def main():
"""
The main config-writer routine
"""
# Generate rock priors
rockpriormu, rockpriorcov = compute_rock_priors(config_petrofn)
# Read in grav data and convert to the standard format.
# For Hugo's data, divide by 10 to convert from um/s^2 to mgal.
gravdata = pd.read_csv(gravdata_fn, names=sensor_colnames,
dtype=float, skiprows=1)
gravdata.val /= 10.0
# Read in mag data and convert to the standard format.
magdata = pd.read_csv(magdata_fn, names=sensor_colnames,
dtype=float, skiprows=1)
# Read in field observation data and convert to the standard format.
fielddata = convert_ground_truth(fielddata_fn)
# Fill remaining fields in config_params
config_params.update({ 'grav_data': gravdata,
'mag_data': magdata,
'field_data': fielddata,
'rockpriormu': rockpriormu,
'rockpriorcov': rockpriorcov, })
write_config(**config_params)
def main():
"""
The main config-writer routine
"""
# Generate rock priors
generate_rock_data(layer_rockprops, source_petrofn, config_petrofn)
rockpriormu, rockpriorcov = compute_rock_priors(config_petrofn)
# Read prospector NPZ
prospect = np.load('synthetic.npz')
gravsynth = prospect['gravReadings'][0]
gravsynth -= np.mean(gravsynth)
# gravsynth += 0.05*np.std(gravsynth)*np.random.normal(size=gravsynth.shape)
with open('gravsynth.csv', 'w') as csvfile:
csvfile.write(
pd.DataFrame(gravsynth).to_csv(index=False, header=False))
magsynth = prospect['magReadings'][0]
magsynth -= np.mean(magsynth)
# magsynth += 0.05*np.std(magsynth)*np.random.normal(size=magsynth.shape)
with open('magsynth.csv', 'w') as csvfile:
csvfile.write(pd.DataFrame(magsynth).to_csv(index=False, header=False))
# Read in grav data and convert to the standard format.
# For Hugo's data, divide by 10 to convert from um/s^2 to mgal.
gravdata = pd.read_csv(gravdata_fn,
names=sensor_colnames,
dtype=float,
skiprows=1)
gravdata.val /= 10.0
# Read in mag data and convert to the standard format.
magdata = pd.read_csv(magdata_fn,
names=sensor_colnames,
dtype=float,
skiprows=1)
# Read in field observation data and convert to the standard format.
fielddata = generate_field_data(layer_rockprops, fielddata_fn, fieldsrc_fn)
return
# Fill remaining fields in config_params
config_params.update({
'grav_data': gravdata,
'mag_data': magdata,
'field_data': fielddata,
'rockpriormu': rockpriormu,
'rockpriorcov': rockpriorcov,
})
write_config(**config_params)
session_id = out['s'][0]
if plus_one:
session_id += 1
return '{}_{}'.format(out['sessionHost'][0], session_id)
with open(weights_list_path, 'r') as fn:
weights_list = json.load(fn)
config_mapping = (dict() if not os.path.exists(config_mapping_path)
else load_pickles(config_mapping_path))
for weights in weights_list:
logger.info('Running with weights: {}'.format(weights))
update_cfg_path = os.path.join(sims_fbs_config_path, 'updated.cfg')
write_config(weights, update_cfg_path)
next_session_id = get_latest_sessionid()
logger.info('Running opsim with session ID: {}'.format(next_session_id))
repo.git.checkout('-b', 'weights/{}'.format(next_session_id))
try:
execute(['./run_opsim.sh {} "{}"'.format(run_dir, opsim_flags)])
except Exception as e:
repo.git.branch('-d', 'weights/{}'.format(next_session_id))
repo.git.checkout('batchrun')
logger.error('Error occurred in running feature based scheduler, '
'please check opsim log files')
logger.error(e)
if os.path.exists(update_cfg_path):
logger.error('Error occurred, removing updated.cfg')
os.remove(update_cfg_path)
def test_write_config():
"""
Unit test of write_config.
"""
write_config(**config_params)