""" The streams module defines the streams API that allows visualizations to generate and respond to events, originating either in Python on the server-side or in Javascript in the Jupyter notebook (client-side). """ import weakref from collections import defaultdict from contextlib import contextmanager from functools import partial from itertools import groupby from numbers import Number from types import FunctionType import numpy as np import pandas as pd import param from .core import util from .core.ndmapping import UniformNdMapping # Types supported by Pointer derived streams pointer_types = (Number, str, tuple, *util.datetime_types) POPUP_POSITIONS = [ "top_right", "top_left", "bottom_left", "bottom_right", "right", "left", "top", "bottom", ] class _SkipTrigger: pass @contextmanager def triggering_streams(streams): """ Temporarily declares the streams as being in a triggered state. Needed by DynamicMap to determine whether to memoize on a Callable, i.e. if a stream has memoization disabled and is in triggered state Callable should disable lookup in the memoization cache. This is done by the dynamicmap_memoization context manager. """ for stream in streams: stream._triggering = True try: yield finally: for stream in streams: stream._triggering = False def streams_list_from_dict(streams): "Converts a streams dictionary into a streams list" params = {} for k, v in streams.items(): v = param.parameterized.transform_reference(v) if isinstance(v, param.Parameter) and v.owner is not None: params[k] = v else: raise TypeError(f'Cannot handle value {v!r} in streams dictionary') return Params.from_params(params) class Stream(param.Parameterized): """ A Stream is simply a parameterized object with parameters that change over time in response to update events and may trigger downstream events on its subscribers. The Stream parameters can be updated using the update method, which will optionally trigger the stream. This will notify the subscribers which may be supplied as a list of callables or added later using the add_subscriber method. The subscribers will be passed a dictionary mapping of the parameters of the stream, which are available on the instance as the ``contents``. Depending on the plotting backend certain streams may interactively subscribe to events and changes by the plotting backend. For this purpose use the LinkedStream baseclass, which enables the linked option by default. A source for the linking may be supplied to the constructor in the form of another viewable object specifying which part of a plot the data should come from. The transient option allows treating stream events as discrete updates, resetting the parameters to their default after the stream has been triggered. A downstream callback can therefore determine whether a stream is active by checking whether the stream values match the default (usually None). The Stream class is meant for subclassing and subclasses should generally add one or more parameters but may also override the transform and reset method to preprocess parameters before they are passed to subscribers and reset them using custom logic respectively. """ # Mapping from a source to a list of streams # WeakKeyDictionary to allow garbage collection # of unreferenced sources registry = weakref.WeakKeyDictionary() # Mapping to define callbacks by backend and Stream type. # e.g. Stream._callbacks['bokeh'][Stream] = Callback _callbacks = defaultdict(dict) @classmethod def define(cls, name, **kwargs): """ Utility to quickly and easily declare Stream classes. Designed for interactive use such as notebooks and shouldn't replace parameterized class definitions in source code that is imported. Takes a stream class name and a set of keywords where each keyword becomes a parameter. If the value is already a parameter, it is simply used otherwise the appropriate parameter type is inferred and declared, using the value as the default. Supported types: bool, int, float, str, dict, tuple and list """ params = {'name': param.String(default=name)} for k, v in kwargs.items(): kws = dict(default=v, constant=True) if isinstance(v, param.Parameter): params[k] = v elif isinstance(v, bool): params[k] = param.Boolean(**kws) elif isinstance(v, int): params[k] = param.Integer(**kws) elif isinstance(v, float): params[k] = param.Number(**kws) elif isinstance(v, str): params[k] = param.String(**kws) elif isinstance(v, dict): params[k] = param.Dict(**kws) elif isinstance(v, tuple): params[k] = param.Tuple(**kws) elif isinstance(v, list): params[k] = param.List(**kws) elif isinstance(v, np.ndarray): params[k] = param.Array(**kws) else: params[k] = param.Parameter(**kws) # Dynamic class creation using type return type(name, (Stream,), params) @classmethod def trigger(cls, streams): """ Given a list of streams, collect all the stream parameters into a dictionary and pass it to the union set of subscribers. Passing multiple streams at once to trigger can be useful when a subscriber may be set multiple times across streams but only needs to be called once. """ # Union of stream contents items = [stream.contents.items() for stream in set(streams)] union = [kv for kvs in items for kv in kvs] klist = [k for k, _ in union] key_clashes = [] for k, v in union: key_count = klist.count(k) try: value_count = union.count((k, v)) except Exception: # If we can't compare values we assume they are not equal value_count = 1 if key_count > 1 and key_count > value_count and k not in key_clashes: key_clashes.append(k) if key_clashes: print(f'Parameter name clashes for keys {key_clashes!r}') # Group subscribers by precedence while keeping the ordering # within each group subscriber_precedence = defaultdict(list) for stream in streams: stream._on_trigger() for precedence, subscriber in stream._subscribers: subscriber_precedence[precedence].append(subscriber) sorted_subscribers = sorted(subscriber_precedence.items(), key=lambda x: x[0]) subscribers = util.unique_iterator([s for _, subscribers in sorted_subscribers for s in subscribers]) with triggering_streams(streams): for subscriber in subscribers: subscriber(**dict(union)) for stream in streams: with util.disable_constant(stream): if stream.transient: stream.reset() def _on_trigger(self): """Called when a stream has been triggered""" @classmethod def _process_streams(cls, streams): """ Processes a list of streams promoting Parameterized objects and methods to Param based streams. """ parameterizeds = defaultdict(set) valid, invalid = [], [] for s in streams: if isinstance(s, partial): s = s.func if isinstance(s, Stream): pass elif isinstance(s, param.Parameter): s = Params(s.owner, [s.name]) elif isinstance(s, param.Parameterized): s = Params(s) elif util.is_param_method(s): if not hasattr(s, "_dinfo"): continue s = ParamMethod(s) elif isinstance(s, FunctionType) and hasattr(s, "_dinfo"): deps = s._dinfo dep_params = list(deps['dependencies']) + list(deps.get('kw', {}).values()) rename = {(p.owner, p.name): k for k, p in deps.get('kw', {}).items()} s = Params(parameters=dep_params, rename=rename) else: deps = param.parameterized.resolve_ref(s) if deps: s = Params(parameters=deps) else: invalid.append(s) continue if isinstance(s, Params): pid = id(s.parameterized) overlap = (set(s.parameters) & parameterizeds[pid]) if overlap: pname = type(s.parameterized).__name__ param.main.param.warning( f'The {sorted([p.name for p in overlap])} parameter(s) ' f'on the {pname} object have ' 'already been supplied in another stream. ' 'Ensure that the supplied streams only specify ' 'each parameter once, otherwise multiple ' 'events will be triggered when the parameter changes.' ) parameterizeds[pid] |= set(s.parameters) valid.append(s) return valid, invalid def __init__(self, rename=None, source=None, subscribers=None, linked=False, transient=False, **params): """ The rename argument allows multiple streams with similar event state to be used by remapping parameter names. Source is an optional argument specifying the HoloViews datastructure that the stream receives events from, as supported by the plotting backend. Some streams are configured to automatically link to the source plot, to disable this set linked=False """ # Source is stored as a weakref to allow it to be garbage collected if subscribers is None: subscribers = [] if rename is None: rename = {} self._source = None if source is None else weakref.ref(source) self._subscribers = [] for subscriber in subscribers: self.add_subscriber(subscriber) self.linked = linked self.transient = transient # Whether this stream is currently triggering its subscribers self._triggering = False # The metadata may provide information about the currently # active event, i.e. the source of the stream values may # indicate where the event originated from self._metadata = {} super().__init__(**params) self._rename = self._validate_rename(rename) if source is not None: if source in self.registry: self.registry[source].append(self) else: self.registry[source] = [self] def clone(self): """Return new stream with identical properties and no subscribers""" return type(self)(**self.contents) @property def subscribers(self): """Property returning the subscriber list""" return [s for p, s in sorted(self._subscribers, key=lambda x: x[0])] def clear(self, policy='all'): """ Clear all subscribers registered to this stream. The default policy of 'all' clears all subscribers. If policy is set to 'user', only subscribers defined by the user are cleared (precedence between zero and one). A policy of 'internal' clears subscribers with precedence greater than unity used internally by HoloViews. """ policies = ['all', 'user', 'internal'] if policy not in policies: raise ValueError(f'Policy for clearing subscribers must be one of {policies}') if policy == 'all': remaining = [] elif policy == 'user': remaining = [(p, s) for (p, s) in self._subscribers if p > 1] else: remaining = [(p, s) for (p, s) in self._subscribers if p <= 1] self._subscribers = remaining def reset(self): """ Resets stream parameters to their defaults. """ with util.disable_constant(self): for k, p in self.param.objects('existing').items(): if k != 'name': setattr(self, k, p.default) def add_subscriber(self, subscriber, precedence=0): """ Register a callable subscriber to this stream which will be invoked either when event is called or when this stream is passed to the trigger classmethod. Precedence allows the subscriber ordering to be controlled. Users should only add subscribers with precedence between zero and one while HoloViews itself reserves the use of higher precedence values. Subscribers with high precedence are invoked later than ones with low precedence. """ if not callable(subscriber): raise TypeError('Subscriber must be a callable.') self._subscribers.append((precedence, subscriber)) def _validate_rename(self, mapping): param_names = [k for k in self.param if k != 'name'] for k, v in mapping.items(): if k not in param_names: raise KeyError(f'Cannot rename {k!r} as it is not a stream parameter') if k != v and v in param_names: raise KeyError(f'Cannot rename to {v!r} as it clashes with a ' 'stream parameter of the same name') return mapping def rename(self, **mapping): """ The rename method allows stream parameters to be allocated to new names to avoid clashes with other stream parameters of the same name. Returns a new clone of the stream instance with the specified name mapping. """ params = {k: v for k, v in self.param.values().items() if k != 'name'} return self.__class__(rename=mapping, source=(self._source() if self._source else None), linked=self.linked, **params) @property def source(self): return self._source() if self._source else None @source.setter def source(self, source): if self.source is not None: source_list = self.registry[self.source] if self in source_list: source_list.remove(self) if not source_list: self.registry.pop(self.source) if source is None: self._source = None return self._source = weakref.ref(source) if source in self.registry: self.registry[source].append(self) else: self.registry[source] = [self] def transform(self): """ Method that can be overwritten by subclasses to process the parameter values before renaming is applied. Returns a dictionary of transformed parameters. """ return {} @property def contents(self): filtered = {k: v for k, v in self.param.values().items() if k != 'name'} return {self._rename.get(k, k): v for (k, v) in filtered.items() if self._rename.get(k, True) is not None} @property def hashkey(self): """ The object the memoization hash is computed from. By default returns the stream contents but can be overridden to provide a custom hash key. """ return self.contents def _set_stream_parameters(self, **kwargs): """ Sets the stream parameters which are expected to be declared constant. """ with util.disable_constant(self): self.param.update(**kwargs) def event(self, **kwargs): """ Update the stream parameters and trigger an event. """ skip = self.update(**kwargs) if skip is not _SkipTrigger: self.trigger([self]) def update(self, **kwargs): """ The update method updates the stream parameters (without any renaming applied) in response to some event. If the stream has a custom transform method, this is applied to transform the parameter values accordingly. To update and trigger, use the event method. """ self._set_stream_parameters(**kwargs) transformed = self.transform() if transformed is None: return _SkipTrigger self._set_stream_parameters(**transformed) def __repr__(self): cls_name = self.__class__.__name__ kwargs = ','.join(f'{k}={v!r}' for (k, v) in self.param.values().items() if k != 'name') if not self._rename: return f'{cls_name}({kwargs})' else: return f'{cls_name}({self._rename!r}, {kwargs})' def __str__(self): return repr(self) class Counter(Stream): """ Simple stream that automatically increments an integer counter parameter every time it is updated. """ counter = param.Integer(default=0, constant=True, bounds=(0, None)) def transform(self): return {'counter': self.counter + 1} class Pipe(Stream): """ A Stream used to pipe arbitrary data to a callback. Unlike other streams memoization can be disabled for a Pipe stream (and is disabled by default). """ data = param.Parameter(default=None, constant=True, doc=""" Arbitrary data being streamed to a DynamicMap callback.""") def __init__(self, data=None, memoize=False, **params): super().__init__(data=data, **params) self._memoize_counter = 0 def send(self, data): """ A convenience method to send an event with data without supplying a keyword. """ self.event(data=data) def _on_trigger(self): self._memoize_counter += 1 @property def hashkey(self): return {'_memoize_key': self._memoize_counter} class Buffer(Pipe): """ Buffer allows streaming and accumulating incoming chunks of rows from tabular datasets. The data may be in the form of a pandas DataFrame, 2D arrays of rows and columns or dictionaries of column arrays. Buffer will accumulate the last N rows, where N is defined by the specified ``length``. The accumulated data is then made available via the ``data`` parameter. A Buffer may also be instantiated with a streamz.StreamingDataFrame or a streamz.StreamingSeries, it will automatically subscribe to events emitted by a streamz object. When streaming a DataFrame will reset the DataFrame index by default making it available to HoloViews elements as dimensions, this may be disabled by setting index=False. The ``following`` argument determines whether any plot which is subscribed to this stream will update the axis ranges when an update is pushed. This makes it possible to control whether zooming is allowed while streaming. """ data = param.Parameter(default=None, constant=True, doc=""" Arbitrary data being streamed to a DynamicMap callback.""") def __init__(self, data, length=1000, index=True, following=True, **params): if isinstance(data, pd.DataFrame): example = data elif isinstance(data, np.ndarray): if data.ndim != 2: raise ValueError("Only 2D array data may be streamed by Buffer.") example = data elif isinstance(data, dict): if not all(isinstance(v, np.ndarray) for v in data.values()): raise ValueError("Data in dictionary must be of array types.") elif len({len(v) for v in data.values()}) > 1: raise ValueError("Columns in dictionary must all be the same length.") example = data else: try: from streamz.dataframe import StreamingDataFrame, StreamingSeries loaded = True except ImportError: try: from streamz.dataframe import ( DataFrame as StreamingDataFrame, Series as StreamingSeries, ) loaded = True except ImportError: loaded = False if not loaded or not isinstance(data, (StreamingDataFrame, StreamingSeries)): raise ValueError("Buffer must be initialized with pandas DataFrame, " "streamz.StreamingDataFrame or streamz.StreamingSeries.") elif isinstance(data, StreamingSeries): data = data.to_frame() example = data.example data.stream.sink(self.send) self.sdf = data params['data'] = example super().__init__(**params) self.length = length self.following = following self._chunk_length = 0 self._count = 0 self._index = index def verify(self, x): """ Verify consistency of dataframes that pass through this stream """ if type(x) != type(self.data): # noqa: E721 raise TypeError(f"Input expected to be of type {type(self.data).__name__}, got {type(x).__name__}.") elif isinstance(x, np.ndarray): if x.ndim != 2: raise ValueError('Streamed array data must be two-dimensional') elif x.shape[1] != self.data.shape[1]: raise ValueError(f"Streamed array data expected to have {self.data.shape[1]} columns, " f"got {x.shape[1]}.") elif isinstance(x, pd.DataFrame) and list(x.columns) != list(self.data.columns): raise IndexError(f"Input expected to have columns {list(self.data.columns)}, got {list(x.columns)}") elif isinstance(x, dict): if any(c not in x for c in self.data): raise IndexError(f"Input expected to have columns {sorted(self.data.keys())}, got {sorted(x.keys())}") elif len({len(v) for v in x.values()}) > 1: raise ValueError("Input columns expected to have the " "same number of rows.") def clear(self): "Clears the data in the stream" if isinstance(self.data, np.ndarray): data = self.data[:, :0] elif isinstance(self.data, pd.DataFrame): data = self.data.iloc[:0] elif isinstance(self.data, dict): data = {k: v[:0] for k, v in self.data.items()} with util.disable_constant(self): self.data = data self.send(data) def _concat(self, data): """ Concatenate and slice the accepted data types to the defined length. """ if isinstance(data, np.ndarray): data_length = len(data) if not self.length: data = np.concatenate([self.data, data]) elif data_length < self.length: prev_chunk = self.data[-(self.length-data_length):] data = np.concatenate([prev_chunk, data]) elif data_length > self.length: data = data[-self.length:] elif isinstance(data, pd.DataFrame): data_length = len(data) if not self.length: data = pd.concat([self.data, data]) elif data_length < self.length: prev_chunk = self.data.iloc[-(self.length-data_length):] data = pd.concat([prev_chunk, data]) elif data_length > self.length: data = data.iloc[-self.length:] elif isinstance(data, dict) and data: data_length = len(next(iter(data.values()))) new_data = {} for k, v in data.items(): if not self.length: new_data[k] = np.concatenate([self.data[k], v]) elif data_length < self.length: prev_chunk = self.data[k][-(self.length-data_length):] new_data[k] = np.concatenate([prev_chunk, v]) elif data_length > self.length: new_data[k] = v[-self.length:] else: new_data[k] = v data = new_data self._chunk_length = data_length return data def update(self, **kwargs): """ Overrides update to concatenate streamed data up to defined length. """ data = kwargs.get('data') if data is not None: self.verify(data) kwargs['data'] = self._concat(data) self._count += 1 return super().update(**kwargs) @property def hashkey(self): return {'hash': (self._count, self._memoize_counter)} class Params(Stream): """ A Stream that watches the changes in the parameters of the supplied Parameterized objects and triggers when they change. """ parameterized = param.ClassSelector(class_=(param.Parameterized, param.parameterized.ParameterizedMetaclass), constant=True, allow_None=True, allow_refs=False, doc=""" Parameterized instance to watch for parameter changes.""") parameters = param.List(default=[], constant=True, doc=""" Parameters on the parameterized to watch.""") def __init__(self, parameterized=None, parameters=None, watch=True, watch_only=False, **params): if parameters is None: parameters = [parameterized.param[p] for p in parameterized.param if p != 'name'] else: parameters = [p if isinstance(p, param.Parameter) else parameterized.param[p] for p in parameters] if 'rename' in params: rename = {} owners = [p.owner for p in parameters] for k, v in params['rename'].items(): if isinstance(k, tuple): rename[k] = v else: rename.update({(o, k): v for o in owners}) params['rename'] = rename if 'linked' not in params: for p in parameters: if isinstance(p.owner, (LinkedStream, Params)) and p.owner.linked: params['linked'] = True self._watch_only = watch_only super().__init__(parameterized=parameterized, parameters=parameters, **params) self._memoize_counter = 0 self._events = [] self._watchers = [] if watch: # Subscribe to parameters keyfn = lambda x: id(x.owner) for _, group in groupby(sorted(parameters, key=keyfn), key=keyfn): group = list(group) watcher = group[0].owner.param.watch(self._watcher, [p.name for p in group]) self._watchers.append(watcher) def unwatch(self): """Stop watching parameters.""" for watcher in self._watchers: watcher.inst.param.unwatch(watcher) self._watchers.clear() @classmethod def from_params(cls, params, **kwargs): """Returns Params streams given a dictionary of parameters Args: params (dict): Dictionary of parameters Returns: List of Params streams """ key_fn = lambda x: id(x[1].owner) streams = [] for _, group in groupby(sorted(params.items(), key=key_fn), key_fn): group = list(group) inst = next(p.owner for _, p in group) if inst is None: continue names = [p.name for _, p in group] rename = {p.name: n for n, p in group} streams.append(cls(inst, names, rename=rename, **kwargs)) return streams def _validate_rename(self, mapping): pnames = [p.name for p in self.parameters] for k, v in mapping.items(): n = k[1] if isinstance(k, tuple) else k if n not in pnames: raise KeyError(f'Cannot rename {n!r} as it is not a stream parameter') if n != v and v in pnames: raise KeyError(f'Cannot rename to {v!r} as it clashes with a ' 'stream parameter of the same name') return mapping def _watcher(self, *events): try: self._events = list(events) self.trigger([self]) finally: self._events = [] def _on_trigger(self): if any(e.type == 'triggered' for e in self._events): self._memoize_counter += 1 @property def hashkey(self): hashkey = {} for p in self.parameters: pkey = (p.owner, p.name) pname = self._rename.get(pkey, p.name) key = ' '.join([str(id(p.owner)), pname]) if self._rename.get(pkey, True) is not None: hashkey[key] = getattr(p.owner, p.name) hashkey['_memoize_key'] = self._memoize_counter return hashkey def reset(self): pass def update(self, **kwargs): if self._rename: owner_updates = defaultdict(dict) for (owner, pname), rname in self._rename.items(): if rname in kwargs: owner_updates[owner][pname] = kwargs[rname] for owner, updates in owner_updates.items(): if isinstance(owner, Stream): owner.update(**updates) else: owner.param.update(**updates) elif isinstance(self.parameterized, Stream): self.parameterized.update(**kwargs) return else: self.parameterized.param.update(**kwargs) @property def contents(self): if self._watch_only: return {} filtered = {(p.owner, p.name): getattr(p.owner, p.name) for p in self.parameters} return {self._rename.get((o, n), n): v for (o, n), v in filtered.items() if self._rename.get((o, n), True) is not None} class ParamMethod(Params): """ A Stream that watches the parameter dependencies on a method of a parameterized class and triggers when one of the parameters change. """ parameterized = param.ClassSelector(class_=(param.Parameterized, param.parameterized.ParameterizedMetaclass), constant=True, allow_None=True, doc=""" Parameterized instance to watch for parameter changes.""") parameters = param.List(default=[], constant=True, doc=""" Parameters on the parameterized to watch.""") def __init__(self, parameterized, parameters=None, watch=True, **params): if not util.is_param_method(parameterized): raise ValueError('ParamMethod stream expects a method on a ' f'parameterized class, found {type(parameterized).__name__}.') method = parameterized parameterized = util.get_method_owner(parameterized) if not parameters: parameters = [p.pobj for p in parameterized.param.method_dependencies(method.__name__)] params['watch_only'] = True super().__init__(parameterized, parameters, watch, **params) class Derived(Stream): """ A Stream that watches the parameters of one or more input streams and produces a result that is a pure function of the input stream values. If exclusive=True, then all streams except the most recently updated are cleared. """ def __init__(self, input_streams, exclusive=False, **params): super().__init__(**params) self.input_streams = [] self._updating = set() self._register_streams(input_streams) self.exclusive = exclusive self.update() def _register_streams(self, streams): """ Register callbacks to watch for changes to input streams """ for stream in streams: self._register_stream(stream) def _register_stream(self, stream): i = len(self.input_streams) def perform_update(stream_index=i, **kwargs): if stream_index in self._updating: return # If exclusive, reset other stream values before triggering event if self.exclusive: for j, input_stream in enumerate(self.input_streams): if stream_index != j: input_stream.reset() self._updating.add(j) try: input_stream.event() finally: self._updating.remove(j) self.event() stream.add_subscriber(perform_update) self.input_streams.append(stream) def _unregister_input_streams(self): """ Unregister callbacks on input streams and clear input streams list """ for stream in self.input_streams: stream.source = None stream.clear() self.input_streams.clear() def append_input_stream(self, stream): """ Add a new input stream """ self._register_stream(stream) @property def constants(self): """ Dict of constants for this instance that should be passed to transform_function Constant values must not change in response to changes in the values of the input streams. They may, however, change in response to other stream property updates. For example, these values may change if the Stream's source element changes """ return {} def transform(self): stream_values = [s.contents for s in self.input_streams] return self.transform_function(stream_values, self.constants) @classmethod def transform_function(cls, stream_values, constants): """ Pure function that transforms input stream param values into the param values of this Derived stream. Args: stream_values: list of dict Current values of the stream params for each input_stream constants: dict Constants as returned by the constants property of an instance of this stream type. Returns: dict dict of new Stream values where the keys match this stream's params """ raise NotImplementedError def __del__(self): self._unregister_input_streams() class History(Stream): """ A Stream that maintains a history of the values of a single input stream """ values = param.List(constant=True, doc=""" List containing the historical values of the input stream""") def __init__(self, input_stream, **params): super().__init__(**params) self.input_stream = input_stream self._register_input_stream() # Trigger event on input stream after registering so that current value is # added to our values list self.input_stream.event() def clone(self): return type(self)(self.input_stream.clone(), **self.contents) def clear_history(self): del self.values[:] def _register_input_stream(self): """ Register callback on input_stream to watch for changes """ def perform_update(**kwargs): self.values.append(kwargs) self.event() self.input_stream.add_subscriber(perform_update) def __del__(self): self.input_stream.source = None self.input_stream.clear() del self.values[:] class SelectionExpr(Derived): selection_expr = param.Parameter(default=None, constant=True) bbox = param.Dict(default=None, constant=True) region_element = param.Parameter(default=None, constant=True) def __init__(self, source, include_region=True, **params): from .core.spaces import DynamicMap from .element import Element from .plotting.util import initialize_dynamic self._index_cols = params.pop('index_cols', None) self.include_region = include_region if isinstance(source, DynamicMap): initialize_dynamic(source) if not ((isinstance(source, DynamicMap) and issubclass(source.type, Element)) or isinstance(source, Element)): raise ValueError( "The source of SelectionExpr must be an instance of an " "Element subclass or a DynamicMap that returns such an " f"instance. Received value of type {type(source)}: {source}" ) input_streams = self._build_selection_streams(source) super().__init__( source=source, input_streams=input_streams, exclusive=True, **params ) def clone(self): return type(self)(self.source, **self.contents) def _build_selection_streams(self, source): from holoviews.core.spaces import DynamicMap if isinstance(source, DynamicMap): element_type = source.type else: element_type = source if element_type: input_streams = [] for stream in element_type._selection_streams: kwargs = dict(source=source) if isinstance(stream, Selection1D): kwargs['index'] = None input_streams.append(stream(**kwargs)) return input_streams else: return [] @property def constants(self): return { "source": self.source, "index_cols": self._index_cols, "include_region": self.include_region, } def transform(self): # Skip index streams if no index_cols are provided for stream in self.input_streams: if (isinstance(stream, Selection1D) and stream._triggering and not self._index_cols): return return super().transform() @classmethod def transform_function(cls, stream_values, constants): hvobj = constants["source"] include_region = constants["include_region"] if hvobj is None: # source is None return dict(selection_expr=None, bbox=None, region_element=None,) from holoviews.core.spaces import DynamicMap # Import after checking for hvobj None to avoid "sys.meta_path is None" # error on shutdown if isinstance(hvobj, DynamicMap): element = hvobj.values()[-1] else: element = hvobj selection_expr = None bbox = None region_element = None for stream_value in stream_values: params = dict(stream_value, index_cols=constants["index_cols"]) selection = element._get_selection_expr_for_stream_value(**params) if selection is None: return selection_expr, bbox, region_element = selection if selection_expr is not None: break for expr_transform in element._transforms[::-1]: if selection_expr is not None: selection_expr = expr_transform(selection_expr) return dict( selection_expr=selection_expr, bbox=bbox, region_element=region_element if include_region else None, ) @property def source(self): return Stream.source.fget(self) @source.setter def source(self, value): # Unregister old selection streams self._unregister_input_streams() # Set new source Stream.source.fset(self, value) # Build selection input streams for new source element if self.source is not None: input_streams = self._build_selection_streams(self.source) else: input_streams = [] # Clear current selection expression state self.update( selection_expr=None, bbox=None, region_element=None, ) # Register callbacks on input streams self._register_streams(input_streams) class SelectionExprSequence(Derived): selection_expr = param.Parameter(default=None, constant=True) region_element = param.Parameter(default=None, constant=True) def __init__( self, source, mode="overwrite", include_region=True, **params ): self.mode = mode self.include_region = include_region sel_expr = SelectionExpr( source, index_cols=params.pop('index_cols'), **params ) self.history_stream = History(sel_expr) input_streams = [self.history_stream] super().__init__( source=source, input_streams=input_streams, **params ) @property def constants(self): return { "source": self.source, "mode": self.mode, "include_region": self.include_region, } def reset(self): self.input_streams[0].clear_history() super().reset() @classmethod def transform_function(cls, stream_values, constants): from .core.spaces import DynamicMap mode = constants["mode"] source = constants["source"] include_region = constants["include_region"] combined_selection_expr = None combined_region_element = None for selection_contents in stream_values[0]["values"]: if selection_contents is None: continue selection_expr = selection_contents['selection_expr'] if not selection_expr: continue region_element = selection_contents['region_element'] # Update combined selection expression if combined_selection_expr is None or mode == "overwrite": if mode == "inverse": combined_selection_expr = ~selection_expr else: combined_selection_expr = selection_expr elif mode == "intersect": combined_selection_expr &= selection_expr elif mode == "union": combined_selection_expr |= selection_expr else: # inverse combined_selection_expr &= ~selection_expr # Update region if isinstance(source, DynamicMap): el_type = source.type else: el_type = source combined_region_element = el_type._merge_regions( combined_region_element, region_element, mode ) return dict( selection_expr=combined_selection_expr, region_element=combined_region_element if include_region else None ) class CrossFilterSet(Derived): selection_expr = param.Parameter(default=None, constant=True) def __init__(self, selection_streams=(), mode="intersection", index_cols=None, **params): self._mode = mode self._index_cols = index_cols input_streams = list(selection_streams) exclusive = mode == "overwrite" super().__init__( input_streams, exclusive=exclusive, **params ) @property def mode(self): return self._mode @mode.setter def mode(self, v): if v != self._mode: self._mode = v self.reset() self.exclusive = self._mode == "overwrite" @property def constants(self): return { "mode": self.mode, "index_cols": self._index_cols } def reset(self): super().reset() for stream in self.input_streams: stream.reset() @classmethod def transform_function(cls, stream_values, constants): from .util.transform import dim index_cols = constants["index_cols"] # Get non-none selection expressions selection_exprs = [sv["selection_expr"] for sv in stream_values] selection_exprs = [expr for expr in selection_exprs if expr is not None] selection_expr = None if len(selection_exprs) > 0: if index_cols: if len(selection_exprs) > 1: vals = set.intersection( *(set(expr.ops[2]['args'][0]) for expr in selection_exprs)) old = selection_exprs[0] selection_expr = dim('new') selection_expr.dimension = old.dimension selection_expr.ops = list(old.ops) selection_expr.ops[2] = dict(selection_expr.ops[2], args=(list(vals),)) else: selection_expr = selection_exprs[0] for expr in selection_exprs[1:]: selection_expr = selection_expr & expr return dict(selection_expr=selection_expr) class LinkedStream(Stream): """ A LinkedStream indicates is automatically linked to plot interactions on a backend via a Renderer. Not all backends may support dynamically supplying stream data. """ def __init__(self, linked=True, popup=None, popup_position="top_right", popup_anchor=None, **params): if popup_position not in POPUP_POSITIONS: raise ValueError( f"Invalid popup_position: {popup_position!r}; " f"expect one of {POPUP_POSITIONS}" ) super().__init__(linked=linked, **params) self.popup = popup self.popup_position = popup_position self.popup_anchor = popup_anchor class PointerX(LinkedStream): """ A pointer position along the x-axis in data coordinates which may be a numeric or categorical dimension. With the appropriate plotting backend, this corresponds to the position of the mouse/trackpad cursor. If the pointer is outside the plot bounds, the position is set to None. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") class PointerY(LinkedStream): """ A pointer position along the y-axis in data coordinates which may be a numeric or categorical dimension. With the appropriate plotting backend, this corresponds to the position of the mouse/trackpad pointer. If the pointer is outside the plot bounds, the position is set to None. """ y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class PointerXY(LinkedStream): """ A pointer position along the x- and y-axes in data coordinates which may numeric or categorical dimensions. With the appropriate plotting backend, this corresponds to the position of the mouse/trackpad pointer. If the pointer is outside the plot bounds, the position values are set to None. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class Draw(PointerXY): """ A series of updating x/y-positions when drawing, together with the current stroke count """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") stroke_count = param.Integer(default=0, constant=True, doc=""" The current drawing stroke count. Increments every time a new stroke is started.""") class SingleTap(PointerXY): """ The x/y-position of a single tap or click in data coordinates. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class Tap(PointerXY): """ The x/y-position of a tap or click in data coordinates. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class MultiAxisTap(LinkedStream): """ The x/y-positions of a tap or click in data coordinates. """ xs = param.Dict(default=None, constant=True, doc=""" Pointer positions along the x-axes in data coordinates""") ys = param.Dict(default=None, constant=True, doc=""" Pointer positions along the y-axes in data coordinates""") class DoubleTap(PointerXY): """ The x/y-position of a double-tap or -click in data coordinates. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class PressUp(PointerXY): """ The x/y position of a mouse pressup event in data coordinates. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class PanEnd(PointerXY): """The x/y position of a the end of a pan event in data coordinates. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class MouseEnter(PointerXY): """ The x/y-position where the mouse/cursor entered the plot area in data coordinates. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class MouseLeave(PointerXY): """ The x/y-position where the mouse/cursor entered the plot area in data coordinates. """ x = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the x-axis in data coordinates""") y = param.ClassSelector(class_=pointer_types, default=None, constant=True, doc=""" Pointer position along the y-axis in data coordinates""") class PlotSize(LinkedStream): """ Returns the dimensions of a plot once it has been displayed. """ width = param.Integer(default=None, constant=True, doc="The width of the plot in pixels") height = param.Integer(default=None, constant=True, doc="The height of the plot in pixels") scale = param.Number(default=1.0, constant=True, doc=""" Scale factor to scale width and height values reported by the stream""") def transform(self): return {'width': int(self.width * self.scale) if self.width else None, 'height': int(self.height * self.scale) if self.height else None} class SelectMode(LinkedStream): mode = param.Selector(default="replace", constant=True, objects=[ "replace", "append", "intersect", "subtract"], doc=""" Defines what should happen when a new selection is made. The default is to replace the existing selection. Other options are to append to theselection, intersect with it or subtract from it.""") class RangeXY(LinkedStream): """ Axis ranges along x- and y-axis in data coordinates. """ x_range = param.Tuple(default=None, length=2, constant=True, doc=""" Range of the x-axis of a plot in data coordinates""") y_range = param.Tuple(default=None, length=2, constant=True, doc=""" Range of the y-axis of a plot in data coordinates""") class RangeX(LinkedStream): """ Axis range along x-axis in data coordinates. """ x_range = param.Tuple(default=None, length=2, constant=True, doc=""" Range of the x-axis of a plot in data coordinates""") def _set_stream_parameters(self, **kwargs): kwargs.pop("y_range", None) super()._set_stream_parameters(**kwargs) class RangeY(LinkedStream): """ Axis range along y-axis in data coordinates. """ y_range = param.Tuple(default=None, length=2, constant=True, doc=""" Range of the y-axis of a plot in data coordinates""") def _set_stream_parameters(self, **kwargs): kwargs.pop("x_range", None) super()._set_stream_parameters(**kwargs) class BoundsXY(LinkedStream): """ A stream representing the bounds of a box selection as an tuple of the left, bottom, right and top coordinates. """ bounds = param.Tuple(default=None, constant=True, length=4, allow_None=True, doc=""" Bounds defined as (left, bottom, right, top) tuple.""") class Lasso(LinkedStream): """ A stream representing a lasso selection in 2D space as a two-column array of coordinates. """ geometry = param.Array(constant=True, doc=""" The coordinates of the lasso geometry as a two-column array.""") class SelectionXY(BoundsXY): """ A stream representing the selection along the x-axis and y-axis. Unlike a BoundsXY stream, this stream returns range or categorical selections. """ bounds = param.Tuple(default=None, constant=True, length=4, allow_None=True, doc=""" Bounds defined as (left, bottom, right, top) tuple.""") x_selection = param.ClassSelector(class_=(tuple, list), allow_None=True, constant=True, doc=""" The current selection along the x-axis, either a numerical range defined as a tuple or a list of categories.""") y_selection = param.ClassSelector(class_=(tuple, list), allow_None=True, constant=True, doc=""" The current selection along the y-axis, either a numerical range defined as a tuple or a list of categories.""") class BoundsX(LinkedStream): """ A stream representing the bounds of a box selection as an tuple of the left and right coordinates. """ boundsx = param.Tuple(default=None, constant=True, length=2, allow_None=True, doc=""" Bounds defined as (left, right) tuple.""") class BoundsY(LinkedStream): """ A stream representing the bounds of a box selection as an tuple of the bottom and top coordinates. """ boundsy = param.Tuple(default=None, constant=True, length=2, allow_None=True, doc=""" Bounds defined as (bottom, top) tuple.""") class Selection1D(LinkedStream): """ A stream representing a 1D selection of objects by their index. """ index = param.List(default=[], allow_None=True, constant=True, doc=""" Indices into a 1D datastructure.""") class PlotReset(LinkedStream): """ A stream signalling when a plot reset event has been triggered. """ resetting = param.Boolean(default=False, constant=True, doc=""" Whether a reset event is being signalled.""") def __init__(self, *args, **params): super().__init__(self, *args, **dict(params, transient=True)) class CDSStream(LinkedStream): """ A Stream that syncs a bokeh ColumnDataSource with python. """ data = param.Dict(constant=True, doc=""" Data synced from Bokeh ColumnDataSource supplied as a dictionary of columns, where each column is a list of values (for point-like data) or list of lists of values (for path-like data).""") class PointDraw(CDSStream): """ Attaches a PointDrawTool and syncs the datasource. add: boolean Whether to allow adding new Points drag: boolean Whether to enable dragging of Points empty_value: int/float/string/None The value to insert on non-position columns when adding a new polygon num_objects: int The number of polygons that can be drawn before overwriting the oldest polygon. styles: dict A dictionary specifying lists of styles to cycle over whenever a new Point glyph is drawn. tooltip: str An optional tooltip to override the default """ data = param.Dict(constant=True, doc=""" Data synced from Bokeh ColumnDataSource supplied as a dictionary of columns, where each column is a list of values (for point-like data) or list of lists of values (for path-like data).""") def __init__(self, empty_value=None, add=True, drag=True, num_objects=0, styles=None, tooltip=None, **params): if styles is None: styles = {} self.add = add self.drag = drag self.empty_value = empty_value self.num_objects = num_objects self.styles = styles self.tooltip = tooltip self.styles = styles super().__init__(**params) @property def element(self): source = self.source if isinstance(source, UniformNdMapping): source = source.last if not self.data: return source.clone([], id=None) return source.clone(self.data, id=None) @property def dynamic(self): from .core.spaces import DynamicMap return DynamicMap(lambda *args, **kwargs: self.element, streams=[self]) class CurveEdit(PointDraw): """ Attaches a PointDraw to the plot which allows editing the Curve when selected. style: dict A dictionary specifying the style of the vertices. tooltip: str An optional tooltip to override the default """ data = param.Dict(constant=True, doc=""" Data synced from Bokeh ColumnDataSource supplied as a dictionary of columns, where each column is a list of values (for point-like data) or list of lists of values (for path-like data).""") def __init__(self, style=None, tooltip=None, **params): if style is None: style = {} self.style = style or {'size': 10} self.tooltip = tooltip super(PointDraw, self).__init__(**params) class PolyDraw(CDSStream): """ Attaches a PolyDrawTool and syncs the datasource. drag: boolean Whether to enable dragging of polygons and paths empty_value: int/float/string/None The value to insert on non-position columns when adding a new polygon num_objects: int The number of polygons that can be drawn before overwriting the oldest polygon. show_vertices: boolean Whether to show the vertices when a polygon is selected styles: dict A dictionary specifying lists of styles to cycle over whenever a new Poly glyph is drawn. tooltip: str An optional tooltip to override the default vertex_style: dict A dictionary specifying the style options for the vertices. The usual bokeh style options apply, e.g. fill_color, line_alpha, size, etc. """ data = param.Dict(constant=True, doc=""" Data synced from Bokeh ColumnDataSource supplied as a dictionary of columns, where each column is a list of values (for point-like data) or list of lists of values (for path-like data).""") def __init__(self, empty_value=None, drag=True, num_objects=0, show_vertices=False, vertex_style=None, styles=None, tooltip=None, **params): if styles is None: styles = {} if vertex_style is None: vertex_style = {} self.drag = drag self.empty_value = empty_value self.num_objects = num_objects self.show_vertices = show_vertices self.vertex_style = vertex_style self.styles = styles self.tooltip = tooltip super().__init__(**params) @property def element(self): source = self.source if isinstance(source, UniformNdMapping): source = source.last data = self.data if not data: return source.clone([], id=None) cols = list(self.data) x, y = source.kdims lookup = {'xs': x.name, 'ys': y.name} data = [{lookup.get(c, c): data[c][i] for c in self.data} for i in range(len(data[cols[0]]))] datatype = source.datatype if source.interface.multi else ['multitabular'] return source.clone(data, datatype=datatype, id=None) @property def dynamic(self): from .core.spaces import DynamicMap return DynamicMap(lambda *args, **kwargs: self.element, streams=[self]) class FreehandDraw(CDSStream): """ Attaches a FreehandDrawTool and syncs the datasource. empty_value: int/float/string/None The value to insert on non-position columns when adding a new polygon num_objects: int The number of polygons that can be drawn before overwriting the oldest polygon. styles: dict A dictionary specifying lists of styles to cycle over whenever a new freehand glyph is drawn. tooltip: str An optional tooltip to override the default """ data = param.Dict(constant=True, doc=""" Data synced from Bokeh ColumnDataSource supplied as a dictionary of columns, where each column is a list of values (for point-like data) or list of lists of values (for path-like data).""") def __init__(self, empty_value=None, num_objects=0, styles=None, tooltip=None, **params): if styles is None: styles = {} self.empty_value = empty_value self.num_objects = num_objects self.styles = styles self.tooltip = tooltip super().__init__(**params) @property def element(self): source = self.source if isinstance(source, UniformNdMapping): source = source.last data = self.data if not data: return source.clone([], id=None) cols = list(self.data) x, y = source.kdims lookup = {'xs': x.name, 'ys': y.name} data = [{lookup.get(c, c): data[c][i] for c in self.data} for i in range(len(data[cols[0]]))] return source.clone(data, id=None) @property def dynamic(self): from .core.spaces import DynamicMap return DynamicMap(lambda *args, **kwargs: self.element, streams=[self]) class BoxEdit(CDSStream): """ Attaches a BoxEditTool and syncs the datasource. empty_value: int/float/string/None The value to insert on non-position columns when adding a new box num_objects: int The number of boxes that can be drawn before overwriting the oldest drawn box. styles: dict A dictionary specifying lists of styles to cycle over whenever a new box glyph is drawn. tooltip: str An optional tooltip to override the default """ data = param.Dict(constant=True, doc=""" Data synced from Bokeh ColumnDataSource supplied as a dictionary of columns, where each column is a list of values (for point-like data) or list of lists of values (for path-like data).""") def __init__(self, empty_value=None, num_objects=0, styles=None, tooltip=None, **params): if styles is None: styles = {} self.empty_value = empty_value self.num_objects = num_objects self.styles = styles self.tooltip = tooltip super().__init__(**params) @property def element(self): from .element import Polygons, Rectangles source = self.source if isinstance(source, UniformNdMapping): source = source.last data = self.data if not data: return source.clone([]) dims = ['x0', 'y0', 'x1', 'y1']+[vd.name for vd in source.vdims] if isinstance(source, Rectangles): data = tuple(data[d] for d in dims) return source.clone(data, id=None) paths = [] for i, (x0, x1, y0, y1) in enumerate(zip(data['x0'], data['x1'], data['y0'], data['y1'])): xs = [x0, x0, x1, x1] ys = [y0, y1, y1, y0] if isinstance(source, Polygons): xs.append(x0) ys.append(y0) vals = [data[vd.name][i] for vd in source.vdims] paths.append((xs, ys, *vals)) datatype = source.datatype if source.interface.multi else ['multitabular'] return source.clone(paths, datatype=datatype, id=None) @property def dynamic(self): from .core.spaces import DynamicMap return DynamicMap(lambda *args, **kwargs: self.element, streams=[self]) class PolyEdit(PolyDraw): """ Attaches a PolyEditTool and syncs the datasource. shared: boolean Whether PolyEditTools should be shared between multiple elements tooltip: str An optional tooltip to override the default vertex_style: dict A dictionary specifying the style options for the vertices. The usual bokeh style options apply, e.g. fill_color, line_alpha, size, etc. """ data = param.Dict(constant=True, doc=""" Data synced from Bokeh ColumnDataSource supplied as a dictionary of columns, where each column is a list of values (for point-like data) or list of lists of values (for path-like data).""") def __init__(self, vertex_style=None, shared=True, **params): if vertex_style is None: vertex_style = {} self.shared = shared super().__init__(vertex_style=vertex_style, **params) def _streams_transform(obj): if isinstance(obj, Pipe): return obj.param.data return obj param.reactive.register_reference_transform(_streams_transform)