def HDF_proj_WKT(hdf_filepath):
# returns the projection of the HDF file in Well Known Text (WKT) format
# first determine the HDF type
hdf_type = HDF_type(hdf_filepath)
if hdf_type == 'hdf4':
# for HDF4 assume corner coordinates are stored in the StructMetadata.0 section
hdf_file = SD(hdf_filepath, SDC.READ)
try:
# access grid metadata section of StructMetadata.0
fattr = hdf_file.attributes(full=1)
structmeta = fattr['StructMetadata.0']
gridmeta = structmeta[0]
# determine the projection GCTP code from the grid metadata
proj_regex = re.compile(r'''Projection=(?P<projection>\w+)''',
re.VERBOSE)
match = proj_regex.search(gridmeta)
proj = match.group('projection')
# support MODIS sinusoidal projection for now, add others later
if proj == 'GCTP_SNSOID':
sinu_proj4 = "+proj=sinu +R=6371007.181 +nadgrids=@null +wktext"
srs = osr.SpatialReference()
srs.ImportFromProj4(sinu_proj4)
return srs.ExportToWkt()
except:
#prjfile = open('/home/rkalyana/GeoEDF/GeoEDF/connector/filter/modis/6933.prj', 'r')
#prj_txt = prjfile.read()
#srs = osr.SpatialReference()
#srs.ImportFromESRI([prj_txt])
#prjfile.close()
#return srs.ExportToWkt()
raise GeoEDFError(
'Error determining the projection or unsupported projection')
else: # HDF5 file; only SMAP files in EASE Grid 2.0 are supported at the moment
hdf_file = h5py.File(hdf_filepath, mode='r')
# check to see if this is a EASE Grid 2.0 file
if 'EASE2_global_projection' in hdf_file.keys():
ease_proj4 = "+proj=cea +lat_0=0 +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m"
srs = osr.SpatialReference()
srs.ImportFromProj4(ease_proj4)
return srs.ExportToWkt()
else:
raise GeoEDFError(
'Error determining the projection or unsupported projection')
def __init__(self,
datatype,
version,
fname,
variables=None,
verbose=False):
self.datatype = datatype
self.version = version
self.fname = fname
self.verbose = verbose
self.dataset = {}
print(version.capitalize())
if version == 'HDF4':
hdf = SD(self.fname, SDC.READ)
if variables is None:
for d in hdf.datasets():
self.dataset[d] = np.array(hdf.select(d).get())
if verbose: print(d)
else:
for d in variables:
self.dataset[d] = np.array(hdf.select(d).get())
if verbose: print('reading', d)
elif version == 'HDF5':
hdf = h5py.File(fname, 'r')
if variables is None:
allvariables = list(hdf.keys())
print('allvariables', allvariables)
for d in allvariables:
self.dataset[d] = np.array(hdf[d])
if verbose: print(d)
else:
for d in variables:
self.dataset[d] = np.array(hdf[d])
if verbose: print(d)
else:
print(datatype, ' not supported')
if verbose:
for key in self.dataset:
print('variable: ',key, ' dimension', self.dataset[key].shape,\
'min, max', np.nanmin(self.dataset[key]),np.nanmax(self.dataset[key]))
print('finished')
def HDF_subdataset_data(hdf_filepath, subdataset_substrs):
# process the names of the subdatasets, finding any that contain a member of
# subdataset_substrs as a substring
# subdataset_substrs is a list
# returned dictionary indexed by subdataset name
# contains data grid and value range
hdf_data = dict()
# first determine the HDF type
hdf_type = HDF_type(hdf_filepath)
if hdf_type == 'hdf4':
hdf_file = SD(hdf_filepath, SDC.READ)
try:
dset_names = hdf_file.datasets().keys()
# loop through input subdataset substrings
for subdset_substr in subdataset_substrs:
# loop through datasets in HDF file
for dset_name in dset_names:
# if substring found
if subdset_substr in dset_name:
# if this subdataset has not been processed before
if dset_name not in hdf_data:
try:
data2D = hdf_file.select(dset_name)
data = data2D[:, :].astype(np.float64)
hdf_data[dset_name] = dict()
hdf_data[dset_name]['data'] = data
#hdf_data[dset_name]['range'] = data2D.getrange()
hdf_data[dset_name][
'fillValue'] = data2D.getfillvalue()
except:
raise GeoEDFError(
'Error retrieving subdataset %s data from HDF file'
% dset_name)
except:
raise GeoEDFError(
'Error retrieving subdatasets from HDF4 file %s' %
hdf_filepath)
else:
hdf_file = h5py.File(hdf_filepath, mode='r')
# assume this follows the structure of HDF-EOS files where all subdatasets are in a "Geophysical_Data" group
if 'Geophysical_Data' in hdf_file.keys():
dset_names = hdf_file['Geophysical_Data'].keys()
# loop through input subdataset substrings
for subdset_substr in subdataset_substrs:
# loop through subdatasets in HDF file
for dset_name in dset_names:
# if substring matches
if subdset_substr in dset_name:
# if subdataset not processed yet
if dset_name not in hdf_data:
try:
# construct fully qualified subdataset name
fq_dset_name = '/Geophysical_Data/%s' % dset_name
data = hdf_file[fq_dset_name]
hdf_data[dset_name] = dict()
hdf_data[dset_name]['data'] = data[:]
hdf_data[dset_name][
'fillValue'] = data.fillvalue
except:
raise GeoEDFError(
'Error retrieving subdataset %s data from HDF file'
% dset_name)
else:
raise GeoEDFError(
'Cannot handle HDF5 files that do not follow the HDF-EOS standards'
)
return hdf_data
def HDF_corner_coords(hdf_filepath):
# return a tuple of upper left and lower right coordinates in lat-lon
# first determine the HDF type
hdf_type = HDF_type(hdf_filepath)
if hdf_type == 'hdf4':
# for HDF4 assume corner coordinates are stored in the StructMetadata.0 section
hdf_file = SD(hdf_filepath, SDC.READ)
try:
# access grid metadata section of StructMetadata.0
fattr = hdf_file.attributes(full=1)
structmeta = fattr['StructMetadata.0']
gridmeta = structmeta[0]
# parse the text to retrieve corner coordinates in meters
ul_regex = re.compile(
r'''UpperLeftPointMtrs=\(
(?P<upper_left_x>[+-]?\d+\.\d+)
,
(?P<upper_left_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = ul_regex.search(gridmeta)
x0 = np.float(match.group('upper_left_x'))
y0 = np.float(match.group('upper_left_y'))
lr_regex = re.compile(
r'''LowerRightMtrs=\(
(?P<lower_right_x>[+-]?\d+\.\d+)
,
(?P<lower_right_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = lr_regex.search(gridmeta)
x1 = np.float(match.group('lower_right_x'))
y1 = np.float(match.group('lower_right_y'))
# construct the projection transformer to convert from meters to lat-lon
# determine the projection GCTP code from the grid metadata
proj_regex = re.compile(r'''Projection=(?P<projection>\w+)''',
re.VERBOSE)
match = proj_regex.search(gridmeta)
proj = match.group('projection')
# support MODIS sinusoidal projection for now, add others later
if proj == 'GCTP_SNSOID':
#sinu = pyproj.Proj("+proj=sinu +R=6371007.181 +nadgrids=@null +wktext")
#wgs84 = pyproj.Proj("+init=EPSG:4326")
#lon0, lat0 = pyproj.transform(sinu, wgs84, x0, y0)
#lon1, lat1 = pyproj.transform(sinu, wgs84, x1, y1)
#return (lon0, lat0, lon1, lat1)
return (x0, y0, x1, y1)
else:
raise GeoEDFError(
'Only MODIS sinusoidal grids are supported currently')
except Exception as e:
#x0, y0, x1, y1 = -17357881.81713629,7324184.56362408,17357881.81713629,-7324184.56362408
#return (x0,y0,x1,y1)
raise GeoEDFError(
'Error retrieving corner coordinates of HDF file')
else: # HDF5 file; only SMAP files in EASE Grid 2.0 are supported at the moment
hdf_file = h5py.File(hdf_filepath, mode='r')
# check to see if this is a EASE Grid 2.0 file
if 'EASE2_global_projection' in hdf_file.keys():
# hardcoded corner coordinates, since this is not stored in the file metadata
x0, y0, x1, y1 = -17357881.81713629, 7324184.56362408, 17357881.81713629, -7324184.56362408
#ease = pyproj.Proj(("+proj=cea +lat_0=0 +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m"))
#wgs84 = pyproj.Proj("+init=EPSG:4326")
#lon0, lat0 = pyproj.transform(ease, wgs84, x0, y0)
#lon1, lat1 = pyproj.transform(ease, wgs84, x1, y1)
#return (lon0, lat0, lon1, lat1)
return (x0, y0, x1, y1)
else:
raise GeoEDFError(
'Only EASE Grid 2.0 HDF5 files are supported currently')
