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