Update dependency, adding permissions (#244)

* Update dependency, adding permissions

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update test.yaml

* Add action commit hash

* Update min actfast version

* Update pyproject.toml

bump min version of scipy

* Update poetry.lock

* Update test.yaml

Try moving this up

* Update pyproject.toml

Move to numpy > 2.1.0?

* Update poetry.lock

* Bump scikit learn

* More scikit bump

* Bump min actfast to newest

* Update test.yaml

Fix permission level

* Initial test of nimbledetach

failing mypy and ruff but just checking python tests cross platform

* Minor fixes

* Pandas added back in

* Fixing some ruff/mypy issues

* Update nimbaldetach.py

Moved to private function outside nimbaldetach

* Starting refactor

* Fixing return

* Update poetry.lock

* Update test.yaml

Add 3.13 back

* refactor intermediary steps

* Update _nimbaldetach.py

* Update test.yaml

Should no longer be needed due to removing skdh dependency

* Update README.md

Remove libomp warning

* Update _nimbaldetach.py

change function name

* Update _nimbaldetach.py

Fixing some docstrings

* Remove pandas

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Asanto32

.github/workflows/test.yaml

@@ -9,29 +9,27 @@ on:
     - main
   pull_request:
 
+
 jobs:
   unit:
     runs-on: ${{ matrix.os }}
+
     strategy:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python_version: ['3.10', '3.11', '3.12']
+        python_version: ['3.10', '3.11', '3.12', '3.13']
         resolution: [lowest-direct, highest]
     steps:
     - uses: actions/checkout@v4
     - name: Install poetry
       run: pipx install poetry
+
     - uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python_version }}
         cache: pip
 
-    - name: Install dependencies on macOS
-      if: runner.os == 'macOS'
-      run: |
-        brew install libomp gfortran
-
     - name: Install dependencies
       run: |
         poetry env use python

README.md

@@ -35,23 +35,14 @@ The main processing pipeline of the wristpy module can be described as follows:
 - **Data calibration**: A post-manufacturer calibration step can be applied, to ensure that the acceleration sensor is measuring 1*g* force during periods of no motion. There are three possible options: `None`, `gradient`, `ggir`.
 - ***Data imputation*** In the special case when dealing with the Actigraph `idle_sleep_mode == enabled`, the gaps in acceleration are filled in after calibration, to avoid biasing the calibration phase.
 - **Metrics Calculation**: Calculates various activity metrics on the calibrated data, namely ENMO (Euclidean norm, minus one), MAD (mean amplitude deviation) <sup>1</sup>, Actigraph activity counts<sup>2</sup>, MIMS (monitor-independent movement summary) unit <sup>3</sup>, and angle-Z (angle of acceleration relative to the *x-y* axis).
-- **Non-wear detection**: We find periods of non-wear based on the acceleration data. Specifically, the standard deviation of the acceleration values in a given time window, along each axis, is used as a threshold to decide `wear` or `not wear`. Additionally, we can use the temperature sensor, when avaialable, to augment the acceleration data. This is used in the CTA (combined temperature and acceleration) algorithm <sup>4</sup>, and in the `skdh` DETACH algorithm <sup>5</sup>. Furthermore, ensemble classification of non-wear periods is possible by providing a list (of any length) of non-wear algorithm options.
+- **Non-wear detection**: We find periods of non-wear based on the acceleration data. Specifically, the standard deviation of the acceleration values in a given time window, along each axis, is used as a threshold to decide `wear` or `not wear`. Additionally, we can use the temperature sensor, when avaialable, to augment the acceleration data. This is used in the CTA (combined temperature and acceleration) algorithm <sup>4</sup>, and in the DETACH algorithm <sup>5</sup>. Furthermore, ensemble classification of non-wear periods is possible by providing a list (of any length) of non-wear algorithm options.
 - **Sleep Detection**: Using the HDCZ<sup>6</sup> and HSPT<sup>7</sup> algorithms to analyze changes in arm angle we are able to find periods of sleep. We find the sleep onset-wakeup times for all sleep windows detected. Any sleep periods that overlap with detected non-wear times are removed, and any remaining sleep periods shorter than 15 minutes (default value) are removed. Additionally, the SIB (sustained inactivity bouts) and the SPT (sleep period time) windows are provided as part of the output to aid in sleep metric post-processing.
 - **Physical activity levels**: Using the chosen physical activity metric (aggregated into time bins, 5 second default) we compute activity levels into the following categories: [`inactive`, `light`, `moderate`, `vigorous`]. The threshold values can be defined by the user, while the default values are chosen based on the specific activity metric and the values found in the literature  <sup>8-10</sup>.
 - **Data output**: The output results can be saved in `.csv` or `.parquet` data formats, with the run-time configuration parameters saved in a `.json` dictionary.
 
 
 ## Installation
 
-> ### ⚠️ Important Note for macOS Users
->
-> **wristpy** depends on `libomp`, a system-level dependency that is not always installed by default on macOS. Install it via:
->
-> ```bash
-> brew install libomp
-> ```
-
-
 Install the `wristpy` package from PyPI via:
 
 ```sh