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Download script from get_band_dos.py
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import os
import sys
import json
import pandas as pd
import matplotlib.pyplot as plt

from mp_api.client import MPRester
from pymatgen.core.structure import Structure
from emmet.core.electronic_structure import BSPathType
from pymatgen.electronic_structure.plotter import BSPlotter, DosPlotter
from pymatgen.electronic_structure.core import Spin
from monty.json import MontyEncoder


API_KEY = os.getenv('MP_APIKEY')
if API_KEY is None:
        print("\nError: Can not get MP API Key from the environment var MP_APIKEY\n")
        exit()

formula = None
kpath = None

def usage():
    print()
    print(f"Usage: python {sys.argv[0]} formula\n")

def update_vars():
    global formula

    argv = sys.argv
    narg = len(argv)
    if narg <= 1:
        print("\nError: Chemical formula must be given as the first arg\n")
        input("Press ENTER to terminate>>\n")
        exit()

    formula = argv[1]
    if narg >= 3: kpath = argv[2]

def get_MPReser(API_KEY = None):
    if not API_KEY: API_KEY = os.getenv('MP_APIKEY')
    if API_KEY is None:
        print("\nError: Can not get MP API Key from the environment var MP_APIKEY\n")
        return None

    mpr = MPRester(API_KEY)
    if mpr is None:
        print(f"\nError: Can not get MPRester using the given API_KEY [{API_KEY}]\n")
        return None

    return mpr

def get_material_ids(formula, mpr):
    search_results = mpr.materials.search(formula = formula, fields = [])
    if not search_results:
        print(f"No data found for {formula}")
        return None

    mid_list = []
    for res in search_results:
        mid_list.append(res.material_id)

    return mid_list


def get_band_data():
    mpr = get_MPReser()
    if not mpr: exit()

    mid_list = get_material_ids(formula, mpr)
    print("Material IDs:", mid_list)

    print()
    for mid in mid_list:
        material_data = mpr.materials.summary.search(material_ids = [mid], fields = [])
        if not material_data:
            print(f"No structure data found for material_id {material_id}")
            continue

        structure_dict = material_data[0].structure.as_dict()
        structure = Structure.from_dict(structure_dict)
        cformula = structure.reduced_formula

        if kpath == "Hinuma":
# -- line-mode, Hinuma et al.:
            try:
                bs = mpr.get_bandstructure_by_material_id(mid, path_type = BSPathType.hinuma)
            except:
                pass
        elif kpath == "Latimer-Munro":
# -- line-mode, Latimer-Munro:
            try:
                bs = mpr.get_bandstructure_by_material_id(mid, path_type = BSPathType.latimer_munro)
            except:
                pass
        elif kpath == "Setyawan-Curtarolo":
# -- uniform:
            try:
                bs = mpr.get_bandstructure_by_material_id(mid, line_mode = False)
            except:
                pass
        else:
# -- line-mode, Setyawan-Curtarolo (default):
            try:
                bs = mpr.get_bandstructure_by_material_id(mid)
            except:
                pass

        if bs is None:
            print(f" Band data is not available: Skip")
            continue

        try:
            dos = mpr.get_dos_by_material_id(mid)
        except:
            pass

        if dos is None:
            print(f" DOS data is not available: Skip")
            continue

        print("Saving JSON files...")
        with open(f'band_{cformula}_{mid}.json', 'w') as f:
            json.dump(bs.as_dict(), f, cls=MontyEncoder, indent = 2)

        with open(f'dos_{cformula}_{mid}.json', 'w') as f:
            json.dump(dos.as_dict(), f, cls=MontyEncoder, indent = 2)

        bs_data = []
        for band in bs.bands[Spin.up]:
            for kpoint, energy in zip(bs.kpoints, band):
                bs_data.append([kpoint.frac_coords, energy])

        print(" CSV files...")
        bs_df = pd.DataFrame(bs_data, columns=['K-Point', 'Energy'])
        bs_df.to_csv(f'band_{cformula}_{mid}.csv', index=False)

        dos_data = []
        for energy, dos_value in zip(dos.energies, dos.densities[Spin.up]):
            dos_data.append([energy, dos_value])

        dos_df = pd.DataFrame(dos_data, columns=['Energy', 'Density of States'])
        dos_df.to_csv(f'dos_{cformula}_{mid}.csv', index=False)

        print(" figure files...")
        bs_plotter = BSPlotter(bs)
        plt_bs = bs_plotter.get_plot()
        fig_bs = plt_bs.figure
        fig_bs.savefig(f'band_{cformula}_{mid}.png')

        dos_plotter = DosPlotter()
        dos_plotter.add_dos("Total DOS", dos)
        plt_dos = dos_plotter.get_plot()
        fig_dos = plt_dos.figure
        fig_dos.savefig(f'dos_{cformula}_{mid}.png')


def main():
    update_vars()

    get_band_data(app, cfg)

    usage()
    print()
    input("Press ENTER to terminate>>\n")
    exit()


if __name__ == "__main__":
    main()