# 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 $
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import run_module_suite, assert_, assert_raises, dec
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):
if callable(getattr(super(SubArray, self),
'__array_finalize__', None)):
super(SubArray, self).__array_finalize__(obj)
self.info = getattr(obj, 'info', {}).copy()
return
def __add__(self, other):
result = super(SubArray, self).__add__(other)
result.info['added'] = result.info.get('added', 0) + 1
return result
def __iadd__(self, other):
result = super(SubArray, self).__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(SubMaskedArray, cls).__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
def _get_series(self):
_view = self.view(MaskedArray)
_view._sharedmask = False
return _view
_series = property(fget=_get_series)
msubarray = MSubArray
class MMatrix(MaskedArray, np.matrix,):
def __new__(cls, data, mask=nomask):
mat = np.matrix(data)
_data = MaskedArray.__new__(cls, data=mat, mask=mask)
return _data
def __array_finalize__(self, obj):
np.matrix.__array_finalize__(self, obj)
MaskedArray.__array_finalize__(self, obj)
return
def _get_series(self):
_view = self.view(MaskedArray)
_view._sharedmask = False
return _view
_series = property(fget=_get_series)
mmatrix = MMatrix
# 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(object):
"""
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))
next = __next__
class ComplicatedSubArray(SubArray):
def __str__(self):
return 'myprefix {0} mypostfix'.format(self.view(SubArray))
def __repr__(self):
# Return a repr that does not start with 'name('
return '<{0} {1}>'.format(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(ComplicatedSubArray, self).__setitem__(
item, self._validate_input(value))
def __getitem__(self, item):
# ensure getter returns our own class also for scalars
value = super(ComplicatedSubArray, self).__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(ComplicatedSubArray, self).__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 TestSubclassing(object):
# Test suite for masked subclasses of ndarray.
def setup(self):
x = np.arange(5, dtype='float')
mx = mmatrix(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, np.matrix))
def test_masked_unary_operations(self):
# Tests masked_unary_operation
(x, mx) = self.data
with np.errstate(divide='ignore'):
assert_(isinstance(log(mx), mmatrix))
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 mmatrix
assert_(isinstance(add(mx, mx), mmatrix))
assert_(isinstance(add(mx, x), mmatrix))
# Result should work
assert_equal(add(mx, x), mx+x)
assert_(isinstance(add(mx, mx)._data, np.matrix))
assert_(isinstance(add.outer(mx, mx), mmatrix))
assert_(isinstance(hypot(mx, mx), mmatrix))
assert_(isinstance(hypot(mx, x), mmatrix))
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), mmatrix))
assert_(isinstance(divide(mx, x), mmatrix))
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),
'masked_{0}(data=[--, 1, --, 3, 4]'.format(SubArray.__name__))
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')
###############################################################################
if __name__ == '__main__':
run_module_suite()