bokeh.util¶

bokeh.util.browser¶

Utility functions for helping with operations involving browsers.

class DummyWebBrowser[source]¶

A “no-op” web-browser controller.

open(url, new=0, autoraise=True)[source]¶

Receive standard arguments and take no action.

get_browser_controller(browser=None)[source]¶

Return a browser controller.

Parameters:	browser (str or None) – browser name, or None (default: None) If passed the string 'none', a dummy web browser controller is returned Otherwise, use the value to select an appropriate controller using the webbrowser standard library module. In the value is None then a system default is used.

Note

If the environment variable BOKEH_BROWSER is set, it will take precedence.

Returns:	a web browser controller
Return type:	controller

view(location, browser=None, new='same', autoraise=True)[source]¶

Open a browser to view the specified location.

Parameters:

location (str) – Location to open If location does not begin with “http:” it is assumed to be a file path on the local filesystem. browser (str or None) – what browser to use (default: None) If None, use the system default browser. new (str) – How to open the location. Valid values are: 'same' - open in the current tab 'tab' - open a new tab in the current window 'window' - open in a new window autoraise (bool) – Whether to automatically raise the location in a new browser window (default: True)

Returns:

None

bokeh.util.callback_manager¶

Provides CallbackManager mixin class for adding an on_change callback interface to classes.

class CallbackManager(*args, **kw)[source]¶

A mixin class to provide an interface for registering and triggering callbacks.

on_change(attr, *callbacks)[source]¶

Add a callback on this object to trigger when attr changes.

Parameters:	attr (str) – an attribute name on this object callback (callable) – a callback function to register
Returns:	None

remove_on_change(attr, *callbacks)[source]¶

Remove a callback from this object

trigger(attr, old, new, hint=None, setter=None)[source]¶

Trigger callbacks for attr on this object.

Parameters:	attr (str) – old (object) – new (object) –
Returns:	None

bokeh.util.compiler¶

Provide functions and classes to help with various JS and CSS compilation.

exception CompilationError(error)[source]¶

A RuntimeError subclass for reporting JS compilation errors.

class AttrDict[source]¶

Provide a dict subclass that supports access by named attributes.

exts = ('.coffee', '.ts', '.tsx', '.js', '.css', '.less')¶

recognized extensions that can be compiled

bokeh.util.dependencies¶

Utilities for checking dependencies

import_optional(mod_name)[source]¶

Attempt to import an optional dependency.

Silently returns None if the requested module is not available.

Parameters:	mod_name (str) – name of the optional module to try to import
Returns:	imported module or None, if import fails

import_required(mod_name, error_msg)[source]¶

Attempt to import a required dependency.

Raises a RuntimeError if the requested module is not available.

Parameters:	mod_name (str) – name of the required module to try to import error_msg (str) – error message to raise when the module is missing
Returns:	imported module
Raises:	RuntimeError

bokeh.util.deprecation¶

bokeh.util.future¶

Utilities for Py2/Py3 interop.

with_metaclass(meta, *bases)[source]¶

Add metaclasses in both Python 2 and Python 3.

Function from jinja2/_compat.py. License: BSD.

Use it like this:

class BaseForm(object):
    pass

class FormType(type):
    pass

class Form(with_metaclass(FormType, BaseForm)):
    pass

This requires a bit of explanation: the basic idea is to make a dummy metaclass for one level of class instantiation that replaces itself with the actual metaclass. Because of internal type checks we also need to make sure that we downgrade the custom metaclass for one level to something closer to type (that’s why __call__ and __init__ comes back from type etc.).

This has the advantage over six.with_metaclass of not introducing dummy classes into the final MRO.

bokeh.util.logconfig¶

Configure the logging system for Bokeh.

By default, logging is not configured, to allow users of Bokeh to have full control over logging policy. However, it is useful to be able to enable logging arbitrarily during when developing Bokeh. This can be accomplished by setting the environment variable BOKEH_PY_LOG_LEVEL. Valid values are, in order of increasing severity:

• debug
• info
• warn
• error
• fatal
• none

The default logging level is none.

bokeh.util.notebook¶

Functions useful for loading Bokeh code and data in IPython notebooks.

get_comms(target_name)[source]¶

Create a Jupyter comms object for a specific target, that can be used to update Bokeh documents in the notebook.

Parameters:	target_name (str) – the target name the Comms object should connect to

Returns

Jupyter Comms

load_notebook(resources=None, verbose=False, hide_banner=False, load_timeout=5000)[source]¶

Prepare the IPython notebook for displaying Bokeh plots.

Parameters:	resources (Resource, optional) – how and where to load BokehJS from (default: CDN) verbose (bool, optional) – whether to report detailed settings (default: False) hide_banner (bool, optional) – whether to hide the Bokeh banner (default: False) load_timeout (int, optional) – Timeout in milliseconds when plots assume load timed out (default: 5000)

Warning

Clearing the output cell containing the published BokehJS resources HTML code may cause Bokeh CSS styling to be removed.

Returns:	None

publish_display_data(data, source='bokeh')[source]¶

Compatibility wrapper for IPython publish_display_data

Later versions of IPython remove the source (first) argument. This function insulates Bokeh library code from this change.

Parameters:	source (str, optional) – the source arg for IPython (default: “bokeh”) data (dict) – the data dict to pass to publish_display_data Typically has the form {'text/html': html}

bokeh.util.options¶

Utilities for specifying, validating, and documenting configuration options.

class Options(kw=None)[source]¶

Leverage the Bokeh properties type system for specifying and validating configuration options.

Subclasses of Options specify a set of configuration options using standard Bokeh properties:

class ConnectOpts(Options):

    host = String(default="127.0.0.1", help="a host value")

    port = Int(default=5590, help="a port value")

Then a ConnectOpts can be created by passing a dictionary containing keys and values corresponding to the configuration options, as well as any additional keys and values. The items corresponding to the properties on ConnectOpts will be *removed* from the dictionary. This can be useful for functions that accept their own set of config keyword arguments in addition to some set of Bokeh model properties.

bokeh.util.paths¶

bokehjsdir(dev=False)[source]¶

Get the location of the bokehjs source files. If dev is True, the files in bokehjs/build are preferred. Otherwise uses the files in bokeh/server/static.

serverdir()[source]¶

Get the location of the server subpackage

bokeh.util.platform¶

Functions for testing what kind of Python or Python environment is in use.

is_notebook()[source]¶

Test whether we are inside an IPython notebook.

Returns

True if we are inside a notebook, otherwise False

is_py3()[source]¶

Test whether we are running Python 3.

Returns

True if we are running Python 3, otherwise False

is_pypy()[source]¶

Test whether we are running PyPy.

Returns

True if we are inside PyPy, otherwise False

bokeh.util.plot_utils¶

bokeh.util.serialization¶

Functions for helping with serialization and deserialization of Bokeh objects.

Certain NunPy array dtypes can be serialized to a binary format for performance and efficiency. The list of supported dtypes is:

• np.uint16
• np.uint32
• np.float64
• np.int16
• np.int8
• np.int32
• np.float32
• np.uint8

array_encoding_disabled(array)[source]¶

Determine whether an array may be binary encoded.

The NumPy array dtypes that can be encoded are:

• np.uint16
• np.uint32
• np.float64
• np.int16
• np.int8
• np.int32
• np.float32
• np.uint8

Parameters:	array (np.ndarray) – the array to check
Returns:	bool

decode_base64_dict(data)[source]¶

Decode a base64 encoded array into a NumPy array.

Parameters:	data (dict) – encoded array data to decode

Data should have the format encoded by encode_base64_dict().

Returns:	np.ndarray

encode_base64_dict(array)[source]¶

Encode a NumPy array using base64:

The encoded format is a dict with the following structure:

{
    '__ndarray__' : << base64 encoded array data >>,
    'shape'       : << array shape >>,
    'dtype'       : << dtype name >>,
}

Parameters:	array (np.ndarray) – an array to encode
Returns:	dict

make_id()[source]¶

Return a new unique ID for a Bokeh object.

Normally this function will return UUIDs to use for identifying Bokeh objects. This is especally important for Bokeh objects stored on a Bokeh server. However, it is convenient to have more human-readable IDs during development, so this behavior can be overridden by setting the environment variable BOKEH_SIMPLE_IDS=yes.

serialize_array(array, force_list=False)[source]¶

Transforms a NumPy array into serialized form.

Parameters:	array (np.ndarray) – the NumPy array to transform force_list (bool, optional) – whether to only output to standard lists This function can encode some dtypes using a binary encoding, but setting this argument to True will override that and cause only standard Python lists to be emitted. (default: False)
Returns:	list or dict

transform_array(array, force_list=False)[source]¶

Transform a NumPy arrays into serialized format

Converts un-serializable dtypes and returns JSON serializable format

Parameters:	array (np.ndarray) – a NumPy array to be transformed force_list (bool, optional) – whether to only output to standard lists This function can encode some dtypes using a binary encoding, but setting this argument to True will override that and cause only standard Python lists to be emitted. (default: False)
Returns:	JSON

transform_array_to_list(array)[source]¶

Transforms a NumPy array into a list of values

Parameters:	array (np.nadarray) – the NumPy array series to transform
Returns:	list or dict

transform_column_source_data(data)[source]¶

Transform ColumnSourceData data to a serialized format

Parameters:	data (dict) – the mapping of names to data columns to transform
Returns:	JSON compatible dict

transform_series(series, force_list=False)[source]¶

Transforms a Pandas series into serialized form

Parameters:	series (pd.Series) – the Pandas series to transform force_list (bool, optional) – whether to only output to standard lists This function can encode some dtypes using a binary encoding, but setting this argument to True will override that and cause only standard Python lists to be emitted. (default: False)
Returns:	list or dict

traverse_data(obj, is_numpy=True, use_numpy=True)[source]¶

Recursively traverse an object until a flat list is found.

If NumPy is available, the flat list is converted to a numpy array and passed to transform_array() to handle nan, inf, and -inf.

Otherwise, iterate through all items, converting non-JSON items

Parameters:	obj (list) – a list of values or lists is_numpy (bool, optional) – Whether NumPy is availanble (default: True if NumPy is importable) use_numpy (bool, optional) – This argument is only useful for testing (default: True)

bokeh.util.session_id¶

Utilities for generating and manipulating session IDs.

A session ID would typically be associated with each browser tab viewing an application or plot. Each session has its own state separate from any other sessions hosted by the server.

check_session_id_signature(session_id, secret_key=None, signed=False)[source]¶

Check the signature of a session ID, returning True if it’s valid.

The server uses this function to check whether a session ID was generated with the correct secret key. If signed sessions are disabled, this function always returns True.

Parameters:	session_id (str) – The session ID to check secret_key (str, optional) – Secret key (default: value of ‘BOKEH_SECRET_KEY’ env var) signed (bool, optional) – Whether to check anything (default: value of ‘BOKEH_SIGN_SESSIONS’ env var)

generate_secret_key()[source]¶

Generate a new securely-generated secret key appropriate for SHA-256 HMAC signatures. This key could be used to sign Bokeh server session IDs for example.

generate_session_id(secret_key=None, signed=False)[source]¶

Generate a random session ID.

Typically, each browser tab connected to a Bokeh application has its own session ID. In production deployments of a Bokeh app, session IDs should be random and unguessable - otherwise users of the app could interfere with one another.

If session IDs are signed with a secret key, the server can verify that the generator of the session ID was “authorized” (the generator had to know the secret key). This can be used to have a separate process, such as another web application, which generates new sessions on a Bokeh server. This other process may require users to log in before redirecting them to the Bokeh server with a valid session ID, for example.

Parameters:	secret_key (str, optional) – Secret key (default: value of ‘BOKEH_SECRET_KEY’ env var) signed (bool, optional) – Whether to sign the session ID (default: value of ‘BOKEH_SIGN_SESSIONS’ env var)

bokeh.util.string¶

Functions useful for string manipulations or encoding.

decode_utf8(u)[source]¶

Decode a sequence of bytes to a UTF-8 string

Parameters:	u (str) – the bytes to decode
Returns:	UTF-8 string

encode_utf8(u)[source]¶

Encode a UTF-8 string to a sequence of bytes.

Parameters:	u (str) – the string to encode
Returns:	bytes

nice_join(seq, sep=', ')[source]¶

Join together sequences of strings into English-friendly phrases using the conjunction or when appropriate.

Parameters:	seq (seq[str]) – a sequence of strings to nicely join sep (str, optional) – a sequence delimiter to use (default: ”, ”)
Returns:	a joined string

Examples

>>> nice_join(["a", "b", "c"])
'a, b or c'

snakify(name, sep='_')[source]¶

Convert CamelCase to snake_case.

bokeh.util.testing¶

Functions to help with testing Bokeh and reporting issues.

create_chart(klass, values, compute_values=True, **kws)[source]¶

Create a new chart class instance with values and the extra kws keyword parameters.

Parameters:	klass (class) – chart class to be created values (iterable) – chart data series compute_values (bool) – if == True underlying chart attributes (e.g., data, ranges, source, etc.) are computed by calling _setup_show, _prepare_show and _show_teardown methods. **kws (refer to klass arguments specification details) –
Returns:	klass chart instance
Return type:	_chart

print_versions()[source]¶

Print the versions for Bokeh and the current Python and OS.

Returns:	None

runtests(args=None)[source]¶

Run the Bokeh tests under the bokeh python directory using pytest.

Does not run tests from bokehjs or examples.

Parameters:	args (list, optional) – command line arguments accepted by py.test e.g. args=[‘-s’, ‘-k charts’] prevents capture of standard out and only runs tests that match charts. For more py.test options see http://pytest.org/latest/usage.html#usage.
Returns:	pytest exitcode
Return type:	int

skipIfPy3(message)[source]¶

unittest decorator to skip a test for Python 3

skipIfPyPy(message)[source]¶

unittest decorator to skip a test for PyPy

bokeh.util.tornado¶

Internal utils related to Tornado

yield_for_all_futures(result)[source]¶

Converts result into a Future by collapsing any futures inside result.

If result is a Future we yield until it’s done, then if the value inside the Future is another Future we yield until it’s done as well, and so on.

bokeh.util.version¶

Provide a version for the Bokeh library.

This module uses versioneer to manage version strings. During development, versioneer will compute a version string from the current git revision. For packaged releases based off tags, the version string is hard coded in the files packaged for distribution.

__version__¶

the version string for this installed Bokeh library

bokeh.util.warnings¶

Provide Bokeh-specific warning subclasses.

The primary use of these subclasses to to force them to be unconditionally displayed to users by default.

exception BokehDeprecationWarning[source]¶

A Bokeh-specific DeprecationWarning subclass.

Used to selectively filter Bokeh deprecations for unconditional display.

exception BokehUserWarning[source]¶

A Bokeh-specific UserWarning subclass.

Used to selectively filter Bokeh warnings for unconditional display.