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Masked arrays add-ons.
A collection of utilities for `numpy.ma`.
:author: Pierre Gerard-Marchant
:contact: pierregm_at_uga_dot_edu
:version: $Id: extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $
s5 Pierre GF Gerard-Marchant ($Author: jarrod.millman $)s 1.0s $Revision: 3473 $s5 $Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $t apply_along_axist
atleast_1dt
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atleast_3dt averaget clump_maskedt clump_unmaskedt column_stackt
compress_colst compress_rowcolst
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Count the number of masked elements along the given axis.
Parameters
----------
arr : array_like
An array with (possibly) masked elements.
axis : int, optional
Axis along which to count. If None (default), a flattened
version of the array is used.
Returns
-------
count : int, ndarray
The total number of masked elements (axis=None) or the number
of masked elements along each slice of the given axis.
See Also
--------
MaskedArray.count : Count non-masked elements.
Examples
--------
>>> import numpy.ma as ma
>>> a = np.arange(9).reshape((3,3))
>>> a = ma.array(a)
>>> a[1, 0] = ma.masked
>>> a[1, 2] = ma.masked
>>> a[2, 1] = ma.masked
>>> a
masked_array(data =
[[0 1 2]
[-- 4 --]
[6 -- 8]],
mask =
[[False False False]
[ True False True]
[False True False]],
fill_value=999999)
>>> ma.count_masked(a)
3
When the `axis` keyword is used an array is returned.
>>> ma.count_masked(a, axis=0)
array([1, 1, 1])
>>> ma.count_masked(a, axis=1)
array([0, 2, 1])
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Empty masked array with all elements masked.
Return an empty masked array of the given shape and dtype, where all the
data are masked.
Parameters
----------
shape : tuple
Shape of the required MaskedArray.
dtype : dtype, optional
Data type of the output.
Returns
-------
a : MaskedArray
A masked array with all data masked.
See Also
--------
masked_all_like : Empty masked array modelled on an existing array.
Examples
--------
>>> import numpy.ma as ma
>>> ma.masked_all((3, 3))
masked_array(data =
[[-- -- --]
[-- -- --]
[-- -- --]],
mask =
[[ True True True]
[ True True True]
[ True True True]],
fill_value=1e+20)
The `dtype` parameter defines the underlying data type.
>>> a = ma.masked_all((3, 3))
>>> a.dtype
dtype('float64')
>>> a = ma.masked_all((3, 3), dtype=np.int32)
>>> a.dtype
dtype('int32')
t mask( R8 t npt emptyR; R6 ( t shapet dtypet a( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR r s /c C s@ t i | ƒ i t ƒ } t i | i d t | i ƒ ƒ| _ | S( sz
Empty masked array with the properties of an existing array.
Return an empty masked array of the same shape and dtype as
the array `arr`, where all the data are masked.
Parameters
----------
arr : ndarray
An array describing the shape and dtype of the required MaskedArray.
Returns
-------
a : MaskedArray
A masked array with all data masked.
Raises
------
AttributeError
If `arr` doesn't have a shape attribute (i.e. not an ndarray)
See Also
--------
masked_all : Empty masked array with all elements masked.
Examples
--------
>>> import numpy.ma as ma
>>> arr = np.zeros((2, 3), dtype=np.float32)
>>> arr
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> ma.masked_all_like(arr)
masked_array(data =
[[-- -- --]
[-- -- --]],
mask =
[[ True True True]
[ True True True]],
fill_value=1e+20)
The dtype of the masked array matches the dtype of `arr`.
>>> arr.dtype
dtype('float32')
>>> ma.masked_all_like(arr).dtype
dtype('float32')
RQ ( RN t
empty_liket viewR, R; RP R6 RQ t _mask( RJ RR ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR ¥ s 2$t _fromnxfunctionc B s) e Z d Z d „ Z d „ Z d „ Z RS( sÃ
Defines a wrapper to adapt NumPy functions to masked arrays.
An instance of `_fromnxfunction` can be called with the same parameters
as the wrapped NumPy function. The docstring of `newfunc` is adapted from
the wrapped function as well, see `getdoc`.
Parameters
----------
funcname : str
The name of the function to be adapted. The function should be
in the NumPy namespace (i.e. ``np.funcname``).
c C s | | _ | i ƒ | _ d S( N( t __name__t getdoct __doc__( t selft funcname( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyt __init__ð s c C se t t | i d ƒ } t | d d ƒ } | o3 | i t i | ƒ } d } d i | | | f ƒ Sd S( s
Retrieve the docstring and signature from the function.
The ``__doc__`` attribute of the function is used as the docstring for
the new masked array version of the function. A note on application
of the function to the mask is appended.
.. warning::
If the function docstring already contained a Notes section, the
new docstring will have two Notes sections instead of appending a note
to the existing section.
Parameters
----------
None
RY sL Notes
-----
The function is applied to both the _data and the _mask, if any.s
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__module__RY R\ RX Rs ( ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyRV ß s c C s` d } xS | t | ƒ j o? x. t | | d ƒ o | | | | | d +q W| d 7} q W| S( s Flatten a sequence in place.i t __iter__i ( Rf t hasattr( RG t k( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyt flatten_inplace< s c O sÓ t | d t d t ƒ} | i } | d j o | | 7} n | | j o t d | | f ƒ ‚ n d g | d } t i | d ƒ } t | ƒ } | i | ƒ t d
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d
d ƒ | | >> a = np.ma.array([1., 2., 3., 4.], mask=[False, False, True, True])
>>> np.ma.average(a, weights=[3, 1, 0, 0])
1.25
>>> x = np.ma.arange(6.).reshape(3, 2)
>>> print x
[[ 0. 1.]
[ 2. 3.]
[ 4. 5.]]
>>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3],
... returned=True)
>>> print avg
[2.66666666667 3.66666666667]
i RK g i RM g ð?RQ Ry s w[s ] * ones(ash, float)s average: weights wrong shape.s( ] * masked_array(ones(ash, float), mask)N( ( i ( ( i ( ( i ( R0 RM RP R^ R: RI t floatt sizeR3 t ravelt umathR. t reducet _dataR/ RN Rf R€ t evalt reprRC R} R7 RB R, R| R8 R> R; (
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#$
#
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$% c C s d „ } | oA | d j o | i ƒ } | i ƒ qc | i d | ƒ | } n t | d | ƒ} | d j o | | ƒ } n t | | | ƒ } | d j o
| } n | S( sA
Compute the median along the specified axis.
Returns the median of the array elements.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : int, optional
Axis along which the medians are computed. The default (None) is
to compute the median along a flattened version of the array.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type will be cast if necessary.
overwrite_input : bool, optional
If True, then allow use of memory of input array (a) for
calculations. The input array will be modified by the call to
median. This will save memory when you do not need to preserve
the contents of the input array. Treat the input as undefined,
but it will probably be fully or partially sorted. Default is
False. Note that, if `overwrite_input` is True, and the input
is not already an `ndarray`, an error will be raised.
Returns
-------
median : ndarray
A new array holding the result is returned unless out is
specified, in which case a reference to out is returned.
Return data-type is `float64` for integers and floats smaller than
`float64`, or the input data-type, otherwise.
See Also
--------
mean
Notes
-----
Given a vector ``V`` with ``N`` non masked values, the median of ``V``
is the middle value of a sorted copy of ``V`` (``Vs``) - i.e.
``Vs[(N-1)/2]``, when ``N`` is odd, or ``{Vs[N/2 - 1] + Vs[N/2]}/2``
when ``N`` is even.
Examples
--------
>>> x = np.ma.array(np.arange(8), mask=[0]*4 + [1]*4)
>>> np.ma.extras.median(x)
1.5
>>> x = np.ma.array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)
>>> np.ma.extras.median(x)
2.5
>>> np.ma.extras.median(x, axis=-1, overwrite_input=True)
masked_array(data = [ 2. 5.],
mask = False,
fill_value = 1e+20)
c S sp t t | ƒ d ƒ } t | d ƒ \ } } | o t | | d ƒ } n t | d | d ƒ } | | i d ƒ S( Ni i i ( R3 R2 t divmodR€ t mean( t datat countst idxt rmdt choice( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyt _median1Di s RK N( R^ R R< R ( RR RK t outt overwrite_inputR² t asortedRš ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR - s <
c C s/ t | ƒ } | i d j o
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Suppress the rows and/or columns of a 2-D array that contain
masked values.
The suppression behavior is selected with the `axis` parameter.
- If axis is None, both rows and columns are suppressed.
- If axis is 0, only rows are suppressed.
- If axis is 1 or -1, only columns are suppressed.
Parameters
----------
axis : int, optional
Axis along which to perform the operation. Default is None.
Returns
-------
compressed_array : ndarray
The compressed array.
Examples
--------
>>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> x
masked_array(data =
[[-- 1 2]
[-- 4 5]
[6 7 8]],
mask =
[[ True False False]
[ True False False]
[False False False]],
fill_value = 999999)
>>> np.ma.extras.compress_rowcols(x)
array([[7, 8]])
>>> np.ma.extras.compress_rowcols(x, 0)
array([[6, 7, 8]])
>>> np.ma.extras.compress_rowcols(x, 1)
array([[1, 2],
[4, 5],
[7, 8]])
i s$ compress2d works for 2D arrays only.i i iÿÿÿÿN( Ni iÿÿÿÿ( R0 R| t NotImplementedErrorR4 R: t anyR t allt nxarrayR~ Rf RP t nonzeroRN R' R R^ ( Rl RK RL t idxrt idxcR7 R R’ ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR ‡ s& /
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c C s
t | d ƒ S( só
Suppress whole rows of a 2-D array that contain masked values.
This is equivalent to ``np.ma.extras.compress_rowcols(a, 0)``, see
`extras.compress_rowcols` for details.
See Also
--------
extras.compress_rowcols
i ( R ( RR ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR
Ë s c C s
t | d ƒ S( sö
Suppress whole columns of a 2-D array that contain masked values.
This is equivalent to ``np.ma.extras.compress_rowcols(a, 1)``, see
`extras.compress_rowcols` for details.
See Also
--------
extras.compress_rowcols
i ( R ( RR ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR Ù s c C sÍ t | ƒ } | i d j o
t d ‚ n t | ƒ } | t j p | i ƒ o | S| i ƒ } | i i ƒ | _ | p t | t
i | d ƒ >> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=np.int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
masked_array(data =
[[0 0 0]
[0 -- 0]
[0 0 0]],
mask =
[[False False False]
[False True False]
[False False False]],
fill_value=999999)
>>> ma.mask_rowcols(a)
masked_array(data =
[[0 -- 0]
[-- -- --]
[0 -- 0]],
mask =
[[False True False]
[ True True True]
[False True False]],
fill_value=999999)
i s$ compress2d works for 2D arrays only.i i iÿÿÿÿN( Ni iÿÿÿÿ(
R0 R| R¶ R4 R: R· Rº RU Ry R7 RN R' R^ ( RR RK RL t maskedval( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR ç s K
'c C s
t | d ƒ S( sœ
Mask rows of a 2D array that contain masked values.
This function is a shortcut to ``mask_rowcols`` with `axis` equal to 0.
See Also
--------
mask_rowcols : Mask rows and/or columns of a 2D array.
masked_where : Mask where a condition is met.
Examples
--------
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=np.int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
masked_array(data =
[[0 0 0]
[0 -- 0]
[0 0 0]],
mask =
[[False False False]
[False True False]
[False False False]],
fill_value=999999)
>>> ma.mask_rows(a)
masked_array(data =
[[0 0 0]
[-- -- --]
[0 0 0]],
mask =
[[False False False]
[ True True True]
[False False False]],
fill_value=999999)
i ( R ( RR RK ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR A s +c C s
t | d ƒ S( sŸ
Mask columns of a 2D array that contain masked values.
This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1.
See Also
--------
mask_rowcols : Mask rows and/or columns of a 2D array.
masked_where : Mask where a condition is met.
Examples
--------
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=np.int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
masked_array(data =
[[0 0 0]
[0 -- 0]
[0 0 0]],
mask =
[[False False False]
[False True False]
[False False False]],
fill_value=999999)
>>> ma.mask_cols(a)
masked_array(data =
[[0 -- 0]
[0 -- 0]
[0 -- 0]],
mask =
[[False True False]
[False True False]
[False True False]],
fill_value=999999)
i ( R ( RR RK ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR n s +c C s¤ | o< | i d j o, | i d j o t | ƒ } t | ƒ } n t i t | d ƒ t | d ƒ ƒ } t | ƒ } t | ƒ } t i | | ƒ } t | d | ƒS( sº
Return the dot product of two arrays.
.. note::
Works only with 2-D arrays at the moment.
This function is the equivalent of `numpy.dot` that takes masked values
into account, see `numpy.dot` for details.
Parameters
----------
a, b : ndarray
Inputs arrays.
strict : bool, optional
Whether masked data are propagated (True) or set to 0 (False) for the
computation. Default is False.
Propagating the mask means that if a masked value appears in a row or
column, the whole row or column is considered masked.
See Also
--------
numpy.dot : Equivalent function for ndarrays.
Examples
--------
>>> a = ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]])
>>> b = ma.array([[1, 2], [3, 4], [5, 6]], mask=[[1, 0], [0, 0], [0, 0]])
>>> np.ma.dot(a, b)
masked_array(data =
[[21 26]
[45 64]],
mask =
[[False False]
[False False]],
fill_value = 999999)
>>> np.ma.dot(a, b, strict=True)
masked_array(data =
[[-- --]
[-- 64]],
mask =
[[ True True]
[ True False]],
fill_value = 999999)
i i RM ( R| R R RN R R3 R5 R8 ( RR t bt strictR¦ t amt bmRL ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR œ s /' $
c C s“ t i | ƒ i } | d | d } | g } | d j o | i d | ƒ n | d j o | i | ƒ n t | ƒ d j o t | ƒ } n | S( s!
Compute the differences between consecutive elements of an array.
This function is the equivalent of `numpy.ediff1d` that takes masked
values into account, see `numpy.ediff1d` for details.
See Also
--------
numpy.ediff1d : Equivalent function for ndarrays.
i iÿÿÿÿi N( R_ t
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c C sq t i | d | d | ƒ} t | t ƒ o3 t | ƒ } | d i t ƒ | d >> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
>>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
>>> intersect1d(x, y)
masked_array(data = [1 3 --],
mask = [False False True],
fill_value = 999999)
i iÿÿÿÿ( R_ R1 R' R< ( RÊ t ar2t
assume_uniquet aux( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR s
!
c C s« | p t | ƒ } t | ƒ } n t i | | f ƒ } | i d j o | S| i ƒ | i ƒ } t i t g | d | d j t g f ƒ } | d | d j } | | S( sâ
Set exclusive-or of 1-D arrays with unique elements.
The output is always a masked array. See `numpy.setxor1d` for more details.
See Also
--------
numpy.setxor1d : Equivalent function for ndarrays.
i i iÿÿÿÿ( R' R_ R1 Rœ R< R3 RE ( RÊ RÌ RÍ RÎ t auxft flagt flag2( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR& / s
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C sÈ | p( t | d t ƒ\ } } t | ƒ } n t i | | f ƒ } | i d d ƒ } | | } | d | d j } t i | t g f ƒ } | i d d ƒ t | ƒ } | o | | S| | | Sd S( s#
Test whether each element of an array is also present in a second
array.
The output is always a masked array. See `numpy.in1d` for more details.
See Also
--------
numpy.in1d : Equivalent function for ndarrays.
Notes
-----
.. versionadded:: 1.4.0
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RÊ RÌ RÍ t rev_idxt art ordert sart equal_adjRÐ t indx( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR I s
c C s t t i | | f ƒ ƒ S( sÀ
Union of two arrays.
The output is always a masked array. See `numpy.union1d` for more details.
See also
--------
numpy.union1d : Equivalent function for ndarrays.
( R' R_ R1 ( RÊ RÌ ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR) m s c C sh | p t | ƒ } t | ƒ } n t | | d t ƒ} | i d j o | St i | ƒ | d j Sd S( sÊ
Set difference of 1D arrays with unique elements.
The output is always a masked array. See `numpy.setdiff1d` for more
details.
See Also
--------
numpy.setdiff1d : Equivalent function for ndarrays.
Examples
--------
>>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1])
>>> np.ma.extras.setdiff1d(x, [1, 2])
masked_array(data = [3 --],
mask = [False True],
fill_value = 999999)
RÍ i N( R' R RE Rœ R_ R0 ( RÊ RÌ RÍ RÎ ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR$ { s c C sq t i | d | d | ƒ} t | t ƒ o3 t | ƒ } | d i t ƒ | d RQ Rç R1 t _retval(
RZ t keyt objst scalarst final_dtypedescrRw t scalarR R R
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Translate slice objects to concatenation along the first axis.
This is the masked array version of `lib.index_tricks.RClass`.
See Also
--------
lib.index_tricks.RClass
Examples
--------
>>> np.ma.mr_[np.ma.array([1,2,3]), 0, 0, np.ma.array([4,5,6])]
array([1, 2, 3, 0, 0, 4, 5, 6])
c C s t i | d ƒ d S( Ni ( R R\ ( RZ ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR\ Ð s ( RW Rt RY R\ ( ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR À s c C so t | ƒ } | t j p t i | ƒ o d d g St i | ƒ } t | ƒ d j o | d d g Sd Sd S( sn
Find the indices of the first and last unmasked values.
Expects a 1-D `MaskedArray`, returns None if all values are masked.
Parameters
----------
arr : array_like
Input 1-D `MaskedArray`
Returns
-------
edges : ndarray or None
The indices of first and last non-masked value in the array.
Returns None if all values are masked.
See Also
--------
flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges
Notes
-----
Only accepts 1-D arrays.
Examples
--------
>>> a = np.arange(10)
>>> mask = (a < 3) | (a > 8) | (a == 5)
>>> ma = np.ma.array(a, mask=m)
>>> np.array(ma[~ma.mask])
array([3, 4, 6, 7, 8])
>>> flatnotmasked_edges(ma)
array([3, 8])
>>> ma = np.ma.array(a, mask=np.ones_like(a))
>>> print flatnotmasked_edges(ma)
None
i iÿÿÿÿN( R4 R: RN R· t flatnonzeroRf R^ ( RR RL t unmasked( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR Ù s * c C sõ t | ƒ } | d j p | i d j o t | ƒ St | ƒ } t t i | i ƒ d t i | g | i ƒ ƒ} t g } t
| i ƒ D] } | | | i | ƒ i ƒ qˆ ~ ƒ t g } t
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Find the indices of the first and last unmasked values along an axis.
If all values are masked, return None. Otherwise, return a list
of two tuples, corresponding to the indices of the first and last
unmasked values respectively.
Parameters
----------
a : array_like
The input array.
axis : int, optional
Axis along which to perform the operation.
If None (default), applies to a flattened version of the array.
Returns
-------
edges : ndarray or list
An array of start and end indexes if there are any masked data in
the array. If there are no masked data in the array, `edges` is a
list of the first and last index.
See Also
--------
flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous
Examples
--------
>>> a = np.arange(9).reshape((3, 3))
>>> m = np.zeros_like(a)
>>> m[1:, 1:] = 1
>>> ma = np.ma.array(a, mask=m)
>>> np.array(ma[~ma.mask])
array([0, 1, 2, 3, 6])
>>> np.ma.extras.notmasked_edges(ma)
array([0, 6])
i RM N( R0 R^ R| R R5 R/ RN t indicesRP RC R~ t mint
compressedRˆ ( RR RK RL R¯ Ro R Rp ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR!
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Find contiguous unmasked data in a masked array along the given axis.
Parameters
----------
a : narray
The input array.
Returns
-------
slice_list : list
A sorted sequence of slices (start index, end index).
See Also
--------
flatnotmasked_edges, notmasked_contiguous, notmasked_edges
Notes
-----
Only accepts 2-D arrays at most.
Examples
--------
>>> a = np.arange(10)
>>> mask = (a < 3) | (a > 8) | (a == 5)
>>> ma = np.ma.array(a, mask=mask)
>>> np.array(ma[~ma.mask])
array([3, 4, 6, 7, 8])
>>> np.ma.extras.flatnotmasked_contiguous(ma)
[slice(3, 4, None), slice(6, 8, None)]
>>> ma = np.ma.array(a, mask=np.ones_like(a))
>>> print np.ma.extras.flatnotmasked_edges(ma)
None
i iÿÿÿÿc S s | \ } } | | S( ( ( t .0R Rl ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyt k s i N( R4 R: Rœ RN R Rf R^ t itertoolst groupbyt enumerateR/ Rä Rg R€ R< ( RR RL R Rš Rw t groupRo t gt tmp( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR ? s %
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c C sÊ t | ƒ } | i } | d j o
t d ‚ n | d j p
| d j o t | ƒ Sg } | d d } d d g } t d d ƒ | | >> a = np.arange(9).reshape((3, 3))
>>> mask = np.zeros_like(a)
>>> mask[1:, 1:] = 1
>>> ma = np.ma.array(a, mask=mask)
>>> np.array(ma[~ma.mask])
array([0, 1, 2, 3, 6])
>>> np.ma.extras.notmasked_contiguous(ma)
[slice(0, 3, None), slice(6, 6, None)]
i s% Currently limited to atmost 2D array.i i N( R0 R| R¶ R^ R R€ R~ RP Rg ( RR RK R Rš t otherR¯ R ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR r s (
c C s§ | i d j o | i ƒ } n | d | d i ƒ } | d d } g } t t i d g | ƒ t i | t | ƒ g ƒ ƒ D] \ } } | t | | ƒ q~ ~ } | S( sv
Finds the clumps (groups of data with the same values) for a 1D bool array.
Returns a series of slices.
i iÿÿÿÿi ( R| R Rº t zipR t chainRf R€ ( RM R¯ Ro t leftt rightt slices( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyt _ezclump s Ac C s~ t | d t ƒ } | t j o t d | i ƒ g St | ƒ } | d t j o | d d d … } n | d d d … } | S( s9
Return list of slices corresponding to the unmasked clumps of a 1-D array.
Parameters
----------
a : ndarray
A one-dimensional masked array.
Returns
-------
slices : list of slice
The list of slices, one for each continuous region of unmasked
elements in `a`.
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
>>> a = np.ma.masked_array(np.arange(10))
>>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked
>>> np.ma.extras.clump_unmasked(a)
[slice(3, 6, None), slice(7, 8, None)]
RU i i Ni ( R] R: R€ Rœ R, R7 ( RR RM R+ Rš ( ( s5 /usr/lib64/python2.6/site-packages/numpy/ma/extras.pyR ¾ s
c C s} t i | ƒ } | t j o g St | ƒ } t | ƒ o? | d t j o | d d d … } qy | d d d … } n | S( sL
Returns a list of slices corresponding to the masked clumps of a 1-D array.
Parameters
----------
a : ndarray
A one-dimensional masked array.
Returns
-------
slices : list of slice
The list of slices, one for each continuous region of masked elements
in `a`.
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
>>> a = np.ma.masked_array(np.arange(10))
>>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked
>>> np.ma.extras.clump_masked(a)
[slice(0, 3, None), slice(6, 7, None), slice(8, None, None)]
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