diff options
Diffstat (limited to 'numpy/ma/extras.py')
| -rw-r--r-- | numpy/ma/extras.py | 100 |
1 files changed, 26 insertions, 74 deletions
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py index ada19ec98..64143d813 100644 --- a/numpy/ma/extras.py +++ b/numpy/ma/extras.py @@ -12,11 +12,18 @@ __revision__ = "$Revision: 3473 $" __date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $' __all__ = ['apply_along_axis', 'atleast_1d', 'atleast_2d', 'atleast_3d', - 'average', 'vstack', 'hstack', 'dstack', 'row_stack', 'column_stack', - 'compress_rowcols', 'compress_rows', 'compress_cols', 'count_masked', - 'dot', 'hsplit', 'mask_rowcols','mask_rows','mask_cols','masked_all', - 'masked_all_like', 'mediff1d', 'median', 'mr_', 'notmasked_edges', - 'notmasked_contiguous', 'stdu', 'varu', + 'average', + 'column_stack','compress_cols','compress_rowcols', 'compress_rows', + 'count_masked', + 'dot','dstack', + 'expand_dims', + 'flatnotmasked_contiguous','flatnotmasked_edges', + 'hsplit','hstack', + 'mask_cols','mask_rowcols','mask_rows','masked_all','masked_all_like', + 'median','mediff1d','mr_', + 'notmasked_contiguous','notmasked_edges', + 'row_stack', + 'vstack', ] from itertools import groupby @@ -82,75 +89,6 @@ def masked_all_like(arr): mask=numeric.ones(arr.shape, bool_)) return a -#####-------------------------------------------------------------------------- -#---- --- New methods --- -#####-------------------------------------------------------------------------- -def varu(a, axis=None, dtype=None): - """Return an unbiased estimate of the variance. - i.e. var = sum((x - x.mean())**2)/(size(x,axis)-1) - - Parameters - ---------- - axis : int, optional - Axis along which to perform the operation. - If None, applies to a flattened version of the array. - dtype : {dtype}, optional - Datatype for the intermediary computation. If not given, - the current dtype is used instead. - - Notes - ----- - The value returned is an unbiased estimate of the true variance. - For the (less standard) biased estimate, use var. - - """ - a = asarray(a) - cnt = a.count(axis=axis) - anom = a.anom(axis=axis, dtype=dtype) - anom *= anom - dvar = anom.sum(axis) / (cnt-1) - if axis is None: - return dvar - dvar.__setmask__(mask_or(a._mask.all(axis), (cnt==1))) - return dvar -# return a.__class__(dvar, -# mask=mask_or(a._mask.all(axis), (cnt==1)), -# fill_value=a._fill_value) - -def stdu(a, axis=None, dtype=None): - """Return an unbiased estimate of the standard deviation. The - standard deviation is the square root of the average of the - squared deviations from the mean, i.e. stdu = sqrt(varu(x)). - - Parameters - ---------- - axis : int, optional - Axis along which to perform the operation. - If None, applies to a flattened version of the array. - dtype : dtype, optional - Datatype for the intermediary computation. - If not given, the current dtype is used instead. - - Notes - ----- - The value returned is an unbiased estimate of the true - standard deviation. For the biased estimate, - use std. - - """ - a = asarray(a) - dvar = a.varu(axis,dtype) - if axis is None: - if dvar is masked: - return masked - else: - # Should we use umath.sqrt instead ? - return sqrt(dvar) - return sqrt(dvar) - - -MaskedArray.stdu = stdu -MaskedArray.varu = varu #####-------------------------------------------------------------------------- #---- --- Standard functions --- @@ -199,6 +137,17 @@ dstack = _fromnxfunction('dstack') hsplit = _fromnxfunction('hsplit') +def expand_dims(a, axis): + """Expands the shape of a by including newaxis before axis. + """ + if not isinstance(a, MaskedArray): + return numpy.expand_dims(a,axis) + elif getmask(a) is nomask: + return numpy.expand_dims(a,axis).view(MaskedArray) + m = getmaskarray(a) + return masked_array(numpy.expand_dims(a,axis), + mask=numpy.expand_dims(m,axis)) + #####-------------------------------------------------------------------------- #---- #####-------------------------------------------------------------------------- @@ -875,3 +824,6 @@ def notmasked_contiguous(a, axis=None): return result ################################################################################ +testmathworks = fix_invalid([1.165 , 0.6268, 0.0751, 0.3516, -0.6965, + numpy.nan]) +expand_dims(testmathworks.mean(0),0)
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