semvec/pypi-packages huggingface dataset:
gtoolkit_bridge 0.3.4
Glamorous Toolkit Python Bridge
Requires Python: <4,>=3.8

# gtoolkit_bridge
# Glamorous Toolkit Python Bridge

This library creates a server in Python that is accessible by Glamorous Toolkit and used to execute Python code
and get results back as proxies or values.

## Quick start

```
import gtoolkit_bridge as pb
args = pb.bridge_args()
pb.setup_bridge(args)
pb.run_bridge()
```

## All in one

```
import gtoolkit_bridge as pb
pb.run_bridge_default()
```

## Command line

```
$ python3 -m gtoolkit_bridge --port 9099 --pharo 0 --method msgpack --log
```

## Output

With logging enabled (log=True).

```
PythonBridge starting
{'port': 9099, 'pharo': 0, 'method': 'msgpack', 'log': True}
HANDLER (MsgPackSocketPlatform): loop func
PythonBridge ready
PYTHON: Start consuming commands
HANDLER (MsgPackSocketPlatform): prim handle message
HANDLER (MsgPackSocketPlatform): loop func
HANDLER (MsgPackSocketPlatform): prim handle message
DESERIALISE (bridge): pb2r9p5j02wd68kmzj1jw6zshk2
HANDLER (MsgPackSocketPlatform): loop func
PYTHON: Executing command pb2r9p5j02wd68kmzj1jw6zshk2
PYTHON: bindings: {'pharoCommandId': 'pb2r9p5j02wd68kmzj1jw6zshk2'}
PYTHON: notify(None,'pb2r9p5j02wd68kmzj1jw6zshk2')

PYTHON: Notify pb2r9p5j02wd68kmzj1jw6zshk2
PYTHON: Finished command execution
HANDLER (MsgPackSocketPlatform): prim handle message
DESERIALISE (bridge): pb2r9p5iy4fmkbyermp1kgy58qf
HANDLER (MsgPackSocketPlatform): loop func
PYTHON: Executing command pb2r9p5iy4fmkbyermp1kgy58qf
PYTHON: bindings: {'pharoCommandId': 'pb2r9p5iy4fmkbyermp1kgy58qf'}
PYTHON: import random
notify(list(map(lambda x: random.randrange(99)+1, range(1,10))), 'pb2r9p5iy4fmkbyermp1kgy58qf')
PYTHON: Notify pb2r9p5iy4fmkbyermp1kgy58qf
PYTHON: Finished command execution
```

# Info

feenk team (gt@feenk.com)

- https://gtoolkit.com
- https://feenk.com
- https://github.com/feenkcom/PythonBridge
viscaSmith 0.4.7
Una libreria per controllare telecamere VISCA tramite UDP
Requires Python: >=3.6


A powerful yet easy-to-use VISCA over IP utility designed to simplify communication with cameras supporting the VISCA protocol. This library provides everything you need to send commands, manage responses, and handle retries over UDP, ensuring reliable camera control.

Key Features:

    Complete VISCA Support: Supports commands like Set, Inquiry, and more.
    Retry Mechanism: Ensures reliable message delivery over UDP with retransmissions.
    Extensible: Built with Python, easily adaptable for advanced use cases.
    Quick Start: Check example01 for a step-by-step implementation.

Whether you're controlling a physical camera or an emulated one, this utility makes the process seamless. Perfect for developers working on broadcasting, streaming, or robotics projects.
hubspot-webhook-model 0.2.0
HubSpot webhook message model
Requires Python: >=3.9

# HubSpot web hooks message models

Models for messages that HubSpot sends as web hook calls to your integrations, written as [msgspec](https://jcristharif.com/msgspec/) models.

# Why

It's easier to operate on objects with parsed properties, and the models themselves are missing from [HubSpot client](https://pypi.org/project/hubspot-api-client/).

# How

Incoming messages are lists of events, each of which is either related to a single object or an association between objects.

Message can be parsed with the following code:

```python
import msgspec.json
from hubspot_webhook_model import Message

data: bytes # request body
model = msgspec.json.decode(data, type=Message)
```

Request signatures should be verified using the raw body

# Caveats

1. Messages contain events related to a single event, which can be user action or one of the internal HubSpot mechanisms. They are typically related to a single object, but not always.
2. Events in a message are not ordered. propertyChange enevts may appear before creation events.
3. The model only works with the new style [generic webhook subscriptions](https://developers.hubspot.com/docs/guides/apps/public-apps/create-generic-webhook-subscriptions).
4. Only a subset of objects is included. Feel free to create Pull Requests with additional models as needed.
valtra 0.1.0
A dynamic dataset loader for PyTorch.
Requires Python: >=3.7

# Valtra (Validation - Train)

Valtra is a mini-project I created because I got sick of keeping track of train and validation/test datasets separately.  
It dynamically switches between training and validation/test data using a simple wrapper around PyTorch’s `DataLoader`.

## 📌 Why "Valtra"?
The name comes from **Val**idation and **Tra**in. Plus, it has a nice ring to it.

## 🚀 Installation
```bash
pip install valtra
```

## 🔧 Usage
```python
import torch
from valtra.dataloader import DynamicDataLoader
from valtra.utils import split_dataset

# Create dummy data
data = torch.randn(1000, 10)
labels = torch.randint(0, 5, (1000,))
dataset = torch.utils.Dataset(data, labels)

# Create DynamicDataLoader
dataloader = split_dataset(dataset, split_ratio=0.8, batch_size=32)

# Training loop (default behavior is training mode)
for batch in dataloader(train=True):
    print("Train batch:", batch[0].shape)

# Evaluation loop
for batch in dataloader(test=True):
    print("Test batch:", batch[0].shape)

# Evaluation loop
for batch in dataloader(other=True):
    print("Other batch:", batch[0].shape)
```

## Contribution
There are many ways to make code more readable in `torch`. If you have another idea feel free to contact me or create an issue!

## 📜 License
MIT License