def __call__(self, kind, x, y):
kind = self.kind or kind
method = getattr(self, kind)
groups = self.groupby
zs = self.kwds.get('z', [])
if not isinstance(zs, list): zs = [zs]
grid = []
if self.row: grid.append(self.row)
if self.col: grid.append(self.col)
groups += grid
if groups or len(zs) > 1:
if self.streaming:
raise NotImplementedError(
"Streaming and groupby not yet implemented")
data = self.data
if not self.gridded and any(g in self.indexes for g in groups):
data = data.reset_index()
dataset = Dataset(data)
if groups:
dataset = dataset.groupby(groups, dynamic=self.dynamic)
if len(zs) > 1:
dimensions = [Dimension(self.group_label, values=zs)
] + dataset.kdims
if self.dynamic:
obj = DynamicMap(
lambda *args: getattr(self, kind)
(x, y, args[0], dataset[args[1:]].data),
kdims=dimensions)
else:
obj = HoloMap(
{(z, ) + k: getattr(self, kind)(x, y, z,
dataset[k])
for k, v in dataset.data.items() for z in zs},
kdims=dimensions)
else:
obj = dataset.map(
lambda ds: getattr(self, kind)(x, y, data=ds.data),
Dataset)
elif len(zs) > 1:
if self.dynamic:
dataset = DynamicMap(
lambda z: getattr(self, kind)
(x, y, z, data=dataset.data),
kdims=[Dimension(self.group_label, values=zs)])
else:
dataset = HoloMap(
{
z: getattr(self, kind)(x, y, z, data=dataset.data)
for z in zs
},
kdims=[self.group_label])
else:
obj = getattr(self, kind)(x, y, data=dataset.data)
if grid:
obj = obj.grid(grid).options(shared_xaxis=True,
shared_yaxis=True)
else:
if self.streaming:
cbcallable = StreamingCallable(partial(method, x, y),
periodic=self.cb)
obj = DynamicMap(cbcallable, streams=[self.stream])
else:
obj = method(x, y)
if not (self.datashade or self.rasterize):
return obj
try:
from holoviews.operation.datashader import datashade, rasterize, dynspread
from datashader import count_cat
except:
raise ImportError('Datashading is not available')
opts = dict(width=self._plot_opts['width'],
height=self._plot_opts['height'],
dynamic=self.dynamic)
if 'cmap' in self._style_opts and self.datashade:
levels = self._plot_opts.get('color_levels')
opts['cmap'] = process_cmap(self._style_opts['cmap'], levels)
opts['color_key'] = opts['cmap']
if self.by:
opts['aggregator'] = count_cat(self.by[0])
if self.aggregator:
opts['aggregator'] = self.aggregator
if self.precompute:
opts['precompute'] = self.precompute
if self.x_sampling:
opts['x_sampling'] = self.x_sampling
if self.y_sampling:
opts['y_sampling'] = self.y_sampling
style = {}
if self.datashade:
operation = datashade
eltype = 'RGB'
else:
operation = rasterize
eltype = 'Image'
if 'cmap' in self._style_opts:
style['cmap'] = self._style_opts['cmap']
if self.crs:
# Apply projection before rasterizing
import cartopy.crs as ccrs
from geoviews import project
projection = self._plot_opts.get('projection',
ccrs.GOOGLE_MERCATOR)
obj = project(obj, projection=projection)
processed = operation(obj, **opts)
if self.dynspread:
if self.datashade:
processed = dynspread(processed,
max_px=self.kwds.get('max_px', 3),
threshold=self.kwds.get(
'threshold', 0.5))
else:
self.warning(
'dynspread may only be applied on datashaded plots, '
'use datashade=True instead of rasterize=True.')
return processed.opts(
{eltype: {
'plot': self._plot_opts,
'style': style
}})
def __call__(self, kind, x, y):
kind = self.kind or kind
method = getattr(self, kind)
groups = self.groupby
zs = self.kwds.get('z', [])
if not isinstance(zs, list): zs = [zs]
grid = []
if self.row: grid.append(self.row)
if self.col: grid.append(self.col)
groups += grid
if groups or len(zs) > 1:
if self.streaming:
raise NotImplementedError(
"Streaming and groupby not yet implemented")
dataset = Dataset(self.data)
if groups:
dataset = dataset.groupby(groups, dynamic=self.dynamic)
if len(zs) > 1:
dimensions = [Dimension(self.group_label, values=zs)
] + dataset.kdims
if self.dynamic:
obj = DynamicMap(
lambda *args: getattr(self, kind)
(x, y, args[0], dataset[args[1:]].data),
kdims=dimensions)
else:
obj = HoloMap(
{(z, ) + k: getattr(self, kind)(x, y, z,
dataset[k])
for k, v in dataset.data.items() for z in zs},
kdims=dimensions)
else:
obj = dataset.map(
lambda ds: getattr(self, kind)(x, y, data=ds.data),
Dataset)
elif len(zs) > 1:
if self.dynamic:
dataset = DynamicMap(
lambda z: getattr(self, kind)
(x, y, z, data=dataset.data),
kdims=[Dimension(self.group_label, values=zs)])
else:
dataset = HoloMap(
{
z: getattr(self, kind)(x, y, z, data=dataset.data)
for z in zs
},
kdims=[self.group_label])
else:
obj = getattr(self, kind)(x, y, data=dataset.data)
if grid:
obj = obj.grid(grid).options(shared_xaxis=True,
shared_yaxis=True)
else:
if self.streaming:
cbcallable = StreamingCallable(partial(method, x, y),
periodic=self.cb)
obj = DynamicMap(cbcallable, streams=[self.stream])
else:
obj = method(x, y)
if not (self.datashade or self.rasterize):
return obj
try:
from holoviews.operation.datashader import datashade, rasterize
from datashader import count_cat
except:
raise ImportError('Datashading is not available')
opts = dict(width=self._plot_opts['width'],
height=self._plot_opts['height'])
if 'cmap' in self._style_opts and self.datashade:
opts['cmap'] = self._style_opts['cmap']
if self.by:
opts['aggregator'] = count_cat(self.by[0])
if self.aggregator:
opts['aggregator'] = self.aggregator
style = {}
if self.datashade:
operation = datashade
eltype = 'RGB'
else:
operation = rasterize
eltype = 'Image'
if 'cmap' in self._style_opts:
style['cmap'] = self._style_opts['cmap']
if self.crs:
# Apply projection before rasterizing
import cartopy.crs as ccrs
from geoviews import project
projection = self._plot_opts.get('projection',
ccrs.GOOGLE_MERCATOR)
obj = project(obj, projection=projection)
return operation(obj, **opts).opts(
{eltype: {
'plot': self._plot_opts,
'style': style
}})
