def extract_TRMM_HDF(hdf_list, layer_indexs, outdir, resolution):
"""
Extracts TRMM products from HDF to tif.
http://pmm.nasa.gov/data-access/data-products
:param hdf_list: list of hdf files or directory with hdfs
:param layer_indexs: list of integer layer indexs
:param outdir: directory to place outputs
:param resolution: The size of a pixel in degrees, either
"0.25", "0.5", "1.0", "5.0" depending on
the specific TRMM product you are extracting.
:return: a list of all files created as output
"""
hdf_list = core.enf_filelist(hdf_list)
output_filelist = []
# load the GPM datatype from the library
datatype = datatype_library()["TRMM_{0}_GLOBAL".format(resolution)]
# for every hdf file in the input list
for hdf in hdf_list:
# extract layers and add the new filepaths to the output filelist
hdf_output_filelist = _extract_HDF_datatype(hdf, layer_indexs, outdir, datatype)
output_filelist += hdf_output_filelist
return output_filelist
def extract_GPM_IMERG(hdf_list, layer_indexs, outdir = None, resolution = "0.1"):
"""
Extracts GPM_IMERG data from its HDF5 format.
:param hdf_list: list of hdf files or directory with hdfs
:param layer_indexs: list of integer layer indexs
:param outdir: directory to place outputs
:param resolution: The size of a pixel in degrees, either
"0.1" or "0.15" depending on GPM product.
:return: a list of all files created as output
Typical contents of a GPM HDF are:
== =========== ================================ ==============
ID layer shape Layer name data type
== =========== ================================ ==============
0 [3600x1800] HQobservationTime (16-bit int)
1 [3600x1800] HQprecipSource (16-bit int)
2 [3600x1800] HQprecipitation (32-bit float)
3 [3600x1800] IRkalmanFilterWeight (16-bit int)
4 [3600x1800] IRprecipitation (32-bit float)
5 [3600x1800] precipitationCal (32-bit float)
6 [3600x1800] precipitationUncal (32-bit float)
7 [3600x1800] probabilityLiquidPrecipitation (16-bit int)
8 [3600x1800] randomError (32-bit float)
== =========== ================================ ==============
"""
hdf_list = core.enf_filelist(hdf_list)
output_filelist = []
# load the GPM datatype from the library
datatype = datatype_library()["GPM_IMERG_{0}_GLOBAL".format(resolution)]
# for every hdf file in the input list
for hdf in hdf_list:
# extract layers and add the new filepaths to the output filelist
hdf_output_filelist = _extract_HDF_datatype(hdf, layer_indexs, outdir,
datatype, nodata_value = -9999.9)
output_filelist += hdf_output_filelist
return output_filelist
def extract_MPE_NetCDF(netcdf_list, layer_indexs, outdir, area):
"""
extracts SMOS data from its native NetCDF format.
:param netcdf_list: list of hdf files or directory with netcdfs
:param layer_indexs: list of integer layer indices
:param outdir: directory to place outputs
:param area: presently only supports "CONUS"
:return: A list of all files created as output
"""
netcdf_list = core.enf_filelist(netcdf_list)
output_filelist = []
# load the GPM datatype from the library
dtype = datatype_library()["MPE_HRAP_{0}".format(area)]
# for every hdf file in the input list
for netcdf in netcdf_list:
data = _extract_NetCDF_layer_data(netcdf, layer_indexs)
for layer_index in layer_indexs:
dataset = data[layer_index]
outpath = core.create_outname(outdir, netcdf, str(layer_index),
"tif")
print("creating dataset at {0}".format(outpath))
_gdal_dataset_to_tif(dataset,
outpath,
cust_projection=dtype.projectionTXT,
cust_geotransform=dtype.geotransform,
force_custom=False,
nodata_value=-1)
output_filelist.append(outpath)
return output_filelist
def extract_MPE_NetCDF(netcdf_list, layer_indexs, outdir, area):
"""
extracts SMOS data from its native NetCDF format.
:param netcdf_list: list of hdf files or directory with netcdfs
:param layer_indexs: list of integer layer indices
:param outdir: directory to place outputs
:param area: presently only supports "CONUS"
:return: A list of all files created as output
"""
netcdf_list = core.enf_filelist(netcdf_list)
output_filelist = []
# load the GPM datatype from the library
dtype = datatype_library()["MPE_HRAP_{0}".format(area)]
# for every hdf file in the input list
for netcdf in netcdf_list:
data = _extract_NetCDF_layer_data(netcdf, layer_indexs)
for layer_index in layer_indexs:
dataset = data[layer_index]
outpath = core.create_outname(outdir, netcdf, str(layer_index), "tif")
print("creating dataset at {0}".format(outpath))
_gdal_dataset_to_tif(dataset, outpath,
cust_projection = dtype.projectionTXT,
cust_geotransform = dtype.geotransform,
force_custom = False,
nodata_value = -1)
output_filelist.append(outpath)
return output_filelist
