semvec/pypi-packages huggingface dataset:
TkThemes 0.1.2
Contains dictionary of custom themes used for creting GUIs with Tkinter
Requires Python: >=3.7

# TkThemes

`TkThemes` is a collection of ready-to-use Tkinter themes for Python GUI applications.  
It provides visually appealing color schemes, fonts, and widget styles to quickly style your Tkinter apps.

---

## Features

- 14+ pre-defined themes including:
  - Dracula, Solarized Light, Nord, Monokai Pro
  - Gruvbox Dark, One Dark Pro, GitHub Light, Oceanic Next
  - Crimson Red, Forest Green, Royal Purple, Classic White
  - Sepia Tone, Cyberpunk, Pastel Sunset
- Theme elements for:
  - Background (`bg`) and foreground (`fg`) colors
  - Entry widget background (`entry_bg`) and foreground (`entry_fg`)
  - Button backgrounds (`btn_bg`, `btn_bg_2`) and active button background (`btn_active_bg`)
  - Plot background (`plot_bg`)
  - Accent color (`accent_color`)
- Pre-defined fonts (`font`) for consistent styling

---

## Installation

You can install directly from GitHub:

```bash
pip install git+https://github.com/TirthrajSG/TkThemes.git
```

Or 

```bash
pip install TkThemes
```

## Usage
```python
from TkThemes import Themes

# Get a theme dictionary
dracula_theme = Themes["Dracula"]

# Access theme properties
bg_color = dracula_theme["bg"]
fg_color = dracula_theme["fg"]
font = dracula_theme["font"]

```
Or import pre-defined theme variables:
```python
from TkThemes import Dracula, Nord, MonokaiPro

print(Nord["accent_color"])  # #88c0d0

```

## Contributing

Feel free to submit new themes, improvements, or bug fixes via pull requests!
Make sure to follow the same structure as existing themes.
lammps-ast 0.1.7
A LAMMPS script parser and sanitizer using Lark
Requires Python: >=3.10

# LAMMPS-AST

**LAMMPS-AST** is a toolset for parsing, analyzing, and processing LAMMPS scripts using an Abstract Syntax Tree (AST) representation. It utilizes [Lark](https://github.com/lark-parser/lark) for parsing LAMMPS input files and provides scripts that leverage Large Language Models (LLMs) for interpreting, modifying, and generating LAMMPS scripts.

## Features

- **LAMMPS AST Parsing**: Uses Lark to generate ASTs from LAMMPS scripts.
- **Example Scripts**: Includes sample usage for different LLM models and prompts.

## Installation

### Clone the Repository

```bash
conda create -n Last_env python=3.11

pip install lammps_ast
conda install graphviz
prelude-parser 0.15.0
Parses XML files exported from Prelude EDC into formats usable by Python.
Requires Python: >=3.10
Keywords: parser, prelude-edc, xml, pandas, polars

# Prelude Parser

[![Tests Status](https://github.com/pbs-data-solutions/prelude-parser/actions/workflows/testing.yml/badge.svg?branch=main&event=push)](https://github.com/pbs-data-solutions/prelude-parser/actions?query=workflow%3ATesting+branch%3Amain+event%3Apush)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/pbs-data-solutions/prelude-parser/main.svg)](https://results.pre-commit.ci/latest/github/pbs-data-solutions/prelude-parser/main)
[![Coverage](https://codecov.io/github/pbs-data-solutions/prelude-parser/coverage.svg?branch=main)](https://codecov.io/gh/pbs-data-solutions/prelude-parser)
[![PyPI version](https://badge.fury.io/py/prelude-parser.svg)](https://badge.fury.io/py/prelude-parser)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/prelude-parser?color=5cc141)](https://github.com/pbs-data-solutions/prelude-parser)

Parses XML files exported from [Prelude EDC](https://preludeedc.com/) into formats Python can use.

## Installation

```sh
pip install prelude-parser
```

Optionally the `pandas` extra can be installed to parse to a Pandas `DataFrame`

```sh
pip install prelude-parser[pandas]
```

Optionally the `polars` extra can be installed to parse to a Polars `DataFrame`

```sh
pip install prelude-parser[polars]
```

All extras can be install with

```sh
pip install prelude-parser[all]
```

## Usage

Parse a Prelude flat XML file to a Python dictionary.

```py
from prelude_parser import parse_to_dict
data = parse_to_dict("physical_examination.xml")
```

Parse a Prelude flat XML file into a list of Python class. The name of the class is taken from the
form name node in the XML file converted to pascal case. For example a <physical_examination> node
will result in a PhysicalExamination class being created.

```py
from prelude_parser import parse_to_classes
data = parse_to_classes("physical_examination.xml")
```

Parse a Prelude flat XML file into a Pandas DataFrame. This works for Prelude flat XML files that
were exported with the "write tables to seperate files" option. In order to use this option
`prelude-parser` either needs to be installed with the `pandas` extra or the `all` extras.

```py
from prelude_parser.pandas import to_dataframe
df = to_dataframe("physical_examination.xml")
```

Parse a Prelude flat XML file into a Polars DataFrame. This works for Prelude flat XML files that
were exported with the "write tables to seperate files" option. In order to use this option
`prelude-parser` either needs to be installed with the `polars` extra or the `all` extras.

```py
from prelude_parser.polars import to_dataframe
df = to_dataframe("physical_examination.xml")
```

## Contributing

Contributions to this project are welcome. If you are interesting in contributing please see our [contributing guide](CONTRIBUTING.md)
dummio 1.10.2
Easiest-possible IO for basic file types.
Requires Python: >=3.10

# dummio

IO for dummies! A unified `save`/`load` interface using the most common and recommendable default options for IO between various object types and file types. For example, instead of
```
with open(file_path, 'r', encoding='utf-8') as file:
    data = json.load(file)
```
you can simply
```
data = dummio.json.load(file_path)
```

Users may pass additional keyword arguments to the underlying IO methods.

## Direct IO vs cloud paths

Most dummio IO calls "just work" against cloud paths like `s3://bucket/key`, `gs://bucket/key`, or `az://container/key`. For example, `dummio.json.load("s3://bucket/key")` will read a json file from an S3 bucket. Notes:
- Shout-out: [universal-pathlib](https://github.com/fsspec/universal_pathlib) powers much of the cloud-iteroperability on our backend.
- Warning: Although we manually run `demo/cloud.py` to ensure basic functionality, current CI unit testing does not cover cloud interactions.

## Standardized IO interface

In some coding applications it is desirable to pass an IO module as an argument to a function. Here it is convenient to pass a dummio submodule, since all dummio submodules have the same `save` and `load` interface, having equivalent signatures (except for differences hidden in `**kwargs`).

## Supported object and file types

So far we support:
- text, pickle, and dill
- simple dictionaries:
    - json
    - orjson
    - yaml
- pandas dataframes:
    - csv
    - feather
    - parquet
- numpy arrays (thin wrapper on numpy.save/load)
- onnx.ModelProto instances
- pydantic models (relying on the built-in json serialization methods)
- mashumaro models inheriting the json or yaml serialization mixins

Filepaths passed to `save` and `load` methods can be of type `str`, `pathlib.Path`, or `universal_pathlib.UPath`.

## Dependencies

[universal-pathlib](https://github.com/fsspec/universal_pathlib) is our only required dependency.

For other dependencies, such as pandas, calling `from dummio.pandas import df_parquet` will raise a helpful message to install pandas if you have not already done so.

## Examples

Basic IO methods can be accessed directly as `dummio.text`, `dummio.json`, etc:.
```
import dummio

text = "hello world"
data = {"key": text}
path = "io_example_file"

# Text
dummio.text.save(text, path=path)
assert text == dummio.text.load(path)

# YAML
dummio.yaml.save(data)
assert data == dummio.yaml.load(path)
```

See `demo/cloud.py` for more many other examples.

## Installation

We're [on pypi](https://pypi.org/project/dummio/), so `pip install dummio`.

If working directly on this repo, consider using the [simplest-possible virtual environment](https://gist.github.com/zkurtz/4c61572b03e667a7596a607706463543).