semvec/pypi-packages huggingface dataset:
usefullibrary 0.1.2
A package containing scripts to help boost your python

# useful_library

This is the github repository for useful_library (a python library).

The latest version can be found here:

https://pypi.org/project/useful_library/0.1.1/

To install this you can use:
```
pip install useful_library
```

Current Features include: 
*	lbl() -  prints what you need letter by letter
useful-library 0.1.1
A package containing scripts to help boost your python

# useful_library

This is the github repository for useful_library (a python library).

The latest version can be found here:

https://pypi.org/project/useful-library/0.1/

To install this you can use:
```
pip install useful-library
```

Current Features include: 
*	lbl() -  prints what you need letter by letter
googlenewspy 0.0.3
Scraping google news
Requires Python: >=3.6

# Google News Py

Search news with python in google news.


## What's necessary
python >=3.6

## Instaling

> pip install googlenewspy

## Using

from googlenewspy import Search\
search = Search()\
text = 'Python'\
search.news(text)

## Features
You can specify witch country and host language in Search object, for better results.
Ex:
> search = Search(host_language='en-US', geolocation='US')\
> search.news('Python')
sentry-chalice 0.3.3
exceptions on chalice
Requires Python: >=3.6

# Sentry-chalice

[![test](https://github.com/cuenca-mx/sentry-chalice/workflows/test/badge.svg)](https://github.com/cuenca-mx/sentry-chalice/actions?query=workflow%3Atest)
[![codecov](https://codecov.io/gh/cuenca-mx/sentry-chalice/branch/main/graph/badge.svg)](https://codecov.io/gh/cuenca-mx/sentry-chalice)
[![PyPI](https://img.shields.io/pypi/v/sentry-chalice.svg)](https://pypi.org/project/sentry-chalice/)

Sentry-Chalice allow the integration of Chalice on sentry.

You can use sentry-chalice integration like this:

```python
import sentry_sdk
from chalice import Chalice

from sentry_chalice import ChaliceIntegration


sentry_sdk.init(
    dsn="https://<key>@<organization>.ingest.sentry.io/<project>",
    integrations=[ChaliceIntegration()]
)

app = Chalice(app_name='appname')

```
sentry-chalice now it works just for views: @app.route.

You can create a route that triggers an error for validate your Sentry installation, like this:

```python
@app.route('/boom')
def boom():
    raise Exception('boom goes the dynamite!')

```

when you enter the route will throw an error that will be captured by Sentry.


## Behavior

- Request data is attached to all events: HTTP method, URL, headers, form data, JSON payloads. Sentry excludes raw bodies and multipart file uploads. Sentry also excludes personally identifiable information (such as user ids, usernames, cookies, authorization headers, IP addresses) unless you set send_default_pii to True.

Each request has a separate scope. Changes to the scope within a view, for example setting a tag, will only apply to events sent as part of the request being handled.
py-common-library 0.1
A package containing scripts to help boost your python

# useful-lib

This is the github repository for useful-lib (a python library).

The latest version can be found here:

https://pypi.org/project/useful-lib/0.1/

To install this you can use:
```
pip install useful-lib
```

Current Features include: 
*	lbl() -  prints what you need letter by letter
SPART-python 1.0.9
Python implementation of the SPART optical radiative transfer model.
Requires Python: >=3.4

# SPART-python

## Overview
This is a Python port of the Matlab code for the SPART radiative transfer model.

The source code can be found at: https://github.com/wirrell/SPART-python

The original code can be found at: https://github.com/peiqiyang/SPART

The model paper is:
The SPART model: A soil-plant-atmosphere radiative transfer model for satellite measurements in the solar spectrum - Yang et al. (2020)

## Installation
`pip install SPART-python`

## Requirements
```
Python 3.4+
NumPy
SciPy
Pandas
```

## Quickstart Example
```
   import SPART
   leafbio = SPART.LeafBiology(40, 10, 0.02, 0.01, 0, 10, 1.5)
   soilpar = SPART.SoilParameters(0.5, 0, 100, 15)
   canopy = SPART.CanopyStructure(3, -0.35, -0.15, 0.05)
   angles = SPART.Angles(40, 0, 0)
   atm = SPART.AtmosphericProperties(0.3246, 0.3480, 1.4116, 1013.25)
   spart = SPART.SPART(soilpar, leafbio, canopy, atm, angles, 'TerraAqua-MODIS',
                 100)
   results = spart.run()  # Pandas DataFrame containing R_TOC, R_TOA, L_TOA
```

## Documentation
Full documentation can be found at https://spart-python.readthedocs.io/