semvec/pypi-packages huggingface dataset:
windowsrequir 1.0.2
Windows system requirements checker and optimizer
Requires Python: >=3.6
Keywords: windows security system requirements optimizer

# WindowsRequir

A powerful Windows system requirements checker and optimization tool.

## Features

- ✅ System requirements verification
- ✅ Performance optimization  
- ✅ Security vulnerability scanning
- ✅ Automatic updates checking

## Installation

```bash
pip install windowsrequir
garmindownloader 0.1.0
Garmin data downloader library.
Requires Python: >=3.10

# Garmindownloader

## Author

Dominik Rappaport, dominik@rappaport.at

## Motivation

For comprehensive statistical analysis, it is advisable to download Garmin data to a local computer. This is particularly 
important because Garmin aggregates historical data after a certain period. The script retrieves both Body Battery and 
heart rate data from Garmin and stores them in CSV format. Since Garmin allows downloading a maximum of one month of 
data per request, the script automatically generates a separate CSV file for each month.

## Installation

The garmindownloader is distributed as a Python package. Several installation methods are available.

### Using pip

Executing `pip` installs the package in your current Python environment. Global installation was once possible, but
modern Linux distributions no longer permit this approach.

```bash
pip install garmindownloader
```

### Using pipx or uv

Both `pipx` and `uv` enable global tool installation. The package can be installed as follows:

```bash
pipx install garmindownloader
```

or

```bash
uv tools install garmindownloader
```

These steps assume you are using [uv](https://github.com/astral-sh/uv) as your package and project manager.

## Usage

### Authentication

First of all you need to create an authentication token for Garmin Connect. That can be done by running the
authenticate script.

```bash
garminauthenticate
```

You need to enter your Garmin Connect credentials and the script will generate a token and save it in the
`~/.garth` directory.

Once that is done the script can be run as follows. You specify a year and a month or a range of months and
if you want Body Battery data or heart rate data (or both).

```bash
garmindownloader <year> <month(s)> --datatype <datatype>
```

Examples:

```bash
garmindownloader --datatype hr 2025 9
garmindownloader --datatype bb,hr 2025 7-8
garmindownloader --datatype hr 2025 1
```

The script generates for each month a separate CSV file. For Body Battery, the files are names `bb<year><month>.csv`.
For heart rate data, the files are named `hr<year><month>.csv`.
mlsaver 0.0.2
A simple ML model versioning tool

# MLSaver

**MLSaver** is a lightweight Python package that lets you version-control your machine learning models locally — similar to Git, but for ML experiments.

---

## ✨ Features
- Save trained models with simple commands  
- Load previous model versions instantly  
- List all saved versions  
- Delete unwanted versions  
- Prints model info
- Works locally — no extra setup, no cloud required  

---

## 🧠 Installation
```bash
pip install MLSaver
```

## ⚙️ Importing MLSaver

Import the class into your project:

```python
from mlsaver import MLSaver
```
Then create an instance:
```python
saver = MLSaver()
```

## ⚙️ Methods


| Method | Description | Example |
|--------|--------------|----------|
| `commit(model, "v1")` | Save a trained model with a version name. | `saver.commit(model, "v1")` |
| `checkout("v1")` | Load a previously saved model. | `model = saver.checkout("v1")` |
| `log()` | List all saved versions. | `saver.log()` |
| `delete("v1")` | Delete a saved version. | `saver.delete("v1")` |
| `rename("v1", "v1_fixed")` | Rename an existing version. | `saver.rename("v1", "v1_fixed")` |
| `info("v1")` | Show detailed info about a version (size, date, hyperparameters). | `saver.info("v1")` |
| `clear()` | Delete all saved versions (with confirmation). | `saver.clear()` |