phono3py-get-kappa

#!/usr/bin/env python


# -------
# Imports
# -------

import csv
import os

import numpy as np

from argparse import ArgumentParser

from Phono3pyPowerTools.Phono3pyIO import Phono3pyKappaHDF5
from Phono3pyPowerTools.Utilities import OpenForCSVWriter


# ----
# Main
# ----

if __name__ == "__main__":
    # Command-line arguments.

    parser = ArgumentParser(
        description = "Get k_latt(T) from Phono3py kappa-*.hdf5 files and save to CSV"
        )

    parser.add_argument(
        metavar = "input_file",
        nargs = '+', dest = 'InputFiles',
        help = "Phono3py kappa-*.hdf5 files to read"
        )

    parser.add_argument(
        "-o", "--output-file",
        metavar = "output_file",
        dest = "OutputFile",
        help = "Output file to write (single input file only)"
        )

    args = parser.parse_args()

    # Sanity check.

    if args.OutputFile is not None and len(args.InputFiles) > 1:
        raise Exception("Error: The -o/--output-file option can only be used when one input file is specified.")

    # Loop over input files.

    for input_file in args.InputFiles:
        # Load input file.

        kappa_hdf5 = Phono3pyKappaHDF5(input_file)

        # Prepare output data.

        header_row = [
            "T [K]",
            "k_xx [W/m.K]", "k_yy [W/m.K]", "k_zz [W/m.K]",
            "k_yz [W/m.K]", "k_xz [W/m.K]", "k_xy [W/m.K]",
            "k_iso [W/m.K]"
            ]

        output_data = []
        
        temps = kappa_hdf5.GetTemperature()

        if kappa_hdf5.IsLBTE:
            if kappa_hdf5.IsWigner:
                # LBTE calculation with Wigner correction.
                
                output_data = [
                    (temps, kappa_hdf5.GetKappa(wigner_mode = 'k_p'), "K_P_LBTE"),
                    (temps, kappa_hdf5.GetKappa(lbte_rta = True, wigner_mode = 'k_p'), "K_P_RTA"),
                    (temps, kappa_hdf5.GetKappa(wigner_mode = 'k_c'), "K_C"),
                    (temps, kappa_hdf5.GetKappa(wigner_mode = 'k_tot'), "K_Tot_LBTE"),
                    (temps, kappa_hdf5.GetKappa(lbte_rta = True, wigner_mode = 'k_tot'), "K_Tot_RTA"),
                    ]
            else:
                # LBTE calculation.
                
                output_data = [
                    (temps, kappa_hdf5.GetKappa(), "LBTE"),
                    (temps, kappa_hdf5.GetKappa(lbte_rta = True), "RTA")
                    ]
        else:
            # RTA calculation with Wigner correction.
            
            if kappa_hdf5.IsWigner:
                output_data = [
                    (temps, kappa_hdf5.GetKappa(wigner_mode = 'k_p'), "K_P"),
                    (temps, kappa_hdf5.GetKappa(wigner_mode = 'k_c'), "K_C"),
                    (temps, kappa_hdf5.GetKappa(wigner_mode = 'k_tot'), "K_Tot")
                    ]
            else:
                # RTA calculation.
                
                output_data = [
                    (temps, kappa_hdf5.GetKappa(), None)
                    ]

        for temps_out, kappa_out, output_suffix in output_data:
            data_rows = []

            for t, k in zip(temps_out, kappa_out):
                k_iso = np.mean(k[:3])

                data_rows.append(
                    [t] + [k_ab for k_ab in k] + [k_iso]
                    )

            # Name for output file.

            output_file = args.OutputFile

            if output_file is None:
                # Generate an output file name based on the input file:
                #   kappa-m*.hdf5 --> kappa-m*.csv

                _, tail = os.path.split(input_file)
                root, _ = os.path.splitext(tail)

                output_file = r"{0}.csv".format(root)

            if output_suffix is not None:
                root, ext = os.path.splitext(output_file)

                output_file = "{0}.{1}{2}".format(root, output_suffix, ext)

            # Write output file.

            with OpenForCSVWriter(output_file) as output_writer:
                output_writer_csv = csv.writer(output_writer)

                output_writer_csv.writerow(header_row)

                for data_row in data_rows:
                    output_writer_csv.writerow(data_row)