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Edit File: test_subclassing.py
# pylint: disable-msg=W0611, W0612, W0511,R0201 """Tests suite for MaskedArray & subclassing. :author: Pierre Gerard-Marchant :contact: pierregm_at_uga_dot_edu :version: $Id: test_subclassing.py 3473 2007-10-29 15:18:13Z jarrod.millman $ """ import numpy as np from numpy.lib.mixins import NDArrayOperatorsMixin from numpy.testing import assert_, assert_raises from numpy.ma.testutils import assert_equal from numpy.ma.core import ( array, arange, masked, MaskedArray, masked_array, log, add, hypot, divide, asarray, asanyarray, nomask ) # 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() return 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): obj = super().__array_wrap__(obj, context) 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) """ __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): 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 = [(i, j) for (i, j) in 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))