semvec/pypi-packages huggingface dataset:
citation-date 0.2.1
Regex date formula and decoder - Philippine Supreme Court Decisions
Requires Python: >=3.13

![Github CI](https://github.com/justmars/citation-date/actions/workflows/ci.yml/badge.svg)

# Citation Date Parser

citation-date is a Python library for parsing and normalizing dates from Philippine legal citations, including dockets, reports, and other references. It handles common typos and multiple date formats with US and UK variants. It is eventually used by the dataset employed in [LawSQL](https://lawsql.com).

## Purpose

The library is designed to:

- Extract dates from text commonly found in legal documents such as Philippine Supreme Court reports (SCRA) and dockets (G.R. No. ...).
- Handle both US (Month Day, Year) and UK (Day Month Year) date formats.
- Normalize dates to standard formats (%B %d, %Y or datetime.date).
- Support dates enclosed in parentheses () or brackets [], and in raw strings.
- Provide regex patterns and utility functions for downstream processing in other citation libraries.

## Key Components

### Regex Patterns

#### US/UK Date Patterns

```py
us = rf"({month}{separator}{day}{separator}{year})"
uk = rf"({day}{separator}{month}{separator}{year})"
```

These patterns detect date-like strings and account for common separators.

#### Report Dates

- `REPORT_DATE_REGEX` matches dates in citations like 1 SCRA 200 (1Dec. 2000).
- Supports naked dates, bracketed [Dec. 1, 2000], or parenthesis (Dec. 1, 2000).

#### Docket Dates

- `DOCKET_DATE_REGEX` matches docket-specific dates such as G.R. No. 12345, Dec,1, 2000.
- `DOCKET_DATE_FORMAT` standardizes formatting to %b. %d, %Y.

## Documentation

See [documentation](https://justmars.github.io/citation-date).
quantgpt-local-runner 0.2.2
Local runner for QuantGPT with WebSocket support
Requires Python: >=3.7

QuantGPT Local Runner
=====================

Local WebSocket-capable runner for QuantGPT experiments.

Usage
-----

```bash
pip install quantgpt-local-runner
quantgpt-run-local --port 8000 --host 0.0.0.0 --log-level debug
```

WebSocket
---------

- Endpoint: `/ws`
- Send JSON with a `code` field containing Python to execute.

Example message:

```json
{"code": "print('hello world')"}
```

Response:

```json
{"status":"completed","content":"hello world\n","timestamp":"..."}
```

Notes
-----

- If available, `numpy`, `pandas`, and `vectorbtpro` are imported into the execution namespace as `np`, `pd`, and `vbt`.
nowcastnet-rewritten 0.1.1
A personal reimplementation of NowcastNet inference framework
Requires Python: <3.11,>=3.10

# NowcastNet-Rewritten

## 1. Introduction

This project is a personal reimplementation of the NowcastNet inference framework. The original research, titled "Skilful nowcasting of extreme precipitation with NowcastNet," by Yuchen Zhang, Mingsheng Long et al., was published in Nature and can be accessed at <https://www.nature.com/articles/s41586-023-06184-4>. Additionally, the original code by Yuchen Zhang is available at <https://doi.org/10.24433/CO.0832447.v1>.

> Q: Why reimplement? A: Just for learning :)

## 2. Getting Started

1. Cloning the repository:

    ```bash
    git clone https://github.com/VioletsOleander/nowcastnet-rewritten.git
    ```

2. Install the dependencies (pick one of the following methods):

    - Sync dependencies using [uv](https://github.com/astral-sh/uv):

        ```bash
        uv sync
        ```

    - Make sure `python>=3.10,<3.11`, and either install from PyPI:

        ```bash
        pip install -U nowcastnet-rewritten
        ```

        or install from local:

        ```bash
        pip install .
        ```

> **Notes:**
>
> - [uv](https://github.com/astral-sh/uv) is recommended for managing dependencies for full reproducibility.
> - To ensure compatibility with this reimplementation's architecture, weights have been modified and are available for download from [Hugging Face](https://huggingface.co/VioletsOleander/nowcastnet-rewritten).

## 3. Usage

To perform inference, run `inference.py` with the required arguments.

To view all available arguments, use:

```bash
python inference.py -h
```

It is recommended to use the `--config_path` option to specify the configuration file, for example:

```bash
python inference.py --config_path ../configs/inference.toml
```

An example configuration file is provided in the `configs/` directory.

You can also specify options directly from the command line, for example:

```bash
python inference.py \
    --case_type normal \
    --device cuda:0 \
    path_to_weights \
    path_to_data \
    path_to_result
```

> **Note:** If `--config_path` is specified, other command line options will be ignored.

## 4. Example Inference Result

1024 x 1024:

![Inference output at 1024×1024 resolution](docs/pictures/1024x1024.png)

512 x 512:

![Inference output at 512x512 resolution](docs/pictures/512x512.png)