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README.rst

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+phys2denoise: A toolbox for physiological metrics calculation
+=============================================================
+
+This package is designed for calculating physiological metrics, derived specifically cardiac and respiratory signals, to use in fMRI denoising.
+
+
+.. image:: https://readthedocs.org/projects/phys2denoise/badge/?version=latest
+   :target: http://phys2denoise.readthedocs.io/en/latest
+.. image:: https://img.shields.io/badge/license-Apache%202-blue.svg
+   :target: http://www.apache.org/licenses/LICENSE-2.0
+.. image:: https://img.shields.io/badge/python-3.6+-blue.svg
+   :target: https://www.python.org/downloads/
+
+
+.. _licensing:
+
+License Information
+-------------------
+
+This codebase is licensed under the Apache License, Version 2.0. The full
+license can be found in the `LICENSE <https://github.com/physiopy/phys2denoise/blob/master/LICENSE>`_ file in the ``phys2denoise`` distribution. You may also
+obtain a copy of the license at: http://www.apache.org/licenses/LICENSE-2.0.

docs/Makefile

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+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = phys2denoise
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/api.rst

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+.. _api_ref:
+API
+===
+
+.. py:module:: phys2denoise
+
+Cardiac data
+------------
+
+.. automodule:: phys2denoise.metrics.cardiac
+   :members: heart_rate, heart_rate_variability, heart_beat_interval, cardiac_phase
+
+Respiratory data
+----------------
+
+.. automodule:: phys2denoise.metrics.chest_belt
+   :members: respiratory_cariance_time, respiratory_pattern_variability, env, respiratory_variance, respiratory_phase
+
+Multimodal data
+---------------
+
+.. autofunction:: phys2denoise.multimodal.retroicor
+
+Response functions
+------------------
+
+.. automodule:: phys2denoise.metrics.responses
+   :members: crf, icrf, rrf
+
+Utilities
+---------
+
+.. automodule:: phys2denoise.metrics.utils
+   :members: print_metric_call, mirrorpad_1d, rms_envelope_1d, apply_lags, apply_function_in_sliding_window, convolve_and_rescale, export_metric

docs/conf.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# Configuration file for the Sphinx documentation builder.
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+import os
+import sys
+
+import matplotlib as mpl
+
+mpl.use("Agg")
+
+# -- Project information -----------------------------------------------------
+
+# Add project name, copyright holder, and author(s)
+project = "phys2denoise"
+copyright = "2024, physiopy"
+author = "physiopy"
+
+# Import project to get version info
+sys.path.insert(0, os.path.abspath(os.path.pardir))
+import phys2denoise  # noqa
+
+# The short X.Y version
+version = phys2denoise.__version__
+# The full version, including alpha/beta/rc tags
+release = phys2denoise.__version__
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "matplotlib.sphinxext.plot_directive",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.doctest",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+]
+
+# Generate the API documentation when building
+autosummary_generate = True
+numpydoc_show_class_members = False
+autoclass_content = "class"
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# The suffix(es) of source filenames.
+source_suffix = ".rst"
+
+# The master toctree document.
+master_doc = "index"
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path .
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "sphinx"
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+import sphinx_rtd_theme  # noqa
+
+html_theme = "sphinx_rtd_theme"
+html_show_sourcelink = False
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "peakdetdoc"
+
+# -- Extension configuration -------------------------------------------------
+intersphinx_mapping = {
+    "matplotlib": ("https://matplotlib.org", None),
+    "numpy": ("https://docs.scipy.org/doc/numpy", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
+}
+
+plot_include_source = True
+plot_formats = [("png", 90)]
+plot_html_show_formats = False
+plot_html_show_source_link = False

docs/index.rst

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+.. include:: ../README.rst
+
+Contents
+--------
+
+.. toctree::
+   :maxdepth: 1
+
+   installation
+   usage
+   api

docs/installation.rst

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+.. _installation_setup:
+
+Installation and setup
+======================
+
+.. _basic_installation:
+
+Basic installation
+------------------
+
+The easiest way to install ``phys2denoise`` is to use ``pip``. Assuming you have
+Python >= 3.6 installed, you can install ``phys2denoise`` by opening a terminal
+and running the following:
+
+.. code-block:: bash
+
+   pip install phys2denoise
+
+.. warning::
+
+   If you encounter an ImportError related to numpy.core.multiarray, please try to update
+   your matplotlib version to 3.9.
+
+Developer installation
+----------------------
+
+This package requires Python >= 3.6. Assuming you have the correct version of
+Python installed, you can install ``phys2denoise`` by opening a terminal and running
+the following:
+
+.. code-block:: bash
+
+   git clone https://github.com/physiopy/phys2denoise.git
+   cd phys2denoise
+   pip install -e .[dev]

docs/requirements.txt

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+-r ../requirements.txt
+sphinx>=1.2
+sphinx_rtd_theme

docs/usage.rst

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+.. _usage:
+
+User guide
+==========
+
+.. toctree::
+   :numbered:
+
+   user_guide/metrics.rst
+   user_guide/exporting.rst

docs/user_guide/exporting.rst

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+.. _usage_exporting:
+
+Exporting physiological data metrics
+------------------------------------
+Another feature of the :py:mod:`phys2denoise` package is the ability to export the computed physiological data metrics to a file, with various parameters.
+This can be done using the :py:func:`export_metric` function, which provides the following capabilities:
+
+- Exporting the computed metrics, resampled at the TR of the fMRI data, along with the original data.
+- Flagging if the exported data is the convolved version or if the metric contains lags of itself, resulting in appropriate file naming.
+- Defining the output file extension and file prefix.
+- Defining the number of timepoints to be considered.
+
+The following example shows how to export the computed respiratory variance time using a Physio object. In the following example,
+we consider that the Physio object is ``resp``, containing the respiratory data, peaks and troughs.
+
+.. code-block:: python
+
+    from phys2denoise.metrics.chest_belt import respiratory_variance_time
+    from phys2denoise.metrics.utils import export_metric
+
+    RVT = respiratory_variance_time(
+        resp.data, resp.peaks, resp.troughs, resp.fs, lags=(0, 4, 8, 12)
+    )
+
+    export_metric(
+        RVT,
+        resp.fs,
+        tr=1.5,
+        fileprefix="data/sub-002_ses-01_task-rest_run-01_RVT",
+        ntp=400,
+        is_convolved=False,
+        has_lags=True,
+    )

docs/user_guide/metrics.rst

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+.. _usage_metrics:
+
+Computing physiological data metrics
+-------------------------------------
+The :py:mod:`phys2denoise` package provides a set of functions to compute physiological data metrics. The set of supported metrics
+includes:
+
+- Cardiac metrics
+    - Cardiac phase
+    - Heart rate
+    - Heart rate variability
+    - Heart beat interval
+- Respiratory metrics
+    - Respiratory phase
+    - Respiratory variance
+    - Respiratory pattern variability
+    - Envelope
+- Multimodal metrics
+    - RETROICOR
+
+All of the metrics computation function definitions, descriptions and references can be found in :ref:`api_ref`.
+
+
+Using a Physio object
+#####################
+
+Physiological data metrics can be easily computed using Physio objects, from the :py:mod:`physutils` module,
+on which the physiological data will be loaded.
+
+The following example shows how to compute the respiratory variance time using a Physio object.
+
+.. code-block:: python
+
+    from physutils import io
+    from phys2denoise.metrics.chest_belt import respiratory_variance_time
+    # peakdet is an example package that provides peak/trough detection for the respiratory signal
+    from peakdet import operations
+
+    # Load the physiological data
+    sample_rate = 1000
+    physio = io.load_physio('path/to/physiological/data', fs=sample_rate)
+
+    # Peak/trough detection for the respiratory signal, using the peakdet package
+    physio = operations.peakfind_physio(physio)
+
+    # Compute RVT
+    physio, rvt = respiratory_variance_time(physio)
+
+:py:func:`respiratory_variance_time` returns a tuple with the updated Physio object and the computed respiratory variance time.
+
+:py:mod:`peakdet` is used in this example as it is also compatible with the Physio object. However, any other peak/trough detection
+package can be used. In this case, the peak and trough values should be stored in the Physio object manually as follows:
+
+.. code-block:: python
+
+    # Store the peak and trough values in the Physio object
+    physio._metadata["peaks"] = peaks
+    physio._metadata["troughs"] = troughs
+
+The benefit of using a Physio object other than the encapsulation of all the desired parameters in a single object is the fact that
+the object retains a history of all the operations performed on it. This allows for easy debugging and reproducibility of the results.
+For further information refer to the :py:mod:`physutils` documentation.
+
+Without using a Physio object
+#############################
+
+However, if the use of the ``Physio`` object from the :py:mod:`physutils` module is not preferred, the metrics can be also computed without it. The following
+example shows how to compute the heart rate and the heart rate variability using the :py:mod:`phys2denoise` package.
+
+.. code-block:: python
+
+    from phys2denoise.metrics.chest_belt import respiratory_variance_time
+
+    # Given that the respiratory signal is stored in `data`, the peaks in `peaks`, the troughs in `troughs`
+    # and the sample rate in `sample_rate`
+    _, rvt = respiratory_variance_time(data, peaks, troughs, sample_rate)