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)