Enable bugbear lint (#5861)

holoviews/annotators.py

@@ -18,7 +18,7 @@
 from .streams import BoxEdit, CurveEdit, PointDraw, PolyDraw, PolyEdit, Selection1D
 
 
-def preprocess(function, current=[]):
+def preprocess(function, current=None):
     """
     Turns a param.depends watch call into a preprocessor method, i.e.
     skips all downstream events triggered by it.
@@ -27,6 +27,8 @@ def preprocess(function, current=[]):
           method which depends on a particular parameter.
           (see https://github.com/pyviz/param/issues/332)
     """
+    if current is None:
+        current = []
     def inner(*args, **kwargs):
         self = args[0]
         self.param._BATCH_WATCH = True

holoviews/core/accessors.py

@@ -93,7 +93,7 @@ class Apply(metaclass=AccessorPipelineMeta):
     def __init__(self, obj, mode=None):
         self._obj = obj
 
-    def __call__(self, apply_function, streams=[], link_inputs=True,
+    def __call__(self, apply_function, streams=None, link_inputs=True,
                  link_dataset=True, dynamic=None, per_element=False, **kwargs):
         """Applies a function to all (Nd)Overlay or Element objects.
 
@@ -139,6 +139,9 @@ def __call__(self, apply_function, streams=[], link_inputs=True,
         from .element import Element
         from .spaces import DynamicMap, HoloMap
 
+        if streams is None:
+            streams = []
+
         if isinstance(self._obj, DynamicMap) and dynamic == False:
             samples = tuple(d.values for d in self._obj.kdims)
             if not all(samples):
@@ -239,22 +242,26 @@ def opts(self, *args, **kwargs):
         kwargs['_method_args'] = args
         return self.__call__('opts', **kwargs)
 
-    def reduce(self, dimensions=[], function=None, spreadfn=None, **kwargs):
+    def reduce(self, dimensions=None, function=None, spreadfn=None, **kwargs):
         """Applies a reduce function to all ViewableElement objects.
 
         See :py:meth:`Dimensioned.opts` and :py:meth:`Apply.__call__`
         for more information.
         """
+        if dimensions is None:
+            dimensions = []
         kwargs['_method_args'] = (dimensions, function, spreadfn)
         kwargs['per_element'] = True
         return self.__call__('reduce', **kwargs)
 
-    def sample(self, samples=[], bounds=None, **kwargs):
+    def sample(self, samples=None, bounds=None, **kwargs):
         """Samples element values at supplied coordinates.
 
         See :py:meth:`Dataset.sample` and :py:meth:`Apply.__call__`
         for more information.
         """
+        if samples is None:
+            samples = []
         kwargs['_method_args'] = (samples, bounds)
         kwargs['per_element'] = True
         return self.__call__('sample', **kwargs)
@@ -358,10 +365,13 @@ def _transform_dimension(self, kdims, vdims, dimension):
             dimension = self._obj.vdims[idx]
         return dimension
 
-    def _create_expression_transform(self, kdims, vdims, exclude=[]):
+    def _create_expression_transform(self, kdims, vdims, exclude=None):
         from ..util.transform import dim
         from .dimension import dimension_name
 
+        if exclude is None:
+            exclude = []
+
         def _transform_expression(expression):
             if dimension_name(expression.dimension) in exclude:
                 dimension = expression.dimension

holoviews/core/boundingregion.py

@@ -102,8 +102,10 @@ def __repr__(self):
         return self.__str__()
 
 
-    def script_repr(self, imports=[], prefix="    "):
+    def script_repr(self, imports=None, prefix="    "):
         # Generate import statement
+        if imports is None:
+            imports = []
         cls = self.__class__.__name__
         mod = self.__module__
         imports.append(f"from {mod} import {cls}")

holoviews/core/data/__init__.py

@@ -433,7 +433,7 @@ def persist(self):
         self._cached = persisted
         return self
 
-    def closest(self, coords=[], **kwargs):
+    def closest(self, coords=None, **kwargs):
         """Snaps coordinate(s) to closest coordinate in Dataset
 
         Args:
@@ -446,6 +446,8 @@ def closest(self, coords=[], **kwargs):
         Raises:
             NotImplementedError: Raised if snapping is not supported
         """
+        if coords is None:
+            coords = []
         if self.ndims > 1:
             raise NotImplementedError("Closest method currently only "
                                       "implemented for 1D Elements")
@@ -719,7 +721,7 @@ def __getitem__(self, slices):
         return data
 
 
-    def sample(self, samples=[], bounds=None, closest=True, **kwargs):
+    def sample(self, samples=None, bounds=None, closest=True, **kwargs):
         """Samples values at supplied coordinates.
 
         Allows sampling of element with a list of coordinates matching
@@ -751,6 +753,8 @@ def sample(self, samples=[], bounds=None, closest=True, **kwargs):
         Returns:
             Element containing the sampled coordinates
         """
+        if samples is None:
+            samples = []
         if kwargs and samples != []:
             raise Exception('Supply explicit list of samples or kwargs, not both.')
         elif kwargs:
@@ -826,7 +830,7 @@ def sample(self, samples=[], bounds=None, closest=True, **kwargs):
         return self.clone(sampled, new_type=Table, datatype=datatype)
 
 
-    def reduce(self, dimensions=[], function=None, spreadfn=None, **reductions):
+    def reduce(self, dimensions=None, function=None, spreadfn=None, **reductions):
         """Applies reduction along the specified dimension(s).
 
         Allows reducing the values along one or more key dimension
@@ -854,6 +858,8 @@ def reduce(self, dimensions=[], function=None, spreadfn=None, **reductions):
         Returns:
             The Dataset after reductions have been applied.
         """
+        if dimensions is None:
+            dimensions = []
         if any(dim in self.vdims for dim in dimensions):
             raise Exception("Reduce cannot be applied to value dimensions")
         function, dims = self._reduce_map(dimensions, function, reductions)
@@ -943,7 +949,7 @@ def aggregate(self, dimensions=None, function=None, spreadfn=None, **kwargs):
                                   new_type=new_type, datatype=datatype)
 
 
-    def groupby(self, dimensions=[], container_type=HoloMap, group_type=None,
+    def groupby(self, dimensions=None, container_type=HoloMap, group_type=None,
                 dynamic=False, **kwargs):
         """Groups object by one or more dimensions
 
@@ -962,6 +968,8 @@ def groupby(self, dimensions=[], container_type=HoloMap, group_type=None,
             Returns object of supplied container_type containing the
             groups. If dynamic=True returns a DynamicMap instead.
         """
+        if dimensions is None:
+            dimensions = []
         if not isinstance(dimensions, list): dimensions = [dimensions]
         if not len(dimensions): dimensions = self.dimensions('key', True)
         if group_type is None: group_type = type(self)

holoviews/core/data/array.py

@@ -106,7 +106,9 @@ def add_dimension(cls, dataset, dimension, dim_pos, values, vdim):
 
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         data = dataset.data
         if len(by) == 1:
             sorting = cls.values(dataset, by[0]).argsort()
@@ -210,7 +212,9 @@ def select(cls, dataset, selection_mask=None, **selection):
 
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
+        if samples is None:
+            samples = []
         data = dataset.data
         mask = False
         for sample in samples:

holoviews/core/data/cudf.py

@@ -325,7 +325,9 @@ def iloc(cls, dataset, index):
 
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         cols = [dataset.get_dimension(d, strict=True).name for d in by]
         return dataset.data.sort_values(by=cols, ascending=not reverse)
 

holoviews/core/data/dask.py

@@ -90,7 +90,9 @@ def range(cls, dataset, dimension):
             return dd.compute(column.min(), column.max())
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         dataset.param.warning('Dask dataframes do not support sorting')
         return dataset.data
 
@@ -249,7 +251,9 @@ def unpack_scalar(cls, dataset, data):
         return data.iat[0,0]
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
+        if samples is None:
+            samples = []
         data = dataset.data
         dims = dataset.dimensions('key', label='name')
         mask = None

holoviews/core/data/dictionary.py

@@ -229,7 +229,9 @@ def mask(cls, dataset, mask, mask_value=np.nan):
 
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         by = [dataset.get_dimension(d).name for d in by]
         if len(by) == 1:
             sorting = cls.values(dataset, by[0]).argsort()
@@ -338,7 +340,9 @@ def select(cls, dataset, selection_mask=None, **selection):
 
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
+        if samples is None:
+            samples = []
         mask = False
         for sample in samples:
             sample_mask = True

holoviews/core/data/grid.py

@@ -297,7 +297,7 @@ def coords(cls, dataset, dim, ordered=False, expanded=False, edges=False):
 
 
     @classmethod
-    def canonicalize(cls, dataset, data, data_coords=None, virtual_coords=[]):
+    def canonicalize(cls, dataset, data, data_coords=None, virtual_coords=None):
         """
         Canonicalize takes an array of values as input and reorients
         and transposes it to match the canonical format expected by
@@ -312,6 +312,8 @@ def canonicalize(cls, dataset, data, data_coords=None, virtual_coords=[]):
         with a virtual integer index. This ensures these coordinates
         are not simply dropped.
         """
+        if virtual_coords is None:
+            virtual_coords = []
         if data_coords is None:
             data_coords = dataset.dimensions('key', label='name')[::-1]
 
@@ -640,10 +642,12 @@ def mask(cls, dataset, mask, mask_val=np.nan):
 
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
         """
         Samples the gridded data into dataset of samples.
         """
+        if samples is None:
+            samples = []
         ndims = dataset.ndims
         dimensions = dataset.dimensions(label='name')
         arrays = [dataset.data[vdim.name] for vdim in dataset.vdims]
@@ -741,7 +745,9 @@ def add_dimension(cls, dataset, dimension, dim_pos, values, vdim):
 
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         if not by or by in [dataset.kdims, dataset.dimensions()]:
             return dataset.data
         else:

holoviews/core/data/ibis.py

@@ -195,7 +195,9 @@ def dtype(cls, dataset, dimension):
     dimension_type = dtype
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         if ibis_version() >= Version("6.0"):
             import ibis
             order = ibis.desc if reverse else ibis.asc
@@ -426,8 +428,10 @@ def select_mask(cls, dataset, selection):
         return predicates
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
         import ibis
+        if samples is None:
+            samples = []
         dims = dataset.dimensions()
         data = dataset.data
         if all(util.isscalar(s) or len(s) == 1 for s in samples):

holoviews/core/data/image.py

@@ -239,7 +239,7 @@ def select(cls, dataset, selection_mask=None, **selection):
 
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
         """
         Sample the Raster along one or both of its dimensions,
         returning a reduced dimensionality type, which is either
@@ -248,6 +248,8 @@ def sample(cls, dataset, samples=[]):
         of the sampled unit indexed by the value in the new_xaxis
         tuple.
         """
+        if samples is None:
+            samples = []
         if len(samples[0]) == 1:
             select = {dataset.kdims[0].name: [s[0] for s in samples]}
             return tuple(dataset.select(**select).columns().values())

holoviews/core/data/multipath.py

@@ -300,7 +300,9 @@ def groupby(cls, dataset, dimensions, container_type, group_type, **kwargs):
             return container_type(grouped_data)
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
+        if samples is None:
+            samples = []
         raise NotImplementedError('Sampling operation on subpaths not supported')
 
     @classmethod
@@ -348,7 +350,9 @@ def dtype(cls, dataset, dimension):
         return ds.interface.dtype(ds, dimension)
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         by = [dataset.get_dimension(d).name for d in by]
         if len(by) == 1:
             sorting = cls.values(dataset, by[0], False).argsort()

holoviews/core/data/pandas.py

@@ -317,7 +317,9 @@ def redim(cls, dataset, dimensions):
 
 
     @classmethod
-    def sort(cls, dataset, by=[], reverse=False):
+    def sort(cls, dataset, by=None, reverse=False):
+        if by is None:
+            by = []
         cols = [dataset.get_dimension(d, strict=True).name for d in by]
 
         if (not isinstance(dataset.data, pd.DataFrame) or
@@ -364,7 +366,9 @@ def values(
 
 
     @classmethod
-    def sample(cls, dataset, samples=[]):
+    def sample(cls, dataset, samples=None):
+        if samples is None:
+            samples = []
         data = dataset.data
         mask = None
         for sample in samples: