semvec/pypi-packages huggingface dataset:
pacote-de-processamento-de-imagens 0.0.1
eita
Requires Python: >=3.8

# Image Processing

Description.
The Package image_processing is used to:
    Processing:
        - Histogram matching
        - Structural similarity
        - Resize image
    Utils:
        - Read Image
        - Save Image 
        - Plot Image
        - Plot Result

## Instalation

Use the package menager [pip](https://pip.pypa.io/en/stable/) to install

## Author
Fabio
mrange 1.2
mrange is used to remove nested range loops
Requires Python: >=3.6

# mrange

Install via 
`conda install numba`
`pip install mrange`

Import via
`from mrange import mrange`

## Description
mrange is used to remove nested range loops
### Input: 
`n_s = (n_1, n_2, ..., n_m)` 
Tuple of integers (or array when using `mrange_array`)
### Output: 
Generator outputting `len(n_s)` values with every call, generating every combination of values in the intervals `[0,1,...,n_s[i]-1]`
### Use:
```
for a, b, ..., m in mrange((n_a, n_b, ..., n_m)):
    ...
Replaces:
for a in range(n_a):
    for b in range(n_b):
       ...
           for m in range(n_m):
               ...
```
## Authors: 
By Michael Schilling, with the help of Francesco Preti
pycoordinates 0.1.1
Create and manipulate coordinates in vector bases

[![build](https://github.com/pulkin/pycoordinates/actions/workflows/test.yml/badge.svg)](https://github.com/pulkin/pycoordinates/actions)
[![pypi](https://img.shields.io/pypi/v/pycoordinates)](https://pypi.org/project/pycoordinates/)

pycoordinates
=============

A tiny implementation of typical coordinate transformations used in physics, materials science,
and quantum chemistry.

Install
-------
```bash
pip install pycoordinates
```

Build from source
-----------------
1. Clone this repository
   ```bash
   git clone https://github.com/pulkin/pycoordinates.git
   cd pycoordinates
   ```
2. Install build system
   ```bash
   pip install build
   ```
3. Build
   ```bash
   python -m build
   ```
4. Install
   ```
   pip install dist/*.tar.gz
   ```
smockrawl 0.3.0
Smockeo API crawler
Requires Python: >=3.5
Keywords: smockeo cobject smoke fire detector api crawl crawler

# smockrawl

Basic Smockeo API crawler.

## Usage:

**Initialisation:**
`mySmockeo = Smockeo(username, password, detectorId, loop, session)`

Where `loop` is an asyncio event loop and `session` an aiohttp session. Have a look at the code in `__main__.py` to
see how this can work. 

**Authentication:**
`mySmockeo.authenticate()`

**Poll the sensor:**
`mySmockeo.poll()`

Following this, properties such as the sensor state, battery level or the last alarm can be read from the object.

An info reading can be printed with:
`mySmockeo.print_status()`
svi-data 0.0.3
Download SVI (social vulnerability index) data from CDC
Requires Python: >=3.6

# svi_data

Code to download 5 year American Community Survey Data estimates and [create the Social Vulnerability Index (SVI) data from CDC](https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html). SVI combines data from several different domains into an overall health index:

![](https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/CDC-SVI-Variables.jpg?_=81002)

This code downloads the data into a pandas dataframe for a given geography (either county, zipcode, or census tract). To install is:

    pip install svi-data

Packages that need to be installed for this to work are the pandas and [census](https://github.com/datamade/census). Also you will need to sign up for a [census API key](https://api.census.gov/data/key_signup.html) (it is free), and will need access to the internet.

An example of importing the SVI data for zipcodes:

    import svi_data
    key = svi_data.get_key('census_api.txt') # read in census api key from text file
    svi_zips = svi_data.get_svi(key,'zip')   # download the SVI data for zipcodes in the US

Note this uses the 2018 SVI version data definition, but allows you to download data for other years and apply that 2018 definition. See the Jupyter notebook `ExampleAnalysis.ipynb` in the root of the github repo illustrating grabbing different variables and different years of data.

# Future Development

In the `/src/prep_acs.py` functions, it has functions to download the ACS data from the FTP site and creates a localized sqllite database with *all* of the census variables for a given year. Future work will incorporate this as a potential way to grab the data to create the SVI or additional variables from the 5 year ACS (which are available at various geographies). 

This work was supported by [Gainwell technologies](https://www.gainwelltechnologies.com/).