def extract_l4_granule(variable, dataset_id='', name='', path='/tmp'):
'''Loads a Level4 gridded Dataset from PODAAC
:param variable: The name of the variable to read from the dataset.
:type variable: :mod:`string`
:param dataset_id: dataset persistent ID. datasetId \
is required for a granule search. Example: \
PODAAC-CCF35-01AD5
:type dataset_id: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:param path: a path on the filesystem to store the granule.
:type path: :mod:`string`
:returns: A :class:`dataset.Dataset` containing the dataset pointed to by
the OpenDAP URL.
:raises: ServerError
'''
podaac = Podaac()
if path is not None:
path = os.path.dirname(os.path.abspath(__file__))
granule_name = podaac.extract_l4_granule(
dataset_id=dataset_id, path=path)
print("Downloaded Level4 Granule '%s' to %s" % (granule_name, path))
path = path + '/' + granule_name
return read_dataset(name, granule_name, variable, path)
def extract_l4_granule(variable, dataset_id='', name='', path='/tmp'):
'''Loads a Level4 gridded Dataset from PODAAC
:param variable: The name of the variable to read from the dataset.
:type variable: :mod:`string`
:param dataset_id: dataset persistent ID. datasetId or \
shortName is required for a granule search. Example: \
PODAAC-ASOP2-25X01
:type dataset_id: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:param path: a path on the filesystem to store the granule.
:type path: :mod:`string`
:returns: A :class:`dataset.Dataset` containing the dataset pointed to by
the OpenDAP URL.
:raises: ServerError
'''
podaac = Podaac()
if path is not None:
path = os.path.dirname(os.path.abspath(__file__))
granule_name = podaac.extract_l4_granule(dataset_id=dataset_id, path=path)
print("Downloaded Level4 Granule '%s' to %s" % (granule_name, path))
path = path + '/' + granule_name
return read_dataset(name, granule_name, variable, path)
def subset_granule(variable, name='', path='/tmp', input_file_path=''):
'''Subset Granule service allows users to Submit subset jobs. \
Use of this service should be preceded by a Granule Search in \
order to identify and generate a list of granules to be subsetted.
:param variable: The name of the variable to read from the dataset.
:type variable: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:param path: (Optional) a path on the filesystem to store the granule.
:type path: :mod:`string`
:param input_file_path: path to a json file which contains the \
the subset request that you want to send to PO.DAAC
:type input_file_path: :mod:`string`
:returns: a token on successful request reception. This can be \
further used to check the status of the request.
'''
podaac = Podaac()
if path is not None:
path = os.path.dirname(os.path.abspath(__file__))
granule_name = podaac.granule_subset(input_file_path, path)
path = path + '/' + granule_name
return read_dataset(name, granule_name, variable, path)
def subset_granule(input_file_path=''):
'''Subset Granule service allows users to Submit subset jobs. \
Use of this service should be preceded by a Granule Search in \
order to identify and generate a list of granules to be subsetted.
:param input_file_path: path to a json file which contains the \
the request that you want to send to PO.DAAC
:type input_file_path: :mod:`string`
:returns: a token on successful request reception. This can be \
further used to check the status of the request.
'''
podaac = Podaac()
status = podaac.subset_status(podaac.granule_subset(input_file_path))
print("Granule subsetting initiated with request tracking token '%s'." % status)
while status is not "done":
print('...')
return status
def subset_granule(input_file_path=''):
'''Subset Granule service allows users to Submit subset jobs. \
Use of this service should be preceded by a Granule Search in \
order to identify and generate a list of granules to be subsetted.
:param input_file_path: path to a json file which contains the \
the request that you want to send to PO.DAAC
:type input_file_path: :mod:`string`
:returns: a token on successful request reception. This can be \
further used to check the status of the request.
'''
podaac = Podaac()
status = podaac.subset_status(podaac.granule_subset(input_file_path))
print("Granule subsetting initiated with request tracking token '%s'." %
status)
while status is not "done":
print('...')
return status
def load_dataset(variable, datasetId='', name=''):
'''Loads a Dataset from PODAAC
:param variable: The name of the variable to read from the dataset.
:type variable: :mod:`string`
:param datasetId: dataset persistent ID. datasetId or \
shortName is required for a granule search. Example: \
PODAAC-ASOP2-25X01
:type datasetId: :mod:`string`
:param shortName: the shorter name for a dataset. \
Either shortName or datasetId is required for a \
granule search. Example: ASCATA-L2-25km
:type shortName: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:returns: A :class:`dataset.Dataset` containing the dataset pointed to by
the OpenDAP URL.
:raises: ServerError
'''
# Downloading the dataset using podaac toolkit
podaac = Podaac()
path = os.path.dirname(os.path.abspath(__file__))
granuleName = podaac.extract_l4_granule(dataset_id=datasetId, path=path)
path = path + '/' + granuleName
d = netcdf_dataset(path, mode='r')
dataset = d.variables[variable]
# By convention, but not by standard, if the dimensions exist, they will be in the order:
# time (t), altitude (z), latitude (y), longitude (x)
# but conventions aren't always followed and all dimensions aren't always present so
# see if we can make some educated deductions before defaulting to just pulling the first three
# columns.
temp_dimensions = map(lambda x: x.lower(), dataset.dimensions)
dataset_dimensions = dataset.dimensions
time = dataset_dimensions[temp_dimensions.index('time') if 'time' in
temp_dimensions else 0]
lat = dataset_dimensions[temp_dimensions.index('lat') if 'lat' in
temp_dimensions else 1]
lon = dataset_dimensions[temp_dimensions.index('lon') if 'lon' in
temp_dimensions else 2]
# Time is given to us in some units since an epoch. We need to convert
# these values to datetime objects. Note that we use the main object's
# time object and not the dataset specific reference to it. We need to
# grab the 'units' from it and it fails on the dataset specific object.
times = np.array(convert_times_to_datetime(d[time]))
lats = np.array(d.variables[lat][:])
lons = np.array(d.variables[lon][:])
values = np.array(dataset[:])
origin = {'source': 'PO.DAAC', 'url': 'podaac.jpl.nasa.gov/ws'}
# Removing the downloaded temporary granule before creating the OCW
# dataset.
d.close()
path = os.path.join(os.path.dirname(__file__), granuleName)
os.remove(path)
return Dataset(lats,
lons,
times,
values,
variable,
name=name,
origin=origin)
def load_level4_granule(variable, datasetId='', name=''):
'''Loads a Level4 gridded Dataset from PODAAC
:param variable: The name of the variable to read from the dataset.
:type variable: :mod:`string`
:param datasetId: dataset persistent ID. datasetId or \
shortName is required for a granule search. Example: \
PODAAC-ASOP2-25X01
:type datasetId: :mod:`string`
:param shortName: the shorter name for a dataset. \
Either shortName or datasetId is required for a \
granule search. Example: ASCATA-L2-25km
:type shortName: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:returns: A :class:`dataset.Dataset` containing the dataset pointed to by
the OpenDAP URL.
:raises: ServerError
'''
# Downloading the dataset using podaac toolkit
podaac = Podaac()
path = os.path.dirname(os.path.abspath(__file__))
granuleName = podaac.extract_l4_granule(
dataset_id=datasetId, path=path)
path = path + '/' + granuleName
d = netcdf_dataset(path, mode='r')
dataset = d.variables[variable]
# By convention, but not by standard, if the dimensions exist, they will be in the order:
# time (t), altitude (z), latitude (y), longitude (x)
# but conventions aren't always followed and all dimensions aren't always present so
# see if we can make some educated deductions before defaulting to just pulling the first three
# columns.
temp_dimensions = list(map(lambda x: x.lower(), dataset.dimensions))
dataset_dimensions = dataset.dimensions
time = dataset_dimensions[temp_dimensions.index(
'time') if 'time' in temp_dimensions else 0]
lat = dataset_dimensions[temp_dimensions.index(
'lat') if 'lat' in temp_dimensions else 1]
lon = dataset_dimensions[temp_dimensions.index(
'lon') if 'lon' in temp_dimensions else 2]
# Time is given to us in some units since an epoch. We need to convert
# these values to datetime objects. Note that we use the main object's
# time object and not the dataset specific reference to it. We need to
# grab the 'units' from it and it fails on the dataset specific object.
times = np.array(convert_times_to_datetime(d[time]))
lats = np.array(d.variables[lat][:])
lons = np.array(d.variables[lon][:])
values = np.array(dataset[:])
origin = {
'source': 'PO.DAAC',
'url': 'podaac.jpl.nasa.gov/ws'
}
# Removing the downloaded temporary granule before creating the OCW
# dataset.
d.close()
path = os.path.join(os.path.dirname(__file__), granuleName)
os.remove(path)
return Dataset(lats, lons, times, values, variable, name=name, origin=origin)
