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Dec 23, 2023
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DOC: Add missing examples to np.ma #25414

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DOC: Add missing examples to np.ma 
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linus-md committed Dec 18, 2023
commit b417c6deed1cf60f7d50dfd293bfa22a66d67df9
18 changes: 15 additions & 3 deletions numpy/ma/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -633,9 +633,10 @@ def filled(a, fill_value=None):

Examples
--------
>>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> import numpy.ma as ma
>>> x = ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> x.filled()
array([[999999, 1, 2],
[999999, 4, 5],
Expand Down Expand Up @@ -5541,6 +5542,17 @@ def round(self, decimals=0, out=None):
--------
numpy.ndarray.round : corresponding function for ndarrays
numpy.around : equivalent function

Examples
--------
>>> import numpy.ma as ma
>>> x = ma.array([1.35, 2.5, 1.5, 1.75, 2.25, 2.75],
... mask=[0, 0, 0, 1, 0, 0])
>>> ma.round(x)
masked_array(data=[1.0, 2.0, 2.0, --, 2.0, 3.0],
mask=[False, False, False, True, False, False],
fill_value=1e+20)

"""
result = self._data.round(decimals=decimals, out=out).view(type(self))
if result.ndim > 0:
Expand Down
154 changes: 124 additions & 30 deletions numpy/ma/extras.py
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Expand Up @@ -69,12 +69,11 @@ def count_masked(arr, axis=None):

Examples
--------
>>> import numpy.ma as ma
>>> a = np.arange(9).reshape((3,3))
>>> a = ma.array(a)
>>> a[1, 0] = ma.masked
>>> a[1, 2] = ma.masked
>>> a[2, 1] = ma.masked
>>> a = np.ma.array(a)
>>> a[1, 0] = np.ma.masked
>>> a[1, 2] = np.ma.masked
>>> a[2, 1] = np.ma.masked
>>> a
masked_array(
data=[[0, 1, 2],
Expand All @@ -84,14 +83,14 @@ def count_masked(arr, axis=None):
[ True, False, True],
[False, True, False]],
fill_value=999999)
>>> ma.count_masked(a)
>>> np.ma.count_masked(a)
3

When the `axis` keyword is used an array is returned.

>>> ma.count_masked(a, axis=0)
>>> np.ma.count_masked(a, axis=0)
array([1, 1, 1])
>>> ma.count_masked(a, axis=1)
>>> np.ma.count_masked(a, axis=1)
array([0, 2, 1])

"""
Expand Down Expand Up @@ -124,8 +123,7 @@ def masked_all(shape, dtype=float):

Examples
--------
>>> import numpy.ma as ma
>>> ma.masked_all((3, 3))
>>> np.ma.masked_all((3, 3))
masked_array(
data=[[--, --, --],
[--, --, --],
Expand All @@ -138,10 +136,10 @@ def masked_all(shape, dtype=float):

The `dtype` parameter defines the underlying data type.

>>> a = ma.masked_all((3, 3))
>>> a = np.ma.masked_all((3, 3))
>>> a.dtype
dtype('float64')
>>> a = ma.masked_all((3, 3), dtype=np.int32)
>>> a = np.ma.masked_all((3, 3), dtype=np.int32)
>>> a.dtype
dtype('int32')

Expand Down Expand Up @@ -179,12 +177,11 @@ def masked_all_like(arr):

Examples
--------
>>> import numpy.ma as ma
>>> arr = np.zeros((2, 3), dtype=np.float32)
>>> arr
array([[0., 0., 0.],
[0., 0., 0.]], dtype=float32)
>>> ma.masked_all_like(arr)
>>> np.ma.masked_all_like(arr)
masked_array(
data=[[--, --, --],
[--, --, --]],
Expand All @@ -197,7 +194,7 @@ def masked_all_like(arr):

>>> arr.dtype
dtype('float32')
>>> ma.masked_all_like(arr).dtype
>>> np.ma.masked_all_like(arr).dtype
dtype('float32')

"""
Expand Down Expand Up @@ -839,6 +836,15 @@ def compress_nd(x, axis=None):
-------
compress_array : ndarray
The compressed array.

Examples
--------
>>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> np.ma.compress_nd(x)
array([[7, 8]])

"""
x = asarray(x)
m = getmask(x)
Expand Down Expand Up @@ -928,6 +934,14 @@ def compress_rows(a):
--------
compress_rowcols

Examples
--------
>>> a = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> np.ma.compress_rows(a)
array([[6, 7, 8]])

"""
a = asarray(a)
if a.ndim != 2:
Expand All @@ -946,6 +960,16 @@ def compress_cols(a):
--------
compress_rowcols

Examples
--------
>>> a = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> np.ma.compress_cols(a)
array([[1, 2],
[4, 5],
[7, 8]])

"""
a = asarray(a)
if a.ndim != 2:
Expand Down Expand Up @@ -998,14 +1022,13 @@ def mask_rowcols(a, axis=None):

Examples
--------
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a = np.ma.masked_equal(a, 1)
>>> a
masked_array(
data=[[0, 0, 0],
Expand All @@ -1015,7 +1038,7 @@ def mask_rowcols(a, axis=None):
[False, True, False],
[False, False, False]],
fill_value=1)
>>> ma.mask_rowcols(a)
>>> np.ma.mask_rowcols(a)
masked_array(
data=[[0, --, 0],
[--, --, --],
Expand Down Expand Up @@ -1055,14 +1078,13 @@ def mask_rows(a, axis=np._NoValue):

Examples
--------
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a = np.ma.masked_equal(a, 1)
>>> a
masked_array(
data=[[0, 0, 0],
Expand All @@ -1073,7 +1095,7 @@ def mask_rows(a, axis=np._NoValue):
[False, False, False]],
fill_value=1)

>>> ma.mask_rows(a)
>>> np.ma.mask_rows(a)
masked_array(
data=[[0, 0, 0],
[--, --, --],
Expand Down Expand Up @@ -1106,14 +1128,13 @@ def mask_cols(a, axis=np._NoValue):

Examples
--------
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a = np.ma.masked_equal(a, 1)
>>> a
masked_array(
data=[[0, 0, 0],
Expand All @@ -1123,7 +1144,7 @@ def mask_cols(a, axis=np._NoValue):
[False, True, False],
[False, False, False]],
fill_value=1)
>>> ma.mask_cols(a)
>>> np.ma.mask_cols(a)
masked_array(
data=[[0, --, 0],
[0, --, 0],
Expand Down Expand Up @@ -1158,6 +1179,14 @@ def ediff1d(arr, to_end=None, to_begin=None):
--------
numpy.ediff1d : Equivalent function for ndarrays.

Examples
--------
>>> arr = np.ma.array([1, 2, 4, 7, 0])
>>> np.ma.ediff1d(arr)
masked_array(data=[ 1, 2, 3, -7],
mask=False,
fill_value=999999)

"""
arr = ma.asanyarray(arr).flat
ed = arr[1:] - arr[:-1]
Expand Down Expand Up @@ -1189,27 +1218,26 @@ def unique(ar1, return_index=False, return_inverse=False):

Examples
--------
>>> import numpy.ma as ma
>>> a = [1, 2, 1000, 2, 3]
>>> mask = [0, 0, 1, 0, 0]
>>> masked_a = ma.masked_array(a, mask)
>>> masked_a = np.ma.masked_array(a, mask)
>>> masked_a
masked_array(data=[1, 2, --, 2, 3],
mask=[False, False, True, False, False],
fill_value=999999)
>>> ma.unique(masked_a)
>>> np.ma.unique(masked_a)
masked_array(data=[1, 2, 3, --],
mask=[False, False, False, True],
fill_value=999999)
>>> ma.unique(masked_a, return_index=True)
>>> np.ma.unique(masked_a, return_index=True)
(masked_array(data=[1, 2, 3, --],
mask=[False, False, False, True],
fill_value=999999), array([0, 1, 4, 2]))
>>> ma.unique(masked_a, return_inverse=True)
>>> np.ma.unique(masked_a, return_inverse=True)
(masked_array(data=[1, 2, 3, --],
mask=[False, False, False, True],
fill_value=999999), array([0, 1, 3, 1, 2]))
>>> ma.unique(masked_a, return_index=True, return_inverse=True)
>>> np.ma.unique(masked_a, return_index=True, return_inverse=True)
(masked_array(data=[1, 2, 3, --],
mask=[False, False, False, True],
fill_value=999999), array([0, 1, 4, 2]), array([0, 1, 3, 1, 2]))
Expand Down Expand Up @@ -1268,6 +1296,15 @@ def setxor1d(ar1, ar2, assume_unique=False):
--------
numpy.setxor1d : Equivalent function for ndarrays.

Examples
--------
>>> ar1 = np.ma.array([1, 2, 3, 2, 4])
>>> ar2 = np.ma.array([2, 3, 5, 7, 5])
>>> np.ma.setxor1d(ar1, ar2)
masked_array(data=[1, 4, 5, 7],
mask=False,
fill_value=999999)

"""
if not assume_unique:
ar1 = unique(ar1)
Expand Down Expand Up @@ -1303,6 +1340,15 @@ def in1d(ar1, ar2, assume_unique=False, invert=False):
-----
.. versionadded:: 1.4.0

Examples
--------
>>> ar1 = np.ma.array([0, 1, 2, 5, 0])
>>> ar2 = [0, 2]
>>> np.ma.in1d(ar1, ar2)
masked_array(data=[ True, False, True, False, True],
mask=False,
fill_value=True)

"""
if not assume_unique:
ar1, rev_idx = unique(ar1, return_inverse=True)
Expand Down Expand Up @@ -1344,6 +1390,15 @@ def isin(element, test_elements, assume_unique=False, invert=False):
-----
.. versionadded:: 1.13.0

Examples
--------
>>> element = np.ma.array([1, 2, 3, 4, 5, 6])
>>> test_elements = [0, 2]
>>> np.ma.isin(element, test_elements)
masked_array(data=[False, True, False, False, False, False],
mask=False,
fill_value=True)

"""
element = ma.asarray(element)
return in1d(element, test_elements, assume_unique=assume_unique,
Expand All @@ -1360,6 +1415,15 @@ def union1d(ar1, ar2):
--------
numpy.union1d : Equivalent function for ndarrays.

Examples
--------
>>> ar1 = np.ma.array([1, 2, 3, 4])
>>> ar2 = np.ma.array([3, 4, 5, 6])
>>> np.ma.union1d(ar1, ar2)
masked_array(data=[1, 2, 3, 4, 5, 6],
mask=False,
fill_value=999999)

"""
return unique(ma.concatenate((ar1, ar2), axis=None))

Expand Down Expand Up @@ -1492,6 +1556,23 @@ def cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None):
--------
numpy.cov

Examples
--------
>>> x = np.ma.array([[0, 1], [1, 1]], mask=[0, 1, 0, 1])
>>> y = np.ma.array([[1, 0], [0, 1]], mask=[0, 0, 1, 1])
>>> np.ma.cov(x, y)
masked_array(
data=[[--, --, --, --],
[--, --, --, --],
[--, --, --, --],
[--, --, --, --]],
mask=[[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True]],
fill_value=1e+20,
dtype=float64)

"""
# Check inputs
if ddof is not None and ddof != int(ddof):
Expand Down Expand Up @@ -1560,6 +1641,19 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, allow_masked=True,
for backwards compatibility with previous versions of this function. These
arguments had no effect on the return values of the function and can be
safely ignored in this and previous versions of numpy.

Examples
--------
>>> x = np.ma.array([[0, 1], [1, 1]], mask=[0, 1, 0, 1])
>>> np.ma.corrcoef(x)
masked_array(
data=[[--, --],
[--, --]],
mask=[[ True, True],
[ True, True]],
fill_value=1e+20,
dtype=float64)

"""
msg = 'bias and ddof have no effect and are deprecated'
if bias is not np._NoValue or ddof is not np._NoValue:
Expand Down
0