def mean(self, axis=None):
'''
Compute tha arithmetic mean along the specified axis.
:param axis: (*int*) Axis along which the standard deviation is computed.
The default is to compute the standard deviation of the flattened array.
returns: (*array_like*) Mean result
'''
if axis is None:
return ArrayMath.mean(self.array)
else:
return MIArray(ArrayMath.mean(self.array, axis))
def mean(self, axis=None):
'''
Compute tha arithmetic mean along the specified axis.
:param axis: (*int*) Axis along which the standard deviation is computed.
The default is to compute the standard deviation of the flattened array.
returns: (*array_like*) Mean result
'''
if axis is None:
return ArrayMath.mean(self.array)
else:
return MIArray(ArrayMath.mean(self.array, axis))
def month_to_season(self, season):
'''
Computes a user-specified three-month seasonal mean
(DJF, JFM, FMA, MAM, AMJ, MJJ, JJA, JAS, ASO, SON, OND, NDJ).
The first average (DJF=JF) and the last average (NDJ=ND) are actually
two-month averages.
The time (leftmost) dimension must be divisible by 12. The data are assumed
to be monthly mean data and the first record is assumed to be January.
:param season: (*string*) A string representing the season to
calculate: e.g., "JFM", "JJA".
:returns: Season averaged data array.
'''
nmonth = self.dimlen(0)
nyear = nmonth / 12
seasons = ['DJF','JFM','FMA','MAM','AMJ','MJJ','JJA','JAS','ASO','SON','OND','NDJ']
season = season.upper()
if not season in seasons:
print 'Season string is not valid: "' + season + '"!'
raise KeyError()
idx = seasons.index(season) - 1
keys = []
keys.append(slice(0,nyear,1))
for i in range(1, self.ndim):
keys.append(slice(None,None,None))
r = self.__getitem__(tuple(keys))
si = idx
for i in range(nyear):
ei = si + 3
if si < 0:
si = 0
if ei > nmonth:
ei = nmonth
keys[0] = slice(si,ei,1)
sdata = self.__getitem__(tuple(keys))
sdata = ArrayMath.mean(sdata.asarray(), 0)
keys[0] = i
r.__setitem__(tuple(keys), sdata)
si += 12
return r
