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authorEric Wieser <wieser.eric@gmail.com>2019-08-19 19:16:44 -0500
committerEric Wieser <wieser.eric@gmail.com>2019-08-19 19:16:44 -0500
commit0f5e376d3eb6118b783cdd3ecd27722c2d1934ba (patch)
treec44850b579cbd27993c45dda1a7922e2d109b24f /numpy/ma
parent483f565d85dadc899f94710531fba8355d554d59 (diff)
parent98bdde643af6443d68a8c6233807b75bd3f0ed80 (diff)
downloadnumpy-0f5e376d3eb6118b783cdd3ecd27722c2d1934ba.tar.gz
Merge remote-tracking branch 'upstream/master' into fix-if-fields
Diffstat (limited to 'numpy/ma')
-rw-r--r--numpy/ma/core.py1314
-rw-r--r--numpy/ma/extras.py272
-rw-r--r--numpy/ma/mrecords.py14
-rw-r--r--numpy/ma/tests/test_core.py26
-rw-r--r--numpy/ma/tests/test_extras.py119
-rw-r--r--numpy/ma/tests/test_mrecords.py2
-rw-r--r--numpy/ma/tests/test_old_ma.py30
-rw-r--r--numpy/ma/tests/test_regression.py4
-rw-r--r--numpy/ma/tests/test_subclassing.py4
-rw-r--r--numpy/ma/timer_comparison.py15
10 files changed, 930 insertions, 870 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py
index 96d7207bd..95b799f6d 100644
--- a/numpy/ma/core.py
+++ b/numpy/ma/core.py
@@ -44,10 +44,9 @@ from numpy.compat import (
getargspec, formatargspec, long, basestring, unicode, bytes
)
from numpy import expand_dims
-from numpy.core.multiarray import normalize_axis_index
from numpy.core.numeric import normalize_axis_tuple
from numpy.core._internal import recursive
-from numpy.core.numeric import pickle
+from numpy.compat import pickle
__all__ = [
@@ -78,7 +77,7 @@ __all__ = [
'maximum_fill_value', 'mean', 'min', 'minimum', 'minimum_fill_value',
'mod', 'multiply', 'mvoid', 'ndim', 'negative', 'nomask', 'nonzero',
'not_equal', 'ones', 'outer', 'outerproduct', 'power', 'prod',
- 'product', 'ptp', 'put', 'putmask', 'rank', 'ravel', 'remainder',
+ 'product', 'ptp', 'put', 'putmask', 'ravel', 'remainder',
'repeat', 'reshape', 'resize', 'right_shift', 'round', 'round_',
'set_fill_value', 'shape', 'sin', 'sinh', 'size', 'soften_mask',
'sometrue', 'sort', 'sqrt', 'squeeze', 'std', 'subtract', 'sum',
@@ -516,18 +515,18 @@ def set_fill_value(a, fill_value):
array([0, 1, 2, 3, 4])
>>> a = ma.masked_where(a < 3, a)
>>> a
- masked_array(data = [-- -- -- 3 4],
- mask = [ True True True False False],
- fill_value=999999)
+ masked_array(data=[--, --, --, 3, 4],
+ mask=[ True, True, True, False, False],
+ fill_value=999999)
>>> ma.set_fill_value(a, -999)
>>> a
- masked_array(data = [-- -- -- 3 4],
- mask = [ True True True False False],
- fill_value=-999)
+ masked_array(data=[--, --, --, 3, 4],
+ mask=[ True, True, True, False, False],
+ fill_value=-999)
Nothing happens if `a` is not a masked array.
- >>> a = range(5)
+ >>> a = list(range(5))
>>> a
[0, 1, 2, 3, 4]
>>> ma.set_fill_value(a, 100)
@@ -689,13 +688,12 @@ def getdata(a, subok=True):
>>> import numpy.ma as ma
>>> a = ma.masked_equal([[1,2],[3,4]], 2)
>>> a
- masked_array(data =
- [[1 --]
- [3 4]],
- mask =
- [[False True]
- [False False]],
- fill_value=999999)
+ masked_array(
+ data=[[1, --],
+ [3, 4]],
+ mask=[[False, True],
+ [False, False]],
+ fill_value=2)
>>> ma.getdata(a)
array([[1, 2],
[3, 4]])
@@ -752,20 +750,19 @@ def fix_invalid(a, mask=nomask, copy=True, fill_value=None):
--------
>>> x = np.ma.array([1., -1, np.nan, np.inf], mask=[1] + [0]*3)
>>> x
- masked_array(data = [-- -1.0 nan inf],
- mask = [ True False False False],
- fill_value = 1e+20)
+ masked_array(data=[--, -1.0, nan, inf],
+ mask=[ True, False, False, False],
+ fill_value=1e+20)
>>> np.ma.fix_invalid(x)
- masked_array(data = [-- -1.0 -- --],
- mask = [ True False True True],
- fill_value = 1e+20)
+ masked_array(data=[--, -1.0, --, --],
+ mask=[ True, False, True, True],
+ fill_value=1e+20)
>>> fixed = np.ma.fix_invalid(x)
>>> fixed.data
- array([ 1.00000000e+00, -1.00000000e+00, 1.00000000e+20,
- 1.00000000e+20])
+ array([ 1.e+00, -1.e+00, 1.e+20, 1.e+20])
>>> x.data
- array([ 1., -1., NaN, Inf])
+ array([ 1., -1., nan, inf])
"""
a = masked_array(a, copy=copy, mask=mask, subok=True)
@@ -803,7 +800,7 @@ class _DomainCheckInterval(object):
def __init__(self, a, b):
"domain_check_interval(a,b)(x) = true where x < a or y > b"
- if (a > b):
+ if a > b:
(a, b) = (b, a)
self.a = a
self.b = b
@@ -1063,7 +1060,7 @@ class _MaskedBinaryOperation(_MaskedUFunc):
if t.shape == ():
t = t.reshape(1)
if m is not nomask:
- m = make_mask(m, copy=1)
+ m = make_mask(m, copy=True)
m.shape = (1,)
if m is nomask:
@@ -1168,7 +1165,7 @@ class _DomainedBinaryOperation(_MaskedUFunc):
if domain is not None:
m |= domain(da, db)
# Take care of the scalar case first
- if (not m.ndim):
+ if not m.ndim:
if m:
return masked
else:
@@ -1200,7 +1197,6 @@ exp = _MaskedUnaryOperation(umath.exp)
conjugate = _MaskedUnaryOperation(umath.conjugate)
sin = _MaskedUnaryOperation(umath.sin)
cos = _MaskedUnaryOperation(umath.cos)
-tan = _MaskedUnaryOperation(umath.tan)
arctan = _MaskedUnaryOperation(umath.arctan)
arcsinh = _MaskedUnaryOperation(umath.arcsinh)
sinh = _MaskedUnaryOperation(umath.sinh)
@@ -1346,9 +1342,9 @@ def make_mask_descr(ndtype):
--------
>>> import numpy.ma as ma
>>> dtype = np.dtype({'names':['foo', 'bar'],
- 'formats':[np.float32, int]})
+ ... 'formats':[np.float32, np.int64]})
>>> dtype
- dtype([('foo', '<f4'), ('bar', '<i4')])
+ dtype([('foo', '<f4'), ('bar', '<i8')])
>>> ma.make_mask_descr(dtype)
dtype([('foo', '|b1'), ('bar', '|b1')])
>>> ma.make_mask_descr(np.float32)
@@ -1381,13 +1377,12 @@ def getmask(a):
>>> import numpy.ma as ma
>>> a = ma.masked_equal([[1,2],[3,4]], 2)
>>> a
- masked_array(data =
- [[1 --]
- [3 4]],
- mask =
- [[False True]
- [False False]],
- fill_value=999999)
+ masked_array(
+ data=[[1, --],
+ [3, 4]],
+ mask=[[False, True],
+ [False, False]],
+ fill_value=2)
>>> ma.getmask(a)
array([[False, True],
[False, False]])
@@ -1402,12 +1397,11 @@ def getmask(a):
>>> b = ma.masked_array([[1,2],[3,4]])
>>> b
- masked_array(data =
- [[1 2]
- [3 4]],
- mask =
- False,
- fill_value=999999)
+ masked_array(
+ data=[[1, 2],
+ [3, 4]],
+ mask=False,
+ fill_value=999999)
>>> ma.nomask
False
>>> ma.getmask(b) == ma.nomask
@@ -1445,13 +1439,12 @@ def getmaskarray(arr):
>>> import numpy.ma as ma
>>> a = ma.masked_equal([[1,2],[3,4]], 2)
>>> a
- masked_array(data =
- [[1 --]
- [3 4]],
- mask =
- [[False True]
- [False False]],
- fill_value=999999)
+ masked_array(
+ data=[[1, --],
+ [3, 4]],
+ mask=[[False, True],
+ [False, False]],
+ fill_value=2)
>>> ma.getmaskarray(a)
array([[False, True],
[False, False]])
@@ -1460,13 +1453,12 @@ def getmaskarray(arr):
>>> b = ma.masked_array([[1,2],[3,4]])
>>> b
- masked_array(data =
- [[1 2]
- [3 4]],
- mask =
- False,
- fill_value=999999)
- >>> >ma.getmaskarray(b)
+ masked_array(
+ data=[[1, 2],
+ [3, 4]],
+ mask=False,
+ fill_value=999999)
+ >>> ma.getmaskarray(b)
array([[False, False],
[False, False]])
@@ -1504,9 +1496,9 @@ def is_mask(m):
>>> import numpy.ma as ma
>>> m = ma.masked_equal([0, 1, 0, 2, 3], 0)
>>> m
- masked_array(data = [-- 1 -- 2 3],
- mask = [ True False True False False],
- fill_value=999999)
+ masked_array(data=[--, 1, --, 2, 3],
+ mask=[ True, False, True, False, False],
+ fill_value=0)
>>> ma.is_mask(m)
False
>>> ma.is_mask(m.mask)
@@ -1527,14 +1519,14 @@ def is_mask(m):
Arrays with complex dtypes don't return True.
>>> dtype = np.dtype({'names':['monty', 'pithon'],
- 'formats':[bool, bool]})
+ ... 'formats':[bool, bool]})
>>> dtype
dtype([('monty', '|b1'), ('pithon', '|b1')])
>>> m = np.array([(True, False), (False, True), (True, False)],
- dtype=dtype)
+ ... dtype=dtype)
>>> m
- array([(True, False), (False, True), (True, False)],
- dtype=[('monty', '|b1'), ('pithon', '|b1')])
+ array([( True, False), (False, True), ( True, False)],
+ dtype=[('monty', '?'), ('pithon', '?')])
>>> ma.is_mask(m)
False
@@ -1562,7 +1554,7 @@ def make_mask(m, copy=False, shrink=True, dtype=MaskType):
Return `m` as a boolean mask, creating a copy if necessary or requested.
The function can accept any sequence that is convertible to integers,
or ``nomask``. Does not require that contents must be 0s and 1s, values
- of 0 are interepreted as False, everything else as True.
+ of 0 are interpreted as False, everything else as True.
Parameters
----------
@@ -1600,7 +1592,7 @@ def make_mask(m, copy=False, shrink=True, dtype=MaskType):
>>> m = np.zeros(4)
>>> m
- array([ 0., 0., 0., 0.])
+ array([0., 0., 0., 0.])
>>> ma.make_mask(m)
False
>>> ma.make_mask(m, shrink=False)
@@ -1616,11 +1608,11 @@ def make_mask(m, copy=False, shrink=True, dtype=MaskType):
>>> arr
[(1, 0), (0, 1), (1, 0), (1, 0)]
>>> dtype = np.dtype({'names':['man', 'mouse'],
- 'formats':[int, int]})
+ ... 'formats':[np.int64, np.int64]})
>>> arr = np.array(arr, dtype=dtype)
>>> arr
array([(1, 0), (0, 1), (1, 0), (1, 0)],
- dtype=[('man', '<i4'), ('mouse', '<i4')])
+ dtype=[('man', '<i8'), ('mouse', '<i8')])
>>> ma.make_mask(arr, dtype=dtype)
array([(True, False), (False, True), (True, False), (True, False)],
dtype=[('man', '|b1'), ('mouse', '|b1')])
@@ -1679,9 +1671,9 @@ def make_mask_none(newshape, dtype=None):
Defining a more complex dtype.
>>> dtype = np.dtype({'names':['foo', 'bar'],
- 'formats':[np.float32, int]})
+ ... 'formats':[np.float32, np.int64]})
>>> dtype
- dtype([('foo', '<f4'), ('bar', '<i4')])
+ dtype([('foo', '<f4'), ('bar', '<i8')])
>>> ma.make_mask_none((3,), dtype=dtype)
array([(False, False), (False, False), (False, False)],
dtype=[('foo', '|b1'), ('bar', '|b1')])
@@ -1751,7 +1743,7 @@ def mask_or(m1, m2, copy=False, shrink=True):
if m1 is m2 and is_mask(m1):
return m1
(dtype1, dtype2) = (getattr(m1, 'dtype', None), getattr(m2, 'dtype', None))
- if (dtype1 != dtype2):
+ if dtype1 != dtype2:
raise ValueError("Incompatible dtypes '%s'<>'%s'" % (dtype1, dtype2))
if dtype1.names is not None:
# Allocate an output mask array with the properly broadcast shape.
@@ -1779,16 +1771,16 @@ def flatten_mask(mask):
Examples
--------
>>> mask = np.array([0, 0, 1])
- >>> flatten_mask(mask)
+ >>> np.ma.flatten_mask(mask)
array([False, False, True])
>>> mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)])
- >>> flatten_mask(mask)
+ >>> np.ma.flatten_mask(mask)
array([False, False, False, True])
>>> mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
>>> mask = np.array([(0, (0, 0)), (0, (0, 1))], dtype=mdtype)
- >>> flatten_mask(mask)
+ >>> np.ma.flatten_mask(mask)
array([False, False, False, False, False, True])
"""
@@ -1873,38 +1865,39 @@ def masked_where(condition, a, copy=True):
>>> a
array([0, 1, 2, 3])
>>> ma.masked_where(a <= 2, a)
- masked_array(data = [-- -- -- 3],
- mask = [ True True True False],
- fill_value=999999)
+ masked_array(data=[--, --, --, 3],
+ mask=[ True, True, True, False],
+ fill_value=999999)
Mask array `b` conditional on `a`.
>>> b = ['a', 'b', 'c', 'd']
>>> ma.masked_where(a == 2, b)
- masked_array(data = [a b -- d],
- mask = [False False True False],
- fill_value=N/A)
+ masked_array(data=['a', 'b', --, 'd'],
+ mask=[False, False, True, False],
+ fill_value='N/A',
+ dtype='<U1')
Effect of the `copy` argument.
>>> c = ma.masked_where(a <= 2, a)
>>> c
- masked_array(data = [-- -- -- 3],
- mask = [ True True True False],
- fill_value=999999)
+ masked_array(data=[--, --, --, 3],
+ mask=[ True, True, True, False],
+ fill_value=999999)
>>> c[0] = 99
>>> c
- masked_array(data = [99 -- -- 3],
- mask = [False True True False],
- fill_value=999999)
+ masked_array(data=[99, --, --, 3],
+ mask=[False, True, True, False],
+ fill_value=999999)
>>> a
array([0, 1, 2, 3])
>>> c = ma.masked_where(a <= 2, a, copy=False)
>>> c[0] = 99
>>> c
- masked_array(data = [99 -- -- 3],
- mask = [False True True False],
- fill_value=999999)
+ masked_array(data=[99, --, --, 3],
+ mask=[False, True, True, False],
+ fill_value=999999)
>>> a
array([99, 1, 2, 3])
@@ -1913,19 +1906,19 @@ def masked_where(condition, a, copy=True):
>>> a = np.arange(4)
>>> a = ma.masked_where(a == 2, a)
>>> a
- masked_array(data = [0 1 -- 3],
- mask = [False False True False],
- fill_value=999999)
+ masked_array(data=[0, 1, --, 3],
+ mask=[False, False, True, False],
+ fill_value=999999)
>>> b = np.arange(4)
>>> b = ma.masked_where(b == 0, b)
>>> b
- masked_array(data = [-- 1 2 3],
- mask = [ True False False False],
- fill_value=999999)
+ masked_array(data=[--, 1, 2, 3],
+ mask=[ True, False, False, False],
+ fill_value=999999)
>>> ma.masked_where(a == 3, b)
- masked_array(data = [-- 1 -- --],
- mask = [ True False True True],
- fill_value=999999)
+ masked_array(data=[--, 1, --, --],
+ mask=[ True, False, True, True],
+ fill_value=999999)
"""
# Make sure that condition is a valid standard-type mask.
@@ -1965,9 +1958,9 @@ def masked_greater(x, value, copy=True):
>>> a
array([0, 1, 2, 3])
>>> ma.masked_greater(a, 2)
- masked_array(data = [0 1 2 --],
- mask = [False False False True],
- fill_value=999999)
+ masked_array(data=[0, 1, 2, --],
+ mask=[False, False, False, True],
+ fill_value=999999)
"""
return masked_where(greater(x, value), x, copy=copy)
@@ -1991,9 +1984,9 @@ def masked_greater_equal(x, value, copy=True):
>>> a
array([0, 1, 2, 3])
>>> ma.masked_greater_equal(a, 2)
- masked_array(data = [0 1 -- --],
- mask = [False False True True],
- fill_value=999999)
+ masked_array(data=[0, 1, --, --],
+ mask=[False, False, True, True],
+ fill_value=999999)
"""
return masked_where(greater_equal(x, value), x, copy=copy)
@@ -2017,9 +2010,9 @@ def masked_less(x, value, copy=True):
>>> a
array([0, 1, 2, 3])
>>> ma.masked_less(a, 2)
- masked_array(data = [-- -- 2 3],
- mask = [ True True False False],
- fill_value=999999)
+ masked_array(data=[--, --, 2, 3],
+ mask=[ True, True, False, False],
+ fill_value=999999)
"""
return masked_where(less(x, value), x, copy=copy)
@@ -2043,9 +2036,9 @@ def masked_less_equal(x, value, copy=True):
>>> a
array([0, 1, 2, 3])
>>> ma.masked_less_equal(a, 2)
- masked_array(data = [-- -- -- 3],
- mask = [ True True True False],
- fill_value=999999)
+ masked_array(data=[--, --, --, 3],
+ mask=[ True, True, True, False],
+ fill_value=999999)
"""
return masked_where(less_equal(x, value), x, copy=copy)
@@ -2069,9 +2062,9 @@ def masked_not_equal(x, value, copy=True):
>>> a
array([0, 1, 2, 3])
>>> ma.masked_not_equal(a, 2)
- masked_array(data = [-- -- 2 --],
- mask = [ True True False True],
- fill_value=999999)
+ masked_array(data=[--, --, 2, --],
+ mask=[ True, True, False, True],
+ fill_value=999999)
"""
return masked_where(not_equal(x, value), x, copy=copy)
@@ -2097,9 +2090,9 @@ def masked_equal(x, value, copy=True):
>>> a
array([0, 1, 2, 3])
>>> ma.masked_equal(a, 2)
- masked_array(data = [0 1 -- 3],
- mask = [False False True False],
- fill_value=999999)
+ masked_array(data=[0, 1, --, 3],
+ mask=[False, False, True, False],
+ fill_value=2)
"""
output = masked_where(equal(x, value), x, copy=copy)
@@ -2128,16 +2121,16 @@ def masked_inside(x, v1, v2, copy=True):
>>> import numpy.ma as ma
>>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]
>>> ma.masked_inside(x, -0.3, 0.3)
- masked_array(data = [0.31 1.2 -- -- -0.4 -1.1],
- mask = [False False True True False False],
- fill_value=1e+20)
+ masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1],
+ mask=[False, False, True, True, False, False],
+ fill_value=1e+20)
The order of `v1` and `v2` doesn't matter.
>>> ma.masked_inside(x, 0.3, -0.3)
- masked_array(data = [0.31 1.2 -- -- -0.4 -1.1],
- mask = [False False True True False False],
- fill_value=1e+20)
+ masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1],
+ mask=[False, False, True, True, False, False],
+ fill_value=1e+20)
"""
if v2 < v1:
@@ -2168,16 +2161,16 @@ def masked_outside(x, v1, v2, copy=True):
>>> import numpy.ma as ma
>>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]
>>> ma.masked_outside(x, -0.3, 0.3)
- masked_array(data = [-- -- 0.01 0.2 -- --],
- mask = [ True True False False True True],
- fill_value=1e+20)
+ masked_array(data=[--, --, 0.01, 0.2, --, --],
+ mask=[ True, True, False, False, True, True],
+ fill_value=1e+20)
The order of `v1` and `v2` doesn't matter.
>>> ma.masked_outside(x, 0.3, -0.3)
- masked_array(data = [-- -- 0.01 0.2 -- --],
- mask = [ True True False False True True],
- fill_value=1e+20)
+ masked_array(data=[--, --, 0.01, 0.2, --, --],
+ mask=[ True, True, False, False, True, True],
+ fill_value=1e+20)
"""
if v2 < v1:
@@ -2222,20 +2215,27 @@ def masked_object(x, value, copy=True, shrink=True):
>>> food = np.array(['green_eggs', 'ham'], dtype=object)
>>> # don't eat spoiled food
>>> eat = ma.masked_object(food, 'green_eggs')
- >>> print(eat)
- [-- ham]
+ >>> eat
+ masked_array(data=[--, 'ham'],
+ mask=[ True, False],
+ fill_value='green_eggs',
+ dtype=object)
>>> # plain ol` ham is boring
>>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object)
>>> eat = ma.masked_object(fresh_food, 'green_eggs')
- >>> print(eat)
- [cheese ham pineapple]
+ >>> eat
+ masked_array(data=['cheese', 'ham', 'pineapple'],
+ mask=False,
+ fill_value='green_eggs',
+ dtype=object)
Note that `mask` is set to ``nomask`` if possible.
>>> eat
- masked_array(data = [cheese ham pineapple],
- mask = False,
- fill_value=?)
+ masked_array(data=['cheese', 'ham', 'pineapple'],
+ mask=False,
+ fill_value='green_eggs',
+ dtype=object)
"""
if isMaskedArray(x):
@@ -2290,16 +2290,16 @@ def masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True):
>>> import numpy.ma as ma
>>> x = np.array([1, 1.1, 2, 1.1, 3])
>>> ma.masked_values(x, 1.1)
- masked_array(data = [1.0 -- 2.0 -- 3.0],
- mask = [False True False True False],
- fill_value=1.1)
+ masked_array(data=[1.0, --, 2.0, --, 3.0],
+ mask=[False, True, False, True, False],
+ fill_value=1.1)
Note that `mask` is set to ``nomask`` if possible.
>>> ma.masked_values(x, 1.5)
- masked_array(data = [ 1. 1.1 2. 1.1 3. ],
- mask = False,
- fill_value=1.5)
+ masked_array(data=[1. , 1.1, 2. , 1.1, 3. ],
+ mask=False,
+ fill_value=1.5)
For integers, the fill value will be different in general to the
result of ``masked_equal``.
@@ -2308,13 +2308,13 @@ def masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True):
>>> x
array([0, 1, 2, 3, 4])
>>> ma.masked_values(x, 2)
- masked_array(data = [0 1 -- 3 4],
- mask = [False False True False False],
- fill_value=2)
+ masked_array(data=[0, 1, --, 3, 4],
+ mask=[False, False, True, False, False],
+ fill_value=2)
>>> ma.masked_equal(x, 2)
- masked_array(data = [0 1 -- 3 4],
- mask = [False False True False False],
- fill_value=999999)
+ masked_array(data=[0, 1, --, 3, 4],
+ mask=[False, False, True, False, False],
+ fill_value=2)
"""
xnew = filled(x, value)
@@ -2348,11 +2348,11 @@ def masked_invalid(a, copy=True):
>>> a[2] = np.NaN
>>> a[3] = np.PINF
>>> a
- array([ 0., 1., NaN, Inf, 4.])
+ array([ 0., 1., nan, inf, 4.])
>>> ma.masked_invalid(a)
- masked_array(data = [0.0 1.0 -- -- 4.0],
- mask = [False False True True False],
- fill_value=1e+20)
+ masked_array(data=[0.0, 1.0, --, --, 4.0],
+ mask=[False, False, True, True, False],
+ fill_value=1e+20)
"""
a = np.array(a, copy=copy, subok=True)
@@ -2513,7 +2513,7 @@ def flatten_structured_array(a):
--------
>>> ndtype = [('a', int), ('b', float)]
>>> a = np.array([(1, 1), (2, 2)], dtype=ndtype)
- >>> flatten_structured_array(a)
+ >>> np.ma.flatten_structured_array(a)
array([[1., 1.],
[2., 2.]])
@@ -2681,17 +2681,13 @@ class MaskedIterator(object):
--------
>>> x = np.ma.array([3, 2], mask=[0, 1])
>>> fl = x.flat
- >>> fl.next()
+ >>> next(fl)
3
- >>> fl.next()
- masked_array(data = --,
- mask = True,
- fill_value = 1e+20)
- >>> fl.next()
+ >>> next(fl)
+ masked
+ >>> next(fl)
Traceback (most recent call last):
- File "<stdin>", line 1, in <module>
- File "/home/ralf/python/numpy/numpy/ma/core.py", line 2243, in next
- d = self.dataiter.next()
+ ...
StopIteration
"""
@@ -3079,7 +3075,7 @@ class MaskedArray(ndarray):
def view(self, dtype=None, type=None, fill_value=None):
"""
- Return a view of the MaskedArray data
+ Return a view of the MaskedArray data.
Parameters
----------
@@ -3093,6 +3089,14 @@ class MaskedArray(ndarray):
type : Python type, optional
Type of the returned view, either ndarray or a subclass. The
default None results in type preservation.
+ fill_value : scalar, optional
+ The value to use for invalid entries (None by default).
+ If None, then this argument is inferred from the passed `dtype`, or
+ in its absence the original array, as discussed in the notes below.
+
+ See Also
+ --------
+ numpy.ndarray.view : Equivalent method on ndarray object.
Notes
-----
@@ -3145,7 +3149,7 @@ class MaskedArray(ndarray):
# also make the mask be a view (so attr changes to the view's
# mask do no affect original object's mask)
# (especially important to avoid affecting np.masked singleton)
- if (getmask(output) is not nomask):
+ if getmask(output) is not nomask:
output._mask = output._mask.view()
# Make sure to reset the _fill_value if needed
@@ -3158,7 +3162,6 @@ class MaskedArray(ndarray):
else:
output.fill_value = fill_value
return output
- view.__doc__ = ndarray.view.__doc__
def __getitem__(self, indx):
"""
@@ -3384,7 +3387,7 @@ class MaskedArray(ndarray):
if mask is masked:
mask = True
- if (current_mask is nomask):
+ if current_mask is nomask:
# Make sure the mask is set
# Just don't do anything if there's nothing to do.
if mask is nomask:
@@ -3445,39 +3448,43 @@ class MaskedArray(ndarray):
_set_mask = __setmask__
- def _get_mask(self):
- """Return the current mask.
+ @property
+ def mask(self):
+ """ Current mask. """
- """
# We could try to force a reshape, but that wouldn't work in some
# cases.
- return self._mask
+ # Return a view so that the dtype and shape cannot be changed in place
+ # This still preserves nomask by identity
+ return self._mask.view()
- mask = property(fget=_get_mask, fset=__setmask__, doc="Mask")
+ @mask.setter
+ def mask(self, value):
+ self.__setmask__(value)
- def _get_recordmask(self):
+ @property
+ def recordmask(self):
"""
- Return the mask of the records.
-
- A record is masked when all the fields are masked.
+ Get or set the mask of the array if it has no named fields. For
+ structured arrays, returns a ndarray of booleans where entries are
+ ``True`` if **all** the fields are masked, ``False`` otherwise:
+ >>> x = np.ma.array([(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)],
+ ... mask=[(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)],
+ ... dtype=[('a', int), ('b', int)])
+ >>> x.recordmask
+ array([False, False, True, False, False])
"""
+
_mask = self._mask.view(ndarray)
if _mask.dtype.names is None:
return _mask
return np.all(flatten_structured_array(_mask), axis=-1)
- def _set_recordmask(self):
- """
- Return the mask of the records.
-
- A record is masked when all the fields are masked.
-
- """
+ @recordmask.setter
+ def recordmask(self, mask):
raise NotImplementedError("Coming soon: setting the mask per records!")
- recordmask = property(fget=_get_recordmask)
-
def harden_mask(self):
"""
Force the mask to hard.
@@ -3508,8 +3515,10 @@ class MaskedArray(ndarray):
self._hardmask = False
return self
- hardmask = property(fget=lambda self: self._hardmask,
- doc="Hardness of the mask")
+ @property
+ def hardmask(self):
+ """ Hardness of the mask """
+ return self._hardmask
def unshare_mask(self):
"""
@@ -3529,8 +3538,10 @@ class MaskedArray(ndarray):
self._sharedmask = False
return self
- sharedmask = property(fget=lambda self: self._sharedmask,
- doc="Share status of the mask (read-only).")
+ @property
+ def sharedmask(self):
+ """ Share status of the mask (read-only). """
+ return self._sharedmask
def shrink_mask(self):
"""
@@ -3551,6 +3562,11 @@ class MaskedArray(ndarray):
array([[False, False],
[False, False]])
>>> x.shrink_mask()
+ masked_array(
+ data=[[1, 2],
+ [3, 4]],
+ mask=False,
+ fill_value=999999)
>>> x.mask
False
@@ -3558,39 +3574,46 @@ class MaskedArray(ndarray):
self._mask = _shrink_mask(self._mask)
return self
- baseclass = property(fget=lambda self: self._baseclass,
- doc="Class of the underlying data (read-only).")
+ @property
+ def baseclass(self):
+ """ Class of the underlying data (read-only). """
+ return self._baseclass
def _get_data(self):
- """Return the current data, as a view of the original
- underlying data.
+ """
+ Returns the underlying data, as a view of the masked array.
+
+ If the underlying data is a subclass of :class:`numpy.ndarray`, it is
+ returned as such.
+ >>> x = np.ma.array(np.matrix([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])
+ >>> x.data
+ matrix([[1, 2],
+ [3, 4]])
+
+ The type of the data can be accessed through the :attr:`baseclass`
+ attribute.
"""
return ndarray.view(self, self._baseclass)
_data = property(fget=_get_data)
data = property(fget=_get_data)
- def _get_flat(self):
- "Return a flat iterator."
+ @property
+ def flat(self):
+ """ Return a flat iterator, or set a flattened version of self to value. """
return MaskedIterator(self)
- def _set_flat(self, value):
- "Set a flattened version of self to value."
+ @flat.setter
+ def flat(self, value):
y = self.ravel()
y[:] = value
- flat = property(fget=_get_flat, fset=_set_flat,
- doc="Flat version of the array.")
-
- def get_fill_value(self):
+ @property
+ def fill_value(self):
"""
- Return the filling value of the masked array.
-
- Returns
- -------
- fill_value : scalar
- The filling value.
+ The filling value of the masked array is a scalar. When setting, None
+ will set to a default based on the data type.
Examples
--------
@@ -3603,8 +3626,17 @@ class MaskedArray(ndarray):
(1e+20+0j)
>>> x = np.ma.array([0, 1.], fill_value=-np.inf)
- >>> x.get_fill_value()
+ >>> x.fill_value
-inf
+ >>> x.fill_value = np.pi
+ >>> x.fill_value
+ 3.1415926535897931 # may vary
+
+ Reset to default:
+
+ >>> x.fill_value = None
+ >>> x.fill_value
+ 1e+20
"""
if self._fill_value is None:
@@ -3618,36 +3650,8 @@ class MaskedArray(ndarray):
return self._fill_value[()]
return self._fill_value
- def set_fill_value(self, value=None):
- """
- Set the filling value of the masked array.
-
- Parameters
- ----------
- value : scalar, optional
- The new filling value. Default is None, in which case a default
- based on the data type is used.
-
- See Also
- --------
- ma.set_fill_value : Equivalent function.
-
- Examples
- --------
- >>> x = np.ma.array([0, 1.], fill_value=-np.inf)
- >>> x.fill_value
- -inf
- >>> x.set_fill_value(np.pi)
- >>> x.fill_value
- 3.1415926535897931
-
- Reset to default:
-
- >>> x.set_fill_value()
- >>> x.fill_value
- 1e+20
-
- """
+ @fill_value.setter
+ def fill_value(self, value=None):
target = _check_fill_value(value, self.dtype)
_fill_value = self._fill_value
if _fill_value is None:
@@ -3657,8 +3661,9 @@ class MaskedArray(ndarray):
# Don't overwrite the attribute, just fill it (for propagation)
_fill_value[()] = target
- fill_value = property(fget=get_fill_value, fset=set_fill_value,
- doc="Filling value.")
+ # kept for compatibility
+ get_fill_value = fill_value.fget
+ set_fill_value = fill_value.fset
def filled(self, fill_value=None):
"""
@@ -3688,9 +3693,9 @@ class MaskedArray(ndarray):
--------
>>> x = np.ma.array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999)
>>> x.filled()
- array([1, 2, -999, 4, -999])
+ array([ 1, 2, -999, 4, -999])
>>> type(x.filled())
- <type 'numpy.ndarray'>
+ <class 'numpy.ndarray'>
Subclassing is preserved. This means that if, e.g., the data part of
the masked array is a recarray, `filled` returns a recarray:
@@ -3755,7 +3760,7 @@ class MaskedArray(ndarray):
>>> x.compressed()
array([0, 1])
>>> type(x.compressed())
- <type 'numpy.ndarray'>
+ <class 'numpy.ndarray'>
"""
data = ndarray.ravel(self._data)
@@ -3797,25 +3802,29 @@ class MaskedArray(ndarray):
Examples
--------
>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
- >>> print(x)
- [[1 -- 3]
- [-- 5 --]
- [7 -- 9]]
+ >>> x
+ masked_array(
+ data=[[1, --, 3],
+ [--, 5, --],
+ [7, --, 9]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
>>> x.compress([1, 0, 1])
- masked_array(data = [1 3],
- mask = [False False],
- fill_value=999999)
+ masked_array(data=[1, 3],
+ mask=[False, False],
+ fill_value=999999)
>>> x.compress([1, 0, 1], axis=1)
- masked_array(data =
- [[1 3]
- [-- --]
- [7 9]],
- mask =
- [[False False]
- [ True True]
- [False False]],
- fill_value=999999)
+ masked_array(
+ data=[[1, 3],
+ [--, --],
+ [7, 9]],
+ mask=[[False, False],
+ [ True, True],
+ [False, False]],
+ fill_value=999999)
"""
# Get the basic components
@@ -4323,75 +4332,59 @@ class MaskedArray(ndarray):
raise MaskError('Cannot convert masked element to a Python long.')
return long(self.item())
-
- def get_imag(self):
+ @property
+ def imag(self):
"""
- Return the imaginary part of the masked array.
-
- The returned array is a view on the imaginary part of the `MaskedArray`
- whose `get_imag` method is called.
-
- Parameters
- ----------
- None
+ The imaginary part of the masked array.
- Returns
- -------
- result : MaskedArray
- The imaginary part of the masked array.
+ This property is a view on the imaginary part of this `MaskedArray`.
See Also
--------
- get_real, real, imag
+ real
Examples
--------
>>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])
- >>> x.get_imag()
- masked_array(data = [1.0 -- 1.6],
- mask = [False True False],
- fill_value = 1e+20)
+ >>> x.imag
+ masked_array(data=[1.0, --, 1.6],
+ mask=[False, True, False],
+ fill_value=1e+20)
"""
result = self._data.imag.view(type(self))
result.__setmask__(self._mask)
return result
- imag = property(fget=get_imag, doc="Imaginary part.")
+ # kept for compatibility
+ get_imag = imag.fget
- def get_real(self):
+ @property
+ def real(self):
"""
- Return the real part of the masked array.
+ The real part of the masked array.
- The returned array is a view on the real part of the `MaskedArray`
- whose `get_real` method is called.
-
- Parameters
- ----------
- None
-
- Returns
- -------
- result : MaskedArray
- The real part of the masked array.
+ This property is a view on the real part of this `MaskedArray`.
See Also
--------
- get_imag, real, imag
+ imag
Examples
--------
>>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])
- >>> x.get_real()
- masked_array(data = [1.0 -- 3.45],
- mask = [False True False],
- fill_value = 1e+20)
+ >>> x.real
+ masked_array(data=[1.0, --, 3.45],
+ mask=[False, True, False],
+ fill_value=1e+20)
"""
result = self._data.real.view(type(self))
result.__setmask__(self._mask)
return result
- real = property(fget=get_real, doc="Real part")
+
+ # kept for compatibility
+ get_real = real.fget
def count(self, axis=None, keepdims=np._NoValue):
"""
@@ -4431,13 +4424,12 @@ class MaskedArray(ndarray):
>>> a = ma.arange(6).reshape((2, 3))
>>> a[1, :] = ma.masked
>>> a
- masked_array(data =
- [[0 1 2]
- [-- -- --]],
- mask =
- [[False False False]
- [ True True True]],
- fill_value = 999999)
+ masked_array(
+ data=[[0, 1, 2],
+ [--, --, --]],
+ mask=[[False, False, False],
+ [ True, True, True]],
+ fill_value=999999)
>>> a.count()
3
@@ -4463,7 +4455,7 @@ class MaskedArray(ndarray):
if m is nomask:
# compare to _count_reduce_items in _methods.py
- if self.shape is ():
+ if self.shape == ():
if axis not in (None, 0):
raise np.AxisError(axis=axis, ndim=self.ndim)
return 1
@@ -4522,12 +4514,20 @@ class MaskedArray(ndarray):
Examples
--------
>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
- >>> print(x)
- [[1 -- 3]
- [-- 5 --]
- [7 -- 9]]
- >>> print(x.ravel())
- [1 -- 3 -- 5 -- 7 -- 9]
+ >>> x
+ masked_array(
+ data=[[1, --, 3],
+ [--, 5, --],
+ [7, --, 9]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
+ >>> x.ravel()
+ masked_array(data=[1, --, 3, --, 5, --, 7, --, 9],
+ mask=[False, True, False, True, False, True, False, True,
+ False],
+ fill_value=999999)
"""
r = ndarray.ravel(self._data, order=order).view(type(self))
@@ -4576,15 +4576,25 @@ class MaskedArray(ndarray):
Examples
--------
>>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])
- >>> print(x)
- [[-- 2]
- [3 --]]
+ >>> x
+ masked_array(
+ data=[[--, 2],
+ [3, --]],
+ mask=[[ True, False],
+ [False, True]],
+ fill_value=999999)
>>> x = x.reshape((4,1))
- >>> print(x)
- [[--]
- [2]
- [3]
- [--]]
+ >>> x
+ masked_array(
+ data=[[--],
+ [2],
+ [3],
+ [--]],
+ mask=[[ True],
+ [False],
+ [False],
+ [ True]],
+ fill_value=999999)
"""
kwargs.update(order=kwargs.get('order', 'C'))
@@ -4641,21 +4651,36 @@ class MaskedArray(ndarray):
Examples
--------
>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
- >>> print(x)
- [[1 -- 3]
- [-- 5 --]
- [7 -- 9]]
+ >>> x
+ masked_array(
+ data=[[1, --, 3],
+ [--, 5, --],
+ [7, --, 9]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
>>> x.put([0,4,8],[10,20,30])
- >>> print(x)
- [[10 -- 3]
- [-- 20 --]
- [7 -- 30]]
+ >>> x
+ masked_array(
+ data=[[10, --, 3],
+ [--, 20, --],
+ [7, --, 30]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
>>> x.put(4,999)
- >>> print(x)
- [[10 -- 3]
- [-- 999 --]
- [7 -- 30]]
+ >>> x
+ masked_array(
+ data=[[10, --, 3],
+ [--, 999, --],
+ [7, --, 30]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
"""
# Hard mask: Get rid of the values/indices that fall on masked data
@@ -4695,14 +4720,14 @@ class MaskedArray(ndarray):
--------
>>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1])
>>> x.ids()
- (166670640, 166659832)
+ (166670640, 166659832) # may vary
If the array has no mask, the address of `nomask` is returned. This address
is typically not close to the data in memory:
>>> x = np.ma.array([1, 2, 3])
>>> x.ids()
- (166691080, 3083169284L)
+ (166691080, 3083169284L) # may vary
"""
if self._mask is nomask:
@@ -4851,13 +4876,12 @@ class MaskedArray(ndarray):
>>> import numpy.ma as ma
>>> x = ma.array(np.eye(3))
>>> x
- masked_array(data =
- [[ 1. 0. 0.]
- [ 0. 1. 0.]
- [ 0. 0. 1.]],
- mask =
- False,
- fill_value=1e+20)
+ masked_array(
+ data=[[1., 0., 0.],
+ [0., 1., 0.],
+ [0., 0., 1.]],
+ mask=False,
+ fill_value=1e+20)
>>> x.nonzero()
(array([0, 1, 2]), array([0, 1, 2]))
@@ -4865,15 +4889,14 @@ class MaskedArray(ndarray):
>>> x[1, 1] = ma.masked
>>> x
- masked_array(data =
- [[1.0 0.0 0.0]
- [0.0 -- 0.0]
- [0.0 0.0 1.0]],
- mask =
- [[False False False]
- [False True False]
- [False False False]],
- fill_value=1e+20)
+ masked_array(
+ data=[[1.0, 0.0, 0.0],
+ [0.0, --, 0.0],
+ [0.0, 0.0, 1.0]],
+ mask=[[False, False, False],
+ [False, True, False],
+ [False, False, False]],
+ fill_value=1e+20)
>>> x.nonzero()
(array([0, 2]), array([0, 2]))
@@ -4890,13 +4913,12 @@ class MaskedArray(ndarray):
>>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
- masked_array(data =
- [[False False False]
- [ True True True]
- [ True True True]],
- mask =
- False,
- fill_value=999999)
+ masked_array(
+ data=[[False, False, False],
+ [ True, True, True],
+ [ True, True, True]],
+ mask=False,
+ fill_value=True)
>>> ma.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
@@ -4978,18 +5000,27 @@ class MaskedArray(ndarray):
Examples
--------
>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
- >>> print(x)
- [[1 -- 3]
- [-- 5 --]
- [7 -- 9]]
- >>> print(x.sum())
+ >>> x
+ masked_array(
+ data=[[1, --, 3],
+ [--, 5, --],
+ [7, --, 9]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
+ >>> x.sum()
25
- >>> print(x.sum(axis=1))
- [4 5 16]
- >>> print(x.sum(axis=0))
- [8 5 12]
+ >>> x.sum(axis=1)
+ masked_array(data=[4, 5, 16],
+ mask=[False, False, False],
+ fill_value=999999)
+ >>> x.sum(axis=0)
+ masked_array(data=[8, 5, 12],
+ mask=[False, False, False],
+ fill_value=999999)
>>> print(type(x.sum(axis=0, dtype=np.int64)[0]))
- <type 'numpy.int64'>
+ <class 'numpy.int64'>
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
@@ -5010,7 +5041,7 @@ class MaskedArray(ndarray):
result = self.filled(0).sum(axis, dtype=dtype, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getmask(out)
- if (outmask is nomask):
+ if outmask is nomask:
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
return out
@@ -5040,8 +5071,11 @@ class MaskedArray(ndarray):
Examples
--------
>>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0])
- >>> print(marr.cumsum())
- [0 1 3 -- -- -- 9 16 24 33]
+ >>> marr.cumsum()
+ masked_array(data=[0, 1, 3, --, --, --, 9, 16, 24, 33],
+ mask=[False, False, False, True, True, True, False, False,
+ False, False],
+ fill_value=999999)
"""
result = self.filled(0).cumsum(axis=axis, dtype=dtype, out=out)
@@ -5089,7 +5123,7 @@ class MaskedArray(ndarray):
result = self.filled(1).prod(axis, dtype=dtype, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getmask(out)
- if (outmask is nomask):
+ if outmask is nomask:
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
return out
@@ -5145,9 +5179,9 @@ class MaskedArray(ndarray):
--------
>>> a = np.ma.array([1,2,3], mask=[False, False, True])
>>> a
- masked_array(data = [1 2 --],
- mask = [False False True],
- fill_value = 999999)
+ masked_array(data=[1, 2, --],
+ mask=[False, False, True],
+ fill_value=999999)
>>> a.mean()
1.5
@@ -5168,7 +5202,7 @@ class MaskedArray(ndarray):
out.flat = result
if isinstance(out, MaskedArray):
outmask = getmask(out)
- if (outmask is nomask):
+ if outmask is nomask:
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = getmask(result)
return out
@@ -5200,9 +5234,9 @@ class MaskedArray(ndarray):
--------
>>> a = np.ma.array([1,2,3])
>>> a.anom()
- masked_array(data = [-1. 0. 1.],
- mask = False,
- fill_value = 1e+20)
+ masked_array(data=[-1., 0., 1.],
+ mask=False,
+ fill_value=1e+20)
"""
m = self.mean(axis, dtype)
@@ -5210,9 +5244,9 @@ class MaskedArray(ndarray):
return m
if not axis:
- return (self - m)
+ return self - m
else:
- return (self - expand_dims(m, axis))
+ return self - expand_dims(m, axis)
def var(self, axis=None, dtype=None, out=None, ddof=0,
keepdims=np._NoValue):
@@ -5327,7 +5361,7 @@ class MaskedArray(ndarray):
out.__setmask__(self._mask)
return out
- def argsort(self, axis=np._NoValue, kind='quicksort', order=None,
+ def argsort(self, axis=np._NoValue, kind=None, order=None,
endwith=True, fill_value=None):
"""
Return an ndarray of indices that sort the array along the
@@ -5347,7 +5381,7 @@ class MaskedArray(ndarray):
Until then, the axis should be given explicitly when
``arr.ndim > 1``, to avoid a FutureWarning.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
- Sorting algorithm.
+ The sorting algorithm used.
order : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
@@ -5382,9 +5416,9 @@ class MaskedArray(ndarray):
--------
>>> a = np.ma.array([3,2,1], mask=[False, False, True])
>>> a
- masked_array(data = [3 2 --],
- mask = [False False True],
- fill_value = 999999)
+ masked_array(data=[3, 2, --],
+ mask=[False, False, True],
+ fill_value=999999)
>>> a.argsort()
array([1, 0, 2])
@@ -5432,15 +5466,19 @@ class MaskedArray(ndarray):
Examples
--------
- >>> x = np.ma.array(arange(4), mask=[1,1,0,0])
+ >>> x = np.ma.array(np.arange(4), mask=[1,1,0,0])
>>> x.shape = (2,2)
- >>> print(x)
- [[-- --]
- [2 3]]
- >>> print(x.argmin(axis=0, fill_value=-1))
- [0 0]
- >>> print(x.argmin(axis=0, fill_value=9))
- [1 1]
+ >>> x
+ masked_array(
+ data=[[--, --],
+ [2, 3]],
+ mask=[[ True, True],
+ [False, False]],
+ fill_value=999999)
+ >>> x.argmin(axis=0, fill_value=-1)
+ array([0, 0])
+ >>> x.argmin(axis=0, fill_value=9)
+ array([1, 1])
"""
if fill_value is None:
@@ -5485,7 +5523,7 @@ class MaskedArray(ndarray):
d = self.filled(fill_value).view(ndarray)
return d.argmax(axis, out=out)
- def sort(self, axis=-1, kind='quicksort', order=None,
+ def sort(self, axis=-1, kind=None, order=None,
endwith=True, fill_value=None):
"""
Sort the array, in-place
@@ -5498,7 +5536,7 @@ class MaskedArray(ndarray):
Axis along which to sort. If None, the array is flattened before
sorting. The default is -1, which sorts along the last axis.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
- Sorting algorithm. Default is 'quicksort'.
+ The sorting algorithm used.
order : list, optional
When `a` is a structured array, this argument specifies which fields
to compare first, second, and so on. This list does not need to
@@ -5506,7 +5544,7 @@ class MaskedArray(ndarray):
endwith : {True, False}, optional
Whether missing values (if any) should be treated as the largest values
(True) or the smallest values (False)
- When the array contains unmasked values at the same extremes of the
+ When the array contains unmasked values sorting at the same extremes of the
datatype, the ordering of these values and the masked values is
undefined.
fill_value : {var}, optional
@@ -5531,23 +5569,29 @@ class MaskedArray(ndarray):
Examples
--------
- >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
+ >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # Default
>>> a.sort()
- >>> print(a)
- [1 3 5 -- --]
+ >>> a
+ masked_array(data=[1, 3, 5, --, --],
+ mask=[False, False, False, True, True],
+ fill_value=999999)
- >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
+ >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # Put missing values in the front
>>> a.sort(endwith=False)
- >>> print(a)
- [-- -- 1 3 5]
+ >>> a
+ masked_array(data=[--, --, 1, 3, 5],
+ mask=[ True, True, False, False, False],
+ fill_value=999999)
- >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
+ >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # fill_value takes over endwith
>>> a.sort(endwith=False, fill_value=3)
- >>> print(a)
- [1 -- -- 3 5]
+ >>> a
+ masked_array(data=[1, --, --, 3, 5],
+ mask=[False, True, True, False, False],
+ fill_value=999999)
"""
if self._mask is nomask:
@@ -5577,6 +5621,10 @@ class MaskedArray(ndarray):
fill_value : {var}, optional
Value used to fill in the masked values.
If None, use the output of `minimum_fill_value`.
+ keepdims : bool, optional
+ If this is set to True, the axes which are reduced are left
+ in the result as dimensions with size one. With this option,
+ the result will broadcast correctly against the array.
Returns
-------
@@ -5613,7 +5661,7 @@ class MaskedArray(ndarray):
result = self.filled(fill_value).min(axis=axis, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getmask(out)
- if (outmask is nomask):
+ if outmask is nomask:
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
else:
@@ -5653,27 +5701,36 @@ class MaskedArray(ndarray):
Examples
--------
>>> x = np.ma.array(np.arange(6), mask=[0 ,1, 0, 0, 0 ,1]).reshape(3, 2)
- >>> print(x)
- [[0 --]
- [2 3]
- [4 --]]
+ >>> x
+ masked_array(
+ data=[[0, --],
+ [2, 3],
+ [4, --]],
+ mask=[[False, True],
+ [False, False],
+ [False, True]],
+ fill_value=999999)
>>> x.mini()
- 0
+ masked_array(data=0,
+ mask=False,
+ fill_value=999999)
>>> x.mini(axis=0)
- masked_array(data = [0 3],
- mask = [False False],
- fill_value = 999999)
- >>> print(x.mini(axis=1))
- [0 2 4]
+ masked_array(data=[0, 3],
+ mask=[False, False],
+ fill_value=999999)
+ >>> x.mini(axis=1)
+ masked_array(data=[0, 2, 4],
+ mask=[False, False, False],
+ fill_value=999999)
There is a small difference between `mini` and `min`:
>>> x[:,1].mini(axis=0)
- masked_array(data = --,
- mask = True,
- fill_value = 999999)
+ masked_array(data=3,
+ mask=False,
+ fill_value=999999)
>>> x[:,1].min(axis=0)
- masked
+ 3
"""
# 2016-04-13, 1.13.0, gh-8764
@@ -5698,6 +5755,10 @@ class MaskedArray(ndarray):
fill_value : {var}, optional
Value used to fill in the masked values.
If None, use the output of maximum_fill_value().
+ keepdims : bool, optional
+ If this is set to True, the axes which are reduced are left
+ in the result as dimensions with size one. With this option,
+ the result will broadcast correctly against the array.
Returns
-------
@@ -5734,7 +5795,7 @@ class MaskedArray(ndarray):
result = self.filled(fill_value).max(axis=axis, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getmask(out)
- if (outmask is nomask):
+ if outmask is nomask:
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
else:
@@ -5762,6 +5823,10 @@ class MaskedArray(ndarray):
but the type will be cast if necessary.
fill_value : {var}, optional
Value used to fill in the masked values.
+ keepdims : bool, optional
+ If this is set to True, the axes which are reduced are left
+ in the result as dimensions with size one. With this option,
+ the result will broadcast correctly against the array.
Returns
-------
@@ -5822,7 +5887,6 @@ class MaskedArray(ndarray):
return out[()]
# Array methods
- clip = _arraymethod('clip', onmask=False)
copy = _arraymethod('copy')
diagonal = _arraymethod('diagonal')
flatten = _arraymethod('flatten')
@@ -5889,7 +5953,7 @@ class MaskedArray(ndarray):
returns bytes not strings.
"""
- return self.tobytes(fill_value, order='C')
+ return self.tobytes(fill_value, order=order)
def tobytes(self, fill_value=None, order='C'):
"""
@@ -5926,7 +5990,7 @@ class MaskedArray(ndarray):
--------
>>> x = np.ma.array(np.array([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])
>>> x.tobytes()
- '\\x01\\x00\\x00\\x00?B\\x0f\\x00?B\\x0f\\x00\\x04\\x00\\x00\\x00'
+ b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00?B\\x0f\\x00\\x00\\x00\\x00\\x00?B\\x0f\\x00\\x00\\x00\\x00\\x00\\x04\\x00\\x00\\x00\\x00\\x00\\x00\\x00'
"""
return self.filled(fill_value).tobytes(order=order)
@@ -5974,14 +6038,20 @@ class MaskedArray(ndarray):
Examples
--------
>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
- >>> print(x)
- [[1 -- 3]
- [-- 5 --]
- [7 -- 9]]
- >>> print(x.toflex())
- [[(1, False) (2, True) (3, False)]
- [(4, True) (5, False) (6, True)]
- [(7, False) (8, True) (9, False)]]
+ >>> x
+ masked_array(
+ data=[[1, --, 3],
+ [--, 5, --],
+ [7, --, 9]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
+ >>> x.toflex()
+ array([[(1, False), (2, True), (3, False)],
+ [(4, True), (5, False), (6, True)],
+ [(7, False), (8, True), (9, False)]],
+ dtype=[('_data', '<i8'), ('_mask', '?')])
"""
# Get the basic dtype.
@@ -6081,12 +6151,11 @@ class mvoid(MaskedArray):
_data.fill_value = fill_value
return _data
- def _get_data(self):
+ @property
+ def _data(self):
# Make sure that the _data part is a np.void
return super(mvoid, self)._data[()]
- _data = property(fget=_get_data)
-
def __getitem__(self, indx):
"""
Get the index.
@@ -6228,15 +6297,14 @@ def isMaskedArray(x):
[ 0., 0., 1.]])
>>> m = ma.masked_values(a, 0)
>>> m
- masked_array(data =
- [[1.0 -- --]
- [-- 1.0 --]
- [-- -- 1.0]],
- mask =
- [[False True True]
- [ True False True]
- [ True True False]],
- fill_value=0.0)
+ masked_array(
+ data=[[1.0, --, --],
+ [--, 1.0, --],
+ [--, --, 1.0]],
+ mask=[[False, True, True],
+ [ True, False, True],
+ [ True, True, False]],
+ fill_value=0.0)
>>> ma.isMaskedArray(a)
False
>>> ma.isMaskedArray(m)
@@ -6400,16 +6468,16 @@ def is_masked(x):
>>> import numpy.ma as ma
>>> x = ma.masked_equal([0, 1, 0, 2, 3], 0)
>>> x
- masked_array(data = [-- 1 -- 2 3],
- mask = [ True False True False False],
- fill_value=999999)
+ masked_array(data=[--, 1, --, 2, 3],
+ mask=[ True, False, True, False, False],
+ fill_value=0)
>>> ma.is_masked(x)
True
>>> x = ma.masked_equal([0, 1, 0, 2, 3], 42)
>>> x
- masked_array(data = [0 1 0 2 3],
- mask = False,
- fill_value=999999)
+ masked_array(data=[0, 1, 0, 2, 3],
+ mask=False,
+ fill_value=42)
>>> ma.is_masked(x)
False
@@ -6669,7 +6737,7 @@ def power(a, b, third=None):
invalid = np.logical_not(np.isfinite(result.view(ndarray)))
# Add the initial mask
if m is not nomask:
- if not (result.ndim):
+ if not result.ndim:
return masked
result._mask = np.logical_or(m, invalid)
# Fix the invalid parts
@@ -6684,7 +6752,7 @@ def power(a, b, third=None):
argmin = _frommethod('argmin')
argmax = _frommethod('argmax')
-def argsort(a, axis=np._NoValue, kind='quicksort', order=None, endwith=True, fill_value=None):
+def argsort(a, axis=np._NoValue, kind=None, order=None, endwith=True, fill_value=None):
"Function version of the eponymous method."
a = np.asanyarray(a)
@@ -6699,7 +6767,7 @@ def argsort(a, axis=np._NoValue, kind='quicksort', order=None, endwith=True, fil
return a.argsort(axis=axis, kind=kind, order=order)
argsort.__doc__ = MaskedArray.argsort.__doc__
-def sort(a, axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None):
+def sort(a, axis=-1, kind=None, order=None, endwith=True, fill_value=None):
"Function version of the eponymous method."
a = np.array(a, copy=True, subok=True)
if axis is None:
@@ -6759,17 +6827,17 @@ def concatenate(arrays, axis=0):
>>> a[1] = ma.masked
>>> b = ma.arange(2, 5)
>>> a
- masked_array(data = [0 -- 2],
- mask = [False True False],
- fill_value = 999999)
+ masked_array(data=[0, --, 2],
+ mask=[False, True, False],
+ fill_value=999999)
>>> b
- masked_array(data = [2 3 4],
- mask = False,
- fill_value = 999999)
+ masked_array(data=[2, 3, 4],
+ mask=False,
+ fill_value=999999)
>>> ma.concatenate([a, b])
- masked_array(data = [0 -- 2 2 3 4],
- mask = [False True False False False False],
- fill_value = 999999)
+ masked_array(data=[0, --, 2, 2, 3, 4],
+ mask=[False, True, False, False, False, False],
+ fill_value=999999)
"""
d = np.concatenate([getdata(a) for a in arrays], axis)
@@ -6924,24 +6992,21 @@ def transpose(a, axes=None):
>>> import numpy.ma as ma
>>> x = ma.arange(4).reshape((2,2))
>>> x[1, 1] = ma.masked
- >>>> x
- masked_array(data =
- [[0 1]
- [2 --]],
- mask =
- [[False False]
- [False True]],
- fill_value = 999999)
+ >>> x
+ masked_array(
+ data=[[0, 1],
+ [2, --]],
+ mask=[[False, False],
+ [False, True]],
+ fill_value=999999)
>>> ma.transpose(x)
- masked_array(data =
- [[0 2]
- [1 --]],
- mask =
- [[False False]
- [False True]],
- fill_value = 999999)
-
+ masked_array(
+ data=[[0, 2],
+ [1, --]],
+ mask=[[False, False],
+ [False, True]],
+ fill_value=999999)
"""
# We can't use 'frommethod', as 'transpose' doesn't take keywords
try:
@@ -6988,39 +7053,39 @@ def resize(x, new_shape):
>>> a = ma.array([[1, 2] ,[3, 4]])
>>> a[0, 1] = ma.masked
>>> a
- masked_array(data =
- [[1 --]
- [3 4]],
- mask =
- [[False True]
- [False False]],
- fill_value = 999999)
+ masked_array(
+ data=[[1, --],
+ [3, 4]],
+ mask=[[False, True],
+ [False, False]],
+ fill_value=999999)
>>> np.resize(a, (3, 3))
- array([[1, 2, 3],
- [4, 1, 2],
- [3, 4, 1]])
+ masked_array(
+ data=[[1, 2, 3],
+ [4, 1, 2],
+ [3, 4, 1]],
+ mask=False,
+ fill_value=999999)
>>> ma.resize(a, (3, 3))
- masked_array(data =
- [[1 -- 3]
- [4 1 --]
- [3 4 1]],
- mask =
- [[False True False]
- [False False True]
- [False False False]],
- fill_value = 999999)
+ masked_array(
+ data=[[1, --, 3],
+ [4, 1, --],
+ [3, 4, 1]],
+ mask=[[False, True, False],
+ [False, False, True],
+ [False, False, False]],
+ fill_value=999999)
A MaskedArray is always returned, regardless of the input type.
>>> a = np.array([[1, 2] ,[3, 4]])
>>> ma.resize(a, (3, 3))
- masked_array(data =
- [[1 2 3]
- [4 1 2]
- [3 4 1]],
- mask =
- False,
- fill_value = 999999)
+ masked_array(
+ data=[[1, 2, 3],
+ [4, 1, 2],
+ [3, 4, 1]],
+ mask=False,
+ fill_value=999999)
"""
# We can't use _frommethods here, as N.resize is notoriously whiny.
@@ -7033,23 +7098,6 @@ def resize(x, new_shape):
return result
-def rank(obj):
- """
- maskedarray version of the numpy function.
-
- .. note::
- Deprecated since 1.10.0
-
- """
- # 2015-04-12, 1.10.0
- warnings.warn(
- "`rank` is deprecated; use the `ndim` function instead. ",
- np.VisibleDeprecationWarning, stacklevel=2)
- return np.ndim(getdata(obj))
-
-rank.__doc__ = np.rank.__doc__
-
-
def ndim(obj):
"""
maskedarray version of the numpy function.
@@ -7111,14 +7159,24 @@ def where(condition, x=_NoValue, y=_NoValue):
>>> x = np.ma.array(np.arange(9.).reshape(3, 3), mask=[[0, 1, 0],
... [1, 0, 1],
... [0, 1, 0]])
- >>> print(x)
- [[0.0 -- 2.0]
- [-- 4.0 --]
- [6.0 -- 8.0]]
- >>> print(np.ma.where(x > 5, x, -3.1416))
- [[-3.1416 -- -3.1416]
- [-- -3.1416 --]
- [6.0 -- 8.0]]
+ >>> x
+ masked_array(
+ data=[[0.0, --, 2.0],
+ [--, 4.0, --],
+ [6.0, --, 8.0]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=1e+20)
+ >>> np.ma.where(x > 5, x, -3.1416)
+ masked_array(
+ data=[[-3.1416, --, -3.1416],
+ [--, -3.1416, --],
+ [6.0, --, 8.0]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=1e+20)
"""
@@ -7198,9 +7256,9 @@ def choose(indices, choices, out=None, mode='raise'):
>>> choice = np.array([[1,1,1], [2,2,2], [3,3,3]])
>>> a = np.array([2, 1, 0])
>>> np.ma.choose(a, choice)
- masked_array(data = [3 2 1],
- mask = False,
- fill_value=999999)
+ masked_array(data=[3, 2, 1],
+ mask=False,
+ fill_value=999999)
"""
def fmask(x):
@@ -7222,7 +7280,7 @@ def choose(indices, choices, out=None, mode='raise'):
# Construct the mask
outputmask = np.choose(c, masks, mode=mode)
outputmask = make_mask(mask_or(outputmask, getmask(indices)),
- copy=0, shrink=True)
+ copy=False, shrink=True)
# Get the choices.
d = np.choose(c, data, mode=mode, out=out).view(MaskedArray)
if out is not None:
@@ -7323,25 +7381,23 @@ def mask_rowcols(a, axis=None):
[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)
+ masked_array(
+ data=[[0, 0, 0],
+ [0, --, 0],
+ [0, 0, 0]],
+ mask=[[False, False, False],
+ [False, True, False],
+ [False, False, False]],
+ fill_value=1)
>>> ma.mask_rowcols(a)
- masked_array(data =
- [[0 -- 0]
- [-- -- --]
- [0 -- 0]],
- mask =
- [[False True False]
- [ True True True]
- [False True False]],
- fill_value=999999)
+ masked_array(
+ data=[[0, --, 0],
+ [--, --, --],
+ [0, --, 0]],
+ mask=[[False, True, False],
+ [ True, True, True],
+ [False, True, False]],
+ fill_value=1)
"""
a = array(a, subok=False)
@@ -7402,24 +7458,22 @@ def dot(a, b, strict=False, out=None):
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]])
+ >>> a = np.ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]])
+ >>> b = np.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)
+ 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)
+ masked_array(
+ data=[[--, --],
+ [--, 64]],
+ mask=[[ True, True],
+ [ True, False]],
+ fill_value=999999)
"""
# !!!: Works only with 2D arrays. There should be a way to get it to run
@@ -7478,7 +7532,7 @@ def outer(a, b):
return masked_array(d)
ma = getmaskarray(a)
mb = getmaskarray(b)
- m = make_mask(1 - np.outer(1 - ma, 1 - mb), copy=0)
+ m = make_mask(1 - np.outer(1 - ma, 1 - mb), copy=False)
return masked_array(d, mask=m)
outer.__doc__ = doc_note(np.outer.__doc__,
"Masked values are replaced by 0.")
@@ -7587,18 +7641,18 @@ def allequal(a, b, fill_value=True):
Examples
--------
- >>> a = ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])
+ >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])
>>> a
- masked_array(data = [10000000000.0 1e-07 --],
- mask = [False False True],
- fill_value=1e+20)
+ masked_array(data=[10000000000.0, 1e-07, --],
+ mask=[False, False, True],
+ fill_value=1e+20)
- >>> b = array([1e10, 1e-7, -42.0])
+ >>> b = np.array([1e10, 1e-7, -42.0])
>>> b
array([ 1.00000000e+10, 1.00000000e-07, -4.20000000e+01])
- >>> ma.allequal(a, b, fill_value=False)
+ >>> np.ma.allequal(a, b, fill_value=False)
False
- >>> ma.allequal(a, b)
+ >>> np.ma.allequal(a, b)
True
"""
@@ -7664,29 +7718,29 @@ def allclose(a, b, masked_equal=True, rtol=1e-5, atol=1e-8):
Examples
--------
- >>> a = ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])
+ >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])
>>> a
- masked_array(data = [10000000000.0 1e-07 --],
- mask = [False False True],
- fill_value = 1e+20)
- >>> b = ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1])
- >>> ma.allclose(a, b)
+ masked_array(data=[10000000000.0, 1e-07, --],
+ mask=[False, False, True],
+ fill_value=1e+20)
+ >>> b = np.ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1])
+ >>> np.ma.allclose(a, b)
False
- >>> a = ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
- >>> b = ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1])
- >>> ma.allclose(a, b)
+ >>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
+ >>> b = np.ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1])
+ >>> np.ma.allclose(a, b)
True
- >>> ma.allclose(a, b, masked_equal=False)
+ >>> np.ma.allclose(a, b, masked_equal=False)
False
Masked values are not compared directly.
- >>> a = ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
- >>> b = ma.array([1.00001e10, 1e-9, 42.0], mask=[0, 0, 1])
- >>> ma.allclose(a, b)
+ >>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
+ >>> b = np.ma.array([1.00001e10, 1e-9, 42.0], mask=[0, 0, 1])
+ >>> np.ma.allclose(a, b)
True
- >>> ma.allclose(a, b, masked_equal=False)
+ >>> np.ma.allclose(a, b, masked_equal=False)
False
"""
@@ -7753,15 +7807,14 @@ def asarray(a, dtype=None, order=None):
--------
>>> x = np.arange(10.).reshape(2, 5)
>>> x
- array([[ 0., 1., 2., 3., 4.],
- [ 5., 6., 7., 8., 9.]])
+ array([[0., 1., 2., 3., 4.],
+ [5., 6., 7., 8., 9.]])
>>> np.ma.asarray(x)
- masked_array(data =
- [[ 0. 1. 2. 3. 4.]
- [ 5. 6. 7. 8. 9.]],
- mask =
- False,
- fill_value = 1e+20)
+ masked_array(
+ data=[[0., 1., 2., 3., 4.],
+ [5., 6., 7., 8., 9.]],
+ mask=False,
+ fill_value=1e+20)
>>> type(np.ma.asarray(x))
<class 'numpy.ma.core.MaskedArray'>
@@ -7801,15 +7854,14 @@ def asanyarray(a, dtype=None):
--------
>>> x = np.arange(10.).reshape(2, 5)
>>> x
- array([[ 0., 1., 2., 3., 4.],
- [ 5., 6., 7., 8., 9.]])
+ array([[0., 1., 2., 3., 4.],
+ [5., 6., 7., 8., 9.]])
>>> np.ma.asanyarray(x)
- masked_array(data =
- [[ 0. 1. 2. 3. 4.]
- [ 5. 6. 7. 8. 9.]],
- mask =
- False,
- fill_value = 1e+20)
+ masked_array(
+ data=[[0., 1., 2., 3., 4.],
+ [5., 6., 7., 8., 9.]],
+ mask=False,
+ fill_value=1e+20)
>>> type(np.ma.asanyarray(x))
<class 'numpy.ma.core.MaskedArray'>
@@ -7953,39 +8005,38 @@ def fromflex(fxarray):
>>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[0] + [1, 0] * 4)
>>> rec = x.toflex()
>>> rec
- array([[(0, False), (1, True), (2, False)],
- [(3, True), (4, False), (5, True)],
- [(6, False), (7, True), (8, False)]],
- dtype=[('_data', '<i4'), ('_mask', '|b1')])
+ array([[(0, False), (1, True), (2, False)],
+ [(3, True), (4, False), (5, True)],
+ [(6, False), (7, True), (8, False)]],
+ dtype=[('_data', '<i8'), ('_mask', '?')])
>>> x2 = np.ma.fromflex(rec)
>>> x2
- masked_array(data =
- [[0 -- 2]
- [-- 4 --]
- [6 -- 8]],
- mask =
- [[False True False]
- [ True False True]
- [False True False]],
- fill_value = 999999)
+ masked_array(
+ data=[[0, --, 2],
+ [--, 4, --],
+ [6, --, 8]],
+ mask=[[False, True, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
Extra fields can be present in the structured array but are discarded:
>>> dt = [('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')]
>>> rec2 = np.zeros((2, 2), dtype=dt)
>>> rec2
- array([[(0, False, 0.0), (0, False, 0.0)],
- [(0, False, 0.0), (0, False, 0.0)]],
- dtype=[('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')])
+ array([[(0, False, 0.), (0, False, 0.)],
+ [(0, False, 0.), (0, False, 0.)]],
+ dtype=[('_data', '<i4'), ('_mask', '?'), ('field3', '<f4')])
>>> y = np.ma.fromflex(rec2)
>>> y
- masked_array(data =
- [[0 0]
- [0 0]],
- mask =
- [[False False]
- [False False]],
- fill_value = 999999)
+ masked_array(
+ data=[[0, 0],
+ [0, 0]],
+ mask=[[False, False],
+ [False, False]],
+ fill_value=999999,
+ dtype=int32)
"""
return masked_array(fxarray['_data'], mask=fxarray['_mask'])
@@ -8086,7 +8137,10 @@ def append(a, b, axis=None):
>>> import numpy.ma as ma
>>> a = ma.masked_values([1, 2, 3], 2)
>>> b = ma.masked_values([[4, 5, 6], [7, 8, 9]], 7)
- >>> print(ma.append(a, b))
- [1 -- 3 4 5 6 -- 8 9]
+ >>> ma.append(a, b)
+ masked_array(data=[1, --, 3, 4, 5, 6, --, 8, 9],
+ mask=[False, True, False, False, False, False, True, False,
+ False],
+ fill_value=999999)
"""
return concatenate([a, b], axis)
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index 3be4d3625..639b3dd1f 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -81,15 +81,14 @@ def count_masked(arr, axis=None):
>>> 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)
+ 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
@@ -132,15 +131,15 @@ def masked_all(shape, dtype=float):
--------
>>> 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)
+ masked_array(
+ data=[[--, --, --],
+ [--, --, --],
+ [--, --, --]],
+ mask=[[ True, True, True],
+ [ True, True, True],
+ [ True, True, True]],
+ fill_value=1e+20,
+ dtype=float64)
The `dtype` parameter defines the underlying data type.
@@ -188,16 +187,16 @@ def masked_all_like(arr):
>>> import numpy.ma as ma
>>> arr = np.zeros((2, 3), dtype=np.float32)
>>> arr
- array([[ 0., 0., 0.],
- [ 0., 0., 0.]], dtype=float32)
+ 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)
+ masked_array(
+ data=[[--, --, --],
+ [--, --, --]],
+ mask=[[ True, True, True],
+ [ True, True, True]],
+ fill_value=1e+20,
+ dtype=float32)
The dtype of the masked array matches the dtype of `arr`.
@@ -391,7 +390,6 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs):
i[axis] = slice(None, None)
outshape = np.asarray(arr.shape).take(indlist)
i.put(indlist, ind)
- j = i.copy()
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
# if res is a number, then we have a smaller output array
asscalar = np.isscalar(res)
@@ -492,28 +490,45 @@ if apply_over_axes.__doc__ is not None:
Examples
--------
- >>> a = ma.arange(24).reshape(2,3,4)
- >>> a[:,0,1] = ma.masked
- >>> a[:,1,:] = ma.masked
- >>> print(a)
- [[[0 -- 2 3]
- [-- -- -- --]
- [8 9 10 11]]
-
- [[12 -- 14 15]
- [-- -- -- --]
- [20 21 22 23]]]
- >>> print(ma.apply_over_axes(ma.sum, a, [0,2]))
- [[[46]
- [--]
- [124]]]
+ >>> a = np.ma.arange(24).reshape(2,3,4)
+ >>> a[:,0,1] = np.ma.masked
+ >>> a[:,1,:] = np.ma.masked
+ >>> a
+ masked_array(
+ data=[[[0, --, 2, 3],
+ [--, --, --, --],
+ [8, 9, 10, 11]],
+ [[12, --, 14, 15],
+ [--, --, --, --],
+ [20, 21, 22, 23]]],
+ mask=[[[False, True, False, False],
+ [ True, True, True, True],
+ [False, False, False, False]],
+ [[False, True, False, False],
+ [ True, True, True, True],
+ [False, False, False, False]]],
+ fill_value=999999)
+ >>> np.ma.apply_over_axes(np.ma.sum, a, [0,2])
+ masked_array(
+ data=[[[46],
+ [--],
+ [124]]],
+ mask=[[[False],
+ [ True],
+ [False]]],
+ fill_value=999999)
Tuple axis arguments to ufuncs are equivalent:
- >>> print(ma.sum(a, axis=(0,2)).reshape((1,-1,1)))
- [[[46]
- [--]
- [124]]]
+ >>> np.ma.sum(a, axis=(0,2)).reshape((1,-1,1))
+ masked_array(
+ data=[[[46],
+ [--],
+ [124]]],
+ mask=[[[False],
+ [ True],
+ [False]]],
+ fill_value=999999)
"""
@@ -558,14 +573,19 @@ def average(a, axis=None, weights=None, returned=False):
1.25
>>> x = np.ma.arange(6.).reshape(3, 2)
- >>> print(x)
- [[ 0. 1.]
- [ 2. 3.]
- [ 4. 5.]]
+ >>> x
+ masked_array(
+ data=[[0., 1.],
+ [2., 3.],
+ [4., 5.]],
+ mask=False,
+ fill_value=1e+20)
>>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3],
... returned=True)
- >>> print(avg)
- [2.66666666667 3.66666666667]
+ >>> avg
+ masked_array(data=[2.6666666666666665, 3.6666666666666665],
+ mask=[False, False],
+ fill_value=1e+20)
"""
a = asarray(a)
@@ -676,9 +696,9 @@ def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
>>> np.ma.median(x)
2.5
>>> np.ma.median(x, axis=-1, overwrite_input=True)
- masked_array(data = [ 2. 5.],
- mask = False,
- fill_value = 1e+20)
+ masked_array(data=[2.0, 5.0],
+ mask=[False, False],
+ fill_value=1e+20)
"""
if not hasattr(a, 'mask'):
@@ -856,15 +876,14 @@ def compress_rowcols(x, axis=None):
... [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)
+ 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.compress_rowcols(x)
array([[7, 8]])
@@ -937,25 +956,24 @@ def mask_rows(a, axis=None):
[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)
+ masked_array(
+ data=[[0, 0, 0],
+ [0, --, 0],
+ [0, 0, 0]],
+ mask=[[False, False, False],
+ [False, True, False],
+ [False, False, False]],
+ fill_value=1)
+
>>> 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)
+ masked_array(
+ data=[[0, 0, 0],
+ [--, --, --],
+ [0, 0, 0]],
+ mask=[[False, False, False],
+ [ True, True, True],
+ [False, False, False]],
+ fill_value=1)
"""
return mask_rowcols(a, 0)
@@ -982,25 +1000,23 @@ def mask_cols(a, axis=None):
[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)
+ masked_array(
+ data=[[0, 0, 0],
+ [0, --, 0],
+ [0, 0, 0]],
+ mask=[[False, False, False],
+ [False, True, False],
+ [False, False, False]],
+ fill_value=1)
>>> 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)
+ masked_array(
+ data=[[0, --, 0],
+ [0, --, 0],
+ [0, --, 0]],
+ mask=[[False, True, False],
+ [False, True, False],
+ [False, True, False]],
+ fill_value=1)
"""
return mask_rowcols(a, 1)
@@ -1078,12 +1094,12 @@ def intersect1d(ar1, ar2, assume_unique=False):
Examples
--------
- >>> 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)
+ >>> x = np.ma.array([1, 3, 3, 3], mask=[0, 0, 0, 1])
+ >>> y = np.ma.array([3, 1, 1, 1], mask=[0, 0, 0, 1])
+ >>> np.ma.intersect1d(x, y)
+ masked_array(data=[1, 3, --],
+ mask=[False, False, True],
+ fill_value=999999)
"""
if assume_unique:
@@ -1216,9 +1232,9 @@ def setdiff1d(ar1, ar2, assume_unique=False):
--------
>>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1])
>>> np.ma.setdiff1d(x, [1, 2])
- masked_array(data = [3 --],
- mask = [False True],
- fill_value = 999999)
+ masked_array(data=[3, --],
+ mask=[False, True],
+ fill_value=999999)
"""
if assume_unique:
@@ -1483,7 +1499,9 @@ class mr_class(MAxisConcatenator):
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])
+ masked_array(data=[1, 2, 3, ..., 4, 5, 6],
+ mask=False,
+ fill_value=999999)
"""
def __init__(self):
@@ -1514,7 +1532,7 @@ def flatnotmasked_edges(a):
See Also
--------
- flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges,
+ flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges
clump_masked, clump_unmasked
Notes
@@ -1524,19 +1542,19 @@ def flatnotmasked_edges(a):
Examples
--------
>>> a = np.ma.arange(10)
- >>> flatnotmasked_edges(a)
- [0,-1]
+ >>> np.ma.flatnotmasked_edges(a)
+ array([0, 9])
>>> mask = (a < 3) | (a > 8) | (a == 5)
>>> a[mask] = np.ma.masked
>>> np.array(a[~a.mask])
array([3, 4, 6, 7, 8])
- >>> flatnotmasked_edges(a)
+ >>> np.ma.flatnotmasked_edges(a)
array([3, 8])
>>> a[:] = np.ma.masked
- >>> print(flatnotmasked_edges(ma))
+ >>> print(np.ma.flatnotmasked_edges(a))
None
"""
@@ -1575,7 +1593,7 @@ def notmasked_edges(a, axis=None):
See Also
--------
- flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous,
+ flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous
clump_masked, clump_unmasked
Examples
@@ -1588,7 +1606,7 @@ def notmasked_edges(a, axis=None):
>>> np.array(am[~am.mask])
array([0, 1, 2, 3, 6])
- >>> np.ma.notmasked_edges(ma)
+ >>> np.ma.notmasked_edges(am)
array([0, 6])
"""
@@ -1620,7 +1638,7 @@ def flatnotmasked_contiguous(a):
See Also
--------
- flatnotmasked_edges, notmasked_contiguous, notmasked_edges,
+ flatnotmasked_edges, notmasked_contiguous, notmasked_edges
clump_masked, clump_unmasked
Notes
@@ -1680,7 +1698,7 @@ def notmasked_contiguous(a, axis=None):
See Also
--------
- flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,
+ flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
clump_masked, clump_unmasked
Notes
@@ -1709,15 +1727,11 @@ def notmasked_contiguous(a, axis=None):
[slice(0, 1, None), slice(2, 4, None), slice(7, 9, None), slice(11, 12, None)]
>>> np.ma.notmasked_contiguous(ma, axis=0)
- [[slice(0, 1, None), slice(2, 3, None)], # column broken into two segments
- [], # fully masked column
- [slice(0, 1, None)],
- [slice(0, 3, None)]]
+ [[slice(0, 1, None), slice(2, 3, None)], [], [slice(0, 1, None)], [slice(0, 3, None)]]
>>> np.ma.notmasked_contiguous(ma, axis=1)
- [[slice(0, 1, None), slice(2, 4, None)], # row broken into two segments
- [slice(3, 4, None)],
- [slice(0, 1, None), slice(3, 4, None)]]
+ [[slice(0, 1, None), slice(2, 4, None)], [slice(3, 4, None)], [slice(0, 1, None), slice(3, 4, None)]]
+
"""
a = asarray(a)
nd = a.ndim
@@ -1789,7 +1803,7 @@ def clump_unmasked(a):
See Also
--------
- flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,
+ flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
notmasked_contiguous, clump_masked
Examples
@@ -1828,7 +1842,7 @@ def clump_masked(a):
See Also
--------
- flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,
+ flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
notmasked_contiguous, clump_unmasked
Examples
diff --git a/numpy/ma/mrecords.py b/numpy/ma/mrecords.py
index b16e1670a..826fb0f64 100644
--- a/numpy/ma/mrecords.py
+++ b/numpy/ma/mrecords.py
@@ -19,7 +19,6 @@ import sys
import warnings
import numpy as np
-import numpy.core.numerictypes as ntypes
from numpy.compat import basestring
from numpy import (
bool_, dtype, ndarray, recarray, array as narray
@@ -167,24 +166,22 @@ class MaskedRecords(MaskedArray, object):
_dict['_baseclass'] = recarray
return
- def _getdata(self):
+ @property
+ def _data(self):
"""
Returns the data as a recarray.
"""
return ndarray.view(self, recarray)
- _data = property(fget=_getdata)
-
- def _getfieldmask(self):
+ @property
+ def _fieldmask(self):
"""
Alias to mask.
"""
return self._mask
- _fieldmask = property(fget=_getfieldmask)
-
def __len__(self):
"""
Returns the length
@@ -224,7 +221,8 @@ class MaskedRecords(MaskedArray, object):
except IndexError:
# Couldn't find a mask: use the default (nomask)
pass
- hasmasked = _mask.view((bool, (len(_mask.dtype) or 1))).any()
+ tp_len = len(_mask.dtype)
+ hasmasked = _mask.view((bool, ((tp_len,) if tp_len else ()))).any()
if (obj.shape or hasmasked):
obj = obj.view(MaskedArray)
obj._baseclass = ndarray
diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py
index 2775b11ec..b72ce56aa 100644
--- a/numpy/ma/tests/test_core.py
+++ b/numpy/ma/tests/test_core.py
@@ -49,7 +49,7 @@ from numpy.ma.core import (
ravel, repeat, reshape, resize, shape, sin, sinh, sometrue, sort, sqrt,
subtract, sum, take, tan, tanh, transpose, where, zeros,
)
-from numpy.core.numeric import pickle
+from numpy.compat import pickle
pi = np.pi
@@ -347,7 +347,7 @@ class TestMaskedArray(object):
m = make_mask(n)
m2 = make_mask(m)
assert_(m is m2)
- m3 = make_mask(m, copy=1)
+ m3 = make_mask(m, copy=True)
assert_(m is not m3)
x1 = np.arange(5)
@@ -374,12 +374,12 @@ class TestMaskedArray(object):
y2a = array(x1, mask=m, copy=1)
assert_(y2a._data.__array_interface__ != x1.__array_interface__)
- #assert_( y2a.mask is not m)
+ #assert_( y2a._mask is not m)
assert_(y2a._mask.__array_interface__ != m.__array_interface__)
assert_(y2a[2] is masked)
y2a[2] = 9
assert_(y2a[2] is not masked)
- #assert_( y2a.mask is not m)
+ #assert_( y2a._mask is not m)
assert_(y2a._mask.__array_interface__ != m.__array_interface__)
assert_(allequal(y2a.mask, 0))
@@ -2401,9 +2401,9 @@ class TestMaskedArrayInPlaceArithmetics(object):
assert_equal(xm, y + 1)
(x, _, xm) = self.floatdata
- id1 = x.data.ctypes._data
+ id1 = x.data.ctypes.data
x += 1.
- assert_(id1 == x.data.ctypes._data)
+ assert_(id1 == x.data.ctypes.data)
assert_equal(x, y + 1.)
def test_inplace_addition_array(self):
@@ -3035,6 +3035,13 @@ class TestMaskedArrayMethods(object):
assert_equal(clipped._data, x.clip(2, 8))
assert_equal(clipped._data, mx._data.clip(2, 8))
+ def test_clip_out(self):
+ # gh-14140
+ a = np.arange(10)
+ m = np.ma.MaskedArray(a, mask=[0, 1] * 5)
+ m.clip(0, 5, out=m)
+ assert_equal(m.mask, [0, 1] * 5)
+
def test_compress(self):
# test compress
a = masked_array([1., 2., 3., 4., 5.], fill_value=9999)
@@ -5203,3 +5210,10 @@ def test_fieldless_void():
mx = np.ma.array(x, mask=x)
assert_equal(mx.dtype, x.dtype)
assert_equal(mx.shape, x.shape)
+
+
+def test_mask_shape_assignment_does_not_break_masked():
+ a = np.ma.masked
+ b = np.ma.array(1, mask=a.mask)
+ b.shape = (1,)
+ assert_equal(a.mask.shape, ())
diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py
index 5243cf714..836770378 100644
--- a/numpy/ma/tests/test_extras.py
+++ b/numpy/ma/tests/test_extras.py
@@ -148,7 +148,7 @@ class TestAverage(object):
ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
assert_equal(2.0, average(ott, axis=0))
assert_equal(2.0, average(ott, weights=[1., 1., 2., 1.]))
- result, wts = average(ott, weights=[1., 1., 2., 1.], returned=1)
+ result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
assert_equal(2.0, result)
assert_(wts == 4.0)
ott[:] = masked
@@ -159,7 +159,7 @@ class TestAverage(object):
assert_equal(average(ott, axis=0), [2.0, 0.0])
assert_equal(average(ott, axis=1).mask[0], [True])
assert_equal([2., 0.], average(ott, axis=0))
- result, wts = average(ott, axis=0, returned=1)
+ result, wts = average(ott, axis=0, returned=True)
assert_equal(wts, [1., 0.])
def test_testAverage2(self):
@@ -200,14 +200,14 @@ class TestAverage(object):
# Yet more tests of average!
a = arange(6)
b = arange(6) * 3
- r1, w1 = average([[a, b], [b, a]], axis=1, returned=1)
+ r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
assert_equal(shape(r1), shape(w1))
assert_equal(r1.shape, w1.shape)
- r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=1)
+ r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=True)
assert_equal(shape(w2), shape(r2))
- r2, w2 = average(ones((2, 2, 3)), returned=1)
+ r2, w2 = average(ones((2, 2, 3)), returned=True)
assert_equal(shape(w2), shape(r2))
- r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=1)
+ r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
assert_equal(shape(w2), shape(r2))
a2d = array([[1, 2], [0, 4]], float)
a2dm = masked_array(a2d, [[False, False], [True, False]])
@@ -891,61 +891,51 @@ class TestMedian(object):
expected)
def test_nan(self):
- with suppress_warnings() as w:
- w.record(RuntimeWarning)
- for mask in (False, np.zeros(6, dtype=bool)):
- dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
- dm.mask = mask
-
- # scalar result
- r = np.ma.median(dm, axis=None)
- assert_(np.isscalar(r))
- assert_array_equal(r, np.nan)
- r = np.ma.median(dm.ravel(), axis=0)
- assert_(np.isscalar(r))
- assert_array_equal(r, np.nan)
-
- r = np.ma.median(dm, axis=0)
- assert_equal(type(r), MaskedArray)
- assert_array_equal(r, [1, np.nan, 3])
- r = np.ma.median(dm, axis=1)
- assert_equal(type(r), MaskedArray)
- assert_array_equal(r, [np.nan, 2])
- r = np.ma.median(dm, axis=-1)
- assert_equal(type(r), MaskedArray)
- assert_array_equal(r, [np.nan, 2])
-
+ for mask in (False, np.zeros(6, dtype=bool)):
dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
- dm[:, 2] = np.ma.masked
- assert_array_equal(np.ma.median(dm, axis=None), np.nan)
- assert_array_equal(np.ma.median(dm, axis=0), [1, np.nan, 3])
- assert_array_equal(np.ma.median(dm, axis=1), [np.nan, 1.5])
- assert_equal([x.category is RuntimeWarning for x in w.log],
- [True]*13)
+ dm.mask = mask
+
+ # scalar result
+ r = np.ma.median(dm, axis=None)
+ assert_(np.isscalar(r))
+ assert_array_equal(r, np.nan)
+ r = np.ma.median(dm.ravel(), axis=0)
+ assert_(np.isscalar(r))
+ assert_array_equal(r, np.nan)
+
+ r = np.ma.median(dm, axis=0)
+ assert_equal(type(r), MaskedArray)
+ assert_array_equal(r, [1, np.nan, 3])
+ r = np.ma.median(dm, axis=1)
+ assert_equal(type(r), MaskedArray)
+ assert_array_equal(r, [np.nan, 2])
+ r = np.ma.median(dm, axis=-1)
+ assert_equal(type(r), MaskedArray)
+ assert_array_equal(r, [np.nan, 2])
+
+ dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
+ dm[:, 2] = np.ma.masked
+ assert_array_equal(np.ma.median(dm, axis=None), np.nan)
+ assert_array_equal(np.ma.median(dm, axis=0), [1, np.nan, 3])
+ assert_array_equal(np.ma.median(dm, axis=1), [np.nan, 1.5])
def test_out_nan(self):
- with warnings.catch_warnings(record=True):
- warnings.filterwarnings('always', '', RuntimeWarning)
- o = np.ma.masked_array(np.zeros((4,)))
- d = np.ma.masked_array(np.ones((3, 4)))
- d[2, 1] = np.nan
- d[2, 2] = np.ma.masked
- assert_equal(np.ma.median(d, 0, out=o), o)
- o = np.ma.masked_array(np.zeros((3,)))
- assert_equal(np.ma.median(d, 1, out=o), o)
- o = np.ma.masked_array(np.zeros(()))
- assert_equal(np.ma.median(d, out=o), o)
+ o = np.ma.masked_array(np.zeros((4,)))
+ d = np.ma.masked_array(np.ones((3, 4)))
+ d[2, 1] = np.nan
+ d[2, 2] = np.ma.masked
+ assert_equal(np.ma.median(d, 0, out=o), o)
+ o = np.ma.masked_array(np.zeros((3,)))
+ assert_equal(np.ma.median(d, 1, out=o), o)
+ o = np.ma.masked_array(np.zeros(()))
+ assert_equal(np.ma.median(d, out=o), o)
def test_nan_behavior(self):
a = np.ma.masked_array(np.arange(24, dtype=float))
a[::3] = np.ma.masked
a[2] = np.nan
- with suppress_warnings() as w:
- w.record(RuntimeWarning)
- assert_array_equal(np.ma.median(a), np.nan)
- assert_array_equal(np.ma.median(a, axis=0), np.nan)
- assert_(w.log[0].category is RuntimeWarning)
- assert_(w.log[1].category is RuntimeWarning)
+ assert_array_equal(np.ma.median(a), np.nan)
+ assert_array_equal(np.ma.median(a, axis=0), np.nan)
a = np.ma.masked_array(np.arange(24, dtype=float).reshape(2, 3, 4))
a.mask = np.arange(a.size) % 2 == 1
@@ -954,39 +944,26 @@ class TestMedian(object):
a[1, 1, 2] = np.nan
# no axis
- with suppress_warnings() as w:
- w.record(RuntimeWarning)
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_array_equal(np.ma.median(a), np.nan)
- assert_(np.isscalar(np.ma.median(a)))
- assert_(w.log[0].category is RuntimeWarning)
+ assert_array_equal(np.ma.median(a), np.nan)
+ assert_(np.isscalar(np.ma.median(a)))
# axis0
b = np.ma.median(aorig, axis=0)
b[2, 3] = np.nan
b[1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.ma.median(a, 0), b)
- assert_equal(len(w), 1)
+ assert_equal(np.ma.median(a, 0), b)
# axis1
b = np.ma.median(aorig, axis=1)
b[1, 3] = np.nan
b[1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.ma.median(a, 1), b)
- assert_equal(len(w), 1)
+ assert_equal(np.ma.median(a, 1), b)
# axis02
b = np.ma.median(aorig, axis=(0, 2))
b[1] = np.nan
b[2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.ma.median(a, (0, 2)), b)
- assert_equal(len(w), 1)
+ assert_equal(np.ma.median(a, (0, 2)), b)
def test_ambigous_fill(self):
# 255 is max value, used as filler for sort
diff --git a/numpy/ma/tests/test_mrecords.py b/numpy/ma/tests/test_mrecords.py
index dbbf1c8a1..94e772d55 100644
--- a/numpy/ma/tests/test_mrecords.py
+++ b/numpy/ma/tests/test_mrecords.py
@@ -23,7 +23,7 @@ from numpy.ma.testutils import (
assert_, assert_equal,
assert_equal_records,
)
-from numpy.core.numeric import pickle
+from numpy.compat import pickle
class TestMRecords(object):
diff --git a/numpy/ma/tests/test_old_ma.py b/numpy/ma/tests/test_old_ma.py
index 2978be22c..7100eccbb 100644
--- a/numpy/ma/tests/test_old_ma.py
+++ b/numpy/ma/tests/test_old_ma.py
@@ -21,7 +21,7 @@ from numpy.ma import (
repeat, resize, shape, sin, sinh, sometrue, sort, sqrt, subtract, sum,
take, tan, tanh, transpose, where, zeros,
)
-from numpy.core.numeric import pickle
+from numpy.compat import pickle
pi = np.pi
@@ -263,14 +263,14 @@ class TestMa(object):
m = make_mask(n)
m2 = make_mask(m)
assert_(m is m2)
- m3 = make_mask(m, copy=1)
+ m3 = make_mask(m, copy=True)
assert_(m is not m3)
x1 = np.arange(5)
y1 = array(x1, mask=m)
assert_(y1._data is not x1)
assert_(allequal(x1, y1._data))
- assert_(y1.mask is m)
+ assert_(y1._mask is m)
y1a = array(y1, copy=0)
# For copy=False, one might expect that the array would just
@@ -280,19 +280,19 @@ class TestMa(object):
y1._mask.__array_interface__)
y2 = array(x1, mask=m3, copy=0)
- assert_(y2.mask is m3)
+ assert_(y2._mask is m3)
assert_(y2[2] is masked)
y2[2] = 9
assert_(y2[2] is not masked)
- assert_(y2.mask is m3)
+ assert_(y2._mask is m3)
assert_(allequal(y2.mask, 0))
y2a = array(x1, mask=m, copy=1)
- assert_(y2a.mask is not m)
+ assert_(y2a._mask is not m)
assert_(y2a[2] is masked)
y2a[2] = 9
assert_(y2a[2] is not masked)
- assert_(y2a.mask is not m)
+ assert_(y2a._mask is not m)
assert_(allequal(y2a.mask, 0))
y3 = array(x1 * 1.0, mask=m)
@@ -318,14 +318,14 @@ class TestMa(object):
assert_(x[3] is masked)
assert_(x[4] is masked)
x[[1, 4]] = [10, 40]
- assert_(x.mask is m)
+ assert_(x._mask is m)
assert_(x[3] is masked)
assert_(x[4] is not masked)
assert_(eq(x, [0, 10, 2, -1, 40]))
x = array(d, mask=m2, copy=True)
x.put([0, 1, 2], [-1, 100, 200])
- assert_(x.mask is not m2)
+ assert_(x._mask is not m2)
assert_(x[3] is masked)
assert_(x[4] is masked)
assert_(eq(x, [-1, 100, 200, 0, 0]))
@@ -570,7 +570,7 @@ class TestMa(object):
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
assert_(eq(2.0, average(ott, axis=0)))
assert_(eq(2.0, average(ott, weights=[1., 1., 2., 1.])))
- result, wts = average(ott, weights=[1., 1., 2., 1.], returned=1)
+ result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
assert_(eq(2.0, result))
assert_(wts == 4.0)
ott[:] = masked
@@ -581,7 +581,7 @@ class TestMa(object):
assert_(eq(average(ott, axis=0), [2.0, 0.0]))
assert_(average(ott, axis=1)[0] is masked)
assert_(eq([2., 0.], average(ott, axis=0)))
- result, wts = average(ott, axis=0, returned=1)
+ result, wts = average(ott, axis=0, returned=True)
assert_(eq(wts, [1., 0.]))
def test_testAverage2(self):
@@ -622,14 +622,14 @@ class TestMa(object):
a = arange(6)
b = arange(6) * 3
- r1, w1 = average([[a, b], [b, a]], axis=1, returned=1)
+ r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
assert_equal(shape(r1), shape(w1))
assert_equal(r1.shape, w1.shape)
- r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=1)
+ r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=True)
assert_equal(shape(w2), shape(r2))
- r2, w2 = average(ones((2, 2, 3)), returned=1)
+ r2, w2 = average(ones((2, 2, 3)), returned=True)
assert_equal(shape(w2), shape(r2))
- r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=1)
+ r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
assert_(shape(w2) == shape(r2))
a2d = array([[1, 2], [0, 4]], float)
a2dm = masked_array(a2d, [[0, 0], [1, 0]])
diff --git a/numpy/ma/tests/test_regression.py b/numpy/ma/tests/test_regression.py
index 54f1bda7d..b83873a5a 100644
--- a/numpy/ma/tests/test_regression.py
+++ b/numpy/ma/tests/test_regression.py
@@ -87,3 +87,7 @@ class TestRegression(object):
# See gh-12464. Indexing with empty list should give empty result.
ma = np.ma.MaskedArray([(1, 1.), (2, 2.), (3, 3.)], dtype='i4,f4')
assert_array_equal(ma[[]], ma[:0])
+
+ def test_masked_array_tostring_fortran(self):
+ ma = np.ma.arange(4).reshape((2,2))
+ assert_array_equal(ma.tostring(order='F'), ma.T.tostring())
diff --git a/numpy/ma/tests/test_subclassing.py b/numpy/ma/tests/test_subclassing.py
index f8ab52bb9..440b36722 100644
--- a/numpy/ma/tests/test_subclassing.py
+++ b/numpy/ma/tests/test_subclassing.py
@@ -66,11 +66,11 @@ class MSubArray(SubArray, MaskedArray):
_data.info = subarr.info
return _data
- def _get_series(self):
+ @property
+ def _series(self):
_view = self.view(MaskedArray)
_view._sharedmask = False
return _view
- _series = property(fget=_get_series)
msubarray = MSubArray
diff --git a/numpy/ma/timer_comparison.py b/numpy/ma/timer_comparison.py
index 68104ed0a..4ad635e38 100644
--- a/numpy/ma/timer_comparison.py
+++ b/numpy/ma/timer_comparison.py
@@ -430,11 +430,10 @@ if __name__ == '__main__':
setup_cur = "import numpy.ma.core as module\n" + setup_base
(nrepeat, nloop) = (10, 10)
- if 1:
- for i in range(1, 8):
- func = 'tester.test_%i()' % i
- cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10)
- cur = np.sort(cur)
- print("#%i" % i + 50*'.')
- print(eval("ModuleTester.test_%i.__doc__" % i))
- print("core_current : %.3f - %.3f" % (cur[0], cur[1]))
+ for i in range(1, 8):
+ func = 'tester.test_%i()' % i
+ cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10)
+ cur = np.sort(cur)
+ print("#%i" % i + 50*'.')
+ print(eval("ModuleTester.test_%i.__doc__" % i))
+ print("core_current : %.3f - %.3f" % (cur[0], cur[1]))
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