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"""Tests suite for MaskedArray & subclassing.
:author: Pierre Gerard-Marchant
:contact: pierregm_at_uga_dot_edu
"""
import numpy as np
from numpy.lib.mixins import NDArrayOperatorsMixin
from numpy.ma.core import (
MaskedArray,
add,
arange,
array,
asanyarray,
asarray,
divide,
hypot,
log,
masked,
masked_array,
nomask,
)
from numpy.ma.testutils import assert_equal
from numpy.testing import assert_, assert_raises
# from numpy.ma.core import (
def assert_startswith(a, b):
# produces a better error message than assert_(a.startswith(b))
assert_equal(a[:len(b)], b)
class SubArray(np.ndarray):
# Defines a generic np.ndarray subclass, that stores some metadata
# in the dictionary `info`.
def __new__(cls, arr, info={}):
x = np.asanyarray(arr).view(cls)
x.info = info.copy()
return x
def __array_finalize__(self, obj):
super().__array_finalize__(obj)
self.info = getattr(obj, 'info', {}).copy()
def __add__(self, other):
result = super().__add__(other)
result.info['added'] = result.info.get('added', 0) + 1
return result
def __iadd__(self, other):
result = super().__iadd__(other)
result.info['iadded'] = result.info.get('iadded', 0) + 1
return result
subarray = SubArray
class SubMaskedArray(MaskedArray):
"""Pure subclass of MaskedArray, keeping some info on subclass."""
def __new__(cls, info=None, **kwargs):
obj = super().__new__(cls, **kwargs)
obj._optinfo['info'] = info
return obj
class MSubArray(SubArray, MaskedArray):
def __new__(cls, data, info={}, mask=nomask):
subarr = SubArray(data, info)
_data = MaskedArray.__new__(cls, data=subarr, mask=mask)
_data.info = subarr.info
return _data
@property
def _series(self):
_view = self.view(MaskedArray)
_view._sharedmask = False
return _view
msubarray = MSubArray
# Also a subclass that overrides __str__, __repr__ and __setitem__, disallowing
# setting to non-class values (and thus np.ma.core.masked_print_option)
# and overrides __array_wrap__, updating the info dict, to check that this
# doesn't get destroyed by MaskedArray._update_from. But this one also needs
# its own iterator...
class CSAIterator:
"""
Flat iterator object that uses its own setter/getter
(works around ndarray.flat not propagating subclass setters/getters
see https://github.com/numpy/numpy/issues/4564)
roughly following MaskedIterator
"""
def __init__(self, a):
self._original = a
self._dataiter = a.view(np.ndarray).flat
def __iter__(self):
return self
def __getitem__(self, indx):
out = self._dataiter.__getitem__(indx)
if not isinstance(out, np.ndarray):
out = out.__array__()
out = out.view(type(self._original))
return out
def __setitem__(self, index, value):
self._dataiter[index] = self._original._validate_input(value)
def __next__(self):
return next(self._dataiter).__array__().view(type(self._original))
class ComplicatedSubArray(SubArray):
def __str__(self):
return f'myprefix {self.view(SubArray)} mypostfix'
def __repr__(self):
# Return a repr that does not start with 'name('
return f'<{self.__class__.__name__} {self}>'
def _validate_input(self, value):
if not isinstance(value, ComplicatedSubArray):
raise ValueError("Can only set to MySubArray values")
return value
def __setitem__(self, item, value):
# validation ensures direct assignment with ndarray or
# masked_print_option will fail
super().__setitem__(item, self._validate_input(value))
def __getitem__(self, item):
# ensure getter returns our own class also for scalars
value = super().__getitem__(item)
if not isinstance(value, np.ndarray): # scalar
value = value.__array__().view(ComplicatedSubArray)
return value
@property
def flat(self):
return CSAIterator(self)
@flat.setter
def flat(self, value):
y = self.ravel()
y[:] = value
def __array_wrap__(self, obj, context=None, return_scalar=False):
obj = super().__array_wrap__(obj, context, return_scalar)
if context is not None and context[0] is np.multiply:
obj.info['multiplied'] = obj.info.get('multiplied', 0) + 1
return obj
class WrappedArray(NDArrayOperatorsMixin):
"""
Wrapping a MaskedArray rather than subclassing to test that
ufunc deferrals are commutative.
See: https://github.com/numpy/numpy/issues/15200)
"""
__slots__ = ('_array', 'attrs')
__array_priority__ = 20
def __init__(self, array, **attrs):
self._array = array
self.attrs = attrs
def __repr__(self):
return f"{self.__class__.__name__}(\n{self._array}\n{self.attrs}\n)"
def __array__(self, dtype=None, copy=None):
return np.asarray(self._array)
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
if method == '__call__':
inputs = [arg._array if isinstance(arg, self.__class__) else arg
for arg in inputs]
return self.__class__(ufunc(*inputs, **kwargs), **self.attrs)
else:
return NotImplemented
class TestSubclassing:
# Test suite for masked subclasses of ndarray.
def setup_method(self):
x = np.arange(5, dtype='float')
mx = msubarray(x, mask=[0, 1, 0, 0, 0])
self.data = (x, mx)
def test_data_subclassing(self):
# Tests whether the subclass is kept.
x = np.arange(5)
m = [0, 0, 1, 0, 0]
xsub = SubArray(x)
xmsub = masked_array(xsub, mask=m)
assert_(isinstance(xmsub, MaskedArray))
assert_equal(xmsub._data, xsub)
assert_(isinstance(xmsub._data, SubArray))
def test_maskedarray_subclassing(self):
# Tests subclassing MaskedArray
(x, mx) = self.data
assert_(isinstance(mx._data, subarray))
def test_masked_unary_operations(self):
# Tests masked_unary_operation
(x, mx) = self.data
with np.errstate(divide='ignore'):
assert_(isinstance(log(mx), msubarray))
assert_equal(log(x), np.log(x))
def test_masked_binary_operations(self):
# Tests masked_binary_operation
(x, mx) = self.data
# Result should be a msubarray
assert_(isinstance(add(mx, mx), msubarray))
assert_(isinstance(add(mx, x), msubarray))
# Result should work
assert_equal(add(mx, x), mx + x)
assert_(isinstance(add(mx, mx)._data, subarray))
assert_(isinstance(add.outer(mx, mx), msubarray))
assert_(isinstance(hypot(mx, mx), msubarray))
assert_(isinstance(hypot(mx, x), msubarray))
def test_masked_binary_operations2(self):
# Tests domained_masked_binary_operation
(x, mx) = self.data
xmx = masked_array(mx.data.__array__(), mask=mx.mask)
assert_(isinstance(divide(mx, mx), msubarray))
assert_(isinstance(divide(mx, x), msubarray))
assert_equal(divide(mx, mx), divide(xmx, xmx))
def test_attributepropagation(self):
x = array(arange(5), mask=[0] + [1] * 4)
my = masked_array(subarray(x))
ym = msubarray(x)
#
z = (my + 1)
assert_(isinstance(z, MaskedArray))
assert_(not isinstance(z, MSubArray))
assert_(isinstance(z._data, SubArray))
assert_equal(z._data.info, {})
#
z = (ym + 1)
assert_(isinstance(z, MaskedArray))
assert_(isinstance(z, MSubArray))
assert_(isinstance(z._data, SubArray))
assert_(z._data.info['added'] > 0)
# Test that inplace methods from data get used (gh-4617)
ym += 1
assert_(isinstance(ym, MaskedArray))
assert_(isinstance(ym, MSubArray))
assert_(isinstance(ym._data, SubArray))
assert_(ym._data.info['iadded'] > 0)
#
ym._set_mask([1, 0, 0, 0, 1])
assert_equal(ym._mask, [1, 0, 0, 0, 1])
ym._series._set_mask([0, 0, 0, 0, 1])
assert_equal(ym._mask, [0, 0, 0, 0, 1])
#
xsub = subarray(x, info={'name': 'x'})
mxsub = masked_array(xsub)
assert_(hasattr(mxsub, 'info'))
assert_equal(mxsub.info, xsub.info)
def test_subclasspreservation(self):
# Checks that masked_array(...,subok=True) preserves the class.
x = np.arange(5)
m = [0, 0, 1, 0, 0]
xinfo = list(zip(x, m))
xsub = MSubArray(x, mask=m, info={'xsub': xinfo})
#
mxsub = masked_array(xsub, subok=False)
assert_(not isinstance(mxsub, MSubArray))
assert_(isinstance(mxsub, MaskedArray))
assert_equal(mxsub._mask, m)
#
mxsub = asarray(xsub)
assert_(not isinstance(mxsub, MSubArray))
assert_(isinstance(mxsub, MaskedArray))
assert_equal(mxsub._mask, m)
#
mxsub = masked_array(xsub, subok=True)
assert_(isinstance(mxsub, MSubArray))
assert_equal(mxsub.info, xsub.info)
assert_equal(mxsub._mask, xsub._mask)
#
mxsub = asanyarray(xsub)
assert_(isinstance(mxsub, MSubArray))
assert_equal(mxsub.info, xsub.info)
assert_equal(mxsub._mask, m)
def test_subclass_items(self):
"""test that getter and setter go via baseclass"""
x = np.arange(5)
xcsub = ComplicatedSubArray(x)
mxcsub = masked_array(xcsub, mask=[True, False, True, False, False])
# getter should return a ComplicatedSubArray, even for single item
# first check we wrote ComplicatedSubArray correctly
assert_(isinstance(xcsub[1], ComplicatedSubArray))
assert_(isinstance(xcsub[1, ...], ComplicatedSubArray))
assert_(isinstance(xcsub[1:4], ComplicatedSubArray))
# now that it propagates inside the MaskedArray
assert_(isinstance(mxcsub[1], ComplicatedSubArray))
assert_(isinstance(mxcsub[1, ...].data, ComplicatedSubArray))
assert_(mxcsub[0] is masked)
assert_(isinstance(mxcsub[0, ...].data, ComplicatedSubArray))
assert_(isinstance(mxcsub[1:4].data, ComplicatedSubArray))
# also for flattened version (which goes via MaskedIterator)
assert_(isinstance(mxcsub.flat[1].data, ComplicatedSubArray))
assert_(mxcsub.flat[0] is masked)
assert_(isinstance(mxcsub.flat[1:4].base, ComplicatedSubArray))
# setter should only work with ComplicatedSubArray input
# first check we wrote ComplicatedSubArray correctly
assert_raises(ValueError, xcsub.__setitem__, 1, x[4])
# now that it propagates inside the MaskedArray
assert_raises(ValueError, mxcsub.__setitem__, 1, x[4])
assert_raises(ValueError, mxcsub.__setitem__, slice(1, 4), x[1:4])
mxcsub[1] = xcsub[4]
mxcsub[1:4] = xcsub[1:4]
# also for flattened version (which goes via MaskedIterator)
assert_raises(ValueError, mxcsub.flat.__setitem__, 1, x[4])
assert_raises(ValueError, mxcsub.flat.__setitem__, slice(1, 4), x[1:4])
mxcsub.flat[1] = xcsub[4]
mxcsub.flat[1:4] = xcsub[1:4]
def test_subclass_nomask_items(self):
x = np.arange(5)
xcsub = ComplicatedSubArray(x)
mxcsub_nomask = masked_array(xcsub)
assert_(isinstance(mxcsub_nomask[1, ...].data, ComplicatedSubArray))
assert_(isinstance(mxcsub_nomask[0, ...].data, ComplicatedSubArray))
assert_(isinstance(mxcsub_nomask[1], ComplicatedSubArray))
assert_(isinstance(mxcsub_nomask[0], ComplicatedSubArray))
def test_subclass_repr(self):
"""test that repr uses the name of the subclass
and 'array' for np.ndarray"""
x = np.arange(5)
mx = masked_array(x, mask=[True, False, True, False, False])
assert_startswith(repr(mx), 'masked_array')
xsub = SubArray(x)
mxsub = masked_array(xsub, mask=[True, False, True, False, False])
assert_startswith(repr(mxsub),
f'masked_{SubArray.__name__}(data=[--, 1, --, 3, 4]')
def test_subclass_str(self):
"""test str with subclass that has overridden str, setitem"""
# first without override
x = np.arange(5)
xsub = SubArray(x)
mxsub = masked_array(xsub, mask=[True, False, True, False, False])
assert_equal(str(mxsub), '[-- 1 -- 3 4]')
xcsub = ComplicatedSubArray(x)
assert_raises(ValueError, xcsub.__setitem__, 0,
np.ma.core.masked_print_option)
mxcsub = masked_array(xcsub, mask=[True, False, True, False, False])
assert_equal(str(mxcsub), 'myprefix [-- 1 -- 3 4] mypostfix')
def test_pure_subclass_info_preservation(self):
# Test that ufuncs and methods conserve extra information consistently;
# see gh-7122.
arr1 = SubMaskedArray('test', data=[1, 2, 3, 4, 5, 6])
arr2 = SubMaskedArray(data=[0, 1, 2, 3, 4, 5])
diff1 = np.subtract(arr1, arr2)
assert_('info' in diff1._optinfo)
assert_(diff1._optinfo['info'] == 'test')
diff2 = arr1 - arr2
assert_('info' in diff2._optinfo)
assert_(diff2._optinfo['info'] == 'test')
class ArrayNoInheritance:
"""Quantity-like class that does not inherit from ndarray"""
def __init__(self, data, units):
self.magnitude = data
self.units = units
def __getattr__(self, attr):
return getattr(self.magnitude, attr)
def test_array_no_inheritance():
data_masked = np.ma.array([1, 2, 3], mask=[True, False, True])
data_masked_units = ArrayNoInheritance(data_masked, 'meters')
# Get the masked representation of the Quantity-like class
new_array = np.ma.array(data_masked_units)
assert_equal(data_masked.data, new_array.data)
assert_equal(data_masked.mask, new_array.mask)
# Test sharing the mask
data_masked.mask = [True, False, False]
assert_equal(data_masked.mask, new_array.mask)
assert_(new_array.sharedmask)
# Get the masked representation of the Quantity-like class
new_array = np.ma.array(data_masked_units, copy=True)
assert_equal(data_masked.data, new_array.data)
assert_equal(data_masked.mask, new_array.mask)
# Test that the mask is not shared when copy=True
data_masked.mask = [True, False, True]
assert_equal([True, False, False], new_array.mask)
assert_(not new_array.sharedmask)
# Get the masked representation of the Quantity-like class
new_array = np.ma.array(data_masked_units, keep_mask=False)
assert_equal(data_masked.data, new_array.data)
# The change did not affect the original mask
assert_equal(data_masked.mask, [True, False, True])
# Test that the mask is False and not shared when keep_mask=False
assert_(not new_array.mask)
assert_(not new_array.sharedmask)
class TestClassWrapping:
# Test suite for classes that wrap MaskedArrays
def setup_method(self):
m = np.ma.masked_array([1, 3, 5], mask=[False, True, False])
wm = WrappedArray(m)
self.data = (m, wm)
def test_masked_unary_operations(self):
# Tests masked_unary_operation
(m, wm) = self.data
with np.errstate(divide='ignore'):
assert_(isinstance(np.log(wm), WrappedArray))
def test_masked_binary_operations(self):
# Tests masked_binary_operation
(m, wm) = self.data
# Result should be a WrappedArray
assert_(isinstance(np.add(wm, wm), WrappedArray))
assert_(isinstance(np.add(m, wm), WrappedArray))
assert_(isinstance(np.add(wm, m), WrappedArray))
# add and '+' should call the same ufunc
assert_equal(np.add(m, wm), m + wm)
assert_(isinstance(np.hypot(m, wm), WrappedArray))
assert_(isinstance(np.hypot(wm, m), WrappedArray))
# Test domained binary operations
assert_(isinstance(np.divide(wm, m), WrappedArray))
assert_(isinstance(np.divide(m, wm), WrappedArray))
assert_equal(np.divide(wm, m) * m, np.divide(m, m) * wm)
# Test broadcasting
m2 = np.stack([m, m])
assert_(isinstance(np.divide(wm, m2), WrappedArray))
assert_(isinstance(np.divide(m2, wm), WrappedArray))
assert_equal(np.divide(m2, wm), np.divide(wm, m2))
def test_mixins_have_slots(self):
mixin = NDArrayOperatorsMixin()
# Should raise an error
assert_raises(AttributeError, mixin.__setattr__, "not_a_real_attr", 1)
m = np.ma.masked_array([1, 3, 5], mask=[False, True, False])
wm = WrappedArray(m)
assert_raises(AttributeError, wm.__setattr__, "not_an_attr", 2)