semvec/pypi-packages huggingface dataset:
lucupy 0.2.8
Lucuma core package for the Gemini Automated Scheduler at: https://github.com/gemini-hlsw/scheduler
Requires Python: <4.0,>=3.11
Keywords: lucuma, scheudule, gpp

Lucupy
#########

This package contains data structures and functions to support all the microservices that make up the Schedule app
for the Gemini Program Plataform (and other auxiliary services such as Env and Resource)

Content
-------

- Minimodel: A small version of GPP's data model. This allows for any services to use common data structures to model programs, observations, etc.
- Helpers: A collection of functions that helps with the handling of data.
- Observatory: An API that allows Observatory-specific behaviour to be added to a service (or sub-service: e.g Collector)
- Sky: A library that calculates night events for the use of visibility in Observations
- Time Utils: Time handling functions
- Types: A collection of variables to use with Python typing
- Decorators: A collection of decorators (Currently empty)

Copyright (c) 2016-2022 Association of Universities for Research in Astronomy, Inc. (AURA)
For license information see LICENSE or https://opensource.org/licenses/BSD-3-Clause
blackdoc-temp 0.4.3.1
run black on documentation code snippets
Requires Python: >=3.10

blackdoc
========

.. image:: https://github.com/keewis/blackdoc/actions/workflows/ci.yml/badge.svg?branch=main
    :target: https://github.com/keewis/blackdoc/actions/workflows/ci.yml
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/python/black
.. image:: https://readthedocs.org/projects/blackdoc/badge/?version=latest
    :target: https://blackdoc.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

**blackdoc** is a tool that applies `black`_ to code in documentation.

It was originally a rewrite of a `gist`_ and called
**black-doctest**. In April 2020, it was renamed to **blackdoc**.

.. _gist: https://gist.github.com/mattharrison/2a1a263597d80e99cf85e898b800ec32
.. _black: https://github.com/psf/black

Installation
------------
Dependencies:

- `black`_
- `more-itertools`_
- `rich`_
- `tomli`_
- `pathspec`_

.. _more-itertools: https://github.com/more-itertools/more-itertools
.. _rich: https://github.com/textualize/rich
.. _tomli: https://github.com/hukkin/tomli
.. _pathspec: https://github.com/cpburnz/python-pathspec

Install it with:

.. code:: bash

    python -m pip install blackdoc

Usage
-----
The commandline interface supports two modes: checking and inplace
reformatting.

.. code:: bash

    python -m blackdoc --help


In inplace reformatting mode, it will reformat the doctest lines and
write them back to disk:

.. code:: bash

    # on explicitly mentioned files
    python -m blackdoc file1.py file2.py
    # on the whole directory
    python -m blackdoc .


When checking, it will report the changed files but will not write them to disk:

.. code:: bash

    python -m blackdoc --check .

It is also possible to use the entrypoint script:

.. code:: bash

    blackdoc --help

pre-commit
----------
This repository defines a ``pre-commit`` hook:

.. code:: yaml

   hooks:
   ...
   - repo: https://github.com/keewis/blackdoc
     rev: 3.8.0
     hooks:
     - id: blackdoc

It is recommended to *pin* ``black`` in order to avoid having different versions for each contributor. To automatically synchronize this pin with the version of the ``black`` hook, use the ``blackdoc-autoupdate-black`` hook:

.. code:: yaml

   hooks:
   ...
   - repo: https://github.com/psf/black
     rev: 23.10.1
     hooks:
     - id: black
   ...
   - repo: https://github.com/keewis/blackdoc
     rev: 3.8.0
     hooks:
     - id: blackdoc
       additional_dependencies: ["black==23.10.1"]
     - id: blackdoc-autoupdate-black

Note that this hook is *not* run on ``pre-commit autoupdate``.
woolworm 0.0.10
Add your description here
Requires Python: >=3.12

# woolworm (Pre-Alpha State)

Hello Northwestern Digitization team (and anyone else who may be following along), welcome to woolworm, your new (hopefully) one-stop shop for digitization.
I have attempted to abstract as much of the intricacies of image transformation in python. At least to the best of my ability.
While we are working on this grant, I will be working on build automation and a CLI for you all so that it can be even easier to use.
The point of this repo is in case I die, it can be developed and such.
Here is my current feature list, where I am open to suggestions or requests, because I like this sort of thing:

## Road to v0.1.0

- [ ] API
  - [x] Load image
  - [x] Deskew
  - [x] Intelligent document binarization/grayscale
  - [x] Tesseract OCR
  - [x] Standalone Ollama LLM OCR
  - [x] Marker Document Understanding LLM OCR
  - [ ] HathiTrust (currently experimental in a standalone script)

## Road to v1.0.0

- [ ] Pipelines
  - [x] Image processing
  - [ ] OCR (do we need a pipeline for this? It is a single function)
  - [ ] HathiTrust (Migrated Brendan's Ruby script to python)
  - [ ] ???
  - [ ] Profit
- [ ] CLI (To be done later)
- [ ] Figure out how the hell I publish a python package

Automation, supercomputing interfacing, remote directories will be handled in a different repository. This is to track one step of the data science process: data cleaning.

## Prerequisites

You will want to familiarize yourself with the absolute basics of calling object-methods. If you want to use any LLM models, you will need to install [Ollama](https://ollama.com/). Feel free to contact me if you need assistance in setting up Ollama.

## Quickstart

If you are extremely impatient, you can get started with two lines of code

```python
from woolworm import Woolworm

Woolworm.Pipelines.process_image("inputfilename.jpg", "outputfilename.jpg")
```

In the backend, it looks like this. You can find this code in the `cookbook` directory

```python
from woolworm import Woolworm

p = woolworm()  # Creates the "woolworm" class

f = "filename.jpg"
base_name = f.replace(".jpg", "")

# Step 1: Load original
img = p.load(f)

# Step 2: de-skew
img = p.deskew_with_hough(img)

# Step 3: This is kinda weird, and currently fine-tuned for use with NU's environmental impact statements
# Long story short, the programming will use some heuristics to detect if the image is a diagram or mostly text
# If the program thinks it is text, it will binarize, if it thinks it is a diagram, it will not.
img = p.binarize_or_gray(img)

p.save_image(img)
```

Sample output:
![Sample Output in a nicely formatted table](assets/output_sample.png)