semvec/pypi-packages huggingface dataset:
spicenet 2.0.0a2
SPICEnet is an artificial neural network that is capable of identifying the mathematical relationship between two values. This library provides a single core implementation for quick usage.
Requires Python: <4.0,>=3.11
Keywords: spicenet, spice-net, neuromorphic, neural network

![](media/spice-net-logo-green-ai.png)

# SPICEnet in Python
SPICEnet is an artificial neural network that is capable of identifying the mathematical relationship between two values. This library provides a single core implementation for quick usage.
For more information about the network look up the pdf in the docs folder.

## Install
```pip install spicenet```

If you want to use the plotting subpackage use this command instead:\
```pip install spicenet[plotting]```

## How to use
Please take a look at the [tutorial notebook](/notebooks/spice_net_tutorial.ipynb).


## Notebooks
The Notebooks you will find in this repository, are examples on how to use this library.

- [tests](/notebooks/tests.ipynb): This notebook is only for test cases, it might not be running. 
- [solar panels](/notebooks/solar_panel_example.ipynb): This notebook shows you how to calculate the voltage of a solar panel by the lux level of the environment.
- [tutorial](/notebooks/spice_net_tutorial.ipynb): Follow this guid on how to use this library

## Dev notes
```poetry install --all-extras```
amutils 0.0.3
Apple Music Utilties
Requires Python: >=3.6

# amutils

Command-line utilities for Apple Music library management and statistics.

## Installation

Clone the repository and ensure you have Python installed.

```bash
pip3 install --user amutils
```

## Usage

```bash
amutils <command> [file]
```

## Commands

- `stat` - Get your library statistics
- `playedtime` - Get library total played time
- `replace` - Replace songs in your library with given music file(s)

## Examples

```bash
# Get library statistics
amutils stat

# Get total played time
amutils playedtime

# Replace a single song
amutils replace path/to/song.m4a

# Replace multiple songs in a folder
amutils replace path/to/folder
```

## Features

- Library statistics tracking
- Total playtime calculation
- Smart song replacement based on metadata matching
- Support for both single file and folder processing
- Automatic metadata comparison and matching

## License

MIT License - see LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
TestPipInstallProgram 0.0.2
Simple calculater
Requires Python: >=3.0


# Test Pip Install Program
    This is a test program for learning to cerate and assemble a pip installable work library

# Content
    The libiray has 4 functions in the simple_calc: addition, subtraction, multiplication and division. 

# Instructions 
    1. Install:
        Pip install TestPipInstallprogram

    2. Use functions:
        In python:
            from TestPipInstallProgram import simple_calc

        Four functions:
            -------
            addition(a,b): adds a and b together.
            --------

            -------
            subtraction(a,b): subtracts b from a.
            --------

            -------
            multiplication(a,b): multiply a and b togehter.
            --------

            -------
            division(a,b): divides a by b.
            --------
Topsis-Anshul-102217060 1.0.3
Implementation of Topsis
Keywords: Topsis, Topsis-Anshul-102217060, Anshul, Topsis-Anshul, 102217060

# Topsis-Anshul
# TOPSIS Implementation

This repository contains a Python implementation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is a powerful multi-criteria decision-making method that assists in ranking a set of alternatives based on their proximity to the ideal solution.

## Table of Contents

1. [Introduction](#introduction)
2. [Usage](#usage)
3. [Command-line Arguments](#command-line-arguments)
6. [Requirements](#requirements)

## Introduction

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a well-established method for decision-making. This Python implementation allows you to easily apply TOPSIS to your decision matrix and obtain a ranked list of alternatives.

### Key Concepts:

- Decision Matrix: Represents alternatives and criteria.
- Weights: Assign importance to criteria.
- Impacts: Indicate whether higher or lower values are favorable.
- Normalization: Ensures all criteria are on a similar scale.
- Ideal and Worst Solutions: Represent best and worst possible outcomes.
- Similarity and Dissimilarity Measures: Calculate proximity to ideal and dissimilarity to worst.
- TOPSIS Score: Combines similarity and dissimilarity measures.
- Ranking: Alternatives are ranked based on TOPSIS scores.

## Usage

1. Ensure you have Python installed on your system.

2. Clone this repository to your local machine:

   bash
   git clone https://github.com/anshulmahajan14/Topsis-Anshul

3. Navigate to the project directory:

      bash
   git clone https://github.com/anshulmahajan14/Topsis-Anshul

   
4. Run the TOPSIS script with the required command-line arguments:

   
      python 102217060.py 102217060-data.csv "1,1,1,2" "+,+,-,+" result.csv



5. The TOPSIS analysis will be performed, and the result will be saved to the specified CSV file.
qingping-iot-cloud 0.2.2
Library for accessing cloud service of Qingping IoT .
Requires Python: >=3.8

# Qingping IoT (cloud)

Library for accessing cloud service of Qingping IoT (instead of Bluetooth).

## Background

This is designed for users who:

- have Qingping IoT supported devices
  - see [docs](https://developer.qingping.co/cloud-to-cloud/specification-guidelines#2-products-list-and-support-note) for full list
  - boils down to ones with native Wi-Fi and selected BT-only via Qingping gateway
- do not want to use Bluetooth to poll devices directly
  - at least 2nd generation of Qingping Air Monitor does not have BT at all
  - for larger houses and offices it's problematic to set up several non-Qingping BT gateways
- do not want to "privatize" devices
  - said operation eliminates access from Qingping IoT mobile app and ability to upgrade firmware
  - it requires local MQTT server and all responsibility is shifted on user

This library is mainly created for use with Home Assistant integration (not yet implemented), but also offers CLI.

The scope of APIs implemented is shown in [API.md](./API.md).

## How to get credentials

Normal users have static *App Key* and *App Secret* tied to their account. 

1. register on [qingpingiot.com](https://www.qingpingiot.com)
2. download mobile app linked there
3. bind devices in app
4. go back to [qingpingiot.com](https://www.qingpingiot.com/devices) and validate that you see all the devices
5. log in to [developer.qingping.co](https://developer.qingping.co/personal/permissionApply) using the same credentials as for the app
6. go to [Access Management](https://developer.qingping.co/personal/permissionApply) and get credentials

---

## Install

[![PyPI: qingping-iot-cloud](https://img.shields.io/pypi/v/qingping_iot_cloud?style=flat-square&label=PyPI%3A%20qingping-iot-cloud)](https://pypi.org/project/qingping-iot-cloud/)

```bash
pip install qingping-iot-cloud
```

## CLI usage

```bash
export QINGPINGIOT_APPKEY=...
export QINGPINGIOT_APPSECRET=...
qingping-iot-cloud -h
qingping-iot-cloud list_devices
```

example response from `list_devices`:

```
AAAAAAAAAAAA: sensor_A (CO2& Temp & RH Monitor)
  timestamp: 1735593815 
  battery: 100 %
  signal: -61 dBm
  temperature: 21.4 °C
  humidity: 56 %
  co2: 959 ppm
BBBBBBBBBBBB: sensor_B (CO2& Temp & RH Monitor)
  timestamp: 1735593520 
  battery: 100 %
  signal: -47 dBm
  temperature: 24.4 °C
  humidity: 42 %
  co2: 876 ppm
CCCCCCCCCCCC: sensor_C (Qingping Air Monitor Lite)
  timestamp: 1735593600 
  battery: 100 %
  temperature: 26.4 °C
  humidity: 44.3 %
  co2: 1053 ppm
  pm25: 12 μg/m³
  pm10: 12 μg/m³
```
physion 1.2.1
Vision Physiology Software
Requires Python: >=3.9.5

<!--             ** Build Instructions **              -->
<!--  python -m build                                  -->
<!--  python -m twine upload --repository pypi dist/*  -->
<!--                                                   -->

<div><img src="./docs/icons/physion.png" alt="physion logo" width="35%" align="right" style="margin-left: 10px"></div>

# Vision Physiology Software

> *An integrated software for cellular and circuit physiology of visual processing during behavior*

--------------------

The software is organized into several modules to perform the acquisition, the preprocessing, the standardization, the visualization, the analysis and the sharing of multimodal neurophysiological recordings.

The different modules are detailed in the [documentation below](README.md#modules-and-documentation) and their integration is summarized on the drawing below:
<p align="center">
  <img src="docs/integrated-solution.svg" width="100%" />
</p>


--------------------

## Install

Create a `"physion"` environment running `python 3.11`, with:

```
conda create -n "physion" python=3.11
```

Then simply:
```
pip install physion
```

- For an installation on an acquisition setup, see the detailed steps in [./docs/install/acquisition.md](./docs/install/acquisition.md)
- For some installation issues, see [./docs/install/troubleshooting.md](./docs/install/troubleshooting.md)

## Usage

Run:
```
python -m physion
```

You will find some pre-defined shortcuts in the [utils/](./src/utils/) folder for different operating systems.

N.B. The program is launched in either "analysis" (by default) or "acquisition" mode. This insures that you can launch several analysis instances while you record with one acquisition instance of the program. To launch the acquisition mode, run: `python -m physion acquisition`.

## Modules and documentation

The different modules of the software are documented in the following links:

- [Visual stimulation](src/physion/visual_stim/README.md)
- [Multimodal Acquisition](src/physion/acquisition/README.md)
- [Intrinsic Imaging](src/physion/intrinsic/README.md)
- [Electrophysiology](src/physion/electrophy/README.md)
- [Calcium imaging](src/physion/imaging/README.md)
- [Pupil tracking](src/physion/pupil/README.md)
- [Face Motion tracking](src/physion/facemotion/README.md)
- [Behavior](src/physion/behavior/README.md) 
- [Assembling pipeline](src/physion/assembling/README.md)
- [Hardware control](src/physion/hardware/README.md)
- [Visualization](src/physion/dataviz/README.md)
- [Analysis](src/physion/analysis/README.md)
- [Data Management](src/physion/utils/management/README.md)
- [Data Sharing](src/physion/utils/sharing/README.md)

## Troubleshooting / Issues

Use the dedicated [Issues](https://github.com/yzerlaut/physion/issues) interface of Github.