semvec/pypi-packages huggingface dataset:
ros2-test-runner 0.0.2
A python package to conveniently collect statistics data in ros2
Keywords: python,ros2,humble,launch


# ROS2 Test Runner

This Python package provides a convenient way to run ROS2 programs multiple times and collect timing and other statistics. It can be used to analyze the performance and behavior of ROS2 systems.

## Features

- Run ROS2 files multiple times with customizable parameters.
- Collect timing statistics such as execution time, latency, and frequency.
- Easy integration with existing ROS2 workflows.


## Usage

1. Change the configuration file (`config.json`) specifying the ROS2 files you want to execute and their parameters. Here's an example:

```json
{
    "packages":[
        {
            "name" : "panda_moveit_config",
            "launch" : "moveit.launch.py",
            "leader" : false
        },
        {
            "name" : "paper_benchmarks",
            "launch" : "benchmark_asynchronous.launch.py",
            "leader" : true
        }
    ],
    "checkpoint" : "<place_your_checkpoint_log_string_here>",
    "terminate" : "<place_your_terminate_log_string_here>",
    "pre_commands" : [
        "cd /home/ros/ws_moveit ", 
        ". install/setup.sh "
    ]
}
```

You need to log checkpoint and terminate string from as RCL_INFO.

#### Checkpoint

When the "checkpoint" keyword is encountered in the stdout, the statistics collection process records a timestamp in the statistics collection file, signifying the occurrence of this event.

#### Terminate

The "terminate" keyword is utilized to signal the end of a current run and initiate a new run in the statistics collection process. 


2. Then run the runner.py

```sh
python3 runner.py
```


4. After execution, you can find the log lines of checkpoints in the `example.txt`. It will contain timing statistics.

## Contributing

Contributions are welcome! If you encounter any issues or have suggestions for improvement, please open an issue on the project's GitHub repository.

## Contact

For any inquiries or support, please contact the project maintainer:

- Tharaka Ratnayake
- Email: tharakasachintha27@gmail.com

We hope this package helps you in analyzing and optimizing your ROS2 systems. Happy coding!
print-torch 1.3
print pytorch tensors and numpy arrays concisely.
Requires Python: >=3.7

# print_torch

It is often overwhelming to `print(tensor)` which may flash your screen and you just don't get any information useful.

Instead, use `pt(tensor)` and it will give you the size, dtype, device, min, max, std, mean, whether has nan, etc. in one line.

```py
>>> import torch
>>> from print_torch import pt
>>> tensor = torch.randn((2,3,4,5))
>>> pt(tensor)
tensor(shape=(2, 3, 4, 5), dtype=float32, min=-2.803, max=2.49, std=1.019, mean=-0.06855, data=[-0.8246, -1.135, 0.1368, 1.56, ... ], device="cpu")                                  >>>
```

It also works for list of numbers and numpy tensors. 

If you have a nested dict/list of pytorch/numpy tensors, you can also `pt` it, and it will list each element which may be again a tensor in one line.

For example, you can just `pt(locals())` in your `forward()` function in the breakpoint to know all the stats of your local variables.

Don't hesitate to add this tool to your arsenal!

```sh
pip install print_torch
```
census-colendi 0.0.2
Colendi data team package
Keywords: python

<h1 align="center">Welcome to Colendi Data Team Package 👋</h1>
<p>
  <img alt="Version" src="https://img.shields.io/badge/version-1.0-blue.svg?cacheSeconds=2592000" />
  <a href="empty" target="_blank">
    <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" />
  </a>
  <a href="#" target="_blank">
    <img alt="License: Colendi Token is provided under Apache License version 2.0." src="https://img.shields.io/badge/License-Colendi Token is provided under Apache License version 2.0.-yellow.svg" />
  </a>
</p>

> The project is used by Colendi Data Team daily code cycle.

### 🏠 [Homepage of Company](https://www.colendi.com/)

### 🏠 [Documentation Page]()


## Architecture

## Install

```sh
pip install census-colendi 
or 
pip3 install census-colendi
```

These packages need to be downloaded for the project to run.

```sh
pandas==1.5.1
setuptools==65.6.3
Werkzeug==2.2.2
```

## 🤝 Contributing

Contributions, issues and feature requests are welcome!
1. Fork this repository
2. Clone to the local machine
3. Create a branch and make changes
4. Commit changes with clear explanation
5. Push changes to Github
6. Open a Pull Request

Don't Forget these by contributing:

1. master branch is the production branch.

2. beta branch is used for the stage environment.

3. alpha branch is used for project development.

## 🔧 Issues & Feedback
Feel free to submit issues and enhancement requests
* email: datateam@colendi.com.
neuronlinear 0.0.1
A small Perceptron package
Requires Python: >=3.6

# Perceptron Package - neuronlinear



![Perceptron](https://images.deepai.org/glossary-terms/perceptron-6168423.jpg)

Welcome to the neuronlinear ! This package is designed to provide a simple and efficient implementation of the Perceptron algorithm for binary classification tasks. Whether you are new to machine learning or an experienced practitioner, the Perceptron Package offers a versatile and easy-to-use tool for your classification needs.

## Features

* Lightweight and efficient implementation of the Perceptron algorithm
* Supports both online and batch learning modes
* Flexible customization options for data preprocessing and feature engineering
* Simple and intuitive API for training and inference
Compatible with Python 3.x

## Installation

You can easily install the Perceptron Package using pip:

```pip install neuronlinear```

## Quick Start
Using the neuronlinear Package is straightforward. Here's a simple example to get you started:

```
from neuronlinear.model import Perceptron, prepare_data


ETA = 0.1 #leaning rate
EPOCHS = 10 #epochs count

# Create a Perceptron object
perceptron = Perceptron(eta=ETA, epochs=EPOCHS)

data = {"x1":[0, 0, 1, 1], "x2" : [0, 1, 0, 1], "y" : [0, 1, 1, 1]}

X, y = prepare_data(data)

perceptron.fit(X, y)
print(f"Prediction for > 0, 1 is {perceptron.predict([0, 1])}")

print(f"Total loss is {model.total_loss}")

```

## Contributing
We welcome contributions from the community! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request on our GitHub repository.

## License

The neuronlinear Package is licensed under the MIT License.

## Acknowledgments
We would like to express our gratitude to the contributors and supporters who have helped make this package possible.

If you find the Perceptron Package useful, please consider giving it a star on GitHub. Your support is greatly appreciated!

> Note: The neuronlinear Package is provided as-is without any warranty. Use at your own risk.
teghoshal 0.0.1
Basic Hello package
Keywords: python,video,stream,video stream,camera stream,sockets


<h1 align="center" color="green">
aws3 Bucket Finder
<p align="center" >
<img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/techghoshal?style=social">
<img alt="Thank" src="https://img.shields.io/badge/Thanks-Nuclei-bl">
</p>
</h1>

## Ues Easy

```bash
nuclei -l target-wordlist.txt -t aws3.yaml 
```

## Screenshot

<h1 align="left">
  <img src="https://github-production-user-asset-6210df.s3.amazonaws.com/85815644/238631984-0697e06d-2cee-4b7f-ab62-14aa41136374.png" alt="s3-bucket-finder" width="700px"></a>
  <br>
</h1>
flask-model-validation 0.1.0
Extension for Flask-SQLALchemy that include validation for models, a easier way to query and more new features.
Requires Python: >=3.11,<4.0

# Flask-model-validation

Flask-model-validation is an extension for [Flask-SQLAlchemy](https://github.com/pallets-eco/flask-sqlalchemy) that adds support for validating the model before commit it to the database. It adds new functions to query and save the model easily.

## Installation

Install using [pip](https://pip.pypa.io)

```bash
pip install Flask-model-validation
```

## Setup the extension
It should be initialized like `Flask-SQLAlchemy` extension.

For more information see the [Flask-SQLAlchemy documentation](https://flask-sqlalchemy.palletsprojects.com/en/3.0.x/)

```python
from flask import Flask
from flask_model_validation import SQLAlchemyModelValidation

app = Flask(__name__)
app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite://"
db = SQLAlchemyModelValidation()
db.init_app(app)
```

## Features
Flask-model-validation includes several new features.

- [Validation](docs/validation.md)
- [Mixins](docs/mixins.md)
- [Saving the model](docs/save.md)
- [Deleting the model](docs/delete.md)
- [Extra model features](docs/model_features.md)
- [query to the database](docs/query.md)


## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)