"""
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
import math
import param
import numpy as np
from numbers import Number
from collections import defaultdict
from .core import util
from contextlib import contextmanager
[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)
@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:
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_=(Number, util.basestring), 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_=(Number, util.basestring), 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_=(Number, util.basestring, tuple), default=None,
constant=True, doc="""
Pointer position along the x-axis in data coordinates""")
y = param.ClassSelector(class_=(Number, util.basestring, tuple), 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.
"""
reset = 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)
