semvec/pypi-packages huggingface dataset:
tuesday-eor 2.3.1
Tools for post-processing and computing summaries of 21cmFAST simulations
License: MIT
Requires Python: >=3.10

===================================================
tuesday - The Ultimate EoR Simulation Data AnalYser
===================================================
A collection of lightcone postprocessing tools such as calculating power spectra, plotting, and making line intensity maps.

|PyPI| |Status| |License| |Version| |Python Version| |Docs| |Code Style| |Codecov|

.. |PyPI| image:: https://badgen.net/pypi/v/tuesday-eor/
   :target: https://pypi.org/project/tuesday-eor
   :alt: PyPI version
.. |Status| image:: https://img.shields.io/pypi/status/tuesday.svg
    :target: https://pypi.org/project/tuesday-eor
    :alt: Status
.. |License| image:: https://img.shields.io/badge/License-MIT-yellow.svg
    :target: https://opensource.org/licenses/MIT
    :alt: License
.. |Version| image:: https://badgen.net/pypi/v/tuesday-eor/
    :target: https://pypi.org/project/tuesday-eor
    :alt: Version
.. |Python Version| image:: https://img.shields.io/pypi/pyversions/tuesday-eor.svg
    :target: https://pypi.python.org/pypi/tuesday-eor/
    :alt: Python Version
.. |Docs| image:: https://readthedocs.org/projects/tuesday/badge/?version=latest
    :target: http://tuesday.readthedocs.io/?badge=latest
    :alt: Documentation Status
.. |Code Style| image:: https://img.shields.io/badge/code%20style-ruff-red.svg
    :target: https://github.com/astral-sh/ruff
.. |Codecov| image:: https://codecov.io/gh/21cmfast/tuesday/branch/main/graph/badge.svg
    :target: https://app.codecov.io/gh/21cmfast/tuesday
    :alt: Code Coverage

Installation
------------
``tuesday`` is available on PyPI and can be installed with standard
tools like ``pip`` or ``uv``::

    $ pip install tuesday-eor

or::

    $ uv pip install tuesday-eor

If you are developing ``tuesday``, we recommend using a virtual environment.
You can create a new environment with ``uv``::

    $ uv venv --python=3.12 [env-name]
    $ source .venv/bin/activate
    $ uv pip install -e ".[dev]"

Then use::
    $ deactivate

to exit the virtual environment.

Documentation
-------------

See the `documentation <https://tuesday.readthedocs.io/en/latest/>`_ for more information on how to use ``tuesday``.

Contribute
----------

Contributions are very welcome.
To learn more, see the `Contributor Guide <https://github.com/21cmfast/tuesday/blob/main/CONTRIBUTING.rst>`_.
shiyunzi 3.1.7
A comprehensive AI toolkit for text, image, and video generation
License: MIT
Requires Python: >=3.8

# SYZ Base Image Viewer

一个基于PyQt6的图片查看器应用。

## 功能特点

- 支持浏览PNG图片
- 图片缩放功能
- 适应窗口显示
- 简洁的用户界面
- 支持图片切换

## 安装

```bash
pip install syz_base
```

## 使用方法

安装后，可以通过以下命令启动应用：

```bash
syz_viewer
```

## 开发环境要求

- Python >= 3.8
- PyQt6 >= 6.6.1
- Pillow >= 10.2.0

## 许可证

MIT License
donut-ai-evaluate 0.1.5
Donut packages for evaluating the output of data entry and redact.
License: MIT
Requires Python: >=3.10

# Donut AI Evaluate

Install the library using

```
pip install donut-ai-evaluate
```


Before running the evaluation, make sure that the google gemini API key is present in the environment variables.

## Data Entry Evaluation

The user can either compare a single field or multiple fields with one another. 

The following code snipped shows an example of how single field comparison works:

```
from donut_ai_evaluate.data_entry import compare_single_field

data = {
    "Name" : 
    {
        "predicted" : "John",
        "ground-truth" : "Jon"
    }
}

comparison_data = compare_single_field(data)

print(comparison_data)
```

The following code snipped shows an example of how multi field comparison works:

```
from donut_ai_evaluate.data_entry import compare_batch_fields

data = [
    {
        "Name" : 
        {
            "predicted" : "John",
            "ground-truth" : "Jon"
        }
    },
    {
        "State" : 
        {
            "predicted" : "Florida",
            "ground-truth" : "Fl"
        }
    },
    {
        "Phone Number" : 
        {
            "predicted" : "922000511",
            "ground-truth" : "92-2000-511"
        }
    }
]

comparison_data = compare_batch_fields(data)

print(comparison_data)
```

## Redact Evaluation

Field redactions can be evaluated using the following code:

```
from donut_ai_evaluate.redact import is_redacted_field_gt,is_gt_field_redacted
from donut_ai_evaluate._common.eval_logger import logger


# Example Ground Truth Bboxes as lists [x, y, w, h]
gt_boxes_raw = [
    [10, 10, 50, 20],   # GT 0
    [100, 100, 80, 30],  # GT 1
    [200, 50, 40, 40]   # GT 2
]

# Example AI Detected Bboxes as lists [x, y, w, h]
# Scenario 1: Perfect match
ai_boxes_perfect_raw = [
    [11, 11, 49, 19],   # AI 0 -> matches GT 0
    [100, 100, 80, 30], # AI 1 -> matches GT 1
    [201, 51, 39, 39],   # AI 2 -> matches GT 2
    [201, 51, 41, 123] , # AI 3 -> matches None
    [300, 41, 41, 123]  # AI 4 -> matches None
]


# Check if each GT is matched by an AI box
is_gt_field_redacted(gt_boxes_raw, ai_boxes_perfect_raw) # Should be [True, True, True]

# Check if each AI box matches a GT box
is_redacted_field_gt(ai_boxes_perfect_raw, gt_boxes_raw) # Should be [True, True, True]

```