def __call__(self, **kwargs) :
# input : {axisName: condition, ...}
# standardize the axisNames
for axisName, condition in kwargs.iteritems() :
del kwargs[axisName]
if type(condition) == tuple :
condition = tuple(sorted(condition[:2]))+condition[2:]
kwargs[Axes.standardize(axisName)] = condition
output = self.extract_data(**kwargs)
# do we need to interpolate along an axis ?
for axisName, condition in kwargs.iteritems() :
# did we ask for a single value for an axis
# yet still have this axis in the output variable ?
# this signifies extract_data returned the neighbouring points
# because this single value is not on the grid
if type(condition) != tuple and axisName in output.axes :
output.data # kludge for grib files
firstSlice = [slice(None)]*len(output.shape)
secondSlice = [slice(None)]*len(output.shape)
firstSlice[output.axes.index(axisName)] = 0
secondSlice[output.axes.index(axisName)] = 1
# linear interpolation !
try :
output = \
(output[secondSlice] - output[firstSlice])/\
(output.axes[axisName].data[1] - \
output.axes[axisName].data[0])\
*(condition - output.axes[axisName].data[0]) \
+ output[firstSlice]
except IndexError :
# sometimes, due to rounding errors, extract_data will return one
# value when two were expected, thus output...[1] fails
output = output[firstSlice]
output.metadata[axisName] = condition
return output
def mean(self, axisNames) : # input can either either be like 'zy' or like ['lev', 'lat'] # turn the 'zy' into ['z', 'y'] axisNames = list(axisNames) for i in range(len(axisNames)) : axisNames[i] = Axes.standardize(axisNames[i]) # levels must be averaged first # 'level' must be at the top of the list if axisNames[i] == 'level' : del axisNames[i] axisNames = ['level'] + axisNames return self.averager(axisNames)