From f3333c38b69d6436ae7d0b2b4dfbcdaacf35dc03 Mon Sep 17 00:00:00 2001 From: Nicolas Legrand Date: Thu, 15 Sep 2022 23:05:53 +0200 Subject: [PATCH] v0.2.3 (#42) * Fix typo * [Git actions] : add linting, type checking and code coverage. * Rename requirements file * Update codecov.yml * Fix Git action for code coverage * Add citation in the text * Git actions: using pytest-cov and another black version * Fix test workflow * added plot_rsp() to plots, backends, init, test_plots * liting fix * First version of the BIDS report module. - Create individual HTML reports, summary dataframes and physio dataframes. - Create group-level HTML reports. * [Git Actions] : Remove unused package from testing. * Fix linting package error. * [tests] : close Matplotlib figures after test to avoid memory overload. * Formatting issue in README * [reports] : Add command line functionalities. * [reports] : Add show_raw parameter and set to False as defaults to save memory. * [reports] : load participants ID automatically if not provided. * [utils] : Add a nan_cleaning function that can be called before peaks detection (optional). * [PPG] : Add a find_clipping function to automatically find clipping threshold. This function is called by ppg_peaks when clipping=True and clipping_threshold="auto". [detection]: ecg_peaks, ppg_peaks and rsp_peaks accept pd.Series as input. Defalt input variable named signal for consistency. * [notebooks] : Clean imports, fix use of interpolate_clipping and use keyword args. * Minor API docs. * [detection] : remove default values for `sfreq` and standardize outputs for the 3 modalities. * Doctrings and minor change in tests. * [hrv] Minor fix: use pd.concat instead of the append method. * [correction] : Add a correct_ectopic_peaks to correct pseudo-ectopic heart beatd in peaks vector. * [plots] : plot_raw accept a `decim` parameter to control raw signal downsamplig. * Use pd.concat * Use pd.concat * Move the tables function into the reports sub-module. Update the reports function to current (non working) version. * [plots] Improve plot_events and its integration with plot_rr. * [plots]: fix plot_evoked and allow to pass arguments to seaborn.lineplot more easily. * [detection]: Add sleepecg peak detector as default for Systole * [plots]: plot_raws now accept an events_params parameter to be passed to plot_rr. * Add sleepecg in the requirements * Drop CI for Python 3.7 * Update pre-commit * [reports]: Add logs for artefatcs in the subject level function. * [test] : Fix error in plot_raw * Fix CI error (black) * Minor fixes * [reports] : Add artefacts visualization at the group level. * [reports] Use Joblib for paralellization. * [detection] - PPG: change `noise_removal` to moving_average and add a `moving_average_length` parameter to control the window length. - Fix interpolate_clipping() as it was not handling thresholds correctly if the provided threshold is not the max/min of the time series. * Fix the test after clipping error. * [requirements]: include Numpy 1.21 * [reports] : The command line function find the relevant files automatically. The signals are automatically trimmed when starting and ending times are provided. Also start using pathlib instead of strings for paths. * Typos and changelogs. * - Fix bug in interpolate_clipping() and add corresponding tests - Linting * [detection] : interpolate_clipping - fix the way first and last values are corrected. Look at all values more/less or equal than thr and substract the smallest diff from the time serie. * [plots] : Minor change in matplotlib defaults * Update pre-commit config * Fix formatting and mypy errors * Add Git action for automated docs deployment (first trial). * [docs] : Fix small errors in the examples * [docs] : Drop Psychopy import in the examples * isort config file * Update the requirement files. * [docs] : - Automatic docs construction now working and moved to https://embodied-computation-group.github.io/systole/ - Minor fixes in the docstrings * Update notebooks * Remove docs api example from the code base. * Update docs link * Remove the findOximeter function #8 * [docs]: link to paper * CI minor updates * [plots]: Fix decim function and error in the docstring examples. * [correction] : Rewrite correct_missed (to correct_missed_rr) and correct_extra (to correct_extra_rr) to better control for RR insertion and deletion. Rewrite correct_rr. All interpolations in a single pass. All function use jit. (see #40). Update pre-commit hooks. * [correction] : Add extensive testing suite for correct_extra_rr, correct_missed_rr and correct_rr (#40). * [correction] : Fix errors in the arrays update for missed and etra peaks. Update the corresponding notebooks. Add entry in changelogs (#40). * Fix mypy errors * [correction] : Update the dcumentation for API changes. * changelogs * [docs] : Add instruction for using dev branch * [reports] : Fix defaults paths and add the patterns parameter for more flexibility in the files selection. * [docs] : Add docs for BIDS folder. * Update requirements.txt. Fix reports tests. * [viewer] : Add first version of Systole viewer for manual peaks correction. * [docs] Myst parser * [viewer] : Use empty path names by default. * [viewer] : more docs and small fix * [docs] : Reorganize folders * [plots] : plot_subspaces accepts an ax argument. * Autodocs link * [test] : Remove notebooks from the test suite now that we have autodocs jobs (for now). * Links * Change version to 0.2.3dev * [reports] : Use the file name from the BIDS folder to save derivatives. This will avoid duplicates in case of different patterns been used. * [reports] : Automatically retrieve file_name template when creating group level reports. * [docs] Image link * [viewer] : Automatically loading and processing data depending on the modality. * [viewer] : Create method for plotting and add some aesthetic features. * [viewer] : Fix several errors during import and add more menues in the Viewer. * [viewer] : Add option to mark the signal or segments of the signal as bads. * [plots] Prepare merge plot_raw and plot_resp and remove tight layout from the deault. * [plots] : Error in plot_raw * [viewer] : Fix save button * [viewer] : Better output when files are not found. * [viewer] : Error with the logs of bad segments. * [plots] : Add possibe `bad_segments` parameter as input for plot_raw and plot_rr. * [plots] : Merge plot_resp into plot raw. Use modality strings defined at the package level. [viewer] : allow full file name to be provided. * [pre-commit] Autoupdate * Typing * docstring * [utils] : Add tools to handle bad segments and use it in the plotting functions. * [requirements] : last pydata theme * Add docs. * Fix bad_segments utills (flake8 error) * requirements (bibtex) * Use sleepecg as default R-peaks detector * [docs] * [docs] * [docs] More examples * [docs] Links * [docs] plotting examples * [docs] * [docs] * [viewer] : Save the entire corrected peaks. Load corrected peaks and bad segments automatically with signal. * [viewer] : fix file name for corrected peaks. * Add import function for manually corrected peaks (#41) * [docs] Pair noteboks with md files * dev version * [viewer] : Fix the way corrected peaks are imported. * [docs] Citations * Rename ref.bib * [reports] : Improve the reports and fix filename error. * [reports] : Save a JSON and the raw signal in the preprocessed folder. * [viewer] : Retrieve the list of participants from the folder names. * [docs] documenting Viewer usages * [docs] remove citation that is not rendering correctly * [docs] images links * version bump Co-authored-by: alexvasilichi Co-authored-by: Leah banellis <94465218+lbanellis@users.noreply.github.com> --- .github/workflows/docs.yml | 35 + .github/workflows/linting.yml | 35 + .github/workflows/test.yml | 16 +- .gitignore | 7 +- .isort.cfg | 2 +- .pre-commit-config.yaml | 10 +- CONTRIBUTING.md | 2 +- MANIFEST.in | 3 + README.rst | 66 +- codecov.yml | 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systole/reports/command_line.py create mode 100644 systole/reports/group_level.html create mode 100644 systole/reports/group_level.py create mode 100644 systole/reports/images/logo.svg create mode 100644 systole/reports/subject_level.html create mode 100644 systole/reports/subject_level.py create mode 100644 systole/reports/tables.py create mode 100644 systole/reports/utils.py create mode 100644 systole/tests/group_level_ses-session1_task-hrd.tsv create mode 100644 systole/tests/test_reports.py create mode 100644 systole/viewer/__init__.py create mode 100644 systole/viewer/viewer.py diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..d36bf897 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,35 @@ +name: Build and Deploy + +on: + push: + branches: + - dev + pull_request: + branches: + - master +permissions: + contents: write + +jobs: + build-and-deploy: + runs-on: ubuntu-latest + steps: + - name: Checkout 🛎️ + uses: actions/checkout@v3 + + - name: Set up Python 3.9 + uses: actions/setup-python@v1 + with: + python-version: 3.9 + + - name: Build + run: | + pip install . + pip install -r requirements-docs.txt + sphinx-build -b html docs/source docs/build/html + + - name: Deploy 🚀 + uses: JamesIves/github-pages-deploy-action@v4 + with: + folder: docs/build/html + BRANCH: gh-pages diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml new file mode 100644 index 00000000..484a630f --- /dev/null +++ b/.github/workflows/linting.yml @@ -0,0 +1,35 @@ +name: Linting + +on: [push, pull_request] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.9] + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - uses: actions/cache@v2 + name: Configure pip caching + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ hashFiles('**/setup.py') }} + restore-keys: | + ${{ runner.os }}-pip- + - name: Install dependencies + run: | + pip install -r requirements-tests.txt + pip install isort flake8 black + pip install mypy==0.971 + pip install types-setuptools + - name: Run tests and coverage + run: | + mypy ./systole/ --ignore-missing-imports + flake8 ./systole/ + black ./systole/ + isort ./systole/ diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index e8e0e93a..7cf19cbf 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,13 +1,13 @@ name: Test -on: [push] +on: [push, pull_request] jobs: test: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.7, 3.8, 3.9] + python-version: [3.8] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} @@ -23,10 +23,12 @@ jobs: ${{ runner.os }}-pip- - name: Install dependencies run: | - pip install -e '.[test]' - pip install -r requirements-test.txt - pip install ipykernel + pip install -r requirements-tests.txt + pip install ipykernel coverage pytest pytest-cov python -m ipykernel install --user --name python3 - - name: Run tests + pip install . + - name: Run tests and coverage run: | - pytest \ No newline at end of file + pytest --cov=./ --cov-report=xml + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 64ba6620..9c834ebb 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ *.pyc *-checkpoint.ipynb +*-checkpoint.md mypyreports/ build/ dist/ @@ -7,5 +8,7 @@ systole.egg-info/ htmlcov/ .coverage .vscode/ -source/auto_examples -source/generated \ No newline at end of file +docs/source/auto_examples +docs/source/generated +docs/source/api +coverage.xml \ No newline at end of file diff --git a/.isort.cfg b/.isort.cfg index 88c43cc2..62d0e28b 100644 --- a/.isort.cfg +++ b/.isort.cfg @@ -1,5 +1,5 @@ [settings] -known_third_party = ecgdetectors,matplotlib,numpy,pandas,plotly,psychopy,pytest,requests,scipy,seaborn,serial,sphinx_bootstrap_theme,tqdm +known_third_party = matplotlib,numpy,pandas,pytest,requests,scipy,seaborn,serial,sphinx_bootstrap_theme,tqdm multi_line_output = 3 include_trailing_comma = True force_grid_wrap = 0 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f4afd70e..e7d4e8b6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,22 +1,22 @@ repos: - repo: https://github.com/pre-commit/mirrors-isort - rev: v5.9.3 + rev: v5.10.1 hooks: - id: isort files: ^systole/ - repo: https://github.com/ambv/black - rev: 21.12b0 + rev: 22.6.0 hooks: - id: black language_version: python3 files: ^systole/ -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v2.3.0 +- repo: https://github.com/pycqa/flake8 + rev: 5.0.4 hooks: - id: flake8 files: ^systole/ - repo: https://github.com/pre-commit/mirrors-mypy - rev: 'v0.920' # Use the sha / tag you want to point at + rev: 'v0.971' hooks: - id: mypy files: ^systole/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9710c41f..fd82ca52 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -26,7 +26,7 @@ Please verify that your issue is not being currently addressed by other issues o While issue reporting is valuable, we strongly encourage users who are inclined to do so to submit patches for new or existing issues via pull requests. This is particularly the case for simple fixes, such as typos or tweaks to documentation, which do not require a heavy investment of time and attention. -Contributors are also encouraged to contribute new code to enhance Systole's functionality, also via pull requests. Please consult the [Systole documentation](https://systole-docs.github.io/) to ensure that any new contribution does not strongly overlap with existing functionality. +Contributors are also encouraged to contribute new code to enhance Systole's functionality, also via pull requests. Please consult the [Systole documentation](https://embodied-computation-group.github.io/systole/#) to ensure that any new contribution does not strongly overlap with existing functionality. The preferred workflow for contributing to Systole is to fork the [GitHub repository](https://github.com/embodied-computation-group/systole), clone it to your local machine, and develop on a feature branch. diff --git a/MANIFEST.in b/MANIFEST.in index fcffc68a..9688fd10 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,3 +2,6 @@ include README.rst include LICENSE include requirements.txt +include systole/reports/subject_level.html +include systole/reports/group_level.html +include systole/reports/images/logo.svg \ No newline at end of file diff --git a/README.rst b/README.rst index 33f74c43..5795fd70 100644 --- a/README.rst +++ b/README.rst @@ -28,7 +28,7 @@ ================ -.. image:: https://github.com/embodied-computation-group/systole/blob/dev/source/images/logo.png +.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/logo.png :align: center ================ @@ -38,10 +38,18 @@ This includes tools for data epoching, artefact detection, artefact correction, variability analyses, circular statistical approaches to analysing cardiac cycles, and synchronising stimulus presentation with different cardiac phases via Psychopy. -The documentation can be found under the following `link `_. +The documentation can be found under the following `link `_. If you have questions, you can ask them in the `Gitter chat `_. +How to cite? +++++++++++++ + +If you are using **Systole** in a publication we ask you to cite the following paper:: + + Legrand et al., (2022). Systole: A python package for cardiac signal synchrony and analysis. Journal of Open Source Software, 7(69), 3832, https://doi.org/10.21105/joss.03832 + + Installation ++++++++++++ @@ -88,6 +96,8 @@ For an introduction to Systole and cardiac signal analysis, you can refer to the - |Colab badge 4| * - Instantaneous and evoked heart rate - |Colab badge 5| + * - Working with BIDS folders + - |Colab badge 6| .. |Colab badge 1| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/embodied-computation-group/systole/blob/dev/source/notebooks/1-PhysiologicalSignals.ipynb @@ -104,6 +114,9 @@ For an introduction to Systole and cardiac signal analysis, you can refer to the .. |Colab badge 5| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/embodied-computation-group/systole/blob/dev/source/notebooks/5-InstantaneousHeartRate.ipynb +.. |Colab badge 6| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/github/embodied-computation-group/systole/blob/dev/source/notebooks/6-WorkingWithBIDSFolders.ipynb + Getting started +++++++++++++++ @@ -127,7 +140,7 @@ The package integrates a set of functions for interactive or non interactive dat plot_raw(signal[60000 : 120000], modality="ecg", backend="bokeh", show_heart_rate=True, show_artefacts=True, figsize=300) -.. image:: https://github.com/embodied-computation-group/systole/blob/dev/source/images/raw.png +.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/raw.png :align: center @@ -145,7 +158,7 @@ Artefacts can be detected and corrected in the RR interval time series or the pe plot_subspaces(peaks, input_type="peaks", backend="bokeh") -.. image:: https://github.com/embodied-computation-group/systole/blob/dev/source/images/subspaces.png +.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/subspaces.png :align: center @@ -163,7 +176,7 @@ Systole implements time-domain, frequency-domain and non-linear HRV indices, as plot_poincare(peaks, input_type="peaks", backend="bokeh", figsize=(200, 200)), ) -.. image:: https://github.com/embodied-computation-group/systole/blob/dev/source/images/hrv.png +.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/hrv.png :align: center @@ -174,6 +187,34 @@ The package natively supports recording of physiological signals from the follow - `Nonin 3012LP Xpod USB pulse oximeter `_ together with the `Nonin 8000SM 'soft-clip' fingertip sensors `_ (USB). - Remote Data Access (RDA) via BrainVision Recorder together with `Brain product ExG amplifier `_ (Ethernet). +Interactive visualization of BIDS structured datasets +===================================================== + +.. code-block:: python + + from systole.viewer import Viewer + + view = Viewer( + input_folder="/BIDS/folder/path/", + pattern="task-mytask", + modality="beh", + signal_type="ECG" + ) + +.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/editor.gif + :align: center + +Inserting and removing peaks +============================ + +.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/peaks.gif + :align: center + +Annotating bad segments +======================= + +.. image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/segments.gif + :align: center Development +++++++++++ @@ -182,13 +223,6 @@ This module was created and is maintained by Nicolas Legrand and Micah Allen (EC This program is provided with NO WARRANTY OF ANY KIND. -Contributors -++++++++++++ - -- Jan C. Brammer (jan.c.brammer@gmail.com) -- Gidon Levakov (gidonlevakov@gmail.com) -- Peter Doggart (peter.doggart@pulseai.io) - Acknowledgements ++++++++++++++++ @@ -200,7 +234,7 @@ Systole was largely inspired by pre-existing toolboxes dedicated to heartrate va * hrv: https://github.com/rhenanbartels/hrv -* pyHVR: https://pyhrv.readthedocs.io/en/latest/index.html +* pyHRV: https://pyhrv.readthedocs.io/en/latest/index.html * ECG-detector: https://github.com/berndporr/py-ecg-detectors @@ -212,11 +246,11 @@ Systole was largely inspired by pre-existing toolboxes dedicated to heartrate va |AU| |lundbeck| |lab| -.. |AU| image:: https://github.com/embodied-computation-group/systole/raw/dev/source/images/au_clinisk_logo.png +.. |AU| image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/au_clinisk_logo.png :width: 100% -.. |lundbeck| image:: https://github.com/embodied-computation-group/systole/raw/dev/source/images/lundbeckfonden_logo.png +.. |lundbeck| image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/lundbeckfonden_logo.png :width: 10% -.. |lab| image:: https://github.com/embodied-computation-group/systole/raw/dev/source/images/LabLogo.png +.. |lab| image:: https://github.com/embodied-computation-group/systole/blob/dev/docs/source/images/LabLogo.png :width: 20% diff --git a/codecov.yml b/codecov.yml index 9fb6f81c..2509e6ef 100644 --- a/codecov.yml +++ b/codecov.yml @@ -1,21 +1,2 @@ -codecov: - token: 11a4b360-4894-4ef4-9f7c-c6d2858453c0 - require_ci_to_pass: yes - -coverage: - precision: 2 - round: down - range: "70...100" - - status: - project: yes - patch: yes - changes: no - -parsers: - gcov: - branch_detection: - conditional: yes - loop: yes - method: no - macro: no +ignore: + - "./systole/tests/*" \ No newline at end of file diff --git a/source/_static/style.css b/docs/source/_static/style.css similarity index 100% rename from source/_static/style.css rename to docs/source/_static/style.css diff --git a/source/api.rst b/docs/source/api.rst similarity index 74% rename from source/api.rst rename to docs/source/api.rst index 1c2ac2f6..24b61ba4 100644 --- a/source/api.rst +++ b/docs/source/api.rst @@ -18,12 +18,13 @@ Correction .. autosummary:: :toctree: generated/correction - correct_extra - correct_missed - interpolate_bads + correct_extra_rr + correct_missed_rr + interpolate_rr correct_rr correct_peaks correct_missed_peaks + correct_ectopic_peaks Detection --------- @@ -37,7 +38,7 @@ Detection ppg_peaks ecg_peaks - res_peaks + rsp_peaks rr_artefacts interpolate_clipping @@ -96,7 +97,33 @@ Recording Oximeter BrainVisionExG - findOximeter + +Reports +------- + +.. currentmodule:: systole.reports + +.. _reports: + +.. autosummary:: + :toctree: generated/reports + + time_table + frequency_table + nonlinear_table + +Viewer +------- + +.. currentmodule:: systole.viewer + +.. _reports: + +.. autosummary:: + :toctree: generated/viewer + + Viewer + Editor Utils ----- @@ -116,3 +143,7 @@ Utils simulate_rr to_neighbour input_conversion + nan_cleaning + find_clipping + get_valid_segments + norm_bad_segments \ No newline at end of file diff --git a/source/changelog.rst b/docs/source/changelog.rst similarity index 97% rename from source/changelog.rst rename to docs/source/changelog.rst index 6e226168..9d212410 100644 --- a/source/changelog.rst +++ b/docs/source/changelog.rst @@ -24,6 +24,8 @@ This page contains information about what has changed in each new version of ``s
+.. include:: releases/v0.2.3.txt + .. include:: releases/v0.2.2.txt .. include:: releases/v0.2.1.txt diff --git a/source/conf.py b/docs/source/conf.py similarity index 92% rename from source/conf.py rename to docs/source/conf.py index 20321192..1ba69702 100644 --- a/source/conf.py +++ b/docs/source/conf.py @@ -11,8 +11,9 @@ # documentation root, use os.path.abspath to make it absolute, like shown here. # import time -import systole + import sphinx_bootstrap_theme +import systole # -- Project information ----------------------------------------------------- @@ -25,11 +26,15 @@ image_scrapers = ("matplotlib",) sphinx_gallery_conf = { - "examples_dirs": "../examples/", + "examples_dirs": "./examples/", "backreferences_dir": "api", "image_scrapers": image_scrapers, } +bibtex_bibfiles = ['refs.bib'] +bibtex_reference_style = "author_year" +bibtex_default_style = "unsrt" + # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be @@ -50,6 +55,7 @@ "sphinx_panels", "myst_nb", "sphinx_gallery.load_style", + "sphinxcontrib.bibtex" ] panels_add_bootstrap_css = False @@ -64,8 +70,7 @@ plot_html_show_formats = False plot_html_show_source_link = False -# source_suffix = ['.rst', '.md'] -source_suffix = ".rst" +source_suffix = ['.rst', '.md'] # The master toctree document. master_doc = "index" @@ -103,7 +108,7 @@ icon="fas fa-box", ), ], - "logo_link": "https://systole-docs.github.io/", + "logo_link": "https://embodied-computation-group.github.io/systole/#", } html_sidebars = { @@ -117,8 +122,8 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] html_css_files = ["https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css"] -html_logo = "images/logo.png" - +html_logo = "images/logo.svg" +html_favicon = "images/logo_small.svg" def setup(app): app.add_css_file('style.css') diff --git a/examples/Artefacts/README.txt b/docs/source/examples/Artefacts/README.txt similarity index 100% rename from examples/Artefacts/README.txt rename to docs/source/examples/Artefacts/README.txt diff --git a/examples/Artefacts/plot_ArtefactsDetection.py b/docs/source/examples/Artefacts/plot_ArtefactsDetection.py similarity index 100% rename from examples/Artefacts/plot_ArtefactsDetection.py rename to docs/source/examples/Artefacts/plot_ArtefactsDetection.py diff --git a/examples/Artefacts/plot_PeaksCorrection.py b/docs/source/examples/Artefacts/plot_PeaksCorrection.py similarity index 100% rename from examples/Artefacts/plot_PeaksCorrection.py rename to docs/source/examples/Artefacts/plot_PeaksCorrection.py diff --git a/examples/Artefacts/plot_RRCorrection.py b/docs/source/examples/Artefacts/plot_RRCorrection.py similarity index 80% rename from examples/Artefacts/plot_RRCorrection.py rename to docs/source/examples/Artefacts/plot_RRCorrection.py index 2758af43..f970ecc4 100644 --- a/examples/Artefacts/plot_RRCorrection.py +++ b/docs/source/examples/Artefacts/plot_RRCorrection.py @@ -5,12 +5,14 @@ This example describes artefacts correction in RR time series. The function `correct_rr()` automatically detect artefacts using the method proposed -by Lipponen & Tarvainen (2019) [#]_. At each iteration, shorts, extra, long, missed -and ectopic beats are corrected using interpolation of the RR time series, and the +by Lipponen & Tarvainen (2019) [#]_. Shorts, extra, long, missed +and ectopic beats are corrected using either inserion/delection of RR intervals or +interpolation of the RR time series, and the detection procedure is run again using cleaned intervals. Importantly, when using this method the signal length can be altered after the interpolation, introducing misalignement with eg. triggers from the experiment. For this reason, it is only -recommended to use it in the context of "bloc design" study or heart rate variability. +recommended to use it in the context of "bloc design" study, when heart rate +variability is measured for a given time interval (usually > 5 minutes). """ @@ -18,16 +20,17 @@ # Licence: GPL v3 #%% +import matplotlib.pyplot as plt import numpy as np import pandas as pd +import seaborn as sns + from systole import import_dataset1 -from systole.detection import ecg_peaks from systole.correction import correct_rr -from systole.utils import input_conversion -from systole.plots import plot_rr, plot_frequency +from systole.detection import ecg_peaks from systole.hrv import frequency_domain -import matplotlib.pyplot as plt -import seaborn as sns +from systole.plots import plot_frequency, plot_rr +from systole.utils import input_conversion #%% Import ECG recording and events triggers ecg_df = import_dataset1(modalities=['ECG', 'Stim']) @@ -69,13 +72,13 @@ # artefacts in the RR intervals and reconstruct the most probable value using time # series interpolation. The number of iteration is set to `2` by default, we add it # here for clarity. -rr_correction = correct_rr(corrupted_rr, n_iterations=2) +corrected_rr, _ = correct_rr(corrupted_rr) #%% The num -plot_rr(rr_correction["clean_rr"], input_type='rr_ms', show_artefacts=True, +plot_rr(corrected_rr, input_type='rr_ms', show_artefacts=True, line=False, figsize=(13, 5)) plt.show() -#%% We can see that after two iterations, most/all of the artefacts have been corrected. +#%% We can see that most/all of the artefacts have been corrected. # This does not means that the new values match exactly the RR intervals, and the new # corrected time series will always slightly differs from the original one. However, we # can estimate how large this difference is by comparing the true, corrupted and @@ -84,7 +87,7 @@ # artefacts, like the high frequency HRV. _, axs = plt.subplots(1, 3, figsize=(13, 5), sharey=True) for i, rr, lab in zip(range(3), - [rr_ms, corrupted_rr, rr_correction["clean_rr"]], + [rr_ms, corrupted_rr, corrected_rr], ["Original", "Corrupted", "Corrected"]): plot_frequency(rr, input_type="rr_ms", ax=axs[i]) axs[i].set_title(lab) @@ -96,31 +99,36 @@ # parameters estimated at each steps. # Clean the RR time series before simulation -initial_rr = correct_rr(rr_ms.copy())["clean_rr"] +corrected_rr, _ = correct_rr(rr_ms.copy()) simulation_df = pd.DataFrame([]) for i in range(20): # Measure HF-HRV for corrupted RR intervals time series - corrupted_rr = initial_rr.copy() + corrupted_rr = corrected_rr.copy() corrupted_rr[np.random.choice(len(corrupted_rr), 50)] *= 2 corrupted_rr[np.random.choice(len(corrupted_rr), 50)] /= 3 corrupted_hrv = frequency_domain(corrupted_rr, input_type="rr_ms") - corrupted_hf = corrupted_hrv[corrupted_hrv.Metric == "power_hf_nu"].Values.iloc[0] + corrupted_hf = corrupted_hrv[corrupted_hrv.Metric == "hf_power_nu"].Values.iloc[0] # Measure HF-HRV for corrected RR intervals time series - corrected = correct_rr(corrupted_rr, n_iterations=2, verbose=False)["clean_rr"] + corrected, _ = correct_rr(corrupted_rr, verbose=False) corrected_hrv = frequency_domain(corrected, input_type="rr_ms") - corrected_hf = corrected_hrv[corrected_hrv.Metric == "power_hf_nu"].Values.iloc[0] - - simulation_df = simulation_df.append( - pd.DataFrame({"HF-HRV (n.u.)": [corrupted_hf, corrected_hf], - "Data Quality": ["Corrupted", "Corrected"] - }) - ) + corrected_hf = corrected_hrv[corrected_hrv.Metric == "hf_power_nu"].Values.iloc[0] + + simulation_df = pd.concat( + [ + simulation_df, + pd.DataFrame( + {"HF-HRV (n.u.)": [corrupted_hf, corrected_hf], + "Data Quality": ["Corrupted", "Corrected"] + } + ) + ] + ) -initial_hrv = frequency_domain(initial_rr, input_type="rr_ms") -initial_hf = initial_hrv[initial_hrv.Metric == "power_hf_nu"].Values.iloc[0] +initial_hrv = frequency_domain(corrected_rr, input_type="rr_ms") +initial_hf = initial_hrv[initial_hrv.Metric == "hf_power_nu"].Values.iloc[0] #%% Simulation results plt.figure(figsize=(5, 8)) diff --git a/docs/source/examples/Plots/README.txt b/docs/source/examples/Plots/README.txt new file mode 100644 index 00000000..8641c912 --- /dev/null +++ b/docs/source/examples/Plots/README.txt @@ -0,0 +1,2 @@ +Plotting +++++++++ \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_circular.py b/docs/source/examples/Plots/plot_circular.py new file mode 100644 index 00000000..9041d38f --- /dev/null +++ b/docs/source/examples/Plots/plot_circular.py @@ -0,0 +1,39 @@ +""" + +Plot circular +============= +The density function can be represented using the area of the bars, the height or +the transparency (alpha). The default behaviour will use the area. Using the heigth +can visually biase the importance of the largest values. Adapted from [#]_. + +The circular mean was adapted from Pingouin's implementation [#]_. + +.. [#] https://jwalton.info/Matplotlib-rose-plots/ + +.. [#] https://pingouin-stats.org/_modules/pingouin/circular.html#circ_mean + +""" + +# Author: Nicolas Legrand +# Licence: GPL v3 + +#%% +# Using a numpy array of angular values as input +# ---------------------------------------------- +import numpy as np +from systole.plots import plot_circular +x = np.random.normal(np.pi, 0.5, 100) +plot_circular(data=x) +#%% +# Using a data frame as input +# --------------------------- +import numpy as np +import pandas as pd +from systole.plots import plot_circular + +# Create angular values (two conditions) +x = np.random.normal(np.pi, 0.5, 100) +y = np.random.uniform(0, np.pi*2, 100) +data = pd.DataFrame(data={'x': x, 'y': y}).melt() + +plot_circular(data=data, y='value', hue='variable') \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_ectopic.py b/docs/source/examples/Plots/plot_ectopic.py new file mode 100644 index 00000000..86720979 --- /dev/null +++ b/docs/source/examples/Plots/plot_ectopic.py @@ -0,0 +1,44 @@ +""" + +Plot ectopic beats +================== + +""" + +# Author: Nicolas Legrand +# Licence: GPL v3 + +from systole.plots import plot_ectopic + +#%% +# Visualizing ectopic subspace from RR time series +# ------------------------------------------------ +from systole import import_rr + +# Import PPG recording as numpy array +rr = import_rr().rr.to_numpy() + +plot_ectopic(rr, input_type="rr_ms") +#%% +# Visualizing ectopic subspace from the `artefact` dictionary generated by :py:func:`systole.detection.rr_artefacts()` +# -------------------------------------------------------------------------------------------------------------------- +from systole.detection import rr_artefacts + +# Use the rr_artefacts function to find ectopic beats +artefacts = rr_artefacts(rr) + +plot_ectopic(artefacts=artefacts) +#%% +# Using Bokeh as plotting backend +# ------------------------------- +from bokeh.io import output_notebook +from bokeh.plotting import show +from systole.detection import rr_artefacts + +output_notebook() + +# Use the rr_artefacts function to find ectopic beats +artefacts = rr_artefacts(rr) +show( + plot_ectopic(artefacts=artefacts, backend="bokeh") +) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_events.py b/docs/source/examples/Plots/plot_events.py new file mode 100644 index 00000000..89b31484 --- /dev/null +++ b/docs/source/examples/Plots/plot_events.py @@ -0,0 +1,53 @@ +""" + +Plot events +=========== + +""" +import numpy as np +import seaborn as sns +from bokeh.io import output_notebook +from bokeh.plotting import show +from systole.detection import ecg_peaks +from systole.plots import plot_events, plot_rr + +from systole import import_dataset1 + +# Author: Nicolas Legrand +# Licence: GPL v3 + +#%% +# Plot events distributions using Matplotlib as plotting backend +# -------------------------------------------------------------- + +ecg_df = import_dataset1(modalities=['ECG', "Stim"]) + +# Get events triggers +triggers_idx = [ + np.where(ecg_df.stim.to_numpy() == 2)[0], + np.where(ecg_df.stim.to_numpy() == 1)[0] +] + +plot_events( + triggers_idx=triggers_idx, labels=["Disgust", "Neutral"], + tmin=-0.5, tmax=10.0, figsize=(13, 3), + palette=[sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]], +) +#%% +# Plot events distributions using Bokeh as plotting backend and add the RR time series +# ------------------------------------------------------------------------------------ + +output_notebook() + +# Peak detection in the ECG signal using the Pan-Tompkins method +signal, peaks = ecg_peaks(ecg_df.ecg, method='pan-tompkins', sfreq=1000) + +# First, we create a RR interval plot +rr_plot = plot_rr(peaks, input_type='peaks', backend='bokeh', figsize=250) + +show( + # Then we add events annotations to this plot using the plot_events function + plot_events(triggers_idx=triggers_idx, backend="bokeh", labels=["Disgust", "Neutral"], + tmin=-0.5, tmax=10.0, palette=[sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]], + ax=rr_plot.children[0]) +) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_evoked.py b/docs/source/examples/Plots/plot_evoked.py new file mode 100644 index 00000000..f001cf8d --- /dev/null +++ b/docs/source/examples/Plots/plot_evoked.py @@ -0,0 +1,111 @@ +""" + +Plot evoked +=========== + +""" + +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +from systole.detection import ecg_peaks +from systole.plots import plot_evoked +from systole.utils import heart_rate, to_epochs + +from systole import import_dataset1 + +# Author: Nicolas Legrand +# Licence: GPL v3 + +#%% +# Plot evoked heart rate across two conditions using the Matplotlib backend +# Here, for the sake of example, we are going to create the same plot three time using three kind of input data: +# * The raw signal + the triggers timing (or a list of in case of multiple conditions). +# * The peaks detection + the triggers timing (or a list of in case of multiple conditions) +# * The epoched signal as a 2d NumPy array (or a list of in case of multiple conditions) +# -------------------------------------------------------------- + +ecg_df = import_dataset1(modalities=['ECG', "Stim"]) + +# Get events triggers +triggers_idx = [ + np.where(ecg_df.stim.to_numpy() == 2)[0], + np.where(ecg_df.stim.to_numpy() == 1)[0] +] + +# Peak detection in the ECG signal using the Pan-Tompkins method +signal, peaks = ecg_peaks(ecg_df.ecg, method='sleepecg', sfreq=1000) + +# Convert to instantaneous heart rate +rr, _ = heart_rate(peaks, kind="cubic", unit="bpm", input_type="peaks") + +# Create list epochs arrays for each condition +hr_epochs, _ = to_epochs( + signal=rr, triggers_idx=triggers_idx, tmin=-1.0, tmax=10.0, + apply_baseline=(-1.0, 0.0) + ) + +fig, axs = plt.subplots(ncols=3, figsize=(15, 5), sharey=True) + +# We define a common set of plotting arguments here +plot_args = { + "backend": "matplotlib", "figsize": (400, 400), + "palette": [sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]], + "tmin": -1.0, "tmax": 10.0, "apply_baseline": (-1.0, 0.0), "decim": 100 + } + +# Using the raw signal and events triggers +plot_evoked( + signal=ecg_df.ecg.to_numpy(), triggers_idx=triggers_idx, modality="ecg", + ax=axs[0], **plot_args + ) + +# Using the detected peaks and events triggers +plot_evoked( + rr=peaks, triggers_idx=triggers_idx, input_type="peaks", ax=axs[1], + **plot_args + ) + +# Using the list of epochs arrays +plot_evoked( + epochs=hr_epochs, ax=axs[2], **plot_args + ) +#%% Plot evoked heart rate across two conditions using Bokeh as plotting backend. +# Here, for the sake of example, we are going to create the same plot three times using three kind of input data: +# * The raw signal + the triggers timing (or a list of in case of multiple conditions). +# * The peaks detection + the triggers timing (or a list of in case of multiple conditions) +# * The epoched signal as a 2d NumPy array (or a list of in case of multiple conditions) +# -------------------------------------------------------------------------------------- +from bokeh.io import output_notebook +from bokeh.layouts import row +from bokeh.plotting import show + +output_notebook() + +# We define a common set of plotting arguments here +plot_args = { + "backend": "bokeh", "figsize": (300, 300), + "palette": [sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]], + "tmin": -1.0, "tmax": 10.0, "apply_baseline": (-1.0, 0.0), "decim": 100 + } + +# Using the raw signal and events triggers +raw_plot = plot_evoked( + signal=ecg_df.ecg.to_numpy(), triggers_idx=triggers_idx, modality="ecg", + **plot_args + ) + +# Using the detected peaks and events triggers +peaks_plot = plot_evoked( + rr=peaks, triggers_idx=triggers_idx, input_type="peaks", **plot_args + ) + +# Using the list of epochs arrays +epochs_plots = plot_evoked(epochs=hr_epochs, **plot_args) + +# Create a Bokeh layout and plot the figures side by side +show( + row( + raw_plot, peaks_plot, epochs_plots + ) + ) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_frequency.py b/docs/source/examples/Plots/plot_frequency.py new file mode 100644 index 00000000..007b5bf7 --- /dev/null +++ b/docs/source/examples/Plots/plot_frequency.py @@ -0,0 +1,34 @@ +""" + +Plot frequency +============== + +""" + + + +# Author: Nicolas Legrand +# Licence: GPL v3 + +#%% +#Visualizing HRV frequency domain from RR time series using Matplotlib as plotting backend +# ---------------------------------------------------------------------------------------- +from systole import import_rr +from systole.plots import plot_frequency + +# Import PPG recording as numpy array +rr = import_rr().rr.to_numpy() +plot_frequency(rr, input_type="rr_ms") + +#%% +#Visualizing HRV frequency domain from RR time series using Bokeh as plotting backend +# ----------------------------------------------------------------------------------- +from systole import import_rr +from systole.plots import plot_frequency +from bokeh.io import output_notebook +from bokeh.plotting import show +output_notebook() + +show( + plot_frequency(rr, input_type="rr_ms", backend="bokeh") +) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_pointcare.py b/docs/source/examples/Plots/plot_pointcare.py new file mode 100644 index 00000000..831533a2 --- /dev/null +++ b/docs/source/examples/Plots/plot_pointcare.py @@ -0,0 +1,34 @@ +""" + +Plot pointcare +============== + +""" + +# Author: Nicolas Legrand +# Licence: GPL v3 + +#%% +# Visualizing poincare plot from RR time series using Matplotlib as plotting backend +# ---------------------------------------------------------------------------------- +from systole import import_rr +from systole.plots import plot_poincare + +# Import PPG recording as numpy array +rr = import_rr().rr.to_numpy() + +plot_poincare(rr, input_type="rr_ms") + +#%% +# Using Bokeh as plotting backend +# ------------------------------- +from bokeh.io import output_notebook +from bokeh.plotting import show +output_notebook() + +from systole import import_rr +from systole.plots import plot_poincare + +show( + plot_poincare(rr, input_type="rr_ms", backend="bokeh") +) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_raw.py b/docs/source/examples/Plots/plot_raw.py new file mode 100644 index 00000000..b2fc0e74 --- /dev/null +++ b/docs/source/examples/Plots/plot_raw.py @@ -0,0 +1,55 @@ +""" + +Plot raw physiological signal +============================= + +""" + +# Author: Nicolas Legrand +# Licence: GPL v3 + + +from bokeh.io import output_notebook +from bokeh.plotting import show +from systole.plots import plot_raw + +from systole import import_dataset1, import_ppg + +#%% +# Plot raw ECG signal +# -------------------- + +# Import PPG recording as pandas data frame +physio_df = import_dataset1(modalities=['ECG', 'Respiration']) + +# Only use the first 60 seconds for demonstration +ecg = physio_df[physio_df.time.between(60, 90)].ecg +plot_raw(ecg, modality='ecg', sfreq=1000, ecg_method='sleepecg') +#%% +# Plot raw PPG signal +# ------------------- +# Import Respiration recording as pandas data frame +rsp = import_dataset1(modalities=['Respiration']) + +# Only use the first 90 seconds for demonstration +rsp = physio_df[physio_df.time.between(500, 600)].respiration +plot_raw(rsp, sfreq=1000, modality="respiration") +#%% +# Plot raw respiratory signal +# --------------------------- + +# Import PPG recording as pandas data frame +ppg = import_ppg() + +# Only use the first 60 seconds for demonstration +plot_raw(ppg[ppg.time<60], sfreq=75) + +#%% +# Using Bokeh as plotting backend +# ------------------------------- + +output_notebook() + +show( + plot_raw(ppg, backend="bokeh", show_heart_rate=True, show_artefacts=True) + ) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_rr.py b/docs/source/examples/Plots/plot_rr.py new file mode 100644 index 00000000..abf4a3fd --- /dev/null +++ b/docs/source/examples/Plots/plot_rr.py @@ -0,0 +1,39 @@ +""" + +Plot instantaneous heart rate +============================= + +""" + +# Author: Nicolas Legrand +# Licence: GPL v3 + +from bokeh.io import output_notebook +from bokeh.plotting import show +from systole.plots import plot_rr + +from systole import import_rr + +#%% +# Plot instantaneous heart rate from a RR interval time series (in milliseconds). +# ------------------------------------------------------------------------------- + +# Import R-R intervals time series +rr = import_rr().rr.values + +plot_rr(rr=rr, input_type="rr_ms"); +#%% +# Only show the interpolated instantaneous heart rate, add a bad segment and change the default unit to beats per minute (BPM). +# ----------------------------------------------------------------------------------------------------------------------------- +plot_rr(rr=rr, input_type="rr_ms", unit="bpm", points=False); +#%% +# Use Bokeh as a plotting backend, only show the scatterplt and highlight artefacts in the RR intervals +# ----------------------------------------------------------------------------------------------------- +output_notebook() + +show( + plot_rr( + rr=rr, input_type="rr_ms", backend="bokeh", + line=False, show_artefacts=True + ) +) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_shortlong.py b/docs/source/examples/Plots/plot_shortlong.py new file mode 100644 index 00000000..49fd13ce --- /dev/null +++ b/docs/source/examples/Plots/plot_shortlong.py @@ -0,0 +1,52 @@ +""" + +Plot short and long invertvals +============================== + +Visualization of short, long, extra and missed intervals detection. + +The artefact detection is based on the method described in [1]_. + +.. [1] Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for + heart rate variability time series artefact correction using novel beat + classification. Journal of Medical Engineering & Technology, 43(3), + 173–181. https://doi.org/10.1080/03091902.2019.1640306 + +""" + +# Author: Nicolas Legrand +# Licence: GPL v3 + +#%% +# Visualizing short/long and missed/extra intervals from a RR time series +# ----------------------------------------------------------------------- +from systole import import_rr +from systole.plots import plot_shortlong + +# Import PPG recording as numpy array +rr = import_rr().rr.to_numpy() + +plot_shortlong(rr) +#%% +# Visualizing ectopic subspace from the `artefact` dictionary +# ----------------------------------------------------------- +from systole.detection import rr_artefacts + +# Use the rr_artefacts function to short/long and extra/missed intervals +artefacts = rr_artefacts(rr) + +plot_shortlong(artefacts=artefacts) + +#%% +# Using Bokeh as plotting backend +# ------------------------------- +from bokeh.io import output_notebook +from bokeh.plotting import show +from systole.detection import rr_artefacts +output_notebook() + +show( + plot_shortlong( + artefacts=artefacts, backend="bokeh" + ) +) \ No newline at end of file diff --git a/docs/source/examples/Plots/plot_subspace.py b/docs/source/examples/Plots/plot_subspace.py new file mode 100644 index 00000000..8f30ad29 --- /dev/null +++ b/docs/source/examples/Plots/plot_subspace.py @@ -0,0 +1,53 @@ +""" + +Plot subspaces to vivualize short/long and ectopic beats +======================================================== + +The artefact detection is based on the method described in [1]_. + +.. [1] Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for + heart rate variability time series artefact correction using novel beat + classification. Journal of Medical Engineering & Technology, 43(3), + 173–181. https://doi.org/10.1080/03091902.2019.1640306 + +""" + +# Author: Nicolas Legrand +# Licence: GPL v3 + +#%% +# Visualizing artefacts from RR time series +# ----------------------------------------- +from systole import import_rr +from systole.plots import plot_subspaces +import matplotlib.pyplot as plt + +# Import PPG recording as numpy array +rr = import_rr().rr.to_numpy() + +_, axs = plt.subplots(ncols=2, figsize=(12, 6)) +plot_subspaces(rr, ax=axs); +#%% +# Visualizing artefacts from the `artefact` dictionary +# ---------------------------------------------------- +from systole.detection import rr_artefacts + +# Use the rr_artefacts function to short/long and extra/missed intervals +artefacts = rr_artefacts(rr) + +_, axs = plt.subplots(ncols=2, figsize=(12, 6)) +plot_subspaces(artefacts=artefacts, ax=axs) + +#%% +# Using Bokeh as plotting backend +# ------------------------------- +from bokeh.io import output_notebook +from bokeh.plotting import show +from systole.detection import rr_artefacts +output_notebook() + +show( + plot_subspaces( + artefacts=artefacts, backend="bokeh", figsize=400 + ) +) \ No newline at end of file diff --git a/examples/README.rst b/docs/source/examples/README.rst similarity index 100% rename from examples/README.rst rename to docs/source/examples/README.rst diff --git a/examples/Recording/README.txt b/docs/source/examples/Recording/README.txt similarity index 100% rename from examples/Recording/README.txt rename to docs/source/examples/Recording/README.txt diff --git a/examples/Recording/plot_HeartBeatrpeggios.py b/docs/source/examples/Recording/plot_HeartBeatrpeggios.py similarity index 77% rename from examples/Recording/plot_HeartBeatrpeggios.py rename to docs/source/examples/Recording/plot_HeartBeatrpeggios.py index 2a35731b..971fc9b0 100644 --- a/examples/Recording/plot_HeartBeatrpeggios.py +++ b/docs/source/examples/Recording/plot_HeartBeatrpeggios.py @@ -3,16 +3,15 @@ ======================================================= This tutorial illustrates how to use the ``Oximeter`` class to trigger stimuli -at different phases of the cardiac cycle using the [Psychopy](https://www.psychopy.org/) -toolbox. The PPG signal is recorded for 30 seconds and peaks are detected -online. Four notes ('C', 'E', 'G', 'Bfl') are played in synch with peak -detection with various delays: no delay, 1/4, 2/4 or 3/4 of the previous +at different phases of the cardiac cycle. The PPG signal is recorded for 30 seconds +and peaks are detected online. Four notes ('C', 'E', 'G', 'Bfl') are played in synch +with peak detection with various delays: no delay, 1/4, 2/4 or 3/4 of the previous cardiac cycle length. While R-R intervals are prone to large changes over longer timescales, such changes are physiologically limited from one heartbeat to the next, limiting variance in the onset synchrony between the tones and the cardiac cycle. On this basis, each presentation time is calibrated based on the previous RR-interval. -This procedure can easily be adapted to create a standard interoception task, e.g. by either presenting -tones at no delay (systole, s+) or a fixed offset (diastole, s-). +This procedure can easily be adapted to create a standard interoception task, e.g. by +either presenting tones at no delay (systole, s+) or a fixed offset (diastole, s-). """ @@ -24,7 +23,6 @@ import matplotlib.pyplot as plt import numpy as np -from psychopy.sound import Sound import seaborn as sns from systole import serialSim from systole.plots import plot_circular, plot_events @@ -56,21 +54,19 @@ oxi = Oximeter(serial=ser, sfreq=75, add_channels=4).setup() #%% -# Create an Oxymeter instance, initialize recording and record for 10 seconds - -beat = Sound("C", secs=0.1) -diastole1 = Sound("E", secs=0.1) -diastole2 = Sound("G", secs=0.1) -diastole3 = Sound("Bfl", secs=0.1) +# Play the different tones at different intervals following the detection of the +# systolic peaks, using the length of the previous RR interval as reference. To kee the +# execution of the example simple we do no pla atual sounds (e.g. using Psychopy) but +# this step can easily be added by adding new lines for each time window. systoleTime1, systoleTime2, systoleTime3 = None, None, None tstart = time.time() while time.time() - tstart < 20: # Check if there are new data to read - while oxi.serial.inWaiting() >= 5: + if oxi.serial.inWaiting() >= 5: - # Convert bytes into list of int + # Read one incoming data point - Convert bytes into list of int paquet = list(oxi.serial.read(5)) if oxi.check(paquet): # Data consistency @@ -78,8 +74,7 @@ # T + 0 if oxi.peaks[-1] == 1: - beat = Sound("C", secs=0.1) - beat.play() + oxi.channels["Channel_0"][-1] = 1 systoleTime1 = time.time() systoleTime2 = time.time() systoleTime3 = time.time() @@ -87,30 +82,21 @@ # T + 1/4 if systoleTime1 is not None: if time.time() - systoleTime1 >= ((oxi.instant_rr[-1] / 4) / 1000): - diastole1 = Sound("E", secs=0.1) - diastole1.play() + oxi.channels["Channel_1"][-1] = 1 systoleTime1 = None # T + 2/4 if systoleTime2 is not None: if time.time() - systoleTime2 >= (((oxi.instant_rr[-1] / 4) * 2) / 1000): - diastole2 = Sound("G", secs=0.1) - diastole2.play() + oxi.channels["Channel_2"][-1] = 1 systoleTime2 = None # T + 3/4 if systoleTime3 is not None: if time.time() - systoleTime3 >= (((oxi.instant_rr[-1] / 4) * 3) / 1000): - diastole3 = Sound("A", secs=0.1) - diastole3.play() + oxi.channels["Channel_3"][-1] = 1 systoleTime3 = None - # Track the note status - oxi.channels["Channel_0"][-1] = beat.status - oxi.channels["Channel_1"][-1] = diastole1.status - oxi.channels["Channel_2"][-1] = diastole2.status - oxi.channels["Channel_3"][-1] = diastole3.status - #%% # Events # -------- @@ -135,7 +121,7 @@ # Plot the event according to its duration (0.1 second) plot_events( triggers=triggers, tmin=0.0, tmax=0.1, sfreq=75, - events_labels=events_labels[i], ax=axs[0], palette=[next(palette)]) + labels=events_labels[i], ax=axs[0], palette=[next(palette)]) axs[0].set_xlabel("") oxi.plot_raw(ax=axs[1]) @@ -164,3 +150,5 @@ for i, density in enumerate(["height", "area", "alpha"]): plot_circular(angles, density=density, ax=axs[i]) axs[i].set_title(f"Density method = {density}") + +# %% diff --git a/examples/Recording/plot_InstantaneousHeartRate.py b/docs/source/examples/Recording/plot_InstantaneousHeartRate.py similarity index 95% rename from examples/Recording/plot_InstantaneousHeartRate.py rename to docs/source/examples/Recording/plot_InstantaneousHeartRate.py index 1fd6d1d5..ab34bf8e 100644 --- a/examples/Recording/plot_InstantaneousHeartRate.py +++ b/docs/source/examples/Recording/plot_InstantaneousHeartRate.py @@ -13,13 +13,12 @@ # Licence: GPL v3 import matplotlib.pyplot as plt -import pandas as pd import numpy as np +import pandas as pd from systole import serialSim -from systole.recording import Oximeter from systole.detection import ppg_peaks from systole.plots import plot_raw, plot_rr - +from systole.recording import Oximeter #%% # Recording @@ -40,7 +39,7 @@ # import serial # ser = serial.Serial('COM4') # Change this value according to your setup -# Create an Oxymeter instance, initialize recording and record for 10 seconds +# Create an Oxymeter instance, initialize recording and record for 30 seconds oxi = Oximeter(serial=ser, sfreq=75).setup() oxi.read(30) @@ -48,7 +47,7 @@ # Plotting # -------- -signal, peaks = ppg_peaks(x=oxi.recording, sfreq=75) +signal, peaks = ppg_peaks(signal=oxi.recording, sfreq=75) fig, ax = plt.subplots(3, 1, figsize=(13, 8), sharex=True) @@ -60,4 +59,4 @@ ax[1].set_ylabel("Peak\n detection") plot_rr(peaks, input_type="peaks", ax=ax[2]) -plt.tight_layout() +plt.tight_layout() \ No newline at end of file diff --git a/examples/Recording/plot_RecordingPPG.py b/docs/source/examples/Recording/plot_RecordingPPG.py similarity index 98% rename from examples/Recording/plot_RecordingPPG.py rename to docs/source/examples/Recording/plot_RecordingPPG.py index 4d59d405..fd982032 100644 --- a/examples/Recording/plot_RecordingPPG.py +++ b/docs/source/examples/Recording/plot_RecordingPPG.py @@ -6,7 +6,7 @@ from `Nonin 3012LP Xpod USB pulse oximeter `_ together with the `Nonin 8000SM 'soft-clip' fingertip sensors `_. This function can easily -be integrated with other stimulus presentation software lie `PsychoPy +be integrated with other stimulus presentation software like `PsychoPy `_ to record cardiac activity during psychological experiments, or to synchronize stimulus delivery around cardiac phases (e.g. systole or diastole). diff --git a/source/getting_started.rst b/docs/source/getting_started.rst similarity index 90% rename from source/getting_started.rst rename to docs/source/getting_started.rst index ea548579..81392e03 100644 --- a/source/getting_started.rst +++ b/docs/source/getting_started.rst @@ -2,12 +2,18 @@ Installation ++++++++++++ -Systole can be installed using pip: +The last stable version of Systole can be installed using pip: .. code-block:: shell pip install systole +If you want to download the `dev` branch instead and try the last features that are currently under development (and probably a bit unstable), use: + +.. code-block:: shell + + pip install “git+https://github.com/embodied-computation-group/systole.git@dev” + The following packages are required to use Systole: * `Numpy `_ (>=1.15) diff --git a/source/images/LabLogo.png b/docs/source/images/LabLogo.png similarity index 100% rename from source/images/LabLogo.png rename to docs/source/images/LabLogo.png diff --git a/source/images/au_clinisk_logo.png b/docs/source/images/au_clinisk_logo.png similarity index 100% rename from source/images/au_clinisk_logo.png rename to docs/source/images/au_clinisk_logo.png diff --git a/source/images/code-solid.png b/docs/source/images/code-solid.png similarity index 100% rename from source/images/code-solid.png rename to docs/source/images/code-solid.png diff --git a/source/images/code-solid.svg b/docs/source/images/code-solid.svg similarity index 100% rename from source/images/code-solid.svg rename to docs/source/images/code-solid.svg diff --git a/source/images/create_figures.py b/docs/source/images/create_figures.py similarity index 100% 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