semvec/pypi-packages huggingface dataset:
ollmcp 0.26.0
CLI for MCP Client for Ollama - An easy-to-use command for interacting with Ollama through MCP
Requires Python: >=3.10

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  <img src="https://github.com/jonigl/mcp-client-for-ollama/blob/main/misc/ollmcp-logo-512.png?raw=true" width="256" />
</p>
<p align="center">
<i>A simple yet powerful Python client for interacting with Model Context Protocol (MCP) servers using Ollama, allowing local LLMs to use tools.</i>
</p>

---
# ollmcp

`ollmcp` is a command-line interface for connecting to MCP servers using Ollama.

![ollmpc usage demo gif](https://raw.githubusercontent.com/jonigl/mcp-client-for-ollama/v0.2.5/misc/ollmcp-demo.gif)

## Installation

```
pip install ollmcp
```

## Usage

```
ollmcp [options]
```

## Description

This is a lightweight package that provides the `ollmcp` command-line interface. It's a convenience wrapper around the main [`mcp-client-for-ollama`](https://github.com/jonigl/mcp-client-for-ollama) package.

The actual implementation is contained in the main package. This package simply provides a more convenient and shorter name for installing the CLI tool.

## License

MIT
erbs 0.2.0
Enhanced sampling in the representation space of atomistic neural networks.
Requires Python: >=3.10
Keywords: enhanced sampling, interatomic potentials, machine-learning, molecular-dynamics

# ERBS - Enhanced Representation Based Sampling

**ERBS** is a JAX-based library for the efficient generation of training data for Machine-Learned Interatomic Potentials (MLIPs).

It implements the Enhanced Representation Based Sampling strategy, which identifies slow, physically relevant collective variables directly from the variance of atomic descriptors and accelerates sampling along these modes using bias potentials.
The library itself ships with a simplified version of the Gaussian Moment descriptor, but is descriptor agostic and can be easily interfaced with others.



## Features

- Automated CV Selection: Automatically identifies slow degrees of freedom via PCA on the descriptor space.
- OPES-Explore: Uses the OPES-Explore formalism to rapidly explore the free energy surface with bounded bias potentials.
- Model Agnostic: Can be used with any atomistic descriptor that matches the required function signature (see Documentation).


## Installation

ERBS is available on PyPI:

```bash
pip install erbs
```

To install the latest development version from GitHub:

```bash
pip install git+https://github.com/your_username/erbs.git
```


## Usage

ERBS integrates with ASE to run enhanced sampling simulations.
A typical workflow involves the typical ASE setup, selecting a descriptor and wrapping the plain energy/forces calculator with ERBS.

```python


dim_reduction = GlobalPCA(pca_components)

dE = units.kB * temperature * barrier_factor
energy_fn_factory = OPESExploreFactory(
    T=temperature, dE=dE, a=band_width
)

base_calc = ...
calc = ERBS(
    base_calc,
    dim_reduction,
    energy_fn_factory,
    n_basis=4, # Uses built-in GM descriptor
    r_min=1.1,
    r_max=6.0,
    dr_threshold=0.5,
    interval=2000,
)
```

Authors

    Moritz René Schäfer

Under the supervision of Johannes Kästner.


## Contributing

We are happy to receive your issues and pull requests!
If you want to add a new dimensionality reduction, energy function or descriptor, please open an issue to discuss the implementation details.



## Acknowledgements

The creation of ERBS was supported by the DFG under Germany's Excellence Strategy - EXC 2075 - 390740016 and the Stuttgart Center for Simulation Science (SimTech).


References

If you use ERBS in your research, please cite the following paper:

TBD
streamlit-gridtable 0.1.0
Streamlit custom component: DataTables (React) wrapper
Requires Python: >=3.7
Keywords: streamlit, component, datatable, react

# streamlit-custom-component

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install streamlit-custom-component
```

## Usage instructions

```python
import streamlit as st

from my_component import my_component

value = my_component()

st.write(value)
```