def __init__(self, name, location, data=None, attributes=None, time=None):
"""
create a UVar object
:param name: the name of the variable (depth, u_velocity, etc.)
:type name: string
:param location: the type of grid element the data is associated with:
'node', 'edge', or 'face'
:param data: The data itself
:type data: 1-d numpy array or array-like object ().
If you have a list or tuple, it should be something that can be
converted to a numpy array (list, etc.)
"""
self.name = name
if location not in ['node', 'edge', 'face', 'boundary']:
raise ValueError("location must be one of: "
"'node', 'edge', 'face', 'boundary'")
self.location = location
if data is None:
# Could be any data type, but we'll default to float
self._data = np.zeros((0, ), dtype=np.float64)
else:
self._data = asarraylike(data)
# FixMe: we need a separate attribute dict -- we really do'nt want all this
# getting mixed up with the python object attributes
if time is None:
# data not time dependent.
self._time = None
else:
self._time = time
self.attributes = {} if attributes is None else attributes
# if the data is a netcdf variable, pull the attributes from there
try:
for attr in data.ncattrs():
self.attributes[attr] = data.getncattr(attr)
except AttributeError: # must not be a netcdf variable
pass
self._cache = OrderedDict()
def __init__(self, name, location, data=None, attributes=None):
"""
create a UVar object
:param name: the name of the variable (depth, u_velocity, etc.)
:type name: string
:param location: the type of grid element the data is associated with:
'node', 'edge', or 'face'
:param data: The data itself
:type data: 1-d numpy array or array-like object ().
If you have a list or tuple, it should be something that can be
converted to a numpy array (list, etc.)
"""
self.name = name
if location not in ['node', 'edge', 'face', 'boundary']:
raise ValueError("location must be one of: "
"'node', 'edge', 'face', 'boundary'")
self.location = location
if data is None:
# Could be any data type, but we'll default to float
self._data = np.zeros((0,), dtype=np.float64)
else:
self._data = asarraylike(data)
# FixMe: we need a separate attribute dict -- we really do'nt want all this
# getting mixed up with the python object attributes
self.attributes = {} if attributes is None else attributes
# if the data is a netcdf variable, pull the attributes from there
try:
for attr in data.ncattrs():
self.attributes[attr] = data.getncattr(attr)
except AttributeError: # must not be a netcdf variable
pass
self._cache = OrderedDict()
def data(self, data):
self._data = asarraylike(data)
def data(self, data):
self._data = asarraylike(data)
