semvec/pypi-packages huggingface dataset:
jaraco.fabric 6.3.1
Fabric tasks by jaraco
Requires Python: >=3.9

.. image:: https://img.shields.io/pypi/v/jaraco.fabric.svg
   :target: https://pypi.org/project/jaraco.fabric

.. image:: https://img.shields.io/pypi/pyversions/jaraco.fabric.svg

.. image:: https://github.com/jaraco/jaraco.fabric/actions/workflows/main.yml/badge.svg
   :target: https://github.com/jaraco/jaraco.fabric/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. .. image:: https://readthedocs.org/projects/PROJECT_RTD/badge/?version=latest
..    :target: https://PROJECT_RTD.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2025-informational
   :target: https://blog.jaraco.com/skeleton

Fabric tasks and helpers. Includes modules implementing
Fabric tasks.

The easiest way to use jaraco.fabric is to install it and
invoke it using ``python -m jaraco.fabric``. For example,
to list the available commands:

    $ python -m jaraco.fabric -l

Or to install MongoDB 3.2 on "somehost":

    $ python -m jaraco.fabric -H somehost mongodb.distro_install:version=3.2
xbmini-py 0.5.1
Python Toolkit for the GCDC HAM
Requires Python: >=3.11

# xbmini-py
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/xbmini-py/0.5.1?logo=python&logoColor=FFD43B)](https://pypi.org/project/xbmini-py/)
[![PyPI](https://img.shields.io/pypi/v/xbmini-py)](https://pypi.org/project/xbmini-py/)
[![PyPI - License](https://img.shields.io/pypi/l/xbmini-py?color=magenta)](https://github.com/sco1/xbmini-py/blob/master/LICENSE)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/sco1/xbmini-py/main.svg)](https://results.pre-commit.ci/latest/github/sco1/xbmini-py/main)

Python Toolkit for the [GCDC HAM](http://www.gcdataconcepts.com/ham.html)

## Known Firmware Compatibility
This package is currently tested against firmware versions `1379`, `2108`, and `2570`, compatibility with other firmware versions is not guaranteed.

## Installation
Install from PyPi with your favorite `pip` invocation:

```bash
$ pip install xbmini-py
```

You can confirm proper installation via the `xbmini` CLI:
<!-- [[[cog
import cog
from subprocess import PIPE, run
out = run(["xbmini", "--help"], stdout=PIPE, encoding="ascii")
cog.out(
    f"```\n$ xbmini --help\n{out.stdout.rstrip()}\n```"
)
]]] -->
```
$ xbmini --help
Usage: xbmini [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  merge  Combine multiple log sessions.
  trim   XBMini log trimming.
```
<!-- [[[end]]] -->

## CLI Usage
### `xbmini merge batch`
Batch combine XBM files for each logger and dump a serialized `XBMLog` instance to a CSV in its respective logger's directory.
#### Input Parameters
| Parameter       | Description                                            | Type         | Default                                |
|-----------------|--------------------------------------------------------|--------------|----------------------------------------|
| `--top-dir`     | Path to top-level log directory to search.<sup>1</sup> | `Path\|None` | GUI Prompt                             |
| `--log-pattern` | XBMini log file glob pattern.<sup>2</sup>              | `str`        | `"*.CSV"`                              |
| `--dry-run`     | Show processing pipeline without processing any files. | `bool`       | `False`                                |
| `--skip-strs`   | Skip files containing any of the provided substrings.  | `list[str]`  | `["processed", "trimmed", "combined"]` |

1. Log searching will be executed recursively starting from the top directory
2. Case sensitivity is deferred to the host OS
quickscrape 0.1.1
A simple library for extracting common data types from web pages
Requires Python: >=3.7

# QuickScrape

A simple Python library for extracting common data types from web pages.

## Installation

```bash
pip install quickscrape
```

## Usage

```python-repl
import quickscrape

# Extract emails from a webpage
emails = quickscrape.extract("email", "https://example.com/contact")

# Extract tables
tables = quickscrape.extract("table", "https://example.com/data")
# Get tables as pandas DataFrames
tables_df = quickscrape.extract("table", "https://example.com/data", output_format="dataframe")

# Extract multiple data types at once
results = quickscrape.extract(["email", "table"], "https://example.com")email", "https://example.com/contact")

# Extract tables
tables = quickscrape.extract("table", "https://example.com/data")
```

## License

MIT

### Development Workflow

1. **Local Development**: Install your package in development mode:

   ```bash
   pip install -e .
   ```
2. **Testing** : Use pytest for writing and running tests.

   ```bash
   pip install pytest
   pytest
   ```
nxviz 0.7.6
Graph Visualization Package
Keywords: graph, networkx, nxviz, visualization

# nxviz: Composable and rational network visualizations in matplotlib

`nxviz` is a package for building _rational_ network visualizations
using matplotlib as a backend.
Inspired heavily by the principles espoused in the grammar of graphics,
nxviz provides ways to _compose_ a graph visualization together
by adhering to the following recipe:

1. Prioritize node placement, mapping data to position and visual properties,
2. Draw in edges, mapping data to visual properties,
3. Add in annotations and highlights on the graph.

`nxviz` is simultaneously a data visualization research project,
art project,
and declarative data visualization tool.
We hope you enjoy using it to build beautiful graph visualizations.

## Installation

### Official Releases

`nxviz` is available on PyPI:

```bash
pip install nxviz
```

It's also available on conda-forge:

```bash
conda install -c conda-forge nxviz
```

### Pre-releases

Pre-releases are done by installing directly from git:

```bash
pip install git+https://github.com/ericmjl/nxviz.git
```

## Quickstart

To make a Circos plot:

```python
# We assume you have a graph G that is a NetworkX graph object.
# In this example, all nodes possess the "group" and "value" node attributes
# where "group" is categorical and "value" is continuous,
# and all edges have the "edge_value" node attribute as well.

import nxviz as nv
ax = nv.circos(
    G,
    group_by="group",
    sort_by="value",
    node_color_by="group",
    edge_alpha_by="edge_value"
)

nv.annotate.circos_group(G, group_by="group")
```

![](images/circos.png)

For more examples, including other plots that can be made,
please see the examples gallery on the docs.
parsdantic 0.2.0
Parsdantic is a powerful Python package that automatically generates Pydantic models from JSON schemas. Enums support **included**
Requires Python: >=3.11

# Parsdantic

Parsdantic is a powerful Python package that automatically generates Pydantic models from JSON schemas. It simplifies the process of data validation and serialization by bridging the gap between JSON Schema and Pydantic.
Features

## Features

- Convert JSON schemas to Pydantic models with a single function call
- Support for nested objects and complex data structures
- Automatic type inference and validation

## Installation

You can install Parsdantic using pip:

```bash
pip install parsdantic
```

## Quick Start
Here's a simple example of how to use Parsdantic:

```python
from parsdantic.parser import parse

schema = {
    "$defs": {
        "Person": {
            "properties": {
                "id": {"title": "Id", "type": "integer"},
                "name": {"title": "Name", "type": "string"},
                "directions": {
                    "items": {"type": "string"},
                    "title": "Directions",
                    "type": "array"
                }
            },
            "required": ["id", "name", "directions"],
            "title": "Person",
            "type": "object"
        }
    },
    "properties": {
        "people": {
            "items": {"$ref": "#/$defs/Person"},
            "title": "People",
            "type": "array"
        }
    },
    "required": ["people"],
    "title": "People",
    "type": "object"
}

People = parse(schema)

instance = People(
    people=[
        {"id": 1, "name": "John", "directions": ["Carrer de les Corts, 1", "Barcelona"]},
        {"id": 2, "name": "Jane", "directions": ["Carrer de les Corts, 2", "Barcelona"]},
    ]
)

print(instance) # People(people=[Person(id=1, name='John', directions=['Carrer de les Corts, 1', 'Barcelona']), Person(id=2, name='Jane', directions=['Carrer de les Corts, 2', 'Barcelona'])])
```
_Note: descriptions, default values, required fields, and Enum types are still not supported._
## Support
You can open an issue on the [GitHub issue tracker](https://github.com/SergiFuster/parsdantic/issues).


## LICENSE
This project is licensed under the MIT License. See the [LICENSE](https://github.com/SergiFuster/parsdantic/blob/main/LICENSE) file for details.
DE-GoogleCloud 0.0.9
Pacote de funcionalidades para o GCP
Requires Python: >=3.9
Keywords: Cloud, Google, DataEng, GCP, Pacote GCP python, Almir J Gomes, almir.jg@hotmail.com

Versão estavel. Efetuas novas consistencias em Upload e Downloads para verificação de pastas e arquivos existem, e tambem o timeout=60 para upload e download