def test_write_dataframe():
from lstchain.io.io import write_dataframe
df = pd.DataFrame(
{
"x": np.random.normal(size=10),
"N": np.random.poisson(5, size=10),
}
)
with tempfile.NamedTemporaryFile() as f:
write_dataframe(df, f.name, "data/awesome_table")
with tables.open_file(f.name) as h5_file:
# make sure nothing else in this group
# (e.g. like pandas writes _i_ tables)
assert h5_file.root.data._v_children.keys() == {"awesome_table"}
table = h5_file.root.data.awesome_table[:]
for col in df.columns:
np.testing.assert_array_equal(table[col], df[col])
# test it's also readable by pandas directly
df_read = pd.read_hdf(f.name, "data/awesome_table")
assert df.equals(df_read)
# and with astropy
t = Table.read(f.name, "data/awesome_table")
for col in df.columns:
np.testing.assert_array_equal(t[col], df[col])
def main():
args = parser.parse_args()
dl1_filename = os.path.abspath(args.input_file)
config = get_standard_config()
if args.config_file is not None:
try:
config = read_configuration_file(os.path.abspath(args.config_file))
except ("Custom configuration could not be loaded !!!"):
pass
dl1_params = pd.read_hdf(dl1_filename, key=dl1_params_lstcam_key)
subarray_info = SubarrayDescription.from_hdf(dl1_filename)
tel_id = config["allowed_tels"][0] if "allowed_tels" in config else 1
focal_length = subarray_info.tel[tel_id].optics.equivalent_focal_length
src_dep_df = pd.concat(get_source_dependent_parameters(
dl1_params, config, focal_length=focal_length),
axis=1)
metadata = global_metadata()
write_dataframe(src_dep_df,
dl1_filename,
dl1_params_src_dep_lstcam_key,
config=config,
meta=metadata)
def test_write_dataframe():
a = np.ones(3)
df = pd.DataFrame(a, columns=['a'])
with tempfile.NamedTemporaryFile() as f:
io.write_dataframe(df, f.name, 'data/awesome_table')
with tables.open_file(f.name) as file:
np.testing.assert_array_equal(file.root.data.awesome_table[:]['a'], a)
def main():
dl1_filename = os.path.abspath(args.input_file)
config = {}
if args.config_file is not None:
try:
config = read_configuration_file(os.path.abspath(args.config_file))
except ("Custom configuration could not be loaded !!!"):
pass
dl1_params = pd.read_hdf(dl1_filename, key=dl1_params_lstcam_key)
src_dep_df = get_source_dependent_parameters(dl1_params, config)
write_dataframe(src_dep_df, dl1_filename, dl1_params_src_dep_lstcam_key)
def test_write_dataframe_index():
"""Test that also an index can be written."""
from lstchain.io.io import write_dataframe
df = pd.DataFrame({
'x': np.random.normal(size=10),
'N': np.random.poisson(5, size=10),
})
df.index.name = 'event_id'
with tempfile.NamedTemporaryFile() as f:
write_dataframe(df, f.name, 'data/awesome_table', index=True)
with tables.open_file(f.name) as file:
table = file.root.data.awesome_table[:]
for col in df.columns:
np.testing.assert_array_equal(table[col], df[col])
np.testing.assert_array_equal(table['event_id'], df.index)
def test_write_dataframe():
from lstchain.io import config, global_metadata
from lstchain.io.io import write_dataframe
df = pd.DataFrame({
"x": np.random.normal(size=10),
"N": np.random.poisson(5, size=10),
})
config = config.get_standard_config()
with tempfile.NamedTemporaryFile() as f:
meta = global_metadata()
write_dataframe(df,
f.name,
"data/awesome_table",
config=config,
meta=meta)
with tables.open_file(f.name) as h5_file:
# make sure nothing else in this group
# (e.g. like pandas writes _i_ tables)
assert h5_file.root.data._v_children.keys() == {"awesome_table"}
table = h5_file.root.data.awesome_table[:]
for col in df.columns:
np.testing.assert_array_equal(table[col], df[col])
# test global metadata and config are properly written
for k in meta.keys():
assert meta[k] == h5_file.root.data.awesome_table.attrs[k]
assert config == h5_file.root.data.awesome_table.attrs["config"]
# test it's also readable by pandas directly
df_read = pd.read_hdf(f.name, "data/awesome_table")
assert df.equals(df_read)
# and with astropy
t = Table.read(f.name, "data/awesome_table")
for col in df.columns:
np.testing.assert_array_equal(t[col], df[col])
def main():
args = parser.parse_args()
custom_config = {}
if args.config_file is not None:
try:
custom_config = read_configuration_file(
os.path.abspath(args.config_file))
except ("Custom configuration could not be loaded !!!"):
pass
config = replace_config(standard_config, custom_config)
data = pd.read_hdf(args.input_file, key=dl1_params_lstcam_key)
if 'lh_fit_config' in config.keys():
lhfit_data = pd.read_hdf(args.input_file,
key=dl1_likelihood_params_lstcam_key)
if np.all(lhfit_data['obs_id'] == data['obs_id']) & np.all(
lhfit_data['event_id'] == data['event_id']):
lhfit_data.drop({'obs_id', 'event_id'}, axis=1, inplace=True)
lhfit_keys = lhfit_data.keys()
data = pd.concat([data, lhfit_data], axis=1)
# if real data, add deltat t to dataframe keys
data = add_delta_t_key(data)
# Dealing with pointing missing values. This happened when `ucts_time` was invalid.
if 'alt_tel' in data.columns and 'az_tel' in data.columns \
and (np.isnan(data.alt_tel).any() or np.isnan(data.az_tel).any()):
# make sure there is a least one good pointing value to interp from.
if np.isfinite(data.alt_tel).any() and np.isfinite(data.az_tel).any():
data = impute_pointing(data)
else:
data.alt_tel = -np.pi / 2.
data.az_tel = -np.pi / 2.
# Get trained RF path for reconstruction:
file_reg_energy = os.path.join(args.path_models, 'reg_energy.sav')
file_cls_gh = os.path.join(args.path_models, 'cls_gh.sav')
if config['disp_method'] == 'disp_vector':
file_disp_vector = os.path.join(args.path_models,
'reg_disp_vector.sav')
elif config['disp_method'] == 'disp_norm_sign':
file_disp_norm = os.path.join(args.path_models, 'reg_disp_norm.sav')
file_disp_sign = os.path.join(args.path_models, 'cls_disp_sign.sav')
subarray_info = SubarrayDescription.from_hdf(args.input_file)
tel_id = config["allowed_tels"][0] if "allowed_tels" in config else 1
focal_length = subarray_info.tel[tel_id].optics.equivalent_focal_length
# Apply the models to the data
# Source-independent analysis
if not config['source_dependent']:
data = filter_events(
data,
filters=config["events_filters"],
finite_params=config['energy_regression_features'] +
config['disp_regression_features'] +
config['particle_classification_features'] +
config['disp_classification_features'],
)
if config['disp_method'] == 'disp_vector':
dl2 = dl1_to_dl2.apply_models(data,
file_cls_gh,
file_reg_energy,
reg_disp_vector=file_disp_vector,
focal_length=focal_length,
custom_config=config)
elif config['disp_method'] == 'disp_norm_sign':
dl2 = dl1_to_dl2.apply_models(data,
file_cls_gh,
file_reg_energy,
reg_disp_norm=file_disp_norm,
cls_disp_sign=file_disp_sign,
focal_length=focal_length,
custom_config=config)
# Source-dependent analysis
if config['source_dependent']:
# if source-dependent parameters are already in dl1 data, just read those data.
if dl1_params_src_dep_lstcam_key in get_dataset_keys(args.input_file):
data_srcdep = get_srcdep_params(args.input_file)
# if not, source-dependent parameters are added now
else:
data_srcdep = pd.concat(dl1_to_dl2.get_source_dependent_parameters(
data, config, focal_length=focal_length),
axis=1)
dl2_srcdep_dict = {}
srcindep_keys = data.keys()
srcdep_assumed_positions = data_srcdep.columns.levels[0]
for i, k in enumerate(srcdep_assumed_positions):
data_with_srcdep_param = pd.concat([data, data_srcdep[k]], axis=1)
data_with_srcdep_param = filter_events(
data_with_srcdep_param,
filters=config["events_filters"],
finite_params=config['energy_regression_features'] +
config['disp_regression_features'] +
config['particle_classification_features'] +
config['disp_classification_features'],
)
if config['disp_method'] == 'disp_vector':
dl2_df = dl1_to_dl2.apply_models(
data_with_srcdep_param,
file_cls_gh,
file_reg_energy,
reg_disp_vector=file_disp_vector,
focal_length=focal_length,
custom_config=config)
elif config['disp_method'] == 'disp_norm_sign':
dl2_df = dl1_to_dl2.apply_models(data_with_srcdep_param,
file_cls_gh,
file_reg_energy,
reg_disp_norm=file_disp_norm,
cls_disp_sign=file_disp_sign,
focal_length=focal_length,
custom_config=config)
dl2_srcdep = dl2_df.drop(srcindep_keys, axis=1)
dl2_srcdep_dict[k] = dl2_srcdep
if i == 0:
dl2_srcindep = dl2_df[srcindep_keys]
os.makedirs(args.output_dir, exist_ok=True)
output_file = os.path.join(
args.output_dir,
os.path.basename(args.input_file).replace('dl1', 'dl2', 1))
if os.path.exists(output_file):
raise IOError(output_file + ' exists, exiting.')
dl1_keys = get_dataset_keys(args.input_file)
if dl1_images_lstcam_key in dl1_keys:
dl1_keys.remove(dl1_images_lstcam_key)
if dl1_params_lstcam_key in dl1_keys:
dl1_keys.remove(dl1_params_lstcam_key)
if dl1_params_src_dep_lstcam_key in dl1_keys:
dl1_keys.remove(dl1_params_src_dep_lstcam_key)
if dl1_likelihood_params_lstcam_key in dl1_keys:
dl1_keys.remove(dl1_likelihood_params_lstcam_key)
metadata = global_metadata()
write_metadata(metadata, output_file)
with open_file(args.input_file, 'r') as h5in:
with open_file(output_file, 'a') as h5out:
# Write the selected DL1 info
for k in dl1_keys:
if not k.startswith('/'):
k = '/' + k
path, name = k.rsplit('/', 1)
if path not in h5out:
grouppath, groupname = path.rsplit('/', 1)
g = h5out.create_group(grouppath,
groupname,
createparents=True)
else:
g = h5out.get_node(path)
h5in.copy_node(k, g, overwrite=True)
# need container to use lstchain.io.add_global_metadata and lstchain.io.add_config_metadata
if not config['source_dependent']:
if 'lh_fit_config' not in config.keys():
write_dl2_dataframe(dl2, output_file, config=config, meta=metadata)
else:
dl2_onlylhfit = dl2[lhfit_keys]
dl2.drop(lhfit_keys, axis=1, inplace=True)
write_dl2_dataframe(dl2, output_file, config=config, meta=metadata)
write_dataframe(dl2_onlylhfit,
output_file,
dl2_likelihood_params_lstcam_key,
config=config,
meta=metadata)
else:
write_dl2_dataframe(dl2_srcindep,
output_file,
config=config,
meta=metadata)
write_dataframe(pd.concat(dl2_srcdep_dict, axis=1),
output_file,
dl2_params_src_dep_lstcam_key,
config=config,
meta=metadata)
def main():
custom_config = {}
if args.config_file is not None:
try:
custom_config = read_configuration_file(
os.path.abspath(args.config_file))
except ("Custom configuration could not be loaded !!!"):
pass
config = replace_config(standard_config, custom_config)
data = pd.read_hdf(args.input_file, key=dl1_params_lstcam_key)
# if real data, add deltat t to dataframe keys
data = add_delta_t_key(data)
# Dealing with pointing missing values. This happened when `ucts_time` was invalid.
if 'alt_tel' in data.columns and 'az_tel' in data.columns \
and (np.isnan(data.alt_tel).any() or np.isnan(data.az_tel).any()):
# make sure there is a least one good pointing value to interp from.
if np.isfinite(data.alt_tel).any() and np.isfinite(data.az_tel).any():
data = impute_pointing(data)
else:
data.alt_tel = -np.pi / 2.
data.az_tel = -np.pi / 2.
#Load the trained RF for reconstruction:
fileE = args.path_models + "/reg_energy.sav"
fileD = args.path_models + "/reg_disp_vector.sav"
fileH = args.path_models + "/cls_gh.sav"
reg_energy = joblib.load(fileE)
reg_disp_vector = joblib.load(fileD)
cls_gh = joblib.load(fileH)
#Apply the models to the data
#Source-independent analysis
if not config['source_dependent']:
data = filter_events(
data,
filters=config["events_filters"],
finite_params=config['regression_features'] +
config['classification_features'],
)
dl2 = dl1_to_dl2.apply_models(data,
cls_gh,
reg_energy,
reg_disp_vector,
custom_config=config)
#Source-dependent analysis
if config['source_dependent']:
data_srcdep = pd.read_hdf(args.input_file,
key=dl1_params_src_dep_lstcam_key)
data_srcdep.columns = pd.MultiIndex.from_tuples([
tuple(col[1:-1].replace('\'', '').replace(' ', '').split(","))
for col in data_srcdep.columns
])
dl2_srcdep_dict = {}
for i, k in enumerate(data_srcdep.columns.levels[0]):
data_with_srcdep_param = pd.concat([data, data_srcdep[k]], axis=1)
data_with_srcdep_param = filter_events(
data_with_srcdep_param,
filters=config["events_filters"],
finite_params=config['regression_features'] +
config['classification_features'],
)
dl2_df = dl1_to_dl2.apply_models(data_with_srcdep_param,
cls_gh,
reg_energy,
reg_disp_vector,
custom_config=config)
dl2_srcdep = dl2_df.drop(data.keys(), axis=1)
dl2_srcdep_dict[k] = dl2_srcdep
if i == 0:
dl2_srcindep = dl2_df.drop(data_srcdep[k].keys(), axis=1)
os.makedirs(args.output_dir, exist_ok=True)
output_file = os.path.join(
args.output_dir,
os.path.basename(args.input_file).replace('dl1', 'dl2'))
if os.path.exists(output_file):
raise IOError(output_file + ' exists, exiting.')
dl1_keys = get_dataset_keys(args.input_file)
if dl1_images_lstcam_key in dl1_keys:
dl1_keys.remove(dl1_images_lstcam_key)
if dl1_params_lstcam_key in dl1_keys:
dl1_keys.remove(dl1_params_lstcam_key)
if dl1_params_src_dep_lstcam_key in dl1_keys:
dl1_keys.remove(dl1_params_src_dep_lstcam_key)
with open_file(args.input_file, 'r') as h5in:
with open_file(output_file, 'a') as h5out:
# Write the selected DL1 info
for k in dl1_keys:
if not k.startswith('/'):
k = '/' + k
path, name = k.rsplit('/', 1)
if path not in h5out:
grouppath, groupname = path.rsplit('/', 1)
g = h5out.create_group(grouppath,
groupname,
createparents=True)
else:
g = h5out.get_node(path)
h5in.copy_node(k, g, overwrite=True)
if not config['source_dependent']:
write_dl2_dataframe(dl2, output_file)
else:
write_dl2_dataframe(dl2_srcindep, output_file)
write_dataframe(pd.concat(dl2_srcdep_dict, axis=1), output_file,
dl2_params_src_dep_lstcam_key)
