semvec/pypi-packages huggingface dataset:
ora-get 0.1.1
Download from ORA
Requires Python: >=3.7

# ora_get

Download an ORA (Oxford Research Archive) collection.

```
ora_get 8d95c423-816c-4a4f-88b6-eb7a040cb40e  # by UUID
```

## License

This codebase is made available under the MIT License, copyright (c) 2023 Tom Russell and contributors. See [./LICENSE](./LICENSE) for details
yz-core2 1.0.61b8
An ID generator for distributed microservices
Requires Python: >=3.6.8

# readme-template

--------------

## Introduction

yzcore 目的为了开发后端服务时，提供一种代码结构规范参考。
可以通过`startproject`和`startapp`两个命令快速创建工程和内部的接口应用模块。
**安装模块**
```shell
$ pip install yz-core2
```
示例：
- 创建工程：
```shell
$ yzcore startproject myproject
```
- 创建工程内部应用：
```shell
$ yzcore startapp myapp ./src/apps/
```

代码结构介绍：
```
.
├── docs		        说明文档、接口文档等文档的存放目录
├── migrations		    数据表迁移文件存放目录
├── src
│   ├── apps 接口应用程序的主目录
│   │   ├── __init__.py
│   │   ├── myapp01
│   │   │   ├── __init__.py
│   │   │   ├── controllers.py  控制层：封装数据交互操作
│   │   │   ├── models.py       模型层：实现数据表与模型的定义
│   │   │   ├── schemas.py      模式层：定义接口数据参数
│   │   │   ├── tests.py        测试文件
│   │   │   └── views.py        视图层：接口定义层
│   │   └── myapp02
│   ├── conf		配置文件的存放目录
│   ├── const		公共常量存放目录
│   ├── tests		测试文件的存放目录
│   ├── main.py		程序的入口文件
│   ├── settings.py	程序的设置文件
│   └── utils		抽离出的公共代码模块存放目录
├── .gitignore
├── requirements.txt
└── README.md
```

## Quick start

Quick Start 部分主要包括两部分内容：简易的安装部署说明(Deployment)和使用案例(Example)。特别是对于一些基础库，必须包括Example模块。


## Documentation

Documentation 部分是核心的文档，对于大型项目可以使用超链接，如使用以下这种形式：

For the full story, head over to the [documentation](https://git.k8s.io/community/contributors/devel#readme).

## 数据库迁移操作
```
# pip install alembic

alembic init migrations                             # 创建迁移环境
alembic revision --autogenerate -m "commit content" # 自动生成迁移文件
alembic upgrade head                                # 升级到最近版本
alembic upgrade <revision_id>                       # 升级到指定版本
alembic downgrade <revision_id>                     # 回退到指定版本
```
radshap 0.0.1
Shapley values for interpreting radiomic models
Requires Python: >=3.7

# radshap

<p align="center">
    <img src="docs/images/radshap_logo.png"/>
</p>

[![Documentation Status](https://readthedocs.org/projects/radshap/badge/?version=latest)](https://radshap.readthedocs.io/en/latest/?badge=latest)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![PyPI version](https://badge.fury.io/py/radshap.svg)](https://badge.fury.io/py/radshap)
[![Downloads](https://pepy.tech/badge/radshap)](https://pepy.tech/project/radhsap)

This repository proposes a python tool for highlighting the contribution of different regions of interest (ROIs) to the predictions of radiomic models.
It estimates the Shapley value of the different ROIs of an image that a trained radiomic model uses to obtain a prediction.

## Graphical abstract

<p align="center">
    <img src="docs/images/graphical_abstract.png"/>
</p>
<b>a.</b> schematic view of a generic aggregated radiomic model - <b>b.</b> computation of a Shapley value for a specific region.

## Install

### Install from source
```
pip install git+https://github.com/ncaptier/radshap.git
```
## Experiments
We provide a jupyter notebook for an illustration with PET images and simple aggregation strategies:
* [Classification of Non-Small Cell Lung Cancer subtype and interpretation with Shapley values](examples/nsclc_subtype_classification.ipynb)

We provide a jupyter notebook for an illustration of a robust strategy for computing Shapley values:
* [Robust Shapley values for explaining multi-region radiomic models with non-optional regions](examples/robust_shapleyvalues.ipynb)

## Examples
**Explanation with Shapley values**
```python
import numpy as np
import joblib
from radshap.shapley import Shapley

model = joblib.load("trained_logistic_regression.joblib")
shap = Shapley(predictor = lambda x: model.predict_proba(x)[:, 1], aggregation = ('mean', None))
shapvalues = shap.explain(X) # X a 2D array of shape (n_instances, n_instance_features)
```

**Robust explanation with Shapley values**
```python
import numpy as np
import joblib
from radshap.shapley import RobustShapley

model = joblib.load("trained_logistic_regression.joblib")
shap = RobustShapley(predictor = lambda x: model.predict_proba(x)[:, 1],
                     aggregation = ('nanmean', None),
                     background_data = Xback) # Xback a 2D array of shape (n_samples_background, n_input_features)
shapvalues = shap.explain(X) # X a 2D array of shape (n_instances, n_instance_features)
```
## License
This project is licensed under a custom open-source license (see the [LICENSE.md](LICENSE.md) file for more details).
## Acknowledgements

This package was created as a part of the PhD project of Nicolas Captier in the [Laboratory of Translational Imaging in Oncology (LITO)](https://www.lito-web.fr/en/) of Institut Curie.
biometatools 1.1.1
Package for meta-analysis of reads compared to features
Requires Python: >=3.4

# biometatools

A repository of tools for performing meta analysis on high throughput
sequencing data and genome features defined as genomic regions. Sequencing
data are most often requested in indexed BAM file format and features in BED
file format.

Examples of some questions that these tools are useful for:

* What is the distribution of reads around the 5' ends of genes?
* What is the distribution of reads along the length of all mRNAs?

# Installation

```
pip install biometatools
```