semvec/pypi-packages huggingface dataset:
dc-converter-tof 0.1.6
Converter
Requires Python: >=3.10

# RAW file converter

Fast converter for raw files using Python and Rust.

## Binding

Binding between Rust and Python is done using [PyO3](https://github.com/PyO3/pyo3), a library like PyBind11 for C++.

Check [Userguide](https://pyo3.rs/).

## Build

Create venv and build the Rust lib. Needs also Rust installation. https://www.rust-lang.org/tools/install

```bash
sudo apt install libopencv-dev clang libclang-dev

python3 -m venv .env
source .env/bin activate
pip install -r requirements.txt

# Test:
maturin develop -r

# Publish:
maturin publish -i python3.10 python3.11

```
vitalpython 0.1.0
VitalPy: A Vital Signal Analysis Package
Requires Python: >=3.9

<h1 align="center">
<img src="https://github.com/SCAI-Lab/VitalPy/assets/33239037/e8a006a2-9f84-4fc4-abd0-e3d9ebfb37d0" width="550">
</h1><br>

# VitalPy: A Vital Signal Analysis Package

Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.

**It provides:**
- PPG preprocessing: Singal quality metrics, baseline extraction, etc.
- PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
- Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.


## Use

VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:

```pip install -r requirements.txt```

Import:

```from src.ppg.PPGSignal import PPGSignal```

Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):

```signal = PPGSignal(waveform_df, verbose=1)```

```signal.check_keypoints()```

Get features:

```signal = PPGSignal(waveform_df, verbose=0)```

```features = signal.extract_features()```



## Example plots for a AuroraBP signal

Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv

#### Mean signal

The following figure shows the mean template computed from all templates within the signal given as input.

<img src="https://github.com/SCAI-Lab/VitalPy/assets/33239037/9ede79d8-2613-47b6-a1ca-b5247c417cfc" width="650">

#### Preprocessing steps

All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.

<img src="https://github.com/SCAI-Lab/VitalPy/assets/33239037/ecc0a2ea-b4e8-4f67-a4b2-05232de1f81c" width="650">

#### PPG Keypoints

Exemplary PPG keypoint extraction.

<img src="https://github.com/SCAI-Lab/VitalPy/assets/33239037/93e17375-e5c3-44a3-a437-63aa9bbb2262" width="650">

## License

VitalPy is available under the General Public License v3.0.

## Citation

If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
> A. Cisnal*et al.* "Robust Feature Selection for Continuous BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," *IEEE J Biomed Health Inform*, Under Review (2023).

```
@unpublished{vitalpy,
  title={Robust Feature Selection for Continuous BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring},
  author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2023},
  note = "Under review"
}
```
tsenum 1.2.0
Timestamp enumerator
Requires Python: >=3.7
Keywords: enumerator,timestamp,tool

# tsenum

[![Tests](https://github.com/aboehm/tsenum/actions/workflows/tests.yml/badge.svg)](https://github.com/aboehm/tsenum/actions/workflows/tests.yml)

A timestamp generator.

## Install

You can use pip to install from the repository

```
pip install tsenum
```

or download sources and run pip from this directory

```
git clone https://github.com/aboehm/tsenum
pip install .
```

## Usage

`tsenum` provides an CLI. For help run:

```
tsenum -h
```

To count 7 days back from yesterday via CLI, run:

```sh
tsenum --offset -1 --count -7 --step day --pattern "%Y-%m-%d: Hello world!"
2016-05-27: Hello world!
2016-05-28: Hello world!
2016-05-29: Hello world!
2016-05-30: Hello world!
2016-05-31: Hello world!
2016-06-01: Hello world!
2016-06-02: Hello world!
```

To do it programmatically:

```python
from datetime import datetime
import tsenum

print(
    tsenum.enumerate_times(
        datetime.now().astimezone(),
        offset=-1,
        count=-7,
        step=tsenum.Step.DAY,
        pattern='%Y-%m-%d'
    )
)
```