semvec/pypi-packages huggingface dataset:
prettyplease 0.13.1
Create pretty corner plots.
Requires Python: >=3.6

Create eye-pleasing cornerplots (and maybe other types of plots in the future).

Usage example(s):

  import numpy as np
  import prettyplease

  # Generate some example data
  rng = np.random.default_rng()
  n_samples = 100000
  n_dim = 4
  x = rng.normal(1.618, 0.2, size=(n_samples, n_dim))
  # x is an ndarray of shape (n_samples, n_dim)

  # With default settings
  fig = prettyplease.corner(x)
  fig.savefig('default.pdf', bbox_inches='tight')

  # With some customization
  labels = [rf'$\theta_{i}$' for i in range(n_dim)]
  fig = prettyplease.corner(x, bins=30, labels=labels, plot_estimates=True, colors='red', n_uncertainty_digits=4, title_loc='center', figsize=(7,7))
  fig.savefig('custom.pdf', bbox_inches='tight')
fastmcp-agents-cli 0.5.10
CLI for running FastMCP and calling tools on it
Requires Python: >=3.13



`uvx fastmcp-agents-cli call tool --config config.yml --tool=get_structure --args '{"depth": 3}'`

`uvx fastmcp-agents-cli list tools --config config.json`

Will auto pick up mcp.json, config.json, mcp.yml, config.yml

`uvx fastmcp-agents-cli call tool get_structure '{"depth": 3}'`

`uvx fastmcp-agents-cli list tools`
sfai-sdk 0.14b0
A zero-config Python CLI to scaffold, containerize, and deploy FastAPI apps
Requires Python: >=3.9

# SFAI SDK

A powerful toolkit for developing, connecting, and deploying AI applications within the Salesforce ecosystem. Build sophisticated FastAPI-powered AI services, deploy seamlessly to Heroku for native Salesforce integration, publish them to MuleSoft, and connect with Salesforce for enhanced agent capabilities. This SDK simplifies the entire AI application lifecycle—from local development to enterprise deployment on Heroku Private Spaces—with zero configuration, Kubernetes or container support, and secure connectivity between AI services and Salesforce.

## Terms of the use
Use of this project with Salesforce is subject to the [TERMS OF USE](https://github.com/salesforce/sfai-sdk/blob/main/TERMS%20OF%20USE)

## 🚀 Features

- Scaffold a FastAPI app with Docker + Helm support
- Deploy to Kubernetes (local or cloud) using Helm
- Persistent context system for app configuration
- Simplified environment management
- Built-in networking tools (port-forwarding, tunnels)
- MuleSoft API integration
- **Seamless Heroku deployment** for Salesforce ecosystem integration

## Salesforce Ecosystem Integration

SFAI SDK is designed to work natively with the Salesforce ecosystem:

- **Heroku Deployment**: Deploy directly to Salesforce's preferred application platform with enterprise features like Private Spaces and internal routing
- **MuleSoft Connectivity**: Publish and deploy APIs to MuleSoft for Salesforce integration
- **Zero Configuration**: Context system manages all environment settings automatically
- **Enterprise Security**: Support for Salesforce SSO and secure network configurations

## Installation

```bash
pip install sfai-sdk
```

### Prerequisites

- Python 3.9+
- Docker
- kubectl
- Helm
- For AWS deployments: AWS CLI configured with appropriate permissions
- For Heroku deployments: Heroku CLI installed

For detailed documentation, see the [SFAI-DOCS](https://opensource.salesforce.com/sfai-sdk/).

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

## License

This project is licensed under the Apache License 2.0 - see the [LICENSE.txt](LICENSE.txt) file for details.
glassInit 0.0.6
glassInit is a way to initialize the parameters of a neural network layer @pytorch. It is suitable for shortcut connections where tensor sizes need to be adjusted. The flow of information between forward and backward propagation is maximized while adjusting the tensor size. 
Requires Python: >=3.8
Keywords: glassInit, initialize, shortcut, PyTorch, AI, neural, NN, connections, Skip Connections, Res

# glassInit
glassInit is a way to initialize the parameters of a neural network layer. It is suitable for shortcut connections where tensor sizes need to be adjusted. The flow of information between forward and backward propagation is maximized while adjusting the tensor size.  

## 思路说明
在神经网络中构建快捷连接/跳跃连接时又是需要改变形状,如果直接使用卷积层/线性层等会因为其初始化方式影响连接中的梯度传播,而如果在各维度上分别使用插值缩放会导致明显的信息也会影响梯度传播. glassInit专用于快捷连接/跳跃连接中用于调整张量尺寸的层,它构建了一个插值矩阵实现在所有维度上一起插值,最大限度的保留了信息.

## Install
```bash
pip install glassInit
```

## Use
```python
from glassInit import glassInit_
...
linear=torch.nn.Linear(512,2048,bias=False)
glassInit_(linear.weight,inDim=...,outDim=0,gain=None,zeroMean=True)
```

大多数时候不需要传入外部gain(默认为None)如果传入外部gain则内部gain不生效.

输入元素数小于输出元素数且关闭稀疏时内部gain为1. 否则内部gain为sqrt(输出元素数/输入元素数)

需根据矩阵各维度的作用填写inDim和outDim,最终加在一起的要填到inDim,输出时仍然分离的要填到outDim

举例来说卷积层的inChannel维度和kernels维度要填到inDim里,outChannel维度要填到outDim里

转置卷积要根据设定参数后的具体行为决定kernels维度要填到哪里,可以先写道inDim中尝试

如果zeroMean为False,返回矩阵将没有小于零的值,并且是插值矩阵.否则,返回矩阵将对插值矩阵逐元素随机取反.  

如果输入元素数大于输出元素数或关闭稀疏,矩阵实现线性插值,否则是插0插值.两种插值均为循环边界条件.  


## HomePage
<https://github.com/PsycheHalo/glassInit/>
mlpotentials 0.0.1
A Python package for machine learning potentials
Requires Python: >=3.8
Keywords: machine learning, potentials, chemistry

# MLPotentials

A Python package for easy usage of machine learning potentials in scientific
computing and molecular simulations.

## Installation

You can install mlpotentials from PyPI:

```bash
pip install mlpotentials
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
for details.
nyanger 0.9.2
Simple logger. Simple to use. Simple to modify.
Requires Python: >=3.8

# Nyanger - simple logger

**Nyanger** is a simple logger designed to be simple to use and simple to modify.

Creation of Nyanger was motivated by the need of a simple logging facility for simple everyday scripts as well as simple logging solution for complex multiprocessing code.

Nyanger designed to be simultaneously: working solution, prototype, and code example.
So use Nyanger as is, extend it with **LogWriters**, or modify source code to fill your needs.

Nyanger consist of 3 modules:
- **async** (for the use with asyncio)
- **process** (for use with code of any complexity, but especially complex multiprocessing/multithreading code)
- **simple** (for plain simple scripts or multithreading code)

# Compatibility
Nyanger compatible with **Linux** (and probably any *NIX), and probably with **Windows** (feel free to test and report any issues)

# Usage
All 3 modules follow same pattern:
1. `Nyanger` is our logger class. You need to get instance of it ether by creating object manually or by calling `get_logger` method. 
2. You must provide list of `LogWriter` objects to `Nyanger` constructor, if `get_logger` called without this list then default console `LogWriter` will be created.
3. You can create your own log writes by implementing `LogWriter` abstract class.
4. You start logger by calling `start()` method.
5. You use it by calling `other()` `info()` `warning()` `error()` `debug()` or `log()` methods of `Nyanger` instance.
6. Before ending your program you're stopping logger by calling `stop()` method.

# Example
Init Nyanger directly:

```python
import nyanger.process as nya
import nyanger.process.log_writers.console_writer as cwr

log: nya.Nyanger

if __name__ == '__main__':
    # Init logger
    log = nya.Nyanger("pur", loging_level=nya.LogLevel.DEBUG, log_writers=[cwr.ConsoleWriter()])

    log.start()
    log.other("Other test pur")
    log.info("Info test put")
    log.warning("Warning test pur")
    log.error("Error test pur")
    log.debug("Debug test pur")
    log.stop()
```

Using get_logger helper:
```python
import nyanger.process.static as nya_stat

# Init logger
log = nya_stat.get_logger("nyan")

if __name__ == '__main__':
    log.start()
    log.other("Other test pur")
    log.info("Info test put")
    log.warning("Warning test pur")
    log.error("Error test pur")
    log.debug("Debug test pur")
    log.stop()
```