semvec/pypi-packages huggingface dataset:
eratools 0.2.0
A package extending xarray for use with ECMWF ERA model level data.

[![xarray](http://img.shields.io/badge/powered%20by-xarray-blue.svg?style=flat)](http://http://xarray.pydata.org/en/stable/)

# eratools

### A package that extends xarray with functionality to work with ECMWF ERA data. 

## Introduction
`eratools` loads [ECMWF](https://www.ecmwf.int/) ERA5 or ERA-Interim data files that contain model level data of temperature, relative humidity, surface pressure, and surface geopotential.
It uses these variables to calculate pressure and temperature data on pressure levels and altitude levels.

Also included is a utility for downloading daily files of ERA5 or ERA-Interim data. 

## Further reading
* ECMWF IFS dosumentation [Cy38r1 Part III](https://www.ecmwf.int/sites/default/files/elibrary/2013/9244-part-iii-dynamics-and-numerical-procedures.pdf)
* ECMWF referenced [paper](http://journals.ametsoc.org/doi/abs/10.1175/1520-0493%281981%29109%3C0758:AEAAMC%3E2.0.CO;2)
* Example [code](https://confluence.ecmwf.int/x/u8RCBQ) on ECMWF's wiki

## Installation

To create a conda environmen and install run:
```
conda env create -f environment.yml
source activate eratools
pip install eratools
```

### Optional Dependencies

If you wish to use the ERA downloading utilities, you will need to follow the instructions [here](https://confluence.ecmwf.int/display/WEBAPI/Access+ECMWF+Public+Datasets)

## Supported Products
* ERA5
* ERA-Interim
topsis-python-souravdlboy 2.8
Python package for Ranking ML models using TOPSIS algorithmic approach
Requires Python: >=3.0

# topsis-python
# Package Description :
Python package for TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) ALGORITHM.  
# Motivation :   
This is a part of project - I made for UCS633 - Data analytics and visualization at TIET.     
@Author : Sourav Kumar    
@Roll no. : 101883068    
# Algorithm :    
### STEP 1 :    
Create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria.   
Apply any preprocessing if required.   
### STEP 2 :   
The matrix is then normalised using the norm.

### STEP 3 :
Calculate the weighted normalised decision matrix.

### STEP 4 :
Determine the worst alternative and the best alternative.

### STEP 5 :
Calculate the L2-distance between the target alternative i and the worst condition.

### STEP 6 :
Calculate the similarity to the worst condition.

### STEP 7 :
Rank the alternatives according to final performance scores.   

### Getting started Locally :  
> Run On Terminal       
```python -m topsis.topsis <filename.csv> <weights> <impacts>```   
  ex. python python -m topsis.topsis topsis.csv 0.25,0.25,0.25,0.25 -,+,+,+     

> Run In IDLE   
```from topsis import topsis```      
```t = topsis.topsis('filepath', [list of weights], [list of impacts])```    
```t.topsis_main()```  

> Run on Jupyter   
Open terminal (cmd)   
```jupyter notebook```   
Create a new python3 file.     
If file <filename.csv> doesn't exists, then make sure to upload the file to jupyter env.    
```from topsis import topsis```      
```t = topsis.topsis('filepath', [list of weights], [list of impacts])```    
```t.topsis_main()```

* ```topsis_main()``` has been specifically designed to inhibit leakeage of inbuilt functions.  
* ```topsis_main(debug=True)``` use this to display all the intermediate matrices.   
* Make sure that ```filename.csv``` is in same directory where package is installed.
### PAPER :
Find the research paper at [arxiv](https://arxiv.org/ftp/arxiv/papers/1412/1412.5077.pdf).

### OUTPUT :
Prints out overall ml models ranked and the best model / alternative.

![output result on jupyter](/images/t.JPG)
![output result on idle](/images/t1.JPG)
![output result on cmd](/images/t2.JPG)
spriteutilkhangvu 1.0.4
An internal project for Intek Institute
Requires Python: >=3.7

## Intro:
&nbsp; The project is generally used for detecting sprites inside an sprite sheets, providing bounding box of each sprite. It can also detect most used color. It can also draw and colorize a mask from the initial image.

## Usage:
#### &ensp; Create a SpriteSheet object using:
##### &ensp;&nbsp; SpriteSheet(fd, background_color=None):

   @fd: the name and path (a string) that references an image file in the local file system;
   OR a pathlib.Path object that references an image file in the local file system ; a file object that MUST implement read(), seek(), and tell() methods, and be opened in binary mode;
   OR a Image object.

   @background_color: an integer if the mode is grayscale;
   OR a tuple (red, green, blue) of integers if the mode is RGB;
   OR a tuple (red, green, blue, alpha) of integers if the mode is RGBA. The alpha element is optional. If not defined, while the image mode is RGBA, the constructor considers the alpha element to be 255.

#### &ensp; Class SpriteSheet provides following methods:

##### &ensp;&nbsp; SpriteSheet.find_most_common_color(image):

	Find most used color in an Image object
	arg: image: MUST be an Image object
	Return most used color in the image with the same format image's mode

##### &ensp;&nbsp; SpriteSheet_object.create_sprite_labels_image():

	Create a mask image of initial image, and add a bounding box around each sprite,
	each sprite also have an unique random uniform color.
	Return an Image object.

##### &ensp;&nbsp; SpriteSheet_object.find_sprites():

	Detect sprites inside the image
	Return a 2D label map and a dict that stores:
	key: sprite's label
	value: its Sprite's object
	arg: image: MUST be an Image object

## Installation:
The project require Python 3.7+ to run

#### &ensp; FOR USER:
##### &emsp; In Terminal, use command:

		 pip3 install spriteutilkhangvu

##### &emsp; Example for usage inside your project:

     from spriteutilkhangvu.spriteutil import SpriteSheet


#### &ensp; FOR DEVELOPMENT:
##### &emsp; Step 1: Clone or Downloads the project, using this command:

     git clone http://gitlab-students.int.intek.edu.vn/sprite-sheet/vdinh.git
##### &emsp; Step 2: Install required libs and tools, using this command in Terminal:

		 pip3 install -r requirements.txt
##### &emsp; Step 3: Edit the source code as you wish.

## Contact:
&emsp;&emsp;&emsp; During the usage of the project, if you have any question, please contact me personally at INTEK HCM City or via my Github page: https://github.com/khagkhangg

## Contributors:
&emsp;&emsp;&emsp; Khang VU from INTEK Institute, HCM City