#!/usr/bin/env python
# coding: utf-8

# Back to the main [Index](index.ipynb) <a id="top"></a>

# # Analyzing wannier90 results with AbiPy

# This notebook shows how to use AbiPy to analyze the output files
# produced by [wannier90](http://www.wannier.org/) and how to use the `ABIWAN.nc` file
# produced by Abinit to interpolate band energies.
# 
# As usual, one can use:
# 
#     abiopen.py FILE_ABIWAN.nc
#     
# with the `--expose` or the `--print` option for a command line interface
# and `--notebook` to generate a jupyter notebook.
# 
# Note: The code in this notebook requires abinit >= 8.9 and abipy >= 0.6
# 
# 
# ## Table of Contents
# [[back to top](#top)]
# 
# - [How to analyze the WOUT file](#How-to-analyze-the-WOUT-file)
# - [Using ABIWAN.nc to interpolate band energies](#Using-ABIWAN.nc-to-interpolate-band-energies)
# 
# Let's start by importing the basic modules needed for this tutorial.

# In[3]:


# Use this at the beginning of your script so that your code will be compatible with python3
from __future__ import print_function, division, unicode_literals

import os

import warnings 
warnings.filterwarnings("ignore")  # Ignore warnings

from abipy import abilab
abilab.enable_notebook() # This line tells AbiPy we are running inside a notebook
import abipy.data as abidata

# This line configures matplotlib to show figures embedded in the notebook.
# Replace `inline` with `notebook` in classic notebook
get_ipython().run_line_magic('matplotlib', 'inline')

# Option available in jupyterlab. See https://github.com/matplotlib/jupyter-matplotlib
#%matplotlib widget  


# ## How to analyze the WOUT file  
# [[back to top](#top)]

# Use `abiopen` to open a `wout` file (the main output file produced by wannier90):

# In[4]:


filepath = os.path.join(abidata.dirpath, "refs", "wannier90", "example01_gaas.wout")

wout = abilab.abiopen(filepath)
print(wout)


# To plot the convergence of the wannier cycle use:

# In[5]:


wout.plot();


# To plot the evolution of the Wannier centers and spread, use:

# In[6]:


wout.plot_centers_spread();


# <div class="alert alert-info" role="alert">
# Alternatively one can use `abiopen.py FILE_MDF.nc -nb` to generate a jupyter notebook directly from the terminal
# or `abiopen.py FILE_MDF.nc -e -sns` to produce matplotlib plots automatically.
# </div>

# ## Using ABIWAN.nc to interpolate band energies
# [[back to top](#top)]
# 
# 
# `ABIWAN.nc` is a netcdf file produced by Abinit after having called *wannier90* in library mode.
# The file contains the unitary transformation and other important parameters associated to the calculations.
# This file can be read by AbiPy and can be used to interpolate band energies with the wannier method.
# 
# As usual, use `abiopen` to open the file:

# In[7]:


filepath = os.path.join(abidata.dirpath, "refs", "wannier90", "tutoplugs_tw90_4", "tw90_4o_DS3_ABIWAN.nc")
abiwan = abilab.abiopen(filepath)
print(abiwan)


# To plot the matrix elements of the KS Hamiltonian in real space in the Wannier Gauge, use:

# In[8]:


abiwan.hwan.plot(title="Matrix elements in real space");


# To interpolate the KS energies along a high-symmetry k-path and construct 
# a new `ElectronBands` object, use:

# In[9]:


ebands_kpath = abiwan.interpolate_ebands()


# In[10]:


ebands_kpath.plot(title="Wannier-interpolated");


# If you need an IBZ sampling instead of a k-path, for instance a 36x36x36 k-mesh, use:

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ebands_kmesh = abiwan.interpolate_ebands(ngkpt=(36, 36, 36))


# As we are dealing with AbiPy objects, we can easily reuse the AbiPy API to plot bands + DOS:

# In[12]:


ebands_kpath.plot_with_edos(ebands_kmesh.get_edos(), title="Wannier-interpolated bands and DOS");


# We can also compare an ab-initio band structure with the Wannier-interpolated results.
# This is useful to understand if our wannier functions are well localized and if the 
# k-mesh used with wannier90 is dense enough.
# 
# In this case, it is just a matter of passing the path to the netcdf file 
# containing the ab-initio band structure to the `get_plotter_from_ebands` method of `abiwan`.
# The function interpolates the band energies using the k-path found in the netcdf file 
# and returns a plotter object:

# In[14]:


import abipy.data as abidata
gsr_path = abidata.ref_file("si_nscf_GSR.nc")

plotter = abiwan.get_plotter_from_ebands(gsr_path)


# Then we call `combiplot` to plot the two band structures on the same figure:

# In[15]:


plotter.combiplot();


# As we can see, the interpolated band structures is not completely on top of the ab-initio
# results. To improve the agreement we should try to reduced the spread and/or increase 
# the density of the k-mesh used in the wannierization procedure.

# Back to the main [Index](index.ipynb)

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