semvec/pypi-packages huggingface dataset:
retricon 1.3.3
A library to create icons similar to Github avatars
Requires Python: >=3
Keywords: image, avatar, icon, identicon

py-retricon
===========

Library to create identicons similar to Github avatars.

Port of node-retricon https://github.com/sehrgut/node-retricon to python.

Usage
=====

    from retricon import retricon
    
    img = retricon("Hello World!")
    img.save('hello.png', 'PNG')


Parameters
==========

    style           Predefined style (Default: None)
    tile_size       Size of a tile (Default: 1)
    tile_color      Tiles color (Default: 0)
    tile_padding    Padding of each tiles (Default: 0)
    bg_color        Background color (Default: None)
    image_padding   Padding of the icon (Default: 0)
    tiles           Number of tiles that make the width of the icon (Default: 5)
    min_fill        Minimum ratio of tile in the image (Default: 0.3)
    max_fill        Maximum ratio of tile in the image (Default: 0.9)
    vertical_sym    Use vertical symmetry (Default: True)
    horizontal_sym  Use horizontal symmetry (Default: False)
    width           Width of the output image in pixels (Default: 500)


Predefined styles
=================

    'github'
    'gravatar'
    'mono'
    'mosaic'
    'mini'
    'window'


Valid color value
=================

    None -> transparent
    0 -> the lighter color chosen for you from the hash
    1 -> the darker color chosen for you from the hash
    An array describing the RGB(A) color (e.g. [125, 75, 250])
    An hexadecimal representation of the color (e.g. "0fd5e4")
midas-palaestrai 3.5.8
This package contains everything needed to use midas with palaestrAI.
Requires Python: >=3.10

# MIDAS Palaestrai Adapter

## Description
This package contains everything needed to use midas with palaestrAI.

## License
This software is released under the GNU Lesser General Public License (LGPL). See the license file for more information about the details.
gymnasium-planar-robotics 1.1.0
A library for reinforcement learning environments in the field of planar robotics
Requires Python: >=3.11
Keywords: Reinforcement Learning, Gymnasium, RL, AI, Robotics, Planar Robotics, Magnetic Levitation

# Gymnasium-Planar-Robotics (GymPR)
This library contains reinforcement learning environments for motion planning and object manipulation in the field of planar robotics. The environments follow either the [Gymnasium](https://gymnasium.farama.org/) API for single-agent RL or the [PettingZoo parallel API](https://pettingzoo.farama.org/api/parallel/) for multi-agent RL. All environments are based on the [MuJoCo](https://mujoco.org/) physics engine. Note that this library depends on the latest MuJoCo Python bindings. 
[mujoco-py](https://github.com/openai/mujoco-py) is not supported.

<img src="https://github.com/ubi-coro/gymnasium-planar-robotics/raw/main/docs/images/visual_abstract.png" />

## Installation
The Gymnasium-Planar-Robotics package can be installed via PIP:
```
pip install gymnasium-planar-robotics
```
To install optional dependencies, to build the documentation, or to run the tests, use:
```
pip install gymnasium-planar-robotics[docs, tests]
```
**Note:** Depending on your shell (e.g. when using Zsh), you may need to use additional quotation marks: 
```
pip install "gymnasium-planar-robotics[docs, tests]"
```

## Documentation
The documentation is available at: [https://ubi-coro.github.io/gymnasium-planar-robotics/](https://ubi-coro.github.io/gymnasium-planar-robotics/)

## License
GymPR is published under the GNU General Public License v3.0.

## Example
The following example shows how to use a trained policy with an example environment that follows the Gymnasium API:

```python
import gymnasium as gym

env = gym.make("BenchmarkPushingEnv-v0", render_mode="human")
observation, info = env.reset(seed=42)

for _ in range(0,100):
    while not terminated and not truncated:
        action = policy(observation)  # custom policy
        observation, reward, terminated, truncated, info = env.step(action)

    observation, info = env.reset()
env.close()
```

## Maintainer
Gymnasium-Planar-Robotics is currently maintained by Lara Bergmann (@lbergmann1).
vscode-workspace-config-generator 1.1.2
Generate VS Code workspace files for multi-package projects
Requires Python: >=3.6

# Workspace Generator

Generate VS Code workspace files for multi-package projects.

## Installation

```bash
pip install vscode_workspace_config_generator
```

## Usage

```bash
# Generate VS Code workspace config for current directory (auto-detect workspace root)
generate-vscode-workspace-config

# Generate VS Code workspace config for specific directory
generate-vscode-workspace-config /path/to/workspace
```

## What it does

- Scans the `src/` directory for packages
- Creates a `.code-workspace` file with folder configurations
- Includes debug configuration for Python remote debugging

## Remote Debugging Setup

To use the remote debugger configuration, add this code to your Python script:

```python
import debugpy; debugpy.listen(5678);print("🛑 Waiting for debugger to attach on port 5678...");debugpy.wait_for_client()
```
workspace-generator 1.0.1
Generate VS Code workspace files for multi-package projects
Requires Python: >=3.6

# Workspace Generator

Generate VS Code workspace files for multi-package projects.

## Installation

```bash
pip install workspace-generator
```

## Usage

```bash
# Generate workspace file for current directory (auto-detect workspace root)
generate-workspace

# Generate workspace file for specific directory
generate-workspace /path/to/workspace
```

## What it does

- Scans the `src/` directory for packages
- Creates a `.code-workspace` file with folder configurations
- Includes debug configuration for Python remote debugging

## Remote Debugging Setup

To use the remote debugger configuration, add this code to your Python script:

```python
import debugpy; debugpy.listen(5678);print("🛑 Waiting for debugger to attach on port 5678...");debugpy.wait_for_client()
```
tox-extra 2.2.0
Performs extra checks before or after running
Requires Python: >=3.9
Keywords: git, tox, tox-plugin

# tox-extra

This [tox plugin](https://github.com/topics/tox-plugin) adds a few extra checks
like:

- [tox-extra](#tox-extra)
  - [Checks Git Dirty Status](#checks-git-dirty-status)
  - [Checks system dependencies using bindep](#checks-system-dependencies-using-bindep)

## Checks Git Dirty Status

It ensures exit code 1 if git reports dirty or untracked files _after_ the run.

Usage example:

```shell
$ tox -e py
...
ERROR: Git reported dirty status. Git should never report dirty status at the end of testing, regardless if status is passed, failed or aborted.
On branch devel
Your branch is up to date with 'origin/devel'.

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	some-untracked.txt

nothing added to commit but untracked files present (use "git add" to track)
__________________________________________ summary ___________________________________________
ERROR:   py: failed
```

The goal of this plugin is to help developers be aware of files modified by tests
or untracked files before they commit the code. This plugin also does not take into
consideration the global `.gitignore`, something that can make git miss reporting
some untracked files, the goal being to assure that when a new developer clones and
runs the tests they do not endup with an unexpected git status.

If you have any cases where you expect to have git report dirty, please
add `--allow-dirty` to the command call to disable this check.

## Checks system dependencies using bindep

If a `bindep.txt` config file is found, tox will run `bindep [profiles]` to
check if dependencies, including test ones, are present. There is no need to
install bindep your self.

This plugin will add the following list of bindep profiles:

- `test` is always added as tox itself is a test tool
- exact tox env name
- tox env name itself split by `-'
- `pythonX.Y` and `pyXY` based on which python current tox env will use

This should allow developers to modify their `bindep.txt` file to include
system dependencies specific to a single tox environment if they wish.

To disable bindep feature, you can define `TOX_EXTRA_BINDEP=0` in your
environment.

## Improves colored output on CI

Multiple tools, including tox and pre-commit fail to property use coloring
when run in a CI evironment like Github Actions. This plugin will inject
additional environment variables in order to ensure that the tool output
remains colored.

It should be noted that this happens only if these variables are not already
defined by the user. This feature should reduce the need of adding extra
environment variables to your pipelines.
actinia-stac-plugin 0.4.0
A (RESTFUL) Flask application which implements STAC collections into actinia
Requires Python: >=3.8
Keywords: processing, earth observation, cloud-based processing, rest api, gis, grass gis, osgeo, stac

<!-- markdownlint-configure-file
{
  "MD013": { "line_length": 150 },
  "MD034": false # no-bare-urls
}
-->

# actinia-stac-plugin

You can run actinia-stac-plugin as actinia-core plugin.

## Installation

For installation or DEV setup, see docker/README.md.

## DEV notes

### Build

__insprired by https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/__

to create a shippable wheel, run

```bash
pip3 install --upgrade pip pep517
python3 -m pep517.build .
```

#### Versioning

https://semver.org/ (MAJOR.MINOR.PATCH)

#### Logging

in any module, import `from actinia_stac_plugin.resources.logging import log` and call logger with `log.info("my info i want to log")`
actinia-satellite-plugin 0.3.0
An actinia-core plugin which adds efficient satellite data handling to actinia-core
Requires Python: >=3.8
Keywords: processing, earth observation, cloud-based processing, rest api, gis, grass gis, osgeo, satellite

# Actinia Satellite Plugin

This actinia plugin is designed for efficient satellite data handling, especially Landsat and Sentinel-2 scenes.
It implements endpoints for on-the-fly NDVI computation and time series import for Landsat and Sentinel-2
scenes in the actinia database.

Note:

    Actinia[1] is an open source REST API for scalable, distributed, high performance
    processing of geographical data that uses GRASS GIS for computational tasks.

    The Actinia service consists of the *Actinia Core* that provides the basic but sophisticated processing service
    and *Actinia plugins* that provide problem specific services like Sentinel-2 and Landsat NDVI computation,
    spatio-temporal statistical analysis and many more.

    [1] https://github.com/mundialis/actinia_core


## Installation

The actinia plugin must be installed in the same environment as actinia core.
Actinia core must be configured to load the installed plugin. When the plugin is
loaded and all plugin endpoints are available in actinia, the interface description
of actinia will be extended with the endpoints of the plugins.

    .. code-block:: bash

        git clone https://github.com/mundialis/actinia_satellite_plugin.git

        cd actinia_satellite_plugin
        pip3 install .

    ..

After installation set the plugin name in the actinia core configuration
and restart the actinia core server.


## Testing locally

```
docker build -f docker/actinia-plugin-tests/Dockerfile -t actinia-plugin-test .
docker run -v </path/to/actinia_satellite_plugin/tests>:/src/actinia_satellite_plugin/tests -it actinia-plugin-test:latest -i

# run tests with
make test
```
actinia-statistic-plugin 0.4.0
Actinia plugin for efficient statistic computing
Requires Python: >=3.8
Keywords: processing, earth observation, cloud-based processing, rest api, gis, grass gis, osgeo, stac

# Actinia Statistic Plugin


This actinia plugin is deigned for to compute raster map layer and raster-time-series statistics
for categorical and continuous data. It provides endpoints to sample raster
time series data.

> **_NOTE:_**
>
>    [Actinia](https://github.com/mundialis/actinia_core) is an open source REST API for scalable, distributed, high performance
    processing of geographical data that uses GRASS GIS for computational tasks.
>
>    The Actinia service consists of the *Actinia Core* that provides the basic but sophisticated processing service
    and *Actinia plugins* that provide problem specific services like Sentinel-2 and Landsat NDVI computation,
    spatio-temporal statistical analysis and many more.

## Installation

The actinia plugin must be installed in the same environment as actinia core.
Actinia core must be configured to load the installed plugin. When the plugin is
loaded and all plugin endpoints are available in actinia.
The interface description of actinia will be extended with the endpoints of the plugins.

```bash
git clone https://github.com/mundialis/actinia_statistic_plugin.git

cd actinia_statistic_plugin
pip3 install -r requirements.txt
python3 setup.py install
```

After installation set the plugin name in the actinia core configuration
and restart the actinia core server.

### Required GRASS GIS Addons

The statistic plugin needs the GRASS GIS addon [t.rast.sample](https://github.com/mundialis/t.rast.sample) to be installed in actinia.


## Testing locally

```
docker build -f docker/actinia-plugin-tests/Dockerfile -t actinia-plugin-test .
docker run -v </path/to/actinia_statistic_plugin/tests>:/src/actinia_statistic_plugin/tests -it actinia-plugin-test:latest -i

# run tests with
make test
```
gpay-sdk 1.0.1
GPay API Python Client Library
Requires Python: >=3.6

# gpay-api-client

A Python client library for the GPay API.

## Official GPay API Documentation

For full API details, see the [GPay API Documentation](https://gpay.ly/banking/doc/index.html).

## Installation

```bash
pip install gpay-sdk
```

## Usage

### Import and Create the Client

```python
from gpay.gpay_api_client import GPayApiClient, BaseUrl

# client: GPayApiClient
client = GPayApiClient(
    api_key='your_api_key',
    secret_key='your_secret_key',
    password='your_password',
    base_url=BaseUrl.STAGING  # or BaseUrl.PRODUCTION
)
```

### Get Wallet Balance

```python
# type: Balance
balance = client.get_balance()  
print(balance.balance, balance.response_time)
```

### Create a Payment Request

```python
# type: PaymentRequest
payment_request = client.create_payment_request(amount='100.00', reference_no='INV-123', description='Invoice Payment')  
print(payment_request.request_id, payment_request.amount)
```

### Check Payment Status

```python
# type: PaymentStatus
status = client.check_payment_status(request_id=payment_request.request_id)  
print(status.is_paid, status.transaction_id)
```

### Send Money

```python
# type: SendMoneyResult
send_result = client.send_money(amount='50.00', wallet_gateway_id='recipient_wallet_id', 
description='Gift', reference_no='GIFT-001')  
print(send_result.transaction_id, send_result.new_balance)
```

### Get Day Statement

```python
# type: Statement
statement = client.get_day_statement(date='2025-06-22')  
print(statement.day_balance)
for tx in statement.day_statement:
    # tx: StatementTransaction
    print(tx.transaction_id, tx.amount)
```

### Check Wallet

```python
 # type: WalletCheck
wallet_info = client.check_wallet(wallet_gateway_id='recipient_wallet_id') 
print(wallet_info.exists, wallet_info.wallet_name)
```

### Get Outstanding Transactions

```python
# type: OutstandingTransactions
outstanding = client.get_outstanding_transactions()  
print(outstanding.outstanding_credit, outstanding.outstanding_debit)
for tx in outstanding.outstanding_transactions:
    # tx: OutstandingTransaction
    print(tx.transaction_id, tx.amount)
```