semvec/pypi-packages huggingface dataset:
grammarian 0.2.1
An easy and fast tool to check english grammar
License: MIT
Requires Python: >=3.13


# Grammarian

Grammarian is a Python package that allows you to easily check the correctness of English words, and also suggests "what the author (potentially) meant" if the word is not correct. 




## Authors

- [@hiko667](https://github.com/hiko667)


## Deployment

To deploy this package, you just need to run following commnd in your terminal:

```bash
pip install grammarian

```


## Usage/Examples

To import grammarian package run
```python
from grammarian import Grammarian

```

Grammarian is a python class. You run 

```python
g = Grammarian()

```
to create an instance of it. Constructor is meant to be empty. To check grammar of any english word run:

```python
g.check_grammar("apple")

```
check_grammar returns an GrammarianCheck class instance. If the word you have given is correct, GrammarianCheck.is_correct will be True, and GrammarianCheck.suggestions will be None. If the word is incorrect, GrammarianCheck.is_correct will be False, and GrammarianCheck.suggestions will be a list of 5 suggested words (in str). Run:
```python
str(g.check_grammar("appleq"))

```
to return a str in format of:
```python
return ' '.join(self.suggestions)
```
to acces all of the words on their own run:
```python
g.check_grammar("appleq")[i]
```
where 'i' variable is by deafault between 0-5. For "appleq" it returns:
```bash
apple appled apples apelet apoplex 
```
as mentioned, i is "by deafult" no higher than 5. Since grammarian 0.1.20 you can set the number of returned elements. If you set the size variable to zero or less, code will just return if its correct.
```python
g.check_grammar("appleq", 10)
```
CuBATS 1.0.0
Computer vision-Based Antigen Target Selector
License: MIT
Requires Python: <3.12,>=3.10
Keywords: digital pathology, whole-slide imaging, patient-specific antigen analysis, spatial antigen heterogeneity

# **C**omp**u**ter vision-**B**ased **A**ntigen **T**arget **S**elector <img src="https://raw.githubusercontent.com/hnu-digihealth/CuBATS/main/docs/images/CuBATS_logo.png" alt="CuBATS" title="CuBATS Logo" width="150" height="150" />


[![codecov](https://codecov.io/gh/hnu-digihealth/CuBATS/graph/badge.svg?token=5R3EBA6JS9)](https://codecov.io/gh/hnu-digihealth/CuBATS)
[![CI](https://github.com/hnu-digihealth/CuBATS/actions/workflows/test.yaml/badge.svg)](https://github.com/hnu-digihealth/CuBATS/actions/workflows/test.yaml)

**CuBATS** (Computer vision-Based Antigen Target Selector) is an open-source computer vision pipeline for patient-specific tumor-associated antigen (TAA) selection to support multi-target CAR T-cell strategies. It analyzes a variable number of **immunohistochemically (IHC) stained whole-slide images (WSIs)**, alongside a single **hematoxylin and eosin (HE) stained WSI** for tumor segmentation. CuBATS systematically quantifies antigen expression and identifies optimal mono-, dual-, or triplet combinations for **multi-targeted CAR T-cell therapy design**&mdash;maximizing spatial tumor coverage while minimizing TAA overlap. CuBATS can be applied to any solid tumor type, given an appropriate tumor segmentation model.

CuBATS integrates WSI registration, tumor segmentation, color deconvolution, quantification, and combinatorial analysis into a unified, streamlined framework. It enables **patient-specific, reproducible, and scalable** TAA selection addressing the challenges of **spatial tumor heterogeneity** and **antigen escape** that currently hinder translation of CAR T-cell therapy to solid tumors.

## Pipeline Overview
CuBATS includes the following steps:
1. **WSI Registration:** Registration and alignment of tissue across all WSIs using [VALIS](https://github.com/MathOnco/valis).
2. **Tumor Segmentation:** Configurable tumor segmentation based on the HE WSI. CuBATS accepts segmentation models in **ONNX** format, enabling tumor-type independent application, if an appropriate model is provided.
3. **Color Deconvolution:** Separation of antigen-specific DAB stain from counterstains.
4. **Quantification of Staining Intensities:** Classification of tissue regions into high-, medium-, low-positive and negative intensity categories.
5. **Combinatorial Analysis:** Evaluation of spatial TAA co-expression to identify optimal mono-, dual-, and triplet TAA combinations that maximize tumor coverage while minimizing TAA overlap.

For full details and installation instructions, see the [Documentation](https://hnu-digihealth.github.io/CuBATS/).

## Contribute to the Project
If you want to contribute to the project please read our [Contributing Guideline](https://github.com/hnu-digihealth/CuBATS/blob/main/CONTRIBUTING.md).


## License
[MIT](https://github.com/hnu-digihealth/CuBATS/blob/main/LICENSE.txt) © 2025 Moritz Dinser, Daniel Hieber
frequenz-dispatch 1.0.2
A highlevel interface for the dispatch API
License: MIT
Requires Python: <4,>=3.11
Keywords: frequenz, python, actor, frequenz-dispatch, dispatch, highlevel, api

# Dispatch Highlevel Interface

[![Build Status](https://github.com/frequenz-floss/frequenz-dispatch-python/actions/workflows/ci.yaml/badge.svg)](https://github.com/frequenz-floss/frequenz-dispatch-python/actions/workflows/ci.yaml)
[![PyPI Package](https://img.shields.io/pypi/v/frequenz-dispatch)](https://pypi.org/project/frequenz-dispatch/)
[![Docs](https://img.shields.io/badge/docs-latest-informational)](https://frequenz-floss.github.io/frequenz-dispatch-python/)

## Introduction

A highlevel interface for the dispatch API.

See [the documentation](https://frequenz-floss.github.io/frequenz-dispatch-python/v0.1/reference/frequenz/dispatch) for more information.

## Usage

The [`Dispatcher` class](https://frequenz-floss.github.io/frequenz-dispatch-python/v0.1/reference/frequenz/dispatch/#frequenz.dispatch.Dispatcher), the main entry point for the API, provides two channels:

* [Lifecycle events](https://frequenz-floss.github.io/frequenz-dispatch-python/v0.1/reference/frequenz/dispatch/#frequenz.dispatch.Dispatcher.lifecycle_events): A channel that sends a message whenever a [Dispatch][frequenz.dispatch.Dispatch] is created, updated or deleted.
* [Running status change](https://frequenz-floss.github.io/frequenz-dispatch-python/v0.1/reference/frequenz/dispatch/#frequenz.dispatch.Dispatcher.running_status_change): Sends a dispatch message whenever a dispatch is ready to be executed according to the schedule or the running status of the dispatch changed in a way that could potentially require the actor to start, stop or reconfigure itself.

### Example using the running status change channel

```python
import os
from unittest.mock import MagicMock
from datetime import timedelta

from frequenz.dispatch import Dispatcher, DispatchInfo, MergeByType

async def create_actor(dispatch: DispatchInfo, receiver: Receiver[DispatchInfo]) -> Actor:
    return MagicMock(dispatch=dispatch, receiver=receiver)

async def run():
    url = os.getenv("DISPATCH_API_URL", "grpc://dispatch.url.goes.here.example.com")
    auth_key = os.getenv("DISPATCH_API_AUTH_KEY", "some-key")
    sign_secret = os.getenv("DISPATCH_API_SIGN_SECRET")

    microgrid_id = 1

    async with Dispatcher(
        microgrid_id=microgrid_id,
        server_url=url,
        auth_key=auth_key,
        sign_secret=sign_secret,
    ) as dispatcher:
        await dispatcher.start_managing(
            dispatch_type="EXAMPLE_TYPE",
            actor_factory=create_actor,
            merge_strategy=MergeByType(),
            retry_interval=timedelta(seconds=10)
        )

        await dispatcher
```

## Supported Platforms

The following platforms are officially supported (tested):

- **Python:** 3.11
- **Operating System:** Ubuntu Linux 20.04
- **Architectures:** amd64, arm64

## Contributing

If you want to know how to build this project and contribute to it, please
check out the [Contributing Guide](CONTRIBUTING.md).