from __future__ import absolute_import, division
from collections import Callable, Iterable
from distutils.version import LooseVersion
import warnings
import param
import numpy as np
import pandas as pd
import xarray as xr
import datashader as ds
import datashader.transfer_functions as tf
import dask.dataframe as dd
ds_version = LooseVersion(ds.__version__)
from datashader.core import bypixel
from datashader.pandas import pandas_pipeline
from datashader.dask import dask_pipeline
from datashape.dispatch import dispatch
from datashape import discover as dsdiscover
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)
from ..core.data import PandasInterface, DaskInterface, XArrayInterface
from ..core.sheetcoords import BoundingBox
from ..core.util import get_param_values, basestring, datetime_types, dt_to_int
from ..element import Image, Path, Curve, Contours, RGB, Graph
from ..streams import RangeXY, PlotSize
from ..plotting.util import fire
[docs]class ResamplingOperation(Operation):
"""
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.NumericTuple(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.NumericTuple(default=None, length=2, doc="""
The x_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 y-axis.""")
y_sampling = param.Number(default=None, doc="""
Specifies the smallest allowed sampling interval along the y-axis.""")
target = param.ClassSelector(class_=Image, doc="""
A target Image 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.""")
link_inputs = param.Boolean(default=True, doc="""
By default, the link_inputs parameter is set to True so that
when applying shade, backends that support linked streams
update RangeXY streams on the inputs of the shade 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.""")
def _get_sampling(self, element, x, y):
target = self.p.target
if target:
x_range, y_range = target.range(x), target.range(y)
height, width = target.dimension_values(2, flat=False).shape
else:
if x is None or y is None:
x_range = self.p.x_range or (-0.5, 0.5)
y_range = self.p.y_range or (-0.5, 0.5)
else:
if self.p.expand or not self.p.x_range:
x_range = self.p.x_range or element.range(x)
else:
x0, x1 = self.p.x_range
ex0, ex1 = element.range(x)
x_range = np.max([x0, ex0]), np.min([x1, ex1])
if x_range[0] == x_range[1]:
x_range = (x_range[0]-0.5, x_range[0]+0.5)
if self.p.expand or not self.p.y_range:
y_range = self.p.y_range or element.range(y)
else:
y0, y1 = self.p.y_range
ey0, ey1 = element.range(y)
y_range = np.max([y0, ey0]), np.min([y1, ey1])
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), dt_to_int(xend)
xtype = 'datetime'
elif not np.isfinite(xstart) and not np.isfinite(xend):
if element.get_dimension_type(x) in datetime_types:
xstart, xend = 0, 10000
xtype = 'datetime'
else:
xstart, xend = 0, 1
elif xstart == xend:
xstart, xend = (xstart-0.5, xend+0.5)
x_range = (xstart, xend)
ytype = 'numeric'
if isinstance(ystart, datetime_types) or isinstance(yend, datetime_types):
ystart, yend = dt_to_int(ystart), dt_to_int(yend)
ytype = 'datetime'
elif not np.isfinite(ystart) and not np.isfinite(yend):
if element.get_dimension_type(y) in datetime_types:
xstart, xend = 0, 10000
xtype = 'datetime'
else:
ystart, yend = 0, 1
elif ystart == yend:
ystart, yend = (ystart-0.5, yend+0.5)
y_range = (ystart, yend)
# 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]))
xunit, yunit = float(xspan)/width, float(yspan)/height
xs, ys = (np.linspace(xstart+xunit/2., xend-xunit/2., width),
np.linspace(ystart+yunit/2., yend-yunit/2., height))
return (x_range, y_range), (xs, ys), (width, height), (xtype, ytype)
[docs]class aggregate(ResamplingOperation):
"""
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 hv.Image or an overlay of
hv.Images by rasterizing it, which provides 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.
"""
aggregator = param.ClassSelector(class_=ds.reductions.Reduction,
default=ds.count())
[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 category and df[category].dtype.name != 'category':
df[category] = df[category].astype('category')
if any(df[d.name].dtype.kind == 'M' for d in (x, y)):
df = df.copy()
for d in (x, y):
if df[d.name].dtype.kind == 'M':
df[d.name] = df[d.name].astype('datetime64[ns]').astype('int64') * 10e-4
return x, y, Dataset(df, kdims=kdims, vdims=vdims), glyph
def _aggregate_ndoverlay(self, element, agg_fn):
"""
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.
"""
# Compute overall bounds
x, y = element.last.dimensions()[0:2]
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = self._get_sampling(element, x, y)
agg_params = dict({k: v for k, v in self.p.items() if k in aggregate.params()},
x_range=x_range, y_range=y_range)
# 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)
return grouped.clone({k: agg_fn1(v) for k, v in grouped.items()})
# 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
def _process(self, element, key=None):
agg_fn = self.p.aggregator
category = agg_fn.column if isinstance(agg_fn, ds.count_cat) else None
if (isinstance(element, NdOverlay) and
((isinstance(agg_fn, (ds.count, ds.sum, ds.mean)) and agg_fn.column not in element.kdims) or
(isinstance(agg_fn, ds.count_cat) and agg_fn.column in element.kdims))):
return self._aggregate_ndoverlay(element, agg_fn)
x, y, data, glyph = self.get_agg_data(element, category)
(x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = self._get_sampling(element, x, y)
if x is None or y is None:
xarray = xr.DataArray(np.full((height, width), np.NaN, dtype=np.float32),
dims=['y', 'x'], coords={'x': xs, 'y': ys})
return self.p.element_type(xarray)
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
column = agg_fn.column
if column and isinstance(agg_fn, ds.count_cat):
name = '%s Count' % agg_fn.column
else:
name = column
vdims = [element.get_dimension(column)(name) if column
else Dimension('Count')]
params = dict(get_param_values(element), kdims=[x, y],
datatype=['xarray'], vdims=vdims)
dfdata = PandasInterface.as_dframe(data)
agg = getattr(cvs, glyph)(dfdata, x.name, y.name, self.p.aggregator)
if 'x_axis' in agg and 'y_axis' in agg:
agg = agg.rename({'x_axis': x, 'y_axis': y})
if xtype == 'datetime':
agg[x.name] = agg[x.name].astype('datetime64[us]')
if ytype == 'datetime':
agg[y.name] = agg[y.name].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[column].data:
cagg = agg.sel(**{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(column)])
[docs]class regrid(ResamplingOperation):
"""
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 unneccessarily padding the output array
with nan values.
"""
aggregator = param.ObjectSelector(default='mean',
objects=['first', 'last', 'mean', 'mode', 'std', 'var', 'min', 'max'], doc="""
Aggregation method.
""")
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'], 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 _process(self, element, key=None):
if ds_version <= '0.5.0':
raise RuntimeError('regrid operation requires datashader>=0.6.0')
x, y = element.kdims
(x_range, y_range), _, (width, height), (xtype, ytype) = self._get_sampling(element, x, y)
coords = tuple(element.dimension_values(d, expanded=False)
for d in [x, y])
coord_dict = {x.name: coords[0], y.name: coords[1]}
dims = [y.name, x.name]
arrays = []
for vd in element.vdims:
if element.interface is XArrayInterface:
xarr = element.data[vd.name]
if 'datetime' in (xtype, ytype):
xarr = xarr.copy()
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) for v in xarr[x.name].values]
if ytype == "datetime":
xarr[y.name] = [dt_to_int(v) for v in xarr[y.name].values]
arrays.append(xarr)
# Disable upsampling if requested
(xstart, xend), (ystart, yend) = (x_range, y_range)
xspan, yspan = (xend-xstart), (yend-ystart)
if not self.p.upsample 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), dt_to_int(x1)
if isinstance(y0, datetime_types):
y0, y1 = dt_to_int(y0), dt_to_int(y1)
exspan, eyspan = (x1-x0), (y1-y0)
width = min([int((xspan/exspan) * len(coords[0])), width])
height = min([int((yspan/eyspan) * len(coords[1])), height])
# Get expanded or bounded ranges
cvs = ds.Canvas(plot_width=width, plot_height=height,
x_range=x_range, y_range=y_range)
regridded = []
for xarr in arrays:
rarray = cvs.raster(xarr, upsample_method=self.p.interpolation,
downsample_method=self.p.aggregator)
if xtype == "datetime":
rarray[x.name] = rarray[x.name].astype('datetime64[us]')
if ytype == "datetime":
rarray[y.name] = rarray[y.name].astype('datetime64[us]')
regridded.append(rarray)
regridded = xr.Dataset({vd.name: xarr for vd, xarr in zip(element.vdims, regridded)})
if xtype == 'datetime':
xstart, xend = np.array([xstart, xend]).astype('datetime64[us]')
if ytype == 'datetime':
ystart, yend = np.array([ystart, yend]).astype('datetime64[us]')
bbox = BoundingBox(points=[(xstart, ystart), (xend, yend)])
return element.clone(regridded, bounds=bbox, datatype=['xarray'])
[docs]class shade(Operation):
"""
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) may be supplied as an
Iterable or a Callable.
"""
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 which returns colors as hex colors, to
be used for the color key of categorical datashader output.
Callable type must allow mapping colors 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.
""")
link_inputs = param.Boolean(default=True, doc="""
By default, the link_inputs parameter is set to True so that
when applying shade, backends that support linked streams
update RangeXY streams on the inputs of the shade 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.""")
[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.T 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.T, 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 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))
def _process(self, element, key=None):
if isinstance(element, NdOverlay):
bounds = element.last.bounds
element = self.concatenate(element)
else:
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)
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)
for d in kdims:
if array[d.name].dtype.kind == 'M':
array[d.name] = array[d.name].astype('datetime64[ns]').astype('int64') * 10e-4
with warnings.catch_warnings():
warnings.filterwarnings('ignore', r'invalid value encountered in true_divide')
if np.isnan(array.data).all():
arr = np.zeros(array.data.shape, dtype=np.uint32)
img = array.copy()
img.data = arr
else:
img = tf.shade(array, **shade_opts)
params = dict(get_param_values(element), kdims=kdims,
bounds=bounds, vdims=RGB.vdims[:])
return RGB(self.uint32_to_uint8(img.data), **params)
[docs]class datashade(aggregate, 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 = aggregate._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('stack operation expect RGB type elements, '
'not %s name.' % 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 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 dynspread(Operation):
"""
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.readthedocs.io/en/latest/api.html#datashader.transfer_functions.dynspread
"""
how = param.ObjectSelector(default='source',
objects=['source', 'over',
'saturate', 'add'], doc="""
The name of the compositing operator to use when combining
pixels.""")
max_px = param.Integer(default=3, doc="""
Maximum number of pixels to spread on all sides.""")
shape = param.ObjectSelector(default='circle', objects=['circle', 'square'],
doc="""
The shape to spread by. Options are 'circle' [default] or 'square'.""")
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.""")
link_inputs = param.Boolean(default=True, doc="""
By default, the link_inputs parameter is set to True so that
when applying dynspread, backends that support linked streams
update RangeXY streams on the inputs of the dynspread 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.""")
@classmethod
def uint8_to_uint32(cls, img):
shape = img.shape
flat_shape = np.multiply.reduce(shape[:2])
rgb = img.reshape((flat_shape, 4)).view('uint32').reshape(shape[:2])
return rgb
def _apply_dynspread(self, array):
img = tf.Image(array)
return tf.dynspread(img, max_px=self.p.max_px,
threshold=self.p.threshold,
how=self.p.how, shape=self.p.shape).data
def _process(self, element, key=None):
if not isinstance(element, RGB):
raise ValueError('dynspread can only be applied to RGB Elements.')
rgb = element.rgb
new_data = {kd.name: rgb.dimension_values(kd, expanded=False)
for kd in rgb.kdims}
rgbarray = np.dstack([element.dimension_values(vd, flat=False)
for vd in element.vdims])
data = self.uint8_to_uint32(rgbarray)
array = self._apply_dynspread(data)
img = datashade.uint32_to_uint8(array)
for i, vd in enumerate(element.vdims):
if i < img.shape[-1]:
new_data[vd.name] = np.flipud(img[..., i])
return element.clone(new_data)
[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)
