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train | BoundingBox.containsbb_exclusive | Returns true if the given BoundingBox x is contained within the
bounding box, where at least one of the boundaries of the box has
to be exclusive. | holoviews/core/boundingregion.py | def containsbb_exclusive(self, x):
"""
Returns true if the given BoundingBox x is contained within the
bounding box, where at least one of the boundaries of the box has
to be exclusive.
"""
left, bottom, right, top = self.aarect().lbrt()
leftx, bottomx, rightx, to... | def containsbb_exclusive(self, x):
"""
Returns true if the given BoundingBox x is contained within the
bounding box, where at least one of the boundaries of the box has
to be exclusive.
"""
left, bottom, right, top = self.aarect().lbrt()
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train | AARectangle.lbrt | Return (left,bottom,right,top) as a tuple. | holoviews/core/boundingregion.py | def lbrt(self):
"""Return (left,bottom,right,top) as a tuple."""
return self._left, self._bottom, self._right, self._top | def lbrt(self):
"""Return (left,bottom,right,top) as a tuple."""
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train | AARectangle.centroid | Return the centroid of the rectangle. | holoviews/core/boundingregion.py | def centroid(self):
"""
Return the centroid of the rectangle.
"""
left, bottom, right, top = self.lbrt()
return (right + left) / 2.0, (top + bottom) / 2.0 | def centroid(self):
"""
Return the centroid of the rectangle.
"""
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train | get_nested_dmaps | Recurses DynamicMap to find DynamicMaps inputs
Args:
dmap: DynamicMap to recurse to look for DynamicMap inputs
Returns:
List of DynamicMap instances that were found | holoviews/core/spaces.py | def get_nested_dmaps(dmap):
"""Recurses DynamicMap to find DynamicMaps inputs
Args:
dmap: DynamicMap to recurse to look for DynamicMap inputs
Returns:
List of DynamicMap instances that were found
"""
if not isinstance(dmap, DynamicMap):
return []
dmaps = [dmap]
for ... | def get_nested_dmaps(dmap):
"""Recurses DynamicMap to find DynamicMaps inputs
Args:
dmap: DynamicMap to recurse to look for DynamicMap inputs
Returns:
List of DynamicMap instances that were found
"""
if not isinstance(dmap, DynamicMap):
return []
dmaps = [dmap]
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train | get_nested_streams | Recurses supplied DynamicMap to find all streams
Args:
dmap: DynamicMap to recurse to look for streams
Returns:
List of streams that were found | holoviews/core/spaces.py | def get_nested_streams(dmap):
"""Recurses supplied DynamicMap to find all streams
Args:
dmap: DynamicMap to recurse to look for streams
Returns:
List of streams that were found
"""
return list({s for dmap in get_nested_dmaps(dmap) for s in dmap.streams}) | def get_nested_streams(dmap):
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Args:
dmap: DynamicMap to recurse to look for streams
Returns:
List of streams that were found
"""
return list({s for dmap in get_nested_dmaps(dmap) for s in dmap.streams}) | [
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train | dynamicmap_memoization | Determine whether the Callable should have memoization enabled
based on the supplied streams (typically by a
DynamicMap). Memoization is disabled if any of the streams require
it it and are currently in a triggered state. | holoviews/core/spaces.py | def dynamicmap_memoization(callable_obj, streams):
"""
Determine whether the Callable should have memoization enabled
based on the supplied streams (typically by a
DynamicMap). Memoization is disabled if any of the streams require
it it and are currently in a triggered state.
"""
memoization... | def dynamicmap_memoization(callable_obj, streams):
"""
Determine whether the Callable should have memoization enabled
based on the supplied streams (typically by a
DynamicMap). Memoization is disabled if any of the streams require
it it and are currently in a triggered state.
"""
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train | HoloMap.overlay | Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
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Args:
dimensions: Dimension(s) of dimensions to group by
Returns:
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"""Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
along the dimension(s).
Args:
dimensions: Dimension(s) of dimensions to group by
Returns:
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"""Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
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Args:
dimensions: Dimension(s) of dimensions to group by
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train | HoloMap.grid | Group by supplied dimension(s) and lay out groups in grid
Groups data by supplied dimension(s) laying the groups along
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Args:
dimensions: Dimension/str or list
Dimension or list of dimensions to group by
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"""Group by supplied dimension(s) and lay out groups in grid
Groups data by supplied dimension(s) laying the groups along
the dimension(s) out in a GridSpace.
Args:
dimensions: Dimension/str or list
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Groups data by supplied dimension(s) laying the groups along
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train | HoloMap.layout | Group by supplied dimension(s) and lay out groups
Groups data by supplied dimension(s) laying the groups along
the dimension(s) out in a NdLayout.
Args:
dimensions: Dimension(s) to group by
Returns:
NdLayout with supplied dimensions | holoviews/core/spaces.py | def layout(self, dimensions=None, **kwargs):
"""Group by supplied dimension(s) and lay out groups
Groups data by supplied dimension(s) laying the groups along
the dimension(s) out in a NdLayout.
Args:
dimensions: Dimension(s) to group by
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dimensions: Dimension(s) to group by
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train | HoloMap.options | Applies simplified option definition returning a new object
Applies options defined in a flat format to the objects
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obj.options(cmap='viridis',... | holoviews/core/spaces.py | def options(self, *args, **kwargs):
"""Applies simplified option definition returning a new object
Applies options defined in a flat format to the objects
returned by the DynamicMap. If the options are to be set
directly on the objects in the HoloMap a simple format may be
used,... | def options(self, *args, **kwargs):
"""Applies simplified option definition returning a new object
Applies options defined in a flat format to the objects
returned by the DynamicMap. If the options are to be set
directly on the objects in the HoloMap a simple format may be
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train | HoloMap.split_overlays | Deprecated method to split overlays inside the HoloMap. | holoviews/core/spaces.py | def split_overlays(self):
"Deprecated method to split overlays inside the HoloMap."
if util.config.future_deprecations:
self.param.warning("split_overlays is deprecated and is now "
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return self._split_overlays() | def split_overlays(self):
"Deprecated method to split overlays inside the HoloMap."
if util.config.future_deprecations:
self.param.warning("split_overlays is deprecated and is now "
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train | HoloMap._split_overlays | Splits overlays inside the HoloMap into list of HoloMaps | holoviews/core/spaces.py | def _split_overlays(self):
"Splits overlays inside the HoloMap into list of HoloMaps"
if not issubclass(self.type, CompositeOverlay):
return None, self.clone()
item_maps = OrderedDict()
for k, overlay in self.data.items():
for key, el in overlay.items():
... | def _split_overlays(self):
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return None, self.clone()
item_maps = OrderedDict()
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train | HoloMap._dimension_keys | Helper for __mul__ that returns the list of keys together with
the dimension labels. | holoviews/core/spaces.py | def _dimension_keys(self):
"""
Helper for __mul__ that returns the list of keys together with
the dimension labels.
"""
return [tuple(zip([d.name for d in self.kdims], [k] if self.ndims == 1 else k))
for k in self.keys()] | def _dimension_keys(self):
"""
Helper for __mul__ that returns the list of keys together with
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"""
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train | HoloMap._dynamic_mul | Implements dynamic version of overlaying operation overlaying
DynamicMaps and HoloMaps where the key dimensions of one is
a strict superset of the other. | holoviews/core/spaces.py | def _dynamic_mul(self, dimensions, other, keys):
"""
Implements dynamic version of overlaying operation overlaying
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"""
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if ... | def _dynamic_mul(self, dimensions, other, keys):
"""
Implements dynamic version of overlaying operation overlaying
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train | HoloMap.collate | Collate allows reordering nested containers
Collation allows collapsing nested mapping types by merging
their dimensions. In simple terms in merges nested containers
into a single merged type.
In the simple case a HoloMap containing other HoloMaps can
easily be joined in this w... | holoviews/core/spaces.py | def collate(self, merge_type=None, drop=[], drop_constant=False):
"""Collate allows reordering nested containers
Collation allows collapsing nested mapping types by merging
their dimensions. In simple terms in merges nested containers
into a single merged type.
In the simple ca... | def collate(self, merge_type=None, drop=[], drop_constant=False):
"""Collate allows reordering nested containers
Collation allows collapsing nested mapping types by merging
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Defaults to all key dimensions
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train | HoloMap.relabel | Clone object and apply new group and/or label.
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label (str, optional): New label to apply to returned object
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train | HoloMap.hist | Computes and adjoins histogram along specified dimension(s).
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dimension: Dimension(s) to compute histogram on
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"""Computes and adjoins histogram along specified dimension(s).
Defaults to first value dimension if present otherwise falls
back to first key dimension.
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train | Callable.noargs | Returns True if the callable takes no arguments | holoviews/core/spaces.py | def noargs(self):
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**overrides: Parameter overrides to apply
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train | DynamicMap.event | Updates attached streams and triggers events
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"""Updates attached streams and triggers events
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**kwargs: Events to update streams with
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train | DynamicMap._style | Applies custom option tree to values return by the callback. | holoviews/core/spaces.py | def _style(self, retval):
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train | DynamicMap._execute_callback | Executes the callback with the appropriate args and kwargs | holoviews/core/spaces.py | def _execute_callback(self, *args):
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Args:
data: New data replacing the existing data
shared_data (bool, optional): Whether to use existing data
new_type (optional): Type to cast object to
link (bool, optional): Whether clone should be linked
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train | DynamicMap._cross_product | Returns a new DynamicMap if the key (tuple form) expresses a
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train | DynamicMap._slice_bounded | Slices bounded DynamicMaps by setting the soft_ranges on
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train | DynamicMap.select | Applies selection by dimension name
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Applies a selection along the dimensions of the object using
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train | DynamicMap._cache | Request that a key/value pair be considered for caching. | holoviews/core/spaces.py | def _cache(self, key, val):
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Request that a key/value pair be considered for caching.
"""
cache_size = (1 if util.dimensionless_contents(self.streams, self.kdims)
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train | DynamicMap.map | Map a function to all objects matching the specs
Recursively replaces elements using a map function when the
specs apply, by default applies to all objects, e.g. to apply
the function to all contained Curve objects:
dmap.map(fn, hv.Curve)
Args:
map_fn: Function... | holoviews/core/spaces.py | def map(self, map_fn, specs=None, clone=True, link_inputs=True):
"""Map a function to all objects matching the specs
Recursively replaces elements using a map function when the
specs apply, by default applies to all objects, e.g. to apply
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Recursively replaces elements using a map function when the
specs apply, by default applies to all objects, e.g. to apply
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train | DynamicMap.relabel | Clone object and apply new group and/or label.
Applies relabeling to children up to the supplied depth.
Args:
label (str, optional): New label to apply to returned object
group (str, optional): New group to apply to returned object
depth (int, optional): Depth to wh... | holoviews/core/spaces.py | def relabel(self, label=None, group=None, depth=1):
"""Clone object and apply new group and/or label.
Applies relabeling to children up to the supplied depth.
Args:
label (str, optional): New label to apply to returned object
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train | DynamicMap._split_overlays | Splits a DynamicMap into its components. Only well defined for
DynamicMap with consistent number and order of layers. | holoviews/core/spaces.py | def _split_overlays(self):
"""
Splits a DynamicMap into its components. Only well defined for
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"""
if not len(self):
raise ValueError('Cannot split DynamicMap before it has been initialized')
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"""
Splits a DynamicMap into its components. Only well defined for
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train | DynamicMap.collate | Unpacks DynamicMap into container of DynamicMaps
Collation allows unpacking DynamicMaps which return Layout,
NdLayout or GridSpace objects into a single such object
containing DynamicMaps. Assumes that the items in the layout
or grid that is returned do not change.
Returns:
... | holoviews/core/spaces.py | def collate(self):
"""Unpacks DynamicMap into container of DynamicMaps
Collation allows unpacking DynamicMaps which return Layout,
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containing DynamicMaps. Assumes that the items in the layout
or grid that is returned do not... | def collate(self):
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Collation allows unpacking DynamicMaps which return Layout,
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train | DynamicMap.groupby | Groups DynamicMap by one or more dimensions
Applies groupby operation over the specified dimensions
returning an object of type container_type (expected to be
dictionary-like) containing the groups.
Args:
dimensions: Dimension(s) to group by
container_type: Type... | holoviews/core/spaces.py | def groupby(self, dimensions=None, container_type=None, group_type=None, **kwargs):
"""Groups DynamicMap by one or more dimensions
Applies groupby operation over the specified dimensions
returning an object of type container_type (expected to be
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... | def groupby(self, dimensions=None, container_type=None, group_type=None, **kwargs):
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Applies groupby operation over the specified dimensions
returning an object of type container_type (expected to be
dictionary-like) containing the groups.
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train | DynamicMap.grid | Groups data by supplied dimension(s) laying the groups along
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Args:
dimensions: Dimension/str or list
Dimension or list of dimensions to group by
Returns:
grid: GridSpace
GridSpace with supplied dimensions | holoviews/core/spaces.py | def grid(self, dimensions=None, **kwargs):
"""
Groups data by supplied dimension(s) laying the groups along
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Args:
dimensions: Dimension/str or list
Dimension or list of dimensions to group by
Returns:
grid: GridSp... | def grid(self, dimensions=None, **kwargs):
"""
Groups data by supplied dimension(s) laying the groups along
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dimensions: Dimension/str or list
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train | DynamicMap.layout | Groups data by supplied dimension(s) laying the groups along
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Args:
dimensions: Dimension/str or list
Dimension or list of dimensions to group by
Returns:
layout: NdLayout
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"""
Groups data by supplied dimension(s) laying the groups along
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Args:
dimensions: Dimension/str or list
Dimension or list of dimensions to group by
Returns:
layout: NdL... | def layout(self, dimensions=None, **kwargs):
"""
Groups data by supplied dimension(s) laying the groups along
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Args:
dimensions: Dimension/str or list
Dimension or list of dimensions to group by
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layout: NdL... | [
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train | DynamicMap.overlay | Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
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Args:
dimensions: Dimension(s) of dimensions to group by
Returns:
NdOverlay object(s) with supplied dimensions | holoviews/core/spaces.py | def overlay(self, dimensions=None, **kwargs):
"""Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
along the dimension(s).
Args:
dimensions: Dimension(s) of dimensions to group by
Returns:
N... | def overlay(self, dimensions=None, **kwargs):
"""Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
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Args:
dimensions: Dimension(s) of dimensions to group by
Returns:
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train | DynamicMap.hist | Computes and adjoins histogram along specified dimension(s).
Defaults to first value dimension if present otherwise falls
back to first key dimension.
Args:
dimension: Dimension(s) to compute histogram on
num_bins (int, optional): Number of bins
bin_range (t... | holoviews/core/spaces.py | def hist(self, dimension=None, num_bins=20, bin_range=None,
adjoin=True, **kwargs):
"""Computes and adjoins histogram along specified dimension(s).
Defaults to first value dimension if present otherwise falls
back to first key dimension.
Args:
dimension: Dimens... | def hist(self, dimension=None, num_bins=20, bin_range=None,
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Defaults to first value dimension if present otherwise falls
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train | DynamicMap.reindex | Reorders key dimensions on DynamicMap
Create a new object with a reordered set of key dimensions.
Dropping dimensions is not allowed on a DynamicMap.
Args:
kdims: List of dimensions to reindex the mapping with
force: Not applicable to a DynamicMap
Returns:
... | holoviews/core/spaces.py | def reindex(self, kdims=[], force=False):
"""Reorders key dimensions on DynamicMap
Create a new object with a reordered set of key dimensions.
Dropping dimensions is not allowed on a DynamicMap.
Args:
kdims: List of dimensions to reindex the mapping with
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kdims: List of dimensions to reindex the mapping with
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train | GridSpace.last | The last of a GridSpace is another GridSpace
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train | GridSpace.shape | Returns the 2D shape of the GridSpace as (rows, cols). | holoviews/core/spaces.py | def shape(self):
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return len(set(k[0] for k in keys)), len(set(k[1] for k in keys)) | def shape(self):
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train | PathPlot._get_hover_data | Initializes hover data based on Element dimension values. | holoviews/plotting/bokeh/path.py | def _get_hover_data(self, data, element):
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train | ContourPlot._get_hover_data | Initializes hover data based on Element dimension values.
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train | ContourPlot._init_glyph | Returns a Bokeh glyph object. | holoviews/plotting/bokeh/path.py | def _init_glyph(self, plot, mapping, properties):
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train | CompositeOverlay.hist | Computes and adjoins histogram along specified dimension(s).
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Args:
dimension: Dimension(s) to compute histogram on
num_bins (int, optional): Number of bins
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"""Computes and adjoins histogram along specified dimension(s).
Defaults to first value dimension if present otherwise falls
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dimension: The dimension to return values for
expanded (bool, optional): Whether to expand values
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dimension: The dimension to return values for
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train | Overlay.get | Get a layer in the Overlay.
Get a particular layer in the Overlay using its path string
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Args:
identifier: Index or path string of the item to return
default: Value to return if no item is found
Returns:
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"""Get a layer in the Overlay.
Get a particular layer in the Overlay using its path string
or an integer index.
Args:
identifier: Index or path string of the item to return
default: Value to return if no item is found
... | def get(self, identifier, default=None):
"""Get a layer in the Overlay.
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identifier: Index or path string of the item to return
default: Value to return if no item is found
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train | Overlay.collapse | Deprecated method to collapse layers in the Overlay. | holoviews/core/overlay.py | def collapse(self, function):
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train | displayable | Predicate that returns whether the object is displayable or not
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"""
Predicate that returns whether the object is displayable or not
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if isinstance(obj, Overlay) and any(isinstance(o, (HoloMap, GridSpace))
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train | overlay_depth | Computes the depth of a DynamicMap overlay if it can be determined
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"""
Computes the depth of a DynamicMap overlay if it can be determined
otherwise return None.
"""
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... | def overlay_depth(obj):
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train | compute_overlayable_zorders | Traverses an overlayable composite container to determine which
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"""
Traverses an overlayable composite container to determine which
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... | def compute_overlayable_zorders(obj, path=[]):
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Traverses an overlayable composite container to determine which
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... | pyviz/holoviews | python | https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/plotting/util.py#L118-L191 | [
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train | is_dynamic_overlay | Traverses a DynamicMap graph and determines if any components
were overlaid dynamically (i.e. by * on a DynamicMap). | holoviews/plotting/util.py | def is_dynamic_overlay(dmap):
"""
Traverses a DynamicMap graph and determines if any components
were overlaid dynamically (i.e. by * on a DynamicMap).
"""
if not isinstance(dmap, DynamicMap):
return False
elif dmap.callback._is_overlay:
return True
else:
return any(is... | def is_dynamic_overlay(dmap):
"""
Traverses a DynamicMap graph and determines if any components
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if not isinstance(dmap, DynamicMap):
return False
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train | split_dmap_overlay | Splits a DynamicMap into the original component layers it was
constructed from by traversing the graph to search for dynamically
overlaid components (i.e. constructed by using * on a DynamicMap).
Useful for assigning subplots of an OverlayPlot the streams that
are responsible for driving their updates. ... | holoviews/plotting/util.py | def split_dmap_overlay(obj, depth=0):
"""
Splits a DynamicMap into the original component layers it was
constructed from by traversing the graph to search for dynamically
overlaid components (i.e. constructed by using * on a DynamicMap).
Useful for assigning subplots of an OverlayPlot the streams th... | def split_dmap_overlay(obj, depth=0):
"""
Splits a DynamicMap into the original component layers it was
constructed from by traversing the graph to search for dynamically
overlaid components (i.e. constructed by using * on a DynamicMap).
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train | initialize_dynamic | Initializes all DynamicMap objects contained by the object | holoviews/plotting/util.py | def initialize_dynamic(obj):
"""
Initializes all DynamicMap objects contained by the object
"""
dmaps = obj.traverse(lambda x: x, specs=[DynamicMap])
for dmap in dmaps:
if dmap.unbounded:
# Skip initialization until plotting code
continue
if not len(dmap):
... | def initialize_dynamic(obj):
"""
Initializes all DynamicMap objects contained by the object
"""
dmaps = obj.traverse(lambda x: x, specs=[DynamicMap])
for dmap in dmaps:
if dmap.unbounded:
# Skip initialization until plotting code
continue
if not len(dmap):
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train | get_plot_frame | Returns the current frame in a mapping given a key mapping.
Args:
obj: Nested Dimensioned object
key_map: Dictionary mapping between dimensions and key value
cached: Whether to allow looking up key in cache
Returns:
The item in the mapping corresponding to the supplied key. | holoviews/plotting/util.py | def get_plot_frame(map_obj, key_map, cached=False):
"""Returns the current frame in a mapping given a key mapping.
Args:
obj: Nested Dimensioned object
key_map: Dictionary mapping between dimensions and key value
cached: Whether to allow looking up key in cache
Returns:
The... | def get_plot_frame(map_obj, key_map, cached=False):
"""Returns the current frame in a mapping given a key mapping.
Args:
obj: Nested Dimensioned object
key_map: Dictionary mapping between dimensions and key value
cached: Whether to allow looking up key in cache
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train | get_nested_plot_frame | Extracts a single frame from a nested object.
Replaces any HoloMap or DynamicMap in the nested data structure,
with the item corresponding to the supplied key.
Args:
obj: Nested Dimensioned object
key_map: Dictionary mapping between dimensions and key value
cached: Whether to allow... | holoviews/plotting/util.py | def get_nested_plot_frame(obj, key_map, cached=False):
"""Extracts a single frame from a nested object.
Replaces any HoloMap or DynamicMap in the nested data structure,
with the item corresponding to the supplied key.
Args:
obj: Nested Dimensioned object
key_map: Dictionary mapping bet... | def get_nested_plot_frame(obj, key_map, cached=False):
"""Extracts a single frame from a nested object.
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with the item corresponding to the supplied key.
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train | undisplayable_info | Generate helpful message regarding an undisplayable object | holoviews/plotting/util.py | def undisplayable_info(obj, html=False):
"Generate helpful message regarding an undisplayable object"
collate = '<tt>collate</tt>' if html else 'collate'
info = "For more information, please consult the Composing Data tutorial (http://git.io/vtIQh)"
if isinstance(obj, HoloMap):
error = "HoloMap... | def undisplayable_info(obj, html=False):
"Generate helpful message regarding an undisplayable object"
collate = '<tt>collate</tt>' if html else 'collate'
info = "For more information, please consult the Composing Data tutorial (http://git.io/vtIQh)"
if isinstance(obj, HoloMap):
error = "HoloMap... | [
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train | compute_sizes | Scales point sizes according to a scaling factor,
base size and size_fn, which will be applied before
scaling. | holoviews/plotting/util.py | def compute_sizes(sizes, size_fn, scaling_factor, scaling_method, base_size):
"""
Scales point sizes according to a scaling factor,
base size and size_fn, which will be applied before
scaling.
"""
if sizes.dtype.kind not in ('i', 'f'):
return None
if scaling_method == 'area':
... | def compute_sizes(sizes, size_fn, scaling_factor, scaling_method, base_size):
"""
Scales point sizes according to a scaling factor,
base size and size_fn, which will be applied before
scaling.
"""
if sizes.dtype.kind not in ('i', 'f'):
return None
if scaling_method == 'area':
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train | get_axis_padding | Process a padding value supplied as a tuple or number and returns
padding values for x-, y- and z-axis. | holoviews/plotting/util.py | def get_axis_padding(padding):
"""
Process a padding value supplied as a tuple or number and returns
padding values for x-, y- and z-axis.
"""
if isinstance(padding, tuple):
if len(padding) == 2:
xpad, ypad = padding
zpad = 0
elif len(padding) == 3:
... | def get_axis_padding(padding):
"""
Process a padding value supplied as a tuple or number and returns
padding values for x-, y- and z-axis.
"""
if isinstance(padding, tuple):
if len(padding) == 2:
xpad, ypad = padding
zpad = 0
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train | get_minimum_span | If lower and high values are equal ensures they are separated by
the defined span. | holoviews/plotting/util.py | def get_minimum_span(low, high, span):
"""
If lower and high values are equal ensures they are separated by
the defined span.
"""
if is_number(low) and low == high:
if isinstance(low, np.datetime64):
span = span * np.timedelta64(1, 's')
low, high = low-span, high+span
... | def get_minimum_span(low, high, span):
"""
If lower and high values are equal ensures they are separated by
the defined span.
"""
if is_number(low) and low == high:
if isinstance(low, np.datetime64):
span = span * np.timedelta64(1, 's')
low, high = low-span, high+span
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train | get_range | Computes the data, soft- and hard-range along a dimension given
an element and a dictionary of ranges. | holoviews/plotting/util.py | def get_range(element, ranges, dimension):
"""
Computes the data, soft- and hard-range along a dimension given
an element and a dictionary of ranges.
"""
if dimension and dimension != 'categorical':
if ranges and dimension.name in ranges:
drange = ranges[dimension.name]['data']
... | def get_range(element, ranges, dimension):
"""
Computes the data, soft- and hard-range along a dimension given
an element and a dictionary of ranges.
"""
if dimension and dimension != 'categorical':
if ranges and dimension.name in ranges:
drange = ranges[dimension.name]['data']
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train | get_sideplot_ranges | Utility to find the range for an adjoined
plot given the plot, the element, the
Element the plot is adjoined to and the
dictionary of ranges. | holoviews/plotting/util.py | def get_sideplot_ranges(plot, element, main, ranges):
"""
Utility to find the range for an adjoined
plot given the plot, the element, the
Element the plot is adjoined to and the
dictionary of ranges.
"""
key = plot.current_key
dims = element.dimensions()
dim = dims[0] if 'frequency' ... | def get_sideplot_ranges(plot, element, main, ranges):
"""
Utility to find the range for an adjoined
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"""
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train | within_range | Checks whether range1 is within the range specified by range2. | holoviews/plotting/util.py | def within_range(range1, range2):
"""Checks whether range1 is within the range specified by range2."""
range1 = [r if isfinite(r) else None for r in range1]
range2 = [r if isfinite(r) else None for r in range2]
return ((range1[0] is None or range2[0] is None or range1[0] >= range2[0]) and
(r... | def within_range(range1, range2):
"""Checks whether range1 is within the range specified by range2."""
range1 = [r if isfinite(r) else None for r in range1]
range2 = [r if isfinite(r) else None for r in range2]
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train | get_dynamic_mode | Returns the common mode of the dynamic maps in given composite object | holoviews/plotting/util.py | def get_dynamic_mode(composite):
"Returns the common mode of the dynamic maps in given composite object"
dynmaps = composite.traverse(lambda x: x, [DynamicMap])
holomaps = composite.traverse(lambda x: x, ['HoloMap'])
dynamic_unbounded = any(m.unbounded for m in dynmaps)
if holomaps:
validate... | def get_dynamic_mode(composite):
"Returns the common mode of the dynamic maps in given composite object"
dynmaps = composite.traverse(lambda x: x, [DynamicMap])
holomaps = composite.traverse(lambda x: x, ['HoloMap'])
dynamic_unbounded = any(m.unbounded for m in dynmaps)
if holomaps:
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train | initialize_unbounded | Initializes any DynamicMaps in unbounded mode. | holoviews/plotting/util.py | def initialize_unbounded(obj, dimensions, key):
"""
Initializes any DynamicMaps in unbounded mode.
"""
select = dict(zip([d.name for d in dimensions], key))
try:
obj.select([DynamicMap], **select)
except KeyError:
pass | def initialize_unbounded(obj, dimensions, key):
"""
Initializes any DynamicMaps in unbounded mode.
"""
select = dict(zip([d.name for d in dimensions], key))
try:
obj.select([DynamicMap], **select)
except KeyError:
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train | save_frames | Utility to export object to files frame by frame, numbered individually.
Will use default backend and figure format by default. | holoviews/plotting/util.py | def save_frames(obj, filename, fmt=None, backend=None, options=None):
"""
Utility to export object to files frame by frame, numbered individually.
Will use default backend and figure format by default.
"""
backend = Store.current_backend if backend is None else backend
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train | dynamic_update | Given a plot, subplot and dynamically generated (Nd)Overlay
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"""
Given a plot, subplot and dynamically generated (Nd)Overlay
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"""
match_spec = get_overlay_spec(overlay,
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train | map_colors | Maps an array of values to RGB hex strings, given
a color range and colormap. | holoviews/plotting/util.py | def map_colors(arr, crange, cmap, hex=True):
"""
Maps an array of values to RGB hex strings, given
a color range and colormap.
"""
if isinstance(crange, np.ndarray):
xsorted = np.argsort(crange)
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arr = xsorted[ypos]
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"""
Maps an array of values to RGB hex strings, given
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"""
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xsorted = np.argsort(crange)
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train | mplcmap_to_palette | Converts a matplotlib colormap to palette of RGB hex strings." | holoviews/plotting/util.py | def mplcmap_to_palette(cmap, ncolors=None, categorical=False):
"""
Converts a matplotlib colormap to palette of RGB hex strings."
"""
from matplotlib.colors import Colormap, ListedColormap
ncolors = ncolors or 256
if not isinstance(cmap, Colormap):
import matplotlib.cm as cm
# A... | def mplcmap_to_palette(cmap, ncolors=None, categorical=False):
"""
Converts a matplotlib colormap to palette of RGB hex strings."
"""
from matplotlib.colors import Colormap, ListedColormap
ncolors = ncolors or 256
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import matplotlib.cm as cm
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train | linear_gradient | Interpolates the color gradient between to hex colors | holoviews/plotting/util.py | def linear_gradient(start_hex, finish_hex, n=10):
"""
Interpolates the color gradient between to hex colors
"""
s = hex2rgb(start_hex)
f = hex2rgb(finish_hex)
gradient = [s]
for t in range(1, n):
curr_vector = [int(s[j] + (float(t)/(n-1))*(f[j]-s[j])) for j in range(3)]
gradi... | def linear_gradient(start_hex, finish_hex, n=10):
"""
Interpolates the color gradient between to hex colors
"""
s = hex2rgb(start_hex)
f = hex2rgb(finish_hex)
gradient = [s]
for t in range(1, n):
curr_vector = [int(s[j] + (float(t)/(n-1))*(f[j]-s[j])) for j in range(3)]
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train | polylinear_gradient | Interpolates the color gradients between a list of hex colors. | holoviews/plotting/util.py | def polylinear_gradient(colors, n):
"""
Interpolates the color gradients between a list of hex colors.
"""
n_out = int(float(n) / (len(colors)-1))
gradient = linear_gradient(colors[0], colors[1], n_out)
if len(colors) == len(gradient):
return gradient
for col in range(1, len(colors... | def polylinear_gradient(colors, n):
"""
Interpolates the color gradients between a list of hex colors.
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n_out = int(float(n) / (len(colors)-1))
gradient = linear_gradient(colors[0], colors[1], n_out)
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train | _list_cmaps | List available colormaps by combining matplotlib, bokeh, and
colorcet colormaps or palettes if available. May also be
narrowed down to a particular provider or list of providers. | holoviews/plotting/util.py | def _list_cmaps(provider=None, records=False):
"""
List available colormaps by combining matplotlib, bokeh, and
colorcet colormaps or palettes if available. May also be
narrowed down to a particular provider or list of providers.
"""
if provider is None:
provider = providers
elif isi... | def _list_cmaps(provider=None, records=False):
"""
List available colormaps by combining matplotlib, bokeh, and
colorcet colormaps or palettes if available. May also be
narrowed down to a particular provider or list of providers.
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train | register_cmaps | Maintain descriptions of colormaps that include the following information:
name - string name for the colormap
category - intended use or purpose, mostly following matplotlib
provider - package providing the colormap directly
source - original source or creator of the colormaps
bg - bas... | holoviews/plotting/util.py | def register_cmaps(category, provider, source, bg, names):
"""
Maintain descriptions of colormaps that include the following information:
name - string name for the colormap
category - intended use or purpose, mostly following matplotlib
provider - package providing the colormap directly
so... | def register_cmaps(category, provider, source, bg, names):
"""
Maintain descriptions of colormaps that include the following information:
name - string name for the colormap
category - intended use or purpose, mostly following matplotlib
provider - package providing the colormap directly
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train | list_cmaps | Return colormap names matching the specified filters. | holoviews/plotting/util.py | def list_cmaps(provider=None, records=False, name=None, category=None, source=None,
bg=None, reverse=None):
"""
Return colormap names matching the specified filters.
"""
# Only uses names actually imported and currently available
available = _list_cmaps(provider=provider, records=True... | def list_cmaps(provider=None, records=False, name=None, category=None, source=None,
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"""
Return colormap names matching the specified filters.
"""
# Only uses names actually imported and currently available
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train | process_cmap | Convert valid colormap specifications to a list of colors. | holoviews/plotting/util.py | def process_cmap(cmap, ncolors=None, provider=None, categorical=False):
"""
Convert valid colormap specifications to a list of colors.
"""
providers_checked="matplotlib, bokeh, or colorcet" if provider is None else provider
if isinstance(cmap, Cycle):
palette = [rgb2hex(c) if isinstance(c, ... | def process_cmap(cmap, ncolors=None, provider=None, categorical=False):
"""
Convert valid colormap specifications to a list of colors.
"""
providers_checked="matplotlib, bokeh, or colorcet" if provider is None else provider
if isinstance(cmap, Cycle):
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train | color_intervals | Maps the supplied colors into bins defined by the supplied levels.
If a clip tuple is defined the bins are clipped to the defined
range otherwise the range is computed from the levels and returned.
Arguments
---------
colors: list
List of colors (usually hex string or named colors)
levels... | holoviews/plotting/util.py | def color_intervals(colors, levels, clip=None, N=255):
"""
Maps the supplied colors into bins defined by the supplied levels.
If a clip tuple is defined the bins are clipped to the defined
range otherwise the range is computed from the levels and returned.
Arguments
---------
colors: list
... | def color_intervals(colors, levels, clip=None, N=255):
"""
Maps the supplied colors into bins defined by the supplied levels.
If a clip tuple is defined the bins are clipped to the defined
range otherwise the range is computed from the levels and returned.
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---------
colors: list
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train | dim_axis_label | Returns an axis label for one or more dimensions. | holoviews/plotting/util.py | def dim_axis_label(dimensions, separator=', '):
"""
Returns an axis label for one or more dimensions.
"""
if not isinstance(dimensions, list): dimensions = [dimensions]
return separator.join([d.pprint_label for d in dimensions]) | def dim_axis_label(dimensions, separator=', '):
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Returns an axis label for one or more dimensions.
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train | attach_streams | Attaches plot refresh to all streams on the object. | holoviews/plotting/util.py | def attach_streams(plot, obj, precedence=1.1):
"""
Attaches plot refresh to all streams on the object.
"""
def append_refresh(dmap):
for stream in get_nested_streams(dmap):
if plot.refresh not in stream._subscribers:
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Attaches plot refresh to all streams on the object.
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def append_refresh(dmap):
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if plot.refresh not in stream._subscribers:
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train | traverse_setter | Traverses the object and sets the supplied attribute on the
object. Supports Dimensioned and DimensionedPlot types. | holoviews/plotting/util.py | def traverse_setter(obj, attribute, value):
"""
Traverses the object and sets the supplied attribute on the
object. Supports Dimensioned and DimensionedPlot types.
"""
obj.traverse(lambda x: setattr(x, attribute, value)) | def traverse_setter(obj, attribute, value):
"""
Traverses the object and sets the supplied attribute on the
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"""
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train | _get_min_distance_numpy | NumPy based implementation of get_min_distance | holoviews/plotting/util.py | def _get_min_distance_numpy(element):
"""
NumPy based implementation of get_min_distance
"""
xys = element.array([0, 1])
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train | get_min_distance | Gets the minimum sampling distance of the x- and y-coordinates
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"""
Gets the minimum sampling distance of the x- and y-coordinates
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"""
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train | get_directed_graph_paths | Computes paths for a directed path which include an arrow to
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"""
Computes paths for a directed path which include an arrow to
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"""
edgepaths = element._split_edgepaths
edges = edgepaths.split(datatype='array', dimensions=edgepaths.kdims)
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"""
Computes paths for a directed path which include an arrow to
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edgepaths = element._split_edgepaths
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train | rgb2hex | Convert RGB(A) tuple to hex. | holoviews/plotting/util.py | def rgb2hex(rgb):
"""
Convert RGB(A) tuple to hex.
"""
if len(rgb) > 3:
rgb = rgb[:-1]
return "#{0:02x}{1:02x}{2:02x}".format(*(int(v*255) for v in rgb)) | def rgb2hex(rgb):
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train | dim_range_key | Returns the key to look up a dimension range. | holoviews/plotting/util.py | def dim_range_key(eldim):
"""
Returns the key to look up a dimension range.
"""
if isinstance(eldim, dim):
dim_name = repr(eldim)
if dim_name.startswith("'") and dim_name.endswith("'"):
dim_name = dim_name[1:-1]
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dim_name = eldim.name
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Returns the key to look up a dimension range.
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dim_name = repr(eldim)
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train | BokehServerWidgets.create_widget | Given a Dimension creates bokeh widgets to select along that
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train | BokehServerWidgets.get_widgets | Creates a set of widgets representing the dimensions on the
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Creates a set of widgets representing the dimensions on the
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"""
widgets = OrderedDict()
mappings = {}
for dim in self.mock_obj.kdims:
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Creates a set of widgets representing the dimensions on the
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widgets = OrderedDict()
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train | BokehServerWidgets.update | Handle update events on bokeh server. | holoviews/plotting/bokeh/widgets.py | def update(self):
"""
Handle update events on bokeh server.
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if not self._queue:
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self._queue = []
dim_label = dim.pprint_label
label, widget = self.widgets[dim_label]
if wid... | def update(self):
"""
Handle update events on bokeh server.
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train | BokehWidget._plot_figure | Returns the figure in html format on the
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Returns the figure in html format on the
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"""
self.plot.update(idx)
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train | ElementPlot._init_tools | Processes the list of tools to be supplied to the plot. | holoviews/plotting/bokeh/element.py | def _init_tools(self, element, callbacks=[]):
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Processes the list of tools to be supplied to the plot.
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tooltips, hover_opts = self._hover_opts(element)
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Processes the list of tools to be supplied to the plot.
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Initializes hover data based on Element dimension values.
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Initializes hover data based on Element dimension values.
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train | ElementPlot._merge_ranges | Given a list of other plots return axes that are shared
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"""
plot_ranges = {}
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"""
dims = element.dimensions()[:2]
if len(dims... | def _get_axis_dims(self, element):
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Initializes Bokeh figure to draw Element into and sets basic
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... | ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655 |
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