Metadata-Version: 2.1
Name: POMFinder
Version: 1.0.0
Summary: Finds POM clusters from PDF data!
Home-page: https://github.com/AndyNano/POMFinder
Author: Andy S. Anker
Author-email: andy@chem.ku.dk
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE.txt

# POMFinder
Welcome to POMFinder!
This is a simple machine learning tool for structure characterisation of polyoxometalate clusters using total scattering Pair 
Distribution Function (PDF) analysis.  
Simply provide a PDF and the model will output best best structural models from it structure catalog which contains 443 polyoxometalate clusters. 

1. [Install](#install)
2. [Usage](#usage)
3. [Authors](#authors)
4. [Cite](#cite)
5. [License](#license)


## Install
To install POMFindeer you will need to have [Python](https://www.python.org/downloads/) or 
[Anaconda](https://www.anaconda.com/products/individual) installed. I recommend running POMFinder on Python version
3.7 or higher. If you have installed Anaconda you can create a new environment and activate it. 
```
conda create --name POMFinder_env python=3.7
conda activate POMFinder_env
```
Now you are ready to install what you actually come for! Currently __POMFinder__ is not avaible through PyPI or conda so the
package needs to be downloaded manually
Run the following command to install the __POMFindeer__ package.  
```
pip install .
or
python setup.py install
```

To verify that __POMFinder__ have been installed properly try calling the help argument.
```
POMFinder --help

>>> usage: POMFinder [-h] -d DATA [-n NYQUIST] [-i QMIN] [-a QMAX] [-m QDAMP] [-f FILE_NAME]       
>>> 
>>> This is a package which takes a directory of PDF files 
>>> or a specific PDF file. It then determines the best structural 
>>> candidates based of a polyoxometalate catalog. Results can
>>> be fitted to the PDF. 
```  
This should output a list of possible arguments for running __POMFinder__ and indicates that it could find the package! 

## Usage
Now that __POMFinder__ is installed and ready to use, lets discuss the possible arguments. The arguments are described in 
greater detail at the end of this section.

| Arg | Description | Default |  
| --- | --- |  --- |  
|  | __Required argument__ | | 
| `-h` or `--help` | Prints help message. |    
| `-n` or `--nyquist` | Is the data nyquist sampled. __bool__ | `-n True`
| `-i` or `--Qmin` | Qmin value of the experimental PDF. __float__ | `-i 0.7` 
| `-a` or `--Qmax` | Qmax value of the experimental PDF. __float__ | `-a 30` 
| `-m` or `--Qdamp` | Qdamp value of the experimental PDF. __float__ | `-m 0.04`
| `-f` or `--file_name` | Name of the output file. __str__ | `-o ''` 
| `-d` or `--data` | A directory of PDFs or a specific PDF file. __str__ | `-d 5` 

For example
```  
POMFinder --data "Experimental_Data/DanMAX_AlphaKeggin.gr" --nyquist "no" --Qmin 0.7 --Qmax 20 --Qdamp 0.02

>>> The 1st guess from the model is:  icsd_427457_1_0.9rscale.xyz with  83.29164981842041 % certaincy
>>> The 2nd guess from the model is:  icsd_427379_0_0.9rscale.xyz with  13.973137736320496 % certaincy
>>> The 3rd guess from the model is:  icsd_281447_0_1.0rscale.xyz with  1.488963421434164 % certaincy
>>> The 4th guess from the model is:  icsd_423775_0_0.9rscale.xyz with  0.9325935505330563 % certaincy
>>> The 5th guess from the model is:  icsd_172542_0_1.1rscale.xyz with  0.22610558662563562 % certaincy

```  
# Authors
__Andy S. Anker__<sup>1</sup>   
__Emil T. S. Kjær__<sup>1</sup>  
__Kirsten M. Ø. Jensen__<sup>1</sup>    
 
<sup>1</sup> Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.   

Should there be any question, desired improvement or bugs please contact us on GitHub or 
through email: __andy@chem.ku.dk__.

# Cite
If you use our code or our results, please consider citing our paper. Thanks in advance!
```
```

# License
This project is licensed under the Apache License Version 2.0, January 2004 - see the [LICENSE](LICENSE) file for details.