def main(args):
print(args.atl08)
atl08file = args.atl08[0].split('/')[-1]
newfilename = 'ATL08_30m_' + atl08file.split('ATL08_')[1]
newatl08file = os.path.join(args.outdir[0], newfilename)
copyfile(args.atl08[0], newatl08file)
h5f = h5py.File(newatl08file, 'a')
gt_list = ['gt1r', 'gt1l', 'gt2r', 'gt2l', 'gt3r', 'gt3l']
for gt in gt_list:
base_key = gt + '/land_segments/30m_segment/'
print('ATL03 Heights')
atl03 = get_atl03_struct(args.atl03[0], gt, args.atl08[0])
print('ATL08 Land Segments')
atl08 = get_atl08_struct(args.atl08[0], gt, atl03)
print('Match ATL08 to ATL03 by segment')
upsampled_atl08_bin = create_atl08_bin(atl03, atl08, res_at=30)
print('Append Data to ATL08')
all_cols = upsampled_atl08_bin.columns
for i in range(0, len(all_cols)):
a = np.array(upsampled_atl08_bin[all_cols[i]])
h5f[base_key + all_cols[i]] = a
h5f.close()
def copy_and_append_atl08(atl03filepath, atl08filepath, out_dir, in_res=30):
atl08file = atl08filepath.split('/')[-1]
newatl08file = out_dir + atl08file
newatl08file = os.join(out_dir, atl08file)
copyfile(atl08filepath, newatl08file)
h5f = h5py.File(newatl08file, 'a')
gt_list = ['gt1r', 'gt1l', 'gt2r', 'gt2l', 'gt3r', 'gt3l']
for gt in gt_list:
base_key = gt + '/land_segments/30m_segment/'
print('ATL03 Heights')
atl03 = get_atl03_struct(atl03filepath, gt, atl08filepath)
print('ATL08 Land Segments')
atl08 = get_atl08_struct(atl08filepath, gt, atl03)
print('Match ATL08 to ATL03 by segment')
upsampled_atl08_bin = create_atl08_bin(atl03, atl08, res_at=in_res)
print('Append Data to ATL08')
all_cols = upsampled_atl08_bin.columns
for i in range(0, len(all_cols)):
a = np.array(upsampled_atl08_bin[all_cols[i]])
h5f[base_key + all_cols[i]] = a
h5f.close()
print('warning: using alt file_03')
print(file_03)
print(file_08)
# if file_03 != '/bigtex_data/data/release/002/ATL03_r002/Finland/ATL03_20181019013011_03120105_002_01.h5':
# continue
if gt_03 != gt_08:
print('gt_03 != gt_08')
print(file_03)
print(file_08)
continue
atl03 = icesatReader.get_atl03_struct(file_03, gt, file_08)
atl03_g = icesatReader.read_atl03_geolocation(file_03, gt)
atl08 = icesatReader.get_atl08_struct(file_08, gt, atl03)
# atl08 = icesatReader.get_atl08_struct(file_08, gt)
# fp = h5.File(file_08, 'r')
# delta_time_08_h5 = np.array(fp[gt + '/land_segments/delta_time'])
# t_08_h5 = delta_time_08_h5 - delta_time_08_h5[0]
# fp.close()
seg_id_beg_08 = np.array(atl08.df.segment_id_beg).astype(int)
seg_id_end_08 = np.array(atl08.df.segment_id_end).astype(int)
t = np.array(atl03.df.time).astype(float)
# t_08 = np.array(atl08.df.time).astype(float)
# t_08 -= t_08[0]
# t_08 = np.array(atl08.df.delta_time).astype(float)
if plot_debug:
# check df_03_ds
fig, ax = ip.make_fig()
ax.plot(df_ground[domain_dataset], df_ground['h_ph'], '.', color=ip.BROWN, label='df_ground h_ph')
ax.plot(df_canopy[domain_dataset], df_canopy['h_ph'], '.', color='C2', label='df_canopy h_ph')
ax.plot(df_03_ds[domain_dataset], df_03_ds[ground_median_dataset], '.', color='r', label=ground_median_dataset)
ax.plot(df_03_ds[domain_dataset], df_03_ds['%dm_canopy_rh_98' % dx], '.', color='C1', label='%dm_canopy_rh_98' % dx)
ax.legend(fontsize=9)
ax.set_title('check df_03_ds vs. df_ground/canopy')
fig.show()
# load in 08, match to 03 ttg
try:
atl08 = ir.get_atl08_struct(file_08, gt, atl03)
except (KeyError, UnboundLocalError) as e:
print(file_08)
print('warning:', e)
continue
df_08 = atl08.df
df_08 = df_08.sort_values(by=[domain_dataset]) # rarely domain can reverse
# assign 1e30 error to np.nan for
# consistency (helps with np.interp)
for key in df_08.columns:
dtype = str(df_08[key].dtype)
# if 'int' in dtype:
# continue
if not ('float' in dtype):
def segment_analysis(header_file_path, kml_bounds_txt1, truthSwathDir,
outFilePath_truth, outFilePath_corrected, epsg_str,
atl03filepath, atl08filepath, gt, min_time, max_time,
outfolder):
# header_file_path =\
# '/LIDAR/server/USERS/eric/1_experiment/Finland_HeaderData.mat'
# kml_bounds_txt1 = '/LIDAR/server/USERS/eric/2_production/kmlBounds.txt'
# epsg_string = '32635'
# Read ATL03 Struct
print('Generate ATL03 Struct')
atl03 = get_atl03_struct(atl03filepath,
gt,
atl08filepath,
epsg=epsg_string,
kml_bounds_txt=kml_bounds_txt1,
header_file_path=header_file_path)
# Read ATL03 Geolocation Subgroup as DF
print('Read Geolocation Subgroup')
geolocation = read_atl03_geolocation(atl03filepath, gt)
atl03.df = append_atl03_geolocation(atl03.df,
geolocation,
fields=['segment_id'])
geolocation, gl_rotation_data, gl_epsg = match_atl_to_atl03(
geolocation, atl03)
# Trim Data by Time
print('Trim Data by Time')
atl03.df = atl03.df[atl03.df['time'] < max_time]
geolocation = geolocation[geolocation['time'] < max_time]
atl03.df = atl03.df[atl03.df['time'] > min_time]
geolocation = geolocation[geolocation['time'] > min_time]
atl03.df = atl03.df.reset_index(drop=True)
geolocation = geolocation.reset_index(drop=True)
# Recalculate alongtrack/crosstrack for shortened granule
atl03.df, rotation_data = get_atl_alongtrack(atl03.df)
atl03.rotationData = rotation_data
# "Match" the geolocation df to the ATL03 struct
print('Match Geolocation to ATL03')
geolocation, gl_rotation_data, gl_epsg = match_atl_to_atl03(
geolocation, atl03)
# Convert the ATL03 Struct to the legacy ATL03 Struct
print('Convert Struct to Legacy')
atl03legacy, rotationData, headerData = convert_atl03_to_legacy(atl03)
# Legacy Truth Swath Inputs
buffer = 50 # Distance in cross-track (meters) around ATL03 track
useExistingTruth = False # Option to use existing truth data if it exists
# truthSwathDir = '/laserpewpew/data/validation/data/Finland/LAS_UTM'
# outFilePath_truth = '/LIDAR/server/USERS/eric/1_experiment/finland_analysis/' +\
# 'las/truth'
createTruthFile = True # Option to create output truth .las file
# Call getAtlTruthSwath (with ACE)
print('Run Legacy Truth Swath')
try:
timeStart = time.time()
atlTruthData = getAtlTruth(atl03legacy, headerData, rotationData,
useExistingTruth, truthSwathDir, buffer,
outFilePath_truth, createTruthFile)
atlTruthData.classification[atlTruthData.classification == 3] = 4
atlTruthData.classification[atlTruthData.classification == 5] = 4
timeEnd = time.time()
timeElapsedTotal = timeEnd - timeStart
timeElapsedMin = np.floor(timeElapsedTotal / 60)
timeElapsedSec = timeElapsedTotal % 60
print(' Script Completed in %d min %d sec.' %
(timeElapsedMin, timeElapsedSec))
print('\n')
# outFilePath_corrected = '/LIDAR/server/USERS/eric/1_experiment/' +\
# 'finland_analysis/las/truth_corrected'
print('Run Legacy Offset Coorection')
atlCorrections = legacy_get_meas_error(atl03legacy, atlTruthData,
rotationData,
outFilePath_corrected)
# Apply ATLCorrections to the Geolocation
geolocation.alongtrack = geolocation.alongtrack +\
atlCorrections.alongTrack
geolocation.crosstrack = geolocation.crosstrack +\
atlCorrections.crossTrack
# Apply ATLCorrectuibs to ATL03 Legacy
atl03legacy.alongTrack = atl03legacy.alongTrack +\
atlCorrections.alongTrack
atl03legacy.crossTrack = atl03legacy.crossTrack +\
atlCorrections.crossTrack
atl03legacy.z = atl03legacy.z +\
atlCorrections.z
# Apply ATLCorrections to ATL03 DF
atl03.df.alongtrack = atl03.df.alongtrack +\
atlCorrections.alongTrack
atl03.df.crosstrack = atl03.df.crosstrack +\
atlCorrections.crossTrack
atl03.df.h_ph = atl03.df.h_ph +\
atlCorrections.z
# Run Superfilter Legacy
superTruth, sortedMeasured = superFilter(atl03legacy,
atlTruthData,
xBuf=5.5,
classCode=[])
# Run Perfect Classifier Legacy and assign to ATL03\
truthgroundclass = 2
truthcanopyclass = [3, 4, 5]
unclassedlist = [6, 9, 13, 18]
measpc, measoc = perfectClassifier(sortedMeasured,
superTruth,
ground=[truthgroundclass],
canopy=truthcanopyclass,
unclassed=unclassedlist,
keepsize=True)
# Sort ATL03 by Along Track
##Create new column for Index1
print('Sort Alongtrack')
atl03.df['index1'] = atl03.df.index
##Sort by Along-track
atl03.df = atl03.df.sort_values(by=['alongtrack'])
##Reset Index
atl03.df = atl03.df.reset_index(drop=True)
##Join PC to DF
atl03.df = pd.concat(
[atl03.df,
pd.DataFrame(measpc, columns=['perfect_class'])],
axis=1)
atl03.df = pd.concat(
[atl03.df,
pd.DataFrame(measoc, columns=['generic_class'])],
axis=1)
##Sort by Index1
atl03.df = atl03.df.sort_values(by=['index1'])
##Reset Index
atl03.df = atl03.df.reset_index(drop=True)
##Remove Index1
atl03.df = atl03.df.drop(columns=['index1'])
# Read ATL09
# atl09 = get_atl09_struct(atl09filepath, gt, atl03)
# df_seg = geolocation.merge(atl09.df, on="segment_id",how='left')
# df_seg = df_seg.fillna(method='ffill',limit=14)
# Assign Segment ID Values to Tuth Data
seg_id_truth, include = estimate_segment_id_legacy(
geolocation, gt, superTruth)
# Calculate Error and Meterics
##Filter Truth Data by Include
alongtrack = superTruth.alongTrack.flatten()
crosstrack = superTruth.crossTrack.flatten()
z = superTruth.z.flatten()
easting = superTruth.easting.flatten()
northing = superTruth.northing.flatten()
classification = superTruth.classification.flatten()
intensity = superTruth.intensity.flatten()
alongtrack = alongtrack[include == 1]
crosstrack = crosstrack[include == 1]
z = z[include == 1]
easting = easting[include == 1]
northing = northing[include == 1]
classification = classification[include == 1]
intensity = intensity[include == 1]
seg_id_truth = seg_id_truth[include == 1]
truth_flag = True
except:
print('Truth Failed, continue with rest of code')
atl03.df['perfect_class'] = np.nan
atl03.df['generic_class'] = np.nan
truth_flag = False
##Create ATLTruth DF
if truth_flag == True:
df_truth = pd.DataFrame(z, columns=['z'])
df_truth = pd.concat(
[df_truth,
pd.DataFrame(crosstrack, columns=['crosstrack'])],
axis=1)
df_truth = pd.concat(
[df_truth,
pd.DataFrame(alongtrack, columns=['alongtrack'])],
axis=1)
df_truth = pd.concat(
[df_truth, pd.DataFrame(easting, columns=['easting'])], axis=1)
df_truth = pd.concat(
[df_truth, pd.DataFrame(northing, columns=['northing'])], axis=1)
df_truth = pd.concat([
df_truth,
pd.DataFrame(classification, columns=['classification'])
],
axis=1)
# df_truth = pd.concat([df_truth,pd.DataFrame(
# intensity,columns=['intensity'])],axis=1)
df_truth = pd.concat(
[df_truth,
pd.DataFrame(seg_id_truth, columns=['segment_id'])],
axis=1)
##Find ATL08 Segment Range
###Read ATL08
atl08 = get_atl08_struct(atl08filepath, gt, atl03)
atl08.df = atl08.df[atl08.df['time'] <= (max_time - min_time)]
atl08.df = atl08.df[atl08.df['time'] >= 0]
atl08.df = atl08.df.reset_index(drop=True)
atl08.df, atl08_rotation_data, atl08_epsg = match_atl_to_atl03(
atl08.df, atl03)
atl03.df = atl03.df.reset_index(drop=True)
geolocation = geolocation.reset_index(drop=True)
###Get ATL08 Keys
atl08_seg = np.array(atl08.df.segment_id_beg)
seg_id = np.array(geolocation.segment_id)
atl08_key_df = pd.DataFrame(atl08_seg, columns=['segment_id'])
atl08_key_df = pd.concat(
[atl08_key_df,
pd.DataFrame(atl08_seg, columns=['segment_id_beg'])],
axis=1)
atl08_key_df = pd.concat(
[atl08_key_df,
pd.DataFrame(atl08_seg, columns=['seg_id'])], axis=1)
key_df = pd.DataFrame(seg_id, columns=['segment_id'])
key_df = key_df.merge(atl08_key_df, on="segment_id", how='left')
key_df = key_df.fillna(method='ffill', limit=4)
max_seg = max(geolocation.segment_id)
min_seg = min(geolocation.segment_id)
key_df = key_df[key_df['segment_id'] <= max_seg]
key_df = key_df[key_df['segment_id'] >= min_seg]
###Merge Geolocation/ATL09
# df_atl09 = df_seg.merge(key_df, on="segment_id", how="left")
###Merge ATL08 Keys to Truth
if truth_flag == True:
df_truth = df_truth.merge(key_df, on="segment_id", how="left")
###Merge ATL08 Keys to ATL03
df_atl03 = atl03.df.merge(key_df, on="segment_id", how="left")
# Calculate Meterics
# Assign Geolocation/ATL09
# zgroup = df_atl09.groupby('segment_id_beg')
# zout = zgroup.aggregate(pd.Series.mode)
# zout = zgroup.aggregate(np.median)
# zout['segment_id_beg'] = zout.index
# zout = zout.reset_index(drop = True)
# zout['segment_id_beg'] = zout['seg_id']
# df_out = df_out.merge(zout, on="segment_id_beg",how='left')
# return df_out
# zout = zout.reset_index().T.drop_duplicates().T
# atl09_list = ['aclr_true','apparent_surf_reflec','backg_c',
# 'backg_theoret','beam_azimuth','beam_elevation','segment_id_beg']
# zout = zout.filter(['segment_id_beg'])
# zout2 = zout2.filter([outfield,'segment_id_beg'])
# df_test3 = atl08.df.merge(zout, on="segment_id_beg",how='left')
# Assign Tif
##Cornie
cornie = '/LIDAR/server/USERS/eric/1_experiment/global_products/' +\
'Corine_LandCover_europe/cornie_landcover_finland_UTM.tif'
data, epsg, ulx, uly, resx, resy = read_geotiff(cornie)
x = np.array(atl08.df.easting)
y = np.array(atl08.df.northing)
result = find_intersecting_values(x, y, data, ulx, uly, resx, resy)
atl08.df['Corine_LC'] = result
##Forest Canopy Height
simard = '/LIDAR/server/USERS/eric/1_experiment/global_products/' +\
'Corine_LandCover_europe/Simard_Forest_Height_Finland_UTM_auto.tif'
data, epsg, ulx, uly, resx, resy = read_geotiff(simard)
x = np.array(atl08.df.easting)
y = np.array(atl08.df.northing)
result = find_intersecting_values(x, y, data, ulx, uly, resx, resy)
atl08.df['Simard_Forest_Height'] = result
##Truth Meterics
###Truth Ground Median
print('Apply meterics')
if truth_flag == True:
atl08.df = calculate_seg_meteric(df_truth, atl08.df, [2], np.median,
'z', 'truth_ground_median')
###Truth Canopy Max 98
atl08.df = calculate_seg_meteric(df_truth, atl08.df, [4], get_max98,
'z', 'truth_canopy_max98')
##Measured Meterics
###ATL03 Ground Median
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [1],
np.median,
'h_ph',
'atl03_ground_median',
classfield='generic_class')
###ATL03 Canopy Max 98
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [2],
get_max98,
'h_ph',
'atl03_canopy_max98',
classfield='generic_class')
else:
atl08.df['truth_ground_median'] = np.nan
atl08.df['truth_canopy_max98'] = np.nan
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [1],
np.median,
'h_ph',
'atl03_ground_median',
classfield='classification')
###ATL03 Canopy Max 98
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [2, 3],
get_max98,
'h_ph',
'atl03_canopy_max98',
classfield='classification')
##Perfect Metericsdemocratic primary count
###ATL03 Ground Median
if truth_flag == True:
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [1],
np.median,
'h_ph',
'pc_ground_median',
classfield='perfect_class')
###ATL03 Canopy Max 98
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [2],
get_max98,
'h_ph',
'pc_canopy_max98',
classfield='perfect_class')
##Truth Size
atl08.df = calculate_seg_meteric(df_truth, atl08.df, [2], np.size, 'z',
'truth_n_ground')
atl08.df = calculate_seg_meteric(df_truth, atl08.df, [4], np.size, 'z',
'truth_n_canopy')
atl08.df = calculate_seg_meteric(df_truth, atl08.df, [0], np.size, 'z',
'truth_n_unclassed')
else:
atl08.df['pc_ground_median'] = np.nan
atl08.df['pc_canopy_max98'] = np.nan
atl08.df['truth_n_ground'] = np.nan
atl08.df['truth_n_unclassed'] = np.nan
##ATL03 Size
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [-1], np.size, 'h_ph',
'atl03_n_unclassified')
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [0], np.size, 'h_ph',
'atl03_n_draggan')
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [1], np.size, 'h_ph',
'atl03_n_ground')
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [2], np.size, 'h_ph',
'atl03_n_canopy')
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [3], np.size, 'h_ph',
'atl03_n_high_canopy')
##GC Size
if truth_flag == True:
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [0],
np.size,
'h_ph',
'gc_n_unclassified',
classfield='generic_class')
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [1],
np.size,
'h_ph',
'gc_n_ground',
classfield='generic_class')
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [2],
np.size,
'h_ph',
'gc_n_canopy',
classfield='generic_class')
else:
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [-1, 0], np.size,
'h_ph', 'gc_n_unclassified')
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [1], np.size,
'h_ph', 'gc_n_ground')
atl08.df = calculate_seg_meteric(df_atl03, atl08.df, [2, 3], np.size,
'h_ph', 'gc_n_canopy')
##PC Size
if truth_flag == True:
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [0],
np.size,
'h_ph',
'pc_n_unclassified',
classfield='perfect_class')
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [1],
np.size,
'h_ph',
'pc_n_ground',
classfield='perfect_class')
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [2],
np.size,
'h_ph',
'pc_n_canopy',
classfield='perfect_class')
else:
atl08.df['pc_n_unclassified'] = np.nan
atl08.df['pc_n_ground'] = np.nan
atl08.df['pc_n_canopy'] = np.nan
##GC Unique Time
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [0],
get_len_unique,
'time',
'gc_nshots_unclassed',
classfield='generic_class')
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [1],
get_len_unique,
'time',
'gc_nshots_ground',
classfield='generic_class')
atl08.df = calculate_seg_meteric(df_atl03,
atl08.df, [2],
get_len_unique,
'time',
'gc_nshots_canopy',
classfield='generic_class')
if truth_flag == True:
atl08.df['alongtrackoffset'] = float(atlCorrections.alongTrack)
atl08.df['crosstrackoffset'] = float(atlCorrections.crossTrack)
atl08.df['zoffset'] = float(atlCorrections.z)
else:
atl08.df['alongtrackoffset'] = np.nan
atl08.df['crosstrackoffset'] = np.nan
atl08.df['zoffset'] = np.nan
# ATL03
outfilename = outfolder + '/mat/atl03/' + atl03.atlFileName + '_' + gt + "_" +\
str(min_time) + '_' + str(max_time) + '.mat'
convert_df_to_mat(atl03.df, outfilename)
outfilename = outfolder + '/pkl/atl03/' + atl03.atlFileName + '_' + gt + "_" +\
str(min_time) + '_' + str(max_time) + '.pkl'
atl03.df.to_pickle(outfilename)
# ATL08
outfilename = outfolder + '/mat/atl08/' + atl08.atlFileName + '_' + gt + "_" +\
str(min_time) + '_' + str(max_time) + '.mat'
convert_df_to_mat(atl08.df, outfilename)
outfilename = outfolder + '/csv/atl08/' + atl08.atlFileName + '_' + gt + "_" +\
str(min_time) + '_' + str(max_time) + '.csv'
atl08.df.to_csv(outfilename)
outfilename = outfolder + '/pkl/atl08/' + atl08.atlFileName + '_' + gt + "_" +\
str(min_time) + '_' + str(max_time) + '.pkl'
atl08.df.to_pickle(outfilename)
# Truth
if truth_flag == True:
truth_file = atl03.atlFileName.split('ATL03_')[1]
outfilename = outfolder + '/mat/truth/' + "truth_" + truth_file +\
'_' + gt + "_" + str(min_time) + '_' + str(max_time) + '.mat'
convert_df_to_mat(df_truth, outfilename)
outfilename = outfolder + '/pkl/truth/' + "truth_" + truth_file +\
'_' + gt + "_" + str(min_time) + '_' + str(max_time) + '.pkl'
df_truth.to_pickle(outfilename)
outfilename = atl03.atlFileName + '_' + gt + "_" + str(min_time) +\
'_' + str(max_time)
generate_atl03_truth_plot(atl03, outfolder, outfilename, df_truth)
outfilename = atl08.atlFileName + '_' + gt + "_" + str(min_time) +\
'_' + str(max_time)
generate_atl08_truth_plot(atl03, atl08, outfolder, outfilename,
df_truth)
else:
outfilename = atl03.atlFileName + '_' + gt + "_" + str(min_time) +\
'_' + str(max_time)
generate_atl03_plot(atl03, outfolder, outfilename)
outfilename = atl08.atlFileName + '_' + gt + "_" + str(min_time) +\
'_' + str(max_time)
generate_atl08_plot(atl03, atl08, outfolder, outfilename)
from shutil import copyfile
import h5py
copyfile(atl08filepath, newatl08file)
# gt1l/land_segments/30m_segment/
h5f = h5py.File(output_folder + atl08file, 'a')
gt_list = ['gt1r', 'gt1l', 'gt2r', 'gt2l', 'gt3r', 'gt3l']
for gt in gt_list:
base_key = gt + '/land_segments/30m_segment/'
print('ATL03 Heights')
atl03 = get_atl03_struct(atl03filepath, gt, atl08filepath)
print('ATL08 Land Segments')
atl08 = get_atl08_struct(atl08filepath, gt, atl03)
print('Match ATL08 to ATL03 by segment')
upsampled_atl08_bin = create_atl08_bin(atl03, atl08, res_at=30)
all_cols = upsampled_atl08_bin.columns
for i in range(0, len(all_cols)):
a = np.array(upsampled_atl08_bin[all_cols[i]])
h5f[base_key + all_cols[i]] = a
h5f.close()
# agg_list = ['atl03,atl03_min,h_ph,min,[1]']
# downsampled_atl03_bin = get_bin_df(atl03.df, 'time', 0.01, agg_list)
