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Dimension parameters now use short names by default
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This follows the suggestion in issue #135.
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@philippjfr
philippjfr committed Jun 5, 2015
1 parent d377bcd commit 8feb7d2
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Showing 37 changed files with 401 additions and 372 deletions.
117 changes: 75 additions & 42 deletions holoviews/core/dimension.py
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Expand Up @@ -365,7 +365,7 @@ class Dimensioned(LabelledData):
class to be associated with dimensions. The contents associated
with dimensions may be partitioned into one of three types
* key_dimensions: These are the dimensions that can be indexed via
* key dimensions: These are the dimensions that can be indexed via
the __getitem__ method. Dimension objects
supporting key dimensions must support indexing
over these dimensions and may also support
Expand All @@ -378,22 +378,29 @@ class to be associated with dimensions. The contents associated
object 'obj', then obj[80,175] indexes a weight
of 80 and height of 175.
* value_dimensions: These dimensions correspond to any data held
Accessed using either kdims or key_dimensions.
* value dimensions: These dimensions correspond to any data held
on the Dimensioned object not in the key
dimensions. Indexing by value dimension is
supported by dimension name (when there are
multiple possible value dimensions); no
slicing semantics is supported and all the
data associated with that dimension will be
returned at once. Note that it is not possible
to mix value_dimensions and deep_dimensions.
to mix value dimensions and deep dimensions.
Accessed using either vdims or value_dimensions.
* deep_dimensions: These are dynamically computed dimensions that
* deep dimensions: These are dynamically computed dimensions that
belong to other Dimensioned objects that are
nested in the data. Objects that support this
should enable the _deep_indexable flag. Note
that it is not possible to mix value_dimensions
and deep_dimensions.
that it is not possible to mix value dimensions
and deep dimensions.
Accessed using either ddims or deep_dimensions.
Dimensioned class support generalized methods for finding the
range and type of values along a particular Dimension. The range
Expand All @@ -405,48 +412,71 @@ class to be associated with dimensions. The contents associated
by the value dimensions and ending with the deep dimensions.
"""

constant_dimensions = param.Dict(default=OrderedDict(), doc="""
A dictionary of Dimension:value pairs providing additional
dimension information about the object.""")
cdims = param.Dict(default=OrderedDict(), doc="""
The constant dimensions defined as a dictionary of Dimension:value
pairs providing additional dimension information about the object.
Aliased with constant_dimensions.""")

key_dimensions = param.List(bounds=(0, None), constant=True, doc="""
The list of dimensions that may be used in indexing (and
potential slicing) semantics. The order of the dimensions
listed here determines the semantics of each component of a
multi-dimensional indexing operation.""")
kdims = param.List(bounds=(0, None), constant=True, doc="""
The key dimensions defined as list of dimensions that may be
used in indexing (and potential slicing) semantics. The order
of the dimensions listed here determines the semantics of each
component of a multi-dimensional indexing operation.
value_dimensions = param.List(bounds=(0, None), constant=True, doc="""
The list of dimensions used to describe the components of the
data. If multiple value dimensions are supplied, a particular
value dimension may be indexed by name after the key
dimensions.""")
Aliased with key_dimensions.""")

vdims = param.List(bounds=(0, None), constant=True, doc="""
The value dimensions defined as the list of dimensions used to
describe the components of the data. If multiple value
dimensions are supplied, a particular value dimension may be
indexed by name after the key dimensions.
Aliased with value_dimensions.""")

group = param.String(default='Dimensioned', constant=True, doc="""
A string describing the data wrapped by the object.""")


__abstract = True
_sorted = False
_dim_groups = ['key_dimensions',
'value_dimensions',
'deep_dimensions']
_dim_groups = ['kdims', 'vdims', 'cdims', 'ddims']
_dim_aliases = dict(key_dimensions='kdims', value_dimensions='vdims',
constant_dimensions='cdims')


# Long-name aliases

@property
def key_dimensions(self): return self.kdims

@property
def value_dimensions(self): return self.vdims

@property
def constant_dimensions(self): return self.cdims

@property
def deep_dimensions(self): return self.ddims

def __init__(self, data, **params):
for group in self._dim_groups[0:2]:
for group in self._dim_groups[0:2]+self._dim_aliases.keys():
if group in params:
if 'constant' in group:
if group in self._dim_aliases:
params[self._dim_aliases[group]] = params.pop(group)
group = self._dim_aliases[group]
if group == 'cdims':
dimensions = {d if isinstance(d, Dimension) else Dimension(d): val
for d, val in params.pop(group)}
else:
dimensions = [d if isinstance(d, Dimension) else Dimension(d)
for d in params.pop(group)]
params[group] = dimensions
super(Dimensioned, self).__init__(data, **params)
self.ndims = len(self.key_dimensions)
constant_dimensions = [(d.name, val) for d, val in self.constant_dimensions.items()]
self._cached_constants = OrderedDict(constant_dimensions)
self._cached_index_names = [d.name for d in self.key_dimensions]
self._cached_value_names = [d.name for d in self.value_dimensions]
self.ndims = len(self.kdims)
cdims = [(d.name, val) for d, val in self.cdims.items()]
self._cached_constants = OrderedDict(cdims)
self._cached_index_names = [d.name for d in self.kdims]
self._cached_value_names = [d.name for d in self.vdims]
self._settings = None


Expand All @@ -462,8 +492,9 @@ def _valid_dimensions(self, dimensions):
raise Exception("Supplied dimensions %s not found." % dim)
return dimensions


@property
def deep_dimensions(self):
def ddims(self):
"The list of deep dimensions"
if self._deep_indexable and len(self):
return self.values()[0].dimensions()
Expand All @@ -478,16 +509,18 @@ def dimensions(self, selection='all', label=False):
by their type, i.e. 'key' or 'value' dimensions.
By default 'all' dimensions are returned.
"""
lambdas = {'key': (lambda x: x.key_dimensions, {'full_breadth': False}),
'value': (lambda x: x.value_dimensions, {}),
'constant': (lambda x: x.constant_dimensions, {})}
lambdas = {'k': (lambda x: x.kdims, {'full_breadth': False}),
'v': (lambda x: x.vdims, {}),
'c': (lambda x: x.cdims, {})}
aliases = {'key': 'k', 'value': 'v', 'constant': 'c'}
if selection == 'all':
dims = [dim for group in self._dim_groups
for dim in getattr(self, group)]
elif isinstance(selection, list):
dims = [dim for group in selection
for dim in getattr(self, '%s_dimensions' % group)]
elif selection in ['key', 'value', 'constant']:
for dim in getattr(self, '%sdims' % aliases.get(group))]
elif aliases.get(selection) in lambdas:
selection = aliases.get(selection, selection)
lmbd, kwargs = lambdas[selection]
key_traversal = self.traverse(lmbd, **kwargs)
dims = [dim for keydims in key_traversal for dim in keydims]
Expand Down Expand Up @@ -540,7 +573,7 @@ def get_dimension_type(self, dim):
def __getitem__(self, key):
"""
Multi-dimensional indexing semantics is determined by the list
of key_dimensions. For instance, the first indexing component
of key dimensions. For instance, the first indexing component
will index the first key dimension.
After the key dimensions are given, *either* a value dimension
Expand All @@ -558,7 +591,7 @@ def select(self, selection_specs=None, **kwargs):
value pairs. Select descends recursively through the
data structure applying the key dimension selection.
The 'value' keyword allows selecting the
value_dimensions on objects which have any declared.
value dimensions on objects which have any declared.
The selection may also be selectively applied to
specific objects by supplying the selection_specs
Expand All @@ -567,7 +600,7 @@ def select(self, selection_specs=None, **kwargs):
"""

# Apply all indexes applying on this object
val_dim = ['value'] if self.value_dimensions else []
val_dim = ['value'] if self.vdims else []
local_dims = self._cached_index_names + val_dim
local_kwargs = {k: v for k, v in kwargs.items()
if k in local_dims}
Expand Down Expand Up @@ -597,15 +630,15 @@ def select(self, selection_specs=None, **kwargs):

if type(selection) is not type(self):
# Apply the selection on the selected object of a different type
val_dim = ['value'] if selection.value_dimensions else []
val_dim = ['value'] if selection.vdims else []
key_dims = selection.dimensions('key', label=True) + val_dim
if any(kw in key_dims for kw in kwargs):
selection = selection.select(selection_specs, **kwargs)
elif selection._deep_indexable:
# Apply the deep selection on each item in local selection
items = []
for k, v in selection.items():
val_dim = ['value'] if v.value_dimensions else []
val_dim = ['value'] if v.vdims else []
key_dims = v.dimensions('key', label=True) + val_dim
if any(kw in key_dims for kw in kwargs):
items.append((k, v.select(selection_specs, **kwargs)))
Expand Down Expand Up @@ -645,7 +678,7 @@ def range(self, dimension, data_range=True):
return (None, None)
soft_range = [r for r in dimension.soft_range
if r is not None]
if dimension in self.key_dimensions or dimension in self.value_dimensions:
if dimension in self.kdims or dimension in self.vdims:
dim_vals = self.dimension_values(dimension.name)
return find_range(dim_vals, soft_range)
dname = dimension.name
Expand Down

1 comment on commit 8feb7d2

@jbednar
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This is a very useful change, and it is definitely good practice to support the old names as aliases. Now that it's supported, though, it would be good to move the aliasing code into the Param package -- having aliases for parameter names surely should be defined at the Param level, as it's in no way specific to key_dimensions or to HoloViews. There is already a feature request open for this functionality
(holoviz/param#112) so any further discussion should occur there.

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