semvec/pypi-packages huggingface dataset:
flask-simple-tsdb 2.1.3.0
Python code for accessing simple_tsdb databases from flask <= 2.1.3.
License: MIT
Requires Python: >=3.8.10
Keywords: simple_tsdb flask flask_simple_tsdb

flask_simple_tsdb
=================
This package provides a library for interacting with a simple_tsdb server to
fetch and write points from a flask app.  This package only works with flask
versions prior to 2.2 (so, the most-recently-supported flask version is 2.1.3).
This is because the way to access app context variables had a breaking change
at 2.2.
MiniCNN 0.1.1
Minimal, well-documented CNN models for image classification. Great for beginners!
License: MIT
Requires Python: >=3.8

# MiniCNN

**MiniCNN** is a minimal, well-documented Python package with beginner-friendly 
**Convolutional Neural Network (CNN)** models using PyTorch.  
Great for **learning**, **tutorials**, and **fast prototyping**.

## Installation

pip install MiniCNN


## Usage

import torch

from MiniCNN import SimpleCNN, TinyCNN

Example: Create a SimpleCNN model for MNIST (10 classes)

model = SimpleCNN(num_classes=10) print(model)

Random input (batch=1, channel=1, size=28x28)

x = torch.randn(1, 1, 28, 28) y = model(x) print(y.shape)  # torch.Size()


## Features
- **SimpleCNN**: Standard small CNN, 2 conv layers + 2 FC layers.
- **TinyCNN**: Extremely small model for quick tests.


## Requirements
- Python 3.8+
- PyTorch >= 2.0.0


## License
MIT License
cloe-etl-adf 1.0.10
Generates the orchestration pipelines for the job template package based on metadata.
License: MIT
Requires Python: <3.12,>=3.11

# CLOE ETL ADF

[![Copier](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/copier-org/copier/master/img/badge/badge-grayscale-inverted-border-orange.json)](https://github.com/copier-org/copier)
[![python](https://img.shields.io/badge/Python-3.11-3776AB.svg?style=flat&logo=python&logoColor=white)](https://www.python.org)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v0.json)](https://github.com/charliermarsh/ruff)
[![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](https://mypy-lang.org/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)

Owner: initions

Generates the orchestration pipelines for the job template package based on metadata.
bin-waterfall 0.1.0
A Python library for waterfall chart generation
License: MIT
Requires Python: >=3.8

安装方法：
pip install bin-waterfall
快速上手：
from bin_waterfall import EnergyProcessor, SpectrumProcessor
#在初始化类之前创建输入和输出目录，示例：
#输入目录存放bin文件
#输出目录会得到程序运行的瀑布图
    input_dir  = "D:/input"
    output_dir="D:/output"
#类初始化
    spec_proc = SpectrumProcessor(
        input_dir=input_dir,
        output_dir=os.path.join(output_dir, "Spectrum"),  # 给子类单独的子目录
        max_freq=60.0,     # 截止频率
        view_mode="side",  # 'side' 或 'top'
        dpi=300
    )
        energy_proc = EnergyProcessor(
        input_dir=input_dir,
        output_dir=os.path.join(output_dir, "Energy"),  # 给子类单独的子目录
        dpi=300,
        block_len=1024
    )
#然后调用对应的run方法即可
fixed-asset-plugin 0.1.0
Add Fixed Asset Parameter to every new Part created
License: MIT
Requires Python: >=3.9

# FixedAssetPlugin

Add Fixed Asset Parameter to every new Part created

## Installation

### InvenTree Plugin Manager

... todo ...

### Command Line 

To install manually via the command line, run the following command:

```bash
pip install fixed-asset-plugin
```

## Configuration

... todo ...

## Usage

... todo ...