semvec/pypi-packages huggingface dataset:
encapsia-cli 0.5.12
Client CLI for talking to an Encapsia system.
License: MIT
Requires Python: <4.0,>=3.8.1
Keywords: encapsia, eSource, EDC, Clinical Trials

# About

![Workflows](https://github.com/encapsia/encapsia-cli/actions/workflows/main.yml/badge.svg)
![PyPI](https://img.shields.io/pypi/v/encapsia-cli?style=flat)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/encapsia-cli)

This package provides command line access to Encapsia over the REST API.

All of these are designed to work with server 1.5 and beyond.

## Autocomplete

Setup autocomplete using the instructions found on <https://github.com/click-contrib/click-completion>

## Tests

### Unit tests

Run:

    poetry run pytest

### Walkthrough Tests

Prerequisite: an instance of ice must be running on your localhost, and valid token for
it must be present in your key store.

See the `walkthrough_tests` directory for bash scripts which exercise the CLI.

Run them e.g. with:

    poetry run bash walkthrough_tests/all.sh

or test specific subcommands with:

    poetry run bash walkthrough_tests/token.sh

Note that these tests are *not* self-verifying; they just provide helpful coverage,
assurance, and working documentation.

## Release checklist

* Run: `poetry run black .`
* Run: `poetry run isort .`
* Run: `poetry run flake8 .`
* Run: `poetry run mypy .`
* Ensure "tests" run ok (see above).
* Capture test output and commit with: `poetry run bash walkthrough_tests/all.sh 2>&1 | poetry run ansi2html -f 80% >WALKTHROUGH.html`
* Ensure git tag and package version are equal.
fastapi-easystart 0.0.7
FastAPI EasyStart simplifies FastAPI project setup with ready-to-use templates and configurations for environment management, CORS, and authentication. It lets you focus on coding by handling the boilerplate, making it easy to launch projects quickly.
License: MIT
Requires Python: >=3.12
Keywords: fastapi, fastapi easy start, fastapi project setup, fastapi jwt, fastapi custom response, FastAPI projects, CORS setup, fastapi modular components

# FastAPI EasyStart

FastAPI EasyStart is a streamlined library designed to help developers quickly set up and configure a FastAPI project with essential components. It simplifies the process of creating APIs by providing ready-to-use templates and configurations for common tasks such as authentication, database integration, and CORS settings.


## Features

- 🚀 Quick Start Templates: Get started with FastAPI in seconds using predefined project structures and configurations.
- 🔧 Environment Management: Easily manage environment variables and settings.
- 🌍 CORS Configuration: Simplify CORS setup with built-in configurations.
- 📦 Modular Components: Use only what you need with modular and customizable components.
- 💡 Custom Exception Classes: Enhance error handling with predefined, customizable exception classes tailored for FastAPI.
- 🔐 Authentication: Easily integrate JWT-based authentication. (Coming Soon)
- 💾 Database Support: Seamless integration with popular databases like SQLite, PostgreSQL, and MongoDB. (Coming Soon)

## Installation

You can install the fastapi-easystart package using either pip or pipenv:

```bash
pip install fastapi-easystart
```

## Quickstart

### 1. Initialize a New FastAPI Project
Begin by creating a new FastAPI project with the default settings and configurations. Run the following command:
```bash
python -m  fastapi_easystart.main init
```
This command will generate the basic project structure, including directories and initial files, based on the easy start template.

### 2. Configure the main.py File
After initializing your project, configure the main.py file to set up the FastAPI application. If you want to either override or merge the existing main.py file with a new configuration, use the following command:
```bash
python -m fastapi_easystart.main config-main
```

### 3. Running the Development Server
Once the project is set up, you can start the development server:
```bash
uvicorn main:app --reload
```

Alternatively, you can use the FastAPI CLI command:

```bash
fastapi dev main.py
```

## Documentation

For full documentation, visit the FastAPI EasyStart Documentation (Coming Soon).

[//]: # (## Contributing)

[//]: # ()
[//]: # (Contributions are welcome! Please see the CONTRIBUTING.md file for more information on how to get involved.)


## License

This project is licensed under a modified MIT License - see the [LICENSE](LICENSE) file for details.

---

This version organizes the information clearly, ensures that placeholders for future content are marked, and maintains a professional tone throughout. Adjust the documentation and URLs when they are available.
knn-j25ng 2.0.3
Implements KNN model using KNeighborsClassifier
License: MIT
Requires Python: >=3.11

# Weekly program no.1 - 자가 학습 KNN

## 프로그램 동작 방식
- [ ] 학습된 모델이 없는 상태에서 출발
- [ ] 사용자는 길이, 무게 데이터를 입력하고 프로그램 예측 값을 출력 (이때 프로그램은 학습 데이터가 없어 임의의 값을 예측)
- [ ] 예측값에 대한 정답 유무 입력
- [ ] 프로그램에 입력된 데이터(훈련데이터)와 정답(타겟) 데이터를 저장
- [ ] 위 과정이 반복 되면서 모델을 진화 시켜 나간다.


## usage
```bash
# install
## use git url
$ pip install git+https://github.com/j25ng/knn_j25ng.git
## use pypi
$ pip install knn-j25ng

# data training
$ fish
🐟 물고기의 길이를 입력하세요 (cm): 10.8
🐟 물고기의 무게를 입력하세요  (g): 8.7
🐟 이 물고기는 빙어입니다.
🐟 예측이 맞습니까? (y/n): y
🐟 예측 성공🥳
```

```bash
# data chart(use matplotlib)
$ chart
### display the figure window ###
```
### chart window
<img src="https://github.com/user-attachments/assets/f585310b-d655-4d6e-a411-8648da14eecc" width=600 />

## data
```bash
$ cd ~/code/data
$ tree
.
├── data.json
└── target.json

0 directories, 2 files
```