semvec/pypi-packages huggingface dataset:
pashehnet 0.1.1
Sensor network simulator publishing to MQTT broker
Requires Python: >=3.8

# PashehNet
Provides a simulated sensor network (SSN) framework for publishing to a target 
system.  The package implements both an API and command line interface to provide 
this service.

This README is intentionally short for tl;dr purposes.  Full documentation can be found [here](https://pashehnet.readthedocs.io).

## Installation

If available from a package repository:
```bash
pip install pashehnet
```

Otherwise you can install directly from the GitHub repo:
```bash
pip install git+https://github.com/zaggyai/pashehnet
```

## Contributing

First, fork the repo if not a current maintainer, or clone if you are!  :)

Make sure your virtual env is up-to-date:
```bash
pip install --upgrade pip wheel setuptools
```

Install package in dev/editable mode:
```bash
pip install -e .
```

Install dev environment dependencies such as `pytest`, `flake8` and supporting deps for tests:
```bash
pip install -r dev-requirements.txt
```

Keep in mind that even if you already have a virtualenv configured from a previous 
fork/clone, its dependencies could be out of date so re-running the installs should 
bring your virtualenv current.

### Environment

You can leverage environment variables within code or CLI config files for sensitive 
data you don't want to check into a repo.  This is always a going concern for any code
that requires client/server configuration.  **Please be careful to never add security 
credentials to a commit** as they're nearly impossible to permanently remove from a repo after 
the fact.

For MQTT broker config, we typically define the following env vars and use them in our code/configs:

- `MQTT_HOST`
- `MQTT_PORT`
- `MQTT_USER`
- `MQTT_PWD`

We like the cross-platform [direnv](https://direnv.net/) tool and have a gitignore exclusion for 
its files, but you do you!  :upside_down_face:

### Opening a Pull Request (PR)

Pull requests are a git workflow for managing branch merges with reviews and 
tests.  You can read more about them in the [GitHub docs](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).  Before opening a PR 
please make sure the following commands complete locally without errors in your 
local dev environment as they will block a PR if not:

```bash
# Run linter
flake8 src/ tests/

# Run all unit tests
# Note we disable deprecation warnings for the time being;
# there's a method to the madness
pytest -W ignore::DeprecationWarning
```
novaml 1.0.0
Build machine learning applications with NovaML; The NEW machine learning library!
Requires Python: >=3.11

# NovaML
Build machine learning applications with NovaML; The NEW machine learning library!

## Note
This library is for learning purposes and not ready for production use!

## Install
```shell
pip install novaml
```

## Usage
```python
import numpy as np
from novaml.models import LinearRegression

x = np.array([1.0, 2.0])
y = np.array([300.0, 500.0])
w_init = np.array([0.0])
b_init = np.array([0.0])
alpha = 1.0e-2
iterations = 10000
lambd = None

linear_regression = LinearRegression()

final_w, final_b, _, _ = linear_regression.train(
    x, y, w_init, b_init, alpha, iterations, lambd
)

yhat = linear_regression.predict(x, final_w, final_b)

print(f"ytrain: {y}")
print(f"yhat: {yhat}")
```

## Examples
Check the [README.md](examples%2FREADME.md) of examples folder.

## License
[MIT License](LICENSE)
streamlit-chunk-file-uploader 1.0.4
Custom component to perform chunked upload in Streamlit.
Requires Python: >=3.7

# streamlit-chunk-file-uploader

[![image](https://img.shields.io/pypi/v/streamlit-chunk-file-uploader.svg)](https://pypi.python.org/pypi/streamlit-chunk-file-uploader)  
[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://chunk-file-uploader.streamlit.app/)  

This is a custom component that allows you to split files and send them from your browser to Streamlit.

## Installation instructions

```sh
pip install streamlit-chunk-file-uploader
```

## Usage instructions

```python
import streamlit as st
from streamlit_chunk_file_uploader import uploader

file = uploader("uploader", key="chunk_uploader", chunk_size=32)
st.write(file)
if file is not None:
    st.download_button(
        "download",
        data=file,
        file_name=file.name,
        type="primary",
    )

```

## About chunk size
When a file is uploaded, a Python script slices the file at the specified chunk size on the browser side and sends it as multiple files to the backend.  
It's important to note that chunk size and request size are different.  
If you have a constraint such as client_max_body_size, you should set it to a value slightly smaller than the constraint size, such as 31MB if the constraint size is 32MB.