semvec/pypi-packages huggingface dataset:
the-hop-test 0.2.0
Run the Hop Test against your python code repository
Requires Python: >=3.8

# The Hop Test

At Hop Labs we strive to write high quality software. It's not easy to measure
this empirically but we can measure some proximate signals for quality. That is
what this tool is trying to achieve. We want to measure every thing we can about
a code repository that is suggestive of high quality code.

For now we only support python projects.

## Installation

You can install as a precommit hook:

```yaml
# .pre-commit-hooks.yaml
repos:
-   repo: https://github.com/hopservices/the-hop-test
    rev: v0.1.2
    hooks:
    -   id: the-hop-test
```

You can install with pip from PyPI:

```shell
pip install the-hop-test
```

## Usage

Run the script in the root of your repository

```shell
hoptest
```

## Configuration

By default, the `hoptest` will read configuration settings from
`./hoptest.toml`, but you can specify where the file is with

```shell
hoptest --config path/to/hoptest.toml
```

An example config file looks like

```toml
# ./hoptest.toml
[tool.hoptest]
license_file = "custom-license.txt"
skip_checks = ["logging"]
```

## Development

To build and publish to PyPI, run the following

```shell
rye build
rye publish --skip-existing
```
obsidian2html 0.1.2
Convert Obsidian-flavoured markdown to HTML
Requires Python: >=3.8

# obsidian2html

Convert Obsidian-flavoured markdown to HTML.

## Installation

```
pip install obsidian2html
```

## Usage

On the command line:

```
obsidian2html infile outfile
```

As a python library:

```python
from obsidian2html import obsidian2html

src = '''
# A simple markdown file

Hello world!
'''

html: str = obsidian2html(src)

print(html)
```

## Features

 - Convert internal links:
   ```
   [[my notes|Notes]] -> <a href="Notes.html">my notes</a>
   [[a non-obsidian-markdown link|example.pdf]] -> <a href="example.pdf">a non-obsidian-markdown link</a>

   ```

## Missing features

Basics:

 - Support for checklists - https://codepen.io/ouroboros8/pen/KKOzmBX
 - comments `%%this is an obsidian markdown inline comment%%`
 - footnotes

Advanced:

 - callouts
 - MathJAX
 - Tables
 - mermaid diagrams

## References

 - [Obsidian's basic formatting syntax](https://help.obsidian.md/Editing+and+formatting/Basic+formatting+syntax)
 - [Obsidian Flavoured Markdown](https://help.obsidian.md/Editing+and+formatting/Obsidian+Flavored+Markdown)
TrajectoryKNN 1.0.0
Trajectory by KNN
Requires Python: >=3.8

# TrajectoryKNN: Single-Cell Trajectory Analysis with k-Nearest Neighbors Algorithm


TrajectoryKNN is a software designed for single-cell trajectory analysis using the k-Nearest Neighbors (KNN) algorithm. It enables researchers to analyze the developmental trajectories of single cells and uncover the underlying dynamics of cellular processes.



## Getting Started

### Prerequisites

Before you begin, ensure you have met the following requirements:
- Python 3.8 or higher
- A compatible operating system (e.g., Windows, macOS, Linux)

### Installation

To install the software, follow these steps:

```
pip install TrajectoryKNN
```


## License 

This project is licensed under the MIT License.

## Contact
For any questions or feedback, please reach out to us at 605547565@qq.com.
tree-values-viewer 0.1.16
A tool to view project tree and file contents
Requires Python: >=3.6

# Tree Values Viewer

A simple and powerful tool to view project tree structure, file contents, and file information.

## Features

- Display project directory structure
- View file contents
- Show file information including line count
- Ignore specific files or directories

## Installation

You can install Tree Values Viewer using pip:

```
pip install tree-values-viewer
```

## Usage

Tree Values Viewer provides three main commands:

1. `tree`: Display the project directory structure
2. `values`: View file contents
3. `values-info`: Show file information including line count

### Viewing Project Structure

To view the project directory structure:

```
view-project tree
```

Example output:
```
|____project_root/
    |____src/
        |____main.py
        |____utils.py
    |____tests/
        |____test_main.py
    |____README.md
```

### Viewing File Contents

To view the contents of all files in the project:

```
view-project values
```

This will display the content of each file, separated by a line of dashes.

### Viewing File Information

To view information about files, including their line count:

```
view-project values-info
```

Example output:
```
+------------------------+------------+
|       File Path        | Line Count |
+------------------------+------------+
| ./src/main.py          |    100     |
| ./src/utils.py         |     50     |
| ./tests/test_main.py   |     75     |
| ./README.md            |     30     |
+------------------------+------------+
Total lines of code: 255
```

### Ignoring Files or Directories

You can ignore specific files or directories using the `--ignore` option:

```
view-project tree --ignore .env,.git,node_modules
view-project values --ignore .env,.git,node_modules
view-project values-info --ignore .env,.git,node_modules
```

This will exclude the specified files or directories from the output.

## Development

### Setting Up the Development Environment

1. Clone the repository:
   ```
   git clone https://github.com/keskinbu/tree-values-viewer.git
   cd tree-values-viewer
   ```

2. Create a virtual environment and activate it:
   ```
   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   ```

3. Install the development dependencies:
   ```
   pip install -r requirements-dev.txt
   ```

### Running Tests

To run the tests:

```
python -m unittest discover tests
```

### Building and Publishing

1. Install the required tools:
   ```
   pip install setuptools wheel twine
   ```

2. Create distribution packages:
   ```
   python setup.py sdist bdist_wheel
   ```

3. Upload to PyPI:
   ```
   twine upload dist/* --repository-url https://upload.pypi.org/legacy/ -u __token__ -p $PYPI_API_TOKEN
   ```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details.
BCscannerBWA 1.0.0
Barcode split for Cyclone
Requires Python: >=3.8

# BCscannerBWA
 
BCscannerBWA software is a one-stop tool for barcode splitting. The software consists of 4 steps, including building reference database, comparing input data to reference database, filtering correction, and output data.



## Getting Started

### Prerequisites

Before you begin, ensure you have met the following requirements:
- Python 3.8 or higher
- A compatible operating system (e.g., Windows, macOS, Linux)

### Installation

To install the software, follow these steps:

```
pip install BCscannerBWA
```


## License 

This project is licensed under the MIT License.

## Contact
For any questions or feedback, please reach out to us at 605547565@qq.com.
sepsesam 0.5.3
SEP Sesam Python API
Requires Python: >=3.6

> [!IMPORTANT]  
> This project is not associated with [SEP](https://www.sep.de/) in any way

> [!NOTE]  
> Not all API endpoints offered by the SEP Sesam REST API (neither v1 nor v2) are implemented.
> Some additional, undocumented endpoints are used, which may be not supported by SEP or could even be removed by SEP without any further notice.

# sepsesam
SEP Sesam Python API

This library is a wrapper for the v1 and v2 APIs of the SEP Sesam backup solution.
See: https://wiki.sep.de/wiki/index.php/4_4_3_Beefalo:Using_SEP_sesam_REST_API

## Install
```bash
python3 -m pip install sepsesam
```

## Example usage
```python
import pprint
import sepsesam.api

cred = {
    "url": "http://sesam.my.domain:11401",
    "username": "Administrator",
    "password": "Abcd1234!"
}

data = {}

with sepsesam.api.Api(**cred) as api:
    data = api.location_list()

pprint.pprint(data)
```

## Contributing
In case you find and bugs or if you have any extensions, please open an issue / pull
request.
AnnotationTransform 1.0.0
Single cell automatic annotation
Requires Python: >=3.8

# AnnotationTransform
 
The software provides a training model function, users can use scRNA data as input, using random forest algorithm to train the model. By selecting and adjusting different parameters and features, users can optimize the performance of the model according to their research needs. Once the model is trained, the software allows users to use the trained model to make predictions on new scATAC data. scATAC data can be entered into the software, processed and analyzed by the model, and predicted results can be obtained. The software provides model evaluation capabilities to help users evaluate the performance and accuracy of trained models, as well as visualization capabilities.
sentier-peakachu 0.2.1
Modeling electricity stuff
Requires Python: >=3.11

# sentier_peakachu

[![PyPI](https://img.shields.io/pypi/v/sentier_peakachu.svg)][pypi status]
[![Status](https://img.shields.io/pypi/status/sentier_peakachu.svg)][pypi status]
[![Python Version](https://img.shields.io/pypi/pyversions/sentier_peakachu)][pypi status]
[![License](https://img.shields.io/pypi/l/sentier-peakachu)][license]

[![Read the documentation at https://sentier-peakachu.readthedocs.io/](https://img.shields.io/readthedocs/sentier_peakachu/latest.svg?label=Read%20the%20Docs)][read the docs]
[![Tests](https://github.com/TimoDiepers/sentier_peakachu/actions/workflows/python-test.yml/badge.svg)][tests]
[![Codecov](https://codecov.io/gh/TimoDiepers/sentier-peakachu/branch/main/graph/badge.svg)][codecov]

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)][pre-commit]
[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)][black]

[pypi status]: https://pypi.org/project/sentier.peakachu/
[read the docs]: https://sentier-peakachu.readthedocs.io/
[tests]: https://github.com/TimoDiepers/sentier_peakachu/actions?workflow=Tests
[codecov]: https://app.codecov.io/gh/TimoDiepers/sentier_peakachu
[pre-commit]: https://github.com/pre-commit/pre-commit
[black]: https://github.com/psf/black

This is a repo for the group work at the DdS Autumn School 2024, working on the integration of electricity markets.

## Installation

You can install _sentier_peakachu_ via [pip] from [PyPI]:

```console
$ pip install sentier_peakachu
```

## Contributing

Contributions are very welcome.
To learn more, see the [Contributor Guide][Contributor Guide].

## License

Distributed under the terms of the [BSD 3 Clause license][License],
_sentier_peakachu_ is free and open source software.

## Issues

If you encounter any problems,
please [file an issue][Issue Tracker] along with a detailed description.

<!-- github-only -->

[command-line reference]: https://sentier_peakachu.readthedocs.io/en/latest/usage.html
[License]: https://github.com/TimoDiepers/sentier_peakachu/blob/main/LICENSE
[Contributor Guide]: https://github.com/TimoDiepers/sentier_peakachu/blob/main/CONTRIBUTING.md
[Issue Tracker]: https://github.com/TimoDiepers/sentier_peakachu/issues

## Building the Documentation

You can build the documentation locally by installing the documentation Conda environment:

```bash
conda env create -f docs/environment.yml
```

activating the environment

```bash
conda activate sphinx_sentier_peakachu
```

and [running the build command](https://www.sphinx-doc.org/en/master/man/sphinx-build.html#sphinx-build):

```bash
sphinx-build docs _build/html --builder=html --jobs=auto --write-all; open _build/html/index.html
```
pulselab 1.2.0
Creates RF envelopes with or without carrier. A timebase and waveform is supplied.
Requires Python: <4.0,>=3.10

Pulselab contains:
    Class Pulse : This defines a single pulse. It is defined in terms of pulse descriptor parameters (e.g. Pulse width). It also has a, created, waveform and timebase.
    Class Pulses : This holds a number of pulses. It has a generated waveform that holds all the pulse.