Source code for holoviews.operation.datashader

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.reductions as rd
import datashader.transfer_functions as tf
import dask.dataframe as dd
from param.parameterized import bothmethod

ds_version = LooseVersion(ds.__version__)

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)
from ..core.data import PandasInterface, 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, RGB, Graph, TriMesh, QuadMesh, Contours)
from ..streams import RangeXY, PlotSize


[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.""")
[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.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.""") 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): inst = super(ResamplingOperation, self_or_cls).instance(**params) inst._precomputed = {} return inst 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, 'ns'), dt_to_int(xend, 'ns') 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, 'ns'), dt_to_int(yend, 'ns') ytype = 'datetime' elif not np.isfinite(ystart) and not np.isfinite(yend): if element.get_dimension_type(y) in datetime_types: ystart, yend = 0, 10000 ytype = '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])) width, height = max([width, 1]), max([height, 1]) 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 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 agg.column 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 inner_element.vdims: field = inner_element.vdims[0].name elif isinstance(inner_element, TriMesh) and inner_element.nodes.kdims: field = inner_element.nodes.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
[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') 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') * 1000. 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] info = self._get_sampling(element, x, y) (x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = info agg_params = dict({k: v for k, v in dict(self.get_param_values(), **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._get_aggregator(element) 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) 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) 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) elif not len(data): xarray = xr.DataArray(np.full((height, width), np.NaN, dtype=np.float32), dims=[y.name, x.name], coords={x.name: xs, y.name: 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 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](name)] else: vdims = 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, 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]/10e5).astype('datetime64[us]') if ytype == 'datetime': agg[y.name] = (agg[y.name]/10e5).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, **dict(params, vdims=vdims)) return NdOverlay(layers, kdims=[data.get_dimension(column)])
[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'], 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 vd in element.vdims: if element.interface is XArrayInterface: 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), _, (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) 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, '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) width = min([int((xspan/exspan) * len(coords[0])), width]) height = min([int((yspan/eyspan) * len(coords[1])), height]) width, height = max([width, 1]), max([height, 1]) 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=self.p.interpolation, downsample_method=agg_fn) # Convert datetime coordinates if xtype == "datetime": rarray[x.name] = (rarray[x.name]/10e5).astype('datetime64[us]') if ytype == "datetime": rarray[y.name] = (rarray[y.name]/10e5).astype('datetime64[us]') regridded[vd] = rarray regridded = xr.Dataset(regridded) # Compute bounds (converting datetimes) if xtype == 'datetime': xstart, xend = (np.array([xstart, xend])/10e5).astype('datetime64[us]') if ytype == 'datetime': ystart, yend = (np.array([ystart, yend])/10e5).astype('datetime64[us]') bbox = BoundingBox(points=[(xstart, ystart), (xend, yend)]) return element.clone(regridded, bounds=bbox, datatype=['xarray']+element.datatype)
[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', None], doc=""" The interpolation method to apply during rasterization.""") def _precompute(self, element): from datashader.utils import mesh if element.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 _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), _, (width, height), (xtype, ytype) = info cvs = ds.Canvas(plot_width=width, plot_height=height, x_range=x_range, y_range=y_range) agg = self.p.aggregator if not (element.vdims or element.nodes.vdims): if not isinstance(agg, (rd.count, rd.any)) and agg.column is not None: raise ValueError("Aggregation column %s not found on TriMesh element. " "The supplied TriMesh does not define any value " "dimensions.") self.p.aggregator = ds.count() if not isinstance(agg, ds.any) else agg return aggregate._process(self, element, key) elif element._plot_id in self._precomputed: precomputed = self._precomputed[element._plot_id] else: precomputed = self._precompute(element) simplices = precomputed['simplices'] pts = precomputed['vertices'] mesh = precomputed['mesh'] if self.p.precompute: self._precomputed = {element._plot_id: precomputed} vdim = element.vdims[0] if element.vdims else element.nodes.vdims[0] interpolate = bool(self.p.interpolation) agg = cvs.trimesh(pts, simplices, agg=self._get_aggregator(element), interp=interpolate, mesh=mesh) params = dict(get_param_values(element), kdims=[x, y], datatype=['xarray'], vdims=[vdim]) 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): return super(quadmesh_rasterize, self)._precompute(element.trimesh())
[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, default=None) interpolation = param.ObjectSelector(default='bilinear', objects=['bilinear', None], doc=""" The interpolation method to apply during rasterization.""") _transforms = [(Image, regrid), (TriMesh, trimesh_rasterize), (QuadMesh, quadmesh_rasterize), (lambda x: (isinstance(x, NdOverlay) and issubclass(x.type, Dataset) and not issubclass(x.type, Image)), aggregate), (Contours, contours_rasterize), (lambda x: (isinstance(x, Dataset) and (not isinstance(x, Image))), aggregate)] def _process(self, element, key=None): for predicate, transform in self._transforms: op_params = dict({k: v for k, v in self.p.items() if k in transform.params() and v is not None}, dynamic=False) op = transform.instance(**op_params) op._precomputed = self._precomputed element = element.map(op, predicate) self._precomputed = op._precomputed return element
[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. """ 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, 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 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) def _process(self, element, key=None): element = element.map(self.to_xarray, Image) if isinstance(element, NdOverlay): bounds = element.last.bounds element = self.concatenate(element) elif isinstance(element, Overlay): return element.map(self._process, [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, min_alpha=self.p.min_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) 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(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('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 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', objects=['source', 'over', 'saturate', 'add'], doc=""" The name of the compositing operator to use when combining pixels.""") shape = param.ObjectSelector(default='circle', objects=['circle', 'square'], doc=""" The shape to spread by. Options are 'circle' [default] or 'square'.""") @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_spreading(self, array): """Apply the spread function using the indicated parameters.""" raise NotImplementedError def _process(self, element, key=None): if not isinstance(element, RGB): raise ValueError('spreading 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_spreading(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]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): img = tf.Image(array) return tf.spread(img, px=self.p.px, how=self.p.how, shape=self.p.shape).data
[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): 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
[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)