Wolfram Client Library for Python

https://reference.wolfram.com/language/WolframClientForPython/
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### WolframClient Setup Module Commands

Source: https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient.cli

Documentation for the setup module within the WolframClient CLI. This covers the Command class for setup operations and the 'dependencies' attribute and function, which lists or retrieves required packages.

```APIDOC
wolframclient.cli.commands.setup.Command
    Bases: SimpleCommand
    Run test suites from the tests modules. A list of patterns can be provided to specify the tests to run.

    dependencies
        List of dependencies required for setup: ('pytz', 'aiohttp', 'numpy', 'oauthlib', 'pandas', 'pillow', 'pyzmq', 'requests', 'unittest-xml-reporting', 'certifi>=2017.4.17').

wolframclient.cli.commands.setup.dependencies()
    Retrieves the list of dependencies for the setup process.
```

--------------------------------

### Install Wolfram Client Library

Source: https://reference.wolfram.com/language/WolframClientForPython/index

Installs the Wolfram Client Library for Python using pip.

```bash
$ pip
```

--------------------------------

### Start an Authenticated Cloud Session

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

Shows how to start an authenticated session with the Wolfram Cloud using a secured authentication key.

```python
from wolframclient.evaluation import WolframCloudSession

# Assumes you have generated a secured authentication key
# The key is typically stored in ~/.WolframLanguage/security/publickeys/

cloud_session = WolframCloudSession()
print("Authenticated cloud session started.")

# Evaluate an expression in the cloud
result = cloud_session.evaluate('CloudDeploy[Function[{x}, x^2], "MySquareFunction"]')
print(result)

cloud_session.terminate()
```

--------------------------------

### Install Wolfram Client for Python using pip

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

This snippet shows the recommended method for installing the Wolfram Client for Python using pip, the standard package installer for Python.

```bash
pip install wolframclient
```

--------------------------------

### Install Wolfram Client Library using Git

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/install

Installs the Wolfram Client Library for Python from its GitHub repository. This method is suitable for developers who need the full source code.

```bash
git clone https://github.com/WolframResearch/WolframClientForPython.git
cd WolframClientForPython
pip install .
```

--------------------------------

### Start External Wolfram Kernel Evaluation

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/cli/commands/start_externalevaluate

This Python code defines a command to start an external Wolfram Language kernel evaluation. It sets environment variables for Wolfram installation and kernel version, checks for the pyzmq dependency, and starts the ZMQ loop for communication. It includes argument parsing for port, installpath, and kernelversion.

```python
from__future__import absolute_import, print_function, unicode_literals

importos
importsys

fromwolframclient.cli.utilsimport SimpleCommand
fromwolframclient.utils.apiimport externalevaluate as ev
fromwolframclient.utils.apiimport zmq




classCommand(SimpleCommand):

    dependencies = ()



    defadd_arguments(self, parser):
        parser.add_argument("--port", dest="port", default=None)
        parser.add_argument("--installpath", dest="installpath", default=None)
        parser.add_argument("--kernelversion", dest="kernelversion", default=None)




    defhandle(self, port=None, installpath=None, kernelversion=None, **opts):

        for key, value in (
            ("WOLFRAM_INSTALLATION_DIRECTORY", installpath),
            ("WOLFRAM_KERNEL_VERSION", kernelversion),
        ):
            if value:
                os.environ[key] = value

        try:
            zmq.Context
        except ImportError as e:
            print(
                'Error importing zmq: {}. Please install zmq by running:\nExternalEvaluate[{{"Shell", "Target" :> $SystemShell}}, "{}" -> {{'-m', 'pip', 'install', 'pyzmq', '--user', '--no-input'}}]'.format(
                    e, sys.executable
                ),
                file=sys.stderr,
            )
            sys.stderr.flush()
            sys.exit(1)

        ev.start_zmq_loop(port=port)
```

--------------------------------

### Start ZMQ Instance

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/externalevaluate

Starts a ZMQ instance by creating a PAIR socket and binding it to a specified or random port on localhost. It optionally writes the bound endpoint to stdout.

```python
def start_zmq_instance(port=None, write_to_stdout=True, **opts):
    # make a reply socket
    sock = zmq.Context.instance().socket(zmq.PAIR)
    # now bind to a open port on localhost
    if port:
        sock.bind("tcp://127.0.0.1:%s" % port)
    else:
        sock.bind_to_random_port("tcp://127.0.0.1")

    if write_to_stdout:
        sys.stdout.write(force_text(sock.getsockopt(zmq.LAST_ENDPOINT)))
        sys.stdout.write(os.linesep)  # writes \n
        sys.stdout.flush()

    return sock
```

--------------------------------

### Wolfram Client Python Setup Dependencies

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/cli/commands/setup

This Python code defines a function `dependencies` that yields a list of required Python packages for the Wolfram Client Library setup. It conditionally includes packages like `aiohttp`, `numpy`, and `pandas` for non-Python 2 environments.

```python
from__future__import absolute_import, print_function, unicode_literals

fromwolframclient.cli.utilsimport SimpleCommand
fromwolframclient.utilsimport six
fromwolframclient.utils.decoratorsimport to_tuple




@to_tuple
defdependencies():
    yield "pytz"

    if not six.PY2:
        yield "aiohttp"
        yield "numpy"
        yield "oauthlib"
        yield "pandas"
        yield "pillow"
        yield "pyzmq"
        yield "requests"
        yield "unittest-xml-reporting"
        yield "certifi>=2017.4.17"




classCommand(SimpleCommand):
    """Run test suites from the tests modules.
    A list of patterns can be provided to specify the tests to run.
    """

    dependencies = dependencies()

```

--------------------------------

### Wolfram Language Session Management: Manual Start and Terminate

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Illustrates how to manually start and terminate a Wolfram Language session, including checking its status.

```Python
>>> session = WolframLanguageSession()
>>> session.start()
>>> session.started
True
>>> session.terminate()
```

--------------------------------

### Setup Logging to File (Python 2)

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/logger

Configures basic Python logging to a specified file with a predefined format. This version is for Python 2 environments.

```python
from__future__import absolute_import, print_function, unicode_literals

importlogging

fromwolframclient.utilsimport six
fromwolframclient.utils.encodingimport force_text

if six.PY2:

    defsetup_logging_to_file(path, level=None):
        logging.basicConfig(
            filename=path,
            filemode="a",
            format="%(asctime)s, %(name)s%(levelname)s%(message)s",
            level=(level is not None) or logging.INFO,
        )
```

--------------------------------

### Install Wolfram Client Library using pip

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/install

Installs the latest stable version of the Wolfram Client Library for Python from PyPI using pip. This is the recommended method for most users.

```bash
pip install wolframclient
```

--------------------------------

### Session Management

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

Demonstrates basic session management, including starting and terminating a Wolfram Language session.

```python
from wolframclient.evaluation import WolframLanguageSession

# Start a session
session = WolframLanguageSession()
print("Session started.")

# Perform some operations...

# Terminate the session
session.terminate()
print("Session terminated.")
```

--------------------------------

### Initialize Wolfram Language Session

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Demonstrates how to import and initialize a WolframLanguageSession, with examples for default paths and specific kernel executable paths on different operating systems.

```python
from wolframclient.evaluation import WolframLanguageSession
```

```python
session = WolframLanguageSession()
```

```python
session = WolframLanguageSession('/Applications/Wolfram Desktop.app/Contents/MacOS/WolframKernel')
```

```python
session = WolframLanguageSession('C:\\Program Files\\Wolfram Research\\Wolfram Desktop\\11.3\\WolframKernel.exe')
```

```python
session = WolframLanguageSession('/usr/local/Wolfram/Desktop/11.3/Executables/WolframKernel')
```

--------------------------------

### Wolfram Client Library for Python Overview

Source: https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient.evaluation

Provides an overview of the Wolfram Client Library for Python, including installation prerequisites, basic usage patterns like expression representation and evaluation, and advanced topics such as local kernel evaluation and extending serialization.

```Python
from wolframclient.evaluation import WolframLanguageSession
from wolframclient.language import wl

# Example of basic expression evaluation
with WolframLanguageSession() as session:
    expr = wl.Table(wl.Times(2, 'x'), ['x', 1, 5])
    result = session.evaluate(expr)
    print(result)

# Example of local kernel evaluation setup
# session = WolframLanguageSession('/path/to/WolframKernel')
# result = session.evaluate(wl.Integrate('x^2', 'x'))
# print(result)
```

--------------------------------

### Setup Logging to File (Python 3)

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/logger

Configures basic Python logging to a specified file with a predefined format. This version is for Python 3 environments and uses FileHandler with UTF-8 encoding.

```python
else:



[docs][](https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient.utils.html#wolframclient.utils.logger.setup_logging_to_file)
    defsetup_logging_to_file(path, level=None):
"""Setup a basic Python logging configuration to a given file."""
        logging.basicConfig(
            format="%(asctime)s, %(name)s%(levelname)s%(message)s",
            handlers=[logging.FileHandler(path, mode="a", encoding="utf-8")],
            level=(level is not None) or logging.INFO,
        )
```

--------------------------------

### Wolfram Client for Python Usage Documentation

Source: https://reference.wolfram.com/language/WolframClientForPython/py-modindex

Provides links to documentation pages covering installation, basic usage (expression representation, evaluation, cloud interactions, serialization), and advanced usage of the Wolfram Client for Python.

```APIDOC
Overview: https://reference.wolfram.com/language/WolframClientForPython/index.html

Installation and Prerequisites:
  Prerequisites: https://reference.wolfram.com/language/WolframClientForPython/docpages/install.html#prerequisites
  Installation: https://reference.wolfram.com/language/WolframClientForPython/docpages/install.html#installation

Basic Usage:
  Expression Representation: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages.html#expression-representation
  Expression Evaluation: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages.html#expression-evaluation
  Wolfram Cloud Interactions: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages.html#wolfram-cloud-interactions
  Serialization: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages.html#serialization

Advanced Usage: https://reference.wolfram.com/language/WolframClientForPython/docpages/advanced_usages.html
```

--------------------------------

### Start Authenticated Cloud Session

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Establishes an authenticated session with the Wolfram Cloud using previously generated credentials. This session is required for cloud evaluations.

```python
from wolframclient.evaluation import SecuredAuthenticationKey, WolframCloudSession
```

```python
sak = SecuredAuthenticationKey(
    'T2ghIEhpISDinIw=',
    'VGhhdCdzIE1ZIHNlY3JldCE=')

session = WolframCloudSession(credentials=sak)
session.start()
session.authorized()
# True
```

--------------------------------

### Start ZMQ Loop

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/externalevaluate

Starts the ZMQ event loop, processing messages until a message limit is reached. It sets up a consumer, handler, and socket writer, and logs side effects.

```python
def start_zmq_loop(
    message_limit=float("inf"), exception_class=None, routes_registry={},
):

    consumer = ExternalEvaluateConsumer(routes_registry=routes_registry)

    handler = to_wl(
        exception_class=exception_class,
        object_processor=lambda serializer, instance: serializer.encode(
            to_external_object(instance, consumer.objects_registry)
        ),
        target_format="wxf",
    )(handle_message)
    socket = start_zmq_instance(**opts)
    stream = SocketWriter(socket)
    messages = 0

    class SideEffectSender(logging.Handler):
        def emit(self, record):
            stream.send_side_effect(record.msg)

    side_effect_logger.addHandler(SideEffectSender())

    # now sit in a while loop, evaluating input
    while messages < message_limit:
        stream.write(handler(socket, consumer=consumer))
        messages += 1
```

--------------------------------

### WXFParser Initialization and Token Generation - Python

Source: https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient.deserializers

Demonstrates how to initialize the WXFParser with a file-like object and iterate through the generated WXF tokens. This is a low-level example for parsing WXF data.

```python
with open('/tmp/data.wxf', 'rb') as fp:
    parser = WXFParser(fp)
    gen = parser.tokens()
    print(next(gen))
```

--------------------------------

### Navigate to Wolfram Client for Python Directory

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/install

This snippet shows how to change the current directory to the Wolfram Client for Python installation path. This is a common step before running commands or scripts related to the library.

```bash
cd
```

--------------------------------

### Parallel Evaluation Example

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/advanced_usages

Demonstrates parallel evaluation of expressions that return the kernel process ID after a delay. It shows how to build a list of expressions and then evaluate them in parallel.

```python
expressions = ['Pause[1]; $ProcessID' for _ in range(10)]
parallel_evaluate(expressions)
```

--------------------------------

### WolframClientForPython start_externalevaluate Command API

Source: https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient.cli

Documentation for the Command class within the start_externalevaluate module. This class is responsible for handling the start external evaluate functionality.

```APIDOC
wolframclient.cli.commands.start_externalevaluate.Command:
  Bases: wolframclient.cli.utils.SimpleCommand
  add_arguments(parser):
    Adds arguments to the parser for this command.
  dependencies = ()
  handle(port=None, installpath=None, kernelversion=None, **opts):
    Handles the execution of the start external evaluate command with specified options.
```

--------------------------------

### WolframClientForPython Utility and Exception Methods

Source: https://reference.wolfram.com/language/WolframClientForPython/genindex

API documentation for utility functions and methods within WolframClientForPython, including data structure manipulation, logging setup, and exception handling.

```APIDOC
externalevaluate.Set()
  - Represents a Set operation.

WolframLanguageException.set_traceback()
  - Sets the traceback for a WolframLanguageException.

WolframClientForPython.language.exceptions.WolframLanguageException.set_traceback()
  - Sets the traceback for a WolframLanguageException.

externalevaluate.SetAttribute()
  - Represents a SetAttribute operation.

immutabledict.setdefault()
  - Returns the value of a key, or sets it to a default if not present.

externalevaluate.SetItem()
  - Represents a SetItem operation.

Settings (class in wolframclient.utils.datastructures)
  - A class for managing settings.

wolframclient.utils.logger.setup_logging_to_file()
  - Sets up logging to a file.

WolframLanguageException.show_traceback()
  - Displays the traceback for a WolframLanguageException.

WolframClientForPython.language.exceptions.WolframLanguageException.show_traceback()
  - Displays the traceback for a WolframLanguageException.

SimpleCommand (class in wolframclient.cli.utils)
  - Represents a simple command in the CLI.
```

--------------------------------

### Deploy API to Get Image Dimensions

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Shows how to deploy a Wolfram Language API that accepts an image and returns its dimensions as a JSON array using ImageDimensions.

```Wolfram Language
CloudDeploy[
  APIFunction[{"image"->"Image"},
    ImageDimensions[#image]&,
    "JSON"
  ],
  CloudObject["api/private/imagedimensions"]
]
```

--------------------------------

### Get Installed Modules

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/require

Retrieves a dictionary of installed Python modules and their versions using pip.

```python
from __future__ import absolute_import, print_function, unicode_literals

from functools import wraps

from wolframclient.utils.api import pip

def installed_modules():
    return {i.key: i.version for i in pip.get_installed_distributions()}
```

--------------------------------

### Get Wolfram Kernel Installation Version

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/environment

Retrieves the installed Wolfram Kernel version from the environment variable WOLFRAM_KERNEL_VERSION. If the variable is not set, it defaults to 12.0.

```python
from__future__import absolute_import, print_function, unicode_literals

fromwolframclient.utilsimport six
fromwolframclient.utils.apiimport os


definstallation_version():

    v = os.environ.get("WOLFRAM_KERNEL_VERSION", None)
    if v:
        return float(v)

    return 12.0
```

--------------------------------

### Combined Python and Kernel Logging Example

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/advanced_usages

Illustrates controlling log levels at both the Python and Wolfram Language kernel levels. It starts a session with kernel logging enabled at INFO level, sends messages from the kernel, and then disables kernel logging.

```python
import logging
from wolframclient.evaluation import WolframLanguageSession

logging.basicConfig(level=logging.INFO)

with WolframLanguageSession(kernel_loglevel=logging.INFO) as session:
    session.evaluate('ClientLibrary`info["ON -- Example message printed from the kernel \nwith log level INFO --"]')
    session.evaluate('ClientLibrary`debug["OFF -- Debug message."]')
    session.evaluate('ClientLibrary`DisableKernelLogging[]')
    session.evaluate('ClientLibrary`fatal["OFF -- Fatal message. Not printed"]')
    session.evaluate('ClientLibrary`info["OFF -- End of kernel evaluation. Not printed"]')
```

--------------------------------

### Install Missing Modules

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/require

Installs any missing Python modules required by the application. It handles installation within virtual environments and user-specific installations.

```python
def require_module(*modules):

    commands = list(missing_requirements(*modules))

    if commands:

        print("Update in progress: pip install %s --user" % " ".join(commands))

        if pip.running_under_virtualenv():
            pip.main(["install", *commands])
        else:
            pip.main(["install", "--user", *commands])
```

--------------------------------

### Get Length of an Object

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/externalevaluate

Returns the length (number of items) of an object. This is a generic way to get the size of collections or sequences.

```python
from wolframclient.utils import externalevaluate

# Example usage:
my_list = [10, 20, 30, 40]

consumer = None # Placeholder for consumer object
length = externalevaluate.Len(consumer, my_list)
print(length) # Output: 4
```

--------------------------------

### System and Global Context Factories

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/public_api

Demonstrates the use of pre-instantiated factories for 'System' and 'Global' contexts for easy symbol representation.

```Python
from wolframclient.language import System, Global

# Represent a symbol in the System context
print(System.ImageIdentify)

# Represent a symbol in the Global context
print(Global.mySymbol)

# Represent a function call in the Global context
print(Global.myFunction('foo'))
```

--------------------------------

### Decorator for Module Requirements

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/require

A decorator factory that ensures specified modules are installed before executing a function. It wraps the target function, performing the module check and installation if necessary.

```python
def require(*modules):
    def outer(func):
        @wraps(func)
        def inner(*args, **kw):
            require_module(*modules)
            return func(*args, **kw)

        return inner

    return outer
```

--------------------------------

### Call Wolfram API with Parameters

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Demonstrates how to initiate a WolframAPICall, set input parameters, perform the call, and retrieve the result. Requires an authenticated session and a deployed API.

```Python
from wolframclient.evaluation import WolframAPICall

# Assuming 'session' is an authenticated Wolfram Language session
call = WolframAPICall(session, ('MyWolframID', 'api/private/xsquared'))
call.set_parameter('x', 4)
result = call.perform()
print(result.get())
```

--------------------------------

### Get Length - WolframClientForPython

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/serializers/wxf

Determines the length of an iterable. If length is provided, it's used directly. Otherwise, it attempts to get the length using safe_len or by converting to a tuple.

```python
def get_length(iterable, length=None):
    if length is not None:
        return iterable, length

    length = safe_len(iterable)

    if length is not None:
        return iterable, length

    iterable = tuple(iterable)

    return iterable, len(iterable)
```

--------------------------------

### Benchmark Report Generation

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/cli/commands/benchmark

Generates a comprehensive benchmark report. It sets up the temporary directory, prepares benchmark data, runs initial deserialization for lazy loading, and then prints formatted tables for binary deserialization times across different complexities and export formats.

```python
def report(self):

        path = tempfile.gettempdir()

        benchmarks = [(c, self.expression_handler(c)) for c in self.complexity]

        self.table_line("dumping results in %s" % path)
        self.table_line()

        # running export to do all lazy loadings
        binary_deserialize(export(1, target_format="wxf"))

        self.table_line("* Binary deserialize")
        self.table_line()

        self.table_line(
            "Memory", *(force_text(c).ljust(self.col_size) for c in self.complexity)
        )
        self.table_divider(len(self.complexity) + 1)

        for label, opts in (("wxf", {}), ("wxf zip", {"compress": True})):

            self.table_line(
                label,
                *(
                    self.formatted_time(

```

--------------------------------

### Check for Missing Requirements

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/require

Yields a list of modules that are either not installed or do not match the specified version.

```python
def missing_requirements(*modules):

    distributions = installed_modules()

    for module in modules:
        version = None
        if isinstance(module, (tuple, list)):
            module, version = module

        if module not in distributions or (version and distributions[module] != version):
            yield version and "{}=={}".format(module, version) or module
```

--------------------------------

### Functional Utilities

Source: https://reference.wolfram.com/language/WolframClientForPython/genindex

Utility functions for functional programming, including getting the first element of a sequence and flattening lists.

```APIDOC
wolframclient.utils.functional.first(seq)
  Returns the first element of a sequence.
  Parameters:
    seq: The input sequence.
  Returns: The first element of the sequence.

wolframclient.utils.functional.flatten(seq)
  Flattens a nested sequence.
  Parameters:
    seq: The sequence to flatten.
  Returns: A flattened sequence.
```

--------------------------------

### Local Session Initialization and Expression Evaluation

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

Demonstrates how to initialize a local Wolfram Language session and evaluate expressions using the Wolfram Client for Python. It covers basic evaluation and options.

```python
from wolframclient.evaluation import WolframLanguageSession

# Initialize a local session
session = WolframLanguageSession()

# Evaluate an expression
result = session.evaluate('2 + 2')
print(result)

# Evaluate an expression with options
result_with_options = session.evaluate('Plot[Sin[x], {x, 0, 2 Pi}]', options={'ImageSize': (300, 200)})
print(result_with_options)

session.terminate()
```

--------------------------------

### Get Boolean Value of an Object

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/externalevaluate

Returns the boolean representation of an object. This is useful for conditional checks in external evaluations.

```python
from wolframclient.utils import externalevaluate

# Example usage:
consumer = None # Placeholder for consumer object

non_empty_list = [1]
empty_list = []

print(f"Boolean of non-empty list: {externalevaluate.Bool(consumer, non_empty_list)}") # Output: True
print(f"Boolean of empty list: {externalevaluate.Bool(consumer, empty_list)}")     # Output: False
```

--------------------------------

### Query Wolfram|Alpha Directly

Source: https://reference.wolfram.com/language/WolframClientForPython/index

Queries Wolfram|Alpha directly from Python to get specific information, like the number of moons of Saturn.

```python
>>> session.evaluate(wl.WolframAlpha("number of moons of Saturn", "Result"))
62
```

--------------------------------

### Import WXF File in Wolfram Desktop

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Demonstrates how to import a WXF file using Wolfram Desktop.

```Wolfram Language
Import("/path/to/file.wxf")
```

--------------------------------

### Get First Element of an Iterable

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/functional

Retrieves the first element from an iterable. If the iterable is empty, it returns a default value.

```python
from wolframclient.utils.functional import first

my_list = [1, 2, 3]
print(first(my_list))  # Output: 1

empty_list = []
print(first(empty_list, default=0))  # Output: 0
```

--------------------------------

### Get Attribute of an Object

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/externalevaluate

Retrieves an attribute from an object, supporting nested attribute access. Used for accessing properties of external objects.

```python
from wolframclient.utils import externalevaluate
from wolframclient.utils.iterate import iterate

# Example usage:
class MyClass:
    def __init__(self):
        self.nested = NestedClass()

class NestedClass:
    def __init__(self):
        self.value = 42

consumer = None # Placeholder for consumer object
obj = MyClass()
attribute_names = ['nested', 'value']
attribute_value = externalevaluate.GetAttribute(consumer, obj, attribute_names)
print(attribute_value) # Output: 42
```

--------------------------------

### Wolfram Client Library for Python - wolframclient.evaluation package structure

Source: https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient

This documentation outlines the structure of the wolframclient.evaluation package, including its subpackages and modules. It provides links to detailed API documentation for each component.

```APIDOC
wolframclient.evaluation package:
  Subpackages:
    - wolframclient.evaluation.cloud package
      - Submodules:
        - wolframclient.evaluation.cloud.asynccloudsession module
        - wolframclient.evaluation.cloud.asyncoauth module
        - wolframclient.evaluation.cloud.base module
        - wolframclient.evaluation.cloud.cloudsession module
        - wolframclient.evaluation.cloud.oauth module
        - wolframclient.evaluation.cloud.request_adapter module
        - wolframclient.evaluation.cloud.server module
    - wolframclient.evaluation.kernel package
      - Submodules:
        - wolframclient.evaluation.kernel.asyncsession module
        - wolframclient.evaluation.kernel.kernelcontroller module
        - wolframclient.evaluation.kernel.localsession module
        - wolframclient.evaluation.kernel.zmqsocket module
  Submodules:
    - wolframclient.evaluation.base module
    - wolframclient.evaluation.pool module
    - wolframclient.evaluation.result module
```

--------------------------------

### WolframClient Benchmark Command Class

Source: https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient.cli

Documentation for the Command class within the wolframclient.cli.commands.benchmark module. This class handles benchmark operations, including argument parsing, expression handling, and reporting.

```APIDOC
wolframclient.cli.commands.benchmark.Command:
  Bases: SimpleCommand

  Attributes:
    col_size: int = 8
    complexity: list[int] = [1, 2, 5, 10, 100, 1000]
    repetitions: int = 10
    title_size: int = 14

  Methods:
    add_arguments(parser):
      Adds command-line arguments to the parser for benchmark configuration.

    expression_handler(complexity):
      Handles the processing of expressions based on a given complexity level.

    formatted_time(function, *args, **opts):
      Measures and formats the execution time of a given function with its arguments and options.

    handle(profile, **opts):
      Main handler for the benchmark command, orchestrating the benchmarking process.

    report():
      Generates and displays the benchmark report.

    stream_generators(path):
      Streams data generators from a specified path.

    table_divider(length):
      Generates a table divider string of a given length.

    table_line(*iterable):
      Formats an iterable into a table row string.
```

--------------------------------

### Generate a Secured Authentication Key

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

Provides instructions on how to generate a secured authentication key for Wolfram Cloud interactions.

```bash
wolframscript -activate-user
```

--------------------------------

### Get Last Element of an Iterable

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/functional

Retrieves the last element from an iterable. If the iterable is empty, it returns a default value. Note: This may not be efficient for all iterable types.

```python
from wolframclient.utils.functional import last

my_list = [1, 2, 3]
print(last(my_list))  # Output: 3

empty_list = []
print(last(empty_list, default=0))  # Output: 0
```

--------------------------------

### Deploy a Wolfram Language API

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

Illustrates the process of deploying a Wolfram Language function as an API in the Wolfram Cloud.

```python
from wolframclient.evaluation import WolframCloudSession

session = WolframCloudSession()

# Deploy a simple Wolfram Language function as an API
api_name = 'SimpleAdder'
api_code = f'CloudDeploy[Function[{{x, y}}, x + y], "{api_name}"]'

result = session.evaluate(api_code)
print(f'API "{api_name}" deployed. Result: {result}')

session.terminate()
```

--------------------------------

### Representing Wolfram Language Symbol

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Demonstrates how to represent a Wolfram Language symbol, such as 'Now', using the `wl` factory.

```Python
wl.Now
```

--------------------------------

### Use WolframAPICall

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

Demonstrates the usage of the WolframAPICall utility for interacting with Wolfram Language APIs.

```python
from wolframclient.api import WolframAPICall

# Example: Calling a hypothetical Wolfram Language API that returns a list
api_url = 'YOUR_WOLFRAM_CLOUD_API_URL/GetData'

api_caller = WolframAPICall()

# Call the API without arguments
result = api_caller.call(api_url)
print(f'Data from API: {result}')

# Call the API with specific arguments
result_with_args = api_caller.call(api_url, arguments={'param1': 'value1', 'param2': 10})
print(f'Data with arguments: {result_with_args}')

```

--------------------------------

### WolframClient for Python API Documentation

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/cli/commands/benchmark

This section outlines the core functionalities and methods available in the Wolfram Client for Python library, including expression handling, evaluation, serialization, and cloud interactions.

```APIDOC
wolframclient.cli.commands.benchmark.Command.handle(profile, **opts)
    Handles the execution of benchmark tests. If 'profile' is True, it uses cProfile to report performance metrics; otherwise, it directly calls the report method.
    Parameters:
        profile (bool): If True, enables profiling.
        **opts: Additional options.

    wolframclient.cli.commands.benchmark.Command.report()
    Generates and displays benchmark reports for various operations.

    safe_import_string_and_call(import_string, function_string, function_args, global_vars)
    Safely imports a module and calls a function within it. Used for dynamic execution of profiling or reporting functions.
    Parameters:
        import_string (str): The string representation of the module to import (e.g., 'cProfile.runctx').
        function_string (str): The name of the function to call within the imported module.
        function_args (dict): Arguments to pass to the function.
        global_vars (dict): Global variables to be available during function execution.

    export(expr, target_format="wxf", **opts)
    Exports a Wolfram Language expression to a specified format. Supports formats like 'wxf' with optional compression.
    Parameters:
        expr: The Wolfram Language expression to export.
        target_format (str): The desired output format (default is 'wxf').
        **opts: Additional options for the export process, such as compression settings.

    binary_deserialize(data, **opts)
    Deserializes binary data into a Wolfram Language expression.
    Parameters:
        data: The binary data to deserialize.
        **opts: Additional options for deserialization.

    stream_generators(path)
    Provides generators for streaming data, likely used in conjunction with export operations.
    Parameters:
        path: Path information relevant to the stream generation.

    formatted_time(func, *args, **kwargs)
    A utility function to time the execution of a given function and format the output.
    Parameters:
        func: The function to execute and time.
        *args: Positional arguments to pass to the function.
        **kwargs: Keyword arguments to pass to the function.

    table_line(*args)
    Writes a line of data to a table format, used for displaying benchmark results.

    table_divider(num_columns)
    Writes a table divider line, used to structure the output table.

    complexity
    Attribute representing the complexity levels for benchmarking.

    col_size
    Attribute representing the column size for formatted output.

    force_text(obj)
    Ensures an object is converted to its text representation.
```

--------------------------------

### Extending Serialization: Writing an Encoder

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/serializers/encoder

Guides on how to extend the serialization capabilities of the Wolfram Client for Python by writing custom encoders for specific Python data types.

```Python
# Example structure for a custom encoder
# from wolframclient.serializers.encoder import WolframFunctionEncoder
# from wolframclient.language import wl

# class CustomObjectEncoder(WolframFunctionEncoder):
#     def __call__(self, obj, serializer):
#         if isinstance(obj, MyCustomClass):
#             return wl.MyWolframFunction(obj.attribute1, obj.attribute2)
#         return super().__call__(obj, serializer)

# To use the custom encoder:
# serializer = WolframSerializer(encoders={'MyCustomClass': CustomObjectEncoder()})

```

--------------------------------

### One-Shot Cloud Evaluation

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/toc

Demonstrates how to perform a single evaluation in the Wolfram Cloud without establishing a persistent session.

```python
from wolframclient.evaluation import WolframCloudSession

# Evaluate an expression directly in the cloud
result = WolframCloudSession().evaluate('1 + 1')
print(result)

```

--------------------------------

### Serializer Methods

Source: https://reference.wolfram.com/language/WolframClientForPython/genindex

Defines methods for generating byte representations of data using different serializer formats like WL and WXF. Includes methods for getting length and properties.

```python
from wolframclient.serializers.base import FormatSerializer
from wolframclient.serializers.wl import WLSerializer
from wolframclient.serializers.wxf import WXFSerializer, get_length
from wolframclient.serializers.encoder import Encoder

# Example usage (conceptual):
# serializer = WLSerializer()
# byte_data = serializer.generate_bytes(data)
# length = get_length(data)
# property = encoder.get_property(key)
```

--------------------------------

### Wolfram Client for Python CLI Dispatch Methods

Source: https://reference.wolfram.com/language/WolframClientForPython/genindex

Documentation for methods related to command dispatching in the CLI, including subcommand argument retrieval and listing available subcommands.

```APIDOC
DispatchCommand.subcommand_args(self, subcommand):
  Description: Retrieves arguments for a specific subcommand.
  Parameters:
    subcommand: The name of the subcommand.
  Returns: A list of argument specifications.

DispatchCommand.subcommands(self):
  Description: Returns a list of available subcommands.
  Returns: A dictionary mapping subcommand names to their configurations.
```

--------------------------------

### Get Item from an Object

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/externalevaluate

Retrieves an item from an object using key-based access (like dictionaries or lists), supporting nested access. Used for accessing elements within collections.

```python
from wolframclient.utils import externalevaluate
from wolframclient.utils.iterate import iterate

# Example usage:
my_dict = {'a': {'b': 100}}

consumer = None # Placeholder for consumer object
item_keys = ['a', 'b']
item_value = externalevaluate.GetItem(consumer, my_dict, item_keys)
print(item_value) # Output: 100
```

--------------------------------

### Representing Wolfram Language Function

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Shows how to construct a Wolfram Language function call, like `Select[PrimeQ, Range[5]]`, using the `wl` factory.

```Python
wl.Select(wl.PrimeQ, wl.Range(5))
```

--------------------------------

### Get Pandas Series Encoder from Index

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/serializers/encoders/pandas

Determines the appropriate encoder for a pandas Series based on its index type, a flag for using time series, and a specified form.

```python
defget_series_encoder_from_index(index, use_ts, form):

```

--------------------------------

### Create Pandas DataFrame

Source: https://reference.wolfram.com/language/WolframClientForPython/index

Demonstrates creating a Pandas DataFrame, which can be used with the Wolfram Client Library.

```python
>>> import pandas
>>> df = pandas.DataFrame({'A': [1, 2], 'B': [11, 12]}, index=['id1', 'id2'])
>>> df
       A   B
id1    1  11
id2    2  12
```

--------------------------------

### Dispatch Class API Documentation

Source: https://reference.wolfram.com/language/WolframClientForPython/_modules/wolframclient/utils/dispatch

API documentation for the Dispatch class, covering its initialization, registration of functions with type annotations, updating mappings, and unregistering implementations.

```APIDOC
class Dispatch:
    """A method dispatcher class allowing for multiple implementations of a function. Each implementation is associated to a specific input type.

    Implementations are registered with the annotation :meth:`~wolframclient.utils.dispatch.Dispatch.dispatch`.

    The Dispatch class is callable, it behaves as a function that uses the implementation corresponding to the input parameter.

    When a type is a subtype, the type and its parents are checked in the order given by :data:`__mro__` (method resolution order).

    *Example:* method :meth:`~wolframclient.utils.dispatch.Dispatch.resolve` applied to an instance of :class:`collections.OrderedDict`,
    check for the first implementation to match with :class:`collections.OrderedDict`, then with :class:`dict`, and ultimately to :data:`object`.

    Once the mapping is determined, it is cached for later use.
    """

    def __init__(self):
        """Initializes the Dispatch object and clears any existing mappings."""
        self.clear()

    def dispatch(self, *args, **opts):
        """Annotate a function and map it to a given set of type(s).

        Declare an implementation to use on :data:`bytearray` input::

            @dispatcher.dispatch(bytearray)
            def my_func(...)

        The default implementation is associated with :data:`object`. Set a default::

            @dispatcher.dispatch(object)
            def my_default_func(...)

        A tuple can be used as input to associate more than one type with a function. Declare a function used for both :data:`bytes` and :data:`bytearray`::

            @dispatcher.dispatch((bytes, bytearray))
            def my_func(...)

        Implementation must be unique. By default, registering the same combination of types will raise an error. Set `replace_existing` to :data:`True` to update the current mapping. Or, set `keep_existing` to :data:`True` to ignore duplicate registration and keep the existing mapping.
        """

        def register(func):
            return self.register(func, *args, **opts)

        return register

    def update(self, dispatch, **opts):
        """Update current mapping with the one from `dispatch`.

        `dispatch` can be a Dispatch instance or a :class:`dict`.
        `**opts` are passed to :meth:`~wolframclient.utils.dispatch.Dispatch.register`
        """
        if isinstance(dispatch, Dispatch):
            dispatchmapping = dispatch.dispatch_dict
        elif isinstance(dispatch, dict):
            dispatchmapping = dispatch
        else:
            raise ValueError("%s is not an instance of Dispatch" % dispatch)
        for t, function in dispatchmapping.items():
            self.register(function, t, **opts)

    def validate_types(self, types):
        """Validates that the provided types are classes."""
        for t in frozenset(flatten(types)):
            if not inspect.isclass(t):
                raise ValueError("%s is not a class" % t)
            yield t

    def register(self, function, types=object, keep_existing=False, replace_existing=False):
        """Equivalent to annotation :meth:`~wolframclient.utils.dispatch.Dispatch.dispatch` but as a function.

        Registers a function with a given set of types.
        
        Args:
            function: The function to register.
            types: The type(s) to associate the function with. Defaults to `object`.
            keep_existing: If True, ignores duplicate registrations and keeps the existing mapping.
            replace_existing: If True, updates the mapping for existing types.
        
        Raises:
            ValueError: If `keep_existing` and `replace_existing` are both True, or if a type is not a class.
            TypeError: If a duplicate registration occurs and `keep_existing` is False.
        """
        if not callable(function):
            raise ValueError("Function %s is not callable" % function)
        if keep_existing and replace_existing:
            raise ValueError(
                "Option values keep_existing and replace_existing cannot be both True."
            )

        self.clear_cache()

        for t in self.validate_types(types):
            if replace_existing:
                self.dispatch_dict[t] = function
            elif t in self.dispatch_dict:
                if not keep_existing:
                    raise TypeError("Duplicated registration for input type(s): {}".format(t))
            else:
                self.dispatch_dict[t] = function

        return function

    def unregister(self, types=object):
        """Remove implementations associated with types."""

        self.clear_cache()

        for t in self.validate_types(types):
            try:
                del self.dispatch_dict[t]
            except KeyError:
                pass

```

--------------------------------

### Representing Function with Options

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Illustrates how to represent a Wolfram Language function with options, such as `ArrayPad` with the `Padding` option.

```Python
wl.ArrayPad([[0]], 1, Padding=1)
```

--------------------------------

### Get Image Dimensions using Wolfram Language

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Loads an image using PIL and then uses the Wolfram Language to compute its dimensions, demonstrating inter-process communication and evaluation.

```Python
>>> from PIL import Image
>>> img = Image.open('/tmp/data.png')
>>> session.evaluate(wl.ImageDimensions(img))
[360, 219]
```

--------------------------------

### Create Parser Utility

Source: https://reference.wolfram.com/language/WolframClientForPython/genindex

The create_parser method from the SimpleCommand class in wolframclient.cli.utils is used for creating command-line argument parsers.

```APIDOC
create_parser()
  - Creates an argument parser for command-line interfaces.
  - Part of the SimpleCommand class in wolframclient.cli.utils.
```

--------------------------------

### WolframClient Refactor Module Commands

Source: https://reference.wolfram.com/language/WolframClientForPython/api/wolframclient.cli

Documentation for the refactor module within the WolframClient CLI. This includes the Command class, its methods for handling operations, managing modules, and running processes, as well as a utility function for waiting on process completion.

```APIDOC
wolframclient.cli.commands.refactor.Command
    Bases: SimpleCommand

    handle(**opts)
        Handles refactoring operations.

    modules
        List of modules associated with refactoring.

    run(pre, *args)
        Executes the refactoring process.

wolframclient.cli.commands.refactor.wait_for_process(processes, raise_errors=True, show_output=False)
    Waits for a list of processes to complete.
    Parameters:
      processes: A list of process objects to wait for.
      raise_errors: If True, raises an exception if any process fails.
      show_output: If True, displays the output of the processes.
```

--------------------------------

### Create Bar Chart and Export to PNG

Source: https://reference.wolfram.com/language/WolframClientForPython/docpages/basic_usages

Demonstrates creating a BarChart from Python data and exporting it to a PNG file using Wolfram Language functions via the WolframClient for Python.

```Python
>>> data = [.3, 2, 4, 5, 5.5]
>>> graphic = wl.BarChart(data)
>>> path = "/tmp/data.png"
>>> png_export = wl.Export(path, graphic, "PNG")
>>> session.evaluate(png_export)
'/tmp/data.png'
```
Tokens:
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