"""
Public API for all plots supported by HoloViews, regardless of
plotting package or backend. Every plotting classes must be a subclass
of this Plot baseclass.
"""
from itertools import groupby, product
from collections import Counter, defaultdict
import numpy as np
import param
from ..core import OrderedDict
from ..core import util, traversal
from ..core.element import Element
from ..core.overlay import Overlay, CompositeOverlay
from ..core.layout import Empty, NdLayout, Layout
from ..core.options import Store, Compositor, SkipRendering
from ..core.overlay import NdOverlay
from ..core.spaces import HoloMap, DynamicMap
from ..core.util import stream_parameters, cartesian_product
from ..element import Table
from .util import (get_dynamic_mode, initialize_unbounded, dim_axis_label,
attach_streams, traverse_setter, get_nested_streams,
compute_overlayable_zorders, get_plot_frame)
[docs]class Plot(param.Parameterized):
"""
Base class of all Plot classes in HoloViews, designed to be
general enough to use any plotting package or backend.
"""
# A list of style options that may be supplied to the plotting
# call
style_opts = []
# Sometimes matplotlib doesn't support the common aliases.
# Use this list to disable any invalid style options
_disabled_opts = []
[docs] def initialize_plot(self, ranges=None):
"""
Initialize the matplotlib figure.
"""
raise NotImplementedError
[docs] def update(self, key):
"""
Update the internal state of the Plot to represent the given
key tuple (where integers represent frames). Returns this
state.
"""
return self.state
@property
def state(self):
"""
The plotting state that gets updated via the update method and
used by the renderer to generate output.
"""
raise NotImplementedError
def __len__(self):
"""
Returns the total number of available frames.
"""
raise NotImplementedError
@classmethod
def lookup_options(cls, obj, group):
plot_class = None
try:
plot_class = Store.renderers[cls.backend].plotting_class(obj)
style_opts = plot_class.style_opts
except SkipRendering:
style_opts = None
node = Store.lookup_options(cls.backend, obj, group)
if group == 'style' and style_opts is not None:
return node.filtered(style_opts)
elif group == 'plot' and plot_class:
return node.filtered(list(plot_class.params().keys()))
else:
return node
[docs]class PlotSelector(object):
"""
Proxy that allows dynamic selection of a plotting class based on a
function of the plotted object. Behaves like a Plot class and
presents the same parameterized interface.
"""
_disabled_opts = []
def __init__(self, selector, plot_classes, allow_mismatch=False):
"""
The selector function accepts a component instance and returns
the appropriate key to index plot_classes dictionary.
"""
self.selector = selector
self.plot_classes = OrderedDict(plot_classes)
interface = self._define_interface(self.plot_classes.values(), allow_mismatch)
self.style_opts, self.plot_options = interface
def _define_interface(self, plots, allow_mismatch):
parameters = [{k:v.precedence for k,v in plot.params().items()
if ((v.precedence is None) or (v.precedence >= 0))}
for plot in plots]
param_sets = [set(params.keys()) for params in parameters]
if not allow_mismatch and not all(pset == param_sets[0] for pset in param_sets):
raise Exception("All selectable plot classes must have identical plot options.")
styles= [plot.style_opts for plot in plots]
if not allow_mismatch and not all(style == styles[0] for style in styles):
raise Exception("All selectable plot classes must have identical style options.")
return styles[0], parameters[0]
def __call__(self, obj, **kwargs):
key = self.selector(obj)
if key not in self.plot_classes:
msg = "Key %s returned by selector not in set: %s"
raise Exception(msg % (key, ', '.join(self.plot_classes.keys())))
return self.plot_classes[key](obj, **kwargs)
def __setattr__(self, label, value):
try:
return super(PlotSelector, self).__setattr__(label, value)
except:
raise Exception("Please set class parameters directly on classes %s"
% ', '.join(str(cls) for cls in self.__dict__['plot_classes'].values()))
def params(self):
return self.plot_options
[docs]class DimensionedPlot(Plot):
"""
DimensionedPlot implements a number of useful methods
to compute dimension ranges and titles containing the
dimension values.
"""
fontsize = param.Parameter(default=None, allow_None=True, doc="""
Specifies various fontsizes of the displayed text.
Finer control is available by supplying a dictionary where any
unmentioned keys reverts to the default sizes, e.g:
{'ticks':20, 'title':15,
'ylabel':5, 'xlabel':5,
'legend':8, 'legend_title':13}
You can set the fontsize of both 'ylabel' and 'xlabel' together
using the 'labels' key.""")
#Allowed fontsize keys
_fontsize_keys = ['xlabel','ylabel', 'labels', 'ticks',
'title', 'legend', 'legend_title', 'xticks',
'yticks']
show_title = param.Boolean(default=True, doc="""
Whether to display the plot title.""")
title_format = param.String(default="{label} {group}\n{dimensions}", doc="""
The formatting string for the title of this plot, allows defining
a label group separator and dimension labels.""")
normalize = param.Boolean(default=True, doc="""
Whether to compute ranges across all Elements at this level
of plotting. Allows selecting normalization at different levels
for nested data containers.""")
projection = param.Parameter(default=None, doc="""
Allows supplying a custom projection to transform the axis
coordinates during display. Example projections include '3d'
and 'polar' projections supported by some backends. Depending
on the backend custom projection objects may be supplied.""")
def __init__(self, keys=None, dimensions=None, layout_dimensions=None,
uniform=True, subplot=False, adjoined=None, layout_num=0,
style=None, subplots=None, dynamic=False, renderer=None, **params):
self.subplots = subplots
self.adjoined = adjoined
self.dimensions = dimensions
self.layout_num = layout_num
self.layout_dimensions = layout_dimensions
self.subplot = subplot
self.keys = keys
self.uniform = uniform
self.dynamic = dynamic
self.drawn = False
self.handles = {}
self.group = None
self.label = None
self.current_frame = None
self.current_key = None
self.ranges = {}
self.renderer = renderer if renderer else Store.renderers[self.backend].instance()
self.comm = None
self._force = False
self._updated = False # Whether the plot should be marked as updated
params = {k: v for k, v in params.items()
if k in self.params()}
super(DimensionedPlot, self).__init__(**params)
def __getitem__(self, frame):
"""
Get the state of the Plot for a given frame number.
"""
if isinstance(frame, int) and frame > len(self):
self.warning("Showing last frame available: %d" % len(self))
if not self.drawn: self.handles['fig'] = self.initialize_plot()
if not isinstance(frame, tuple):
frame = self.keys[frame]
self.update_frame(frame)
return self.state
def _get_frame(self, key):
"""
Required on each MPLPlot type to get the data corresponding
just to the current frame out from the object.
"""
pass
[docs] def matches(self, spec):
"""
Matches a specification against the current Plot.
"""
if callable(spec) and not isinstance(spec, type): return spec(self)
elif isinstance(spec, type): return isinstance(self, spec)
else:
raise ValueError("Matching specs have to be either a type or a callable.")
[docs] def traverse(self, fn=None, specs=None, full_breadth=True):
"""
Traverses any nested DimensionedPlot returning a list
of all plots that match the specs. The specs should
be supplied as a list of either Plot types or callables,
which should return a boolean given the plot class.
"""
accumulator = []
matches = specs is None
if not matches:
for spec in specs:
matches = self.matches(spec)
if matches: break
if matches:
accumulator.append(fn(self) if fn else self)
# Assumes composite objects are iterables
if hasattr(self, 'subplots') and self.subplots:
for el in self.subplots.values():
if el is None:
continue
accumulator += el.traverse(fn, specs, full_breadth)
if not full_breadth: break
return accumulator
def _frame_title(self, key, group_size=2, separator='\n'):
"""
Returns the formatted dimension group strings
for a particular frame.
"""
if self.layout_dimensions is not None:
dimensions, key = zip(*self.layout_dimensions.items())
elif not self.dynamic and (not self.uniform or len(self) == 1) or self.subplot:
return ''
else:
key = key if isinstance(key, tuple) else (key,)
dimensions = self.dimensions
dimension_labels = [dim.pprint_value_string(k) for dim, k in
zip(dimensions, key)]
groups = [', '.join(dimension_labels[i*group_size:(i+1)*group_size])
for i in range(len(dimension_labels))]
return util.bytes_to_unicode(separator.join(g for g in groups if g))
def _fontsize(self, key, label='fontsize', common=True):
if not self.fontsize: return {}
if not isinstance(self.fontsize, dict):
return {label:self.fontsize} if common else {}
unknown_keys = set(self.fontsize.keys()) - set(self._fontsize_keys)
if unknown_keys:
msg = "Popping unknown keys %r from fontsize dictionary.\nValid keys: %r"
self.warning(msg % (list(unknown_keys), self._fontsize_keys))
for key in unknown_keys: self.fontsize.pop(key, None)
if key in self.fontsize:
return {label:self.fontsize[key]}
elif key in ['ylabel', 'xlabel'] and 'labels' in self.fontsize:
return {label:self.fontsize['labels']}
elif key in ['xticks', 'yticks'] and 'ticks' in self.fontsize:
return {label:self.fontsize['ticks']}
else:
return {}
[docs] def compute_ranges(self, obj, key, ranges):
"""
Given an object, a specific key and the normalization options
this method will find the specified normalization options on
the appropriate OptionTree, group the elements according to
the selected normalization option (i.e. either per frame or
over the whole animation) and finally compute the dimension
ranges in each group. The new set of ranges is returned.
"""
all_table = all(isinstance(el, Table) for el in obj.traverse(lambda x: x, [Element]))
if obj is None or not self.normalize or all_table:
return OrderedDict()
# Get inherited ranges
ranges = self.ranges if ranges is None else dict(ranges)
# Get element identifiers from current object and resolve
# with selected normalization options
norm_opts = self._get_norm_opts(obj)
# Traverse displayed object if normalization applies
# at this level, and ranges for the group have not
# been supplied from a composite plot
return_fn = lambda x: x if isinstance(x, Element) else None
for group, (axiswise, framewise) in norm_opts.items():
elements = []
# Skip if ranges are cached or already computed by a
# higher-level container object.
framewise = framewise or self.dynamic or len(elements) == 1
if group in ranges and (not framewise or ranges is not self.ranges):
continue
elif not framewise: # Traverse to get all elements
elements = obj.traverse(return_fn, [group])
elif key is not None: # Traverse to get elements for each frame
frame = self._get_frame(key)
elements = [] if frame is None else frame.traverse(return_fn, [group])
# Only compute ranges if not axiswise on a composite plot
# or not framewise on a Overlay or ElementPlot
if (not (axiswise and not isinstance(obj, HoloMap)) or
(not framewise and isinstance(obj, HoloMap))):
self._compute_group_range(group, elements, ranges)
self.ranges.update(ranges)
return ranges
def _get_norm_opts(self, obj):
"""
Gets the normalization options for a LabelledData object by
traversing the object for to find elements and their ids.
The id is then used to select the appropriate OptionsTree,
accumulating the normalization options into a dictionary.
Returns a dictionary of normalization options for each
element in the tree.
"""
norm_opts = {}
# Get all elements' type.group.label specs and ids
type_val_fn = lambda x: (x.id, (type(x).__name__, util.group_sanitizer(x.group, escape=False),
util.label_sanitizer(x.label, escape=False))) \
if isinstance(x, Element) else None
element_specs = {(idspec[0], idspec[1]) for idspec in obj.traverse(type_val_fn)
if idspec is not None}
# Group elements specs by ID and override normalization
# options sequentially
key_fn = lambda x: -1 if x[0] is None else x[0]
id_groups = groupby(sorted(element_specs, key=key_fn), key_fn)
for gid, element_spec_group in id_groups:
gid = None if gid == -1 else gid
group_specs = [el for _, el in element_spec_group]
backend = self.renderer.backend
optstree = Store.custom_options(
backend=backend).get(gid, Store.options(backend=backend))
# Get the normalization options for the current id
# and match against customizable elements
for opts in optstree:
path = tuple(opts.path.split('.')[1:])
applies = any(path == spec[:i] for spec in group_specs
for i in range(1, 4))
if applies and 'norm' in opts.groups:
nopts = opts['norm'].options
if 'axiswise' in nopts or 'framewise' in nopts:
norm_opts.update({path: (nopts.get('axiswise', False),
nopts.get('framewise', False))})
element_specs = [spec for _, spec in element_specs]
norm_opts.update({spec: (False, False) for spec in element_specs
if not any(spec[:i] in norm_opts.keys() for i in range(1, 4))})
return norm_opts
@staticmethod
def _compute_group_range(group, elements, ranges):
# Iterate over all elements in a normalization group
# and accumulate their ranges into the supplied dictionary.
elements = [el for el in elements if el is not None]
group_ranges = OrderedDict()
for el in elements:
if isinstance(el, (Empty, Table)): continue
for dim in el.dimensions('ranges', label=True):
dim_range = el.range(dim)
if dim not in group_ranges:
group_ranges[dim] = []
group_ranges[dim].append(dim_range)
ranges[group] = OrderedDict((k, util.max_range(v)) for k, v in group_ranges.items())
@classmethod
def _traverse_options(cls, obj, opt_type, opts, specs=None, keyfn=None, defaults=True):
"""
Traverses the supplied object getting all options in opts for
the specified opt_type and specs. Also takes into account the
plotting class defaults for plot options. If a keyfn is
supplied the returned options will be grouped by the returned
keys.
"""
def lookup(x):
"""
Looks up options for object, including plot defaults,
keyfn determines returned key otherwise None key is used.
"""
options = cls.lookup_options(x, opt_type)
selected = {o: options.options[o]
for o in opts if o in options.options}
if opt_type == 'plot' and defaults:
plot = Store.registry[cls.backend].get(type(x))
selected['defaults'] = {o: getattr(plot, o) for o in opts
if o not in selected and hasattr(plot, o)}
key = keyfn(x) if keyfn else None
return (key, selected)
# Traverse object and accumulate options by key
traversed = obj.traverse(lookup, specs)
options = defaultdict(lambda: defaultdict(list))
default_opts = defaultdict(lambda: defaultdict(list))
for key, opts in traversed:
defaults = opts.pop('defaults', {})
for opt, v in opts.items():
options[key][opt].append(v)
for opt, v in defaults.items():
default_opts[key][opt].append(v)
# Merge defaults into dictionary if not explicitly specified
for key, opts in default_opts.items():
for opt, v in opts.items():
if opt not in options[key]:
options[key][opt] = v
return options if keyfn else options[None]
def _get_projection(cls, obj):
"""
Uses traversal to find the appropriate projection
for a nested object. Respects projections set on
Overlays before considering Element based settings,
before finally looking up the default projection on
the plot type. If more than one non-None projection
type is found an exception is raised.
"""
isoverlay = lambda x: isinstance(x, CompositeOverlay)
opts = cls._traverse_options(obj, 'plot', ['projection'],
[CompositeOverlay, Element],
keyfn=isoverlay)
from_overlay = not all(p is None for p in opts[True]['projection'])
projections = opts[from_overlay]['projection']
custom_projs = [p for p in projections if p is not None]
if len(set(custom_projs)) > 1:
raise Exception("An axis may only be assigned one projection type")
return custom_projs[0] if custom_projs else None
def update(self, key):
if len(self) == 1 and key == 0 and not self.drawn:
return self.initialize_plot()
item = self.__getitem__(key)
self.traverse(lambda x: setattr(x, '_updated', True))
return item
[docs] def refresh(self, **kwargs):
"""
Refreshes the plot by rerendering it and then pushing
the updated data if the plot has an associated Comm.
"""
traverse_setter(self, '_force', True)
key = self.current_key if self.current_key else self.keys[0]
dim_streams = [stream for stream in self.streams
if any(c in self.dimensions for c in stream.contents)]
stream_params = stream_parameters(dim_streams)
key = tuple(None if d in stream_params else k
for d, k in zip(self.dimensions, key))
stream_key = util.wrap_tuple_streams(key, self.dimensions, self.streams)
# Update if not top-level, batched or an ElementPlot
if not self.top_level or isinstance(self, GenericElementPlot):
self.update(stream_key)
if self.comm is not None and self.top_level:
self.push()
[docs] def push(self):
"""
Pushes updated plot data via the Comm.
"""
if self.comm is None:
raise Exception('Renderer does not have a comm.')
diff = self.renderer.diff(self)
self.comm.send(diff)
[docs] def init_comm(self):
"""
Initializes comm and attaches streams.
"""
comm = None
if self.dynamic or self.renderer.widget_mode == 'live':
comm = self.renderer.comms[self.renderer.mode][0](self)
return comm
def __len__(self):
"""
Returns the total number of available frames.
"""
return len(self.keys)
[docs]class GenericElementPlot(DimensionedPlot):
"""
Plotting baseclass to render contents of an Element. Implements
methods to get the correct frame given a HoloMap, axis labels and
extents and titles.
"""
apply_ranges = param.Boolean(default=True, doc="""
Whether to compute the plot bounds from the data itself.""")
apply_extents = param.Boolean(default=True, doc="""
Whether to apply extent overrides on the Elements""")
# A dictionary mapping of the plot methods used to draw the
# glyphs corresponding to the ElementPlot, can support two
# keyword arguments a 'single' implementation to draw an individual
# plot and a 'batched' method to draw multiple Elements at once
_plot_methods = {}
# Declares the options that are propagated from sub-elements of the
# plot, mostly useful for inheriting options from individual
# Elements on an OverlayPlot. Enabled by default in v1.7.
_propagate_options = []
v17_option_propagation = True
def __init__(self, element, keys=None, ranges=None, dimensions=None,
batched=False, overlaid=0, cyclic_index=0, zorder=0, style=None,
overlay_dims={}, stream_sources=[], **params):
self.zorder = zorder
self.cyclic_index = cyclic_index
self.overlaid = overlaid
self.batched = batched
self.overlay_dims = overlay_dims
if not isinstance(element, (HoloMap, DynamicMap)):
self.hmap = HoloMap(initial_items=(0, element),
kdims=['Frame'], id=element.id)
else:
self.hmap = element
if overlaid:
self.stream_sources = stream_sources
else:
self.stream_sources = compute_overlayable_zorders(self.hmap)
plot_element = self.hmap.last
if self.batched and not isinstance(self, GenericOverlayPlot):
plot_element = plot_element.last
dynamic = isinstance(element, DynamicMap) and not element.unbounded
self.top_level = keys is None
if self.top_level:
dimensions = self.hmap.kdims
values = [d.values for d in dimensions]
if dynamic and values and all(values):
keys = list(zip(*cartesian_product(values)))
else:
keys = list(self.hmap.data.keys())
self.style = self.lookup_options(plot_element, 'style') if style is None else style
plot_opts = self.lookup_options(plot_element, 'plot').options
if self.v17_option_propagation:
inherited = self._traverse_options(plot_element, 'plot',
self._propagate_options,
defaults=False)
plot_opts.update(**{k: v[0] for k, v in inherited.items()})
super(GenericElementPlot, self).__init__(keys=keys, dimensions=dimensions,
dynamic=dynamic,
**dict(params, **plot_opts))
streams = []
if isinstance(self.hmap, DynamicMap):
streams = get_nested_streams(self.hmap)
self.streams = streams
if self.top_level:
self.comm = self.init_comm()
self.traverse(lambda x: setattr(x, 'comm', self.comm))
# Attach streams if not overlaid and not a batched ElementPlot
if not (self.overlaid or (self.batched and not isinstance(self, GenericOverlayPlot))):
attach_streams(self, self.hmap)
# Update plot and style options for batched plots
if self.batched:
self.ordering = util.layer_sort(self.hmap)
overlay_opts = self.lookup_options(self.hmap.last, 'plot').options.items()
opts = {k: v for k, v in overlay_opts if k in self.params()}
self.set_param(**opts)
self.style = self.lookup_options(plot_element, 'style').max_cycles(len(self.ordering))
else:
self.ordering = []
[docs] def get_zorder(self, overlay, key, el):
"""
Computes the z-order of element in the NdOverlay
taking into account possible batching of elements.
"""
spec = util.get_overlay_spec(overlay, key, el)
return self.ordering.index(spec)
def _updated_zorders(self, overlay):
specs = [util.get_overlay_spec(overlay, key, el)
for key, el in overlay.data.items()]
self.ordering = sorted(set(self.ordering+specs))
return [self.ordering.index(spec) for spec in specs]
def _get_frame(self, key):
if isinstance(self.hmap, DynamicMap) and self.overlaid and self.current_frame:
self.current_key = key
return self.current_frame
elif key == self.current_key and not self._force:
return self.current_frame
cached = self.current_key is None
key_map = dict(zip([d.name for d in self.dimensions], key))
frame = get_plot_frame(self.hmap, key_map, cached)
traverse_setter(self, '_force', False)
if not key in self.keys and self.dynamic:
self.keys.append(key)
self.current_frame = frame
self.current_key = key
return frame
[docs] def get_extents(self, view, ranges):
"""
Gets the extents for the axes from the current View. The globally
computed ranges can optionally override the extents.
"""
ndims = len(view.dimensions())
num = 6 if self.projection == '3d' else 4
if self.apply_ranges:
if ranges:
dims = view.dimensions()
x0, x1 = ranges[dims[0].name]
if ndims > 1:
y0, y1 = ranges[dims[1].name]
else:
y0, y1 = (np.NaN, np.NaN)
if self.projection == '3d':
if len(dims) > 2:
z0, z1 = ranges[dims[2].name]
else:
z0, z1 = np.NaN, np.NaN
else:
x0, x1 = view.range(0)
y0, y1 = view.range(1) if ndims > 1 else (np.NaN, np.NaN)
if self.projection == '3d':
z0, z1 = view.range(2)
if self.projection == '3d':
range_extents = (x0, y0, z0, x1, y1, z1)
else:
range_extents = (x0, y0, x1, y1)
else:
range_extents = (np.NaN,) * num
if self.apply_extents:
norm_opts = self.lookup_options(view, 'norm').options
if norm_opts.get('framewise', False) or self.dynamic:
extents = view.extents
else:
extent_list = self.hmap.traverse(lambda x: x.extents, [Element])
extents = util.max_extents(extent_list, self.projection == '3d')
else:
extents = (np.NaN,) * num
if getattr(self, 'shared_axes', False) and self.subplot:
return util.max_extents([range_extents, extents], self.projection == '3d')
else:
max_extent = []
for l1, l2 in zip(range_extents, extents):
if (isinstance(l2, util.datetime_types)
or (l2 is not None and np.isfinite(l2))):
max_extent.append(l2)
else:
max_extent.append(l1)
return tuple(max_extent)
def _get_axis_labels(self, dimensions, xlabel=None, ylabel=None, zlabel=None):
if dimensions and xlabel is None:
xlabel = dim_axis_label(dimensions[0]) if dimensions[0] else ''
if len(dimensions) >= 2 and ylabel is None:
ylabel = dim_axis_label(dimensions[1]) if dimensions[1] else ''
if self.projection == '3d' and len(dimensions) >= 3 and zlabel is None:
zlabel = dim_axis_label(dimensions[2]) if dimensions[2] else ''
return xlabel, ylabel, zlabel
def _format_title(self, key, dimensions=True, separator='\n'):
frame = self._get_frame(key)
if frame is None: return None
type_name = type(frame).__name__
group = frame.group if frame.group != type_name else ''
label = frame.label
if self.layout_dimensions:
dim_title = self._frame_title(key, separator=separator)
title = dim_title
else:
if dimensions:
dim_title = self._frame_title(key, separator=separator)
else:
dim_title = ''
title_format = util.bytes_to_unicode(self.title_format)
title = title_format.format(label=util.bytes_to_unicode(label),
group=util.bytes_to_unicode(group),
type=type_name,
dimensions=dim_title)
return title.strip(' \n')
[docs] def update_frame(self, key, ranges=None):
"""
Set the plot(s) to the given frame number. Operates by
manipulating the matplotlib objects held in the self._handles
dictionary.
If n is greater than the number of available frames, update
using the last available frame.
"""
[docs]class GenericOverlayPlot(GenericElementPlot):
"""
Plotting baseclass to render (Nd)Overlay objects. It implements
methods to handle the creation of ElementPlots, coordinating style
groupings and zorder for all layers across a HoloMap. It also
allows collapsing of layers via the Compositor.
"""
batched = param.Boolean(default=True, doc="""
Whether to plot Elements NdOverlay in a batched plotting call
if possible. Disables legends and zorder may not be preserved.""")
legend_limit = param.Integer(default=25, doc="""
Number of rendered glyphs before legends are disabled.""")
show_legend = param.Boolean(default=True, doc="""
Whether to show legend for the plot.""")
style_grouping = param.Integer(default=2,
doc="""The length of the type.group.label
spec that will be used to group Elements into style groups, i.e.
a style_grouping value of 1 will group just by type, a value of 2
will group by type and group and a value of 3 will group by the
full specification.""")
_passed_handles = []
def __init__(self, overlay, ranges=None, batched=True, keys=None, **params):
super(GenericOverlayPlot, self).__init__(overlay, ranges=ranges, keys=keys,
batched=batched, **params)
# Apply data collapse
self.hmap = self._apply_compositor(self.hmap, ranges, self.keys)
self.subplots = self._create_subplots(ranges)
self.traverse(lambda x: setattr(x, 'comm', self.comm))
self.top_level = keys is None
if self.top_level:
self.comm = self.init_comm()
self.traverse(lambda x: setattr(x, 'comm', self.comm))
self.traverse(lambda x: attach_streams(self, x.hmap, 2),
[GenericElementPlot])
def _apply_compositor(self, holomap, ranges=None, keys=None, dimensions=None):
"""
Given a HoloMap compute the appropriate (mapwise or framewise)
ranges in order to apply the Compositor collapse operations in
display mode (data collapse should already have happened).
"""
# Compute framewise normalization
defaultdim = holomap.ndims == 1 and holomap.kdims[0].name != 'Frame'
if keys and ranges and dimensions and not defaultdim:
dim_inds = [dimensions.index(d) for d in holomap.kdims]
sliced_keys = [tuple(k[i] for i in dim_inds) for k in keys]
frame_ranges = OrderedDict([(slckey, self.compute_ranges(holomap, key, ranges[key]))
for key, slckey in zip(keys, sliced_keys) if slckey in holomap.data.keys()])
else:
mapwise_ranges = self.compute_ranges(holomap, None, None)
frame_ranges = OrderedDict([(key, self.compute_ranges(holomap, key, mapwise_ranges))
for key in holomap.data.keys()])
ranges = frame_ranges.values()
return Compositor.collapse(holomap, (ranges, frame_ranges.keys()), mode='display')
def _create_subplots(self, ranges):
# Check if plot should be batched
ordering = util.layer_sort(self.hmap)
registry = Store.registry[self.renderer.backend]
batched = self.batched and type(self.hmap.last) is NdOverlay
stream_sources = self.stream_sources
if batched:
batchedplot = registry.get(type(self.hmap.last.last))
if (batched and batchedplot and 'batched' in batchedplot._plot_methods and
(not self.show_legend or len(ordering) > self.legend_limit)):
self.batched = True
keys, vmaps = [()], [self.hmap]
param_vals = dict(self.get_param_values())
propagate = {opt: param_vals[opt] for opt in self._propagate_options
if opt in param_vals}
else:
self.batched = False
keys, vmaps = self.hmap.split_overlays()
# Compute global ordering
length = self.style_grouping
group_fn = lambda x: (x.type.__name__, x.last.group, x.last.label)
map_lengths = Counter()
for m in vmaps:
map_lengths[group_fn(m)[:length]] += 1
zoffset = 0
overlay_type = 1 if self.hmap.type == Overlay else 2
group_counter = Counter()
subplots = OrderedDict()
for (key, vmap) in zip(keys, vmaps):
opts = {'overlaid': overlay_type}
if self.hmap.type == Overlay:
style_key = (vmap.type.__name__,) + key
else:
if not isinstance(key, tuple): key = (key,)
style_key = group_fn(vmap) + key
opts['overlay_dims'] = OrderedDict(zip(self.hmap.last.kdims, key))
if self.batched:
vtype = type(vmap.last.last)
oidx = 0
else:
vtype = type(vmap.last)
oidx = ordering.index(style_key)
plottype = registry.get(vtype, None)
if plottype is None:
self.warning("No plotting class for %s type and %s backend "
"found. " % (vtype.__name__, self.renderer.backend))
continue
# Get zorder and style counter
group_key = style_key[:length]
zorder = self.zorder + oidx + zoffset
cyclic_index = group_counter[group_key]
group_counter[group_key] += 1
group_length = map_lengths[group_key]
if issubclass(plottype, GenericOverlayPlot):
opts['show_legend'] = self.show_legend
if not any(len(frame) for frame in vmap):
self.warning('%s is empty and will be skipped during plotting'
% vmap.last)
continue
elif self.batched and 'batched' in plottype._plot_methods:
opts['batched'] = self.batched
opts['overlaid'] = self.overlaid
opts.update(propagate)
if len(ordering) > self.legend_limit:
opts['show_legend'] = False
style = self.lookup_options(vmap.last, 'style').max_cycles(group_length)
passed_handles = {k: v for k, v in self.handles.items()
if k in self._passed_handles}
plotopts = dict(opts, cyclic_index=cyclic_index,
invert_axes=self.invert_axes,
dimensions=self.dimensions, keys=self.keys,
layout_dimensions=self.layout_dimensions,
ranges=ranges, show_title=self.show_title,
style=style, uniform=self.uniform,
fontsize=self.fontsize,
renderer=self.renderer, stream_sources=stream_sources,
zorder=zorder, adjoined=self.adjoined, **passed_handles)
if not isinstance(key, tuple): key = (key,)
subplots[key] = plottype(vmap, **plotopts)
if (not isinstance(plottype, PlotSelector) and
issubclass(plottype, GenericOverlayPlot)):
zoffset += len(set([k for o in vmap for k in o.keys()])) - 1
if not subplots:
raise SkipRendering("%s backend could not plot any Elements "
"in the Overlay." % self.renderer.backend)
return subplots
def get_extents(self, overlay, ranges):
extents = []
items = overlay.items()
if self.batched and self.subplots:
subplot = list(self.subplots.values())[0]
subplots = [(k, subplot) for k in overlay.data.keys()]
else:
subplots = self.subplots.items()
for key, subplot in subplots:
found = False
layer = overlay.data.get(key, None)
if isinstance(self.hmap, DynamicMap) and layer is None:
for _, layer in items:
if isinstance(layer, subplot.hmap.type):
found = True
break
if not found:
layer = None
if layer and subplot.apply_ranges:
if isinstance(layer, CompositeOverlay):
sp_ranges = ranges
else:
sp_ranges = util.match_spec(layer, ranges) if ranges else {}
extents.append(subplot.get_extents(layer, sp_ranges))
return util.max_extents(extents, self.projection == '3d')
class GenericCompositePlot(DimensionedPlot):
def __init__(self, layout, keys=None, dimensions=None, **params):
if 'uniform' not in params:
params['uniform'] = traversal.uniform(layout)
self.top_level = keys is None
if self.top_level:
dimensions, keys = traversal.unique_dimkeys(layout)
dynamic, unbounded = get_dynamic_mode(layout)
if unbounded:
initialize_unbounded(layout, dimensions, keys[0])
self.layout = layout
super(GenericCompositePlot, self).__init__(keys=keys,
dynamic=dynamic,
dimensions=dimensions,
**params)
nested_streams = layout.traverse(lambda x: get_nested_streams(x),
[DynamicMap])
self.streams = list(set([s for streams in nested_streams for s in streams]))
def _get_frame(self, key):
"""
Creates a clone of the Layout with the nth-frame for each
Element.
"""
cached = self.current_key is None
layout_frame = self.layout.clone(shared_data=False)
if key == self.current_key and not self._force:
return self.current_frame
else:
self.current_key = key
key_map = dict(zip([d.name for d in self.dimensions], key))
for path, item in self.layout.items():
frame = item.map(lambda x: get_plot_frame(x, key_map, cached=cached),
['DynamicMap', 'HoloMap'])
if frame is not None:
layout_frame[path] = frame
traverse_setter(self, '_force', False)
self.current_frame = layout_frame
return layout_frame
def _format_title(self, key, dimensions=True, separator='\n'):
dim_title = self._frame_title(key, 3, separator) if dimensions else ''
layout = self.layout
type_name = type(self.layout).__name__
group = util.bytes_to_unicode(layout.group if layout.group != type_name else '')
label = util.bytes_to_unicode(layout.label)
title = util.bytes_to_unicode(self.title_format).format(label=label,
group=group,
type=type_name,
dimensions=dim_title)
return title.strip(' \n')
[docs]class GenericLayoutPlot(GenericCompositePlot):
"""
A GenericLayoutPlot accepts either a Layout or a NdLayout and
displays the elements in a cartesian grid in scanline order.
"""
transpose = param.Boolean(default=False, doc="""
Whether to transpose the layout when plotting. Switches
from row-based left-to-right and top-to-bottom scanline order
to column-based top-to-bottom and left-to-right order.""")
def __init__(self, layout, **params):
if not isinstance(layout, (NdLayout, Layout)):
raise ValueError("GenericLayoutPlot only accepts Layout objects.")
if len(layout.values()) == 0:
raise SkipRendering(warn=False)
super(GenericLayoutPlot, self).__init__(layout, **params)
self.subplots = {}
self.rows, self.cols = layout.shape[::-1] if self.transpose else layout.shape
self.coords = list(product(range(self.rows),
range(self.cols)))
