semvec/pypi-packages huggingface dataset:
ecsbatchcli 0.1.3
A CLI tool for batch management of Volcano Engine ECS instances
Requires Python: >=3.6

# ECSBatchCLI - 火山引擎ECS批量管理CLI工具

ECSBatchCLI是一款基于Python的命令行工具，用于高效、自动化地批量创建和销毁火山引擎云主机（ECS实例）。通过读取用户定义的YAML配置文件和接收命令行参数，简化操作流程，减少人工干预，满足特定业务场景下对资源快速响应和成本控制的需求。

## 主要功能

- **批量创建云主机**：通过简单的命令行参数，批量创建符合特定配置的云主机
- **批量销毁云主机**：安全、精确地批量销毁特定范围内的云主机
- **配置管理**：通过YAML配置文件管理常用的云主机配置模板
- **结果反馈**：清晰展示每次批量操作的执行结果和潜在问题

## 安装

### 要求

- Python 3.6+
- 火山引擎账号和访问凭证（AK/SK）
- 火山引擎SDK（volcengine-python-sdk>=1.0.0）

### 安装火山引擎SDK

详细安装方法请参考[火山引擎SDK安装指南](VOLCENGINE_SDK_INSTALLATION.md)。

简要步骤：
```bash
# 安装火山引擎Python SDK
pip install volcengine-python-sdk>=1.0.0
```

### 通过pip安装

```bash
pip install ecsbatchcli
```

### 从源码安装

```bash
git clone https://github.com/volcengine/ecsbatchcli.git
cd ecsbatchcli
pip install -e .
```

**注意**：从源码安装时，您仍需要单独安装火山引擎SDK。

## 快速开始

### 1. 创建配置文件

在当前目录下创建`config.yaml`文件，或使用`--config`参数指定配置文件路径。配置文件示例：

```yaml
credentials:
  access_key_id: "your_access_key_id"
  secret_access_key: "your_secret_access_key"

profiles:
  - name: "test-profile"
    region_id: "cn-beijing"
    availability_zone_id: "cn-beijing-a"
    image_id: "image-xxxxxxxxx"
    security_group_id: "sg-xxxxxxxxx"
    vpc_id: "vpc-xxxxxxxxx"
    subnet_id: "subnet-xxxxxxxxx"
    default_instance_type: "ecs.g1ie.large"
    system_disk_type: "ESSD_PL0"
    system_disk_size_gb: 40
    hostname_prefix: "test-server"
    project_id: "project-xxxxxxxxx"
```

### 2. 批量创建云主机

```bash
ecsbatchcli create --config-group test-profile --instance-type ecs.g1ie.large --count 5
```

### 3. 批量销毁云主机

```bash
ecsbatchcli delete --config-group test-profile --count 3
```

## 详细文档

更多详细用法和配置选项，请参考[完整文档](docs/usage.md)。

## 许可证

本项目采用MIT许可证。详见[LICENSE](LICENSE)文件。
Adaptive-MCMC 0.1.3
Adaptive Markov Chain Monte Carlo (MCMC) algorithms
Requires Python: >=3.10

# Adaptive-MCMC

Adaptive Markov Chain Monte Carlo (MCMC) algorithms

## Install

```{bash}
pip install adaptive-mcmc
```

## Examples

```{python}
import numpy as np
from adaptive_mcmc.mala import ESJDMALA
from scipy.stats import multivariate_normal

dim = 20
cov = 0.5 * np.eye(dim) + 0.5
inv_cov = np.linalg.inv(cov)


def logp(x):
    return multivariate_normal.logpdf(x, mean=np.zeros(dim), cov=cov)


def grad_logp(x):
    return -inv_cov @ x


mala = ESJDMALA(logp, grad_logp, initial_sample=np.zeros(dim), eps0=0.5)
samples, acc, eps_hist, esjd_hist = mala.run(20_000)
print(f"Acceptance: {acc:.3f}, Final epsilon = {eps_hist[-1]:.4f}")

pmala = PrecondESJDMALA(logp, grad_logp, initial_sample=np.zeros(dim), initial_covariance=np.eye(dim), eps0=0.5)
samples, acc, eps_hist, esjd_hist = pmala.run(20_000)
print(f"Acceptance: {acc:.3f}, Final epsilon = {eps_hist[-1]:.4f}")
```
audiomerger 0.1.0
A simple package to merge audio files using base64 encoding
Requires Python: >=3.6

# AudioMerger

A simple Python package to merge audio files using base64 encoding.

## Installation

```bash
pip install audiomerger
```

## Usage

```python
from audiomerger import base64_merge_audio

# Open your audio files
with open('mic_audio.wav', 'rb') as audio_a, open('system_audio.wav', 'rb') as audio_b:
    # Merge the audio files
    merged_audio = base64_merge_audio(audio_a, audio_b)
    
    # Save the merged audio
    with open('merged_audio.wav', 'wb') as f:
        f.write(merged_audio)
```

## Requirements

- Python 3.6 or higher
- requests>=2.25.0

## License

This project is licensed under the MIT License - see the LICENSE file for details.
scLiTr 1.0.1
Package for an analysis of lineage-tracing scRNA-Seq data
Requires Python: >=3.8

<p align="center"><a href="https://sclitr.readthedocs.io/en/latest/"><img src="docs/source/logo.png" height="250"></a></p>

# scLiTr

![Tests](https://github.com/kharchenkolab/scLiTr/actions/workflows/tests.yml/badge.svg)
![PyPI](https://img.shields.io/pypi/v/sclitr)
![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)

scLiTr is a Python package for analysis of lineage tracing coupled with single-cell RNA-Seq.

The main key of the package are clonal embeddings — vector representations of the whole clones
in low dimensional space (clone2vec). These representations is a dropout-robust and cluster-free
way of representation of heterogeneity within clonal behaviour for cell type tree-free hypothesis
generation regarding cells' multipotency.

clone2vec builds representation of clones in exact same way with popular word embedding algorithm — word2vec —
via construction two-layers fully connected neural network (it uses Skip-Gram architecture) that
aims to predict neighbour cells clonal labellings by clonal label of cells. As a result, clones that exist in
similar context in gene expression space will have similar weights in this neural network, and these weights
will be used as embedding for further analysis.

## Installation

scLiTr might be installed via `pip` (takes 1-2 minutes on Google Colab):
```bash
pip install sclitr
```
or the latest development version can be installed from GitHub using:
```bash
pip install git+https://github.com/kharchenkolab/scLiTr
```

## System requirements

`scLiTr` requires Python 3.8 or later with packages listed in [setup.cfg](setup.cfg) file. The package was successfully tested
on the following systems:

* macOS Sonoma 14.5 (Apple M1 Chip @ 3.20GHz × 8, 16GB RAM) — *MacBook Air M1*,
* Ubuntu 18.04.5 LTS, 64-bit (Intel Xeon @ 2.60GHz × 32, 256GB RAM) — *PowerEdge server*,
* Ubuntu 22.04.3 LTS, 64-bit (Intel Xeon @ 2.20GHz × 2, 13GB RAM) — *Google Colab*.

## Documentation and tutorials

Please visit [documentation web-site](https://sclitr.readthedocs.io/en/latest/) to check out API description and a few
tutorials with analysis.

An example with the dataset from [Weinreb et al., 2020](https://www.science.org/doi/10.1126/science.aaw3381) is available on
[Google Colab](https://colab.research.google.com/github/kharchenkolab/scLiTr/blob/main/docs/source/Basic_usage.ipynb)
(takes about 45 minutes on the CPU, of which approximately 30 minutes are clone2vec latent representation construction).

## clones2cells

For interactive exploration of clonal and gene expression embeddings together we recommend using
our simple tool [clones2cells](https://github.com/serjisa/clones2cells_app).