semvec/pypi-packages huggingface dataset:
dashcam-gpx-converter 0.3.0
Convert 70mai dashcam GPS logs into GPX tracks
License: MIT
Requires Python: >=3.9
Keywords: dashcam, gpx, gps, converter, 70mai

# Dashcam GPX Converter

Convert 70mai dashcam GPS logs into GPX tracks for easy mapping and analysis

## Installation

```bash
pip install dashcam-gpx-converter
```

## Usage

```bash
dashcam2gpx -s 250 GPSData000001.txt -o GPSData000001.gpx -v
```

- `GPSData000001.txt`: Your dashcam log file, including the `.txt` extension.
- `-o, --output`: Optional GPX output file name (e.g., GPSData000001.gpx).
- `-v, --verbose`: Enable debug logging.
- `-i, --info`: Show file info.
- `-s, --segments-limit`: Maximum number of track segments to include (default: 0). Set to **250** for easier importing into **Dawarich**.

## Development

```bash
git clone https://github.com/max2697/dashcam-gpx-converter.git
cd dashcam-gpx-converter
poetry install --with dev
poetry run dashcam2gpx -s 250 GPSData000001.txt
poetry run poe lint
```
SI-QPOT 0.1.0
An implementation of SI-QPOT: Statistical Inference for Quadratic Programming-based Feature Selection after Optimal Transport-based Domain Adaptation
License: MIT
Requires Python: >=3.10

# SI-QPOT: Statistical Inference for Quadratic Programming-based Feature Selection after Optimal Transport-based Domain Adaptation

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SI-QPOT is a Python package that implements a selective inference (SI) framework for conducting valid statistical inference after QP-based Feature Selection algorithms in the presence of domain adaptation (DA). The main idea is to leverages the SI framework and employs a divide-and conquer approach to efficiently compute the $p$ -value. Our proposed methods provides valid $p$-value for FS-DA results, by keeping the false positive rate (FPR) under control, while also maximizing the true positive rate (TPR), i.e., lowering the false negative rate (FNR).

## Requirements
This package has the following requirements:

    cvxpy
    mpmath
    numpy
    POT
    scikit-learn
    scipy

## Installation

### Package Installation
This package can be installed using pip:
```bash
$ pip install si_qpot
```

## Usage

We provide several Jupyter notebooks demonstrating how to use the stand-da package in action.

- Examples for conducting inference for QP-based Feature Selection after OT-based DA
```
>> ex1_feature_selection_after_DA.ipynb
```
- Check the uniformity of the pivot
```
>> ex2_validity_of_p_value.ipynb
```