semvec/pypi-packages huggingface dataset:
numbatalib 0.1.0
TA-Lib reimplemented in pure Python + Numba.
Requires Python: >=3.10
Keywords: ta-lib, talib, technical-analysis, indicators, numba

# numbatalib

Reimplement TA-Lib (technical analysis indicators) in **pure Python + Numba**, aiming for:

- algorithmic parity with the local installed `talib` (ground-truth reference for tests/benchmarks)
- near-C performance for large vectors (Numba JIT)
- no compiled extension at install time (no `libta-lib` dependency)

## Status

- Implemented TA-Lib Core functions: **161 / 161**
- Parity tests: `generated/pytest_results.txt`
- Parity + benchmark results: `generated/parity_results.csv`, `generated/bench_results.csv`
- Status tracker: `port_checklist.csv`

Note: the local `talib` wheel in this environment does not expose `ACCBANDS`, `AVGDEV`, `IMI`; those are validated against upstream TA-Lib C (see `generated/parity_results_upstream_c.csv`).

## Install

After publishing to PyPI:

```bash
pip install numbatalib
```

## Usage

`numbatalib` exposes TA-Lib function names dynamically:

```python
import numpy as np
import numbatalib as ta

x = np.random.default_rng(0).normal(size=1000).cumsum()

sma = ta.SMA(x, timeperiod=20)
rsi = ta.RSI(x, timeperiod=14)
```

## TA-Lib compatible API (minimal habit cost)

If you want `talib`-like **APIs + error messages**, use the compatibility shim:

```python
import numpy as np
import numbatalib.talib as talib  # or: import numbatalib.compat.talib as talib

x = np.random.default_rng(0).normal(size=1000).cumsum()

talib.SMA(x, timeperiod=20)
talib.stream.SMA(x, timeperiod=20)        # streaming scalar
from numbatalib.talib import abstract
abstract.Function("SMA")(x, timeperiod=20)
```

Notes:
- `set_compatibility/get_compatibility` and `set_unstable_period/get_unstable_period` are supported (matching TA-Lib behavior for EMA/RSI/CMO and unstable-period masking).

## Dev

- Run parity tests vs installed `talib`: `pytest -q`
- Regenerate parity + speed CSVs and update checklist: `python tools/compare_vs_talib.py --bench --write-checklist`
daotonomos-sdk 0.2.1
Python SDK for DAOtonomos - Advanced Autonomous Governance
Requires Python: >=3.8

# DAOtonomos Python SDK

## Install
```bash
pip install daotonomos-sdk
```

## CLI
```bash
daotonomos --help
```

## Terminal Dashboard (TUI)
```bash
daotonomos dashboard --config ./dashboard.json
```

Example `dashboard.json`:
```json
{
  "ws_url": "ws://127.0.0.1:9944",
  "treasury_account": "5F...",
  "default_seed": "//Alice"
}
```

The TUI exposes all SDK functions: governance, AI, analytics, oracles, cross-chain, ZK, quantum, treasury.
npmlp-core 1.0.0
A modular Deep Learning framework built from scratch using NumPy
Requires Python: >=3.9

# npmlp-core: Modular Deep Learning Framework from Scratch

A lightweight, modular deep learning library built entirely in NumPy. This project implements the core components of modern neural networks, including backpropagation, vectorized optimization, and advanced regularization techniques, without the use of high-level frameworks like PyTorch or TensorFlow.

## Installation

```bash
pip install npmlp-core
```

## Quick Start

```python
import numpy as np
from mytorch.models import MLP1
from mytorch.optim import SGD
from mytorch.nn import CrossEntropyLoss

# Create a model (input: 784, output: 10 classes)
model = MLP1(784, 10)

# Set up optimizer and loss
optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion = CrossEntropyLoss()

# Training step
x = np.random.randn(32, 784)  # batch of 32
y = np.random.randint(0, 10, 32)  # labels

# Forward pass
output = model.forward(x)
loss = criterion.forward(output, y)

# Backward pass
grad = criterion.backward()
model.backward(grad)

# Update weights
optimizer.step()
optimizer.zero_grad()
```

## Key Features

- **Core Layers**: Fully functional Linear layers with comprehensive backpropagation.
- **Advanced Activations**: Implementation of ReLU, Sigmoid, Tanh, and modern industry-standard functions like GELU (Gaussian Error Linear Units) and Swish.
- **Regularization**: Vectorized BatchNorm1d with training/inference mode logic and running statistics.
- **Optimizers**: Stochastic Gradient Descent (SGD) with configurable Momentum.
- **Loss Functions**: MSELoss and CrossEntropyLoss with numerically stable Softmax integration.
- **Architectures**: Pre-configured Multi-Layer Perceptron (MLP) models ranging from shallow (MLP0) to deep (MLP4) configurations.

## Technical Highlights

### 1. Vectorized Backpropagation

Every layer in this library implements its own forward and backward pass. The gradients are calculated using vectorized matrix calculus, ensuring high performance on the CPU.

### 2. Numerical Stability

The Softmax and CrossEntropy implementations include numerical stability techniques (such as the max-subtraction method) to prevent floating-point overflow during exponentiation.

### 3. Training vs. Inference Mode

The BatchNorm1d layer manages global running means and variances using exponential moving averages (momentum), ensuring the models perform accurately during evaluation on single samples.

## Project Structure

```text
mlp-core/
├── src/
│   └── mytorch/
│       ├── nn/         # Layers (Linear, BatchNorm) and Activations
│       ├── optim/      # SGD with Momentum
│       ├── models/     # MLP Architectures
│       └── loss.py     # MSE and Cross-Entropy
├── requirements.txt    # Minimal dependencies (NumPy, SciPy)
└── README.md
wtflow 0.5.0
Add your description here
Requires Python: >=3.10

[![test](https://github.com/ido123net/wtflow/actions/workflows/test.yml/badge.svg)](https://github.com/ido123net/wtflow/actions/workflows/test.yml)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/ido123net/wtflow/main.svg)](https://results.pre-commit.ci/latest/github/ido123net/wtflow/main)

# What The Flow (wtflow)

Wtflow is a powerful asynchronous workflow runner that orchestrates complex tasks and dependencies with ease.
cmmt 0.1.1
AI-powered commit messages and branch naming.
Requires Python: >=3.13

# cmmt

[![PyPI version](https://badge.fury.io/py/cmmt.svg)](https://badge.fury.io/py/cmmt)

`cmmt` 是一个由 AI 驱动的命令行工具，旨在帮助您自动生成符合 [Conventional Commits](https://www.conventionalcommits.org/) 规范的 Git 提交消息和分支名称。

## 核心功能

- **AI 驱动**: 利用 OpenAI 的强大语言模型，根据您的代码变更 (`git diff`) 生成高质量的提交消息。
- **遵循规范**: 严格遵循 Conventional Commits 规范，提升您代码库的可读性和可维护性。
- **分支命名**: 自动建议清晰、规范的分支名称。
- **高度可配置**: 您可以通过 `~/.cmmt.yml` 文件自定义模型、提示语、忽略文件等。
- **上下文感知**: 能够获取 `git status`, `git diff`, `git log` 以及项目文件结构作为上下文，生成更精准的提交信息。
- **交互式与非交互式**: 支持交互式确认，也支持 `-y` 参数进行自动化操作。

## 安装部署

您可以通过 `pip` 来安装 `cmmt`:

```bash
pip install cmmt
```

或者，如果您使用 `uv`:

```bash
uv pip install cmmt
```

安装完成后，您需要初始化配置文件并设置您的 OpenAI API 密钥：

```bash
cmmt --init
```

该命令会引导您在 `~/.cmmt.yml` 文件中设置 API 密钥、模型、以及其他可选配置。

## 使用方法

在您的 Git 仓库中，当您已经将变更添加到暂存区后，运行以下命令：

```bash
git add .
cmmt
```

`cmmt` 将会分析您的变更，并生成建议的提交消息。

### 常用选项

- **生成分支名称**:
  ```bash
  cmmt -b
  ```
- **自动确认并提交**:
  ```bash
  cmmt -y
  ```
- **提交后自动推送到远程仓库**:
  ```bash
  cmmt -p
  ```
- **组合使用**:
  ```bash
  cmmt -b -y -p
  ```
- **提供额外信息**:
  您可以通过 `-e` 参数为 AI 提供额外的上下文信息。
  ```bash
  cmmt -e "This change is part of the new authentication feature."
  ```

## 配置文件

`cmmt` 的所有配置都存储在 `~/.cmmt.yml` 文件中。您可以通过 `cmmt --init` 命令来初始化该文件，也可以手动编辑它。

以下是一个配置文件的示例：

```yaml
openai_api_key: "sk-..."
model: "gpt-4o"
base_url: null
max_tokens: 2048
ignore_files:
  - "package-lock.json"
  - "*.log"
extra_info: "Always respond in Chinese."
git_log_level: "brief" # Can be 'none', 'brief', 'detailed'
git_log_count: 10 # -1 for all
project_structure_enabled: true
project_structure_max_depth: 3
project_structure_ignore:
  - "node_modules/"
force_think: false
```

## 许可协议

该项目基于 [MIT License](LICENSE) 开源。