semvec/pypi-packages huggingface dataset:
libcasm-configuration 2.2.0
CASM configuration comparison and enumeration
Requires Python: <3.14,>=3.9

<img alt="Shows the CASM logo" src="https://raw.githubusercontent.com/prisms-center/CASMcode_global/main/python/doc/_static/logo.svg" width="600" />

#### libcasm-configuration

The libcasm-configuration package is the CASM configuration comparison and enumeration module. This includes:

- Classes for representing supercells, configurations, clusters, and occupation events
- Methods for comparing and enumerating unique configurations, clusters, occupation events, and local environments
- Methods for generating orbits of symmetrically equivalent configurations, clusters, and occupation events
- Methods for copying configurations to make sub- or super-configurations
- Methods for generating symmetry groups, and constructing and applying symmetry representations
- Methods for performing irreducible space decompositions and finding symmetry adapted order parameters
- Methods for creating configurations with properties from mapped structures

#### Install

    pip install libcasm-configuration


#### Dependencies

For symmetry group classification, we use the [spglib](https://spglib.readthedocs.io/en/latest/) package.


#### Usage

See the [libcasm docs](https://prisms-center.github.io/CASMcode_pydocs/libcasm/overview/latest/).


#### About CASM

The libcasm-configuration package is part of the [CASM](https://prisms-center.github.io/CASMcode_docs/) open source software package, which is designed to perform first-principles statistical mechanical studies of multi-component crystalline solids.

CASM is developed by the Van der Ven group, originally at the University of Michigan and currently at the University of California Santa Barbara.

For more information, see the [CASM homepage](https://prisms-center.github.io/CASMcode_docs/).


#### License

GNU Lesser General Public License (LGPL). Please see the file LICENSE for details.
purr-petra-cli 0.1.0
Add your description here
Requires Python: >=3.13

# purr_petra_cli

| <img src="./docs/purrio.png" alt="purrio logo" height="100"/> | <img src="./docs/snp.png" alt="purrio logo" width="200"/>| **PETRA** |
|:--:|:--:|:--:|


Use **purr_petra_cli** to query Petra projects* from the command line and generate well-centric json. This is a stripped-down implementation of most export functionality of [purr_petra](https://github.com/rbhughes/purr_petra).


```
- core
- dst
- formation
- ip
- perforation
- production
- raster_log
- survey
- vector_log
- well
- zone
```


* _Each [Petra](https://www.spglobal.com/commodityinsights/en/ci/products/petra-geological-analysis.html) "project" is a semi-structured collection
  of E&P assets that interoperate with its own
  [DBISAM](https://www.elevatesoft.com/products?category=dbisam)
  database. From an IT perspective, Petra is a distributed collection of
  user-managed databases "on the network" containing millions of assets._


## WHO IS THIS FOR?

This was developed as part of a Databricks geospatial catalog pipeline. It is useful for quickly exporting data from Petra, particularly if you don't have a license or want to avoid Petra's cumbersome ASCII export.


## LIMITATIONS:

If you are trying to egress lots of data from a large Petra project, this is NOT the tool for you. It relies solely on ODBC via Python. You are better off exporting/parsing a .PPF file.

**purr_petra_cli** should be fine for smaller projects ~ roughly those with < 100000 wells. Use the `--uwis` filter to limit state/county. It's slow but memory-safe.


## QUICKSTART:

1. `pip install purr_petra_cli`
2. launch console "as Administrator" and run `prep-purr-petra`
3. `start-purr-petra`
4. visit `http://localhost:8070/docs` for auto-generated docs
python-bugger 0.5.2
Practice debugging, by intentionally introducing bugs into an existing codebase.
Requires Python: >=3.9


![py-bugger logo](https://raw.githubusercontent.com/ehmatthes/py-bugger/main/assets/logo_raw_bordered.png)

py-bugger
===

When people learn debugging, they typically have to learn it by focusing on whatever bugs come up in their code. They don't get to work on specific kinds of errors, and they don't get the chance to progress from simple to more complex bugs. This is quite different from how we teach and learn just about any other skill.

`py-bugger` lets you intentionally introduce specific kinds and numbers of bugs to a working project. You can introduce bugs to a project with a single file, or a much larger project. This is much different from the typical process of waiting for your next bug to show up, or introducing a bug yourself. `py-bugger` gives people a structured way to learn debugging, just as we approach all other areas of programming.

Full documentation is at [https://py-bugger.readthedocs.io/](https://py-bugger.readthedocs.io/en/latest/).

Installation
---

```sh
$ pip install python-bugger
```

Note: The package name is python-bugger, because py-bugger was unavailable on PyPI.

## Basic usage

If you don't specify a target directory or file, `py-bugger` will look at all *.py* files in the current directory before deciding where to insert a bug. If the directory is a Git repository, it will follow the rules in *.gitignore*. It will also avoid introducing bugs into test directories and virtual environments that follow familiar naming patterns.

`py-bugger` creates bugs that induce specific exceptions. Here's how to create a bug that generates a `ModuleNotFoundError`:

```sh
$ py-bugger -e ModuleNotFoundError
Introducing a ModuleNotFoundError...
  Modified file.
```

When you run the project again, it should fail with a `ModuleNotFoundError`.

For more details, see the [Quick Start](https://py-bugger.readthedocs.io/en/latest/quick_start/) and [Usage](https://py-bugger.readthedocs.io/en/latest/usage/) pages in the official [docs](https://py-bugger.readthedocs.io/en/latest/).


Contributing
---

If you're interested in this project, please feel free to get in touch. If you have general feedback or just want to see the project progress, please share your thoughts in the [Initial feedback](https://github.com/ehmatthes/py-bugger/discussions/7) discussion. Also, feel free to [open a new issue](https://github.com/ehmatthes/py-bugger/issues/new). The [contributing](https://py-bugger.readthedocs.io/en/latest/contributing/) section in the official docs has more information about how to contribute.
libcasm-monte 2.2.0
CASM building blocks for Monte Carlo simulations
Requires Python: <3.14,>=3.9

<img alt="Shows the CASM logo" src="https://raw.githubusercontent.com/prisms-center/CASMcode_global/main/python/doc/_static/logo.svg" width="600" />

#### libcasm-monte

The libcasm-monte package provides useful building blocks for Monte Carlo simulations. This includes:

- Sampling classes and functions
- Equilibration, convergence checking, and statistics calculation
- Generic results IO
- Supercell index conversions
- Generic event definitions, construction, and selection


#### Install

    pip install libcasm-monte


#### Usage

See the [libcasm docs](https://prisms-center.github.io/CASMcode_pydocs/libcasm/overview/latest/).


#### About CASM

The libcasm-monte package is part of the [CASM](https://prisms-center.github.io/CASMcode_docs/) open source software package, which is designed to perform first-principles statistical mechanical studies of multi-component crystalline solids.

CASM is developed by the Van der Ven group, originally at the University of Michigan and currently at the University of California Santa Barbara.

For more information, see the [CASM homepage](https://prisms-center.github.io/CASMcode_docs/).


#### License

GNU Lesser General Public License (LGPL). Please see the file LICENSE for details.
matiss 1.0.0
Marco's Time Saving Stuff

# MATISS

**Marco's time saving stuff for Python**

This rapidly evolving Python package is a collection of functions and classes to aid the analysis of genomics data. It includes functions for plotting with matplotlib and seaborn, to interact with the Google Cloud Platform, for statistical testing, and others.

## Installation
Install MATISS through PIP with:
```
pip install matiss
```

## Author
Marco Teixeira \
mcarvalh@broadinstitute.org
wake-arena-cli 1.4.0
Wake Arena CLI tool
Requires Python: !=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8

# Wake Arena CLI
Wake Arena command line interface to operate projects and vulnerability checks based on [Wake](https://github.com/Ackee-Blockchain/wake) testing tool.


## Quick start 🚀
1. Install wake are na cli
```shell
pip install wake-arena-cli
```
2. Initialize the CLI
```shell
wake-arena init
```
3. (optional) If you don't have any Wake version installed, use `wake install` and `wake use` command to activate specific version you want
```shell
wake-arena wake install
wake-arena wake use
```
4. Perform security audit using remote Wake execution
```shell
wake-arena check
```

## Env parameters 🚩
| Env                   | Description                                                        |
|-----------------------|--------------------------------------------------------------------|
| `WAKE_ARENA_API_KEY`  | Uses api key instead of configured authentication                  |
| `WAKE_ARENA_PROJECT`  | Project id. CLI will use this project instead of configured one    |