Source code for holoviews.plotting.plot

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.
import uuid
import warnings

from collections import Counter, defaultdict
from functools import partial
from itertools import groupby, product

import numpy as np
import param

from panel.config import config
from import push
from import state
    from import unlocked
except Exception:
    from import unlocked
from pyviz_comms import JupyterComm

from ..selection import NoOpSelectionDisplay
from ..core import OrderedDict
from ..core import util, traversal
from import Dataset, disable_pipeline
from ..core.element import Element, Element3D
from ..core.overlay import Overlay, CompositeOverlay
from ..core.layout import Empty, NdLayout, Layout
from ..core.options import Store, Compositor, SkipRendering, lookup_options
from ..core.overlay import NdOverlay
from ..core.spaces import HoloMap, DynamicMap
from ..core.util import LooseVersion, stream_parameters, isfinite
from ..element import Table, Graph
from ..streams import Stream, RangeXY, RangeX, RangeY
from ..util.transform import dim
from .util import (
    get_dynamic_mode, initialize_unbounded, dim_axis_label,
    attach_streams, traverse_setter, get_nested_streams,
    compute_overlayable_zorders, get_nested_plot_frame,
    split_dmap_overlay, get_axis_padding, get_range, get_minimum_span,
    get_plot_frame, scale_fontsize, dynamic_update

[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. """ backend = None # 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 = [] def __init__(self, renderer=None, root=None, **params): params = {k: v for k, v in params.items() if k in self.param} super().__init__(**params) self.renderer = renderer if renderer else Store.renderers[self.backend].instance() self._force = False self._comm = None self._document = None self._root = None self._pane = None self._triggering = [] self._trigger = [] self.set_root(root) @property def state(self): """ The plotting state that gets updated via the update method and used by the renderer to generate output. """ raise NotImplementedError
[docs] def set_root(self, root): """ Sets the root model on all subplots. """ if root is None: return for plot in self.traverse(lambda x: x): plot._root = root
@property def root(self): if self._root: return self._root elif 'plot' in self.handles and self.top_level: return self.state else: return None @property def document(self): return self._document @document.setter def document(self, doc): if (doc and hasattr(doc, 'on_session_destroyed') and self.root is self.handles.get('plot') and not isinstance(self, GenericAdjointLayoutPlot)): doc.on_session_destroyed(self._session_destroy) from .bokeh.util import bokeh_version if self._document and bokeh_version >= LooseVersion('2.4.0'): if isinstance(self._document.callbacks._session_destroyed_callbacks, set): self._document.callbacks._session_destroyed_callbacks.discard(self._session_destroy) else: self._document.callbacks._session_destroyed_callbacks.pop(self._session_destroy, None) elif self._document: if isinstance(self._document._session_destroyed_callbacks, set): self._document._session_destroyed_callbacks.discard(self._session_destroy) else: self._document._session_destroyed_callbacks.pop(self._session_destroy, None) self._document = doc if self.subplots: for plot in self.subplots.values(): if plot is not None: plot.document = doc @property def pane(self): return self._pane @pane.setter def pane(self, pane): if (config.console_output != 'disable' and self.root and self.root.ref['id'] not in state._handles and isinstance(self.comm, JupyterComm)): from IPython.display import display handle = display(display_id=uuid.uuid4().hex) state._handles[self.root.ref['id']] = (handle, []) self._pane = pane if self.subplots: for plot in self.subplots.values(): if plot is not None: plot.pane = pane if not plot.root: continue for cb in getattr(plot, 'callbacks', []): if hasattr(pane, '_on_error') and getattr(cb, 'comm', None): cb.comm._on_error = partial(pane._on_error, plot.root.ref['id']) elif self.root: for cb in getattr(self, 'callbacks', []): if hasattr(pane, '_on_error') and getattr(cb, 'comm', None): cb.comm._on_error = partial(pane._on_error, self.root.ref['id']) @property def comm(self): return self._comm @comm.setter def comm(self, comm): self._comm = comm if self.subplots: for plot in self.subplots.values(): if plot is not None: plot.comm = comm
[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
[docs] def cleanup(self): """ Cleans up references to the plot on the attached Stream subscribers. """ plots = self.traverse(lambda x: x, [Plot]) for plot in plots: if not isinstance(plot, (GenericCompositePlot, GenericElementPlot, GenericOverlayPlot)): continue for stream in set(plot.streams): stream._subscribers = [ (p, subscriber) for p, subscriber in stream._subscribers if not util.is_param_method(subscriber) or util.get_method_owner(subscriber) not in plots ]
def _session_destroy(self, session_context): self.cleanup()
[docs] def refresh(self, **kwargs): """ Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm. """ if self.renderer.mode == 'server' and not state._unblocked(self.document): # If we do not have the Document lock, schedule refresh as callback self._triggering += [s for p in self.traverse(lambda x: x, [Plot]) for s in getattr(p, 'streams', []) if s._triggering] if self.document and self.document.session_context: self.document.add_next_tick_callback(self.refresh) return # Ensure that server based tick callbacks maintain stream triggering state for s in self._triggering: s._triggering = True try: 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) self._trigger_refresh(stream_key) if self.top_level: self.push() except Exception as e: raise e finally: # Reset triggering state for s in self._triggering: s._triggering = False self._triggering = []
def _trigger_refresh(self, key): "Triggers update to a plot on a refresh event" # Update if not top-level, batched or an ElementPlot if not self.top_level or isinstance(self, GenericElementPlot): with unlocked(): self.update(key)
[docs] def push(self): """ Pushes plot updates to the frontend. """ root = self._root if (root and self.pane is not None and root.ref['id'] in self.pane._plots): child_pane = self.pane._plots[root.ref['id']][1] else: child_pane = None if self.renderer.backend != 'bokeh' and child_pane is not None: child_pane.object = self.renderer.get_plot_state(self) elif (self.renderer.mode != 'server' and root and 'embedded' not in root.tags and self.document and self.comm): push(self.document, self.comm)
@property def id(self): return if self.comm else id(self.state) def __len__(self): """ Returns the total number of available frames. """ raise NotImplementedError @classmethod def lookup_options(cls, obj, group): return lookup_options(obj, group, cls.backend)
[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 selection_display(self, obj): plt_class = self.get_plot_class(obj) return getattr(plt_class, 'selection_display', None) def _define_interface(self, plots, allow_mismatch): parameters = [{k:v.precedence for k,v in plot.param.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.") plot_params = {p: v for params in parameters for p, v in params.items()} return [s for style in styles for s in style], plot_params def __call__(self, obj, **kwargs): plot_class = self.get_plot_class(obj) return plot_class(obj, **kwargs) def get_plot_class(self, obj): 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] def __setattr__(self, label, value): try: return super().__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 @property def param(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 font sizes of the displayed text. Finer control is available by supplying a dictionary where any unmentioned keys revert to the default sizes, e.g: {'ticks':20, 'title':15, 'ylabel':5, 'xlabel':5, 'zlabel':5, 'legend':8, 'legend_title':13} You can set the font size of 'zlabel', 'ylabel' and 'xlabel' together using the 'labels' key.""") fontscale = param.Number(default=None, doc=""" Scales the size of all fonts.""") #Allowed fontsize keys _fontsize_keys = ['xlabel','ylabel', 'zlabel', 'clabel', 'labels', 'xticks', 'yticks', 'zticks', 'cticks', 'ticks', 'minor_xticks', 'minor_yticks', 'minor_ticks', 'title', 'legend', 'legend_title', ] show_title = param.Boolean(default=True, doc=""" Whether to display the plot title.""") title = 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, **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 if keys is None else list(keys) self.uniform = uniform self.dynamic = dynamic self.drawn = False self.handles = {} = None self.label = None self.current_frame = None self.current_key = None self.ranges = {} self._updated = False # Whether the plot should be marked as updated super().__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.param.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 _format_title(self, key, dimensions=True, separator='\n'): label, group, type_name, dim_title = self._format_title_components( key, dimensions=True, separator='\n' ) title = util.bytes_to_unicode(self.title).format( label=util.bytes_to_unicode(label), group=util.bytes_to_unicode(group), type=type_name, dimensions=dim_title ) return title.strip(' \n') def _format_title_components(self, key, dimensions=True, separator='\n'): """ Determine components of title as used by _format_title method. To be overridden in child classes. Return signature: (label, group, type_name, dim_title) """ return (self.label,, type(self).__name__, '') def _get_fontsize_defaults(self): """ Should returns default fontsize for the following keywords: * ticks * minor_ticks * label * title * legend * legend_title However may also provide more specific defaults for specific axis label or ticks, e.g. clabel or xticks. """ return {} def _fontsize(self, key, label='fontsize', common=True): if not self.fontsize and not self.fontscale: return {} elif not isinstance(self.fontsize, dict) and self.fontsize is not None and common: return {label: scale_fontsize(self.fontsize, self.fontscale)} fontsize = self.fontsize if isinstance(self.fontsize, dict) else {} unknown_keys = set(fontsize.keys()) - set(self._fontsize_keys) if unknown_keys: msg = "Popping unknown keys %r from fontsize dictionary.\nValid keys: %r" self.param.warning(msg % (list(unknown_keys), self._fontsize_keys)) for key in unknown_keys: fontsize.pop(key, None) defaults = self._get_fontsize_defaults() size = None if key in fontsize: size = fontsize[key] elif key in ['zlabel', 'ylabel', 'xlabel', 'clabel']: size = fontsize.get('labels', defaults.get(key, defaults.get('label'))) elif key in ['xticks', 'yticks', 'zticks', 'cticks']: size = fontsize.get('ticks', defaults.get(key, defaults.get('ticks'))) elif key in ['minor_xticks', 'minor_yticks']: size = fontsize.get('minor_ticks', defaults.get(key, defaults.get('minor_ticks'))) elif key in ('legend', 'legend_title', 'title'): size = defaults.get(key) if size is None: return {} return {label: scale_fontsize(size, self.fontscale)}
[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. """ prev_frame = getattr(self, 'prev_frame', None) 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 {k: dict(v) for k, v in ranges.items()} # 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, robust) in norm_opts.items(): axiswise = (not getattr(self, 'shared_axes', True)) or (axiswise) 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 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, framewise, axiswise, robust, self.top_level, prev_frame) self.ranges.update(ranges) return ranges
def _get_norm_opts(self, obj): """ Gets the normalization options for a LabelledData object by traversing the object 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' specs and ids type_val_fn = lambda x: (, (type(x).__name__, util.group_sanitizer(, 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 popts = opts['plot'].options if 'axiswise' in nopts or 'framewise' in nopts or 'clim_percentile' in popts: norm_opts.update({path: (nopts.get('axiswise', False), nopts.get('framewise', False), popts.get('clim_percentile', False))}) element_specs = [spec for _, spec in element_specs] norm_opts.update({spec: (False, 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 @classmethod def _merge_group_ranges(cls, ranges): hard_range = util.max_range(ranges['hard'], combined=False) soft_range = util.max_range(ranges['soft']) robust_range = util.max_range(ranges.get('robust', [])) data_range = util.max_range(ranges['data']) combined = util.dimension_range(data_range[0], data_range[1], hard_range, soft_range) dranges = {'data': data_range, 'hard': hard_range, 'soft': soft_range, 'combined': combined, 'robust': robust_range, 'values': ranges} if 'factors' in ranges: all_factors = ranges['factors'] factor_dtypes = {fs.dtype for fs in all_factors} if all_factors else [] dtype = list(factor_dtypes)[0] if len(factor_dtypes) == 1 else None expanded = [v for fctrs in all_factors for v in fctrs] if dtype is not None: try: # Try to keep the same dtype expanded = np.array(expanded, dtype=dtype) except Exception: pass dranges['factors'] = util.unique_array(expanded) return dranges @classmethod def _compute_group_range(cls, group, elements, ranges, framewise, axiswise, robust, top_level, prev_frame): # 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] data_ranges = {} robust_ranges = {} categorical_dims = [] for el in elements: for el_dim in el.dimensions('ranges'): if hasattr(el, 'interface'): if isinstance(el, Graph) and el_dim in el.nodes.dimensions(): dtype = el.nodes.interface.dtype(el.nodes, el_dim) else: dtype = el.interface.dtype(el, el_dim) elif hasattr(el, '__len__') and len(el): dtype = el.dimension_values(el_dim).dtype else: dtype = None if all(util.isfinite(r) for r in el_dim.range): data_range = (None, None) elif dtype is not None and dtype.kind in 'SU': data_range = ('', '') elif isinstance(el, Graph) and el_dim in el.kdims[:2]: data_range = el.nodes.range(2, dimension_range=False) elif el_dim.values: ds = Dataset(el_dim.values, el_dim) data_range = ds.range(el_dim, dimension_range=False) else: data_range = el.range(el_dim, dimension_range=False) data_ranges[(el, el_dim)] = data_range if dtype is not None and dtype.kind == 'uif' and robust: percentile = 2 if isinstance(robust, bool) else robust robust_ranges[(el, el_dim)] = ( dim(el_dim, np.nanpercentile, percentile).apply(el), dim(el_dim, np.nanpercentile, percentile).apply(el) ) if (any(isinstance(r, str) for r in data_range) or (el_dim.type is not None and issubclass(el_dim.type, str)) or (dtype is not None and dtype.kind in 'SU')): categorical_dims.append(el_dim) prev_ranges = ranges.get(group, {}) group_ranges = OrderedDict() for el in elements: if isinstance(el, (Empty, Table)): continue opts = cls.lookup_options(el, 'style') plot_opts = cls.lookup_options(el, 'plot') opt_kwargs = dict(opts.kwargs, **plot_opts.kwargs) if not opt_kwargs.get('apply_ranges', True): continue # Compute normalization for color dim transforms for k, v in opt_kwargs.items(): if not isinstance(v, dim) or ('color' not in k and k != 'magnitude'): continue if isinstance(v, dim) and v.applies(el): dim_name = repr(v) if dim_name in prev_ranges and not framewise: continue values = v.apply(el, all_values=True) factors = None if values.dtype.kind == 'M': drange = values.min(), values.max() elif util.isscalar(values): drange = values, values elif values.dtype.kind in 'US': factors = util.unique_array(values) elif len(values) == 0: drange = np.NaN, np.NaN else: try: with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered') drange = (np.nanmin(values), np.nanmax(values)) except: factors = util.unique_array(values) if dim_name not in group_ranges: group_ranges[dim_name] = { 'id': [], 'data': [], 'hard': [], 'soft': [] } if factors is not None: if 'factors' not in group_ranges[dim_name]: group_ranges[dim_name]['factors'] = [] group_ranges[dim_name]['factors'].append(factors) else: group_ranges[dim_name]['data'].append(drange) group_ranges[dim_name]['id'].append(id(el)) # Compute dimension normalization for el_dim in el.dimensions('ranges'): dim_name = if dim_name in prev_ranges and not framewise: continue data_range = data_ranges[(el, el_dim)] if dim_name not in group_ranges: group_ranges[dim_name] = { 'id': [], 'data': [], 'hard': [], 'soft': [], 'robust': [] } group_ranges[dim_name]['data'].append(data_range) group_ranges[dim_name]['hard'].append(el_dim.range) group_ranges[dim_name]['soft'].append(el_dim.soft_range) if (el, el_dim) in robust_ranges: group_ranges[dim_name]['robust'].append(robust_ranges[(el, el_dim)]) if el_dim in categorical_dims: if 'factors' not in group_ranges[dim_name]: group_ranges[dim_name]['factors'] = [] if el_dim.values not in ([], None): values = el_dim.values elif el_dim in el: if isinstance(el, Graph) and el_dim in el.kdims[:2]: # Graph start/end normalization should include all node indices values = el.nodes.dimension_values(2, expanded=False) else: values = el.dimension_values(el_dim, expanded=False) elif isinstance(el, Graph) and el_dim in el.nodes: values = el.nodes.dimension_values(el_dim, expanded=False) if (isinstance(values, np.ndarray) and values.dtype.kind == 'O' and all(isinstance(v, (np.ndarray)) for v in values)): values = np.concatenate(values) if len(values) else [] factors = util.unique_array(values) group_ranges[dim_name]['factors'].append(factors) group_ranges[dim_name]['id'].append(id(el)) # Avoid merging ranges with non-matching types group_dim_ranges = defaultdict(dict) for gdim, values in group_ranges.items(): matching = True for t, rs in values.items(): if t in ('factors', 'id'): continue matching &= ( len({'date' if isinstance(v, util.datetime_types) else 'number' for rng in rs for v in rng if util.isfinite(v)}) < 2 ) if matching: group_dim_ranges[gdim] = values # Merge ranges across elements dim_ranges = [] for gdim, values in group_dim_ranges.items(): dranges = cls._merge_group_ranges(values) dim_ranges.append((gdim, dranges)) # Merge local ranges into global range dictionary if prev_ranges and not (top_level or axiswise) and framewise and prev_frame is not None: # Partially update global ranges with local changes prev_ids = prev_frame.traverse(lambda o: id(o)) for d, dranges in dim_ranges: values = prev_ranges.get(d, {}).get('values', None) if values is None or 'id' not in values: for g, drange in dranges.items(): if d not in prev_ranges: prev_ranges[d] = {} prev_ranges[d][g] = drange continue ids = values.get('id') # Filter out ranges of updated elements and append new ranges merged = {} for g, drange in dranges['values'].items(): filtered = [r for i, r in zip(ids, values[g]) if i not in prev_ids] filtered += drange merged[g] = filtered prev_ranges[d] = cls._merge_group_ranges(merged) elif prev_ranges and not (framewise and (top_level or axiswise)): # Combine local with global range for d, dranges in dim_ranges: for g, drange in dranges.items(): prange = prev_ranges.get(d, {}).get(g, None) if prange is None: if d not in prev_ranges: prev_ranges[d] = {} prev_ranges[d][g] = drange elif g in ('factors', 'values'): prev_ranges[d][g] = drange else: prev_ranges[d][g] = util.max_range([prange, drange], combined=g=='hard') else: # Override global range ranges[group] = OrderedDict(dim_ranges) @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 = OrderedDict() default_opts = defaultdict(lambda: defaultdict(list)) for key, opts in traversed: defaults = opts.pop('defaults', {}) if key not in options: options[key] = {} for opt, v in opts.items(): if opt not in options[key]: options[key][opt] = [] 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) element3d = obj.traverse(lambda x: x, [Element3D]) if element3d: return '3d' opts = cls._traverse_options(obj, 'plot', ['projection'], [CompositeOverlay, Element], keyfn=isoverlay) from_overlay = not all(p is None for p in opts.get(True, {}).get('projection', [])) projections = opts.get(from_overlay, {}).get('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
[docs] def update(self, key): if len(self) == 1 and ((key == 0) or (key == self.keys[0])) and not self.drawn: return self.initialize_plot() item = self.__getitem__(key) self.traverse(lambda x: setattr(x, '_updated', True)) return item
def __len__(self): """ Returns the total number of available frames. """ return len(self.keys)
class CallbackPlot(object): backend = None def _construct_callbacks(self): """ Initializes any callbacks for streams which have defined the plotted object as a source. """ source_streams = [] cb_classes = set() registry = list(Stream.registry.items()) callbacks = Stream._callbacks[self.backend] for source in self.link_sources: streams = [ s for src, streams in registry for s in streams if src is source or (src._plot_id is not None and src._plot_id == source._plot_id)] cb_classes |= {(callbacks[type(stream)], stream) for stream in streams if type(stream) in callbacks and stream.linked and stream.source is not None} cbs = [] sorted_cbs = sorted(cb_classes, key=lambda x: id(x[0])) for cb, group in groupby(sorted_cbs, lambda x: x[0]): cb_streams = [s for _, s in group] for cb_stream in cb_streams: if cb_stream not in source_streams: source_streams.append(cb_stream) cbs.append(cb(self, cb_streams, source)) return cbs, source_streams @property def link_sources(self): "Returns potential Link or Stream sources." if isinstance(self, GenericOverlayPlot): zorders = [] elif self.batched: zorders = list(range(self.zorder, self.zorder+len(self.hmap.last))) else: zorders = [self.zorder] if isinstance(self, GenericOverlayPlot) and not self.batched: sources = [] elif not self.static or isinstance(self.hmap, DynamicMap): sources = [o for i, inputs in self.stream_sources.items() for o in inputs if i in zorders] else: sources = [self.hmap.last] return sources
[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""") bgcolor = param.ClassSelector(class_=(str, tuple), default=None, doc=""" If set bgcolor overrides the background color of the axis.""") default_span = param.ClassSelector(default=2.0, class_=(int, float, tuple), doc=""" Defines the span of an axis if the axis range is zero, i.e. if the lower and upper end of an axis are equal or no range is defined at all. For example if there is a single datapoint at 0 a default_span of 2.0 will result in axis ranges spanning from -1 to 1.""") hooks = param.HookList(default=[], doc=""" Optional list of hooks called when finalizing a plot. The hook is passed the plot object and the displayed element, and other plotting handles can be accessed via plot.handles.""") invert_axes = param.Boolean(default=False, doc=""" Whether to invert the x- and y-axis""") invert_xaxis = param.Boolean(default=False, doc=""" Whether to invert the plot x-axis.""") invert_yaxis = param.Boolean(default=False, doc=""" Whether to invert the plot y-axis.""") logx = param.Boolean(default=False, doc=""" Whether the x-axis of the plot will be a log axis.""") logy = param.Boolean(default=False, doc=""" Whether the y-axis of the plot will be a log axis.""") padding = param.ClassSelector(default=0.1, class_=(int, float, tuple), doc=""" Fraction by which to increase auto-ranged extents to make datapoints more visible around borders. To compute padding, the axis whose screen size is largest is chosen, and the range of that axis is increased by the specified fraction along each axis. Other axes are then padded ensuring that the amount of screen space devoted to padding is equal for all axes. If specified as a tuple, the int or float values in the tuple will be used for padding in each axis, in order (x,y or x,y,z). For example, for padding=0.2 on a 800x800-pixel plot, an x-axis with the range [0,10] will be padded by 20% to be [-1,11], while a y-axis with a range [0,1000] will be padded to be [-100,1100], which should make the padding be approximately the same number of pixels. But if the same plot is changed to have a height of only 200, the y-range will then be [-400,1400] so that the y-axis padding will still match that of the x-axis. It is also possible to declare non-equal padding value for the lower and upper bound of an axis by supplying nested tuples, e.g. padding=(0.1, (0, 0.1)) will pad the x-axis lower and upper bound as well as the y-axis upper bound by a fraction of 0.1 while the y-axis lower bound is not padded at all.""") show_legend = param.Boolean(default=True, doc=""" Whether to show legend for the plot.""") show_grid = param.Boolean(default=False, doc=""" Whether to show a Cartesian grid on the plot.""") xaxis = param.ObjectSelector(default='bottom', objects=['top', 'bottom', 'bare', 'top-bare', 'bottom-bare', None, True, False], doc=""" Whether and where to display the xaxis. The "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='left', objects=['left', 'right', 'bare', 'left-bare', 'right-bare', None, True, False], doc=""" Whether and where to display the yaxis. The "bare" options allow suppressing all axis labels, including ticks and ylabel. Valid options are 'left', 'right', 'bare', 'left-bare' and 'right-bare'.""") xlabel = param.String(default=None, doc=""" An explicit override of the x-axis label, if set takes precedence over the dimension label.""") ylabel = param.String(default=None, doc=""" An explicit override of the y-axis label, if set takes precedence over the dimension label.""") xlim = param.Tuple(default=(np.nan, np.nan), length=2, doc=""" User-specified x-axis range limits for the plot, as a tuple (low,high). If specified, takes precedence over data and dimension ranges.""") ylim = param.Tuple(default=(np.nan, np.nan), length=2, doc=""" User-specified x-axis range limits for the plot, as a tuple (low,high). If specified, takes precedence over data and dimension ranges.""") zlim = param.Tuple(default=(np.nan, np.nan), length=2, doc=""" User-specified z-axis range limits for the plot, as a tuple (low,high). If specified, takes precedence over data and dimension ranges.""") xrotation = param.Integer(default=None, bounds=(0, 360), doc=""" Rotation angle of the xticks.""") yrotation = param.Integer(default=None, bounds=(0, 360), doc=""" Rotation angle of the yticks.""") xticks = param.Parameter(default=None, doc=""" Ticks along x-axis specified as an integer, explicit list of tick locations, or bokeh Ticker object. If set to None default bokeh ticking behavior is applied.""") yticks = param.Parameter(default=None, doc=""" Ticks along y-axis specified as an integer, explicit list of tick locations, or bokeh Ticker object. If set to None default bokeh ticking behavior is applied.""") # 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 _deprecations = { 'color_index': ( "The `color_index` parameter is deprecated in favor of color " "style mapping, e.g. `color=dim('color')` or `line_color=dim('color')`" ), 'size_index': ( "The `size_index` parameter is deprecated in favor of size " "style mapping, e.g. `size=dim('size')**2`." ), 'scaling_method': ( "The `scaling_method` parameter is deprecated in favor of size " "style mapping, e.g. `size=dim('size')**2` for area scaling." ), 'scaling_factor': ( "The `scaling_factor` parameter is deprecated in favor of size " "style mapping, e.g. `size=dim('size')*10`." ), 'size_fn': ( "The `size_fn` parameter is deprecated in favor of size " "style mapping, e.g. `size=abs(dim('size'))`." ), } _selection_display = NoOpSelectionDisplay() 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={}, streams=None, **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'], 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 keys = list( = 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() if k not in plot_opts}) applied_params = dict(params, **plot_opts) for p, pval in applied_params.items(): if p in self.param and p in self._deprecations and pval is not None: self.param.warning(self._deprecations[p]) super().__init__(keys=keys, dimensions=dimensions, dynamic=dynamic, **applied_params) self.streams = get_nested_streams(self.hmap) if streams is None else streams # 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.param} self.param.set_param(**opts) = 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] 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 and not any(s._triggering for s in self.streams) key_map = dict(zip([ 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 len(key) == self.hmap.ndims and self.dynamic: self.keys.append(key) self.current_frame = frame self.current_key = key return frame def _execute_hooks(self, element): """ Executes finalize hooks """ for hook in self.hooks: try: hook(self, element) except Exception as e: self.param.warning("Plotting hook %r could not be " "applied:\n\n %s" % (hook, e))
[docs] def get_aspect(self, xspan, yspan): """ Should define the aspect ratio of the plot. """
[docs] def get_padding(self, obj, extents): """ Computes padding along the axes taking into account the plot aspect. """ (x0, y0, z0, x1, y1, z1) = extents padding_opt = self.lookup_options(obj, 'plot').kwargs.get('padding') if self.overlaid: padding = 0 elif padding_opt is None: if self.param.objects('existing')['padding'].default is not self.padding: padding = self.padding else: opts = self._traverse_options( obj, 'plot', ['padding'], specs=[Element], defaults=True ) padding = opts.get('padding') if padding: padding = padding[0] else: padding = self.padding else: padding = padding_opt xpad, ypad, zpad = get_axis_padding(padding) if not self.overlaid and not self.batched: xspan = x1-x0 if util.is_number(x0) and util.is_number(x1) else None yspan = y1-y0 if util.is_number(y0) and util.is_number(y1) else None aspect = self.get_aspect(xspan, yspan) if aspect > 1: xpad = tuple(xp/aspect for xp in xpad) if isinstance(xpad, tuple) else xpad/aspect else: ypad = tuple(yp*aspect for yp in ypad) if isinstance(ypad, tuple) else ypad*aspect return xpad, ypad, zpad
def _get_range_extents(self, element, ranges, range_type, xdim, ydim, zdim): dims = element.dimensions() ndims = len(dims) xdim = xdim or (dims[0] if ndims else None) ydim = ydim or (dims[1] if ndims > 1 else None) if isinstance(self.projection, str) and self.projection == '3d': zdim = zdim or (dims[2] if ndims > 2 else None) else: zdim = None (x0, x1), xsrange, xhrange = get_range(element, ranges, xdim) (y0, y1), ysrange, yhrange = get_range(element, ranges, ydim) (z0, z1), zsrange, zhrange = get_range(element, ranges, zdim) trigger = False if not self.overlaid and not self.batched: xspan, yspan, zspan = (v/2. for v in get_axis_padding(self.default_span)) mx0, mx1 = get_minimum_span(x0, x1, xspan) if x0 != mx0 or x1 != mx1: x0, x1 = mx0, mx1 trigger = True my0, my1 = get_minimum_span(y0, y1, yspan) if y0 != my0 or y1 != my1: y0, y1 = my0, my1 trigger = True mz0, mz1 = get_minimum_span(z0, z1, zspan) xpad, ypad, zpad = self.get_padding(element, (x0, y0, z0, x1, y1, z1)) if range_type == 'soft': x0, x1 = xsrange elif range_type == 'hard': x0, x1 = xhrange elif xdim == 'categorical': x0, x1 = '', '' elif range_type == 'combined': x0, x1 = util.dimension_range(x0, x1, xhrange, xsrange, xpad, self.logx) if range_type == 'soft': y0, y1 = ysrange elif range_type == 'hard': y0, y1 = yhrange elif range_type == 'combined': y0, y1 = util.dimension_range(y0, y1, yhrange, ysrange, ypad, self.logy) elif ydim == 'categorical': y0, y1 = '', '' elif ydim is None: y0, y1 = np.NaN, np.NaN if isinstance(self.projection, str) and self.projection == '3d': if range_type == 'soft': z0, z1 = zsrange elif range_type == 'data': z0, z1 = zhrange elif range_type=='combined': z0, z1 = util.dimension_range(z0, z1, zhrange, zsrange, zpad, self.logz) elif zdim == 'categorical': z0, z1 = '', '' elif zdim is None: z0, z1 = np.NaN, np.NaN return (x0, y0, z0, x1, y1, z1) if not self.drawn: for stream in getattr(self, 'source_streams', []): if (isinstance(stream, (RangeX, RangeY, RangeXY)) and trigger and stream not in self._trigger): self._trigger.append(stream) return (x0, y0, x1, y1)
[docs] def get_extents(self, element, ranges, range_type='combined', xdim=None, ydim=None, zdim=None): """ Gets the extents for the axes from the current Element. The globally computed ranges can optionally override the extents. The extents are computed by combining the data ranges, extents and dimension ranges. Each of these can be obtained individually by setting the range_type to one of: * 'data': Just the data ranges * 'extents': Element.extents * 'soft': Dimension.soft_range values * 'hard': Dimension.range values To obtain the combined range, which includes range padding the default may be used: * 'combined': All the range types combined and padding applied This allows Overlay plots to obtain each range and combine them appropriately for all the objects in the overlay. """ num = 6 if (isinstance(self.projection, str) and self.projection == '3d') else 4 if self.apply_extents and range_type in ('combined', 'extents'): norm_opts = self.lookup_options(element, 'norm').options if norm_opts.get('framewise', False) or self.dynamic: extents = element.extents else: extent_list = self.hmap.traverse(lambda x: x.extents, [Element]) extents = util.max_extents( extent_list, isinstance(self.projection, str) and self.projection == '3d' ) else: extents = (np.NaN,) * num if range_type == 'extents': return extents if self.apply_ranges: range_extents = self._get_range_extents(element, ranges, range_type, xdim, ydim, zdim) else: range_extents = (np.NaN,) * num if getattr(self, 'shared_axes', False) and self.subplot: combined = util.max_extents( [range_extents, extents], isinstance(self.projection, str) and self.projection == '3d' ) else: max_extent = [] for l1, l2 in zip(range_extents, extents): if isfinite(l2): max_extent.append(l2) else: max_extent.append(l1) combined = tuple(max_extent) if isinstance(self.projection, str) and self.projection == '3d': x0, y0, z0, x1, y1, z1 = combined else: x0, y0, x1, y1 = combined x0, x1 = util.dimension_range(x0, x1, self.xlim, (None, None)) y0, y1 = util.dimension_range(y0, y1, self.ylim, (None, None)) if not self.drawn: x_range, y_range = ((y0, y1), (x0, x1)) if self.invert_axes else ((x0, x1), (y0, y1)) for stream in getattr(self, 'source_streams', []): if isinstance(stream, RangeX): params = {'x_range': x_range} elif isinstance(stream, RangeY): params = {'y_range': y_range} elif isinstance(stream, RangeXY): params = {'x_range': x_range, 'y_range': y_range} else: continue stream.update(**params) if stream not in self._trigger and (self.xlim or self.ylim): self._trigger.append(stream) if isinstance(self.projection, str) and self.projection == '3d': z0, z1 = util.dimension_range(z0, z1, self.zlim, (None, None)) return (x0, y0, z0, x1, y1, z1) return (x0, y0, x1, y1)
def _get_axis_labels(self, dimensions, xlabel=None, ylabel=None, zlabel=None): if self.xlabel is not None: xlabel = self.xlabel elif dimensions and xlabel is None: xdims = dimensions[0] xlabel = dim_axis_label(xdims) if xdims else '' if self.ylabel is not None: ylabel = self.ylabel elif len(dimensions) >= 2 and ylabel is None: ydims = dimensions[1] ylabel = dim_axis_label(ydims) if ydims else '' if getattr(self, 'zlabel', None) is not None: zlabel = self.zlabel elif (isinstance(self.projection, str) and 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_components(self, key, dimensions=True, separator='\n'): frame = self._get_frame(key) if frame is None: return ('', '', '', '') type_name = type(frame).__name__ group = if != type_name else '' label = frame.label if self.layout_dimensions or dimensions: dim_title = self._frame_title(key, separator=separator) else: dim_title = '' return (label, group, type_name, dim_title)
[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 spec that will be used to group Elements into style groups. 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, group_counter=None, **params): if 'projection' not in params: params['projection'] = self._get_projection(overlay) super().__init__(overlay, ranges=ranges, keys=keys, batched=batched, **params) # Apply data collapse self.hmap = self._apply_compositor(self.hmap, ranges, self.keys) self.map_lengths = Counter() self.group_counter = Counter() if group_counter is None else group_counter self.cyclic_index_lookup = {} self.zoffset = 0 self.subplots = self._create_subplots(ranges) self.traverse(lambda x: setattr(x, 'comm', self.comm)) self.top_level = keys is None self.dynamic_subplots = [] if self.top_level: 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]) else: mapwise_ranges = self.compute_ranges(holomap, None, None) frame_ranges = OrderedDict([(key, self.compute_ranges(holomap, key, mapwise_ranges)) for key in]) ranges = frame_ranges.values() with disable_pipeline(): collapsed = Compositor.collapse(holomap, (ranges, frame_ranges.keys()), mode='display') return collapsed def _create_subplots(self, ranges): # Check if plot should be batched ordering = util.layer_sort(self.hmap) batched = self.batched and type(self.hmap.last) is NdOverlay if batched: backend = self.renderer.backend batchedplot = Store.registry[backend].get(self.hmap.last.type) 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] else: self.batched = False keys, vmaps = self.hmap._split_overlays() if isinstance(self.hmap, DynamicMap): dmap_streams = [get_nested_streams(layer) for layer in split_dmap_overlay(self.hmap)] else: dmap_streams = [None]*len(keys) # Compute global ordering length = self.style_grouping group_fn = lambda x: (x.type.__name__,, x.last.label) for m in vmaps: self.map_lengths[group_fn(m)[:length]] += 1 subplots = OrderedDict() for (key, vmap, streams) in zip(keys, vmaps, dmap_streams): subplot = self._create_subplot(key, vmap, streams, ranges) if subplot is None: continue if not isinstance(key, tuple): key = (key,) subplots[key] = subplot if isinstance(subplot, GenericOverlayPlot): self.zoffset += len(subplot.subplots.keys()) - 1 if not subplots: raise SkipRendering("%s backend could not plot any Elements " "in the Overlay." % self.renderer.backend) return subplots def _create_subplot(self, key, obj, streams, ranges): registry = Store.registry[self.renderer.backend] ordering = util.layer_sort(self.hmap) overlay_type = 1 if self.hmap.type == Overlay else 2 group_fn = lambda x: (x.type.__name__,, x.last.label) opts = {'overlaid': overlay_type} if self.hmap.type == Overlay: style_key = (obj.type.__name__,) + key if self.overlay_dims: opts['overlay_dims'] = self.overlay_dims else: if not isinstance(key, tuple): key = (key,) style_key = group_fn(obj) + key opts['overlay_dims'] = OrderedDict(zip(self.hmap.last.kdims, key)) if self.batched: vtype = type(obj.last.last) oidx = 0 else: vtype = type(obj.last) if style_key not in ordering: ordering.append(style_key) oidx = ordering.index(style_key) plottype = registry.get(vtype, None) if plottype is None: self.param.warning( "No plotting class for %s type and %s backend " "found. " % (vtype.__name__, self.renderer.backend)) return None # Get zorder and style counter length = self.style_grouping group_key = style_key[:length] zorder = self.zorder + oidx + self.zoffset cyclic_index = self.group_counter[group_key] self.cyclic_index_lookup[style_key] = cyclic_index self.group_counter[group_key] += 1 group_length = self.map_lengths[group_key] if not isinstance(plottype, PlotSelector) and issubclass(plottype, GenericOverlayPlot): opts['group_counter'] = self.group_counter opts['show_legend'] = self.show_legend if not any(len(frame) for frame in obj): self.param.warning('%s is empty and will be skipped ' 'during plotting' % obj.last) return None elif self.batched and 'batched' in plottype._plot_methods: param_vals = dict(self.param.get_param_values()) propagate = {opt: param_vals[opt] for opt in self._propagate_options if opt in param_vals} 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(obj.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, streams=streams, renderer=self.renderer, adjoined=self.adjoined, stream_sources=self.stream_sources, projection=self.projection, fontscale=self.fontscale, zorder=zorder, root=self.root, **passed_handles) return plottype(obj, **plotopts) def _match_subplot(self, key, subplot, items, element): found = False temp_items = list(items) while not found: idx, spec, exact = dynamic_update(self, subplot, key, element, temp_items) if idx is not None: if not exact: exact_matches = [ dynamic_update(self, subplot, k, element, temp_items) for k in self.subplots ] exact_matches = [m for m in exact_matches if m[-1]] if exact_matches: idx = exact_matches[0][0] _, el = temp_items.pop(idx) continue found = True if idx is not None: idx = items.index(temp_items.pop(idx)) return idx, spec, exact def _create_dynamic_subplots(self, key, items, ranges, **init_kwargs): """ Handles the creation of new subplots when a DynamicMap returns a changing set of elements in an Overlay. """ length = self.style_grouping group_fn = lambda x: (x.type.__name__,, x.last.label) for i, (k, obj) in enumerate(items): vmap = self.hmap.clone([(key, obj)]) self.map_lengths[group_fn(vmap)[:length]] += 1 subplot = self._create_subplot(k, vmap, [], ranges) if subplot is None: continue self.subplots[k] = subplot subplot.initialize_plot(ranges, **init_kwargs) subplot.update_frame(key, ranges, element=obj) self.dynamic_subplots.append(subplot) def _update_subplot(self, subplot, spec): """ Updates existing subplots when the subplot has been assigned to plot an element that is not an exact match to the object it was initially assigned. """ # See if the precise spec has already been assigned a cyclic # index otherwise generate a new one if spec in self.cyclic_index_lookup: cyclic_index = self.cyclic_index_lookup[spec] else: group_key = spec[:self.style_grouping] self.group_counter[group_key] += 1 cyclic_index = self.group_counter[group_key] self.cyclic_index_lookup[spec] = cyclic_index subplot.cyclic_index = cyclic_index if subplot.overlay_dims: odim_key = util.wrap_tuple(spec[-1]) new_dims = zip(subplot.overlay_dims, odim_key) subplot.overlay_dims = util.OrderedDict(new_dims) def _get_subplot_extents(self, overlay, ranges, range_type): """ Iterates over all subplots and collects the extents of each. """ if range_type == 'combined': extents = {'extents': [], 'soft': [], 'hard': [], 'data': []} else: extents = {range_type: []} items = overlay.items() if self.batched and self.subplots: subplot = list(self.subplots.values())[0] subplots = [(k, subplot) for k in] else: subplots = self.subplots.items() for key, subplot in subplots: found = False if subplot is None: continue layer =, 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 is None or not subplot.apply_ranges: continue if isinstance(layer, CompositeOverlay): sp_ranges = ranges else: sp_ranges = util.match_spec(layer, ranges) if ranges else {} for rt in extents: extent = subplot.get_extents(layer, sp_ranges, range_type=rt) extents[rt].append(extent) return extents
[docs] def get_extents(self, overlay, ranges, range_type='combined'): subplot_extents = self._get_subplot_extents(overlay, ranges, range_type) zrange = isinstance(self.projection, str) and self.projection == '3d' extents = {k: util.max_extents(rs, zrange) for k, rs in subplot_extents.items()} if range_type != 'combined': return extents[range_type] # Unpack extents if len(extents['data']) == 6: x0, y0, z0, x1, y1, z1 = extents['data'] sx0, sy0, sz0, sx1, sy1, sz1 = extents['soft'] hx0, hy0, hz0, hx1, hy1, hz1 = extents['hard'] else: x0, y0, x1, y1 = extents['data'] sx0, sy0, sx1, sy1 = extents['soft'] hx0, hy0, hx1, hy1 = extents['hard'] z0, z1 = np.NaN, np.NaN # Apply minimum span xspan, yspan, zspan = (v/2. for v in get_axis_padding(self.default_span)) x0, x1 = get_minimum_span(x0, x1, xspan) y0, y1 = get_minimum_span(y0, y1, yspan) z0, z1 = get_minimum_span(z0, z1, zspan) # Apply padding xpad, ypad, zpad = self.get_padding(overlay, (x0, y0, z0, x1, y1, z1)) x0, x1 = util.dimension_range(x0, x1, (hx0, hx1), (sx0, sx1), xpad, self.logx) y0, y1 = util.dimension_range(y0, y1, (hy0, hy1), (sy0, sy1), ypad, self.logy) if len(extents['data']) == 6: z0, z1 = util.dimension_range(z0, z1, (hz0, hz1), (sz0, sz1), zpad, self.logz) padded = (x0, y0, z0, x1, y1, z1) else: padded = (x0, y0, x1, y1) # Combine with Element.extents combined = util.max_extents([padded, extents['extents']], zrange) if isinstance(self.projection, str) and self.projection == '3d': x0, y0, z0, x1, y1, z1 = combined else: x0, y0, x1, y1 = combined # Apply xlim, ylim, zlim plot option x0, x1 = util.dimension_range(x0, x1, self.xlim, (None, None)) y0, y1 = util.dimension_range(y0, y1, self.ylim, (None, None)) if isinstance(self.projection, str) and self.projection == '3d': z0, z1 = util.dimension_range(z0, z1, getattr(self, 'zlim', (None, None)), (None, None)) return (x0, y0, z0, x1, y1, z1) return (x0, y0, x1, y1)
[docs]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().__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])) self._link_dimensioned_streams() def _link_dimensioned_streams(self): """ Should perform any linking required to update titles when dimensioned streams change. """ 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([ for d in self.dimensions], key)) for path, item in self.layout.items(): frame = get_nested_plot_frame(item, key_map, cached) 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_components(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( if != type_name else '') label = util.bytes_to_unicode(layout.label) return (label, group, type_name, dim_title)
[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().__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)))
[docs]class GenericAdjointLayoutPlot(Plot): """ AdjointLayoutPlot allows placing up to three Views in a number of predefined and fixed layouts, which are defined by the layout_dict class attribute. This allows placing subviews next to a main plot in either a 'top' or 'right' position. """ layout_dict = {'Single': {'positions': ['main']}, 'Dual': {'positions': ['main', 'right']}, 'Triple': {'positions': ['main', 'right', 'top']}}