semvec/pypi-packages huggingface dataset:
octodir 1.1.0
Tool for downloading directories from Github repositories
Requires Python: >=3.0
Keywords: github directory download

<div align="center">
    <img src="https://raw.githubusercontent.com/Jalkhov/octodir/main/logo.png">
</div>

Tool for downloading directories from Github repositories.

ALERT: Not authenticated requests have limits, fixing for add API Key support.

# Installation
  ```
  $ pip install octodir
  ```

# Use

## From the console

```
$ octodir
```

**You will be asked for the following data:**

* **Full folder url:** Direct url to the target folder
  * **Example:** `https://github.com/Jalkhov/octodir/tree/stable/octodir`
* **Output folder**: Absolute path of the output directory
  * You can enter a dot to download in the current working directory
* **API key**: Personal Github Token for prevent requests limit

## In code

```python
from octodir import Octodir

target = 'https://github.com/Jalkhov/Octodir/tree/stable/octodir'
folder = '.' # Current working directory
api_key = '<PERSONAL_GITHUB_TOKEN>'

Octo = Octodir(target, folder, api_key=api_key)
Octo.dowload_folder()
```

# Support me <3

<a href="https://www.buymeacoffee.com/Jalkhov" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a>
fastlingo 1.2.0
FastLingo is a command-line tool designed to translate and manage app metadata within Fastlane projects.
Requires Python: >=3.7
Keywords: fastlane,localization,translation,localize,translate,localization-management

<div align="center">
  <img src=".github/assets/logo.png" height="128">

[![PyPI version](https://badge.fury.io/py/fastlingo.svg)](https://badge.fury.io/py/fastlingo)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)

</div>

FastLingo is a command-line tool designed to translate iOS app metadata using a variety of translation sources. It is built on top of [Fastlane](https://fastlane.tools) and [Deliver](https://docs.fastlane.tools/actions/deliver/). FastLingo is currently supports the following translation sources:

- [Google Cloud Translation API](https://cloud.google.com/translate)

Full documentation can be found [here](https://fastlingo.readthedocs.io/en/latest/).

### Features

- Multilingual app metadata translation.
- Configurable translation sources.
- Translation caching for enhanced performance and fewer API calls.
- Seamless integration with Fastlane metadata folder structure.

### Installation

To install FastLingo, execute the following command:

```bash
pip install fastlingo
```

### Configuration

Before using FastLingo, configure your preferred translation source by establishing API keys and endpoints. Generate a template configuration file by executing:

```bash
fastlingo init
```

This action creates a `FastLingo.toml` file in the current directory, which can then be edited to include your API keys and endpoints. For example, to configure the Google Cloud Translation API, add the following to your configuration file:

```toml
translation_service = "google"
source_language = "ENGLISH"
target_languages = [ "AUTO",]
metadata_folder = "your/metadata/folder"
fields_to_translate = [ "name", "description" ]

[api_keys]
google = "service/account/key.json"

[cache]
cache_folder = ".fastlingo-cache"
cache_enabled = true
```

### How to Use

To translate your app metadata, run:

```bash
fastlingo translate
```

This will translate all the metadata fields specified in your configuration file for each target language.

### Contributing

Pull requests are welcome. Additions to the translation source list are encouraged.

### License

FastLingo is available under the MIT license.
nb-move-imports 0.4.5
Move import statements in jupyter notebook to the first cell
Requires Python: >=3.8.9,<4.0.0

# nb_move_imports

------------------------------
[![PyPI version](https://badge.fury.io/py/nb_move_imports.svg)](https://badge.fury.io/py/nb_move_imports)
[![Python version](https://img.shields.io/badge/python-≥3.8-blue.svg)](https://pypi.org/project/kedro/)
[![Release Pipeline](https://github.com/AnH0ang/nb_move_imports/actions/workflows/release.yml/badge.svg)](https://github.com/AnH0ang/nb_move_imports/actions/workflows/release.yml)
[![Test](https://github.com/AnH0ang/nb_move_imports/actions/workflows/test.yml/badge.svg)](https://github.com/AnH0ang/nb_move_imports/actions/workflows/test.yml)
[![Code Quality](https://github.com/AnH0ang/nb_move_imports/actions/workflows/code_quality.yml/badge.svg)](https://github.com/AnH0ang/nb_move_imports/actions/workflows/code_quality.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/STATWORX/statworx-theme/blob/master/LICENSE)
![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)

Move import statements in jupyter notebook to the first cell

## Use nb_move_imports

To run the script on a specific jupyter notebook run:

```console
nb_move_imports path/to/notebook.ipynb
```

## Skip processing of cells

In order to skip a cell you have to tag it with the `IGNORE_MV_IMPORTS` tag.

## Precommit Hook

Add this section to your `pre-commit-config.yaml` so that the nb_move_imports script is executed before each commit with pre-commit.

```yaml
- repo: https://github.com/AnH0ang/nb_move_imports
  rev: 0.4.5
  hooks:
    - id: nb_move_imports
      name: nb_move_imports
```
ixbrl-parse 0.10.1
Parse iXBRL files, can present in RDF
Requires Python: >=3.6


# `ixbrl-parse`

## Introduction

- Python library code, parses iXBRL files.
- Script `ixbrl-dump` emits iXBRL tagged data in a semi-human-readable dump.
- Script `ixbrl-report` emits iXBRL tagged data in a human-readable report.
  This involves downloading referenced XBRL schema to get the human-readable
  fact labels.
- Script `ixbrl-to-rdf` emits iXBRL tagged data in RDF.
- Script `ixbrl-to-csv` outputs iXBRL tagged data in a CSV format.
- Script `ixbrl-to-json` emits iXBRL tagged data in JSON.
- Script `ixbrl-to-xbrl` emits iXBRL tagged data as an XBRL instance.
- Script `ixbrl-to-kv` emits iXBRL in a key-value form easily consumed
  by scripts.
- Script `ixbrl-markdown` emits iXBRL tagged data in a markdown report.

## Sample data

There's a bunch of sample iXBRL files grabbed from various places in
the `ixbrl` directory: US 10-K and 10-Q filings, a few random things
from UK companies house, and a couple of sample ESEF filings.  This is the
data I've tested with.

Also, `accts.html` is a sample file created using
[`gnucash-ixbrl`](https://github.com/cybermaggedon/gnucash-ixbrl).

## Installation

```
pip3 install git+https://github.com/cybermaggedon/ixbrl-parse
```

## Usage

Parse iXBRL and output in RDF (default n3 format):
```
ixbrl-to-rdf accts.html
```

Parse iXBRL and output in RDF/XML:
```
ixbrl-to-rdf accts.html --format xml
```

Parse iXBRL and output in CSV:
```
ixbrl-to-csv accts.html
```

Parse iXBRL and output in JSON:
```
ixbrl-to-json accts.html
```

Schema labels in JSON:
```
ixbrl-to-json ixbrl/10k/lyft-20201231.htm -f labeled \
    -b https://www.sec.gov/Archives/edgar/data/1759509/000175950921000011/lyft-20201231.htm
```

Dump iXBRL values:
```
ixbrl-dump accts.html
```

Human-readable report:
```
ixbrl-report accts.html
```

Human-readable report from SEC EDGAR.  Note need to tell ixbrl-report the
URL of the original report in order to know where to fetch the
custom schema (relative URLs are used):

```
ixbrl-report ixbrl/10k/lyft-20201231.htm \
    -b https://www.sec.gov/Archives/edgar/data/1759509/000175950921000011/lyft-20201231.htm
```

Dump iXBRL as XBRL:
```
ixbrl-to-xbrl accts.html
```

## API

The `ixbrl-to-csv` file is a good starting point if you want to see how
the API works.

## What next?

This loads into a Redland RDF sqlite3 store:

```
ixbrl-to-rdf -i accts.html -f ntriples > accts.ntriples
rdfproc -n -s sqlite accts.db parse accts.ntriples ntriples
rdfproc -s sqlite accts.db print | head
```

I run a SPARQL store across the data, and view it with
[LodLive](https://github.com/LodLive/LodLive).

![Screenshot of LodLive](docs/screenshot.png)