+                    "DataFrames.")
             simplices = element.dframe(simplex_dims)
             verts = element.nodes.dframe(vert_dims)
         for c, dtype in zip(simplices.columns[:3], simplices.dtypes):

holoviews/operation/element.py

@@ -711,7 +711,7 @@ def _process(self, element, key=None):
         if self.p.dimension:
             selected_dim = self.p.dimension
         else:
-            selected_dim = [d.name for d in element.vdims + element.kdims][0]
+            selected_dim = next(d.name for d in element.vdims + element.kdims)
         dim = element.get_dimension(selected_dim)
 
         if hasattr(element, 'interface'):

holoviews/plotting/bokeh/callbacks.py

@@ -1023,7 +1023,7 @@ def _update_cds_vdims(self, data):
             if dim in data:
                 continue
             values = element.dimension_values(d)
-            if len(values) != len(list(data.values())[0]):
+            if len(values) != len(next(iter(data.values()))):
                 values = np.concatenate([values, [stream.empty_value]])
             data[dim] = values
 

holoviews/plotting/bokeh/chart.py

@@ -155,7 +155,7 @@ def get_batched_data(self, element, ranges):
                 continue
 
             # Apply static styles
-            nvals = len(list(eldata.values())[0])
+            nvals = len(next(iter(eldata.values())))
             sdata, smapping = expand_batched_style(style, self._batched_style_opts,
                                                    elmapping, nvals)
             if 'angle' in sdata and '__angle' not in data and 'marker' in data:
@@ -864,7 +864,7 @@ def _add_color_data(self, ds, ranges, style, cdim, data, mapping, factors, color
         for k, cd in cdata.items():
             if isinstance(cmapper, CategoricalColorMapper) and cd.dtype.kind in 'uif':
                 cd = categorize_array(cd, cdim)
-            if k not in data or len(data[k]) != [len(data[key]) for key in data if key != k][0]:
+            if k not in data or len(data[k]) != next(len(data[key]) for key in data if key != k):
                 data[k].append(cd)
             else:
                 data[k][-1] = cd

holoviews/plotting/bokeh/element.py

@@ -361,7 +361,7 @@ def _get_hover_data(self, data, element, dimensions=None):
         for k, v in self.overlay_dims.items():
             dim = util.dimension_sanitizer(k.name)
             if dim not in data:
-                data[dim] = [v for _ in range(len(list(data.values())[0]))]
+                data[dim] = [v] * len(next(iter(data.values())))
 
     def _shared_axis_range(self, plots, specs, range_type, axis_type, pos):
         """
@@ -1400,15 +1400,15 @@ def _apply_transforms(self, element, data, ranges, style, group=None):
                 elif isinstance(element, Graph) and v in element.nodes:
                     v = dim(element.nodes.get_dimension(v))
                 elif any(d==v for d in self.overlay_dims):
-                    v = dim([d for d in self.overlay_dims if d==v][0])
+                    v = dim(next(d for d in self.overlay_dims if d==v))
 
             if (not isinstance(v, dim) or (group is not None and not k.startswith(group))):
                 continue
             elif (not v.applies(element) and v.dimension not in self.overlay_dims):
                 new_style.pop(k)
                 self.param.warning(
-                    'Specified {} dim transform {!r} could not be applied, '
-                    'as not all dimensions could be resolved.'.format(k, v))
+                    f'Specified {k} dim transform {v!r} could not be applied, '
+                    'as not all dimensions could be resolved.')
                 continue
 
             if v.dimension in self.overlay_dims:
@@ -1433,7 +1433,7 @@ def _apply_transforms(self, element, data, ranges, style, group=None):
                                      'to overlay your data along the dimension.'.format(
                                          style=k, dim=v.dimension, element=element,
                                          backend=self.renderer.backend))
-                elif data and len(val) != len(list(data.values())[0]):
+                elif data and len(val) != len(next(iter(data.values()))):
                     if isinstance(element, VectorField):
                         val = np.tile(val, 3)
                     elif isinstance(element, Path) and not isinstance(element, Contours):
@@ -1638,7 +1638,7 @@ def _update_glyph(self, renderer, properties, mapping, glyph, source, data):
                             prop = 'value' if 'label' in lp else 'field'
                             label = {prop: legend}
                         elif isinstance(item.label, dict):
-                            label = {list(item.label)[0]: legend}
+                            label = {next(iter(item.label)): legend}
                         else:
                             label = {'value': legend}
                         item.label = label

holoviews/plotting/bokeh/path.py

@@ -72,7 +72,7 @@ def _get_hover_data(self, data, element):
         for k, v in self.overlay_dims.items():
             dim = util.dimension_sanitizer(k.name)
             if dim not in data:
-                data[dim] = [v for _ in range(len(list(data.values())[0]))]
+                data[dim] = [v] * len(next(iter(data.values())))
 
 
     def get_data(self, element, ranges, style):
@@ -159,7 +159,7 @@ def get_batched_data(self, element, ranges=None):
                 continue
 
             # Apply static styles
-            nvals = len(list(eldata.values())[0])
+            nvals = len(next(iter(eldata.values())))
             sdata, smapping = expand_batched_style(style, self._batched_style_opts,
                                                    elmapping, nvals)
             elmapping.update({k: v for k, v in smapping.items() if k not in elmapping})
@@ -219,7 +219,7 @@ def _get_hover_data(self, data, element):
         for k, v in self.overlay_dims.items():
             dim = util.dimension_sanitizer(k.name)
             if dim not in data:
-                data[dim] = [v for _ in range(len(list(data.values())[0]))]
+                data[dim] = [v] * len(next(iter(data.values())))
 
     def get_data(self, element, ranges, style):
         if self._has_holes is None:

holoviews/plotting/mpl/chart.py

@@ -335,11 +335,9 @@ def initialize_plot(self, ranges=None):
             if 'vmin' in style:
                 raise ValueError('Mapping a continuous dimension to a '
                                  'color on a HistogramPlot is not '
-                                 'supported by the {backend} backend. '
+                                 f'supported by the {self.renderer.backend} backend. '
                                  'To map a dimension to a color supply '
-                                 'an explicit list of rgba colors.'.format(
-                                     backend=self.renderer.backend
-                                 )
+                                 'an explicit list of rgba colors.'
                 )
 
         # Plot bars and make any adjustments
@@ -631,8 +629,8 @@ def _compute_styles(self, element, ranges, style):
             if sizes is None:
                 eltype = type(element).__name__
                 self.param.warning(
-                    '{} dimension is not numeric, cannot use to '
-                    'scale {} size.'.format(sdim.pprint_label, eltype))
+                    f'{sdim.pprint_label} dimension is not numeric, cannot use to '
+                    f'scale {eltype} size.')
             else:
                 style['s'] = sizes
         style['edgecolors'] = style.pop('edgecolors', 'none')