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 PropertyCallbackManager and EventCallbackManager mixin classes for adding on_change and on_event callback interfaces to classes.

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

A mixin class to provide an interface for registering and triggering event callbacks on the Python side.

class PropertyCallbackManager(*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.

class CoffeeScript(code, file=None)[source]¶

An implementation for a Bokeh custom model in CoffeeSript.

Example

class MyExt(Model):
    __implementation__ = CoffeeScript(""" <CoffeeScript code> """)

Note that CoffeeScript is the default implementation language for custom model implementations. The following is equivalent to example above:

class MyExt(Model):
    __implementation__ == """ <some coffeescript code> """

class CustomModel(cls)[source]¶

Represent a custom (user-defined) Bokeh model.

class FromFile(path)[source]¶

A custom model implementation read from a separate source file.

Parameters:	path (str) – The path to the file containing the extension source code

class Implementation[source]¶

Base class for representing Bokeh custom model implementations.

class Inline(code, file=None)[source]¶

Base class for representing Bokeh custom model implementations that may be given as inline code in some language.

Parameters:	code (str) – The source code for the implementation file (str, optional) – A file path to a file containing the source text (default: None)

class JavaScript(code, file=None)[source]¶

An implementation for a Bokeh custom model in JavaSript

Example

class MyExt(Model):
    __implementation__ = Javacript(""" <Javactipt code> """)

class Less(code, file=None)[source]¶

An implementation of a Less CSS style sheet.

class TypeScript(code, file=None)[source]¶

An implementation for a Bokeh custom model in TypeSript

Example

class MyExt(Model):
    __implementation__ = TypeScript(""" <TypeSctipt code> """)

bundle_models(models)[source]¶

Create a bundle of models.

calc_cache_key()[source]¶

Generate a key to cache a custom extension implementation with.

There is no metadata other than the Model classes, so this is the only base to generate a cache key.

We build the model keys from the list of model.full_name. This is not ideal but possibly a better solution can be found found later.

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

recognized extensions that can be compiled

bokeh.util.dependencies¶

Utilities for checking dependencies

detect_phantomjs()[source]¶

Detect if PhantomJS is avaiable in PATH.

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.

basicConfig(*args, **kwargs)[source]¶

A logging.basicConfig() wrapper that also undoes the default Bokeh-specific configuration.

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_py3()[source]¶

Test whether we are running Python 3.

Returns

True if we are running Python 3, 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.int8
• np.int32
• np.float64
• np.uint8
• np.uint32
• np.uint16
• np.float32
• np.int16

array_encoding_disabled(array)[source]¶

Determine whether an array may be binary encoded.

The NumPy array dtypes that can be encoded are:

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

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

convert_datetime_type(obj)[source]¶

Convert any recognized date, datetime or time delta value to floating point milliseconds

Date and Datetime values are converted to milliseconds since epoch.

TimeDeleta values are converted to absolute milliseconds.

Arg:

obj (object) : the object to convert

Returns:	milliseconds
Return type:	float

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

encode_binary_dict(array, buffers)[source]¶

Send a numpy array as an unencoded binary buffer

The encoded format is a dict with the following structure:

{
    '__buffer__' :  << an ID to locate the buffer >>,
    'shape'      : << array shape >>,
    'dtype'      : << dtype name >>,
    'order'      : << byte order at origin (little or big)>>
}

Parameters:	array (np.ndarray) – an array to encode buffers (set) – Set to add buffers to This is an “out” parameter. The values it contains will be modified in-place.
Returns:	dict

is_datetime_type(obj)[source]¶

Whether an object is any date, datetime, or time delta type recognized by Bokeh.

Arg:

obj (object) : the object to test

Returns:	True if obj is a datetime type
Return type:	bool

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, buffers=None)[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) buffers (set, optional) – If binary buffers are desired, the buffers parameter may be provided, and any columns that may be sent as binary buffers will be added to the set. If None, then only base64 encodinfg will be used (default: None) If force_list is True, then this value will be ignored, and no buffers will be generated. This is an “out” parameter. The values it contains will be modified in-place.

Returns:

list or dict

transform_array(array, force_list=False, buffers=None)[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) buffers (set, optional) – If binary buffers are desired, the buffers parameter may be provided, and any columns that may be sent as binary buffers will be added to the set. If None, then only base64 encodinfg will be used (default: None) If force_list is True, then this value will be ignored, and no buffers will be generated. This is an “out” parameter. The values it contains will be modified in-place.

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, buffers=None, cols=None)[source]¶

Transform ColumnSourceData data to a serialized format

Parameters:

data (dict) – the mapping of names to data columns to transform buffers (set, optional) – If binary buffers are desired, the buffers parameter may be provided, and any columns that may be sent as binary buffers will be added to the set. If None, then only base64 encodinfg will be used (default: None) This is an “out” parameter. The values it contains will be modified in-place. cols (list[str], optional) – Optional list of subset of columns to transform. If None, all columns will be transformed (default: None)

Returns:

JSON compatible dict

transform_series(series, force_list=False, buffers=None)[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) buffers (set, optional) – If binary buffers are desired, the buffers parameter may be provided, and any columns that may be sent as binary buffers will be added to the set. If None, then only base64 encodinfg will be used (default: None) If force_list is True, then this value will be ignored, and no buffers will be generated. This is an “out” parameter. The values it contains will be modified in-place.

Returns:

list or dict

traverse_data(obj, use_numpy=True, buffers=None)[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 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

format_docstring(docstring, *args, **kwargs)[source]¶

Safely format docstrings.

When Python is executed with the -OO option, doc strings are removed and replaced the value None. This function guards against applying the string formatting options in that case.

Parameters:	docstring (str or None) – The docstring to format, or None args (tuple) – string formatting arguments for the docsring kwargs (dict) – string formatting arguments for the docsring
Returns:	str or None

indent(text, n=2, ch=' ')[source]¶

Indent all the lines in a given block of text by a specified ammount.

Parameters:	text (str) – The text to indent n (int, optional) – The amount to indent each line by (default: 2) ch (char, optional) – What character to fill the indentation with (default: ” ”)

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.

skipIfPy3(message)[source]¶

unittest decorator to skip a test for Python 3

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 full version string for this installed Bokeh library

__base_version__¶

The base version string , without any “dev”, “rc” or local build information appended.

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.