text: 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
  
  
  class ChartPlot(ElementPlot):
      """
      Baseclass to plot Chart elements.
      """
  
  
  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
  
  
  
  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)
  
  
  
  class SideAreaPlot(AdjoinedPlot, AreaPlot):
  
      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.""")
  
      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'.""")
  
  
  
  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)
  
  
  
  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)
  
  
  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']
  
  
  
  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'.""")
  
  