Source code for holoviews.plotting.mpl.element

import math

import param
import numpy as np
import matplotlib as mpl
import matplotlib.colors as mpl_colors
from matplotlib import ticker
from matplotlib.dates import date2num

from ...core import util
from ...core import (OrderedDict, NdOverlay, DynamicMap,
                     CompositeOverlay, Element3D, Element)
from ...core.options import abbreviated_exception
from ..plot import GenericElementPlot, GenericOverlayPlot
from ..util import dynamic_update, process_cmap
from .plot import MPLPlot, mpl_rc_context
from .util import wrap_formatter
from distutils.version import LooseVersion

class ElementPlot(GenericElementPlot, MPLPlot):

    apply_ticks = param.Boolean(default=True, doc="""
        Whether to apply custom ticks.""")

    aspect = param.Parameter(default='square', doc="""
        The aspect ratio mode of the plot. By default, a plot may
        select its own appropriate aspect ratio but sometimes it may
        be necessary to force a square aspect ratio (e.g. to display
        the plot as an element of a grid). The modes 'auto' and
        'equal' correspond to the axis modes of the same name in
        matplotlib, a numeric value may also be passed.""")

    invert_zaxis = param.Boolean(default=False, doc="""
        Whether to invert the plot z-axis.""")

    labelled = param.List(default=['x', 'y'], doc="""
        Whether to plot the 'x' and 'y' labels.""")

    logz  = param.Boolean(default=False, doc="""
         Whether to apply log scaling to the y-axis of the Chart.""")

    zaxis = param.Boolean(default=True, doc="""
        Whether to display the z-axis.""")

    zrotation = param.Integer(default=0, bounds=(0, 360), doc="""
        Rotation angle of the zticks.""")

    zticks = param.Parameter(default=None, doc="""
        Ticks along z-axis specified as an integer, explicit list of
        tick locations, list of tuples containing the locations and
        labels or a matplotlib tick locator object. If set to None
        default matplotlib ticking behavior is applied.""")

    # Element Plots should declare the valid style options for matplotlib call
    style_opts = []

    # Whether plot has axes, disables setting axis limits, labels and ticks
    _has_axes = True

    def __init__(self, element, **params):
        super(ElementPlot, self).__init__(element, **params)
        check = self.hmap.last
        if isinstance(check, CompositeOverlay):
            check = check.values()[0] # Should check if any are 3D plots
        if isinstance(check, Element3D):
            self.projection = '3d'

        for hook in self.initial_hooks:
                hook(self, element)
            except Exception as e:
                self.warning("Plotting hook %r could not be applied:\n\n %s" % (hook, e))

    def _finalize_axis(self, key, element=None, title=None, dimensions=None, ranges=None, xticks=None,
                       yticks=None, zticks=None, xlabel=None, ylabel=None, zlabel=None):
        Applies all the axis settings before the axis or figure is returned.
        Only plots with zorder 0 get to apply their settings.

        When the number of the frame is supplied as n, this method looks
        up and computes the appropriate title, axis labels and axis bounds.
        if element is None:
            element = self._get_frame(key)
        self.current_frame = element
        if not dimensions and element and not self.subplots:
            el = element.traverse(lambda x: x, [Element])
            if el: dimensions = el[0].dimensions()
        axis = self.handles['axis']

        subplots = list(self.subplots.values()) if self.subplots else []
        if self.zorder == 0 and key is not None:
            if self.bgcolor:
                if LooseVersion(mpl.__version__) <= '1.5.9':

            # Apply title
            title = self._format_title(key)
            if self.show_title and title is not None:
                fontsize = self._fontsize('title')
                if 'title' in self.handles:
                    self.handles['title'] = axis.set_title(title, **fontsize)

            # Apply subplot label

            # Apply axis options if axes are enabled
            if element and not any(not sp._has_axes for sp in [self] + subplots):
                # Set axis labels
                if dimensions:
                    self._set_labels(axis, dimensions, xlabel, ylabel, zlabel)

                if not subplots:
                    legend = axis.get_legend()
                    if legend:
                        self.handles["bbox_extra_artists"] += [legend]

                # Apply log axes
                if self.logx:
                if self.logy:

                if not self.projection == '3d':
                    self._set_axis_position(axis, 'x', self.xaxis)
                    self._set_axis_position(axis, 'y', self.yaxis)

                # Apply ticks
                if self.apply_ticks:
                    self._finalize_ticks(axis, dimensions, xticks, yticks, zticks)

                # Set axes limits
                self._set_axis_limits(axis, element, subplots, ranges)

            # Apply aspects
            if self.aspect is not None and self.projection != 'polar' and not self.adjoined:
                self._set_aspect(axis, self.aspect)

        if not subplots and not self.drawn:

        return super(ElementPlot, self)._finalize_axis(key)

    def _finalize_ticks(self, axis, dimensions, xticks, yticks, zticks):
        Finalizes the ticks on the axes based on the supplied ticks
        and Elements. Sets the axes position as well as tick positions,
        labels and fontsize.
        ndims = len(dimensions) if dimensions else 0
        xdim = dimensions[0] if ndims else None
        ydim = dimensions[1] if ndims > 1 else None

        # Tick formatting
        if xdim:
            self._set_axis_formatter(axis.xaxis, xdim)
        if ydim:
            self._set_axis_formatter(axis.yaxis, ydim)
        if self.projection == '3d':
            zdim = dimensions[2] if ndims > 2 else None
            if zdim:
                self._set_axis_formatter(axis.zaxis, zdim)

        xticks = xticks if xticks else self.xticks
        self._set_axis_ticks(axis.xaxis, xticks, log=self.logx,

        yticks = yticks if yticks else self.yticks
        self._set_axis_ticks(axis.yaxis, yticks, log=self.logy,

        if self.projection == '3d':
            zticks = zticks if zticks else self.zticks
            self._set_axis_ticks(axis.zaxis, zticks, log=self.logz,

        for ax, ax_obj in zip('xy', [axis.xaxis, axis.yaxis]):
            tick_fontsize = self._fontsize('%sticks' % ax,'labelsize',common=False)
            if tick_fontsize: ax_obj.set_tick_params(**tick_fontsize)

    def _finalize_artist(self, element):
        Allows extending the _finalize_axis method with Element
        specific options.

    def _set_labels(self, axes, dimensions, xlabel=None, ylabel=None, zlabel=None):
        Sets the labels of the axes using the supplied list of dimensions.
        Optionally explicit labels may be supplied to override the dimension
        xlabel, ylabel, zlabel = self._get_axis_labels(dimensions, xlabel, ylabel, zlabel)
        if self.invert_axes:
            xlabel, ylabel = ylabel, xlabel
        if xlabel and self.xaxis and 'x' in self.labelled:
            axes.set_xlabel(xlabel, **self._fontsize('xlabel'))
        if ylabel and self.yaxis and 'y' in self.labelled:
            axes.set_ylabel(ylabel, **self._fontsize('ylabel'))
        if zlabel and self.zaxis and 'z' in self.labelled:
            axes.set_zlabel(zlabel, **self._fontsize('zlabel'))

    def _set_axis_formatter(self, axis, dim):
        Set axis formatter based on dimension formatter.
        if isinstance(dim, list): dim = dim[0]
        formatter = None
        if dim.value_format:
            formatter = dim.value_format
        elif dim.type in dim.type_formatters:
            formatter = dim.type_formatters[dim.type]
        if formatter:

    def _set_aspect(self, axes, aspect):
        Set the aspect on the axes based on the aspect setting.
        if isinstance(aspect, util.basestring) and aspect != 'square':

        (x0, x1), (y0, y1) = axes.get_xlim(), axes.get_ylim()
        xsize = np.log(x1) - np.log(x0) if self.logx else x1-x0
        ysize = np.log(y1) - np.log(y0) if self.logy else y1-y0
        xsize = max(abs(xsize), 1e-30)
        ysize = max(abs(ysize), 1e-30)
        data_ratio = 1./(ysize/xsize)
        if aspect != 'square':
            data_ratio = data_ratio/aspect

    def _set_axis_limits(self, axis, view, subplots, ranges):
        Compute extents for current view and apply as axis limits
        # Extents
        scalex, scaley = True, True
        extents = self.get_extents(view, ranges)
        if extents and not self.overlaid:
            coords = [coord if np.isreal(coord) or isinstance(coord, np.datetime64) else np.NaN for coord in extents]
            coords = [date2num(util.dt64_to_dt(c)) if isinstance(c, np.datetime64) else c
                      for c in coords]
            valid_lim = lambda c: util.isnumeric(c) and not np.isnan(c)
            if self.projection == '3d' or len(extents) == 6:
                l, b, zmin, r, t, zmax = coords
                if self.invert_zaxis or any(p.invert_zaxis for p in subplots):
                    zmin, zmax = zmax, zmin
                if zmin != zmax:
                    if valid_lim(zmin):
                    if valid_lim(zmax):
                l, b, r, t = coords

            if self.invert_axes:
                l, b, r, t = b, l, t, r

            if self.invert_xaxis or any(p.invert_xaxis for p in subplots):
                r, l = l, r
            if l != r:
                lims = {}
                if valid_lim(l):
                    lims['left'] = l
                    scalex = False
                if valid_lim(r):
                    lims['right'] = r
                    scalex = False
                if lims:
            if self.invert_yaxis or any(p.invert_yaxis for p in subplots):
                t, b = b, t
            if b != t:
                lims = {}
                if valid_lim(b):
                    lims['bottom'] = b
                    scaley = False
                if valid_lim(t):
                    lims['top'] = t
                    scaley = False
                if lims:
        axis.autoscale_view(scalex=scalex, scaley=scaley)

    def _set_axis_position(self, axes, axis, option):
        Set the position and visibility of the xaxis or yaxis by
        supplying the axes object, the axis to set, i.e. 'x' or 'y'
        and an option to specify the position and visibility of the axis.
        The option may be None, 'bare' or positional, i.e. 'left' and
        'right' for the yaxis and 'top' and 'bottom' for the xaxis.
        May also combine positional and 'bare' into for example 'left-bare'.
        positions = {'x': ['bottom', 'top'], 'y': ['left', 'right']}[axis]
        axis = axes.xaxis if axis == 'x' else axes.yaxis
        if option in [None, False]:
            for pos in positions:
            if option is True:
                option = positions[0]
            if 'bare' in option:
            if option != 'bare':
                option = option.split('-')[0]
        if not self.overlaid and not self.show_frame and self.projection != 'polar':
            pos = (positions[1] if (option and (option == 'bare' or positions[0] in option))
                   else positions[0])

    def _set_axis_ticks(self, axis, ticks, log=False, rotation=0):
        Allows setting the ticks for a particular axis either with
        a tuple of ticks, a tick locator object, an integer number
        of ticks, a list of tuples containing positions and labels
        or a list of positions. Also supports enabling log ticking
        if an integer number of ticks is supplied and setting a
        rotation for the ticks.
        if isinstance(ticks, (list, tuple)) and all(isinstance(l, list) for l in ticks):
        elif isinstance(ticks, ticker.Locator):
        elif not ticks and ticks is not None:
        elif isinstance(ticks, int):
            if log:
                locator = ticker.LogLocator(numticks=ticks,
                locator = ticker.MaxNLocator(ticks)
        elif isinstance(ticks, (list, tuple)):
            labels = None
            if all(isinstance(t, tuple) for t in ticks):
                ticks, labels = zip(*ticks)
            if labels:
        for tick in axis.get_ticklabels():

    def update_frame(self, key, ranges=None, element=None):
        Set the plot(s) to the given frame number.  Operates by
        manipulating the matplotlib objects held in the self._handles

        If n is greater than the number of available frames, update
        using the last available frame.
        reused = isinstance(self.hmap, DynamicMap) and self.overlaid
        if not reused and element is None:
            element = self._get_frame(key)
        elif element is not None:
            self.current_key = key
            self.current_frame = element

        if element is not None:
            self.set_param(**self.lookup_options(element, 'plot').options)
        axis = self.handles['axis']

        axes_visible = element is not None or self.overlaid
        axis.xaxis.set_visible(axes_visible and self.xaxis)
        axis.yaxis.set_visible(axes_visible and self.yaxis)
        axis.patch.set_alpha(np.min([int(axes_visible), 1]))

        for hname, handle in self.handles.items():
            hideable = hasattr(handle, 'set_visible')
            if hname not in ['axis', 'fig'] and hideable:
                handle.set_visible(element is not None)
        if element is None:

        ranges = self.compute_ranges(self.hmap, key, ranges)
        ranges = util.match_spec(element, ranges)

        label = element.label if self.show_legend else ''
        style = dict(label=label, zorder=self.zorder, **[self.cyclic_index])
        axis_kwargs = self.update_handles(key, axis, element, ranges, style)
        self._finalize_axis(key, element=element, ranges=ranges,
                            **(axis_kwargs if axis_kwargs else {}))

    def initialize_plot(self, ranges=None):
        element = self.hmap.last
        ax = self.handles['axis']
        key = list([-1]
        dim_map = dict(zip(( for d in self.hmap.kdims), key))
        key = tuple(dim_map.get(, None) for d in self.dimensions)
        ranges = self.compute_ranges(self.hmap, key, ranges)
        self.current_ranges = ranges
        self.current_frame = element
        self.current_key = key

        ranges = util.match_spec(element, ranges)

        style = dict(zorder=self.zorder, **[self.cyclic_index])
        if self.show_legend:
            style['label'] = element.label

        plot_data, plot_kwargs, axis_kwargs = self.get_data(element, ranges, style)

        with abbreviated_exception():
            handles = self.init_artists(ax, plot_data, plot_kwargs)

        return self._finalize_axis(self.keys[-1], element=element, ranges=ranges,

    def init_artists(self, ax, plot_args, plot_kwargs):
        Initializes the artist based on the plot method declared on
        the plot.
        plot_method = self._plot_methods.get('batched' if self.batched else 'single')
        plot_fn = getattr(ax, plot_method)
        artist = plot_fn(*plot_args, **plot_kwargs)
        return {'artist': artist[0] if isinstance(artist, list) and
                len(artist) == 1 else artist}

    def update_handles(self, key, axis, element, ranges, style):
        Update the elements of the plot.
        plot_data, plot_kwargs, axis_kwargs = self.get_data(element, ranges, style)

        with abbreviated_exception():
            handles = self.init_artists(axis, plot_data, plot_kwargs)
        return axis_kwargs

    def teardown_handles(self):
        If no custom update_handles method is supplied this method
        is called to tear down any previous handles before replacing
        if 'artist' in self.handles:

class ColorbarPlot(ElementPlot):

    colorbar = param.Boolean(default=False, doc="""
        Whether to draw a colorbar.""")

    color_levels = param.Integer(default=None, doc="""
        Number of discrete colors to use when colormapping.""")

    clipping_colors = param.Dict(default={}, doc="""
        Dictionary to specify colors for clipped values, allows
        setting color for NaN values and for values above and below
        the min and max value. The min, max or NaN color may specify
        an RGB(A) color as a color hex string of the form #FFFFFF or
        #FFFFFFFF or a length 3 or length 4 tuple specifying values in
        the range 0-1 or a named HTML color.""")

    cbar_padding = param.Number(default=0.01, doc="""
        Padding between colorbar and other plots.""")

    cbar_ticks = param.Parameter(default=None, doc="""
        Ticks along colorbar-axis specified as an integer, explicit
        list of tick locations, list of tuples containing the
        locations and labels or a matplotlib tick locator object. If
        set to None default matplotlib ticking behavior is

    cbar_width = param.Number(default=0.05, doc="""
        Width of the colorbar as a fraction of the main plot""")

    symmetric = param.Boolean(default=False, doc="""
        Whether to make the colormap symmetric around zero.""")

    _colorbars = {}

    _default_nan = '#8b8b8b'

    def __init__(self, *args, **kwargs):
        super(ColorbarPlot, self).__init__(*args, **kwargs)
        self._cbar_extend = 'neither'

    def _adjust_cbar(self, cbar, label, dim):
        noalpha = math.floor([self.cyclic_index].get('alpha', 1)) == 1
        if (cbar.solids and noalpha):
        if isinstance(self.cbar_ticks, ticker.Locator):
        elif self.cbar_ticks == 0:
        elif isinstance(self.cbar_ticks, int):
            locator = ticker.MaxNLocator(self.cbar_ticks)
        elif isinstance(self.cbar_ticks, list):
            if all(isinstance(t, tuple) for t in self.cbar_ticks):
                ticks, labels = zip(*self.cbar_ticks)
                ticks, labels = zip(*[(t, dim.pprint_value(t))
                                        for t in self.cbar_ticks])

    def _finalize_artist(self, element):
        artist = self.handles.get('artist', None)
        if artist and self.colorbar:

    def _draw_colorbar(self, dim=None, redraw=True):
        element = self.hmap.last
        artist = self.handles.get('artist', None)
        fig = self.handles['fig']
        axis = self.handles['axis']
        ax_colorbars, position = ColorbarPlot._colorbars.get(id(axis), ([], None))
        specs = [spec[:2] for _, _, spec, _ in ax_colorbars]
        spec = util.get_spec(element)

        if position is None or not redraw:
            if redraw:
            bbox = axis.get_position()
            l, b, w, h = bbox.x0, bbox.y0, bbox.width, bbox.height
            l, b, w, h = position

        # Get colorbar label
        dim = element.get_dimension(dim)
        if dim:
            label = dim.pprint_label
        elif element.vdims:
            label = element.vdims[0].pprint_label
        elif dim is None:
            label = ''

        padding = self.cbar_padding
        width = self.cbar_width
        if spec[:2] not in specs:
            offset = len(ax_colorbars)
            scaled_w = w*width
            cax = fig.add_axes([l+w+padding+(scaled_w+padding+w*0.15)*offset,
                                b, scaled_w, h])
            cbar = fig.colorbar(artist, cax=cax, ax=axis, extend=self._cbar_extend)
            self._adjust_cbar(cbar, label, dim)
            self.handles['cax'] = cax
            self.handles['cbar'] = cbar
            ylabel = cax.yaxis.get_label()
            self.handles['bbox_extra_artists'] += [cax, ylabel]
            ax_colorbars.append((artist, cax, spec, label))

        for i, (artist, cax, spec, label) in enumerate(ax_colorbars):
            scaled_w = w*width
                              b, scaled_w, h])

        ColorbarPlot._colorbars[id(axis)] = (ax_colorbars, (l, b, w, h))

    def _norm_kwargs(self, element, ranges, opts, vdim, prefix=''):
        Returns valid color normalization kwargs
        to be passed to matplotlib plot function.
        clim = opts.pop(prefix+'clims', None)
        values = np.asarray(element.dimension_values(vdim))
        if clim is None:
            if not len(values):
                clim = (0, 0)
                categorical = False
            elif values.dtype.kind in 'uif':
                clim = ranges[] if in ranges else element.range(vdim)
                if self.logz:
                    # Lower clim must be >0 when logz=True
                    # Choose the maximum between the lowest non-zero value
                    # and the overall range
                    if clim[0] == 0:
                        clim = (values[values!=0].min(), clim[1])
                if self.symmetric:
                    clim = -np.abs(clim).max(), np.abs(clim).max()
                categorical = False
                clim = (0, len(np.unique(values))-1)
                categorical = True
            categorical = values.dtype.kind not in 'uif'

        if self.logz:
            if self.symmetric:
                norm = mpl_colors.SymLogNorm(vmin=clim[0], vmax=clim[1],
                norm = mpl_colors.LogNorm(vmin=clim[0], vmax=clim[1])
            opts[prefix+'norm'] = norm
        opts[prefix+'vmin'] = clim[0]
        opts[prefix+'vmax'] = clim[1]

        # Check whether the colorbar should indicate clipping
        if values.dtype.kind not in 'OSUM':
            ncolors = self.color_levels
                el_min, el_max = np.nanmin(values), np.nanmax(values)
            except ValueError:
                el_min, el_max = -np.inf, np.inf
            ncolors = clim[-1]+1
            el_min, el_max = -np.inf, np.inf
        vmin = -np.inf if opts[prefix+'vmin'] is None else opts[prefix+'vmin']
        vmax = np.inf if opts[prefix+'vmax'] is None else opts[prefix+'vmax']
        if el_min < vmin and el_max > vmax:
            self._cbar_extend = 'both'
        elif el_min < vmin:
            self._cbar_extend = 'min'
        elif el_max > vmax:
            self._cbar_extend = 'max'

        # Define special out-of-range colors on colormap
        cmap = opts.get(prefix+'cmap', 'viridis')
        colors = {}
        for k, val in self.clipping_colors.items():
            if val == 'transparent':
                colors[k] = {'color': 'w', 'alpha': 0}
            elif isinstance(val, tuple):
                colors[k] = {'color': val[:3],
                             'alpha': val[3] if len(val) > 3 else 1}
            elif isinstance(val, util.basestring):
                color = val
                alpha = 1
                if color.startswith('#') and len(color) == 9:
                    alpha = int(color[-2:], 16)/255.
                    color = color[:-2]
                colors[k] = {'color': color, 'alpha': alpha}

        if not isinstance(cmap, mpl_colors.Colormap):
            if isinstance(cmap, dict):
                factors = np.unique(values)
                palette = [cmap.get(f, colors.get('NaN', {'color': self._default_nan})['color'])
                           for f in factors]
                palette = process_cmap(cmap, ncolors, categorical=categorical)
            cmap = mpl_colors.ListedColormap(palette)
        if 'max' in colors: cmap.set_over(**colors['max'])
        if 'min' in colors: cmap.set_under(**colors['min'])
        if 'NaN' in colors: cmap.set_bad(**colors['NaN'])
        opts[prefix+'cmap'] = cmap

class LegendPlot(ElementPlot):

    show_legend = param.Boolean(default=True, doc="""
        Whether to show legend for the plot.""")

    legend_cols = param.Integer(default=None, doc="""
       Number of legend columns in the legend.""")

    legend_position = param.ObjectSelector(objects=['inner', 'right',
                                                    'bottom', 'top',
                                                    'left', 'best',
                                           default='inner', doc="""
        Allows selecting between a number of predefined legend position
        options. The predefined options may be customized in the
        legend_specs class attribute. By default, 'inner', 'right',
        'bottom', 'top', 'left', 'best', 'top_right', 'top_left',
        'bottom_right' and 'bottom_left' are supported.""")

    legend_specs = {'inner': {},
                    'best': {},
                    'left':   dict(bbox_to_anchor=(-.15, 1), loc=1),
                    'right':  dict(bbox_to_anchor=(1.05, 1), loc=2),
                    '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., -0.25, 1., .102),
                    'top_right': dict(loc=1),
                    'top_left': dict(loc=2),
                    'bottom_left': dict(loc=3),
                    'bottom_right': dict(loc=4)}

[docs]class OverlayPlot(LegendPlot, GenericOverlayPlot): """ OverlayPlot supports compositors processing of Overlays across maps. """ _passed_handles = ['fig', 'axis'] _propagate_options = ['aspect', 'fig_size', 'xaxis', 'yaxis', 'zaxis', 'labelled', 'bgcolor', 'fontsize', 'invert_axes', 'show_frame', 'show_grid', 'logx', 'logy', 'logz', 'xticks', 'yticks', 'zticks', 'xrotation', 'yrotation', 'zrotation', 'invert_xaxis', 'invert_yaxis', 'invert_zaxis', 'title_format'] def __init__(self, overlay, ranges=None, **params): if 'projection' not in params: params['projection'] = self._get_projection(overlay) super(OverlayPlot, self).__init__(overlay, ranges=ranges, **params) def _finalize_artist(self, element): for subplot in self.subplots.values(): subplot._finalize_artist(element) def _adjust_legend(self, overlay, axis): """ Accumulate the legend handles and labels for all subplots and set up the legend """ legend_data = [] dimensions = overlay.kdims title = ', '.join([ for d in dimensions]) for key, subplot in self.subplots.items(): element =, False) if not subplot.show_legend or not element: continue title = ', '.join([ for d in dimensions]) handle = subplot.traverse(lambda p: p.handles['artist'], [lambda p: 'artist' in p.handles]) if isinstance(overlay, NdOverlay): key = (dim.pprint_value(k) for k, dim in zip(key, dimensions)) label = ','.join([str(k) + dim.unit if dim.unit else str(k) for dim, k in zip(dimensions, key)]) if handle: legend_data.append((handle, label)) else: if isinstance(subplot, OverlayPlot): legend_data += subplot.handles.get('legend_data', {}).items() elif element.label and handle: legend_data.append((handle, element.label)) all_handles, all_labels = list(zip(*legend_data)) if legend_data else ([], []) data = OrderedDict() used_labels = [] for handle, label in zip(all_handles, all_labels): # Ensure that artists with multiple handles are supported if isinstance(handle, list): handle = tuple(handle) if handle and (handle not in data) and label and label not in used_labels: data[handle] = label used_labels.append(label) if (not len(set(data.values())) > 0) or not self.show_legend: legend = axis.get_legend() if legend: legend.set_visible(False) else: leg_spec = self.legend_specs[self.legend_position] if self.legend_cols: leg_spec['ncol'] = self.legend_cols leg = axis.legend(list(data.keys()), list(data.values()), title=title, scatterpoints=1, **dict(leg_spec, **self._fontsize('legend'))) title_fontsize = self._fontsize('legend_title') if title_fontsize: leg.get_title().set_fontsize(title_fontsize['fontsize']) frame = leg.get_frame() frame.set_facecolor('1.0') frame.set_edgecolor('0.0') frame.set_linewidth('1.0') leg.set_zorder(10e6) self.handles['legend'] = leg self.handles['bbox_extra_artists'].append(leg) self.handles['legend_data'] = data @mpl_rc_context def initialize_plot(self, ranges=None): axis = self.handles['axis'] key = self.keys[-1] element = self._get_frame(key) ranges = self.compute_ranges(self.hmap, key, ranges) for k, subplot in self.subplots.items(): subplot.initialize_plot(ranges=ranges) if isinstance(element, CompositeOverlay): frame = element.get(k, None) subplot.current_frame = frame if self.show_legend and element is not None: self._adjust_legend(element, axis) return self._finalize_axis(key, element=element, ranges=ranges, title=self._format_title(key)) @mpl_rc_context def update_frame(self, key, ranges=None, element=None): axis = self.handles['axis'] reused = isinstance(self.hmap, DynamicMap) and self.overlaid if element is None and not reused: element = self._get_frame(key) elif element is not None: self.current_frame = element self.current_key = key empty = element is None if isinstance(self.hmap, DynamicMap): range_obj = element else: range_obj = self.hmap items = [] if element is None else element.items() if not empty: ranges = self.compute_ranges(range_obj, key, ranges) for k, subplot in self.subplots.items(): el = None if empty else element.get(k, None) if isinstance(self.hmap, DynamicMap) and not empty: idx, spec, exact = dynamic_update(self, subplot, k, element, items) if idx is not None: _, el = items.pop(idx) if not exact: self._update_subplot(subplot, spec) subplot.update_frame(key, ranges, el) if isinstance(self.hmap, DynamicMap) and items: self._create_dynamic_subplots(key, items, ranges) if self.show_legend and not empty: self._adjust_legend(element, axis) self._finalize_axis(key, element=element, ranges=ranges)