semvec/pypi-packages huggingface dataset:
hashname-cli 0.1.0
Generate deterministic human-readable names from any input
Requires Python: >=3.8
Keywords: cli, hash, name, deterministic, human-readable

# hashname-cli

Generate deterministic human-readable names from any input — hashes, UUIDs, IPs, commit SHAs, anything.

Same input always produces the same name. Like Docker container names, but for everything.

## Install

```bash
pip install hashname-cli
```

## Usage

```bash
# Basic naming
hashname name "abc123"              # → keen-spark
hashname name "192.168.1.1"        # → golden-reef

# More words for uniqueness
hashname name "abc123" -w 3        # → keen-cosmic-spark

# Capitalize
hashname name "abc123" -c          # → Keen-Spark

# Custom separator
hashname name "abc123" -s "_"      # → keen_spark

# Multiple names from one input
hashname name "server" -n 5

# Batch processing
hashname batch id1 id2 id3
echo -e "hash1\nhash2" | hashname batch

# JSON output
hashname name "abc123" --json

# Verify determinism
hashname verify "abc123"

# Word list stats
hashname stats
```

## Why?

- **Log readability**: Replace `a4995e18227e62fa` with `bold-ember`
- **Quick identification**: Same hash = same name, every time
- **No collisions needed**: It's a label, not a UUID
- **Agent-friendly**: `--json` output, predictable CLI

## For AI Agents

See [SKILL.md](SKILL.md) for agent-optimized documentation.
noisy-load-profiles 0.1.8
A Python package for adding realistic noise to electrical load or current profiles. Useful for studying robustness to noise of algorithms
Requires Python: >=3.12

# Overview
This is a package to ADD realistic sources of noise to electrical load profiles. 
This is useful for testing the robustness of algorithms to noise, to enhance the realism of synthetic data, or to generate augmented data for machine learning purposes.

The overal approach is as follows:
- The package contains various `Perturbations`, such as perturbations to add Gaussian or Ornstein-Uhlenbeck noise, or to simulate measurement deadbands (and many others)
- You decide which perturbations you want to use, and you add them to a `Pipeline` object.
- You can call `pipeline.apply(profiles)`, and the pipeline sequentially applies the various perturbations to the given profiles.

Note, the package expects `profiles` as a 2D array (timesteps X measurement devices)

## Install
`pip install noisy_load_profiles`

## Examples
On our [Github](https://github.com/Flinverdaasdonk/noisy_load_profiles) We have examples that demonstrate basic usage, advanced usage, and how to construct new Perturbations.

Below we show the most barebones example of a pipeline applying two types of noise.

```
from noisy_load_profiles import Pipeline, perturbations
import numpy as np


# initialize some profiles
timesteps = 10
n_profiles = 2
profiles = np.ones((timesteps, n_profiles)) # 2 profiles with 10 timesteps each; example


# Initialize some pertubations
gaussian_noise = perturbations.GaussianNoise(mean=0.0, std=0.01, seed=42)
deadband = perturbations.PercentualDeadBand(seed=42)

# construct the pipeline
pipeline = Pipeline([gaussian_noise, deadband])


# apply the perturbation to the profiles
perturbed_profiles = pipeline.apply(profiles)

```
tomskit-cli 0.3.0
tomskit CLI: Create projects based on tomskit development framework, focusing on business logic
Requires Python: >=3.12

# tomskit-cli

**tomskit CLI**：基于 tomskit 开发框架的项目脚手架工具，一键生成项目结构与常用组件，让你专注业务逻辑。

## 功能特性

- **创建新项目**：支持 FastAPI、Celery 或两者兼备的完整项目模板
- **添加模块**：在已有项目中快速生成完整模块（Model + Service + Controller + Schema）
- **添加组件**：按需添加 Celery 任务、FastAPI 扩展、独立 Service
- **数据库迁移**：为项目初始化 Alembic 迁移目录与配置
- **Claude Code 支持**：生成 CLAUDE.md 等 AI 辅助开发配置

## 安装

需要 Python 3.12+，推荐使用 [uv](https://docs.astral.sh/uv/) 或 pip：

```bash
# 使用 uv（推荐）
uv tool install tomskit-cli

# 或使用 pip
pip install tomskit-cli
```

安装后可直接使用 `tomskit` 命令。

## 快速开始

### 创建新项目

```bash
# 创建完整项目（FastAPI + Celery，默认）
tomskit init my-app

# 指定项目描述与类型
tomskit init my-app -d "我的后端服务" -t full

# 仅 FastAPI
tomskit init my-api -t fastapi

# 仅 Celery
tomskit init my-worker -t celery

# 指定输出目录
tomskit init my-app -o /path/to/output
```

**项目类型**（`-t` / `--type`）：

| 类型       | 说明                |
| ---------- | ------------------- |
| `full`     | FastAPI + Celery（默认） |
| `fastapi`  | 仅 FastAPI Web 应用 |
| `celery`   | 仅 Celery 异步任务  |

创建完成后，按终端提示执行：

```bash
cd my-app
uv sync
cp .env.example .env
# 编辑 .env 配置数据库和 Redis
```

### 在已有项目中添加内容

需在项目根目录（含 `main.py` 或 `app/`）下执行：

```bash
# 创建完整模块（Model + Service + Controller + Schema）
tomskit add module user

# 创建 Celery 异步任务
tomskit add task send_email

# 创建 FastAPI 扩展
tomskit add extension cache

# 创建独立 Service
tomskit add service payment
```

### 初始化数据库迁移

```bash
# 在当前目录对应的项目中初始化
tomskit migrations

# 指定项目目录
tomskit migrations -d /path/to/project
```

### 初始化 Claude Code 支持

```bash
tomskit claude init
# 可选：-d /path/to/project
```

会在项目根目录生成 `CLAUDE.md` 等文件，便于 AI 辅助开发。

## 命令一览

| 命令 | 说明 |
|------|------|
| `tomskit init <name>` | 创建新项目 |
| `tomskit add module <name>` | 添加完整模块 |
| `tomskit add task <name>` | 添加 Celery 任务 |
| `tomskit add extension <name>` | 添加 FastAPI 扩展 |
| `tomskit add service <name>` | 添加独立 Service |
| `tomskit migrations` | 初始化数据库迁移 |
| `tomskit claude init` | 初始化 Claude Code 支持 |

**通用选项**：

- `-y` / `--yes`：跳过交互提示，使用默认值
- `-d` / `--dir`：指定项目目录（在 `add`、`migrations`、`claude init` 等子命令中）
- `-o` / `--output`：`init` 时指定目标目录
- `-t` / `--type`：`init` 时指定项目类型（full / fastapi / celery）
- `-d` / `--description`：`init` 时指定项目描述

## Shell 自动补全

安装 [argcomplete](https://github.com/kislyuk/argcomplete) 后，可启用 Tab 补全：

**Bash**（写入 `~/.bashrc`）：

```bash
eval "$(register-python-argcomplete tomskit)"
```

**Zsh**（写入 `~/.zshrc`）：

```zsh
autoload -U bashcompinit
bashcompinit
eval "$(register-python-argcomplete tomskit)"
```

## 开发

```bash
# 克隆仓库后
cd tomskit-cli
uv sync

# 以可编辑方式运行
uv run tomskit --help
```

构建与发布见项目内 `Makefile`（如 `make build`、`make publish`）。

## 许可证与作者

- **作者**：aioai (ai@aioai.cc)
- **版本**：见 [pyproject.toml](pyproject.toml)