Source code for holoviews.streams

"""
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 uuid
from numbers import Number
from collections import defaultdict
from contextlib import contextmanager

import param
import numpy as np

from .core import util
from .core.ndmapping import UniformNdMapping

# Types supported by Pointer derived streams
pointer_types = (Number, util.basestring, tuple)+util.datetime_types


[docs]@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 except: raise finally: for stream in streams: stream._triggering = False
[docs]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 id to a list of streams registry = defaultdict(list) # Mapping to define callbacks by backend and Stream type. # e.g. Stream._callbacks['bokeh'][Stream] = Callback _callbacks = defaultdict(dict)
[docs] @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)
[docs] @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 streams] union = [kv for kvs in items for kv in kvs] klist = [k for k, _ in union] clashes = set([k for k in klist if klist.count(k) > 1]) if clashes: param.main.warning('Parameter name clashes for keys: %r' % clashes) # Group subscribers by precedence while keeping the ordering # within each group subscriber_precedence = defaultdict(list) for stream in streams: 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 __init__(self, rename={}, source=None, subscribers=[], 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 """ self._source = source self._subscribers = [] for subscriber in subscribers: self.add_subscriber(subscriber) self.linked = linked self._rename = self._validate_rename(rename) 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(Stream, self).__init__(**params) if source is not None: self.registry[id(source)].append(self) @property def subscribers(self): """Property returning the subscriber list""" return [s for p, s in sorted(self._subscribers, key=lambda x: x[0])]
[docs] 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('Policy for clearing subscribers must be one of %s' % 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
[docs] def reset(self): """ Resets stream parameters to their defaults. """ with util.disable_constant(self): for k, p in self.params().items(): if k != 'name': setattr(self, k, p.default)
[docs] 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.params().keys() if k != 'name'] for k, v in mapping.items(): if k not in param_names: raise KeyError('Cannot rename %r as it is not a stream parameter' % k) if v in param_names: raise KeyError('Cannot rename to %r as it clashes with a ' 'stream parameter of the same name' % v) return mapping
[docs] 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.get_param_values() if k != 'name'} return self.__class__(rename=mapping, source=self._source, linked=self.linked, **params)
@property def source(self): return self._source @source.setter def source(self, source): if self._source: source_list = self.registry[id(self._source)] if self in source_list: source_list.remove(self) self._source = source self.registry[id(source)].append(self)
[docs] 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.get_param_values() 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.set_param(**kwargs)
[docs] def event(self, **kwargs): """ Update the stream parameters and trigger an event. """ self.update(**kwargs) self.trigger([self])
[docs] 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: self._set_stream_parameters(**transformed)
def __repr__(self): cls_name = self.__class__.__name__ kwargs = ','.join('%s=%r' % (k, v) for (k, v) in self.get_param_values() if k != 'name') if not self._rename: return '%s(%s)' % (cls_name, kwargs) else: return '%s(%r, %s)' % (cls_name, self._rename, kwargs) def __str__(self): return repr(self)
[docs]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}
[docs]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(Pipe, self).__init__(data=data, **params) self._memoize = memoize
[docs] def send(self, data): """ A convenience method to send an event with data without supplying a keyword. """ self.event(data=data)
@property def hashkey(self): if self._memoize: return self.contents return {'hash': uuid.uuid4().hex}
[docs]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. """ def __init__(self, data, length=1000, index=True, **params): if (util.pd and isinstance(data, util.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(set(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 if index and (util.pd and isinstance(example, util.pd.DataFrame)): example = example.reset_index() params['data'] = example super(Buffer, self).__init__(**params) self.length = length self._chunk_length = 0 self._count = 0 self._index = index
[docs] def verify(self, x): """ Verify consistency of dataframes that pass through this stream """ if type(x) != type(self.data): raise TypeError("Input expected to be of type %s, got %s." % (type(self.data).__name__, 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("Streamed array data expeced to have %d columns, " "got %d." % (self.data.shape[1], x.shape[1])) elif util.pd and isinstance(x, util.pd.DataFrame) and list(x.columns) != list(self.data.columns): raise IndexError("Input expected to have columns %s, got %s" % (list(self.data.columns), list(x.columns))) elif isinstance(x, dict): if any(c not in x for c in self.data): raise IndexError("Input expected to have columns %s, got %s" % (sorted(self.data.keys()), sorted(x.keys()))) elif len(set(len(v) for v in x.values())) > 1: raise ValueError("Input columns expected to have the " "same number of rows.")
[docs] def clear(self): "Clears the data in the stream" if isinstance(self.data, np.ndarray): data = self.data[:, :0] elif util.pd and isinstance(self.data, util.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 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 util.pd and isinstance(data, util.pd.DataFrame): data_length = len(data) if data_length < self.length: prev_chunk = self.data.iloc[-(self.length-data_length):] data = util.pd.concat([prev_chunk, data]) elif data_length > self.length: data = data.iloc[-self.length:] elif isinstance(data, dict) and data: data_length = len(list(data.values())[0]) new_data = {} for k, v in data.items(): if 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
[docs] def update(self, **kwargs): """ Overrides update to concatenate streamed data up to defined length. """ data = kwargs.get('data') if data is not None: if util.pd and isinstance(data, util.pd.DataFrame) and self._index: data = data.reset_index() self.verify(data) kwargs['data'] = self._concat(data) self._count += 1 super(Buffer, self).update(**kwargs)
@property def hashkey(self): return {'hash': self._count}
[docs]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, **params): super(LinkedStream, self).__init__(linked=linked, **params)
[docs]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""")
[docs]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""")
[docs]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""")
[docs]class Draw(PointerXY): """ A series of updating x/y-positions when drawing, together with the current stroke count """ stroke_count = param.Integer(default=0, constant=True, doc=""" The current drawing stroke count. Increments every time a new stroke is started.""")
[docs]class SingleTap(PointerXY): """ The x/y-position of a single tap or click in data coordinates. """
[docs]class Tap(PointerXY): """ The x/y-position of a tap or click in data coordinates. """
[docs]class DoubleTap(PointerXY): """ The x/y-position of a double-tap or -click in data coordinates. """
[docs]class MouseEnter(PointerXY): """ The x/y-position where the mouse/cursor entered the plot area in data coordinates. """
[docs]class MouseLeave(PointerXY): """ The x/y-position where the mouse/cursor entered the plot area in data coordinates. """
[docs]class PlotSize(LinkedStream): """ Returns the dimensions of a plot once it has been displayed. """ width = param.Integer(None, constant=True, doc="The width of the plot in pixels") height = param.Integer(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), 'height': int(self.height * self.scale)}
[docs]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""")
[docs]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""")
[docs]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""")
[docs]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 Bounds(BoundsXY): def __init__(self, *args, **kwargs): self.warning('Bounds is deprecated use BoundsXY instead.') super(Bounds, self).__init__(*args, **kwargs)
[docs]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.""")
[docs]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.""")
[docs]class Selection1D(LinkedStream): """ A stream representing a 1D selection of objects by their index. """ index = param.List(default=[], constant=True, doc=""" Indices into a 1D datastructure.""")
[docs]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(PlotReset, self).__init__(self, *args, **dict(params, transient=True))
[docs]class ParamValues(Stream): """ A Stream based on the parameter values of some other parameterized object, whether it is a parameterized class or a parameterized instance. The update method enables the stream to update the parameters of the specified object. """ def __init__(self, obj, **params): self._obj = obj super(ParamValues, self).__init__(**params) @property def contents(self): if isinstance(self._obj, type): remapped = {k: getattr(self._obj, k) for k in self._obj.params().keys() if k != 'name'} else: remapped = {k: v for k, v in self._obj.get_param_values() if k != 'name'} return remapped
[docs] def update(self, **kwargs): """ The update method updates the parameters of the specified object. If trigger is enabled, the trigger classmethod is invoked on this Stream instance to execute its subscribers. """ if isinstance(self._obj, type): for name in self._obj.params().keys(): if name in kwargs: setattr(self._obj, name, kwargs[name]) else: self._obj.set_param(**kwargs)
def __repr__(self): cls_name = self.__class__.__name__ return '%s(%r)' % (cls_name, self._obj) def __str__(self): return repr(self)
class PositionX(PointerX): def __init__(self, **params): self.warning('PositionX stream deprecated: use PointerX instead') super(PositionX, self).__init__(**params) class PositionY(PointerY): def __init__(self, **params): self.warning('PositionY stream deprecated: use PointerY instead') super(PositionY, self).__init__(**params) class PositionXY(PointerXY): def __init__(self, **params): self.warning('PositionXY stream deprecated: use PointerXY instead') super(PositionXY, self).__init__(**params)
[docs]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).""")
[docs]class PointDraw(CDSStream): """ Attaches a PointAddTool 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 """ def __init__(self, empty_value=None, drag=True, **params): self.drag = drag self.empty_value = empty_value super(PointDraw, self).__init__(**params) @property def element(self): source = self.source if isinstance(source, UniformNdMapping): source = source.last if not self.data: return source.clone([]) return source.clone(self.data) @property def dynamic(self): from .core.spaces import DynamicMap return DynamicMap(lambda *args, **kwargs: self.element, streams=[self])
[docs]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 """ def __init__(self, empty_value=None, drag=True, **params): self.drag = drag self.empty_value = empty_value super(PolyDraw, self).__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([]) 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) @property def dynamic(self): from .core.spaces import DynamicMap return DynamicMap(lambda *args, **kwargs: self.element, streams=[self])
[docs]class BoxEdit(CDSStream): """ Attaches a BoxEditTool and syncs the datasource. """ @property def element(self): from .element import Polygons source = self.source if isinstance(source, UniformNdMapping): source = source.last data = self.data if not data: return source.clone([]) paths = [] for (x0, x1, y0, y1) in 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) paths.append(np.column_stack((xs, ys))) return source.clone(paths) @property def dynamic(self): from .core.spaces import DynamicMap return DynamicMap(lambda *args, **kwargs: self.element, streams=[self])
[docs]class PolyEdit(PolyDraw): """ Attaches a PolyEditTool and syncs the datasource. shared: boolean Whether PolyEditTools should be shared between multiple elements 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. """ def __init__(self, vertex_style={}, shared=True, **params): self.shared = shared self.vertex_style = vertex_style super(PolyEdit, self).__init__(**params)