def read_atl09_ancillary_data(atl09filepath):
# Iterate through keys for ancillary data
subgroup = '/ancillary_data/'
keys = getH5Keys(atl09filepath, subgroup)
key_info = get_H5_keys_info(atl09filepath, subgroup)
byte_encoded = ['control', 'data_start_utc', 'data_end_utc',
'granule_start_utc', 'granule_end_utc']
# Read each key, put it in pandas df
for idx, key in enumerate(keys):
data = readAtlH5(atl09filepath, subgroup + '/' + key + '/')
if np.isin(key, ['release', 'version']):
data = int(data)
if np.isin(key, byte_encoded):
data = data[0]
data = data.decode('utf-8')
if key_info[idx][1] != 'Group':
if len(key.split('/')) == 1:
if idx == 0:
df = pd.Series(data,
index=[key.split('/')[-1]],
dtype=object)
else:
df = pd.concat(
[df, pd.Series(data,
index=[key.split('/')[-1]],
dtype=object)])
return df
def check_for_matching_segments(atl09file, atl03file_list, atl09basepath,
atl03basepath):
atl03_overlap_with_atl09 = pd.DataFrame(
columns=['starting_seg', 'ending_seg'])
if len(atl03file_list) == 0:
print('WARNING No matching atl03 file found')
else:
atl09_segment_ids = readAtlH5(atl09basepath + atl09file,
'profile_1/high_rate/segment_id')
sc_orient = readAtlH5(atl09basepath + atl09file,
'orbit_info/sc_orient')
if len(sc_orient) == 1 and sc_orient[0] == 0:
gt = 'gt1l'
elif len(sc_orient) == 1 and sc_orient[0] == 1:
gt = 'gt1r'
else:
print(
'WARNING the spacecraft transitions orientation during this granule'
)
for atl03file in atl03file_list:
atl03_segment_ids = readAtlH5(atl03basepath + atl03file,
gt + '/geolocation/segment_id')
segments_in_both = np.isin(atl09_segment_ids, atl03_segment_ids)
if np.any(segments_in_both):
starting_seg = atl09_segment_ids[segments_in_both][0]
ending_seg = atl09_segment_ids[segments_in_both][-1]
atl03_overlap_with_atl09.loc[atl03file,
'starting_seg'] = starting_seg
atl03_overlap_with_atl09.loc[atl03file,
'ending_seg'] = ending_seg
return atl03_overlap_with_atl09
def read_atl09_orbit_info(atl09filepath):
subgroup = '/orbit_info/'
keys = getH5Keys(atl09filepath, subgroup)
key_info = get_H5_keys_info(atl09filepath, subgroup)
# Read each key, put it in pandas df
for idx, key in enumerate(keys):
data = readAtlH5(atl09filepath, subgroup + '/' + key + '/')
if idx == 0:
df = pd.Series(data, index=[key], dtype=object)
else:
df_key = pd.Series(data, index=[key], dtype=object)
df = pd.concat([df, df_key])
return df
def read_atl09_hr_profile(atl09filepath, gt):
# Iterate through keys for "Land Segments"
subgroup = 'profile_' + gt[2] + '/high_rate/'
keys = getH5Keys(atl09filepath, subgroup)
key_info = get_H5_keys_info(atl09filepath, subgroup)
# Read each key, put it in pandas df
for idx, key in enumerate(keys):
data = readAtlH5(atl09filepath, subgroup + '/' + key + '/')
if key == 'ds_layers':
ds_layers = np.array(data)
elif key == 'ds_va_bin_h':
ds_va_bin_h = np.array(data)
elif key == 'cab_prof':
cab_prof = np.array(data)
elif key == 'density_pass1':
density_pass1 = np.array(data)
elif key == 'density_pass2':
density_pass2 = np.array(data)
else:
if key_info[idx][1] != 'Group':
if idx == 0:
df = pd.DataFrame(data, columns=[key.split('/')[-1]])
else:
if len(data.shape) == 2:
cols = data.shape[1]
for idx2 in range(0, cols):
df = pd.concat(
[df,pd.DataFrame(data[:,idx2],columns=\
[key.split('/')[-1] +'_' +
str(idx2)])],axis=1)
else:
df = pd.concat(
[df,pd.DataFrame(data,columns=\
[key.split('/')[-1]])],axis=1)
return df, ds_layers, ds_va_bin_h, cab_prof, density_pass1, density_pass2