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  <h1 align="center">YAIV</h3>
  <h3 align="center">Yet another Ab Initio Visualizer...</h3>
  <p align="center">
    A general purpose tool for condensed matter data analysis.
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    <a href="https://github.com/mgamigo/YAIV/tree/main/Tutorial"><strong>Explore the tutorials:</strong></a>
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    <a href="https://github.com/mgamigo/YAIV/blob/main/Tutorial/Plot_module.ipynb">Plotting</a>
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    <a href="https://github.com/mgamigo/YAIV/blob/main/Tutorial/Convergence_module.ipynb">Convergence</a>  
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    <a href="https://github.com/mgamigo/YAIV/blob/main/Tutorial/Utils_module.ipynb">Utilities</a>
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<!-- TABLE OF CONTENTS -->
<details>
  <summary>Table of Contents</summary>
  <ol>
    <li>
      <a href="#about-the-project">About The Project</a>
      <ul>
        <li><a href="#why">Why?</a></li>
      </ul>
    </li>
    <li><a href="#installation">Installation</a></li>
    <li>
	<a href="#current-tools">Current tools</a>
	<ul>
            <li><a href="#i-plot-module">Plotting tools</a></li>
	    <li><a href="#ii-convergence-module">Convergence analysis</a></li>
	    <li><a href="#iii-utils-module">Other utilities</a></li>
        </ul>
    </li>
    <li><a href="#examples">Examples</a></li>
    <li><a href="#roadmap">Roadmap</a></li>
  </ol>
</details>

<!-- ABOUT THE PROJECT -->
## About The Project
YAIV is a collection of tools for plotting results of condensed matter ab initio codes such as *Quantum Espresso, VASP, Wannier90, Wannier Tools...* Although it can be used from the command line, the main intention of YAIV is to be used within JupyterLab, thereby allowing users to centralize the data analysis of a whole project into a single file. The goal is to provide both *(1)* fast and easy plotting defaults to glance over results, while *(2)* being flexible and powerful enough to generate publication-ready figures.

<!-- 
![gif demo](../media/demo.gif?raw=true)
-->
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### Why?
> *A single file to rule them all...*

Most of the tools contained on YAIV are nothing more than glorified python scripts I needed during my PhD. Although python libraries for ab initio data analysis already exist, I found many of them being disigned to work within the command line (often required to be run from a certain directory). YAIV is aimed at providing useful ab initio analysis functionalities to those people willing to use a single JupyterLab file to organize their projects.

YAIV also intends to provide enough flexibility and modularity for most scenarios. To this end, useful tools are also provided in order to scrape data from the output of a variety of codes. Then, users can either further process the raw data or plot it in any desired way.

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---

## Installation

#### Create an isolated python enviroment
In case you want to create your own python enviroment and have it available in JupyterLab.
```sh
    python -m venv yaiv_env                             #Create yor new enviroment
    source yaiv_env/bin/activate                        #Load the enviroment
    pip install ipykernel                               #In order to create a Jupyter Kernel for this enviroment
    python -m ipykernel install --user --name=YAIV      #Install your new kernel with your desired name
    jupyter kernelspec list                             #Check that the new installed kernel appears
```
Now your new installed Kernel should be available in JupyterLab. You can select Kernel clicking at the top-right corner of JupyterLab.

#### Installing YAIV
You can either install from pip as:
```sh
   pip install yaiv
```

   Or cloning the git repository:
   
```sh
   git clone https://github.com/mgamigo/YAIV.git
   cd YAIV
   pip install .
```

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---

## Current tools

All the functions are properly documented (remember that in JupyterLab all the documentation can be conviniently accesed with the **shift + tab** shortcut).
<!--
All the tools are demostrated in the **[tutorials](Tutorial)**, here is a brief summary of the main modules of YAIV and their current tools:
-->

### I. Grep module
This module provides text-scraping utilities for extracting (grepping) structural and spectral information from first-principles calculation outputs. It supports common DFT packages such as Quantum ESPRESSO and VASP.
```py
from yaiv.grep import kpointsEnergies
spectrum = kpointsEnergies("OUTCAR")
spectrum.eigenvalues.shape
-----
(100, 32)
```

### II. Utils module
This module provides general-purpose utility functions that are used across various classes and methods in the codebase. They are also intended to be reusable by the user for custom workflows, especially when combined with the data extraction tools.

### III. Spectrum module
This module defines core classes for representing and plotting the eigenvalue spectrum of periodic operators, such as electronic bands or phonon frequencies, across a set of k-points. It also supports reciprocal lattice handling and coordinate transformations.
```py
from yaiv.spectrum import electronBands
bands = electronBands("data/qe/Si.bands.pwo")
bands.eigenvalues.shape
---
(100, 32)
---
bands.plot()
---
(Basic Figure)
```

### IV. Plot module
This module provides plotting utilities for visualizing eigenvalue spectra from periodic systems. It supports electronic and vibrational spectra obtained from common ab initio codes such as Quantum ESPRESSO and VASP.
```py
from yaiv.spectrum import electronBands
from yaiv import plot
bands = electronBands("OUTCAR")
plot.bands(bands)
---
(Decorated Figure)
```


<!--
---
## Examples
Here are some simple examples:
```py
plot.bands(file='DATA/bands/QE/results_bands/CsV3Sb5.bands.pwo',  #raw Quantum Espresso output file with the band structure
           KPATH='DATA/bands/KPATH',   #File with the Kpath (in order to plot the ticks at the High symmetry points)
           aux_file='DATA/bands/QE/results_scf/CsV3Sb5.scf.pwo', #File needed to read the number of electrons and lattice parameters
           title='Electronic bandstructures')    # A title of your liking
```
<img src="../media/bands.png" width="600">

```py
plot.phonons(file='DATA/phonons/2x2x2/results_matdyn/CsV3Sb5.freq.gp', #raw data file with the phonon spectrum
            KPATH='DATA/bands/KPATH',                                 #File with the Kpath (in order to plot the ticks at the High symmetry points)
            ph_out='DATA/phonons/2x2x2/results_ph/CsV3Sb5.ph.pwo',    #File with the phonon grid points and lattice vectors.
            title='Phonon spectra with the (2x2x2) grid highlighted!',   # A title of your liking
            grid=True,color='navy',linewidth=1)                        #Non-mandatory customization
```
<img src="../media/phonon.png" width="600">


```py
conv.kgrid.analysis(data='DATA/convergence/Kgrid/',         #Folder with your DFT outputs
		    title='K-grid convergence analysis')    #A title of your liking
```
<img src="../media/convergence.png" width="800">


```py
conv.wannier.w90(data='DATA/convergence/wannier90/NbGe2.wout',     #Wannier90 output file
                 title='Wannier minimization (66 WF)')             #A title of your liking
```
<img src="../media/wannier.png" width="800">

Combining YAIV tools with the usual **matplotlib sintax** one can generate complex plots as this one (check the [tutorial](Tutorial/Plot_module.ipynb)):

<img src="../media/collage.png" width="800">


_(For more examples, please refer to the [Tutorials](Tutorial))._

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<!---
---

## Roadmap

- [x] Grep module
    - [x] Electronic and phonon band strucutres.
    - [x] Fermi level.
    - [x] Real and reciprocal space lattice.
    - [x] Total energy and decomposition.
    - [x] Stress tensor.
    - [x] K-paths.
    - [ ] Projections over orbitals...
    - [ ] ...


- [x] Plot module
    - [x] Plotting phonon and electronic spectra
    - [x] Comparing spectrums
    - [ ] ...
    - [ ] Plotting surface DOS generated by WannierTools (ARPES simulations)
    - [ ] Plotting contour energy DOS generated by WannierTools
    - [ ] 3D Band structure plots

- [x] Utils module
    - [x] Grep tools to scrape data form OUTPUT files
    - [x] Transformation tools for easy changing of coordinates
    - [ ] ...
- [x] Convergence analysis tools
    - [x] Quantum Espresso self consistent calculations
    - [x] Quantum Espresso phonon spectra
    - [x] Wannierizations for Wannier90
    - [ ] ...
- [ ] Crystall structure analysis tools
    - [ ] Symmetry analysis
    - [ ] Visualization tools
    - [ ] ...
- [ ] Charge density wave analysis
    - [ ] Reading Quantum Espresso outputs
    - [ ] Distort crystal structures according to a given phonon
    - [ ] Linear combinations of condensing modes
    - [ ] Computing Born–Oppenheimer energy landscapes
    - [ ] ...
- [ ] ...
-->

##### Built With

[![NumPy][numpy.js]][numpy-url]
[![Pint][pint.js]][pint-url]
[![Matplotlib][matplo.js]][matplo-url]
[![ASE][ase.js]][ase-url] 

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<!-- MARKDOWN LINKS & IMAGES -->
[numpy-url]: https://numpy.org/
[numpy.js]: https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white

[pint-url]: https://pint.readthedocs.io/en/stable/
[pint.js]: https://img.shields.io/badge/Pint-%23006f5c.svg?style=for-the-badge&logoColor=FF6719

[matplo-url]: https://matplotlib.org/
[matplo.js]: https://img.shields.io/badge/Matplotlib-%23000000.svg?style=for-the-badge&logo=Matplotlib&logoColor=white

[ase-url]: https://wiki.fysik.dtu.dk/ase/
[ase.js]: https://img.shields.io/badge/ASE-%23006f5c.svg?style=for-the-badge&logoColor=FF6719
