def add_geoid_layer(dataFolder,
year,
fileIndex,
resolution,
projection,
geoidGeometry,
printStatus=True):
if printStatus:
print(" Step 5: Adding geoid layer to file...")
if 'EPSG' in projection:
baseDirectory = 'Resampled_' + str(
resolution) + 'm_' + projection.split(':')[1]
else:
baseDirectory = 'Resampled_' + str(resolution) + 'm'
regridded_folder = os.path.join(
dataFolder, baseDirectory,
'OMG_Ice_GLISTIN-A_L3_' + '{:02d}'.format(fileIndex))
regridded_file = ref.indexAndYearToFileID(fileIndex, year) + '.nc'
regridded_filepath = os.path.join(regridded_folder, regridded_file)
print(' Reading the data geometry')
dataset = xr.open_dataset(regridded_filepath)
lon, lat, espg = read_lat_long(dataset)
print(' Finding a geoid subset around the swath')
# step 3: find geoid subset around the swath extent
gXsubset, gYsubset, geoidSubset = create_geoid_subset(
geoidGeometry, lon, lat)
print(' Making an interpolation grid for the geoid')
# step 4: make an interpolation function for the geoid subset
set_int = interp2d(gXsubset, gYsubset, geoidSubset)
print(' Interpolating the geoid onto the grid points')
# step 5: for each point in the swath, find the value in the geoid grid
geoid = interpolate_geoid_on_swath(lon, lat, set_int)
print(' Geoid added to the dataset')
dataset['geoid'] = (['y', 'x'], geoid)
os.remove(
regridded_filepath
) #remove the old file so that the new one can be saved in its place
dataset.to_netcdf(regridded_filepath)
def generateDownloadLinks(swathIndices,yearList):
dataDownloadLinks=[]
metadataDownloadLinks=[]
years=[]
swathIDs=[]
swathIndexLong=[]
for index in swathIndices:
for year in yearList:
if year==2016 and index in ref.swathIndicesMissingIn2016():
print('Note: Swath index ' + str(index) + ' is not available in 2016')
else:
fileID = ref.indexAndYearToFileID(index, year)
years.append(year)
swathID = ref.fileNameToSwathID(fileID)
swathIDs.append(swathID)
dataLink = ref.swathIDtoDataLink(swathID)
metadatalink = ref.swathIDtoMetadataLink(swathID)
dataDownloadLinks.append(dataLink)
metadataDownloadLinks.append(metadatalink)
swathIndexLong.append(index)
return(dataDownloadLinks,metadataDownloadLinks,years,swathIDs,swathIndexLong)
def save_resample(dataFolder, year, fileIndex, resolution, projection, x, y,
output_grid, stdev_grid, count_grid, lon_grid, lat_grid):
if 'EPSG:326' in projection:
baseDirectory = 'Resampled_' + str(resolution) + 'm'
else:
baseDirectory = 'Resampled_' + str(
resolution) + 'm_' + projection.split(':')[1]
# output_folder = os.path.join(dataFolder,baseDirectory,'OMG_Ice_GLISTIN-A_L3_'+ '{:02d}'.format(fileIndex))
output_folder = dataFolder.joinpath(
baseDirectory, 'OMG_Ice_GLISTIN-A_L3_' + '{:02d}'.format(fileIndex))
output_file = ref.indexAndYearToFileID(fileIndex, year) + '.nc'
# if output_file in os.listdir(output_folder):
# os.remove(os.path.join(output_folder,output_file))
if output_folder.joinpath(output_file).is_file():
print(' Removing old version of ' + output_file)
output_folder.joinpath(output_file).unlink()
swath = xr.Dataset( \
{'elevation': (['y', 'x'], output_grid),
'standard_deviation': (['y', 'x'], stdev_grid),
'count': (['y', 'x'], count_grid),
'projection':'',
'longitude': (('y', 'x'), lon_grid),
'latitude': (('y', 'x'), lat_grid)}, \
coords={'y': y, \
'x': x })
swath['projection'].attrs['EPSG'] = projection
#swath.to_netcdf(os.path.join(output_folder,output_file))
print(' Saving to NetCDF ' + str(output_file))
swath.to_netcdf(str(output_folder.joinpath(output_file)))
def add_DEM_metadata(dataFolder,
year,
fileIndex,
resolution,
projection,
addGeoid,
printStatus=True):
if printStatus:
print(" Step 6: Adding DEM metadata to file...")
if 'EPSG' in projection:
baseDirectory = 'Resampled_' + str(
resolution) + 'm_' + projection.split(':')[1]
else:
baseDirectory = 'Resampled_' + str(resolution) + 'm'
#regridded_folder = os.path.join(dataFolder, baseDirectory,'OMG_Ice_GLISTIN-A_L3_' + '{:02d}'.format(fileIndex))
regridded_folder = dataFolder.joinpath(
baseDirectory, 'OMG_Ice_GLISTIN-A_L3_' + '{:02d}'.format(fileIndex))
regridded_file = ref.indexAndYearToFileID(fileIndex, year) + '.nc'
#regridded_filepath = os.path.join(regridded_folder,regridded_file)
regridded_filepath = regridded_folder.joinpath(regridded_file)
#step 1: read in the variables from the regridded file
variables, variableNames, coordinates, coordinateNames, projection = read_regridded_swath(
regridded_filepath)
#step 2: generate a new swath with the variable and coordinates
dataset = generate_new_dataset(variables, variableNames, coordinates,
coordinateNames, projection, addGeoid)
#step 3: add in the global metadata values
dataset = write_global_attributes(dataset, dataFolder, regridded_filepath,
resolution, variables, variableNames,
coordinates, coordinateNames, projection)
#step 4: add in the global metadata values
dataset = write_variables_attributes(dataset, variables, variableNames,
coordinates, coordinateNames,
projection, addGeoid)
#step 5: add in the projection information
dataset = write_projection_attributes(dataset, coordinates,
coordinateNames, projection,
resolution)
#step 6: add in the coordinate attributes
dataset = write_coordinates_attributes(dataset, coordinates,
coordinateNames)
#step 7: add in the geoid attributes if geoid is added
if addGeoid:
dataset = write_geoid_attributes(dataset, variables, variableNames,
projection)
# os.remove(regridded_filepath) # remove the old file so that the new one can be saved in its place
try:
regridded_filepath.unlink()
except:
print('could not unlink ' + str(regridded_filepath))
print(' ... aborting!')
exit()
print(" Saving updated NetCDF file.")
dataset.to_netcdf(str(regridded_filepath))
def read_swath_and_create_geometry(dataFolder, fileIndex, year, printStatus):
if printStatus:
print(
' Step 2: Reading in the binary grid and creating the original geometry'
)
print(' Reading in binary data from file')
fileID = ref.indexAndYearToFileID(fileIndex, year)
metadata_dictionary = read_metadata_dictionary(dataFolder, fileID)
swathID = ref.fileNameToSwathID(fileID)
dataPath = dataFolder.joinpath('Raw', str(year), 'Data',
swathID + '.hgt.grd')
g = np.fromfile(str(dataPath), dtype='<f4')
# cast to float 2
g = g.astype(np.dtype('<f2'))
grid = np.reshape(g, (metadata_dictionary['GRD Latitude Lines'],
metadata_dictionary['GRD Longitude Samples']))
if printStatus:
print(' Preparing original geometry of the swath from metadata')
grid = np.where(grid > grid.min(), grid, np.nan)
min_swath_lon = metadata_dictionary['GRD Starting Longitude']
max_swath_lon = metadata_dictionary['GRD Starting Longitude'] + metadata_dictionary['GRD Longitude Samples'] * \
metadata_dictionary['GRD Longitude Spacing']
min_swath_lat = metadata_dictionary['GRD Starting Latitude'] + metadata_dictionary['GRD Latitude Lines'] * \
metadata_dictionary['GRD Latitude Spacing']
max_swath_lat = metadata_dictionary['GRD Starting Latitude']
lats = np.linspace(min_swath_lat, max_swath_lat,
metadata_dictionary['GRD Latitude Lines'])
lons = np.linspace(min_swath_lon, max_swath_lon,
metadata_dictionary['GRD Longitude Samples'])
grid = np.flipud(grid)
if printStatus:
# print(" The grid shape is (" + str(len(lats)) + "," + str(len(lons)) + ")")
print(" The grid shape is (" + str(np.shape(grid)[0]) +
"," + str(np.shape(grid)[1]) + ")")
# Original Area definition in swath geometry:
Lons, Lats = np.meshgrid(lons, lats)
Lons = np.reshape(Lons, (np.size(Lons), ))
Lats = np.reshape(Lats, (np.size(Lats), ))
grid = np.reshape(grid, (np.size(grid), ))
# Remove nans so averaging is ubiquitous
non_nans = np.invert(np.isnan(grid))
if printStatus:
print(' Removed ' + str(np.sum(np.isnan(grid))) +
' nan points out of ' + str(np.size(grid)) + ' grid points')
Lons = Lons[non_nans]
Lats = Lats[non_nans]
grid = grid[non_nans]
area_original = geometry.SwathDefinition(lons=Lons, lats=Lats)
return (area_original, grid)
def find_common_index_extent(dataFolder,
fileIndex,
printStatus,
useMetadata=False):
if useMetadata:
if printStatus:
print(
' Step 1: Finding a common extent for all DEMs with index '
+ str(fileIndex))
min_lon = 360
max_lon = -360
min_lat = 90
max_lat = -90
addFileData = True
for year in [2016, 2017, 2018, 2019]:
if year == 2016:
if fileIndex in ref.fileIndicesMissingIn2016():
addFileData = False
else:
addFileData = True
if addFileData:
fileID = ref.indexAndYearToFileID(fileIndex, year)
metadata_dictionary = read_metadata_dictionary(
dataFolder, fileID)
min_swath_lon = metadata_dictionary['GRD Starting Longitude']
max_swath_lon = metadata_dictionary[
'GRD Starting Longitude'] + metadata_dictionary[
'GRD Longitude Samples'] * metadata_dictionary[
'GRD Longitude Spacing']
min_swath_lat = metadata_dictionary[
'GRD Starting Latitude'] + metadata_dictionary[
'GRD Latitude Lines'] * metadata_dictionary[
'GRD Latitude Spacing']
max_swath_lat = metadata_dictionary['GRD Starting Latitude']
min_lon = np.min([min_lon, min_swath_lon])
max_lon = np.max([max_lon, max_swath_lon])
min_lat = np.min([min_lat, min_swath_lat])
max_lat = np.max([max_lat, max_swath_lat])
if printStatus:
print(' Longitude extents -> Min: ' +
'{:.06f}'.format(min_lon) + ' Max: ' +
'{:.06f}'.format(max_lon))
print(' Latitude extents -> Min: ' +
'{:.06f}'.format(min_lat) + ' Max: ' +
'{:.06f}'.format(max_lat))
else:
if printStatus:
print(
' Step 1: Finding a common extent for all DEMs with index '
+ str(fileIndex))
saved_extent = ref.indexToCommonExtent(fileIndex)
min_lon = saved_extent[0]
max_lon = saved_extent[1]
min_lat = saved_extent[2]
max_lat = saved_extent[3]
if printStatus:
print(' Longitude extents -> Min: ' +
'{:.06f}'.format(min_lon) + ' Max: ' +
'{:.06f}'.format(max_lon))
print(' Latitude extents -> Min: ' +
'{:.06f}'.format(min_lat) + ' Max: ' +
'{:.06f}'.format(max_lat))
return (min_lon, max_lon, min_lat, max_lat)