Source code for holoviews.plotting.mpl.hex_tiles

import numpy as np
import param

from .element import ColorbarPlot

[docs]class HexTilesPlot(ColorbarPlot): aggregator = param.Callable(default=np.size, doc=""" Aggregation function used to compute bin values. Any NumPy reduction is allowed, defaulting to np.size to count the number of values in each bin.""") gridsize = param.ClassSelector(default=50, class_=(int, tuple), doc=""" Number of hexagonal bins along x- and y-axes. Defaults to uniform sampling along both axes when setting and integer but independent bin sampling can be specified a tuple of integers corresponding to the number of bins along each axis.""") max_scale = param.Number(default=0.9, bounds=(0, None), doc=""" When size_index is enabled this defines the maximum size of each bin relative to uniform tile size, i.e. for a value of 1, the largest bin will match the size of bins when scaling is disabled. Setting value larger than 1 will result in overlapping bins.""") min_count = param.Number(default=None, doc=""" The display threshold before a bin is shown, by default bins with a count of less than 1 are hidden.""") style_opts = ['cmap', 'edgecolors', 'alpha', 'linewidths', 'marginals'] _nonvectorized_styles = style_opts _plot_methods = dict(single='hexbin') def get_data(self, element, ranges, style): if not element.vdims: element = element.add_dimension('Count', 0, np.ones(len(element)), True) xs, ys = (element.dimension_values(i) for i in range(2)) args = (ys, xs) if self.invert_axes else (xs, ys) args += (element.dimension_values(2),) cdim = element.vdims[0] agg = np.sum if self.aggregator is np.size else self.aggregator self._norm_kwargs(element, ranges, style, cdim) style['reduce_C_function'] = agg style['vmin'], style['vmax'] = cdim.range style['xscale'] = 'log' if self.logx else 'linear' style['yscale'] = 'log' if self.logy else 'linear' style['gridsize'] = self.gridsize style['mincnt'] = -1 if self.min_count == 0 else self.min_count return args, style, {}