from __future__ import absolute_import, division
import warnings
from collections import Callable
from functools import partial
import param
import numpy as np
import pandas as pd
import xarray as xr
import datashader as ds
import datashader.reductions as rd
import datashader.transfer_functions as tf
import dask.dataframe as dd
from param.parameterized import bothmethod
try:
from datashader.bundling import (directly_connect_edges as connect_edges,
hammer_bundle)
except:
hammer_bundle, connect_edges = object, object
from ..core import (Operation, Element, Dimension, NdOverlay,
CompositeOverlay, Dataset, Overlay, OrderedDict)
from ..core.data import PandasInterface, XArrayInterface, DaskInterface, cuDFInterface
from ..core.util import (
Iterable, LooseVersion, basestring, cftime_types, cftime_to_timestamp,
datetime_types, dt_to_int, isfinite, get_param_values, max_range)
from ..element import (Image, Path, Curve, RGB, Graph, TriMesh,
QuadMesh, Contours, Spikes, Area, Spread,
Segments, Scatter, Points, Polygons)
from ..element.util import connect_tri_edges_pd
from ..streams import RangeXY, PlotSize
ds_version = LooseVersion(ds.__version__)
[docs]class LinkableOperation(Operation):
"""
Abstract baseclass for operations supporting linked inputs.
"""
link_inputs = param.Boolean(default=True, doc="""
By default, the link_inputs parameter is set to True so that
when applying an operation, backends that support linked
streams update RangeXY streams on the inputs of the operation.
Disable when you do not want the resulting plot to be
interactive, e.g. when trying to display an interactive plot a
second time.""")
_allow_extra_keywords=True
[docs]class ResamplingOperation(LinkableOperation):
"""
Abstract baseclass for resampling operations
"""
dynamic = param.Boolean(default=True, doc="""
Enables dynamic processing by default.""")
expand = param.Boolean(default=True, doc="""
Whether the x_range and y_range should be allowed to expand
beyond the extent of the data. Setting this value to True is
useful for the case where you want to ensure a certain size of
output grid, e.g. if you are doing masking or other arithmetic
on the grids. A value of False ensures that the grid is only
just as large as it needs to be to contain the data, which will
be faster and use less memory if the resulting aggregate is
being overlaid on a much larger background.""")
height = param.Integer(default=400, doc="""
The height of the output image in pixels.""")
width = param.Integer(default=400, doc="""
The width of the output image in pixels.""")
x_range = param.Tuple(default=None, length=2, doc="""
The x_range as a tuple of min and max x-value. Auto-ranges
if set to None.""")
y_range = param.Tuple(default=None, length=2, doc="""
The y-axis range as a tuple of min and max y value. Auto-ranges
if set to None.""")
x_sampling = param.Number(default=None, doc="""
Specifies the smallest allowed sampling interval along the x axis.""")
y_sampling = param.Number(default=None, doc="""
Specifies the smallest allowed sampling interval along the y axis.""")
target = param.ClassSelector(class_=Dataset, doc="""
A target Dataset which defines the desired x_range, y_range,
width and height.
""")
streams = param.List(default=[PlotSize, RangeXY], doc="""
List of streams that are applied if dynamic=True, allowing
for dynamic interaction with the plot.""")
element_type = param.ClassSelector(class_=(Dataset,), instantiate=False,
is_instance=False, default=Image,
doc="""
The type of the returned Elements, must be a 2D Dataset type.""")
precompute = param.Boolean(default=False, doc="""
Whether to apply precomputing operations. Precomputing can
speed up resampling operations by avoiding unnecessary
recomputation if the supplied element does not change between
calls. The cost of enabling this option is that the memory
used to represent this internal state is not freed between
calls.""")
@bothmethod
def instance(self_or_cls,**params):
filtered = {k:v for k,v in params.items() if k in self_or_cls.param}
inst = super(ResamplingOperation, self_or_cls).instance(**filtered)
inst._precomputed = {}
return inst
def _get_sampling(self, element, x, y, ndim=2, default=None):
target = self.p.target
if not isinstance(x, list) and x is not None:
x = [x]
if not isinstance(y, list) and y is not None:
y = [y]
if target:
x0, y0, x1, y1 = target.bounds.lbrt()
x_range, y_range = (x0, x1), (y0, y1)
height, width = target.dimension_values(2, flat=False).shape
else:
if x is None:
x_range = self.p.x_range or (-0.5, 0.5)
elif self.p.expand or not self.p.x_range:
if self.p.x_range and all(isfinite(v) for v in self.p.x_range):
x_range = self.p.x_range
else:
x_range = max_range([element.range(xd) for xd in x])
else:
x0, x1 = self.p.x_range
ex0, ex1 = max_range([element.range(xd) for xd in x])
x_range = (np.nanmin([np.nanmax([x0, ex0]), ex1]),
np.nanmax([np.nanmin([x1, ex1]), ex0]))
if (y is None and ndim == 2):
y_range = self.p.y_range or default or (-0.5, 0.5)
elif self.p.expand or not self.p.y_range:
if self.p.y_range and all(isfinite(v) for v in self.p.y_range):
y_range = self.p.y_range
elif default is None:
y_range = max_range([element.range(yd) for yd in y])
else:
y_range = default
else:
y0, y1 = self.p.y_range
if default is None:
ey0, ey1 = max_range([element.range(yd) for yd in y])
else:
ey0, ey1 = default
y_range = (np.nanmin([np.nanmax([y0, ey0]), ey1]),
np.nanmax([np.nanmin([y1, ey1]), ey0]))
width, height = self.p.width, self.p.height
(xstart, xend), (ystart, yend) = x_range, y_range
xtype = 'numeric'
if isinstance(xstart, datetime_types) or isinstance(xend, datetime_types):
xstart, xend = dt_to_int(xstart, 'ns'), dt_to_int(xend, 'ns')
xtype = 'datetime'
elif not np.isfinite(xstart) and not np.isfinite(xend):
xstart, xend = 0, 0
if x and element.get_dimension_type(x[0]) in datetime_types:
xtype = 'datetime'
ytype = 'numeric'
if isinstance(ystart, datetime_types) or isinstance(yend, datetime_types):
ystart, yend = dt_to_int(ystart, 'ns'), dt_to_int(yend, 'ns')
ytype = 'datetime'
elif not np.isfinite(ystart) and not np.isfinite(yend):
ystart, yend = 0, 0
if y and element.get_dimension_type(y[0]) in datetime_types:
ytype = 'datetime'
# Compute highest allowed sampling density
xspan = xend - xstart
yspan = yend - ystart
if self.p.x_sampling:
width = int(min([(xspan/self.p.x_sampling), width]))
if self.p.y_sampling:
height = int(min([(yspan/self.p.y_sampling), height]))
if xstart == xend or width == 0:
xunit, width = 0, 0
else:
xunit = float(xspan)/width
if ystart == yend or height == 0:
yunit, height = 0, 0
else:
yunit = float(yspan)/height
xs, ys = (np.linspace(xstart+xunit/2., xend-xunit/2., width),
np.linspace(ystart+yunit/2., yend-yunit/2., height))
return ((xstart, xend), (ystart, yend)), (xs, ys), (width, height), (xtype, ytype)
def _dt_transform(self, x_range, y_range, xs, ys, xtype, ytype):
(xstart, xend), (ystart, yend) = x_range, y_range
if xtype == 'datetime':
xstart, xend = (np.array([xstart, xend])/1e3).astype('datetime64[us]')
xs = (xs/1e3).astype('datetime64[us]')
if ytype == 'datetime':
ystart, yend = (np.array([ystart, yend])/1e3).astype('datetime64[us]')
ys = (ys/1e3).astype('datetime64[us]')
return ((xstart, xend), (ystart, yend)), (xs, ys)
[docs]class AggregationOperation(ResamplingOperation):
"""
AggregationOperation extends the ResamplingOperation defining an
aggregator parameter used to define a datashader Reduction.
"""
aggregator = param.ClassSelector(class_=(ds.reductions.Reduction, basestring),
default=ds.count(), doc="""
Datashader reduction function used for aggregating the data.
The aggregator may also define a column to aggregate; if
no column is defined the first value dimension of the element
will be used. May also be defined as a string.""")
_agg_methods = {
'any': rd.any,
'count': rd.count,
'first': rd.first,
'last': rd.last,
'mode': rd.mode,
'mean': rd.mean,
'sum': rd.sum,
'var': rd.var,
'std': rd.std,
'min': rd.min,
'max': rd.max
}
def _get_aggregator(self, element, add_field=True):
agg = self.p.aggregator
if isinstance(agg, basestring):
if agg not in self._agg_methods:
agg_methods = sorted(self._agg_methods)
raise ValueError("Aggregation method '%r' is not known; "
"aggregator must be one of: %r" %
(agg, agg_methods))
agg = self._agg_methods[agg]()
elements = element.traverse(lambda x: x, [Element])
if add_field and getattr(agg, 'column', False) is None and not isinstance(agg, (rd.count, rd.any)):
if not elements:
raise ValueError('Could not find any elements to apply '
'%s operation to.' % type(self).__name__)
inner_element = elements[0]
if isinstance(inner_element, TriMesh) and inner_element.nodes.vdims:
field = inner_element.nodes.vdims[0].name
elif inner_element.vdims:
field = inner_element.vdims[0].name
elif isinstance(element, NdOverlay):
field = element.kdims[0].name
else:
raise ValueError("Could not determine dimension to apply "
"'%s' operation to. Declare the dimension "
"to aggregate as part of the datashader "
"aggregator." % type(self).__name__)
agg = type(agg)(field)
return agg
def _empty_agg(self, element, x, y, width, height, xs, ys, agg_fn, **params):
x = x.name if x else 'x'
y = y.name if x else 'y'
xarray = xr.DataArray(np.full((height, width), np.NaN),
dims=[y, x], coords={x: xs, y: ys})
if width == 0:
params['xdensity'] = 1
if height == 0:
params['ydensity'] = 1
el = self.p.element_type(xarray, **params)
if isinstance(agg_fn, ds.count_cat):
vals = element.dimension_values(agg_fn.column, expanded=False)
dim = element.get_dimension(agg_fn.column)
return NdOverlay({v: el for v in vals}, dim)
return el
def _get_agg_params(self, element, x, y, agg_fn, bounds):
params = dict(get_param_values(element), kdims=[x, y],
datatype=['xarray'], bounds=bounds)
category = None
if hasattr(agg_fn, 'reduction'):
category = agg_fn.cat_column
agg_fn = agg_fn.reduction
column = agg_fn.column if agg_fn else None
if column:
dims = [d for d in element.dimensions('ranges') if d == column]
if not dims:
raise ValueError("Aggregation column '%s' not found on '%s' element. "
"Ensure the aggregator references an existing "
"dimension." % (column,element))
name = '%s Count' % column if isinstance(agg_fn, ds.count_cat) else column
vdims = [dims[0].clone(name)]
elif category:
vdims = Dimension('%s Count' % category)
else:
vdims = Dimension('Count')
params['vdims'] = vdims
return params
[docs]class aggregate(AggregationOperation):
"""
aggregate implements 2D binning for any valid HoloViews Element
type using datashader. I.e., this operation turns a HoloViews
Element or overlay of Elements into an Image or an overlay of
Images by rasterizing it. This allows quickly aggregating large
datasets computing a fixed-sized representation independent
of the original dataset size.
By default it will simply count the number of values in each bin
but other aggregators can be supplied implementing mean, max, min
and other reduction operations.
The bins of the aggregate are defined by the width and height and
the x_range and y_range. If x_sampling or y_sampling are supplied
the operation will ensure that a bin is no smaller than the minimum
sampling distance by reducing the width and height when zoomed in
beyond the minimum sampling distance.
By default, the PlotSize stream is applied when this operation
is used dynamically, which means that the height and width
will automatically be set to match the inner dimensions of
the linked plot.
"""
[docs] @classmethod
def get_agg_data(cls, obj, category=None):
"""
Reduces any Overlay or NdOverlay of Elements into a single
xarray Dataset that can be aggregated.
"""
paths = []
if isinstance(obj, Graph):
obj = obj.edgepaths
kdims = list(obj.kdims)
vdims = list(obj.vdims)
dims = obj.dimensions()[:2]
if isinstance(obj, Path):
glyph = 'line'
for p in obj.split(datatype='dataframe'):
paths.append(p)
elif isinstance(obj, CompositeOverlay):
element = None
for key, el in obj.data.items():
x, y, element, glyph = cls.get_agg_data(el)
dims = (x, y)
df = PandasInterface.as_dframe(element)
if isinstance(obj, NdOverlay):
df = df.assign(**dict(zip(obj.dimensions('key', True), key)))
paths.append(df)
if element is None:
dims = None
else:
kdims += element.kdims
vdims = element.vdims
elif isinstance(obj, Element):
glyph = 'line' if isinstance(obj, Curve) else 'points'
paths.append(PandasInterface.as_dframe(obj))
if dims is None or len(dims) != 2:
return None, None, None, None
else:
x, y = dims
if len(paths) > 1:
if glyph == 'line':
path = paths[0][:1]
if isinstance(path, dd.DataFrame):
path = path.compute()
empty = path.copy()
empty.iloc[0, :] = (np.NaN,) * empty.shape[1]
paths = [elem for p in paths for elem in (p, empty)][:-1]
if all(isinstance(path, dd.DataFrame) for path in paths):
df = dd.concat(paths)
else:
paths = [p.compute() if isinstance(p, dd.DataFrame) else p for p in paths]
df = pd.concat(paths)
else:
df = paths[0] if paths else pd.DataFrame([], columns=[x.name, y.name])
if category and df[category].dtype.name != 'category':
df[category] = df[category].astype('category')
is_custom = isinstance(df, dd.DataFrame) or cuDFInterface.applies(df)
if any((not is_custom and len(df[d.name]) and isinstance(df[d.name].values[0], cftime_types)) or
df[d.name].dtype.kind == 'M' for d in (x, y)):
df = df.copy()
for d in (x, y):
vals = df[d.name]
if not is_custom and len(vals) and isinstance(vals.values[0], cftime_types):
vals = cftime_to_timestamp(vals, 'ns')
elif df[d.name].dtype.kind == 'M':
vals = vals.astype('datetime64[ns]')
else:
continue
df[d.name] = vals.astype('int64')
return x, y, Dataset(df, kdims=kdims, vdims=vdims), glyph
def _process(self, element, key=None):
agg_fn = self._get_aggregator(element)
if hasattr(agg_fn, 'cat_column'):
category = agg_fn.cat_column
else:
category = agg_fn.column if isinstance(agg_fn, ds.count_cat) else None
if overlay_aggregate.applies(element, agg_fn):
params = dict(
{p: v for p, v in self.param.get_param_values() if p != 'name'},
dynamic=False, **{p: v for p, v in self.p.items()
if p not in ('name', 'dynamic')})
return overlay_aggregate(element, **params)
if element._plot_id in self._precomputed:
x, y, data, glyph = self._precomputed[element._plot_id]
else:
x, y, data, glyph = self.get_agg_data(element, category)
if self.p.precompute:
self._precomputed[element._plot_id] = x, y, data, glyph
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = self._get_sampling(element, x, y)
((x0, x1), (y0, y1)), (xs, ys) = self._dt_transform(x_range, y_range, xs, ys, xtype, ytype)
params = self._get_agg_params(element, x, y, agg_fn, (x0, y0, x1, y1))
if x is None or y is None or width == 0 or height == 0:
return self._empty_agg(element, x, y, width, height, xs, ys, agg_fn, **params)
elif not getattr(data, 'interface', None) is DaskInterface and not len(data):
empty_val = 0 if isinstance(agg_fn, ds.count) else np.NaN
xarray = xr.DataArray(np.full((height, width), empty_val),
dims=[y.name, x.name], coords={x.name: xs, y.name: ys})
return self.p.element_type(xarray, **params)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
dfdata = PandasInterface.as_dframe(data)
agg = getattr(cvs, glyph)(dfdata, x.name, y.name, agg_fn)
if 'x_axis' in agg.coords and 'y_axis' in agg.coords:
agg = agg.rename({'x_axis': x, 'y_axis': y})
if xtype == 'datetime':
agg[x.name] = (agg[x.name]/1e3).astype('datetime64[us]')
if ytype == 'datetime':
agg[y.name] = (agg[y.name]/1e3).astype('datetime64[us]')
if agg.ndim == 2:
# Replacing x and y coordinates to avoid numerical precision issues
eldata = agg if ds_version > '0.5.0' else (xs, ys, agg.data)
return self.p.element_type(eldata, **params)
else:
layers = {}
for c in agg.coords[agg_fn.column].data:
cagg = agg.sel(**{agg_fn.column: c})
eldata = cagg if ds_version > '0.5.0' else (xs, ys, cagg.data)
layers[c] = self.p.element_type(eldata, **params)
return NdOverlay(layers, kdims=[data.get_dimension(agg_fn.column)])
[docs]class overlay_aggregate(aggregate):
"""
Optimized aggregation for NdOverlay objects by aggregating each
Element in an NdOverlay individually avoiding having to concatenate
items in the NdOverlay. Works by summing sum and count aggregates and
applying appropriate masking for NaN values. Mean aggregation
is also supported by dividing sum and count aggregates. count_cat
aggregates are grouped by the categorical dimension and a separate
aggregate for each category is generated.
"""
@classmethod
def applies(cls, element, agg_fn):
return (isinstance(element, NdOverlay) and
((isinstance(agg_fn, (ds.count, ds.sum, ds.mean)) and
(agg_fn.column is None or agg_fn.column not in element.kdims)) or
(isinstance(agg_fn, ds.count_cat) and agg_fn.column in element.kdims)))
def _process(self, element, key=None):
agg_fn = self._get_aggregator(element)
if not self.applies(element, agg_fn):
raise ValueError('overlay_aggregate only handles aggregation '
'of NdOverlay types with count, sum or mean '
'reduction.')
# Compute overall bounds
dims = element.last.dimensions()[0:2]
ndims = len(dims)
if ndims == 1:
x, y = dims[0], None
else:
x, y = dims
info = self._get_sampling(element, x, y, ndims)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
((x0, x1), (y0, y1)), _ = self._dt_transform(x_range, y_range, xs, ys, xtype, ytype)
agg_params = dict({k: v for k, v in dict(self.param.get_param_values(),
**self.p).items()
if k in aggregate.param},
x_range=(x0, x1), y_range=(y0, y1))
bbox = (x0, y0, x1, y1)
# Optimize categorical counts by aggregating them individually
if isinstance(agg_fn, ds.count_cat):
agg_params.update(dict(dynamic=False, aggregator=ds.count()))
agg_fn1 = aggregate.instance(**agg_params)
if element.ndims == 1:
grouped = element
else:
grouped = element.groupby([agg_fn.column], container_type=NdOverlay,
group_type=NdOverlay)
groups = []
for k, v in grouped.items():
agg = agg_fn1(v)
groups.append((k, agg.clone(agg.data, bounds=bbox)))
return grouped.clone(groups)
# Create aggregate instance for sum, count operations, breaking mean
# into two aggregates
column = agg_fn.column or 'Count'
if isinstance(agg_fn, ds.mean):
agg_fn1 = aggregate.instance(**dict(agg_params, aggregator=ds.sum(column)))
agg_fn2 = aggregate.instance(**dict(agg_params, aggregator=ds.count()))
else:
agg_fn1 = aggregate.instance(**agg_params)
agg_fn2 = None
is_sum = isinstance(agg_fn1.aggregator, ds.sum)
# Accumulate into two aggregates and mask
agg, agg2, mask = None, None, None
mask = None
for v in element:
# Compute aggregates and mask
new_agg = agg_fn1.process_element(v, None)
if is_sum:
new_mask = np.isnan(new_agg.data[column].values)
new_agg.data = new_agg.data.fillna(0)
if agg_fn2:
new_agg2 = agg_fn2.process_element(v, None)
if agg is None:
agg = new_agg
if is_sum: mask = new_mask
if agg_fn2: agg2 = new_agg2
else:
agg.data += new_agg.data
if is_sum: mask &= new_mask
if agg_fn2: agg2.data += new_agg2.data
# Divide sum by count to compute mean
if agg2 is not None:
agg2.data.rename({'Count': agg_fn.column}, inplace=True)
with np.errstate(divide='ignore', invalid='ignore'):
agg.data /= agg2.data
# Fill masked with with NaNs
if is_sum:
agg.data[column].values[mask] = np.NaN
return agg.clone(bounds=bbox)
[docs]class area_aggregate(AggregationOperation):
"""
Aggregates Area elements by filling the area between zero and
the y-values if only one value dimension is defined and the area
between the curves if two are provided.
"""
def _process(self, element, key=None):
x, y = element.dimensions()[:2]
agg_fn = self._get_aggregator(element)
default = None
if not self.p.y_range:
y0, y1 = element.range(1)
if len(element.vdims) > 1:
y0, _ = element.range(2)
elif y0 >= 0:
y0 = 0
elif y1 <= 0:
y1 = 0
default = (y0, y1)
ystack = element.vdims[1].name if len(element.vdims) > 1 else None
info = self._get_sampling(element, x, y, ndim=2, default=default)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
((x0, x1), (y0, y1)), (xs, ys) = self._dt_transform(x_range, y_range, xs, ys, xtype, ytype)
df = PandasInterface.as_dframe(element)
if isinstance(agg_fn, (ds.count, ds.any)):
vdim = type(agg_fn).__name__
else:
vdim = element.get_dimension(agg_fn.column)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
params = dict(get_param_values(element), kdims=[x, y], vdims=vdim,
datatype=['xarray'], bounds=(x0, y0, x1, y1))
if width == 0 or height == 0:
return self._empty_agg(element, x, y, width, height, xs, ys, agg_fn, **params)
agg = cvs.area(df, x.name, y.name, agg_fn, axis=0, y_stack=ystack)
if xtype == "datetime":
agg[x.name] = (agg[x.name]/1e3).astype('datetime64[us]')
return self.p.element_type(agg, **params)
[docs]class spread_aggregate(area_aggregate):
"""
Aggregates Spread elements by filling the area between the lower
and upper error band.
"""
def _process(self, element, key=None):
x, y = element.dimensions()[:2]
df = PandasInterface.as_dframe(element)
if df is element.data:
df = df.copy()
pos, neg = element.vdims[1:3] if len(element.vdims) > 2 else element.vdims[1:2]*2
yvals = df[y.name]
df[y.name] = yvals+df[pos.name]
df['_lower'] = yvals-df[neg.name]
area = element.clone(df, vdims=[y, '_lower']+element.vdims[3:], new_type=Area)
return super(spread_aggregate, self)._process(area, key=None)
[docs]class spikes_aggregate(AggregationOperation):
"""
Aggregates Spikes elements by drawing individual line segments
over the entire y_range if no value dimension is defined and
between zero and the y-value if one is defined.
"""
spike_length = param.Number(default=None, allow_None=True, doc="""
If numeric, specifies the length of each spike, overriding the
vdims values (if present).""")
offset = param.Number(default=0., doc="""
The offset of the lower end of each spike.""")
def _process(self, element, key=None):
agg_fn = self._get_aggregator(element)
x, y = element.kdims[0], None
spike_length = 0.5 if self.p.spike_length is None else self.p.spike_length
if element.vdims and self.p.spike_length is None:
x, y = element.dimensions()[:2]
rename_dict = {'x': x.name, 'y':y.name}
if not self.p.y_range:
y0, y1 = element.range(1)
if y0 >= 0:
default = (0, y1)
elif y1 <= 0:
default = (y0, 0)
else:
default = (y0, y1)
else:
default = None
else:
x, y = element.kdims[0], None
default = (float(self.p.offset),
float(self.p.offset + spike_length))
rename_dict = {'x': x.name}
info = self._get_sampling(element, x, y, ndim=1, default=default)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
((x0, x1), (y0, y1)), (xs, ys) = self._dt_transform(x_range, y_range, xs, ys, xtype, ytype)
value_cols = [] if agg_fn.column is None else [agg_fn.column]
if y is None:
df = element.dframe([x]+value_cols).copy()
y = Dimension('y')
df['y0'] = float(self.p.offset)
df['y1'] = float(self.p.offset + spike_length)
yagg = ['y0', 'y1']
if not self.p.expand: height = 1
else:
df = element.dframe([x, y]+value_cols).copy()
df['y0'] = np.array(0, df.dtypes[y.name])
yagg = ['y0', y.name]
if xtype == 'datetime':
df[x.name] = df[x.name].astype('datetime64[us]').astype('int64')
if isinstance(agg_fn, (ds.count, ds.any)):
vdim = type(agg_fn).__name__
else:
vdim = element.get_dimension(agg_fn.column)
params = dict(get_param_values(element), kdims=[x, y], vdims=vdim,
datatype=['xarray'], bounds=(x0, y0, x1, y1))
if width == 0 or height == 0:
return self._empty_agg(element, x, y, width, height, xs, ys, agg_fn, **params)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
agg = cvs.line(df, x.name, yagg, agg_fn, axis=1).rename(rename_dict)
if xtype == "datetime":
agg[x.name] = (agg[x.name]/1e3).astype('datetime64[us]')
return self.p.element_type(agg, **params)
[docs]class segments_aggregate(AggregationOperation):
"""
Aggregates Segments elements.
"""
def _process(self, element, key=None):
agg_fn = self._get_aggregator(element)
x0d, y0d, x1d, y1d = element.kdims
info = self._get_sampling(element, [x0d, x1d], [y0d, y1d], ndim=1)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
((x0, x1), (y0, y1)), (xs, ys) = self._dt_transform(x_range, y_range, xs, ys, xtype, ytype)
df = element.interface.as_dframe(element)
if xtype == 'datetime':
df[x0d.name] = df[x0d.name].astype('datetime64[us]').astype('int64')
df[x1d.name] = df[x1d.name].astype('datetime64[us]').astype('int64')
if ytype == 'datetime':
df[y0d.name] = df[y0d.name].astype('datetime64[us]').astype('int64')
df[y1d.name] = df[y1d.name].astype('datetime64[us]').astype('int64')
if isinstance(agg_fn, (ds.count, ds.any)):
vdim = type(agg_fn).__name__
else:
vdim = element.get_dimension(agg_fn.column)
params = dict(get_param_values(element), kdims=[x0d, y0d], vdims=vdim,
datatype=['xarray'], bounds=(x0, y0, x1, y1))
if width == 0 or height == 0:
return self._empty_agg(element, x0d, y0d, width, height, xs, ys, agg_fn, **params)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
agg = cvs.line(df, [x0d.name, x1d.name], [y0d.name, y1d.name], agg_fn, axis=1)
xdim, ydim = list(agg.dims)[:2][::-1]
if xtype == "datetime":
agg[xdim] = (agg[xdim]/1e3).astype('datetime64[us]')
if ytype == "datetime":
agg[ydim] = (agg[ydim]/1e3).astype('datetime64[us]')
params['kdims'] = [xdim, ydim]
return self.p.element_type(agg, **params)
[docs]class regrid(AggregationOperation):
"""
regrid allows resampling a HoloViews Image type using specified
up- and downsampling functions defined using the aggregator and
interpolation parameters respectively. By default upsampling is
disabled to avoid unnecessarily upscaling an image that has to be
sent to the browser. Also disables expanding the image beyond its
original bounds avoiding unnecessarily padding the output array
with NaN values.
"""
aggregator = param.ClassSelector(default=ds.mean(),
class_=(ds.reductions.Reduction, basestring))
expand = param.Boolean(default=False, doc="""
Whether the x_range and y_range should be allowed to expand
beyond the extent of the data. Setting this value to True is
useful for the case where you want to ensure a certain size of
output grid, e.g. if you are doing masking or other arithmetic
on the grids. A value of False ensures that the grid is only
just as large as it needs to be to contain the data, which will
be faster and use less memory if the resulting aggregate is
being overlaid on a much larger background.""")
interpolation = param.ObjectSelector(default='nearest',
objects=['linear', 'nearest', 'bilinear', None, False], doc="""
Interpolation method""")
upsample = param.Boolean(default=False, doc="""
Whether to allow upsampling if the source array is smaller
than the requested array. Setting this value to True will
enable upsampling using the interpolation method, when the
requested width and height are larger than what is available
on the source grid. If upsampling is disabled (the default)
the width and height are clipped to what is available on the
source array.""")
def _get_xarrays(self, element, coords, xtype, ytype):
x, y = element.kdims
dims = [y.name, x.name]
irregular = any(element.interface.irregular(element, d)
for d in dims)
if irregular:
coord_dict = {x.name: (('y', 'x'), coords[0]),
y.name: (('y', 'x'), coords[1])}
else:
coord_dict = {x.name: coords[0], y.name: coords[1]}
arrays = {}
for i, vd in enumerate(element.vdims):
if element.interface is XArrayInterface:
if element.interface.packed(element):
xarr = element.data[..., i]
else:
xarr = element.data[vd.name]
if 'datetime' in (xtype, ytype):
xarr = xarr.copy()
if dims != xarr.dims and not irregular:
xarr = xarr.transpose(*dims)
elif irregular:
arr = element.dimension_values(vd, flat=False)
xarr = xr.DataArray(arr, coords=coord_dict, dims=['y', 'x'])
else:
arr = element.dimension_values(vd, flat=False)
xarr = xr.DataArray(arr, coords=coord_dict, dims=dims)
if xtype == "datetime":
xarr[x.name] = [dt_to_int(v, 'ns') for v in xarr[x.name].values]
if ytype == "datetime":
xarr[y.name] = [dt_to_int(v, 'ns') for v in xarr[y.name].values]
arrays[vd.name] = xarr
return arrays
def _process(self, element, key=None):
if ds_version <= '0.5.0':
raise RuntimeError('regrid operation requires datashader>=0.6.0')
# Compute coords, anges and size
x, y = element.kdims
coords = tuple(element.dimension_values(d, expanded=False) for d in [x, y])
info = self._get_sampling(element, x, y)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
# Disable upsampling by clipping size and ranges
(xstart, xend), (ystart, yend) = (x_range, y_range)
xspan, yspan = (xend-xstart), (yend-ystart)
interp = self.p.interpolation or None
if interp == 'bilinear': interp = 'linear'
if not (self.p.upsample or interp is None) and self.p.target is None:
(x0, x1), (y0, y1) = element.range(0), element.range(1)
if isinstance(x0, datetime_types):
x0, x1 = dt_to_int(x0, 'ns'), dt_to_int(x1, 'ns')
if isinstance(y0, datetime_types):
y0, y1 = dt_to_int(y0, 'ns'), dt_to_int(y1, 'ns')
exspan, eyspan = (x1-x0), (y1-y0)
if np.isfinite(exspan) and exspan > 0 and xspan > 0:
width = max([min([int((xspan/exspan) * len(coords[0])), width]), 1])
else:
width = 0
if np.isfinite(eyspan) and eyspan > 0 and yspan > 0:
height = max([min([int((yspan/eyspan) * len(coords[1])), height]), 1])
else:
height = 0
xunit = float(xspan)/width if width else 0
yunit = float(yspan)/height if height else 0
xs, ys = (np.linspace(xstart+xunit/2., xend-xunit/2., width),
np.linspace(ystart+yunit/2., yend-yunit/2., height))
# Compute bounds (converting datetimes)
((x0, x1), (y0, y1)), (xs, ys) = self._dt_transform(x_range, y_range, xs, ys, xtype, ytype)
params = dict(bounds=(x0, y0, x1, y1))
if width == 0 or height == 0:
if width == 0:
params['xdensity'] = 1
if height == 0:
params['ydensity'] = 1
return element.clone((xs, ys, np.zeros((height, width))), **params)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
# Apply regridding to each value dimension
regridded = {}
arrays = self._get_xarrays(element, coords, xtype, ytype)
agg_fn = self._get_aggregator(element, add_field=False)
for vd, xarr in arrays.items():
rarray = cvs.raster(xarr, upsample_method=interp,
downsample_method=agg_fn)
# Convert datetime coordinates
if xtype == "datetime":
rarray[x.name] = (rarray[x.name]/1e3).astype('datetime64[us]')
if ytype == "datetime":
rarray[y.name] = (rarray[y.name]/1e3).astype('datetime64[us]')
regridded[vd] = rarray
regridded = xr.Dataset(regridded)
return element.clone(regridded, datatype=['xarray']+element.datatype, **params)
[docs]class contours_rasterize(aggregate):
"""
Rasterizes the Contours element by weighting the aggregation by
the iso-contour levels if a value dimension is defined, otherwise
default to any aggregator.
"""
aggregator = param.ClassSelector(default=ds.mean(),
class_=(ds.reductions.Reduction, basestring))
def _get_aggregator(self, element, add_field=True):
agg = self.p.aggregator
if not element.vdims and agg.column is None and not isinstance(agg, (rd.count, rd.any)):
return ds.any()
return super(contours_rasterize, self)._get_aggregator(element, add_field)
[docs]class trimesh_rasterize(aggregate):
"""
Rasterize the TriMesh element using the supplied aggregator. If
the TriMesh nodes or edges define a value dimension, will plot
filled and shaded polygons; otherwise returns a wiremesh of the
data.
"""
aggregator = param.ClassSelector(default=ds.mean(),
class_=(ds.reductions.Reduction, basestring))
interpolation = param.ObjectSelector(default='bilinear',
objects=['bilinear', 'linear', None, False], doc="""
The interpolation method to apply during rasterization.""")
def _precompute(self, element, agg):
from datashader.utils import mesh
if element.vdims and getattr(agg, 'column', None) not in element.nodes.vdims:
simplices = element.dframe([0, 1, 2, 3])
verts = element.nodes.dframe([0, 1])
elif element.nodes.vdims:
simplices = element.dframe([0, 1, 2])
verts = element.nodes.dframe([0, 1, 3])
for c, dtype in zip(simplices.columns[:3], simplices.dtypes):
if dtype.kind != 'i':
simplices[c] = simplices[c].astype('int')
return {'mesh': mesh(verts, simplices), 'simplices': simplices,
'vertices': verts}
def _precompute_wireframe(self, element, agg):
if hasattr(element, '_wireframe'):
segments = element._wireframe.data
else:
segments = connect_tri_edges_pd(element)
element._wireframe = Dataset(segments, datatype=['dataframe', 'dask'])
return {'segments': segments}
def _process(self, element, key=None):
if isinstance(element, TriMesh):
x, y = element.nodes.kdims[:2]
else:
x, y = element.kdims
info = self._get_sampling(element, x, y)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
agg = self.p.aggregator
interp = self.p.interpolation or None
precompute = self.p.precompute
if interp == 'linear': interp = 'bilinear'
wireframe = False
if (not (element.vdims or (isinstance(element, TriMesh) and element.nodes.vdims))) and ds_version <= '0.6.9':
self.p.aggregator = ds.any() if isinstance(agg, ds.any) or agg == 'any' else ds.count()
return aggregate._process(self, element, key)
elif ((not interp and (isinstance(agg, (ds.any, ds.count)) or
agg in ['any', 'count']))
or not (element.vdims or element.nodes.vdims)):
wireframe = True
precompute = False # TriMesh itself caches wireframe
agg = self._get_aggregator(element) if isinstance(agg, (ds.any, ds.count)) else ds.any()
vdim = 'Count' if isinstance(agg, ds.count) else 'Any'
elif getattr(agg, 'column', None):
if agg.column in element.vdims:
vdim = element.get_dimension(agg.column)
elif isinstance(element, TriMesh) and agg.column in element.nodes.vdims:
vdim = element.nodes.get_dimension(agg.column)
else:
raise ValueError("Aggregation column %s not found on TriMesh element."
% agg.column)
else:
if isinstance(element, TriMesh) and element.nodes.vdims:
vdim = element.nodes.vdims[0]
else:
vdim = element.vdims[0]
agg = self._get_aggregator(element)
if element._plot_id in self._precomputed:
precomputed = self._precomputed[element._plot_id]
elif wireframe:
precomputed = self._precompute_wireframe(element, agg)
else:
precomputed = self._precompute(element, agg)
params = dict(get_param_values(element), kdims=[x, y],
datatype=['xarray'], vdims=[vdim])
if width == 0 or height == 0:
if width == 0: params['xdensity'] = 1
if height == 0: params['ydensity'] = 1
bounds = (x_range[0], y_range[0], x_range[1], y_range[1])
return Image((xs, ys, np.zeros((height, width))), bounds=bounds, **params)
if wireframe:
segments = precomputed['segments']
else:
simplices = precomputed['simplices']
pts = precomputed['vertices']
mesh = precomputed['mesh']
if precompute:
self._precomputed = {element._plot_id: precomputed}
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
if wireframe:
agg = cvs.line(segments, x=['x0', 'x1', 'x2', 'x0'],
y=['y0', 'y1', 'y2', 'y0'], axis=1,
agg=agg).rename({'x': x.name, 'y': y.name})
else:
interpolate = bool(self.p.interpolation)
agg = cvs.trimesh(pts, simplices, agg=agg,
interp=interpolate, mesh=mesh)
return Image(agg, **params)
[docs]class quadmesh_rasterize(trimesh_rasterize):
"""
Rasterize the QuadMesh element using the supplied aggregator.
Simply converts to a TriMesh and lets trimesh_rasterize
handle the actual rasterization.
"""
def _precompute(self, element, agg):
if ds_version <= '0.7.0':
return super(quadmesh_rasterize, self)._precompute(element.trimesh(), agg)
def _process(self, element, key=None):
if ds_version <= '0.7.0':
return super(quadmesh_rasterize, self)._process(element, key)
if element.interface.datatype != 'xarray':
element = element.clone(datatype=['xarray'])
data = element.data
x, y = element.kdims
agg_fn = self._get_aggregator(element)
info = self._get_sampling(element, x, y)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
if xtype == 'datetime':
data[x.name] = data[x.name].astype('datetime64[us]').astype('int64')
if ytype == 'datetime':
data[y.name] = data[y.name].astype('datetime64[us]').astype('int64')
# Compute bounds (converting datetimes)
((x0, x1), (y0, y1)), (xs, ys) = self._dt_transform(
x_range, y_range, xs, ys, xtype, ytype
)
params = dict(get_param_values(element), datatype=['xarray'],
bounds=(x0, y0, x1, y1))
if width == 0 or height == 0:
return self._empty_agg(element, x, y, width, height, xs, ys, agg_fn, **params)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
vdim = getattr(agg_fn, 'column', element.vdims[0].name)
agg = cvs.quadmesh(data[vdim], x.name, y.name, agg_fn)
xdim, ydim = list(agg.dims)[:2][::-1]
if xtype == "datetime":
agg[xdim] = (agg[xdim]/1e3).astype('datetime64[us]')
if ytype == "datetime":
agg[ydim] = (agg[ydim]/1e3).astype('datetime64[us]')
return Image(agg, **params)
[docs]class shade(LinkableOperation):
"""
shade applies a normalization function followed by colormapping to
an Image or NdOverlay of Images, returning an RGB Element.
The data must be in the form of a 2D or 3D DataArray, but NdOverlays
of 2D Images will be automatically converted to a 3D array.
In the 2D case data is normalized and colormapped, while a 3D
array representing categorical aggregates will be supplied a color
key for each category. The colormap (cmap) for the 2D case may be
supplied as an Iterable or a Callable.
"""
alpha = param.Integer(default=255, bounds=(0, 255), doc="""
Value between 0 - 255 representing the alpha value to use for
colormapped pixels that contain data (i.e. non-NaN values).
Regardless of this value, ``NaN`` values are set to be fully
transparent when doing colormapping.""")
cmap = param.ClassSelector(class_=(Iterable, Callable, dict), doc="""
Iterable or callable which returns colors as hex colors
or web color names (as defined by datashader), to be used
for the colormap of single-layer datashader output.
Callable type must allow mapping colors between 0 and 1.
The default value of None reverts to Datashader's default
colormap.""")
color_key = param.ClassSelector(class_=(Iterable, Callable, dict), doc="""
Iterable or callable that returns colors as hex colors, to
be used for the color key of categorical datashader output.
Callable type must allow mapping colors for supplied values
between 0 and 1.""")
normalization = param.ClassSelector(default='eq_hist',
class_=(basestring, Callable),
doc="""
The normalization operation applied before colormapping.
Valid options include 'linear', 'log', 'eq_hist', 'cbrt',
and any valid transfer function that accepts data, mask, nbins
arguments.""")
clims = param.NumericTuple(default=None, length=2, doc="""
Min and max data values to use for colormap interpolation, when
wishing to override autoranging.
""")
min_alpha = param.Number(default=40, bounds=(0, 255), doc="""
The minimum alpha value to use for non-empty pixels when doing
colormapping, in [0, 255]. Use a higher value to avoid
undersaturation, i.e. poorly visible low-value datapoints, at
the expense of the overall dynamic range..""")
[docs] @classmethod
def concatenate(cls, overlay):
"""
Concatenates an NdOverlay of Image types into a single 3D
xarray Dataset.
"""
if not isinstance(overlay, NdOverlay):
raise ValueError('Only NdOverlays can be concatenated')
xarr = xr.concat([v.data.transpose() for v in overlay.values()],
pd.Index(overlay.keys(), name=overlay.kdims[0].name))
params = dict(get_param_values(overlay.last),
vdims=overlay.last.vdims,
kdims=overlay.kdims+overlay.last.kdims)
return Dataset(xarr.transpose(), datatype=['xarray'], **params)
[docs] @classmethod
def uint32_to_uint8(cls, img):
"""
Cast uint32 RGB image to 4 uint8 channels.
"""
return np.flipud(img.view(dtype=np.uint8).reshape(img.shape + (4,)))
[docs] @classmethod
def uint32_to_uint8_xr(cls, img):
"""
Cast uint32 xarray DataArray to 4 uint8 channels.
"""
new_array = img.values.view(dtype=np.uint8).reshape(img.shape + (4,))
coords = OrderedDict(list(img.coords.items())+[('band', [0, 1, 2, 3])])
return xr.DataArray(new_array, coords=coords, dims=img.dims+('band',))
[docs] @classmethod
def rgb2hex(cls, rgb):
"""
Convert RGB(A) tuple to hex.
"""
if len(rgb) > 3:
rgb = rgb[:-1]
return "#{0:02x}{1:02x}{2:02x}".format(*(int(v*255) for v in rgb))
@classmethod
def to_xarray(cls, element):
if issubclass(element.interface, XArrayInterface):
return element
data = tuple(element.dimension_values(kd, expanded=False)
for kd in element.kdims)
data += tuple(element.dimension_values(vd, flat=False)
for vd in element.vdims)
dtypes = [dt for dt in element.datatype if dt != 'xarray']
return element.clone(data, datatype=['xarray']+dtypes,
bounds=element.bounds,
xdensity=element.xdensity,
ydensity=element.ydensity)
def _process(self, element, key=None):
element = element.map(self.to_xarray, Image)
if isinstance(element, NdOverlay):
bounds = element.last.bounds
xdensity = element.last.xdensity
ydensity = element.last.ydensity
element = self.concatenate(element)
elif isinstance(element, Overlay):
return element.map(partial(shade._process, self), [Element])
else:
xdensity = element.xdensity
ydensity = element.ydensity
bounds = element.bounds
vdim = element.vdims[0].name
array = element.data[vdim]
kdims = element.kdims
# Compute shading options depending on whether
# it is a categorical or regular aggregate
shade_opts = dict(how=self.p.normalization,
min_alpha=self.p.min_alpha,
alpha=self.p.alpha)
if element.ndims > 2:
kdims = element.kdims[1:]
categories = array.shape[-1]
if not self.p.color_key:
pass
elif isinstance(self.p.color_key, dict):
shade_opts['color_key'] = self.p.color_key
elif isinstance(self.p.color_key, Iterable):
shade_opts['color_key'] = [c for i, c in
zip(range(categories), self.p.color_key)]
else:
colors = [self.p.color_key(s) for s in np.linspace(0, 1, categories)]
shade_opts['color_key'] = map(self.rgb2hex, colors)
elif not self.p.cmap:
pass
elif isinstance(self.p.cmap, Callable):
colors = [self.p.cmap(s) for s in np.linspace(0, 1, 256)]
shade_opts['cmap'] = map(self.rgb2hex, colors)
else:
shade_opts['cmap'] = self.p.cmap
if self.p.clims:
shade_opts['span'] = self.p.clims
elif ds_version > '0.5.0' and self.p.normalization != 'eq_hist':
shade_opts['span'] = element.range(vdim)
params = dict(get_param_values(element), kdims=kdims,
bounds=bounds, vdims=RGB.vdims[:],
xdensity=xdensity, ydensity=ydensity)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', r'invalid value encountered in true_divide')
if np.isnan(array.data).all():
xd, yd = kdims[:2]
arr = np.zeros(array.data.shape[:2]+(4,), dtype=np.uint8)
coords = {xd.name: element.data.coords[xd.name],
yd.name: element.data.coords[yd.name],
'band': [0, 1, 2, 3]}
img = xr.DataArray(arr, coords=coords, dims=(yd.name, xd.name, 'band'))
return RGB(img, **params)
else:
img = tf.shade(array, **shade_opts)
return RGB(self.uint32_to_uint8_xr(img), **params)
[docs]class geometry_rasterize(AggregationOperation):
"""
Rasterizes geometries by converting them to spatialpandas.
"""
aggregator = param.ClassSelector(default=ds.mean(),
class_=(ds.reductions.Reduction, basestring))
def _get_aggregator(self, element, add_field=True):
agg = self.p.aggregator
if (not (element.vdims or isinstance(agg, basestring)) and
agg.column is None and not isinstance(agg, (rd.count, rd.any))):
return ds.count()
return super(geometry_rasterize, self)._get_aggregator(element, add_field)
def _process(self, element, key=None):
agg_fn = self._get_aggregator(element)
xdim, ydim = element.kdims
info = self._get_sampling(element, xdim, ydim)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info
x0, x1 = x_range
y0, y1 = y_range
params = self._get_agg_params(element, xdim, ydim, agg_fn, (x0, y0, x1, y1))
if width == 0 or height == 0:
return self._empty_agg(element, xdim, ydim, width, height, xs, ys, agg_fn, **params)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
if element._plot_id in self._precomputed:
data, col = self._precomputed[element._plot_id]
else:
if element.interface.datatype != 'spatialpandas':
element = element.clone(datatype=['spatialpandas'])
data = element.data
col = element.interface.geo_column(data)
if self.p.precompute:
self._precomputed[element._plot_id] = (data, col)
if isinstance(agg_fn, ds.count_cat):
data[agg_fn.column] = data[agg_fn.column].astype('category')
if isinstance(element, Polygons):
agg = cvs.polygons(data, geometry=col, agg=agg_fn)
elif isinstance(element, Path):
agg = cvs.line(data, geometry=col, agg=agg_fn)
elif isinstance(element, Points):
agg = cvs.points(data, geometry=col, agg=agg_fn)
agg = agg.rename({'x': xdim.name, 'y': ydim.name})
if agg.ndim == 2:
return self.p.element_type(agg, **params)
else:
layers = {}
for c in agg.coords[agg_fn.column].data:
cagg = agg.sel(**{agg_fn.column: c})
layers[c] = self.p.element_type(cagg, **params)
return NdOverlay(layers, kdims=[element.get_dimension(agg_fn.column)])
[docs]class rasterize(AggregationOperation):
"""
Rasterize is a high-level operation that will rasterize any
Element or combination of Elements, aggregating them with the supplied
aggregator and interpolation method.
The default aggregation method depends on the type of Element but
usually defaults to the count of samples in each bin. Other
aggregators can be supplied implementing mean, max, min and other
reduction operations.
The bins of the aggregate are defined by the width and height and
the x_range and y_range. If x_sampling or y_sampling are supplied
the operation will ensure that a bin is no smaller than the minimum
sampling distance by reducing the width and height when zoomed in
beyond the minimum sampling distance.
By default, the PlotSize and RangeXY streams are applied when this
operation is used dynamically, which means that the width, height,
x_range and y_range will automatically be set to match the inner
dimensions of the linked plot and the ranges of the axes.
"""
aggregator = param.ClassSelector(class_=(ds.reductions.Reduction, basestring),
default='default')
interpolation = param.ObjectSelector(
default='default', objects=['default', 'linear', 'nearest', 'bilinear', None, False], doc="""
The interpolation method to apply during rasterization.
Default depends on element type""")
_transforms = [(Image, regrid),
(Polygons, geometry_rasterize),
(lambda x: (isinstance(x, Path) and
x.interface.datatype == 'spatialpandas'),
geometry_rasterize),
(TriMesh, trimesh_rasterize),
(QuadMesh, quadmesh_rasterize),
(lambda x: (isinstance(x, NdOverlay) and
issubclass(x.type, (Scatter, Points, Curve, Path))),
aggregate),
(Spikes, spikes_aggregate),
(Area, area_aggregate),
(Spread, spread_aggregate),
(Segments, segments_aggregate),
(Contours, contours_rasterize),
(Graph, aggregate),
(Scatter, aggregate),
(Points, aggregate),
(Curve, aggregate),
(Path, aggregate),
(type(None), shade) # To handle parameters of datashade
]
def _process(self, element, key=None):
# Potentially needs traverse to find element types first?
all_allowed_kws = set()
all_supplied_kws = set()
for predicate, transform in self._transforms:
merged_param_values = dict(self.param.get_param_values(), **self.p)
# If aggregator or interpolation are 'default', pop parameter so
# datashader can choose the default aggregator itself
for k in ['aggregator', 'interpolation']:
if merged_param_values.get(k, None) == 'default':
merged_param_values.pop(k)
op_params = dict({k: v for k, v in merged_param_values.items()
if not (v is None and k == 'aggregator')},
dynamic=False)
extended_kws = dict(op_params, **self.p.extra_keywords())
all_supplied_kws |= set(extended_kws)
all_allowed_kws |= set(transform.param)
# Collect union set of consumed. Versus union of available.
op = transform.instance(**{k:v for k,v in extended_kws.items()
if k in transform.param})
op._precomputed = self._precomputed
element = element.map(op, predicate)
self._precomputed = op._precomputed
unused_params = list(all_supplied_kws - all_allowed_kws)
if unused_params:
self.param.warning('Parameter(s) [%s] not consumed by any element rasterizer.'
% ', '.join(unused_params))
return element
[docs]class datashade(rasterize, shade):
"""
Applies the aggregate and shade operations, aggregating all
elements in the supplied object and then applying normalization
and colormapping the aggregated data returning RGB elements.
See aggregate and shade operations for more details.
"""
def _process(self, element, key=None):
agg = rasterize._process(self, element, key)
shaded = shade._process(self, agg, key)
return shaded
[docs]class stack(Operation):
"""
The stack operation allows compositing multiple RGB Elements using
the defined compositing operator.
"""
compositor = param.ObjectSelector(objects=['add', 'over', 'saturate', 'source'],
default='over', doc="""
Defines how the compositing operation combines the images""")
def uint8_to_uint32(self, element):
img = np.dstack([element.dimension_values(d, flat=False)
for d in element.vdims])
if img.shape[2] == 3: # alpha channel not included
alpha = np.ones(img.shape[:2])
if img.dtype.name == 'uint8':
alpha = (alpha*255).astype('uint8')
img = np.dstack([img, alpha])
if img.dtype.name != 'uint8':
img = (img*255).astype(np.uint8)
N, M, _ = img.shape
return img.view(dtype=np.uint32).reshape((N, M))
def _process(self, overlay, key=None):
if not isinstance(overlay, CompositeOverlay):
return overlay
elif len(overlay) == 1:
return overlay.last if isinstance(overlay, NdOverlay) else overlay.get(0)
imgs = []
for rgb in overlay:
if not isinstance(rgb, RGB):
raise TypeError("The stack operation expects elements of type RGB, "
"not '%s'." % type(rgb).__name__)
rgb = rgb.rgb
dims = [kd.name for kd in rgb.kdims][::-1]
coords = {kd.name: rgb.dimension_values(kd, False)
for kd in rgb.kdims}
imgs.append(tf.Image(self.uint8_to_uint32(rgb), coords=coords, dims=dims))
try:
imgs = xr.align(*imgs, join='exact')
except ValueError:
raise ValueError('RGB inputs to the stack operation could not be aligned; '
'ensure they share the same grid sampling.')
stacked = tf.stack(*imgs, how=self.p.compositor)
arr = shade.uint32_to_uint8(stacked.data)[::-1]
data = (coords[dims[1]], coords[dims[0]], arr[:, :, 0],
arr[:, :, 1], arr[:, :, 2])
if arr.shape[-1] == 4:
data = data + (arr[:, :, 3],)
return rgb.clone(data, datatype=[rgb.interface.datatype]+rgb.datatype)
[docs]class SpreadingOperation(LinkableOperation):
"""
Spreading expands each pixel in an Image based Element a certain
number of pixels on all sides according to a given shape, merging
pixels using a specified compositing operator. This can be useful
to make sparse plots more visible.
"""
how = param.ObjectSelector(default='source' if ds_version <= '0.11.1' else None,
objects=[None, 'source', 'over', 'saturate', 'add', 'max', 'min'], doc="""
The name of the compositing operator to use when combining
pixels. Default of None uses 'over' operator for RGB elements
and 'add' operator for aggregate arrays.""")
shape = param.ObjectSelector(default='circle', objects=['circle', 'square'],
doc="""
The shape to spread by. Options are 'circle' [default] or 'square'.""")
_per_element = True
@classmethod
def uint8_to_uint32(cls, img):
shape = img.shape
flat_shape = np.multiply.reduce(shape[:2])
if shape[-1] == 3:
img = np.dstack([img, np.ones(shape[:2], dtype='uint8')*255])
rgb = img.reshape((flat_shape, 4)).view('uint32').reshape(shape[:2])
return rgb
def _apply_spreading(self, array):
"""Apply the spread function using the indicated parameters."""
raise NotImplementedError
def _preprocess_rgb(self, element):
rgbarray = np.dstack([element.dimension_values(vd, flat=False)
for vd in element.vdims])
if rgbarray.dtype.kind == 'f':
rgbarray = rgbarray * 255
return tf.Image(self.uint8_to_uint32(rgbarray.astype('uint8')))
def _process(self, element, key=None):
if isinstance(element, RGB):
rgb = element.rgb
data = self._preprocess_rgb(rgb)
elif isinstance(element, Image):
data = element.clone(datatype=['xarray']).data[element.vdims[0].name]
else:
raise ValueError('spreading can only be applied to Image or RGB Elements.')
kwargs = {}
array = self._apply_spreading(data)
if isinstance(element, RGB):
img = datashade.uint32_to_uint8(array.data)[::-1]
new_data = {
kd.name: rgb.dimension_values(kd, expanded=False)
for kd in rgb.kdims
}
vdims = rgb.vdims+[rgb.alpha_dimension] if len(rgb.vdims) == 3 else rgb.vdims
kwargs['vdims'] = vdims
new_data[tuple(vd.name for vd in vdims)] = img
else:
new_data = array
return element.clone(new_data, xdensity=element.xdensity,
ydensity=element.ydensity, **kwargs)
[docs]class spread(SpreadingOperation):
"""
Spreading expands each pixel in an Image based Element a certain
number of pixels on all sides according to a given shape, merging
pixels using a specified compositing operator. This can be useful
to make sparse plots more visible.
See the datashader documentation for more detail:
http://datashader.org/api.html#datashader.transfer_functions.spread
"""
px = param.Integer(default=1, doc="""
Number of pixels to spread on all sides.""")
def _apply_spreading(self, array):
return tf.spread(array, px=self.p.px, how=self.p.how, shape=self.p.shape)
[docs]class dynspread(SpreadingOperation):
"""
Spreading expands each pixel in an Image based Element a certain
number of pixels on all sides according to a given shape, merging
pixels using a specified compositing operator. This can be useful
to make sparse plots more visible. Dynamic spreading determines
how many pixels to spread based on a density heuristic.
See the datashader documentation for more detail:
http://datashader.org/api.html#datashader.transfer_functions.dynspread
"""
max_px = param.Integer(default=3, doc="""
Maximum number of pixels to spread on all sides.""")
threshold = param.Number(default=0.5, bounds=(0,1), doc="""
When spreading, determines how far to spread.
Spreading starts at 1 pixel, and stops when the fraction
of adjacent non-empty pixels reaches this threshold.
Higher values give more spreading, up to the max_px
allowed.""")
def _apply_spreading(self, array):
return tf.dynspread(
array, max_px=self.p.max_px, threshold=self.p.threshold,
how=self.p.how, shape=self.p.shape
)
[docs]def split_dataframe(path_df):
"""
Splits a dataframe of paths separated by NaNs into individual
dataframes.
"""
splits = np.where(path_df.iloc[:, 0].isnull())[0]+1
return [df for df in np.split(path_df, splits) if len(df) > 1]
class _connect_edges(Operation):
split = param.Boolean(default=False, doc="""
Determines whether bundled edges will be split into individual edges
or concatenated with NaN separators.""")
def _bundle(self, position_df, edges_df):
raise NotImplementedError('_connect_edges is an abstract baseclass '
'and does not implement any actual bundling.')
def _process(self, element, key=None):
index = element.nodes.kdims[2].name
rename_edges = {d.name: v for d, v in zip(element.kdims[:2], ['source', 'target'])}
rename_nodes = {d.name: v for d, v in zip(element.nodes.kdims[:2], ['x', 'y'])}
position_df = element.nodes.redim(**rename_nodes).dframe([0, 1, 2]).set_index(index)
edges_df = element.redim(**rename_edges).dframe([0, 1])
paths = self._bundle(position_df, edges_df)
paths = paths.rename(columns={v: k for k, v in rename_nodes.items()})
paths = split_dataframe(paths) if self.p.split else [paths]
return element.clone((element.data, element.nodes, paths))
[docs]class bundle_graph(_connect_edges, hammer_bundle):
"""
Iteratively group edges and return as paths suitable for datashading.
Breaks each edge into a path with multiple line segments, and
iteratively curves this path to bundle edges into groups.
"""
def _bundle(self, position_df, edges_df):
from datashader.bundling import hammer_bundle
return hammer_bundle.__call__(self, position_df, edges_df, **self.p)
[docs]class directly_connect_edges(_connect_edges, connect_edges):
"""
Given a Graph object will directly connect all nodes.
"""
def _bundle(self, position_df, edges_df):
return connect_edges.__call__(self, position_df, edges_df)
