Running GeoBIPy to invert Resolve data

import os
import sys
import pathlib
from datetime import timedelta
import time
import numpy as np
from geobipy import Inference3D
from geobipy import user_parameters
from geobipy import get_prng

def checkCommandArguments():
    """Check the users command line arguments. """
    import argparse
    # warnings.filterwarnings('error')

    Parser = argparse.ArgumentParser(description="GeoBIPy",
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    Parser.add_argument('--index', default=0, type=int, help='job array index 0-18')
    Parser.add_argument('--data', default=None, help="Data type. Choose from ['skytem_512', 'tempest', 'resolve']")
    Parser.add_argument('--model', default=None, help="Model type. Choose from ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']")

    return Parser.parse_args()

np.random.seed(0)

args = checkCommandArguments()
sys.path.append(os.getcwd())

models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
data_type = "Resolve"
model_type = models[args.index]

The directory where HDF files will be stored %%

file_path = os.path.join(data_type, model_type)
pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)

for filename in os.listdir(file_path):
    try:
        if os.path.isfile(file_path) or os.path.islink(file_path):
            os.unlink(file_path)
    except Exception as e:
        print('Failed to delete %s. Reason: %s' % (file_path, e))

output_directory = file_path

data_filename = data_type + '_' + model_type

supplementary = "..//..//supplementary//"

parameter_file = supplementary + "//options_files//{}_options".format(data_type)
inputFile = pathlib.Path(parameter_file)
assert inputFile.exists(), Exception("Cannot find input file {}".format(inputFile))

output_directory = pathlib.Path(output_directory)
assert output_directory.exists(), Exception("Make sure the output directory exists {}".format(output_directory))

print('Using user input file {}'.format(parameter_file))
print('Output files will be produced at {}'.format(output_directory))

kwargs = user_parameters.read(inputFile)

kwargs['n_markov_chains'] = 5000

kwargs['data_filename'] = supplementary + '//data//' + data_filename + '.csv'
kwargs['system_filename'] = supplementary + "//data//" + kwargs['system_filename']

# Everyone needs the system classes read in early.
data = kwargs['data_type']._initialize_sequential_reading(kwargs['data_filename'], kwargs['system_filename'])

# Start keeping track of time.
t0 = time.time()

seed = 146100583096709124601953385843316024947
prng = get_prng(seed=seed)

inference3d = Inference3D(data, prng=prng)
inference3d.create_hdf5(directory=output_directory, **kwargs)

print("Created hdf5 files in {} h:m:s".format(str(timedelta(seconds=time.time()-t0))))

inference3d.infer(index=30, **kwargs)

Using user input file ..//..//supplementary////options_files//Resolve_options
Output files will be produced at Resolve/glacial
Creating HDF5 files, this may take a few minutes...
Files are being created for data files ..//..//supplementary////data//Resolve_glacial.csv and system files ..//..//supplementary////data//..//data/FdemSystem2.stm
Created hdf5 file for line 0.0 with 79 data points
Created hdf5 files 79 total data points
Created hdf5 files in 0:00:00.164874 h:m:s
i=5000, k=1, acc=*49.200, 0.006 s/Model, 28.026 s Elapsed, eta=--:--:-- h:m:s

Remaining Points -30/1 || Elapsed Time: 0:00:28.993323 h:m:s || ETA 0:00:00.935268 h:m:s