semvec/pypi-packages huggingface dataset:
gitlabfs 1.1.0
Mount projects in GitLab as a file system
Requires Python: >=3.5

# gitlabfs

gitlabfs allows you to mount all projects in a company's GitLab instance as a file system.

## About

This project was conceived out of a need to easily browse code in many different repositories within a company. While GitLab does have builtin browsing and search, it's just not as convenient to use as common tools like `find` and `grep`.

It sets out to solve that by exposing an entire GitLab instance as a file system with the following hierarchy:

```
/
  /user
    /project
      /master
        README.md
      /feature
        /abc
          /src
            main.py
      /v1.0
        main.py
  /group
    /subgroup
      /project
```

This allows you to freely browse all of the code in a GitLab instance without having to clone everything. All of the files are loaded from the GitLab API as you access them. The only caveat is that it is a read-only view.

## Installation

Install the latest version of gitlabfs from [PyPI](https://pypi.org/project/gitlabfs/):

    pip install gitlabfs

## Usage

1. Go to your profile page in GitLab and [generate a new access token](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html) with the `api` scope.
2. Create a directory where you would like to access your projects from, e.g. `~/gitlab`.
3. Mount GitLab at that directory:

    ```
    glfs https://gitlab.mycompany.com ~/gitlab
    ```

  You will be asked to provide your access token and then the GitLab hierarchy will be mounted.

## Configuration

Various options can be passed to configure the properties of the file system:

* `--tags`: Include tags in the list of repository refs.
* `--users`: List user repositories as well.
* `--file-times`: Better approximate file modification times with commit metadata.
* `--cache-expiry=SEC`: Expire the cache after this many seconds (default: 60).
* `--fresh-project-tree`: Enable cache expiry for the project tree.
* `--debug`: Enable verbose logging for development purposes.

Many of these are not enabled by default because they may result in worse performance or more clutter.

The access token must be provided via a command line prompt by default, but can also be passed via the `GITLAB_TOKEN` environment variable.

## Limitations

* Not designed for GitLab instances with a huge number of projects (e.g. gitlab.com).
* File modification times are not accurate since they are not exposed by the GitLab API.

## Development

The file system can be easily run locally with [Pipenv](https://github.com/pypa/pipenv):

```
pipenv install
pipenv run ./glfs <url> <mountpoint> [options]
```

And the PyPI package is published using the following commands:

```
pipenv run python setup.py sdist bdist bdist_wheel
pipenv run twine upload dist/*
```

## License

MIT License
hasl 2.3.1
Communications Module for HomeAssistant SL Sensor

Home Assistant SL Sensor (HASL) Communication Library
===============================

This module provides the low level communications functionality used by the [Home Assistant SL Sensor](https://github.com/DSorlov/hasl-platform).

>__NOTE__: This code is really not supported for use outside of Home Assistant, even if that should be possible in it self.

If you're reading this on the project's github page, the package is available on [PyPI](https://pypi.org/project/hasl/).
pyannote.features.shennong 0.1
A feature extraction extension for the pyannote framework

# pyannote-shennong-features
A feature extractor for Pyannote that is based on the Shennong library

## Installation

This library depends on Shennong, which is available on conda. 
Thus, make sure you're in a conda environment **where pyannote-audio has been installed already** and run

```yaml
conda install -c coml shennong
```

Then you can pip-install the library in a regular way:

```
pip install pyannote.features.shennong
```

## Pyannote Configuration

To use features from this library, this is the kind of configuration you
should add your `config.yml` file:

```yaml
feature_extraction:
   name: pyannote.features.ShennongMfcc
   params:
       coefs: 19
       e: True
       D: True
       DD: True
       mfccWindowType: 'hanning'
       mfccLowFreq: 20
       mfccHighFreq: -100 # Real value will be (f_nyquist - 100)
       with_pitch: True
       with_cmvn: False
       duration: 0.025
       step: 0.010
       sample_rate: 16000
       pitchFmin: 20
       pitchFmax: 500
```