semvec/pypi-packages huggingface dataset:
flet-color-picker 0.1.0
Pick colors in Flet apps.
Requires Python: >=3.10

# flet-color-picker

[![pypi](https://img.shields.io/pypi/v/flet-color-picker.svg)](https://pypi.python.org/pypi/flet-color-picker)
[![downloads](https://static.pepy.tech/badge/flet-color-picker/month)](https://pepy.tech/project/flet-color-picker)
[![license](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://github.com/flet-dev/flet/blob/main/sdk/python/packages/flet-color-picker/LICENSE)

A [Flet](https://flet.dev) extension package for picking colors.

It is based on the [flutter_colorpicker](https://pub.dev/packages/flutter_colorpicker) Flutter package.

## Documentation

Detailed documentation to this package can be found [here](https://docs.flet.dev/color_picker/).

## Platform Support

| Platform | Windows | macOS | Linux | iOS | Android | Web |
|----------|---------|-------|-------|-----|---------|-----|
| Supported|    ✅    |   ✅   |   ✅   |  ✅  |    ✅    |  ✅  |

## Usage

### Installation

To install the `flet-color-picker` package and add it to your project dependencies:

- Using `uv`:
    ```bash
    uv add flet-color-picker
    ```

- Using `pip`:
    ```bash
    pip install flet-color-picker
    ```
    After this, you will have to manually add this package to your `requirements.txt` or `pyproject.toml`.

### Examples

For examples, see [these](https://github.com/flet-dev/flet/tree/main/sdk/python/examples/controls/color_picker).
envaudit 0.1.0
Environment variable validator and manager
Requires Python: >=3.9
Keywords: cli, devops, dotenv, env, environment, validation

# envaudit

Environment variable validator and manager.

Check, validate, compare, and manage environment variables from the command line.

## Installation

```bash
pip install envaudit-cli
```

## Usage

### Validate a .env file

```bash
envaudit validate .env
# ✓ .env is valid (12 variables)

envaudit validate broken.env
# ✗ broken.env has 2 error(s):
#   • Line 3: Missing '=' in 'INVALID_LINE'
#   • Line 7: Invalid variable name '123BAD'
```

### Check required variables

```bash
envaudit required DATABASE_URL API_KEY SECRET
# ✗ Missing 1 required variable(s):
#   • SECRET

# Also check a .env file
envaudit required DATABASE_URL -f .env
```

### Compare .env against template

```bash
envaudit compare .env.example .env
# ✗ Missing 2 variable(s) from .env.example:
#   • NEW_FEATURE_FLAG
#   • ANALYTICS_KEY
# ⚠ 1 extra variable(s) not in .env.example:
#   • DEBUG
```

### Generate export statements

```bash
# For bash/zsh
eval "$(envaudit export .env)"

# For fish
envaudit export .env --shell fish | source

# For PowerShell
envaudit export .env --shell powershell | Invoke-Expression
```

### Show .env contents (with masking)

```bash
envaudit show .env
# DATABASE_URL=postgres://localhost:5432/myapp
# API_KEY=sk-1***************
# DEBUG=true
```

### Get a single variable

```bash
envaudit get DATABASE_URL
# postgres://localhost:5432/myapp

envaudit get MISSING --default "fallback"
# fallback
```

### Dump environment variables

```bash
# All variables
envaudit dump

# Filter by pattern
envaudit dump -f "AWS|AZURE"

# Show without masking
envaudit dump --no-mask
```

### Security audit

```bash
envaudit audit .env
# Auditing: .env (12 variables)
#
# [WARNING] 2 variable(s) have empty values
#   • OPTIONAL_KEY
#   • LEGACY_TOKEN
#
# [WARNING] 1 variable(s) appear to have placeholder values
#   • API_URL
#
# [INFO] 4 variable(s) appear to contain sensitive data
#   • API_KEY
#   • SECRET_TOKEN
#   • DATABASE_PASSWORD
#   • JWT_SECRET
```

## JSON Output

All commands support `--json` for machine-readable output:

```bash
envaudit validate .env --json
```

```json
{
  "file": ".env",
  "valid": true,
  "variables": 12,
  "errors": []
}
```

## For AI Agents

See [SKILL.md](SKILL.md) for agent-optimized documentation.

## License

MIT
ironcore-alloy 0.14.0
Python bindings to the IronCore Labs Alloy SDK. Used for encrypting and decrypting embeddings and their related metadata.
Requires Python: >=3.9

# ironcore_alloy

[![PyPI - Version](https://img.shields.io/pypi/v/ironcore-alloy.svg)](https://pypi.org/project/ironcore-alloy)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ironcore-alloy.svg)](https://pypi.org/project/ironcore-alloy)

Python bindings to the IronCore Labs Alloy SDK. Python API documentation is available [here](https://ironcore-alloy.readthedocs.io). General documentation of the various products and broader information about how to use the SDK is available [here](https://docs.ironcorelabs.com).

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install ironcore-alloy
```

## Local

If you're trying to run local tests and `pytest` is looking for a missing `python`, try `hatch env remove test && hatch env create test` to get hatch to rebuild its test environment.

## License

`ironcore-alloy` is licensed under the [GNU Affero General Public License](https://github.com/IronCoreLabs/ironoxide/blob/main/LICENSE). We also offer commercial licenses - [email](mailto:info@ironcorelabs.com) for more information or check pricing on our [website](https://ironcorelabs.com/).
MC-SPS 0.1.1
A Python tool for performing site permutation search on fixed atomic geometries.
Requires Python: >=3.7


# Monte Carlo Site Permutation Search

Toolkit for optimizing atomic decoration on fixed geometries. MEGNet and M3GNet provide rapid estimation of total energy, which can accurately rank the energetic favorability of various configurations. Simulated annealing and basin hopping techniques assist in discovery of locally and globally minimal configurations.  
Functionality and results of this software is documented at https://doi.org/10.21203/rs.3.rs-3740642/v1. 
  
## Requirements:
  * ASE
  * Numpy
  * MEGNet or M3GNet  


## Installation:
  From inside of the MC-SPS root directory, run the following python command.
  * python setup.py install


## Usage:
  * The following SPS routines can be imported from the mcsps module, sps\_fixed, sps\_vacancy, sps\_cluster. Structure lattice, atomic basis, and temperature trajectory are supplied directly to the SPS routines.
  * Examples documenting the package usage are located in the examples directory.
  * The main\*.py scripts can be run in the background, and output can be monitored through the trajectory output file and structure output file. Trajectory output is updated upon each accepted configuration. Structure output is updated each time a structure is identified with a lower total energy.
  * Visualization tools and advanced features for documenting site occupation factors and saving multiple favorable structures are coming soon. 


## Examples:
  * GaAs - 3x3x3 Gallium Arsenide supercell. Demonstrates the most basic features of the SPS package.
  * Rb8Ga27Sb19 - Superstructural ordering predicted on the elemental type-I clathrate geometry
  * Vacancy ordered SPS optimization examples coming soon.