semvec/pypi-packages huggingface dataset:
autocat 2022.8.5
Tools for automated generation of catalyst structures and sequential learning
License: MIT

# AutoCat

AutoCat is a suite of python tools for **sequential learning for materials applications**
and **automating structure generation for DFT catalysis studies.**
Documentation for the package can be found
[here](https://aced-differentiate.github.io/auto_cat).

Development of this package stems from [ACED](https://www.cmu.edu/aced/), as part of the
ARPA-E DIFFERENTIATE program.

## Installation

There are two options for installation, either via `pip` or from the repo directly.

### `pip` (recommended)

If you are planning on strictly using AutoCat rather than contributing to development,
 we recommend using `pip` within a virtual environment (e.g.
 [`conda`](https://docs.conda.io/en/latest/)
 ). This can be done as follows:

```
pip install autocat
```

### Github (for developers)

Alternatively, if you would like to contribute to the development of this software,
AutoCat can be installed via a clone from Github. First, you'll need to clone the
github repo to your local machine (or wherever you'd like to use AutoCat) using
`git clone`. Once the repo has been cloned, you can install AutoCat as an editable
package by changing into the created directory (the one with `setup.py`) and installing
via:
```
pip install -e .
```

## Contributing

Contributions through issues, feature requests, and pull requests are welcome.
Guidelines are provided [here](CONTRIBUTING.md).

## Acknowledgements

The code presented herein was funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001211 and in part by the National Science Foundation, under Award Number CBET-1554273. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
magic-storage 1.1.0
Small Python 3 library providing wrapper classes for storage (caching) of test resources (and unintentionally other types of resources too).
License: MIT
Requires Python: >=3.9
Keywords: python-3,python-3.9,python-3.10

<h2 align="center">Magic Storage cooler than you think!</h2>

---

<p align="center">
    <a href="https://pypi.org/project/magic-storage/"><img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/magic_storage"></a>
    <a href="https://pycqa.github.io/isort/"><img alt="isort" src="https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336"></a>
    <a href="https://github.com/Argmaster/magic-storage/actions"><img alt="Test Status" src="https://github.com/Argmaster/magic-storage/workflows/Test%20suite%20CI%20run/badge.svg"></a>
    <a href="https://github.com/Argmaster/magic-storage/actions"><img alt="Docs Status" src="https://github.com/Argmaster/magic-storage/workflows/Deploy%20documentation/badge.svg"></a>
    <a href="https://github.com/Argmaster/magic-storage/actions"><img alt="Docs Status" src="https://github.com/Argmaster/magic-storage/workflows/Deploy%20documentation/badge.svg"></a>
    <a href="https://pypi.org/project/magic-storage/"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/magic-storage"></a>
    <a href="https://github.com/Argmaster/magic-storage/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/Argmaster/magic-storage"></a>
    <a href="https://github.com/Argmaster/magic-storage/pulls"><img alt="GitHub pull requests" src="https://img.shields.io/github/issues-pr/Argmaster/magic-storage"></a>
    <a href="https://github.com/Argmaster/magic-storage/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/Argmaster/magic-storage"></a>
    <img alt="GitHub tag (latest SemVer)" src="https://img.shields.io/github/v/tag/Argmaster/magic-storage?label=version">
</p>

Magic storage is a Python library that provides tools to easily write, read and
delete resources for testing. This applies, of course, to resources that are
difficult to obtain but not very expensive to store locally and, in addition,
do not change. A good example are responses from REST APIs or at least those of
them that are not live data.

The library consists of a set of classes that implement storage using the file
system and temporary storage in RAM. All tools can be accessed through the
MagicStorage class.

## Installing

Install and update using pip:

```
$ pip install -U magic_storage
```

## Example

```python
from typing import Any
from magic_storage import MagicStorage


def very_expensive_get() -> Any:
    ...


response = (
    MagicStorage()
    .filesystem(__file__)
    .cache_if_missing("Nice thing", lambda: very_expensive_get())
)

```

## Documentation

Online documentation is available on
[Github pages](https://argmaster.github.io/magic-storage/).