refactor: enable RUF005

holoviews/core/__init__.py

@@ -40,6 +40,7 @@ def public(obj):
                    SheetCoordinateSystem, AttrTree]
     return any([issubclass(obj, bc) for bc in baseclasses])
 
-_public = list({_k for _k, _v in locals().items() if public(_v)})
-__all__ = _public + ["boundingregion", "dimension", "layer", "layout",
-                     "ndmapping", "operation", "options", "sheetcoords", "tree", "element"]
+__all__ = [
+    *{_k for _k, _v in locals().items() if public(_v)},
+    "boundingregion", "dimension", "layer", "layout", "ndmapping", "operation", "options", "sheetcoords", "tree", "element"
+]

holoviews/core/accessors.py

@@ -56,9 +56,7 @@ def pipelined_call(*args, **kwargs):
 
                     if isinstance(result, Dataset):
                         result._pipeline = inst_pipeline.instance(
-                            operations=inst_pipeline.operations + [
-                                init_op, call_op
-                            ],
+                            operations=[*inst_pipeline.operations, init_op, call_op],
                             output_type=type(result),
                         )
                     elif isinstance(result, MultiDimensionalMapping):
@@ -69,9 +67,7 @@ def pipelined_call(*args, **kwargs):
                                 args=[key],
                             )
                             element._pipeline = inst_pipeline.instance(
-                                operations=inst_pipeline.operations + [
-                                    init_op, call_op, getitem_op
-                                ],
+                                operations=[*inst_pipeline.operations, init_op, call_op, getitem_op],
                                 output_type=type(result),
                             )
             finally:
@@ -431,7 +427,7 @@ def __call__(self, specs=None, **dimensions):
             if renames:
                 data = obj.interface.redim(obj, renames)
             transform = self._create_expression_transform(kdims, vdims, list(renames.values()))
-            transforms = obj._transforms + [transform]
+            transforms = [*obj._transforms, transform]
             clone = obj.clone(data, kdims=kdims, vdims=vdims, transforms=transforms)
             if self._obj.dimensions(label='name') == clone.dimensions(label='name'):
                 # Ensure that plot_id is inherited as long as dimension

holoviews/core/data/__init__.py

@@ -206,7 +206,7 @@ def pipelined_fn(*args, **kwargs):
                 if not in_method:
                     if isinstance(result, Dataset):
                         result._pipeline = inst_pipeline.instance(
-                            operations=inst_pipeline.operations + [op],
+                            operations=[*inst_pipeline.operations, op],
                             output_type=type(result),
                         )
 
@@ -219,9 +219,7 @@ def pipelined_fn(*args, **kwargs):
                                     args=[key]
                                 )
                                 element._pipeline = inst_pipeline.instance(
-                                    operations=inst_pipeline.operations + [
-                                        op, getitem_op
-                                    ],
+                                    operations=[*inst_pipeline.operations, op, getitem_op],
                                     output_type=type(result),
                                 )
             finally:
@@ -343,7 +341,7 @@ def __init__(self, data, kdims=None, vdims=None, **kwargs):
             kwargs=dict(kwargs, kdims=self.kdims, vdims=self.vdims),
         )
         self._pipeline = input_pipeline.instance(
-            operations=input_pipeline.operations + [init_op],
+            operations=[*input_pipeline.operations, init_op],
             output_type=type(self),
         )
         self._transforms = input_transforms or []
@@ -606,8 +604,8 @@ def select(self, selection_expr=None, selection_specs=None, **selection):
 
         if selection_specs is not None and not isinstance(selection_specs, (list, tuple)):
             selection_specs = [selection_specs]
-        selection = {dim_name: sel for dim_name, sel in selection.items()
-                     if dim_name in self.dimensions()+['selection_mask']}
+        sel_dims = (*self.dimensions(), 'selection_mask')
+        selection = {dim: sel for dim, sel in selection.items() if dim in sel_dims}
         if (selection_specs and not any(self.matches(sp) for sp in selection_specs)
             or (not selection and not selection_expr)):
             return self
@@ -811,7 +809,7 @@ def sample(self, samples=None, bounds=None, closest=True, **kwargs):
                 reindexed = selection.clone(new_type=Dataset, datatype=datatype).reindex(kdims)
                 selection = tuple(reindexed.columns(kdims+self.vdims).values())
 
-            datatype = list(core_util.unique_iterator(self.datatype+['dataframe', 'dict']))
+            datatype = list(core_util.unique_iterator([*self.datatype, 'dataframe', 'dict']))
             return self.clone(selection, kdims=kdims, new_type=new_type,
                               datatype=datatype)
 
@@ -1185,7 +1183,7 @@ def clone(self, data=None, shared_data=True, new_type=None, link=True,
             Cloned object
         """
         if 'datatype' not in overrides:
-            datatypes = [self.interface.datatype] + self.datatype
+            datatypes = [self.interface.datatype, *self.datatype]
             overrides['datatype'] = list(core_util.unique_iterator(datatypes))
 
         if data is None:

holoviews/core/data/array.py

@@ -247,7 +247,7 @@ def assign(cls, dataset, new_data):
             idx = dataset.get_dimension_index(d)
             data[:, idx] = arr
         new_cols = [arr for d, arr in new_data.items() if dataset.get_dimension(d) is None]
-        return np.column_stack([data]+new_cols)
+        return np.column_stack([data, *new_cols])
 
 
     @classmethod

holoviews/core/data/grid.py

@@ -122,7 +122,7 @@ def init(cls, eltype, data, kdims, vdims):
                 if shape[-1] != len(vdims):
                     raise error('The shape of the value array does not match the number of value dimensions.')
                 shape = shape[:-1]
-            if (not expected and shape == (1,)) or (len(set((shape,)+shapes)) == 1 and len(shape) > 1):
+            if (not expected and shape == (1,)) or (len(shape) > 1 and len({shape, *shapes}) == 1):
                 # If empty or an irregular mesh
                 pass
             elif len(shape) != len(expected):

holoviews/core/data/image.py

@@ -253,7 +253,7 @@ def sample(cls, dataset, samples=None):
         if len(samples[0]) == 1:
             select = {dataset.kdims[0].name: [s[0] for s in samples]}
             return tuple(dataset.select(**select).columns().values())
-        return [c+(dataset.data[dataset._coord2matrix(c)],) for c in samples]
+        return [(*c, dataset.data[dataset._coord2matrix(c)]) for c in samples]
 
 
     @classmethod

holoviews/core/data/interface.py

@@ -39,7 +39,7 @@ def __getitem__(self, index):
                     args=[index],
                 )
                 res._pipeline = self.dataset.pipeline.instance(
-                    operations=self.dataset.pipeline.operations + [getitem_op],
+                    operations=[*self.dataset.pipeline.operations, getitem_op],
                     output_type=type(self.dataset)
                 )
         finally:
@@ -91,7 +91,7 @@ def _perform_getitem(cls, dataset, index):
             kdims = [d for d in dims if d in kdims]
             vdims = [d for d in dims if d in vdims]
 
-        datatypes = util.unique_iterator([dataset.interface.datatype]+dataset.datatype)
+        datatypes = util.unique_iterator([dataset.interface.datatype, *dataset.datatype])
         datatype = [dt for dt in datatypes if dt in Interface.interfaces and
                     not Interface.interfaces[dt].gridded]
         if not datatype: datatype = ['dataframe', 'dictionary']
@@ -118,7 +118,7 @@ def _perform_getitem(cls, dataset, indices):
         params = {}
         if hasattr(ds, 'bounds'):
             params['bounds'] = None
-        return dataset.clone(selected, datatype=[ds.interface.datatype]+ds.datatype, **params)
+        return dataset.clone(selected, datatype=[ds.interface.datatype, *ds.datatype], **params)
 
 
 class Interface(param.Parameterized):

holoviews/core/data/multipath.py

@@ -567,8 +567,8 @@ def ensure_ring(geom, values=None):
         values = geom
 
     breaks = np.where(np.isnan(geom.astype('float')).sum(axis=1))[0]
-    starts = [0] + list(breaks+1)
-    ends = list(breaks-1) + [len(geom)-1]
+    starts = [0, *(breaks + 1)]
+    ends = [*(breaks - 1), len(geom) - 1]
     zipped = zip(geom[starts], geom[ends], ends, values[starts])
     unpacked = tuple(zip(*[(v, i+1) for s, e, i, v in zipped
                      if (s!=e).any()]))

holoviews/core/data/spatialpandas.py

@@ -815,7 +815,7 @@ def to_spatialpandas(data, xdim, ydim, columns=None, geom='point'):
         converted['geometry'] = GeoSeries(geom_array)
     else:
         converted['geometry'] = GeoSeries(single_array([]))
-    return GeoDataFrame(converted, columns=['geometry']+columns)
+    return GeoDataFrame(converted, columns=['geometry', *columns])
 
 
 def to_geom_dict(eltype, data, kdims, vdims, interface=None):

holoviews/core/data/xarray.py

@@ -267,7 +267,7 @@ def validate(cls, dataset, vdims=True):
             nonmatching = [f'{kd}: {dims}' for kd, dims in irregular[1:]
                            if set(dims) != set(irregular[0][1])]
             if nonmatching:
-                nonmatching = ['{}: {}'.format(*irregular[0])] + nonmatching
+                nonmatching = ['{}: {}'.format(*irregular[0]), *nonmatching]
                 raise DataError("The dimensions of coordinate arrays "
                                 "on irregular data must match. The "
                                 "following kdims were found to have "

holoviews/core/dimension.py

@@ -1067,7 +1067,7 @@ def select(self, selection_specs=None, **kwargs):
             selection_specs = [selection_specs]
 
         # Apply all indexes applying on this object
-        vdims = self.vdims+['value'] if self.vdims else []
+        vdims = [*self.vdims, 'value'] if self.vdims else []
         kdims = self.kdims
         local_kwargs = {k: v for k, v in kwargs.items()
                         if k in kdims+vdims}
@@ -1106,14 +1106,14 @@ def select(self, selection_specs=None, **kwargs):
             return selection
         elif type(selection) is not type(self) and isinstance(selection, Dimensioned):
             # Apply the selection on the selected object of a different type
-            dimensions = selection.dimensions() + ['value']
+            dimensions = [*selection.dimensions(), 'value']
             if any(kw in dimensions for kw in kwargs):
                 selection = selection.select(selection_specs=selection_specs, **kwargs)
         elif isinstance(selection, Dimensioned) and selection._deep_indexable:
             # Apply the deep selection on each item in local selection
             items = []
             for k, v in selection.items():
-                dimensions = v.dimensions() + ['value']
+                dimensions = [*v.dimensions(), 'value']
                 if any(kw in dimensions for kw in kwargs):
                     items.append((k, v.select(selection_specs=selection_specs, **kwargs)))
                 else:
@@ -1308,7 +1308,7 @@ class ViewableTree(AttrTree, Dimensioned):
     def __init__(self, items=None, identifier=None, parent=None, **kwargs):
         if items and all(isinstance(item, Dimensioned) for item in items):
             items = self._process_items(items)
-        params = {p: kwargs.pop(p) for p in list(self.param)+['id', 'plot_id'] if p in kwargs}
+        params = {p: kwargs.pop(p) for p in [*self.param, 'id', 'plot_id'] if p in kwargs}
 
         AttrTree.__init__(self, items, identifier, parent, **kwargs)
         Dimensioned.__init__(self, self.data, **params)
@@ -1355,7 +1355,7 @@ def _deduplicate_items(cls, items):
         counts = defaultdict(lambda: 0)
         for path, item in items:
             if counter[path] > 1:
-                path = path + (util.int_to_roman(counts[path]+1),)
+                path = (*path, util.int_to_roman(counts[path] + 1))
             else:
                 inc = 1
                 while counts[path]:

holoviews/core/layout.py

@@ -46,7 +46,7 @@ def __lshift__(self, other):
         if isinstance(other, (ViewableElement, NdMapping, Empty)):
             return AdjointLayout([self, other])
         elif isinstance(other, AdjointLayout):
-            return AdjointLayout(other.data.values()+[self])
+            return AdjointLayout([*other.data.values(), self])
         else:
             raise TypeError(f'Cannot append {type(other).__name__} to a AdjointLayout')
 

holoviews/core/operation.py

@@ -155,9 +155,7 @@ def _apply(self, element, key=None):
             and isinstance(element, Dataset) and not in_method):
             ret._dataset = element.dataset.clone()
             ret._pipeline = element_pipeline.instance(
-                operations=element_pipeline.operations + [
-                    self.instance(**self.p)
-                ],
+                operations=[*element_pipeline.operations, self.instance(**self.p)],
             )
             ret._transforms = element._transforms
         return ret

holoviews/core/options.py

@@ -767,7 +767,7 @@ def closest(self, obj, group, defaults=True, backend=None):
         # Try to get a cache hit in the backend lookup cache
         backend = backend or Store.current_backend
         cache = Store._lookup_cache.get(backend, {})
-        cache_key = opts_spec+(group, defaults, id(self.root))
+        cache_key = (*opts_spec, group, defaults, id(self.root))
         if cache_key in cache:
             return cache[cache_key]
 
@@ -1344,7 +1344,7 @@ def add_style_opts(cls, component, new_options, backend=None):
             for option in new_options:
                 if option not in cls.registry[backend][component].style_opts:
                     plot_class = cls.registry[backend][component]
-                    plot_class.style_opts = sorted(plot_class.style_opts+[option])
+                    plot_class.style_opts = sorted([*plot_class.style_opts, option])
         cls._options[backend][component.name] = Options(
             'style', merge_keywords=True, allowed_keywords=new_options
         )
@@ -1592,7 +1592,7 @@ def validate_spec(cls, spec, backends=None):
                 error_key = (error.invalid_keyword,
                              error.allowed_keywords.target,
                              error.group_name)
-                error_info[error_key+(backend,)] = error.allowed_keywords
+                error_info[(*error_key, backend)] = error.allowed_keywords
                 backend_errors[error_key].add(backend)
 
         for ((keyword, target, group_name), backend_error) in backend_errors.items():

holoviews/core/overlay.py

@@ -328,5 +328,7 @@ def decollate(self):
         return decollate(self)
 
 
-__all__ = list({_k for _k, _v in locals().items()
-                    if isinstance(_v, type) and issubclass(_v, Dimensioned)}) + ['Overlayable']
+__all__ = [
+    *{_k for _k, _v in locals().items() if isinstance(_v, type) and issubclass(_v, Dimensioned)},
+    "Overlayable"
+]

holoviews/core/spaces.py

@@ -297,7 +297,7 @@ def __lshift__(self, other):
         if isinstance(other, (ViewableElement, UniformNdMapping, Empty)):
             return AdjointLayout([self, other])
         elif isinstance(other, AdjointLayout):
-            return AdjointLayout(other.data+[self])
+            return AdjointLayout([*other.data, self])
         else:
             raise TypeError(f'Cannot append {type(other).__name__} to a AdjointLayout')
 
@@ -1048,7 +1048,7 @@ def clone(self, data=None, shared_data=True, new_type=None, link=True,
                 overrides['plot_id'] = self._plot_id
         clone = super(UniformNdMapping, self).clone(
             callback, shared_data, new_type, link,
-            *(data,) + args, **overrides)
+            *(data, *args), **overrides)
 
         # Ensure the clone references this object to ensure
         # stream sources are inherited
@@ -1747,7 +1747,7 @@ def __lshift__(self, other):
         if isinstance(other, (ViewableElement, UniformNdMapping)):
             return AdjointLayout([self, other])
         elif isinstance(other, AdjointLayout):
-            return AdjointLayout(other.data+[self])
+            return AdjointLayout([*other.data, self])
         else:
             raise TypeError(f'Cannot append {type(other).__name__} to a AdjointLayout')
 

holoviews/core/tree.py

@@ -160,7 +160,7 @@ def _propagate(self, path, val):
         else:
             self.data[path] = val
         if self.parent is not None:
-            self.parent._propagate((self.identifier,)+path, val)
+            self.parent._propagate((self.identifier, *path), val)
 
 
     def __setitem__(self, identifier, val):

holoviews/core/util.py

@@ -994,7 +994,7 @@ def max_range(ranges, combined=True):
         with warnings.catch_warnings():
             warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
             values = [tuple(np.nan if v is None else v for v in r) for r in ranges]
-            if any(isinstance(v, datetime_types) and not isinstance(v, cftime_types+(dt.time,))
+            if any(isinstance(v, datetime_types) and not isinstance(v, (*cftime_types, dt.time))
                           for r in values for v in r):
                 converted = []
                 for l, h in values:
@@ -1281,7 +1281,7 @@ def dimension_sort(odict, kdims, vdims, key_index):
     indexes = [(dimensions[i], int(i not in range(ndims)),
                     i if i in range(ndims) else i-ndims)
                 for i in key_index]
-    cached_values = {d.name: [None]+list(d.values) for d in dimensions}
+    cached_values = {d.name: [None, *d.values] for d in dimensions}
 
     if len(set(key_index)) != len(key_index):
         raise ValueError("Cannot sort on duplicated dimensions")
@@ -1434,8 +1434,8 @@ def get_overlay_spec(o, k, v):
     Gets the type.group.label + key spec from an Element in an Overlay.
     """
     k = wrap_tuple(k)
-    return ((type(v).__name__, v.group, v.label) + k if len(o.kdims) else
-            (type(v).__name__,) + k)
+    return ((type(v).__name__, v.group, v.label, *k) if len(o.kdims) else
+            (type(v).__name__, *k))
 
 
 def layer_sort(hmap):
@@ -1887,9 +1887,9 @@ def make_path_unique(path, counts, new):
             path = path[:-1]
         else:
             added = True
-        path = path + (int_to_roman(count),)
+        path = (*path, int_to_roman(count))
     if len(path) == 1:
-        path = path + (int_to_roman(counts.get(path, 1)),)
+        path = (*path, int_to_roman(counts.get(path, 1)))
     if path not in counts:
         counts[path] = 1
     return path