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train | Renderer.plotting_class | Given an object or Element class, return the suitable plotting
class needed to render it with the current renderer. | holoviews/plotting/renderer.py | def plotting_class(cls, obj):
"""
Given an object or Element class, return the suitable plotting
class needed to render it with the current renderer.
"""
if isinstance(obj, AdjointLayout) or obj is AdjointLayout:
obj = Layout
if isinstance(obj, type):
... | def plotting_class(cls, obj):
"""
Given an object or Element class, return the suitable plotting
class needed to render it with the current renderer.
"""
if isinstance(obj, AdjointLayout) or obj is AdjointLayout:
obj = Layout
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train | Renderer.html_assets | Returns JS and CSS and for embedding of widgets. | holoviews/plotting/renderer.py | def html_assets(cls, core=True, extras=True, backends=None, script=False):
"""
Returns JS and CSS and for embedding of widgets.
"""
if backends is None:
backends = [cls.backend] if cls.backend else []
# Get all the widgets and find the set of required js widget files... | def html_assets(cls, core=True, extras=True, backends=None, script=False):
"""
Returns JS and CSS and for embedding of widgets.
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backends = [cls.backend] if cls.backend else []
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train | Renderer.save | Save a HoloViews object to file, either using an explicitly
supplied format or to the appropriate default. | holoviews/plotting/renderer.py | def save(self_or_cls, obj, basename, fmt='auto', key={}, info={}, options=None, **kwargs):
"""
Save a HoloViews object to file, either using an explicitly
supplied format or to the appropriate default.
"""
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raise Exception('Renderer does not support sav... | def save(self_or_cls, obj, basename, fmt='auto', key={}, info={}, options=None, **kwargs):
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Save a HoloViews object to file, either using an explicitly
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train | Renderer.load_nb | Loads any resources required for display of plots
in the Jupyter notebook | holoviews/plotting/renderer.py | def load_nb(cls, inline=True):
"""
Loads any resources required for display of plots
in the Jupyter notebook
"""
with param.logging_level('ERROR'):
cls.notebook_context = True
cls.comm_manager = JupyterCommManager | def load_nb(cls, inline=True):
"""
Loads any resources required for display of plots
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"""
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train | Renderer._delete_plot | Deletes registered plots and calls Plot.cleanup | holoviews/plotting/renderer.py | def _delete_plot(cls, plot_id):
"""
Deletes registered plots and calls Plot.cleanup
"""
plot = cls._plots.get(plot_id)
if plot is None:
return
plot.cleanup()
del cls._plots[plot_id] | def _delete_plot(cls, plot_id):
"""
Deletes registered plots and calls Plot.cleanup
"""
plot = cls._plots.get(plot_id)
if plot is None:
return
plot.cleanup()
del cls._plots[plot_id] | [
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train | image_overlay._match | Return the strength of the match (None if no match) | holoviews/operation/element.py | def _match(cls, el, spec):
"Return the strength of the match (None if no match)"
spec_dict = dict(zip(['type', 'group', 'label'], spec.split('.')))
if not isinstance(el, Image) or spec_dict['type'] != 'Image':
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sanit... | def _match(cls, el, spec):
"Return the strength of the match (None if no match)"
spec_dict = dict(zip(['type', 'group', 'label'], spec.split('.')))
if not isinstance(el, Image) or spec_dict['type'] != 'Image':
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train | image_overlay._match_overlay | Given a raster or input overlay, generate a list of matched
elements (None if no match) and corresponding tuple of match
strength values. | holoviews/operation/element.py | def _match_overlay(self, raster, overlay_spec):
"""
Given a raster or input overlay, generate a list of matched
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strength values.
"""
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"""
Given a raster or input overlay, generate a list of matched
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train | MultiInterface._inner_dataset_template | Returns a Dataset template used as a wrapper around the data
contained within the multi-interface dataset. | holoviews/core/data/multipath.py | def _inner_dataset_template(cls, dataset):
"""
Returns a Dataset template used as a wrapper around the data
contained within the multi-interface dataset.
"""
from . import Dataset
vdims = dataset.vdims if getattr(dataset, 'level', None) is None else []
return Data... | def _inner_dataset_template(cls, dataset):
"""
Returns a Dataset template used as a wrapper around the data
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"""
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vdims = dataset.vdims if getattr(dataset, 'level', None) is None else []
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train | MultiInterface.isscalar | Tests if dimension is scalar in each subpath. | holoviews/core/data/multipath.py | def isscalar(cls, dataset, dim):
"""
Tests if dimension is scalar in each subpath.
"""
if not dataset.data:
return True
ds = cls._inner_dataset_template(dataset)
isscalar = []
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isscalar.append(d... | def isscalar(cls, dataset, dim):
"""
Tests if dimension is scalar in each subpath.
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train | MultiInterface.select | Applies selectiong on all the subpaths. | holoviews/core/data/multipath.py | def select(cls, dataset, selection_mask=None, **selection):
"""
Applies selectiong on all the subpaths.
"""
if not dataset.data:
return []
ds = cls._inner_dataset_template(dataset)
data = []
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se... | def select(cls, dataset, selection_mask=None, **selection):
"""
Applies selectiong on all the subpaths.
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train | MultiInterface.select_paths | Allows selecting paths with usual NumPy slicing index. | holoviews/core/data/multipath.py | def select_paths(cls, dataset, selection):
"""
Allows selecting paths with usual NumPy slicing index.
"""
return [s[0] for s in np.array([{0: p} for p in dataset.data])[selection]] | def select_paths(cls, dataset, selection):
"""
Allows selecting paths with usual NumPy slicing index.
"""
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train | MultiInterface.shape | Returns the shape of all subpaths, making it appear like a
single array of concatenated subpaths separated by NaN values. | holoviews/core/data/multipath.py | def shape(cls, dataset):
"""
Returns the shape of all subpaths, making it appear like a
single array of concatenated subpaths separated by NaN values.
"""
if not dataset.data:
return (0, len(dataset.dimensions()))
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"""
Returns the shape of all subpaths, making it appear like a
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train | MultiInterface.length | Returns the length of the multi-tabular dataset making it appear
like a single array of concatenated subpaths separated by NaN
values. | holoviews/core/data/multipath.py | def length(cls, dataset):
"""
Returns the length of the multi-tabular dataset making it appear
like a single array of concatenated subpaths separated by NaN
values.
"""
if not dataset.data:
return 0
length = 0
ds = cls._inner_dataset_template(d... | def length(cls, dataset):
"""
Returns the length of the multi-tabular dataset making it appear
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"""
if not dataset.data:
return 0
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train | MultiInterface.values | Returns a single concatenated array of all subpaths separated
by NaN values. If expanded keyword is False an array of arrays
is returned. | holoviews/core/data/multipath.py | def values(cls, dataset, dimension, expanded, flat):
"""
Returns a single concatenated array of all subpaths separated
by NaN values. If expanded keyword is False an array of arrays
is returned.
"""
if not dataset.data:
return np.array([])
values = []
... | def values(cls, dataset, dimension, expanded, flat):
"""
Returns a single concatenated array of all subpaths separated
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is returned.
"""
if not dataset.data:
return np.array([])
values = []
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train | MultiInterface.split | Splits a multi-interface Dataset into regular Datasets using
regular tabular interfaces. | holoviews/core/data/multipath.py | def split(cls, dataset, start, end, datatype, **kwargs):
"""
Splits a multi-interface Dataset into regular Datasets using
regular tabular interfaces.
"""
objs = []
if datatype is None:
for d in dataset.data[start: end]:
objs.append(dataset.clon... | def split(cls, dataset, start, end, datatype, **kwargs):
"""
Splits a multi-interface Dataset into regular Datasets using
regular tabular interfaces.
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dimensions: Dimensions to return as columns
multi_index: Convert key dimensions to (multi-)index
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dimensions: Dimensions to return as columns
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dimensions: Dimensions to return as columns
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train | Element.array | Convert dimension values to columnar array.
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train | Element.table | Deprecated method to convert any Element to a Table. | holoviews/core/element.py | def table(self, datatype=None):
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train | Element.mapping | Deprecated method to convert data to dictionary | holoviews/core/element.py | def mapping(self, kdims=None, vdims=None, **kwargs):
"Deprecated method to convert data to dictionary"
if config.future_deprecations:
self.param.warning(
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train | Tabular.pprint_cell | Formatted contents of table cell.
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row (int): Integer index of table row
col (int): Integer index of table column
Returns:
Formatted table cell contents | holoviews/core/element.py | def pprint_cell(self, row, col):
"""Formatted contents of table cell.
Args:
row (int): Integer index of table row
col (int): Integer index of table column
Returns:
Formatted table cell contents
"""
ndims = self.ndims
if col >= self.co... | def pprint_cell(self, row, col):
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row (int): Integer index of table row
col (int): Integer index of table column
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Formatted table cell contents
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train | Collator.static_dimensions | Return all constant dimensions. | holoviews/core/element.py | def static_dimensions(self):
"""
Return all constant dimensions.
"""
dimensions = []
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dimensions.append(dim)
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train | Collator._add_dimensions | Recursively descend through an Layout and NdMapping objects
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"""
Recursively descend through an Layout and NdMapping objects
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"""
if isinstance(item, Layout):
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train | _kde_support | Establish support for a kernel density estimate. | holoviews/operation/stats.py | def _kde_support(bin_range, bw, gridsize, cut, clip):
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kmin, kmax = bin_range[0] - bw * cut, bin_range[1] + bw * cut
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train | MPLRenderer.show | Renders the supplied object and displays it using the active
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Renders the supplied object and displays it using the active
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train | MPLRenderer.diff | Returns the latest plot data to update an existing plot. | holoviews/plotting/mpl/renderer.py | def diff(self, plot):
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Returns the latest plot data to update an existing plot.
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train | MPLRenderer._figure_data | Render matplotlib figure object and return the corresponding
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Render matplotlib figure object and return the corresponding
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Render matplotlib figure object and return the corresponding
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train | MPLRenderer._anim_data | Render a matplotlib animation object and return the corresponding data. | holoviews/plotting/mpl/renderer.py | def _anim_data(self, anim, fmt):
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Render a matplotlib animation object and return the corresponding data.
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train | MPLRenderer._compute_bbox | Compute the tight bounding box for each figure once, reducing
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function of the number of frames.
Tight bounding box computing code here mirrors:
matplotlib.backend_bases.FigureCanvasBase.print_figure
as it hasn't been fact... | holoviews/plotting/mpl/renderer.py | def _compute_bbox(self, fig, kw):
"""
Compute the tight bounding box for each figure once, reducing
number of required canvas draw calls from N*2 to N+1 as a
function of the number of frames.
Tight bounding box computing code here mirrors:
matplotlib.backend_bases.Figure... | def _compute_bbox(self, fig, kw):
"""
Compute the tight bounding box for each figure once, reducing
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train | MPLRenderer.load_nb | Initialize matplotlib backend | holoviews/plotting/mpl/renderer.py | def load_nb(cls, inline=True):
"""
Initialize matplotlib backend
"""
import matplotlib.pyplot as plt
backend = plt.get_backend()
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"""
Initialize matplotlib backend
"""
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train | cleanup_custom_options | Cleans up unused custom trees if all objects referencing the
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train | options_policy | Context manager to temporarily set the skip_invalid and warn_on_skip
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"""
Context manager to temporarily set the skip_invalid and warn_on_skip
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settings = (Options.skip_invalid, Options.warn_on_skip)
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yield
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Context manager to temporarily set the skip_invalid and warn_on_skip
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settings = (Options.skip_invalid, Options.warn_on_skip)
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train | OptionError.format_options_error | Return a fuzzy match message based on the OptionError | holoviews/core/options.py | def format_options_error(self):
"""
Return a fuzzy match message based on the OptionError
"""
allowed_keywords = self.allowed_keywords
target = allowed_keywords.target
matches = allowed_keywords.fuzzy_match(self.invalid_keyword)
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"""
Return a fuzzy match message based on the OptionError
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allowed_keywords = self.allowed_keywords
target = allowed_keywords.target
matches = allowed_keywords.fuzzy_match(self.invalid_keyword)
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train | AbbreviatedException.print_traceback | Print the traceback of the exception wrapped by the AbbreviatedException. | holoviews/core/options.py | def print_traceback(self):
"""
Print the traceback of the exception wrapped by the AbbreviatedException.
"""
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Print the traceback of the exception wrapped by the AbbreviatedException.
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train | Options.filtered | Return a new Options object that is filtered by the specified
list of keys. Mutating self.kwargs to filter is unsafe due to
the option expansion that occurs on initialization. | holoviews/core/options.py | def filtered(self, allowed):
"""
Return a new Options object that is filtered by the specified
list of keys. Mutating self.kwargs to filter is unsafe due to
the option expansion that occurs on initialization.
"""
kws = {k:v for k,v in self.kwargs.items() if k in allowed}
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Return a new Options object that is filtered by the specified
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train | Options.max_cycles | Truncates all contained Palette objects to a maximum number
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"""
Truncates all contained Palette objects to a maximum number
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"""
kwargs = {kw: (arg[num] if isinstance(arg, Palette) else arg)
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"""
Truncates all contained Palette objects to a maximum number
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train | Options.cyclic | Returns True if the options cycle, otherwise False | holoviews/core/options.py | def cyclic(self):
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train | OptionTree._merge_options | Computes a merged Options object for the given group
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new Options which are passed in. | holoviews/core/options.py | def _merge_options(self, identifier, group_name, options):
"""
Computes a merged Options object for the given group
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"""
if group_name not in self.groups:
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train | OptionTree.find | Find the closest node or path to an the arbitrary path that is
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type. | holoviews/core/options.py | def find(self, path, mode='node'):
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Find the closest node or path to an the arbitrary path that is
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Find the closest node or path to an the arbitrary path that is
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train | OptionTree.closest | This method is designed to be called from the root of the
tree. Given any LabelledData object, this method will return
the most appropriate Options object, including inheritance.
In addition, closest supports custom options by checking the
object | holoviews/core/options.py | def closest(self, obj, group, defaults=True):
"""
This method is designed to be called from the root of the
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train | OptionTree.options | Using inheritance up to the root, get the complete Options
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"""
Using inheritance up to the root, get the complete Options
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"""
if target is None:
target = self.path
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target = self.path
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Returns the single strongest matching compositor operation
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train | Compositor.collapse_element | Finds any applicable compositor and applies it. | holoviews/core/options.py | def collapse_element(cls, overlay, ranges=None, mode='data', backend=None):
"""
Finds any applicable compositor and applies it.
"""
from .element import Element
from .overlay import Overlay, CompositeOverlay
unpack = False
if not isinstance(overlay, CompositeOverl... | def collapse_element(cls, overlay, ranges=None, mode='data', backend=None):
"""
Finds any applicable compositor and applies it.
"""
from .element import Element
from .overlay import Overlay, CompositeOverlay
unpack = False
if not isinstance(overlay, CompositeOverl... | [
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train | Compositor.collapse | Given a map of Overlays, apply all applicable compositors. | holoviews/core/options.py | def collapse(cls, holomap, ranges=None, mode='data'):
"""
Given a map of Overlays, apply all applicable compositors.
"""
# No potential compositors
if cls.definitions == []:
return holomap
# Apply compositors
clone = holomap.clone(shared_data=False)
... | def collapse(cls, holomap, ranges=None, mode='data'):
"""
Given a map of Overlays, apply all applicable compositors.
"""
# No potential compositors
if cls.definitions == []:
return holomap
# Apply compositors
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train | Compositor.map | Applies compositor operations to any HoloViews element or container
using the map method. | holoviews/core/options.py | def map(cls, obj, mode='data', backend=None):
"""
Applies compositor operations to any HoloViews element or container
using the map method.
"""
from .overlay import CompositeOverlay
element_compositors = [c for c in cls.definitions if len(c._pattern_spec) == 1]
ov... | def map(cls, obj, mode='data', backend=None):
"""
Applies compositor operations to any HoloViews element or container
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"""
from .overlay import CompositeOverlay
element_compositors = [c for c in cls.definitions if len(c._pattern_spec) == 1]
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train | Compositor._slice_match_level | Find the match strength for a list of overlay items that must
be exactly the same length as the pattern specification. | holoviews/core/options.py | def _slice_match_level(self, overlay_items):
"""
Find the match strength for a list of overlay items that must
be exactly the same length as the pattern specification.
"""
level = 0
for spec, el in zip(self._pattern_spec, overlay_items):
if spec[0] != type(el)... | def _slice_match_level(self, overlay_items):
"""
Find the match strength for a list of overlay items that must
be exactly the same length as the pattern specification.
"""
level = 0
for spec, el in zip(self._pattern_spec, overlay_items):
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train | Compositor.match_level | Given an overlay, return the match level and applicable slice
of the overall overlay. The level an integer if there is a
match or None if there is no match.
The level integer is the number of matching components. Higher
values indicate a stronger match. | holoviews/core/options.py | def match_level(self, overlay):
"""
Given an overlay, return the match level and applicable slice
of the overall overlay. The level an integer if there is a
match or None if there is no match.
The level integer is the number of matching components. Higher
values indicate... | def match_level(self, overlay):
"""
Given an overlay, return the match level and applicable slice
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The level integer is the number of matching components. Higher
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train | Compositor.apply | Apply the compositor on the input with the given input ranges. | holoviews/core/options.py | def apply(self, value, input_ranges, backend=None):
"""
Apply the compositor on the input with the given input ranges.
"""
from .overlay import CompositeOverlay
if backend is None: backend = Store.current_backend
kwargs = {k: v for k, v in self.kwargs.items() if k != 'out... | def apply(self, value, input_ranges, backend=None):
"""
Apply the compositor on the input with the given input ranges.
"""
from .overlay import CompositeOverlay
if backend is None: backend = Store.current_backend
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train | Store.set_current_backend | Use this method to set the backend to run the switch hooks | holoviews/core/options.py | def set_current_backend(cls, backend):
"Use this method to set the backend to run the switch hooks"
for hook in cls._backend_switch_hooks:
hook(backend)
cls.current_backend = backend | def set_current_backend(cls, backend):
"Use this method to set the backend to run the switch hooks"
for hook in cls._backend_switch_hooks:
hook(backend)
cls.current_backend = backend | [
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train | Store.load | Equivalent to pickle.load except that the HoloViews trees is
restored appropriately. | holoviews/core/options.py | def load(cls, filename):
"""
Equivalent to pickle.load except that the HoloViews trees is
restored appropriately.
"""
cls.load_counter_offset = StoreOptions.id_offset()
val = pickle.load(filename)
cls.load_counter_offset = None
return val | def load(cls, filename):
"""
Equivalent to pickle.load except that the HoloViews trees is
restored appropriately.
"""
cls.load_counter_offset = StoreOptions.id_offset()
val = pickle.load(filename)
cls.load_counter_offset = None
return val | [
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train | Store.loads | Equivalent to pickle.loads except that the HoloViews trees is
restored appropriately. | holoviews/core/options.py | def loads(cls, pickle_string):
"""
Equivalent to pickle.loads except that the HoloViews trees is
restored appropriately.
"""
cls.load_counter_offset = StoreOptions.id_offset()
val = pickle.loads(pickle_string)
cls.load_counter_offset = None
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"""
Equivalent to pickle.loads except that the HoloViews trees is
restored appropriately.
"""
cls.load_counter_offset = StoreOptions.id_offset()
val = pickle.loads(pickle_string)
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train | Store.dump | Equivalent to pickle.dump except that the HoloViews option
tree is saved appropriately. | holoviews/core/options.py | def dump(cls, obj, file, protocol=0):
"""
Equivalent to pickle.dump except that the HoloViews option
tree is saved appropriately.
"""
cls.save_option_state = True
pickle.dump(obj, file, protocol=protocol)
cls.save_option_state = False | def dump(cls, obj, file, protocol=0):
"""
Equivalent to pickle.dump except that the HoloViews option
tree is saved appropriately.
"""
cls.save_option_state = True
pickle.dump(obj, file, protocol=protocol)
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train | Store.dumps | Equivalent to pickle.dumps except that the HoloViews option
tree is saved appropriately. | holoviews/core/options.py | def dumps(cls, obj, protocol=0):
"""
Equivalent to pickle.dumps except that the HoloViews option
tree is saved appropriately.
"""
cls.save_option_state = True
val = pickle.dumps(obj, protocol=protocol)
cls.save_option_state = False
return val | def dumps(cls, obj, protocol=0):
"""
Equivalent to pickle.dumps except that the HoloViews option
tree is saved appropriately.
"""
cls.save_option_state = True
val = pickle.dumps(obj, protocol=protocol)
cls.save_option_state = False
return val | [
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train | Store.info | Show information about a particular object or component class
including the applicable style and plot options. Returns None if
the object is not parameterized. | holoviews/core/options.py | def info(cls, obj, ansi=True, backend='matplotlib', visualization=True,
recursive=False, pattern=None, elements=[]):
"""
Show information about a particular object or component class
including the applicable style and plot options. Returns None if
the object is not parameter... | def info(cls, obj, ansi=True, backend='matplotlib', visualization=True,
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Show information about a particular object or component class
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train | Store.lookup | Given an object, lookup the corresponding customized option
tree if a single custom tree is applicable. | holoviews/core/options.py | def lookup(cls, backend, obj):
"""
Given an object, lookup the corresponding customized option
tree if a single custom tree is applicable.
"""
ids = set([el for el in obj.traverse(lambda x: x.id) if el is not None])
if len(ids) == 0:
raise Exception("Object do... | def lookup(cls, backend, obj):
"""
Given an object, lookup the corresponding customized option
tree if a single custom tree is applicable.
"""
ids = set([el for el in obj.traverse(lambda x: x.id) if el is not None])
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train | Store.transfer_options | Transfers options for all backends from one object to another.
Drops any options defined in the supplied drop list. | holoviews/core/options.py | def transfer_options(cls, obj, new_obj, backend=None):
"""
Transfers options for all backends from one object to another.
Drops any options defined in the supplied drop list.
"""
backend = cls.current_backend if backend is None else backend
type_name = type(new_obj).__nam... | def transfer_options(cls, obj, new_obj, backend=None):
"""
Transfers options for all backends from one object to another.
Drops any options defined in the supplied drop list.
"""
backend = cls.current_backend if backend is None else backend
type_name = type(new_obj).__nam... | [
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train | Store.add_style_opts | Given a component such as an Element (e.g. Image, Curve) or a
container (e.g Layout) specify new style options to be
accepted by the corresponding plotting class.
Note: This is supplied for advanced users who know which
additional style keywords are appropriate for the
correspon... | holoviews/core/options.py | def add_style_opts(cls, component, new_options, backend=None):
"""
Given a component such as an Element (e.g. Image, Curve) or a
container (e.g Layout) specify new style options to be
accepted by the corresponding plotting class.
Note: This is supplied for advanced users who kno... | def add_style_opts(cls, component, new_options, backend=None):
"""
Given a component such as an Element (e.g. Image, Curve) or a
container (e.g Layout) specify new style options to be
accepted by the corresponding plotting class.
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"""
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"""
Specify a display hook that will be applied to objects of type
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"""
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train | StoreOptions.stop_recording_skipped | Stop collecting OptionErrors recorded with the
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"""
Stop collecting OptionErrors recorded with the
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raise Exception('Cannot stop recording before it is started')
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"""
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specs[spec_key] = {}
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"""
Recursively propagate an id through an object for components
matching the applied_keys. This method can only be called if
there is a tree with a matching id in Store.custom_options
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"""
Apply the given option specs to the supplied options tree.
"""
for key in sorted(spec.keys()):
if isinstance(spec[key], (list, tuple)):
customization = {v.key:v for v in spec[key]}
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Apply the given option specs to the supplied options tree.
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"""
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Expands compositor definition keys into {type}.{group}
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"""
expanded_spec={}
applied_keys = []
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Expands compositor definition keys into {type}.{group}
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"""
Returns the appropriate set of customized subtree clones for
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integer ids a new OptionTree is built.
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>>> style={'Curve':{'linewidth':10 }}
>>> merged = StoreOptions.merge_options(['style'], options, style=style)
>>> s... | holoviews/core/options.py | def merge_options(cls, groups, options=None,**kwargs):
"""
Given a full options dictionary and options groups specified
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>>> style={'Curve':{'linewidth':10 }}
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train | StoreOptions.state | Method to capture and restore option state. When called
without any state supplied, the current state is
returned. Then if this state is supplied back in a later call
using the same object, the original state is restored. | holoviews/core/options.py | def state(cls, obj, state=None):
"""
Method to capture and restore option state. When called
without any state supplied, the current state is
returned. Then if this state is supplied back in a later call
using the same object, the original state is restored.
"""
i... | def state(cls, obj, state=None):
"""
Method to capture and restore option state. When called
without any state supplied, the current state is
returned. Then if this state is supplied back in a later call
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train | StoreOptions.options | Context-manager for temporarily setting options on an object
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left in exactly the same state they were in before the context
manager was used.
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"""
Context-manager for temporarily setting options on an object
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context manager exits, both the object and the Store will be
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Context-manager for temporarily setting options on an object
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train | StoreOptions.id_offset | Compute an appropriate offset for future id values given the set
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"""
Compute an appropriate offset for future id values given the set
of ids currently defined across backends.
"""
max_ids = []
for backend in Store.renderers.keys():
store_ids = Store.custom_options(backend=backend).keys()
max_... | def id_offset(cls):
"""
Compute an appropriate offset for future id values given the set
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"""
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train | StoreOptions.update_backends | Given the id_mapping from previous ids to new ids and the new
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stay linked with the current object. | holoviews/core/options.py | def update_backends(cls, id_mapping, custom_trees, backend=None):
"""
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custom tree dictionary, update the current backend with the
supplied trees and update the keys in the remaining backends to
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train | StoreOptions.set_options | Pure Python function for customize HoloViews objects in terms of
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The options specification is a dictionary containing the target
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such a key is 'Image' which would customize all... | holoviews/core/options.py | def set_options(cls, obj, options=None, backend=None, **kwargs):
"""
Pure Python function for customize HoloViews objects in terms of
their style, plot and normalization options.
The options specification is a dictionary containing the target
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"""
Pure Python function for customize HoloViews objects in terms of
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The options specification is a dictionary containing the target
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train | is_color | Checks if supplied object is a valid color spec. | holoviews/plotting/mpl/util.py | def is_color(color):
"""
Checks if supplied object is a valid color spec.
"""
if not isinstance(color, basestring):
return False
elif RGB_HEX_REGEX.match(color):
return True
elif color in COLOR_ALIASES:
return True
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Checks if supplied object is a valid color spec.
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train | validate | Validates a style and associated value.
Arguments
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style: str
The style to validate (e.g. 'color', 'size' or 'marker')
value:
The style value to validate
vectorized: bool
Whether validator should allow vectorized setting
Returns
-------
valid: boolean or N... | holoviews/plotting/mpl/util.py | def validate(style, value, vectorized=True):
"""
Validates a style and associated value.
Arguments
---------
style: str
The style to validate (e.g. 'color', 'size' or 'marker')
value:
The style value to validate
vectorized: bool
Whether validator should allow vectorized... | def validate(style, value, vectorized=True):
"""
Validates a style and associated value.
Arguments
---------
style: str
The style to validate (e.g. 'color', 'size' or 'marker')
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The style value to validate
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train | filter_styles | Filters styles which are specific to a particular artist, e.g.
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edges.
Arguments
---------
style: dict
Dictionary of styles and values
group: str
Group within the styles to filter for
other_groups: list
... | holoviews/plotting/mpl/util.py | def filter_styles(style, group, other_groups, blacklist=[]):
"""
Filters styles which are specific to a particular artist, e.g.
for a GraphPlot this will filter options specific to the nodes and
edges.
Arguments
---------
style: dict
Dictionary of styles and values
group: str
... | def filter_styles(style, group, other_groups, blacklist=[]):
"""
Filters styles which are specific to a particular artist, e.g.
for a GraphPlot this will filter options specific to the nodes and
edges.
Arguments
---------
style: dict
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train | wrap_formatter | Wraps formatting function or string in
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"""
Wraps formatting function or string in
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args = [arg for arg in _getargspec(formatter).args
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Wraps formatting function or string in
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train | axis_overlap | Tests whether two axes overlap vertically | holoviews/plotting/mpl/util.py | def axis_overlap(ax1, ax2):
"""
Tests whether two axes overlap vertically
"""
b1, t1 = ax1.get_position().intervaly
b2, t2 = ax2.get_position().intervaly
return t1 > b2 and b1 < t2 | def axis_overlap(ax1, ax2):
"""
Tests whether two axes overlap vertically
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b1, t1 = ax1.get_position().intervaly
b2, t2 = ax2.get_position().intervaly
return t1 > b2 and b1 < t2 | [
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train | resolve_rows | Recursively iterate over lists of axes merging
them by their vertical overlap leaving a list
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"""
Recursively iterate over lists of axes merging
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train | fix_aspect | Calculate heights and widths of axes and adjust
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vspace=0.2, hspace=0.2):
"""
Calculate heights and widths of axes and adjust
the size of the figure to match the aspect.
"""
fig.canvas.draw()
w, h = fig.get_size_inches()
# Compute maximum height and width of ea... | def fix_aspect(fig, nrows, ncols, title=None, extra_artists=[],
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"""
Calculate heights and widths of axes and adjust
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train | get_tight_bbox | Compute a tight bounding box around all the artists in the figure. | holoviews/plotting/mpl/util.py | def get_tight_bbox(fig, bbox_extra_artists=[], pad=None):
"""
Compute a tight bounding box around all the artists in the figure.
"""
renderer = fig.canvas.get_renderer()
bbox_inches = fig.get_tightbbox(renderer)
bbox_artists = bbox_extra_artists[:]
bbox_artists += fig.get_default_bbox_extra_... | def get_tight_bbox(fig, bbox_extra_artists=[], pad=None):
"""
Compute a tight bounding box around all the artists in the figure.
"""
renderer = fig.canvas.get_renderer()
bbox_inches = fig.get_tightbbox(renderer)
bbox_artists = bbox_extra_artists[:]
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train | get_raster_array | Return the array data from any Raster or Image type | holoviews/plotting/mpl/util.py | def get_raster_array(image):
"""
Return the array data from any Raster or Image type
"""
if isinstance(image, RGB):
rgb = image.rgb
data = np.dstack([np.flipud(rgb.dimension_values(d, flat=False))
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else:
data = image.dimension_val... | def get_raster_array(image):
"""
Return the array data from any Raster or Image type
"""
if isinstance(image, RGB):
rgb = image.rgb
data = np.dstack([np.flipud(rgb.dimension_values(d, flat=False))
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train | ring_coding | Produces matplotlib Path codes for exterior and interior rings
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"""
Produces matplotlib Path codes for exterior and interior rings
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train | polygons_to_path_patches | Converts Polygons into list of lists of matplotlib.patches.PathPatch
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train | BokehPlot.link_sources | Returns potential Link or Stream sources. | holoviews/plotting/bokeh/plot.py | def link_sources(self):
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Initializes any callbacks for streams which have defined
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train | BokehPlot.set_root | Sets the root model on all subplots. | holoviews/plotting/bokeh/plot.py | def set_root(self, root):
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Sets the root model on all subplots.
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train | BokehPlot._init_datasource | Initializes a data source to be passed into the bokeh glyph. | holoviews/plotting/bokeh/plot.py | def _init_datasource(self, data):
"""
Initializes a data source to be passed into the bokeh glyph.
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data = self._postprocess_data(data)
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Initializes a data source to be passed into the bokeh glyph.
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train | BokehPlot._postprocess_data | Applies necessary type transformation to the data before
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Applies necessary type transformation to the data before
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train | BokehPlot._update_datasource | Update datasource with data for a new frame. | holoviews/plotting/bokeh/plot.py | def _update_datasource(self, source, data):
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Update datasource with data for a new frame.
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Iterates over all subplots and updates existing CustomJS
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Cleans up references to the plot after the plot has been
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size = super(BokehPlot, self)._fontsize(key, label, common)
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train | BokehPlot.sync_sources | Syncs data sources between Elements, which draw data
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"""
Syncs data sources between Elements, which draw data
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train | CompositePlot._link_dimensioned_streams | Should perform any linking required to update titles when dimensioned
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train | GridPlot.update_frame | Update the internal state of the Plot to represent the given
key tuple (where integers represent frames). Returns this
state. | holoviews/plotting/bokeh/plot.py | def update_frame(self, key, ranges=None):
"""
Update the internal state of the Plot to represent the given
key tuple (where integers represent frames). Returns this
state.
"""
ranges = self.compute_ranges(self.layout, key, ranges)
for coord in self.layout.keys(ful... | def update_frame(self, key, ranges=None):
"""
Update the internal state of the Plot to represent the given
key tuple (where integers represent frames). Returns this
state.
"""
ranges = self.compute_ranges(self.layout, key, ranges)
for coord in self.layout.keys(ful... | [
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] | pyviz/holoviews | python | https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/plotting/bokeh/plot.py#L760-L773 | [
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... | ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655 |
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