diff options
Diffstat (limited to 'numpy/ma/core.py')
| -rw-r--r-- | numpy/ma/core.py | 1009 |
1 files changed, 543 insertions, 466 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py index 96d7207bd..63a61599c 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -516,18 +516,18 @@ 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 +689,12 @@ 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 +751,19 @@ 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) @@ -1346,9 +1344,9 @@ 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 +1379,12 @@ 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 +1399,11 @@ 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 +1441,12 @@ 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 +1455,12 @@ 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 +1498,9 @@ 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 +1521,14 @@ 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 @@ -1600,7 +1594,7 @@ 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 +1610,11 @@ 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 +1673,9 @@ 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')]) @@ -1779,16 +1773,16 @@ 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 +1867,39 @@ 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 +1908,19 @@ 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 +1960,9 @@ 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 +1986,9 @@ 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 +2012,9 @@ 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 +2038,9 @@ 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 +2064,9 @@ 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 +2092,9 @@ 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 +2123,16 @@ 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 +2163,16 @@ 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 +2217,27 @@ 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 +2292,16 @@ 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 +2310,13 @@ 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 +2350,11 @@ 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 +2515,7 @@ 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.]]) @@ -2684,9 +2686,7 @@ class MaskedIterator(object): >>> fl.next() 3 >>> fl.next() - masked_array(data = --, - mask = True, - fill_value = 1e+20) + masked >>> fl.next() Traceback (most recent call last): File "<stdin>", line 1, in <module> @@ -3551,6 +3551,11 @@ 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 @@ -3639,7 +3644,7 @@ class MaskedArray(ndarray): -inf >>> x.set_fill_value(np.pi) >>> x.fill_value - 3.1415926535897931 + 3.1415926535897931 # may vary Reset to default: @@ -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 @@ -4348,9 +4357,9 @@ class MaskedArray(ndarray): -------- >>> 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) + masked_array(data=[1.0, --, 1.6], + mask=[False, True, False], + fill_value=1e+20) """ result = self._data.imag.view(type(self)) @@ -4383,9 +4392,9 @@ class MaskedArray(ndarray): -------- >>> 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) + masked_array(data=[1.0, --, 3.45], + mask=[False, True, False], + fill_value=1e+20) """ result = self._data.real.view(type(self)) @@ -4431,13 +4440,12 @@ 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 @@ -4522,12 +4530,20 @@ 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 +4592,25 @@ 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 +4667,36 @@ 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 +4736,14 @@ 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 +4892,12 @@ 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 +4905,14 @@ 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 +4929,12 @@ 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 +5016,27 @@ 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} @@ -5040,8 +5087,11 @@ 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) @@ -5145,9 +5195,9 @@ 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 @@ -5200,9 +5250,9 @@ 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) @@ -5382,9 +5432,9 @@ 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 +5482,19 @@ 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: @@ -5531,23 +5585,29 @@ 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: @@ -5653,27 +5713,36 @@ 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 @@ -5926,7 +5995,7 @@ 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 +6043,20 @@ 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. @@ -6228,15 +6303,14 @@ 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 +6474,16 @@ 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 @@ -6759,17 +6833,17 @@ 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 +6998,21 @@ 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 +7059,39 @@ 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. @@ -7111,14 +7182,24 @@ 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 +7279,9 @@ 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): @@ -7323,25 +7404,23 @@ 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 +7481,22 @@ 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 @@ -7587,18 +7664,18 @@ 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 +7741,29 @@ 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 +7830,14 @@ 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 +7877,14 @@ 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 +8028,38 @@ 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 +8160,10 @@ 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) |