def test_OA_request():
short_name = 'ATL06'
date_range = ['2018-10-14', '2018-10-16']
bbox = [-121, 48, -120, 49]
region = ipd.Icesat2Data(short_name, bbox, date_range)
region.visualize_spatial_extent(elevplot=True)
assert region_a.OA_data.empty == False
from icepyx import icesat2data as ipd
short_name = 'ATL06'
date_range = ['2018-10-14', '2020-04-04']
# bounding box for DEM
bbox = [-75, -76.5, -74.5, -76]
#[-76.5, -79.5, -76, -79]
#[-122, 47, -120, 48]
#[-121.87893182746436, 48.70258466806347, -121.79542720391288, 48.7704252035505]
#bbox = [(-55, 68), (-55, 71), (-48, 71), (-48, 68), (-55, 68)]
#print (len(bbox))
#reg_a = ipd.Icesat2Data('ATL06', [-55, 68, -48, 71], ['2019-02-20', '2019-02-28'])
#print (reg_a.file_meta())
region = ipd.Icesat2Data(short_name, bbox, date_range)
print('product: ', region.dataset)
print('dates: ', region.dates)
print('start time: ', region.start_time)
print('end time: ', region.end_time)
print('version: ', region.dataset_version)
print('extent: ', region.spatial_extent)
# search for available granules and provide basic summary info about them
print('\nDATA:')
print('\n'.join([str(item) for item in region.avail_granules().items()]))
# visualize data extents
region.visualize_spatial_extent(elevplot=True)
def read_atl03(lat, lon, date_range, delta_m, path="new_ATL03", extent=None):
"""
Read a ATL03 file based and retieve individual photons in a window around a
desired latitide, longitud and a range of dates.
"""
# Spatial extend
if extent == None:
window_lat = delta_lat(lat, lon, delta_m)
window_lon = delta_lon(lat, lon, delta_m)
spatial_extent = [
lon - window_lon, lat - window_lat, lon + window_lon,
lat + window_lat
]
else:
spatial_extent = extent
spatial_extent = [
float(x) for x in spatial_extent
] # This line has to be remove after solving Issue 82 in Icepyx
# Retreiving the data
region_a = ipd.Icesat2Data('ATL03', spatial_extent, date_range)
region_a.avail_granules(ids=True)
region_a.earthdata_login(user, earthdata_emails[user])
region_a.order_vars.append(var_list=['lat_ph', "lon_ph", "h_ph"])
region_a.subsetparams(Coverage=region_a.order_vars.wanted)
region_a.order_granules()
region_a.download_granules(path)
flist = file_in_dir(path)
assert len(
flist
) > 0, "There are not available granules for these parameters. Check that the h5 files were download in path"
dataframes = pd.DataFrame(
columns=["h_ph", "lon_ph", "lat_ph", "ground_track"])
for file in flist:
fname = path + "/" + file
with h5py.File(fname, 'r') as fi:
for my_gt in filter(fi.keys(), ["gt"]):
lat_ph = fi[my_gt]['heights']["lat_ph"][:]
lon_ph = fi[my_gt]['heights']["lon_ph"][:]
h_ph = fi[my_gt]['heights']["h_ph"][:]
df = pd.DataFrame.from_dict({
"h_ph": h_ph,
"lon_ph": lon_ph,
"lat_ph": lat_ph,
"ground_track": [my_gt] * len(h_ph)
})
if extent == None:
df = df[(df["lat_ph"] < lat + window_lat)
& (df["lon_ph"] < lon + window_lon) &
(df["lat_ph"] > lat - window_lat) &
(df["lon_ph"] > lon - window_lon)]
else:
df = df[(df["lat_ph"] < extent[3])
& (df["lon_ph"] < extent[2]) &
(df["lat_ph"] > extent[1]) &
(df["lon_ph"] > extent[0])]
dataframes = dataframes.append(df, ignore_index=True)
return dataframes
def main():
#-- Read the system arguments listed after the program
long_options = [
'HELP', 'DIR=', 'EXTENT=', 'DATE=', 'USER='******'EMAIL=', 'noDownload'
]
optlist, arglist = getopt.getopt(sys.argv[1:], 'HD:E:T:U:E:N',
long_options)
#-- Set default settings
ddir = '/home/jovyan/data'
short_name = 'ATL06'
spatial_extent = [31.5, -70.56, 33.73, -69.29]
date_range = ['2020-03-30', '2020-04-1']
user = ''
email = ''
download = True
#-- read commandline inputs
for opt, arg in optlist:
if opt in ("-H", "--HELP"):
run_help()
sys.exit('Done.')
elif opt in ("-D", "--DIR"):
ddir = os.path.expanduser(arg)
elif opt in ("-E", "--EXTENT"):
spatial_extent = [
float(i)
for i in arg.replace('[', '').replace(']', '').split(',')
]
elif opt in ("-T", "--DATE"):
date_range = arg.replace('[',
'').replace(']',
'').replace("'",
"").split(',')
elif opt in ("-U", "--USER"):
user = arg
elif opt in ("-E", "--EMAIL"):
email = arg
elif opt in ("N", "--noDownload"):
download = False
if download:
#-- login to earth data and get data
region_a = ipd.Icesat2Data(short_name, spatial_extent, date_range)
region_a.earthdata_login(user, email)
#-- put data order
region_a.order_vars.append(var_list=['count'])
#-- download data
region_a.download_granules(ddir)
#-- Get list of files
file_list = os.listdir(ddir)
files = [f for f in file_list if f.endswith('.h5')]
#-- Loop through files, read specified file, and save histogram as numpy array
for f in files:
print(f)
#-- read specified file
FILE_NAME = os.path.join(ddir, f)
fid = h5py.File(FILE_NAME, mode='r')
#-- determine which beam is the strong beam (left or right)
if fid['gt1l'].attrs['atlas_beam_type'] == 'strong':
strong_id = 'l'
else:
strong_id = 'r'
#-- loop all three beam pairs and save all three
for i in range(1, 4):
#-- read count
count = fid['gt%i%s/residual_histogram/count' % (i, strong_id)][:]
lat_mean = fid['gt%i%s/residual_histogram/lat_mean' %
(i, strong_id)][:]
lon_mean = fid['gt%i%s/residual_histogram/lon_mean' %
(i, strong_id)][:]
h_li = fid['gt%i%s/land_ice_segments/h_li' % (i, strong_id)][:]
h_lat = fid['gt%i%s/land_ice_segments/latitude' %
(i, strong_id)][:]
h_lon = fid['gt%i%s/land_ice_segments/longitude' %
(i, strong_id)][:]
path_hist = os.path.join(ddir, 'hist')
if not os.path.exists(path_hist):
os.makedirs(path_hist)
path_lon = os.path.join(ddir, 'lon')
if not os.path.exists(path_lon):
os.makedirs(path_lon)
path_lat = os.path.join(ddir, 'lat')
if not os.path.exists(path_lat):
os.makedirs(path_lat)
#-- save numpy arrays
np.save(
os.path.join(
path_hist,
f.replace('.h5', '_hist_gt%i%s.npy' % (i, strong_id))),
count)
np.save(
os.path.join(
path_lat,
f.replace('.h5', '_lat_mean_gt%i%s.npy' % (i, strong_id))),
lat_mean)
np.save(
os.path.join(
path_lon,
f.replace('.h5', '_lon_mean_gt%i%s.npy' % (i, strong_id))),
lon_mean)
np.save(
os.path.join(
ddir, f.replace('.h5',
'_h_li_gt%i%s.npy' % (i, strong_id))),
h_li)
np.save(
os.path.join(
ddir, f.replace('.h5',
'_h_lat_gt%i%s.npy' % (i, strong_id))),
h_lat)
np.save(
os.path.join(
ddir, f.replace('.h5',
'_h_lon_gt%i%s.npy' % (i, strong_id))),
h_lon)
#-- close hdf5 file
fid.close()