Package 'reticulate'

Description

Reticulate provides S3 Ops Group Generic Methods for Python objects. The methods invoke the equivalent python method of the object.

Usage

## S3 method for class 'python.builtin.object'
e1 == e2

## S3 method for class 'python.builtin.object'
e1 != e2

## S3 method for class 'python.builtin.object'
e1 < e2

## S3 method for class 'python.builtin.object'
e1 > e2

## S3 method for class 'python.builtin.object'
e1 >= e2

## S3 method for class 'python.builtin.object'
e1 <= e2

## S3 method for class 'python.builtin.object'
e1 + e2

## S3 method for class 'python.builtin.object'
e1 - e2

## S3 method for class 'python.builtin.object'
e1 * e2

## S3 method for class 'python.builtin.object'
e1 / e2

## S3 method for class 'python.builtin.object'
e1 %/% e2

## S3 method for class 'python.builtin.object'
e1 %% e2

## S3 method for class 'python.builtin.object'
e1 ^ e2

## S3 method for class 'python.builtin.object'
e1 & e2

## S3 method for class 'python.builtin.object'
e1 | e2

## S3 method for class 'python.builtin.object'
!e1

## S3 method for class 'python.builtin.object'
x %*% y

Arguments

Value

Result from evaluating the Python expression. If either of the arguments to the operator was a Python object with convert=FALSE, then the result will also be a Python object with convert=FALSE set. Otherwise, the result will be converted to an R object if possible.

Operator Mappings

Note: If the initial Python method invoked raises a NotImplemented Exception, the Python interpreter will attempt to use the reflected variant of the method from the second argument. The arithmetic operators will call the equivalent double underscore (dunder) method with an "r" prefix. For instance, when evaluating the expression x + y, if ⁠type(x).__add__(x, y)⁠ raises a NotImplemented exception, then the interpreter will attempt ⁠type(y).__radd__(y, x)⁠. The comparison operators follow a different sequence of fallbacks; refer to the Python documentation for more details.

Description

Reshape (reindex) a multi-dimensional array, using row-major (C-style) reshaping semantics by default.

Usage

array_reshape(x, dim, order = c("C", "F"))

Arguments

Details

This function differs from e.g. dim(x) <- dim in a very important way: by default, array_reshape() will fill the new dimensions in row-major (C-style) ordering, while dim<-() will fill new dimensions in column-major (Fortran-style) ordering. This is done to be consistent with libraries like NumPy, Keras, and TensorFlow, which default to this sort of ordering when reshaping arrays. See the examples for why this difference may be important.

Examples

## Not run: 
# let's construct a 2x2 array from a vector of 4 elements
x <- 1:4

# rearrange will fill the array row-wise
array_reshape(x, c(2, 2))
#      [,1] [,2]
# [1,]    1    2
# [2,]    3    4
# setting the dimensions 'fills' the array col-wise
dim(x) <- c(2, 2)
x
#      [,1] [,2]
# [1,]    1    3
# [2,]    2    4

## End(Not run)

Description

Traverse a Python iterator or generator

Usage

as_iterator(x)

iterate(it, f = base::identity, simplify = TRUE)

iter_next(it, completed = NULL)

Arguments

Details

Simplification is only attempted all elements are length 1 vectors of type "character", "complex", "double", "integer", or "logical".

Value

For iterate(), A list or vector containing the results of calling f on each item in x (invisibly); For iter_next(), the next value in the iteration (or the sentinel completed value if the iteration is complete).

Description

Convert Python bytes to an R character vector

Usage

## S3 method for class 'python.builtin.bytes'
as.character(x, encoding = "utf-8", errors = "strict", ...)

Arguments

Description

Tools for managing Python conda environments.

Usage

conda_list(conda = "auto")

conda_create(
  envname = NULL,
  packages = NULL,
  ...,
  forge = TRUE,
  channel = character(),
  environment = NULL,
  conda = "auto",
  python_version = miniconda_python_version(),
  additional_create_args = character()
)

conda_clone(envname, ..., clone = "base", conda = "auto")

conda_export(
  envname,
  file = if (json) "environment.json" else "environment.yml",
  json = FALSE,
  ...,
  conda = "auto"
)

conda_remove(envname, packages = NULL, conda = "auto")

conda_install(
  envname = NULL,
  packages,
  forge = TRUE,
  channel = character(),
  pip = FALSE,
  pip_options = character(),
  pip_ignore_installed = FALSE,
  conda = "auto",
  python_version = NULL,
  additional_create_args = character(),
  additional_install_args = character(),
  ...
)

conda_binary(conda = "auto")

conda_exe(conda = "auto")

conda_version(conda = "auto")

conda_update(conda = "auto")

conda_python(envname = NULL, conda = "auto", all = FALSE)

conda_search(
  matchspec,
  forge = TRUE,
  channel = character(),
  conda = "auto",
  ...
)

condaenv_exists(envname = NULL, conda = "auto")

Arguments

Value

conda_list() returns an R data.frame, with name giving the name of the associated environment, and python giving the path to the Python binary associated with that environment.

conda_create() returns the path to the Python binary associated with the newly-created conda environment.

conda_clone() returns the path to Python within the newly-created conda environment.

conda_export() returns the path to the exported environment definition, invisibly.

conda_search() returns an R data.frame describing packages that matched against matchspec. The data frame will usually include fields name giving the package name, version giving the package version, build giving the package build, and channel giving the channel the package is hosted on.

Finding Conda

Most of reticulate's conda APIs accept a conda parameter, used to control the conda binary used in their operation. When conda = "auto", reticulate will attempt to automatically find a conda installation. The following locations are searched, in order:

1. The location specified by the reticulate.conda_binary R option,

2. The location specified by the RETICULATE_CONDA environment variable,

3. The miniconda_path() location (if it exists),

4. The program PATH,

5. A set of pre-defined locations where conda is typically installed.

To force reticulate to use a particular conda binary, we recommend setting:

options(reticulate.conda_binary = "/path/to/conda")

This can be useful if your conda installation lives in a location that reticulate is unable to automatically discover.

Description

Configure a Python environment, satisfying the Python dependencies of any loaded R packages.

Usage

configure_environment(package = NULL, force = FALSE)

Arguments

Details

Normally, this function should only be used by package authors, who want to ensure that their package dependencies are installed in the active Python environment. For example:

.onLoad <- function(libname, pkgname) {
  reticulate::configure_environment(pkgname)
}

If the Python session has not yet been initialized, or if the user is not using the default Miniconda Python installation, no action will be taken. Otherwise, reticulate will take this as a signal to install any required Python dependencies into the user's Python environment.

If you'd like to disable reticulate's auto-configure behavior altogether, you can set the environment variable:

RETICULATE_AUTOCONFIGURE = FALSE

e.g. in your ⁠~/.Renviron⁠ or similar.

Note that, in the case where the Python session has not yet been initialized, reticulate will automatically ensure your required Python dependencies are installed after the Python session is initialized (when appropriate).

Description

Create a Python dictionary object, including a dictionary whose keys are other Python objects rather than character vectors.

Usage

dict(..., convert = FALSE)

py_dict(keys, values, convert = FALSE)

Arguments

Value

A Python dictionary

Note

The returned dictionary will not automatically convert its elements from Python to R. You can do manual conversion with the py_to_r() function or pass convert = TRUE to request automatic conversion.

Description

This provides a reticulate engine for knitr, suitable for usage when attempting to render Python chunks. Using this engine allows for shared state between Python chunks in a document – that is, variables defined by one Python chunk can be used by later Python chunks.

Usage

eng_python(options)

Arguments

Details

The engine can be activated by setting (for example)

knitr::knit_engines$set(python = reticulate::eng_python)

Typically, this will be set within a document's setup chunk, or by the environment requesting that Python chunks be processed by this engine. Note that knitr (since version 1.18) will use the reticulate engine by default when executing Python chunks within an R Markdown document.

Supported knitr chunk options

For most options, reticulate's python engine behaves the same as the default R engine included in knitr, but they might not support all the same features. Options in italic are equivalent to knitr, but with modified behavior.

• eval (TRUE, logical): If TRUE, all expressions in the chunk are evaluated. If FALSE, no expression is evaluated. Unlike knitr's R engine, it doesn't support numeric values indicating the expressions to evaluate.

• echo (TRUE, logical): Whether to display the source code in the output document. Unlike knitr's R engine, it doesn't support numeric values indicating the expressions to display.

• results ('markup', character): Controls how to display the text results. Note that this option only applies to normal text output (not warnings, messages, or errors). The behavior should be identical to knitr's R engine.

• collapse (FALSE, logical): Whether to, if possible, collapse all the source and output blocks from one code chunk into a single block (by default, they are written to separate blocks). This option only applies to Markdown documents.

• error (TRUE, logical): Whether to preserve errors. If FALSE evaluation stops on errors. (Note that RMarkdown sets it to FALSE).

• warning (TRUE, logical): Whether to preserve warnings in the output. If FALSE, all warnings will be suppressed. Doesn't support indices.

• include (TRUE, logical): Whether to include the chunk output in the output document. If FALSE, nothing will be written into the output document, but the code is still evaluated and plot files are generated if there are any plots in the chunk, so you can manually insert figures later.

• dev: The graphical device to generate plot files. See knitr documentation for additional information.

• base.dir (NULL; character): An absolute directory under which the plots are generated.

• strip.white (TRUE; logical): Whether to remove blank lines in the beginning or end of a source code block in the output.

• dpi (72; numeric): The DPI (dots per inch) for bitmap devices (dpi * inches = pixels).

• fig.width, fig.height (both are 7; numeric): Width and height of the plot (in inches), to be used in the graphics device.

• label: The chunk label for each chunk is assumed to be unique within the document. This is especially important for cache and plot filenames, because these filenames are based on chunk labels. Chunks without labels will be assigned labels like unnamed-chunk-i, where i is an incremental number.

Python engine only options

• jupyter_compat (FALSE, logical): If TRUE then, like in Jupyter notebooks, only the last expression in the chunk is printed to the output.

• out.width.px, out.height.px (810, 400, both integers): Width and height of the plot in the output document, which can be different with its physical fig.width and fig.height, i.e., plots can be scaled in the output document. Unlike knitr's out.width, this is always set in pixels.

• altair.fig.width, altair.fig.height: If set, is used instead of out.width.px and out.height.px when writing Altair charts.

Description

Import the specified Python module, making it available for use from R.

Usage

import(module, as = NULL, convert = TRUE, delay_load = FALSE)

import_main(convert = TRUE, delay_load = FALSE)

import_builtins(convert = TRUE, delay_load = FALSE)

import_from_path(module, path = ".", convert = TRUE, delay_load = FALSE)

Arguments

Value

An R object wrapping a Python module. Module attributes can be accessed via the $ operator, or via py_get_attr().

Python Built-ins

Python's built-in functions (e.g. len()) can be accessed via Python's built-in module. Because the name of this module has changed between Python 2 and Python 3, we provide the function import_builtins() to abstract over that name change.

Delay Load

The delay_load parameter accepts a variety of inputs. If you just need to ensure your module is lazy-loaded (e.g. because you are a package author and want to avoid initializing Python before the user has explicitly requested it), then passing TRUE is normally the right choice.

You can also provide a named list: "before_load", "on_load" and "on_error" can be functions , which act as callbacks to be run when the module is later loaded. "environment" can be a character vector of preferred python environment names to search for and use. For example:

delay_load = list(

  # run before the module is loaded
  before_load = function() { ... }

  # run immediately after the module is loaded
  on_load = function() { ... }

  # run if an error occurs during module import
  on_error = function(error) { ... }

  environment = c("r-preferred-venv1", "r-preferred-venv2")
)

Alternatively, if you supply only a single function, that will be treated as an on_load handler.

Import from Path

import_from_path() can be used in you need to import a module from an arbitrary filesystem path. This is most commonly used when importing modules bundled with an R package – for example:

path <- system.file("python", package = <package>)
reticulate::import_from_path(<module>, path = path, delay_load = TRUE)

Examples

## Not run: 
main <- import_main()
sys <- import("sys")

## End(Not run)

Description

Download the Miniconda installer, and use it to install Miniconda.

Usage

install_miniconda(path = miniconda_path(), update = TRUE, force = FALSE)

Arguments

Details

For arm64 builds of R on macOS, install_miniconda() will use binaries from miniforge instead.

Note

If you encounter binary incompatibilities between R and Miniconda, a scripted build and installation of Python from sources can be performed by install_python()

See Also

Other miniconda-tools: miniconda_uninstall(), miniconda_update()

Description

Download and install Python, using the pyenv. and pyenv-win projects.

Usage

install_python(
  version = "3.10:latest",
  list = FALSE,
  force = FALSE,
  optimized = TRUE
)

Arguments

Details

In general, it is recommended that Python virtual environments are created using the copies of Python installed by install_python(). For example:

library(reticulate)
version <- "3.9.12"
install_python(version)
virtualenv_create("my-environment", version = version)
use_virtualenv("my-environment")

# There is also support for a ":latest" suffix to select the latest patch release
install_python("3.9:latest") # install latest patch available at python.org

# select the latest 3.9.* patch installed locally
virtualenv_create("my-environment", version = "3.9:latest")

Note

On macOS and Linux this will build Python from sources, which may take a few minutes. Installation will be faster if some build dependencies are preinstalled. See https://github.com/pyenv/pyenv/wiki#suggested-build-environment for example commands you can run to pre-install system dependencies (requires administrator privileges).

If optimized = TRUE, (the default) Python is build with:

PYTHON_CONFIGURE_OPTS="--enable-shared --enable-optimizations --with-lto"
PYTHON_CFLAGS="-march=native -mtune=native"

If optimized = FALSE, Python is built with:

PYTHON_CONFIGURE_OPTS=--enable-shared

On Windows, prebuilt installers from https://www.python.org are used.

Description

The path to the Miniconda installation to use. By default, an OS-specific path is used. If you'd like to instead set your own path, you can set the RETICULATE_MINICONDA_PATH environment variable.

Usage

miniconda_path()

Description

Uninstall Miniconda.

Usage

miniconda_uninstall(path = miniconda_path())

Arguments

See Also

Other miniconda-tools: install_miniconda(), miniconda_update()

Description

Update Miniconda to the latest version.

Usage

miniconda_update(path = miniconda_path())

Arguments

See Also

Other miniconda-tools: install_miniconda(), miniconda_uninstall()

Description

This generic enables passing a python.builtin.type object as the 2nd argument to base::inherits().

Usage

## S3 method for class 'python.builtin.type'
nameOfClass(x)

Arguments

Value

A scalar string matching the S3 class of objects constructed from the type.

Examples

## Not run: 
  numpy <- import("numpy")
  x <- r_to_py(array(1:3))
  inherits(x, numpy$ndarray)

## End(Not run)

Description

Create NumPy arrays and convert the data type and in-memory ordering of existing NumPy arrays.

Usage

np_array(data, dtype = NULL, order = "C")

Arguments

Value

A NumPy array object.

Description

The py object provides a means for interacting with the Python main session directly from R. Python objects accessed through py are automatically converted into R objects, and can be used with any other R functions as needed.

Usage

py

Format

An R object acting as an interface to the Python main module.

Description

Check if Python is available on this system

Usage

py_available(initialize = FALSE)

py_numpy_available(initialize = FALSE)

Arguments

Value

Logical indicating whether Python is initialized.

Note

The py_numpy_available function is a superset of the py_available function (it calls py_available first before checking for NumPy).

Description

Equivalent to bool(x) in Python, or ⁠not not x⁠.

Usage

py_bool(x)

Arguments

Details

If the Python object defines a ⁠__bool__⁠ method, then that is invoked. Otherwise, if the object defines a ⁠__len__⁠ method, then TRUE is returned if the length is nonzero. If neither ⁠__len__⁠ nor ⁠__bool__⁠ are defined, then the Python object is considered TRUE.

Value

An R scalar logical: TRUE or FALSE. If x is a null pointer or Python is not initialized, FALSE is returned.

Description

Capture and return Python output

Usage

py_capture_output(expr, type = c("stdout", "stderr"))

Arguments

Value

Character vector with output

Description

Get or (re)set the last Python error encountered.

Usage

py_clear_last_error()

py_last_error(exception)

Arguments

Value

For py_last_error(), NULL if no error has yet been encountered. Otherwise, a named list with entries:

• "type": R string, name of the exception class.

• "value": R string, formatted exception message.

• "traceback": R character vector, the formatted python traceback,

• "message": The full formatted raised exception, as it would be printed in Python. Includes the traceback, type, and value.

• "r_trace": A data.frame with class rlang_trace and columns:

  • call: The R callstack, full_call, summarized for pretty printing.

  • full_call: The R callstack. (Output of sys.calls() at the error callsite).

  • parent: The parent of each frame in callstack. (Output of sys.parents() at the error callsite).

  • Additional columns for internals use: namespace, visible, scope.

And attribute "exception", a 'python.builtin.Exception' object.

The named list has class "py_error", and has a default print method that is the equivalent of cat(py_last_error()$message).

Examples

## Not run: 

# see last python exception with R traceback
reticulate::py_last_error()

# see the full R callstack from the last Python exception
reticulate::py_last_error()$r_trace$full_call

# run python code that might error,
# without modifying the user-visible python exception

safe_len <- function(x) {
  last_err <- py_last_error()
  tryCatch({
    # this might raise a python exception if x has no `__len__` method.
    import_builtins()$len(x)
  }, error = function(e) {
    # py_last_error() was overwritten, is now "no len method for 'object'"
    py_last_error(last_err) # restore previous exception
    -1L
  })
}

safe_len(py_eval("object"))

## End(Not run)

Description

Retrieve information about the version of Python currently being used by reticulate.

Usage

py_config()

Details

If Python has not yet been initialized, then calling py_config() will force the initialization of Python. See py_discover_config() for more details.

Value

Information about the version of Python in use, as an R list with class "py_config".

Description

Delete an attribute of a Python object

Usage

py_del_attr(x, name)

Arguments

Description

This function enables callers to check which versions of Python will be discovered on a system as well as which one will be chosen for use with reticulate.

Usage

py_discover_config(required_module = NULL, use_environment = NULL)

Arguments

Details

The order of discovery is documented in vignette("versions"), also available online here

Value

Python configuration object.

Description

The builtin constant Ellipsis

Usage

py_ellipsis()

Description

Evaluate a single Python expression, in a way analogous to the Python eval() built-in function.

Usage

py_eval(code, convert = TRUE)

Arguments

Value

The result produced by evaluating code, converted to an R object when convert is set to TRUE.

Caveats

py_eval() only supports evaluation of 'simple' Python expressions. Other expressions (e.g. assignments) will fail; e.g.

> py_eval("x = 1")
Error in py_eval_impl(code, convert) :
  SyntaxError: invalid syntax (reticulate_eval, line 1)

and this mirrors what one would see in a regular Python interpreter:

>>> eval("x = 1")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<string>", line 1
x = 1
^
  SyntaxError: invalid syntax

The py_run_string() method can be used if the evaluation of arbitrary Python code is required.

Description

Get the path to the Python executable that reticulate has been configured to use. If Python has already been initialized, then reticulate will choose the currently-active copy of Python.

Usage

py_exe()

Details

This can occasionally be useful if you'd like to interact with Python (or its modules) via a subprocess; for example you might choose to install a package with pip:

system2(py_exe(), c("-m", "pip", "install", "numpy"))

and so you can also have greater control over how these modules are invoked.

Value

The path to the Python executable reticulate has been configured to use.

Description

This function could wrap an R function in a Python function with the same signature. Note that the signature of the R function must not contain esoteric Python-incompatible constructs.

Usage

py_func(f)

Arguments

Value

A Python function that calls the R function f with the same signature.

Description

This function can be used to generate R wrapper for a specified Python function while allowing to inject custom code for critical parts of the wrapper generation, such as process the any part of the docs obtained from py_function_docs() and append additional roxygen fields. The result from execution of python_function is assigned to a variable called python_function_result that can also be processed by postprocess_fn before writing the closing curly braces for the generated wrapper function.

Usage

py_function_custom_scaffold(
  python_function,
  r_function = NULL,
  additional_roxygen_fields = NULL,
  process_docs_fn = function(docs) docs,
  process_param_fn = function(param, docs) param,
  process_param_doc_fn = function(param_doc, docs) param_doc,
  postprocess_fn = function() {
 },
  file_name = NULL
)

Arguments

Examples

## Not run: 

library(tensorflow)
library(stringr)

# Example of a `process_param_fn` to cast parameters with default values
# that contains "L" to integers
process_int_param_fn <- function(param, docs) {
  # Extract the list of parameters that have integer values as default
  int_params <- gsub(
    " = [-]?[0-9]+L",
    "",
    str_extract_all(docs$signature, "[A-z]+ = [-]?[0-9]+L")[[1]])
  # Explicitly cast parameter in the list obtained above to integer
  if (param %in% int_params) {
    param <- paste0("as.integer(", param, ")")
  }
  param
}

# Note that since the default value of parameter `k` is `1L`. It is wrapped
# by `as.integer()` to ensure it's casted to integer before sending it to `tf$nn$top_k`
# for execution. We then print out the python function result.
py_function_custom_scaffold(
  "tf$nn$top_k",
  r_function = "top_k",
  process_param_fn = process_int_param_fn,
  postprocess_fn = function() { "print(python_function_result)" })


## End(Not run)

Description

Get an attribute of a Python object

Usage

py_get_attr(x, name, silent = FALSE)

Arguments

Value

Attribute of Python object

Description

Access an item from a Python object, similar to how x[key] might be used in Python code to access an item indexed by key on an object x. The object's ⁠__getitem__()⁠ ⁠__setitem__()⁠ or ⁠__delitem__()⁠ method will be called.

Usage

py_get_item(x, key, silent = FALSE)

py_set_item(x, key, value)

py_del_item(x, key)

## S3 method for class 'python.builtin.object'
x[...]

## S3 replacement method for class 'python.builtin.object'
x[...] <- value

Arguments

Value

For py_get_item() and [, the return value from the x.__getitem__() method. For py_set_item(), py_del_item() and ⁠[<-⁠, the mutate object x is returned.

Note

The py_get_item() always returns an unconverted python object, while [ will automatically attempt to convert the object if x was created with convert = TRUE.

Examples

## Not run: 

## get/set/del item from Python dict
x <- r_to_py(list(abc = "xyz"))

#'   # R expression    | Python expression
# -------------------- | -----------------
 x["abc"]              # x["abc"]
 x["abc"] <- "123"     # x["abc"] = "123"
 x["abc"] <- NULL      # del x["abc"]
 x["abc"] <- py_none() # x["abc"] = None

## get item from Python list
x <- r_to_py(list("a", "b", "c"))
x[0]

## slice a NumPy array
x <- np_array(array(1:64, c(4, 4, 4)))

# R expression | Python expression
# ------------ | -----------------
  x[0]         # x[0]
  x[, 0]       # x[:, 0]
  x[, , 0]     # x[:, :, 0]

  x[NA:2]      # x[:2]
  x[`:2`]      # x[:2]

  x[2:NA]      # x[2:]
  x[`2:`]      # x[2:]

  x[NA:NA:2]   # x[::2]
  x[`::2`]     # x[::2]

  x[1:3:2]     # x[1:3:2]
  x[`1:3:2`]   # x[1:3:2]


## End(Not run)

Description

Check whether a Python object x has an attribute name.

Usage

py_has_attr(x, name)

Arguments

Value

TRUE if the object has the attribute name, and FALSE otherwise.

Description

Documentation for Python Objects

Usage

py_help(object)

Arguments

Description

Get a globally unique identifier for a Python object.

Usage

py_id(object)

Arguments

Value

Unique identifer (as string) or NULL

Note

In the current implementation of CPython this is the memory address of the object.

Description

Install Python packages into a virtual environment or Conda environment.

Usage

py_install(
  packages,
  envname = NULL,
  method = c("auto", "virtualenv", "conda"),
  conda = "auto",
  python_version = NULL,
  pip = FALSE,
  ...,
  pip_ignore_installed = ignore_installed,
  ignore_installed = FALSE
)

Arguments

Details

On Linux and OS X the "virtualenv" method will be used by default ("conda" will be used if virtualenv isn't available). On Windows, the "conda" method is always used.

See Also

conda_install(), for installing packages into conda environments. virtualenv_install(), for installing packages into virtual environments.

Description

Check if a Python object is a null externalptr

Usage

py_is_null_xptr(x)

py_validate_xptr(x)

Arguments

Details

When Python objects are serialized within a persisted R environment (e.g. .RData file) they are deserialized into null externalptr objects (since the Python session they were originally connected to no longer exists). This function allows you to safely check whether whether a Python object is a null externalptr.

The py_validate function is a convenience function which calls py_is_null_xptr and throws an error in the case that the xptr is NULL.

Value

Logical indicating whether the object is a null externalptr

Description

Create a Python iterator from an R function

Usage

py_iterator(fn, completed = NULL, prefetch = 0L)

Arguments

Details

Python generators are functions that implement the Python iterator protocol. In Python, values are returned using the yield keyword. In R, values are simply returned from the function.

In Python, the yield keyword enables successive iterations to use the state of previous iterations. In R, this can be done by returning a function that mutates its enclosing environment via the ⁠<<-⁠ operator. For example:

sequence_generator <- function(start) {
  value <- start
  function() {
    value <<- value + 1
    value
  }
}

Then create an iterator using py_iterator():

g <- py_iterator(sequence_generator(10))

Value

Python iterator which calls the R function for each iteration.

Ending Iteration

In Python, returning from a function without calling yield indicates the end of the iteration. In R however, return is used to yield values, so the end of iteration is indicated by a special return value (NULL by default, however this can be changed using the completed parameter). For example:

sequence_generator <-function(start) {
  value <- start
  function() {
    value <<- value + 1
    if (value < 100)
      value
    else
      NULL
  }
}

Threading

Some Python APIs use generators to parallellize operations by calling the generator on a background thread and then consuming its results on the foreground thread. The py_iterator() function creates threadsafe iterators by ensuring that the R function is always called on the main thread (to be compatible with R's single-threaded runtime) even if the generator is run on a background thread.

Description

Get the length of a Python object. This is equivalent to calling the Python builtin len() function on the object.

Usage

py_len(x, default = NULL)

Arguments

Details

Not all Python objects have a defined length. For objects without a defined length, calling py_len() will throw an error. If you'd like to instead infer a default length in such cases, you can set the default argument to e.g. 1L, to treat Python objects without a ⁠__len__⁠ method as having length one.

Value

The length of the object, as a numeric value.

Description

List all attributes of a Python object

Usage

py_list_attributes(x)

Arguments

Value

Character vector of attributes

Description

List the Python packages that are installed in the requested Python environment.

Usage

py_list_packages(
  envname = NULL,
  type = c("auto", "virtualenv", "conda"),
  python = NULL
)

Arguments

Details

When envname is NULL, reticulate will use the "default" version of Python, as reported by py_exe(). This implies that you can call py_list_packages() without arguments in order to list the installed Python packages in the version of Python currently used by reticulate.

Value

An R data.frame, with columns:

package

The package name.

version

The package version.

requirement

The package requirement.

channel

(Conda only) The channel associated with this package.

Description

This function is helpful when you need to provide a callback to a Python library which may invoke the callback on a background thread. As R functions must run on the main thread, wrapping the R function with py_main_thread_func() will ensure that R code is only executed on the main thread.

Usage

py_main_thread_func(f)

Arguments

Value

A Python function that delegates to the passed R function, which is guaranteed to always be called on the main thread.

Description

Note that this function will also attempt to initialize Python before checking if the requested module is available.

Usage

py_module_available(module)

Arguments

Value

TRUE if the module is available and can be loaded; FALSE otherwise.

Description

Get a reference to the Python None object.

Usage

py_none()

Description

This is equivalent to calling str(object) or repr(object) in Python.

Usage

py_repr(object)

py_str(object, ...)

Arguments

Details

In Python, calling print() invokes the builtin str(), while auto-printing an object at the REPL invokes the builtin repr().

In R, the default print method for python objects invokes py_repr(), and the default format() and as.character() methods invoke py_str().

For historical reasons, py_str() is also an R S3 method that allows R authors to customize the the string representation of a Python object from R. New code is recommended to provide a format() and/or print() S3 R method for python objects instead.

The default implementation will call PyObject_Str on the object.

Value

Character vector

Description

Execute code within the scope of the __main__ Python module.

Usage

py_run_string(code, local = FALSE, convert = TRUE)

py_run_file(file, local = FALSE, convert = TRUE, prepend_path = TRUE)

Arguments

Value

A Python dictionary of objects. When local is FALSE, this dictionary captures the state of the Python main module after running the provided code. Otherwise, only the variables defined and used are captured.

Description

Save and load Python objects.

Usage

py_save_object(object, filename, pickle = "pickle", ...)

py_load_object(filename, pickle = "pickle", ..., convert = TRUE)

Arguments

Details

Python objects are serialized using the pickle module – see https://docs.python.org/3/library/pickle.html for more details.

Description

Set an attribute of a Python object

Usage

py_set_attr(x, name, value)

Arguments

Description

Set various random seeds required to ensure reproducible results. The provided seed value will establish a new random seed for Python and NumPy, and will also (by default) disable hash randomization.

Usage

py_set_seed(seed, disable_hash_randomization = TRUE)

Arguments

Details

This function does not set the R random seed, for that you should call set.seed().

Description

Suppress Python warnings for an expression

Usage

py_suppress_warnings(expr)

Arguments

Value

Result of evaluating expression

Description

Convert to Python Unicode Object

Usage

py_unicode(str)

Arguments

Details

By default R character vectors are converted to Python strings. In Python 3 these values are unicode objects however in Python 2 they are 8-bit string objects. This function enables you to obtain a Python unicode object from an R character vector when running under Python 2 (under Python 3 a standard Python string object is returned).

Description

Get the version of Python currently being used by reticulate.

Usage

py_version()

Value

The version of Python currently used, or NULL if Python has not yet been initialized by reticulate.

Description

Create a python class

Usage

PyClass(classname, defs = list(), inherit = NULL)

Arguments

Examples

## Not run: 
Hi <- PyClass("Hi", list(
  name = NULL,
  `__init__` = function(self, name) {
    self$name <- name
    NULL
  },
  say_hi = function(self) {
    paste0("Hi ", self$name)
  }
))

a <- Hi("World")

## End(Not run)

Description

Convert between Python and R objects

Usage

r_to_py(x, convert = FALSE)

py_to_r(x)

Arguments

Value

An R object, as converted from the Python object.

Description

This function provides a Python REPL in the R session, which can be used to interactively run Python code. All code executed within the REPL is run within the Python main module, and any generated Python objects will persist in the Python session after the REPL is detached.

Usage

repl_python(
  module = NULL,
  quiet = getOption("reticulate.repl.quiet", default = FALSE),
  input = NULL
)

Arguments

Details

When working with R and Python scripts interactively, one can activate the Python REPL with repl_python(), run Python code, and later run exit to return to the R console.

Magics

A handful of magics are supported in repl_python():

Lines prefixed with ! are executed as system commands:

• !cmd --arg1 --arg2: Execute arbitrary system commands

Magics start with a ⁠%⁠ prefix. Supported magics include:

• ⁠%conda ...⁠ executes a conda command in the active conda environment

• ⁠%pip ...⁠ executes pip for the active python.

• ⁠%load⁠, ⁠%loadpy⁠, ⁠%run⁠ executes a python file.

• ⁠%system⁠, ⁠!!⁠ executes a system command and capture output

• ⁠%env⁠: read current environment variables.

  • ⁠%env name⁠: read environment variable 'name'.

  • ⁠%env name=val⁠, ⁠%env name val⁠: set environment variable 'name' to 'val'. val elements in {} are interpolated using f-strings (required Python >= 3.6).

• ⁠%cd <dir>⁠ change working directory.

  • ⁠%cd -⁠: change to previous working directory (as set by ⁠%cd⁠).

  • ⁠%cd -3⁠: change to 3rd most recent working directory (as set by ⁠%cd⁠).

  • ⁠%cd -foo/bar⁠: change to most recent working directory matching "foo/bar" regex (in history of directories set via ⁠%cd⁠).

• ⁠%pwd⁠: print current working directory.

• ⁠%dhist⁠: print working directory history.

Additionally, the output of system commands can be captured in a variable, e.g.:

• x = !ls

where x will be a list of strings, consisting of stdout output split in "\n" (stderr is not captured).

Example

# enter the Python REPL, create a dictionary, and exit
repl_python()
dictionary = {'alpha': 1, 'beta': 2}
exit

# access the created dictionary from R
py$dictionary
# $alpha
# [1] 1
#
# $beta
# [1] 2

See Also

py, for accessing objects created using the Python REPL.

Description

Evaluate a Python script within the Python main module, then make all public (non-module) objects within the main Python module available within the specified R environment.

Usage

source_python(file, envir = parent.frame(), convert = TRUE)

Arguments

Details

To prevent assignment of objects into R, pass NULL for the envir parameter.

Description

Create a Python tuple object

Usage

tuple(..., convert = FALSE)

Arguments

Value

A Python tuple

Note

The returned tuple will not automatically convert its elements from Python to R. You can do manual conversion with the py_to_r() function or pass convert = TRUE to request automatic conversion.

Description

Select the version of Python to be used by reticulate.

Usage

use_python(python, required = NULL)

use_python_version(version, required = NULL)

use_virtualenv(virtualenv = NULL, required = NULL)

use_condaenv(condaenv = NULL, conda = "auto", required = NULL)

use_miniconda(condaenv = NULL, required = NULL)

Arguments

Details

The reticulate package initializes its Python bindings lazily – that is, it does not initialize its Python bindings until an API that explicitly requires Python to be loaded is called. This allows users and package authors to request particular versions of Python by calling use_python() or one of the other helper functions documented in this help file.

RETICULATE_PYTHON

The RETICULATE_PYTHON environment variable can also be used to control which copy of Python reticulate chooses to bind to. It should be set to the path to a Python interpreter, and that interpreter can either be:

• A standalone system interpreter,

• Part of a virtual environment,

• Part of a Conda environment.

When set, this will override any other requests to use a particular copy of Python. Setting this in ⁠~/.Renviron⁠ (or optionally, a project .Renviron) can be a useful way of forcing reticulate to use a particular version of Python.

Caveats

Note that the requests for a particular version of Python via use_python() and friends only persist for the active session; they must be re-run in each new R session as appropriate.

If use_python() (or one of the other ⁠use_*()⁠ functions) are called multiple times, the most recently-requested version of Python will be used. Note that any request to use_python() will always be overridden by the RETICULATE_PYTHON environment variable, if set.

The py_config() function will also provide a short note describing why reticulate chose to select the version of Python that was ultimately activated.

Description

R functions for managing Python virtual environments.

Usage

virtualenv_create(
  envname = NULL,
  python = virtualenv_starter(version),
  ...,
  version = NULL,
  packages = "numpy",
  requirements = NULL,
  force = FALSE,
  module = getOption("reticulate.virtualenv.module"),
  system_site_packages = getOption("reticulate.virtualenv.system_site_packages", default
    = FALSE),
  pip_version = getOption("reticulate.virtualenv.pip_version", default = NULL),
  setuptools_version = getOption("reticulate.virtualenv.setuptools_version", default =
    NULL),
  extra = getOption("reticulate.virtualenv.extra", default = NULL)
)

virtualenv_install(
  envname = NULL,
  packages = NULL,
  ignore_installed = FALSE,
  pip_options = character(),
  requirements = NULL,
  ...,
  python_version = NULL
)

virtualenv_remove(envname = NULL, packages = NULL, confirm = interactive())

virtualenv_list()

virtualenv_root()

virtualenv_python(envname = NULL)

virtualenv_exists(envname = NULL)

virtualenv_starter(version = NULL, all = FALSE)

Arguments

Details

Virtual environments are by default located at ⁠~/.virtualenvs⁠ (accessed with the virtualenv_root() function). You can change the default location by defining the WORKON_HOME environment variable.

Virtual environments are created from another "starter" or "seed" Python already installed on the system. Suitable Pythons installed on the system are found by virtualenv_starter().

Description

The with method for objects of type python.builtin.object implements the context manager protocol used by the Python with statement. The passed object must implement the context manager (__enter__ and __exit__ methods.

Usage

## S3 method for class 'python.builtin.object'
with(data, expr, as = NULL, ...)

Arguments