Choose file or directory to open via 'Python'

Description

Choose a directory, one or multiple files to open, or choose a file to save.

Usage

choose_fileopen(
  initialfile = NULL,
  multiple = FALSE,
  title = ifelse(multiple, "Choose Files", "Choose a File"),
  message = "",
  verbose = FALSE,
  force = FALSE
)

choose_filesave()

choose_directory(
  initialdir = NULL,
  title = "Choose a Directory",
  message = "",
  verbose = FALSE
)

Arguments

Details

Base-R has file.choose function to choose files. However, users cannot select multiple files nor directories. These functions fill the gap by using 'Python' 'tkinter' package. Please make sure that one-time setup function configure_conda has executed before running these functions.

The functions must run as interactive mode. If you run the functions on a server, most likely you will get nothing. The functions themselves do not check if you are running under interactive sessions. You must check by yourself.

Value

User-selected paths. If the users select nothing, then NULL will be returned. For multiple file selection, multiple paths will be returned.

Examples

if(interactive()) {
  choose_fileopen(multiple = TRUE)
}

'Miniconda' environment

Description

These functions/variables are used to configure 'Miniconda' environment.

Usage

CONDAENV_NAME(env_name)

conda_path()

conda_bin()

env_path(env_name = NA)

list_pkgs(..., env_name = NA)

configure_matlab(matlab, python_ver = "auto")

configure_conda(
  python_ver = "auto",
  packages = NULL,
  matlab = NULL,
  update = FALSE,
  force = FALSE,
  standalone = FALSE,
  env_name = CONDAENV_NAME()
)

conda_tos(channel, agree = TRUE, silent_fail = FALSE)

remove_conda(ask = TRUE, env_name = NA)

add_packages(packages = NULL, python_ver = "auto", ..., env_name = NA)

ensure_rpymat(verbose = TRUE, cache = TRUE, env_name = NA)

matlab_engine()

call_matlab(
  fun,
  ...,
  .options = getOption("rpymat.matlab_opt", "-nodesktop -nojvm"),
  .debug = getOption("rpymat.debug", FALSE)
)

Arguments

Value

None

Background & Objectives

Package reticulate provides sophisticated tool-sets that allow us to call python functions within R. However, the installation of 'Miniconda' and python can be tricky on many platforms, for example, the 'M1' chip, or some other 'ARM' machines. The package rpymat provides easier approach to configure on these machines with totally isolated environments. Any modifications to this environment will not affect your other set ups.

Since 2014, 'Matlab' has introduced its official compiler for python. The package rpymat provides a simple approach to link the compiler, provided that you have proper versions of 'Matlab' installed. Here is a list of 'Matlab' versions with official compilers and their corresponding python versions.

Configuration

If 'Matlab' compiler is not to be installed, In most of the cases, function configure_conda with default arguments automatically downloads the latest 'Miniconda' and configures the latest python. If any other versions of 'Miniconda' is ought to be installed, please set options "reticulate.miniconda.url" to change the source location.

If 'Matlab' is to be installed, please specify the 'Matlab' path when running configure_conda. If the environment has been setup, configure_matlab can link the 'Matlab' compilers without removing the existing environment. For 'ARM' users, unfortunately, there will be no 'Matlab' support as the compilers are written for the 'Intel' chips.

Initialization

Once conda and python environment has been installed, make sure you run ensure_rpymat() before running any python code. This function will make sure correct compiler is linked to your current R session.

Examples

# The script will interactively install \code{conda} to `R_user_dir`
## Not run: 

# Install conda and python 3.9

configure_conda(python_ver = '3.9')


# Add packages h5py, pandas, jupyter

add_packages(c('h5py', 'pandas', 'jupyter'))

# Add pip packages

add_packages("itk", pip = TRUE)

# Initialize the isolated environment

ensure_rpymat()


# Remove the environment

remove_conda()


## End(Not run)

Whether supports customized 'conda' environment

Description

Whether supports customized 'conda' environment

Usage

custom_env_support()

Value

true or false on whether multiple customized environment is supported

Install, register, launch 'Jupyter' notebook to the virtual environment

Description

Install, register, launch 'Jupyter' notebook to the virtual environment

Usage

add_jupyter(..., register_R = TRUE)

jupyter_bin()

jupyter_register_R(
  user = NULL,
  name = "ir",
  displayname = "R",
  rprofile = NULL,
  prefix = NULL,
  sys_prefix = NULL,
  verbose = getOption("verbose")
)

jupyter_options(
  root_dir,
  host = "127.0.0.1",
  port = 8888,
  open_browser = FALSE,
  token = rand_string(),
  base_url = "/jupyter/"
)

jupyter_launch(
  host = "127.0.0.1",
  port = 8888,
  open_browser = TRUE,
  workdir = getwd(),
  async = FALSE,
  ...,
  dry_run = FALSE
)

jupyter_check_launch(
  port = 8888,
  host = "127.0.0.1",
  open_browser = TRUE,
  workdir = getwd(),
  async = "auto",
  ...
)

jupyter_server_list()

jupyter_server_stop(port, ...)

jupyter_server_stop_all(...)

Arguments

Value

jupyter_bin returns the 'Jupyter' notebook binary path; jupyter_options returns the 'Jupyter' configuration in strings; jupyter_server_list returns a table of existing local 'Jupyter' server hosts, ports, and tokens; jupyter_check_launch returns true if a new server has been created, or false if there has been an existing server at the port; other functions return nothing.

Examples

## Not run: 

# Requires installation of conda
library(rpymat)

# Install conda, if you have done so, skip
configure_conda()


# Install Jupyter notebook
add_jupyter(register_R = TRUE)


# Utility functions
jupyter_bin()

# Please install `dipsaus` package to enable `async=TRUE` with
# better experience
jupyter_launch(async = FALSE, open_browser = TRUE)



## End(Not run)

Get 'Python' built-in object

Description

Get 'Python' built-in object

Usage

py_builtin(name, convert = FALSE)

Arguments

Value

A python built-in object specified by name

Examples

if(interactive() && dir.exists(env_path())) {


# ------ Basic case: use python `int` as an R function ---------
py_int <- py_builtin("int", convert = TRUE)

# a is an R object now
a <- py_int(9)
print(a)
class(a)

# ------ Use python `int` as a Python function -----------------
py_int2 <- py_builtin("int", convert = FALSE)

# b in a python object
b <- py_int2(9)

# There is no '[1] ' when printing
print(b)
class(b)

# convert to R object
py_to_r(b)



}

List in 'Python'

Description

List in 'Python'

Usage

py_list(..., convert = FALSE)

Arguments

Value

List instance, or an R vector if converted

Examples

if(interactive() && dir.exists(env_path())) {

  py_list(list(1,2,3))
  py_list(c(1,2,3))

  py_list(array(1:9, c(3,3)))
  py_list(list(list(1:3), letters[1:3]))

}

Slice index in 'Python' arrays

Description

Slice index in 'Python' arrays

Usage

py_slice(...)

Arguments

Value

Index slice instance

Examples

if(interactive() && dir.exists(env_path())) {

  x <- np_array(array(seq(20), c(4, 5)))

  # equivalent to x[::2]
  x[py_slice(NULL, NULL, 2L)]

}

Read data frame from a 'xlsx' file

Description

Tries to use 'readxl' package or 'pandas' to read data frame.

Usage

read_xlsx(
  path,
  sheet = NULL,
  method = c("auto", "pandas", "readxl"),
  n_max = Inf,
  ...
)

Arguments

Value

A data.frame table

Examples

## Not run: 

rpymat::read_xlsx("Book1.xlsx", sheet = 1)

rpymat::read_xlsx("Book1.xlsx", sheet = "sheet1")


## End(Not run)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

glue

glue

Enable interactive 'python' from R

Description

Allows users to type 'python' command from R console for quick code evaluation or debugging.

Usage

repl_python(..., env_name = NA)

Arguments

Value

See repl_python

Wrappers around 'reticulate' package

Description

Almost the same with 'reticulate' functions, with rpymat enabled by default and some minor changes (see parameter convert and local)

Usage

import_main(convert = FALSE)

tuple(..., convert = FALSE)

py_tuple(..., convert = FALSE)

py_help(object)

np_array(data, ...)

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

r_to_py(x, convert = FALSE)

py_to_r(x)

py_to_r_wrapper(x)

py_str(object, ...)

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

py_bool(x)

py_dict(keys, values, convert = FALSE)

py_call(x, ...)

py_del_attr(x, name)

py_del_item(x, name)

py_eval(code, convert = FALSE)

py_get_attr(x, name, silent = FALSE)

py_set_attr(x, name, value)

py_get_item(x, key, silent = FALSE)

py_set_item(x, name, value)

py_len(x, default = NULL)

py_none()

Arguments

Value

'Python' built-in objects

Examples

library(rpymat)
if(interactive() && dir.exists(env_path())) {

  # tuple
  x <- tuple(1, 2, "a")
  print(x)

  # convert to R object
  py_to_r(x)

  # convert R object to python
  y <- r_to_py(list(a = 1, b = "s"))

  # get element
  py_get_item(y, "a")

  # get missing element
  py_get_item(y, "c", silent = TRUE)

}

Get 'Python' main process environment

Description

py automatically converts 'Python' objects to R objects. import_main does not convert by default; see 'Examples' for details.

Usage

py

Format

An object of class NULL of length 0.

Value

The 'Python' main process as a module

Examples

if(interactive() && dir.exists(env_path())) {

py_no_convert <- rpymat::import_main(convert = FALSE)

py$a <- matrix(seq_len(16), 4)

py_no_convert$a

py$a

}

Execute command with additional environments

Description

Enables 'conda' environment

Usage

cmd_create(command, shell, use_glue = TRUE)

cmd_set_env(command, key, value, quote = TRUE, quote_type = "cmd")

cmd_set_workdir(command, workdir)

cmd_set_conda(command, conda_path, env_path)

cmd_build(command, .env = parent.frame(), ...)

detect_shell(suggest = NULL)

run_command(
  command,
  shell = detect_shell(),
  use_glue = FALSE,
  enable_conda = TRUE,
  stdout = "",
  stderr = "",
  stdin = "",
  input = NULL,
  env_list = list(),
  wait = TRUE,
  timeout = 0,
  ...,
  workdir = getwd(),
  dry_run = FALSE,
  print_cmd = dry_run,
  glue_env = parent.frame(),
  env_name = NA
)

Arguments

Value

All the functions return a list with class rpymat_system_command except for run_command, which returns the exit code by system2.

Examples

run_command("conda install -y numpy", dry_run = TRUE)


a <- "This is a message"
run_command('echo "{a}"', dry_run = TRUE,
            enable_conda = FALSE, use_glue = TRUE)


## Not run: 

# Use `jupyter_launch()` instead. This is just a demonstration
run_command('"{jupyter_bin()}" server list', use_glue = TRUE)


## End(Not run)

Run 'Python' script

Description

A wrapper of py_run_file, but with rpymat enabled

Usage

run_script(
  x,
  work_dir = NULL,
  local = FALSE,
  convert = FALSE,
  globals = list()
)

run_pyscript(
  x,
  work_dir = NULL,
  local = FALSE,
  convert = FALSE,
  globals = list(),
  env_name = NA,
  force_child_process = FALSE,
  ...
)

run_pystring(
  code,
  work_dir = NULL,
  local = FALSE,
  convert = FALSE,
  globals = list()
)

Arguments

Value

The values returned by py_run_file

Examples

## Not run: 

# Please configure conda environment first

x <- tempfile()
writeLines(c(
  "import re",
  "zipcode = re.findall(r'[0-9]{5,6}', r.address)"
), con = x)

address <- '2341 Main St., 72381'
rpymat::run_script(x)

py$zipcode


## End(Not run)