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import types
import Numeric as _nx
from Numeric import asarray, reshape, argmin, argmax, compress
from scipy.stats import mean, median
from scipy import isnan, amin, amax, inf, isfinite
def _asarray1d(arr):
"""Ensure 1d array for one array.
"""
m = asarray(arr)
if len(m.shape)==0:
m = reshape(m,(1,))
return m
def nansum(x,axis=-1):
"""Sum the array over the given axis treating nans as missing values.
"""
x = _asarray1d(x).copy()
_nx.putmask(x,isnan(x),0)
return _nx.sum(x,axis)
def nanmin(x,axis=-1):
"""Find the minimium over the given axis ignoring nans.
"""
x = _asarray1d(x).copy()
_nx.putmask(x,isnan(x),inf)
return amin(x,axis)
def nanargmin(x,axis=-1):
"""Find the indices of the minimium over the given axis ignoring nans.
"""
x = _asarray1d(x).copy()
_nx.putmask(x,isnan(x),inf)
return argmin(x,axis)
def nanmax(x,axis=-1):
"""Find the maximum over the given axis ignoring nans.
"""
x = _asarray1d(x).copy()
_nx.putmask(x,isnan(x),-inf)
return amax(x,axis)
def nanargmax(x,axis=-1):
"""Find the maximum over the given axis ignoring nans.
"""
x = _asarray1d(x).copy()
_nx.putmask(x,isnan(x),-inf)
return argmax(x,axis)
def nanmean(x):
"""Find the mean of x ignoring nans.
fixme: should be fixed to work along an axis.
"""
x = _asarray1d(x).copy()
y = compress(isfinite(x), x)
return mean(y)
def nanmedian(x):
"""Find the median over the given axis ignoring nans.
fixme: should be fixed to work along an axis.
"""
x = _asarray1d(x).copy()
y = compress(isfinite(x), x)
return median(y)
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