semvec/pypi-packages huggingface dataset:
bias-adjustment 1.0.6
Bias Adjusment by Quantile Mapping
License: MIT
Requires Python: >=3.8.1,<3.9
Keywords: climatology,data analysis,quantile mapping,bias correction

# Bias Adjusment by Quantile Mapping

Bias adjustment techniques:
- Quantile Mapping
- Detrended Quantile Mapping
- Quantile Delta Mapping ([Cannon et al., 2015](https://doi.org/10.1175/JCLI-D-14-00754.1))


## Usage
```python
from bias_adjustment import BiasAdjustment
```
ImageStack-SVG 1.0.1
Create Images by Stacking them
License: MIT
Requires Python: >=3
Keywords: image,imagestack,imagestack-svg,imagestack_svg,svg

# ImageStack Python

### A simple way to create images using svg and jinja2 templating

[![Build Status](https://github.com/skillor/imagestack-svg-python/actions/workflows/test-python.yml/badge.svg)](https://github.com/skillor/imagestack-svg-python/actions/workflows/test-python.yml) [![PyPi version](https://badgen.net/pypi/v/ImageStack-SVG/)](https://pypi.org/project/ImageStack-SVG)

## Installation

### Install with pip

    pip install imagestack-svg

### Running without display

    xvfb-run python3 {your application}
    
## Test Design

You can test your SVG Design here: https://skillor.github.io/imagestack-svg-python/

## Usage

    from imagestack_svg.imagecreator import ImageCreator
    from imagestack_svg.imageresolve import ImageStackResolveString
    ig = ImageCreator()
    s = ImageStackResolveString('<text>{{ dummy }}</text>')
    s(dummy='Hello World!')
    print(await ig.create_inner_svg(s))
    with open('test.png', 'wb') as f:
        f.write((await ig.create_bytes(s)).read())


more examples here: https://github.com/skillor/imagestack-svg-python/blob/master/test.py
python-classy 0.0.2
Yet another simple dataclass-like object for python.
License: MIT
Requires Python: >=3.11,<3.12

# Python-Classy
Yet another simplest dataclass-like object for Python.  
[![codecov](https://codecov.io/gh/E5presso/python-classy/branch/main/graph/badge.svg?token=F6WD2Y1976)](https://codecov.io/gh/E5presso/python-classy)
<br>

## **Python-Classy** is a simplest dataclass with a several **restriction**.  
> 1. **Constructor** of Classy object only available with **keyword argument**.  
> 2. Classy object is not *"Auto Hashable"* nor *"Auto Equatable"*. You have to implement *compute_hash(self)* or *equals(self, __o: object)* method on your own.  
> 3. Classy object has to be decorated with **mutability decorator**. You need to set @mutable or @immutable decorator in your class definition.  
> ```python
> from classy import Classy, mutable, immutable
>
> @mutable  # You have to decorate with this when you want to keep this object mutable
> class MutableClass(Classy):
>     name: str
> 
> @immutable  # You have to decorate with this when you want to keep this object immutable
> class ImmutableClass(Classy):
>     name: str
> 
> a: MutableClass = MutableClass(name="John")  # Classy object must be constructed with keyword argument.
> a.name = "Sarah"  # It is OK to mutate it's field with @mutable decorator.
> 
> b: ImmutableClass = ImmutableClass(name="John")  # Classy object must be constructed with keyword argument.
> b.name = "Sarah"  # This will raise 'dataclasses.FrozenInstanceError'
> assert a == a  # This will raise NotImplementedError. You have to implement equals(self, __o: object) -> bool:
>```  

<br>

## Why **Python-Classy** has restriction?  
> + **Python-Classy** is designed to develop domain models with minimum overhead.  
> + **Python-Classy** is a just a wrapper for *dataclasses.dataclass* and uses *typing.dataclass_transform*  
> + Also, **Python-Classy** is only uses pure python builtins, So you can keep your domain area safe from other dependencies.  
> + **Python-Classy** doesn't use metaclass to modify default type instantiator of your class. So It is truly pure **Plain Old Python Object**.  
> + I think **explicit** is better than **implicit**.  
> + So, **Python-Classy** restricts its own constructor to use **keyword argument only**. This may helps your project's coding convention.  
> + And also helps your teammate to prevent their **tiny mistake** (wrong placing argument's position)  

<br>

## What **Python-Classy** can actually do?
> Pretty much nothing.  
> But **Python-Classy** has some features might useful for your project.  
> 1. Python-Classy has *.dict* or *.json* property to serialize.  
> 2. Python-Classy has classmethod *default()*. So, you can create object with default value.  
> 3. Python-Classy has classmethod *from_dict()* or *from_json()* to deserialize your object.  
> 4. You can change default json serializer to override *serialize()* and *deserialize()* (currently use builtin json module)
> 5. You can override *equals()* or *compute_hash()* to support hashing and equatable.
doing-cli 1.5.3
CLI tool to simplify the development workflow on azure devops
License: MIT
Requires Python: >=3.7

![pytest](https://github.com/ing-bank/doing-cli/workflows/Development/badge.svg)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/doing-cli)
![PyPI](https://img.shields.io/pypi/v/doing-cli)
![PyPI - License](https://img.shields.io/pypi/l/doing-cli)
![PyPI - Downloads](https://img.shields.io/pypi/dm/doing-cli)
![GitHub contributors](https://img.shields.io/github/contributors-anon/ing-bank/doing-cli)

# doing-cli: Azure Devops on the command line

CLI app to help speed up development on Azure DevOps, built on top of [Azure DevOps CLI](https://learn.microsoft.com/en-us/azure/devops/cli/?view=azure-devops). Allows for a more streamlined repo/issue workflow. Inspired by [cli.github.com](https://cli.github.com/). 

## Installation

`doing` requires python 3.7+, [azure cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli) and the [devops extension](https://docs.microsoft.com/en-us/azure/devops/cli/?view=azure-devops). To install:

```shell
pip3 install doing-cli
```

For more details and setup see the [installation guide](https://ing-bank.github.io/doing-cli/get_started/install/).

## Documentation

See [ing-bank.github.io/doing-cli/](https://ing-bank.github.io/doing-cli/).

## Maintainers

- Joao Pinto Moura [@operte](https://github.com/operte)