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Merge pull request #1066 from ioam/deprecate_mdims
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Renamed conversion interface mdims argument to groupby
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@jlstevens
jlstevens authored Jan 17, 2017
2 parents 56deba7 + 15cdc5c commit 52f219c
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Showing 2 changed files with 59 additions and 46 deletions.
33 changes: 23 additions & 10 deletions holoviews/core/data/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,25 +68,38 @@ class DataConversion(object):
def __init__(self, element):
self._element = element

def __call__(self, new_type, kdims=None, vdims=None, mdims=None,
def __call__(self, new_type, kdims=None, vdims=None, groupby=None,
sort=False, **kwargs):
"""
Generic conversion method for Column based types. Supply the
Columns based type to convert to and optionally the
key dimensions (kdims), value dimensions (vdims) and HoloMap
key dimensions (mdims). Converted Columns can be automatically
sorted via the sort option and kwargs can be passed through.
Generic conversion method for Dataset based Element
types. Supply the Dataset Element type to convert to and
optionally the key dimensions (kdims), value dimensions
(vdims) and the dimensions. to group over. Converted Columns
can be automatically sorted via the sort option and kwargs can
bepassed through.
"""
if 'mdims' in kwargs:
if groupby:
raise ValueError('Cannot supply both mdims and groupby')
else:
self._element.warning("'mdims' keyword has been renamed "
"to groupby; the name mdims is "
"deprecated and will be removed "
"after version 1.7.")
groupby = kwargs['mdims']

if kdims is None:
kdims = self._element.kdims
elif kdims and not isinstance(kdims, list): kdims = [kdims]
if vdims is None:
vdims = self._element.vdims
if vdims and not isinstance(vdims, list): vdims = [vdims]
if mdims is None:
mdims = [d for d in self._element.kdims if d not in kdims+vdims]
if groupby is None:
groupby = [d for d in self._element.kdims if d not in kdims+vdims]
elif groupby and not isinstance(groupby, list):
groupby = [groupby]

selected = self._element.reindex(mdims+kdims, vdims)
selected = self._element.reindex(groupby+kdims, vdims)
params = {'kdims': [selected.get_dimension(kd) for kd in kdims],
'vdims': [selected.get_dimension(vd) for vd in vdims],
'label': selected.label}
Expand All @@ -96,7 +109,7 @@ def __call__(self, new_type, kdims=None, vdims=None, mdims=None,
if len(kdims) == selected.ndims:
element = new_type(selected, **params)
return element.sort() if sort else element
group = selected.groupby(mdims, container_type=HoloMap,
group = selected.groupby(groupby, container_type=HoloMap,
group_type=new_type, **params)
if sort:
return group.map(lambda x: x.sort(), [new_type])
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72 changes: 36 additions & 36 deletions holoviews/element/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,33 +15,33 @@ class ElementConversion(DataConversion):
types.
"""

def bars(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Bars, kdims, vdims, mdims, **kwargs)
def bars(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Bars, kdims, vdims, groupby, **kwargs)

def box(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(BoxWhisker, kdims, vdims, mdims, **kwargs)
def box(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(BoxWhisker, kdims, vdims, groupby, **kwargs)

def bivariate(self, kdims=None, vdims=None, mdims=None, **kwargs):
def bivariate(self, kdims=None, vdims=None, groupby=None, **kwargs):
from ..interface.seaborn import Bivariate
return self(Bivariate, kdims, vdims, mdims, **kwargs)
return self(Bivariate, kdims, vdims, groupby, **kwargs)

def curve(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Curve, kdims, vdims, mdims, sort=True, **kwargs)
def curve(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Curve, kdims, vdims, groupby, sort=True, **kwargs)

def errorbars(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(ErrorBars, kdims, vdims, mdims, sort=True, **kwargs)
def errorbars(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(ErrorBars, kdims, vdims, groupby, sort=True, **kwargs)

def distribution(self, dim=None, mdims=[], **kwargs):
def distribution(self, dim=None, groupby=[], **kwargs):
from ..interface.seaborn import Distribution
if dim is None:
if self._element.vdims:
dim = self._element.vdims[0]
else:
raise Exception('Must supply an explicit value dimension '
'if no value dimensions are defined ')
if mdims:
reindexed = self._element.reindex(mdims, [dim])
return reindexed.groupby(mdims, HoloMap, Distribution, **kwargs)
if groupby:
reindexed = self._element.reindex(groupby, [dim])
return reindexed.groupby(groupby, HoloMap, Distribution, **kwargs)
else:
element = self._element
params = dict(vdims=[element.get_dimension(dim)],
Expand All @@ -51,44 +51,44 @@ def distribution(self, dim=None, mdims=[], **kwargs):
return Distribution((element.dimension_values(dim),),
**dict(params, **kwargs))

def heatmap(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(HeatMap, kdims, vdims, mdims, **kwargs)
def heatmap(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(HeatMap, kdims, vdims, groupby, **kwargs)

def image(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(GridImage, kdims, vdims, mdims, **kwargs)
def image(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(GridImage, kdims, vdims, groupby, **kwargs)

def points(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Points, kdims, vdims, mdims, **kwargs)
def points(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Points, kdims, vdims, groupby, **kwargs)

def raster(self, kdims=None, vdims=None, mdims=None, **kwargs):
def raster(self, kdims=None, vdims=None, groupby=None, **kwargs):
heatmap = self.heatmap(kdims, vdims, **kwargs)
return Raster(heatmap.data, **dict(self._element.get_param_values(onlychanged=True)))

def regression(self, kdims=None, vdims=None, mdims=None, **kwargs):
def regression(self, kdims=None, vdims=None, groupby=None, **kwargs):
from ..interface.seaborn import Regression
return self(Regression, kdims, vdims, mdims, **kwargs)
return self(Regression, kdims, vdims, groupby, **kwargs)

def scatter(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Scatter, kdims, vdims, mdims, **kwargs)
def scatter(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Scatter, kdims, vdims, groupby, **kwargs)

def scatter3d(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Scatter3D, kdims, vdims, mdims, **kwargs)
def scatter3d(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Scatter3D, kdims, vdims, groupby, **kwargs)

def spikes(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Spikes, kdims, vdims, mdims, **kwargs)
def spikes(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Spikes, kdims, vdims, groupby, **kwargs)

def spread(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Spread, kdims, vdims, mdims, sort=True, **kwargs)
def spread(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Spread, kdims, vdims, groupby, sort=True, **kwargs)

def surface(self, kdims=None, vdims=None, mdims=None, **kwargs):
def surface(self, kdims=None, vdims=None, groupby=None, **kwargs):
heatmap = self.heatmap(kdims, vdims, **kwargs)
return Surface(heatmap.data, **dict(self._table.get_param_values(onlychanged=True)))

def trisurface(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(Trisurface, kdims, vdims, mdims, **kwargs)
def trisurface(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(Trisurface, kdims, vdims, groupby, **kwargs)

def vectorfield(self, kdims=None, vdims=None, mdims=None, **kwargs):
return self(VectorField, kdims, vdims, mdims, **kwargs)
def vectorfield(self, kdims=None, vdims=None, groupby=None, **kwargs):
return self(VectorField, kdims, vdims, groupby, **kwargs)


Dataset._conversion_interface = ElementConversion
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