Package 'actinet'

Load Actinet Model

Description

Load Actinet Model

Usage

ac_load_model(
  classifier = c("walmsley", "willetts"),
  model_path = NULL,
  check_md5 = TRUE,
  force_download = FALSE,
  as_python = TRUE
)

ac_model_filename(classifier = c("walmsley", "willetts"))

ac_download_model(
  model_path,
  classifier = c("walmsley", "willetts"),
  check_md5 = TRUE,
  ...
)

Arguments

classifier	type of the model: either walmsley or willetts
model_path	the file path to the model. If on disk, this can be re-used and not re-downloaded. If NULL, will download to the temporary directory
check_md5	Do a MD5 checksum on the file
force_download	force a download of the model, even if the file exists
as_python	Keep model object as a python object
...	for ac_download_model, additional arguments to pass to curl::curl_download()

Value

A model from Python. ac_download_model returns a model file path.

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Minute Level actinet output

Description

Minute Level actinet output

Usage

acti_net(...)

py_acti_net(...)

Arguments

...	arguments to pass to actinet

Value

A minute-level data.frame

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Run Actinet Model on Data

Description

Run Actinet Model on Data

Run actinet with Python

Usage

actinet(
  file,
  outdir = tempfile(),
  classifier = NULL,
  sample_rate = NULL,
  model_path = NULL,
  pytorch_device = NULL,
  no_hmm = FALSE,
  require_sleep_above = NULL,
  single_sleep_block = FALSE,
  force_download = FALSE,
  exclude_first_last = NULL,
  exclude_wear_below = NULL,
  csv_start_row = NULL,
  csv_txyz = NULL,
  csv_txyz_idxs = NULL,
  csv_date_format = NULL,
  calibration_stdtol_min = NULL,
  plot_activity = FALSE,
  cache_classifier = FALSE,
  verbose = TRUE
)

py_actinet(
  ...,
  pyenv_function = function() {
     actinet::py_require_actinet()
 },
  show = TRUE
)

Arguments

file	accelerometry file to process, including CSV, CWA, GT3X, and GENEActiv bin files
outdir	folder location to save output files
classifier	Enter custom activity classifier file to use. Default: walmsley (Walmsley (2020) https://doi.org/10.1136/bjsports-2021-104050 annotations of activity intensity). Can also enter path to local classifier (.joblib.lzma) file.
sample_rate	Sample rate for measurement, otherwise inferred.
model_path	the file path to the model. If on disk, this can be re-used and not re-downloaded. If NULL, will download to the temporary directory
pytorch_device	torch device to use, e.g.: 'cpu' or 'cuda:0'. Default: 'mps' if available, otherwise 'cpu'
no_hmm	Disable HMM post-processing
require_sleep_above	Require sleep blocks to exceed a minimum duration, otherwise be classified as sedentary. Pass values as strings, e.g.: '2H', '30min'. Default: None (no requirement)
single_sleep_block	Recognize only one sleep block per day, all other sleep blocks will be converted to sedentary
force_download	Force download of classifier file
exclude_first_last	first,last,both Exclude first, last or both days of data. Default: None (no exclusion)
exclude_wear_below	Exclude days with wear time below threshold. Pass values as strings, e.g.: '12H', '30min'. Default: None (no exclusion)
csv_start_row	Row number to start reading a CSV file. Default: 1 (First row)
csv_txyz	CSV_TXYZ Column names for time, x, y, z in CSV files. Comma_ separated string. Default: 'time,x,y,z'
csv_txyz_idxs	Column indices for time,x,y,z (0_indexed, e.g., '0,1,2,3'). Overrides csv_txyz.
csv_date_format	Date time format for csv file when reading a csv file. See https://docs.python.org/3/library/datetime.html#strftime_and_strptime_format_codes for more possible codes. Default: '%Y-%m-%d %H:%M:%S.%f' (e.g. '2023-10-01 12:34:56.789')
calibration_stdtol_min	Minimum standard deviation tolerance (g) for detecting stationary periods for calibration. Default: None
plot_activity	Plot the predicted activity labels
cache_classifier	Download and cache classifier file and model modules for offline usage
verbose	print diagnostic messages
...	arguments to pass to actinet
pyenv_function	function that loads the forest Python package. By default, it uses reticulate::py_import("actinet") to import the package. If this function has an args argument, the output of pyenv_function will be re-assigned to args.
show	Logical, whether to show the standard output on the screen while the child process is running, passed to callr::r()

Value

A list of the results (data.frame), summary of the results, adjusted summary of the results, and information about the data.

Examples

actinet_check_result = function() {
  res = try({suppressWarnings(actinet_check())})
  if (inherits(res, "try-error")) {
    res = FALSE
  }
  res
}

  file = system.file("extdata/P30_wrist100.csv.gz", package = "actinet")
  if (actinet_check_result()) {
    out = try({actinet(file = file)})
    if (!inherits(out, "try-error")) {
      data = readr::read_csv(out$outfiles[1])
      daily_data = readr::read_csv(out$outfiles[3])
    }
  }

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Check the actinet Python Module

Description

Check the actinet Python Module

Usage

have_actinet()

actinet_check()

actinet_version()

Value

A logical value indicating whether the actinet Python module is available.

Examples

if (have_actinet()) {
     actinet_version()
  }

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Command for py_require for actinet

Description

Command for py_require for actinet

Usage

py_require_actinet(...)

Arguments

...	arguments to pass to reticulate::py_require()

Value

A logical value indicating whether the package is available.

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