semvec/pypi-packages huggingface dataset:
uphy-upgen 1.0.3
U-Phy Generator
Requires Python: >=3.8

# U-Phy Device Generator

The U-Phy Device Generator is a tool designed to create fieldbus
device description files and U-Phy source code directly from device
models generated by the U-Phy Device Builder.

Maintaining synchronization between source code and description files
can be challenging. Therefore, having a tool that generates both from
a single source of truth is extremely beneficial.

## Developing

Python Poetry is recommended. See
https://python-poetry.org/docs/#installation for installation
instructions.

    $ cd /path/to/upgen
    $ poetry install
    $ poetry shell
uphy-controller 1.0.3
U-Phy Controller
Requires Python: >=3.10

# U-Phy Controller

This contain a python based U-Phy device controller that act as a fieldbus master/controller.

Supported fieldbuses:

- modbus

## Install

```sh
uv pip install uphy-controller
```

## Workflow

```sh
# Start up controller against an existing device
uphy-controller modbus --model [PATH_TO_MODEL] --target [UUID]:[HOST]:[PORT]
```


```sh
# Start up controller against discovered devices that expose their model via
# mdns.
uphy-controller mdns
```
KerasCrafterGPT 0.0.2
Toolkit for building Keras models with the help of GPT
Keywords: keras, gpt, tensorflow, openai, machine, learning, chatgpt, ai

# KerasCrafterGPT

KerasCrafterGPT aims to solve the struggles of guessing model configurations and hyperparameters when developing machine learning projects. Instead of manually tuning architectures and settings, you simply provide your training data (`x` and `y`), and this library leverages large language models (LLMs) specifically ChatGPT to automatically generate suitable layers and hyperparameters for your dataset.

The workflow is as follows:

- You provide your training data.
- The library uses ChatGPT to draft a Keras model architecture and select hyperparameters tailored to your data.
- It runs a training session and evaluates the results.
- The training results are then passed back to the LLM, which attempts to further improve the model configuration in subsequent iterations.

Another major challenge in machine learning experimentation is tracking changes to models and understanding how those changes affect results. KerasCrafterGPT addresses this by using the LLM to log and manage conversational context, so you can see how each modification impacts performance over time.

This toolkit wraps the OpenAI Chat Completions API and provides helpers for drafting model architectures, managing experiments, and logging conversational context when iterating on training runs.

## Installation

```bash
pip install KerasCrafterGPT
```

## Quick start

```python
from KerasCrafterGPT import KerasCrafterGPT

model = KerasCrafterGPT(train_x, train_y, api_key="sk-...")
model.fit(max_iterations=3)
```

## Documentation

### KerasCrafterGPT Arguments

```python
KerasCrafterGPT(
	train_x,                      # Required. Training input data (numpy array or tensor)
	train_y,                      # Required. Training target data (numpy array or tensor)
	api_key=None,                 # Required. OpenAI API key for LLM access
	model="gpt-4o-mini",         # Optional. OpenAI model name (default: "gpt-4o-mini")
	history_path="chat_history.jsonl", # Optional. Path to log conversational/model history (default: "chat_history.jsonl")
	continue_from_history=False,  # Optional. If True, resumes conversation/model history from previous runs (default: False)
)
```

## Contributing

Contributions, suggestions, and bug reports are welcome! Feel free to open issues or submit pull requests to help improve KerasCrafterGPT.
pymdrive 1.0.0
Add your description here
Requires Python: >=3.9

# PyMDrive

PyMDrive is a python driver for MDrive motors. It has wrappers for standard commands (home, move absolute, move relative, etc.) and any other MCode command can be added easily.

## Installation

It is easiest to install with pip: `pip install pymdrive`

You can also install with [uv](https://docs.astral.sh/uv/) by cloning the repo and using `uv sync`.

## Usage

To get started do

```python
from pymdrive import MdriveAxis, MdriveComm

COMM = MdriveComm("COM3")
await COMM.start()

X = MdriveAxis(comm=COMM, name="X")
Y = MdriveAxis(comm=COMM, name="Y")
```

This initializes the COMM and starts its asynchronous queue for processing commands without race conditions. The COMM is then passed to Axis instances where the names are those specified on the motors memory.

From there, you can send standard commands

```python
await X.move_relative(1000)

await Y.move_absolute(0)
```

There's a `testing.ipynb` notebook in the `tests` directory with these and other commands.