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train | LayoutPlot._create_subplots | Plot all the views contained in the AdjointLayout Object using axes
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"""
Plot all the views contained in the AdjointLayout Object using axes
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supplied by LayoutPlot - the purpose of the call is to
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"""
Plot all the views contained in the AdjointLayout Object using axes
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train | LayoutPlot._compute_grid | Computes an empty grid to position the plots on by expanding
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train | LayoutPlot.update_frame | Update the internal state of the Plot to represent the given
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train | BokehRenderer.get_plot | Given a HoloViews Viewable return a corresponding plot instance.
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Creates a bokeh app from a HoloViews object or plot. By
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train | BokehRenderer.server_doc | Get a bokeh Document with the plot attached. May supply
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Get a bokeh Document with the plot attached. May supply
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msg = Protocol("1.0").create("PATCH-DOC", events, use_buf... | def diff(self, plot, binary=True):
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train | BokehRenderer.plot_options | Given a holoviews object and a percentage size, apply heuristics
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train | BokehRenderer.get_size | Return the display size associated with a plot before
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Return the display size associated with a plot before
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train | BokehRenderer.load_nb | Loads the bokeh notebook resources. | holoviews/plotting/bokeh/renderer.py | def load_nb(cls, inline=True):
"""
Loads the bokeh notebook resources.
"""
LOAD_MIME_TYPE = bokeh.io.notebook.LOAD_MIME_TYPE
bokeh.io.notebook.LOAD_MIME_TYPE = MIME_TYPES['jlab-hv-load']
load_notebook(hide_banner=True, resources=INLINE if inline else CDN)
bokeh.io... | def load_nb(cls, inline=True):
"""
Loads the bokeh notebook resources.
"""
LOAD_MIME_TYPE = bokeh.io.notebook.LOAD_MIME_TYPE
bokeh.io.notebook.LOAD_MIME_TYPE = MIME_TYPES['jlab-hv-load']
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train | merge_option_dicts | Update the old_opts option dictionary with the options defined in
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old_opts = {'a': {'x': 'old', 'y': 'old'}}
... | holoviews/core/util.py | def merge_option_dicts(old_opts, new_opts):
"""
Update the old_opts option dictionary with the options defined in
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Update the old_opts option dictionary with the options defined in
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train | merge_options_to_dict | Given a collection of Option objects or partial option dictionaries,
merge everything to a single dictionary. | holoviews/core/util.py | def merge_options_to_dict(options):
"""
Given a collection of Option objects or partial option dictionaries,
merge everything to a single dictionary.
"""
merged_options = {}
for obj in options:
if isinstance(obj,dict):
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train | deprecated_opts_signature | Utility to help with the deprecation of the old .opts method signature
Returns whether opts.apply_groups should be used (as a bool) and the
corresponding options. | holoviews/core/util.py | def deprecated_opts_signature(args, kwargs):
"""
Utility to help with the deprecation of the old .opts method signature
Returns whether opts.apply_groups should be used (as a bool) and the
corresponding options.
"""
from .options import Options
groups = set(Options._option_groups)
opts ... | def deprecated_opts_signature(args, kwargs):
"""
Utility to help with the deprecation of the old .opts method signature
Returns whether opts.apply_groups should be used (as a bool) and the
corresponding options.
"""
from .options import Options
groups = set(Options._option_groups)
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train | deephash | Given an object, return a hash using HashableJSON. This hash is not
architecture, Python version or platform independent. | holoviews/core/util.py | def deephash(obj):
"""
Given an object, return a hash using HashableJSON. This hash is not
architecture, Python version or platform independent.
"""
try:
return hash(json.dumps(obj, cls=HashableJSON, sort_keys=True))
except:
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"""
Given an object, return a hash using HashableJSON. This hash is not
architecture, Python version or platform independent.
"""
try:
return hash(json.dumps(obj, cls=HashableJSON, sort_keys=True))
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train | tree_attribute | Predicate that returns True for custom attributes added to AttrTrees
that are not methods, properties or internal attributes.
These custom attributes start with a capitalized character when
applicable (not applicable to underscore or certain unicode characters) | holoviews/core/util.py | def tree_attribute(identifier):
"""
Predicate that returns True for custom attributes added to AttrTrees
that are not methods, properties or internal attributes.
These custom attributes start with a capitalized character when
applicable (not applicable to underscore or certain unicode characters)
... | def tree_attribute(identifier):
"""
Predicate that returns True for custom attributes added to AttrTrees
that are not methods, properties or internal attributes.
These custom attributes start with a capitalized character when
applicable (not applicable to underscore or certain unicode characters)
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train | argspec | Returns an ArgSpec object for functions, staticmethods, instance
methods, classmethods and partials.
Note that the args list for instance and class methods are those as
seen by the user. In other words, the first argument which is
conventionally called 'self' or 'cls' is omitted in these cases. | holoviews/core/util.py | def argspec(callable_obj):
"""
Returns an ArgSpec object for functions, staticmethods, instance
methods, classmethods and partials.
Note that the args list for instance and class methods are those as
seen by the user. In other words, the first argument which is
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Returns an ArgSpec object for functions, staticmethods, instance
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train | validate_dynamic_argspec | Utility used by DynamicMap to ensure the supplied callback has an
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If validation succeeds, returns a list of strings to be zipped with
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"""
Utility used by DynamicMap to ensure the supplied callback has an
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Utility used by DynamicMap to ensure the supplied callback has an
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train | callable_name | Attempt to return a meaningful name identifying a callable or generator | holoviews/core/util.py | def callable_name(callable_obj):
"""
Attempt to return a meaningful name identifying a callable or generator
"""
try:
if (isinstance(callable_obj, type)
and issubclass(callable_obj, param.ParameterizedFunction)):
return callable_obj.__name__
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"""
Attempt to return a meaningful name identifying a callable or generator
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try:
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train | process_ellipses | Helper function to pad a __getitem__ key with the right number of
empty slices (i.e :) when the key contains an Ellipsis (...).
If the vdim_selection flag is true, check if the end of the key
contains strings or Dimension objects in obj. If so, extra padding
will not be applied for the value dimensions... | holoviews/core/util.py | def process_ellipses(obj, key, vdim_selection=False):
"""
Helper function to pad a __getitem__ key with the right number of
empty slices (i.e :) when the key contains an Ellipsis (...).
If the vdim_selection flag is true, check if the end of the key
contains strings or Dimension objects in obj. If ... | def process_ellipses(obj, key, vdim_selection=False):
"""
Helper function to pad a __getitem__ key with the right number of
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If the vdim_selection flag is true, check if the end of the key
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train | capitalize_unicode_name | Turns a string such as 'capital delta' into the shortened,
capitalized version, in this case simply 'Delta'. Used as a
transform in sanitize_identifier. | holoviews/core/util.py | def capitalize_unicode_name(s):
"""
Turns a string such as 'capital delta' into the shortened,
capitalized version, in this case simply 'Delta'. Used as a
transform in sanitize_identifier.
"""
index = s.find('capital')
if index == -1: return s
tail = s[index:].replace('capital', '').stri... | def capitalize_unicode_name(s):
"""
Turns a string such as 'capital delta' into the shortened,
capitalized version, in this case simply 'Delta'. Used as a
transform in sanitize_identifier.
"""
index = s.find('capital')
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train | asarray | Converts arraylike objects to NumPy ndarray types. Errors if
object is not arraylike and strict option is enabled. | holoviews/core/util.py | def asarray(arraylike, strict=True):
"""
Converts arraylike objects to NumPy ndarray types. Errors if
object is not arraylike and strict option is enabled.
"""
if isinstance(arraylike, np.ndarray):
return arraylike
elif isinstance(arraylike, list):
return np.asarray(arraylike, dt... | def asarray(arraylike, strict=True):
"""
Converts arraylike objects to NumPy ndarray types. Errors if
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"""
if isinstance(arraylike, np.ndarray):
return arraylike
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train | isnat | Checks if the value is a NaT. Should only be called on datetimelike objects. | holoviews/core/util.py | def isnat(val):
"""
Checks if the value is a NaT. Should only be called on datetimelike objects.
"""
if (isinstance(val, (np.datetime64, np.timedelta64)) or
(isinstance(val, np.ndarray) and val.dtype.kind == 'M')):
if numpy_version >= '1.13':
return np.isnat(val)
else... | def isnat(val):
"""
Checks if the value is a NaT. Should only be called on datetimelike objects.
"""
if (isinstance(val, (np.datetime64, np.timedelta64)) or
(isinstance(val, np.ndarray) and val.dtype.kind == 'M')):
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train | isfinite | Helper function to determine if scalar or array value is finite extending
np.isfinite with support for None, string, datetime types. | holoviews/core/util.py | def isfinite(val):
"""
Helper function to determine if scalar or array value is finite extending
np.isfinite with support for None, string, datetime types.
"""
if not np.isscalar(val):
val = asarray(val, strict=False)
if val is None:
return False
elif isinstance(val, np.ndar... | def isfinite(val):
"""
Helper function to determine if scalar or array value is finite extending
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"""
if not np.isscalar(val):
val = asarray(val, strict=False)
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train | isdatetime | Whether the array or scalar is recognized datetime type. | holoviews/core/util.py | def isdatetime(value):
"""
Whether the array or scalar is recognized datetime type.
"""
if isinstance(value, np.ndarray):
return (value.dtype.kind == "M" or
(value.dtype.kind == "O" and len(value) and
isinstance(value[0], datetime_types)))
else:
retur... | def isdatetime(value):
"""
Whether the array or scalar is recognized datetime type.
"""
if isinstance(value, np.ndarray):
return (value.dtype.kind == "M" or
(value.dtype.kind == "O" and len(value) and
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else:
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train | find_minmax | Takes (a1, a2) and (b1, b2) as input and returns
(np.nanmin(a1, b1), np.nanmax(a2, b2)). Used to calculate
min and max values of a number of items. | holoviews/core/util.py | def find_minmax(lims, olims):
"""
Takes (a1, a2) and (b1, b2) as input and returns
(np.nanmin(a1, b1), np.nanmax(a2, b2)). Used to calculate
min and max values of a number of items.
"""
try:
limzip = zip(list(lims), list(olims), [np.nanmin, np.nanmax])
limits = tuple([float(fn([l... | def find_minmax(lims, olims):
"""
Takes (a1, a2) and (b1, b2) as input and returns
(np.nanmin(a1, b1), np.nanmax(a2, b2)). Used to calculate
min and max values of a number of items.
"""
try:
limzip = zip(list(lims), list(olims), [np.nanmin, np.nanmax])
limits = tuple([float(fn([l... | [
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train | find_range | Safely finds either the numerical min and max of
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the last value in the sorted list of values. | holoviews/core/util.py | def find_range(values, soft_range=[]):
"""
Safely finds either the numerical min and max of
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the last value in the sorted list of values.
"""
try:
values = np.array(values)
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train | max_range | Computes the maximal lower and upper bounds from a list bounds.
Args:
ranges (list of tuples): A list of range tuples
combined (boolean, optional): Whether to combine bounds
Whether range should be computed on lower and upper bound
independently or both at once
Returns:
... | holoviews/core/util.py | def max_range(ranges, combined=True):
"""
Computes the maximal lower and upper bounds from a list bounds.
Args:
ranges (list of tuples): A list of range tuples
combined (boolean, optional): Whether to combine bounds
Whether range should be computed on lower and upper bound
... | def max_range(ranges, combined=True):
"""
Computes the maximal lower and upper bounds from a list bounds.
Args:
ranges (list of tuples): A list of range tuples
combined (boolean, optional): Whether to combine bounds
Whether range should be computed on lower and upper bound
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train | range_pad | Pads the range by a fraction of the interval | holoviews/core/util.py | def range_pad(lower, upper, padding=None, log=False):
"""
Pads the range by a fraction of the interval
"""
if padding is not None and not isinstance(padding, tuple):
padding = (padding, padding)
if is_number(lower) and is_number(upper) and padding is not None:
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Pads the range by a fraction of the interval
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if padding is not None and not isinstance(padding, tuple):
padding = (padding, padding)
if is_number(lower) and is_number(upper) and padding is not None:
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train | dimension_range | Computes the range along a dimension by combining the data range
with the Dimension soft_range and range. | holoviews/core/util.py | def dimension_range(lower, upper, hard_range, soft_range, padding=None, log=False):
"""
Computes the range along a dimension by combining the data range
with the Dimension soft_range and range.
"""
lower, upper = range_pad(lower, upper, padding, log)
lower = max_range([(lower, None), (soft_range... | def dimension_range(lower, upper, hard_range, soft_range, padding=None, log=False):
"""
Computes the range along a dimension by combining the data range
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"""
lower, upper = range_pad(lower, upper, padding, log)
lower = max_range([(lower, None), (soft_range... | [
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train | max_extents | Computes the maximal extent in 2D and 3D space from
list of 4-tuples or 6-tuples. If zrange is enabled
all extents are converted to 6-tuples to compute
x-, y- and z-limits. | holoviews/core/util.py | def max_extents(extents, zrange=False):
"""
Computes the maximal extent in 2D and 3D space from
list of 4-tuples or 6-tuples. If zrange is enabled
all extents are converted to 6-tuples to compute
x-, y- and z-limits.
"""
if zrange:
num = 6
inds = [(0, 3), (1, 4), (2, 5)]
... | def max_extents(extents, zrange=False):
"""
Computes the maximal extent in 2D and 3D space from
list of 4-tuples or 6-tuples. If zrange is enabled
all extents are converted to 6-tuples to compute
x-, y- and z-limits.
"""
if zrange:
num = 6
inds = [(0, 3), (1, 4), (2, 5)]
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train | int_to_alpha | Generates alphanumeric labels of form A-Z, AA-ZZ etc. | holoviews/core/util.py | def int_to_alpha(n, upper=True):
"Generates alphanumeric labels of form A-Z, AA-ZZ etc."
casenum = 65 if upper else 97
label = ''
count= 0
if n == 0: return str(chr(n + casenum))
while n >= 0:
mod, div = n % 26, n
for _ in range(count):
div //= 26
div %= 26
... | def int_to_alpha(n, upper=True):
"Generates alphanumeric labels of form A-Z, AA-ZZ etc."
casenum = 65 if upper else 97
label = ''
count= 0
if n == 0: return str(chr(n + casenum))
while n >= 0:
mod, div = n % 26, n
for _ in range(count):
div //= 26
div %= 26
... | [
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train | unique_iterator | Returns an iterator containing all non-duplicate elements
in the input sequence. | holoviews/core/util.py | def unique_iterator(seq):
"""
Returns an iterator containing all non-duplicate elements
in the input sequence.
"""
seen = set()
for item in seq:
if item not in seen:
seen.add(item)
yield item | def unique_iterator(seq):
"""
Returns an iterator containing all non-duplicate elements
in the input sequence.
"""
seen = set()
for item in seq:
if item not in seen:
seen.add(item)
yield item | [
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train | unique_array | Returns an array of unique values in the input order.
Args:
arr (np.ndarray or list): The array to compute unique values on
Returns:
A new array of unique values | holoviews/core/util.py | def unique_array(arr):
"""
Returns an array of unique values in the input order.
Args:
arr (np.ndarray or list): The array to compute unique values on
Returns:
A new array of unique values
"""
if not len(arr):
return np.asarray(arr)
elif pd:
if isinstance(arr,... | def unique_array(arr):
"""
Returns an array of unique values in the input order.
Args:
arr (np.ndarray or list): The array to compute unique values on
Returns:
A new array of unique values
"""
if not len(arr):
return np.asarray(arr)
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if isinstance(arr,... | [
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train | match_spec | Matches the group.label specification of the supplied
element against the supplied specification dictionary
returning the value of the best match. | holoviews/core/util.py | def match_spec(element, specification):
"""
Matches the group.label specification of the supplied
element against the supplied specification dictionary
returning the value of the best match.
"""
match_tuple = ()
match = specification.get((), {})
for spec in [type(element).__name__,
... | def match_spec(element, specification):
"""
Matches the group.label specification of the supplied
element against the supplied specification dictionary
returning the value of the best match.
"""
match_tuple = ()
match = specification.get((), {})
for spec in [type(element).__name__,
... | [
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train | merge_dimensions | Merges lists of fully or partially overlapping dimensions by
merging their values.
>>> from holoviews import Dimension
>>> dim_list = [[Dimension('A', values=[1, 2, 3]), Dimension('B')],
... [Dimension('A', values=[2, 3, 4])]]
>>> dimensions = merge_dimensions(dim_list)
>>> dimensio... | holoviews/core/util.py | def merge_dimensions(dimensions_list):
"""
Merges lists of fully or partially overlapping dimensions by
merging their values.
>>> from holoviews import Dimension
>>> dim_list = [[Dimension('A', values=[1, 2, 3]), Dimension('B')],
... [Dimension('A', values=[2, 3, 4])]]
>>> dimen... | def merge_dimensions(dimensions_list):
"""
Merges lists of fully or partially overlapping dimensions by
merging their values.
>>> from holoviews import Dimension
>>> dim_list = [[Dimension('A', values=[1, 2, 3]), Dimension('B')],
... [Dimension('A', values=[2, 3, 4])]]
>>> dimen... | [
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train | dimension_sort | Sorts data by key using usual Python tuple sorting semantics
or sorts in categorical order for any categorical Dimensions. | holoviews/core/util.py | def dimension_sort(odict, kdims, vdims, key_index):
"""
Sorts data by key using usual Python tuple sorting semantics
or sorts in categorical order for any categorical Dimensions.
"""
sortkws = {}
ndims = len(kdims)
dimensions = kdims+vdims
indexes = [(dimensions[i], int(i not in range(nd... | def dimension_sort(odict, kdims, vdims, key_index):
"""
Sorts data by key using usual Python tuple sorting semantics
or sorts in categorical order for any categorical Dimensions.
"""
sortkws = {}
ndims = len(kdims)
dimensions = kdims+vdims
indexes = [(dimensions[i], int(i not in range(nd... | [
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train | sort_topologically | Stackless topological sorting.
graph = {
3: [1],
5: [3],
4: [2],
6: [4],
}
sort_topologically(graph)
[[1, 2], [3, 4], [5, 6]] | holoviews/core/util.py | def sort_topologically(graph):
"""
Stackless topological sorting.
graph = {
3: [1],
5: [3],
4: [2],
6: [4],
}
sort_topologically(graph)
[[1, 2], [3, 4], [5, 6]]
"""
levels_by_name = {}
names_by_level = defaultdict(list)
def add_level_to_name(nam... | def sort_topologically(graph):
"""
Stackless topological sorting.
graph = {
3: [1],
5: [3],
4: [2],
6: [4],
}
sort_topologically(graph)
[[1, 2], [3, 4], [5, 6]]
"""
levels_by_name = {}
names_by_level = defaultdict(list)
def add_level_to_name(nam... | [
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train | is_cyclic | Return True if the directed graph g has a cycle. The directed graph
should be represented as a dictionary mapping of edges for each node. | holoviews/core/util.py | def is_cyclic(graph):
"""
Return True if the directed graph g has a cycle. The directed graph
should be represented as a dictionary mapping of edges for each node.
"""
path = set()
def visit(vertex):
path.add(vertex)
for neighbour in graph.get(vertex, ()):
if neighbo... | def is_cyclic(graph):
"""
Return True if the directed graph g has a cycle. The directed graph
should be represented as a dictionary mapping of edges for each node.
"""
path = set()
def visit(vertex):
path.add(vertex)
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train | one_to_one | Return True if graph contains only one to one mappings. The
directed graph should be represented as a dictionary mapping of
edges for each node. Nodes should be passed a simple list. | holoviews/core/util.py | def one_to_one(graph, nodes):
"""
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directed graph should be represented as a dictionary mapping of
edges for each node. Nodes should be passed a simple list.
"""
edges = itertools.chain.from_iterable(graph.values())
return len(graph)... | def one_to_one(graph, nodes):
"""
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train | get_overlay_spec | Gets the type.group.label + key spec from an Element in an Overlay. | holoviews/core/util.py | def get_overlay_spec(o, k, v):
"""
Gets the type.group.label + key spec from an Element in an Overlay.
"""
k = wrap_tuple(k)
return ((type(v).__name__, v.group, v.label) + k if len(o.kdims) else
(type(v).__name__,) + k) | def get_overlay_spec(o, k, v):
"""
Gets the type.group.label + key spec from an Element in an Overlay.
"""
k = wrap_tuple(k)
return ((type(v).__name__, v.group, v.label) + k if len(o.kdims) else
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train | layer_sort | Find a global ordering for layers in a HoloMap of CompositeOverlay
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"""
Find a global ordering for layers in a HoloMap of CompositeOverlay
types.
"""
orderings = {}
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okeys = [get_overlay_spec(o, k, v) for k, v in o.data.items()]
if len(okeys) == 1 and not okeys[0] in orderings:
orderings[okeys[0]] = []
els... | def layer_sort(hmap):
"""
Find a global ordering for layers in a HoloMap of CompositeOverlay
types.
"""
orderings = {}
for o in hmap:
okeys = [get_overlay_spec(o, k, v) for k, v in o.data.items()]
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train | layer_groups | Splits a global ordering of Layers into groups based on a slice of
the spec. The grouping behavior can be modified by changing the
length of spec the entries are grouped by. | holoviews/core/util.py | def layer_groups(ordering, length=2):
"""
Splits a global ordering of Layers into groups based on a slice of
the spec. The grouping behavior can be modified by changing the
length of spec the entries are grouped by.
"""
group_orderings = defaultdict(list)
for el in ordering:
group_orderings[... | def layer_groups(ordering, length=2):
"""
Splits a global ordering of Layers into groups based on a slice of
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length of spec the entries are grouped by.
"""
group_orderings = defaultdict(list)
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train | group_select | Given a list of key tuples to select, groups them into sensible
chunks to avoid duplicating indexing operations. | holoviews/core/util.py | def group_select(selects, length=None, depth=None):
"""
Given a list of key tuples to select, groups them into sensible
chunks to avoid duplicating indexing operations.
"""
if length == None and depth == None:
length = depth = len(selects[0])
getter = operator.itemgetter(depth-length)
... | def group_select(selects, length=None, depth=None):
"""
Given a list of key tuples to select, groups them into sensible
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"""
if length == None and depth == None:
length = depth = len(selects[0])
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train | iterative_select | Takes the output of group_select selecting subgroups iteratively,
avoiding duplicating select operations. | holoviews/core/util.py | def iterative_select(obj, dimensions, selects, depth=None):
"""
Takes the output of group_select selecting subgroups iteratively,
avoiding duplicating select operations.
"""
ndims = len(dimensions)
depth = depth if depth is not None else ndims
items = []
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... | def iterative_select(obj, dimensions, selects, depth=None):
"""
Takes the output of group_select selecting subgroups iteratively,
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ndims = len(dimensions)
depth = depth if depth is not None else ndims
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train | find_file | Find a file given folder and filename. If the filename can be
resolved directly returns otherwise walks the supplied folder. | holoviews/core/util.py | def find_file(folder, filename):
"""
Find a file given folder and filename. If the filename can be
resolved directly returns otherwise walks the supplied folder.
"""
matches = []
if os.path.isabs(filename) and os.path.isfile(filename):
return filename
for root, _, filenames in os.wal... | def find_file(folder, filename):
"""
Find a file given folder and filename. If the filename can be
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"""
matches = []
if os.path.isabs(filename) and os.path.isfile(filename):
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train | is_dataframe | Checks whether the supplied data is of DataFrame type. | holoviews/core/util.py | def is_dataframe(data):
"""
Checks whether the supplied data is of DataFrame type.
"""
dd = None
if 'dask' in sys.modules and 'pandas' in sys.modules:
import dask.dataframe as dd
return((pd is not None and isinstance(data, pd.DataFrame)) or
(dd is not None and isinstance(data, ... | def is_dataframe(data):
"""
Checks whether the supplied data is of DataFrame type.
"""
dd = None
if 'dask' in sys.modules and 'pandas' in sys.modules:
import dask.dataframe as dd
return((pd is not None and isinstance(data, pd.DataFrame)) or
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train | is_series | Checks whether the supplied data is of Series type. | holoviews/core/util.py | def is_series(data):
"""
Checks whether the supplied data is of Series type.
"""
dd = None
if 'dask' in sys.modules:
import dask.dataframe as dd
return((pd is not None and isinstance(data, pd.Series)) or
(dd is not None and isinstance(data, dd.Series))) | def is_series(data):
"""
Checks whether the supplied data is of Series type.
"""
dd = None
if 'dask' in sys.modules:
import dask.dataframe as dd
return((pd is not None and isinstance(data, pd.Series)) or
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train | is_param_method | Whether the object is a method on a parameterized object.
Args:
obj: Object to check
has_deps (boolean, optional): Check for dependencies
Whether to also check whether the method has been annotated
with param.depends
Returns:
A boolean value indicating whether the obje... | holoviews/core/util.py | def is_param_method(obj, has_deps=False):
"""Whether the object is a method on a parameterized object.
Args:
obj: Object to check
has_deps (boolean, optional): Check for dependencies
Whether to also check whether the method has been annotated
with param.depends
Returns:
... | def is_param_method(obj, has_deps=False):
"""Whether the object is a method on a parameterized object.
Args:
obj: Object to check
has_deps (boolean, optional): Check for dependencies
Whether to also check whether the method has been annotated
with param.depends
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train | disable_constant | Temporarily set parameters on Parameterized object to
constant=False. | holoviews/core/util.py | def disable_constant(parameterized):
"""
Temporarily set parameters on Parameterized object to
constant=False.
"""
params = parameterized.params().values()
constants = [p.constant for p in params]
for p in params:
p.constant = False
try:
yield
except:
raise
... | def disable_constant(parameterized):
"""
Temporarily set parameters on Parameterized object to
constant=False.
"""
params = parameterized.params().values()
constants = [p.constant for p in params]
for p in params:
p.constant = False
try:
yield
except:
raise
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train | get_ndmapping_label | Function to get the first non-auxiliary object
label attribute from an NdMapping. | holoviews/core/util.py | def get_ndmapping_label(ndmapping, attr):
"""
Function to get the first non-auxiliary object
label attribute from an NdMapping.
"""
label = None
els = itervalues(ndmapping.data)
while label is None:
try:
el = next(els)
except StopIteration:
return None... | def get_ndmapping_label(ndmapping, attr):
"""
Function to get the first non-auxiliary object
label attribute from an NdMapping.
"""
label = None
els = itervalues(ndmapping.data)
while label is None:
try:
el = next(els)
except StopIteration:
return None... | [
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train | stream_name_mapping | Return a complete dictionary mapping between stream parameter names
to their applicable renames, excluding parameters listed in
exclude_params.
If reverse is True, the mapping is from the renamed strings to the
original stream parameter names. | holoviews/core/util.py | def stream_name_mapping(stream, exclude_params=['name'], reverse=False):
"""
Return a complete dictionary mapping between stream parameter names
to their applicable renames, excluding parameters listed in
exclude_params.
If reverse is True, the mapping is from the renamed strings to the
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Return a complete dictionary mapping between stream parameter names
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train | rename_stream_kwargs | Given a stream and a kwargs dictionary of parameter values, map to
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train | stream_parameters | Given a list of streams, return a flat list of parameter name,
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train | dimensionless_contents | Return a list of stream parameters that have not been associated
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"""
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"""
names = stream_parameters(streams, no_duplicates)
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train | unbound_dimensions | Return a list of dimensions that have not been associated with
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"""
Return a list of dimensions that have not been associated with
any streams.
"""
params = stream_parameters(streams, no_duplicates)
return [d for d in kdims if d not in params] | def unbound_dimensions(streams, kdims, no_duplicates=True):
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train | wrap_tuple_streams | Fills in tuple keys with dimensioned stream values as appropriate. | holoviews/core/util.py | def wrap_tuple_streams(unwrapped, kdims, streams):
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"""
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train | drop_streams | Drop any dimensioned streams from the keys and kdims. | holoviews/core/util.py | def drop_streams(streams, kdims, keys):
"""
Drop any dimensioned streams from the keys and kdims.
"""
stream_params = stream_parameters(streams)
inds, dims = zip(*[(ind, kdim) for ind, kdim in enumerate(kdims)
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get = operator.itemgetter(*inds)... | def drop_streams(streams, kdims, keys):
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train | itervalues | Get value iterator from dictionary for Python 2 and 3 | holoviews/core/util.py | def itervalues(obj):
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return iter(obj.values()) if sys.version_info.major == 3 else obj.itervalues() | def itervalues(obj):
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train | iterkeys | Get key iterator from dictionary for Python 2 and 3 | holoviews/core/util.py | def iterkeys(obj):
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return iter(obj.keys()) if sys.version_info.major == 3 else obj.iterkeys() | def iterkeys(obj):
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train | get_path | Gets a path from an Labelled object or from a tuple of an existing
path and a labelled object. The path strings are sanitized and
capitalized. | holoviews/core/util.py | def get_path(item):
"""
Gets a path from an Labelled object or from a tuple of an existing
path and a labelled object. The path strings are sanitized and
capitalized.
"""
sanitizers = [group_sanitizer, label_sanitizer]
if isinstance(item, tuple):
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if item.lab... | def get_path(item):
"""
Gets a path from an Labelled object or from a tuple of an existing
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train | make_path_unique | Given a path, a list of existing paths and counts for each of the
existing paths. | holoviews/core/util.py | def make_path_unique(path, counts, new):
"""
Given a path, a list of existing paths and counts for each of the
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"""
added = False
while any(path == c[:i] for c in counts for i in range(1, len(c)+1)):
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counts[path] += 1
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"""
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train | cartesian_product | Efficient cartesian product of a list of 1D arrays returning the
expanded array views for each dimensions. By default arrays are
flattened, which may be controlled with the flat flag. The array
views can be turned into regular arrays with the copy flag. | holoviews/core/util.py | def cartesian_product(arrays, flat=True, copy=False):
"""
Efficient cartesian product of a list of 1D arrays returning the
expanded array views for each dimensions. By default arrays are
flattened, which may be controlled with the flat flag. The array
views can be turned into regular arrays with the... | def cartesian_product(arrays, flat=True, copy=False):
"""
Efficient cartesian product of a list of 1D arrays returning the
expanded array views for each dimensions. By default arrays are
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train | cross_index | Allows efficiently indexing into a cartesian product without
expanding it. The values should be defined as a list of iterables
making up the cartesian product and a linear index, returning
the cross product of the values at the supplied index. | holoviews/core/util.py | def cross_index(values, index):
"""
Allows efficiently indexing into a cartesian product without
expanding it. The values should be defined as a list of iterables
making up the cartesian product and a linear index, returning
the cross product of the values at the supplied index.
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"""
Allows efficiently indexing into a cartesian product without
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the cross product of the values at the supplied index.
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train | arglexsort | Returns the indices of the lexicographical sorting
order of the supplied arrays. | holoviews/core/util.py | def arglexsort(arrays):
"""
Returns the indices of the lexicographical sorting
order of the supplied arrays.
"""
dtypes = ','.join(array.dtype.str for array in arrays)
recarray = np.empty(len(arrays[0]), dtype=dtypes)
for i, array in enumerate(arrays):
recarray['f%s' % i] = array
... | def arglexsort(arrays):
"""
Returns the indices of the lexicographical sorting
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"""
dtypes = ','.join(array.dtype.str for array in arrays)
recarray = np.empty(len(arrays[0]), dtype=dtypes)
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train | dimensioned_streams | Given a DynamicMap return all streams that have any dimensioned
parameters i.e parameters also listed in the key dimensions. | holoviews/core/util.py | def dimensioned_streams(dmap):
"""
Given a DynamicMap return all streams that have any dimensioned
parameters i.e parameters also listed in the key dimensions.
"""
dimensioned = []
for stream in dmap.streams:
stream_params = stream_parameters([stream])
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"""
Given a DynamicMap return all streams that have any dimensioned
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train | expand_grid_coords | Expand the coordinates along a dimension of the gridded
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"""
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train | dt64_to_dt | Safely converts NumPy datetime64 to a datetime object. | holoviews/core/util.py | def dt64_to_dt(dt64):
"""
Safely converts NumPy datetime64 to a datetime object.
"""
ts = (dt64 - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's')
return dt.datetime.utcfromtimestamp(ts) | def dt64_to_dt(dt64):
"""
Safely converts NumPy datetime64 to a datetime object.
"""
ts = (dt64 - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's')
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train | bound_range | Computes a bounding range and density from a number of samples
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"""
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train | validate_regular_sampling | Validates regular sampling of a 1D array ensuring that the difference
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Validates regular sampling of a 1D array ensuring that the difference
in sampling steps is at most rtol times the smallest sampling step.
Returns a boolean indicating whether the sampling is regular.
"""
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Validates regular sampling of a 1D array ensuring that the difference
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Returns a boolean indicating whether the sampling is regular.
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train | compute_density | Computes a grid density given the edges and number of samples.
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computing a density for the given time_unit. | holoviews/core/util.py | def compute_density(start, end, length, time_unit='us'):
"""
Computes a grid density given the edges and number of samples.
Handles datetime grids correctly by computing timedeltas and
computing a density for the given time_unit.
"""
if isinstance(start, int): start = float(start)
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Computes a grid density given the edges and number of samples.
Handles datetime grids correctly by computing timedeltas and
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train | date_range | Computes a date range given a start date, end date and the number
of samples. | holoviews/core/util.py | def date_range(start, end, length, time_unit='us'):
"""
Computes a date range given a start date, end date and the number
of samples.
"""
step = (1./compute_density(start, end, length, time_unit))
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start = start.to_datetime64()
step = np.time... | def date_range(start, end, length, time_unit='us'):
"""
Computes a date range given a start date, end date and the number
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"""
step = (1./compute_density(start, end, length, time_unit))
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train | dt_to_int | Converts a datetime type to an integer with the supplied time unit. | holoviews/core/util.py | def dt_to_int(value, time_unit='us'):
"""
Converts a datetime type to an integer with the supplied time unit.
"""
if pd:
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value = value.to_timestamp()
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"""
Converts a datetime type to an integer with the supplied time unit.
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train | cftime_to_timestamp | Converts cftime to timestamp since epoch in milliseconds
Non-standard calendars (e.g. Julian or no leap calendars)
are converted to standard Gregorian calendar. This can cause
extra space to be added for dates that don't exist in the original
calendar. In order to handle these dates correctly a custom ... | holoviews/core/util.py | def cftime_to_timestamp(date, time_unit='us'):
"""Converts cftime to timestamp since epoch in milliseconds
Non-standard calendars (e.g. Julian or no leap calendars)
are converted to standard Gregorian calendar. This can cause
extra space to be added for dates that don't exist in the original
calend... | def cftime_to_timestamp(date, time_unit='us'):
"""Converts cftime to timestamp since epoch in milliseconds
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train | search_indices | Given a set of values returns the indices of each of those values
in the source array. | holoviews/core/util.py | def search_indices(values, source):
"""
Given a set of values returns the indices of each of those values
in the source array.
"""
orig_indices = source.argsort()
return orig_indices[np.searchsorted(source[orig_indices], values)] | def search_indices(values, source):
"""
Given a set of values returns the indices of each of those values
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"""
orig_indices = source.argsort()
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train | compute_edges | Computes edges as midpoints of the bin centers. The first and
last boundaries are equidistant from the first and last midpoints
respectively. | holoviews/core/util.py | def compute_edges(edges):
"""
Computes edges as midpoints of the bin centers. The first and
last boundaries are equidistant from the first and last midpoints
respectively.
"""
edges = np.asarray(edges)
if edges.dtype.kind == 'i':
edges = edges.astype('f')
midpoints = (edges[:-1]... | def compute_edges(edges):
"""
Computes edges as midpoints of the bin centers. The first and
last boundaries are equidistant from the first and last midpoints
respectively.
"""
edges = np.asarray(edges)
if edges.dtype.kind == 'i':
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train | mimebundle_to_html | Converts a MIME bundle into HTML. | holoviews/core/util.py | def mimebundle_to_html(bundle):
"""
Converts a MIME bundle into HTML.
"""
if isinstance(bundle, tuple):
data, metadata = bundle
else:
data = bundle
html = data.get('text/html', '')
if 'application/javascript' in data:
js = data['application/javascript']
html +... | def mimebundle_to_html(bundle):
"""
Converts a MIME bundle into HTML.
"""
if isinstance(bundle, tuple):
data, metadata = bundle
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data = bundle
html = data.get('text/html', '')
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js = data['application/javascript']
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train | numpy_scalar_to_python | Converts a NumPy scalar to a regular python type. | holoviews/core/util.py | def numpy_scalar_to_python(scalar):
"""
Converts a NumPy scalar to a regular python type.
"""
scalar_type = type(scalar)
if np.issubclass_(scalar_type, np.float_):
return float(scalar)
elif np.issubclass_(scalar_type, np.int_):
return int(scalar)
return scalar | def numpy_scalar_to_python(scalar):
"""
Converts a NumPy scalar to a regular python type.
"""
scalar_type = type(scalar)
if np.issubclass_(scalar_type, np.float_):
return float(scalar)
elif np.issubclass_(scalar_type, np.int_):
return int(scalar)
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train | closest_match | Recursively iterates over type, group, label and overlay key,
finding the closest matching spec. | holoviews/core/util.py | def closest_match(match, specs, depth=0):
"""
Recursively iterates over type, group, label and overlay key,
finding the closest matching spec.
"""
new_specs = []
match_lengths = []
for i, spec in specs:
if spec[0] == match[0]:
new_specs.append((i, spec[1:]))
else:... | def closest_match(match, specs, depth=0):
"""
Recursively iterates over type, group, label and overlay key,
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"""
new_specs = []
match_lengths = []
for i, spec in specs:
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train | sanitize_identifier_fn.add_aliases | Conveniently add new aliases as keyword arguments. For instance
you can add a new alias with add_aliases(short='Longer string') | holoviews/core/util.py | def add_aliases(self_or_cls, **kwargs):
"""
Conveniently add new aliases as keyword arguments. For instance
you can add a new alias with add_aliases(short='Longer string')
"""
self_or_cls.aliases.update({v:k for k,v in kwargs.items()}) | def add_aliases(self_or_cls, **kwargs):
"""
Conveniently add new aliases as keyword arguments. For instance
you can add a new alias with add_aliases(short='Longer string')
"""
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train | sanitize_identifier_fn.remove_aliases | Remove a list of aliases. | holoviews/core/util.py | def remove_aliases(self_or_cls, aliases):
"""
Remove a list of aliases.
"""
for k,v in self_or_cls.aliases.items():
if v in aliases:
self_or_cls.aliases.pop(k) | def remove_aliases(self_or_cls, aliases):
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train | sanitize_identifier_fn.prefixed | Whether or not the identifier will be prefixed.
Strings that require the prefix are generally not recommended. | holoviews/core/util.py | def prefixed(self, identifier, version):
"""
Whether or not the identifier will be prefixed.
Strings that require the prefix are generally not recommended.
"""
invalid_starting = ['Mn', 'Mc', 'Nd', 'Pc']
if identifier.startswith('_'): return True
return((identifi... | def prefixed(self, identifier, version):
"""
Whether or not the identifier will be prefixed.
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invalid_starting = ['Mn', 'Mc', 'Nd', 'Pc']
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train | sanitize_identifier_fn.remove_diacritics | Remove diacritics and accents from the input leaving other
unicode characters alone. | holoviews/core/util.py | def remove_diacritics(self_or_cls, identifier):
"""
Remove diacritics and accents from the input leaving other
unicode characters alone."""
chars = ''
for c in identifier:
replacement = unicodedata.normalize('NFKD', c).encode('ASCII', 'ignore')
if replacem... | def remove_diacritics(self_or_cls, identifier):
"""
Remove diacritics and accents from the input leaving other
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chars = ''
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train | sanitize_identifier_fn.shortened_character_name | Given a unicode character c, return the shortened unicode name
(as a list of tokens) by applying the eliminations,
substitutions and transforms. | holoviews/core/util.py | def shortened_character_name(self_or_cls, c, eliminations=[], substitutions={}, transforms=[]):
"""
Given a unicode character c, return the shortened unicode name
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substitutions and transforms.
"""
name = unicodedata.name... | def shortened_character_name(self_or_cls, c, eliminations=[], substitutions={}, transforms=[]):
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train | sanitize_identifier_fn._process_underscores | Strip underscores to make sure the number is correct after join | holoviews/core/util.py | def _process_underscores(self, tokens):
"Strip underscores to make sure the number is correct after join"
groups = [[str(''.join(el))] if b else list(el)
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flattened = [el for group in groups for el in group]
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train | sanitize_identifier_fn.sanitize | Accumulate blocks of hex and separate blocks by underscores | holoviews/core/util.py | def sanitize(self, name, valid_fn):
"Accumulate blocks of hex and separate blocks by underscores"
invalid = {'\a':'a','\b':'b', '\v':'v','\f':'f','\r':'r'}
for cc in filter(lambda el: el in name, invalid.keys()):
raise Exception("Please use a raw string or escape control code '\%s'"
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for cc in filter(lambda el: el in name, invalid.keys()):
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train | ImageInterface.values | The set of samples available along a particular dimension. | holoviews/core/data/image.py | def values(cls, dataset, dim, expanded=True, flat=True, compute=True):
"""
The set of samples available along a particular dimension.
"""
dim_idx = dataset.get_dimension_index(dim)
if dim_idx in [0, 1]:
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The set of samples available along a particular dimension.
"""
dim_idx = dataset.get_dimension_index(dim)
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train | ImageInterface.select | Slice the underlying numpy array in sheet coordinates. | holoviews/core/data/image.py | def select(cls, dataset, selection_mask=None, **selection):
"""
Slice the underlying numpy array in sheet coordinates.
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selection = {k: slice(*sel) if isinstance(sel, tuple) else sel
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Slice the underlying numpy array in sheet coordinates.
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... | holoviews/core/data/image.py | def sample(cls, dataset, samples=[]):
"""
Sample the Raster along one or both of its dimensions,
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"""
Sample the Raster along one or both of its dimensions,
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train | ImageInterface.unpack_scalar | Given a dataset object and data in the appropriate format for
the interface, return a simple scalar. | holoviews/core/data/image.py | def unpack_scalar(cls, dataset, data):
"""
Given a dataset object and data in the appropriate format for
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"""
if np.isscalar(data) or len(data) != 1:
return data
key = list(data.keys())[0]
if len(data[key]) == 1 a... | def unpack_scalar(cls, dataset, data):
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Given a dataset object and data in the appropriate format for
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train | mandel | Given the real and imaginary parts of a complex number,
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set given a fixed number of iterations. | examples/gallery/apps/bokeh/mandelbrot.py | def mandel(x, y, max_iters):
"""
Given the real and imaginary parts of a complex number,
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set given a fixed number of iterations.
"""
i = 0
c = complex(x,y)
z = 0.0j
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Given the real and imaginary parts of a complex number,
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train | ErrorBars.range | Return the lower and upper bounds of values along dimension.
Range of the y-dimension includes the symmetric or assymetric
error.
Args:
dimension: The dimension to compute the range on.
data_range (bool): Compute range from data values
dimension_range (bool)... | holoviews/element/chart.py | def range(self, dim, data_range=True, dimension_range=True):
"""Return the lower and upper bounds of values along dimension.
Range of the y-dimension includes the symmetric or assymetric
error.
Args:
dimension: The dimension to compute the range on.
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train | Histogram.values | Property to access the Histogram values provided for backward compatibility | holoviews/element/chart.py | def values(self):
"Property to access the Histogram values provided for backward compatibility"
if util.config.future_deprecations:
self.param.warning('Histogram.values is deprecated in favor of '
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return self.dimension... | def values(self):
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train | Histogram.edges | Property to access the Histogram edges provided for backward compatibility | holoviews/element/chart.py | def edges(self):
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if util.config.future_deprecations:
self.param.warning('Histogram.edges is deprecated in favor of '
'common dimension_values method.')
return self.interface.co... | def edges(self):
"Property to access the Histogram edges provided for backward compatibility"
if util.config.future_deprecations:
self.param.warning('Histogram.edges is deprecated in favor of '
'common dimension_values method.')
return self.interface.co... | [
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train | Area.stack | Stacks an (Nd)Overlay of Area or Curve Elements by offsetting
their baselines. To stack a HoloMap or DynamicMap use the map
method. | holoviews/element/chart.py | def stack(cls, areas):
"""
Stacks an (Nd)Overlay of Area or Curve Elements by offsetting
their baselines. To stack a HoloMap or DynamicMap use the map
method.
"""
if not len(areas):
return areas
baseline = np.zeros(len(areas.values()[0]))
stack... | def stack(cls, areas):
"""
Stacks an (Nd)Overlay of Area or Curve Elements by offsetting
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method.
"""
if not len(areas):
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train | Plot.cleanup | Cleans up references to the plot on the attached Stream
subscribers. | holoviews/plotting/plot.py | def cleanup(self):
"""
Cleans up references to the plot on the attached Stream
subscribers.
"""
plots = self.traverse(lambda x: x, [Plot])
for plot in plots:
if not isinstance(plot, (GenericCompositePlot, GenericElementPlot, GenericOverlayPlot)):
... | def cleanup(self):
"""
Cleans up references to the plot on the attached Stream
subscribers.
"""
plots = self.traverse(lambda x: x, [Plot])
for plot in plots:
if not isinstance(plot, (GenericCompositePlot, GenericElementPlot, GenericOverlayPlot)):
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train | DimensionedPlot.matches | Matches a specification against the current Plot. | holoviews/plotting/plot.py | def matches(self, spec):
"""
Matches a specification against the current Plot.
"""
if callable(spec) and not isinstance(spec, type): return spec(self)
elif isinstance(spec, type): return isinstance(self, spec)
else:
raise ValueError("Matching specs have to be ... | def matches(self, spec):
"""
Matches a specification against the current Plot.
"""
if callable(spec) and not isinstance(spec, type): return spec(self)
elif isinstance(spec, type): return isinstance(self, spec)
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train | DimensionedPlot.traverse | Traverses any nested DimensionedPlot returning a list
of all plots that match the specs. The specs should
be supplied as a list of either Plot types or callables,
which should return a boolean given the plot class. | holoviews/plotting/plot.py | def traverse(self, fn=None, specs=None, full_breadth=True):
"""
Traverses any nested DimensionedPlot returning a list
of all plots that match the specs. The specs should
be supplied as a list of either Plot types or callables,
which should return a boolean given the plot class.
... | def traverse(self, fn=None, specs=None, full_breadth=True):
"""
Traverses any nested DimensionedPlot returning a list
of all plots that match the specs. The specs should
be supplied as a list of either Plot types or callables,
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train | DimensionedPlot._frame_title | Returns the formatted dimension group strings
for a particular frame. | holoviews/plotting/plot.py | def _frame_title(self, key, group_size=2, separator='\n'):
"""
Returns the formatted dimension group strings
for a particular frame.
"""
if self.layout_dimensions is not None:
dimensions, key = zip(*self.layout_dimensions.items())
elif not self.dynamic and (no... | def _frame_title(self, key, group_size=2, separator='\n'):
"""
Returns the formatted dimension group strings
for a particular frame.
"""
if self.layout_dimensions is not None:
dimensions, key = zip(*self.layout_dimensions.items())
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] | pyviz/holoviews | python | https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/plotting/plot.py#L309-L325 | [
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