Source code for holoviews.plotting.mpl.chart

from __future__ import unicode_literals

from itertools import product
from distutils.version import LooseVersion

import numpy as np
import matplotlib as mpl
from matplotlib import cm
from matplotlib.collections import LineCollection
from matplotlib.dates import DateFormatter, date2num

mpl_version = LooseVersion(mpl.__version__)

import param

from ...core import OrderedDict, Dimension, Store
from ...core.util import (
    match_spec, unique_iterator, basestring, max_range, isfinite, datetime_types
)
from ...element import Raster, HeatMap
from ...operation import interpolate_curve
from ..plot import PlotSelector
from ..util import compute_sizes, get_sideplot_ranges, get_min_distance
from .element import ElementPlot, ColorbarPlot, LegendPlot
from .path  import PathPlot
from .plot import AdjoinedPlot, mpl_rc_context


[docs]class ChartPlot(ElementPlot): """ Baseclass to plot Chart elements. """
[docs]class CurvePlot(ChartPlot): """ CurvePlot can plot Curve and ViewMaps of Curve, which can be displayed as a single frame or animation. Axes, titles and legends are automatically generated from dim_info. If the dimension is set to cyclic in the dim_info it will rotate the curve so that minimum y values are at the minimum x value to make the plots easier to interpret. """ autotick = param.Boolean(default=False, doc=""" Whether to let matplotlib automatically compute tick marks or to allow the user to control tick marks.""") interpolation = param.ObjectSelector(objects=['linear', 'steps-mid', 'steps-pre', 'steps-post'], default='linear', doc=""" Defines how the samples of the Curve are interpolated, default is 'linear', other options include 'steps-mid', 'steps-pre' and 'steps-post'.""") relative_labels = param.Boolean(default=False, doc=""" If plotted quantity is cyclic and center_cyclic is enabled, will compute tick labels relative to the center.""") show_grid = param.Boolean(default=False, doc=""" Enable axis grid.""") show_legend = param.Boolean(default=True, doc=""" Whether to show legend for the plot.""") style_opts = ['alpha', 'color', 'visible', 'linewidth', 'linestyle', 'marker', 'ms'] _plot_methods = dict(single='plot') def get_data(self, element, ranges, style): if 'steps' in self.interpolation: element = interpolate_curve(element, interpolation=self.interpolation) xs = element.dimension_values(0) ys = element.dimension_values(1) dims = element.dimensions() if xs.dtype.kind == 'M': dimtype = element.get_dimension_type(0) dt_format = Dimension.type_formatters.get(dimtype, '%Y-%m-%d %H:%M:%S') dims[0] = dims[0](value_format=DateFormatter(dt_format)) coords = (ys, xs) if self.invert_axes else (xs, ys) return coords, style, {'dimensions': dims} def init_artists(self, ax, plot_args, plot_kwargs): xs, ys = plot_args if xs.dtype.kind == 'M': artist = ax.plot_date(xs, ys, '-', **plot_kwargs)[0] else: artist = ax.plot(xs, ys, **plot_kwargs)[0] return {'artist': artist} def update_handles(self, key, axis, element, ranges, style): artist = self.handles['artist'] (xs, ys), style, axis_kwargs = self.get_data(element, ranges, style) artist.set_xdata(xs) artist.set_ydata(ys) return axis_kwargs
[docs]class ErrorPlot(ChartPlot): """ ErrorPlot plots the ErrorBar Element type and supporting both horizontal and vertical error bars via the 'horizontal' plot option. """ style_opts = ['ecolor', 'elinewidth', 'capsize', 'capthick', 'barsabove', 'lolims', 'uplims', 'xlolims', 'errorevery', 'xuplims', 'alpha', 'linestyle', 'linewidth', 'markeredgecolor', 'markeredgewidth', 'markerfacecolor', 'markersize', 'solid_capstyle', 'solid_joinstyle', 'dashes', 'color'] _plot_methods = dict(single='errorbar') def init_artists(self, ax, plot_data, plot_kwargs): handles = ax.errorbar(*plot_data, **plot_kwargs) bottoms, tops = None, None if mpl_version >= str('2.0'): _, caps, verts = handles if caps: bottoms, tops = caps else: _, (bottoms, tops), verts = handles return {'bottoms': bottoms, 'tops': tops, 'verts': verts[0]} def get_data(self, element, ranges, style): style['fmt'] = 'none' dims = element.dimensions() xs, ys = (element.dimension_values(i) for i in range(2)) yerr = element.array(dimensions=dims[2:4]) if self.invert_axes: coords = (ys, xs) err_key = 'xerr' else: coords = (xs, ys) err_key = 'yerr' style[err_key] = yerr.T if len(dims) > 3 else yerr[:, 0] return coords, style, {} def update_handles(self, key, axis, element, ranges, style): bottoms = self.handles['bottoms'] tops = self.handles['tops'] verts = self.handles['verts'] _, style, axis_kwargs = self.get_data(element, ranges, style) xs, ys, neg_error = (element.dimension_values(i) for i in range(3)) samples = len(xs) pos_error = element.dimension_values(3) if len(element.dimensions()) > 3 else neg_error if self.invert_axes: bxs, bys = ys - neg_error, xs txs, tys = ys + pos_error, xs new_arrays = [np.array([[bxs[i], xs[i]], [txs[i], xs[i]]]) for i in range(samples)] else: bxs, bys = xs, ys - neg_error txs, tys = xs, ys + pos_error new_arrays = [np.array([[xs[i], bys[i]], [xs[i], tys[i]]]) for i in range(samples)] verts.set_paths(new_arrays) if bottoms: bottoms.set_xdata(bxs) bottoms.set_ydata(bys) if tops: tops.set_xdata(txs) tops.set_ydata(tys) return axis_kwargs
class AreaPlot(ChartPlot): show_legend = param.Boolean(default=False, doc=""" Whether to show legend for the plot.""") style_opts = ['color', 'facecolor', 'alpha', 'edgecolor', 'linewidth', 'hatch', 'linestyle', 'joinstyle', 'fill', 'capstyle', 'interpolate'] _plot_methods = dict(single='fill_between') def get_data(self, element, ranges, style): xs = element.dimension_values(0) ys = [element.dimension_values(vdim) for vdim in element.vdims] return tuple([xs]+ys), style, {} def init_artists(self, ax, plot_data, plot_kwargs): fill_fn = ax.fill_betweenx if self.invert_axes else ax.fill_between stack = fill_fn(*plot_data, **plot_kwargs) return {'artist': stack} def get_extents(self, element, ranges): vdims = element.vdims vdim = vdims[0].name ranges[vdim] = max_range([ranges[vd.name] for vd in vdims]) return super(AreaPlot, self).get_extents(element, ranges)
[docs]class SpreadPlot(AreaPlot): """ SpreadPlot plots the Spread Element type. """ show_legend = param.Boolean(default=False, doc=""" Whether to show legend for the plot.""") def __init__(self, element, **params): super(SpreadPlot, self).__init__(element, **params) def get_data(self, element, ranges, style): xs = element.dimension_values(0) mean = element.dimension_values(1) neg_error = element.dimension_values(2) pos_idx = 3 if len(element.dimensions()) > 3 else 2 pos_error = element.dimension_values(pos_idx) return (xs, mean-neg_error, mean+pos_error), style, {} def get_extents(self, element, ranges): return ChartPlot.get_extents(self, element, ranges)
[docs]class HistogramPlot(ChartPlot): """ HistogramPlot can plot DataHistograms and ViewMaps of DataHistograms, which can be displayed as a single frame or animation. """ style_opts = ['alpha', 'color', 'align', 'visible', 'facecolor', 'edgecolor', 'log', 'capsize', 'error_kw', 'hatch', 'linewidth'] def __init__(self, histograms, **params): self.center = False self.cyclic = False super(HistogramPlot, self).__init__(histograms, **params) if self.invert_axes: self.axis_settings = ['ylabel', 'xlabel', 'yticks'] else: self.axis_settings = ['xlabel', 'ylabel', 'xticks'] val_dim = self.hmap.last.get_dimension(1) self.cyclic_range = val_dim.range if val_dim.cyclic else None @mpl_rc_context def initialize_plot(self, ranges=None): hist = self.hmap.last key = self.keys[-1] ranges = self.compute_ranges(self.hmap, key, ranges) el_ranges = match_spec(hist, ranges) # Get plot ranges and values dims = hist.dimensions()[:2] edges, hvals, widths, lims, isdatetime = self._process_hist(hist) if isdatetime and not dims[0].value_format: dt_format = Dimension.type_formatters[np.datetime64] dims[0] = dims[0](value_format=DateFormatter(dt_format)) style = self.style[self.cyclic_index] if self.invert_axes: self.offset_linefn = self.handles['axis'].axvline self.plotfn = self.handles['axis'].barh else: self.offset_linefn = self.handles['axis'].axhline self.plotfn = self.handles['axis'].bar # Plot bars and make any adjustments legend = hist.label if self.show_legend else '' bars = self.plotfn(edges, hvals, widths, zorder=self.zorder, label=legend, align='edge', **style) self.handles['artist'] = self._update_plot(self.keys[-1], hist, bars, lims, ranges) # Indexing top ticks = self._compute_ticks(hist, edges, widths, lims) ax_settings = self._process_axsettings(hist, lims, ticks) ax_settings['dimensions'] = dims return self._finalize_axis(self.keys[-1], ranges=el_ranges, element=hist, **ax_settings) def _process_hist(self, hist): """ Get data from histogram, including bin_ranges and values. """ self.cyclic = hist.get_dimension(0).cyclic x = hist.kdims[0] edges = hist.interface.coords(hist, x, edges=True) values = hist.dimension_values(1) hist_vals = np.array(values) xlim = hist.range(0) ylim = hist.range(1) isdatetime = False if edges.dtype.kind == 'M' or isinstance(edges[0], datetime_types): edges = date2num([v.tolist() if isinstance(v, np.datetime64) else v for v in edges]) xlim = date2num([v.tolist() if isinstance(v, np.datetime64) else v for v in xlim]) isdatetime = True widths = np.diff(edges) return edges[:-1], hist_vals, widths, xlim+ylim, isdatetime def _compute_ticks(self, element, edges, widths, lims): """ Compute the ticks either as cyclic values in degrees or as roughly evenly spaced bin centers. """ if self.xticks is None or not isinstance(self.xticks, int): return None if self.cyclic: x0, x1, _, _ = lims xvals = np.linspace(x0, x1, self.xticks) labels = ["%.0f" % np.rad2deg(x) + '\N{DEGREE SIGN}' for x in xvals] elif self.xticks: dim = element.get_dimension(0) inds = np.linspace(0, len(edges), self.xticks, dtype=np.int) edges = list(edges) + [edges[-1] + widths[-1]] xvals = [edges[i] for i in inds] labels = [dim.pprint_value(v) for v in xvals] return [xvals, labels] def get_extents(self, element, ranges): x0, y0, x1, y1 = super(HistogramPlot, self).get_extents(element, ranges) ylow, yhigh = element.get_dimension(1).range y0 = ylow if isfinite(ylow) else np.nanmin([0, y0]) y1 = yhigh if isfinite(yhigh) else np.nanmax([0, y1]) return (x0, y0, x1, y1) def _process_axsettings(self, hist, lims, ticks): """ Get axis settings options including ticks, x- and y-labels and limits. """ axis_settings = dict(zip(self.axis_settings, [None, None, (None if self.overlaid else ticks)])) return axis_settings def _update_plot(self, key, hist, bars, lims, ranges): """ Process bars can be subclassed to manually adjust bars after being plotted. """ return bars def _update_artists(self, key, hist, edges, hvals, widths, lims, ranges): """ Update all the artists in the histogram. Subclassable to allow updating of further artists. """ plot_vals = zip(self.handles['artist'], edges, hvals, widths) for bar, edge, height, width in plot_vals: if self.invert_axes: bar.set_y(edge) bar.set_width(height) bar.set_height(width) else: bar.set_x(edge) bar.set_height(height) bar.set_width(width) def update_handles(self, key, axis, element, ranges, style): # Process values, axes and style edges, hvals, widths, lims, _ = self._process_hist(element) ticks = self._compute_ticks(element, edges, widths, lims) ax_settings = self._process_axsettings(element, lims, ticks) self._update_artists(key, element, edges, hvals, widths, lims, ranges) return ax_settings
class SideHistogramPlot(AdjoinedPlot, HistogramPlot): bgcolor = param.Parameter(default=(1, 1, 1, 0), doc=""" Make plot background invisible.""") offset = param.Number(default=0.2, bounds=(0,1), doc=""" Histogram value offset for a colorbar.""") show_grid = param.Boolean(default=False, doc=""" Whether to overlay a grid on the axis.""") def _process_hist(self, hist): """ Subclassed to offset histogram by defined amount. """ edges, hvals, widths, lims, isdatetime = super(SideHistogramPlot, self)._process_hist(hist) offset = self.offset * lims[3] hvals *= 1-self.offset hvals += offset lims = lims[0:3] + (lims[3] + offset,) return edges, hvals, widths, lims, isdatetime def _update_artists(self, n, element, edges, hvals, widths, lims, ranges): super(SideHistogramPlot, self)._update_artists(n, element, edges, hvals, widths, lims, ranges) self._update_plot(n, element, self.handles['artist'], lims, ranges) def _update_plot(self, key, element, bars, lims, ranges): """ Process the bars and draw the offset line as necessary. If a color map is set in the style of the 'main' ViewableElement object, color the bars appropriately, respecting the required normalization settings. """ main = self.adjoined.main _, y1 = element.range(1) offset = self.offset * y1 range_item, main_range, dim = get_sideplot_ranges(self, element, main, ranges) # Check if plot is colormapped plot_type = Store.registry['matplotlib'].get(type(range_item)) if isinstance(plot_type, PlotSelector): plot_type = plot_type.get_plot_class(range_item) opts = self.lookup_options(range_item, 'plot') if plot_type and issubclass(plot_type, ColorbarPlot): cidx = opts.options.get('color_index', None) cdim = None if cidx is None else range_item.get_dimension(cidx) else: cdim = None # Get colormapping options if isinstance(range_item, (HeatMap, Raster)) or cdim: style = self.lookup_options(range_item, 'style')[self.cyclic_index] cmap = cm.get_cmap(style.get('cmap')) main_range = style.get('clims', main_range) else: cmap = None if offset and ('offset_line' not in self.handles): self.handles['offset_line'] = self.offset_linefn(offset, linewidth=1.0, color='k') elif offset: self._update_separator(offset) if cmap is not None: self._colorize_bars(cmap, bars, element, main_range, dim) return bars def _colorize_bars(self, cmap, bars, element, main_range, dim): """ Use the given cmap to color the bars, applying the correct color ranges as necessary. """ cmap_range = main_range[1] - main_range[0] lower_bound = main_range[0] colors = np.array(element.dimension_values(dim)) colors = (colors - lower_bound) / (cmap_range) for c, bar in zip(colors, bars): bar.set_facecolor(cmap(c)) bar.set_clip_on(False) def _update_separator(self, offset): """ Compute colorbar offset and update separator line if map is non-zero. """ offset_line = self.handles['offset_line'] if offset == 0: offset_line.set_visible(False) else: offset_line.set_visible(True) if self.invert_axes: offset_line.set_xdata(offset) else: offset_line.set_ydata(offset)
[docs]class PointPlot(ChartPlot, ColorbarPlot): """ Note that the 'cmap', 'vmin' and 'vmax' style arguments control how point magnitudes are rendered to different colors. """ color_index = param.ClassSelector(default=None, class_=(basestring, int), allow_None=True, doc=""" Index of the dimension from which the color will the drawn""") size_index = param.ClassSelector(default=None, class_=(basestring, int), allow_None=True, doc=""" Index of the dimension from which the sizes will the drawn.""") scaling_method = param.ObjectSelector(default="area", objects=["width", "area"], doc=""" Determines whether the `scaling_factor` should be applied to the width or area of each point (default: "area").""") scaling_factor = param.Number(default=1, bounds=(0, None), doc=""" Scaling factor which is applied to either the width or area of each point, depending on the value of `scaling_method`.""") show_grid = param.Boolean(default=False, doc=""" Whether to draw grid lines at the tick positions.""") size_fn = param.Callable(default=np.abs, doc=""" Function applied to size values before applying scaling, to remove values lower than zero.""") style_opts = ['alpha', 'color', 'edgecolors', 'facecolors', 'linewidth', 'marker', 'size', 'visible', 'cmap', 'vmin', 'vmax', 'norm'] _disabled_opts = ['size'] _plot_methods = dict(single='scatter') def get_data(self, element, ranges, style): xs, ys = (element.dimension_values(i) for i in range(2)) self._compute_styles(element, ranges, style) return (ys, xs) if self.invert_axes else (xs, ys), style, {} def _compute_styles(self, element, ranges, style): cdim = element.get_dimension(self.color_index) color = style.pop('color', None) cmap = style.get('cmap', None) if cdim and cmap: cs = element.dimension_values(self.color_index) # Check if numeric otherwise treat as categorical if cs.dtype.kind in 'uif': style['c'] = cs else: categories = np.unique(cs) xsorted = np.argsort(categories) ypos = np.searchsorted(categories, cs) style['c'] = xsorted[ypos] self._norm_kwargs(element, ranges, style, cdim) elif color: style['c'] = color style['edgecolors'] = style.pop('edgecolors', style.pop('edgecolor', 'none')) sdim = element.get_dimension(self.size_index) if sdim: sizes = element.dimension_values(self.size_index) ms = style['s'] if 's' in style else mpl.rcParams['lines.markersize'] sizes = compute_sizes(sizes, self.size_fn, self.scaling_factor, self.scaling_method, ms) if sizes is None: eltype = type(element).__name__ self.warning('%s dimension is not numeric, cannot ' 'use to scale %s size.' % (sdim.pprint_label, eltype)) else: style['s'] = sizes style['edgecolors'] = style.pop('edgecolors', 'none') def update_handles(self, key, axis, element, ranges, style): paths = self.handles['artist'] (xs, ys), style, _ = self.get_data(element, ranges, style) paths.set_offsets(np.column_stack([xs, ys])) sdim = element.get_dimension(self.size_index) if sdim: paths.set_sizes(style['s']) cdim = element.get_dimension(self.color_index) if cdim: paths.set_clim((style['vmin'], style['vmax'])) paths.set_array(style['c']) if 'norm' in style: paths.norm = style['norm']
[docs]class VectorFieldPlot(ColorbarPlot): """ Renders vector fields in sheet coordinates. The vectors are expressed in polar coordinates and may be displayed according to angle alone (with some common, arbitrary arrow length) or may be true polar vectors. The color or magnitude can be mapped onto any dimension using the color_index and size_index. The length of the arrows is controlled by the 'scale' style option. The scaling of the arrows may also be controlled via the normalize_lengths and rescale_lengths plot option, which will normalize the lengths to a maximum of 1 and scale them according to the minimum distance respectively. """ color_index = param.ClassSelector(default=None, class_=(basestring, int), allow_None=True, doc=""" Index of the dimension from which the color will the drawn""") size_index = param.ClassSelector(default=None, class_=(basestring, int), allow_None=True, doc=""" Index of the dimension from which the sizes will the drawn.""") arrow_heads = param.Boolean(default=True, doc=""" Whether or not to draw arrow heads. If arrowheads are enabled, they may be customized with the 'headlength' and 'headaxislength' style options.""") normalize_lengths = param.Boolean(default=True, doc=""" Whether to normalize vector magnitudes automatically. If False, it will be assumed that the lengths have already been correctly normalized.""") rescale_lengths = param.Boolean(default=True, doc=""" Whether the lengths will be rescaled to take into account the smallest non-zero distance between two vectors.""") style_opts = ['alpha', 'color', 'edgecolors', 'facecolors', 'linewidth', 'marker', 'visible', 'cmap', 'scale', 'headlength', 'headaxislength', 'pivot', 'width','headwidth', 'norm'] _plot_methods = dict(single='quiver') def get_data(self, element, ranges, style): input_scale = style.pop('scale', 1.0) xidx, yidx = (1, 0) if self.invert_axes else (0, 1) xs = element.dimension_values(xidx) if len(element.data) else [] ys = element.dimension_values(yidx) if len(element.data) else [] radians = element.dimension_values(2) if len(element.data) else [] if self.invert_axes: radians = radians+1.5*np.pi angles = list(np.rad2deg(radians)) if self.rescale_lengths: min_dist = get_min_distance(element) input_scale = input_scale / min_dist mag_dim = element.get_dimension(self.size_index) if mag_dim: magnitudes = element.dimension_values(mag_dim) _, max_magnitude = ranges[mag_dim.name] if self.normalize_lengths and max_magnitude != 0: magnitudes = magnitudes / max_magnitude else: magnitudes = np.ones(len(xs)) args = (xs, ys, magnitudes, [0.0] * len(element)) if self.color_index: colors = element.dimension_values(self.color_index) args += (colors,) cdim = element.get_dimension(self.color_index) self._norm_kwargs(element, ranges, style, cdim) style['clim'] = (style.pop('vmin'), style.pop('vmax')) style.pop('color', None) if 'pivot' not in style: style['pivot'] = 'mid' if not self.arrow_heads: style['headaxislength'] = 0 style.update(dict(scale=input_scale, angles=angles, units='x', scale_units='x')) return args, style, {} def update_handles(self, key, axis, element, ranges, style): args, style, axis_kwargs = self.get_data(element, ranges, style) # Set magnitudes, angles and colors if supplied. quiver = self.handles['artist'] quiver.set_offsets(np.column_stack(args[:2])) quiver.U = args[2] quiver.angles = style['angles'] if self.color_index: quiver.set_array(args[-1]) quiver.set_clim(style['clim']) return axis_kwargs
class BarPlot(LegendPlot): group_index = param.Integer(default=0, doc=""" Index of the dimension in the supplied Bars Element, which will be laid out into groups.""") category_index = param.Integer(default=1, doc=""" Index of the dimension in the supplied Bars Element, which will be laid out into categories.""") stack_index = param.Integer(default=2, doc=""" Index of the dimension in the supplied Bars Element, which will stacked.""") padding = param.Number(default=0.2, doc=""" Defines the padding between groups.""") color_by = param.List(default=['category'], doc=""" Defines how the Bar elements colored. Valid options include any permutation of 'group', 'category' and 'stack'.""") show_legend = param.Boolean(default=True, doc=""" Whether to show legend for the plot.""") xticks = param.Integer(0, precedence=-1) style_opts = ['alpha', 'color', 'align', 'visible', 'edgecolor', 'log', 'facecolor', 'capsize', 'error_kw', 'hatch'] legend_specs = dict(LegendPlot.legend_specs, **{ 'top': dict(bbox_to_anchor=(0., 1.02, 1., .102), ncol=3, loc=3, mode="expand", borderaxespad=0.), 'bottom': dict(ncol=3, mode="expand", loc=2, bbox_to_anchor=(0., -0.4, 1., .102), borderaxespad=0.1)}) _dimensions = OrderedDict([('group', 0), ('category',1), ('stack',2)]) def __init__(self, element, **params): super(BarPlot, self).__init__(element, **params) self.values, self.bar_dimensions = self._get_values() def _get_values(self): """ Get unique index value for each bar """ gi, ci, si =self.group_index, self.category_index, self.stack_index ndims = self.hmap.last.ndims dims = self.hmap.last.kdims dimensions = [] values = {} for vidx, vtype in zip([gi, ci, si], self._dimensions): if vidx < ndims: dim = dims[vidx] dimensions.append(dim) vals = self.hmap.dimension_values(dim.name) else: dimensions.append(None) vals = [None] values[vtype] = list(unique_iterator(vals)) return values, dimensions def _compute_styles(self, element, style_groups): """ Computes color and hatch combinations by any combination of the 'group', 'category' and 'stack'. """ style = self.lookup_options(element, 'style')[0] sopts = [] for sopt in ['color', 'hatch']: if sopt in style: sopts.append(sopt) style.pop(sopt, None) color_groups = [] for sg in style_groups: color_groups.append(self.values[sg]) style_product = list(product(*color_groups)) wrapped_style = self.lookup_options(element, 'style').max_cycles(len(style_product)) color_groups = {k:tuple(wrapped_style[n][sopt] for sopt in sopts) for n,k in enumerate(style_product)} return style, color_groups, sopts def get_extents(self, element, ranges): ngroups = len(self.values['group']) vdim = element.vdims[0].name if self.stack_index in range(element.ndims): return 0, 0, ngroups, np.NaN else: vrange = ranges[vdim] return 0, np.nanmin([vrange[0], 0]), ngroups, vrange[1] @mpl_rc_context def initialize_plot(self, ranges=None): element = self.hmap.last vdim = element.vdims[0] axis = self.handles['axis'] key = self.keys[-1] ranges = self.compute_ranges(self.hmap, key, ranges) ranges = match_spec(element, ranges) self.handles['artist'], self.handles['xticks'], xdims = self._create_bars(axis, element) return self._finalize_axis(key, ranges=ranges, xticks=self.handles['xticks'], element=element, dimensions=[xdims, vdim]) def _finalize_ticks(self, axis, element, xticks, yticks, zticks): """ Apply ticks with appropriate offsets. """ yalignments = None if xticks is not None: ticks, labels, yalignments = zip(*sorted(xticks, key=lambda x: x[0])) xticks = (list(ticks), list(labels)) super(BarPlot, self)._finalize_ticks(axis, element, xticks, yticks, zticks) if yalignments: for t, y in zip(axis.get_xticklabels(), yalignments): t.set_y(y) def _create_bars(self, axis, element): # Get style and dimension information values = self.values gi, ci, si = self.group_index, self.category_index, self.stack_index gdim, cdim, sdim = [element.kdims[i] if i < element.ndims else None for i in (gi, ci, si) ] indices = dict(zip(self._dimensions, (gi, ci, si))) style_groups = [sg for sg in self.color_by if indices[sg] < element.ndims] style_opts, color_groups, sopts = self._compute_styles(element, style_groups) dims = element.dimensions('key', label=True) ndims = len(dims) xdims = [d for d in [cdim, gdim] if d is not None] # Compute widths width = (1-(2.*self.padding)) / len(values['category']) # Initialize variables xticks = [] val_key = [None] * ndims style_key = [None] * len(style_groups) label_key = [None] * len(style_groups) labels = [] bars = {} # Iterate over group, category and stack dimension values # computing xticks and drawing bars and applying styles for gidx, grp_name in enumerate(values['group']): if grp_name is not None: grp = gdim.pprint_value(grp_name) if 'group' in style_groups: idx = style_groups.index('group') label_key[idx] = str(grp) style_key[idx] = grp_name val_key[gi] = grp_name if ci < ndims: yalign = -0.04 else: yalign = 0 xticks.append((gidx+0.5, grp, yalign)) for cidx, cat_name in enumerate(values['category']): xpos = gidx+self.padding+(cidx*width) if cat_name is not None: cat = gdim.pprint_value(cat_name) if 'category' in style_groups: idx = style_groups.index('category') label_key[idx] = str(cat) style_key[idx] = cat_name val_key[ci] = cat_name xticks.append((xpos+width/2., cat, 0)) prev = 0 for stk_name in values['stack']: if stk_name is not None: if 'stack' in style_groups: idx = style_groups.index('stack') stk = gdim.pprint_value(stk_name) label_key[idx] = str(stk) style_key[idx] = stk_name val_key[si] = stk_name vals = element.sample([tuple(val_key)]).dimension_values(element.vdims[0].name) val = float(vals[0]) if len(vals) else np.NaN label = ', '.join(label_key) style = dict(style_opts, label='' if label in labels else label, **dict(zip(sopts, color_groups[tuple(style_key)]))) bar = axis.bar([xpos], [val], width=width, bottom=prev, **style) # Update variables bars[tuple(val_key)] = bar prev += val if isfinite(val) else 0 labels.append(label) title = [element.kdims[indices[cg]].pprint_label for cg in self.color_by if indices[cg] < ndims] if self.show_legend and any(len(l) for l in labels): leg_spec = self.legend_specs[self.legend_position] if self.legend_cols: leg_spec['ncol'] = self.legend_cols axis.legend(title=', '.join(title), **leg_spec) return bars, xticks, xdims def update_handles(self, key, axis, element, ranges, style): dims = element.dimensions('key', label=True) ndims = len(dims) ci, gi, si = self.category_index, self.group_index, self.stack_index val_key = [None] * ndims for g in self.values['group']: if g is not None: val_key[gi] = g for c in self.values['category']: if c is not None: val_key[ci] = c prev = 0 for s in self.values['stack']: if s is not None: val_key[si] = s bar = self.handles['artist'].get(tuple(val_key)) if bar: vals = element.sample([tuple(val_key)]).dimension_values(element.vdims[0].name) height = float(vals[0]) if len(vals) else np.NaN bar[0].set_height(height) bar[0].set_y(prev) prev += height if isfinite(height) else 0 return {'xticks': self.handles['xticks']} class SpikesPlot(PathPlot, ColorbarPlot): aspect = param.Parameter(default='square', doc=""" The aspect ratio mode of the plot. Allows setting an explicit aspect ratio as width/height as well as 'square' and 'equal' options.""") color_index = param.ClassSelector(default=None, allow_None=True, class_=(basestring, int), doc=""" Index of the dimension from which the color will the drawn""") spike_length = param.Number(default=0.1, doc=""" The length of each spike if Spikes object is one dimensional.""") position = param.Number(default=0., doc=""" The position of the lower end of each spike.""") style_opts = PathPlot.style_opts + ['cmap'] def init_artists(self, ax, plot_args, plot_kwargs): line_segments = LineCollection(*plot_args, **plot_kwargs) ax.add_collection(line_segments) return {'artist': line_segments} def get_extents(self, element, ranges): l, b, r, t = super(SpikesPlot, self).get_extents(element, ranges) if len(element.dimensions()) == 1: b, t = self.position, self.position+self.spike_length else: b = np.nanmin([0, b]) t = np.nanmax([0, t]) return l, b, r, t def get_data(self, element, ranges, style): dimensions = element.dimensions(label=True) ndims = len(dimensions) pos = self.position if ndims > 1: data = [[(x, pos), (x, pos+y)] for x, y in element.array()] else: height = self.spike_length data = [[(x[0], pos), (x[0], pos+height)] for x in element.array()] if self.invert_axes: data = [(line[0][::-1], line[1][::-1]) for line in data] dims = element.dimensions() clean_spikes = [] for spike in data: xs, ys = zip(*spike) cols = [] for i, vs in enumerate((xs, ys)): vs = np.array(vs) if vs.dtype.kind == 'M' and i < len(dims): dt_format = Dimension.type_formatters[np.datetime64] dims[i] = dims[i](value_format=DateFormatter(dt_format)) vs = date2num([v.tolist() if isinstance(v, np.datetime64) else v for v in vs]) cols.append(vs) clean_spikes.append(np.column_stack(cols)) cdim = element.get_dimension(self.color_index) if cdim: style['array'] = element.dimension_values(cdim) self._norm_kwargs(element, ranges, style, cdim) style['clim'] = style.pop('vmin'), style.pop('vmax') return (clean_spikes,), style, {'dimensions': dims} def update_handles(self, key, axis, element, ranges, style): artist = self.handles['artist'] (data,), kwargs, axis_kwargs = self.get_data(element, ranges, style) artist.set_paths(data) artist.set_visible(style.get('visible', True)) if 'array' in kwargs: artist.set_clim((kwargs['vmin'], kwargs['vmax'])) artist.set_array(kwargs['array']) if 'norm' in kwargs: artist.norm = kwargs['norm'] return axis_kwargs class SideSpikesPlot(AdjoinedPlot, SpikesPlot): bgcolor = param.Parameter(default=(1, 1, 1, 0), doc=""" Make plot background invisible.""") border_size = param.Number(default=0, doc=""" The size of the border expressed as a fraction of the main plot.""") subplot_size = param.Number(default=0.1, doc=""" The size subplots as expressed as a fraction of the main plot.""") spike_length = param.Number(default=1, doc=""" The length of each spike if Spikes object is one dimensional.""") xaxis = param.ObjectSelector(default='bare', objects=['top', 'bottom', 'bare', 'top-bare', 'bottom-bare', None], doc=""" Whether and where to display the xaxis, bare options allow suppressing all axis labels including ticks and xlabel. Valid options are 'top', 'bottom', 'bare', 'top-bare' and 'bottom-bare'.""") yaxis = param.ObjectSelector(default='bare', objects=['left', 'right', 'bare', 'left-bare', 'right-bare', None], doc=""" Whether and where to display the yaxis, bare options allow suppressing all axis labels including ticks and ylabel. Valid options are 'left', 'right', 'bare' 'left-bare' and 'right-bare'.""")