semvec/pypi-packages huggingface dataset:
sproclib 3.0.1
Standard Process Control Library for chemical process control - Semantic API for chemical plant design using familiar ML patterns
License: MIT
Requires Python: >=3.8
Keywords: process control, chemical engineering, PID, simulation, optimization, semantic API, machine learning

# SPROCLIB - Standard Process Control Library

A comprehensive Python library for chemical process control, providing essential classes and functions for PID control, process modeling, simulation, optimization, and advanced control techniques.

**SPROCLIB provides a semantic API for chemical plant design that uses familiar patterns from machine learning frameworks like TensorFlow and Keras.**

## Installation

```bash
pip install sproclib
```

## Features

- **Semantic Plant Design**: Intuitive API similar to ML frameworks for building complex chemical processes
- **Process Units**: CSTR, pumps, heat exchangers, distillation columns, reactors, and tanks
- **Economic Optimization**: Built-in optimization algorithms for cost minimization and profit maximization
- **PID Controllers**: Classical and advanced PID control implementations with auto-tuning
- **Analysis Tools**: Transfer functions, simulation, and system identification
- **Advanced Control**: Model predictive control, state-space controllers, and IMC
- **Transport Models**: Continuous and batch transport for liquids and solids

## Quick Start

Create and optimize a chemical plant in just a few lines:

```python
from sproclib.unit.plant import ChemicalPlant
from sproclib.unit.pump import CentrifugalPump
from sproclib.unit.reactor import CSTR

# Define plant
plant = ChemicalPlant(name="Process Plant")

# Add units
plant.add(CentrifugalPump(H0=50.0, eta=0.75), name="feed_pump")
plant.add(CSTR(V=150.0, k0=7.2e10), name="reactor")

# Connect units
plant.connect("feed_pump", "reactor", "feed_stream")

# Configure optimization
plant.compile(
   optimizer="economic",
   loss="total_cost",
   metrics=["profit", "conversion"]
)

# Optimize operations
plant.optimize(target_production=1000.0)
```

## Advanced Example

```python
# Traditional PID control example
import sproclib as spc

# Create a PID controller
controller = spc.PIDController(kp=1.0, ki=0.1, kd=0.05)

# Create a tank model
tank = spc.Tank(volume=100, area=10)

# Simulate step response
response = spc.step_response(tank, time_span=100)
```

## Requirements

- Python 3.8+
- NumPy >= 1.20.0
- SciPy >= 1.7.0
- Matplotlib >= 3.3.0

## License

MIT License

## Author

Thorsten Gressling <gressling@paramus.ai>
lsiwh37249_discordbot 0.1.0
Default template for PDM package
License: MIT
Requires Python: ==3.11.*

### PDM TEST

```
pdm run python src/discordbot/app.py

OUTPUT 
>> PDM GitHub repository: https://github.com/pdm-project
>> PDM has 19 public repositories
```

### 📌 Discord 출석체크봇
### ⏰ 기간: 2025.03.10 ~ 2025.03.19
### 🛠 기술 스택: Python
### 👥 인원: 1명

### 🔍 프로젝트 개요
매일 반복되는 퇴실 확인 업무의 효율화를 위해, 간단한 키워드 입력만으로 수강생의 퇴실 여부를 확인할 수 있는 Discord 봇을 개발했습니다.
복잡한 로그인과 수십 회의 클릭, 입력 과정을 대체하여 사용자 편의성과 관리 효율성을 높였습니다.

### 🎯 주요 기능
Discord 명령어 기반 퇴실 확인

키워드 입력만으로 퇴실 상태 간편 조회

PM 등 외부 외출 시에도 어디서든 확인 가능

### ✅ 프로젝트 성과
업무 시간 단축:
기존 수작업 대비 초단위로 업무 완료 가능, 반복적인 클릭 및 입력 작업 제거

데이터 접근성 향상:
장소에 구애받지 않고 실시간으로 출결 정보 확인 가능, 프로세스 간소화