# Argname, description, dtype, default
ARGS = [
('cube-path', 'path to the seismic cube in HDF5 format', str, None),
('savedir', 'path to save files to', str, '_placeholder_'),
('metrics', 'metrics to compute for quality map creation', str, ['support_hellinger']),
('add-prefix', 'whether to prepend cube name to the saved file names', str2bool, True),
('save-txt', 'whether to save point cloud of quality map to disk', str2bool, False),
]
if __name__ == '__main__':
config = make_config(MSG, ARGS, os.path.basename(__file__).split('.')[0])
if config['savedir'] == '_placeholder_':
config['savedir'] = os.path.dirname(config['cube-path'])
geometry = SeismicGeometry(config['cube-path'])
geometry.make_quality_map([0.1, 0.15, 0.2, 0.4, 0.5], config['metrics'])
prefix = '' if config['add-prefix'] is False else geometry.short_name + '_'
safe_mkdir(config['savedir'])
plot_image(geometry.quality_map, cmap='Reds',
xlabel='INLINE_3D', ylabel='CROSSLINE_3D', title='',
savepath=os.path.join(config['savedir'], f'{prefix}quality_map.png'))
if config['save-txt']:
save_point_cloud(geometry.quality_map,
os.path.join(config['savedir'], f'{prefix}quality_map.txt'),
geometry)
cm = HorizonMetrics(carcass)
for metric_name in config['metrics']:
kwargs = copy(LOCAL_KWARGS) if metric_name.startswith(
'local') else copy(SUPPORT_KWARGS)
kwargs = {} if metric_name.startswith('insta') else kwargs
savepath = os.path.join(config['savedir'],
f'{prefix}carcass_{metric_name}')
metric = cm.evaluate(metric_name, **kwargs)
metric = metric if len(carcass) > (
len(horizon) // 10) else enlarge_carcass_metric(
metric, geometry)
plot_image(metric,
figsize=(20, 20),
cmap=METRIC_CMAP,
zmin=-1,
zmax=1,
fill_color='black',
savepath=savepath + '.png')
row_dict[f'carcass_{metric_name}'] = np.nanmean(metric)
if config['save-files']:
horizon.dump(os.path.join(config['savedir'],
f'{horizon.name}_carcass'),
add_height=False)
else:
row_dict = {
**row_dict,
'carcass_name': '',
'carcass_path': '',
**{metric_name: 0
kwargs = copy(LOCAL_KWARGS) if metric_name.startswith(
'local') else copy(SUPPORT_KWARGS)
kwargs = {} if metric_name.startswith('insta') else kwargs
savepath = os.path.join(
config['savedir'],
f'{prefix}{other_prefix}_{metric_name}')
metric = om.evaluate(metric_name, **kwargs)
if other.is_carcass:
metric = enlarge_carcass_metric(metric, geometry)
plot_image(metric,
figsize=(20, 20),
cmap=METRIC_CMAP,
zmin=-1,
zmax=1,
fill_color='black',
xlabel='INLINE_3D',
ylabel='CROSSLINE_3D',
savepath=savepath + '.png')
row_dict[f'{other_prefix}_{metric_name}'] = np.nanmean(
metric)
if config['save-files']:
shutil.copy2(
other.path,
os.path.join(config['savedir'],
f'{horizon.name}_{other_prefix}'))
else:
row_dict = {
**row_dict,
row_dict = {
**row_dict,
'carcass_name': carcass.name,
'carcass_path': os.path.join(*carcass.path.split('/')[-2:]),
}
cm = HorizonMetrics(carcass)
for metric_name in config['metrics']:
kwargs = copy(LOCAL_KWARGS) if metric_name.startswith('local') else copy(SUPPORT_KWARGS)
kwargs = {} if metric_name.startswith('insta') else kwargs
savepath = os.path.join(config['savedir'], f'{prefix}carcass_{metric_name}')
metric = cm.evaluate(metric_name, **kwargs)
plot_image(enlarge_carcass_metric(metric, geometry), figsize=(20, 20),
cmap=METRIC_CMAP, zmin=-1, zmax=1, fill_color='black',
savepath=savepath + '.png')
row_dict[f'carcass_{metric_name}'] = np.nanmean(metric)
if config['save-files']:
horizon.dump(os.path.join(config['savedir'], f'{horizon.name}_carcass'), add_height=False)
else:
row_dict = {
**row_dict,
'carcass_name': '',
'carcass_path': '',
**{metric_name: 0 for metric_name in config['metrics']},
}
dataframe.append(row_dict)
pd.DataFrame(dataframe).to_csv(os.path.join(config['savedir'], 'report.csv'), sep=',', index=False)