semvec/pypi-packages huggingface dataset:
wintoastlistener 1.0.0
A python library implemented by python3, for listening to Toast message notifications on windows.
Requires Python: >=3.8

# WinToastListener
[README_CN](./README.md)  
A python library implemented by python3, for listening to Toast message notifications on windows.

![Demo](./images/example.gif)  

## Supported platforms  
**Supported**  
Windows10 and above  

**Untested**  
Windows8  
P.S.: I don't have the system and can't test it. Welcome to test the relevant system and let me know the conclusion.  

**Not supported**  
Windows7 and below  

## Installation

`pip install wintoastlistener`

## Minimum usage example

```python
from wintoastlistener import ToastListener


def example_callback(event_data, resources):
    print(event_data)
    print(resources)


listener = ToastListener(callback=example_callback)
listener.listen()
```

[More examples](./examples)

## Document
[Document](https://github.com/Gu-f/WinToastListener/wiki/Document)
wwpdb.utils.seqdb-v2 0.14
wwPDB Sequence search utilities
Requires Python: >=3.6

# OneDep Sequence Search/Retrieval Library

Master: [![Build Status](https://dev.azure.com/wwPDB/wwPDB%20Python%20Projects/_apis/build/status/wwPDB.py-wwpdb_utils_seqdb_v2?branchName=master)](https://dev.azure.com/wwPDB/wwPDB%20Python%20Projects/_build/latest?definitionId=10&branchName=master)

Develop: [![Build Status](https://dev.azure.com/wwPDB/wwPDB%20Python%20Projects/_apis/build/status/wwPDB.py-wwpdb_utils_seqdb_v2?branchName=develop)](https://dev.azure.com/wwPDB/wwPDB%20Python%20Projects/_build/latest?definitionId=10&branchName=develop)

## Introduction

This repository povides the tools and access to remote sequence search and retrieval.

### Installation

Download the library source software from the project repository:

```bash

git clone --recurse-submodules https://github.com/wwpdb/py-wwpdb_utils_seqdb_v2.git

```

Optionally, run test suite using the Tox test runner

```bash
python setup.py test

or simply run

tox

If the environment variable MOCKREQUESTS is set, the tests will not contact any remote servers.


Installation is via the program [pip](https://pypi.python.org/pypi/pip).

```bash
pip install wwpdb.utils.seqdb_v2

or from the local repository:

pip install .
```
cspot 1.2.0
CELL SPOTTER (CSPOT): A scalable framework for automated processing of highly multiplexed tissue images
Requires Python: <3.12,>=3.9
Keywords: image analysis, multiplex imaging, single cell analysis



[![Downloads](https://static.pepy.tech/badge/cspot)](https://pepy.tech/project/cspot)
[![docs](https://github.com/nirmallab/cspot/actions/workflows/docs.yml/badge.svg)](https://github.com/nirmallab/cspot/actions/workflows/docs.yml)
[![build-unix-mac-win](https://github.com/nirmallab/cspot/actions/workflows/build-unix-mac-win.yml/badge.svg)](https://github.com/nirmallab/cspot/actions/workflows/build-unix-mac-win.yml)

# 🐊 Getting Started with CSPOT 
Kindly note that **CSPOT is not a plug-and-play solution**. It's a framework that requires significant upfront investment of time from potential users for training and validating deep learning models, which can then be utilized in a plug-and-play manner for processing large volumes of similar multiplexed imaging data.

## System Requirements:

**Hardware :**  
`CSPOT` comprises two modules: training and prediction. Training can be efficiently executed on a standard laptop without the need for a GPU. However, for predictions, leveraging a GPU significantly enhances processing speed (particularly for large images).

**Software :**  
This package is supported for Windows (10, 11), macOS (Sonoma, Ventura) and Linux (Ubuntu 16.04). 
 
**Dependencies :** 
The `pyproject.toml` file contains a comprehensive list of dependencies.

## Installation Guide:

**There are two ways to set it up based on how you would like to run the program**  
- Using an interactive environment like Jupyter Notebooks  
- Using Command Line Interface  
  
Before we set up CSPOT, we highly recommend using a environment manager like Conda. Using an environment manager like Conda allows you to create and manage isolated environments with specific package versions and dependencies. 
  
**Download and Install the right [conda](https://docs.conda.io/en/latest/miniconda.html) based on the opertating system that you are using**

## **Create a new conda environment**

```
# use the terminal (mac/linux) and anaconda promt (windows) to run the following command
conda create --name cspot -y python=3.9
conda activate cspot
```

**Install `cspot` within the conda environment.**

```
pip install cspot
```
The installation time for `cspot` generally falls under 5 minutes, based on internet speed and connectivity.

## **Interactive Mode**
Using IDE or Jupyter notebooks

```python
pip install notebook

# open the notebook and import CSPOT
import cspot as cs
# Go to the tutorial section to follow along
```

## **Command Line Interface**
```
wget https://github.com/nirmalLab/cspot/archive/main.zip
unzip main.zip 
cd cspot-main/cspot 
# Go to the tutorial section to follow along

```

## **Docker Container**
```
docker pull nirmallab/cspot:cspot
# Go to the tutorial section to follow along
```
satio-pc 0.0.14
Sentinel-2 features processing
Requires Python: >=3.8

# satio-pc
Compute Sentinel features on Planetary Computer

## Install 

```
pip install git+https://github.com/dzanaga/satio-pc.git
```

## Usage

Check [loading notebook](https://github.com/dzanaga/satio-pc/blob/main/notebooks/01-Load_S2_clean_timeseries_WorldCover.ipynb)
tes-chp 0.2.1
CHP models implemented in tespy (for WWNW)
Requires Python: >=3.10

# TESpy-based CHP

***Important: This model is still unfinished and under active development! Use it with caution.***

This project contains a CHP implemetation using TESpy. To install the package run

```bash
pip install tes-chp
```

The CHP is implemented as TESpy subsystem. A simple usage could look like:

```py
from tes_chp import CHP
from tespy.networks import Network

net = Network(fluids=["H2O", "CH4", "H2", "O2", "CO2", "Ar", "N2"], p_unit='bar', T_unit='C', iterinfo=False)
chp = CHP("CHP")
net.add_subsys(chp)

chp.set_attr(P=-1e6)

mode= "design"
net.solve(mode=mode, design_path=".")
net.save("tmp")
net.print_results()

chp.set_attr(P=-5e5)

mode= "offdesign"
net.solve(mode=mode, design_path="tmp")
net.print_results()
```