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values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | simple_name_generator | Simple name_generator designed for HoloViews objects.
Objects are labeled with {group}-{label} for each nested
object, based on a depth-first search. Adjacent objects with
identical representations yield only a single copy of the
representation, to avoid long names for the common case of
a contain... | holoviews/core/io.py | def simple_name_generator(obj):
"""
Simple name_generator designed for HoloViews objects.
Objects are labeled with {group}-{label} for each nested
object, based on a depth-first search. Adjacent objects with
identical representations yield only a single copy of the
representation, to avoid lon... | def simple_name_generator(obj):
"""
Simple name_generator designed for HoloViews objects.
Objects are labeled with {group}-{label} for each nested
object, based on a depth-first search. Adjacent objects with
identical representations yield only a single copy of the
representation, to avoid lon... | [
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train | Exporter.encode | Classmethod that applies conditional encoding based on
mime-type. Given an entry as returned by __call__ return the
data in the appropriate encoding. | holoviews/core/io.py | def encode(cls, entry):
"""
Classmethod that applies conditional encoding based on
mime-type. Given an entry as returned by __call__ return the
data in the appropriate encoding.
"""
(data, info) = entry
if info['mime_type'] in cls.utf8_mime_types:
retu... | def encode(cls, entry):
"""
Classmethod that applies conditional encoding based on
mime-type. Given an entry as returned by __call__ return the
data in the appropriate encoding.
"""
(data, info) = entry
if info['mime_type'] in cls.utf8_mime_types:
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train | Exporter._filename | Add the file extension if not already present | holoviews/core/io.py | def _filename(self_or_cls, filename):
"Add the file extension if not already present"
if not filename.endswith(self_or_cls.file_ext):
return '%s.%s' % (filename, self_or_cls.file_ext)
else:
return filename | def _filename(self_or_cls, filename):
"Add the file extension if not already present"
if not filename.endswith(self_or_cls.file_ext):
return '%s.%s' % (filename, self_or_cls.file_ext)
else:
return filename | [
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train | Exporter._merge_metadata | Returns a merged metadata info dictionary from the supplied
function and additional dictionaries | holoviews/core/io.py | def _merge_metadata(self_or_cls, obj, fn, *dicts):
"""
Returns a merged metadata info dictionary from the supplied
function and additional dictionaries
"""
merged = dict([(k,v) for d in dicts for (k,v) in d.items()])
return dict(merged, **fn(obj)) if fn else merged | def _merge_metadata(self_or_cls, obj, fn, *dicts):
"""
Returns a merged metadata info dictionary from the supplied
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"""
merged = dict([(k,v) for d in dicts for (k,v) in d.items()])
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train | Unpickler.collect | Given a list or NdMapping type containing file paths return a
Layout of Collators, which can be called to load a given set
of files using the current Importer.
If supplied as a list each file is expected to disambiguate
itself with contained metadata. If an NdMapping type is
sup... | holoviews/core/io.py | def collect(self_or_cls, files, drop=[], metadata=True):
"""
Given a list or NdMapping type containing file paths return a
Layout of Collators, which can be called to load a given set
of files using the current Importer.
If supplied as a list each file is expected to disambiguat... | def collect(self_or_cls, files, drop=[], metadata=True):
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Given a list or NdMapping type containing file paths return a
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train | FileArchive.parse_fields | Returns the format fields otherwise raise exception | holoviews/core/io.py | def parse_fields(cls, formatter):
"Returns the format fields otherwise raise exception"
if formatter is None: return []
try:
parse = list(string.Formatter().parse(formatter))
return set(f for f in list(zip(*parse))[1] if f is not None)
except:
raise S... | def parse_fields(cls, formatter):
"Returns the format fields otherwise raise exception"
if formatter is None: return []
try:
parse = list(string.Formatter().parse(formatter))
return set(f for f in list(zip(*parse))[1] if f is not None)
except:
raise S... | [
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train | FileArchive.add | If a filename is supplied, it will be used. Otherwise, a
filename will be generated from the supplied object. Note that
if the explicit filename uses the {timestamp} field, it will
be formatted upon export.
The data to be archived is either supplied explicitly as
'data' or autom... | holoviews/core/io.py | def add(self, obj=None, filename=None, data=None, info={}, **kwargs):
"""
If a filename is supplied, it will be used. Otherwise, a
filename will be generated from the supplied object. Note that
if the explicit filename uses the {timestamp} field, it will
be formatted upon export.... | def add(self, obj=None, filename=None, data=None, info={}, **kwargs):
"""
If a filename is supplied, it will be used. Otherwise, a
filename will be generated from the supplied object. Note that
if the explicit filename uses the {timestamp} field, it will
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train | FileArchive._unique_name | Find a unique basename for a new file/key where existing is
either a list of (basename, ext) pairs or an absolute path to
a directory.
By default, uniqueness is enforced depending on the state of
the unique_name parameter (for export names). If force is
True, this parameter is i... | holoviews/core/io.py | def _unique_name(self, basename, ext, existing, force=False):
"""
Find a unique basename for a new file/key where existing is
either a list of (basename, ext) pairs or an absolute path to
a directory.
By default, uniqueness is enforced depending on the state of
the uniqu... | def _unique_name(self, basename, ext, existing, force=False):
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Find a unique basename for a new file/key where existing is
either a list of (basename, ext) pairs or an absolute path to
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train | FileArchive.export | Export the archive, directory or file. | holoviews/core/io.py | def export(self, timestamp=None, info={}):
"""
Export the archive, directory or file.
"""
tval = tuple(time.localtime()) if timestamp is None else timestamp
tstamp = time.strftime(self.timestamp_format, tval)
info = dict(info, timestamp=tstamp)
export_name = self... | def export(self, timestamp=None, info={}):
"""
Export the archive, directory or file.
"""
tval = tuple(time.localtime()) if timestamp is None else timestamp
tstamp = time.strftime(self.timestamp_format, tval)
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train | FileArchive.contents | Print the current (unexported) contents of the archive | holoviews/core/io.py | def contents(self, maxlen=70):
"Print the current (unexported) contents of the archive"
lines = []
if len(self._files) == 0:
print("Empty %s" % self.__class__.__name__)
return
fnames = [self._truncate_name(maxlen=maxlen, *k) for k in self._files]
max_len ... | def contents(self, maxlen=70):
"Print the current (unexported) contents of the archive"
lines = []
if len(self._files) == 0:
print("Empty %s" % self.__class__.__name__)
return
fnames = [self._truncate_name(maxlen=maxlen, *k) for k in self._files]
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train | FileArchive.listing | Return a list of filename entries currently in the archive | holoviews/core/io.py | def listing(self):
"Return a list of filename entries currently in the archive"
return ['.'.join([f,ext]) if ext else f for (f,ext) in self._files.keys()] | def listing(self):
"Return a list of filename entries currently in the archive"
return ['.'.join([f,ext]) if ext else f for (f,ext) in self._files.keys()] | [
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train | progress | A helper utility to display a progress bar when iterating over a
collection of a fixed length or a generator (with a declared
length).
If enum=True, then equivalent to enumerate with a progress bar. | holoviews/ipython/widgets.py | def progress(iterator, enum=False, length=None):
"""
A helper utility to display a progress bar when iterating over a
collection of a fixed length or a generator (with a declared
length).
If enum=True, then equivalent to enumerate with a progress bar.
"""
progress = ProgressBar()
length... | def progress(iterator, enum=False, length=None):
"""
A helper utility to display a progress bar when iterating over a
collection of a fixed length or a generator (with a declared
length).
If enum=True, then equivalent to enumerate with a progress bar.
"""
progress = ProgressBar()
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train | PlotlyWidget._plot_figure | Returns the figure in html format on the
first call and | holoviews/plotting/plotly/widgets.py | def _plot_figure(self, idx, fig_format='json'):
"""
Returns the figure in html format on the
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"""
self.plot.update(idx)
if self.embed:
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self.plot.update(idx)
if self.embed:
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train | SheetCoordinateSystem.__equalize_densities | Calculate the true density along x, and adjust the top and
bottom bounds so that the density along y will be equal.
Returns (adjusted_bounds, true_density) | holoviews/core/sheetcoords.py | def __equalize_densities(self,nominal_bounds,nominal_density):
"""
Calculate the true density along x, and adjust the top and
bottom bounds so that the density along y will be equal.
Returns (adjusted_bounds, true_density)
"""
left,bottom,right,top = nominal_bounds.lbrt(... | def __equalize_densities(self,nominal_bounds,nominal_density):
"""
Calculate the true density along x, and adjust the top and
bottom bounds so that the density along y will be equal.
Returns (adjusted_bounds, true_density)
"""
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train | SheetCoordinateSystem.sheet2matrix | Convert a point (x,y) in Sheet coordinates to continuous
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y, and float_col to x.
Valid for scalar or array x and y.
Note about Bounds For a Sheet with
BoundingBox(points=((-0.5,-0.5),(0.5,... | holoviews/core/sheetcoords.py | def sheet2matrix(self,x,y):
"""
Convert a point (x,y) in Sheet coordinates to continuous
matrix coordinates.
Returns (float_row,float_col), where float_row corresponds to
y, and float_col to x.
Valid for scalar or array x and y.
Note about Bounds For a Sheet wi... | def sheet2matrix(self,x,y):
"""
Convert a point (x,y) in Sheet coordinates to continuous
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Returns (float_row,float_col), where float_row corresponds to
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train | SheetCoordinateSystem.sheet2matrixidx | Convert a point (x,y) in sheet coordinates to the integer row
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"""
Convert a point (x,y) in sheet coordinates to the integer row
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Convert a point (x,y) in sheet coordinates to the integer row
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train | SheetCoordinateSystem.matrix2sheet | Convert a floating-point location (float_row,float_col) in
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sheet coordinates.
Valid for scalar or array float_row and float_col.
Inverse of sheet2matrix(). | holoviews/core/sheetcoords.py | def matrix2sheet(self,float_row,float_col):
"""
Convert a floating-point location (float_row,float_col) in
matrix coordinates to its corresponding location (x,y) in
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"""
Convert a floating-point location (float_row,float_col) in
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train | SheetCoordinateSystem.matrixidx2sheet | Return (x,y) where x and y are the floating point coordinates
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"""
Return (x,y) where x and y are the floating point coordinates
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matrix cell represents a 0.2 by 0.2 region, then the center
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NOTE: This is N... | def matrixidx2sheet(self,row,col):
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train | SheetCoordinateSystem.closest_cell_center | Given arbitrary sheet coordinates, return the sheet coordinates
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"""
Given arbitrary sheet coordinates, return the sheet coordinates
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"""
return self.matrixidx2sheet(*self.sheet2matrixidx(x,y)) | def closest_cell_center(self,x,y):
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train | SheetCoordinateSystem.sheetcoordinates_of_matrixidx | Return x,y where x is a vector of sheet coordinates
representing the x-center of each matrix cell, and y
represents the corresponding y-center of the cell. | holoviews/core/sheetcoords.py | def sheetcoordinates_of_matrixidx(self):
"""
Return x,y where x is a vector of sheet coordinates
representing the x-center of each matrix cell, and y
represents the corresponding y-center of the cell.
"""
rows,cols = self.shape
return self.matrixidx2sheet(np.arang... | def sheetcoordinates_of_matrixidx(self):
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Return x,y where x is a vector of sheet coordinates
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train | Slice.findinputslice | Gets the matrix indices of a slice within an array of size
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"""
Gets the matrix indices of a slice within an array of size
sheetshape from a sliceshape, positioned at coord.
"""
center_row, center_col = coord
n_rows, n_cols = sliceshape
sheet_rows, sheet_cols = sheetshape
... | def findinputslice(coord, sliceshape, sheetshape):
"""
Gets the matrix indices of a slice within an array of size
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"""
center_row, center_col = coord
n_rows, n_cols = sliceshape
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train | Slice.positionlesscrop | Return the correct slice for a weights/mask matrix at this
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correct submatrix of the weights or mask in case the unit is
near the edge of the sheet). | holoviews/core/sheetcoords.py | def positionlesscrop(self,x,y,sheet_coord_system):
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Return the correct slice for a weights/mask matrix at this
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Return the correct slice for a weights/mask matrix at this
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train | Slice.positionedcrop | Offset the bounds_template to this cf's location and store the
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Offset the bounds_template to this cf's location and store the
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Also stores the input_sheet_slice for access by C.
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cf_row,cf_col = sheet_coord_system.sheet2matrixidx(x,y)
bo... | def positionedcrop(self,x,y,sheet_coord_system):
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train | Slice.crop_to_sheet | Crop the slice to the SheetCoordinateSystem's bounds. | holoviews/core/sheetcoords.py | def crop_to_sheet(self,sheet_coord_system):
"Crop the slice to the SheetCoordinateSystem's bounds."
maxrow,maxcol = sheet_coord_system.shape
self[0] = max(0,self[0])
self[1] = min(maxrow,self[1])
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self[3] = min(maxcol,self[3]) | def crop_to_sheet(self,sheet_coord_system):
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self[0] = max(0,self[0])
self[1] = min(maxrow,self[1])
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train | Slice._createoddslicespec | Create the 'odd' Slice that best approximates the specified
sheet-coordinate bounds.
The supplied bounds are translated to have a center at the
center of one of the sheet's units (we arbitrarily use the
center unit), and then these bounds are converted to a slice
in such a way t... | holoviews/core/sheetcoords.py | def _createoddslicespec(bounds,scs,min_matrix_radius):
"""
Create the 'odd' Slice that best approximates the specified
sheet-coordinate bounds.
The supplied bounds are translated to have a center at the
center of one of the sheet's units (we arbitrarily use the
center un... | def _createoddslicespec(bounds,scs,min_matrix_radius):
"""
Create the 'odd' Slice that best approximates the specified
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train | Slice._boundsspec2slicespec | Convert an iterable boundsspec (supplying l,b,r,t of a
BoundingRegion) into a Slice specification.
Includes all units whose centers are within the specified
sheet-coordinate bounds specified by boundsspec.
Exact inverse of _slicespec2boundsspec(). | holoviews/core/sheetcoords.py | def _boundsspec2slicespec(boundsspec,scs):
"""
Convert an iterable boundsspec (supplying l,b,r,t of a
BoundingRegion) into a Slice specification.
Includes all units whose centers are within the specified
sheet-coordinate bounds specified by boundsspec.
Exact inverse of ... | def _boundsspec2slicespec(boundsspec,scs):
"""
Convert an iterable boundsspec (supplying l,b,r,t of a
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train | Slice._slicespec2boundsspec | Convert an iterable slicespec (supplying r1,r2,c1,c2 of a
Slice) into a BoundingRegion specification.
Exact inverse of _boundsspec2slicespec(). | holoviews/core/sheetcoords.py | def _slicespec2boundsspec(slicespec,scs):
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Convert an iterable slicespec (supplying r1,r2,c1,c2 of a
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Exact inverse of _boundsspec2slicespec().
"""
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left,bottom = scs.matrix2sheet(r2,c1)
... | def _slicespec2boundsspec(slicespec,scs):
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Convert an iterable slicespec (supplying r1,r2,c1,c2 of a
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train | SankeyPlot.get_extents | A Chord plot is always drawn on a unit circle. | holoviews/plotting/mpl/sankey.py | def get_extents(self, element, ranges, range_type='combined'):
"""
A Chord plot is always drawn on a unit circle.
"""
if range_type == 'extents':
return element.nodes.extents
xdim, ydim = element.nodes.kdims[:2]
xpad = .05 if self.label_index is None else 0.25... | def get_extents(self, element, ranges, range_type='combined'):
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train | PandasInterface.unpack_scalar | Given a dataset object and data in the appropriate format for
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train | PandasInterface.as_dframe | Returns the data of a Dataset as a dataframe avoiding copying
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train | escape_vals | Escapes a list of values to a string, converting to
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"""
Escapes a list of values to a string, converting to
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"""
# Ints formatted as floats to disambiguate with counter mode
ints, floats = "%.1f", "%.10f"
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Escapes a list of values to a string, converting to
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train | NdWidget.save_json | Saves frames data into a json file at the
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"""
Saves frames data into a json file at the
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"""
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train | renderer | Helper utility to access the active renderer for a given extension. | holoviews/util/__init__.py | def renderer(name):
"""
Helper utility to access the active renderer for a given extension.
"""
try:
if name not in Store.renderers:
extension(name)
return Store.renderers[name]
except ImportError:
msg = ('Could not find a {name!r} renderer, available renderers ar... | def renderer(name):
"""
Helper utility to access the active renderer for a given extension.
"""
try:
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train | save | Saves the supplied object to file.
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inferred from the file extension. Otherwise an explicit format
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html... | holoviews/util/__init__.py | def save(obj, filename, fmt='auto', backend=None, **kwargs):
"""
Saves the supplied object to file.
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default and if the filename is a string the output format will be
inferred from the file extension. Otherwise an explicit format
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Saves the supplied object to file.
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train | render | Renders the HoloViews object to the corresponding object in the
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The backend defaults to the currently declared default
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... | holoviews/util/__init__.py | def render(obj, backend=None, **kwargs):
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Renders the HoloViews object to the corresponding object in the
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The backend defaults to the currently declared default
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Renders the HoloViews object to the corresponding object in the
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train | opts._group_kwargs_to_options | Format option group kwargs into canonical options format | holoviews/util/__init__.py | def _group_kwargs_to_options(cls, obj, kwargs):
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train | opts._apply_groups_to_backend | Apply the groups to a single specified backend | holoviews/util/__init__.py | def _apply_groups_to_backend(cls, obj, options, backend, clone):
"Apply the groups to a single specified backend"
obj_handle = obj
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obj_handle = obj.map(lambda x: x.clone(id=None))
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train | opts._grouped_backends | Group options by backend and filter out output group appropriately | holoviews/util/__init__.py | def _grouped_backends(cls, options, backend):
"Group options by backend and filter out output group appropriately"
if options is None:
return [(backend or Store.current_backend, options)]
dfltdict = defaultdict(dict)
for spec, groups in options.items():
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Applies options on an object or nested group of objects,
returning a new object with the options applied. This method
accepts the separate option namespaces explicitly (i.e 'plot',
'style' and 'norm').
If the options are to be ... | holoviews/util/__init__.py | def apply_groups(cls, obj, options=None, backend=None, clone=True, **kwargs):
"""Applies nested options definition grouped by type.
Applies options on an object or nested group of objects,
returning a new object with the options applied. This method
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train | opts._cellmagic | Deprecated, not expected to be used by any current code | holoviews/util/__init__.py | def _cellmagic(cls, options, obj, strict=False):
"Deprecated, not expected to be used by any current code"
options, failure = cls._process_magic(options, strict)
if failure: return obj
if not isinstance(obj, Dimensioned):
return obj
else:
return StoreOptio... | def _cellmagic(cls, options, obj, strict=False):
"Deprecated, not expected to be used by any current code"
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train | opts._linemagic | Deprecated, not expected to be used by any current code | holoviews/util/__init__.py | def _linemagic(cls, options, strict=False, backend=None):
"Deprecated, not expected to be used by any current code"
backends = None if backend is None else [backend]
options, failure = cls._process_magic(options, strict, backends=backends)
if failure: return
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"Deprecated, not expected to be used by any current code"
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train | opts.defaults | Set default options for a session.
Set default options for a session. whether in a Python script or
a Jupyter notebook.
Args:
*options: Option objects used to specify the defaults.
backend: The plotting extension the options apply to | holoviews/util/__init__.py | def defaults(cls, *options, **kwargs):
"""Set default options for a session.
Set default options for a session. whether in a Python script or
a Jupyter notebook.
Args:
*options: Option objects used to specify the defaults.
backend: The plotting extension the opti... | def defaults(cls, *options, **kwargs):
"""Set default options for a session.
Set default options for a session. whether in a Python script or
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"""
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train | opts._element_keywords | Returns a dictionary of element names to allowed keywords | holoviews/util/__init__.py | def _element_keywords(cls, backend, elements=None):
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mapping = {}
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train | Dynamic._get_streams | Generates a list of streams to attach to the returned DynamicMap.
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of supplied stream classes and instances are processed and
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train | Dynamic._eval_kwargs | Evaluates any parameterized methods in the kwargs | holoviews/util/__init__.py | def _eval_kwargs(self):
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train | Dynamic._dynamic_operation | Generate function to dynamically apply the operation.
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"""
Generate function to dynamically apply the operation.
Wraps an existing HoloMap or DynamicMap.
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train | Dynamic._make_dynamic | Accepts a HoloMap and a dynamic callback function creating
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"""
Accepts a HoloMap and a dynamic callback function creating
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"""
if isinstance(hmap, ViewableElement):
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if isinstance(hmap, ViewableElement):
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train | PlotlyPlot._trigger_refresh | Triggers update to a plot on a refresh event | holoviews/plotting/plotly/plot.py | def _trigger_refresh(self, key):
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if self.top_level:
self.update(key)
else:
self.current_key = None
self.current_frame = None | def _trigger_refresh(self, key):
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if self.top_level:
self.update(key)
else:
self.current_key = None
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train | norm | Unity-based normalization to scale data into 0-1 range.
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Args:
values: Array of values to be normalized
min (float, optional): Lower bound of normalization range
max (float, optional): Upper bound of normalization range
Returns:
Array of nor... | holoviews/util/transform.py | def norm(values, min=None, max=None):
"""Unity-based normalization to scale data into 0-1 range.
(values - min) / (max - min)
Args:
values: Array of values to be normalized
min (float, optional): Lower bound of normalization range
max (float, optional): Upper bound of normaliza... | def norm(values, min=None, max=None):
"""Unity-based normalization to scale data into 0-1 range.
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values: Array of values to be normalized
min (float, optional): Lower bound of normalization range
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train | bin | Bins data into declared bins
Bins data into declared bins. By default each bin is labelled
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Args:
values: Array of values to be binned
bins: List or array containing the bin boundaries
labels: List of labe... | holoviews/util/transform.py | def bin(values, bins, labels=None):
"""Bins data into declared bins
Bins data into declared bins. By default each bin is labelled
with bin center values but an explicit list of bin labels may be
defined.
Args:
values: Array of values to be binned
bins: List or array containing the ... | def bin(values, bins, labels=None):
"""Bins data into declared bins
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values: Array of values to be binned
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Args:
values: Array of values to be categorized
categories: List or dict of categories to map inputs to
default: Default value... | holoviews/util/transform.py | def categorize(values, categories, default=None):
"""Maps discrete values to supplied categories.
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Args:
values: Array of values to be categorized
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train | dim.bin | Bins continuous values.
Bins continuous using the provided bins and assigns labels
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Args:
bins: List or array containing the bin boundaries
labels: List of labels to assign to each bin
... | holoviews/util/transform.py | def bin(self, bins, labels=None):
"""Bins continuous values.
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Args:
bins: List or array containing the bin boundaries
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bins: List or array containing the bin boundaries
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train | SankeyPlot._compute_quads | Computes the node quad glyph data.x | holoviews/plotting/bokeh/sankey.py | def _compute_quads(self, element, data, mapping):
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Computes the node quad glyph data.x
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train | SankeyPlot._compute_labels | Computes labels for the nodes and adds it to the data. | holoviews/plotting/bokeh/sankey.py | def _compute_labels(self, element, data, mapping):
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train | SankeyPlot._patch_hover | Replace edge start and end hover data with label_index data. | holoviews/plotting/bokeh/sankey.py | def _patch_hover(self, element, data):
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train | set_style | Select a style by name, e.g. set_style('default'). To revert to the
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train | ParamFilter.regexp_filter | Builds a parameter filter using the supplied pattern (may be a
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Builds a parameter filter using the supplied pattern (may be a
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train | InfoPrinter.heading | Turn the supplied heading text into a suitable heading with
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heading_color = cls.headings[level] if ansi else '%s'
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train | InfoPrinter.info | Show information about an object in the given category. ANSI
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train | PrettyPrinter.recurse | Recursive function that builds up an ASCII tree given an
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train | PrettyPrinter.node_info | Given a node, return relevant information. | holoviews/core/pprint.py | def node_info(cls_or_slf, node, attrpath, attrpaths, siblings, level, value_dims):
"""
Given a node, return relevant information.
"""
opts = None
if hasattr(node, 'children'):
(lvl, lines) = (level, [(level, cls_or_slf.component_type(node))])
opts = cls_or... | def node_info(cls_or_slf, node, attrpath, attrpaths, siblings, level, value_dims):
"""
Given a node, return relevant information.
"""
opts = None
if hasattr(node, 'children'):
(lvl, lines) = (level, [(level, cls_or_slf.component_type(node))])
opts = cls_or... | [
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train | PrettyPrinter.element_info | Return the information summary for an Element. This consists
of the dotted name followed by an value dimension names. | holoviews/core/pprint.py | def element_info(cls_or_slf, node, siblings, level, value_dims):
"""
Return the information summary for an Element. This consists
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"""
info = cls_or_slf.component_type(node)
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Return the information summary for an Element. This consists
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train | split_path | Split a Path type containing a single NaN separated path into
multiple subpaths. | holoviews/element/util.py | def split_path(path):
"""
Split a Path type containing a single NaN separated path into
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"""
path = path.split(0, 1)[0]
values = path.dimension_values(0)
splits = np.concatenate([[0], np.where(np.isnan(values))[0]+1, [None]])
subpaths = []
data = PandasInterface.as_... | def split_path(path):
"""
Split a Path type containing a single NaN separated path into
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path = path.split(0, 1)[0]
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train | compute_slice_bounds | Given a 2D selection consisting of slices/coordinates, a
SheetCoordinateSystem and the shape of the array returns a new
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xidx, yidx = slices
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xidx, yidx = slices
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train | reduce_fn | Aggregation function to get the first non-zero value. | holoviews/element/util.py | def reduce_fn(x):
"""
Aggregation function to get the first non-zero value.
"""
values = x.values if pd and isinstance(x, pd.Series) else x
for v in values:
if not is_nan(v):
return v
return np.NaN | def reduce_fn(x):
"""
Aggregation function to get the first non-zero value.
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values = x.values if pd and isinstance(x, pd.Series) else x
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train | circular_layout | Lay out nodes on a circle and add node index. | holoviews/element/util.py | def circular_layout(nodes):
"""
Lay out nodes on a circle and add node index.
"""
N = len(nodes)
if not N:
return ([], [], [])
circ = np.pi/N*np.arange(N)*2
x = np.cos(circ)
y = np.sin(circ)
return (x, y, nodes) | def circular_layout(nodes):
"""
Lay out nodes on a circle and add node index.
"""
N = len(nodes)
if not N:
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train | quadratic_bezier | Compute quadratic bezier spline given start and end coordinate and
two control points. | holoviews/element/util.py | def quadratic_bezier(start, end, c0=(0, 0), c1=(0, 0), steps=50):
"""
Compute quadratic bezier spline given start and end coordinate and
two control points.
"""
steps = np.linspace(0, 1, steps)
sx, sy = start
ex, ey = end
cx0, cy0 = c0
cx1, cy1 = c1
xs = ((1-steps)**3*sx + 3*((1-... | def quadratic_bezier(start, end, c0=(0, 0), c1=(0, 0), steps=50):
"""
Compute quadratic bezier spline given start and end coordinate and
two control points.
"""
steps = np.linspace(0, 1, steps)
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ex, ey = end
cx0, cy0 = c0
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train | connect_edges_pd | Given a Graph element containing abstract edges compute edge
segments directly connecting the source and target nodes. This
operation depends on pandas and is a lot faster than the pure
NumPy equivalent. | holoviews/element/util.py | def connect_edges_pd(graph):
"""
Given a Graph element containing abstract edges compute edge
segments directly connecting the source and target nodes. This
operation depends on pandas and is a lot faster than the pure
NumPy equivalent.
"""
edges = graph.dframe()
edges.index.name = 'grap... | def connect_edges_pd(graph):
"""
Given a Graph element containing abstract edges compute edge
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train | connect_edges | Given a Graph element containing abstract edges compute edge
segments directly connecting the source and target nodes. This
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Given a Graph element containing abstract edges compute edge
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paths = []
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train | categorical_aggregate2d._get_coords | Get the coordinates of the 2D aggregate, maintaining the correct
sorting order. | holoviews/element/util.py | def _get_coords(self, obj):
"""
Get the coordinates of the 2D aggregate, maintaining the correct
sorting order.
"""
xdim, ydim = obj.dimensions(label=True)[:2]
xcoords = obj.dimension_values(xdim, False)
ycoords = obj.dimension_values(ydim, False)
# Deter... | def _get_coords(self, obj):
"""
Get the coordinates of the 2D aggregate, maintaining the correct
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"""
xdim, ydim = obj.dimensions(label=True)[:2]
xcoords = obj.dimension_values(xdim, False)
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train | categorical_aggregate2d._aggregate_dataset | Generates a gridded Dataset from a column-based dataset and
lists of xcoords and ycoords | holoviews/element/util.py | def _aggregate_dataset(self, obj, xcoords, ycoords):
"""
Generates a gridded Dataset from a column-based dataset and
lists of xcoords and ycoords
"""
dim_labels = obj.dimensions(label=True)
vdims = obj.dimensions()[2:]
xdim, ydim = dim_labels[:2]
shape = (... | def _aggregate_dataset(self, obj, xcoords, ycoords):
"""
Generates a gridded Dataset from a column-based dataset and
lists of xcoords and ycoords
"""
dim_labels = obj.dimensions(label=True)
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train | categorical_aggregate2d._process | Generates a categorical 2D aggregate by inserting NaNs at all
cross-product locations that do not already have a value assigned.
Returns a 2D gridded Dataset object. | holoviews/element/util.py | def _process(self, obj, key=None):
"""
Generates a categorical 2D aggregate by inserting NaNs at all
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Returns a 2D gridded Dataset object.
"""
if isinstance(obj, Dataset) and obj.interface.gridded:
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"""
Generates a categorical 2D aggregate by inserting NaNs at all
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if isinstance(obj, Dataset) and obj.interface.gridded:
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train | TablePlot.initialize_plot | Initializes a new plot object with the last available frame. | holoviews/plotting/bokeh/tabular.py | def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
"""
Initializes a new plot object with the last available frame.
"""
# Get element key and ranges for frame
element = self.hmap.last
key = self.keys[-1]
self.current_frame = element
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"""
Initializes a new plot object with the last available frame.
"""
# Get element key and ranges for frame
element = self.hmap.last
key = self.keys[-1]
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train | TablePlot.update_frame | Updates an existing plot with data corresponding
to the key. | holoviews/plotting/bokeh/tabular.py | def update_frame(self, key, ranges=None, plot=None):
"""
Updates an existing plot with data corresponding
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"""
element = self._get_frame(key)
self._get_title_div(key, '12pt')
# Cache frame object id to skip updating data if unchanged
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"""
Updates an existing plot with data corresponding
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"""
element = self._get_frame(key)
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train | Raster.dimension_values | The set of samples available along a particular dimension. | holoviews/element/raster.py | def dimension_values(self, dim, expanded=True, flat=True):
"""
The set of samples available along a particular dimension.
"""
dim_idx = self.get_dimension_index(dim)
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train | Raster.sample | Sample the Raster along one or both of its dimensions,
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... | holoviews/element/raster.py | def sample(self, samples=[], **sample_values):
"""
Sample the Raster along one or both of its dimensions,
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... | def sample(self, samples=[], **sample_values):
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Sample the Raster along one or both of its dimensions,
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train | Raster.reduce | Reduces the Raster using functions provided via the
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Optionally a label_prefix can be provided to prepend to
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Reduces the Raster using functions provided via the
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... | def reduce(self, dimensions=None, function=None, **reduce_map):
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Reduces the Raster using functions provided via the
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train | Image.clone | Returns a clone of the object with matching parameter values
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If shared_data is set to True and no data explicitly supplied,
the clone will share data with the original. May also supply
a new_type, which will inherit all shared parameters. | holoviews/element/raster.py | def clone(self, data=None, shared_data=True, new_type=None, link=True,
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"""
Returns a clone of the object with matching parameter values
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If shared_data is set to True and no data explicitly supplied,
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of type.group.label strings, types or functions) ... | holoviews/element/raster.py | def select(self, selection_specs=None, **selection):
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Allows selecting data by the slices, sets and scalar values
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train | Image.sample | Allows sampling of an Image as an iterator of coordinates
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just the selected samples. Alternatively may supply kwargs to
sample a coordinate on an object. On an Image the coordinates
are continuously indexed and will always sn... | holoviews/element/raster.py | def sample(self, samples=[], **kwargs):
"""
Allows sampling of an Image as an iterator of coordinates
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train | Image.closest | Given a single coordinate or multiple coordinates as
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the dimension closest will find the closest actual x/y
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"""
Given a single coordinate or multiple coordinates as
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"""
Returns an raster element or raw numpy array from a PNG image
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train | HSV.rgb | Conversion from HSV to RGB. | holoviews/element/raster.py | def rgb(self):
"""
Conversion from HSV to RGB.
"""
coords = tuple(self.dimension_values(d, expanded=False)
for d in self.kdims)
data = [self.dimension_values(d, flat=False)
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hsv = self.hsv_to_rgb(*data[:3])
... | def rgb(self):
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train | QuadMesh.trimesh | Converts a QuadMesh into a TriMesh. | holoviews/element/raster.py | def trimesh(self):
"""
Converts a QuadMesh into a TriMesh.
"""
# Generate vertices
xs = self.interface.coords(self, 0, edges=True)
ys = self.interface.coords(self, 1, edges=True)
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train | notebook_extension.completions_sorting_key | Fixed version of IPyton.completer.completions_sorting_key | holoviews/ipython/__init__.py | def completions_sorting_key(cls, word):
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if word.startswith('__'): prio1 = 2
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if word.endswith('='): prio1 = -1
if word.startswith('%%'):
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if word.startswith('__'): prio1 = 2
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train | notebook_extension._get_resources | Finds the list of resources from the keyword parameters and pops
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"""
Finds the list of resources from the keyword parameters and pops
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"""
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disabled = []
for resource in ['holoviews'] + list(Store.renderers.keys()):
if resource i... | def _get_resources(self, args, params):
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train | notebook_extension.load_hvjs | Displays javascript and CSS to initialize HoloViews widgets. | holoviews/ipython/__init__.py | def load_hvjs(cls, logo=False, bokeh_logo=False, mpl_logo=False, plotly_logo=False,
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"""
Displays javascript and CSS to initialize HoloViews widgets.
"""
import jinja2
# Evaluate load_notebook.html template wi... | def load_hvjs(cls, logo=False, bokeh_logo=False, mpl_logo=False, plotly_logo=False,
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"""
Displays javascript and CSS to initialize HoloViews widgets.
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train | notebook_extension.tab_completion_docstring | Generates a docstring that can be used to enable tab-completion
of resources. | holoviews/ipython/__init__.py | def tab_completion_docstring(self_or_cls):
"""
Generates a docstring that can be used to enable tab-completion
of resources.
"""
elements = ['%s=Boolean' %k for k in list(Store.renderers.keys())]
for name, p in self_or_cls.params().items():
param_type = p.__cl... | def tab_completion_docstring(self_or_cls):
"""
Generates a docstring that can be used to enable tab-completion
of resources.
"""
elements = ['%s=Boolean' %k for k in list(Store.renderers.keys())]
for name, p in self_or_cls.params().items():
param_type = p.__cl... | [
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train | OptsCompleter.setup_completer | Get the dictionary of valid completions | holoviews/ipython/magics.py | def setup_completer(cls):
"Get the dictionary of valid completions"
try:
for element in Store.options().keys():
options = Store.options()['.'.join(element)]
plotkws = options['plot'].allowed_keywords
stylekws = options['style'].allowed_keywords... | def setup_completer(cls):
"Get the dictionary of valid completions"
try:
for element in Store.options().keys():
options = Store.options()['.'.join(element)]
plotkws = options['plot'].allowed_keywords
stylekws = options['style'].allowed_keywords... | [
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train | OptsCompleter.dotted_completion | Supply the appropriate key in Store.options and supply
suggestions for further completion. | holoviews/ipython/magics.py | def dotted_completion(cls, line, sorted_keys, compositor_defs):
"""
Supply the appropriate key in Store.options and supply
suggestions for further completion.
"""
completion_key, suggestions = None, []
tokens = [t for t in reversed(line.replace('.', ' ').split())]
... | def dotted_completion(cls, line, sorted_keys, compositor_defs):
"""
Supply the appropriate key in Store.options and supply
suggestions for further completion.
"""
completion_key, suggestions = None, []
tokens = [t for t in reversed(line.replace('.', ' ').split())]
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] | pyviz/holoviews | python | https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/ipython/magics.py#L175-L202 | [
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