semvec/pypi-packages huggingface dataset:
ncopa 0.2.0
ncopa: nginx.conf parser
Requires Python: >=3.12

# ncopa

ncopa is short for nginx.conf parser. It offers a Python library for parsing an nginx.conf file.


# Install

```bash
pip install ncopa
```


# Usage

```python
import ncopa

with open("nginx.conf") as stream:
    content = stream.read()

directives = ncopa.parse(content)
```

`directives` is a list of `ncopa.Directive` objects.


# The `ntree` Command

This package provides the `ntree` command which summarizes a nginx.conf directives in a tree format. 
To use it:

```bash
ntree <path to nginx.conf>
```
BlockDAG 1.1.3
BlockDAGs for structured signature generation.
Requires Python: >=3.6

[![License: MIT](https://img.shields.io/badge/License-MIT-informational.svg)](https://opensource.org/licenses/MIT)
![Pylint: 10](https://github.com/pritchardn/BlockDAG/actions/workflows/pylint.yml/badge.svg)
![Tests: Passing](https://github.com/pritchardn/BlockDAG/actions/workflows/python-package.yml/badge.svg)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6999445.svg)](https://doi.org/10.5281/zenodo.6999445)


# BlockDAG
BlockDAGs for structured signature generation.

A [Merkle-Tree](https://en.wikipedia.org/wiki/Merkle_tree) is a binary tree of hash nodes.
This module implements an extension of this data-structure which differs in that:
- Data can be included at any node in the structure
- The structure between nodes can form a directed acyclic graph

Note, this is, in principle, a superset of a MerkleTree.

One creates a BlockDAG by providing a dictionary of vertices (id : dict()) 
and list of edges (id: id tuples).

For example (available in `examples/EX0.py`). Consider the following Graph:

<img src="https://github.com/pritchardn/BlockDAG/blob/main/img/ActualDAG.JPG"  />

Once processed, the following HashDAG is generated.

<img src="https://github.com/pritchardn/BlockDAG/blob/main/img/HashDAG.JPG"  />

Changing the values of a single vertex, or edges the graph signature will be completely different.
Moreover, the hashes of all descendent vertices will be completely different too.

The main method `build_block_dag` requires:
- A dictionary of vertices, each with a unqiue key, and dictionary of fields as values
- A list of tuples, describing the edges in the graph.
- A hash function (returns the hex-digest of the provided data. `Hashlib` provides many)
- A list of keys used to inclusively filter the data contained in each vertex. The values selected
are placed in a MerkleTree, the root of which is used as the `datahash` for that vertex
- A boolean, `append_hashes` which if True, will add the `hash` and graph `signature` to the 
original vertex dictionary.

`bulid_block_dag` returns a dictionary with the `datahash`, `parenthashes` and `blockhash` for each vertex
in the original graph, along with a whole-graph signature.

The whole-graph `signature` is computed by collecting the hashes of all leaf nodes (no outgoing edges)
then inserting them into a MerkleTree, and taking the root.

## Usage

Install it:

`pip install blockdag`

or clone it.

There are several examples in `examples` to get started.
You can compare blockdags to find differing vertices (by signature, not label) and print dags
nicely.

## Requirements / Assumptions

- Vertex keys are assumed to be unique
- The fields 'hash' and 'signature' will be overwritten if `append_hashes` is set to `True`
- While it will not cause any issues, cycles will be ignored in the original graph.

## Contributions
Feel free to help out, submit issues and make merge requests.
Phyloki 0.5.51
Annotate phylogenetic trees like never before
Requires Python: >=3.12

![Python3](https://img.shields.io/badge/Language-Python3-steelblue)
![Biopython](https://img.shields.io/badge/Dependecy-Biopython-steelblue)
![Pandas](https://img.shields.io/badge/Dependecy-Pandas-steelblue)
![OS](https://img.shields.io/badge/OS-_Windows_|_Mac_|_Linux-steelblue)
![License](https://img.shields.io/badge/License-MIT-steelblue)

> Phyloki simplifies phylogenetic tree annotation in microbiology and virology by fetching metadata from NCBI GenBank using accession numbers. It also reinstates organism names in trees constructed with IQ-TREE, retrieves host information about microorganisms, and prepares annotation datasets for further visualization in iTOL.

## Table of contents

- [Features](#features)
- [Installation](#installation)
- [Usage Guide](#usage-guide)
- [Contributing](#contributing)
- [Contact](#contact)

## Features
### Sequence Downloading
- Facilitates the retrieval of sequences from NCBI GenBank using specified accession numbers.
### Metadata Fetching
- Downloads metadata from NCBI GenBank (Accession Number; Organism Name; Country; Year; Host)
### Organism Name Reintegration
- Enhances IQ-TREE constructed trees by replacing accession numbers with the corresponding organism names for clarity and context.
### Host Information Retrieval
- Gathers host data for each microorganism, including the host's taxonomic order.
### Annotation Dataset Preparation for iTOL
- Utilizes the collected host information to prepare detailed annotation datasets, optimizing visualization in iTOL.

## Installation

```python
pip install phyloki
```

## Usage Guide

Demonstrational data is based on the recent paper about identifying novel hantavirus in bats

🔗 Visit [Phyloki wiki](https://github.com/iliapopov17/phyloki/wiki) page

## Contributing
Contributions are welcome! If you have any ideas, bug fixes, or enhancements, feel free to open an issue or submit a pull request.

## Contact
For any inquiries or support, feel free to contact me via [email](mailto:iljapopov17@gmail.com)

Happy tree annotating! 🌳
manylocales 0.1.0
AI translations in your CI/CD pipeline
Requires Python: >=3.8
Keywords: gettext, i18n, internationalization, l10n, localization, translation

# ManyLocales

Collect and translate texts with AI using a single command.

ManyLocales is an AI-powered tool designed to automate translations in source code. It uses translation providers like ChatGPT to translate `.po` files right after extracting them from your source code.

## Features

- **Collect Strings**: Uses the `gettext` toolset and `polib` Python package to efficiently collect strings from your source files.
- **Automatic Translation**: Translates `.po` files using AI.
- **Locale Management**: Supports multiple locales and handles merging with existing translations.

## Installation

TBD

## Usage

Run ManyLocales with the following command:

```bash
manylocales
```

### Command-line Options

- `-w`, `--workdir`: Change to this directory before executing the command.
TBD

## Configuration

TBD