semvec/pypi-packages huggingface dataset:
pyweaver 0.0.10
Web based Python visual programming

# PyWeaver
PyWeaver is a visual code editor for Python that leverages web technologies. It is inspired by Jupyter Notebook and Simulink. The project is under development.
Currently working with Python 2. Python 3 support comming soon.

## Motivation
I spend most of my time analyzing data (mostly time series) of chemical process plants. I wanted a tool that optimizes the workflow of importing, cleaning and analyzing data by expliciting the relationship of the variables involved and defining the flow of information.
I wanted the tool to have enough flexibility to display different type of plots and UI components and perform any kind of calculation.

Ultimately, PyWeaver will have the functionality to store and grow a library of computational nodes that users will be able to drag & drop to define their workflows. They will also be able to easily develop their own nodes to fit their specific needs.

## Installation
If you want to run the current development version:
Clone the repo, cd into client, npm i and npm run serve.
Also, cd into server, run python main.py.

This will be improved shortly.

![pyweaver_1](https://user-images.githubusercontent.com/6884660/57988776-70d97e00-7a68-11e9-9bb5-7f304aec0bac.PNG)

## How it works

Each computational node is defined by a Python function, an HTML snippet that determines what is displayed in the UI and a Java Script object containing any functions needed by the UI. All the code is introduced via the web client and processed by the server:

Python function:
![pyweaver_2](https://user-images.githubusercontent.com/6884660/57988857-5eac0f80-7a69-11e9-94e3-8884114598fd.PNG)
The server analyzes the code, finds the inputs and outputs of each function and creates the sockets in the UI to specify how to connect the variables.

HTML Display:
![pyweaver_3](https://user-images.githubusercontent.com/6884660/57988860-68357780-7a69-11e9-8ee4-18c6ad9742a3.PNG)
PyWeaver leverages the power of Vue.js, mxgraph, codemirror and Vuetify. I am currently working on integrating Plotly.

Result:
![pyweaver_4](https://user-images.githubusercontent.com/6884660/57988881-9dda6080-7a69-11e9-92cd-19dca7ef42b1.PNG)
lfdata 0.0.4
Tools for interacting with GT's LF AWESOME Receiver data

# LF Data

This project provides a set of useful tools for interacting with data taken from
the LF AWESOME receivers maintained by the LF Radio Lab at Georgia Tech. This
data is available publicly at [Waldo World](https://waldo.world/). 

## Installation

Run the following to install:

```python
pip install lfdata
```

## Example Usage

```python
import lf

# Load an entire .mat file
data = lf.data.rx.load_rx_data("path_to_mat_file")

# Load a specific variable or set of variables
variables = ["station_name", "call_sign", "data"]
data = lf.data.rx.load_rx_data("path_to_mat_file", variables)

# Create an LFData object from two mat files or dictionaries
mat_files = ["amplitude.mat", "phase.mat"]
data = lf.data.rx.LFData(mat_files)

# Evaluate the quality of your data with the EvalLF object
data_eval = lf.data.rxquality.EvalLF(data)
```
nersights 0.0.1
Typer, build great CLIs. Easy to code. Based on Python type hints.
Requires Python: >=3.6

<p align="center">
    <em>`nersights`, Debug annotated Named Entity Recognition (NER) data for inconsitencies and get insights on improving the quality of your data.</em>
</p>
<p align="center">
<a href="https://dev.azure.com/kabirkhan1137/nersights/_build" target="_blank">
    <img src="https://dev.azure.com/kabirkhan1137/nersights/_apis/build/status/kabirkhan.nersights?branchName=master" alt="Build Status">
</a>
<a href="https://codecov.io/gh/kabirkhan/nersights">
  <img src="https://codecov.io/gh/kabirkhan/nersights/branch/master/graph/badge.svg" alt="Coverage"/>
</a>
<a href="https://pypi.org/project/nersights" target="_blank">
    <img src="https://badge.fury.io/py/nersights.svg" alt="Package version">
</a>
</p>

---

**Documentation**: <a href="https://kabirkhan.github.io/nersights" target="_blank">https://kabirkhan.github.io/nersights</a>

**Source Code**: <a href="https://github.com/kabirkhan/nersights" target="_blank">https://github.com/kabirkhan/nersights</a>

---

`nersights` is a library to help you fix your annotated NER data and identify examples that are hardest for your model to predict so you can strategically prioritize the examples you annotate. 

The key features are:

* **Data Validation and Cleanup**: Easily Validate the format of your NER data. Filter overlapping Entity Annotations, fix missing properties.
* **Model Insights**: Analyze how well your model does on your Dataset. Identify the top errors your model is making so you can prioritize data collection and correction strategically.
* **Model Insights**: Analyze how well your model does on your Dataset. Identify the top errors your model is making so you can prioritize data collection and correction strategically.
* **Dataset Management**: `nersights` provides a `Dataset` class to manage the train/dev/test split of your data and apply the same functions across all splits in your data + a concatenation of all examples. Operate inplace to consistently transform your data.
* **Serializable Dataset**: Serialize and Deserialize your data to and from JSON to the `nersights` type system. 
* **Type Hints**: Comprehensive Typing system based on Python 3.6+ Type Hints

## Requirements

Python 3.6+

Python 3.6+

`nersights` is built on a few comprehensive, high-performing packages.

* <a href="https://spacy.io" class="external-link" target="_blank">spaCy</a>
* <a href="https://pydantic-docs.helpmanual.io/" class="external-link" target="_blank">Pydantic (Type system and JSON Serialization)</a>
* <a href="https://typer.tiangolo.com" class="external-link" target="_blank">Typer (CLI)</a>.


## Installation

<div class="termy">

```console
$ pip install nersights
---> 100%
Successfully installed nersights
```

</div>

## License

This project is licensed under the terms of the MIT license.
pydtp 0.1.1
A simple protocol based on json representation of python dictionaries

# Python Dictionary Transfer Protocol 
`pydtp` is a package defining a protocol for transparently 
accessing a remote server with function calls passing 
lightweight arguments. 

`pydtp` defines three classes:
 - a `Server` class based on `socketserver.TCPServer` 
 - an abstract  `Handler` class to implement to define the Server-responses
 - an abstract `Client` class to implement to define the Client interface 

The functions implemented on the Client class are redirected to 
the server class encoding the arguments and the return value in 
the most transparent way. 

### Dependencies 
The package is developed for `Python3`. 

The package requires `numpy` plus the standard python libraries 
for both the client and server sides. 

## Example
Define the client implementing two functions, 
that just redirect the functions to the `query`:
```
import pydtp 

class SimpleClient (pydtp.Client) :
  def add_one ( self, num ):
    return self.query ( 'add_one', [num] ) 

  def capitalize ( self, *strings):
    return self.query ( 'capitalize', [strings] ) 
```

The `query` function hides the complexity and allow to 
call functions implemented in a `Handler`: 
```
import pydtp 

class SimpleHandler (pydtp.Handler):
  def add_one ( self, num ):
    return num + 1 

  def capitalize ( self, strings ):
    return {k: k.capitalize() for k in strings} 
```

The server is then instantiated and started on `localhost` and on a port.
```
server = pydtp.Server ( ('localhost', 12345), SimpleHandler )
server.start() 
```

The client class is instantiated indicating the address and the port
```
client = SimpleClient ( ('localhost', 12345) ) 
client.add_one ( 3 ) ## == 4
client.capitalize ( 'test' ) ## == "Test"
```
tinycircuits-sx1505 0.0.3
SX1505 Joystick & Rotary Python library
Requires Python: >=3.6

# TinyCircuits SX1505 Python

This repository holds a python package and examples meant for using the TinyCircuits [Joystick Wireling](https://tinycircuits.com/collections/wireling-input-output/products/joystick-wireling) and [Rotary Wireling](https://tinycircuits.com/collections/wireling-input-output/products/rotary-switch-wireling) with a Raspberry Pi and Wireling Pi Hat.

To install the package, use:

```
pip3 install tinycircuits-sx1505
```

or if your default is Python 3:

```
pip install tinycircuits-sx1505
```

Software written by: [TinyCircuits](https://tinycircuits.com/) for use with TinyCircuits hardware